Python 3 Help.

Scenario: You have a data set that is about ~ 20000 values long. You want to create a loop that starts at the 1st data point and calculates the trend over 15 years. NOW the challenge is that you want to rerun through this data set, but instead of starting at 1, you start at 2 and calculate the trend over 15 years etc etc. To simplify, dates 0-15 --> calculate trend , dates 16-30 calculate trend ....all the way till the end then you start over BUT instead of starting at 0 you start at 1...so dates 1-16 calculate trend. Note that we wont end at the same value.

I have some code below but I am gettign a lot of errors and I don't know how to account for the fact that we won't always end at the same date.

At the end of the loop I am to have a list of all possible trends collected (should be a VERY long list)

d1=ds2['time'].isel(time=0). #this is to select time variable in the data set DATA = ds['DATA'] #this is to select the data from the imported data set time_len=len(ds2['time']) num_mon=15*12 #so all the data points are in monthly incraments you want to account for that you want it for 15 years timeline=np.arange(0,15*12,1) #not sure if I need this. trends_collected=[] #list of trends i=0 while i <=21456: #this is the amount of months in the data. I looked at the length of the time and subtracted 15 years from it for tt in range(i,time_len-i,num_mon): time_data = ds['DATA'].isel(time=slice(tt,tt+num_mon)) slope, intercept, r_value, p_value, std_err = stats.linregress(timeline,time_data) #calculate a linear regression for data trends_collected.append(slope) i+=1

In the bold I get an error " all the input array dimensions except for the concatenation axis must match exactly "