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.venv/Lib/site-packages/pandas/tests/plotting/test_boxplot_method.py

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+""" Test cases for .boxplot method """
+
+import itertools
+import string
+
+import numpy as np
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import (
+    DataFrame,
+    MultiIndex,
+    Series,
+    date_range,
+    timedelta_range,
+)
+import pandas._testing as tm
+from pandas.tests.plotting.common import (
+    TestPlotBase,
+    _check_plot_works,
+)
+
+import pandas.plotting as plotting
+
+pytestmark = pytest.mark.slow
+
+
+@td.skip_if_no_mpl
+class TestDataFramePlots(TestPlotBase):
+    def test_boxplot_legacy1(self):
+        df = DataFrame(
+            np.random.randn(6, 4),
+            index=list(string.ascii_letters[:6]),
+            columns=["one", "two", "three", "four"],
+        )
+        df["indic"] = ["foo", "bar"] * 3
+        df["indic2"] = ["foo", "bar", "foo"] * 2
+
+        _check_plot_works(df.boxplot, return_type="dict")
+        _check_plot_works(df.boxplot, column=["one", "two"], return_type="dict")
+        # _check_plot_works adds an ax so catch warning. see GH #13188
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, column=["one", "two"], by="indic")
+        _check_plot_works(df.boxplot, column="one", by=["indic", "indic2"])
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, by="indic")
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, by=["indic", "indic2"])
+        _check_plot_works(plotting._core.boxplot, data=df["one"], return_type="dict")
+        _check_plot_works(df.boxplot, notch=1, return_type="dict")
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, by="indic", notch=1)
+
+    def test_boxplot_legacy2(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)
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, by="X")
+
+        # When ax is supplied and required number of axes is 1,
+        # passed ax should be used:
+        fig, ax = self.plt.subplots()
+        axes = df.boxplot("Col1", by="X", ax=ax)
+        ax_axes = ax.axes
+        assert ax_axes is axes
+
+        fig, ax = self.plt.subplots()
+        axes = df.groupby("Y").boxplot(ax=ax, return_type="axes")
+        ax_axes = ax.axes
+        assert ax_axes is axes["A"]
+
+        # Multiple columns with an ax argument should use same figure
+        fig, ax = self.plt.subplots()
+        with tm.assert_produces_warning(UserWarning):
+            axes = df.boxplot(
+                column=["Col1", "Col2"], by="X", ax=ax, return_type="axes"
+            )
+        assert axes["Col1"].get_figure() is fig
+
+        # When by is None, check that all relevant lines are present in the
+        # dict
+        fig, ax = self.plt.subplots()
+        d = df.boxplot(ax=ax, return_type="dict")
+        lines = list(itertools.chain.from_iterable(d.values()))
+        assert len(ax.get_lines()) == len(lines)
+
+    def test_boxplot_return_type_none(self):
+        # GH 12216; return_type=None & by=None -> axes
+        result = self.hist_df.boxplot()
+        assert isinstance(result, self.plt.Axes)
+
+    def test_boxplot_return_type_legacy(self):
+        # 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"],
+        )
+        msg = "return_type must be {'axes', 'dict', 'both'}"
+        with pytest.raises(ValueError, match=msg):
+            df.boxplot(return_type="NOT_A_TYPE")
+
+        result = df.boxplot()
+        self._check_box_return_type(result, "axes")
+
+        with tm.assert_produces_warning(False):
+            result = df.boxplot(return_type="dict")
+        self._check_box_return_type(result, "dict")
+
+        with tm.assert_produces_warning(False):
+            result = df.boxplot(return_type="axes")
+        self._check_box_return_type(result, "axes")
+
+        with tm.assert_produces_warning(False):
+            result = df.boxplot(return_type="both")
+        self._check_box_return_type(result, "both")
+
+    def test_boxplot_axis_limits(self):
+        def _check_ax_limits(col, ax):
+            y_min, y_max = ax.get_ylim()
