BUG: SparseDataFrame construction with lists not coercing to dtype

[jreback]

coercion happens

In [12]: df = pd.SparseDataFrame({'a': [1, 0, 0],
    ...:                          'b': [0, 1, 0],
    ...:                          'c': [0, 0, 1]}, dtype='uint8', default_fill_value=0)

In [13]: df.dtypes
Out[13]: 
a    int64
b    int64
c    int64
dtype: object

respected here

In [23]: df = pd.SparseDataFrame({'a': pd.SparseSeries([1, 0, 0], dtype='uint8'),
    ...:                          'b': pd.SparseSeries([0, 1, 0], dtype='uint8'),
    ...:                          'c': pd.SparseSeries([0, 0, 1], dtype='uint8')}, default_fill_value=0)

In [24]: df.dtypes
Out[24]: 
a    uint8
b    uint8
c    uint8
dtype: object

In [25]: df.default_fill_value
Out[25]: 0

And here

In [15]: df = pd.DataFrame({'a': [1, 0, 0],
    ...:                          'b': [0, 1, 0],
    ...:                          'c': [0, 0, 1]}, dtype='uint8').to_sparse(fill_value=0)

In [17]: df.dtypes
Out[17]: 
a    uint8
b    uint8
c    uint8
dtype: object

In [18]: df.default_fill_value
Out[18]: 0

[ucals]

As the last one we did was about types, I'd like to try this one :)

[jreback]

@ucals sure!