我目前有以下两个DataFrame:
raw_data =
Time F1 F2 F3
2082-05-03 00:00:59.961599999 -83.769997 29.430000 29.400000
2082-05-03 00:02:00.009600000 -84.209999 28.940001 28.870001
2082-05-03 00:02:59.971200000 -84.339996 28.280001 28.320000
outage_by_timeofday_num = (由raw_data制作)(忽略破折号 - 它们仅用于对齐)
F1 F2 F3
Time
2082-05-03 00:00:00 0 1 1
2082-05-03 01:00:00 0 1 1
我已经能够使用以下代码(下面)按时间对raw_data DataFrame进行排序和平均,但我无法对outage_by_timeofday_num DataFrame执行相同的操作:
这有效:
raw_data = pd.read_excel(r'/Users/linnk ....
raw_data[u'Time']= pd.to_datetime(raw_data['Time'], unit='d')
raw_data.set_index(pd.DatetimeIndex(raw_data[u'Time']), inplace=True)
raw_data.Time = pd.to_datetime(raw_data.Time)
def time_cat(t):
hour = t.hour
if(hour >= 5 and hour < 9):
return 'Morning (5AM-9AM)'
elif(hour >= 9 and hour < 18):
return 'Day (9AM-6PM)'
elif(hour >= 18 and hour < 22):
return 'Evening (6PM-10PM)'
else:
return 'Night (10PM-5AM)'
by_timeofday = raw_data.groupby(raw_data.Time.apply(time_cat)).mean()
和by_timeofday输出为:
F1 F2 F3
Time
Day (9AM-6PM) -47.301852 23.070963 22.981000
Evening (6PM-10PM) -50.033000 24.011667 23.921833
Morning (5AM-9AM) -62.481130 48.417866 48.537197
Night (10PM-5AM) -71.372613 -71.289763 53.957411 \
然而,这不起作用:
outage_by_hour_num.Time= pd.to_datetime(outage_by_hour_num.Time)
outage_by_timeofday = outage_by_hour_num.groupby(outage_by_hour_num.Time.apply(time_cat)).sum(axis=1, numeric_only=True)
这会出错: AttributeError:'DataFrame'对象没有属性'Time'
有人可以帮我发现我的错误/编辑我需要按照与排序raw_data相同的方式对outage_by_timeofday_num DataFrame进行排序吗? 如果它可能有用,则outage_by_timeofday_num按以下方式进行:
ave_data = raw_data.resample('h', how='mean')
ave_data.index.name=u'Time'
summary_ave_data = ave_data.copy()
summary_ave_data['Hourly Substation Average'] = summary_ave_data.mean(numeric_only=True, axis=1)
outage_by_hour = summary_ave_data >= 0.05
outage_by_hour_num= outage_by_hour.astype(int)
答案 0 :(得分:0)
您摆脱了ave_data.index.name=u'Time'
中的“时间”列。
改为:
ave_data.set_index('Time', drop=False, inplace=True)
这确保您将其设置为索引,但保留“时间”列。