8th day of python challenges 111-117

venv/lib/python3.6/site-packages/pandas/tests/plotting/common.py

@@ -0,0 +1,540 @@
+#!/usr/bin/env python
+# coding: utf-8
+
+import os
+import warnings
+
+import numpy as np
+from numpy import random
+
+from pandas.util._decorators import cache_readonly
+import pandas.util._test_decorators as td
+
+from pandas.core.dtypes.api import is_list_like
+
+from pandas import DataFrame, Series
+import pandas.util.testing as tm
+from pandas.util.testing import assert_is_valid_plot_return_object, ensure_clean
+
+
+"""
+This is a common base class used for various plotting tests
+"""
+
+
+@td.skip_if_no_mpl
+class TestPlotBase:
+    def setup_method(self, method):
+
+        import matplotlib as mpl
+        from pandas.plotting._matplotlib import compat
+
+        mpl.rcdefaults()
+
+        self.mpl_ge_2_2_3 = compat._mpl_ge_2_2_3()
+        self.mpl_ge_3_0_0 = compat._mpl_ge_3_0_0()
+        self.mpl_ge_3_1_0 = compat._mpl_ge_3_1_0()
+
+        self.bp_n_objects = 7
+        self.polycollection_factor = 2
+        self.default_figsize = (6.4, 4.8)
+        self.default_tick_position = "left"
+
+        n = 100
+        with tm.RNGContext(42):
+            gender = np.random.choice(["Male", "Female"], size=n)
+            classroom = np.random.choice(["A", "B", "C"], size=n)
+
+            self.hist_df = DataFrame(
+                {
+                    "gender": gender,
+                    "classroom": classroom,
+                    "height": random.normal(66, 4, size=n),
+                    "weight": random.normal(161, 32, size=n),
+                    "category": random.randint(4, size=n),
+                }
+            )
+
+        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 teardown_method(self, method):
+        tm.close()
+
+    @cache_readonly
+    def plt(self):
+        import matplotlib.pyplot as plt
+
+        return plt
+
+    @cache_readonly
+    def colorconverter(self):
+        import matplotlib.colors as colors
+
+        return colors.colorConverter
+
+    def _check_legend_labels(self, axes, labels=None, visible=True):
+        """
+        Check each axes has expected legend labels
+
+        Parameters
+        ----------
+        axes : matplotlib Axes object, or its list-like
+        labels : list-like
+            expected legend labels
+        visible : bool
+            expected legend visibility. labels are checked only when visible is
+            True
+        """
+
+        if visible and (labels is None):
+            raise ValueError("labels must be specified when visible is True")
+        axes = self._flatten_visible(axes)
+        for ax in axes:
+            if visible:
+                assert ax.get_legend() is not None
+                self._check_text_labels(ax.get_legend().get_texts(), labels)
+            else:
+                assert ax.get_legend() is None
+
+    def _check_data(self, xp, rs):
+        """
+        Check each axes has identical lines
+
+        Parameters
+        ----------
+        xp : matplotlib Axes object
+        rs : matplotlib Axes object
+        """
+        xp_lines = xp.get_lines()
+        rs_lines = rs.get_lines()
+
+        def check_line(xpl, rsl):
+            xpdata = xpl.get_xydata()
+            rsdata = rsl.get_xydata()
+            tm.assert_almost_equal(xpdata, rsdata)
+
+        assert len(xp_lines) == len(rs_lines)
+        [check_line(xpl, rsl) for xpl, rsl in zip(xp_lines, rs_lines)]
+        tm.close()
+
+    def _check_visible(self, collections, visible=True):
+        """
+        Check each artist is visible or not
+
+        Parameters
+        ----------
+        collections : matplotlib Artist or its list-like
+            target Artist or its list or collection
+        visible : bool
+            expected visibility
+        """
+        from matplotlib.collections import Collection
+
+        if not isinstance(collections, Collection) and not is_list_like(collections):
+            collections = [collections]
+
+        for patch in collections:
+            assert patch.get_visible() == visible
+
+    def _get_colors_mapped(self, series, colors):
+        unique = series.unique()
+        # unique and colors length can be differed
+        # depending on slice value
+        mapped = dict(zip(unique, colors))
+        return [mapped[v] for v in series.values]
+
+    def _check_colors(
+        self, collections, linecolors=None, facecolors=None, mapping=None
+    ):
+        """
+        Check each artist has expected line colors and face colors
+
+        Parameters
+        ----------
+        collections : list-like
+            list or collection of target artist
+        linecolors : list-like which has the same length as collections
+            list of expected line colors
+        facecolors : list-like which has the same length as collections
+            list of expected face colors
+        mapping : Series
+            Series used for color grouping key
+            used for andrew_curves, parallel_coordinates, radviz test
+        """
+
+        from matplotlib.lines import Line2D
+        from matplotlib.collections import Collection, PolyCollection, LineCollection
+
+        conv = self.colorconverter
+        if linecolors is not None:
+
+            if mapping is not None:
+                linecolors = self._get_colors_mapped(mapping, linecolors)
+                linecolors = linecolors[: len(collections)]
+
+            assert len(collections) == len(linecolors)
+            for patch, color in zip(collections, linecolors):
+                if isinstance(patch, Line2D):
+                    result = patch.get_color()
+                    # Line2D may contains string color expression
+                    result = conv.to_rgba(result)
+                elif isinstance(patch, (PolyCollection, LineCollection)):
+                    result = tuple(patch.get_edgecolor()[0])
+                else:
+                    result = patch.get_edgecolor()
+
+                expected = conv.to_rgba(color)
+                assert result == expected
+
+        if facecolors is not None:
+
+            if mapping is not None:
+                facecolors = self._get_colors_mapped(mapping, facecolors)
+                facecolors = facecolors[: len(collections)]
+
+            assert len(collections) == len(facecolors)
+            for patch, color in zip(collections, facecolors):
+                if isinstance(patch, Collection):
+                    # returned as list of np.array
+                    result = patch.get_facecolor()[0]
+                else:
+                    result = patch.get_facecolor()
+
+                if isinstance(result, np.ndarray):
+                    result = tuple(result)
+
+                expected = conv.to_rgba(color)
+                assert result == expected
+
+    def _check_text_labels(self, texts, expected):
+        """
+        Check each text has expected labels
+
+        Parameters
+        ----------
+        texts : matplotlib Text object, or its list-like
+            target text, or its list
+        expected : str or list-like which has the same length as texts
+            expected text label, or its list
+        """
+        if not is_list_like(texts):
+            assert texts.get_text() == expected
+        else:
+            labels = [t.get_text() for t in texts]
+            assert len(labels) == len(expected)
+            for label, e in zip(labels, expected):
+                assert label == e
+
+    def _check_ticks_props(
+        self, axes, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None
+    ):
+        """
+        Check each axes has expected tick properties
+
+        Parameters
+        ----------
+        axes : matplotlib Axes object, or its list-like
+        xlabelsize : number
+            expected xticks font size
+        xrot : number
+            expected xticks rotation
+        ylabelsize : number
+            expected yticks font size
+        yrot : number
+            expected yticks rotation
+        """
+        from matplotlib.ticker import NullFormatter
+
+        axes = self._flatten_visible(axes)
+        for ax in axes:
+            if xlabelsize or xrot:
+                if isinstance(ax.xaxis.get_minor_formatter(), NullFormatter):
+                    # If minor ticks has NullFormatter, rot / fontsize are not
+                    # retained
+                    labels = ax.get_xticklabels()
+                else:
+                    labels = ax.get_xticklabels() + ax.get_xticklabels(minor=True)
+
+                for label in labels:
+                    if xlabelsize is not None:
+                        tm.assert_almost_equal(label.get_fontsize(), xlabelsize)
+                    if xrot is not None:
+                        tm.assert_almost_equal(label.get_rotation(), xrot)
+
+            if ylabelsize or yrot:
+                if isinstance(ax.yaxis.get_minor_formatter(), NullFormatter):
+                    labels = ax.get_yticklabels()
+                else:
+                    labels = ax.get_yticklabels() + ax.get_yticklabels(minor=True)
+
+                for label in labels:
+                    if ylabelsize is not None:
+                        tm.assert_almost_equal(label.get_fontsize(), ylabelsize)
+                    if yrot is not None:
+                        tm.assert_almost_equal(label.get_rotation(), yrot)
+
+    def _check_ax_scales(self, axes, xaxis="linear", yaxis="linear"):
+        """
+        Check each axes has expected scales
+
+        Parameters
+        ----------
+        axes : matplotlib Axes object, or its list-like
+        xaxis : {'linear', 'log'}
+            expected xaxis scale
+        yaxis :  {'linear', 'log'}
+            expected yaxis scale
+        """
+        axes = self._flatten_visible(axes)
+        for ax in axes:
+            assert ax.xaxis.get_scale() == xaxis
+            assert ax.yaxis.get_scale() == yaxis
+
+    def _check_axes_shape(self, axes, axes_num=None, layout=None, figsize=None):
+        """
+        Check expected number of axes is drawn in expected layout
+
+        Parameters
+        ----------
+        axes : matplotlib Axes object, or its list-like
+        axes_num : number
+            expected number of axes. Unnecessary axes should be set to
+            invisible.
+        layout :  tuple
+            expected layout, (expected number of rows , columns)
+        figsize : tuple
+            expected figsize. default is matplotlib default
+        """
+        from pandas.plotting._matplotlib.tools import _flatten
+
+        if figsize is None:
+            figsize = self.default_figsize
+        visible_axes = self._flatten_visible(axes)
+
+        if axes_num is not None:
+            assert len(visible_axes) == axes_num
+            for ax in visible_axes:
+                # check something drawn on visible axes
+                assert len(ax.get_children()) > 0
+
+        if layout is not None:
+            result = self._get_axes_layout(_flatten(axes))
+            assert result == layout
+
+        tm.assert_numpy_array_equal(
+            visible_axes[0].figure.get_size_inches(),
+            np.array(figsize, dtype=np.float64),
+        )
+
+    def _get_axes_layout(self, axes):
+        x_set = set()
+        y_set = set()
+        for ax in axes:
+            # check axes coordinates to estimate layout
+            points = ax.get_position().get_points()
+            x_set.add(points[0][0])
+            y_set.add(points[0][1])
+        return (len(y_set), len(x_set))
+
+    def _flatten_visible(self, axes):
+        """
+        Flatten axes, and filter only visible
+
+        Parameters
+        ----------
+        axes : matplotlib Axes object, or its list-like
+
+        """
+        from pandas.plotting._matplotlib.tools import _flatten
+
+        axes = _flatten(axes)
+        axes = [ax for ax in axes if ax.get_visible()]
+        return axes
+
+    def _check_has_errorbars(self, axes, xerr=0, yerr=0):
+        """
+        Check axes has expected number of errorbars
+
+        Parameters
+        ----------
+        axes : matplotlib Axes object, or its list-like
+        xerr : number
+            expected number of x errorbar
+        yerr : number
+            expected number of y errorbar
+        """
+        axes = self._flatten_visible(axes)
+        for ax in axes:
+            containers = ax.containers
+            xerr_count = 0
+            yerr_count = 0
+            for c in containers:
+                has_xerr = getattr(c, "has_xerr", False)
+                has_yerr = getattr(c, "has_yerr", False)
+                if has_xerr:
+                    xerr_count += 1
+                if has_yerr:
+                    yerr_count += 1
+            assert xerr == xerr_count
+            assert yerr == yerr_count
+
+    def _check_box_return_type(
+        self, returned, return_type, expected_keys=None, check_ax_title=True
+    ):
+        """
+        Check box returned type is correct
+
+        Parameters
+        ----------
+        returned : object to be tested, returned from boxplot
+        return_type : str
+            return_type passed to boxplot
+        expected_keys : list-like, optional
+            group labels in subplot case. If not passed,
+            the function checks assuming boxplot uses single ax
+        check_ax_title : bool
+            Whether to check the ax.title is the same as expected_key
+            Intended to be checked by calling from ``boxplot``.
+            Normal ``plot`` doesn't attach ``ax.title``, it must be disabled.
+        """
+        from matplotlib.axes import Axes
+
+        types = {"dict": dict, "axes": Axes, "both": tuple}
+        if expected_keys is None:
+            # should be fixed when the returning default is changed
+            if return_type is None:
+                return_type = "dict"
+
+            assert isinstance(returned, types[return_type])
+            if return_type == "both":
+                assert isinstance(returned.ax, Axes)
+                assert isinstance(returned.lines, dict)
+        else:
+            # should be fixed when the returning default is changed
+            if return_type is None:
+                for r in self._flatten_visible(returned):
+                    assert isinstance(r, Axes)
+                return
+
+            assert isinstance(returned, Series)
+
+            assert sorted(returned.keys()) == sorted(expected_keys)
+            for key, value in returned.items():
+                assert isinstance(value, types[return_type])
+                # check returned dict has correct mapping
+                if return_type == "axes":
+                    if check_ax_title:
+                        assert value.get_title() == key
+                elif return_type == "both":
+                    if check_ax_title:
+                        assert value.ax.get_title() == key
+                    assert isinstance(value.ax, Axes)
+                    assert isinstance(value.lines, dict)
+                elif return_type == "dict":
+                    line = value["medians"][0]
+                    axes = line.axes
+                    if check_ax_title:
+                        assert axes.get_title() == key
+                else:
+                    raise AssertionError
+
+    def _check_grid_settings(self, obj, kinds, kws={}):
+        # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
+
+        import matplotlib as mpl
+
+        def is_grid_on():
+            xticks = self.plt.gca().xaxis.get_major_ticks()
+            yticks = self.plt.gca().yaxis.get_major_ticks()
+            # for mpl 2.2.2, gridOn and gridline.get_visible disagree.
+            # for new MPL, they are the same.
+
+            if self.mpl_ge_3_1_0:
+                xoff = all(not g.gridline.get_visible() for g in xticks)
+                yoff = all(not g.gridline.get_visible() for g in yticks)
+            else:
+                xoff = all(not g.gridOn for g in xticks)
+                yoff = all(not g.gridOn for g in yticks)
+
+            return not (xoff and yoff)
+
+        spndx = 1
+        for kind in kinds:
+
+            self.plt.subplot(1, 4 * len(kinds), spndx)
+            spndx += 1
+            mpl.rc("axes", grid=False)
+            obj.plot(kind=kind, **kws)
+            assert not is_grid_on()
+
+            self.plt.subplot(1, 4 * len(kinds), spndx)
+            spndx += 1
+            mpl.rc("axes", grid=True)
+            obj.plot(kind=kind, grid=False, **kws)
+            assert not is_grid_on()
+
+            if kind != "pie":
+                self.plt.subplot(1, 4 * len(kinds), spndx)
+                spndx += 1
+                mpl.rc("axes", grid=True)
+                obj.plot(kind=kind, **kws)
+                assert is_grid_on()
+
+                self.plt.subplot(1, 4 * len(kinds), spndx)
+                spndx += 1
+                mpl.rc("axes", grid=False)
+                obj.plot(kind=kind, grid=True, **kws)
+                assert is_grid_on()
+
+    def _unpack_cycler(self, rcParams, field="color"):
+        """
+        Auxiliary function for correctly unpacking cycler after MPL >= 1.5
+        """
+        return [v[field] for v in rcParams["axes.prop_cycle"]]
+
+
+def _check_plot_works(f, filterwarnings="always", **kwargs):
+    import matplotlib.pyplot as plt
+
+    ret = None
+    with warnings.catch_warnings():
+        warnings.simplefilter(filterwarnings)
+        try:
+            try:
+                fig = kwargs["figure"]
+            except KeyError:
+                fig = plt.gcf()
+
+            plt.clf()
+
+            ax = kwargs.get("ax", fig.add_subplot(211))  # noqa
+            ret = f(**kwargs)
+
+            assert_is_valid_plot_return_object(ret)
+
+            try:
+                kwargs["ax"] = fig.add_subplot(212)
+                ret = f(**kwargs)
+            except Exception:
+                pass
+            else:
+                assert_is_valid_plot_return_object(ret)
+
+            with ensure_clean(return_filelike=True) as path:
+                plt.savefig(path)
+        finally:
+            tm.close(fig)
+
+        return ret
+
+
+def curpath():
+    pth, _ = os.path.split(os.path.abspath(__file__))
+    return pth

