Можно ли нарисовать boxplot matplotlib с учетом значений процентилей вместо исходных входных данных?

вот обновленная версия этой полезной процедуры. Установка вершин напрямую работает как для заполненных ящиков (patchArtist=True), так и для незаполненных.

def customized_box_plot(percentiles, axes, redraw = True, *args, **kwargs):
    """
    Generates a customized boxplot based on the given percentile values
    """
    n_box = len(percentiles)
    box_plot = axes.boxplot([[-9, -4, 2, 4, 9],]*n_box, *args, **kwargs) 
    # Creates len(percentiles) no of box plots

    min_y, max_y = float('inf'), -float('inf')

    for box_no, pdata in enumerate(percentiles):
        if len(pdata) == 6:
            (q1_start, q2_start, q3_start, q4_start, q4_end, fliers_xy) = pdata
        elif len(pdata) == 5:
            (q1_start, q2_start, q3_start, q4_start, q4_end) = pdata
            fliers_xy = None
        else:
            raise ValueError("Percentile arrays for customized_box_plot must have either 5 or 6 values")

        # Lower cap
        box_plot['caps'][2*box_no].set_ydata([q1_start, q1_start])
        # xdata is determined by the width of the box plot

        # Lower whiskers
        box_plot['whiskers'][2*box_no].set_ydata([q1_start, q2_start])

        # Higher cap
        box_plot['caps'][2*box_no + 1].set_ydata([q4_end, q4_end])

        # Higher whiskers
        box_plot['whiskers'][2*box_no + 1].set_ydata([q4_start, q4_end])

        # Box
        path = box_plot['boxes'][box_no].get_path()
        path.vertices[0][1] = q2_start
        path.vertices[1][1] = q2_start
        path.vertices[2][1] = q4_start
        path.vertices[3][1] = q4_start
        path.vertices[4][1] = q2_start

        # Median
        box_plot['medians'][box_no].set_ydata([q3_start, q3_start])

        # Outliers
        if fliers_xy is not None and len(fliers_xy[0]) != 0:
            # If outliers exist
            box_plot['fliers'][box_no].set(xdata = fliers_xy[0],
                                           ydata = fliers_xy[1])

            min_y = min(q1_start, min_y, fliers_xy[1].min())
            max_y = max(q4_end, max_y, fliers_xy[1].max())

        else:
            min_y = min(q1_start, min_y)
            max_y = max(q4_end, max_y)

        # The y axis is rescaled to fit the new box plot completely with 10% 
        # of the maximum value at both ends
        axes.set_ylim([min_y*1.1, max_y*1.1])

    # If redraw is set to true, the canvas is updated.
    if redraw:
        ax.figure.canvas.draw()

    return box_plot

вот подход снизу вверх, где box_plot создается с помощью matplotlib vline, Rectangle, и нормально plot функции

def boxplot(df, ax=None, box_width=0.2, whisker_size=20, mean_size=10, median_size = 10 , line_width=1.5, xoffset=0,
                     color=0):
    """Plots a boxplot from existing percentiles.

    Parameters
    ----------
    df: pandas DataFrame
    ax: pandas AxesSubplot
        if to plot on en existing axes
    box_width: float
    whisker_size: float
        size of the bar at the end of each whisker
    mean_size: float
        size of the mean symbol
    color: int or rgb(list)
        If int particular color of property cycler is taken. Example of rgb: [1,0,0] (red)

    Returns
    -------
    f, a, boxes, vlines, whisker_tips, mean, median
    """

    if type(color) == int:
        color = plt.rcParams['axes.prop_cycle'].by_key()['color'][color]

    if ax:
        a = ax
        f = a.get_figure()
    else:
        f, a = plt.subplots()

    boxes = []
    vlines = []
    xn = []
    for row in df.iterrows():
        x = row[0] + xoffset
        xn.append(x)

        # box
        y = row[1][25]
        height = row[1][75] - row[1][25]
        box = plt.Rectangle((x - box_width / 2, y), box_width, height)
        a.add_patch(box)
        boxes.append(box)

        # whiskers
        y = (row[1][95] + row[1][5]) / 2
        vl = a.vlines(x, row[1][5], row[1][95])
        vlines.append(vl)

    for b in boxes:
        b.set_linewidth(line_width)
        b.set_facecolor([1, 1, 1, 1])
        b.set_edgecolor(color)
        b.set_zorder(2)

    for vl in vlines:
        vl.set_color(color)
        vl.set_linewidth(line_width)
        vl.set_zorder(1)

    whisker_tips = []
    if whisker_size:
        g, = a.plot(xn, df[5], ls='')
        whisker_tips.append(g)

        g, = a.plot(xn, df[95], ls='')
        whisker_tips.append(g)

    for wt in whisker_tips:
        wt.set_markeredgewidth(line_width)
        wt.set_color(color)
        wt.set_markersize(whisker_size)
        wt.set_marker('_')

    mean = None
    if mean_size:
        g, = a.plot(xn, df['mean'], ls='')
        g.set_marker('o')
        g.set_markersize(mean_size)
        g.set_zorder(20)
        g.set_markerfacecolor('None')
        g.set_markeredgewidth(line_width)
        g.set_markeredgecolor(color)
        mean = g

    median = None
    if median_size:
        g, = a.plot(xn, df['median'], ls='')
        g.set_marker('_')
        g.set_markersize(median_size)
        g.set_zorder(20)
        g.set_markeredgewidth(line_width)
        g.set_markeredgecolor(color)
        median = g

    a.set_ylim(np.nanmin(df), np.nanmax(df))
    return f, a, boxes, vlines, whisker_tips, mean, median

вот как это выглядит в действии:

import numpy as np
import pandas as pd
import matplotlib.pylab as plt

nopts = 12
df = pd.DataFrame()
df['mean'] = np.random.random(nopts) + 7
df['median'] = np.random.random(nopts) + 7
df[5] = np.random.random(nopts) + 4
df[25] = np.random.random(nopts) + 6
df[75] = np.random.random(nopts) + 8
df[95] = np.random.random(nopts) + 10
out = boxplot(df)