# being a bit too dynamic
# pylint: disable=E1101
from __future__ import division

import warnings
import re
from collections import namedtuple
from distutils.version import LooseVersion

import numpy as np

from pandas.util._decorators import cache_readonly
import pandas.core.common as com
from pandas.core.base import PandasObject
from pandas.core.config import get_option
from pandas.core.dtypes.missing import isna, notna, remove_na_arraylike
from pandas.core.dtypes.common import (
    is_list_like,
    is_integer,
    is_number,
    is_hashable,
    is_iterator)
from pandas.core.dtypes.generic import ABCSeries, ABCDataFrame

from pandas.core.generic import _shared_docs, _shared_doc_kwargs
from pandas.core.index import Index, MultiIndex

from pandas.core.indexes.period import PeriodIndex
from pandas.compat import range, lrange, map, zip, string_types
import pandas.compat as compat
from pandas.io.formats.printing import pprint_thing
from pandas.util._decorators import Appender

from pandas.plotting._compat import (_mpl_ge_1_3_1,
                                     _mpl_ge_1_5_0,
                                     _mpl_ge_2_0_0)
from pandas.plotting._style import (plot_params,
                                    _get_standard_colors)
from pandas.plotting._tools import (_subplots, _flatten, table,
                                    _handle_shared_axes, _get_all_lines,
                                    _get_xlim, _set_ticks_props,
                                    format_date_labels)

try:
    from pandas.plotting import _converter
except ImportError:
    _HAS_MPL = False
else:
    _HAS_MPL = True
    if get_option('plotting.matplotlib.register_converters'):
        _converter.register(explicit=True)


def _raise_if_no_mpl():
    # TODO(mpl_converter): remove once converter is explicit
    if not _HAS_MPL:
        raise ImportError("matplotlib is required for plotting.")


def _get_standard_kind(kind):
    return {'density': 'kde'}.get(kind, kind)


def _gca(rc=None):
    import matplotlib.pyplot as plt
    with plt.rc_context(rc):
        return plt.gca()


def _gcf():
    import matplotlib.pyplot as plt
    return plt.gcf()


class MPLPlot(object):
    """
    Base class for assembling a pandas plot using matplotlib

    Parameters
    ----------
    data :

    """

    @property
    def _kind(self):
        """Specify kind str. Must be overridden in child class"""
        raise NotImplementedError

    _layout_type = 'vertical'
    _default_rot = 0
    orientation = None
    _pop_attributes = ['label', 'style', 'logy', 'logx', 'loglog',
                       'mark_right', 'stacked']
    _attr_defaults = {'logy': False, 'logx': False, 'loglog': False,
                      'mark_right': True, 'stacked': False}

    def __init__(self, data, kind=None, by=None, subplots=False, sharex=None,
                 sharey=False, use_index=True,
                 figsize=None, grid=None, legend=True, rot=None,
                 ax=None, fig=None, title=None, xlim=None, ylim=None,
                 xticks=None, yticks=None,
                 sort_columns=False, fontsize=None,
                 secondary_y=False, colormap=None,
                 table=False, layout=None, **kwds):

        _raise_if_no_mpl()
        _converter._WARN = False
        self.data = data
        self.by = by

        self.kind = kind

        self.sort_columns = sort_columns

        self.subplots = subplots

        if sharex is None:
            if ax is None:
                self.sharex = True
            else:
                # if we get an axis, the users should do the visibility
                # setting...
                self.sharex = False
        else:
            self.sharex = sharex

        self.sharey = sharey
        self.figsize = figsize
        self.layout = layout

        self.xticks = xticks
        self.yticks = yticks
        self.xlim = xlim
        self.ylim = ylim
        self.title = title
        self.use_index = use_index

        self.fontsize = fontsize

        if rot is not None:
            self.rot = rot
            # need to know for format_date_labels since it's rotated to 30 by
            # default
            self._rot_set = True
        else:
            self._rot_set = False
            self.rot = self._default_rot

        if grid is None:
            grid = False if secondary_y else self.plt.rcParams['axes.grid']

        self.grid = grid
        self.legend = legend
        self.legend_handles = []
        self.legend_labels = []

        for attr in self._pop_attributes:
            value = kwds.pop(attr, self._attr_defaults.get(attr, None))
            setattr(self, attr, value)

        self.ax = ax
        self.fig = fig
        self.axes = None

        # parse errorbar input if given
        xerr = kwds.pop('xerr', None)
        yerr = kwds.pop('yerr', None)
        self.errors = {}
        for kw, err in zip(['xerr', 'yerr'], [xerr, yerr]):
            self.errors[kw] = self._parse_errorbars(kw, err)

        if not isinstance(secondary_y, (bool, tuple, list, np.ndarray, Index)):
            secondary_y = [secondary_y]
        self.secondary_y = secondary_y

        # ugly TypeError if user passes matplotlib's `cmap` name.
        # Probably better to accept either.
        if 'cmap' in kwds and colormap:
            raise TypeError("Only specify one of `cmap` and `colormap`.")
        elif 'cmap' in kwds:
            self.colormap = kwds.pop('cmap')
        else:
            self.colormap = colormap

        self.table = table

        self.kwds = kwds

        self._validate_color_args()

    def _validate_color_args(self):
        if 'color' not in self.kwds and 'colors' in self.kwds:
            warnings.warn(("'colors' is being deprecated. Please use 'color'"
                           "instead of 'colors'"))
            colors = self.kwds.pop('colors')
            self.kwds['color'] = colors

        if ('color' in self.kwds and self.nseries == 1 and
                not is_list_like(self.kwds['color'])):
            # support series.plot(color='green')
            self.kwds['color'] = [self.kwds['color']]

        if ('color' in self.kwds and isinstance(self.kwds['color'], tuple) and
                self.nseries == 1 and len(self.kwds['color']) in (3, 4)):
            # support RGB and RGBA tuples in series plot
            self.kwds['color'] = [self.kwds['color']]

        if ('color' in self.kwds or 'colors' in self.kwds) and \
                self.colormap is not None:
            warnings.warn("'color' and 'colormap' cannot be used "
                          "simultaneously. Using 'color'")

        if 'color' in self.kwds and self.style is not None:
            if is_list_like(self.style):
                styles = self.style
            else:
                styles = [self.style]
            # need only a single match
            for s in styles:
                if re.match('^[a-z]+?', s) is not None:
                    raise ValueError(
                        "Cannot pass 'style' string with a color "
                        "symbol and 'color' keyword argument. Please"
                        " use one or the other or pass 'style' "
                        "without a color symbol")

    def _iter_data(self, data=None, keep_index=False, fillna=None):
        if data is None:
            data = self.data
        if fillna is not None:
            data = data.fillna(fillna)

        # TODO: unused?
        # if self.sort_columns:
        #     columns = com._try_sort(data.columns)
        # else:
        #     columns = data.columns

        for col, values in data.iteritems():
            if keep_index is True:
                yield col, values
            else:
                yield col, values.values

    @property
    def nseries(self):
        if self.data.ndim == 1:
            return 1
        else:
            return self.data.shape[1]

    def draw(self):
        self.plt.draw_if_interactive()

    def generate(self):
        self._args_adjust()
        self._compute_plot_data()
        self._setup_subplots()
        self._make_plot()
        self._add_table()
        self._make_legend()
        self._adorn_subplots()

        for ax in self.axes:
            self._post_plot_logic_common(ax, self.data)
            self._post_plot_logic(ax, self.data)

    def _args_adjust(self):
        pass

    def _has_plotted_object(self, ax):
        """check whether ax has data"""
        return (len(ax.lines) != 0 or
                len(ax.artists) != 0 or
                len(ax.containers) != 0)

    def _maybe_right_yaxis(self, ax, axes_num):
        if not self.on_right(axes_num):
            # secondary axes may be passed via ax kw
            return self._get_ax_layer(ax)

        if hasattr(ax, 'right_ax'):
            # if it has right_ax proparty, ``ax`` must be left axes
            return ax.right_ax
        elif hasattr(ax, 'left_ax'):
            # if it has left_ax proparty, ``ax`` must be right axes
            return ax
        else:
            # otherwise, create twin axes
            orig_ax, new_ax = ax, ax.twinx()
            # TODO: use Matplotlib public API when available
            new_ax._get_lines = orig_ax._get_lines
            new_ax._get_patches_for_fill = orig_ax._get_patches_for_fill
            orig_ax.right_ax, new_ax.left_ax = new_ax, orig_ax

            if not self._has_plotted_object(orig_ax):  # no data on left y
                orig_ax.get_yaxis().set_visible(False)
            return new_ax

    def _setup_subplots(self):
        if self.subplots:
            fig, axes = _subplots(naxes=self.nseries,
                                  sharex=self.sharex, sharey=self.sharey,
                                  figsize=self.figsize, ax=self.ax,
                                  layout=self.layout,
                                  layout_type=self._layout_type)
        else:
            if self.ax is None:
                fig = self.plt.figure(figsize=self.figsize)
                axes = fig.add_subplot(111)
            else:
                fig = self.ax.get_figure()
                if self.figsize is not None:
                    fig.set_size_inches(self.figsize)
                axes = self.ax

        axes = _flatten(axes)

        if self.logx or self.loglog:
            [a.set_xscale('log') for a in axes]
        if self.logy or self.loglog:
            [a.set_yscale('log') for a in axes]

        self.fig = fig
        self.axes = axes

    @property
    def result(self):
        """
        Return result axes
        """
        if self.subplots:
            if self.layout is not None and not is_list_like(self.ax):
                return self.axes.reshape(*self.layout)
            else:
                return self.axes
        else:
            sec_true = isinstance(self.secondary_y, bool) and self.secondary_y
            all_sec = (is_list_like(self.secondary_y) and
                       len(self.secondary_y) == self.nseries)
            if (sec_true or all_sec):
                # if all data is plotted on secondary, return right axes
                return self._get_ax_layer(self.axes[0], primary=False)
            else:
                return self.axes[0]

    def _compute_plot_data(self):
        data = self.data

        if isinstance(data, ABCSeries):
            label = self.label
            if label is None and data.name is None:
                label = 'None'
            data = data.to_frame(name=label)

        # GH16953, _convert is needed as fallback, for ``Series``
        # with ``dtype == object``
        data = data._convert(datetime=True, timedelta=True)
        numeric_data = data.select_dtypes(include=[np.number,
                                                   "datetime",
                                                   "datetimetz",
                                                   "timedelta"])

        try:
            is_empty = numeric_data.empty
        except AttributeError:
            is_empty = not len(numeric_data)

        # no empty frames or series allowed
        if is_empty:
            raise TypeError('Empty {0!r}: no numeric data to '
                            'plot'.format(numeric_data.__class__.__name__))

        self.data = numeric_data

    def _make_plot(self):
        raise com.AbstractMethodError(self)

    def _add_table(self):
        if self.table is False:
            return
        elif self.table is True:
            data = self.data.transpose()
        else:
            data = self.table
        ax = self._get_ax(0)
        table(ax, data)

    def _post_plot_logic_common(self, ax, data):
        """Common post process for each axes"""

        def get_label(i):
            try:
                return pprint_thing(data.index[i])
            except Exception:
                return ''

        if self.orientation == 'vertical' or self.orientation is None:
            if self._need_to_set_index:
                xticklabels = [get_label(x) for x in ax.get_xticks()]
                ax.set_xticklabels(xticklabels)
            self._apply_axis_properties(ax.xaxis, rot=self.rot,
                                        fontsize=self.fontsize)
            self._apply_axis_properties(ax.yaxis, fontsize=self.fontsize)

            if hasattr(ax, 'right_ax'):
                self._apply_axis_properties(ax.right_ax.yaxis,
                                            fontsize=self.fontsize)

        elif self.orientation == 'horizontal':
            if self._need_to_set_index:
                yticklabels = [get_label(y) for y in ax.get_yticks()]
                ax.set_yticklabels(yticklabels)
            self._apply_axis_properties(ax.yaxis, rot=self.rot,
                                        fontsize=self.fontsize)
            self._apply_axis_properties(ax.xaxis, fontsize=self.fontsize)

            if hasattr(ax, 'right_ax'):
                self._apply_axis_properties(ax.right_ax.yaxis,
                                            fontsize=self.fontsize)
        else:  # pragma no cover
            raise ValueError

    def _post_plot_logic(self, ax, data):
        """Post process for each axes. Overridden in child classes"""
        pass

    def _adorn_subplots(self):
        """Common post process unrelated to data"""
        if len(self.axes) > 0:
            all_axes = self._get_subplots()
            nrows, ncols = self._get_axes_layout()
            _handle_shared_axes(axarr=all_axes, nplots=len(all_axes),
                                naxes=nrows * ncols, nrows=nrows,
                                ncols=ncols, sharex=self.sharex,
                                sharey=self.sharey)

        for ax in self.axes:
            if self.yticks is not None:
                ax.set_yticks(self.yticks)

            if self.xticks is not None:
                ax.set_xticks(self.xticks)

            if self.ylim is not None:
                ax.set_ylim(self.ylim)

            if self.xlim is not None:
                ax.set_xlim(self.xlim)

            ax.grid(self.grid)

        if self.title:
            if self.subplots:
                if is_list_like(self.title):
                    if len(self.title) != self.nseries:
                        msg = ('The length of `title` must equal the number '
                               'of columns if using `title` of type `list` '
                               'and `subplots=True`.\n'
                               'length of title = {}\n'
                               'number of columns = {}').format(
                            len(self.title), self.nseries)
                        raise ValueError(msg)

                    for (ax, title) in zip(self.axes, self.title):
                        ax.set_title(title)
                else:
                    self.fig.suptitle(self.title)
            else:
                if is_list_like(self.title):
                    msg = ('Using `title` of type `list` is not supported '
                           'unless `subplots=True` is passed')
                    raise ValueError(msg)
                self.axes[0].set_title(self.title)

    def _apply_axis_properties(self, axis, rot=None, fontsize=None):
        labels = axis.get_majorticklabels() + axis.get_minorticklabels()
        for label in labels:
            if rot is not None:
                label.set_rotation(rot)
            if fontsize is not None:
                label.set_fontsize(fontsize)

