TST: Clean plotting tests (pandas-dev#53917)

* TST: Clean plotting tests

* Fix random state

Author: mroeschke

pandas/tests/plotting/test_boxplot_method.py

@@ -28,6 +28,12 @@
 mpl = pytest.importorskip("matplotlib")
 
 
+def _check_ax_limits(col, ax):
+    y_min, y_max = ax.get_ylim()
+    assert y_min <= col.min()
+    assert y_max >= col.max()
+
+
 class TestDataFramePlots:
     def test_stacked_boxplot_set_axis(self):
         # GH2980
@@ -88,18 +94,30 @@ def test_boxplot_legacy2(self):
         with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
             _check_plot_works(df.boxplot, by="X")
 
+    def test_boxplot_legacy2_with_ax(self):
+        df = DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"])
+        df["X"] = Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
+        df["Y"] = Series(["A"] * 10)
         # When ax is supplied and required number of axes is 1,
         # passed ax should be used:
-        fig, ax = mpl.pyplot.subplots()
+        _, ax = mpl.pyplot.subplots()
         axes = df.boxplot("Col1", by="X", ax=ax)
         ax_axes = ax.axes
         assert ax_axes is axes
 
+    def test_boxplot_legacy2_with_ax_return_type(self):
+        df = DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"])
+        df["X"] = Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
+        df["Y"] = Series(["A"] * 10)
         fig, ax = mpl.pyplot.subplots()
         axes = df.groupby("Y").boxplot(ax=ax, return_type="axes")
         ax_axes = ax.axes
         assert ax_axes is axes["A"]
 
+    def test_boxplot_legacy2_with_multi_col(self):
+        df = DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"])
+        df["X"] = Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
+        df["Y"] = Series(["A"] * 10)
         # Multiple columns with an ax argument should use same figure
         fig, ax = mpl.pyplot.subplots()
         with tm.assert_produces_warning(UserWarning):
@@ -108,9 +126,13 @@ def test_boxplot_legacy2(self):
             )
         assert axes["Col1"].get_figure() is fig
 
+    def test_boxplot_legacy2_by_none(self):
+        df = DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"])
+        df["X"] = Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
+        df["Y"] = Series(["A"] * 10)
         # When by is None, check that all relevant lines are present in the
         # dict
-        fig, ax = mpl.pyplot.subplots()
+        _, ax = mpl.pyplot.subplots()
         d = df.boxplot(ax=ax, return_type="dict")
         lines = list(itertools.chain.from_iterable(d.values()))
         assert len(ax.get_lines()) == len(lines)
@@ -135,24 +157,20 @@ def test_boxplot_return_type_legacy(self):
         result = df.boxplot()
         _check_box_return_type(result, "axes")
 
-        with tm.assert_produces_warning(False):
-            result = df.boxplot(return_type="dict")
-        _check_box_return_type(result, "dict")
-
-        with tm.assert_produces_warning(False):
-            result = df.boxplot(return_type="axes")
-        _check_box_return_type(result, "axes")
+    @pytest.mark.parametrize("return_type", ["dict", "axes", "both"])
+    def test_boxplot_return_type_legacy_return_type(self, return_type):
+        # API change in https://github.com/pandas-dev/pandas/pull/7096
 
+        df = DataFrame(
+            np.random.randn(6, 4),
+            index=list(string.ascii_letters[:6]),
+            columns=["one", "two", "three", "four"],
+        )
         with tm.assert_produces_warning(False):
-            result = df.boxplot(return_type="both")
-        _check_box_return_type(result, "both")
+            result = df.boxplot(return_type=return_type)
+        _check_box_return_type(result, return_type)
 
     def test_boxplot_axis_limits(self, hist_df):
-        def _check_ax_limits(col, ax):
-            y_min, y_max = ax.get_ylim()
-            assert y_min <= col.min()
-            assert y_max >= col.max()
-
         df = hist_df.copy()
         df["age"] = np.random.randint(1, 20, df.shape[0])
         # One full row
@@ -161,6 +179,9 @@ def _check_ax_limits(col, ax):
         _check_ax_limits(df["weight"], weight_ax)
         assert weight_ax._sharey == height_ax
 