+            assert y_min <= col.min()
+            assert y_max >= col.max()
+
+        df = self.hist_df.copy()
+        df["age"] = np.random.randint(1, 20, df.shape[0])
+        # One full row
+        height_ax, weight_ax = df.boxplot(["height", "weight"], by="category")
+        _check_ax_limits(df["height"], height_ax)
+        _check_ax_limits(df["weight"], weight_ax)
+        assert weight_ax._sharey == height_ax
+
+        # 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]
+        dummy_ax = p[1, 1]
+
+        _check_ax_limits(df["height"], height_ax)
+        _check_ax_limits(df["weight"], weight_ax)
+        _check_ax_limits(df["age"], age_ax)
+        assert weight_ax._sharey == height_ax
+        assert age_ax._sharey == height_ax
+        assert dummy_ax._sharey is None
+
+    def test_boxplot_empty_column(self):
+        df = DataFrame(np.random.randn(20, 4))
+        df.loc[:, 0] = np.nan
+        _check_plot_works(df.boxplot, return_type="axes")
+
+    def test_figsize(self):
+        df = DataFrame(np.random.rand(10, 5), columns=["A", "B", "C", "D", "E"])
+        result = df.boxplot(return_type="axes", figsize=(12, 8))
+        assert result.figure.bbox_inches.width == 12
+        assert result.figure.bbox_inches.height == 8
+
+    def test_fontsize(self):
+        df = DataFrame({"a": [1, 2, 3, 4, 5, 6]})
+        self._check_ticks_props(
+            df.boxplot("a", fontsize=16), xlabelsize=16, ylabelsize=16
+        )
+
+    def test_boxplot_numeric_data(self):
+        # GH 22799
+        df = DataFrame(
+            {
+                "a": date_range("2012-01-01", periods=100),
+                "b": np.random.randn(100),
+                "c": np.random.randn(100) + 2,
+                "d": date_range("2012-01-01", periods=100).astype(str),
+                "e": date_range("2012-01-01", periods=100, tz="UTC"),
+                "f": timedelta_range("1 days", periods=100),
+            }
+        )
+        ax = df.plot(kind="box")
+        assert [x.get_text() for x in ax.get_xticklabels()] == ["b", "c"]
+
+    @pytest.mark.parametrize(
+        "colors_kwd, expected",
+        [
+            (
+                {"boxes": "r", "whiskers": "b", "medians": "g", "caps": "c"},
+                {"boxes": "r", "whiskers": "b", "medians": "g", "caps": "c"},
+            ),
+            ({"boxes": "r"}, {"boxes": "r"}),
+            ("r", {"boxes": "r", "whiskers": "r", "medians": "r", "caps": "r"}),
+        ],
+    )
+    def test_color_kwd(self, colors_kwd, expected):
+        # GH: 26214
+        df = DataFrame(np.random.rand(10, 2))
+        result = df.boxplot(color=colors_kwd, return_type="dict")
+        for k, v in expected.items():
+            assert result[k][0].get_color() == v
+
+    @pytest.mark.parametrize(
+        "scheme,expected",
+        [
+            (
+                "dark_background",
+                {
+                    "boxes": "#8dd3c7",
+                    "whiskers": "#8dd3c7",
+                    "medians": "#bfbbd9",
+                    "caps": "#8dd3c7",
+                },
+            ),
+            (
+                "default",
+                {
+                    "boxes": "#1f77b4",
+                    "whiskers": "#1f77b4",
+                    "medians": "#2ca02c",
+                    "caps": "#1f77b4",
+                },
+            ),
+        ],
+    )
+    def test_colors_in_theme(self, scheme, expected):
+        # GH: 40769
+        df = DataFrame(np.random.rand(10, 2))
+        import matplotlib.pyplot as plt
+
+        plt.style.use(scheme)
+        result = df.plot.box(return_type="dict")
+        for k, v in expected.items():
+            assert result[k][0].get_color() == v
+
+    @pytest.mark.parametrize(
+        "dict_colors, msg",
+        [({"boxes": "r", "invalid_key": "r"}, "invalid key 'invalid_key'")],
+    )
+    def test_color_kwd_errors(self, dict_colors, msg):
+        # GH: 26214
+        df = DataFrame(np.random.rand(10, 2))
+        with pytest.raises(ValueError, match=msg):
+            df.boxplot(color=dict_colors, return_type="dict")
+
+    @pytest.mark.parametrize(
