有没有一种方法可以聚合（例如求和、平均）pandas系列中的区间？我用了冗长而不优雅的方式来实现，但是我觉得应该有一种内置的方法可以做到这一点，因为人们经常使用pandas进行时间序列分析（这不是时间序列）。也许可以利用pd.Interval对象来实现？
import numpy as np
import pandas as pd
from collections import OrderedDict
data = pd.Series({0.0: 0.0027077328584676173,0.0041841004184100415: 0.002791971614505515,0.008368200836820083: 0.0029836729636686537,0.012552301255230124: 0.00310210056994597,0.016736401673640166: 0.003123592522767784,0.02092050209205021: 0.0031416073125521624,0.025104602510460247: 0.0031485613317755977,0.02928870292887029: 0.0031557637903831775,0.03347280334728033: 0.00326313482475896,0.03765690376569037: 0.003301728046497936,0.04184100418410042: 0.0033366539533600335,0.046025104602510455: 0.0033702672902052584,0.050209205020920494: 0.0033868374139273388,0.05439330543933054: 0.003396846064522763,0.05857740585774058: 0.0034311293771955432,0.06276150627615062: 0.0034554861912855788,0.06694560669456066: 0.0035232368934153594,0.0711297071129707: 0.0035777792118595695,0.07531380753138074: 0.003592954000065738,0.0794979079497908: 0.00361514147677572,0.08368200836820083: 0.003623149988144421,0.08786610878661087: 0.0037193787063349777,0.09205020920502091: 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0.0035866395461282012,1.0: 0.0035637164646374914})
interval_points = np.linspace(0,1,11)
interval_aggregate = OrderedDict()
for i in range(len(interval_points) - 1):
lower = interval_points[i]
upper = interval_points[i+1]
if i == 0:
interval_aggregate[(lower, upper)] = data[data.index.map(lambda x: lower <= x < upper)].sum()
else:
interval_aggregate[(lower, upper)] = data[data.index.map(lambda x: lower < x <= upper)].sum()
aggregate_data = pd.Series(interval_aggregate)
aggregate_data.sum()
# 1.0