    @property
    def legend_title(self):
        if not isinstance(self.data.columns, MultiIndex):
            name = self.data.columns.name
            if name is not None:
                name = pprint_thing(name)
            return name
        else:
            stringified = map(pprint_thing,
                              self.data.columns.names)
            return ','.join(stringified)

    def _add_legend_handle(self, handle, label, index=None):
        if label is not None:
            if self.mark_right and index is not None:
                if self.on_right(index):
                    label = label + ' (right)'
            self.legend_handles.append(handle)
            self.legend_labels.append(label)

    def _make_legend(self):
        ax, leg = self._get_ax_legend(self.axes[0])

        handles = []
        labels = []
        title = ''

        if not self.subplots:
            if leg is not None:
                title = leg.get_title().get_text()
                handles = leg.legendHandles
                labels = [x.get_text() for x in leg.get_texts()]

            if self.legend:
                if self.legend == 'reverse':
                    self.legend_handles = reversed(self.legend_handles)
                    self.legend_labels = reversed(self.legend_labels)

                handles += self.legend_handles
                labels += self.legend_labels
                if self.legend_title is not None:
                    title = self.legend_title

            if len(handles) > 0:
                ax.legend(handles, labels, loc='best', title=title)

        elif self.subplots and self.legend:
            for ax in self.axes:
                if ax.get_visible():
                    ax.legend(loc='best')

    def _get_ax_legend(self, ax):
        leg = ax.get_legend()
        other_ax = (getattr(ax, 'left_ax', None) or
                    getattr(ax, 'right_ax', None))
        other_leg = None
        if other_ax is not None:
            other_leg = other_ax.get_legend()
        if leg is None and other_leg is not None:
            leg = other_leg
            ax = other_ax
        return ax, leg

    @cache_readonly
    def plt(self):
        import matplotlib.pyplot as plt
        return plt

    @staticmethod
    def mpl_ge_1_3_1():
        return _mpl_ge_1_3_1()

    @staticmethod
    def mpl_ge_1_5_0():
        return _mpl_ge_1_5_0()

    _need_to_set_index = False

    def _get_xticks(self, convert_period=False):
        index = self.data.index
        is_datetype = index.inferred_type in ('datetime', 'date',
                                              'datetime64', 'time')

        if self.use_index:
            if convert_period and isinstance(index, PeriodIndex):
                self.data = self.data.reindex(index=index.sort_values())
                x = self.data.index.to_timestamp()._mpl_repr()
            elif index.is_numeric():
                """
                Matplotlib supports numeric values or datetime objects as
                xaxis values. Taking LBYL approach here, by the time
                matplotlib raises exception when using non numeric/datetime
                values for xaxis, several actions are already taken by plt.
                """
                x = index._mpl_repr()
            elif is_datetype:
                self.data = self.data[notna(self.data.index)]
                self.data = self.data.sort_index()
                x = self.data.index._mpl_repr()
            else:
                self._need_to_set_index = True
                x = lrange(len(index))
        else:
            x = lrange(len(index))

        return x

    @classmethod
    def _plot(cls, ax, x, y, style=None, is_errorbar=False, **kwds):
        mask = isna(y)
        if mask.any():
            y = np.ma.array(y)
            y = np.ma.masked_where(mask, y)

        if isinstance(x, Index):
            x = x._mpl_repr()

        if is_errorbar:
            if 'xerr' in kwds:
                kwds['xerr'] = np.array(kwds.get('xerr'))
            if 'yerr' in kwds:
                kwds['yerr'] = np.array(kwds.get('yerr'))
            return ax.errorbar(x, y, **kwds)
        else:
            # prevent style kwarg from going to errorbar, where it is
            # unsupported
            if style is not None:
                args = (x, y, style)
            else:
                args = (x, y)
            return ax.plot(*args, **kwds)

    def _get_index_name(self):
        if isinstance(self.data.index, MultiIndex):
            name = self.data.index.names
            if com._any_not_none(*name):
                name = ','.join(pprint_thing(x) for x in name)
            else:
                name = None
        else:
            name = self.data.index.name
            if name is not None:
                name = pprint_thing(name)

        return name

    @classmethod
    def _get_ax_layer(cls, ax, primary=True):
        """get left (primary) or right (secondary) axes"""
        if primary:
            return getattr(ax, 'left_ax', ax)
        else:
            return getattr(ax, 'right_ax', ax)

    def _get_ax(self, i):
        # get the twinx ax if appropriate
        if self.subplots:
            ax = self.axes[i]
            ax = self._maybe_right_yaxis(ax, i)
            self.axes[i] = ax
        else:
            ax = self.axes[0]
            ax = self._maybe_right_yaxis(ax, i)

        ax.get_yaxis().set_visible(True)
        return ax

    def on_right(self, i):
        if isinstance(self.secondary_y, bool):
            return self.secondary_y

        if isinstance(self.secondary_y, (tuple, list, np.ndarray, Index)):
            return self.data.columns[i] in self.secondary_y

    def _apply_style_colors(self, colors, kwds, col_num, label):
        """
        Manage style and color based on column number and its label.
        Returns tuple of appropriate style and kwds which "color" may be added.
        """
        style = None
        if self.style is not None:
            if isinstance(self.style, list):
                try:
                    style = self.style[col_num]
                except IndexError:
                    pass
            elif isinstance(self.style, dict):
                style = self.style.get(label, style)
            else:
                style = self.style

        has_color = 'color' in kwds or self.colormap is not None
        nocolor_style = style is None or re.match('[a-z]+', style) is None
        if (has_color or self.subplots) and nocolor_style:
            kwds['color'] = colors[col_num % len(colors)]
        return style, kwds

    def _get_colors(self, num_colors=None, color_kwds='color'):
        if num_colors is None:
            num_colors = self.nseries

        return _get_standard_colors(num_colors=num_colors,
                                    colormap=self.colormap,
                                    color=self.kwds.get(color_kwds))

    def _parse_errorbars(self, label, err):
        """
        Look for error keyword arguments and return the actual errorbar data
        or return the error DataFrame/dict

        Error bars can be specified in several ways:
            Series: the user provides a pandas.Series object of the same
                    length as the data
            ndarray: provides a np.ndarray of the same length as the data
            DataFrame/dict: error values are paired with keys matching the
                    key in the plotted DataFrame
            str: the name of the column within the plotted DataFrame
        """

        if err is None:
            return None

        from pandas import DataFrame, Series

        def match_labels(data, e):
            e = e.reindex(data.index)
            return e

        # key-matched DataFrame
        if isinstance(err, DataFrame):

            err = match_labels(self.data, err)
        # key-matched dict
        elif isinstance(err, dict):
            pass

        # Series of error values
        elif isinstance(err, Series):
            # broadcast error series across data
            err = match_labels(self.data, err)
            err = np.atleast_2d(err)
            err = np.tile(err, (self.nseries, 1))

        # errors are a column in the dataframe
        elif isinstance(err, string_types):
            evalues = self.data[err].values
            self.data = self.data[self.data.columns.drop(err)]
            err = np.atleast_2d(evalues)
            err = np.tile(err, (self.nseries, 1))

        elif is_list_like(err):
            if is_iterator(err):
                err = np.atleast_2d(list(err))
            else:
                # raw error values
                err = np.atleast_2d(err)

            err_shape = err.shape

            # asymmetrical error bars
            if err.ndim == 3:
                if (err_shape[0] != self.nseries) or \
                        (err_shape[1] != 2) or \
                        (err_shape[2] != len(self.data)):
                    msg = "Asymmetrical error bars should be provided " + \
                        "with the shape (%u, 2, %u)" % \
                        (self.nseries, len(self.data))
                    raise ValueError(msg)

            # broadcast errors to each data series
            if len(err) == 1:
                err = np.tile(err, (self.nseries, 1))

        elif is_number(err):
            err = np.tile([err], (self.nseries, len(self.data)))

        else:
            msg = "No valid {label} detected".format(label=label)
            raise ValueError(msg)

        return err

    def _get_errorbars(self, label=None, index=None, xerr=True, yerr=True):
        from pandas import DataFrame
        errors = {}

        for kw, flag in zip(['xerr', 'yerr'], [xerr, yerr]):
            if flag:
                err = self.errors[kw]
                # user provided label-matched dataframe of errors
                if isinstance(err, (DataFrame, dict)):
                    if label is not None and label in err.keys():
                        err = err[label]
                    else:
                        err = None
                elif index is not None and err is not None:
                    err = err[index]

                if err is not None:
                    errors[kw] = err
        return errors

    def _get_subplots(self):
        from matplotlib.axes import Subplot
        return [ax for ax in self.axes[0].get_figure().get_axes()
                if isinstance(ax, Subplot)]

    def _get_axes_layout(self):
        axes = self._get_subplots()
        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))


class PlanePlot(MPLPlot):
    """
    Abstract class for plotting on plane, currently scatter and hexbin.
    """

    _layout_type = 'single'

    def __init__(self, data, x, y, **kwargs):
        MPLPlot.__init__(self, data, **kwargs)
        if x is None or y is None:
            raise ValueError(self._kind + ' requires and x and y column')
        if is_integer(x) and not self.data.columns.holds_integer():
            x = self.data.columns[x]
        if is_integer(y) and not self.data.columns.holds_integer():
            y = self.data.columns[y]
        if len(self.data[x]._get_numeric_data()) == 0:
            raise ValueError(self._kind + ' requires x column to be numeric')
        if len(self.data[y]._get_numeric_data()) == 0:
            raise ValueError(self._kind + ' requires y column to be numeric')

        self.x = x
        self.y = y

    @property
    def nseries(self):
        return 1

    def _post_plot_logic(self, ax, data):
        x, y = self.x, self.y
        ax.set_ylabel(pprint_thing(y))
        ax.set_xlabel(pprint_thing(x))


class ScatterPlot(PlanePlot):
    _kind = 'scatter'

    def __init__(self, data, x, y, s=None, c=None, **kwargs):
        if s is None:
            # hide the matplotlib default for size, in case we want to change
            # the handling of this argument later
            s = 20
        super(ScatterPlot, self).__init__(data, x, y, s=s, **kwargs)
        if is_integer(c) and not self.data.columns.holds_integer():
            c = self.data.columns[c]
        self.c = c

    def _make_plot(self):
        x, y, c, data = self.x, self.y, self.c, self.data
        ax = self.axes[0]

        c_is_column = is_hashable(c) and c in self.data.columns

        # plot a colorbar only if a colormap is provided or necessary
        cb = self.kwds.pop('colorbar', self.colormap or c_is_column)

        # pandas uses colormap, matplotlib uses cmap.
        cmap = self.colormap or 'Greys'
        cmap = self.plt.cm.get_cmap(cmap)
        color = self.kwds.pop("color", None)
        if c is not None and color is not None:
            raise TypeError('Specify exactly one of `c` and `color`')
        elif c is None and color is None:
            c_values = self.plt.rcParams['patch.facecolor']
        elif color is not None:
            c_values = color
        elif c_is_column:
            c_values = self.data[c].values
        else:
            c_values = c

        if self.legend and hasattr(self, 'label'):
            label = self.label
        else:
            label = None
        scatter = ax.scatter(data[x].values, data[y].values, c=c_values,
                             label=label, cmap=cmap, **self.kwds)
        if cb:
            img = ax.collections[0]
            kws = dict(ax=ax)
            if self.mpl_ge_1_3_1():
                kws['label'] = c if c_is_column else ''
            self.fig.colorbar(img, **kws)

        if label is not None:
            self._add_legend_handle(scatter, label)
        else:
            self.legend = False

        errors_x = self._get_errorbars(label=x, index=0, yerr=False)
        errors_y = self._get_errorbars(label=y, index=0, xerr=False)
        if len(errors_x) > 0 or len(errors_y) > 0:
            err_kwds = dict(errors_x, **errors_y)
            err_kwds['ecolor'] = scatter.get_facecolor()[0]
            ax.errorbar(data[x].values, data[y].values,
                        linestyle='none', **err_kwds)


class HexBinPlot(PlanePlot):
    _kind = 'hexbin'

    def __init__(self, data, x, y, C=None, **kwargs):
        super(HexBinPlot, self).__init__(data, x, y, **kwargs)
        if is_integer(C) and not self.data.columns.holds_integer():
            C = self.data.columns[C]
        self.C = C

    def _make_plot(self):
        x, y, data, C = self.x, self.y, self.data, self.C
        ax = self.axes[0]
        # pandas uses colormap, matplotlib uses cmap.
        cmap = self.colormap or 'BuGn'
        cmap = self.plt.cm.get_cmap(cmap)
        cb = self.kwds.pop('colorbar', True)

        if C is None:
            c_values = None
        else:
            c_values = data[C].values

        ax.hexbin(data[x].values, data[y].values, C=c_values, cmap=cmap,
                  **self.kwds)
        if cb:
            img = ax.collections[0]
            self.fig.colorbar(img, ax=ax)

    def _make_legend(self):
        pass


class LinePlot(MPLPlot):
    _kind = 'line'
    _default_rot = 0
    orientation = 'vertical'

    def __init__(self, data, **kwargs):
        MPLPlot.__init__(self, data, **kwargs)
        if self.stacked:
            self.data = self.data.fillna(value=0)
        self.x_compat = plot_params['x_compat']
        if 'x_compat' in self.kwds:
            self.x_compat = bool(self.kwds.pop('x_compat'))

    def _is_ts_plot(self):
        # this is slightly deceptive
        return not self.x_compat and self.use_index and self._use_dynamic_x()

    def _use_dynamic_x(self):
        from pandas.plotting._timeseries import _use_dynamic_x
        return _use_dynamic_x(self._get_ax(0), self.data)

    def _make_plot(self):
        if self._is_ts_plot():
            from pandas.plotting._timeseries import _maybe_convert_index
            data = _maybe_convert_index(self._get_ax(0), self.data)

            x = data.index      # dummy, not used
            plotf = self._ts_plot
            it = self._iter_data(data=data, keep_index=True)
        else:
            x = self._get_xticks(convert_period=True)
            plotf = self._plot
            it = self._iter_data()

        stacking_id = self._get_stacking_id()
        is_errorbar = com._any_not_none(*self.errors.values())

        colors = self._get_colors()
        for i, (label, y) in enumerate(it):
            ax = self._get_ax(i)
            kwds = self.kwds.copy()
            style, kwds = self._apply_style_colors(colors, kwds, i, label)

            errors = self._get_errorbars(label=label, index=i)
            kwds = dict(kwds, **errors)

            label = pprint_thing(label)  # .encode('utf-8')
            kwds['label'] = label

            newlines = plotf(ax, x, y, style=style, column_num=i,
                             stacking_id=stacking_id,
                             is_errorbar=is_errorbar,
                             **kwds)
            self._add_legend_handle(newlines[0], label, index=i)

            if not _mpl_ge_2_0_0():
                lines = _get_all_lines(ax)
                left, right = _get_xlim(lines)
                ax.set_xlim(left, right)

