我面临的任务是在时间序列的等距时间间隔内找到时间序列中的测量值最小的确切时间。
我尝试使用df.groupby(pd.TimeGrouper('time_interval')).idxmin()来执行此任务，但我遇到此方法的意外（可能是错误的）行为：
在数据框上使用df.groupby(pd.TimeGrouper('time_interval')).idxmin()方法时，日期时间索引包含（至少）两行之间大于重采样间隔的一个间隔，它会生成一个完全空的数据帧，而不是用'NaT'填充其他间隔（如df.groupby(pd.TimeGrouper('time_interval')).xmin()用“NaN”填充额外的间隔。
有没有人知道这个问题的解决方法（或者这个方法可能有错误修复）？我在帖子的最后给出了一个最小的工作示例和一些内联讨论。
干杯，
西蒙
python版本：Python 3.6.0 :: Anaconda 4.3.1（64位）
pandas版本：0.19.2
import datetime
import pandas as pd
timestamp_list = [1493992554.897, 1493999093.997, 1493999108.733, 1493999116.101, 1493999117.943, 1493999119.785, 1493999121.627, 1493999123.469, 1493999125.311, 1493999127.153, 1493999128.995, 1493999130.837, 1493999132.679, 1493999134.521, 1493999136.363, 1493999138.205, 1493999140.047, 1493999141.889, 1493999143.731, 1493999145.573, 1493999147.415, 1493999149.257, 1493999151.099, 1493999152.941, 1493999154.783, 1493999156.625, 1493999158.467, 1493999160.309, 1493999162.151, 1493999163.993]
value_list = [2.52962e-41, 2.52962e-41, 11.9625, 12.033420000000001, 12.069, 12.0784, 12.080933333333334, 12.080549999999999, 12.080233333333332, 12.078975, 12.033750000000001, 11.9472, 11.910966666666667, 11.902700000000001, 11.899766666666666, 11.898925, 11.898733333333332, 11.8987, 11.921174999999998, 11.982775, 12.010975000000002, 12.019466666666666, 12.021700000000001, 12.0224, 12.0225, 12.0226, 11.95525, 11.776133333333334, 11.65815, 11.624400000000001]
dt_list = [datetime.datetime.fromtimestamp(x) for x in timestamp_list]
time_frame = pd.DataFrame(index=dt_list, data=value_list)
time_frame.columns = ['value']
time_frame.head()
# Out[11]:
# value
# 2017-05-05 15:55:54.897 2.529620e-41 <- Large time diff (larger than resample length)
# 2017-05-05 17:44:53.997 2.529620e-41 <-
# 2017-05-05 17:45:08.733 1.196250e+01
# 2017-05-05 17:45:16.101 1.203342e+01
# 2017-05-05 17:45:17.943 1.206900e+01
# I want to resample this dataframe and determine the min in each interval
# this works fine:
tf_resampled_min = time_frame.groupby(pd.TimeGrouper('60000L')).min()
tf_resampled_min.head()
#Out[13]:
# value
#2017-05-05 15:55:00 2.529620e-41
#2017-05-05 15:56:00 NaN
#2017-05-05 15:57:00 NaN
#2017-05-05 15:58:00 NaN
#2017-05-05 15:59:00 NaN
# I also want to determine the exact time the mmin occured, and here I encounter a problem:
tf_resampled_idxmin = time_frame.groupby(pd.TimeGrouper('60000L')).idxmin()
tf_resampled_idxmin.head()
#Out[14]:
#Empty DataFrame
#Columns: []
#Index: []
# I expected something like:
#
#2017-05-05 15:55:00 2017-05-05 15:55:54.897
#2017-05-05 15:56:00 NaT
#2017-05-05 15:57:00 NaT
#2017-05-05 15:58:00 NaT
#2017-05-05 15:59:00 NaT
# With this output I would still be able to determine the minidx in the valid regions, but with the empty dataframe, all information is lost.
