我有几天(甚至几周)的数据,每天以完全相同的时间间隔拍摄,并且想要计算平均日时间曲线。 到目前为止,我尝试过每日平均值,但是我每天都有一个平均值...我需要的是在每个可用时间平均所有可用天数的值。知道正确的命令很可能很容易。不幸的是,我对熊猫很新。 即使只是提示在哪里查看文档也会很棒!
Time some value
2010-08-31 12:30:00 33.910
2010-08-31 12:40:00 33.250
2010-08-31 12:50:00 30.500
2010-08-31 13:00:00 27.065
2010-08-31 13:10:00 25.610
...
2013-06-07 02:10:00 16.970
2013-06-07 02:20:00 16.955
2013-06-07 02:30:00 17.000
2013-06-07 02:40:00 17.015
2013-06-07 02:50:00 16.910
答案 0 :(得分:1)
您可以hours
和minutes
以及groupby
mean
尝试transform
:
print df
Time some value
0 2010-08-31 12:30:00 33.910
1 2010-08-31 12:40:00 33.250
2 2010-08-31 12:50:00 30.500
3 2010-08-31 13:00:00 27.065
4 2010-08-31 13:10:00 25.610
5 2013-06-07 02:10:00 16.970
6 2013-06-07 02:20:00 16.955
7 2013-06-07 02:30:00 17.000
8 2013-06-07 02:40:00 17.015
9 2013-06-07 02:50:00 16.910
#convert column time to datetime
df['Time'] = pd.to_datetime(df['Time'])
#set index from column Time
df = df.set_index('Time')
print df
some value
Time
2010-08-31 12:30:00 33.910
2010-08-31 12:40:00 33.250
2010-08-31 12:50:00 30.500
2010-08-31 13:00:00 27.065
2010-08-31 13:10:00 25.610
2013-06-07 02:10:00 16.970
2013-06-07 02:20:00 16.955
2013-06-07 02:30:00 17.000
2013-06-07 02:40:00 17.015
2013-06-07 02:50:00 16.910
print df.groupby([df.index.hour, df.index.minute])['some value'].transform('mean')
Time
2010-08-31 12:30:00 33.910
2010-08-31 12:40:00 33.250
2010-08-31 12:50:00 30.500
2010-08-31 13:00:00 27.065
2010-08-31 13:10:00 25.610
2013-06-07 02:10:00 16.970
2013-06-07 02:20:00 16.955
2013-06-07 02:30:00 17.000
2013-06-07 02:40:00 17.015
2013-06-07 02:50:00 16.910
dtype: float64
下一个解决方案未将index
设置为Datetimeindex
,使用dt.hour
和dt.minute
并创建新列newCol
:
print df
Time some value
0 2010-08-31 12:30:00 33.910
1 2010-08-31 12:40:00 33.250
2 2010-08-31 12:50:00 30.500
3 2010-08-31 13:00:00 27.065
4 2010-08-31 13:10:00 25.610
5 2013-06-07 02:10:00 16.970
6 2013-06-07 02:20:00 16.955
7 2013-06-07 02:30:00 17.000
8 2013-06-07 02:40:00 17.015
9 2013-06-07 02:50:00 16.910
#convert column time to datetime
df['Time'] = pd.to_datetime(df['Time'])
print df
Time some value
0 2010-08-31 12:30:00 33.910
1 2010-08-31 12:40:00 33.250
2 2010-08-31 12:50:00 30.500
3 2010-08-31 13:00:00 27.065
4 2010-08-31 13:10:00 25.610
5 2013-06-07 02:10:00 16.970
6 2013-06-07 02:20:00 16.955
7 2013-06-07 02:30:00 17.000
8 2013-06-07 02:40:00 17.015
9 2013-06-07 02:50:00 16.910
df['newCol'] = df.groupby([df['Time'].dt.hour, df['Time'].dt.minute])['some value']
.transform('mean')
print df
Time some value newCol
0 2010-08-31 12:30:00 33.910 33.910
1 2010-08-31 12:40:00 33.250 33.250
2 2010-08-31 12:50:00 30.500 30.500
3 2010-08-31 13:00:00 27.065 27.065
4 2010-08-31 13:10:00 25.610 25.610
5 2013-06-07 02:10:00 16.970 16.970
6 2013-06-07 02:20:00 16.955 16.955
7 2013-06-07 02:30:00 17.000 17.000
8 2013-06-07 02:40:00 17.015 17.015
9 2013-06-07 02:50:00 16.910 16.910