    @classmethod
    def _plot(cls, ax, x, y, style=None, column_num=None,
              stacking_id=None, **kwds):
        # column_num is used to get the target column from protf in line and
        # area plots
        if column_num == 0:
            cls._initialize_stacker(ax, stacking_id, len(y))
        y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])
        lines = MPLPlot._plot(ax, x, y_values, style=style, **kwds)
        cls._update_stacker(ax, stacking_id, y)
        return lines

    @classmethod
    def _ts_plot(cls, ax, x, data, style=None, **kwds):
        from pandas.plotting._timeseries import (_maybe_resample,
                                                 _decorate_axes,
                                                 format_dateaxis)
        # accept x to be consistent with normal plot func,
        # x is not passed to tsplot as it uses data.index as x coordinate
        # column_num must be in kwds for stacking purpose
        freq, data = _maybe_resample(data, ax, kwds)

        # Set ax with freq info
        _decorate_axes(ax, freq, kwds)
        # digging deeper
        if hasattr(ax, 'left_ax'):
            _decorate_axes(ax.left_ax, freq, kwds)
        if hasattr(ax, 'right_ax'):
            _decorate_axes(ax.right_ax, freq, kwds)
        ax._plot_data.append((data, cls._kind, kwds))

        lines = cls._plot(ax, data.index, data.values, style=style, **kwds)
        # set date formatter, locators and rescale limits
        format_dateaxis(ax, ax.freq, data.index)
        return lines

    def _get_stacking_id(self):
        if self.stacked:
            return id(self.data)
        else:
            return None

    @classmethod
    def _initialize_stacker(cls, ax, stacking_id, n):
        if stacking_id is None:
            return
        if not hasattr(ax, '_stacker_pos_prior'):
            ax._stacker_pos_prior = {}
        if not hasattr(ax, '_stacker_neg_prior'):
            ax._stacker_neg_prior = {}
        ax._stacker_pos_prior[stacking_id] = np.zeros(n)
        ax._stacker_neg_prior[stacking_id] = np.zeros(n)

    @classmethod
    def _get_stacked_values(cls, ax, stacking_id, values, label):
        if stacking_id is None:
            return values
        if not hasattr(ax, '_stacker_pos_prior'):
            # stacker may not be initialized for subplots
            cls._initialize_stacker(ax, stacking_id, len(values))

        if (values >= 0).all():
            return ax._stacker_pos_prior[stacking_id] + values
        elif (values <= 0).all():
            return ax._stacker_neg_prior[stacking_id] + values

        raise ValueError('When stacked is True, each column must be either '
                         'all positive or negative.'
                         '{0} contains both positive and negative values'
                         .format(label))

    @classmethod
    def _update_stacker(cls, ax, stacking_id, values):
        if stacking_id is None:
            return
        if (values >= 0).all():
            ax._stacker_pos_prior[stacking_id] += values
        elif (values <= 0).all():
            ax._stacker_neg_prior[stacking_id] += values

    def _post_plot_logic(self, ax, data):
        condition = (not self._use_dynamic_x() and
                     data.index.is_all_dates and
                     not self.subplots or
                     (self.subplots and self.sharex))

        index_name = self._get_index_name()

        if condition:
            # irregular TS rotated 30 deg. by default
            # probably a better place to check / set this.
            if not self._rot_set:
                self.rot = 30
            format_date_labels(ax, rot=self.rot)

        if index_name is not None and self.use_index:
            ax.set_xlabel(index_name)


class AreaPlot(LinePlot):
    _kind = 'area'

    def __init__(self, data, **kwargs):
        kwargs.setdefault('stacked', True)
        data = data.fillna(value=0)
        LinePlot.__init__(self, data, **kwargs)

        if not self.stacked:
            # use smaller alpha to distinguish overlap
            self.kwds.setdefault('alpha', 0.5)

        if self.logy or self.loglog:
            raise ValueError("Log-y scales are not supported in area plot")

    @classmethod
    def _plot(cls, ax, x, y, style=None, column_num=None,
              stacking_id=None, is_errorbar=False, **kwds):

        if column_num == 0:
            cls._initialize_stacker(ax, stacking_id, len(y))
        y_values = cls._get_stacked_values(ax, stacking_id, y, kwds['label'])

        # need to remove label, because subplots uses mpl legend as it is
        line_kwds = kwds.copy()
        if cls.mpl_ge_1_5_0():
            line_kwds.pop('label')
        lines = MPLPlot._plot(ax, x, y_values, style=style, **line_kwds)

        # get data from the line to get coordinates for fill_between
        xdata, y_values = lines[0].get_data(orig=False)

        # unable to use ``_get_stacked_values`` here to get starting point
        if stacking_id is None:
            start = np.zeros(len(y))
        elif (y >= 0).all():
            start = ax._stacker_pos_prior[stacking_id]
        elif (y <= 0).all():
            start = ax._stacker_neg_prior[stacking_id]
        else:
            start = np.zeros(len(y))

        if 'color' not in kwds:
            kwds['color'] = lines[0].get_color()

        rect = ax.fill_between(xdata, start, y_values, **kwds)
        cls._update_stacker(ax, stacking_id, y)

        # LinePlot expects list of artists
        res = [rect] if cls.mpl_ge_1_5_0() else lines
        return res

    def _add_legend_handle(self, handle, label, index=None):
        if not self.mpl_ge_1_5_0():
            from matplotlib.patches import Rectangle
            # Because fill_between isn't supported in legend,
            # specifically add Rectangle handle here
            alpha = self.kwds.get('alpha', None)
            handle = Rectangle((0, 0), 1, 1, fc=handle.get_color(),
                               alpha=alpha)
        LinePlot._add_legend_handle(self, handle, label, index=index)

    def _post_plot_logic(self, ax, data):
        LinePlot._post_plot_logic(self, ax, data)

        if self.ylim is None:
            if (data >= 0).all().all():
                ax.set_ylim(0, None)
            elif (data <= 0).all().all():
                ax.set_ylim(None, 0)


class BarPlot(MPLPlot):
    _kind = 'bar'
    _default_rot = 90
    orientation = 'vertical'

    def __init__(self, data, **kwargs):
        # we have to treat a series differently than a
        # 1-column DataFrame w.r.t. color handling
        self._is_series = isinstance(data, ABCSeries)
        self.bar_width = kwargs.pop('width', 0.5)
        pos = kwargs.pop('position', 0.5)
        kwargs.setdefault('align', 'center')
        self.tick_pos = np.arange(len(data))

        self.bottom = kwargs.pop('bottom', 0)
        self.left = kwargs.pop('left', 0)

        self.log = kwargs.pop('log', False)
        MPLPlot.__init__(self, data, **kwargs)

        if self.stacked or self.subplots:
            self.tickoffset = self.bar_width * pos
            if kwargs['align'] == 'edge':
                self.lim_offset = self.bar_width / 2
            else:
                self.lim_offset = 0
        else:
            if kwargs['align'] == 'edge':
                w = self.bar_width / self.nseries
                self.tickoffset = self.bar_width * (pos - 0.5) + w * 0.5
                self.lim_offset = w * 0.5
            else:
                self.tickoffset = self.bar_width * pos
                self.lim_offset = 0

        self.ax_pos = self.tick_pos - self.tickoffset

    def _args_adjust(self):
        if is_list_like(self.bottom):
            self.bottom = np.array(self.bottom)
        if is_list_like(self.left):
            self.left = np.array(self.left)

    @classmethod
    def _plot(cls, ax, x, y, w, start=0, log=False, **kwds):
        return ax.bar(x, y, w, bottom=start, log=log, **kwds)

    @property
    def _start_base(self):
        return self.bottom

    def _make_plot(self):
        import matplotlib as mpl

        colors = self._get_colors()
        ncolors = len(colors)

        pos_prior = neg_prior = np.zeros(len(self.data))
        K = self.nseries

        for i, (label, y) in enumerate(self._iter_data(fillna=0)):
            ax = self._get_ax(i)
            kwds = self.kwds.copy()
            if self._is_series:
                kwds['color'] = colors
            else:
                kwds['color'] = colors[i % ncolors]

            errors = self._get_errorbars(label=label, index=i)
            kwds = dict(kwds, **errors)

            label = pprint_thing(label)

            if (('yerr' in kwds) or ('xerr' in kwds)) \
                    and (kwds.get('ecolor') is None):
                kwds['ecolor'] = mpl.rcParams['xtick.color']

            start = 0
            if self.log and (y >= 1).all():
                start = 1
            start = start + self._start_base

            if self.subplots:
                w = self.bar_width / 2
                rect = self._plot(ax, self.ax_pos + w, y, self.bar_width,
                                  start=start, label=label,
                                  log=self.log, **kwds)
                ax.set_title(label)
            elif self.stacked:
                mask = y > 0
                start = np.where(mask, pos_prior, neg_prior) + self._start_base
                w = self.bar_width / 2
                rect = self._plot(ax, self.ax_pos + w, y, self.bar_width,
                                  start=start, label=label,
                                  log=self.log, **kwds)
                pos_prior = pos_prior + np.where(mask, y, 0)
                neg_prior = neg_prior + np.where(mask, 0, y)
            else:
                w = self.bar_width / K
                rect = self._plot(ax, self.ax_pos + (i + 0.5) * w, y, w,
                                  start=start, label=label,
                                  log=self.log, **kwds)
            self._add_legend_handle(rect, label, index=i)

    def _post_plot_logic(self, ax, data):
        if self.use_index:
            str_index = [pprint_thing(key) for key in data.index]
        else:
            str_index = [pprint_thing(key) for key in range(data.shape[0])]
        name = self._get_index_name()

        s_edge = self.ax_pos[0] - 0.25 + self.lim_offset
        e_edge = self.ax_pos[-1] + 0.25 + self.bar_width + self.lim_offset

        self._decorate_ticks(ax, name, str_index, s_edge, e_edge)

    def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge):
        ax.set_xlim((start_edge, end_edge))
        ax.set_xticks(self.tick_pos)
        ax.set_xticklabels(ticklabels)
        if name is not None and self.use_index:
            ax.set_xlabel(name)


class BarhPlot(BarPlot):
    _kind = 'barh'
    _default_rot = 0
    orientation = 'horizontal'

    @property
    def _start_base(self):
        return self.left

    @classmethod
    def _plot(cls, ax, x, y, w, start=0, log=False, **kwds):
        return ax.barh(x, y, w, left=start, log=log, **kwds)

    def _decorate_ticks(self, ax, name, ticklabels, start_edge, end_edge):
        # horizontal bars
        ax.set_ylim((start_edge, end_edge))
        ax.set_yticks(self.tick_pos)
        ax.set_yticklabels(ticklabels)
        if name is not None and self.use_index:
            ax.set_ylabel(name)


class HistPlot(LinePlot):
    _kind = 'hist'

    def __init__(self, data, bins=10, bottom=0, **kwargs):
        self.bins = bins        # use mpl default
        self.bottom = bottom
        # Do not call LinePlot.__init__ which may fill nan
        MPLPlot.__init__(self, data, **kwargs)

    def _args_adjust(self):
        if is_integer(self.bins):
            # create common bin edge
            values = (self.data._convert(datetime=True)._get_numeric_data())
            values = np.ravel(values)
            values = values[~isna(values)]

            hist, self.bins = np.histogram(
                values, bins=self.bins,
                range=self.kwds.get('range', None),
                weights=self.kwds.get('weights', None))

        if is_list_like(self.bottom):
            self.bottom = np.array(self.bottom)

    @classmethod
    def _plot(cls, ax, y, style=None, bins=None, bottom=0, column_num=0,
              stacking_id=None, **kwds):
        if column_num == 0:
            cls._initialize_stacker(ax, stacking_id, len(bins) - 1)
        y = y[~isna(y)]

        base = np.zeros(len(bins) - 1)
        bottom = bottom + \
            cls._get_stacked_values(ax, stacking_id, base, kwds['label'])
        # ignore style
        n, bins, patches = ax.hist(y, bins=bins, bottom=bottom, **kwds)
        cls._update_stacker(ax, stacking_id, n)
        return patches

    def _make_plot(self):
        colors = self._get_colors()
        stacking_id = self._get_stacking_id()

        for i, (label, y) in enumerate(self._iter_data()):
            ax = self._get_ax(i)

            kwds = self.kwds.copy()

            label = pprint_thing(label)
            kwds['label'] = label

            style, kwds = self._apply_style_colors(colors, kwds, i, label)
            if style is not None:
                kwds['style'] = style

            kwds = self._make_plot_keywords(kwds, y)
            artists = self._plot(ax, y, column_num=i,
                                 stacking_id=stacking_id, **kwds)
            self._add_legend_handle(artists[0], label, index=i)

    def _make_plot_keywords(self, kwds, y):
        """merge BoxPlot/KdePlot properties to passed kwds"""
        # y is required for KdePlot
        kwds['bottom'] = self.bottom
        kwds['bins'] = self.bins
        return kwds

    def _post_plot_logic(self, ax, data):
        if self.orientation == 'horizontal':
            ax.set_xlabel('Frequency')
        else:
            ax.set_ylabel('Frequency')

    @property
    def orientation(self):
        if self.kwds.get('orientation', None) == 'horizontal':
            return 'horizontal'
        else:
            return 'vertical'


_kde_docstring = """
        Generate Kernel Density Estimate plot using Gaussian kernels.