venv/lib/python3.6/site-packages/pandas/tests/plotting/test_backend.py

@@ -0,0 +1,38 @@
+import pytest
+
+import pandas
+
+
+def test_matplotlib_backend_error():
+    msg = (
+        "matplotlib is required for plotting when the default backend "
+        '"matplotlib" is selected.'
+    )
+    try:
+        import matplotlib  # noqa
+    except ImportError:
+        with pytest.raises(ImportError, match=msg):
+            pandas.set_option("plotting.backend", "matplotlib")
+
+
+def test_backend_is_not_module():
+    msg = (
+        '"not_an_existing_module" does not seem to be an installed module. '
+        "A pandas plotting backend must be a module that can be imported"
+    )
+    with pytest.raises(ValueError, match=msg):
+        pandas.set_option("plotting.backend", "not_an_existing_module")
+
+
+def test_backend_is_correct(monkeypatch):
+    monkeypatch.setattr(
+        "pandas.core.config_init.importlib.import_module", lambda name: None
+    )
+    pandas.set_option("plotting.backend", "correct_backend")
+    assert pandas.get_option("plotting.backend") == "correct_backend"
+
+    # Restore backend for other tests (matplotlib can be not installed)
+    try:
+        pandas.set_option("plotting.backend", "matplotlib")
+    except ImportError:
+        pass

venv/lib/python3.6/site-packages/pandas/tests/plotting/test_boxplot_method.py

@@ -0,0 +1,399 @@
+# coding: utf-8
+
+import itertools
+import string
+
+import numpy as np
+from numpy import random
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import DataFrame, MultiIndex, Series
+from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
+import pandas.util.testing as tm
+
+import pandas.plotting as plotting
+
+""" Test cases for .boxplot method """
+
+
+@td.skip_if_no_mpl
+class TestDataFramePlots(TestPlotBase):
+    @pytest.mark.slow
+    def test_boxplot_legacy1(self):
+        df = DataFrame(
+            np.random.randn(6, 4),
+            index=list(string.ascii_letters[:6]),
+            columns=["one", "two", "three", "four"],
+        )
+        df["indic"] = ["foo", "bar"] * 3
+        df["indic2"] = ["foo", "bar", "foo"] * 2
+
+        _check_plot_works(df.boxplot, return_type="dict")
+        _check_plot_works(df.boxplot, column=["one", "two"], return_type="dict")
+        # _check_plot_works adds an ax so catch warning. see GH #13188
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, column=["one", "two"], by="indic")
+        _check_plot_works(df.boxplot, column="one", by=["indic", "indic2"])
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, by="indic")
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, by=["indic", "indic2"])
+        _check_plot_works(plotting._core.boxplot, data=df["one"], return_type="dict")
+        _check_plot_works(df.boxplot, notch=1, return_type="dict")
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, by="indic", notch=1)
+
+    @pytest.mark.slow
+    def test_boxplot_legacy2(self):
+        df = DataFrame(np.random.rand(10, 2), columns=["Col1", "Col2"])
+        df["X"] = Series(["A", "A", "A", "A", "A", "B", "B", "B", "B", "B"])
+        df["Y"] = Series(["A"] * 10)
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.boxplot, by="X")
+
+        # When ax is supplied and required number of axes is 1,
+        # passed ax should be used:
+        fig, ax = self.plt.subplots()
+        axes = df.boxplot("Col1", by="X", ax=ax)
+        ax_axes = ax.axes
+        assert ax_axes is axes
+
+        fig, ax = self.plt.subplots()
+        axes = df.groupby("Y").boxplot(ax=ax, return_type="axes")
+        ax_axes = ax.axes
+        assert ax_axes is axes["A"]
+
+        # Multiple columns with an ax argument should use same figure
+        fig, ax = self.plt.subplots()
+        with tm.assert_produces_warning(UserWarning):
+            axes = df.boxplot(
+                column=["Col1", "Col2"], by="X", ax=ax, return_type="axes"
+            )
+        assert axes["Col1"].get_figure() is fig
+
+        # When by is None, check that all relevant lines are present in the
+        # dict
+        fig, ax = self.plt.subplots()
+        d = df.boxplot(ax=ax, return_type="dict")
+        lines = list(itertools.chain.from_iterable(d.values()))
+        assert len(ax.get_lines()) == len(lines)
+
+    @pytest.mark.slow
+    def test_boxplot_return_type_none(self):
+        # GH 12216; return_type=None & by=None -> axes
+        result = self.hist_df.boxplot()
+        assert isinstance(result, self.plt.Axes)
+
+    @pytest.mark.slow
+    def test_boxplot_return_type_legacy(self):
+        # API change in https://github.com/pandas-dev/pandas/pull/7096
+        import matplotlib as mpl  # noqa
+
+        df = DataFrame(
+            np.random.randn(6, 4),
+            index=list(string.ascii_letters[:6]),
+            columns=["one", "two", "three", "four"],
+        )
+        with pytest.raises(ValueError):
+            df.boxplot(return_type="NOTATYPE")
+
+        result = df.boxplot()
+        self._check_box_return_type(result, "axes")
+
+        with tm.assert_produces_warning(False):
+            result = df.boxplot(return_type="dict")
+        self._check_box_return_type(result, "dict")
+
+        with tm.assert_produces_warning(False):
+            result = df.boxplot(return_type="axes")
+        self._check_box_return_type(result, "axes")
+
+        with tm.assert_produces_warning(False):
+            result = df.boxplot(return_type="both")
+        self._check_box_return_type(result, "both")
+
+    @pytest.mark.slow
+    def test_boxplot_axis_limits(self):
+        def _check_ax_limits(col, ax):
+            y_min, y_max = ax.get_ylim()
+            assert y_min <= col.min()
+            assert y_max >= col.max()
+
+        df = self.hist_df.copy()
+        df["age"] = np.random.randint(1, 20, df.shape[0])
+        # One full row
+        height_ax, weight_ax = df.boxplot(["height", "weight"], by="category")
+        _check_ax_limits(df["height"], height_ax)
+        _check_ax_limits(df["weight"], weight_ax)
+        assert weight_ax._sharey == height_ax
+
+        # Two rows, one partial
+        p = df.boxplot(["height", "weight", "age"], by="category")
+        height_ax, weight_ax, age_ax = p[0, 0], p[0, 1], p[1, 0]
+        dummy_ax = p[1, 1]
+
+        _check_ax_limits(df["height"], height_ax)
+        _check_ax_limits(df["weight"], weight_ax)
+        _check_ax_limits(df["age"], age_ax)
+        assert weight_ax._sharey == height_ax
+        assert age_ax._sharey == height_ax
+        assert dummy_ax._sharey is None
+
+    @pytest.mark.slow
+    def test_boxplot_empty_column(self):
+        df = DataFrame(np.random.randn(20, 4))
+        df.loc[:, 0] = np.nan
+        _check_plot_works(df.boxplot, return_type="axes")
+
+    @pytest.mark.slow
+    def test_figsize(self):
+        df = DataFrame(np.random.rand(10, 5), columns=["A", "B", "C", "D", "E"])
+        result = df.boxplot(return_type="axes", figsize=(12, 8))
+        assert result.figure.bbox_inches.width == 12
+        assert result.figure.bbox_inches.height == 8
+
+    def test_fontsize(self):
+        df = DataFrame({"a": [1, 2, 3, 4, 5, 6]})
+        self._check_ticks_props(
+            df.boxplot("a", fontsize=16), xlabelsize=16, ylabelsize=16
+        )
+
+
+@td.skip_if_no_mpl
+class TestDataFrameGroupByPlots(TestPlotBase):
+    @pytest.mark.slow
+    def test_boxplot_legacy1(self):
+        grouped = self.hist_df.groupby(by="gender")
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(grouped.boxplot, return_type="axes")
+        self._check_axes_shape(list(axes.values), axes_num=2, layout=(1, 2))
+        axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
+        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
+
+    @pytest.mark.slow
+    def test_boxplot_legacy2(self):
+        tuples = zip(string.ascii_letters[:10], range(10))
+        df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
+        grouped = df.groupby(level=1)
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(grouped.boxplot, return_type="axes")
+        self._check_axes_shape(list(axes.values), axes_num=10, layout=(4, 3))
+
+        axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
+        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
+
+    @pytest.mark.slow
+    def test_boxplot_legacy3(self):
+        tuples = zip(string.ascii_letters[:10], range(10))
+        df = DataFrame(np.random.rand(10, 3), index=MultiIndex.from_tuples(tuples))
+        grouped = df.unstack(level=1).groupby(level=0, axis=1)
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(grouped.boxplot, return_type="axes")
+        self._check_axes_shape(list(axes.values), axes_num=3, layout=(2, 2))
+        axes = _check_plot_works(grouped.boxplot, subplots=False, return_type="axes")
+        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
+
+    @pytest.mark.slow
+    def test_grouped_plot_fignums(self):
+        n = 10
+        weight = Series(np.random.normal(166, 20, size=n))
+        height = Series(np.random.normal(60, 10, size=n))
+        with tm.RNGContext(42):
+            gender = np.random.choice(["male", "female"], size=n)
+        df = DataFrame({"height": height, "weight": weight, "gender": gender})
+        gb = df.groupby("gender")
+
+        res = gb.plot()
+        assert len(self.plt.get_fignums()) == 2
+        assert len(res) == 2
+        tm.close()
+
+        res = gb.boxplot(return_type="axes")
+        assert len(self.plt.get_fignums()) == 1
+        assert len(res) == 2
+        tm.close()
+
+        # now works with GH 5610 as gender is excluded
+        res = df.groupby("gender").hist()
+        tm.close()
+
+    @pytest.mark.slow
+    def test_grouped_box_return_type(self):
+        df = self.hist_df
+
+        # old style: return_type=None
+        result = df.boxplot(by="gender")
+        assert isinstance(result, np.ndarray)
+        self._check_box_return_type(
+            result, None, expected_keys=["height", "weight", "category"]
+        )
+
+        # now for groupby
+        result = df.groupby("gender").boxplot(return_type="dict")
+        self._check_box_return_type(result, "dict", expected_keys=["Male", "Female"])
+
+        columns2 = "X B C D A G Y N Q O".split()
+        df2 = DataFrame(random.randn(50, 10), columns=columns2)
+        categories2 = "A B C D E F G H I J".split()
+        df2["category"] = categories2 * 5
+
+        for t in ["dict", "axes", "both"]:
+            returned = df.groupby("classroom").boxplot(return_type=t)
+            self._check_box_return_type(returned, t, expected_keys=["A", "B", "C"])
+
+            returned = df.boxplot(by="classroom", return_type=t)
+            self._check_box_return_type(
+                returned, t, expected_keys=["height", "weight", "category"]
+            )
+
+            returned = df2.groupby("category").boxplot(return_type=t)
+            self._check_box_return_type(returned, t, expected_keys=categories2)
+
+            returned = df2.boxplot(by="category", return_type=t)
+            self._check_box_return_type(returned, t, expected_keys=columns2)
+
+    @pytest.mark.slow
+    def test_grouped_box_layout(self):
+        df = self.hist_df
+
+        msg = "Layout of 1x1 must be larger than required size 2"
+        with pytest.raises(ValueError, match=msg):
+            df.boxplot(column=["weight", "height"], by=df.gender, layout=(1, 1))
+
+        msg = "The 'layout' keyword is not supported when 'by' is None"
+        with pytest.raises(ValueError, match=msg):
+            df.boxplot(
+                column=["height", "weight", "category"],
+                layout=(2, 1),
+                return_type="dict",
+            )
+
+        msg = "At least one dimension of layout must be positive"
+        with pytest.raises(ValueError, match=msg):
+            df.boxplot(column=["weight", "height"], by=df.gender, layout=(-1, -1))
+
+        # _check_plot_works adds an ax so catch warning. see GH #13188
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("gender").boxplot, column="height", return_type="dict"
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=2, layout=(1, 2))
+
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("category").boxplot, column="height", return_type="dict"
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))
+
+        # GH 6769
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("classroom").boxplot, column="height", return_type="dict"
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
+
+        # GH 5897
+        axes = df.boxplot(
+            column=["height", "weight", "category"], by="gender", return_type="axes"
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
+        for ax in [axes["height"]]:
+            self._check_visible(ax.get_xticklabels(), visible=False)
+            self._check_visible([ax.xaxis.get_label()], visible=False)
+        for ax in [axes["weight"], axes["category"]]:
+            self._check_visible(ax.get_xticklabels())
+            self._check_visible([ax.xaxis.get_label()])
+
+        box = df.groupby("classroom").boxplot(
+            column=["height", "weight", "category"], return_type="dict"
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(2, 2))
+
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("category").boxplot,
+                column="height",
+                layout=(3, 2),
+                return_type="dict",
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))
+        with tm.assert_produces_warning(UserWarning):
+            box = _check_plot_works(
+                df.groupby("category").boxplot,
+                column="height",
+                layout=(3, -1),
+                return_type="dict",
+            )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(3, 2))
+
+        box = df.boxplot(
+            column=["height", "weight", "category"], by="gender", layout=(4, 1)
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(4, 1))
+
+        box = df.boxplot(
+            column=["height", "weight", "category"], by="gender", layout=(-1, 1)
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(3, 1))
+
+        box = df.groupby("classroom").boxplot(
+            column=["height", "weight", "category"], layout=(1, 4), return_type="dict"
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 4))
+
+        box = df.groupby("classroom").boxplot(  # noqa
+            column=["height", "weight", "category"], layout=(1, -1), return_type="dict"
+        )
+        self._check_axes_shape(self.plt.gcf().axes, axes_num=3, layout=(1, 3))
+
+    @pytest.mark.slow
+    def test_grouped_box_multiple_axes(self):
+        # GH 6970, GH 7069
+        df = self.hist_df
+
+        # check warning to ignore sharex / sharey
+        # this check should be done in the first function which
+        # passes multiple axes to plot, hist or boxplot
+        # location should be changed if other test is added
+        # which has earlier alphabetical order
+        with tm.assert_produces_warning(UserWarning):
+            fig, axes = self.plt.subplots(2, 2)
+            df.groupby("category").boxplot(column="height", return_type="axes", ax=axes)
+            self._check_axes_shape(self.plt.gcf().axes, axes_num=4, layout=(2, 2))
+
+        fig, axes = self.plt.subplots(2, 3)
+        with tm.assert_produces_warning(UserWarning):
+            returned = df.boxplot(
+                column=["height", "weight", "category"],
+                by="gender",
+                return_type="axes",
+                ax=axes[0],
+            )
+        returned = np.array(list(returned.values))
+        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
+        tm.assert_numpy_array_equal(returned, axes[0])
+        assert returned[0].figure is fig
+
+        # draw on second row
+        with tm.assert_produces_warning(UserWarning):
+            returned = df.groupby("classroom").boxplot(
+                column=["height", "weight", "category"], return_type="axes", ax=axes[1]
+            )
+        returned = np.array(list(returned.values))
+        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
+        tm.assert_numpy_array_equal(returned, axes[1])
+        assert returned[0].figure is fig
+
+        with pytest.raises(ValueError):
+            fig, axes = self.plt.subplots(2, 3)
+            # pass different number of axes from required
+            with tm.assert_produces_warning(UserWarning):
+                axes = df.groupby("classroom").boxplot(ax=axes)
+
+    def test_fontsize(self):
+        df = DataFrame({"a": [1, 2, 3, 4, 5, 6], "b": [0, 0, 0, 1, 1, 1]})
+        self._check_ticks_props(
+            df.boxplot("a", by="b", fontsize=16), xlabelsize=16, ylabelsize=16
+        )

