我有一个包含[key，datetime，receiver，score]属性的DataFrame。我想在5分钟内按日期和接收者重新采样数据。增量。我的方法如下。首先，我将“ datetime”设置为适当的“ date”和“ time”类型。然后，我对“日期”和“接收者”进行分组，并使用一个应用函数对每个组进行重采样。
如果删除“重新采样”并仅返回“ x”，则可以看到数据已正确分组并传递给“处理”功能（请参见下文）。
key datetime receiver score date time
0 9IIWNCEZD 2017-01-03 08:36:09 A -2.013896e+08 2017-01-03 08:36:09
5 ZEU7GZP47 2017-01-03 08:36:23 A -2.013668e+08 2017-01-03 08:36:23
6 ZEYSUQEI1 2017-01-03 08:36:27 A -2.013640e+08 2017-01-03 08:36:27
10 KW5FYJPIT 2017-01-03 08:36:38 A -2.013632e+08 2017-01-03 08:36:38
17 CE9RZFN5S 2017-01-03 08:36:49 A -2.013631e+08 2017-01-03 08:36:49
21 YQ7KSTNSC 2017-01-03 09:09:32 A -2.029635e+08 2017-01-03 09:09:32
key datetime receiver score date time
1 10E1WQXUI 2017-01-03 08:36:11 B -50020185.32 2017-01-03 08:36:11
key datetime receiver score date time
2 EHB0FM863 2017-01-03 08:36:12 C -1.008293e+08 2017-01-03 08:36:12
8 KW0UKT04Y 2017-01-03 08:36:35 C -1.007854e+08 2017-01-03 08:36:35
key datetime receiver score date time
3 EHFLTCXJX 2017-01-03 08:36:14 D -90002925.25 2017-01-03 08:36:14
12 YD2EHEZUE 2017-01-03 08:36:39 D -90001925.25 2017-01-03 08:36:39
18 KWJ83RTOH 2017-01-03 08:36:50 D -90001725.25 2017-01-03 08:36:50
key datetime receiver score date time
4 VHYI21ALA 2017-01-03 08:36:15 E -1.006858e+08 2017-01-03 08:36:15
9 YCXT3OAGJ 2017-01-03 08:36:36 E -1.006308e+08 2017-01-03 08:36:36
11 PUSYD2TBQ 2017-01-03 08:36:38 E -1.006268e+08 2017-01-03 08:36:38
13 3VR53M1VB 2017-01-03 08:36:40 E -1.006264e+08 2017-01-03 08:36:40
16 PV254K83I 2017-01-03 08:36:47 E -1.006258e+08 2017-01-03 08:36:47
19 3W4X8U610 2017-01-03 08:36:53 E -1.005406e+08 2017-01-03 08:36:53
20 DS1EUQNUE 2017-01-03 09:07:34 E -1.005189e+08 2017-01-03 09:07:34
25 T5ZOVXHGW 2017-01-03 10:17:53 E -1.005244e+08 2017-01-03 10:17:53
key datetime receiver score date time
7 IRBW5Z94D 2017-01-03 08:36:31 F -1.001900e+08 2017-01-03 08:36:31
14 CE0L7Y8E0 2017-01-03 08:36:40 F -1.001320e+08 2017-01-03 08:36:40
15 YD6ZV5P8A 2017-01-03 08:36:43 F -1.001270e+08 2017-01-03 08:36:43
29 PUXJQTNW2 2017-01-03 10:28:35 F -1.012220e+08 2017-01-03 10:28:35
key datetime receiver score date time
24 L0VF2ZUFX 2017-01-04 09:14:37 A -2.026835e+08 2017-01-04 09:14:37
30 OCTPWAQOH 2017-01-04 10:51:29 A -2.025107e+08 2017-01-04 10:51:29
key datetime receiver score date time
23 FBJRWFDKB 2017-01-04 09:12:43 B -44649416.6 2017-01-04 09:12:43
key datetime receiver score date time
22 JVEE0WOVC 2017-01-04 09:10:32 D -88645751.82 2017-01-04 09:10:32
key datetime receiver score date time
28 KWA1CAK36 2017-01-04 10:28:35 E -1.005225e+08 2017-01-04 10:28:35
key datetime receiver score date time
26 8IO0DWFDA 2017-01-04 10:22:38 F -1.012222e+08 2017-01-04 10:22:38
27 RK21L5E69 2017-01-04 10:27:46 F -1.012221e+08 2017-01-04 10:27:46
