Python pandas.util.testing.assert_sp_array_equal() Examples
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code examples of pandas.util.testing.assert_sp_array_equal().
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Example #1
| Source File: test_array.py From vnpy_crypto with MIT License | 6 votes |
|
def test_sparseseries_roundtrip(self):
# GH 13999
for kind in ['integer', 'block']:
for fill in [1, np.nan, 0]:
arr = SparseArray([np.nan, 1, np.nan, 2, 3], kind=kind,
fill_value=fill)
res = SparseArray(SparseSeries(arr))
tm.assert_sp_array_equal(arr, res)
arr = SparseArray([0, 0, 0, 1, 1, 2], dtype=np.int64,
kind=kind, fill_value=fill)
res = SparseArray(SparseSeries(arr), dtype=np.int64)
tm.assert_sp_array_equal(arr, res)
res = SparseArray(SparseSeries(arr))
tm.assert_sp_array_equal(arr, res)
for fill in [True, False, np.nan]:
arr = SparseArray([True, False, True, True], dtype=np.bool,
kind=kind, fill_value=fill)
res = SparseArray(SparseSeries(arr))
tm.assert_sp_array_equal(arr, res)
res = SparseArray(SparseSeries(arr))
tm.assert_sp_array_equal(arr, res)
Example #2
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_astype(self):
# float -> float
arr = SparseArray([None, None, 0, 2])
result = arr.astype("Sparse[float32]")
expected = SparseArray([None, None, 0, 2], dtype=np.dtype('float32'))
tm.assert_sp_array_equal(result, expected)
dtype = SparseDtype("float64", fill_value=0)
result = arr.astype(dtype)
expected = SparseArray._simple_new(np.array([0., 2.],
dtype=dtype.subtype),
IntIndex(4, [2, 3]),
dtype)
tm.assert_sp_array_equal(result, expected)
dtype = SparseDtype("int64", 0)
result = arr.astype(dtype)
expected = SparseArray._simple_new(np.array([0, 2], dtype=np.int64),
IntIndex(4, [2, 3]),
dtype)
tm.assert_sp_array_equal(result, expected)
arr = SparseArray([0, np.nan, 0, 1], fill_value=0)
with pytest.raises(ValueError, match='NA'):
arr.astype('Sparse[i8]')
Example #3
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_take_filling_all_nan(self):
sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan])
# XXX: did the default kind from take change?
result = sparse.take(np.array([1, 0, -1]))
expected = SparseArray([np.nan, np.nan, np.nan], kind='block')
tm.assert_sp_array_equal(result, expected)
result = sparse.take(np.array([1, 0, -1]), fill_value=True)
expected = SparseArray([np.nan, np.nan, np.nan], kind='block')
tm.assert_sp_array_equal(result, expected)
with pytest.raises(IndexError):
sparse.take(np.array([1, -6]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]), fill_value=True)
Example #4
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_getslice_tuple(self):
dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0])
sparse = SparseArray(dense)
res = sparse[4:, ]
exp = SparseArray(dense[4:, ])
tm.assert_sp_array_equal(res, exp)
sparse = SparseArray(dense, fill_value=0)
res = sparse[4:, ]
exp = SparseArray(dense[4:, ], fill_value=0)
tm.assert_sp_array_equal(res, exp)
with pytest.raises(IndexError):
sparse[4:, :]
with pytest.raises(IndexError):
# check numpy compat
dense[4:, :]
Example #5
| Source File: test_series.py From recruit with Apache License 2.0 | 6 votes |
|
def test_dropna(self):
sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0)
sp_valid = sp.dropna()
expected = sp.to_dense().dropna()
expected = expected[expected != 0]
exp_arr = pd.SparseArray(expected.values, fill_value=0, kind='block')
tm.assert_sp_array_equal(sp_valid.values, exp_arr)
tm.assert_index_equal(sp_valid.index, expected.index)
assert len(sp_valid.sp_values) == 2
result = self.bseries.dropna()
expected = self.bseries.to_dense().dropna()
assert not isinstance(result, SparseSeries)
tm.assert_series_equal(result, expected)
Example #6
| Source File: test_array.py From recruit with Apache License 2.0 | 6 votes |
|
def test_take_filling_all_nan(self):
sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan])
