base
参数。
一天是24小时,因此以12为基数的分组将从中午开始-中午。重新采样为您提供了介于两天之间的所有时间,因此如果您不需要完整的基础,可以.dropna(how='all')
。 (我假设您有一个DatetimeIndex
,如果没有,则可以使用resample的on
参数指定日期时间列。)
df.resample('24H',base=12).mean()
#df.groupby(pd.Grouper(level=0,base=12,freq='24H')).mean() # Equivalent
1 2 3
0
2014-03-31 12:00:00 54.20 41.30 52.233333
2014-04-01 12:00:00 50.75 39.35 34.950000
2014-04-02 12:00:00 NaN NaN NaN
2014-04-03 12:00:00 NaN NaN NaN
2014-04-04 12:00:00 NaN NaN NaN
... ... ... ...
2016-11-26 12:00:00 NaN NaN NaN
2016-11-27 12:00:00 NaN NaN NaN
2016-11-28 12:00:00 NaN NaN NaN
2016-11-29 12:00:00 NaN NaN NaN
2016-11-30 12:00:00 17.80 15.45 40.450000
,
您可以减去时间和分组依据:
df.groupby((df.index - pd.to_timedelta('12:00:00')).normalize()).mean()
,
您可以将小时数更改12小时,然后按天重新采样。
from io import StringIO
import pandas as pd
data = """
2014-04-01 09:00:00,52.9,41.1,36.3
2014-04-01 10:00:00,56.4,41.6,70.8
2014-04-01 11:00:00,53.3,41.2,49.6
2014-04-01 12:00:00,50.4,39.5,36.6
2014-04-01 13:00:00,51.1,39.2,33.3
2016-11-30 16:00:00,16.0,13.5,36.6
2016-11-30 17:00:00,19.6,17.4,44.3
"""
df = pd.read_csv(StringIO(data),sep=',',header=None,index_col=0)
df.index = pd.to_datetime(df.index)
# shift by 12 hours
df.index = df.index - pd.Timedelta(hours=12)
# resample and drop na rows
df.resample('D').mean().dropna()
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