Python Pandas- How do I subset time intervals into smaller ones?-CodePudding

Let's imagine I have a timeseries dataframe of temperature sensor data that goes by 30 min intervals. How do I basically subset each 30 min interval into smaller 5 min intervals while accounting for the difference of temperature drops between each interval?

I imagine that doing something like this could work:

30 min intervals:

        interval 1: temp = 30 
        interval 2: temp = 25

5 min intervals:

        interval 1: temp = 30
        interval 2: temp = 29
        interval 3: temp = 28
        interval 4: temp = 27
        interval 5: temp = 26
        interval 6: temp = 25

CodePudding user response：

I would do it with a resample of the data frame to a lower time resolution ("6T" in this case, with T meaning minutes), this will create new rows for missing time steps with nan values then you can fill those nan somehow, for what you describe I think a linear interpolation can be enough.

Here you have a simple example that I think can match the data you describe.

import pandas as pd

df = pd.DataFrame({"temp":[30, 25, 20, 18]}, index = pd.date_range("2021-12-01 12:00:00", "2021-12-01 13:59:00", freq = "30T"))

#This resample will preserve your values at their original time indexes, and will create new rows for the intermediate
#datetime full of nans
#the .last() is just used to select the value for each time-step, you could also use mean o max o min or mean as there is just one value for each time step so it would get you the same.
df = df.resample("6T").last()

#it really depends on how you want to implement the change over time of the data, but as you described a linear 
#variation, what you can use is a simple linear interpolation between values with the method interpolate
df.interpolate()

CodePudding user response：

It would be helpful if you could include an example of two things:

What does your data look like currently?
What would you like it too look like after you are done?

Otherwise it isn't clear what you're asking or what the problem is, so it's harder to help!