+        "props, expected",
+        [
+            ("boxprops", "boxes"),
+            ("whiskerprops", "whiskers"),
+            ("capprops", "caps"),
+            ("medianprops", "medians"),
+        ],
+    )
+    def test_specified_props_kwd(self, props, expected):
+        # GH 30346
+        df = DataFrame({k: np.random.random(100) for k in "ABC"})
+        kwd = {props: {"color": "C1"}}
+        result = df.boxplot(return_type="dict", **kwd)
+
+        assert result[expected][0].get_color() == "C1"
+
+
+@td.skip_if_no_mpl
+class TestDataFrameGroupByPlots(TestPlotBase):
+    def test_boxplot_legacy1(self):
+        grouped = self.hist_df.groupby(by="gender")
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(grouped.boxplot, return_type="axes")
+        self._check_axes_shape(list(axes.values), axes_num=2, layout=(1, 2))
+        axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
+        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
+
+    def test_boxplot_legacy2(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)
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(grouped.boxplot, return_type="axes")
+        self._check_axes_shape(list(axes.values), axes_num=10, layout=(4, 3))
+
+        axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
+        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
+
+    def test_boxplot_legacy3(self):
+        tuples = zip(string.ascii_letters[:10], range(10))
+        df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
+        grouped = df.unstack(level=1).groupby(level=0, axis=1)
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(grouped.boxplot, return_type="axes")
+        self._check_axes_shape(list(axes.values), axes_num=3, layout=(2, 2))
+        axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
+        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
+
+    def test_grouped_plot_fignums(self):
+        n = 10
+        weight = Series(np.random.normal(166, 20, size=n))
+        height = Series(np.random.normal(60, 10, size=n))
+        with tm.RNGContext(42):
+            gender = np.random.choice(["male", "female"], size=n)
+        df = DataFrame({"height": height, "weight": weight, "gender": gender})
+        gb = df.groupby("gender")
+
+        res = gb.plot()
+        assert len(self.plt.get_fignums()) == 2
+        assert len(res) == 2
+        tm.close()
+
+        res = gb.boxplot(return_type="axes")
+        assert len(self.plt.get_fignums()) == 1
+        assert len(res) == 2
+        tm.close()
+
+        # now works with GH 5610 as gender is excluded
+        res = df.groupby("gender").hist()
+        tm.close()
+
+    def test_grouped_box_return_type(self):
+        df = self.hist_df
+
+        # old style: return_type=None
+        result = df.boxplot(by="gender")
+        assert isinstance(result, np.ndarray)
+        self._check_box_return_type(
+            result, None, expected_keys=["height", "weight", "category"]
+        )
+
+        # now for groupby
+        result = df.groupby("gender").boxplot(return_type="dict")
+        self._check_box_return_type(result, "dict", expected_keys=["Male", "Female"])
+
+        columns2 = "X B C D A G Y N Q O".split()
+        df2 = DataFrame(np.random.randn(50, 10), columns=columns2)
+        categories2 = "A B C D E F G H I J".split()
+        df2["category"] = categories2 * 5
+
+        for t in ["dict", "axes", "both"]:
+            returned = df.groupby("classroom").boxplot(return_type=t)
+            self._check_box_return_type(returned, t, expected_keys=["A", "B", "C"])
+
+            returned = df.boxplot(by="classroom", return_type=t)
+            self._check_box_return_type(
+                returned, t, expected_keys=["height", "weight", "category"]
+            )
+
+            returned = df2.groupby("category").boxplot(return_type=t)
+            self._check_box_return_type(returned, t, expected_keys=categories2)
+
+            returned = df2.boxplot(by="category", return_type=t)
+            self._check_box_return_type(returned, t, expected_keys=columns2)
+
+    def test_grouped_box_layout(self):
