2017-04-19-100647 class fumbling.ipynb

Kernel: Python 2 (SageMath)

import pandas as pd 
import numpy as nb

netatmo = pd.read_csv('NetAtmo_2016.csv', parse_dates=True, index_col=1)

netatmo['time']= netatmo.index
netatmo['time'].diff(1)

Timezone : America/Los_Angeles
2016-02-19 13:26:00        NaT
2016-02-19 13:27:00   00:01:00
2016-02-19 13:27:00   00:00:00
2016-02-19 13:31:00   00:04:00
2016-02-19 13:36:00   00:05:00
2016-02-19 13:41:00   00:05:00
2016-02-19 13:46:00   00:05:00
2016-02-19 13:51:00   00:05:00
2016-02-19 13:56:00   00:05:00
2016-02-19 14:02:00   00:06:00
2016-02-19 14:07:00   00:05:00
2016-02-19 14:12:00   00:05:00
2016-02-19 14:17:00   00:05:00
2016-02-19 14:22:00   00:05:00
2016-02-19 14:27:00   00:05:00
2016-02-19 14:32:00   00:05:00
2016-02-19 14:37:00   00:05:00
2016-02-19 14:42:00   00:05:00
2016-02-19 14:47:00   00:05:00
2016-02-19 14:52:00   00:05:00
2016-02-19 14:57:00   00:05:00
2016-02-19 15:02:00   00:05:00
2016-02-19 15:07:00   00:05:00
2016-02-19 15:12:00   00:05:00
2016-02-19 15:17:00   00:05:00
2016-02-19 15:22:00   00:05:00
2016-02-19 15:27:00   00:05:00
2016-02-19 15:32:00   00:05:00
2016-02-19 15:37:00   00:05:00
2016-02-19 15:42:00   00:05:00
                        ...   
2016-12-31 21:30:00   00:06:00
2016-12-31 21:35:00   00:05:00
2016-12-31 21:40:00   00:05:00
2016-12-31 21:45:00   00:05:00
2016-12-31 21:50:00   00:05:00
2016-12-31 21:55:00   00:05:00
2016-12-31 22:00:00   00:05:00
2016-12-31 22:05:00   00:05:00
2016-12-31 22:10:00   00:05:00
2016-12-31 22:15:00   00:05:00
2016-12-31 22:20:00   00:05:00
2016-12-31 22:25:00   00:05:00
2016-12-31 22:30:00   00:05:00
2016-12-31 22:35:00   00:05:00
2016-12-31 22:40:00   00:05:00
2016-12-31 22:45:00   00:05:00
2016-12-31 22:50:00   00:05:00
2016-12-31 22:55:00   00:05:00
2016-12-31 23:00:00   00:05:00
2016-12-31 23:05:00   00:05:00
2016-12-31 23:10:00   00:05:00
2016-12-31 23:15:00   00:05:00
2016-12-31 23:20:00   00:05:00
2016-12-31 23:25:00   00:05:00
2016-12-31 23:30:00   00:05:00
2016-12-31 23:35:00   00:05:00
2016-12-31 23:40:00   00:05:00
2016-12-31 23:45:00   00:05:00
2016-12-31 23:50:00   00:05:00
2016-12-31 23:55:00   00:05:00
Name: time, dtype: timedelta64[ns]

netatmo['time'].diff(1).sum()

Timedelta('316 days 10:29:00')

netatmo['time'].diff(1).sum() / nb.timedelta64(1, 'h')

7594.4833333333336