Apr-04-2019, 05:22 PM
Hi all,
I am new to python and I have learned using data frames. I have imported some data series and I was able to set the index to be time based. That makes me picking time durations easier. A few questions though more
a. How I can pick based on time stamps durations?(assume a start point in time X) Pick all rows that are within 1 second.
b. I want then to pick measurements based on a rolling window(previous start point+ a displacement).
I can do this easily in python with a for loop and having indexes increasing gradually. But is this the most efficient way to do that?
c. Now since I am splitting my initial data set to a X new datasets what will be the most efficient data structure to keep that new vectors? So for a long vector that is time indexed I will split it to Y new vectors (they will be overlapping as I described at point b). How I can store them? In other words can my function still return a data frame that makes calculations way easier?
I would like to thank you in advance for your reply.
Regards
Alex
I am new to python and I have learned using data frames. I have imported some data series and I was able to set the index to be time based. That makes me picking time durations easier. A few questions though more
a. How I can pick based on time stamps durations?(assume a start point in time X) Pick all rows that are within 1 second.
b. I want then to pick measurements based on a rolling window(previous start point+ a displacement).
I can do this easily in python with a for loop and having indexes increasing gradually. But is this the most efficient way to do that?
c. Now since I am splitting my initial data set to a X new datasets what will be the most efficient data structure to keep that new vectors? So for a long vector that is time indexed I will split it to Y new vectors (they will be overlapping as I described at point b). How I can store them? In other words can my function still return a data frame that makes calculations way easier?
I would like to thank you in advance for your reply.
Regards
Alex