感谢@tacaswell的评论，我能够找到所需的文档，并使用Matplotlib 1.4.3提出一个示例。然而，这个示例不会自动将图缩放到正确的大小。

import matplotlib.pyplot as plt

item = {}

item["label"] = 'box' # not required
item["mean"] = 5 # not required
item["med"] = 5.5
item["q1"] = 3.5
item["q3"] = 7.5
#item["cilo"] = 5.3 # not required
#item["cihi"] = 5.7 # not required
item["whislo"] = 2.0 # required
item["whishi"] = 8.0 # required
item["fliers"] = [] # required if showfliers=True

stats = [item]

fig, axes = plt.subplots(1, 1)
axes.bxp(stats)
axes.set_title('Default')
y_axis = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
y_values = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"]
plt.yticks(y_axis, y_values)

文档的相关链接：

• Axes.bxp（）函数
• boxplot_stats数据结构
• other examples using Axes.bxp

在旧版本中，您必须通过单独更改箱线图元素来手动执行此操作：

Mean=[3.4] #mean
IQR=[3.0,3.9] #inter quantile range
CL=[2.0,5.0] #confidence limit
A=np.random.random(50)
D=plt.boxplot(A) # a simple case with just one variable to boxplot
D['medians'][0].set_ydata(Mean)
D['boxes'][0]._xy[[0,1,4], 1]=IQR[0]
D['boxes'][0]._xy[[2,3],1]=IQR[1]
D['whiskers'][0].set_ydata(np.array([IQR[0], CL[0]]))
D['whiskers'][1].set_ydata(np.array([IQR[1], CL[1]]))
D['caps'][0].set_ydata(np.array([CL[0], CL[0]]))
D['caps'][1].set_ydata(np.array([CL[1], CL[1]]))
_=plt.ylim(np.array(CL)+[-0.1*np.ptp(CL), 0.1*np.ptp(CL)]) #reset the limit

参考@MKroehnert和Boxplot drawer function在https://matplotlib.org/gallery/statistics/bxp.html的回答，以下内容可能会有所帮助：

import matplotlib.pyplot as plt

stats = [{
    "label": 'A',  # not required
    "mean":  5,  # not required
    "med": 5.5,
    "q1": 3.5,
    "q3": 7.5,
    # "cilo": 5.3 # not required
    # "cihi": 5.7 # not required
    "whislo": 2.0,  # required
    "whishi": 8.0,  # required
    "fliers": []  # required if showfliers=True
    }]

fs = 10  # fontsize

fig, axes = plt.subplots(nrows=1, ncols=1, figsize=(6, 6), sharey=True)
axes.bxp(stats)
axes.set_title('Boxplot for precalculated statistics', fontsize=fs)
plt.show()