正如问题中所建议的，一个可能的解决方案可能存在于vspan函数中。然而，使用hspan为y轴添加多个阴影区域似乎比使用vspan和x轴更容易。后者需要更多的调整。更多细节可以在我建议的解决方案后找到。在

下面的图由下面的代码片段和函数multiShades生成：

绘图：

片段：### Setup from the question ###

import plotly

import cufflinks as cf

from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot

import pandas as pd

import numpy as np

from IPython.display import HTML

from IPython.core.display import display, HTML

import copy

# setup

init_notebook_mode(connected=True)

np.random.seed(123)

cf.set_config_file(theme='pearl')

# Random data using cufflinks

df = cf.datagen.lines()

fig = df.iplot(asFigure=True, kind='scatter',

xTitle='Dates',yTitle='Returns',title='Returns',

vspan={'x0':'2015-01-11','x1':'2015-02-22','color':'rgba(30,30,30,0.3)','fill':True,'opacity':.4})

### ANSWER ###

xStart = ['2015-01-11', '2015-02-08', '2015-03-08', '2015-04-05']

xStop = ['2015-01-25', '2015-02-22', '2015-03-22', '2015-04-10']

def multiShades(plot, x0, x1):

""" Adds shaded areas for specified dates in a plotly plot.

The lines of the areas are set to transparent using rgba(0,0,0,0)

"""

# get start and end dates

x0 = xStart

x1 = xStop

# get dict from tuple made by vspan()

xElem = fig['layout']['shapes'][0]

# container (list) for dicts / shapes

shp_lst=[]

# make dicts according to x0 and X1

# and edit elements of those dicts

for i in range(0,len(x0)):

shp_lst.append(copy.deepcopy(xElem))

shp_lst[i]['x0'] = x0[i]

shp_lst[i]['x1'] = x1[i]

shp_lst[i]['line']['color'] = 'rgba(0,0,0,0)'

# replace shape in fig with multiple new shapes

fig['layout']['shapes']= tuple(shp_lst)

return(fig)

fig = multiShades(plot=fig, x0=xStart, x1=xStop)

iplot(fig)

一些细节：

函数vspan'用以下形式的字典填充元组fig['layout']['shapes']：

^{pr2}$

我的函数只需获取字典，生成若干副本，根据函数参数编辑这些副本，然后用函数中的新元组替换原始元组。在

挑战：