合并两个 Pandas 数据帧，通过平均在一个时间列上重新采样

Question

我的问题与这个问题有些相似，但有几个关键区别：

Combine two Pandas dataframes, resample on one time column, interpolate

我有两个数据集由不同的采集系统以不同的采样率同时采集 - 一个每 1 秒采集一次数据 (df2)，第二个每 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来自 11 分钟采样频率数据帧（df1）。此数据帧中的数据将是来自 df1 的原始数据，来自 1 秒数据帧 (df2) 的数据在相关 11 分钟期间平均并附加到 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？

Answer 1

我认为你需要concat + resample：

df2 = pd.concat([df1, df2.resample('11T').mean()], axis=1)

替代方案是使用concat + groupby + Grouper：

df2 = pd.concat([df1, df2.groupby(pd.Grouper(freq='11T')).mean()], axis=1)

为测试创建了较小的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