Python 如何使用大量NaN按日期时间分组

我从传感器收集了大量数据集。我想使用平均值按每五分钟对每列数据进行分组。存在大量NaN值。有人能帮我吗？此处显示了数据信息和前20行：

 datasets.info()

 [20 rows x 11 columns]
 <class 'pandas.core.frame.DataFrame'>
 RangeIndex: 1083433 entries, 0 to 1083432
 Data columns (total 11 columns):
 Time                1083433 non-null object
 Temp (back)         132639 non-null object
 Temp (front)        104623 non-null object
 Temp (out)          104797 non-null object
 Humidity (back)     132610 non-null object
 Humidity (front)    104718 non-null object
 Humidity (out)      104798 non-null object
 CO2 (front)         104713 non-null object
 CO2 (out)           104790 non-null object
 Door Open           104677 non-null object
 Motion              104415 non-null object
 dtypes: object(11)
 memory usage: 90.9+ MB  

print(datasets.head(20)) 

                     Time Temp (back)  ... Door Open Motion
 0   2018-09-06T07:52:09+02:00       24.79  ...       NaN    NaN
 1   2018-09-06T07:52:09+02:00         NaN  ...       NaN    NaN
 2   2018-09-06T07:53:07+02:00       24.79  ...       NaN    NaN
 3   2018-09-06T07:53:07+02:00         NaN  ...       NaN    NaN
 4   2018-09-06T07:54:06+02:00       24.79  ...       NaN    NaN
 5   2018-09-06T07:54:06+02:00         NaN  ...       NaN    NaN
 6   2018-09-06T07:55:04+02:00        24.8  ...       NaN    NaN
 7   2018-09-06T07:55:04+02:00         NaN  ...       NaN    NaN
 8   2018-09-06T07:56:03+02:00       24.77  ...       NaN    NaN
 9   2018-09-06T07:56:03+02:00         NaN  ...       NaN    NaN
 10  2018-09-06T07:57:01+02:00       24.77  ...       NaN    NaN
 11  2018-09-06T07:57:01+02:00         NaN  ...       NaN    NaN
 12  2018-09-06T07:58:00+02:00       24.77  ...       NaN    NaN
 13  2018-09-06T07:58:00+02:00         NaN  ...       NaN    NaN
 14  2018-09-06T07:58:59+02:00       24.79  ...       NaN    NaN
 15  2018-09-06T07:58:59+02:00         NaN  ...       NaN    NaN
 16  2018-09-06T07:59:58+02:00       24.77  ...       NaN    NaN
 17  2018-09-06T07:59:58+02:00         NaN  ...       NaN    NaN
 18  2018-09-06T08:00:57+02:00       24.82  ...       NaN    NaN
 19  2018-09-06T08:00:57+02:00         NaN  ...       NaN    NaN

 datasets['Time']=pd.to_datetime(datasets['Time'])
 datasets.groupby(pd.Grouper(freq='5Min')).mean()

  TypeError: Only valid with DatetimeIndex, TimedeltaIndex or 
  PeriodIndex, but got an instance of 'RangeIndex'

datasets.info（）
[20行x 11列]
范围索引：1083433个条目，0到1083432
数据列（共11列）：
时间1083433非空对象
临时（返回）132639非空对象
临时（前）104623非空对象
临时（输出）104797非空对象
湿度（背面）132610非空对象
湿度（前）104718非零对象
湿度（输出）104798非空对象
CO2（前）104713非空对象
CO2（输出）104790非空对象
门打开104677非空对象
运动104415非空对象
数据类型：对象（11）
内存使用率：90.9+MB
打印（数据集.标题（20））
时间温度（返回）。。。开门动作
0 2018-09-06T07:52:09+02:00 24.79。。。楠楠
2018-09-06T07:52:09+02:00南。。。楠楠
2018-09-06T07:53:07+02:00 24.79。。。楠楠
3 2018-09-06T07:53:07+02:00南。。。楠楠
4 2018-09-06T07:54:06+02:00 24.79。。。楠楠
5 2018-09-06T07:54:06+02:00南。。。楠楠
6 2018-09-06T07:55:04+02:00 24.8。。。楠楠
7 2018-09-06T07:55:04+02:00南。。。楠楠
8 2018-09-06T07:56:03+02:00 24.77。。。楠楠
9 2018-09-06T07:56:03+02:00南。。。楠楠
10 2018-09-06T07:57:01+02:00 24.77。。。楠楠
11 2018-09-06T07:57:01+02:00南。。。楠楠
12 2018-09-06T07:58:00+02:00 24.77。。。楠楠
13 2018-09-06T07:58:00+02:00南。。。楠楠
14 2018-09-06T07:58:59+02:00 24.79。。。楠楠
15 2018-09-06T07:58:59+02:00南。。。楠楠
16 2018-09-06T07:59:58+02:00 24.77。。。楠楠
17 2018-09-06T07:59:58+02:00南。。。楠楠
18 2018-09-06T08:00:57+02:00 24.82。。。楠楠
19 2018-09-06T08:00:57+02:00南。。。楠楠
数据集['Time']=pd.to_datetime（数据集['Time']）
数据集.groupby（pd.Grouper（freq='5Min'））.mean（）
TypeError：仅对DatetimeIndex、TimedeltaIndex或
PeriodIndex，但获得了“RangeIndex”的实例

使用以下代码datasets=pd.read_CSV（'grafana_data_export.CSV'，delimiter='；'，dtype=str，header=0）从CSV导入数据集使用以下代码datasets=pd.read_CSV（'grafana_data_export.CSV'，delimiter='；'，dtype=str header=0）从CSV导入数据集