python 2.7 - How to get the duration of a threshold breach in a pandas data frame? -


i'm looking suggested approach following time efficient in pandas. let's have dataframe looks this:

[timestamp]             [val]  2017-08-19 22:28:42.000  151  2017-08-19 22:28:42.001  127  2017-08-19 22:29:42.000  149  2017-08-19 22:34:10.000  127  2017-08-19 22:35:10.000  126  2017-08-19 22:36:10.000  132  2017-08-19 22:37:10.000  129  2017-08-19 22:39:10.000  124

how duration when val exceeds 127?

so i'd expect answer of:

22:28:42 -> 22:28:42.001 22:29:42 -> 22:34:10.000 22:36:10 -> 22:39:10.000

i @ these date ranges , carry out actions like: how many datapoint there between dates value above 127

first sort data timestamp

>> df['timestamp'] = pd.to_datetime(df['timestamp']) >> df = df.sort_values('timestamp')

then find positions val changes lte or gt 127

>> df['changed'] = (df['val'] > 127).astype(int).diff().fillna(1).astype(int) >> df     timestamp                   val     changed 0   2017-08-19 22:28:42.000     151     1 1   2017-08-19 22:28:42.001     127     -1 2   2017-08-19 22:29:42.000     149     1 3   2017-08-19 22:34:10.000     127     -1 4   2017-08-19 22:35:10.000     126     0 5   2017-08-19 22:36:10.000     132     1 6   2017-08-19 22:37:10.000     129     0 7   2017-08-19 22:39:10.000     124     -1

above, particular timestamp

  • -1 means val changed lte 127
  • +1 means val changed gt 127

finally construct time intervals need

>> pd.dataframe({ >>     't_0': df.loc[df.changed ==  1, 'timestamp'].reset_index(drop=true),  >>     't_n': df.loc[df.changed == -1, 'timestamp'].reset_index(drop=true)})      t_n                         t_0 0   2017-08-19 22:28:42.001     2017-08-19 22:28:42 1   2017-08-19 22:34:10.000     2017-08-19 22:29:42 2   2017-08-19 22:39:10.000     2017-08-19 22:36:10

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