python - Matching range of timestamps in pandas -

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i having trouble doing in pandas

df1 (my left join):

name |     timestart        | timeend               | values   | order john   12/24/2014 08:10:32    12/24/2014 08:14:21        2        1 john   12/24/2014 08:15:03    12/24/2014 08:22:49        2        2

df2

name |     timestart        | timeend               | values john   12/24/2014 08:12:57    12/24/2014 08:13:31        8

timestart in df2 greater timestart in df1 (this typically happens during interaction). , match, have less next row of data individual.

here thought process. shift row columns see if match. compare df2 timestart > initial timestart on df1 less next row timestart (same name).

df1.sort(['name', 'timestart'], ascending=[1, 1], inplace = true) df1['name_r'] = df1['name'].shift(-1) df1['matching row'] = np.where((df1['name_r'] == df1['name']), 1, 0) df1['next timestamp'] = np.where(df1['matching row'] == 1, df1['timestart'].shift(-1), np.datetime64('nat')) df1['test'] = np.where(df2['timestart'] > df1['timestart'] < df1['next timestamp'], 1, 0)

edit - possible asof command? trick name has match, closest timestamp timestart on each file/dataframe.

there "asof join" in pandas 0.19. example, ignore starting time , join recent ending time.

pd.merge_asof(df1, df2, on='timeend')

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