        In statistics, `kernel density estimation`_ (KDE) is a non-parametric
        way to estimate the probability density function (PDF) of a random
        variable. This function uses Gaussian kernels and includes automatic
        bandwith determination.

        .. _kernel density estimation:
            https://en.wikipedia.org/wiki/Kernel_density_estimation

        Parameters
        ----------
        bw_method : str, scalar or callable, optional
            The method used to calculate the estimator bandwidth. This can be
            'scott', 'silverman', a scalar constant or a callable.
            If None (default), 'scott' is used.
            See :class:`scipy.stats.gaussian_kde` for more information.
        ind : NumPy array or integer, optional
            Evaluation points for the estimated PDF. If None (default),
            1000 equally spaced points are used. If `ind` is a NumPy array, the
            KDE is evaluated at the points passed. If `ind` is an integer,
            `ind` number of equally spaced points are used.
        **kwds : optional
            Additional keyword arguments are documented in
            :meth:`pandas.%(this-datatype)s.plot`.

        Returns
        -------
        axes : matplotlib.axes.Axes or numpy.ndarray of them

        See Also
        --------
        scipy.stats.gaussian_kde : Representation of a kernel-density
            estimate using Gaussian kernels. This is the function used
            internally to estimate the PDF.
        %(sibling-datatype)s.plot.kde : Generate a KDE plot for a
            %(sibling-datatype)s.

        Examples
        --------
        %(examples)s
        """


class KdePlot(HistPlot):
    _kind = 'kde'
    orientation = 'vertical'

    def __init__(self, data, bw_method=None, ind=None, **kwargs):
        MPLPlot.__init__(self, data, **kwargs)
        self.bw_method = bw_method
        self.ind = ind

    def _args_adjust(self):
        pass

    def _get_ind(self, y):
        if self.ind is None:
            # np.nanmax() and np.nanmin() ignores the missing values
            sample_range = np.nanmax(y) - np.nanmin(y)
            ind = np.linspace(np.nanmin(y) - 0.5 * sample_range,
                              np.nanmax(y) + 0.5 * sample_range, 1000)
        elif is_integer(self.ind):
            sample_range = np.nanmax(y) - np.nanmin(y)
            ind = np.linspace(np.nanmin(y) - 0.5 * sample_range,
                              np.nanmax(y) + 0.5 * sample_range, self.ind)
        else:
            ind = self.ind
        return ind

    @classmethod
    def _plot(cls, ax, y, style=None, bw_method=None, ind=None,
              column_num=None, stacking_id=None, **kwds):
        from scipy.stats import gaussian_kde
        from scipy import __version__ as spv

        y = remove_na_arraylike(y)

        if LooseVersion(spv) >= '0.11.0':
            gkde = gaussian_kde(y, bw_method=bw_method)
        else:
            gkde = gaussian_kde(y)
            if bw_method is not None:
                msg = ('bw_method was added in Scipy 0.11.0.' +
                       ' Scipy version in use is {spv}.'.format(spv=spv))
                warnings.warn(msg)

        y = gkde.evaluate(ind)
        lines = MPLPlot._plot(ax, ind, y, style=style, **kwds)
        return lines

    def _make_plot_keywords(self, kwds, y):
        kwds['bw_method'] = self.bw_method
        kwds['ind'] = self._get_ind(y)
        return kwds

    def _post_plot_logic(self, ax, data):
        ax.set_ylabel('Density')


class PiePlot(MPLPlot):
    _kind = 'pie'
    _layout_type = 'horizontal'

    def __init__(self, data, kind=None, **kwargs):
        data = data.fillna(value=0)
        if (data < 0).any().any():
            raise ValueError("{0} doesn't allow negative values".format(kind))
        MPLPlot.__init__(self, data, kind=kind, **kwargs)

    def _args_adjust(self):
        self.grid = False
        self.logy = False
        self.logx = False
        self.loglog = False

    def _validate_color_args(self):
        pass

    def _make_plot(self):
        colors = self._get_colors(
            num_colors=len(self.data), color_kwds='colors')
        self.kwds.setdefault('colors', colors)

        for i, (label, y) in enumerate(self._iter_data()):
            ax = self._get_ax(i)
            if label is not None:
                label = pprint_thing(label)
                ax.set_ylabel(label)

            kwds = self.kwds.copy()

            def blank_labeler(label, value):
                if value == 0:
                    return ''
                else:
                    return label

            idx = [pprint_thing(v) for v in self.data.index]
            labels = kwds.pop('labels', idx)
            # labels is used for each wedge's labels
            # Blank out labels for values of 0 so they don't overlap
            # with nonzero wedges
            if labels is not None:
                blabels = [blank_labeler(l, value) for
                           l, value in zip(labels, y)]
            else:
                blabels = None
            results = ax.pie(y, labels=blabels, **kwds)

            if kwds.get('autopct', None) is not None:
                patches, texts, autotexts = results
            else:
                patches, texts = results
                autotexts = []

            if self.fontsize is not None:
                for t in texts + autotexts:
                    t.set_fontsize(self.fontsize)

            # leglabels is used for legend labels
            leglabels = labels if labels is not None else idx
            for p, l in zip(patches, leglabels):
                self._add_legend_handle(p, l)


class BoxPlot(LinePlot):
    _kind = 'box'
    _layout_type = 'horizontal'

    _valid_return_types = (None, 'axes', 'dict', 'both')
    # namedtuple to hold results
    BP = namedtuple("Boxplot", ['ax', 'lines'])

    def __init__(self, data, return_type='axes', **kwargs):
        # Do not call LinePlot.__init__ which may fill nan
        if return_type not in self._valid_return_types:
            raise ValueError(
                "return_type must be {None, 'axes', 'dict', 'both'}")

        self.return_type = return_type
        MPLPlot.__init__(self, data, **kwargs)

    def _args_adjust(self):
        if self.subplots:
            # Disable label ax sharing. Otherwise, all subplots shows last
            # column label
            if self.orientation == 'vertical':
                self.sharex = False
            else:
                self.sharey = False

    @classmethod
    def _plot(cls, ax, y, column_num=None, return_type='axes', **kwds):
        if y.ndim == 2:
            y = [remove_na_arraylike(v) for v in y]
            # Boxplot fails with empty arrays, so need to add a NaN
            #   if any cols are empty
            # GH 8181
            y = [v if v.size > 0 else np.array([np.nan]) for v in y]
        else:
            y = remove_na_arraylike(y)
        bp = ax.boxplot(y, **kwds)

        if return_type == 'dict':
            return bp, bp
        elif return_type == 'both':
            return cls.BP(ax=ax, lines=bp), bp
        else:
            return ax, bp

    def _validate_color_args(self):
        if 'color' in self.kwds:
            if self.colormap is not None:
                warnings.warn("'color' and 'colormap' cannot be used "
                              "simultaneously. Using 'color'")
            self.color = self.kwds.pop('color')

            if isinstance(self.color, dict):
                valid_keys = ['boxes', 'whiskers', 'medians', 'caps']
                for key, values in compat.iteritems(self.color):
                    if key not in valid_keys:
                        raise ValueError("color dict contains invalid "
                                         "key '{0}' "
                                         "The key must be either {1}"
                                         .format(key, valid_keys))
        else:
            self.color = None

        # get standard colors for default
        colors = _get_standard_colors(num_colors=3,
                                      colormap=self.colormap,
                                      color=None)
        # use 2 colors by default, for box/whisker and median
        # flier colors isn't needed here
        # because it can be specified by ``sym`` kw
        self._boxes_c = colors[0]
        self._whiskers_c = colors[0]
        self._medians_c = colors[2]
        self._caps_c = 'k'          # mpl default

    def _get_colors(self, num_colors=None, color_kwds='color'):
        pass

    def maybe_color_bp(self, bp):
        if isinstance(self.color, dict):
            boxes = self.color.get('boxes', self._boxes_c)
            whiskers = self.color.get('whiskers', self._whiskers_c)
            medians = self.color.get('medians', self._medians_c)
            caps = self.color.get('caps', self._caps_c)
        else:
            # Other types are forwarded to matplotlib
            # If None, use default colors
            boxes = self.color or self._boxes_c
            whiskers = self.color or self._whiskers_c
            medians = self.color or self._medians_c
            caps = self.color or self._caps_c

        from matplotlib.artist import setp
        setp(bp['boxes'], color=boxes, alpha=1)
        setp(bp['whiskers'], color=whiskers, alpha=1)
        setp(bp['medians'], color=medians, alpha=1)
        setp(bp['caps'], color=caps, alpha=1)

    def _make_plot(self):
        if self.subplots:
            from pandas.core.series import Series
            self._return_obj = Series()

            for i, (label, y) in enumerate(self._iter_data()):
                ax = self._get_ax(i)
                kwds = self.kwds.copy()

                ret, bp = self._plot(ax, y, column_num=i,
                                     return_type=self.return_type, **kwds)
                self.maybe_color_bp(bp)
                self._return_obj[label] = ret

                label = [pprint_thing(label)]
                self._set_ticklabels(ax, label)
        else:
            y = self.data.values.T
            ax = self._get_ax(0)
            kwds = self.kwds.copy()

            ret, bp = self._plot(ax, y, column_num=0,
                                 return_type=self.return_type, **kwds)
            self.maybe_color_bp(bp)
            self._return_obj = ret

            labels = [l for l, _ in self._iter_data()]
            labels = [pprint_thing(l) for l in labels]
            if not self.use_index:
                labels = [pprint_thing(key) for key in range(len(labels))]
            self._set_ticklabels(ax, labels)

    def _set_ticklabels(self, ax, labels):
        if self.orientation == 'vertical':
            ax.set_xticklabels(labels)
        else:
            ax.set_yticklabels(labels)

    def _make_legend(self):
        pass

    def _post_plot_logic(self, ax, data):
        pass

    @property
    def orientation(self):
        if self.kwds.get('vert', True):
            return 'vertical'
        else:
            return 'horizontal'

    @property
    def result(self):
        if self.return_type is None:
            return super(BoxPlot, self).result
        else:
            return self._return_obj


# kinds supported by both dataframe and series
_common_kinds = ['line', 'bar', 'barh',
                 'kde', 'density', 'area', 'hist', 'box']
# kinds supported by dataframe
_dataframe_kinds = ['scatter', 'hexbin']
# kinds supported only by series or dataframe single column
_series_kinds = ['pie']
_all_kinds = _common_kinds + _dataframe_kinds + _series_kinds

_klasses = [LinePlot, BarPlot, BarhPlot, KdePlot, HistPlot, BoxPlot,
            ScatterPlot, HexBinPlot, AreaPlot, PiePlot]

_plot_klass = {}
for klass in _klasses:
    _plot_klass[klass._kind] = klass


def _plot(data, x=None, y=None, subplots=False,
          ax=None, kind='line', **kwds):
    kind = _get_standard_kind(kind.lower().strip())
    if kind in _all_kinds:
        klass = _plot_klass[kind]
    else:
        raise ValueError("%r is not a valid plot kind" % kind)

    if kind in _dataframe_kinds:
        if isinstance(data, ABCDataFrame):
            plot_obj = klass(data, x=x, y=y, subplots=subplots, ax=ax,
                             kind=kind, **kwds)
        else:
            raise ValueError("plot kind %r can only be used for data frames"
                             % kind)

    elif kind in _series_kinds:
        if isinstance(data, ABCDataFrame):
            if y is None and subplots is False:
                msg = "{0} requires either y column or 'subplots=True'"
                raise ValueError(msg.format(kind))
            elif y is not None:
                if is_integer(y) and not data.columns.holds_integer():
                    y = data.columns[y]
                # converted to series actually. copy to not modify
                data = data[y].copy()
                data.index.name = y
        plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)
    else:
        if isinstance(data, ABCDataFrame):
            data_cols = data.columns
            if x is not None:
                if is_integer(x) and not data.columns.holds_integer():
                    x = data_cols[x]
                elif not isinstance(data[x], ABCSeries):
                    raise ValueError("x must be a label or position")
                data = data.set_index(x)

            if y is not None:
                # check if we have y as int or list of ints
                int_ylist = is_list_like(y) and all(is_integer(c) for c in y)
                int_y_arg = is_integer(y) or int_ylist
                if int_y_arg and not data.columns.holds_integer():
                    y = data_cols[y]

                label_kw = kwds['label'] if 'label' in kwds else False
                for kw in ['xerr', 'yerr']:
                    if (kw in kwds) and \
                        (isinstance(kwds[kw], string_types) or
                            is_integer(kwds[kw])):
                        try:
                            kwds[kw] = data[kwds[kw]]
                        except (IndexError, KeyError, TypeError):
                            pass

