1#@!#!123s

: / usr / local / lib64 / python3.6 / site-packages / pandas / tests / plotting /
Filename : test_frame.py
back
""" Test cases for DataFrame.plot """

from datetime import date, datetime
import itertools
import string
import warnings

import numpy as np
from numpy.random import rand, randn
import pytest

import pandas.util._test_decorators as td

from pandas.core.dtypes.api import is_list_like

import pandas as pd
from pandas import DataFrame, MultiIndex, PeriodIndex, Series, bdate_range, date_range
import pandas._testing as tm
from pandas.core.arrays import integer_array
from pandas.tests.plotting.common import TestPlotBase, _check_plot_works

from pandas.io.formats.printing import pprint_thing
import pandas.plotting as plotting


@td.skip_if_no_mpl
class TestDataFramePlots(TestPlotBase):
    def setup_method(self, method):
        TestPlotBase.setup_method(self, method)
        import matplotlib as mpl

        mpl.rcdefaults()

        self.tdf = tm.makeTimeDataFrame()
        self.hexbin_df = DataFrame(
            {
                "A": np.random.uniform(size=20),
                "B": np.random.uniform(size=20),
                "C": np.arange(20) + np.random.uniform(size=20),
            }
        )

    def _assert_ytickslabels_visibility(self, axes, expected):
        for ax, exp in zip(axes, expected):
            self._check_visible(ax.get_yticklabels(), visible=exp)

    def _assert_xtickslabels_visibility(self, axes, expected):
        for ax, exp in zip(axes, expected):
            self._check_visible(ax.get_xticklabels(), visible=exp)

    @pytest.mark.xfail(reason="Waiting for PR 34334", strict=True)
    @pytest.mark.slow
    def test_plot(self):
        from pandas.plotting._matplotlib.compat import _mpl_ge_3_1_0

        df = self.tdf
        _check_plot_works(df.plot, grid=False)
        # _check_plot_works adds an ax so catch warning. see GH #13188
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(df.plot, subplots=True)
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))

        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(df.plot, subplots=True, layout=(-1, 2))
        self._check_axes_shape(axes, axes_num=4, layout=(2, 2))

        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(df.plot, subplots=True, use_index=False)
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))

        df = DataFrame({"x": [1, 2], "y": [3, 4]})
        if _mpl_ge_3_1_0():
            msg = "'Line2D' object has no property 'blarg'"
        else:
            msg = "Unknown property blarg"
        with pytest.raises(AttributeError, match=msg):
            df.plot.line(blarg=True)

        df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))

        _check_plot_works(df.plot, use_index=True)
        _check_plot_works(df.plot, sort_columns=False)
        _check_plot_works(df.plot, yticks=[1, 5, 10])
        _check_plot_works(df.plot, xticks=[1, 5, 10])
        _check_plot_works(df.plot, ylim=(-100, 100), xlim=(-100, 100))

        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(df.plot, subplots=True, title="blah")

        # We have to redo it here because _check_plot_works does two plots,
        # once without an ax kwarg and once with an ax kwarg and the new sharex
        # behaviour does not remove the visibility of the latter axis (as ax is
        # present).  see: https://github.com/pandas-dev/pandas/issues/9737

        axes = df.plot(subplots=True, title="blah")
        self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
        # axes[0].figure.savefig("test.png")
        for ax in axes[:2]:
            self._check_visible(ax.xaxis)  # xaxis must be visible for grid
            self._check_visible(ax.get_xticklabels(), visible=False)
            self._check_visible(ax.get_xticklabels(minor=True), visible=False)
            self._check_visible([ax.xaxis.get_label()], visible=False)
        for ax in [axes[2]]:
            self._check_visible(ax.xaxis)
            self._check_visible(ax.get_xticklabels())
            self._check_visible([ax.xaxis.get_label()])
            self._check_ticks_props(ax, xrot=0)

        _check_plot_works(df.plot, title="blah")

        tuples = zip(string.ascii_letters[:10], range(10))
        df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
        _check_plot_works(df.plot, use_index=True)

        # unicode
        index = MultiIndex.from_tuples(
            [
                ("\u03b1", 0),
                ("\u03b1", 1),
                ("\u03b2", 2),
                ("\u03b2", 3),
                ("\u03b3", 4),
                ("\u03b3", 5),
                ("\u03b4", 6),
                ("\u03b4", 7),
            ],
            names=["i0", "i1"],
        )
        columns = MultiIndex.from_tuples(
            [("bar", "\u0394"), ("bar", "\u0395")], names=["c0", "c1"]
        )
        df = DataFrame(np.random.randint(0, 10, (8, 2)), columns=columns, index=index)
        _check_plot_works(df.plot, title="\u03A3")

        # GH 6951
        # Test with single column
        df = DataFrame({"x": np.random.rand(10)})
        axes = _check_plot_works(df.plot.bar, subplots=True)
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

        axes = _check_plot_works(df.plot.bar, subplots=True, layout=(-1, 1))
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
        # When ax is supplied and required number of axes is 1,
        # passed ax should be used:
        fig, ax = self.plt.subplots()
        axes = df.plot.bar(subplots=True, ax=ax)
        assert len(axes) == 1
        result = ax.axes
        assert result is axes[0]

    def test_integer_array_plot(self):
        # GH 25587
        arr = integer_array([1, 2, 3, 4], dtype="UInt32")

        s = Series(arr)
        _check_plot_works(s.plot.line)
        _check_plot_works(s.plot.bar)
        _check_plot_works(s.plot.hist)
        _check_plot_works(s.plot.pie)

        df = DataFrame({"x": arr, "y": arr})
        _check_plot_works(df.plot.line)
        _check_plot_works(df.plot.bar)
        _check_plot_works(df.plot.hist)
        _check_plot_works(df.plot.pie, y="y")
        _check_plot_works(df.plot.scatter, x="x", y="y")
        _check_plot_works(df.plot.hexbin, x="x", y="y")

    def test_mpl2_color_cycle_str(self):
        # GH 15516
        colors = ["C" + str(x) for x in range(10)]
        df = DataFrame(randn(10, 3), columns=["a", "b", "c"])
        for c in colors:
            _check_plot_works(df.plot, color=c)

    def test_color_single_series_list(self):
        # GH 3486
        df = DataFrame({"A": [1, 2, 3]})
        _check_plot_works(df.plot, color=["red"])

    def test_rgb_tuple_color(self):
        # GH 16695
        df = DataFrame({"x": [1, 2], "y": [3, 4]})
        _check_plot_works(df.plot, x="x", y="y", color=(1, 0, 0))
        _check_plot_works(df.plot, x="x", y="y", color=(1, 0, 0, 0.5))

    def test_color_empty_string(self):
        df = DataFrame(randn(10, 2))
        with pytest.raises(ValueError):
            df.plot(color="")

    def test_color_and_style_arguments(self):
        df = DataFrame({"x": [1, 2], "y": [3, 4]})
        # passing both 'color' and 'style' arguments should be allowed
        # if there is no color symbol in the style strings:
        ax = df.plot(color=["red", "black"], style=["-", "--"])
        # check that the linestyles are correctly set:
        linestyle = [line.get_linestyle() for line in ax.lines]
        assert linestyle == ["-", "--"]
        # check that the colors are correctly set:
        color = [line.get_color() for line in ax.lines]
        assert color == ["red", "black"]
        # passing both 'color' and 'style' arguments should not be allowed
        # if there is a color symbol in the style strings:
        with pytest.raises(ValueError):
            df.plot(color=["red", "black"], style=["k-", "r--"])

    def test_nonnumeric_exclude(self):
        df = DataFrame({"A": ["x", "y", "z"], "B": [1, 2, 3]})
        ax = df.plot()
        assert len(ax.get_lines()) == 1  # B was plotted

    @pytest.mark.slow
    def test_implicit_label(self):
        df = DataFrame(randn(10, 3), columns=["a", "b", "c"])
        ax = df.plot(x="a", y="b")
        self._check_text_labels(ax.xaxis.get_label(), "a")

    @pytest.mark.slow
    def test_donot_overwrite_index_name(self):
        # GH 8494
        df = DataFrame(randn(2, 2), columns=["a", "b"])
        df.index.name = "NAME"
        df.plot(y="b", label="LABEL")
        assert df.index.name == "NAME"

    @pytest.mark.slow
    def test_plot_xy(self):
        # columns.inferred_type == 'string'
        df = self.tdf
        self._check_data(df.plot(x=0, y=1), df.set_index("A")["B"].plot())
        self._check_data(df.plot(x=0), df.set_index("A").plot())
        self._check_data(df.plot(y=0), df.B.plot())
        self._check_data(df.plot(x="A", y="B"), df.set_index("A").B.plot())
        self._check_data(df.plot(x="A"), df.set_index("A").plot())
        self._check_data(df.plot(y="B"), df.B.plot())

        # columns.inferred_type == 'integer'
        df.columns = np.arange(1, len(df.columns) + 1)
        self._check_data(df.plot(x=1, y=2), df.set_index(1)[2].plot())
        self._check_data(df.plot(x=1), df.set_index(1).plot())
        self._check_data(df.plot(y=1), df[1].plot())

        # figsize and title
        ax = df.plot(x=1, y=2, title="Test", figsize=(16, 8))
        self._check_text_labels(ax.title, "Test")
        self._check_axes_shape(ax, axes_num=1, layout=(1, 1), figsize=(16.0, 8.0))

        # columns.inferred_type == 'mixed'
        # TODO add MultiIndex test

    @pytest.mark.slow
    @pytest.mark.parametrize(
        "input_log, expected_log", [(True, "log"), ("sym", "symlog")]
    )
    def test_logscales(self, input_log, expected_log):
        df = DataFrame({"a": np.arange(100)}, index=np.arange(100))

        ax = df.plot(logy=input_log)
        self._check_ax_scales(ax, yaxis=expected_log)
        assert ax.get_yscale() == expected_log

        ax = df.plot(logx=input_log)
        self._check_ax_scales(ax, xaxis=expected_log)
        assert ax.get_xscale() == expected_log

        ax = df.plot(loglog=input_log)
        self._check_ax_scales(ax, xaxis=expected_log, yaxis=expected_log)
        assert ax.get_xscale() == expected_log
        assert ax.get_yscale() == expected_log

    @pytest.mark.parametrize("input_param", ["logx", "logy", "loglog"])
    def test_invalid_logscale(self, input_param):
        # GH: 24867
        df = DataFrame({"a": np.arange(100)}, index=np.arange(100))

        msg = "Boolean, None and 'sym' are valid options, 'sm' is given."
        with pytest.raises(ValueError, match=msg):
            df.plot(**{input_param: "sm"})

    @pytest.mark.slow
    def test_xcompat(self):
        import pandas as pd

        df = self.tdf
        ax = df.plot(x_compat=True)
        lines = ax.get_lines()
        assert not isinstance(lines[0].get_xdata(), PeriodIndex)

        tm.close()
        pd.plotting.plot_params["xaxis.compat"] = True
        ax = df.plot()
        lines = ax.get_lines()
        assert not isinstance(lines[0].get_xdata(), PeriodIndex)

        tm.close()
        pd.plotting.plot_params["x_compat"] = False

        ax = df.plot()
        lines = ax.get_lines()
        assert not isinstance(lines[0].get_xdata(), PeriodIndex)
        assert isinstance(PeriodIndex(lines[0].get_xdata()), PeriodIndex)

        tm.close()
        # useful if you're plotting a bunch together
        with pd.plotting.plot_params.use("x_compat", True):
            ax = df.plot()
            lines = ax.get_lines()
            assert not isinstance(lines[0].get_xdata(), PeriodIndex)

        tm.close()
        ax = df.plot()
        lines = ax.get_lines()
        assert not isinstance(lines[0].get_xdata(), PeriodIndex)
        assert isinstance(PeriodIndex(lines[0].get_xdata()), PeriodIndex)

    def test_period_compat(self):
        # GH 9012
        # period-array conversions
        df = DataFrame(
            np.random.rand(21, 2),
            index=bdate_range(datetime(2000, 1, 1), datetime(2000, 1, 31)),
            columns=["a", "b"],
        )

        df.plot()
        self.plt.axhline(y=0)
        tm.close()

    def test_unsorted_index(self):
        df = DataFrame(
            {"y": np.arange(100)}, index=np.arange(99, -1, -1), dtype=np.int64
        )
        ax = df.plot()
        lines = ax.get_lines()[0]
        rs = lines.get_xydata()
        rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64, name="y")
        tm.assert_series_equal(rs, df.y, check_index_type=False)
        tm.close()

        df.index = pd.Index(np.arange(99, -1, -1), dtype=np.float64)
        ax = df.plot()
        lines = ax.get_lines()[0]
        rs = lines.get_xydata()
        rs = Series(rs[:, 1], rs[:, 0], dtype=np.int64, name="y")
        tm.assert_series_equal(rs, df.y)

    def test_unsorted_index_lims(self):
        df = DataFrame({"y": [0.0, 1.0, 2.0, 3.0]}, index=[1.0, 0.0, 3.0, 2.0])
        ax = df.plot()
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        assert xmin <= np.nanmin(lines[0].get_data()[0])
        assert xmax >= np.nanmax(lines[0].get_data()[0])

        df = DataFrame(
            {"y": [0.0, 1.0, np.nan, 3.0, 4.0, 5.0, 6.0]},
            index=[1.0, 0.0, 3.0, 2.0, np.nan, 3.0, 2.0],
        )
        ax = df.plot()
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        assert xmin <= np.nanmin(lines[0].get_data()[0])
        assert xmax >= np.nanmax(lines[0].get_data()[0])

        df = DataFrame({"y": [0.0, 1.0, 2.0, 3.0], "z": [91.0, 90.0, 93.0, 92.0]})
        ax = df.plot(x="z", y="y")
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        assert xmin <= np.nanmin(lines[0].get_data()[0])
        assert xmax >= np.nanmax(lines[0].get_data()[0])

    @pytest.mark.slow
    def test_subplots(self):
        df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))

        for kind in ["bar", "barh", "line", "area"]:
            axes = df.plot(kind=kind, subplots=True, sharex=True, legend=True)
            self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
            assert axes.shape == (3,)

            for ax, column in zip(axes, df.columns):
                self._check_legend_labels(ax, labels=[pprint_thing(column)])

            for ax in axes[:-2]:
                self._check_visible(ax.xaxis)  # xaxis must be visible for grid
                self._check_visible(ax.get_xticklabels(), visible=False)
                if not (kind == "bar" and self.mpl_ge_3_1_0):
                    # change https://github.com/pandas-dev/pandas/issues/26714
                    self._check_visible(ax.get_xticklabels(minor=True), visible=False)
                self._check_visible(ax.xaxis.get_label(), visible=False)
                self._check_visible(ax.get_yticklabels())

            self._check_visible(axes[-1].xaxis)
            self._check_visible(axes[-1].get_xticklabels())
            self._check_visible(axes[-1].get_xticklabels(minor=True))
            self._check_visible(axes[-1].xaxis.get_label())
            self._check_visible(axes[-1].get_yticklabels())

            axes = df.plot(kind=kind, subplots=True, sharex=False)
            for ax in axes:
                self._check_visible(ax.xaxis)
                self._check_visible(ax.get_xticklabels())
                self._check_visible(ax.get_xticklabels(minor=True))
                self._check_visible(ax.xaxis.get_label())
                self._check_visible(ax.get_yticklabels())

            axes = df.plot(kind=kind, subplots=True, legend=False)
            for ax in axes:
                assert ax.get_legend() is None

    def test_groupby_boxplot_sharey(self):
        # https://github.com/pandas-dev/pandas/issues/20968
        # sharey can now be switched check whether the right
        # pair of axes is turned on or off

        df = DataFrame(
            {
                "a": [-1.43, -0.15, -3.70, -1.43, -0.14],
                "b": [0.56, 0.84, 0.29, 0.56, 0.85],
                "c": [0, 1, 2, 3, 1],
            },
            index=[0, 1, 2, 3, 4],
        )

        # behavior without keyword
        axes = df.groupby("c").boxplot()
        expected = [True, False, True, False]
        self._assert_ytickslabels_visibility(axes, expected)

        # set sharey=True should be identical
        axes = df.groupby("c").boxplot(sharey=True)
        expected = [True, False, True, False]
        self._assert_ytickslabels_visibility(axes, expected)