venv/lib/python3.6/site-packages/pandas/tests/plotting/test_converter.py

@@ -0,0 +1,375 @@
+from datetime import date, datetime
+import subprocess
+import sys
+
+import numpy as np
+import pytest
+
+import pandas._config.config as cf
+
+from pandas.compat.numpy import np_datetime64_compat
+
+from pandas import Index, Period, Series, Timestamp, date_range
+import pandas.util.testing as tm
+
+from pandas.plotting import (
+    deregister_matplotlib_converters,
+    register_matplotlib_converters,
+)
+from pandas.tseries.offsets import Day, Micro, Milli, Second
+
+try:
+    from pandas.plotting._matplotlib import converter
+except ImportError:
+    # try / except, rather than skip, to avoid internal refactoring
+    # causing an improprer skip
+    pass
+
+pytest.importorskip("matplotlib.pyplot")
+
+
+def test_initial_warning():
+    code = (
+        "import pandas as pd; import matplotlib.pyplot as plt; "
+        "s = pd.Series(1, pd.date_range('2000', periods=12)); "
+        "fig, ax = plt.subplots(); "
+        "ax.plot(s.index, s.values)"
+    )
+    call = [sys.executable, "-c", code]
+    out = subprocess.check_output(call, stderr=subprocess.STDOUT).decode()
+    assert "Using an implicitly" in out
+
+
+def test_timtetonum_accepts_unicode():
+    assert converter.time2num("00:01") == converter.time2num("00:01")
+
+
+class TestRegistration:
+    def test_register_by_default(self):
+        # Run in subprocess to ensure a clean state
+        code = (
+            "'import matplotlib.units; "
+            "import pandas as pd; "
+            "units = dict(matplotlib.units.registry); "
+            "assert pd.Timestamp in units)'"
+        )
+        call = [sys.executable, "-c", code]
+        assert subprocess.check_call(call) == 0
+
+    def test_warns(self):
+        plt = pytest.importorskip("matplotlib.pyplot")
+        s = Series(range(12), index=date_range("2017", periods=12))
+        _, ax = plt.subplots()
+
+        # Set to the "warning" state, in case this isn't the first test run
+        converter._WARN = True
+        with tm.assert_produces_warning(FutureWarning, check_stacklevel=False) as w:
+            ax.plot(s.index, s.values)
+            plt.close()
+
+        assert len(w) == 1
+        assert "Using an implicitly registered datetime converter" in str(w[0])
+
+    def test_registering_no_warning(self):
+        plt = pytest.importorskip("matplotlib.pyplot")
+        s = Series(range(12), index=date_range("2017", periods=12))
+        _, ax = plt.subplots()
+
+        # Set to the "warn" state, in case this isn't the first test run
+        converter._WARN = True
+        register_matplotlib_converters()
+        with tm.assert_produces_warning(None) as w:
+            ax.plot(s.index, s.values)
+
+        assert len(w) == 0
+
+    def test_pandas_plots_register(self):
+        pytest.importorskip("matplotlib.pyplot")
+        s = Series(range(12), index=date_range("2017", periods=12))
+        # Set to the "warn" state, in case this isn't the first test run
+        converter._WARN = True
+        with tm.assert_produces_warning(None) as w:
+            s.plot()
+
+        assert len(w) == 0
+
+    def test_matplotlib_formatters(self):
+        units = pytest.importorskip("matplotlib.units")
+        assert Timestamp in units.registry
+
+        ctx = cf.option_context("plotting.matplotlib.register_converters", False)
+        with ctx:
+            assert Timestamp not in units.registry
+
+        assert Timestamp in units.registry
+
+    def test_option_no_warning(self):
+        pytest.importorskip("matplotlib.pyplot")
+        ctx = cf.option_context("plotting.matplotlib.register_converters", False)
+        plt = pytest.importorskip("matplotlib.pyplot")
+        s = Series(range(12), index=date_range("2017", periods=12))
+        _, ax = plt.subplots()
+
+        converter._WARN = True
+        # Test without registering first, no warning
+        with ctx:
+            with tm.assert_produces_warning(None) as w:
+                ax.plot(s.index, s.values)
+
+        assert len(w) == 0
+
+        # Now test with registering
+        converter._WARN = True
+        register_matplotlib_converters()
+        with ctx:
+            with tm.assert_produces_warning(None) as w:
+                ax.plot(s.index, s.values)
+
+        assert len(w) == 0
+
+    def test_registry_resets(self):
+        units = pytest.importorskip("matplotlib.units")
+        dates = pytest.importorskip("matplotlib.dates")
+
+        # make a copy, to reset to
+        original = dict(units.registry)
+
+        try:
+            # get to a known state
+            units.registry.clear()
+            date_converter = dates.DateConverter()
+            units.registry[datetime] = date_converter
+            units.registry[date] = date_converter
+
+            register_matplotlib_converters()
+            assert units.registry[date] is not date_converter
+            deregister_matplotlib_converters()
+            assert units.registry[date] is date_converter
+
+        finally:
+            # restore original stater
+            units.registry.clear()
+            for k, v in original.items():
+                units.registry[k] = v
+
+    def test_old_import_warns(self):
+        with tm.assert_produces_warning(FutureWarning) as w:
+            from pandas.tseries import converter
+
+            converter.register()
+
+        assert len(w)
+        assert "pandas.plotting.register_matplotlib_converters" in str(w[0].message)
+
+
+class TestDateTimeConverter:
+    def setup_method(self, method):
+        self.dtc = converter.DatetimeConverter()
+        self.tc = converter.TimeFormatter(None)
+
+    def test_convert_accepts_unicode(self):
+        r1 = self.dtc.convert("12:22", None, None)
+        r2 = self.dtc.convert("12:22", None, None)
+        assert r1 == r2, "DatetimeConverter.convert should accept unicode"
+
+    def test_conversion(self):
+        rs = self.dtc.convert(["2012-1-1"], None, None)[0]
+        xp = datetime(2012, 1, 1).toordinal()
+        assert rs == xp
+
+        rs = self.dtc.convert("2012-1-1", None, None)
+        assert rs == xp
+
+        rs = self.dtc.convert(date(2012, 1, 1), None, None)
+        assert rs == xp
+
+        rs = self.dtc.convert(datetime(2012, 1, 1).toordinal(), None, None)
+        assert rs == xp
+
+        rs = self.dtc.convert("2012-1-1", None, None)
+        assert rs == xp
+
+        rs = self.dtc.convert(Timestamp("2012-1-1"), None, None)
+        assert rs == xp
+
+        # also testing datetime64 dtype (GH8614)
+        rs = self.dtc.convert(np_datetime64_compat("2012-01-01"), None, None)
+        assert rs == xp
+
+        rs = self.dtc.convert(
+            np_datetime64_compat("2012-01-01 00:00:00+0000"), None, None
+        )
+        assert rs == xp
+
+        rs = self.dtc.convert(
+            np.array(
+                [
+                    np_datetime64_compat("2012-01-01 00:00:00+0000"),
+                    np_datetime64_compat("2012-01-02 00:00:00+0000"),
+                ]
+            ),
+            None,
+            None,
+        )
+        assert rs[0] == xp
+
+        # we have a tz-aware date (constructed to that when we turn to utc it
+        # is the same as our sample)
+        ts = Timestamp("2012-01-01").tz_localize("UTC").tz_convert("US/Eastern")
+        rs = self.dtc.convert(ts, None, None)
+        assert rs == xp
+
+        rs = self.dtc.convert(ts.to_pydatetime(), None, None)
+        assert rs == xp
+
+        rs = self.dtc.convert(Index([ts - Day(1), ts]), None, None)
+        assert rs[1] == xp
+
+        rs = self.dtc.convert(Index([ts - Day(1), ts]).to_pydatetime(), None, None)
+        assert rs[1] == xp
+
+    def test_conversion_float(self):
+        decimals = 9
+
+        rs = self.dtc.convert(Timestamp("2012-1-1 01:02:03", tz="UTC"), None, None)
+        xp = converter.dates.date2num(Timestamp("2012-1-1 01:02:03", tz="UTC"))
+        tm.assert_almost_equal(rs, xp, decimals)
+
+        rs = self.dtc.convert(
+            Timestamp("2012-1-1 09:02:03", tz="Asia/Hong_Kong"), None, None
+        )
+        tm.assert_almost_equal(rs, xp, decimals)
+
+        rs = self.dtc.convert(datetime(2012, 1, 1, 1, 2, 3), None, None)
+        tm.assert_almost_equal(rs, xp, decimals)
+
+    def test_conversion_outofbounds_datetime(self):
+        # 2579
+        values = [date(1677, 1, 1), date(1677, 1, 2)]
+        rs = self.dtc.convert(values, None, None)
+        xp = converter.dates.date2num(values)
+        tm.assert_numpy_array_equal(rs, xp)
+        rs = self.dtc.convert(values[0], None, None)
+        xp = converter.dates.date2num(values[0])
+        assert rs == xp
+
+        values = [datetime(1677, 1, 1, 12), datetime(1677, 1, 2, 12)]
+        rs = self.dtc.convert(values, None, None)
+        xp = converter.dates.date2num(values)
+        tm.assert_numpy_array_equal(rs, xp)
+        rs = self.dtc.convert(values[0], None, None)
+        xp = converter.dates.date2num(values[0])
+        assert rs == xp
+
+    @pytest.mark.parametrize(
+        "time,format_expected",
+        [
+            (0, "00:00"),  # time2num(datetime.time.min)
+            (86399.999999, "23:59:59.999999"),  # time2num(datetime.time.max)
+            (90000, "01:00"),
+            (3723, "01:02:03"),
+            (39723.2, "11:02:03.200"),
+        ],
+    )
+    def test_time_formatter(self, time, format_expected):
+        # issue 18478
+        result = self.tc(time)
+        assert result == format_expected
+
+    def test_dateindex_conversion(self):
+        decimals = 9
+
+        for freq in ("B", "L", "S"):
+            dateindex = tm.makeDateIndex(k=10, freq=freq)
+            rs = self.dtc.convert(dateindex, None, None)
+            xp = converter.dates.date2num(dateindex._mpl_repr())
+            tm.assert_almost_equal(rs, xp, decimals)
+
+    def test_resolution(self):
+        def _assert_less(ts1, ts2):
+            val1 = self.dtc.convert(ts1, None, None)
+            val2 = self.dtc.convert(ts2, None, None)
+            if not val1 < val2:
+                raise AssertionError("{0} is not less than {1}.".format(val1, val2))
+
+        # Matplotlib's time representation using floats cannot distinguish
+        # intervals smaller than ~10 microsecond in the common range of years.
+        ts = Timestamp("2012-1-1")
+        _assert_less(ts, ts + Second())
+        _assert_less(ts, ts + Milli())
+        _assert_less(ts, ts + Micro(50))
+
+    def test_convert_nested(self):
+        inner = [Timestamp("2017-01-01"), Timestamp("2017-01-02")]
+        data = [inner, inner]
+        result = self.dtc.convert(data, None, None)
+        expected = [self.dtc.convert(x, None, None) for x in data]
+        assert (np.array(result) == expected).all()
+
+
+class TestPeriodConverter:
+    def setup_method(self, method):
+        self.pc = converter.PeriodConverter()
+
+        class Axis:
+            pass
+
+        self.axis = Axis()
+        self.axis.freq = "D"
+
+    def test_convert_accepts_unicode(self):
+        r1 = self.pc.convert("2012-1-1", None, self.axis)
+        r2 = self.pc.convert("2012-1-1", None, self.axis)
+        assert r1 == r2
+
+    def test_conversion(self):
+        rs = self.pc.convert(["2012-1-1"], None, self.axis)[0]
+        xp = Period("2012-1-1").ordinal
+        assert rs == xp
+
+        rs = self.pc.convert("2012-1-1", None, self.axis)
+        assert rs == xp
+
+        rs = self.pc.convert([date(2012, 1, 1)], None, self.axis)[0]
+        assert rs == xp
+
+        rs = self.pc.convert(date(2012, 1, 1), None, self.axis)
+        assert rs == xp
+
+        rs = self.pc.convert([Timestamp("2012-1-1")], None, self.axis)[0]
+        assert rs == xp
+
+        rs = self.pc.convert(Timestamp("2012-1-1"), None, self.axis)
+        assert rs == xp
+
+        rs = self.pc.convert(np_datetime64_compat("2012-01-01"), None, self.axis)
+        assert rs == xp
+
+        rs = self.pc.convert(
+            np_datetime64_compat("2012-01-01 00:00:00+0000"), None, self.axis
+        )
+        assert rs == xp
+
+        rs = self.pc.convert(
+            np.array(
+                [
+                    np_datetime64_compat("2012-01-01 00:00:00+0000"),
+                    np_datetime64_compat("2012-01-02 00:00:00+0000"),
+                ]
+            ),
+            None,
+            self.axis,
+        )
+        assert rs[0] == xp
+
+    def test_integer_passthrough(self):
+        # GH9012
+        rs = self.pc.convert([0, 1], None, self.axis)
+        xp = [0, 1]
+        assert rs == xp
+
+    def test_convert_nested(self):
+        data = ["2012-1-1", "2012-1-2"]
+        r1 = self.pc.convert([data, data], None, self.axis)
+        r2 = [self.pc.convert(data, None, self.axis) for _ in range(2)]
+        assert r1 == r2

