Sparse becomes float under fancy indexing

[jnothman]

>>> pd.SparseSeries([False, True], dtype=bool)[[0, 1]]
0    0.0
1    1.0
dtype: float64
BlockIndex
Block locations: array([1], dtype=int32)
Block lengths: array([1], dtype=int32)

Expected output

0    False
1    True
dtype: bool
BlockIndex
Block locations: array([1], dtype=int32)
Block lengths: array([1], dtype=int32)

Output of pd.versions()

pandas: 0.18.1

should alone be relevant.

Apologies I've not checked if this is fixed by #13849 or elsewhere. Just passing on issues from
scikit-learn/scikit-learn#7352.

[jreback]

in master/0.19.0 (rc1 comes out this week)

http://pandas-docs.github.io/pandas-docs-travis/whatsnew.html#sparse-changes

In [1]: pd.SparseSeries([False, True], dtype=bool)[[0, 1]]
Out[1]: 
0    False
1     True
dtype: bool
BlockIndex
Block locations: array([1], dtype=int32)
Block lengths: array([1], dtype=int32)

[jnothman]

Great.

On 7 September 2016 at 10:15, Jeff Reback notifications@github.com wrote:

in master/0.19.0 (rc1 comes out this week)

In [1]: pd.SparseSeries([False, True], dtype=bool)[[0, 1]]
Out[1]:
0 False
1 True
dtype: bool
BlockIndex
Block locations: array([1], dtype=int32)
Block lengths: array([1], dtype=int32)

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