'combine first' in pandas produces NA error
Found the best solution:
pd.tools.merge.concat([test.construction,test.ops],join='outer')
Joins along the date index and keeps the different columns.  To the extent the column names are the same, it will join 'inner' or 'outer' as specified.
I have two dataframes, each with a series of dates as the index. The dates to not overlap (in other words one date range from, say, 2013-01-01 through 2016-06-15 by month and the second DataFrame will start on 2016-06-15 and run quarterly through 2035-06-15.  
Most of the column names overlap (i.e. are the same) and the join just does fine. However, there is one columns in each DataFrame that I would like to preserve as 'belonging' to the original DataFrame so that I have them both available for future use.  I gave each a different name. For example, DF1 has a column entitled opselapsed_time and DF2 has a column entitled constructionelapsed_time.
When I try to combine DF1 and DF2 together using the command DF1.combine_first(DF2) or vice versa I get this error:  ValueError: Cannot convert NA to integer.
Could someone please give me advice on how best to resolve?
Do I need to just stick with using a merge/join type solution instead of combine_first?