有没有可能在给定百分位数值的情况下画出matplotlib的boxplot，而不是原始输入？

Question 1

在我看来， boxplot() 方法需要一连串的原始值（数字）作为输入，然后从中计算出百分位数来画出boxplot（s）。

我希望有一种方法，可以通过传递百分位数，得到相应的 boxplot 。

假设我已经运行了几个基准，对于每个基准我都测量了延迟（浮点值）。此外，我还预先计算了这些数值的百分位数。

因此，对于每个基准，我都有第25、50、75个百分位数以及最小和最大值。

现在，鉴于这些数据，我想画出基准的箱形图。

Question 2

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，它可以用来根据百分位数值绘制方框和晶须。只有离群值需要原始数据，而这是可选的。

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()
这产生了以下图像。

Question 3


          
           为了只用百分位数值和异常值（如果有的话）绘制箱形图，我做了一个
           
            customized_box_plot
           
           的函数，它基本上修改了基本箱形图中的属性（从一个很小的样本数据中生成），使其根据你的百分位数值来适应。
          
          
           
            The
            
             customized_box_plot
            
            function
           
          
          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
使用逆向逻辑（代码在最后），我从这里提取了百分位数的值例子
>>> 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，它将是每个百分位数的第6个元素。
还要注意的是，所有通常的附加kwargs/args都可以使用，因为它们只是被传递到boxplot方法里面。
>>> fig, ax = plt.subplots()
>>> b = customized_box_plot(percentiles, ax, redraw=True, notch=0, sym='+', vert=1, whis=1.5)
>>> plt.show()
该boxplot方法返回一个字典，将boxplot的组件映射到所创建的各个matplotlib.lines.Line2D实例。
引用matplotlib.pyplot.boxplot文档中的话。
这个字典有以下的键（假设是垂直的boxplots）。
boxes：boxplot的主体，显示四分位数和中位数的置信区间（如果启用）。
中位数：每个盒子的中位数的水平线。
晶须：延伸到最极端的n个离群数据点的垂直线。 帽：晶须两端的水平线。
异常值：代表超出晶须的数据的点（异常值）。
平均值：代表平均值的点或线。
For 例子 observe the boxplot of a tiny sample data of [-9, -4, 2, 4, 9]
>>> b = ax.boxplot([[-9, -4, 2, 4, 9],])
{'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()
The matplotlib.lines.Line2D对象有两个方法，我将在我的函数中广泛使用。set_xdata ( or set_ydata)和get_xdata ( or get_ydata ).
使用这些方法，我们可以改变基础箱形图的组成线的位置，以符合你的百分位值（这就是customized_box_plot函数的作用）。在改变了组成线的位置后，你可以用figure.canvas.draw()重新绘制画布。
总结了从百分位数到各种Line2D对象的坐标的映射关系。
The max ( q4_end - end of 4th quartile ) corresponds to the top most cap Line2D object.
The min ( q1_start - start of the 1st quartile ) corresponds to the lowermost most cap Line2D object.
The median corresponds to the ( q3_start ) median Line2D object.
The 2 whiskers lie between the ends of the boxes and extreme caps ( q1_start and q2_start - lower whisker; q4_start and q4_end - upper whisker )
The box is actually an interesting n shaped line bounded by a cap at the lower portion. The extremes of the n shaped line correspond to the q2_start and the q4_start.
The Central x coordinates ( for multiple box plots are usually 1, 2, 3... )
The library automatically calculates the bounding x coordinates based on the width specified.
INVERSE FUNCTION TO RETRIEVE THE PERCENTILES FROM THE boxplot DICT:
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
我之所以没有做一个完全自定义的boxplot方法，是因为，内置的盒式图提供的许多功能不能完全复制。
另外，如果我可能不必要地解释了一些可能太明显的东西，请原谅我。

Question 4


          
           
            
             
              
               
                这里是这个有用的程序的更新版本。直接设置顶点似乎对填充的盒子（patchArtist=True）和未填充的盒子都有效。
               
               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

Question 5


          
           
            
             
              
               
                
                 下面是一个自下而上的方法，使用matplotlib的
                 
                  vline
                 
                 、
                 
                  Rectangle
                 
                 和正常的
                 
                  plot
                 
                 函数来建立box_plot。
                
                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
这是它在行动中的样子。
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