venv/lib/python3.6/site-packages/pandas/tests/plotting/test_groupby.py

@@ -0,0 +1,69 @@
+# coding: utf-8
+
+""" Test cases for GroupBy.plot """
+
+
+import numpy as np
+
+import pandas.util._test_decorators as td
+
+from pandas import DataFrame, Series
+from pandas.tests.plotting.common import TestPlotBase
+import pandas.util.testing as tm
+
+
+@td.skip_if_no_mpl
+class TestDataFrameGroupByPlots(TestPlotBase):
+    def test_series_groupby_plotting_nominally_works(self):
+        n = 10
+        weight = Series(np.random.normal(166, 20, size=n))
+        height = Series(np.random.normal(60, 10, size=n))
+        with tm.RNGContext(42):
+            gender = np.random.choice(["male", "female"], size=n)
+
+        weight.groupby(gender).plot()
+        tm.close()
+        height.groupby(gender).hist()
+        tm.close()
+        # Regression test for GH8733
+        height.groupby(gender).plot(alpha=0.5)
+        tm.close()
+
+    def test_plotting_with_float_index_works(self):
+        # GH 7025
+        df = DataFrame(
+            {"def": [1, 1, 1, 2, 2, 2, 3, 3, 3], "val": np.random.randn(9)},
+            index=[1.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 3.0],
+        )
+
+        df.groupby("def")["val"].plot()
+        tm.close()
+        df.groupby("def")["val"].apply(lambda x: x.plot())
+        tm.close()
+
+    def test_hist_single_row(self):
+        # GH10214
+        bins = np.arange(80, 100 + 2, 1)
+        df = DataFrame({"Name": ["AAA", "BBB"], "ByCol": [1, 2], "Mark": [85, 89]})
+        df["Mark"].hist(by=df["ByCol"], bins=bins)
+        df = DataFrame({"Name": ["AAA"], "ByCol": [1], "Mark": [85]})
+        df["Mark"].hist(by=df["ByCol"], bins=bins)
+
+    def test_plot_submethod_works(self):
+        df = DataFrame({"x": [1, 2, 3, 4, 5], "y": [1, 2, 3, 2, 1], "z": list("ababa")})
+        df.groupby("z").plot.scatter("x", "y")
+        tm.close()
+        df.groupby("z")["x"].plot.line()
+        tm.close()
+
+    def test_plot_kwargs(self):
+
+        df = DataFrame({"x": [1, 2, 3, 4, 5], "y": [1, 2, 3, 2, 1], "z": list("ababa")})
+
+        res = df.groupby("z").plot(kind="scatter", x="x", y="y")
+        # check that a scatter plot is effectively plotted: the axes should
+        # contain a PathCollection from the scatter plot (GH11805)
+        assert len(res["a"].collections) == 1
+
+        res = df.groupby("z").plot.scatter(x="x", y="y")
+        assert len(res["a"].collections) == 1

venv/lib/python3.6/site-packages/pandas/tests/plotting/test_hist_method.py

@@ -0,0 +1,445 @@
+# coding: utf-8
+
+""" Test cases for .hist method """
+
+import numpy as np
+from numpy.random import randn
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import DataFrame, Series
+from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
+import pandas.util.testing as tm
+
+
+@td.skip_if_no_mpl
+class TestSeriesPlots(TestPlotBase):
+    def setup_method(self, method):
+        TestPlotBase.setup_method(self, method)
+        import matplotlib as mpl
+
+        mpl.rcdefaults()
+
+        self.ts = tm.makeTimeSeries()
+        self.ts.name = "ts"
+
+    @pytest.mark.slow
+    def test_hist_legacy(self):
+        _check_plot_works(self.ts.hist)
+        _check_plot_works(self.ts.hist, grid=False)
+        _check_plot_works(self.ts.hist, figsize=(8, 10))
+        # _check_plot_works adds an ax so catch warning. see GH #13188
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(self.ts.hist, by=self.ts.index.month)
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5)
+
+        fig, ax = self.plt.subplots(1, 1)
+        _check_plot_works(self.ts.hist, ax=ax)
+        _check_plot_works(self.ts.hist, ax=ax, figure=fig)
+        _check_plot_works(self.ts.hist, figure=fig)
+        tm.close()
+
+        fig, (ax1, ax2) = self.plt.subplots(1, 2)
+        _check_plot_works(self.ts.hist, figure=fig, ax=ax1)
+        _check_plot_works(self.ts.hist, figure=fig, ax=ax2)
+
+        with pytest.raises(ValueError):
+            self.ts.hist(by=self.ts.index, figure=fig)
+
+    @pytest.mark.slow
+    def test_hist_bins_legacy(self):
+        df = DataFrame(np.random.randn(10, 2))
+        ax = df.hist(bins=2)[0][0]
+        assert len(ax.patches) == 2
+
+    @pytest.mark.slow
+    def test_hist_layout(self):
+        df = self.hist_df
+        with pytest.raises(ValueError):
+            df.height.hist(layout=(1, 1))
+
+        with pytest.raises(ValueError):
+            df.height.hist(layout=[1, 1])
+
+    @pytest.mark.slow
+    def test_hist_layout_with_by(self):
+        df = self.hist_df
+
+        # _check_plot_works adds an `ax` kwarg to the method call
+        # so we get a warning about an axis being cleared, even
+        # though we don't explicing pass one, see GH #13188
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.gender, layout=(2, 1))
+        self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.gender, layout=(3, -1))
+        self._check_axes_shape(axes, axes_num=2, layout=(3, 1))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.category, layout=(4, 1))
+        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.category, layout=(2, -1))
+        self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.category, layout=(3, -1))
+        self._check_axes_shape(axes, axes_num=4, layout=(3, 2))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.category, layout=(-1, 4))
+        self._check_axes_shape(axes, axes_num=4, layout=(1, 4))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.classroom, layout=(2, 2))
+        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
+
+        axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7))
+        self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 7))
+
+    @pytest.mark.slow
+    def test_hist_no_overlap(self):
+        from matplotlib.pyplot import subplot, gcf
+
+        x = Series(randn(2))
+        y = Series(randn(2))
+        subplot(121)
+        x.hist()
+        subplot(122)
+        y.hist()
+        fig = gcf()
+        axes = fig.axes
+        assert len(axes) == 2
+
+    @pytest.mark.slow
+    def test_hist_by_no_extra_plots(self):
+        df = self.hist_df
+        axes = df.height.hist(by=df.gender)  # noqa
+        assert len(self.plt.get_fignums()) == 1
+
+    @pytest.mark.slow
+    def test_plot_fails_when_ax_differs_from_figure(self):
+        from pylab import figure
+
+        fig1 = figure()
+        fig2 = figure()
+        ax1 = fig1.add_subplot(111)
+        with pytest.raises(AssertionError):
+            self.ts.hist(ax=ax1, figure=fig2)
+
+
+@td.skip_if_no_mpl
+class TestDataFramePlots(TestPlotBase):
+    @pytest.mark.slow
+    def test_hist_df_legacy(self):
+        from matplotlib.patches import Rectangle
+
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(self.hist_df.hist)
+
+        # make sure layout is handled
+        df = DataFrame(randn(100, 3))
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.hist, grid=False)
+        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
+        assert not axes[1, 1].get_visible()
+
+        df = DataFrame(randn(100, 1))
+        _check_plot_works(df.hist)
+
+        # make sure layout is handled
+        df = DataFrame(randn(100, 6))
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.hist, layout=(4, 2))
+        self._check_axes_shape(axes, axes_num=6, layout=(4, 2))
+
+        # make sure sharex, sharey is handled
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.hist, sharex=True, sharey=True)
+
+        # handle figsize arg
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.hist, figsize=(8, 10))
+
+        # check bins argument
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(df.hist, bins=5)
+
+        # make sure xlabelsize and xrot are handled
+        ser = df[0]
+        xf, yf = 20, 18
+        xrot, yrot = 30, 40
+        axes = ser.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot)
+        self._check_ticks_props(
+            axes, xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot
+        )
+
+        xf, yf = 20, 18
+        xrot, yrot = 30, 40
+        axes = df.hist(xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot)
+        self._check_ticks_props(
+            axes, xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot
+        )
+
+        tm.close()
+
+        ax = ser.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 = ser.hist(log=True)
+        # scale of y must be 'log'
+        self._check_ax_scales(ax, yaxis="log")
+
+        tm.close()
+
+        # propagate attr exception from matplotlib.Axes.hist
+        with pytest.raises(AttributeError):
+            ser.hist(foo="bar")
+
+    @pytest.mark.slow
+    def test_hist_non_numerical_raises(self):
+        # gh-10444
+        df = DataFrame(np.random.rand(10, 2))
+        df_o = df.astype(np.object)
+
+        msg = "hist method requires numerical columns, nothing to plot."
+        with pytest.raises(ValueError, match=msg):
+            df_o.hist()
+
+    @pytest.mark.slow
+    def test_hist_layout(self):
+        df = DataFrame(randn(100, 3))
+
+        layout_to_expected_size = (
+            {"layout": None, "expected_size": (2, 2)},  # default is 2x2
+            {"layout": (2, 2), "expected_size": (2, 2)},
+            {"layout": (4, 1), "expected_size": (4, 1)},
+            {"layout": (1, 4), "expected_size": (1, 4)},
+            {"layout": (3, 3), "expected_size": (3, 3)},
+            {"layout": (-1, 4), "expected_size": (1, 4)},
+            {"layout": (4, -1), "expected_size": (4, 1)},
+            {"layout": (-1, 2), "expected_size": (2, 2)},
+            {"layout": (2, -1), "expected_size": (2, 2)},
+        )
+
+        for layout_test in layout_to_expected_size:
+            axes = df.hist(layout=layout_test["layout"])
+            expected = layout_test["expected_size"]
+            self._check_axes_shape(axes, axes_num=3, layout=expected)
+
+        # layout too small for all 4 plots
+        with pytest.raises(ValueError):
+            df.hist(layout=(1, 1))
+
+        # invalid format for layout
+        with pytest.raises(ValueError):
+            df.hist(layout=(1,))
+        with pytest.raises(ValueError):
+            df.hist(layout=(-1, -1))
+
+    @pytest.mark.slow
+    # GH 9351
+    def test_tight_layout(self):
+        df = DataFrame(randn(100, 3))
+        _check_plot_works(df.hist)
+        self.plt.tight_layout()
+
+        tm.close()
+
+
+@td.skip_if_no_mpl
+class TestDataFrameGroupByPlots(TestPlotBase):
+    @pytest.mark.slow
+    def test_grouped_hist_legacy(self):
+        from matplotlib.patches import Rectangle
+        from pandas.plotting._matplotlib.hist import _grouped_hist
+
+        df = DataFrame(randn(500, 2), columns=["A", "B"])
+        df["C"] = np.random.randint(0, 4, 500)
+        df["D"] = ["X"] * 500
+
+        axes = _grouped_hist(df.A, by=df.C)
+        self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
+
+        tm.close()
+        axes = df.hist(by=df.C)
+        self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
+
+        tm.close()
+        # group by a key with single value
+        axes = df.hist(by="D", rot=30)
+        self._check_axes_shape(axes, axes_num=1, layout=(1, 1))
+        self._check_ticks_props(axes, xrot=30)
+
+        tm.close()
+        # make sure kwargs to hist are handled
+        xf, yf = 20, 18
+        xrot, yrot = 30, 40
+
+        axes = _grouped_hist(
+            df.A,
+            by=df.C,
+            cumulative=True,
+            bins=4,
+            xlabelsize=xf,
+            xrot=xrot,
+            ylabelsize=yf,
+            yrot=yrot,
+            density=True,
+        )
+        # height of last bin (index 5) must be 1.0
+        for ax in axes.ravel():
+            rects = [x for x in ax.get_children() if isinstance(x, Rectangle)]
+            height = rects[-1].get_height()
+            tm.assert_almost_equal(height, 1.0)
+        self._check_ticks_props(
+            axes, xlabelsize=xf, xrot=xrot, ylabelsize=yf, yrot=yrot
+        )
+
+        tm.close()
+        axes = _grouped_hist(df.A, by=df.C, log=True)
+        # scale of y must be 'log'
+        self._check_ax_scales(axes, yaxis="log")
+
+        tm.close()
+        # propagate attr exception from matplotlib.Axes.hist
+        with pytest.raises(AttributeError):
+            _grouped_hist(df.A, by=df.C, foo="bar")
+
+        with tm.assert_produces_warning(FutureWarning):
+            df.hist(by="C", figsize="default")
+
+    @pytest.mark.slow
+    def test_grouped_hist_legacy2(self):
+        n = 10
+        weight = Series(np.random.normal(166, 20, size=n))
+        height = Series(np.random.normal(60, 10, size=n))
+        with tm.RNGContext(42):
+            gender_int = np.random.choice([0, 1], size=n)
+        df_int = DataFrame({"height": height, "weight": weight, "gender": gender_int})
+        gb = df_int.groupby("gender")
+        axes = gb.hist()
+        assert len(axes) == 2
+        assert len(self.plt.get_fignums()) == 2
+        tm.close()
+
+    @pytest.mark.slow
+    def test_grouped_hist_layout(self):
+        df = self.hist_df
+        msg = "Layout of 1x1 must be larger than required size 2"
+        with pytest.raises(ValueError, match=msg):
+            df.hist(column="weight", by=df.gender, layout=(1, 1))
+
+        msg = "Layout of 1x3 must be larger than required size 4"
+        with pytest.raises(ValueError, match=msg):
+            df.hist(column="height", by=df.category, layout=(1, 3))
+
+        msg = "At least one dimension of layout must be positive"
+        with pytest.raises(ValueError, match=msg):
+            df.hist(column="height", by=df.category, layout=(-1, -1))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(
+                df.hist, column="height", by=df.gender, layout=(2, 1)
+            )
+        self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(
+                df.hist, column="height", by=df.gender, layout=(2, -1)
+            )
+        self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
+
+        axes = df.hist(column="height", by=df.category, layout=(4, 1))
+        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
+
+        axes = df.hist(column="height", by=df.category, layout=(-1, 1))
+        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
+
+        axes = df.hist(column="height", by=df.category, layout=(4, 2), figsize=(12, 8))
+        self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 8))
+        tm.close()
+
+        # GH 6769
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(
+                df.hist, column="height", by="classroom", layout=(2, 2)
+            )
+        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
+
+        # without column
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.hist, by="classroom")
+        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
+
+        axes = df.hist(by="gender", layout=(3, 5))
+        self._check_axes_shape(axes, axes_num=2, layout=(3, 5))
+
+        axes = df.hist(column=["height", "weight", "category"])
+        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
+
+    @pytest.mark.slow
+    def test_grouped_hist_multiple_axes(self):
+        # GH 6970, GH 7069
+        df = self.hist_df
+
+        fig, axes = self.plt.subplots(2, 3)
+        returned = df.hist(column=["height", "weight", "category"], ax=axes[0])
+        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
+        tm.assert_numpy_array_equal(returned, axes[0])
+        assert returned[0].figure is fig
+        returned = df.hist(by="classroom", ax=axes[1])
+        self._check_axes_shape(returned, axes_num=3, layout=(1, 3))
+        tm.assert_numpy_array_equal(returned, axes[1])
+        assert returned[0].figure is fig
+
+        with pytest.raises(ValueError):
+            fig, axes = self.plt.subplots(2, 3)
+            # pass different number of axes from required
+            axes = df.hist(column="height", ax=axes)
+
+    @pytest.mark.slow
+    def test_axis_share_x(self):
+        df = self.hist_df
+        # GH4089
+        ax1, ax2 = df.hist(column="height", by=df.gender, sharex=True)
+
+        # share x
+        assert ax1._shared_x_axes.joined(ax1, ax2)
+        assert ax2._shared_x_axes.joined(ax1, ax2)
+
+        # don't share y
+        assert not ax1._shared_y_axes.joined(ax1, ax2)
+        assert not ax2._shared_y_axes.joined(ax1, ax2)
+
+    @pytest.mark.slow
+    def test_axis_share_y(self):
+        df = self.hist_df
+        ax1, ax2 = df.hist(column="height", by=df.gender, sharey=True)
+
+        # share y
+        assert ax1._shared_y_axes.joined(ax1, ax2)
+        assert ax2._shared_y_axes.joined(ax1, ax2)
+
+        # don't share x
+        assert not ax1._shared_x_axes.joined(ax1, ax2)
+        assert not ax2._shared_x_axes.joined(ax1, ax2)
+
+    @pytest.mark.slow
+    def test_axis_share_xy(self):
+        df = self.hist_df
+        ax1, ax2 = df.hist(column="height", by=df.gender, sharex=True, sharey=True)
+
+        # share both x and y
+        assert ax1._shared_x_axes.joined(ax1, ax2)
+        assert ax2._shared_x_axes.joined(ax1, ax2)
+
+        assert ax1._shared_y_axes.joined(ax1, ax2)
+        assert ax2._shared_y_axes.joined(ax1, ax2)