+        df = self.hist_df
+
+        msg = "Layout of 1x1 must be larger than required size 2"
+        with pytest.raises(ValueError, match=msg):
+            df.boxplot(column=["weight", "height"], by=df.gender, layout=(1, 1))
+
+        msg = "The 'layout' keyword is not supported when 'by' is None"
+        with pytest.raises(ValueError, match=msg):
+            df.boxplot(
+                column=["height", "weight", "category"],
+                layout=(2, 1),
+                return_type="dict",
+            )
+
+        msg = "At least one dimension of layout must be positive"
+        with pytest.raises(ValueError, match=msg):
+            df.boxplot(column=["weight", "height"], by=df.gender, layout=(-1, -1))
+
+        # _check_plot_works adds an ax so catch warning. see GH #13188
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("gender").boxplot, column="height", return_type="dict"
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2))
+
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("category").boxplot, column="height", return_type="dict"
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))
+
+        # GH 6769
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("classroom").boxplot, column="height", return_type="dict"
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
+
+        # GH 5897
+        axes = df.boxplot(
+            column=["height", "weight", "category"], by="gender", return_type="axes"
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
+        for ax in [axes["height"]]:
+            self._check_visible(ax.get_xticklabels(), visible=False)
+            self._check_visible([ax.xaxis.get_label()], visible=False)
+        for ax in [axes["weight"], axes["category"]]:
+            self._check_visible(ax.get_xticklabels())
+            self._check_visible([ax.xaxis.get_label()])
+
+        box = df.groupby("classroom").boxplot(
+            column=["height", "weight", "category"], return_type="dict"
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
+
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("category").boxplot,
+                column="height",
+                layout=(3, 2),
+                return_type="dict",
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("category").boxplot,
+                column="height",
+                layout=(3, -1),
+                return_type="dict",
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))
+
+        box = df.boxplot(
+            column=["height", "weight", "category"], by="gender", layout=(4, 1)
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1))
+
+        box = df.boxplot(
+            column=["height", "weight", "category"], by="gender", layout=(-1, 1)
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(3, 1))
+
+        box = df.groupby("classroom").boxplot(
+            column=["height", "weight", "category"], layout=(1, 4), return_type="dict"
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4))
+
+        box = df.groupby("classroom").boxplot(  # noqa
+            column=["height", "weight", "category"], layout=(1, -1), return_type="dict"
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 3))
+
+    def test_grouped_box_multiple_axes(self):
+        # GH 6970, GH 7069
+        df = self.hist_df
+
+        # check warning to ignore sharex / sharey
+        # this check should be done in the first function which
+        # passes multiple axes to plot, hist or boxplot
+        # location should be changed if other test is added
+        # which has earlier alphabetical order
+        with tm.assert_produces_warning(UserWarning):
+            fig, axes = self.plt.subplots(2, 2)
+            df.groupby("category").boxplot(column="height", return_type="axes", ax=axes)
+            self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))
+
+        fig, axes = self.plt.subplots(2, 3)
+        with tm.assert_produces_warning(UserWarning):
+            returned = df.boxplot(
+                column=["height", "weight", "category"],
+                by="gender",
+                return_type="axes",
+                ax=axes[0],