但是，如果我包括重新采样，则该行为是奇怪的（请参阅底部的打印输出）。看起来列逐渐被删除，直到将空的DataFrame传递给'process'函数，然后该函数出错。我知道“应用”实际上在第一行/列上调用了两次函数，但是由于我不认为自己对任何数据进行了变异，所以我不了解发生了什么。注意：我不仅在寻找解决方案，还试图了解其行为。
s = pd.DataFrame([["9IIWNCEZD","2017-01-03 08:36:09","A",-201389609],["10E1WQXUI","2017-01-03 08:36:11","B",-50020185.32],["EHB0FM863","2017-01-03 08:36:12","C",-100829267.43],["EHFLTCXJX","2017-01-03 08:36:14","D",-90002925.25],["VHYI21ALA","2017-01-03 08:36:15","E",-100685818.41],["ZEU7GZP47","2017-01-03 08:36:23","A",-201366792.15],["ZEYSUQEI1","2017-01-03 08:36:27","A",-201363981.95999998],["IRBW5Z94D","2017-01-03 08:36:31","F",-100190030.42],["KW0UKT04Y","2017-01-03 08:36:35","C",-100785367.43],["YCXT3OAGJ","2017-01-03 08:36:36","E",-100630818.41],["KW5FYJPIT","2017-01-03 08:36:38","A",-201363181.95999998],["PUSYD2TBQ","2017-01-03 08:36:38","E",-100626818.41],["YD2EHEZUE","2017-01-03 08:36:39","D",-90001925.25],["3VR53M1VB","2017-01-03 08:36:40","E",-100626418.41],["CE0L7Y8E0","2017-01-03 08:36:40","F",-100132011.16],["YD6ZV5P8A","2017-01-03 08:36:43","F",-100127011.16],["PV254K83I","2017-01-03 08:36:47","E",-100625778.41],["CE9RZFN5S","2017-01-03 08:36:49","A",-201363081.95999998],["KWJ83RTOH","2017-01-03 08:36:50","D",-90001725.25],["3W4X8U610","2017-01-03 08:36:53","E",-100540645.57],["DS1EUQNUE","2017-01-03 09:07:34","E",-100518856.89999999],["YQ7KSTNSC","2017-01-03 09:09:32","A",-202963512.17000002],["JVEE0WOVC","2017-01-03 09:10:32","D",-88645751.82],["FBJRWFDKB","2017-01-03 09:12:43","B",-44649416.6],["L0VF2ZUFX","2017-01-03 09:14:37","A",-202683512.17000002],["T5ZOVXHGW","2017-01-03 10:17:53","E",-100524437.18999998],["8IO0DWFDA","2017-01-03 10:22:38","F",-101222150.92999999],["RK21L5E69","2017-01-03 10:27:46","F",-101222144.03999999],["KWA1CAK36","2017-01-03 10:28:35","E",-100522494.62],["PUXJQTNW2","2017-01-03 10:28:35","F",-101221964.32],["OCTPWAQOH","2017-01-03 10:51:29","A",-202510655.58]],columns=["key","datetime","receiver","score"])
s["date"] = pd.to_datetime(s["datetime"]).dt.date
s["time"] = pd.to_datetime(s["datetime"]).dt.time
data_YMD = s.copy()
i=0
def process(x):
global i
if i<=6:
print(x)
y = x.resample("5T", on="time").max()
return y
data15 = data_YMD.groupby(by=["date","receiver"]).apply(lambda x: process(x))
哪个会产生以下输出（从'print'语句'输出）和错误：
key datetime receiver score date time
0 9IIWNCEZD 2017-01-03 08:36:09 A -2.013896e+08 2017-01-03 08:36:09
5 ZEU7GZP47 2017-01-03 08:36:23 A -2.013668e+08 2017-01-03 08:36:23
6 ZEYSUQEI1 2017-01-03 08:36:27 A -2.013640e+08 2017-01-03 08:36:27
10 KW5FYJPIT 2017-01-03 08:36:38 A -2.013632e+08 2017-01-03 08:36:38
17 CE9RZFN5S 2017-01-03 08:36:49 A -2.013631e+08 2017-01-03 08:36:49
21 YQ7KSTNSC 2017-01-03 09:09:32 A -2.029635e+08 2017-01-03 09:09:32
key datetime receiver score date time
0 9IIWNCEZD 2017-01-03 08:36:09 A -2.013896e+08 2017-01-03 08:36:09
5 ZEU7GZP47 2017-01-03 08:36:23 A -2.013668e+08 2017-01-03 08:36:23