        # sharey=False, all yticklabels should be visible
        axes = df.groupby("c").boxplot(sharey=False)
        expected = [True, True, True, True]
        self._assert_ytickslabels_visibility(axes, expected)

    def test_groupby_boxplot_sharex(self):
        # https://github.com/pandas-dev/pandas/issues/20968
        # sharex can now be switched check whether the right
        # pair of axes is turned on or off

        df = DataFrame(
            {
                "a": [-1.43, -0.15, -3.70, -1.43, -0.14],
                "b": [0.56, 0.84, 0.29, 0.56, 0.85],
                "c": [0, 1, 2, 3, 1],
            },
            index=[0, 1, 2, 3, 4],
        )

        # behavior without keyword
        axes = df.groupby("c").boxplot()
        expected = [True, True, True, True]
        self._assert_xtickslabels_visibility(axes, expected)

        # set sharex=False should be identical
        axes = df.groupby("c").boxplot(sharex=False)
        expected = [True, True, True, True]
        self._assert_xtickslabels_visibility(axes, expected)

        # sharex=True, yticklabels should be visible
        # only for bottom plots
        axes = df.groupby("c").boxplot(sharex=True)
        expected = [False, False, True, True]
        self._assert_xtickslabels_visibility(axes, expected)

    @pytest.mark.xfail(reason="Waiting for PR 34334", strict=True)
    @pytest.mark.slow
    def test_subplots_timeseries(self):
        idx = date_range(start="2014-07-01", freq="M", periods=10)
        df = DataFrame(np.random.rand(10, 3), index=idx)

        for kind in ["line", "area"]:
            axes = df.plot(kind=kind, subplots=True, sharex=True)
            self._check_axes_shape(axes, axes_num=3, layout=(3, 1))

            for ax in axes[:-2]:
                # GH 7801
                self._check_visible(ax.xaxis)  # xaxis must be visible for grid
                self._check_visible(ax.get_xticklabels(), visible=False)
                self._check_visible(ax.get_xticklabels(minor=True), visible=False)
                self._check_visible(ax.xaxis.get_label(), visible=False)
                self._check_visible(ax.get_yticklabels())

            self._check_visible(axes[-1].xaxis)
            self._check_visible(axes[-1].get_xticklabels())
            self._check_visible(axes[-1].get_xticklabels(minor=True))
            self._check_visible(axes[-1].xaxis.get_label())
            self._check_visible(axes[-1].get_yticklabels())
            self._check_ticks_props(axes, xrot=0)

            axes = df.plot(kind=kind, subplots=True, sharex=False, rot=45, fontsize=7)
            for ax in axes:
                self._check_visible(ax.xaxis)
                self._check_visible(ax.get_xticklabels())
                self._check_visible(ax.get_xticklabels(minor=True))
                self._check_visible(ax.xaxis.get_label())
                self._check_visible(ax.get_yticklabels())
                self._check_ticks_props(ax, xlabelsize=7, xrot=45, ylabelsize=7)

    def test_subplots_timeseries_y_axis(self):
        # GH16953
        data = {
            "numeric": np.array([1, 2, 5]),
            "timedelta": [
                pd.Timedelta(-10, unit="s"),
                pd.Timedelta(10, unit="m"),
                pd.Timedelta(10, unit="h"),
            ],
            "datetime_no_tz": [
                pd.to_datetime("2017-08-01 00:00:00"),
                pd.to_datetime("2017-08-01 02:00:00"),
                pd.to_datetime("2017-08-02 00:00:00"),
            ],
            "datetime_all_tz": [
                pd.to_datetime("2017-08-01 00:00:00", utc=True),
                pd.to_datetime("2017-08-01 02:00:00", utc=True),
                pd.to_datetime("2017-08-02 00:00:00", utc=True),
            ],
            "text": ["This", "should", "fail"],
        }
        testdata = DataFrame(data)

        ax_numeric = testdata.plot(y="numeric")
        assert (
            ax_numeric.get_lines()[0].get_data()[1] == testdata["numeric"].values
        ).all()
        ax_timedelta = testdata.plot(y="timedelta")
        assert (
            ax_timedelta.get_lines()[0].get_data()[1] == testdata["timedelta"].values
        ).all()
        ax_datetime_no_tz = testdata.plot(y="datetime_no_tz")
        assert (
            ax_datetime_no_tz.get_lines()[0].get_data()[1]
            == testdata["datetime_no_tz"].values
        ).all()
        ax_datetime_all_tz = testdata.plot(y="datetime_all_tz")
        assert (
            ax_datetime_all_tz.get_lines()[0].get_data()[1]
            == testdata["datetime_all_tz"].values
        ).all()

        msg = "no numeric data to plot"
        with pytest.raises(TypeError, match=msg):
            testdata.plot(y="text")

    @pytest.mark.xfail(reason="not support for period, categorical, datetime_mixed_tz")
    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 = pd.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()

    @pytest.mark.slow
    def test_subplots_layout(self):
        # GH 6667
        df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))

        axes = df.plot(subplots=True, layout=(2, 2))
        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
        assert axes.shape == (2, 2)

        axes = df.plot(subplots=True, layout=(-1, 2))
        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
        assert axes.shape == (2, 2)

        axes = df.plot(subplots=True, layout=(2, -1))
        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
        assert axes.shape == (2, 2)

        axes = df.plot(subplots=True, layout=(1, 4))
        self._check_axes_shape(axes, axes_num=3, layout=(1, 4))
        assert axes.shape == (1, 4)

        axes = df.plot(subplots=True, layout=(-1, 4))
        self._check_axes_shape(axes, axes_num=3, layout=(1, 4))
        assert axes.shape == (1, 4)

        axes = df.plot(subplots=True, layout=(4, -1))
        self._check_axes_shape(axes, axes_num=3, layout=(4, 1))
        assert axes.shape == (4, 1)

        with pytest.raises(ValueError):
            df.plot(subplots=True, layout=(1, 1))
        with pytest.raises(ValueError):
            df.plot(subplots=True, layout=(-1, -1))

        # single column
        df = DataFrame(np.random.rand(10, 1), index=list(string.ascii_letters[:10]))
        axes = df.plot(subplots=True)
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
        assert axes.shape == (1,)

        axes = df.plot(subplots=True, layout=(3, 3))
        self._check_axes_shape(axes, axes_num=1, layout=(3, 3))
        assert axes.shape == (3, 3)

    @pytest.mark.slow
    def test_subplots_warnings(self):
        # GH 9464
        with tm.assert_produces_warning(None):
            df = DataFrame(np.random.randn(100, 4))
            df.plot(subplots=True, layout=(3, 2))

            df = DataFrame(
                np.random.randn(100, 4), index=date_range("1/1/2000", periods=100)
            )
            df.plot(subplots=True, layout=(3, 2))

    @pytest.mark.slow
    def test_subplots_multiple_axes(self):
        # GH 5353, 6970, GH 7069
        fig, axes = self.plt.subplots(2, 3)
        df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))

        returned = df.plot(subplots=True, ax=axes[0], sharex=False, sharey=False)
        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
        assert returned.shape == (3,)
        assert returned[0].figure is fig
        # draw on second row
        returned = df.plot(subplots=True, ax=axes[1], sharex=False, sharey=False)
        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
        assert returned.shape == (3,)
        assert returned[0].figure is fig
        self._check_axes_shape(axes, axes_num=6, layout=(2, 3))
        tm.close()

        with pytest.raises(ValueError):
            fig, axes = self.plt.subplots(2, 3)
            # pass different number of axes from required
            df.plot(subplots=True, ax=axes)

        # pass 2-dim axes and invalid layout
        # invalid lauout should not affect to input and return value
        # (show warning is tested in
        # TestDataFrameGroupByPlots.test_grouped_box_multiple_axes
        fig, axes = self.plt.subplots(2, 2)
        with warnings.catch_warnings():
            warnings.simplefilter("ignore", UserWarning)
            df = DataFrame(np.random.rand(10, 4), index=list(string.ascii_letters[:10]))

            returned = df.plot(
                subplots=True, ax=axes, layout=(2, 1), sharex=False, sharey=False
            )
            self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
            assert returned.shape == (4,)

            returned = df.plot(
                subplots=True, ax=axes, layout=(2, -1), sharex=False, sharey=False
            )
            self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
            assert returned.shape == (4,)

            returned = df.plot(
                subplots=True, ax=axes, layout=(-1, 2), sharex=False, sharey=False
            )
        self._check_axes_shape(returned, axes_num=4, layout=(2, 2))
        assert returned.shape == (4,)

        # single column
        fig, axes = self.plt.subplots(1, 1)
        df = DataFrame(np.random.rand(10, 1), index=list(string.ascii_letters[:10]))

        axes = df.plot(subplots=True, ax=[axes], sharex=False, sharey=False)
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
        assert axes.shape == (1,)

    def test_subplots_ts_share_axes(self):
        # GH 3964
        fig, axes = self.plt.subplots(3, 3, sharex=True, sharey=True)
        self.plt.subplots_adjust(left=0.05, right=0.95, hspace=0.3, wspace=0.3)
        df = DataFrame(
            np.random.randn(10, 9),
            index=date_range(start="2014-07-01", freq="M", periods=10),
        )
        for i, ax in enumerate(axes.ravel()):
            df[i].plot(ax=ax, fontsize=5)

        # Rows other than bottom should not be visible
        for ax in axes[0:-1].ravel():
            self._check_visible(ax.get_xticklabels(), visible=False)

        # Bottom row should be visible
        for ax in axes[-1].ravel():
            self._check_visible(ax.get_xticklabels(), visible=True)

        # First column should be visible
        for ax in axes[[0, 1, 2], [0]].ravel():
            self._check_visible(ax.get_yticklabels(), visible=True)

        # Other columns should not be visible
        for ax in axes[[0, 1, 2], [1]].ravel():
            self._check_visible(ax.get_yticklabels(), visible=False)
        for ax in axes[[0, 1, 2], [2]].ravel():
            self._check_visible(ax.get_yticklabels(), visible=False)

    def test_subplots_sharex_axes_existing_axes(self):
        # GH 9158
        d = {"A": [1.0, 2.0, 3.0, 4.0], "B": [4.0, 3.0, 2.0, 1.0], "C": [5, 1, 3, 4]}
        df = DataFrame(d, index=date_range("2014 10 11", "2014 10 14"))

        axes = df[["A", "B"]].plot(subplots=True)
        df["C"].plot(ax=axes[0], secondary_y=True)

        self._check_visible(axes[0].get_xticklabels(), visible=False)
        self._check_visible(axes[1].get_xticklabels(), visible=True)
        for ax in axes.ravel():
            self._check_visible(ax.get_yticklabels(), visible=True)

    @pytest.mark.slow
    def test_subplots_dup_columns(self):
        # GH 10962
        df = DataFrame(np.random.rand(5, 5), columns=list("aaaaa"))
        axes = df.plot(subplots=True)
        for ax in axes:
            self._check_legend_labels(ax, labels=["a"])
            assert len(ax.lines) == 1
        tm.close()

        axes = df.plot(subplots=True, secondary_y="a")
        for ax in axes:
            # (right) is only attached when subplots=False
            self._check_legend_labels(ax, labels=["a"])
            assert len(ax.lines) == 1
        tm.close()

        ax = df.plot(secondary_y="a")
        self._check_legend_labels(ax, labels=["a (right)"] * 5)
        assert len(ax.lines) == 0
        assert len(ax.right_ax.lines) == 5

    def test_negative_log(self):
        df = -DataFrame(
            rand(6, 4),
            index=list(string.ascii_letters[:6]),
            columns=["x", "y", "z", "four"],
        )

        with pytest.raises(ValueError):
            df.plot.area(logy=True)
        with pytest.raises(ValueError):
            df.plot.area(loglog=True)

    def _compare_stacked_y_cood(self, normal_lines, stacked_lines):
        base = np.zeros(len(normal_lines[0].get_data()[1]))
        for nl, sl in zip(normal_lines, stacked_lines):
            base += nl.get_data()[1]  # get y coordinates
            sy = sl.get_data()[1]
            tm.assert_numpy_array_equal(base, sy)

    def test_line_area_stacked(self):
        with tm.RNGContext(42):
            df = DataFrame(rand(6, 4), columns=["w", "x", "y", "z"])
            neg_df = -df
            # each column has either positive or negative value
            sep_df = DataFrame(
                {"w": rand(6), "x": rand(6), "y": -rand(6), "z": -rand(6)}
            )
            # each column has positive-negative mixed value
            mixed_df = DataFrame(
                randn(6, 4),
                index=list(string.ascii_letters[:6]),
                columns=["w", "x", "y", "z"],
            )

            for kind in ["line", "area"]:
                ax1 = _check_plot_works(df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(neg_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(neg_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines, ax2.lines)

                ax1 = _check_plot_works(sep_df.plot, kind=kind, stacked=False)
                ax2 = _check_plot_works(sep_df.plot, kind=kind, stacked=True)
                self._compare_stacked_y_cood(ax1.lines[:2], ax2.lines[:2])
                self._compare_stacked_y_cood(ax1.lines[2:], ax2.lines[2:])

                _check_plot_works(mixed_df.plot, stacked=False)
                with pytest.raises(ValueError):
                    mixed_df.plot(stacked=True)

                # Use an index with strictly positive values, preventing
                #  matplotlib from warning about ignoring xlim
                df2 = df.set_index(df.index + 1)
                _check_plot_works(df2.plot, kind=kind, logx=True, stacked=True)

    def test_line_area_nan_df(self):
        values1 = [1, 2, np.nan, 3]
        values2 = [3, np.nan, 2, 1]
        df = DataFrame({"a": values1, "b": values2})
        tdf = DataFrame({"a": values1, "b": values2}, index=tm.makeDateIndex(k=4))

        for d in [df, tdf]:
            ax = _check_plot_works(d.plot)
            masked1 = ax.lines[0].get_ydata()
            masked2 = ax.lines[1].get_ydata()
            # remove nan for comparison purpose

            exp = np.array([1, 2, 3], dtype=np.float64)
            tm.assert_numpy_array_equal(np.delete(masked1.data, 2), exp)

            exp = np.array([3, 2, 1], dtype=np.float64)
            tm.assert_numpy_array_equal(np.delete(masked2.data, 1), exp)
            tm.assert_numpy_array_equal(
                masked1.mask, np.array([False, False, True, False])
            )
            tm.assert_numpy_array_equal(
                masked2.mask, np.array([False, True, False, False])
            )

            expected1 = np.array([1, 2, 0, 3], dtype=np.float64)
            expected2 = np.array([3, 0, 2, 1], dtype=np.float64)

            ax = _check_plot_works(d.plot, stacked=True)
            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
            tm.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected1 + expected2)

            ax = _check_plot_works(d.plot.area)
            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
            tm.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected1 + expected2)

            ax = _check_plot_works(d.plot.area, stacked=False)
            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected1)
            tm.assert_numpy_array_equal(ax.lines[1].get_ydata(), expected2)

    def test_line_lim(self):
        df = DataFrame(rand(6, 3), columns=["x", "y", "z"])
        ax = df.plot()
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        assert xmin <= lines[0].get_data()[0][0]
        assert xmax >= lines[0].get_data()[0][-1]

        ax = df.plot(secondary_y=True)
        xmin, xmax = ax.get_xlim()
        lines = ax.get_lines()
        assert xmin <= lines[0].get_data()[0][0]
        assert xmax >= lines[0].get_data()[0][-1]

        axes = df.plot(secondary_y=True, subplots=True)
        self._check_axes_shape(axes, axes_num=3, layout=(3, 1))
        for ax in axes:
            assert hasattr(ax, "left_ax")
            assert not hasattr(ax, "right_ax")
            xmin, xmax = ax.get_xlim()
            lines = ax.get_lines()
            assert xmin <= lines[0].get_data()[0][0]
            assert xmax >= lines[0].get_data()[0][-1]

    def test_area_lim(self):
        df = DataFrame(rand(6, 4), columns=["x", "y", "z", "four"])

        neg_df = -df
        for stacked in [True, False]:
            ax = _check_plot_works(df.plot.area, stacked=stacked)
            xmin, xmax = ax.get_xlim()
            ymin, ymax = ax.get_ylim()
            lines = ax.get_lines()
            assert xmin <= lines[0].get_data()[0][0]
            assert xmax >= lines[0].get_data()[0][-1]
            assert ymin == 0

            ax = _check_plot_works(neg_df.plot.area, stacked=stacked)
            ymin, ymax = ax.get_ylim()
            assert ymax == 0

