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Fix nonzero of a SparseArray #21175

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Nov 17, 2018
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1 change: 1 addition & 0 deletions doc/source/whatsnew/v0.24.0.txt
Original file line number Diff line number Diff line change
Expand Up @@ -1353,6 +1353,7 @@ Sparse
- Bug in ``DataFrame.groupby`` not including ``fill_value`` in the groups for non-NA ``fill_value`` when grouping by a sparse column (:issue:`5078`)
- Bug in unary inversion operator (``~``) on a ``SparseSeries`` with boolean values. The performance of this has also been improved (:issue:`22835`)
- Bug in :meth:`SparseArary.unique` not returning the unique values (:issue:`19595`)
- Bug in ``SparseArray.nonzero`` and `SparseDataFrame.dropna` returning shifted/invalid results (:issue:`21172`)
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can you add :func: to reference the api of these

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sure


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^^^^^^^^^^^^^
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16 changes: 16 additions & 0 deletions pandas/tests/arrays/sparse/test_array.py
Original file line number Diff line number Diff line change
Expand Up @@ -747,6 +747,22 @@ def test_fillna_overlap(self):
exp = SparseArray([1, 3, 3, 3, 3], fill_value=0, dtype=np.float64)
tm.assert_sp_array_equal(res, exp)

def test_nonzero(self):
sa = pd.SparseArray([
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can you add the issue number

float('nan'),
float('nan'),
1, 0, 0,
2, 0, 0, 0,
3, 0, 0
])
tm.assert_numpy_array_equal(np.array([2, 5, 9], dtype=np.int32),
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can you use

result = 
expected = 
tm.assert_numpy_array_equal(result, expected)

sa.nonzero()[0])

sa = pd.SparseArray(
[0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
tm.assert_numpy_array_equal(np.array([2, 5, 9], dtype=np.int32),
sa.nonzero()[0])


class TestSparseArrayAnalytics(object):

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8 changes: 8 additions & 0 deletions pandas/tests/sparse/frame/test_frame.py
Original file line number Diff line number Diff line change
Expand Up @@ -1350,3 +1350,11 @@ def test_assign_with_sparse_frame(self):

for column in res.columns:
assert type(res[column]) is SparseSeries

def test_dropna(self):
tm.assert_sp_frame_equal(
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use the result= and expected= format

add the issue number as a comment

pd.SparseDataFrame({"F2": [0, 1]}),
pd.SparseDataFrame(
{"F1": [float('nan'), float('nan')], "F2": [0, 1]}
).dropna(axis=1, inplace=False, how='all')
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can you test for inplace=True/False, how='all', 'any'

via parametrization

)