venv/lib/python3.6/site-packages/pandas/tests/plotting/test_misc.py

@@ -0,0 +1,419 @@
+# coding: utf-8
+
+""" Test cases for misc plot functions """
+
+import numpy as np
+from numpy import random
+from numpy.random import randn
+import pytest
+
+import pandas.util._test_decorators as td
+
+from pandas import DataFrame, Series
+from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
+import pandas.util.testing as tm
+
+import pandas.plotting as plotting
+
+
+@td.skip_if_mpl
+def test_import_error_message():
+    # GH-19810
+    df = DataFrame({"A": [1, 2]})
+
+    with pytest.raises(ImportError, match="No module named 'matplotlib'"):
+        df.plot()
+
+
+def test_get_accessor_args():
+    func = plotting._core.PlotAccessor._get_call_args
+
+    msg = "Called plot accessor for type list, expected Series or DataFrame"
+    with pytest.raises(TypeError, match=msg):
+        func(backend_name="", data=[], args=[], kwargs={})
+
+    with tm.assert_produces_warning(FutureWarning, check_stacklevel=False):
+        x, y, kind, kwargs = func(
+            backend_name="", data=Series(), args=["line", None], kwargs={}
+        )
+    assert x is None
+    assert y is None
+    assert kind == "line"
+    assert kwargs == {"ax": None}
+
+    x, y, kind, kwargs = func(
+        backend_name="",
+        data=DataFrame(),
+        args=["x"],
+        kwargs={"y": "y", "kind": "bar", "grid": False},
+    )
+    assert x == "x"
+    assert y == "y"
+    assert kind == "bar"
+    assert kwargs == {"grid": False}
+
+    x, y, kind, kwargs = func(
+        backend_name="pandas.plotting._matplotlib", data=Series(), args=[], kwargs={}
+    )
+    assert x is None
+    assert y is None
+    assert kind == "line"
+    assert len(kwargs) == 22
+
+
+@td.skip_if_no_mpl
+class TestSeriesPlots(TestPlotBase):
+    def setup_method(self, method):
+        TestPlotBase.setup_method(self, method)
+        import matplotlib as mpl
+
+        mpl.rcdefaults()
+
+        self.ts = tm.makeTimeSeries()
+        self.ts.name = "ts"
+
+    @pytest.mark.slow
+    def test_autocorrelation_plot(self):
+        from pandas.plotting import autocorrelation_plot
+
+        _check_plot_works(autocorrelation_plot, series=self.ts)
+        _check_plot_works(autocorrelation_plot, series=self.ts.values)
+
+        ax = autocorrelation_plot(self.ts, label="Test")
+        self._check_legend_labels(ax, labels=["Test"])
+
+    @pytest.mark.slow
+    def test_lag_plot(self):
+        from pandas.plotting import lag_plot
+
+        _check_plot_works(lag_plot, series=self.ts)
+        _check_plot_works(lag_plot, series=self.ts, lag=5)
+
+    @pytest.mark.slow
+    def test_bootstrap_plot(self):
+        from pandas.plotting import bootstrap_plot
+
+        _check_plot_works(bootstrap_plot, series=self.ts, size=10)
+
+
+@td.skip_if_no_mpl
+class TestDataFramePlots(TestPlotBase):
+    @td.skip_if_no_scipy
+    def test_scatter_matrix_axis(self):
+        scatter_matrix = plotting.scatter_matrix
+
+        with tm.RNGContext(42):
+            df = DataFrame(randn(100, 3))
+
+        # we are plotting multiples on a sub-plot
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(
+                scatter_matrix, filterwarnings="always", frame=df, range_padding=0.1
+            )
+        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
+
+        # GH 5662
+        expected = ["-2", "0", "2"]
+        self._check_text_labels(axes0_labels, expected)
+        self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
+
+        df[0] = (df[0] - 2) / 3
+
+        # we are plotting multiples on a sub-plot
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(
+                scatter_matrix, filterwarnings="always", frame=df, range_padding=0.1
+            )
+        axes0_labels = axes[0][0].yaxis.get_majorticklabels()
+        expected = ["-1.0", "-0.5", "0.0"]
+        self._check_text_labels(axes0_labels, expected)
+        self._check_ticks_props(axes, xlabelsize=8, xrot=90, ylabelsize=8, yrot=0)
+
+    @pytest.mark.slow
+    def test_andrews_curves(self, iris):
+        from pandas.plotting import andrews_curves
+        from matplotlib import cm
+
+        df = iris
+
+        _check_plot_works(andrews_curves, frame=df, class_column="Name")
+
+        rgba = ("#556270", "#4ECDC4", "#C7F464")
+        ax = _check_plot_works(
+            andrews_curves, frame=df, class_column="Name", color=rgba
+        )
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
+        )
+
+        cnames = ["dodgerblue", "aquamarine", "seagreen"]
+        ax = _check_plot_works(
+            andrews_curves, frame=df, class_column="Name", color=cnames
+        )
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
+        )
+
+        ax = _check_plot_works(
+            andrews_curves, frame=df, class_column="Name", colormap=cm.jet
+        )
+        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
+        )
+
+        length = 10
+        df = DataFrame(
+            {
+                "A": random.rand(length),
+                "B": random.rand(length),
+                "C": random.rand(length),
+                "Name": ["A"] * length,
+            }
+        )
+
+        _check_plot_works(andrews_curves, frame=df, class_column="Name")
+
+        rgba = ("#556270", "#4ECDC4", "#C7F464")
+        ax = _check_plot_works(
+            andrews_curves, frame=df, class_column="Name", color=rgba
+        )
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
+        )
+
+        cnames = ["dodgerblue", "aquamarine", "seagreen"]
+        ax = _check_plot_works(
+            andrews_curves, frame=df, class_column="Name", color=cnames
+        )
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
+        )
+
+        ax = _check_plot_works(
+            andrews_curves, frame=df, class_column="Name", colormap=cm.jet
+        )
+        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
+        )
+
+        colors = ["b", "g", "r"]
+        df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors})
+        ax = andrews_curves(df, "Name", color=colors)
+        handles, labels = ax.get_legend_handles_labels()
+        self._check_colors(handles, linecolors=colors)
+
+        with tm.assert_produces_warning(FutureWarning):
+            andrews_curves(data=df, class_column="Name")
+
+    @pytest.mark.slow
+    def test_parallel_coordinates(self, iris):
+        from pandas.plotting import parallel_coordinates
+        from matplotlib import cm
+
+        df = iris
+
+        ax = _check_plot_works(parallel_coordinates, frame=df, class_column="Name")
+        nlines = len(ax.get_lines())
+        nxticks = len(ax.xaxis.get_ticklabels())
+
+        rgba = ("#556270", "#4ECDC4", "#C7F464")
+        ax = _check_plot_works(
+            parallel_coordinates, frame=df, class_column="Name", color=rgba
+        )
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=rgba, mapping=df["Name"][:10]
+        )
+
+        cnames = ["dodgerblue", "aquamarine", "seagreen"]
+        ax = _check_plot_works(
+            parallel_coordinates, frame=df, class_column="Name", color=cnames
+        )
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=cnames, mapping=df["Name"][:10]
+        )
+
+        ax = _check_plot_works(
+            parallel_coordinates, frame=df, class_column="Name", colormap=cm.jet
+        )
+        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
+        self._check_colors(
+            ax.get_lines()[:10], linecolors=cmaps, mapping=df["Name"][:10]
+        )
+
+        ax = _check_plot_works(
+            parallel_coordinates, frame=df, class_column="Name", axvlines=False
+        )
+        assert len(ax.get_lines()) == (nlines - nxticks)
+
+        colors = ["b", "g", "r"]
+        df = DataFrame({"A": [1, 2, 3], "B": [1, 2, 3], "C": [1, 2, 3], "Name": colors})
+        ax = parallel_coordinates(df, "Name", color=colors)
+        handles, labels = ax.get_legend_handles_labels()
+        self._check_colors(handles, linecolors=colors)
+
+        with tm.assert_produces_warning(FutureWarning):
+            parallel_coordinates(data=df, class_column="Name")
+        with tm.assert_produces_warning(FutureWarning):
+            parallel_coordinates(df, "Name", colors=colors)
+
+    # not sure if this is indicative of a problem
+    @pytest.mark.filterwarnings("ignore:Attempting to set:UserWarning")
+    def test_parallel_coordinates_with_sorted_labels(self):
+        """ For #15908 """
+        from pandas.plotting import parallel_coordinates
+
+        df = DataFrame(
+            {
+                "feat": [i for i in range(30)],
+                "class": [2 for _ in range(10)]
+                + [3 for _ in range(10)]
+                + [1 for _ in range(10)],
+            }
+        )
+        ax = parallel_coordinates(df, "class", sort_labels=True)
+        polylines, labels = ax.get_legend_handles_labels()
+        color_label_tuples = zip(
+            [polyline.get_color() for polyline in polylines], labels
+        )
+        ordered_color_label_tuples = sorted(color_label_tuples, key=lambda x: x[1])
+        prev_next_tupels = zip(
+            [i for i in ordered_color_label_tuples[0:-1]],
+            [i for i in ordered_color_label_tuples[1:]],
+        )
+        for prev, nxt in prev_next_tupels:
+            # labels and colors are ordered strictly increasing
+            assert prev[1] < nxt[1] and prev[0] < nxt[0]
+
+    @pytest.mark.slow
+    def test_radviz(self, iris):
+        from pandas.plotting import radviz
+        from matplotlib import cm
+
+        df = iris
+        _check_plot_works(radviz, frame=df, class_column="Name")
+
+        rgba = ("#556270", "#4ECDC4", "#C7F464")
+        ax = _check_plot_works(radviz, frame=df, class_column="Name", color=rgba)
+        # skip Circle drawn as ticks
+        patches = [p for p in ax.patches[:20] if p.get_label() != ""]
+        self._check_colors(patches[:10], facecolors=rgba, mapping=df["Name"][:10])
+
+        cnames = ["dodgerblue", "aquamarine", "seagreen"]
+        _check_plot_works(radviz, frame=df, class_column="Name", color=cnames)
+        patches = [p for p in ax.patches[:20] if p.get_label() != ""]
+        self._check_colors(patches, facecolors=cnames, mapping=df["Name"][:10])
+
+        _check_plot_works(radviz, frame=df, class_column="Name", colormap=cm.jet)
+        cmaps = [cm.jet(n) for n in np.linspace(0, 1, df["Name"].nunique())]
+        patches = [p for p in ax.patches[:20] if p.get_label() != ""]
+        self._check_colors(patches, facecolors=cmaps, mapping=df["Name"][:10])
+
+        colors = [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 1.0, 1.0], [1.0, 0.0, 0.0, 1.0]]
+        df = DataFrame(
+            {"A": [1, 2, 3], "B": [2, 1, 3], "C": [3, 2, 1], "Name": ["b", "g", "r"]}
+        )
+        ax = radviz(df, "Name", color=colors)
+        handles, labels = ax.get_legend_handles_labels()
+        self._check_colors(handles, facecolors=colors)
+
+    @pytest.mark.slow
+    def test_subplot_titles(self, iris):
+        df = iris.drop("Name", axis=1).head()
+        # Use the column names as the subplot titles
+        title = list(df.columns)
+
+        # Case len(title) == len(df)
+        plot = df.plot(subplots=True, title=title)
+        assert [p.get_title() for p in plot] == title
+
+        # Case len(title) > len(df)
+        msg = (
+            "The length of `title` must equal the number of columns if"
+            " using `title` of type `list` and `subplots=True`"
+        )
+        with pytest.raises(ValueError, match=msg):
+            df.plot(subplots=True, title=title + ["kittens > puppies"])
+
+        # Case len(title) < len(df)
+        with pytest.raises(ValueError, match=msg):
+            df.plot(subplots=True, title=title[:2])
+
+        # Case subplots=False and title is of type list
+        msg = (
+            "Using `title` of type `list` is not supported unless"
+            " `subplots=True` is passed"
+        )
+        with pytest.raises(ValueError, match=msg):
+            df.plot(subplots=False, title=title)
+
+        # Case df with 3 numeric columns but layout of (2,2)
+        plot = df.drop("SepalWidth", axis=1).plot(
+            subplots=True, layout=(2, 2), title=title[:-1]
+        )
+        title_list = [ax.get_title() for sublist in plot for ax in sublist]
+        assert title_list == title[:3] + [""]
+
+    def test_get_standard_colors_random_seed(self):
+        # GH17525
+        df = DataFrame(np.zeros((10, 10)))
+
+        # Make sure that the random seed isn't reset by _get_standard_colors
+        plotting.parallel_coordinates(df, 0)
+        rand1 = random.random()
+        plotting.parallel_coordinates(df, 0)
+        rand2 = random.random()
+        assert rand1 != rand2
+
+        # Make sure it produces the same colors every time it's called
+        from pandas.plotting._matplotlib.style import _get_standard_colors
+
+        color1 = _get_standard_colors(1, color_type="random")
+        color2 = _get_standard_colors(1, color_type="random")
+        assert color1 == color2
+
+    def test_get_standard_colors_default_num_colors(self):
+        from pandas.plotting._matplotlib.style import _get_standard_colors
+
+        # Make sure the default color_types returns the specified amount
+        color1 = _get_standard_colors(1, color_type="default")
+        color2 = _get_standard_colors(9, color_type="default")
+        color3 = _get_standard_colors(20, color_type="default")
+        assert len(color1) == 1
+        assert len(color2) == 9
+        assert len(color3) == 20
+
+    def test_plot_single_color(self):
+        # Example from #20585. All 3 bars should have the same color
+        df = DataFrame(
+            {
+                "account-start": ["2017-02-03", "2017-03-03", "2017-01-01"],
+                "client": ["Alice Anders", "Bob Baker", "Charlie Chaplin"],
+                "balance": [-1432.32, 10.43, 30000.00],
+                "db-id": [1234, 2424, 251],
+                "proxy-id": [525, 1525, 2542],
+                "rank": [52, 525, 32],
+            }
+        )
+        ax = df.client.value_counts().plot.bar()
+        colors = [rect.get_facecolor() for rect in ax.get_children()[0:3]]
+        assert all(color == colors[0] for color in colors)
+
+    def test_get_standard_colors_no_appending(self):
+        # GH20726
+
+        # Make sure not to add more colors so that matplotlib can cycle
+        # correctly.
+        from matplotlib import cm
+        from pandas.plotting._matplotlib.style import _get_standard_colors
+
+        color_before = cm.gnuplot(range(5))
+        color_after = _get_standard_colors(1, color=color_before)
+        assert len(color_after) == len(color_before)
+
+        df = DataFrame(np.random.randn(48, 4), columns=list("ABCD"))
+
+        color_list = cm.gnuplot(np.linspace(0, 1, 16))
+        p = df.A.plot.bar(figsize=(16, 7), color=color_list)
+        assert p.patches[1].get_facecolor() == p.patches[17].get_facecolor()