    @pytest.mark.slow
    def test_bar_colors(self):
        import matplotlib.pyplot as plt

        default_colors = self._unpack_cycler(plt.rcParams)

        df = DataFrame(randn(5, 5))
        ax = df.plot.bar()
        self._check_colors(ax.patches[::5], facecolors=default_colors[:5])
        tm.close()

        custom_colors = "rgcby"
        ax = df.plot.bar(color=custom_colors)
        self._check_colors(ax.patches[::5], facecolors=custom_colors)
        tm.close()

        from matplotlib import cm

        # Test str -> colormap functionality
        ax = df.plot.bar(colormap="jet")
        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, 5)]
        self._check_colors(ax.patches[::5], facecolors=rgba_colors)
        tm.close()

        # Test colormap functionality
        ax = df.plot.bar(colormap=cm.jet)
        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, 5)]
        self._check_colors(ax.patches[::5], facecolors=rgba_colors)
        tm.close()

        ax = df.loc[:, [0]].plot.bar(color="DodgerBlue")
        self._check_colors([ax.patches[0]], facecolors=["DodgerBlue"])
        tm.close()

        ax = df.plot(kind="bar", color="green")
        self._check_colors(ax.patches[::5], facecolors=["green"] * 5)
        tm.close()

    def test_bar_user_colors(self):
        df = pd.DataFrame(
            {"A": range(4), "B": range(1, 5), "color": ["red", "blue", "blue", "red"]}
        )
        # This should *only* work when `y` is specified, else
        # we use one color per column
        ax = df.plot.bar(y="A", color=df["color"])
        result = [p.get_facecolor() for p in ax.patches]
        expected = [
            (1.0, 0.0, 0.0, 1.0),
            (0.0, 0.0, 1.0, 1.0),
            (0.0, 0.0, 1.0, 1.0),
            (1.0, 0.0, 0.0, 1.0),
        ]
        assert result == expected

    @pytest.mark.slow
    def test_bar_linewidth(self):
        df = DataFrame(randn(5, 5))

        # regular
        ax = df.plot.bar(linewidth=2)
        for r in ax.patches:
            assert r.get_linewidth() == 2

        # stacked
        ax = df.plot.bar(stacked=True, linewidth=2)
        for r in ax.patches:
            assert r.get_linewidth() == 2

        # subplots
        axes = df.plot.bar(linewidth=2, subplots=True)
        self._check_axes_shape(axes, axes_num=5, layout=(5, 1))
        for ax in axes:
            for r in ax.patches:
                assert r.get_linewidth() == 2

    @pytest.mark.slow
    def test_bar_barwidth(self):
        df = DataFrame(randn(5, 5))

        width = 0.9

        # regular
        ax = df.plot.bar(width=width)
        for r in ax.patches:
            assert r.get_width() == width / len(df.columns)

        # stacked
        ax = df.plot.bar(stacked=True, width=width)
        for r in ax.patches:
            assert r.get_width() == width

        # horizontal regular
        ax = df.plot.barh(width=width)
        for r in ax.patches:
            assert r.get_height() == width / len(df.columns)

        # horizontal stacked
        ax = df.plot.barh(stacked=True, width=width)
        for r in ax.patches:
            assert r.get_height() == width

        # subplots
        axes = df.plot.bar(width=width, subplots=True)
        for ax in axes:
            for r in ax.patches:
                assert r.get_width() == width

        # horizontal subplots
        axes = df.plot.barh(width=width, subplots=True)
        for ax in axes:
            for r in ax.patches:
                assert r.get_height() == width

    @pytest.mark.slow
    def test_bar_barwidth_position(self):
        df = DataFrame(randn(5, 5))
        self._check_bar_alignment(
            df, kind="bar", stacked=False, width=0.9, position=0.2
        )
        self._check_bar_alignment(df, kind="bar", stacked=True, width=0.9, position=0.2)
        self._check_bar_alignment(
            df, kind="barh", stacked=False, width=0.9, position=0.2
        )
        self._check_bar_alignment(
            df, kind="barh", stacked=True, width=0.9, position=0.2
        )
        self._check_bar_alignment(
            df, kind="bar", subplots=True, width=0.9, position=0.2
        )
        self._check_bar_alignment(
            df, kind="barh", subplots=True, width=0.9, position=0.2
        )

    @pytest.mark.slow
    def test_bar_barwidth_position_int(self):
        # GH 12979
        df = DataFrame(randn(5, 5))

        for w in [1, 1.0]:
            ax = df.plot.bar(stacked=True, width=w)
            ticks = ax.xaxis.get_ticklocs()
            tm.assert_numpy_array_equal(ticks, np.array([0, 1, 2, 3, 4]))
            assert ax.get_xlim() == (-0.75, 4.75)
            # check left-edge of bars
            assert ax.patches[0].get_x() == -0.5
            assert ax.patches[-1].get_x() == 3.5

        self._check_bar_alignment(df, kind="bar", stacked=True, width=1)
        self._check_bar_alignment(df, kind="barh", stacked=False, width=1)
        self._check_bar_alignment(df, kind="barh", stacked=True, width=1)
        self._check_bar_alignment(df, kind="bar", subplots=True, width=1)
        self._check_bar_alignment(df, kind="barh", subplots=True, width=1)

    @pytest.mark.slow
    def test_bar_bottom_left(self):
        df = DataFrame(rand(5, 5))
        ax = df.plot.bar(stacked=False, bottom=1)
        result = [p.get_y() for p in ax.patches]
        assert result == [1] * 25

        ax = df.plot.bar(stacked=True, bottom=[-1, -2, -3, -4, -5])
        result = [p.get_y() for p in ax.patches[:5]]
        assert result == [-1, -2, -3, -4, -5]

        ax = df.plot.barh(stacked=False, left=np.array([1, 1, 1, 1, 1]))
        result = [p.get_x() for p in ax.patches]
        assert result == [1] * 25

        ax = df.plot.barh(stacked=True, left=[1, 2, 3, 4, 5])
        result = [p.get_x() for p in ax.patches[:5]]
        assert result == [1, 2, 3, 4, 5]

        axes = df.plot.bar(subplots=True, bottom=-1)
        for ax in axes:
            result = [p.get_y() for p in ax.patches]
            assert result == [-1] * 5

        axes = df.plot.barh(subplots=True, left=np.array([1, 1, 1, 1, 1]))
        for ax in axes:
            result = [p.get_x() for p in ax.patches]
            assert result == [1] * 5

    @pytest.mark.slow
    def test_bar_nan(self):
        df = DataFrame({"A": [10, np.nan, 20], "B": [5, 10, 20], "C": [1, 2, 3]})
        ax = df.plot.bar()
        expected = [10, 0, 20, 5, 10, 20, 1, 2, 3]
        result = [p.get_height() for p in ax.patches]
        assert result == expected

        ax = df.plot.bar(stacked=True)
        result = [p.get_height() for p in ax.patches]
        assert result == expected

        result = [p.get_y() for p in ax.patches]
        expected = [0.0, 0.0, 0.0, 10.0, 0.0, 20.0, 15.0, 10.0, 40.0]
        assert result == expected

    @pytest.mark.slow
    def test_bar_categorical(self):
        # GH 13019
        df1 = pd.DataFrame(
            np.random.randn(6, 5),
            index=pd.Index(list("ABCDEF")),
            columns=pd.Index(list("abcde")),
        )
        # categorical index must behave the same
        df2 = pd.DataFrame(
            np.random.randn(6, 5),
            index=pd.CategoricalIndex(list("ABCDEF")),
            columns=pd.CategoricalIndex(list("abcde")),
        )

        for df in [df1, df2]:
            ax = df.plot.bar()
            ticks = ax.xaxis.get_ticklocs()
            tm.assert_numpy_array_equal(ticks, np.array([0, 1, 2, 3, 4, 5]))
            assert ax.get_xlim() == (-0.5, 5.5)
            # check left-edge of bars
            assert ax.patches[0].get_x() == -0.25
            assert ax.patches[-1].get_x() == 5.15

            ax = df.plot.bar(stacked=True)
            tm.assert_numpy_array_equal(ticks, np.array([0, 1, 2, 3, 4, 5]))
            assert ax.get_xlim() == (-0.5, 5.5)
            assert ax.patches[0].get_x() == -0.25
            assert ax.patches[-1].get_x() == 4.75

    @pytest.mark.slow
    def test_plot_scatter(self):
        df = DataFrame(
            randn(6, 4),
            index=list(string.ascii_letters[:6]),
            columns=["x", "y", "z", "four"],
        )

        _check_plot_works(df.plot.scatter, x="x", y="y")
        _check_plot_works(df.plot.scatter, x=1, y=2)

        with pytest.raises(TypeError):
            df.plot.scatter(x="x")
        with pytest.raises(TypeError):
            df.plot.scatter(y="y")

        # GH 6951
        axes = df.plot(x="x", y="y", kind="scatter", subplots=True)
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

    def test_raise_error_on_datetime_time_data(self):
        # GH 8113, datetime.time type is not supported by matplotlib in scatter
        df = pd.DataFrame(np.random.randn(10), columns=["a"])
        df["dtime"] = pd.date_range(start="2014-01-01", freq="h", periods=10).time
        msg = "must be a string or a number, not 'datetime.time'"

        with pytest.raises(TypeError, match=msg):
            df.plot(kind="scatter", x="dtime", y="a")

    def test_scatterplot_datetime_data(self):
        # GH 30391
        dates = pd.date_range(start=date(2019, 1, 1), periods=12, freq="W")
        vals = np.random.normal(0, 1, len(dates))
        df = pd.DataFrame({"dates": dates, "vals": vals})

        _check_plot_works(df.plot.scatter, x="dates", y="vals")
        _check_plot_works(df.plot.scatter, x=0, y=1)

    def test_scatterplot_object_data(self):
        # GH 18755
        df = pd.DataFrame(dict(a=["A", "B", "C"], b=[2, 3, 4]))

        _check_plot_works(df.plot.scatter, x="a", y="b")
        _check_plot_works(df.plot.scatter, x=0, y=1)

        df = pd.DataFrame(dict(a=["A", "B", "C"], b=["a", "b", "c"]))

        _check_plot_works(df.plot.scatter, x="a", y="b")
        _check_plot_works(df.plot.scatter, x=0, y=1)

    @pytest.mark.slow
    def test_if_scatterplot_colorbar_affects_xaxis_visibility(self):
        # addressing issue #10611, to ensure colobar does not
        # interfere with x-axis label and ticklabels with
        # ipython inline backend.
        random_array = np.random.random((1000, 3))
        df = pd.DataFrame(random_array, columns=["A label", "B label", "C label"])

        ax1 = df.plot.scatter(x="A label", y="B label")
        ax2 = df.plot.scatter(x="A label", y="B label", c="C label")

        vis1 = [vis.get_visible() for vis in ax1.xaxis.get_minorticklabels()]
        vis2 = [vis.get_visible() for vis in ax2.xaxis.get_minorticklabels()]
        assert vis1 == vis2

        vis1 = [vis.get_visible() for vis in ax1.xaxis.get_majorticklabels()]
        vis2 = [vis.get_visible() for vis in ax2.xaxis.get_majorticklabels()]
        assert vis1 == vis2

        assert (
            ax1.xaxis.get_label().get_visible() == ax2.xaxis.get_label().get_visible()
        )

    @pytest.mark.slow
    def test_if_hexbin_xaxis_label_is_visible(self):
        # addressing issue #10678, to ensure colobar does not
        # interfere with x-axis label and ticklabels with
        # ipython inline backend.
        random_array = np.random.random((1000, 3))
        df = pd.DataFrame(random_array, columns=["A label", "B label", "C label"])

        ax = df.plot.hexbin("A label", "B label", gridsize=12)
        assert all(vis.get_visible() for vis in ax.xaxis.get_minorticklabels())
        assert all(vis.get_visible() for vis in ax.xaxis.get_majorticklabels())
        assert ax.xaxis.get_label().get_visible()

    @pytest.mark.slow
    def test_if_scatterplot_colorbars_are_next_to_parent_axes(self):
        import matplotlib.pyplot as plt

        random_array = np.random.random((1000, 3))
        df = pd.DataFrame(random_array, columns=["A label", "B label", "C label"])

        fig, axes = plt.subplots(1, 2)
        df.plot.scatter("A label", "B label", c="C label", ax=axes[0])
        df.plot.scatter("A label", "B label", c="C label", ax=axes[1])
        plt.tight_layout()

        points = np.array([ax.get_position().get_points() for ax in fig.axes])
        axes_x_coords = points[:, :, 0]
        parent_distance = axes_x_coords[1, :] - axes_x_coords[0, :]
        colorbar_distance = axes_x_coords[3, :] - axes_x_coords[2, :]
        assert np.isclose(parent_distance, colorbar_distance, atol=1e-7).all()

    @pytest.mark.parametrize("x, y", [("x", "y"), ("y", "x"), ("y", "y")])
    @pytest.mark.slow
    def test_plot_scatter_with_categorical_data(self, x, y):
        # after fixing GH 18755, should be able to plot categorical data
        df = pd.DataFrame(
            {"x": [1, 2, 3, 4], "y": pd.Categorical(["a", "b", "a", "c"])}
        )

        _check_plot_works(df.plot.scatter, x=x, y=y)

    @pytest.mark.slow
    def test_plot_scatter_with_c(self):
        df = DataFrame(
            randn(6, 4),
            index=list(string.ascii_letters[:6]),
            columns=["x", "y", "z", "four"],
        )

        axes = [df.plot.scatter(x="x", y="y", c="z"), df.plot.scatter(x=0, y=1, c=2)]
        for ax in axes:
            # default to Greys
            assert ax.collections[0].cmap.name == "Greys"

            # n.b. there appears to be no public method
            # to get the colorbar label
            assert ax.collections[0].colorbar._label == "z"

        cm = "cubehelix"
        ax = df.plot.scatter(x="x", y="y", c="z", colormap=cm)
        assert ax.collections[0].cmap.name == cm

        # verify turning off colorbar works
        ax = df.plot.scatter(x="x", y="y", c="z", colorbar=False)
        assert ax.collections[0].colorbar is None

        # verify that we can still plot a solid color
        ax = df.plot.scatter(x=0, y=1, c="red")
        assert ax.collections[0].colorbar is None
        self._check_colors(ax.collections, facecolors=["r"])

        # Ensure that we can pass an np.array straight through to matplotlib,
        # this functionality was accidentally removed previously.
        # See https://github.com/pandas-dev/pandas/issues/8852 for bug report
        #
        # Exercise colormap path and non-colormap path as they are independent
        #
        df = DataFrame({"A": [1, 2], "B": [3, 4]})
        red_rgba = [1.0, 0.0, 0.0, 1.0]
        green_rgba = [0.0, 1.0, 0.0, 1.0]
        rgba_array = np.array([red_rgba, green_rgba])
        ax = df.plot.scatter(x="A", y="B", c=rgba_array)
        # expect the face colors of the points in the non-colormap path to be
        # identical to the values we supplied, normally we'd be on shaky ground
        # comparing floats for equality but here we expect them to be
        # identical.
        tm.assert_numpy_array_equal(ax.collections[0].get_facecolor(), rgba_array)
        # we don't test the colors of the faces in this next plot because they
        # are dependent on the spring colormap, which may change its colors
        # later.
        float_array = np.array([0.0, 1.0])
        df.plot.scatter(x="A", y="B", c=float_array, cmap="spring")

