我在这篇文章（图2）中看到了这个精美的箱线图。
正如您所见，这是一个箱线图，在上面叠加了一些黑色点的散点图：x表示黑色点（随机排序），y是所关注的变量。我想使用Matplotlib做类似的图形，但我不知道从哪里开始。到目前为止，我在网上找到的箱线图差别很大，都是这样的：
Matplotlib文档： http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.boxplot
着色箱线图的方法： https://github.com/jbmouret/matplotlib_for_papers#colored-boxes
您要找的是一种在x轴上添加抖动的方法。
类似于从这里获取的以下内容：
bp = titanic.boxplot(column='age', by='pclass', grid=False)
for i in [1,2,3]:
y = titanic.age[titanic.pclass==i].dropna()
# Add some random "jitter" to the x-axis
x = np.random.normal(i, 0.04, size=len(y))
plot(x, y, 'r.', alpha=0.2)
引用链接：
向箱线图添加附加信息的一种方法是覆盖实际数据；这通常适用于小或中等大小的数据系列。当数据密集时，上面使用的一些技巧有助于可视化：
- 减小 alpha 水平以使点部分透明
- 沿 x 轴添加随机“抖动”以避免过度叠加
代码如下：
import pylab as P
import numpy as np
# Define data
# Define numBoxes
P.figure()
bp = P.boxplot(data)
for i in range(numBoxes):
y = data[i]
x = np.random.normal(1+i, 0.04, size=len(y))
P.plot(x, y, 'r.', alpha=0.2)
P.show()
扩展Kyrubas的解决方案并仅使用matplotlib进行绘图部分（有时我难以使用matplotlib对pandas图进行格式化）。
from matplotlib import cm
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
# initialize dataframe
n = 200
ngroup = 3
df = pd.DataFrame({'data': np.random.rand(n), 'group': map(np.floor, np.random.rand(n) * ngroup)})
group = 'group'
column = 'data'
grouped = df.groupby(group)
names, vals, xs = [], [] ,[]
for i, (name, subdf) in enumerate(grouped):
names.append(name)
vals.append(subdf[column].tolist())
xs.append(np.random.normal(i+1, 0.04, subdf.shape[0]))
plt.boxplot(vals, labels=names)
ngroup = len(vals)
clevels = np.linspace(0., 1., ngroup)
for x, val, clevel in zip(xs, vals, clevels):
plt.scatter(x, val, c=cm.prism(clevel), alpha=0.4)
'group': list(map(np.floor, np.random.rand(n) * ngroup)) - jss367import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style="whitegrid")
tips = sns.load_dataset("tips")
ax = sns.boxplot(x="day", y="total_bill", data=tips, showfliers = False)
ax = sns.swarmplot(x="day", y="total_bill", data=tips, color=".25")
plt.show()
scattered_boxplot（并将其添加为plt.Axes的方法），这样您就可以始终使用scattered_boxplot而不是boxplot：fig, ax = plt.subplots(figsize=(5, 6))
ax.scattered_boxplot(x=[np.array([1,2,3]*50),np.array([1.1,2.2,3.3])])
函数scattered_boxplot可以仅使用matplotlib定义如下：
import matplotlib.pyplot as plt
import numpy as np
from numbers import Number
def scattered_boxplot(ax, x, notch=None, sym=None, vert=None, whis=None, positions=None, widths=None, patch_artist=None, bootstrap=None, usermedians=None, conf_intervals=None, meanline=None, showmeans=None, showcaps=None, showbox=None,
showfliers="unif",
hide_points_within_whiskers=False,
boxprops=None, labels=None, flierprops=None, medianprops=None, meanprops=None, capprops=None, whiskerprops=None, manage_ticks=True, autorange=False, zorder=None, *, data=None):
if showfliers=="classic":
classic_fliers=True
else:
classic_fliers=False
ax.boxplot(x, notch=notch, sym=sym, vert=vert, whis=whis, positions=positions, widths=widths, patch_artist=patch_artist, bootstrap=bootstrap, usermedians=usermedians, conf_intervals=conf_intervals, meanline=meanline, showmeans=showmeans, showcaps=showcaps, showbox=showbox,
showfliers=classic_fliers,
boxprops=boxprops, labels=labels, flierprops=flierprops, medianprops=medianprops, meanprops=meanprops, capprops=capprops, whiskerprops=whiskerprops, manage_ticks=manage_ticks, autorange=autorange, zorder=zorder,data=data)
N=len(x)
datashape_message = ("List of boxplot statistics and `{0}` "
"values must have same the length")
# check position
if positions is None:
positions = list(range(1, N + 1))
elif len(positions) != N:
raise ValueError(datashape_message.format("positions"))
positions = np.array(positions)
if len(positions) > 0 and not isinstance(positions[0], Number):
raise TypeError("positions should be an iterable of numbers")
# width
if widths is None:
widths = [np.clip(0.15 * np.ptp(positions), 0.15, 0.5)] * N
elif np.isscalar(widths):
widths = [widths] * N
elif len(widths) != N:
raise ValueError(datashape_message.format("widths"))
if hide_points_within_whiskers:
import matplotlib.cbook as cbook
from matplotlib import rcParams
if whis is None:
whis = rcParams['boxplot.whiskers']
if bootstrap is None:
bootstrap = rcParams['boxplot.bootstrap']
bxpstats = cbook.boxplot_stats(x, whis=whis, bootstrap=bootstrap,
labels=labels, autorange=autorange)
for i in range(N):
if hide_points_within_whiskers:
xi=bxpstats[i]['fliers']
else:
xi=x[i]
if showfliers=="unif":
jitter=np.random.uniform(-widths[i]*0.5,widths[i]*0.5,size=np.size(xi))
elif showfliers=="normal":
jitter=np.random.normal(loc=0.0, scale=widths[i]*0.1,size=np.size(xi))
elif showfliers==False or showfliers=="classic":
return
else:
raise NotImplementedError("showfliers='"+str(showfliers)+"' is not implemented. You can choose from 'unif', 'normal', 'classic' and False")
plt.scatter(positions[i]+jitter,xi,alpha=0.2,marker="o", facecolors='none', edgecolors="k")
并且可以通过添加方法到plt.Axes中实现
setattr(plt.Axes, "scattered_boxplot", scattered_boxplot)
一个人仍然可以访问所有箱线图选项，并且还可以选择用于水平抖动的散点分布（例如showfliers="unif"），并且可以选择是否显示在须之外的异常值（例如hide_points_within_whiskers=False）。
这个解决方案已经相当不错了。另一种选择是直接更改matplotlib的源代码，主要是在这一行：https://github.com/matplotlib/matplotlib/blob/9765379ce6e7343070e815afc0988874041b98e2/lib/matplotlib/axes/_axes.py#L4006
seaborn。https://python-graph-gallery.com/36-add-jitter-over-boxplot-seaborn/ - Eike P.