I have a time series dataset that has several variables for a state/province for fixed periods of time. That is for state A, there are samples from April 2017 to July 2019. Of course, I thought adding precipitation and temperature variables would be a great idea. I tried finding some relevant external data but most of it is abstract and spread out. How would one simulate dynamic data in Python with varying means, highs and lows for say six months on a daily basis, taking into account average temperatures/precipitation for each month?
So if I have the mean temperatures (C) as below for state A:
year Jan Feb Mar Apr May Jun 2017 5.5 6.0 12.0 15.0 20.0 25.0
I would like data to be simulated as below without really doing for each month since that would make the whole task very tedious:
Duration Temp 2017-01-01 5.0 2017-01-02 5.1 2017-01-03 4.9 . 2017-03-01 7.8 2017-03-02 9.0 2017-03-03 9.5 . 2017-06-30 26.7
Are there ways to achieve this in Python (or R)?