我的问题有点类似于这个问题，但有一些关键的区别：
Combine two Pandas dataframes, resample on one time column, interpolate
我有两个不同采集系统同时采集的数据集，采样率不同 - 一个采集数据每秒一次（df2），第二个采集数据每11分钟采集一次数据（df1）。我想创建一个包含两个数据集的数据帧，其中组合数据帧的时间索引将是11分钟采样频率数据帧（df1）的时间索引。此数据帧中的数据将是来自df1的原始数据，其中来自1秒数据帧（df2）的数据在相关的11分钟周期内取平均值并附加到df1。
以下是一些示例数据：
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
todays_date = datetime.now().date()
index1 = pd.date_range(todays_date-timedelta(10), periods=10, freq='11min')
index2 = pd.date_range(todays_date-timedelta(10), periods=6000, freq='S')
columns1 = [15, 17, 19, 21, 24, 27, 30, 34, 38, 43, 48, 54, 60, 67, 75, 84,
94, 105, 118, 132, 148, 166, 186, 208, 233, 261, 292, 327, 366, 410, 459,
514, 576, 645, 722, 809, 906]
columns2 = [103.73, 111.469, 119.786, 128.723, 138.327, 148.647, 159.737,
171.655, 184.462, 198.224, 213.013, 228.905, 245.984, 264.336, 284.057,
305.25, 328.024, 352.497, 378.797, 407.058, 437.427, 470.063, 505.133,
542.82, 583.319, 626.839, 673.606, 723.862, 777.868, 835.903, 898.268,
965.286, 1037.304, 1114.695, 1197.86, 1287.23, 1383.267, 1486.47, 1597.372,
1716.548, 1844.616, 1982.239, 2130.13, 2289.054, 2459.835, 2643.358,
2840.573, 3052.502, 3280.243, 3524.975, 3787.966, 4070.578, 4374.274,
4700.629, 5051.333, 5428.202, 5833.189, 6268.39, 6736.061, 7238.624,
7778.682, 8359.033, 8982.682, 9652.861, 10373.039, 11146.949, 11978.599,
12872.296, 13832.67, 14864.696, 15973.718, 17165.483, 18446.161, 19822.39,
21301.296, 22890.539, 24598.352, 26433.582]
data1 = np.random.rand(10, 37)*1000
data2 = np.random.rand(6000, 78)*1000
df1 = pd.DataFrame(data1, index=index1, columns=columns1)
df2 = pd.DataFrame(data2, index=index2, columns=columns2)
最简单的方法是什么？
答案 0 :(得分：3)
df2 = pd.concat([df1, df2.resample('11T').mean()], axis=1)
替代方法是使用concat + groupby + Grouper：
df2 = pd.concat([df1, df2.groupby(pd.Grouper(freq='11T')).mean()], axis=1)
为了测试创建了较小的DataFrames，df2中的频率更改为1.1Min：
np.random.seed(123)
todays_date = datetime.now().date()
index1 = pd.date_range(todays_date-timedelta(10), periods=2, freq='11min')
index2 = pd.date_range(todays_date-timedelta(10), periods=20, freq='1.1Min')
columns1 = [15, 17]
columns2 = [103.73, 111.469, 119.78]
data1 = np.random.randint(10, size=(2, 2))
data2 = np.random.randint(3, size=(20, 3))
df1 = pd.DataFrame(data1, index=index1, columns=columns1)
df2 = pd.DataFrame(data2, index=index2, columns=columns2)
print (df1)
15 17
2017-04-29 00:00:00 2 2
2017-04-29 00:11:00 6 1
print (df2)
103.730 111.469 119.780
2017-04-29 00:00:00 2 1 2
2017-04-29 00:01:06 1 0 1
2017-04-29 00:02:12 2 1 0
2017-04-29 00:03:18 2 0 1
2017-04-29 00:04:24 2 1 0
2017-04-29 00:05:30 0 0 0
2017-04-29 00:06:36 1 2 0
2017-04-29 00:07:42 2 0 0
2017-04-29 00:08:48 1 0 1
2017-04-29 00:09:54 0 0 0
2017-04-29 00:11:00 2 1 1
2017-04-29 00:12:06 2 2 2
2017-04-29 00:13:12 1 0 0
2017-04-29 00:14:18 2 1 0
2017-04-29 00:15:24 2 2 2
2017-04-29 00:16:30 2 1 2
2017-04-29 00:17:36 0 1 0
2017-04-29 00:18:42 2 0 2
2017-04-29 00:19:48 1 2 0
2017-04-29 00:20:54 2 2 0
df3 = pd.concat([df1, df2.resample('11T').mean()], axis=1)
print (df3)
15.000 17.000 103.730 111.469 119.780
2017-04-29 00:00:00 2 2 1.3 0.5 0.5
2017-04-29 00:11:00 6 1 1.6 1.2 0.9
df3 = pd.concat([df1, df2.groupby(pd.Grouper(freq='11T')).mean()], axis=1)
print (df3)
15.000 17.000 103.730 111.469 119.780
2017-04-29 00:00:00 2 2 1.3 0.5 0.5
2017-04-29 00:11:00 6 1 1.6 1.2 0.9