boxplot.py

from collections import namedtuple
import warnings

from matplotlib.artist import setp
import numpy as np

from pandas.core.dtypes.generic import ABCSeries
from pandas.core.dtypes.missing import remove_na_arraylike

import pandas as pd

from pandas import IndexSlice, Index, MultiIndex
from pandas.io.formats.printing import pprint_thing
from pandas.plotting._matplotlib import converter
from pandas.plotting._matplotlib.core import LinePlot, MPLPlot
from pandas.plotting._matplotlib.style import _get_standard_colors
from pandas.plotting._matplotlib.tools import _flatten, _subplots


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 self.color.items():
                    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

        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:
            self._return_obj = pd.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().result
        else:
            return self._return_obj


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)

    result = pd.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


def boxplot(
    data,
    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

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

    if isinstance(data, ABCSeries):
        data = data.to_frame("x")
        column = "x"

    def _get_colors():
        #  num_colors=3 is required as method maybe_color_bp takes the colors
        #  in positions 0 and 2.
        return _get_standard_colors(color=kwds.get("color"), num_colors=3)

    def maybe_color_bp(bp):
        if "color" not in kwds:
            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 {}
            with plt.rc_context(rc):
                ax = plt.gca()
        data = data._get_numeric_data()

        # if columns is None, use all numeric columns of data; if data columns
        # is multiIndex, which means a groupby has been applied before, select
        # data using new grouped column names; if data columns is Index, select
        # data simply using columns
        if columns is None:
            columns = data.columns
        elif isinstance(data.columns, MultiIndex):

            # reselect columns with after-groupby multi-index columns
            data = data.loc[:, IndexSlice[:, columns]]
            columns = data.columns
        elif isinstance(data.columns, Index):
            data = data.loc[:, columns]
            columns = data.columns

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

    return result


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  # no warning for pandas plots
    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 boxplot_frame_groupby(
    grouped,
    subplots=True,
    column=None,
    fontsize=None,
    rot=0,
    grid=True,
    ax=None,
    figsize=None,
    layout=None,
    sharex=False,
    sharey=True,
    **kwds
):
    converter._WARN = False  # no warning for pandas plots
    if subplots is True:
        naxes = len(grouped)
        fig, axes = _subplots(
            naxes=naxes,
            squeeze=False,
            ax=ax,
            sharex=sharex,
            sharey=sharey,
            figsize=figsize,
            layout=layout,
        )
        axes = _flatten(axes)

        ret = pd.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:
        keys, frames = zip(*grouped)
        if grouped.axis == 0:
            df = pd.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