venv/lib/python3.6/site-packages/pandas/tests/plotting/test_series.py

@@ -0,0 +1,890 @@
+# coding: utf-8
+
+""" Test cases for Series.plot """
+
+
+from datetime import datetime
+from itertools import chain
+
+import numpy as np
+from numpy.random import randn
+import pytest
+
+import pandas.util._test_decorators as td
+
+import pandas as pd
+from pandas import DataFrame, Series, date_range
+from pandas.tests.plotting.common import TestPlotBase, _check_plot_works
+import pandas.util.testing as tm
+
+import pandas.plotting as plotting
+
+
+@td.skip_if_no_mpl
+class TestSeriesPlots(TestPlotBase):
+    def setup_method(self, method):
+        TestPlotBase.setup_method(self, method)
+        import matplotlib as mpl
+
+        mpl.rcdefaults()
+
+        self.ts = tm.makeTimeSeries()
+        self.ts.name = "ts"
+
+        self.series = tm.makeStringSeries()
+        self.series.name = "series"
+
+        self.iseries = tm.makePeriodSeries()
+        self.iseries.name = "iseries"
+
+    @pytest.mark.slow
+    def test_plot(self):
+        _check_plot_works(self.ts.plot, label="foo")
+        _check_plot_works(self.ts.plot, use_index=False)
+        axes = _check_plot_works(self.ts.plot, rot=0)
+        self._check_ticks_props(axes, xrot=0)
+
+        ax = _check_plot_works(self.ts.plot, style=".", logy=True)
+        self._check_ax_scales(ax, yaxis="log")
+
+        ax = _check_plot_works(self.ts.plot, style=".", logx=True)
+        self._check_ax_scales(ax, xaxis="log")
+
+        ax = _check_plot_works(self.ts.plot, style=".", loglog=True)
+        self._check_ax_scales(ax, xaxis="log", yaxis="log")
+
+        _check_plot_works(self.ts[:10].plot.bar)
+        _check_plot_works(self.ts.plot.area, stacked=False)
+        _check_plot_works(self.iseries.plot)
+
+        for kind in ["line", "bar", "barh", "kde", "hist", "box"]:
+            _check_plot_works(self.series[:5].plot, kind=kind)
+
+        _check_plot_works(self.series[:10].plot.barh)
+        ax = _check_plot_works(Series(randn(10)).plot.bar, color="black")
+        self._check_colors([ax.patches[0]], facecolors=["black"])
+
+        # GH 6951
+        ax = _check_plot_works(self.ts.plot, subplots=True)
+        self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
+
+        ax = _check_plot_works(self.ts.plot, subplots=True, layout=(-1, 1))
+        self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
+        ax = _check_plot_works(self.ts.plot, subplots=True, layout=(1, -1))
+        self._check_axes_shape(ax, axes_num=1, layout=(1, 1))
+
+    @pytest.mark.slow
+    def test_plot_figsize_and_title(self):
+        # figsize and title
+        _, ax = self.plt.subplots()
+        ax = self.series.plot(title="Test", figsize=(16, 8), ax=ax)
+        self._check_text_labels(ax.title, "Test")
+        self._check_axes_shape(ax, axes_num=1, layout=(1, 1), figsize=(16, 8))
+
+    def test_dont_modify_rcParams(self):
+        # GH 8242
+        key = "axes.prop_cycle"
+        colors = self.plt.rcParams[key]
+        _, ax = self.plt.subplots()
+        Series([1, 2, 3]).plot(ax=ax)
+        assert colors == self.plt.rcParams[key]
+
+    def test_ts_line_lim(self):
+        fig, ax = self.plt.subplots()
+        ax = self.ts.plot(ax=ax)
+        xmin, xmax = ax.get_xlim()
+        lines = ax.get_lines()
+        assert xmin <= lines[0].get_data(orig=False)[0][0]
+        assert xmax >= lines[0].get_data(orig=False)[0][-1]
+        tm.close()
+
+        ax = self.ts.plot(secondary_y=True, ax=ax)
+        xmin, xmax = ax.get_xlim()
+        lines = ax.get_lines()
+        assert xmin <= lines[0].get_data(orig=False)[0][0]
+        assert xmax >= lines[0].get_data(orig=False)[0][-1]
+
+    def test_ts_area_lim(self):
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.area(stacked=False, ax=ax)
+        xmin, xmax = ax.get_xlim()
+        line = ax.get_lines()[0].get_data(orig=False)[0]
+        assert xmin <= line[0]
+        assert xmax >= line[-1]
+        tm.close()
+
+        # GH 7471
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.area(stacked=False, x_compat=True, ax=ax)
+        xmin, xmax = ax.get_xlim()
+        line = ax.get_lines()[0].get_data(orig=False)[0]
+        assert xmin <= line[0]
+        assert xmax >= line[-1]
+        tm.close()
+
+        tz_ts = self.ts.copy()
+        tz_ts.index = tz_ts.tz_localize("GMT").tz_convert("CET")
+        _, ax = self.plt.subplots()
+        ax = tz_ts.plot.area(stacked=False, x_compat=True, ax=ax)
+        xmin, xmax = ax.get_xlim()
+        line = ax.get_lines()[0].get_data(orig=False)[0]
+        assert xmin <= line[0]
+        assert xmax >= line[-1]
+        tm.close()
+
+        _, ax = self.plt.subplots()
+        ax = tz_ts.plot.area(stacked=False, secondary_y=True, ax=ax)
+        xmin, xmax = ax.get_xlim()
+        line = ax.get_lines()[0].get_data(orig=False)[0]
+        assert xmin <= line[0]
+        assert xmax >= line[-1]
+
+    def test_label(self):
+        s = Series([1, 2])
+        _, ax = self.plt.subplots()
+        ax = s.plot(label="LABEL", legend=True, ax=ax)
+        self._check_legend_labels(ax, labels=["LABEL"])
+        self.plt.close()
+        _, ax = self.plt.subplots()
+        ax = s.plot(legend=True, ax=ax)
+        self._check_legend_labels(ax, labels=["None"])
+        self.plt.close()
+        # get name from index
+        s.name = "NAME"
+        _, ax = self.plt.subplots()
+        ax = s.plot(legend=True, ax=ax)
+        self._check_legend_labels(ax, labels=["NAME"])
+        self.plt.close()
+        # override the default
+        _, ax = self.plt.subplots()
+        ax = s.plot(legend=True, label="LABEL", ax=ax)
+        self._check_legend_labels(ax, labels=["LABEL"])
+        self.plt.close()
+        # Add lebel info, but don't draw
+        _, ax = self.plt.subplots()
+        ax = s.plot(legend=False, label="LABEL", ax=ax)
+        assert ax.get_legend() is None  # Hasn't been drawn
+        ax.legend()  # draw it
+        self._check_legend_labels(ax, labels=["LABEL"])
+
+    def test_line_area_nan_series(self):
+        values = [1, 2, np.nan, 3]
+        s = Series(values)
+        ts = Series(values, index=tm.makeDateIndex(k=4))
+
+        for d in [s, ts]:
+            ax = _check_plot_works(d.plot)
+            masked = ax.lines[0].get_ydata()
+            # remove nan for comparison purpose
+            exp = np.array([1, 2, 3], dtype=np.float64)
+            tm.assert_numpy_array_equal(np.delete(masked.data, 2), exp)
+            tm.assert_numpy_array_equal(
+                masked.mask, np.array([False, False, True, False])
+            )
+
+            expected = np.array([1, 2, 0, 3], dtype=np.float64)
+            ax = _check_plot_works(d.plot, stacked=True)
+            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
+            ax = _check_plot_works(d.plot.area)
+            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
+            ax = _check_plot_works(d.plot.area, stacked=False)
+            tm.assert_numpy_array_equal(ax.lines[0].get_ydata(), expected)
+
+    def test_line_use_index_false(self):
+        s = Series([1, 2, 3], index=["a", "b", "c"])
+        s.index.name = "The Index"
+        _, ax = self.plt.subplots()
+        ax = s.plot(use_index=False, ax=ax)
+        label = ax.get_xlabel()
+        assert label == ""
+        _, ax = self.plt.subplots()
+        ax2 = s.plot.bar(use_index=False, ax=ax)
+        label2 = ax2.get_xlabel()
+        assert label2 == ""
+
+    @pytest.mark.slow
+    def test_bar_log(self):
+        expected = np.array([1e-1, 1e0, 1e1, 1e2, 1e3, 1e4])
+
+        _, ax = self.plt.subplots()
+        ax = Series([200, 500]).plot.bar(log=True, ax=ax)
+        tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
+        tm.close()
+
+        _, ax = self.plt.subplots()
+        ax = Series([200, 500]).plot.barh(log=True, ax=ax)
+        tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected)
+        tm.close()
+
+        # GH 9905
+        expected = np.array([1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1])
+
+        _, ax = self.plt.subplots()
+        ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind="bar", ax=ax)
+        ymin = 0.0007943282347242822
+        ymax = 0.12589254117941673
+        res = ax.get_ylim()
+        tm.assert_almost_equal(res[0], ymin)
+        tm.assert_almost_equal(res[1], ymax)
+        tm.assert_numpy_array_equal(ax.yaxis.get_ticklocs(), expected)
+        tm.close()
+
+        _, ax = self.plt.subplots()
+        ax = Series([0.1, 0.01, 0.001]).plot(log=True, kind="barh", ax=ax)
+        res = ax.get_xlim()
+        tm.assert_almost_equal(res[0], ymin)
+        tm.assert_almost_equal(res[1], ymax)
+        tm.assert_numpy_array_equal(ax.xaxis.get_ticklocs(), expected)
+
+    @pytest.mark.slow
+    def test_bar_ignore_index(self):
+        df = Series([1, 2, 3, 4], index=["a", "b", "c", "d"])
+        _, ax = self.plt.subplots()
+        ax = df.plot.bar(use_index=False, ax=ax)
+        self._check_text_labels(ax.get_xticklabels(), ["0", "1", "2", "3"])
+
+    def test_bar_user_colors(self):
+        s = Series([1, 2, 3, 4])
+        ax = s.plot.bar(color=["red", "blue", "blue", "red"])
+        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
+
+    def test_rotation(self):
+        df = DataFrame(randn(5, 5))
+        # Default rot 0
+        _, ax = self.plt.subplots()
+        axes = df.plot(ax=ax)
+        self._check_ticks_props(axes, xrot=0)
+
+        _, ax = self.plt.subplots()
+        axes = df.plot(rot=30, ax=ax)
+        self._check_ticks_props(axes, xrot=30)
+
+    def test_irregular_datetime(self):
+        rng = date_range("1/1/2000", "3/1/2000")
+        rng = rng[[0, 1, 2, 3, 5, 9, 10, 11, 12]]
+        ser = Series(randn(len(rng)), rng)
+        _, ax = self.plt.subplots()
+        ax = ser.plot(ax=ax)
+        xp = datetime(1999, 1, 1).toordinal()
+        ax.set_xlim("1/1/1999", "1/1/2001")
+        assert xp == ax.get_xlim()[0]
+
+    def test_unsorted_index_xlim(self):
+        ser = Series(
+            [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 = self.plt.subplots()
+        ax = ser.plot(ax=ax)
+        xmin, xmax = ax.get_xlim()
+        lines = ax.get_lines()
+        assert xmin <= np.nanmin(lines[0].get_data(orig=False)[0])
+        assert xmax >= np.nanmax(lines[0].get_data(orig=False)[0])
+
+    @pytest.mark.slow
+    def test_pie_series(self):
+        # if sum of values is less than 1.0, pie handle them as rate and draw
+        # semicircle.
+        series = Series(
+            np.random.randint(1, 5), index=["a", "b", "c", "d", "e"], name="YLABEL"
+        )
+        ax = _check_plot_works(series.plot.pie)
+        self._check_text_labels(ax.texts, series.index)
+        assert ax.get_ylabel() == "YLABEL"
+
+        # without wedge labels
+        ax = _check_plot_works(series.plot.pie, labels=None)
+        self._check_text_labels(ax.texts, [""] * 5)
+
+        # with less colors than elements
+        color_args = ["r", "g", "b"]
