python 3D numpy array time index
Posting the psuedo solution I used:
The problem here is the lack of date-time indexing for 3d array data (i.e. satillite, radar). Whilst there is time series functions in pandas there is not for arrays (as far as i'm aware). 
This solution was possible because the data files I use have date-time in the name e.g. '200401010000' is 'yyyymmddhhMM'. 

construct a 3d array with all the data (missing times in places).
Using the list of data file (os.listdir), create a list of timestamps (length matches 3d array length)
create dfa using timestamps (2) as dfa index and create a column 'inx' of running integers (range(0, len(array) = integers = index of 3d array)
create a datetime index using data start and end times and known frequency of data (no missing datetimes). Create a new dfb using this as the index. 
dfb in (4) left merge with dfa in (3). Aka dfa now has accurate datetime index and 'inx' column containg the 3d array index postition and with nan's at missing data. 

Using this you can then resmaple the df, to for example 1 day, taking the Min and Max of 'inx'. This gives you the start - end position for your array functions. 
You can also insert arrays of nans at mising datetimes (i.e. 'inx' min max = nan) so that your 3d array matches the length of actual datetimes. 
Comment if you have Q's or if you know of a better solution / package to this problem.
Is there a way to index a 3 dimensional array using some form of time index (datetime etc.) on the 3rd dimension?
My problem is that I am doing time series analysis on several thousand radar images and I need to get, for example, monthly averages. However if i simply average over every 31 arrays in the 3rd dimension it becomes inacurate due to shorter months and missing data etc.