+            )
+        returned = np.array(list(returned.values))
+        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
+        tm.assert_numpy_array_equal(returned, axes[0])
+        assert returned[0].figure is fig
+
+        # draw on second row
+        with tm.assert_produces_warning(UserWarning):
+            returned = df.groupby("classroom").boxplot(
+                column=["height", "weight", "category"], return_type="axes", ax=axes[1]
+            )
+        returned = np.array(list(returned.values))
+        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
+        tm.assert_numpy_array_equal(returned, axes[1])
+        assert returned[0].figure is fig
+
+        msg = "The number of passed axes must be 3, the same as the output plot"
+        with pytest.raises(ValueError, match=msg):
+            fig, axes = self.plt.subplots(2, 3)
+            # pass different number of axes from required
+            with tm.assert_produces_warning(UserWarning):
+                axes = df.groupby("classroom").boxplot(ax=axes)
+
+    def test_fontsize(self):
+        df = DataFrame({"a": [1, 2, 3, 4, 5, 6], "b": [0, 0, 0, 1, 1, 1]})
+        self._check_ticks_props(
+            df.boxplot("a", by="b", fontsize=16), xlabelsize=16, ylabelsize=16
+        )
+
+    @pytest.mark.parametrize(
+        "col, expected_xticklabel",
+        [
+            ("v", ["(a, v)", "(b, v)", "(c, v)", "(d, v)", "(e, v)"]),
+            (["v"], ["(a, v)", "(b, v)", "(c, v)", "(d, v)", "(e, v)"]),
+            ("v1", ["(a, v1)", "(b, v1)", "(c, v1)", "(d, v1)", "(e, v1)"]),
+            (
+                ["v", "v1"],
+                [
+                    "(a, v)",
+                    "(a, v1)",
+                    "(b, v)",
+                    "(b, v1)",
+                    "(c, v)",
+                    "(c, v1)",
+                    "(d, v)",
+                    "(d, v1)",
+                    "(e, v)",
+                    "(e, v1)",
+                ],
+            ),
+            (
+                None,
+                [
+                    "(a, v)",
+                    "(a, v1)",
+                    "(b, v)",
+                    "(b, v1)",
+                    "(c, v)",
+                    "(c, v1)",
+                    "(d, v)",
+                    "(d, v1)",
+                    "(e, v)",
+                    "(e, v1)",
+                ],
+            ),
+        ],
+    )
+    def test_groupby_boxplot_subplots_false(self, col, expected_xticklabel):
+        # GH 16748
+        df = DataFrame(
+            {
+                "cat": np.random.choice(list("abcde"), 100),
+                "v": np.random.rand(100),
+                "v1": np.random.rand(100),
+            }
+        )
+        grouped = df.groupby("cat")
+
+        axes = _check_plot_works(
+            grouped.boxplot, subplots=False, column=col, return_type="axes"
+        )
+
+        result_xticklabel = [x.get_text() for x in axes.get_xticklabels()]
+        assert expected_xticklabel == result_xticklabel
+
+    def test_groupby_boxplot_object(self):
+        # GH 43480
+        df = self.hist_df.astype("object")
+        grouped = df.groupby("gender")
+        msg = "boxplot method requires numerical columns, nothing to plot"
+        with pytest.raises(ValueError, match=msg):
+            _check_plot_works(grouped.boxplot, subplots=False)
+
+    def test_boxplot_multiindex_column(self):
+        # GH 16748
+        arrays = [
+            ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
+            ["one", "two", "one", "two", "one", "two", "one", "two"],
+        ]
+        tuples = list(zip(*arrays))
+        index = MultiIndex.from_tuples(tuples, names=["first", "second"])
+        df = DataFrame(np.random.randn(3, 8), index=["A", "B", "C"], columns=index)
+
+        col = [("bar", "one"), ("bar", "two")]
+        axes = _check_plot_works(df.boxplot, column=col, return_type="axes")
+
+        expected_xticklabel = ["(bar, one)", "(bar, two)"]
+        result_xticklabel = [x.get_text() for x in axes.get_xticklabels()]
+        assert expected_xticklabel == result_xticklabel