unwanted type conversion in pandas.DataFrame.update

Question

Is there any reason why pandas changes the type of columns from int to float in update, and can I prevent it from doing it? Here is some example code of the problem

Answer 1

here's the reason for this: since you are effectively masking certain values on a column and replace them (with your updates), some values could become `nan

in an integer array this is impossible, so numeric dtypes are apriori converted to float (for efficiency), as checking first is more expensive that doing this

a change of dtype back is possible...just not in the code right now, therefor this a bug (a bit non-trivial to fix though): github.com/pydata/pandas/issues/4094