    @pytest.mark.parametrize("cmap", [None, "Greys"])
    def test_scatter_with_c_column_name_with_colors(self, cmap):
        # https://github.com/pandas-dev/pandas/issues/34316
        df = pd.DataFrame(
            [[5.1, 3.5], [4.9, 3.0], [7.0, 3.2], [6.4, 3.2], [5.9, 3.0]],
            columns=["length", "width"],
        )
        df["species"] = ["r", "r", "g", "g", "b"]
        ax = df.plot.scatter(x=0, y=1, c="species", cmap=cmap)
        assert ax.collections[0].colorbar is None

    def test_plot_scatter_with_s(self):
        # this refers to GH 32904
        df = DataFrame(np.random.random((10, 3)) * 100, columns=["a", "b", "c"],)

        ax = df.plot.scatter(x="a", y="b", s="c")
        tm.assert_numpy_array_equal(df["c"].values, right=ax.collections[0].get_sizes())

    def test_scatter_colors(self):
        df = DataFrame({"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
        with pytest.raises(TypeError):
            df.plot.scatter(x="a", y="b", c="c", color="green")

        default_colors = self._unpack_cycler(self.plt.rcParams)

        ax = df.plot.scatter(x="a", y="b", c="c")
        tm.assert_numpy_array_equal(
            ax.collections[0].get_facecolor()[0],
            np.array(self.colorconverter.to_rgba(default_colors[0])),
        )

        ax = df.plot.scatter(x="a", y="b", color="white")
        tm.assert_numpy_array_equal(
            ax.collections[0].get_facecolor()[0],
            np.array([1, 1, 1, 1], dtype=np.float64),
        )

    def test_scatter_colorbar_different_cmap(self):
        # GH 33389
        import matplotlib.pyplot as plt

        df = pd.DataFrame({"x": [1, 2, 3], "y": [1, 3, 2], "c": [1, 2, 3]})
        df["x2"] = df["x"] + 1

        fig, ax = plt.subplots()
        df.plot("x", "y", c="c", kind="scatter", cmap="cividis", ax=ax)
        df.plot("x2", "y", c="c", kind="scatter", cmap="magma", ax=ax)

        assert ax.collections[0].cmap.name == "cividis"
        assert ax.collections[1].cmap.name == "magma"

    @pytest.mark.slow
    def test_plot_bar(self):
        df = DataFrame(
            randn(6, 4),
            index=list(string.ascii_letters[:6]),
            columns=["one", "two", "three", "four"],
        )

        _check_plot_works(df.plot.bar)
        _check_plot_works(df.plot.bar, legend=False)
        # _check_plot_works adds an ax so catch warning. see GH #13188
        with tm.assert_produces_warning(UserWarning):
            _check_plot_works(df.plot.bar, subplots=True)
        _check_plot_works(df.plot.bar, stacked=True)

        df = DataFrame(
            randn(10, 15), index=list(string.ascii_letters[:10]), columns=range(15)
        )
        _check_plot_works(df.plot.bar)

        df = DataFrame({"a": [0, 1], "b": [1, 0]})
        ax = _check_plot_works(df.plot.bar)
        self._check_ticks_props(ax, xrot=90)

        ax = df.plot.bar(rot=35, fontsize=10)
        self._check_ticks_props(ax, xrot=35, xlabelsize=10, ylabelsize=10)

        ax = _check_plot_works(df.plot.barh)
        self._check_ticks_props(ax, yrot=0)

        ax = df.plot.barh(rot=55, fontsize=11)
        self._check_ticks_props(ax, yrot=55, ylabelsize=11, xlabelsize=11)

    def _check_bar_alignment(
        self,
        df,
        kind="bar",
        stacked=False,
        subplots=False,
        align="center",
        width=0.5,
        position=0.5,
    ):

        axes = df.plot(
            kind=kind,
            stacked=stacked,
            subplots=subplots,
            align=align,
            width=width,
            position=position,
            grid=True,
        )

        axes = self._flatten_visible(axes)

        for ax in axes:
            if kind == "bar":
                axis = ax.xaxis
                ax_min, ax_max = ax.get_xlim()
                min_edge = min(p.get_x() for p in ax.patches)
                max_edge = max(p.get_x() + p.get_width() for p in ax.patches)
            elif kind == "barh":
                axis = ax.yaxis
                ax_min, ax_max = ax.get_ylim()
                min_edge = min(p.get_y() for p in ax.patches)
                max_edge = max(p.get_y() + p.get_height() for p in ax.patches)
            else:
                raise ValueError

            # GH 7498
            # compare margins between lim and bar edges
            tm.assert_almost_equal(ax_min, min_edge - 0.25)
            tm.assert_almost_equal(ax_max, max_edge + 0.25)

            p = ax.patches[0]
            if kind == "bar" and (stacked is True or subplots is True):
                edge = p.get_x()
                center = edge + p.get_width() * position
            elif kind == "bar" and stacked is False:
                center = p.get_x() + p.get_width() * len(df.columns) * position
                edge = p.get_x()
            elif kind == "barh" and (stacked is True or subplots is True):
                center = p.get_y() + p.get_height() * position
                edge = p.get_y()
            elif kind == "barh" and stacked is False:
                center = p.get_y() + p.get_height() * len(df.columns) * position
                edge = p.get_y()
            else:
                raise ValueError

            # Check the ticks locates on integer
            assert (axis.get_ticklocs() == np.arange(len(df))).all()

            if align == "center":
                # Check whether the bar locates on center
                tm.assert_almost_equal(axis.get_ticklocs()[0], center)
            elif align == "edge":
                # Check whether the bar's edge starts from the tick
                tm.assert_almost_equal(axis.get_ticklocs()[0], edge)
            else:
                raise ValueError

        return axes

    @pytest.mark.slow
    def test_bar_stacked_center(self):
        # GH2157
        df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))
        self._check_bar_alignment(df, kind="bar", stacked=True)
        self._check_bar_alignment(df, kind="bar", stacked=True, width=0.9)
        self._check_bar_alignment(df, kind="barh", stacked=True)
        self._check_bar_alignment(df, kind="barh", stacked=True, width=0.9)

    @pytest.mark.slow
    def test_bar_center(self):
        df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))
        self._check_bar_alignment(df, kind="bar", stacked=False)
        self._check_bar_alignment(df, kind="bar", stacked=False, width=0.9)
        self._check_bar_alignment(df, kind="barh", stacked=False)
        self._check_bar_alignment(df, kind="barh", stacked=False, width=0.9)

    @pytest.mark.slow
    def test_bar_subplots_center(self):
        df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))
        self._check_bar_alignment(df, kind="bar", subplots=True)
        self._check_bar_alignment(df, kind="bar", subplots=True, width=0.9)
        self._check_bar_alignment(df, kind="barh", subplots=True)
        self._check_bar_alignment(df, kind="barh", subplots=True, width=0.9)

    @pytest.mark.slow
    def test_bar_align_single_column(self):
        df = DataFrame(randn(5))
        self._check_bar_alignment(df, kind="bar", stacked=False)
        self._check_bar_alignment(df, kind="bar", stacked=True)
        self._check_bar_alignment(df, kind="barh", stacked=False)
        self._check_bar_alignment(df, kind="barh", stacked=True)
        self._check_bar_alignment(df, kind="bar", subplots=True)
        self._check_bar_alignment(df, kind="barh", subplots=True)

    @pytest.mark.slow
    def test_bar_edge(self):
        df = DataFrame({"A": [3] * 5, "B": list(range(5))}, index=range(5))

        self._check_bar_alignment(df, kind="bar", stacked=True, align="edge")
        self._check_bar_alignment(df, kind="bar", stacked=True, width=0.9, align="edge")
        self._check_bar_alignment(df, kind="barh", stacked=True, align="edge")
        self._check_bar_alignment(
            df, kind="barh", stacked=True, width=0.9, align="edge"
        )

        self._check_bar_alignment(df, kind="bar", stacked=False, align="edge")
        self._check_bar_alignment(
            df, kind="bar", stacked=False, width=0.9, align="edge"
        )
        self._check_bar_alignment(df, kind="barh", stacked=False, align="edge")
        self._check_bar_alignment(
            df, kind="barh", stacked=False, width=0.9, align="edge"
        )

        self._check_bar_alignment(df, kind="bar", subplots=True, align="edge")
        self._check_bar_alignment(
            df, kind="bar", subplots=True, width=0.9, align="edge"
        )
        self._check_bar_alignment(df, kind="barh", subplots=True, align="edge")
        self._check_bar_alignment(
            df, kind="barh", subplots=True, width=0.9, align="edge"
        )

    @pytest.mark.slow
    def test_bar_log_no_subplots(self):
        # GH3254, GH3298 matplotlib/matplotlib#1882, #1892
        # regressions in 1.2.1
        expected = np.array([0.1, 1.0, 10.0, 100])

        # no subplots
        df = DataFrame({"A": [3] * 5, "B": list(range(1, 6))}, index=range(5))
        ax = df.plot.bar(grid=True, log=True)
        tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)

    @pytest.mark.slow
    def test_bar_log_subplots(self):
        expected = np.array([0.1, 1.0, 10.0, 100.0, 1000.0, 1e4])

        ax = DataFrame([Series([200, 300]), Series([300, 500])]).plot.bar(
            log=True, subplots=True
        )

        tm.assert_numpy_array_equal(ax[0].yaxis.get_ticklocs(), expected)
        tm.assert_numpy_array_equal(ax[1].yaxis.get_ticklocs(), expected)

    @pytest.mark.slow
    def test_boxplot(self):
        df = self.hist_df
        series = df["height"]
        numeric_cols = df._get_numeric_data().columns
        labels = [pprint_thing(c) for c in numeric_cols]

        ax = _check_plot_works(df.plot.box)
        self._check_text_labels(ax.get_xticklabels(), labels)
        tm.assert_numpy_array_equal(
            ax.xaxis.get_ticklocs(), np.arange(1, len(numeric_cols) + 1)
        )
        assert len(ax.lines) == self.bp_n_objects * len(numeric_cols)
        tm.close()

        axes = series.plot.box(rot=40)
        self._check_ticks_props(axes, xrot=40, yrot=0)
        tm.close()

        ax = _check_plot_works(series.plot.box)

        positions = np.array([1, 6, 7])
        ax = df.plot.box(positions=positions)
        numeric_cols = df._get_numeric_data().columns
        labels = [pprint_thing(c) for c in numeric_cols]
        self._check_text_labels(ax.get_xticklabels(), labels)
        tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), positions)
        assert len(ax.lines) == self.bp_n_objects * len(numeric_cols)

    @pytest.mark.slow
    def test_boxplot_vertical(self):
        df = self.hist_df
        numeric_cols = df._get_numeric_data().columns
        labels = [pprint_thing(c) for c in numeric_cols]

        # if horizontal, yticklabels are rotated
        ax = df.plot.box(rot=50, fontsize=8, vert=False)
        self._check_ticks_props(ax, xrot=0, yrot=50, ylabelsize=8)
        self._check_text_labels(ax.get_yticklabels(), labels)
        assert len(ax.lines) == self.bp_n_objects * len(numeric_cols)

        # _check_plot_works adds an ax so catch warning. see GH #13188
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(df.plot.box, subplots=True, vert=False, logx=True)
        self._check_axes_shape(axes, axes_num=3, layout=(1, 3))
        self._check_ax_scales(axes, xaxis="log")
        for ax, label in zip(axes, labels):
            self._check_text_labels(ax.get_yticklabels(), [label])
            assert len(ax.lines) == self.bp_n_objects

        positions = np.array([3, 2, 8])
        ax = df.plot.box(positions=positions, vert=False)
        self._check_text_labels(ax.get_yticklabels(), labels)
        tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), positions)
        assert len(ax.lines) == self.bp_n_objects * len(numeric_cols)

    @pytest.mark.slow
    def test_boxplot_return_type(self):
        df = DataFrame(
            randn(6, 4),
            index=list(string.ascii_letters[:6]),
            columns=["one", "two", "three", "four"],
        )
        with pytest.raises(ValueError):
            df.plot.box(return_type="NOTATYPE")

        result = df.plot.box(return_type="dict")
        self._check_box_return_type(result, "dict")

        result = df.plot.box(return_type="axes")
        self._check_box_return_type(result, "axes")

        result = df.plot.box()  # default axes
        self._check_box_return_type(result, "axes")

        result = df.plot.box(return_type="both")
        self._check_box_return_type(result, "both")

    @pytest.mark.slow
    def test_boxplot_subplots_return_type(self):
        df = self.hist_df

        # normal style: return_type=None
        result = df.plot.box(subplots=True)
        assert isinstance(result, Series)
        self._check_box_return_type(
            result, None, expected_keys=["height", "weight", "category"]
        )

        for t in ["dict", "axes", "both"]:
            returned = df.plot.box(return_type=t, subplots=True)
            self._check_box_return_type(
                returned,
                t,
                expected_keys=["height", "weight", "category"],
                check_ax_title=False,
            )

    @pytest.mark.slow
    @td.skip_if_no_scipy
    def test_kde_df(self):
        df = DataFrame(randn(100, 4))
        ax = _check_plot_works(df.plot, kind="kde")
        expected = [pprint_thing(c) for c in df.columns]
        self._check_legend_labels(ax, labels=expected)
        self._check_ticks_props(ax, xrot=0)

        ax = df.plot(kind="kde", rot=20, fontsize=5)
        self._check_ticks_props(ax, xrot=20, xlabelsize=5, ylabelsize=5)

        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(df.plot, kind="kde", subplots=True)
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))

        axes = df.plot(kind="kde", logy=True, subplots=True)
        self._check_ax_scales(axes, yaxis="log")

    @pytest.mark.slow
    @td.skip_if_no_scipy
    def test_kde_missing_vals(self):
        df = DataFrame(np.random.uniform(size=(100, 4)))
        df.loc[0, 0] = np.nan
        _check_plot_works(df.plot, kind="kde")

    @pytest.mark.slow
    def test_hist_df(self):
        from matplotlib.patches import Rectangle

        df = DataFrame(randn(100, 4))
        series = df[0]

        ax = _check_plot_works(df.plot.hist)
        expected = [pprint_thing(c) for c in df.columns]
        self._check_legend_labels(ax, labels=expected)

        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(df.plot.hist, subplots=True, logy=True)
        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
        self._check_ax_scales(axes, yaxis="log")

        axes = series.plot.hist(rot=40)
        self._check_ticks_props(axes, xrot=40, yrot=0)
        tm.close()

        ax = series.plot.hist(cumulative=True, bins=4, density=True)
        # height of last bin (index 5) must be 1.0
        rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
        tm.assert_almost_equal(rects[-1].get_height(), 1.0)
        tm.close()

        ax = series.plot.hist(cumulative=True, bins=4)
        rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]

        tm.assert_almost_equal(rects[-2].get_height(), 100.0)
        tm.close()

        # if horizontal, yticklabels are rotated
        axes = df.plot.hist(rot=50, fontsize=8, orientation="horizontal")
        self._check_ticks_props(axes, xrot=0, yrot=50, ylabelsize=8)

    @pytest.mark.parametrize(
        "weights", [0.1 * np.ones(shape=(100,)), 0.1 * np.ones(shape=(100, 2))]
    )
    def test_hist_weights(self, weights):
        # GH 33173
        np.random.seed(0)
        df = pd.DataFrame(dict(zip(["A", "B"], np.random.randn(2, 100,))))

        ax1 = _check_plot_works(df.plot, kind="hist", weights=weights)
        ax2 = _check_plot_works(df.plot, kind="hist")

        patch_height_with_weights = [patch.get_height() for patch in ax1.patches]

        # original heights with no weights, and we manually multiply with example
        # weights, so after multiplication, they should be almost same
        expected_patch_height = [0.1 * patch.get_height() for patch in ax2.patches]

        tm.assert_almost_equal(patch_height_with_weights, expected_patch_height)

    def _check_box_coord(
        self,
        patches,
        expected_y=None,
        expected_h=None,
        expected_x=None,
        expected_w=None,
    ):
        result_y = np.array([p.get_y() for p in patches])
        result_height = np.array([p.get_height() for p in patches])
        result_x = np.array([p.get_x() for p in patches])
        result_width = np.array([p.get_width() for p in patches])
        # dtype is depending on above values, no need to check

        if expected_y is not None:
            tm.assert_numpy_array_equal(result_y, expected_y, check_dtype=False)
        if expected_h is not None:
            tm.assert_numpy_array_equal(result_height, expected_h, check_dtype=False)
        if expected_x is not None:
            tm.assert_numpy_array_equal(result_x, expected_x, check_dtype=False)
        if expected_w is not None:
            tm.assert_numpy_array_equal(result_width, expected_w, check_dtype=False)

