I have a pandas DataFrame where each cell contains a python dict.

>>> data = {'Q':{'X':{2:2010}, 'Y':{2:2011, 3:2009}},'R':{'X':{1:2013}}}
>>> frame = DataFrame(data)
>>> frame
                    Q          R
X           {2: 2010}  {1: 2013}
Y  {2: 2011, 3: 2009}        NaN

I'd like to replace the NaN with an empty dict, to get this result:

                    Q          R
X           {2: 2010}  {1: 2013}
Y  {2: 2011, 3: 2009}        {}

However, because the fillna function interprets empty dict not as a scalar value but as a mapping of column --> value, it does NOTHING if I simply do this (i.e. it doesn't work):

>>> frame.fillna(inplace=True, value={})
                    Q          R
X           {2: 2010}  {1: 2013}
Y  {2: 2011, 3: 2009}        NaN

Is there any way to use fillna to accomplish what I want? Do I have to iterate through the entire DataFrame or construct a silly dict with all my columns mapped to empty dict?

I was able to use DataFrame.applymap in this way:

>>> from pandas import isnull
>>> frame=frame.applymap(lambda x: {} if isnull(x) else x)
>>> frame
                    Q          R
X           {2: 2010}  {1: 2013}
Y  {2: 2011, 3: 2009}         {}

This solution avoids the pitfalls in both EdChum's solution (where all NaN cells wind up pointing at same underlying dict object in memory, preventing them from being updated independently from one another) and Shashank's (where a potentially large data structure needs to be constructed with nested dicts, just to specify a single empty dict value).