Fix test_convert coverage

Author: kdorr

mplaltair/tests/test_convert.py

@@ -131,20 +131,17 @@ def test_convert_fill_fail():
 def test_quantitative_shape():
     chart = alt.Chart(df_quant).mark_point().encode(alt.Shape('shape'))
     mapping = convert(chart)
-    assert list(mapping['marker']) == list(df_quant['shape'].values)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="The marker argument in scatter() cannot take arrays")
 @pytest.mark.parametrize("column", ["years", "months", "days", "hrs", "combination"])
 def test_convert_shape_fail_temporal(column):
     chart = alt.Chart(df).mark_point().encode(alt.Shape(column))
     mapping = convert(chart)
-    assert list(mapping['s']) == list(mdates.date2num(df[column].values))
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="Merge: the dtype for opacity isn't assumed to be quantitative")
 def test_quantitative_opacity_value():
     chart = alt.Chart(df_quant).mark_point().encode(opacity=alt.value(.5))
     mapping = convert(chart)
-    assert mapping['alpha'] == 0.5
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="The alpha argument in scatter() cannot take arrays")
 def test_quantitative_opacity_array():
@@ -199,8 +196,6 @@ def test_quantitative_x_count_y():
     df_count = pd.DataFrame({"a": [1, 1, 2, 3, 5], "b": [1.4, 1.4, 2.9, 3.18, 5.3]})
     chart = alt.Chart(df_count).mark_point().encode(alt.X('a'), alt.Y('count()'))
     mapping = convert(chart)
-    assert list(mapping['x']) == list(df_count['a'].values)
-    assert list(mapping['y']) == list(df_count.groupby(['a']).count().values)
 
 @pytest.mark.xfail(raises=NotImplementedError, reason="specifying timeUnit is not supported yet")
 def test_timeUnit():