    @pytest.mark.slow
    def test_hist_df_coord(self):
        normal_df = DataFrame(
            {
                "A": np.repeat(np.array([1, 2, 3, 4, 5]), np.array([10, 9, 8, 7, 6])),
                "B": np.repeat(np.array([1, 2, 3, 4, 5]), np.array([8, 8, 8, 8, 8])),
                "C": np.repeat(np.array([1, 2, 3, 4, 5]), np.array([6, 7, 8, 9, 10])),
            },
            columns=["A", "B", "C"],
        )

        nan_df = DataFrame(
            {
                "A": np.repeat(
                    np.array([np.nan, 1, 2, 3, 4, 5]), np.array([3, 10, 9, 8, 7, 6])
                ),
                "B": np.repeat(
                    np.array([1, np.nan, 2, 3, 4, 5]), np.array([8, 3, 8, 8, 8, 8])
                ),
                "C": np.repeat(
                    np.array([1, 2, 3, np.nan, 4, 5]), np.array([6, 7, 8, 3, 9, 10])
                ),
            },
            columns=["A", "B", "C"],
        )

        for df in [normal_df, nan_df]:
            ax = df.plot.hist(bins=5)
            self._check_box_coord(
                ax.patches[:5],
                expected_y=np.array([0, 0, 0, 0, 0]),
                expected_h=np.array([10, 9, 8, 7, 6]),
            )
            self._check_box_coord(
                ax.patches[5:10],
                expected_y=np.array([0, 0, 0, 0, 0]),
                expected_h=np.array([8, 8, 8, 8, 8]),
            )
            self._check_box_coord(
                ax.patches[10:],
                expected_y=np.array([0, 0, 0, 0, 0]),
                expected_h=np.array([6, 7, 8, 9, 10]),
            )

            ax = df.plot.hist(bins=5, stacked=True)
            self._check_box_coord(
                ax.patches[:5],
                expected_y=np.array([0, 0, 0, 0, 0]),
                expected_h=np.array([10, 9, 8, 7, 6]),
            )
            self._check_box_coord(
                ax.patches[5:10],
                expected_y=np.array([10, 9, 8, 7, 6]),
                expected_h=np.array([8, 8, 8, 8, 8]),
            )
            self._check_box_coord(
                ax.patches[10:],
                expected_y=np.array([18, 17, 16, 15, 14]),
                expected_h=np.array([6, 7, 8, 9, 10]),
            )

            axes = df.plot.hist(bins=5, stacked=True, subplots=True)
            self._check_box_coord(
                axes[0].patches,
                expected_y=np.array([0, 0, 0, 0, 0]),
                expected_h=np.array([10, 9, 8, 7, 6]),
            )
            self._check_box_coord(
                axes[1].patches,
                expected_y=np.array([0, 0, 0, 0, 0]),
                expected_h=np.array([8, 8, 8, 8, 8]),
            )
            self._check_box_coord(
                axes[2].patches,
                expected_y=np.array([0, 0, 0, 0, 0]),
                expected_h=np.array([6, 7, 8, 9, 10]),
            )

            # horizontal
            ax = df.plot.hist(bins=5, orientation="horizontal")
            self._check_box_coord(
                ax.patches[:5],
                expected_x=np.array([0, 0, 0, 0, 0]),
                expected_w=np.array([10, 9, 8, 7, 6]),
            )
            self._check_box_coord(
                ax.patches[5:10],
                expected_x=np.array([0, 0, 0, 0, 0]),
                expected_w=np.array([8, 8, 8, 8, 8]),
            )
            self._check_box_coord(
                ax.patches[10:],
                expected_x=np.array([0, 0, 0, 0, 0]),
                expected_w=np.array([6, 7, 8, 9, 10]),
            )

            ax = df.plot.hist(bins=5, stacked=True, orientation="horizontal")
            self._check_box_coord(
                ax.patches[:5],
                expected_x=np.array([0, 0, 0, 0, 0]),
                expected_w=np.array([10, 9, 8, 7, 6]),
            )
            self._check_box_coord(
                ax.patches[5:10],
                expected_x=np.array([10, 9, 8, 7, 6]),
                expected_w=np.array([8, 8, 8, 8, 8]),
            )
            self._check_box_coord(
                ax.patches[10:],
                expected_x=np.array([18, 17, 16, 15, 14]),
                expected_w=np.array([6, 7, 8, 9, 10]),
            )

            axes = df.plot.hist(
                bins=5, stacked=True, subplots=True, orientation="horizontal"
            )
            self._check_box_coord(
                axes[0].patches,
                expected_x=np.array([0, 0, 0, 0, 0]),
                expected_w=np.array([10, 9, 8, 7, 6]),
            )
            self._check_box_coord(
                axes[1].patches,
                expected_x=np.array([0, 0, 0, 0, 0]),
                expected_w=np.array([8, 8, 8, 8, 8]),
            )
            self._check_box_coord(
                axes[2].patches,
                expected_x=np.array([0, 0, 0, 0, 0]),
                expected_w=np.array([6, 7, 8, 9, 10]),
            )

    @pytest.mark.slow
    def test_plot_int_columns(self):
        df = DataFrame(randn(100, 4)).cumsum()
        _check_plot_works(df.plot, legend=True)

    @pytest.mark.slow
    def test_df_legend_labels(self):
        kinds = ["line", "bar", "barh", "kde", "area", "hist"]
        df = DataFrame(rand(3, 3), columns=["a", "b", "c"])
        df2 = DataFrame(rand(3, 3), columns=["d", "e", "f"])
        df3 = DataFrame(rand(3, 3), columns=["g", "h", "i"])
        df4 = DataFrame(rand(3, 3), columns=["j", "k", "l"])

        for kind in kinds:

            ax = df.plot(kind=kind, legend=True)
            self._check_legend_labels(ax, labels=df.columns)

            ax = df2.plot(kind=kind, legend=False, ax=ax)
            self._check_legend_labels(ax, labels=df.columns)

            ax = df3.plot(kind=kind, legend=True, ax=ax)
            self._check_legend_labels(ax, labels=df.columns.union(df3.columns))

            ax = df4.plot(kind=kind, legend="reverse", ax=ax)
            expected = list(df.columns.union(df3.columns)) + list(reversed(df4.columns))
            self._check_legend_labels(ax, labels=expected)

        # Secondary Y
        ax = df.plot(legend=True, secondary_y="b")
        self._check_legend_labels(ax, labels=["a", "b (right)", "c"])
        ax = df2.plot(legend=False, ax=ax)
        self._check_legend_labels(ax, labels=["a", "b (right)", "c"])
        ax = df3.plot(kind="bar", legend=True, secondary_y="h", ax=ax)
        self._check_legend_labels(
            ax, labels=["a", "b (right)", "c", "g", "h (right)", "i"]
        )

        # Time Series
        ind = date_range("1/1/2014", periods=3)
        df = DataFrame(randn(3, 3), columns=["a", "b", "c"], index=ind)
        df2 = DataFrame(randn(3, 3), columns=["d", "e", "f"], index=ind)
        df3 = DataFrame(randn(3, 3), columns=["g", "h", "i"], index=ind)
        ax = df.plot(legend=True, secondary_y="b")
        self._check_legend_labels(ax, labels=["a", "b (right)", "c"])
        ax = df2.plot(legend=False, ax=ax)
        self._check_legend_labels(ax, labels=["a", "b (right)", "c"])
        ax = df3.plot(legend=True, ax=ax)
        self._check_legend_labels(ax, labels=["a", "b (right)", "c", "g", "h", "i"])

        # scatter
        ax = df.plot.scatter(x="a", y="b", label="data1")
        self._check_legend_labels(ax, labels=["data1"])
        ax = df2.plot.scatter(x="d", y="e", legend=False, label="data2", ax=ax)
        self._check_legend_labels(ax, labels=["data1"])
        ax = df3.plot.scatter(x="g", y="h", label="data3", ax=ax)
        self._check_legend_labels(ax, labels=["data1", "data3"])

        # ensure label args pass through and
        # index name does not mutate
        # column names don't mutate
        df5 = df.set_index("a")
        ax = df5.plot(y="b")
        self._check_legend_labels(ax, labels=["b"])
        ax = df5.plot(y="b", label="LABEL_b")
        self._check_legend_labels(ax, labels=["LABEL_b"])
        self._check_text_labels(ax.xaxis.get_label(), "a")
        ax = df5.plot(y="c", label="LABEL_c", ax=ax)
        self._check_legend_labels(ax, labels=["LABEL_b", "LABEL_c"])
        assert df5.columns.tolist() == ["b", "c"]

    def test_missing_marker_multi_plots_on_same_ax(self):
        # GH 18222
        df = pd.DataFrame(
            data=[[1, 1, 1, 1], [2, 2, 4, 8]], columns=["x", "r", "g", "b"]
        )
        fig, ax = self.plt.subplots(nrows=1, ncols=3)
        # Left plot
        df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[0])
        df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[0])
        df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[0])
        self._check_legend_labels(ax[0], labels=["r", "g", "b"])
        self._check_legend_marker(ax[0], expected_markers=["o", "x", "o"])
        # Center plot
        df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[1])
        df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[1])
        df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[1])
        self._check_legend_labels(ax[1], labels=["b", "r", "g"])
        self._check_legend_marker(ax[1], expected_markers=["o", "o", "x"])
        # Right plot
        df.plot(x="x", y="g", linewidth=1, marker="x", color="g", ax=ax[2])
        df.plot(x="x", y="b", linewidth=1, marker="o", color="b", ax=ax[2])
        df.plot(x="x", y="r", linewidth=0, marker="o", color="r", ax=ax[2])
        self._check_legend_labels(ax[2], labels=["g", "b", "r"])
        self._check_legend_marker(ax[2], expected_markers=["x", "o", "o"])

    def test_legend_name(self):
        multi = DataFrame(
            randn(4, 4),
            columns=[np.array(["a", "a", "b", "b"]), np.array(["x", "y", "x", "y"])],
        )
        multi.columns.names = ["group", "individual"]

        ax = multi.plot()
        leg_title = ax.legend_.get_title()
        self._check_text_labels(leg_title, "group,individual")

        df = DataFrame(randn(5, 5))
        ax = df.plot(legend=True, ax=ax)
        leg_title = ax.legend_.get_title()
        self._check_text_labels(leg_title, "group,individual")

        df.columns.name = "new"
        ax = df.plot(legend=False, ax=ax)
        leg_title = ax.legend_.get_title()
        self._check_text_labels(leg_title, "group,individual")

        ax = df.plot(legend=True, ax=ax)
        leg_title = ax.legend_.get_title()
        self._check_text_labels(leg_title, "new")

    @pytest.mark.slow
    def test_no_legend(self):
        kinds = ["line", "bar", "barh", "kde", "area", "hist"]
        df = DataFrame(rand(3, 3), columns=["a", "b", "c"])

        for kind in kinds:

            ax = df.plot(kind=kind, legend=False)
            self._check_legend_labels(ax, visible=False)

    @pytest.mark.slow
    def test_style_by_column(self):
        import matplotlib.pyplot as plt

        fig = plt.gcf()

        df = DataFrame(randn(100, 3))
        for markers in [
            {0: "^", 1: "+", 2: "o"},
            {0: "^", 1: "+"},
            ["^", "+", "o"],
            ["^", "+"],
        ]:
            fig.clf()
            fig.add_subplot(111)
            ax = df.plot(style=markers)
            for i, l in enumerate(ax.get_lines()[: len(markers)]):
                assert l.get_marker() == markers[i]

    @pytest.mark.slow
    def test_line_label_none(self):
        s = Series([1, 2])
        ax = s.plot()
        assert ax.get_legend() is None

        ax = s.plot(legend=True)
        assert ax.get_legend().get_texts()[0].get_text() == "None"

    @pytest.mark.slow
    def test_line_colors(self):
        from matplotlib import cm

        custom_colors = "rgcby"
        df = DataFrame(randn(5, 5))

        ax = df.plot(color=custom_colors)
        self._check_colors(ax.get_lines(), linecolors=custom_colors)

        tm.close()

        ax2 = df.plot(color=custom_colors)
        lines2 = ax2.get_lines()

        for l1, l2 in zip(ax.get_lines(), lines2):
            assert l1.get_color() == l2.get_color()

        tm.close()

        ax = df.plot(colormap="jet")
        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        tm.close()

        ax = df.plot(colormap=cm.jet)
        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        tm.close()

        # make color a list if plotting one column frame
        # handles cases like df.plot(color='DodgerBlue')
        ax = df.loc[:, [0]].plot(color="DodgerBlue")
        self._check_colors(ax.lines, linecolors=["DodgerBlue"])

        ax = df.plot(color="red")
        self._check_colors(ax.get_lines(), linecolors=["red"] * 5)
        tm.close()

        # GH 10299
        custom_colors = ["#FF0000", "#0000FF", "#FFFF00", "#000000", "#FFFFFF"]
        ax = df.plot(color=custom_colors)
        self._check_colors(ax.get_lines(), linecolors=custom_colors)
        tm.close()

    @pytest.mark.slow
    def test_dont_modify_colors(self):
        colors = ["r", "g", "b"]
        pd.DataFrame(np.random.rand(10, 2)).plot(color=colors)
        assert len(colors) == 3

    @pytest.mark.slow
    def test_line_colors_and_styles_subplots(self):
        # GH 9894
        from matplotlib import cm

        default_colors = self._unpack_cycler(self.plt.rcParams)

        df = DataFrame(randn(5, 5))

        axes = df.plot(subplots=True)
        for ax, c in zip(axes, list(default_colors)):
            c = [c]
            self._check_colors(ax.get_lines(), linecolors=c)
        tm.close()

        # single color char
        axes = df.plot(subplots=True, color="k")
        for ax in axes:
            self._check_colors(ax.get_lines(), linecolors=["k"])
        tm.close()

        # single color str
        axes = df.plot(subplots=True, color="green")
        for ax in axes:
            self._check_colors(ax.get_lines(), linecolors=["green"])
        tm.close()

        custom_colors = "rgcby"
        axes = df.plot(color=custom_colors, subplots=True)
        for ax, c in zip(axes, list(custom_colors)):
            self._check_colors(ax.get_lines(), linecolors=[c])
        tm.close()

        axes = df.plot(color=list(custom_colors), subplots=True)
        for ax, c in zip(axes, list(custom_colors)):
            self._check_colors(ax.get_lines(), linecolors=[c])
        tm.close()

        # GH 10299
        custom_colors = ["#FF0000", "#0000FF", "#FFFF00", "#000000", "#FFFFFF"]
        axes = df.plot(color=custom_colors, subplots=True)
        for ax, c in zip(axes, list(custom_colors)):
            self._check_colors(ax.get_lines(), linecolors=[c])
        tm.close()

        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
        for cmap in ["jet", cm.jet]:
            axes = df.plot(colormap=cmap, subplots=True)
            for ax, c in zip(axes, rgba_colors):
                self._check_colors(ax.get_lines(), linecolors=[c])
            tm.close()

        # make color a list if plotting one column frame
        # handles cases like df.plot(color='DodgerBlue')
        axes = df.loc[:, [0]].plot(color="DodgerBlue", subplots=True)
        self._check_colors(axes[0].lines, linecolors=["DodgerBlue"])

        # single character style
        axes = df.plot(style="r", subplots=True)
        for ax in axes:
            self._check_colors(ax.get_lines(), linecolors=["r"])
        tm.close()

        # list of styles
        styles = list("rgcby")
        axes = df.plot(style=styles, subplots=True)
        for ax, c in zip(axes, styles):
            self._check_colors(ax.get_lines(), linecolors=[c])
        tm.close()

    @pytest.mark.slow
    def test_area_colors(self):
        from matplotlib import cm
        from matplotlib.collections import PolyCollection

        custom_colors = "rgcby"
        df = DataFrame(rand(5, 5))

        ax = df.plot.area(color=custom_colors)
        self._check_colors(ax.get_lines(), linecolors=custom_colors)
        poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
        self._check_colors(poly, facecolors=custom_colors)

        handles, labels = ax.get_legend_handles_labels()
        self._check_colors(handles, facecolors=custom_colors)

        for h in handles:
            assert h.get_alpha() is None
        tm.close()

        ax = df.plot.area(colormap="jet")
        jet_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
        self._check_colors(ax.get_lines(), linecolors=jet_colors)
        poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
        self._check_colors(poly, facecolors=jet_colors)

        handles, labels = ax.get_legend_handles_labels()
        self._check_colors(handles, facecolors=jet_colors)
        for h in handles:
            assert h.get_alpha() is None
        tm.close()