6 ZEYSUQEI1 2017-01-03 08:36:27 A -2.013640e+08 2017-01-03 08:36:27
10 KW5FYJPIT 2017-01-03 08:36:38 A -2.013632e+08 2017-01-03 08:36:38
17 CE9RZFN5S 2017-01-03 08:36:49 A -2.013631e+08 2017-01-03 08:36:49
21 YQ7KSTNSC 2017-01-03 09:09:32 A -2.029635e+08 2017-01-03 09:09:32
key datetime score time
0 9IIWNCEZD 2017-01-03 08:36:09 -2.013896e+08 08:36:09
5 ZEU7GZP47 2017-01-03 08:36:23 -2.013668e+08 08:36:23
6 ZEYSUQEI1 2017-01-03 08:36:27 -2.013640e+08 08:36:27
10 KW5FYJPIT 2017-01-03 08:36:38 -2.013632e+08 08:36:38
17 CE9RZFN5S 2017-01-03 08:36:49 -2.013631e+08 08:36:49
21 YQ7KSTNSC 2017-01-03 09:09:32 -2.029635e+08 09:09:32
key datetime score time
0 9IIWNCEZD 2017-01-03 08:36:09 -2.013896e+08 08:36:09
5 ZEU7GZP47 2017-01-03 08:36:23 -2.013668e+08 08:36:23
6 ZEYSUQEI1 2017-01-03 08:36:27 -2.013640e+08 08:36:27
10 KW5FYJPIT 2017-01-03 08:36:38 -2.013632e+08 08:36:38
17 CE9RZFN5S 2017-01-03 08:36:49 -2.013631e+08 08:36:49
21 YQ7KSTNSC 2017-01-03 09:09:32 -2.029635e+08 09:09:32
Traceback (most recent call last):
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 725, in apply
result = self._python_apply_general(f)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 742, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 237, in apply
res = f(group)
File "main.py", line 112, in <lambda>
data15 = data_YMD.groupby(by=["date","receiver"]).apply(lambda x: process(x))
File "main.py", line 109, in process
y = x.resample("5T", on="time").max()
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/generic.py", line 8449, in resample
level=level,
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/resample.py", line 1306, in resample
return tg._get_resampler(obj, kind=kind)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/resample.py", line 1443, in _get_resampler
"but got an instance of %r" % type(ax).__name__
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "main.py", line 112, in <module>
data15 = data_YMD.groupby(by=["date","receiver"]).apply(lambda x: process(x))
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 737, in apply
return self._python_apply_general(f)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/groupby/groupby.py", line 742, in _python_apply_general
keys, values, mutated = self.grouper.apply(f, self._selected_obj, self.axis)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/groupby/ops.py", line 237, in apply
res = f(group)
File "main.py", line 112, in <lambda>
data15 = data_YMD.groupby(by=["date","receiver"]).apply(lambda x: process(x))
File "main.py", line 109, in process
y = x.resample("5T", on="time").max()
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/generic.py", line 8449, in resample
level=level,
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/resample.py", line 1306, in resample
