Search Missing Timestamp and display in python?

Question

Here is my some Dataset which having Time,Temperature1,Temperature2

Timestamp.             Temperature1.        Temperature2
09/01/2016 00:00:08          53.         


        

Answer 1

you can use resample('30S', base=8) method:

In [20]: x.resample('30S', base=8).mean()
Out[20]:
                                 Temperature1    Temperature2
Timestamp
2016-09-01 00:00:08                      53.4            45.5
2016-09-01 00:00:38                      53.5            45.2
2016-09-01 00:01:08                      54.6            43.2
2016-09-01 00:01:38                      55.2            46.3
2016-09-01 00:02:08                      54.5            45.5
2016-09-01 00:02:38                       NaN             NaN
2016-09-01 00:03:08                       NaN             NaN
2016-09-01 00:03:38                       NaN             NaN
2016-09-01 00:04:08                      54.2            35.5
2016-09-01 00:04:38                       NaN             NaN
2016-09-01 00:05:08                      52.4            45.7
2016-09-01 00:05:38                      53.4            45.2

The solution above assumes that the Timestamp is of datetime dtype and that it has been set as index. If Timestamp is a regular column (not index), then starting from Pandas 0.19.0 we can resample on regular columns (it must be of datetime dtype), using on='column_name' parameter:

In [26]: x.resample('30S', on='Timestamp', base=8).mean()
Out[26]:
                                 Temperature1    Temperature2
Timestamp
2016-09-01 00:00:08                      53.4            45.5
2016-09-01 00:00:38                      53.5            45.2
2016-09-01 00:01:08                      54.6            43.2
2016-09-01 00:01:38                      55.2            46.3
2016-09-01 00:02:08                      54.5            45.5
2016-09-01 00:02:38                       NaN             NaN
2016-09-01 00:03:08                       NaN             NaN
2016-09-01 00:03:38                       NaN             NaN
2016-09-01 00:04:08                      54.2            35.5
2016-09-01 00:04:38                       NaN             NaN
2016-09-01 00:05:08                      52.4            45.7
2016-09-01 00:05:38                      53.4            45.2

if you need to find your base value dynamically you can do it this way:

In [21]: x.index[0].second
Out[21]: 8

from docs:

base : int, default 0

For frequencies that evenly subdivide 1 day, the “origin” of the aggregated intervals. For example, for 5min frequency, base could range from 0 through 4.

Defaults to 0