截至2020年，有一个比公认答案中的方法更好的方法。
matplotlib.axes.Axes类提供了一个bxp方法，该方法可用于根据百分位值绘制盒线和须线。原始数据仅用于离群值，这是可选的。
示例：

import matplotlib.pyplot as plt

fig, ax = plt.subplots()
boxes = [
    {
        'label' : "Male height",
        'whislo': 162.6,    # Bottom whisker position
        'q1'    : 170.2,    # First quartile (25th percentile)
        'med'   : 175.7,    # Median         (50th percentile)
        'q3'    : 180.4,    # Third quartile (75th percentile)
        'whishi': 187.8,    # Top whisker position
        'fliers': []        # Outliers
    }
]
ax.bxp(boxes, showfliers=False)
ax.set_ylabel("cm")
plt.savefig("boxplot.png")
plt.close()

这将生成以下图像：

为了只使用百分位值和异常值（如果有的话）绘制箱形图，我制作了一个customized_box_plot函数，它基本上修改了基本箱形图（从微小的样本数据生成）中的属性，使其符合您的百分位值。

customized_box_plot函数

def customized_box_plot(percentiles, axes, redraw = True, *args, **kwargs):
    """
    Generates a customized boxplot based on the given percentile values
    """
    
    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, (q1_start, 
                 q2_start,
                 q3_start,
                 q4_start,
                 q4_end,
                 fliers_xy) in enumerate(percentiles):
        
        # 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
        box_plot['boxes'][box_no].set_ydata([q2_start, 
                                             q2_start, 
                                             q4_start,
                                             q4_start,
                                             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

用途

使用逆逻辑（最后的代码），我从这个例子中提取了百分位值

>>> percentiles
(-1.0597368367634488, 0.3977683984966961, 1.0298955252405229, 1.6693981537742526, 3.4951447843464449)
(-0.90494930553559483, 0.36916539612108634, 1.0303658700697103, 1.6874542731392828, 3.4951447843464449)
(0.13744105279440233, 1.3300645202649739, 2.6131540656339483, 4.8763411136047647, 9.5751914834437937)
(0.22786243898199182, 1.4120860286080519, 2.637650402506837, 4.9067126578493259, 9.4660357513550899)
(0.0064696168078617741, 0.30586770128093388, 0.70774153557312702, 1.5241965711101928, 3.3092932063051976)
(0.007009744579241136, 0.28627373934008982, 0.66039691869500572, 1.4772725266672091, 3.221716765477217)
(-2.2621660374110544, 5.1901313713883352, 7.7178532139979357, 11.277744848353247, 20.155971739152388)
(-2.2621660374110544, 5.1884411864079532, 7.3357079047721054, 10.792299385806913, 18.842012119715388)
(2.5417888074435702, 5.885996170695587, 7.7271286220368598, 8.9207423361593179, 10.846938621419374)
(2.5971767318505856, 5.753551925927133, 7.6569980004033464, 8.8161056254143233, 10.846938621419374)

请注意，为了保持简短，我没有显示离群值向量，这些向量将是每个百分位数组的第6个元素。
还要注意，所有常见的附加kwargs / args都可以使用，因为它们只是传递给boxplot方法：

>>> fig, ax = plt.subplots()
>>> b = customized_box_plot(percentiles, ax, redraw=True, notch=0, sym='+', vert=1, whis=1.5)
>>> plt.show()

解释

boxplot方法返回一个字典，该字典将箱线图的组件Map到所创建的各个matplotlib.lines.Line2D示例。
引用matplotlib.pyplot.boxplot文档：
该字典具有以下键（假设垂直箱线图）：
箱形图：箱形图的主体，显示四分位数和中位数的置信区间（如果启用）。
medians：每个框的中间位置的水平线。
whiskers：延伸到最极端的垂直线，n-离群数据点。caps：触须末端的水平线。
飞行器：表示延伸超出须线的数据的点（离群值）。
均值：表示均值的点或线。
例如，观察boxplot的微小样本数据[-9, -4, 2, 4, 9]

>>> b = ax.boxplot([[-9, -4, 2, 4, 9],])
>>> b
{'boxes': [<matplotlib.lines.Line2D at 0x7fe1f5b21350>],
'caps': [<matplotlib.lines.Line2D at 0x7fe1f54d4e50>,
<matplotlib.lines.Line2D at 0x7fe1f54d0e50>],
'fliers': [<matplotlib.lines.Line2D at 0x7fe1f5b317d0>],
'means': [],
'medians': [<matplotlib.lines.Line2D at 0x7fe1f63549d0>],
'whiskers': [<matplotlib.lines.Line2D at 0x7fe1f5b22e10>,
             <matplotlib.lines.Line2D at 0x7fe20c54a510>]} 

>>> plt.show()

matplotlib.lines.Line2D对象有两个方法，我将在我的函数中广泛使用。set_xdata（或set_ydata）和get_xdata（或get_ydata）。
使用这些方法，我们可以改变基本箱线图的组成线的位置，使其符合您的百分位值（这就是customized_box_plot函数所做的）。

汇总了各个Line2D对象从百分位到坐标的Map。

Y坐标：

• max（q4_end-第四个四分位数的末尾）对应于最顶部的帽Line2D对象。
• min（q1_start-第一四分位数的开始）对应于最低的最大帽Line2D对象。
• 中值对应于（q3_start）中值Line2D对象。
• 2个须状物位于盒的端部和极端帽之间（q1_start和q2_start-下部须状物;q4_start和q4_end-上须）
• 这个盒子实际上是一个有趣的n形状的线，在下部有一个帽。n形状的线的极端对应于q2_start和q4_start。

X坐标：

• 中心x坐标（对于多个箱线图，通常为1、2、3……）
• 库会根据指定的宽度自动计算边界x坐标。

逆函数从箱线图DICT中检索百分位数：

def get_percentiles_from_box_plots(bp):
    percentiles = []
    for i in range(len(bp['boxes'])):
        percentiles.append((bp['caps'][2*i].get_ydata()[0],
                           bp['boxes'][i].get_ydata()[0],
                           bp['medians'][i].get_ydata()[0],
                           bp['boxes'][i].get_ydata()[2],
                           bp['caps'][2*i + 1].get_ydata()[0],
                           (bp['fliers'][i].get_xdata(),
                            bp['fliers'][i].get_ydata())))
    return percentiles

注意：我没有制作一个完全自定义的箱线图方法的原因是，内置箱线图提供的许多功能无法完全复制。
另外，如果我可能不必要地解释了一些可能太明显的事情，请原谅我。

下面是这个有用例程的更新版本。直接设置顶点似乎对填充框（patchArtist=True）和未填充框都有效。

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

下面是一种自底向上的方法，其中box_plot是使用matplotlib的vline、Rectangle和普通plot函数构建的

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

这是它在实际操作中的样子：

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)

如果你喜欢Plotly，你可以用途：

import plotly.graph_objs as go

# Define the box plot parameters
q1 = 1.0
med = 2.5
mean=2.6
q3 = 3.5
lowerfence = 0.5
upperfence = 4.5
fliers = [0.0, 5.0, 6.0]
sd = 0
fig= go.Figure()
fig.add_trace(go.Box(x=["day 1"],
y=[fliers],
q1=[q1],
median=[med],
q3=[q3],
lowerfence=[lowerfence],
upperfence=[upperfence],
mean=[mean],
sd=[sd],
name="day 1",
boxpoints="outliers",
showlegend=True))

Resulted BoxPlot