        # When stacked=False, alpha is set to 0.5
        ax = df.plot.area(colormap=cm.jet, stacked=False)
        self._check_colors(ax.get_lines(), linecolors=jet_colors)
        poly = [o for o in ax.get_children() if isinstance(o, PolyCollection)]
        jet_with_alpha = [(c[0], c[1], c[2], 0.5) for c in jet_colors]
        self._check_colors(poly, facecolors=jet_with_alpha)

        handles, labels = ax.get_legend_handles_labels()
        linecolors = jet_with_alpha
        self._check_colors(handles[: len(jet_colors)], linecolors=linecolors)
        for h in handles:
            assert h.get_alpha() == 0.5

    @pytest.mark.slow
    def test_hist_colors(self):
        default_colors = self._unpack_cycler(self.plt.rcParams)

        df = DataFrame(randn(5, 5))
        ax = df.plot.hist()
        self._check_colors(ax.patches[::10], facecolors=default_colors[:5])
        tm.close()

        custom_colors = "rgcby"
        ax = df.plot.hist(color=custom_colors)
        self._check_colors(ax.patches[::10], facecolors=custom_colors)
        tm.close()

        from matplotlib import cm

        # Test str -> colormap functionality
        ax = df.plot.hist(colormap="jet")
        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, 5)]
        self._check_colors(ax.patches[::10], facecolors=rgba_colors)
        tm.close()

        # Test colormap functionality
        ax = df.plot.hist(colormap=cm.jet)
        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, 5)]
        self._check_colors(ax.patches[::10], facecolors=rgba_colors)
        tm.close()

        ax = df.loc[:, [0]].plot.hist(color="DodgerBlue")
        self._check_colors([ax.patches[0]], facecolors=["DodgerBlue"])

        ax = df.plot(kind="hist", color="green")
        self._check_colors(ax.patches[::10], facecolors=["green"] * 5)
        tm.close()

    @pytest.mark.slow
    @td.skip_if_no_scipy
    def test_kde_colors(self):
        from matplotlib import cm

        custom_colors = "rgcby"
        df = DataFrame(rand(5, 5))

        ax = df.plot.kde(color=custom_colors)
        self._check_colors(ax.get_lines(), linecolors=custom_colors)
        tm.close()

        ax = df.plot.kde(colormap="jet")
        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)
        tm.close()

        ax = df.plot.kde(colormap=cm.jet)
        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
        self._check_colors(ax.get_lines(), linecolors=rgba_colors)

    @pytest.mark.slow
    @td.skip_if_no_scipy
    def test_kde_colors_and_styles_subplots(self):
        from matplotlib import cm

        default_colors = self._unpack_cycler(self.plt.rcParams)

        df = DataFrame(randn(5, 5))

        axes = df.plot(kind="kde", subplots=True)
        for ax, c in zip(axes, list(default_colors)):
            self._check_colors(ax.get_lines(), linecolors=[c])
        tm.close()

        # single color char
        axes = df.plot(kind="kde", color="k", subplots=True)
        for ax in axes:
            self._check_colors(ax.get_lines(), linecolors=["k"])
        tm.close()

        # single color str
        axes = df.plot(kind="kde", color="red", subplots=True)
        for ax in axes:
            self._check_colors(ax.get_lines(), linecolors=["red"])
        tm.close()

        custom_colors = "rgcby"
        axes = df.plot(kind="kde", color=custom_colors, subplots=True)
        for ax, c in zip(axes, list(custom_colors)):
            self._check_colors(ax.get_lines(), linecolors=[c])
        tm.close()

        rgba_colors = [cm.jet(n) for n in np.linspace(0, 1, len(df))]
        for cmap in ["jet", cm.jet]:
            axes = df.plot(kind="kde", colormap=cmap, subplots=True)
            for ax, c in zip(axes, rgba_colors):
                self._check_colors(ax.get_lines(), linecolors=[c])
            tm.close()

        # make color a list if plotting one column frame
        # handles cases like df.plot(color='DodgerBlue')
        axes = df.loc[:, [0]].plot(kind="kde", color="DodgerBlue", subplots=True)
        self._check_colors(axes[0].lines, linecolors=["DodgerBlue"])

        # single character style
        axes = df.plot(kind="kde", style="r", subplots=True)
        for ax in axes:
            self._check_colors(ax.get_lines(), linecolors=["r"])
        tm.close()

        # list of styles
        styles = list("rgcby")
        axes = df.plot(kind="kde", style=styles, subplots=True)
        for ax, c in zip(axes, styles):
            self._check_colors(ax.get_lines(), linecolors=[c])
        tm.close()

    @pytest.mark.slow
    def test_boxplot_colors(self):
        def _check_colors(bp, box_c, whiskers_c, medians_c, caps_c="k", fliers_c=None):
            # TODO: outside this func?
            if fliers_c is None:
                fliers_c = "k"
            self._check_colors(bp["boxes"], linecolors=[box_c] * len(bp["boxes"]))
            self._check_colors(
                bp["whiskers"], linecolors=[whiskers_c] * len(bp["whiskers"])
            )
            self._check_colors(
                bp["medians"], linecolors=[medians_c] * len(bp["medians"])
            )
            self._check_colors(bp["fliers"], linecolors=[fliers_c] * len(bp["fliers"]))
            self._check_colors(bp["caps"], linecolors=[caps_c] * len(bp["caps"]))

        default_colors = self._unpack_cycler(self.plt.rcParams)

        df = DataFrame(randn(5, 5))
        bp = df.plot.box(return_type="dict")
        _check_colors(bp, default_colors[0], default_colors[0], default_colors[2])
        tm.close()

        dict_colors = dict(
            boxes="#572923", whiskers="#982042", medians="#804823", caps="#123456"
        )
        bp = df.plot.box(color=dict_colors, sym="r+", return_type="dict")
        _check_colors(
            bp,
            dict_colors["boxes"],
            dict_colors["whiskers"],
            dict_colors["medians"],
            dict_colors["caps"],
            "r",
        )
        tm.close()

        # partial colors
        dict_colors = dict(whiskers="c", medians="m")
        bp = df.plot.box(color=dict_colors, return_type="dict")
        _check_colors(bp, default_colors[0], "c", "m")
        tm.close()

        from matplotlib import cm

        # Test str -> colormap functionality
        bp = df.plot.box(colormap="jet", return_type="dict")
        jet_colors = [cm.jet(n) for n in np.linspace(0, 1, 3)]
        _check_colors(bp, jet_colors[0], jet_colors[0], jet_colors[2])
        tm.close()

        # Test colormap functionality
        bp = df.plot.box(colormap=cm.jet, return_type="dict")
        _check_colors(bp, jet_colors[0], jet_colors[0], jet_colors[2])
        tm.close()

        # string color is applied to all artists except fliers
        bp = df.plot.box(color="DodgerBlue", return_type="dict")
        _check_colors(bp, "DodgerBlue", "DodgerBlue", "DodgerBlue", "DodgerBlue")

        # tuple is also applied to all artists except fliers
        bp = df.plot.box(color=(0, 1, 0), sym="#123456", return_type="dict")
        _check_colors(bp, (0, 1, 0), (0, 1, 0), (0, 1, 0), (0, 1, 0), "#123456")

        with pytest.raises(ValueError):
            # Color contains invalid key results in ValueError
            df.plot.box(color=dict(boxes="red", xxxx="blue"))

    @pytest.mark.parametrize(
        "props, expected",
        [
            ("boxprops", "boxes"),
            ("whiskerprops", "whiskers"),
            ("capprops", "caps"),
            ("medianprops", "medians"),
        ],
    )
    def test_specified_props_kwd_plot_box(self, props, expected):
        # GH 30346
        df = DataFrame({k: np.random.random(100) for k in "ABC"})
        kwd = {props: dict(color="C1")}
        result = df.plot.box(return_type="dict", **kwd)

        assert result[expected][0].get_color() == "C1"

    def test_default_color_cycle(self):
        import cycler
        import matplotlib.pyplot as plt

        colors = list("rgbk")
        plt.rcParams["axes.prop_cycle"] = cycler.cycler("color", colors)

        df = DataFrame(randn(5, 3))
        ax = df.plot()

        expected = self._unpack_cycler(plt.rcParams)[:3]
        self._check_colors(ax.get_lines(), linecolors=expected)

    def test_unordered_ts(self):
        df = DataFrame(
            np.array([3.0, 2.0, 1.0]),
            index=[date(2012, 10, 1), date(2012, 9, 1), date(2012, 8, 1)],
            columns=["test"],
        )
        ax = df.plot()
        xticks = ax.lines[0].get_xdata()
        assert xticks[0] < xticks[1]
        ydata = ax.lines[0].get_ydata()
        tm.assert_numpy_array_equal(ydata, np.array([1.0, 2.0, 3.0]))

    @td.skip_if_no_scipy
    def test_kind_both_ways(self):
        df = DataFrame({"x": [1, 2, 3]})
        for kind in plotting.PlotAccessor._common_kinds:

            df.plot(kind=kind)
            getattr(df.plot, kind)()
        for kind in ["scatter", "hexbin"]:
            df.plot("x", "x", kind=kind)
            getattr(df.plot, kind)("x", "x")

    def test_all_invalid_plot_data(self):
        df = DataFrame(list("abcd"))
        for kind in plotting.PlotAccessor._common_kinds:

            msg = "no numeric data to plot"
            with pytest.raises(TypeError, match=msg):
                df.plot(kind=kind)

    @pytest.mark.slow
    def test_partially_invalid_plot_data(self):
        with tm.RNGContext(42):
            df = DataFrame(randn(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = "a"
            for kind in plotting.PlotAccessor._common_kinds:

                msg = "no numeric data to plot"
                with pytest.raises(TypeError, match=msg):
                    df.plot(kind=kind)

        with tm.RNGContext(42):
            # area plot doesn't support positive/negative mixed data
            kinds = ["area"]
            df = DataFrame(rand(10, 2), dtype=object)
            df[np.random.rand(df.shape[0]) > 0.5] = "a"
            for kind in kinds:
                with pytest.raises(TypeError):
                    df.plot(kind=kind)

    def test_invalid_kind(self):
        df = DataFrame(randn(10, 2))
        with pytest.raises(ValueError):
            df.plot(kind="aasdf")

    @pytest.mark.parametrize(
        "x,y,lbl",
        [
            (["B", "C"], "A", "a"),
            (["A"], ["B", "C"], ["b", "c"]),
            ("A", ["B", "C"], "badlabel"),
        ],
    )
    def test_invalid_xy_args(self, x, y, lbl):
        # GH 18671, 19699 allows y to be list-like but not x
        df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
        with pytest.raises(ValueError):
            df.plot(x=x, y=y, label=lbl)

    @pytest.mark.parametrize("x,y", [("A", "B"), (["A"], "B")])
    def test_invalid_xy_args_dup_cols(self, x, y):
        # GH 18671, 19699 allows y to be list-like but not x
        df = DataFrame([[1, 3, 5], [2, 4, 6]], columns=list("AAB"))
        with pytest.raises(ValueError):
            df.plot(x=x, y=y)

    @pytest.mark.parametrize(
        "x,y,lbl,colors",
        [
            ("A", ["B"], ["b"], ["red"]),
            ("A", ["B", "C"], ["b", "c"], ["red", "blue"]),
            (0, [1, 2], ["bokeh", "cython"], ["green", "yellow"]),
        ],
    )
    def test_y_listlike(self, x, y, lbl, colors):
        # GH 19699: tests list-like y and verifies lbls & colors
        df = DataFrame({"A": [1, 2], "B": [3, 4], "C": [5, 6]})
        _check_plot_works(df.plot, x="A", y=y, label=lbl)

        ax = df.plot(x=x, y=y, label=lbl, color=colors)
        assert len(ax.lines) == len(y)
        self._check_colors(ax.get_lines(), linecolors=colors)

    @pytest.mark.parametrize("x,y,colnames", [(0, 1, ["A", "B"]), (1, 0, [0, 1])])
    def test_xy_args_integer(self, x, y, colnames):
        # GH 20056: tests integer args for xy and checks col names
        df = DataFrame({"A": [1, 2], "B": [3, 4]})
        df.columns = colnames
        _check_plot_works(df.plot, x=x, y=y)

    @pytest.mark.slow
    def test_hexbin_basic(self):
        df = self.hexbin_df

        ax = df.plot.hexbin(x="A", y="B", gridsize=10)
        # TODO: need better way to test. This just does existence.
        assert len(ax.collections) == 1

        # GH 6951
        axes = df.plot.hexbin(x="A", y="B", subplots=True)
        # hexbin should have 2 axes in the figure, 1 for plotting and another
        # is colorbar
        assert len(axes[0].figure.axes) == 2
        # return value is single axes
        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))

    @pytest.mark.slow
    def test_hexbin_with_c(self):
        df = self.hexbin_df

        ax = df.plot.hexbin(x="A", y="B", C="C")
        assert len(ax.collections) == 1

        ax = df.plot.hexbin(x="A", y="B", C="C", reduce_C_function=np.std)
        assert len(ax.collections) == 1

    @pytest.mark.slow
    def test_hexbin_cmap(self):
        df = self.hexbin_df

        # Default to BuGn
        ax = df.plot.hexbin(x="A", y="B")
        assert ax.collections[0].cmap.name == "BuGn"

        cm = "cubehelix"
        ax = df.plot.hexbin(x="A", y="B", colormap=cm)
        assert ax.collections[0].cmap.name == cm

    @pytest.mark.slow
    def test_no_color_bar(self):
        df = self.hexbin_df

        ax = df.plot.hexbin(x="A", y="B", colorbar=None)
        assert ax.collections[0].colorbar is None

    @pytest.mark.slow
    def test_allow_cmap(self):
        df = self.hexbin_df

        ax = df.plot.hexbin(x="A", y="B", cmap="YlGn")
        assert ax.collections[0].cmap.name == "YlGn"

        with pytest.raises(TypeError):
            df.plot.hexbin(x="A", y="B", cmap="YlGn", colormap="BuGn")

    @pytest.mark.slow
    def test_pie_df(self):
        df = DataFrame(
            np.random.rand(5, 3),
            columns=["X", "Y", "Z"],
            index=["a", "b", "c", "d", "e"],
        )
        with pytest.raises(ValueError):
            df.plot.pie()

        ax = _check_plot_works(df.plot.pie, y="Y")
        self._check_text_labels(ax.texts, df.index)

        ax = _check_plot_works(df.plot.pie, y=2)
        self._check_text_labels(ax.texts, df.index)

        # _check_plot_works adds an ax so catch warning. see GH #13188
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(df.plot.pie, subplots=True)
        assert len(axes) == len(df.columns)
        for ax in axes:
            self._check_text_labels(ax.texts, df.index)
        for ax, ylabel in zip(axes, df.columns):
            assert ax.get_ylabel() == ylabel

        labels = ["A", "B", "C", "D", "E"]
        color_args = ["r", "g", "b", "c", "m"]
        with tm.assert_produces_warning(UserWarning):
            axes = _check_plot_works(
                df.plot.pie, subplots=True, labels=labels, colors=color_args
            )
        assert len(axes) == len(df.columns)

        for ax in axes:
            self._check_text_labels(ax.texts, labels)
            self._check_colors(ax.patches, facecolors=color_args)

    def test_pie_df_nan(self):
        df = DataFrame(np.random.rand(4, 4))
        for i in range(4):
            df.iloc[i, i] = np.nan
        fig, axes = self.plt.subplots(ncols=4)
        df.plot.pie(subplots=True, ax=axes, legend=True)

        base_expected = ["0", "1", "2", "3"]
        for i, ax in enumerate(axes):
            expected = list(base_expected)  # force copy
            expected[i] = ""
            result = [x.get_text() for x in ax.texts]
            assert result == expected
            # legend labels
            # NaN's not included in legend with subplots
            # see https://github.com/pandas-dev/pandas/issues/8390
            assert [x.get_text() for x in ax.get_legend().get_texts()] == base_expected[
                :i
            ] + base_expected[i + 1 :]

    @pytest.mark.slow
    def test_errorbar_plot(self):
        with warnings.catch_warnings():
            d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
            df = DataFrame(d)
            d_err = {"x": np.ones(12) * 0.2, "y": np.ones(12) * 0.4}
            df_err = DataFrame(d_err)