+        ax = _check_plot_works(series.plot.pie, colors=color_args)
+
+        color_expected = ["r", "g", "b", "r", "g"]
+        self._check_colors(ax.patches, facecolors=color_expected)
+
+        # with labels and colors
+        labels = ["A", "B", "C", "D", "E"]
+        color_args = ["r", "g", "b", "c", "m"]
+        ax = _check_plot_works(series.plot.pie, labels=labels, colors=color_args)
+        self._check_text_labels(ax.texts, labels)
+        self._check_colors(ax.patches, facecolors=color_args)
+
+        # with autopct and fontsize
+        ax = _check_plot_works(
+            series.plot.pie, colors=color_args, autopct="%.2f", fontsize=7
+        )
+        pcts = ["{0:.2f}".format(s * 100) for s in series.values / float(series.sum())]
+        expected_texts = list(chain.from_iterable(zip(series.index, pcts)))
+        self._check_text_labels(ax.texts, expected_texts)
+        for t in ax.texts:
+            assert t.get_fontsize() == 7
+
+        # includes negative value
+        with pytest.raises(ValueError):
+            series = Series([1, 2, 0, 4, -1], index=["a", "b", "c", "d", "e"])
+            series.plot.pie()
+
+        # includes nan
+        series = Series([1, 2, np.nan, 4], index=["a", "b", "c", "d"], name="YLABEL")
+        ax = _check_plot_works(series.plot.pie)
+        self._check_text_labels(ax.texts, ["a", "b", "", "d"])
+
+    def test_pie_nan(self):
+        s = Series([1, np.nan, 1, 1])
+        _, ax = self.plt.subplots()
+        ax = s.plot.pie(legend=True, ax=ax)
+        expected = ["0", "", "2", "3"]
+        result = [x.get_text() for x in ax.texts]
+        assert result == expected
+
+    @pytest.mark.slow
+    def test_hist_df_kwargs(self):
+        df = DataFrame(np.random.randn(10, 2))
+        _, ax = self.plt.subplots()
+        ax = df.plot.hist(bins=5, ax=ax)
+        assert len(ax.patches) == 10
+
+    @pytest.mark.slow
+    def test_hist_df_with_nonnumerics(self):
+        # GH 9853
+        with tm.RNGContext(1):
+            df = DataFrame(np.random.randn(10, 4), columns=["A", "B", "C", "D"])
+        df["E"] = ["x", "y"] * 5
+        _, ax = self.plt.subplots()
+        ax = df.plot.hist(bins=5, ax=ax)
+        assert len(ax.patches) == 20
+
+        _, ax = self.plt.subplots()
+        ax = df.plot.hist(ax=ax)  # bins=10
+        assert len(ax.patches) == 40
+
+    @pytest.mark.slow
+    def test_hist_legacy(self):
+        _check_plot_works(self.ts.hist)
+        _check_plot_works(self.ts.hist, grid=False)
+        _check_plot_works(self.ts.hist, figsize=(8, 10))
+        # _check_plot_works adds an ax so catch warning. see GH #13188
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(self.ts.hist, by=self.ts.index.month)
+        with tm.assert_produces_warning(UserWarning):
+            _check_plot_works(self.ts.hist, by=self.ts.index.month, bins=5)
+
+        fig, ax = self.plt.subplots(1, 1)
+        _check_plot_works(self.ts.hist, ax=ax)
+        _check_plot_works(self.ts.hist, ax=ax, figure=fig)
+        _check_plot_works(self.ts.hist, figure=fig)
+        tm.close()
+
+        fig, (ax1, ax2) = self.plt.subplots(1, 2)
+        _check_plot_works(self.ts.hist, figure=fig, ax=ax1)
+        _check_plot_works(self.ts.hist, figure=fig, ax=ax2)
+
+        with pytest.raises(ValueError):
+            self.ts.hist(by=self.ts.index, figure=fig)
+
+    @pytest.mark.slow
+    def test_hist_bins_legacy(self):
+        df = DataFrame(np.random.randn(10, 2))
+        ax = df.hist(bins=2)[0][0]
+        assert len(ax.patches) == 2
+
+    @pytest.mark.slow
+    def test_hist_layout(self):
+        df = self.hist_df
+        with pytest.raises(ValueError):
+            df.height.hist(layout=(1, 1))
+
+        with pytest.raises(ValueError):
+            df.height.hist(layout=[1, 1])
+
+    @pytest.mark.slow
+    def test_hist_layout_with_by(self):
+        df = self.hist_df
+
+        # _check_plot_works adds an ax so catch warning. see GH #13188
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.gender, layout=(2, 1))
+        self._check_axes_shape(axes, axes_num=2, layout=(2, 1))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.gender, layout=(3, -1))
+        self._check_axes_shape(axes, axes_num=2, layout=(3, 1))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.category, layout=(4, 1))
+        self._check_axes_shape(axes, axes_num=4, layout=(4, 1))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.category, layout=(2, -1))
+        self._check_axes_shape(axes, axes_num=4, layout=(2, 2))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.category, layout=(3, -1))
+        self._check_axes_shape(axes, axes_num=4, layout=(3, 2))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.category, layout=(-1, 4))
+        self._check_axes_shape(axes, axes_num=4, layout=(1, 4))
+
+        with tm.assert_produces_warning(UserWarning):
+            axes = _check_plot_works(df.height.hist, by=df.classroom, layout=(2, 2))
+        self._check_axes_shape(axes, axes_num=3, layout=(2, 2))
+
+        axes = df.height.hist(by=df.category, layout=(4, 2), figsize=(12, 7))
+        self._check_axes_shape(axes, axes_num=4, layout=(4, 2), figsize=(12, 7))
+
+    @pytest.mark.slow
+    def test_hist_no_overlap(self):
+        from matplotlib.pyplot import subplot, gcf
+
+        x = Series(randn(2))
+        y = Series(randn(2))
+        subplot(121)
+        x.hist()
+        subplot(122)
+        y.hist()
+        fig = gcf()
+        axes = fig.axes
+        assert len(axes) == 2
+
+    @pytest.mark.slow
+    def test_hist_secondary_legend(self):
+        # GH 9610
+        df = DataFrame(np.random.randn(30, 4), columns=list("abcd"))
+
+        # primary -> secondary
+        _, ax = self.plt.subplots()
+        ax = df["a"].plot.hist(legend=True, ax=ax)
+        df["b"].plot.hist(ax=ax, legend=True, secondary_y=True)
+        # both legends are dran on left ax
+        # left and right axis must be visible
+        self._check_legend_labels(ax, labels=["a", "b (right)"])
+        assert ax.get_yaxis().get_visible()
+        assert ax.right_ax.get_yaxis().get_visible()
+        tm.close()
+
+        # secondary -> secondary
+        _, ax = self.plt.subplots()
+        ax = df["a"].plot.hist(legend=True, secondary_y=True, ax=ax)
+        df["b"].plot.hist(ax=ax, legend=True, secondary_y=True)
+        # both legends are draw on left ax
+        # left axis must be invisible, right axis must be visible
+        self._check_legend_labels(ax.left_ax, labels=["a (right)", "b (right)"])
+        assert not ax.left_ax.get_yaxis().get_visible()
+        assert ax.get_yaxis().get_visible()
+        tm.close()
+
+        # secondary -> primary
+        _, ax = self.plt.subplots()
+        ax = df["a"].plot.hist(legend=True, secondary_y=True, ax=ax)
+        # right axes is returned
+        df["b"].plot.hist(ax=ax, legend=True)
+        # both legends are draw on left ax
+        # left and right axis must be visible
+        self._check_legend_labels(ax.left_ax, labels=["a (right)", "b"])
+        assert ax.left_ax.get_yaxis().get_visible()
+        assert ax.get_yaxis().get_visible()
+        tm.close()
+
+    @pytest.mark.slow
+    def test_df_series_secondary_legend(self):
+        # GH 9779
+        df = DataFrame(np.random.randn(30, 3), columns=list("abc"))
+        s = Series(np.random.randn(30), name="x")
+
+        # primary -> secondary (without passing ax)
+        _, ax = self.plt.subplots()
+        ax = df.plot(ax=ax)
+        s.plot(legend=True, secondary_y=True, ax=ax)
+        # both legends are dran on left ax
+        # left and right axis must be visible
+        self._check_legend_labels(ax, labels=["a", "b", "c", "x (right)"])
+        assert ax.get_yaxis().get_visible()
+        assert ax.right_ax.get_yaxis().get_visible()
+        tm.close()
+
+        # primary -> secondary (with passing ax)
+        _, ax = self.plt.subplots()
+        ax = df.plot(ax=ax)
+        s.plot(ax=ax, legend=True, secondary_y=True)
+        # both legends are dran on left ax
+        # left and right axis must be visible
+        self._check_legend_labels(ax, labels=["a", "b", "c", "x (right)"])
+        assert ax.get_yaxis().get_visible()
+        assert ax.right_ax.get_yaxis().get_visible()
+        tm.close()
+
+        # secondary -> secondary (without passing ax)
+        _, ax = self.plt.subplots()
+        ax = df.plot(secondary_y=True, ax=ax)
+        s.plot(legend=True, secondary_y=True, ax=ax)
+        # both legends are dran on left ax
+        # left axis must be invisible and right axis must be visible
+        expected = ["a (right)", "b (right)", "c (right)", "x (right)"]
+        self._check_legend_labels(ax.left_ax, labels=expected)
+        assert not ax.left_ax.get_yaxis().get_visible()
+        assert ax.get_yaxis().get_visible()
+        tm.close()
+
+        # secondary -> secondary (with passing ax)
+        _, ax = self.plt.subplots()
+        ax = df.plot(secondary_y=True, ax=ax)
+        s.plot(ax=ax, legend=True, secondary_y=True)
+        # both legends are dran on left ax
+        # left axis must be invisible and right axis must be visible
+        expected = ["a (right)", "b (right)", "c (right)", "x (right)"]
+        self._check_legend_labels(ax.left_ax, expected)
+        assert not ax.left_ax.get_yaxis().get_visible()
+        assert ax.get_yaxis().get_visible()
+        tm.close()
+
+        # secondary -> secondary (with passing ax)
+        _, ax = self.plt.subplots()
+        ax = df.plot(secondary_y=True, mark_right=False, ax=ax)
+        s.plot(ax=ax, legend=True, secondary_y=True)
+        # both legends are dran on left ax
+        # left axis must be invisible and right axis must be visible
+        expected = ["a", "b", "c", "x (right)"]
+        self._check_legend_labels(ax.left_ax, expected)
+        assert not ax.left_ax.get_yaxis().get_visible()
+        assert ax.get_yaxis().get_visible()
+        tm.close()
+
+    @pytest.mark.slow
+    @pytest.mark.parametrize(
+        "input_logy, expected_scale", [(True, "log"), ("sym", "symlog")]
+    )
+    def test_secondary_logy(self, input_logy, expected_scale):
+        # GH 25545
+        s1 = Series(np.random.randn(30))
+        s2 = Series(np.random.randn(30))
+
+        # GH 24980
+        ax1 = s1.plot(logy=input_logy)
+        ax2 = s2.plot(secondary_y=True, logy=input_logy)
+
+        assert ax1.get_yscale() == expected_scale
+        assert ax2.get_yscale() == expected_scale
+
+    @pytest.mark.slow
+    def test_plot_fails_with_dupe_color_and_style(self):
+        x = Series(randn(2))
+        with pytest.raises(ValueError):
+            _, ax = self.plt.subplots()
+            x.plot(style="k--", color="k", ax=ax)
+
+    @pytest.mark.slow
+    @td.skip_if_no_scipy
+    def test_hist_kde(self):
+
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.hist(logy=True, ax=ax)
+        self._check_ax_scales(ax, yaxis="log")
+        xlabels = ax.get_xticklabels()
+        # ticks are values, thus ticklabels are blank
