我想使用CSV文件计算每个小时的平均值：
以下是我的数据集：
Timestamp Temperature
9/1/2016 0:00:08 53.8
9/1/2016 0:00:38 53.8
9/1/2016 0:01:08 53.8
9/1/2016 0:01:38 53.8
9/1/2016 0:02:08 53.8
9/1/2016 0:02:38 54.1
9/1/2016 0:03:08 54.1
9/1/2016 0:03:38 54.1
9/1/2016 0:04:38 54
9/1/2016 0:05:38 54
9/1/2016 0:06:08 54
9/1/2016 0:06:38 54
9/1/2016 0:07:08 54
9/1/2016 0:07:38 54
9/1/2016 0:08:08 54.1
9/1/2016 0:08:38 54.1
9/1/2016 0:09:38 54.1
9/1/2016 0:10:32 54
9/1/2016 0:11:02 54
9/1/2016 0:11:32 54
9/1/2016 0:00:08 54
9/2/2016 0:00:20 32
9/2/2016 0:00:50 32
9/2/2016 0:01:20 32
9/2/2016 0:01:50 32
9/2/2016 0:02:20 32
9/2/2016 0:02:50 32
9/2/2016 0:03:20 32
9/2/2016 0:03:50 32
9/2/2016 0:04:20 32
9/2/2016 0:04:50 32
9/2/2016 0:05:20 32
9/2/2016 0:05:50 32
9/2/2016 0:06:20 32
9/2/2016 0:06:50 32
9/2/2016 0:07:20 32
9/2/2016 0:07:50 32
这是我计算每日平均值的代码，但我想要每小时：
from datetime import datetime
import pandas
def same_day(date_string): # Remove year
return datetime.strptime(date_string, "%m/%d/%Y %H:%M%S").strftime(%m%d')
df = pandas.read_csv('/home/kk/Desktop/cal_Avg.csv',index_col=0,usecols=[0, 1], names=['Timestamp', 'Discharge'],converters={'Timestamp': same_day})
print(df.groupby(level=0).mean())
我想要的输出如下：
Timestamp Temp * Avg
9/1/2016 0:00:08 53.8
9/1/2016 0:00:38 53.8 ?avg for this hour
9/1/2016 0:01:08 53.8
9/1/2016 0:01:38 53.8 ?avg for this hour
9/1/2016 0:02:08 53.8
9/1/2016 0:02:38 54.1
现在我想要特定小时的平均值，Min
期望的输出：
这里我只打印了5个小时的输出日期01-09-2016和02-09-16
010900 54.362727 45.497273
010901 54.723276 45.068103
010902 54.746847 45.370270
010903 54.833913 44.931304
010904 54.971053 44.835088
010905 55.519444 44.459259
020901 31.742553 55.640426
020902 31.495556 55.655556
020903 31.304348 55.442609
020904 31.200000 55.437273
020905 31.294382 55.442697
具体日期和具体时间？ 我如何存档？
答案 0 :(得分：0)
我认为您首先需要read_csv参数index_col=[0]用于读取第一列到index和parse_dates=[0]用于解析第一列到DatetimeIndex：
df = pd.read_csv('filename', index_col=[0], parse_dates=[0],, usecols=[0,1])
print (df)
Temperature
Timestamp
2016-09-01 00:00:08 53.8
2016-09-01 00:00:38 53.8
2016-09-01 00:01:08 53.8
2016-09-01 00:01:38 53.8
2016-09-01 00:02:08 53.8
2016-09-01 00:02:38 54.1
2016-09-01 00:03:08 54.1
...
...
然后在hours之前使用resample并汇总Resampler.mean，但在NaN中获取DatetimeIndex个缺失数据：
print (df.resample('H').mean())
Temperature
Timestamp
2016-09-01 00:00:00 53.980952
2016-09-01 01:00:00 NaN
2016-09-01 02:00:00 NaN
2016-09-01 03:00:00 NaN
2016-09-01 04:00:00 NaN
2016-09-01 05:00:00 NaN
2016-09-01 06:00:00 NaN
2016-09-01 07:00:00 NaN
2016-09-01 08:00:00 NaN
2016-09-01 09:00:00 NaN
2016-09-01 10:00:00 NaN
2016-09-01 11:00:00 NaN
2016-09-01 12:00:00 NaN
2016-09-01 13:00:00 NaN
2016-09-01 14:00:00 NaN
2016-09-01 15:00:00 NaN
2016-09-01 16:00:00 NaN
2016-09-01 17:00:00 NaN
2016-09-01 18:00:00 NaN
2016-09-01 19:00:00 NaN
2016-09-01 20:00:00 NaN
2016-09-01 21:00:00 NaN
2016-09-01 22:00:00 NaN
2016-09-01 23:00:00 NaN
2016-09-02 00:00:00 32.000000
另一种解决方案是通过此minutes转换为seconds和hours来删除groupby和array：
print (df.index.values.astype('<M8[h]'))
['2016-09-01T00' '2016-09-01T00' '2016-09-01T00' '2016-09-01T00'
'2016-09-01T00' '2016-09-01T00' '2016-09-01T00' '2016-09-01T00'
'2016-09-01T00' '2016-09-01T00' '2016-09-01T00' '2016-09-01T00'
'2016-09-01T00' '2016-09-01T00' '2016-09-01T00' '2016-09-01T00'
'2016-09-01T00' '2016-09-01T00' '2016-09-01T00' '2016-09-01T00'
'2016-09-01T00' '2016-09-02T00' '2016-09-02T00' '2016-09-02T00'
'2016-09-02T00' '2016-09-02T00' '2016-09-02T00' '2016-09-02T00'
'2016-09-02T00' '2016-09-02T00' '2016-09-02T00' '2016-09-02T00'
'2016-09-02T00' '2016-09-02T00' '2016-09-02T00' '2016-09-02T00'
'2016-09-02T00']
print (df.groupby([df.index.values.astype('<M8[h]')]).mean())
Temperature
2016-09-01 53.980952
2016-09-02 32.000000
此外，如果需要按月计算，则可以DatetimeIndex.strftime DatetimeIndex.hour groupby print (df.index.strftime('%m%d%H'))
['090100' '090100' '090100' '090100' '090100' '090100' '090100' '090100'
'090100' '090100' '090100' '090100' '090100' '090100' '090100' '090100'
'090100' '090100' '090100' '090100' '090100' '090200' '090200' '090200'
'090200' '090200' '090200' '090200' '090200' '090200' '090200' '090200'
'090200' '090200' '090200' '090200' '090200']
print (df.groupby([df.index.strftime('%m%d%H')]).mean())
Temperature
090100 53.980952
090200 32.000000
生成日期和时间：
groupby或者，如果需要仅按{{3}} print (df.index.hour)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]
print (df.groupby([df.index.hour]).mean())
Temperature
0 44.475676
的小时<div ng-if="dataFeteched"><directive-abc ></directive-abc></div>表示：
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答案 1 :(得分：0)
我首先要定义一个新列hour以提高可读性，然后groupBy
df = pd.DataFrame.from_csv('/home/kk/Desktop/cal_Avg.csv',index_col=None)
df['hour']=df['Timestamp'].apply(lambda s:s[:-3])
df[['hour','Temprature']].groupBy('hour').mean()