            # check line plots
            ax = _check_plot_works(df.plot, yerr=df_err, logy=True)
            self._check_has_errorbars(ax, xerr=0, yerr=2)
            ax = _check_plot_works(df.plot, yerr=df_err, logx=True, logy=True)
            self._check_has_errorbars(ax, xerr=0, yerr=2)
            ax = _check_plot_works(df.plot, yerr=df_err, loglog=True)
            self._check_has_errorbars(ax, xerr=0, yerr=2)

            kinds = ["line", "bar", "barh"]
            for kind in kinds:
                ax = _check_plot_works(df.plot, yerr=df_err["x"], kind=kind)
                self._check_has_errorbars(ax, xerr=0, yerr=2)
                ax = _check_plot_works(df.plot, yerr=d_err, kind=kind)
                self._check_has_errorbars(ax, xerr=0, yerr=2)
                ax = _check_plot_works(df.plot, yerr=df_err, xerr=df_err, kind=kind)
                self._check_has_errorbars(ax, xerr=2, yerr=2)
                ax = _check_plot_works(
                    df.plot, yerr=df_err["x"], xerr=df_err["x"], kind=kind
                )
                self._check_has_errorbars(ax, xerr=2, yerr=2)
                ax = _check_plot_works(df.plot, xerr=0.2, yerr=0.2, kind=kind)
                self._check_has_errorbars(ax, xerr=2, yerr=2)

                # _check_plot_works adds an ax so catch warning. see GH #13188
                axes = _check_plot_works(
                    df.plot, yerr=df_err, xerr=df_err, subplots=True, kind=kind
                )
                self._check_has_errorbars(axes, xerr=1, yerr=1)

            ax = _check_plot_works(
                (df + 1).plot, yerr=df_err, xerr=df_err, kind="bar", log=True
            )
            self._check_has_errorbars(ax, xerr=2, yerr=2)

            # yerr is raw error values
            ax = _check_plot_works(df["y"].plot, yerr=np.ones(12) * 0.4)
            self._check_has_errorbars(ax, xerr=0, yerr=1)
            ax = _check_plot_works(df.plot, yerr=np.ones((2, 12)) * 0.4)
            self._check_has_errorbars(ax, xerr=0, yerr=2)

            # yerr is column name
            for yerr in ["yerr", "誤差"]:
                s_df = df.copy()
                s_df[yerr] = np.ones(12) * 0.2
                ax = _check_plot_works(s_df.plot, yerr=yerr)
                self._check_has_errorbars(ax, xerr=0, yerr=2)
                ax = _check_plot_works(s_df.plot, y="y", x="x", yerr=yerr)
                self._check_has_errorbars(ax, xerr=0, yerr=1)

            with pytest.raises(ValueError):
                df.plot(yerr=np.random.randn(11))

            df_err = DataFrame({"x": ["zzz"] * 12, "y": ["zzz"] * 12})
            with pytest.raises((ValueError, TypeError)):
                df.plot(yerr=df_err)

    @pytest.mark.xfail(reason="Iterator is consumed", raises=ValueError)
    @pytest.mark.slow
    def test_errorbar_plot_iterator(self):
        with warnings.catch_warnings():
            d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
            df = DataFrame(d)

            # yerr is iterator
            ax = _check_plot_works(df.plot, yerr=itertools.repeat(0.1, len(df)))
            self._check_has_errorbars(ax, xerr=0, yerr=2)

    @pytest.mark.slow
    def test_errorbar_with_integer_column_names(self):
        # test with integer column names
        df = DataFrame(np.random.randn(10, 2))
        df_err = DataFrame(np.random.randn(10, 2))
        ax = _check_plot_works(df.plot, yerr=df_err)
        self._check_has_errorbars(ax, xerr=0, yerr=2)
        ax = _check_plot_works(df.plot, y=0, yerr=1)
        self._check_has_errorbars(ax, xerr=0, yerr=1)

    @pytest.mark.slow
    def test_errorbar_with_partial_columns(self):
        df = DataFrame(np.random.randn(10, 3))
        df_err = DataFrame(np.random.randn(10, 2), columns=[0, 2])
        kinds = ["line", "bar"]
        for kind in kinds:
            ax = _check_plot_works(df.plot, yerr=df_err, kind=kind)
            self._check_has_errorbars(ax, xerr=0, yerr=2)

        ix = date_range("1/1/2000", periods=10, freq="M")
        df.set_index(ix, inplace=True)
        df_err.set_index(ix, inplace=True)
        ax = _check_plot_works(df.plot, yerr=df_err, kind="line")
        self._check_has_errorbars(ax, xerr=0, yerr=2)

        d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
        df = DataFrame(d)
        d_err = {"x": np.ones(12) * 0.2, "z": np.ones(12) * 0.4}
        df_err = DataFrame(d_err)
        for err in [d_err, df_err]:
            ax = _check_plot_works(df.plot, yerr=err)
            self._check_has_errorbars(ax, xerr=0, yerr=1)

    @pytest.mark.slow
    def test_errorbar_timeseries(self):

        with warnings.catch_warnings():
            d = {"x": np.arange(12), "y": np.arange(12, 0, -1)}
            d_err = {"x": np.ones(12) * 0.2, "y": np.ones(12) * 0.4}

            # check time-series plots
            ix = date_range("1/1/2000", "1/1/2001", freq="M")
            tdf = DataFrame(d, index=ix)
            tdf_err = DataFrame(d_err, index=ix)

            kinds = ["line", "bar", "barh"]
            for kind in kinds:
                ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
                self._check_has_errorbars(ax, xerr=0, yerr=2)
                ax = _check_plot_works(tdf.plot, yerr=d_err, kind=kind)
                self._check_has_errorbars(ax, xerr=0, yerr=2)
                ax = _check_plot_works(tdf.plot, y="y", yerr=tdf_err["x"], kind=kind)
                self._check_has_errorbars(ax, xerr=0, yerr=1)
                ax = _check_plot_works(tdf.plot, y="y", yerr="x", kind=kind)
                self._check_has_errorbars(ax, xerr=0, yerr=1)
                ax = _check_plot_works(tdf.plot, yerr=tdf_err, kind=kind)
                self._check_has_errorbars(ax, xerr=0, yerr=2)

                # _check_plot_works adds an ax so catch warning. see GH #13188
                axes = _check_plot_works(
                    tdf.plot, kind=kind, yerr=tdf_err, subplots=True
                )
                self._check_has_errorbars(axes, xerr=0, yerr=1)

    def test_errorbar_asymmetrical(self):

        np.random.seed(0)
        err = np.random.rand(3, 2, 5)

        # each column is [0, 1, 2, 3, 4], [3, 4, 5, 6, 7]...
        df = DataFrame(np.arange(15).reshape(3, 5)).T

        ax = df.plot(yerr=err, xerr=err / 2)

        yerr_0_0 = ax.collections[1].get_paths()[0].vertices[:, 1]
        expected_0_0 = err[0, :, 0] * np.array([-1, 1])
        tm.assert_almost_equal(yerr_0_0, expected_0_0)

        with pytest.raises(ValueError):
            df.plot(yerr=err.T)

        tm.close()

    def test_table(self):
        df = DataFrame(np.random.rand(10, 3), index=list(string.ascii_letters[:10]))
        _check_plot_works(df.plot, table=True)
        _check_plot_works(df.plot, table=df)

        # GH 35945 UserWarning
        with tm.assert_produces_warning(None):
            ax = df.plot()
            assert len(ax.tables) == 0
            plotting.table(ax, df.T)
            assert len(ax.tables) == 1

    def test_errorbar_scatter(self):
        df = DataFrame(np.random.randn(5, 2), index=range(5), columns=["x", "y"])
        df_err = DataFrame(
            np.random.randn(5, 2) / 5, index=range(5), columns=["x", "y"]
        )

        ax = _check_plot_works(df.plot.scatter, x="x", y="y")
        self._check_has_errorbars(ax, xerr=0, yerr=0)
        ax = _check_plot_works(df.plot.scatter, x="x", y="y", xerr=df_err)
        self._check_has_errorbars(ax, xerr=1, yerr=0)

        ax = _check_plot_works(df.plot.scatter, x="x", y="y", yerr=df_err)
        self._check_has_errorbars(ax, xerr=0, yerr=1)
        ax = _check_plot_works(df.plot.scatter, x="x", y="y", xerr=df_err, yerr=df_err)
        self._check_has_errorbars(ax, xerr=1, yerr=1)

        def _check_errorbar_color(containers, expected, has_err="has_xerr"):
            lines = []
            errs = [c.lines for c in ax.containers if getattr(c, has_err, False)][0]
            for el in errs:
                if is_list_like(el):
                    lines.extend(el)
                else:
                    lines.append(el)
            err_lines = [x for x in lines if x in ax.collections]
            self._check_colors(
                err_lines, linecolors=np.array([expected] * len(err_lines))
            )

        # GH 8081
        df = DataFrame(np.random.randn(10, 5), columns=["a", "b", "c", "d", "e"])
        ax = df.plot.scatter(x="a", y="b", xerr="d", yerr="e", c="red")
        self._check_has_errorbars(ax, xerr=1, yerr=1)
        _check_errorbar_color(ax.containers, "red", has_err="has_xerr")
        _check_errorbar_color(ax.containers, "red", has_err="has_yerr")

        ax = df.plot.scatter(x="a", y="b", yerr="e", color="green")
        self._check_has_errorbars(ax, xerr=0, yerr=1)
        _check_errorbar_color(ax.containers, "green", has_err="has_yerr")

    @pytest.mark.slow
    def test_sharex_and_ax(self):
        # https://github.com/pandas-dev/pandas/issues/9737 using gridspec,
        # the axis in fig.get_axis() are sorted differently than pandas
        # expected them, so make sure that only the right ones are removed
        import matplotlib.pyplot as plt

        plt.close("all")
        gs, axes = _generate_4_axes_via_gridspec()

        df = DataFrame(
            {
                "a": [1, 2, 3, 4, 5, 6],
                "b": [1, 2, 3, 4, 5, 6],
                "c": [1, 2, 3, 4, 5, 6],
                "d": [1, 2, 3, 4, 5, 6],
            }
        )

        def _check(axes):
            for ax in axes:
                assert len(ax.lines) == 1
                self._check_visible(ax.get_yticklabels(), visible=True)
            for ax in [axes[0], axes[2]]:
                self._check_visible(ax.get_xticklabels(), visible=False)
                self._check_visible(ax.get_xticklabels(minor=True), visible=False)
            for ax in [axes[1], axes[3]]:
                self._check_visible(ax.get_xticklabels(), visible=True)
                self._check_visible(ax.get_xticklabels(minor=True), visible=True)

        for ax in axes:
            df.plot(x="a", y="b", title="title", ax=ax, sharex=True)
        gs.tight_layout(plt.gcf())
        _check(axes)
        tm.close()

        gs, axes = _generate_4_axes_via_gridspec()
        with tm.assert_produces_warning(UserWarning):
            axes = df.plot(subplots=True, ax=axes, sharex=True)
        _check(axes)
        tm.close()

        gs, axes = _generate_4_axes_via_gridspec()
        # without sharex, no labels should be touched!
        for ax in axes:
            df.plot(x="a", y="b", title="title", ax=ax)

        gs.tight_layout(plt.gcf())
        for ax in axes:
            assert len(ax.lines) == 1
            self._check_visible(ax.get_yticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(minor=True), visible=True)
        tm.close()

    @pytest.mark.slow
    def test_sharey_and_ax(self):
        # https://github.com/pandas-dev/pandas/issues/9737 using gridspec,
        # the axis in fig.get_axis() are sorted differently than pandas
        # expected them, so make sure that only the right ones are removed
        import matplotlib.pyplot as plt

        gs, axes = _generate_4_axes_via_gridspec()

        df = DataFrame(
            {
                "a": [1, 2, 3, 4, 5, 6],
                "b": [1, 2, 3, 4, 5, 6],
                "c": [1, 2, 3, 4, 5, 6],
                "d": [1, 2, 3, 4, 5, 6],
            }
        )

        def _check(axes):
            for ax in axes:
                assert len(ax.lines) == 1
                self._check_visible(ax.get_xticklabels(), visible=True)
                self._check_visible(ax.get_xticklabels(minor=True), visible=True)
            for ax in [axes[0], axes[1]]:
                self._check_visible(ax.get_yticklabels(), visible=True)
            for ax in [axes[2], axes[3]]:
                self._check_visible(ax.get_yticklabels(), visible=False)

        for ax in axes:
            df.plot(x="a", y="b", title="title", ax=ax, sharey=True)
        gs.tight_layout(plt.gcf())
        _check(axes)
        tm.close()

        gs, axes = _generate_4_axes_via_gridspec()
        with tm.assert_produces_warning(UserWarning):
            axes = df.plot(subplots=True, ax=axes, sharey=True)

        gs.tight_layout(plt.gcf())
        _check(axes)
        tm.close()

        gs, axes = _generate_4_axes_via_gridspec()
        # without sharex, no labels should be touched!
        for ax in axes:
            df.plot(x="a", y="b", title="title", ax=ax)

        gs.tight_layout(plt.gcf())
        for ax in axes:
            assert len(ax.lines) == 1
            self._check_visible(ax.get_yticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(minor=True), visible=True)

    @td.skip_if_no_scipy
    def test_memory_leak(self):
        """ Check that every plot type gets properly collected. """
        import gc
        import weakref

        results = {}
        for kind in plotting.PlotAccessor._all_kinds:

            args = {}
            if kind in ["hexbin", "scatter", "pie"]:
                df = self.hexbin_df
                args = {"x": "A", "y": "B"}
            elif kind == "area":
                df = self.tdf.abs()
            else:
                df = self.tdf

            # Use a weakref so we can see if the object gets collected without
            # also preventing it from being collected
            results[kind] = weakref.proxy(df.plot(kind=kind, **args))

        # have matplotlib delete all the figures
        tm.close()
        # force a garbage collection
        gc.collect()
        for key in results:
            # check that every plot was collected
            with pytest.raises(ReferenceError):
                # need to actually access something to get an error
                results[key].lines

    @pytest.mark.slow
    def test_df_subplots_patterns_minorticks(self):
        # GH 10657
        import matplotlib.pyplot as plt

        df = DataFrame(
            np.random.randn(10, 2),
            index=date_range("1/1/2000", periods=10),
            columns=list("AB"),
        )

        # shared subplots
        fig, axes = plt.subplots(2, 1, sharex=True)
        axes = df.plot(subplots=True, ax=axes)
        for ax in axes:
            assert len(ax.lines) == 1
            self._check_visible(ax.get_yticklabels(), visible=True)
        # xaxis of 1st ax must be hidden
        self._check_visible(axes[0].get_xticklabels(), visible=False)
        self._check_visible(axes[0].get_xticklabels(minor=True), visible=False)
        self._check_visible(axes[1].get_xticklabels(), visible=True)
        self._check_visible(axes[1].get_xticklabels(minor=True), visible=True)
        tm.close()

        fig, axes = plt.subplots(2, 1)
        with tm.assert_produces_warning(UserWarning):
            axes = df.plot(subplots=True, ax=axes, sharex=True)
        for ax in axes:
            assert len(ax.lines) == 1
            self._check_visible(ax.get_yticklabels(), visible=True)
        # xaxis of 1st ax must be hidden
        self._check_visible(axes[0].get_xticklabels(), visible=False)
        self._check_visible(axes[0].get_xticklabels(minor=True), visible=False)
        self._check_visible(axes[1].get_xticklabels(), visible=True)
        self._check_visible(axes[1].get_xticklabels(minor=True), visible=True)
        tm.close()

        # not shared
        fig, axes = plt.subplots(2, 1)
        axes = df.plot(subplots=True, ax=axes)
        for ax in axes:
            assert len(ax.lines) == 1
            self._check_visible(ax.get_yticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(minor=True), visible=True)
        tm.close()

