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':'-01-11','x1':'-02-22','color':'rgba(30,30,30,0.3)','fill':True,'opacity':.4})
### ANSWER ###
xStart = ['-01-11', '-02-08', '-03-08', '-04-05']
xStop = ['-01-25', '-02-22', '-03-22', '-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：
