can pandas autocorr handle irregularly sample timeseries data?
This is not quite a programming question.
Ideally, your measure of autocorrelation would use data measured at the same frequency/same time interval between observations.  Any autocorr function in any programming package will simply measure the correlation between the series and whatever lag you want.  It will not correct for irregular frequencies.
You would have to fix this yourself but 1) setting up a series with a regular frequency, 2) mapping the actual values you have to the date structure, 3) interpolating values where you have gaps/NaN, and then 4) running your autocorr.  
Long story short, autocorr would not do all this work for you.   
If I have misunderstood the problem you are worried about, let me know. It would be helpful to know a little more about the sampling frequencies.  I have had to deal with things like this a lot.
I have a dataframe with datetime index, where the data was sampled irregularly (the datetime index has gaps, and even where there aren't gaps the spacing between samples varies).
If I do:
df['my column'].autocorr(my_lag)
will this work?  Does autocorr know how to handle irregularly sampled datetime data?