+        self._check_text_labels(xlabels, [""] * len(xlabels))
+        ylabels = ax.get_yticklabels()
+        self._check_text_labels(ylabels, [""] * len(ylabels))
+
+        _check_plot_works(self.ts.plot.kde)
+        _check_plot_works(self.ts.plot.density)
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.kde(logy=True, ax=ax)
+        self._check_ax_scales(ax, yaxis="log")
+        xlabels = ax.get_xticklabels()
+        self._check_text_labels(xlabels, [""] * len(xlabels))
+        ylabels = ax.get_yticklabels()
+        self._check_text_labels(ylabels, [""] * len(ylabels))
+
+    @pytest.mark.slow
+    @td.skip_if_no_scipy
+    def test_kde_kwargs(self):
+        sample_points = np.linspace(-100, 100, 20)
+        _check_plot_works(self.ts.plot.kde, bw_method="scott", ind=20)
+        _check_plot_works(self.ts.plot.kde, bw_method=None, ind=20)
+        _check_plot_works(self.ts.plot.kde, bw_method=None, ind=np.int(20))
+        _check_plot_works(self.ts.plot.kde, bw_method=0.5, ind=sample_points)
+        _check_plot_works(self.ts.plot.density, bw_method=0.5, ind=sample_points)
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.kde(logy=True, bw_method=0.5, ind=sample_points, ax=ax)
+        self._check_ax_scales(ax, yaxis="log")
+        self._check_text_labels(ax.yaxis.get_label(), "Density")
+
+    @pytest.mark.slow
+    @td.skip_if_no_scipy
+    def test_kde_missing_vals(self):
+        s = Series(np.random.uniform(size=50))
+        s[0] = np.nan
+        axes = _check_plot_works(s.plot.kde)
+
+        # gh-14821: check if the values have any missing values
+        assert any(~np.isnan(axes.lines[0].get_xdata()))
+
+    @pytest.mark.slow
+    def test_hist_kwargs(self):
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.hist(bins=5, ax=ax)
+        assert len(ax.patches) == 5
+        self._check_text_labels(ax.yaxis.get_label(), "Frequency")
+        tm.close()
+
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.hist(orientation="horizontal", ax=ax)
+        self._check_text_labels(ax.xaxis.get_label(), "Frequency")
+        tm.close()
+
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.hist(align="left", stacked=True, ax=ax)
+        tm.close()
+
+    @pytest.mark.slow
+    @td.skip_if_no_scipy
+    def test_hist_kde_color(self):
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.hist(logy=True, bins=10, color="b", ax=ax)
+        self._check_ax_scales(ax, yaxis="log")
+        assert len(ax.patches) == 10
+        self._check_colors(ax.patches, facecolors=["b"] * 10)
+
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.kde(logy=True, color="r", ax=ax)
+        self._check_ax_scales(ax, yaxis="log")
+        lines = ax.get_lines()
+        assert len(lines) == 1
+        self._check_colors(lines, ["r"])
+
+    @pytest.mark.slow
+    def test_boxplot_series(self):
+        _, ax = self.plt.subplots()
+        ax = self.ts.plot.box(logy=True, ax=ax)
+        self._check_ax_scales(ax, yaxis="log")
+        xlabels = ax.get_xticklabels()
+        self._check_text_labels(xlabels, [self.ts.name])
+        ylabels = ax.get_yticklabels()
+        self._check_text_labels(ylabels, [""] * len(ylabels))
+
+    @pytest.mark.slow
+    def test_kind_both_ways(self):
+        s = Series(range(3))
+        kinds = (
+            plotting.PlotAccessor._common_kinds + plotting.PlotAccessor._series_kinds
+        )
+        _, ax = self.plt.subplots()
+        for kind in kinds:
+
+            s.plot(kind=kind, ax=ax)
+            getattr(s.plot, kind)()
+
+    @pytest.mark.slow
+    def test_invalid_plot_data(self):
+        s = Series(list("abcd"))
+        _, ax = self.plt.subplots()
+        for kind in plotting.PlotAccessor._common_kinds:
+
+            msg = "no numeric data to plot"
+            with pytest.raises(TypeError, match=msg):
+                s.plot(kind=kind, ax=ax)
+
+    @pytest.mark.slow
+    def test_valid_object_plot(self):
+        s = Series(range(10), dtype=object)
+        for kind in plotting.PlotAccessor._common_kinds:
+            _check_plot_works(s.plot, kind=kind)
+
+    def test_partially_invalid_plot_data(self):
+        s = Series(["a", "b", 1.0, 2])
+        _, ax = self.plt.subplots()
+        for kind in plotting.PlotAccessor._common_kinds:
+
+            msg = "no numeric data to plot"
+            with pytest.raises(TypeError, match=msg):
+                s.plot(kind=kind, ax=ax)
+
+    def test_invalid_kind(self):
+        s = Series([1, 2])
+        with pytest.raises(ValueError):
+            s.plot(kind="aasdf")
+
+    @pytest.mark.slow
+    def test_dup_datetime_index_plot(self):
+        dr1 = date_range("1/1/2009", periods=4)
+        dr2 = date_range("1/2/2009", periods=4)
+        index = dr1.append(dr2)
+        values = randn(index.size)
+        s = Series(values, index=index)
+        _check_plot_works(s.plot)
+
+    @pytest.mark.slow
+    def test_errorbar_plot(self):
+
+        s = Series(np.arange(10), name="x")
+        s_err = np.random.randn(10)
+        d_err = DataFrame(randn(10, 2), index=s.index, columns=["x", "y"])
+        # test line and bar plots
+        kinds = ["line", "bar"]
+        for kind in kinds:
+            ax = _check_plot_works(s.plot, yerr=Series(s_err), kind=kind)
+            self._check_has_errorbars(ax, xerr=0, yerr=1)
+            ax = _check_plot_works(s.plot, yerr=s_err, kind=kind)
+            self._check_has_errorbars(ax, xerr=0, yerr=1)
+            ax = _check_plot_works(s.plot, yerr=s_err.tolist(), kind=kind)
+            self._check_has_errorbars(ax, xerr=0, yerr=1)
+            ax = _check_plot_works(s.plot, yerr=d_err, kind=kind)
+            self._check_has_errorbars(ax, xerr=0, yerr=1)
+            ax = _check_plot_works(s.plot, xerr=0.2, yerr=0.2, kind=kind)
+            self._check_has_errorbars(ax, xerr=1, yerr=1)
+
+        ax = _check_plot_works(s.plot, xerr=s_err)
+        self._check_has_errorbars(ax, xerr=1, yerr=0)
+
+        # test time series plotting
+        ix = date_range("1/1/2000", "1/1/2001", freq="M")
+        ts = Series(np.arange(12), index=ix, name="x")
+        ts_err = Series(np.random.randn(12), index=ix)
+        td_err = DataFrame(randn(12, 2), index=ix, columns=["x", "y"])
+
+        ax = _check_plot_works(ts.plot, yerr=ts_err)
+        self._check_has_errorbars(ax, xerr=0, yerr=1)
+        ax = _check_plot_works(ts.plot, yerr=td_err)
+        self._check_has_errorbars(ax, xerr=0, yerr=1)
+
+        # check incorrect lengths and types
+        with pytest.raises(ValueError):
+            s.plot(yerr=np.arange(11))
+
+        s_err = ["zzz"] * 10
+        with pytest.raises(TypeError):
+            s.plot(yerr=s_err)
+
+    def test_table(self):
+        _check_plot_works(self.series.plot, table=True)
+        _check_plot_works(self.series.plot, table=self.series)
+
+    @pytest.mark.slow
+    def test_series_grid_settings(self):
+        # Make sure plot defaults to rcParams['axes.grid'] setting, GH 9792
+        self._check_grid_settings(
+            Series([1, 2, 3]),
+            plotting.PlotAccessor._series_kinds + plotting.PlotAccessor._common_kinds,
+        )
+
+    @pytest.mark.slow
+    def test_standard_colors(self):
+        from pandas.plotting._matplotlib.style import _get_standard_colors
+
+        for c in ["r", "red", "green", "#FF0000"]:
+            result = _get_standard_colors(1, color=c)
+            assert result == [c]
+
+            result = _get_standard_colors(1, color=[c])
+            assert result == [c]
+
+            result = _get_standard_colors(3, color=c)
+            assert result == [c] * 3
+
+            result = _get_standard_colors(3, color=[c])
+            assert result == [c] * 3
+
+    @pytest.mark.slow
+    def test_standard_colors_all(self):
+        import matplotlib.colors as colors
+        from pandas.plotting._matplotlib.style import _get_standard_colors
+
+        # multiple colors like mediumaquamarine
+        for c in colors.cnames:
+            result = _get_standard_colors(num_colors=1, color=c)
+            assert result == [c]
+
+            result = _get_standard_colors(num_colors=1, color=[c])
+            assert result == [c]
+
+            result = _get_standard_colors(num_colors=3, color=c)
+            assert result == [c] * 3
+
+            result = _get_standard_colors(num_colors=3, color=[c])
+            assert result == [c] * 3
+
+        # single letter colors like k
+        for c in colors.ColorConverter.colors:
+            result = _get_standard_colors(num_colors=1, color=c)
+            assert result == [c]
+
+            result = _get_standard_colors(num_colors=1, color=[c])
+            assert result == [c]
+
+            result = _get_standard_colors(num_colors=3, color=c)
+            assert result == [c] * 3
+
+            result = _get_standard_colors(num_colors=3, color=[c])
+            assert result == [c] * 3
+
+    def test_series_plot_color_kwargs(self):
+        # GH1890
+        _, ax = self.plt.subplots()
+        ax = Series(np.arange(12) + 1).plot(color="green", ax=ax)
+        self._check_colors(ax.get_lines(), linecolors=["green"])
+
+    def test_time_series_plot_color_kwargs(self):
+        # #1890
+        _, ax = self.plt.subplots()
+        ax = Series(np.arange(12) + 1, index=date_range("1/1/2000", periods=12)).plot(
+            color="green", ax=ax
+        )
+        self._check_colors(ax.get_lines(), linecolors=["green"])
+
+    def test_time_series_plot_color_with_empty_kwargs(self):
+        import matplotlib as mpl
+
+        def_colors = self._unpack_cycler(mpl.rcParams)
+        index = date_range("1/1/2000", periods=12)
+        s = Series(np.arange(1, 13), index=index)
+
+        ncolors = 3
+
+        _, ax = self.plt.subplots()
+        for i in range(ncolors):
+            ax = s.plot(ax=ax)
+        self._check_colors(ax.get_lines(), linecolors=def_colors[:ncolors])
+
+    def test_xticklabels(self):
+        # GH11529
+        s = Series(np.arange(10), index=["P{i:02d}".format(i=i) for i in range(10)])
+        _, ax = self.plt.subplots()
+        ax = s.plot(xticks=[0, 3, 5, 9], ax=ax)
+        exp = ["P{i:02d}".format(i=i) for i in [0, 3, 5, 9]]
+        self._check_text_labels(ax.get_xticklabels(), exp)
+
+    def test_custom_business_day_freq(self):
+        # GH7222
+        from pandas.tseries.offsets import CustomBusinessDay
+
+        s = Series(
+            range(100, 121),
+            index=pd.bdate_range(
+                start="2014-05-01",
+                end="2014-06-01",
+                freq=CustomBusinessDay(holidays=["2014-05-26"]),
+            ),
+        )
+
+        _check_plot_works(s.plot)
+
+    @pytest.mark.xfail
+    def test_plot_accessor_updates_on_inplace(self):
+        s = Series([1, 2, 3, 4])
+        _, ax = self.plt.subplots()
+        ax = s.plot(ax=ax)
+        before = ax.xaxis.get_ticklocs()
+
+        s.drop([0, 1], inplace=True)
+        _, ax = self.plt.subplots()
+        after = ax.xaxis.get_ticklocs()
+        tm.assert_numpy_array_equal(before, after)