                # don't overwrite
                data = data[y].copy()

                if isinstance(data, ABCSeries):
                    label_name = label_kw or y
                    data.name = label_name
                else:
                    match = is_list_like(label_kw) and len(label_kw) == len(y)
                    if label_kw and not match:
                        raise ValueError(
                            "label should be list-like and same length as y"
                        )
                    label_name = label_kw or data.columns
                    data.columns = label_name

        plot_obj = klass(data, subplots=subplots, ax=ax, kind=kind, **kwds)

    plot_obj.generate()
    plot_obj.draw()
    return plot_obj.result


df_kind = """- 'scatter' : scatter plot
        - 'hexbin' : hexbin plot"""
series_kind = ""

df_coord = """x : label or position, default None
    y : label, position or list of label, positions, default None
        Allows plotting of one column versus another"""
series_coord = ""

df_unique = """stacked : boolean, default False in line and
        bar plots, and True in area plot. If True, create stacked plot.
    sort_columns : boolean, default False
        Sort column names to determine plot ordering
    secondary_y : boolean or sequence, default False
        Whether to plot on the secondary y-axis
        If a list/tuple, which columns to plot on secondary y-axis"""
series_unique = """label : label argument to provide to plot
    secondary_y : boolean or sequence of ints, default False
        If True then y-axis will be on the right"""

df_ax = """ax : matplotlib axes object, default None
    subplots : boolean, default False
        Make separate subplots for each column
    sharex : boolean, default True if ax is None else False
        In case subplots=True, share x axis and set some x axis labels to
        invisible; defaults to True if ax is None otherwise False if an ax
        is passed in; Be aware, that passing in both an ax and sharex=True
        will alter all x axis labels for all axis in a figure!
    sharey : boolean, default False
        In case subplots=True, share y axis and set some y axis labels to
        invisible
    layout : tuple (optional)
        (rows, columns) for the layout of subplots"""
series_ax = """ax : matplotlib axes object
        If not passed, uses gca()"""

df_note = """- If `kind` = 'scatter' and the argument `c` is the name of a dataframe
      column, the values of that column are used to color each point.
    - If `kind` = 'hexbin', you can control the size of the bins with the
      `gridsize` argument. By default, a histogram of the counts around each
      `(x, y)` point is computed. You can specify alternative aggregations
      by passing values to the `C` and `reduce_C_function` arguments.
      `C` specifies the value at each `(x, y)` point and `reduce_C_function`
      is a function of one argument that reduces all the values in a bin to
      a single number (e.g. `mean`, `max`, `sum`, `std`)."""
series_note = ""

_shared_doc_df_kwargs = dict(klass='DataFrame', klass_obj='df',
                             klass_kind=df_kind, klass_coord=df_coord,
                             klass_ax=df_ax, klass_unique=df_unique,
                             klass_note=df_note)
_shared_doc_series_kwargs = dict(klass='Series', klass_obj='s',
                                 klass_kind=series_kind,
                                 klass_coord=series_coord, klass_ax=series_ax,
                                 klass_unique=series_unique,
                                 klass_note=series_note)

_shared_docs['plot'] = """
    Make plots of %(klass)s using matplotlib / pylab.

    *New in version 0.17.0:* Each plot kind has a corresponding method on the
    ``%(klass)s.plot`` accessor:
    ``%(klass_obj)s.plot(kind='line')`` is equivalent to
    ``%(klass_obj)s.plot.line()``.

    Parameters
    ----------
    data : %(klass)s
    %(klass_coord)s
    kind : str
        - 'line' : line plot (default)
        - 'bar' : vertical bar plot
        - 'barh' : horizontal bar plot
        - 'hist' : histogram
        - 'box' : boxplot
        - 'kde' : Kernel Density Estimation plot
        - 'density' : same as 'kde'
        - 'area' : area plot
        - 'pie' : pie plot
        %(klass_kind)s
    %(klass_ax)s
    figsize : a tuple (width, height) in inches
    use_index : boolean, default True
        Use index as ticks for x axis
    title : string or list
        Title to use for the plot. If a string is passed, print the string at
        the top of the figure. If a list is passed and `subplots` is True,
        print each item in the list above the corresponding subplot.
    grid : boolean, default None (matlab style default)
        Axis grid lines
    legend : False/True/'reverse'
        Place legend on axis subplots
    style : list or dict
        matplotlib line style per column
    logx : boolean, default False
        Use log scaling on x axis
    logy : boolean, default False
        Use log scaling on y axis
    loglog : boolean, default False
        Use log scaling on both x and y axes
    xticks : sequence
        Values to use for the xticks
    yticks : sequence
        Values to use for the yticks
    xlim : 2-tuple/list
    ylim : 2-tuple/list
    rot : int, default None
        Rotation for ticks (xticks for vertical, yticks for horizontal plots)
    fontsize : int, default None
        Font size for xticks and yticks
    colormap : str or matplotlib colormap object, default None
        Colormap to select colors from. If string, load colormap with that name
        from matplotlib.
    colorbar : boolean, optional
        If True, plot colorbar (only relevant for 'scatter' and 'hexbin' plots)
    position : float
        Specify relative alignments for bar plot layout.
        From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)
    table : boolean, Series or DataFrame, default False
        If True, draw a table using the data in the DataFrame and the data will
        be transposed to meet matplotlib's default layout.
        If a Series or DataFrame is passed, use passed data to draw a table.
    yerr : DataFrame, Series, array-like, dict and str
        See :ref:`Plotting with Error Bars <visualization.errorbars>` for
        detail.
    xerr : same types as yerr.
    %(klass_unique)s
    mark_right : boolean, default True
        When using a secondary_y axis, automatically mark the column
        labels with "(right)" in the legend
    `**kwds` : keywords
        Options to pass to matplotlib plotting method

    Returns
    -------
    axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them

    Notes
    -----

    - See matplotlib documentation online for more on this subject
    - If `kind` = 'bar' or 'barh', you can specify relative alignments
      for bar plot layout by `position` keyword.
      From 0 (left/bottom-end) to 1 (right/top-end). Default is 0.5 (center)
    %(klass_note)s

    """


@Appender(_shared_docs['plot'] % _shared_doc_df_kwargs)
def plot_frame(data, x=None, y=None, kind='line', ax=None,
               subplots=False, sharex=None, sharey=False, layout=None,
               figsize=None, use_index=True, title=None, grid=None,
               legend=True, style=None, logx=False, logy=False, loglog=False,
               xticks=None, yticks=None, xlim=None, ylim=None,
               rot=None, fontsize=None, colormap=None, table=False,
               yerr=None, xerr=None,
               secondary_y=False, sort_columns=False,
               **kwds):
    return _plot(data, kind=kind, x=x, y=y, ax=ax,
                 subplots=subplots, sharex=sharex, sharey=sharey,
                 layout=layout, figsize=figsize, use_index=use_index,
                 title=title, grid=grid, legend=legend,
                 style=style, logx=logx, logy=logy, loglog=loglog,
                 xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim,
                 rot=rot, fontsize=fontsize, colormap=colormap, table=table,
                 yerr=yerr, xerr=xerr,
                 secondary_y=secondary_y, sort_columns=sort_columns,
                 **kwds)


@Appender(_shared_docs['plot'] % _shared_doc_series_kwargs)
def plot_series(data, kind='line', ax=None,                    # Series unique
                figsize=None, use_index=True, title=None, grid=None,
                legend=False, style=None, logx=False, logy=False, loglog=False,
                xticks=None, yticks=None, xlim=None, ylim=None,
                rot=None, fontsize=None, colormap=None, table=False,
                yerr=None, xerr=None,
                label=None, secondary_y=False,                 # Series unique
                **kwds):

    import matplotlib.pyplot as plt
    if ax is None and len(plt.get_fignums()) > 0:
        ax = _gca()
        ax = MPLPlot._get_ax_layer(ax)
    return _plot(data, kind=kind, ax=ax,
                 figsize=figsize, use_index=use_index, title=title,
                 grid=grid, legend=legend,
                 style=style, logx=logx, logy=logy, loglog=loglog,
                 xticks=xticks, yticks=yticks, xlim=xlim, ylim=ylim,
                 rot=rot, fontsize=fontsize, colormap=colormap, table=table,
                 yerr=yerr, xerr=xerr,
                 label=label, secondary_y=secondary_y,
                 **kwds)


_shared_docs['boxplot'] = """
    Make a box plot from DataFrame columns.

    Make a box-and-whisker plot from DataFrame columns, optionally grouped
    by some other columns. A box plot is a method for graphically depicting
    groups of numerical data through their quartiles.
    The box extends from the Q1 to Q3 quartile values of the data,
    with a line at the median (Q2). The whiskers extend from the edges
    of box to show the range of the data. The position of the whiskers
    is set by default to `1.5 * IQR (IQR = Q3 - Q1)` from the edges of the box.
    Outlier points are those past the end of the whiskers.

    For further details see
    Wikipedia's entry for `boxplot <https://en.wikipedia.org/wiki/Box_plot>`_.

    Parameters
    ----------
    column : str or list of str, optional
        Column name or list of names, or vector.
        Can be any valid input to :meth:`pandas.DataFrame.groupby`.
    by : str or array-like, optional
        Column in the DataFrame to :meth:`pandas.DataFrame.groupby`.
        One box-plot will be done per value of columns in `by`.
    ax : object of class matplotlib.axes.Axes, optional
        The matplotlib axes to be used by boxplot.
    fontsize : float or str
        Tick label font size in points or as a string (e.g., `large`).
    rot : int or float, default 0
        The rotation angle of labels (in degrees)
        with respect to the screen coordinate sytem.
    grid : boolean, default True
        Setting this to True will show the grid.
    figsize : A tuple (width, height) in inches
        The size of the figure to create in matplotlib.
    layout : tuple (rows, columns), optional
        For example, (3, 5) will display the subplots
        using 3 columns and 5 rows, starting from the top-left.
    return_type : {'axes', 'dict', 'both'} or None, default 'axes'
        The kind of object to return. The default is ``axes``.

        * 'axes' returns the matplotlib axes the boxplot is drawn on.
        * 'dict' returns a dictionary whose values are the matplotlib
          Lines of the boxplot.
        * 'both' returns a namedtuple with the axes and dict.
        * when grouping with ``by``, a Series mapping columns to
          ``return_type`` is returned.

          If ``return_type`` is `None`, a NumPy array
          of axes with the same shape as ``layout`` is returned.
    **kwds
        All other plotting keyword arguments to be passed to
        :func:`matplotlib.pyplot.boxplot`.

    Returns
    -------
    result :

        The return type depends on the `return_type` parameter:

        * 'axes' : object of class matplotlib.axes.Axes
        * 'dict' : dict of matplotlib.lines.Line2D objects
        * 'both' : a nametuple with strucure (ax, lines)

        For data grouped with ``by``:

        * :class:`~pandas.Series`
        * :class:`~numpy.array` (for ``return_type = None``)

    See Also
    --------
    Series.plot.hist: Make a histogram.
    matplotlib.pyplot.boxplot : Matplotlib equivalent plot.

    Notes
    -----
    Use ``return_type='dict'`` when you want to tweak the appearance
    of the lines after plotting. In this case a dict containing the Lines
    making up the boxes, caps, fliers, medians, and whiskers is returned.

    Examples
    --------

    Boxplots can be created for every column in the dataframe
    by ``df.boxplot()`` or indicating the columns to be used:

    .. plot::
        :context: close-figs

        >>> np.random.seed(1234)
        >>> df = pd.DataFrame(np.random.randn(10,4),
        ...                   columns=['Col1', 'Col2', 'Col3', 'Col4'])
        >>> boxplot = df.boxplot(column=['Col1', 'Col2', 'Col3'])

    Boxplots of variables distributions grouped by the values of a third
    variable can be created using the option ``by``. For instance:

    .. plot::
        :context: close-figs

        >>> df = pd.DataFrame(np.random.randn(10, 2),
        ...                   columns=['Col1', 'Col2'])
        >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
        ...                      'B', 'B', 'B', 'B', 'B'])
        >>> boxplot = df.boxplot(by='X')

    A list of strings (i.e. ``['X', 'Y']``) can be passed to boxplot
    in order to group the data by combination of the variables in the x-axis:

    .. plot::
        :context: close-figs

        >>> df = pd.DataFrame(np.random.randn(10,3),
        ...                   columns=['Col1', 'Col2', 'Col3'])
        >>> df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A',
        ...                      'B', 'B', 'B', 'B', 'B'])
        >>> df['Y'] = pd.Series(['A', 'B', 'A', 'B', 'A',
        ...                      'B', 'A', 'B', 'A', 'B'])
        >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by=['X', 'Y'])

    The layout of boxplot can be adjusted giving a tuple to ``layout``:

    .. plot::
        :context: close-figs

        >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
        ...                      layout=(2, 1))

    Additional formatting can be done to the boxplot, like suppressing the grid
    (``grid=False``), rotating the labels in the x-axis (i.e. ``rot=45``)
    or changing the fontsize (i.e. ``fontsize=15``):

    .. plot::
        :context: close-figs

        >>> boxplot = df.boxplot(grid=False, rot=45, fontsize=15)

    The parameter ``return_type`` can be used to select the type of element
    returned by `boxplot`.  When ``return_type='axes'`` is selected,
    the matplotlib axes on which the boxplot is drawn are returned:

        >>> boxplot = df.boxplot(column=['Col1','Col2'], return_type='axes')
        >>> type(boxplot)
        <class 'matplotlib.axes._subplots.AxesSubplot'>

    When grouping with ``by``, a Series mapping columns to ``return_type``
    is returned:

        >>> boxplot = df.boxplot(column=['Col1', 'Col2'], by='X',
        ...                      return_type='axes')
        >>> type(boxplot)
        <class 'pandas.core.series.Series'>

    If ``return_type`` is `None`, a NumPy array of axes with the same shape
    as ``layout`` is returned:

        >>> boxplot =  df.boxplot(column=['Col1', 'Col2'], by='X',
        ...                       return_type=None)
        >>> type(boxplot)
        <class 'numpy.ndarray'>
    """


@Appender(_shared_docs['boxplot'] % _shared_doc_kwargs)
def boxplot(data, column=None, by=None, ax=None, fontsize=None,
            rot=0, grid=True, figsize=None, layout=None, return_type=None,
            **kwds):

    # validate return_type:
    if return_type not in BoxPlot._valid_return_types:
        raise ValueError("return_type must be {'axes', 'dict', 'both'}")

    from pandas import Series, DataFrame
    if isinstance(data, Series):
        data = DataFrame({'x': data})
        column = 'x'

    def _get_colors():
        return _get_standard_colors(color=kwds.get('color'), num_colors=1)

    def maybe_color_bp(bp):
        if 'color' not in kwds:
            from matplotlib.artist import setp
            setp(bp['boxes'], color=colors[0], alpha=1)
            setp(bp['whiskers'], color=colors[0], alpha=1)
            setp(bp['medians'], color=colors[2], alpha=1)

    def plot_group(keys, values, ax):
        keys = [pprint_thing(x) for x in keys]
        values = [np.asarray(remove_na_arraylike(v)) for v in values]
        bp = ax.boxplot(values, **kwds)
        if fontsize is not None:
            ax.tick_params(axis='both', labelsize=fontsize)
        if kwds.get('vert', 1):
            ax.set_xticklabels(keys, rotation=rot)
        else:
            ax.set_yticklabels(keys, rotation=rot)
        maybe_color_bp(bp)

        # Return axes in multiplot case, maybe revisit later # 985
        if return_type == 'dict':
            return bp
        elif return_type == 'both':
            return BoxPlot.BP(ax=ax, lines=bp)
        else:
            return ax

    colors = _get_colors()
    if column is None:
        columns = None
    else:
        if isinstance(column, (list, tuple)):
            columns = column
        else:
            columns = [column]

    if by is not None:
        # Prefer array return type for 2-D plots to match the subplot layout
        # https://github.com/pandas-dev/pandas/pull/12216#issuecomment-241175580
        result = _grouped_plot_by_column(plot_group, data, columns=columns,
                                         by=by, grid=grid, figsize=figsize,
                                         ax=ax, layout=layout,
                                         return_type=return_type)
    else:
        if return_type is None:
            return_type = 'axes'
        if layout is not None:
            raise ValueError("The 'layout' keyword is not supported when "
                             "'by' is None")

        if ax is None:
            rc = {'figure.figsize': figsize} if figsize is not None else {}
            ax = _gca(rc)
        data = data._get_numeric_data()
        if columns is None:
            columns = data.columns
        else:
            data = data[columns]

        result = plot_group(columns, data.values.T, ax)
        ax.grid(grid)

    return result


@Appender(_shared_docs['boxplot'] % _shared_doc_kwargs)
def boxplot_frame(self, column=None, by=None, ax=None, fontsize=None, rot=0,
                  grid=True, figsize=None, layout=None,
                  return_type=None, **kwds):
    import matplotlib.pyplot as plt
    _converter._WARN = False
    ax = boxplot(self, column=column, by=by, ax=ax, fontsize=fontsize,
                 grid=grid, rot=rot, figsize=figsize, layout=layout,
                 return_type=return_type, **kwds)
    plt.draw_if_interactive()
    return ax


def scatter_plot(data, x, y, by=None, ax=None, figsize=None, grid=False,
                 **kwargs):
    """
    Make a scatter plot from two DataFrame columns

    Parameters
    ----------
    data : DataFrame
    x : Column name for the x-axis values
    y : Column name for the y-axis values
    ax : Matplotlib axis object
    figsize : A tuple (width, height) in inches
    grid : Setting this to True will show the grid
    kwargs : other plotting keyword arguments
        To be passed to scatter function

    Returns
    -------
    fig : matplotlib.Figure
    """
    import matplotlib.pyplot as plt

    kwargs.setdefault('edgecolors', 'none')

    def plot_group(group, ax):
        xvals = group[x].values
        yvals = group[y].values
        ax.scatter(xvals, yvals, **kwargs)
        ax.grid(grid)

    if by is not None:
        fig = _grouped_plot(plot_group, data, by=by, figsize=figsize, ax=ax)
    else:
        if ax is None:
            fig = plt.figure()
            ax = fig.add_subplot(111)
        else:
            fig = ax.get_figure()
        plot_group(data, ax)
        ax.set_ylabel(pprint_thing(y))
        ax.set_xlabel(pprint_thing(x))

        ax.grid(grid)

    return fig


def hist_frame(data, column=None, by=None, grid=True, xlabelsize=None,
               xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False,
               sharey=False, figsize=None, layout=None, bins=10, **kwds):
    """
    Make a histogram of the DataFrame's.

    A `histogram`_ is a representation of the distribution of data.
    This function calls :meth:`matplotlib.pyplot.hist`, on each series in
    the DataFrame, resulting in one histogram per column.

    .. _histogram: https://en.wikipedia.org/wiki/Histogram

    Parameters
    ----------
    data : DataFrame
        The pandas object holding the data.
    column : string or sequence
        If passed, will be used to limit data to a subset of columns.
    by : object, optional
        If passed, then used to form histograms for separate groups.
    grid : boolean, default True
        Whether to show axis grid lines.
    xlabelsize : int, default None
        If specified changes the x-axis label size.
    xrot : float, default None
        Rotation of x axis labels. For example, a value of 90 displays the
        x labels rotated 90 degrees clockwise.
    ylabelsize : int, default None
        If specified changes the y-axis label size.
    yrot : float, default None
        Rotation of y axis labels. For example, a value of 90 displays the
        y labels rotated 90 degrees clockwise.
    ax : Matplotlib axes object, default None
        The axes to plot the histogram on.
    sharex : boolean, default True if ax is None else False
        In case subplots=True, share x axis and set some x axis labels to
        invisible; defaults to True if ax is None otherwise False if an ax
        is passed in.
        Note that passing in both an ax and sharex=True will alter all x axis
        labels for all subplots in a figure.
    sharey : boolean, default False
        In case subplots=True, share y axis and set some y axis labels to
        invisible.
    figsize : tuple
        The size in inches of the figure to create. Uses the value in
        `matplotlib.rcParams` by default.
    layout : tuple, optional
        Tuple of (rows, columns) for the layout of the histograms.
    bins : integer or sequence, default 10
        Number of histogram bins to be used. If an integer is given, bins + 1
        bin edges are calculated and returned. If bins is a sequence, gives
        bin edges, including left edge of first bin and right edge of last
        bin. In this case, bins is returned unmodified.
    **kwds
        All other plotting keyword arguments to be passed to
        :meth:`matplotlib.pyplot.hist`.

    Returns
    -------
    axes : matplotlib.AxesSubplot or numpy.ndarray of them

    See Also
    --------
    matplotlib.pyplot.hist : Plot a histogram using matplotlib.

    Examples
    --------

    .. plot::
        :context: close-figs

        This example draws a histogram based on the length and width of
        some animals, displayed in three bins

        >>> df = pd.DataFrame({
        ...     'length': [1.5, 0.5, 1.2, 0.9, 3],
        ...     'width': [0.7, 0.2, 0.15, 0.2, 1.1]
        ...     }, index= ['pig', 'rabbit', 'duck', 'chicken', 'horse'])
        >>> hist = df.hist(bins=3)
    """
    _raise_if_no_mpl()
    _converter._WARN = False
    if by is not None:
        axes = grouped_hist(data, column=column, by=by, ax=ax, grid=grid,
                            figsize=figsize, sharex=sharex, sharey=sharey,
                            layout=layout, bins=bins, xlabelsize=xlabelsize,
                            xrot=xrot, ylabelsize=ylabelsize,
                            yrot=yrot, **kwds)
        return axes

    if column is not None:
        if not isinstance(column, (list, np.ndarray, Index)):
            column = [column]
        data = data[column]
    data = data._get_numeric_data()
    naxes = len(data.columns)

    fig, axes = _subplots(naxes=naxes, ax=ax, squeeze=False,
                          sharex=sharex, sharey=sharey, figsize=figsize,
                          layout=layout)
    _axes = _flatten(axes)

    for i, col in enumerate(com._try_sort(data.columns)):
        ax = _axes[i]
        ax.hist(data[col].dropna().values, bins=bins, **kwds)
        ax.set_title(col)
        ax.grid(grid)

    _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot,
                     ylabelsize=ylabelsize, yrot=yrot)
    fig.subplots_adjust(wspace=0.3, hspace=0.3)

    return axes


def hist_series(self, by=None, ax=None, grid=True, xlabelsize=None,
                xrot=None, ylabelsize=None, yrot=None, figsize=None,
                bins=10, **kwds):
    """
    Draw histogram of the input series using matplotlib

    Parameters
    ----------
    by : object, optional
        If passed, then used to form histograms for separate groups
    ax : matplotlib axis object
        If not passed, uses gca()
    grid : boolean, default True
        Whether to show axis grid lines
    xlabelsize : int, default None
        If specified changes the x-axis label size
    xrot : float, default None
        rotation of x axis labels
    ylabelsize : int, default None
        If specified changes the y-axis label size
    yrot : float, default None
        rotation of y axis labels
    figsize : tuple, default None
        figure size in inches by default
    bins : integer or sequence, default 10
        Number of histogram bins to be used. If an integer is given, bins + 1
        bin edges are calculated and returned. If bins is a sequence, gives
        bin edges, including left edge of first bin and right edge of last
        bin. In this case, bins is returned unmodified.
    bins: integer, default 10
        Number of histogram bins to be used
    `**kwds` : keywords
        To be passed to the actual plotting function

    See Also
    --------
    matplotlib.axes.Axes.hist : Plot a histogram using matplotlib.

    """
    import matplotlib.pyplot as plt

    if by is None:
        if kwds.get('layout', None) is not None:
            raise ValueError("The 'layout' keyword is not supported when "
                             "'by' is None")
        # hack until the plotting interface is a bit more unified
        fig = kwds.pop('figure', plt.gcf() if plt.get_fignums() else
                       plt.figure(figsize=figsize))
        if (figsize is not None and tuple(figsize) !=
                tuple(fig.get_size_inches())):
            fig.set_size_inches(*figsize, forward=True)
        if ax is None:
            ax = fig.gca()
        elif ax.get_figure() != fig:
            raise AssertionError('passed axis not bound to passed figure')
        values = self.dropna().values

        ax.hist(values, bins=bins, **kwds)
        ax.grid(grid)
        axes = np.array([ax])

        _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot,
                         ylabelsize=ylabelsize, yrot=yrot)

    else:
        if 'figure' in kwds:
            raise ValueError("Cannot pass 'figure' when using the "
                             "'by' argument, since a new 'Figure' instance "
                             "will be created")
        axes = grouped_hist(self, by=by, ax=ax, grid=grid, figsize=figsize,
                            bins=bins, xlabelsize=xlabelsize, xrot=xrot,
                            ylabelsize=ylabelsize, yrot=yrot, **kwds)

    if hasattr(axes, 'ndim'):
        if axes.ndim == 1 and len(axes) == 1:
            return axes[0]
    return axes


def grouped_hist(data, column=None, by=None, ax=None, bins=50, figsize=None,
                 layout=None, sharex=False, sharey=False, rot=90, grid=True,
                 xlabelsize=None, xrot=None, ylabelsize=None, yrot=None,
                 **kwargs):
    """
    Grouped histogram

    Parameters
    ----------
    data: Series/DataFrame
    column: object, optional
    by: object, optional
    ax: axes, optional
    bins: int, default 50
    figsize: tuple, optional
    layout: optional
    sharex: boolean, default False
    sharey: boolean, default False
    rot: int, default 90
    grid: bool, default True
    kwargs: dict, keyword arguments passed to matplotlib.Axes.hist

    Returns
    -------
    axes: collection of Matplotlib Axes
    """
    _raise_if_no_mpl()
    _converter._WARN = False

    def plot_group(group, ax):
        ax.hist(group.dropna().values, bins=bins, **kwargs)

    xrot = xrot or rot

    fig, axes = _grouped_plot(plot_group, data, column=column,
                              by=by, sharex=sharex, sharey=sharey, ax=ax,
                              figsize=figsize, layout=layout, rot=rot)

    _set_ticks_props(axes, xlabelsize=xlabelsize, xrot=xrot,
                     ylabelsize=ylabelsize, yrot=yrot)

    fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9,
                        hspace=0.5, wspace=0.3)
    return axes


def boxplot_frame_groupby(grouped, subplots=True, column=None, fontsize=None,
                          rot=0, grid=True, ax=None, figsize=None,
                          layout=None, **kwds):
    """
    Make box plots from DataFrameGroupBy data.