# The Problem is indeed the time gap between the first two entries. If I remove them, I get:
timestamp_list2 = [1493999093.997, 1493999108.733, 1493999116.101, 1493999117.943, 1493999119.785, 1493999121.627, 1493999123.469, 1493999125.311, 1493999127.153, 1493999128.995, 1493999130.837, 1493999132.679, 1493999134.521, 1493999136.363, 1493999138.205, 1493999140.047, 1493999141.889, 1493999143.731, 1493999145.573, 1493999147.415, 1493999149.257, 1493999151.099, 1493999152.941, 1493999154.783, 1493999156.625, 1493999158.467, 1493999160.309, 1493999162.151, 1493999163.993]
value_list2 = [2.52962e-41, 11.9625, 12.033420000000001, 12.069, 12.0784, 12.080933333333334, 12.080549999999999, 12.080233333333332, 12.078975, 12.033750000000001, 11.9472, 11.910966666666667, 11.902700000000001, 11.899766666666666, 11.898925, 11.898733333333332, 11.8987, 11.921174999999998, 11.982775, 12.010975000000002, 12.019466666666666, 12.021700000000001, 12.0224, 12.0225, 12.0226, 11.95525, 11.776133333333334, 11.65815, 11.624400000000001]
dt_list2 = [datetime.datetime.fromtimestamp(x) for x in timestamp_list2]
time_frame2 = pd.DataFrame(index=dt_list2, data=value_list2)
time_frame2.columns = ['value']
tf_resampled_idxmin2 = time_frame2.groupby(pd.TimeGrouper('60000L')).idxmin()
tf_resampled_idxmin2.head()
#Out[20]:
# value
#2017-05-05 17:44:00 2017-05-05 17:44:53.997
#2017-05-05 17:45:00 2017-05-05 17:45:41.889
#2017-05-05 17:46:00 2017-05-05 17:46:03.993
答案 0 :(得分：1)
我找到了解决问题的方法：
import datetime
import pandas as pd
import numpy as np
timestamp_list = [1493992554.897, 1493999093.997, 1493999108.733, 1493999116.101, 1493999117.943, 1493999119.785, 1493999121.627, 1493999123.469, 1493999125.311, 1493999127.153, 1493999128.995, 1493999130.837, 1493999132.679, 1493999134.521, 1493999136.363, 1493999138.205, 1493999140.047, 1493999141.889, 1493999143.731, 1493999145.573, 1493999147.415, 1493999149.257, 1493999151.099, 1493999152.941, 1493999154.783, 1493999156.625, 1493999158.467, 1493999160.309, 1493999162.151, 1493999163.993]
value_list = [2.52962e-41, 2.52962e-41, 11.9625, 12.033420000000001, 12.069, 12.0784, 12.080933333333334, 12.080549999999999, 12.080233333333332, 12.078975, 12.033750000000001, 11.9472, 11.910966666666667, 11.902700000000001, 11.899766666666666, 11.898925, 11.898733333333332, 11.8987, 11.921174999999998, 11.982775, 12.010975000000002, 12.019466666666666, 12.021700000000001, 12.0224, 12.0225, 12.0226, 11.95525, 11.776133333333334, 11.65815, 11.624400000000001]
dt_list = [datetime.datetime.fromtimestamp(x) for x in timestamp_list]
time_frame = pd.DataFrame(index=dt_list, data=value_list)
time_frame.columns = ['value']
tf_resampled_idxmin = time_frame.resample("60000L").agg([lambda x: np.argmin(x) if len(x) > 0 else np.datetime64('NaT')])
print(tf_resampled_idxmin)
# value
# <lambda>
#2017-05-05 15:55:00 2017-05-05 15:55:54.897
#2017-05-05 15:56:00 NaT
#2017-05-05 16:23:00 NaT
#2017-05-05 16:24:00 NaT
#... ...
#2017-05-05 17:17:00 NaT
#2017-05-05 17:18:00 NaT
#2017-05-05 17:43:00 NaT
#2017-05-05 17:44:00 2017-05-05 17:44:53.997
#2017-05-05 17:45:00 2017-05-05 17:45:41.889
#2017-05-05 17:46:00 2017-05-05 17:46:03.993
诀窍是使用.agg（[np.argmin（）]）和lambda函数实现自己的idxmin（）版本以捕获空列表的情况。