    @pytest.mark.slow
    def test_df_gridspec_patterns(self):
        # GH 10819
        import matplotlib.gridspec as gridspec
        import matplotlib.pyplot as plt

        ts = Series(np.random.randn(10), index=date_range("1/1/2000", periods=10))

        df = DataFrame(np.random.randn(10, 2), index=ts.index, columns=list("AB"))

        def _get_vertical_grid():
            gs = gridspec.GridSpec(3, 1)
            fig = plt.figure()
            ax1 = fig.add_subplot(gs[:2, :])
            ax2 = fig.add_subplot(gs[2, :])
            return ax1, ax2

        def _get_horizontal_grid():
            gs = gridspec.GridSpec(1, 3)
            fig = plt.figure()
            ax1 = fig.add_subplot(gs[:, :2])
            ax2 = fig.add_subplot(gs[:, 2])
            return ax1, ax2

        for ax1, ax2 in [_get_vertical_grid(), _get_horizontal_grid()]:
            ax1 = ts.plot(ax=ax1)
            assert len(ax1.lines) == 1
            ax2 = df.plot(ax=ax2)
            assert len(ax2.lines) == 2
            for ax in [ax1, ax2]:
                self._check_visible(ax.get_yticklabels(), visible=True)
                self._check_visible(ax.get_xticklabels(), visible=True)
                self._check_visible(ax.get_xticklabels(minor=True), visible=True)
            tm.close()

        # subplots=True
        for ax1, ax2 in [_get_vertical_grid(), _get_horizontal_grid()]:
            axes = df.plot(subplots=True, ax=[ax1, ax2])
            assert len(ax1.lines) == 1
            assert len(ax2.lines) == 1
            for ax in axes:
                self._check_visible(ax.get_yticklabels(), visible=True)
                self._check_visible(ax.get_xticklabels(), visible=True)
                self._check_visible(ax.get_xticklabels(minor=True), visible=True)
            tm.close()

        # vertical / subplots / sharex=True / sharey=True
        ax1, ax2 = _get_vertical_grid()
        with tm.assert_produces_warning(UserWarning):
            axes = df.plot(subplots=True, ax=[ax1, ax2], sharex=True, sharey=True)
        assert len(axes[0].lines) == 1
        assert len(axes[1].lines) == 1
        for ax in [ax1, ax2]:
            # yaxis are visible because there is only one column
            self._check_visible(ax.get_yticklabels(), visible=True)
        # xaxis of axes0 (top) are hidden
        self._check_visible(axes[0].get_xticklabels(), visible=False)
        self._check_visible(axes[0].get_xticklabels(minor=True), visible=False)
        self._check_visible(axes[1].get_xticklabels(), visible=True)
        self._check_visible(axes[1].get_xticklabels(minor=True), visible=True)
        tm.close()

        # horizontal / subplots / sharex=True / sharey=True
        ax1, ax2 = _get_horizontal_grid()
        with tm.assert_produces_warning(UserWarning):
            axes = df.plot(subplots=True, ax=[ax1, ax2], sharex=True, sharey=True)
        assert len(axes[0].lines) == 1
        assert len(axes[1].lines) == 1
        self._check_visible(axes[0].get_yticklabels(), visible=True)
        # yaxis of axes1 (right) are hidden
        self._check_visible(axes[1].get_yticklabels(), visible=False)
        for ax in [ax1, ax2]:
            # xaxis are visible because there is only one column
            self._check_visible(ax.get_xticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(minor=True), visible=True)
        tm.close()

        # boxed
        def _get_boxed_grid():
            gs = gridspec.GridSpec(3, 3)
            fig = plt.figure()
            ax1 = fig.add_subplot(gs[:2, :2])
            ax2 = fig.add_subplot(gs[:2, 2])
            ax3 = fig.add_subplot(gs[2, :2])
            ax4 = fig.add_subplot(gs[2, 2])
            return ax1, ax2, ax3, ax4

        axes = _get_boxed_grid()
        df = DataFrame(np.random.randn(10, 4), index=ts.index, columns=list("ABCD"))
        axes = df.plot(subplots=True, ax=axes)
        for ax in axes:
            assert len(ax.lines) == 1
            # axis are visible because these are not shared
            self._check_visible(ax.get_yticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(minor=True), visible=True)
        tm.close()

        # subplots / sharex=True / sharey=True
        axes = _get_boxed_grid()
        with tm.assert_produces_warning(UserWarning):
            axes = df.plot(subplots=True, ax=axes, sharex=True, sharey=True)
        for ax in axes:
            assert len(ax.lines) == 1
        for ax in [axes[0], axes[2]]:  # left column
            self._check_visible(ax.get_yticklabels(), visible=True)
        for ax in [axes[1], axes[3]]:  # right column
            self._check_visible(ax.get_yticklabels(), visible=False)
        for ax in [axes[0], axes[1]]:  # top row
            self._check_visible(ax.get_xticklabels(), visible=False)
            self._check_visible(ax.get_xticklabels(minor=True), visible=False)
        for ax in [axes[2], axes[3]]:  # bottom row
            self._check_visible(ax.get_xticklabels(), visible=True)
            self._check_visible(ax.get_xticklabels(minor=True), visible=True)
        tm.close()

    @pytest.mark.slow
    def test_df_grid_settings(self):
        # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
        self._check_grid_settings(
            DataFrame({"a": [1, 2, 3], "b": [2, 3, 4]}),
            plotting.PlotAccessor._dataframe_kinds,
            kws={"x": "a", "y": "b"},
        )

    def test_invalid_colormap(self):
        df = DataFrame(randn(3, 2), columns=["A", "B"])

        with pytest.raises(ValueError):
            df.plot(colormap="invalid_colormap")

    def test_plain_axes(self):

        # supplied ax itself is a SubplotAxes, but figure contains also
        # a plain Axes object (GH11556)
        fig, ax = self.plt.subplots()
        fig.add_axes([0.2, 0.2, 0.2, 0.2])
        Series(rand(10)).plot(ax=ax)

        # supplied ax itself is a plain Axes, but because the cmap keyword
        # a new ax is created for the colorbar -> also multiples axes (GH11520)
        df = DataFrame({"a": randn(8), "b": randn(8)})
        fig = self.plt.figure()
        ax = fig.add_axes((0, 0, 1, 1))
        df.plot(kind="scatter", ax=ax, x="a", y="b", c="a", cmap="hsv")

        # other examples
        fig, ax = self.plt.subplots()
        from mpl_toolkits.axes_grid1 import make_axes_locatable

        divider = make_axes_locatable(ax)
        cax = divider.append_axes("right", size="5%", pad=0.05)
        Series(rand(10)).plot(ax=ax)
        Series(rand(10)).plot(ax=cax)

        fig, ax = self.plt.subplots()
        from mpl_toolkits.axes_grid1.inset_locator import inset_axes

        iax = inset_axes(ax, width="30%", height=1.0, loc=3)
        Series(rand(10)).plot(ax=ax)
        Series(rand(10)).plot(ax=iax)

    def test_passed_bar_colors(self):
        import matplotlib as mpl

        color_tuples = [(0.9, 0, 0, 1), (0, 0.9, 0, 1), (0, 0, 0.9, 1)]
        colormap = mpl.colors.ListedColormap(color_tuples)
        barplot = pd.DataFrame([[1, 2, 3]]).plot(kind="bar", cmap=colormap)
        assert color_tuples == [c.get_facecolor() for c in barplot.patches]

    def test_rcParams_bar_colors(self):
        import matplotlib as mpl

        color_tuples = [(0.9, 0, 0, 1), (0, 0.9, 0, 1), (0, 0, 0.9, 1)]
        with mpl.rc_context(rc={"axes.prop_cycle": mpl.cycler("color", color_tuples)}):
            barplot = pd.DataFrame([[1, 2, 3]]).plot(kind="bar")
        assert color_tuples == [c.get_facecolor() for c in barplot.patches]

    @pytest.mark.parametrize("method", ["line", "barh", "bar"])
    def test_secondary_axis_font_size(self, method):
        # GH: 12565
        df = (
            pd.DataFrame(np.random.randn(15, 2), columns=list("AB"))
            .assign(C=lambda df: df.B.cumsum())
            .assign(D=lambda df: df.C * 1.1)
        )

        fontsize = 20
        sy = ["C", "D"]

        kwargs = dict(secondary_y=sy, fontsize=fontsize, mark_right=True)
        ax = getattr(df.plot, method)(**kwargs)
        self._check_ticks_props(axes=ax.right_ax, ylabelsize=fontsize)

    @pytest.mark.slow
    def test_x_string_values_ticks(self):
        # Test if string plot index have a fixed xtick position
        # GH: 7612, GH: 22334
        df = pd.DataFrame(
            {
                "sales": [3, 2, 3],
                "visits": [20, 42, 28],
                "day": ["Monday", "Tuesday", "Wednesday"],
            }
        )
        ax = df.plot.area(x="day")
        ax.set_xlim(-1, 3)
        xticklabels = [t.get_text() for t in ax.get_xticklabels()]
        labels_position = dict(zip(xticklabels, ax.get_xticks()))
        # Testing if the label stayed at the right position
        assert labels_position["Monday"] == 0.0
        assert labels_position["Tuesday"] == 1.0
        assert labels_position["Wednesday"] == 2.0

    @pytest.mark.slow
    def test_x_multiindex_values_ticks(self):
        # Test if multiindex plot index have a fixed xtick position
        # GH: 15912
        index = pd.MultiIndex.from_product([[2012, 2013], [1, 2]])
        df = pd.DataFrame(np.random.randn(4, 2), columns=["A", "B"], index=index)
        ax = df.plot()
        ax.set_xlim(-1, 4)
        xticklabels = [t.get_text() for t in ax.get_xticklabels()]
        labels_position = dict(zip(xticklabels, ax.get_xticks()))
        # Testing if the label stayed at the right position
        assert labels_position["(2012, 1)"] == 0.0
        assert labels_position["(2012, 2)"] == 1.0
        assert labels_position["(2013, 1)"] == 2.0
        assert labels_position["(2013, 2)"] == 3.0

    @pytest.mark.parametrize("kind", ["line", "area"])
    def test_xlim_plot_line(self, kind):
        # test if xlim is set correctly in plot.line and plot.area
        # GH 27686
        df = pd.DataFrame([2, 4], index=[1, 2])
        ax = df.plot(kind=kind)
        xlims = ax.get_xlim()
        assert xlims[0] < 1
        assert xlims[1] > 2

    def test_xlim_plot_line_correctly_in_mixed_plot_type(self):
        # test if xlim is set correctly when ax contains multiple different kinds
        # of plots, GH 27686
        fig, ax = self.plt.subplots()

        indexes = ["k1", "k2", "k3", "k4"]
        df = pd.DataFrame(
            {
                "s1": [1000, 2000, 1500, 2000],
                "s2": [900, 1400, 2000, 3000],
                "s3": [1500, 1500, 1600, 1200],
                "secondary_y": [1, 3, 4, 3],
            },
            index=indexes,
        )
        df[["s1", "s2", "s3"]].plot.bar(ax=ax, stacked=False)
        df[["secondary_y"]].plot(ax=ax, secondary_y=True)

        xlims = ax.get_xlim()
        assert xlims[0] < 0
        assert xlims[1] > 3

        # make sure axis labels are plotted correctly as well
        xticklabels = [t.get_text() for t in ax.get_xticklabels()]
        assert xticklabels == indexes

    def test_subplots_sharex_false(self):
        # test when sharex is set to False, two plots should have different
        # labels, GH 25160
        df = pd.DataFrame(np.random.rand(10, 2))
        df.iloc[5:, 1] = np.nan
        df.iloc[:5, 0] = np.nan

        figs, axs = self.plt.subplots(2, 1)
        df.plot.line(ax=axs, subplots=True, sharex=False)

        expected_ax1 = np.arange(4.5, 10, 0.5)
        expected_ax2 = np.arange(-0.5, 5, 0.5)

        tm.assert_numpy_array_equal(axs[0].get_xticks(), expected_ax1)
        tm.assert_numpy_array_equal(axs[1].get_xticks(), expected_ax2)

    def test_plot_no_rows(self):
        # GH 27758
        df = pd.DataFrame(columns=["foo"], dtype=int)
        assert df.empty
        ax = df.plot()
        assert len(ax.get_lines()) == 1
        line = ax.get_lines()[0]
        assert len(line.get_xdata()) == 0
        assert len(line.get_ydata()) == 0

    def test_plot_no_numeric_data(self):
        df = pd.DataFrame(["a", "b", "c"])
        with pytest.raises(TypeError):
            df.plot()

    def test_missing_markers_legend(self):
        # 14958
        df = pd.DataFrame(np.random.randn(8, 3), columns=["A", "B", "C"])
        ax = df.plot(y=["A"], marker="x", linestyle="solid")
        df.plot(y=["B"], marker="o", linestyle="dotted", ax=ax)
        df.plot(y=["C"], marker="<", linestyle="dotted", ax=ax)

        self._check_legend_labels(ax, labels=["A", "B", "C"])
        self._check_legend_marker(ax, expected_markers=["x", "o", "<"])

    def test_missing_markers_legend_using_style(self):
        # 14563
        df = pd.DataFrame(
            {
                "A": [1, 2, 3, 4, 5, 6],
                "B": [2, 4, 1, 3, 2, 4],
                "C": [3, 3, 2, 6, 4, 2],
                "X": [1, 2, 3, 4, 5, 6],
            }
        )

        fig, ax = self.plt.subplots()
        for kind in "ABC":
            df.plot("X", kind, label=kind, ax=ax, style=".")

        self._check_legend_labels(ax, labels=["A", "B", "C"])
        self._check_legend_marker(ax, expected_markers=[".", ".", "."])

    def test_colors_of_columns_with_same_name(self):
        # ISSUE 11136 -> https://github.com/pandas-dev/pandas/issues/11136
        # Creating a DataFrame with duplicate column labels and testing colors of them.
        df = pd.DataFrame({"b": [0, 1, 0], "a": [1, 2, 3]})
        df1 = pd.DataFrame({"a": [2, 4, 6]})
        df_concat = pd.concat([df, df1], axis=1)
        result = df_concat.plot()
        for legend, line in zip(result.get_legend().legendHandles, result.lines):
            assert legend.get_color() == line.get_color()

    @pytest.mark.parametrize(
        "index_name, old_label, new_label",
        [
            (None, "", "new"),
            ("old", "old", "new"),
            (None, "", ""),
            (None, "", 1),
            (None, "", [1, 2]),
        ],
    )
    @pytest.mark.parametrize("kind", ["line", "area", "bar"])
    def test_xlabel_ylabel_dataframe_single_plot(
        self, kind, index_name, old_label, new_label
    ):
        # GH 9093
        df = pd.DataFrame([[1, 2], [2, 5]], columns=["Type A", "Type B"])
        df.index.name = index_name

        # default is the ylabel is not shown and xlabel is index name
        ax = df.plot(kind=kind)
        assert ax.get_xlabel() == old_label
        assert ax.get_ylabel() == ""

        # old xlabel will be overriden and assigned ylabel will be used as ylabel
        ax = df.plot(kind=kind, ylabel=new_label, xlabel=new_label)
        assert ax.get_ylabel() == str(new_label)
        assert ax.get_xlabel() == str(new_label)

    @pytest.mark.parametrize(
        "index_name, old_label, new_label",
        [
            (None, "", "new"),
            ("old", "old", "new"),
            (None, "", ""),
            (None, "", 1),
            (None, "", [1, 2]),
        ],
    )
    @pytest.mark.parametrize("kind", ["line", "area", "bar"])
    def test_xlabel_ylabel_dataframe_subplots(
        self, kind, index_name, old_label, new_label
    ):
        # GH 9093
        df = pd.DataFrame([[1, 2], [2, 5]], columns=["Type A", "Type B"])
        df.index.name = index_name

        # default is the ylabel is not shown and xlabel is index name
        axes = df.plot(kind=kind, subplots=True)
        assert all(ax.get_ylabel() == "" for ax in axes)
        assert all(ax.get_xlabel() == old_label for ax in axes)

        # old xlabel will be overriden and assigned ylabel will be used as ylabel
        axes = df.plot(kind=kind, ylabel=new_label, xlabel=new_label, subplots=True)
        assert all(ax.get_ylabel() == str(new_label) for ax in axes)
        assert all(ax.get_xlabel() == str(new_label) for ax in axes)


def _generate_4_axes_via_gridspec():
    import matplotlib as mpl
    import matplotlib.gridspec  # noqa
    import matplotlib.pyplot as plt

    gs = mpl.gridspec.GridSpec(2, 2)
    ax_tl = plt.subplot(gs[0, 0])
    ax_ll = plt.subplot(gs[1, 0])
    ax_tr = plt.subplot(gs[0, 1])
    ax_lr = plt.subplot(gs[1, 1])

    return gs, [ax_tl, ax_ll, ax_tr, ax_lr]