SparseSeries.value_counts doesn't include fill_value counts
Based on a suggestion from an user at stackoverflow.com, I am reporting a bug / enhancement request for sparse data frames. Please let me know if you need any more information.

Thanks in advance.

I am encountering a TypeError with a pandas sparse data frame when I use the value_counts method. I have listed the versions of the packages that I am using.

Python 2.7.6 |Anaconda 1.9.1 (x86_64)| (default, Jan 10 2014, 11:23:15) 
[GCC 4.0.1 (Apple Inc. build 5493)] on darwin
Type "help", "copyright", "credits" or "license" for more information.

> > > import pandas
> > > print pandas.**version**
> > > 0.13.1
> > > import numpy
> > > print numpy.**version**
> > > 1.8.0
> > > 
> > > dense_df = pandas.DataFrame(numpy.zeros((10, 10))
> > >                                ,columns=['x%d' % ix for ix in range(10)])
> > > dense_df['x5'] = [1.0, 0.0, 0.0, 1.0, 2.1, 3.0, 0.0, 0.0, 0.0, 0.0]
> > > print dense_df['x5'].value_counts()
> > > 0.0    6
> > > 1.0    2
> > > 3.0    1
> > > 2.1    1
> > > dtype: int64
> > > 
> > > sparse_df = dense_df.to_sparse(fill_value=0) # Tried fill_value=0.0 also
> > > print sparse_df.density
> > > 0.04
> > > 
> > > print sparse_df['x5'].value_counts()
> > > Traceback (most recent call last):
> > >  File "<stdin>", line 1, in <module>
> > >  File "//anaconda/lib/python2.7/site-packages/pandas/core/series.py", line 1156, in     value_counts
> > >     normalize=normalize, bins=bins)
> > >  File "//anaconda/lib/python2.7/site-packages/pandas/core/algorithms.py", line 231, in value_counts
> > >     values = com._ensure_object(values)
> > >   File "generated.pyx", line 112, in pandas.algos.ensure_object (pandas/algos.c:38788)
> > >   File "generated.pyx", line 117, in pandas.algos.ensure_object (pandas/algos.c:38695)
> > >   File "//anaconda/lib/python2.7/site-packages/pandas/sparse/array.py", line 377, in astype
> > >     raise TypeError('Can only support floating point data for now')
> > > TypeError: Can only support floating point data for now