return tg._get_resampler(obj, kind=kind)
File "/home/runner/.local/share/virtualenvs/python3/lib/python3.7/site-packages/pandas/core/resample.py", line 1443, in _get_resampler
"but got an instance of %r" % type(ax).__name__
TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'
后续注意事项：
1）问题可能与“ groupby”有关。如果不是我调用groupby（'date'）而不是调用“ groupby（'date'，'receiver'）”，然后调用应用执行groupby（'receiver'）等的函数，则如下所示。但这似乎很混乱，我想知道为什么有效吗？
def process(x):
def scoop(y):
return y.set_index(pd.DatetimeIndex(y["datetime"])) \
.resample("5T").max()
x = x.groupby("receiver").apply(lambda y: scoop(y))
return x
data15 = data_YMD.groupby(by=["date"]).apply(lambda x: process(x))
答案 0 :(得分：1)
您得到的错误是您需要对非datatime dtype列进行重新采样。
将s ['datetime']转换为datetime dtype并在s ['datetime']上重新采样，如下所示：
s["date"] = pd.to_datetime(s["datetime"]).dt.date
s["time"] = pd.to_datetime(s["datetime"]).dt.time
s['datetime'] = pd.to_datetime(s['datetime'])
data_YMD = s.copy()
i=0
def process(x):
global i
if i<=6:
print(x)
y = x.resample("5T", on="datetime").max()
return y
data15 = data_YMD.groupby(by=["date","receiver"]).apply(lambda x: process(x))
答案 1 :(得分：1)
您的代码有两个独立的问题：
resample需要一个日期时间列，而您的'date'不是。这就是为什么在跟踪结束时出现错误TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'的原因。看这个：
s["date"] = pd.to_datetime(s["datetime"]).dt.date
s["time"] = pd.to_datetime(s["datetime"]).dt.time
dt.date和dt.time正在提取表示日期和时间的字符串。但是是字符串，而不是日期时间对象。因此，您无法在任何这些列上resample。
组合groupby().apply()试图将使用apply生成的结果数据帧合并到多索引数据帧中。但是apply使用的函数正在使用resample，看来这很难重新加入数据帧。如果仅固定点1，则将得到一个KeyError，如下所示：ValueError: Key 2017-01-03 00:00:00 not in level Index([2017-01-03], dtype='object', name='date')。
如何解决所有这些问题？首先，将"datetime"设置为实际的日期时间列（如果尚未创建）：
s["datetime"] = pd.to_datetime(s["datetime"])
要解决问题2，可以使用pandas Grouper，它提供了有用的重采样操作，因此无需使用resample。
data_YMD = s.copy()
data15 = data_YMD.groupby(by=[pd.Grouper(key="receiver"), pd.Grouper(key="datetime", freq="5T")]).max()
data15输出为：
key score date
receiver datetime
A 2017-01-03 08:35:00 ZEYSUQEI1 -2.013631e+08 2017-01-03
2017-01-03 09:05:00 YQ7KSTNSC -2.029635e+08 2017-01-03
2017-01-03 09:10:00 L0VF2ZUFX -2.026835e+08 2017-01-03
2017-01-03 10:50:00 OCTPWAQOH -2.025107e+08 2017-01-03
B 2017-01-03 08:35:00 10E1WQXUI -5.002019e+07 2017-01-03
2017-01-03 09:10:00 FBJRWFDKB -4.464942e+07 2017-01-03
C 2017-01-03 08:35:00 KW0UKT04Y -1.007854e+08 2017-01-03
D 2017-01-03 08:35:00 YD2EHEZUE -9.000173e+07 2017-01-03
2017-01-03 09:10:00 JVEE0WOVC -8.864575e+07 2017-01-03
E 2017-01-03 08:35:00 YCXT3OAGJ -1.005406e+08 2017-01-03
2017-01-03 09:05:00 DS1EUQNUE -1.005189e+08 2017-01-03
2017-01-03 10:15:00 T5ZOVXHGW -1.005244e+08 2017-01-03
2017-01-03 10:25:00 KWA1CAK36 -1.005225e+08 2017-01-03
F 2017-01-03 08:35:00 YD6ZV5P8A -1.001270e+08 2017-01-03
2017-01-03 10:20:00 8IO0DWFDA -1.012222e+08 2017-01-03
2017-01-03 10:25:00 RK21L5E69 -1.012220e+08 2017-01-03