    Parameters
    ----------
    grouped : Grouped DataFrame
    subplots :
        * ``False`` - no subplots will be used
        * ``True`` - create a subplot for each group
    column : column name or list of names, or vector
        Can be any valid input to groupby
    fontsize : int or string
    rot : label rotation angle
    grid : Setting this to True will show the grid
    ax : Matplotlib axis object, default None
    figsize : A tuple (width, height) in inches
    layout : tuple (optional)
        (rows, columns) for the layout of the plot
    `**kwds` : Keyword Arguments
        All other plotting keyword arguments to be passed to
        matplotlib's boxplot function

    Returns
    -------
    dict of key/value = group key/DataFrame.boxplot return value
    or DataFrame.boxplot return value in case subplots=figures=False

    Examples
    --------
    >>> import pandas
    >>> import numpy as np
    >>> import itertools
    >>>
    >>> tuples = [t for t in itertools.product(range(1000), range(4))]
    >>> index = pandas.MultiIndex.from_tuples(tuples, names=['lvl0', 'lvl1'])
    >>> data = np.random.randn(len(index),4)
    >>> df = pandas.DataFrame(data, columns=list('ABCD'), index=index)
    >>>
    >>> grouped = df.groupby(level='lvl1')
    >>> boxplot_frame_groupby(grouped)
    >>>
    >>> grouped = df.unstack(level='lvl1').groupby(level=0, axis=1)
    >>> boxplot_frame_groupby(grouped, subplots=False)
    """
    _raise_if_no_mpl()
    _converter._WARN = False
    if subplots is True:
        naxes = len(grouped)
        fig, axes = _subplots(naxes=naxes, squeeze=False,
                              ax=ax, sharex=False, sharey=True,
                              figsize=figsize, layout=layout)
        axes = _flatten(axes)

        from pandas.core.series import Series
        ret = Series()
        for (key, group), ax in zip(grouped, axes):
            d = group.boxplot(ax=ax, column=column, fontsize=fontsize,
                              rot=rot, grid=grid, **kwds)
            ax.set_title(pprint_thing(key))
            ret.loc[key] = d
        fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1,
                            right=0.9, wspace=0.2)
    else:
        from pandas.core.reshape.concat import concat
        keys, frames = zip(*grouped)
        if grouped.axis == 0:
            df = concat(frames, keys=keys, axis=1)
        else:
            if len(frames) > 1:
                df = frames[0].join(frames[1::])
            else:
                df = frames[0]
        ret = df.boxplot(column=column, fontsize=fontsize, rot=rot,
                         grid=grid, ax=ax, figsize=figsize,
                         layout=layout, **kwds)
    return ret


def _grouped_plot(plotf, data, column=None, by=None, numeric_only=True,
                  figsize=None, sharex=True, sharey=True, layout=None,
                  rot=0, ax=None, **kwargs):
    from pandas import DataFrame

    if figsize == 'default':
        # allowed to specify mpl default with 'default'
        warnings.warn("figsize='default' is deprecated. Specify figure"
                      "size by tuple instead", FutureWarning, stacklevel=4)
        figsize = None

    grouped = data.groupby(by)
    if column is not None:
        grouped = grouped[column]

    naxes = len(grouped)
    fig, axes = _subplots(naxes=naxes, figsize=figsize,
                          sharex=sharex, sharey=sharey, ax=ax,
                          layout=layout)

    _axes = _flatten(axes)

    for i, (key, group) in enumerate(grouped):
        ax = _axes[i]
        if numeric_only and isinstance(group, DataFrame):
            group = group._get_numeric_data()
        plotf(group, ax, **kwargs)
        ax.set_title(pprint_thing(key))

    return fig, axes


def _grouped_plot_by_column(plotf, data, columns=None, by=None,
                            numeric_only=True, grid=False,
                            figsize=None, ax=None, layout=None,
                            return_type=None, **kwargs):
    grouped = data.groupby(by)
    if columns is None:
        if not isinstance(by, (list, tuple)):
            by = [by]
        columns = data._get_numeric_data().columns.difference(by)
    naxes = len(columns)
    fig, axes = _subplots(naxes=naxes, sharex=True, sharey=True,
                          figsize=figsize, ax=ax, layout=layout)

    _axes = _flatten(axes)

    ax_values = []

    for i, col in enumerate(columns):
        ax = _axes[i]
        gp_col = grouped[col]
        keys, values = zip(*gp_col)
        re_plotf = plotf(keys, values, ax, **kwargs)
        ax.set_title(col)
        ax.set_xlabel(pprint_thing(by))
        ax_values.append(re_plotf)
        ax.grid(grid)

    from pandas.core.series import Series
    result = Series(ax_values, index=columns)

    # Return axes in multiplot case, maybe revisit later # 985
    if return_type is None:
        result = axes

    byline = by[0] if len(by) == 1 else by
    fig.suptitle('Boxplot grouped by {byline}'.format(byline=byline))
    fig.subplots_adjust(bottom=0.15, top=0.9, left=0.1, right=0.9, wspace=0.2)

    return result


class BasePlotMethods(PandasObject):

    def __init__(self, data):
        self._data = data

    def __call__(self, *args, **kwargs):
        raise NotImplementedError


class SeriesPlotMethods(BasePlotMethods):
    """Series plotting accessor and method

    Examples
    --------
    >>> s.plot.line()
    >>> s.plot.bar()
    >>> s.plot.hist()

    Plotting methods can also be accessed by calling the accessor as a method
    with the ``kind`` argument:
    ``s.plot(kind='line')`` is equivalent to ``s.plot.line()``
    """

    def __call__(self, kind='line', ax=None,
                 figsize=None, use_index=True, title=None, grid=None,
                 legend=False, style=None, logx=False, logy=False,
                 loglog=False, xticks=None, yticks=None,
                 xlim=None, ylim=None,
                 rot=None, fontsize=None, colormap=None, table=False,
                 yerr=None, xerr=None,
                 label=None, secondary_y=False, **kwds):
        return plot_series(self._data, kind=kind, ax=ax, figsize=figsize,
                           use_index=use_index, title=title, grid=grid,
                           legend=legend, style=style, logx=logx, logy=logy,
                           loglog=loglog, xticks=xticks, yticks=yticks,
                           xlim=xlim, ylim=ylim, rot=rot, fontsize=fontsize,
                           colormap=colormap, table=table, yerr=yerr,
                           xerr=xerr, label=label, secondary_y=secondary_y,
                           **kwds)
    __call__.__doc__ = plot_series.__doc__

    def line(self, **kwds):
        """
        Line plot

        Parameters
        ----------
        `**kwds` : optional
            Additional keyword arguments are documented in
            :meth:`pandas.Series.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them

        Examples
        --------

        .. plot::
            :context: close-figs

            >>> s = pd.Series([1, 3, 2])
            >>> s.plot.line()
        """
        return self(kind='line', **kwds)

    def bar(self, **kwds):
        """
        Vertical bar plot

        Parameters
        ----------
        `**kwds` : optional
            Additional keyword arguments are documented in
            :meth:`pandas.Series.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
        """
        return self(kind='bar', **kwds)

    def barh(self, **kwds):
        """
        Horizontal bar plot

        Parameters
        ----------
        `**kwds` : optional
            Additional keyword arguments are documented in
            :meth:`pandas.Series.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
        """
        return self(kind='barh', **kwds)

    def box(self, **kwds):
        """
        Boxplot

        Parameters
        ----------
        `**kwds` : optional
            Additional keyword arguments are documented in
            :meth:`pandas.Series.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
        """
        return self(kind='box', **kwds)

    def hist(self, bins=10, **kwds):
        """
        Histogram

        Parameters
        ----------
        bins: integer, default 10
            Number of histogram bins to be used
        `**kwds` : optional
            Additional keyword arguments are documented in
            :meth:`pandas.Series.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
        """
        return self(kind='hist', bins=bins, **kwds)

    @Appender(_kde_docstring % {
        'this-datatype': 'Series',
        'sibling-datatype': 'DataFrame',
        'examples': """
        Given a Series of points randomly sampled from an unknown
        distribution, estimate its PDF using KDE with automatic
        bandwidth determination and plot the results, evaluating them at
        1000 equally spaced points (default):

        .. plot::
            :context: close-figs

            >>> s = pd.Series([1, 2, 2.5, 3, 3.5, 4, 5])
            >>> ax = s.plot.kde()

        A scalar bandwidth can be specified. Using a small bandwidth value can
        lead to overfitting, while using a large bandwidth value may result
        in underfitting:

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(bw_method=0.3)

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(bw_method=3)

        Finally, the `ind` parameter determines the evaluation points for the
        plot of the estimated PDF:

        .. plot::
            :context: close-figs

            >>> ax = s.plot.kde(ind=[1, 2, 3, 4, 5])
        """.strip()
    })
    def kde(self, bw_method=None, ind=None, **kwds):
        return self(kind='kde', bw_method=bw_method, ind=ind, **kwds)

    density = kde

    def area(self, **kwds):
        """
        Area plot

        Parameters
        ----------
        `**kwds` : optional
            Additional keyword arguments are documented in
            :meth:`pandas.Series.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
        """
        return self(kind='area', **kwds)

    def pie(self, **kwds):
        """
        Pie chart

        Parameters
        ----------
        `**kwds` : optional
            Additional keyword arguments are documented in
            :meth:`pandas.Series.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
        """
        return self(kind='pie', **kwds)


class FramePlotMethods(BasePlotMethods):
    """DataFrame plotting accessor and method

    Examples
    --------
    >>> df.plot.line()
    >>> df.plot.scatter('x', 'y')
    >>> df.plot.hexbin()

    These plotting methods can also be accessed by calling the accessor as a
    method with the ``kind`` argument:
    ``df.plot(kind='line')`` is equivalent to ``df.plot.line()``
    """

    def __call__(self, x=None, y=None, kind='line', ax=None,
                 subplots=False, sharex=None, sharey=False, layout=None,
                 figsize=None, use_index=True, title=None, grid=None,
                 legend=True, style=None, logx=False, logy=False, loglog=False,
                 xticks=None, yticks=None, xlim=None, ylim=None,
                 rot=None, fontsize=None, colormap=None, table=False,
                 yerr=None, xerr=None,
                 secondary_y=False, sort_columns=False, **kwds):
        return plot_frame(self._data, kind=kind, x=x, y=y, ax=ax,
                          subplots=subplots, sharex=sharex, sharey=sharey,
                          layout=layout, figsize=figsize, use_index=use_index,
                          title=title, grid=grid, legend=legend, style=style,
                          logx=logx, logy=logy, loglog=loglog, xticks=xticks,
                          yticks=yticks, xlim=xlim, ylim=ylim, rot=rot,
                          fontsize=fontsize, colormap=colormap, table=table,
                          yerr=yerr, xerr=xerr, secondary_y=secondary_y,
                          sort_columns=sort_columns, **kwds)
    __call__.__doc__ = plot_frame.__doc__

    def line(self, x=None, y=None, **kwds):
        """
        Plot DataFrame columns as lines.

        This function is useful to plot lines using DataFrame's values
        as coordinates.

        Parameters
        ----------
        x : int or str, optional
            Columns to use for the horizontal axis.
            Either the location or the label of the columns to be used.
            By default, it will use the DataFrame indices.
        y : int, str, or list of them, optional
            The values to be plotted.
            Either the location or the label of the columns to be used.
            By default, it will use the remaining DataFrame numeric columns.
        **kwds
            Keyword arguments to pass on to :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or :class:`numpy.ndarray`
            Returns an ndarray when ``subplots=True``.

        See Also
        --------
        matplotlib.pyplot.plot : Plot y versus x as lines and/or markers.

        Examples
        --------

        .. plot::
            :context: close-figs

            The following example shows the populations for some animals
            over the years.

            >>> df = pd.DataFrame({
            ...    'pig': [20, 18, 489, 675, 1776],
            ...    'horse': [4, 25, 281, 600, 1900]
            ...    }, index=[1990, 1997, 2003, 2009, 2014])
            >>> lines = df.plot.line()

        .. plot::
           :context: close-figs

           An example with subplots, so an array of axes is returned.

           >>> axes = df.plot.line(subplots=True)
           >>> type(axes)
           <class 'numpy.ndarray'>

        .. plot::
            :context: close-figs

            The following example shows the relationship between both
            populations.

            >>> lines = df.plot.line(x='pig', y='horse')
        """
        return self(kind='line', x=x, y=y, **kwds)

    def bar(self, x=None, y=None, **kwds):
        """
        Vertical bar plot.

        A bar plot is a plot that presents categorical data with
        rectangular bars with lengths proportional to the values that they
        represent. A bar plot shows comparisons among discrete categories. One
        axis of the plot shows the specific categories being compared, and the
        other axis represents a measured value.

        Parameters
        ----------
        x : label or position, optional
            Allows plotting of one column versus another. If not specified,
            the index of the DataFrame is used.
        y : label or position, optional
            Allows plotting of one column versus another. If not specified,
            all numerical columns are used.
        **kwds
            Additional keyword arguments are documented in
            :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.axes.Axes or np.ndarray of them
            An ndarray is returned with one :class:`matplotlib.axes.Axes`
            per column when ``subplots=True``.

        See Also
        --------
        pandas.DataFrame.plot.barh : Horizontal bar plot.
        pandas.DataFrame.plot : Make plots of a DataFrame.
        matplotlib.pyplot.bar : Make a bar plot with matplotlib.

        Examples
        --------
        Basic plot.

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
            >>> ax = df.plot.bar(x='lab', y='val', rot=0)

        Plot a whole dataframe to a bar plot. Each column is assigned a
        distinct color, and each row is nested in a group along the
        horizontal axis.

        .. plot::
            :context: close-figs

            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
            >>> index = ['snail', 'pig', 'elephant',
            ...          'rabbit', 'giraffe', 'coyote', 'horse']
            >>> df = pd.DataFrame({'speed': speed,
            ...                    'lifespan': lifespan}, index=index)
            >>> ax = df.plot.bar(rot=0)

        Instead of nesting, the figure can be split by column with
        ``subplots=True``. In this case, a :class:`numpy.ndarray` of
        :class:`matplotlib.axes.Axes` are returned.

