Description
Pandas version checks
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I have checked that this issue has not already been reported.
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I have confirmed this bug exists on the latest version of pandas.
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I have confirmed this bug exists on the main branch of pandas.
Reproducible Example
Two different errors depending on dtype.
df = pd.DataFrame([[1., 0., 1.5], [0., 2., 0.]], dtype=pd.SparseDtype(float))
df.iloc[0]Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/indexing.py", line 967, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/indexing.py", line 1522, in _getitem_axis
return self.obj._ixs(key, axis=axis)
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/frame.py", line 3424, in _ixs
new_values = self._mgr.fast_xs(i)
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/internals/managers.py", line 1012, in fast_xs
result[rl] = blk.iget((i, loc))
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/arrays/sparse/array.py", line 610, in __setitem__
raise TypeError(msg)
TypeError: SparseArray does not support item assignment via setitemdf = pd.DataFrame([[1, 0, 1], [0, 2, 0]], dtype=pd.SparseDtype(int))
df.iloc[0]Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/indexing.py", line 967, in __getitem__
return self._getitem_axis(maybe_callable, axis=axis)
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/indexing.py", line 1522, in _getitem_axis
return self.obj._ixs(key, axis=axis)
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/frame.py", line 3424, in _ixs
new_values = self._mgr.fast_xs(i)
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/internals/managers.py", line 1003, in fast_xs
result = cls._empty((n,), dtype=dtype)
File "/Users/devin/anaconda3/envs/sw310/lib/python3.10/site-packages/pandas/core/arrays/base.py", line 1545, in _empty
raise NotImplementedError(
NotImplementedError: Default 'empty' implementation is invalid for dtype='Sparse[int64, 0]'Issue Description
Getting exceptions from fast_xs when trying to use iloc on a sparse DataFrame. For a float dtype, the exception is for setitem but I am only looking to get an item. It looks like fast_xs constructs an empty SparseArray and then uses setitem, which is causing the error. For an int dtype, the empty object cannot be constructed.
Expected Behavior
I would expect a sparse Series to be returned.
Installed Versions
INSTALLED VERSIONS
commit : 06d2301
python : 3.10.0.final.0
python-bits : 64
OS : Darwin
OS-release : 21.2.0
Version : Darwin Kernel Version 21.2.0: Sun Nov 28 20:28:54 PST 2021; root:xnu-8019.61.5~1/RELEASE_X86_64
machine : x86_64
processor : i386
byteorder : little
LC_ALL : None
LANG : en_US.UTF-8
LOCALE : en_US.UTF-8
pandas : 1.4.1
numpy : 1.21.5
pytz : 2021.3
dateutil : 2.8.2
pip : 21.2.4
setuptools : 58.0.4
Cython : 0.29.25
pytest : 6.2.4
hypothesis : None
sphinx : 4.4.0
blosc : None
feather : None
xlsxwriter : None
lxml.etree : None
html5lib : None
pymysql : None
psycopg2 : None
jinja2 : 3.0.2
IPython : None
pandas_datareader: None
bs4 : None
bottleneck : 1.3.2
fastparquet : None
fsspec : None
gcsfs : None
matplotlib : 3.5.1
numba : None
numexpr : 2.8.0
odfpy : None
openpyxl : None
pandas_gbq : None
pyarrow : None
pyreadstat : None
pyxlsb : None
s3fs : None
scipy : 1.8.0
sqlalchemy : None
tables : None
tabulate : None
xarray : 2022.3.0
xlrd : None
xlwt : None
zstandard : None