+    def test_boxplot_axis_limits_two_rows(self, hist_df):
+        df = hist_df.copy()
+        df["age"] = np.random.randint(1, 20, df.shape[0])
         # Two rows, one partial
         p = df.boxplot(["height", "weight", "age"], by="category")
         height_ax, weight_ax, age_ax = p[0, 0], p[0, 1], p[1, 0]
@@ -275,7 +296,7 @@ def test_color_kwd_errors(self, dict_colors, msg):
     )
     def test_specified_props_kwd(self, props, expected):
         # GH 30346
-        df = DataFrame({k: np.random.random(100) for k in "ABC"})
+        df = DataFrame({k: np.random.random(10) for k in "ABC"})
         kwd = {props: {"color": "C1"}}
         result = df.boxplot(return_type="dict", **kwd)
 
@@ -285,9 +306,9 @@ def test_specified_props_kwd(self, props, expected):
     def test_plot_xlabel_ylabel(self, vert):
         df = DataFrame(
             {
-                "a": np.random.randn(100),
-                "b": np.random.randn(100),
-                "group": np.random.choice(["group1", "group2"], 100),
+                "a": np.random.randn(10),
+                "b": np.random.randn(10),
+                "group": np.random.choice(["group1", "group2"], 10),
             }
         )
         xlabel, ylabel = "x", "y"
@@ -299,9 +320,9 @@ def test_plot_xlabel_ylabel(self, vert):
     def test_boxplot_xlabel_ylabel(self, vert):
         df = DataFrame(
             {
-                "a": np.random.randn(100),
-                "b": np.random.randn(100),
-                "group": np.random.choice(["group1", "group2"], 100),
+                "a": np.random.randn(10),
+                "b": np.random.randn(10),
+                "group": np.random.choice(["group1", "group2"], 10),
             }
         )
         xlabel, ylabel = "x", "y"
@@ -313,9 +334,9 @@ def test_boxplot_xlabel_ylabel(self, vert):
     def test_boxplot_group_xlabel_ylabel(self, vert):
         df = DataFrame(
             {
-                "a": np.random.randn(100),
-                "b": np.random.randn(100),
-                "group": np.random.choice(["group1", "group2"], 100),
+                "a": np.random.randn(10),
+                "b": np.random.randn(10),
+                "group": np.random.choice(["group1", "group2"], 10),
             }
         )
         xlabel, ylabel = "x", "y"
@@ -325,6 +346,15 @@ def test_boxplot_group_xlabel_ylabel(self, vert):
             assert subplot.get_ylabel() == ylabel
         mpl.pyplot.close()
 
+    @pytest.mark.parametrize("vert", [True, False])
+    def test_boxplot_group_no_xlabel_ylabel(self, vert):
+        df = DataFrame(
+            {
+                "a": np.random.randn(10),
+                "b": np.random.randn(10),
+                "group": np.random.choice(["group1", "group2"], 10),
+            }
+        )
         ax = df.boxplot(by="group", vert=vert)
         for subplot in ax:
             target_label = subplot.get_xlabel() if vert else subplot.get_ylabel()
@@ -338,6 +368,9 @@ def test_boxplot_legacy1(self, hist_df):
         with tm.assert_produces_warning(UserWarning, check_stacklevel=False):
             axes = _check_plot_works(grouped.boxplot, return_type="axes")
         _check_axes_shape(list(axes.values), axes_num=2, layout=(1, 2))
+
+    def test_boxplot_legacy1_return_type(self, hist_df):
+        grouped = hist_df.groupby(by="gender")
         axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
         _check_axes_shape(axes, axes_num=1, layout=(1, 1))
 