        .. plot::
            :context: close-figs

            >>> axes = df.plot.bar(rot=0, subplots=True)
            >>> axes[1].legend(loc=2)  # doctest: +SKIP

        Plot a single column.

        .. plot::
            :context: close-figs

            >>> ax = df.plot.bar(y='speed', rot=0)

        Plot only selected categories for the DataFrame.

        .. plot::
            :context: close-figs

            >>> ax = df.plot.bar(x='lifespan', rot=0)
        """
        return self(kind='bar', x=x, y=y, **kwds)

    def barh(self, x=None, y=None, **kwds):
        """
        Make a horizontal bar plot.

        A horizontal bar plot is a plot that presents quantitative data with
        rectangular bars with lengths proportional to the values that they
        represent. A bar plot shows comparisons among discrete categories. One
        axis of the plot shows the specific categories being compared, and the
        other axis represents a measured value.

        Parameters
        ----------
        x : label or position, default DataFrame.index
            Column to be used for categories.
        y : label or position, default All numeric columns in dataframe
            Columns to be plotted from the DataFrame.
        **kwds
            Keyword arguments to pass on to :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them.

        See Also
        --------
        pandas.DataFrame.plot.bar: Vertical bar plot.
        pandas.DataFrame.plot : Make plots of DataFrame using matplotlib.
        matplotlib.axes.Axes.bar : Plot a vertical bar plot using matplotlib.

        Examples
        --------
        Basic example

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
            >>> ax = df.plot.barh(x='lab', y='val')

        Plot a whole DataFrame to a horizontal bar plot

        .. plot::
            :context: close-figs

            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
            >>> index = ['snail', 'pig', 'elephant',
            ...          'rabbit', 'giraffe', 'coyote', 'horse']
            >>> df = pd.DataFrame({'speed': speed,
            ...                    'lifespan': lifespan}, index=index)
            >>> ax = df.plot.barh()

        Plot a column of the DataFrame to a horizontal bar plot

        .. plot::
            :context: close-figs

            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
            >>> index = ['snail', 'pig', 'elephant',
            ...          'rabbit', 'giraffe', 'coyote', 'horse']
            >>> df = pd.DataFrame({'speed': speed,
            ...                    'lifespan': lifespan}, index=index)
            >>> ax = df.plot.barh(y='speed')

        Plot DataFrame versus the desired column

        .. plot::
            :context: close-figs

            >>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
            >>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
            >>> index = ['snail', 'pig', 'elephant',
            ...          'rabbit', 'giraffe', 'coyote', 'horse']
            >>> df = pd.DataFrame({'speed': speed,
            ...                    'lifespan': lifespan}, index=index)
            >>> ax = df.plot.barh(x='lifespan')
        """
        return self(kind='barh', x=x, y=y, **kwds)

    def box(self, by=None, **kwds):
        r"""
        Make a box plot of the DataFrame columns.

        A box plot is a method for graphically depicting groups of numerical
        data through their quartiles.
        The box extends from the Q1 to Q3 quartile values of the data,
        with a line at the median (Q2). The whiskers extend from the edges
        of box to show the range of the data. The position of the whiskers
        is set by default to 1.5*IQR (IQR = Q3 - Q1) from the edges of the
        box. Outlier points are those past the end of the whiskers.

        For further details see Wikipedia's
        entry for `boxplot <https://en.wikipedia.org/wiki/Box_plot>`__.

        A consideration when using this chart is that the box and the whiskers
        can overlap, which is very common when plotting small sets of data.

        Parameters
        ----------
        by : string or sequence
            Column in the DataFrame to group by.
        **kwds : optional
            Additional keywords are documented in
            :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them

        See Also
        --------
        pandas.DataFrame.boxplot: Another method to draw a box plot.
        pandas.Series.plot.box: Draw a box plot from a Series object.
        matplotlib.pyplot.boxplot: Draw a box plot in matplotlib.

        Examples
        --------
        Draw a box plot from a DataFrame with four columns of randomly
        generated data.

        .. plot::
            :context: close-figs

            >>> data = np.random.randn(25, 4)
            >>> df = pd.DataFrame(data, columns=list('ABCD'))
            >>> ax = df.plot.box()
        """
        return self(kind='box', by=by, **kwds)

    def hist(self, by=None, bins=10, **kwds):
        """
        Draw one histogram of the DataFrame's columns.

        A histogram is a representation of the distribution of data.
        This function groups the values of all given Series in the DataFrame
        into bins and draws all bins in one :class:`matplotlib.axes.Axes`.
        This is useful when the DataFrame's Series are in a similar scale.

        Parameters
        ----------
        by : str or sequence, optional
            Column in the DataFrame to group by.
        bins : int, default 10
            Number of histogram bins to be used.
        **kwds
            Additional keyword arguments are documented in
            :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.AxesSubplot histogram.

        See Also
        --------
        DataFrame.hist : Draw histograms per DataFrame's Series.
        Series.hist : Draw a histogram with Series' data.

        Examples
        --------
        When we draw a dice 6000 times, we expect to get each value around 1000
        times. But when we draw two dices and sum the result, the distribution
        is going to be quite different. A histogram illustrates those
        distributions.

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame(
            ...     np.random.randint(1, 7, 6000),
            ...     columns = ['one'])
            >>> df['two'] = df['one'] + np.random.randint(1, 7, 6000)
            >>> ax = df.plot.hist(bins=12, alpha=0.5)
        """
        return self(kind='hist', by=by, bins=bins, **kwds)

    @Appender(_kde_docstring % {
        'this-datatype': 'DataFrame',
        'sibling-datatype': 'Series',
        'examples': """
        Given several Series of points randomly sampled from unknown
        distributions, estimate their PDFs using KDE with automatic
        bandwidth determination and plot the results, evaluating them at
        1000 equally spaced points (default):

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame({
            ...     'x': [1, 2, 2.5, 3, 3.5, 4, 5],
            ...     'y': [4, 4, 4.5, 5, 5.5, 6, 6],
            ... })
            >>> ax = df.plot.kde()

        A scalar bandwidth can be specified. Using a small bandwidth value can
        lead to overfitting, while using a large bandwidth value may result
        in underfitting:

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(bw_method=0.3)

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(bw_method=3)

        Finally, the `ind` parameter determines the evaluation points for the
        plot of the estimated PDF:

        .. plot::
            :context: close-figs

            >>> ax = df.plot.kde(ind=[1, 2, 3, 4, 5, 6])
        """.strip()
    })
    def kde(self, bw_method=None, ind=None, **kwds):
        return self(kind='kde', bw_method=bw_method, ind=ind, **kwds)

    density = kde

    def area(self, x=None, y=None, **kwds):
        """
        Area plot

        Parameters
        ----------
        x, y : label or position, optional
            Coordinates for each point.
        `**kwds` : optional
            Additional keyword arguments are documented in
            :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them
        """
        return self(kind='area', x=x, y=y, **kwds)

    def pie(self, y=None, **kwds):
        """
        Generate a pie plot.

        A pie plot is a proportional representation of the numerical data in a
        column. This function wraps :meth:`matplotlib.pyplot.pie` for the
        specified column. If no column reference is passed and
        ``subplots=True`` a pie plot is drawn for each numerical column
        independently.

        Parameters
        ----------
        y : int or label, optional
            Label or position of the column to plot.
            If not provided, ``subplots=True`` argument must be passed.
        **kwds
            Keyword arguments to pass on to :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : matplotlib.axes.Axes or np.ndarray of them.
            A NumPy array is returned when `subplots` is True.

        See Also
        --------
        Series.plot.pie : Generate a pie plot for a Series.
        DataFrame.plot : Make plots of a DataFrame.

        Examples
        --------
        In the example below we have a DataFrame with the information about
        planet's mass and radius. We pass the the 'mass' column to the
        pie function to get a pie plot.

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame({'mass': [0.330, 4.87 , 5.97],
            ...                    'radius': [2439.7, 6051.8, 6378.1]},
            ...                   index=['Mercury', 'Venus', 'Earth'])
            >>> plot = df.plot.pie(y='mass', figsize=(5, 5))

        .. plot::
            :context: close-figs

            >>> plot = df.plot.pie(subplots=True, figsize=(6, 3))

        """
        return self(kind='pie', y=y, **kwds)

    def scatter(self, x, y, s=None, c=None, **kwds):
        """
        Create a scatter plot with varying marker point size and color.

        The coordinates of each point are defined by two dataframe columns and
        filled circles are used to represent each point. This kind of plot is
        useful to see complex correlations between two variables. Points could
        be for instance natural 2D coordinates like longitude and latitude in
        a map or, in general, any pair of metrics that can be plotted against
        each other.

        Parameters
        ----------
        x : int or str
            The column name or column position to be used as horizontal
            coordinates for each point.
        y : int or str
            The column name or column position to be used as vertical
            coordinates for each point.
        s : scalar or array_like, optional
            The size of each point. Possible values are:

            - A single scalar so all points have the same size.

            - A sequence of scalars, which will be used for each point's size
              recursively. For instance, when passing [2,14] all points size
              will be either 2 or 14, alternatively.

        c : str, int or array_like, optional
            The color of each point. Possible values are:

            - A single color string referred to by name, RGB or RGBA code,
              for instance 'red' or '#a98d19'.

            - A sequence of color strings referred to by name, RGB or RGBA
              code, which will be used for each point's color recursively. For
              intance ['green','yellow'] all points will be filled in green or
              yellow, alternatively.

            - A column name or position whose values will be used to color the
              marker points according to a colormap.

        **kwds
            Keyword arguments to pass on to :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        axes : :class:`matplotlib.axes.Axes` or numpy.ndarray of them

        See Also
        --------
        matplotlib.pyplot.scatter : scatter plot using multiple input data
            formats.

        Examples
        --------
        Let's see how to draw a scatter plot using coordinates from the values
        in a DataFrame's columns.

        .. plot::
            :context: close-figs

            >>> df = pd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
            ...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
            ...                   columns=['length', 'width', 'species'])
            >>> ax1 = df.plot.scatter(x='length',
            ...                       y='width',
            ...                       c='DarkBlue')

        And now with the color determined by a column as well.

        .. plot::
            :context: close-figs

            >>> ax2 = df.plot.scatter(x='length',
            ...                       y='width',
            ...                       c='species',
            ...                       colormap='viridis')
        """
        return self(kind='scatter', x=x, y=y, c=c, s=s, **kwds)

    def hexbin(self, x, y, C=None, reduce_C_function=None, gridsize=None,
               **kwds):
        """
        Generate a hexagonal binning plot.

        Generate a hexagonal binning plot of `x` versus `y`. If `C` is `None`
        (the default), this is a histogram of the number of occurrences
        of the observations at ``(x[i], y[i])``.

        If `C` is specified, specifies values at given coordinates
        ``(x[i], y[i])``. These values are accumulated for each hexagonal
        bin and then reduced according to `reduce_C_function`,
        having as default the NumPy's mean function (:meth:`numpy.mean`).
        (If `C` is specified, it must also be a 1-D sequence
        of the same length as `x` and `y`, or a column label.)

        Parameters
        ----------
        x : int or str
            The column label or position for x points.
        y : int or str
            The column label or position for y points.
        C : int or str, optional
            The column label or position for the value of `(x, y)` point.
        reduce_C_function : callable, default `np.mean`
            Function of one argument that reduces all the values in a bin to
            a single number (e.g. `np.mean`, `np.max`, `np.sum`, `np.std`).
        gridsize : int or tuple of (int, int), default 100
            The number of hexagons in the x-direction.
            The corresponding number of hexagons in the y-direction is
            chosen in a way that the hexagons are approximately regular.
            Alternatively, gridsize can be a tuple with two elements
            specifying the number of hexagons in the x-direction and the
            y-direction.
        **kwds
            Additional keyword arguments are documented in
            :meth:`pandas.DataFrame.plot`.

        Returns
        -------
        matplotlib.AxesSubplot
            The matplotlib ``Axes`` on which the hexbin is plotted.

        See Also
        --------
        DataFrame.plot : Make plots of a DataFrame.
        matplotlib.pyplot.hexbin : hexagonal binning plot using matplotlib,
            the matplotlib function that is used under the hood.

        Examples
        --------
        The following examples are generated with random data from
        a normal distribution.

        .. plot::
            :context: close-figs

            >>> n = 10000
            >>> df = pd.DataFrame({'x': np.random.randn(n),
            ...                    'y': np.random.randn(n)})
            >>> ax = df.plot.hexbin(x='x', y='y', gridsize=20)

        The next example uses `C` and `np.sum` as `reduce_C_function`.
        Note that `'observations'` values ranges from 1 to 5 but the result
        plot shows values up to more than 25. This is because of the
        `reduce_C_function`.

        .. plot::
            :context: close-figs

            >>> n = 500
            >>> df = pd.DataFrame({
            ...     'coord_x': np.random.uniform(-3, 3, size=n),
            ...     'coord_y': np.random.uniform(30, 50, size=n),
            ...     'observations': np.random.randint(1,5, size=n)
            ...     })
            >>> ax = df.plot.hexbin(x='coord_x',
            ...                     y='coord_y',
            ...                     C='observations',
            ...                     reduce_C_function=np.sum,
            ...                     gridsize=10,
            ...                     cmap="viridis")
        """
        if reduce_C_function is not None:
            kwds['reduce_C_function'] = reduce_C_function
        if gridsize is not None:
            kwds['gridsize'] = gridsize
        return self(kind='hexbin', x=x, y=y, C=C, **kwds)
