Using pandas.merge_asof when one merge column contains NaNs
I did two methods:
1) I set the Nan's to -1. (there was no id that had -1 in the other dataset). Then put them back to Nan after.
2) I removed the records associated with the Nan's for that column, and put the records back in after.
I tried to compare results (and reset the indices, sort by timestamp), but I kept getting false. Both should give the same result regardless.
New to pandas - I have been trying to use the pandas.merge_asof to join two datasets together by shared ID first, then merge by nearest timestamp to the timestamp in df1. 
The issue is that I have discovered is that both left_on and right_on must be int. I have one column that contains NaNs and they must remain. Floats was also ineffective. From my research on Stackoverflow, I found out that the latest version of Pandas, 24.02 has this functionality where you simply convert the column to Int64. However, the version of pandas I have available at work is 23.xx and cannot be upgraded at this time. 
What is my easiest option? If I were to simply remove the rows associated with the NaNs values in the one column, could I simply add them back later, and then change the dtype back from int to object? Would this disrupt anything?