@@ -350,6 +383,11 @@ def test_boxplot_legacy2(self):
             axes = _check_plot_works(grouped.boxplot, return_type="axes")
         _check_axes_shape(list(axes.values), axes_num=10, layout=(4, 3))
 
+    @pytest.mark.slow
+    def test_boxplot_legacy2_return_type(self):
+        tuples = zip(string.ascii_letters[:10], range(10))
+        df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
+        grouped = df.groupby(level=1)
         axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
         _check_axes_shape(axes, axes_num=1, layout=(1, 1))
 
@@ -387,8 +425,14 @@ def test_grouped_plot_fignums(self):
         assert len(res) == 2
         tm.close()
 
+    def test_grouped_plot_fignums_excluded_col(self):
+        n = 10
+        weight = Series(np.random.normal(166, 20, size=n))
+        height = Series(np.random.normal(60, 10, size=n))
+        gender = np.random.RandomState(42).choice(["male", "female"], size=n)
+        df = DataFrame({"height": height, "weight": weight, "gender": gender})
         # now works with GH 5610 as gender is excluded
-        res = df.groupby("gender").hist()
+        df.groupby("gender").hist()
         tm.close()
 
     @pytest.mark.slow
@@ -545,10 +589,14 @@ def test_grouped_box_multiple_axes(self, hist_df):
         # location should be changed if other test is added
         # which has earlier alphabetical order
         with tm.assert_produces_warning(UserWarning):
-            fig, axes = mpl.pyplot.subplots(2, 2)
+            _, axes = mpl.pyplot.subplots(2, 2)
             df.groupby("category").boxplot(column="height", return_type="axes", ax=axes)
             _check_axes_shape(mpl.pyplot.gcf().axes, axes_num=4, layout=(2, 2))
 
+    @pytest.mark.slow
+    def test_grouped_box_multiple_axes_on_fig(self, hist_df):
+        # GH 6970, GH 7069
+        df = hist_df
         fig, axes = mpl.pyplot.subplots(2, 3)
         with tm.assert_produces_warning(UserWarning):
             returned = df.boxplot(
@@ -572,6 +620,10 @@ def test_grouped_box_multiple_axes(self, hist_df):
         tm.assert_numpy_array_equal(returned, axes[1])
         assert returned[0].figure is fig
 
+    @pytest.mark.slow
+    def test_grouped_box_multiple_axes_ax_error(self, hist_df):
+        # GH 6970, GH 7069
+        df = hist_df
         msg = "The number of passed axes must be 3, the same as the output plot"
         with pytest.raises(ValueError, match=msg):
             fig, axes = mpl.pyplot.subplots(2, 3)

pandas/tests/plotting/test_converter.py

@@ -222,17 +222,15 @@ def test_conversion_float(self, dtc):
         rs = dtc.convert(datetime(2012, 1, 1, 1, 2, 3), None, None)
         tm.assert_almost_equal(rs, xp, rtol=rtol)
 
-    def test_conversion_outofbounds_datetime(self, dtc):
+    @pytest.mark.parametrize(
+        "values",
+        [
+            [date(1677, 1, 1), date(1677, 1, 2)],
+            [datetime(1677, 1, 1, 12), datetime(1677, 1, 2, 12)],
+        ],
+    )
+    def test_conversion_outofbounds_datetime(self, dtc, values):
         # 2579
-        values = [date(1677, 1, 1), date(1677, 1, 2)]
-        rs = dtc.convert(values, None, None)
-        xp = converter.mdates.date2num(values)
-        tm.assert_numpy_array_equal(rs, xp)
-        rs = dtc.convert(values[0], None, None)
-        xp = converter.mdates.date2num(values[0])
-        assert rs == xp
-
-        values = [datetime(1677, 1, 1, 12), datetime(1677, 1, 2, 12)]
         rs = dtc.convert(values, None, None)
         xp = converter.mdates.date2num(values)
         tm.assert_numpy_array_equal(rs, xp)