# XXX: did the default kind from take change?
result = sparse.take(np.array([1, 0, -1]))
expected = SparseArray([np.nan, np.nan, np.nan], kind='block')
tm.assert_sp_array_equal(result, expected)
result = sparse.take(np.array([1, 0, -1]), fill_value=True)
expected = SparseArray([np.nan, np.nan, np.nan], kind='block')
tm.assert_sp_array_equal(result, expected)
with pytest.raises(IndexError):
sparse.take(np.array([1, -6]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]), fill_value=True)
Example #7
| Source File: test_array.py From recruit with Apache License 2.0 | 6 votes |
|
def test_astype(self):
# float -> float
arr = SparseArray([None, None, 0, 2])
result = arr.astype("Sparse[float32]")
expected = SparseArray([None, None, 0, 2], dtype=np.dtype('float32'))
tm.assert_sp_array_equal(result, expected)
dtype = SparseDtype("float64", fill_value=0)
result = arr.astype(dtype)
expected = SparseArray._simple_new(np.array([0., 2.],
dtype=dtype.subtype),
IntIndex(4, [2, 3]),
dtype)
tm.assert_sp_array_equal(result, expected)
dtype = SparseDtype("int64", 0)
result = arr.astype(dtype)
expected = SparseArray._simple_new(np.array([0, 2], dtype=np.int64),
IntIndex(4, [2, 3]),
dtype)
tm.assert_sp_array_equal(result, expected)
arr = SparseArray([0, np.nan, 0, 1], fill_value=0)
with pytest.raises(ValueError, match='NA'):
arr.astype('Sparse[i8]')
Example #8
| Source File: test_array.py From recruit with Apache License 2.0 | 6 votes |
|
def test_getslice_tuple(self):
dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0])
sparse = SparseArray(dense)
res = sparse[4:, ]
exp = SparseArray(dense[4:, ])
tm.assert_sp_array_equal(res, exp)
sparse = SparseArray(dense, fill_value=0)
res = sparse[4:, ]
exp = SparseArray(dense[4:, ], fill_value=0)
tm.assert_sp_array_equal(res, exp)
with pytest.raises(IndexError):
sparse[4:, :]
with pytest.raises(IndexError):
# check numpy compat
dense[4:, :]
Example #9
| Source File: test_series.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 6 votes |
|
def test_dropna(self):
sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0)
sp_valid = sp.dropna()
expected = sp.to_dense().dropna()
expected = expected[expected != 0]
exp_arr = pd.SparseArray(expected.values, fill_value=0, kind='block')
tm.assert_sp_array_equal(sp_valid.values, exp_arr)
tm.assert_index_equal(sp_valid.index, expected.index)
assert len(sp_valid.sp_values) == 2
result = self.bseries.dropna()
expected = self.bseries.to_dense().dropna()
assert not isinstance(result, SparseSeries)
tm.assert_series_equal(result, expected)
Example #10
| Source File: test_array.py From recruit with Apache License 2.0 | 6 votes |
|
def test_map():
arr = SparseArray([0, 1, 2])
expected = SparseArray([10, 11, 12], fill_value=10)
# dict
result = arr.map({0: 10, 1: 11, 2: 12})
tm.assert_sp_array_equal(result, expected)
# series
result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
tm.assert_sp_array_equal(result, expected)
# function
result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
expected = SparseArray([10, 11, 12], fill_value=10)
tm.assert_sp_array_equal(result, expected)
Example #11
| Source File: test_series.py From vnpy_crypto with MIT License | 6 votes |
|
def test_dropna(self):
sp = SparseSeries([0, 0, 0, nan, nan, 5, 6], fill_value=0)
sp_valid = sp.dropna()
expected = sp.to_dense().dropna()
expected = expected[expected != 0]
exp_arr = pd.SparseArray(expected.values, fill_value=0, kind='block')
tm.assert_sp_array_equal(sp_valid.values, exp_arr)
tm.assert_index_equal(sp_valid.index, expected.index)
assert len(sp_valid.sp_values) == 2
result = self.bseries.dropna()
expected = self.bseries.to_dense().dropna()
assert not isinstance(result, SparseSeries)
tm.assert_series_equal(result, expected)
Example #12
| Source File: test_array.py From vnpy_crypto with MIT License | 6 votes |
|
def test_getslice_tuple(self):
dense = np.array([np.nan, 0, 3, 4, 0, 5, np.nan, np.nan, 0])
sparse = SparseArray(dense)
res = sparse[4:, ]
exp = SparseArray(dense[4:, ])
tm.assert_sp_array_equal(res, exp)
sparse = SparseArray(dense, fill_value=0)
res = sparse[4:, ]
exp = SparseArray(dense[4:, ], fill_value=0)
tm.assert_sp_array_equal(res, exp)
with pytest.raises(IndexError):
sparse[4:, :]
with pytest.raises(IndexError):
# check numpy compat
dense[4:, :]
Example #13
| Source File: test_array.py From vnpy_crypto with MIT License | 6 votes |
|
def test_take_filling_all_nan(self):
sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan])
result = sparse.take(np.array([1, 0, -1]))
expected = SparseArray([np.nan, np.nan, np.nan])
tm.assert_sp_array_equal(result, expected)
result = sparse.take(np.array([1, 0, -1]), fill_value=True)
expected = SparseArray([np.nan, np.nan, np.nan])
tm.assert_sp_array_equal(result, expected)
with pytest.raises(IndexError):
sparse.take(np.array([1, -6]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]), fill_value=True)
Example #14
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_constructor_spindex_dtype_scalar_broadcasts(self):
arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]),
fill_value=0, dtype=None)
exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None)
tm.assert_sp_array_equal(arr, exp)
assert arr.dtype == SparseDtype(np.int64)
assert arr.fill_value == 0
Example #15
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_constructor_spindex_dtype_scalar(self, sparse_index):
# scalar input
arr = SparseArray(data=1, sparse_index=sparse_index, dtype=None)
exp = SparseArray([1], dtype=None)
tm.assert_sp_array_equal(arr, exp)
assert arr.dtype == SparseDtype(np.int64)
assert arr.fill_value == 0
arr = SparseArray(data=1, sparse_index=IntIndex(1, [0]), dtype=None)
exp = SparseArray([1], dtype=None)
tm.assert_sp_array_equal(arr, exp)
assert arr.dtype == SparseDtype(np.int64)
assert arr.fill_value == 0
Example #16
| Source File: test_series.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_to_sparse():
# https://github.com/pandas-dev/pandas/issues/22389
arr = pd.SparseArray([1, 2, None, 3])
result = pd.Series(arr).to_sparse()
assert len(result) == 4
tm.assert_sp_array_equal(result.values, arr, check_kind=False)
Example #17
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_scalar_with_index_infer_dtype(self, scalar, dtype):
# GH 19163
arr = SparseArray(scalar, index=[1, 2, 3], fill_value=scalar)
exp = SparseArray([scalar, scalar, scalar], fill_value=scalar)
tm.assert_sp_array_equal(arr, exp)
assert arr.dtype == dtype
assert exp.dtype == dtype
Example #18
| Source File: test_indexing.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def tests_indexing_with_sparse(self, kind, fill):
# see gh-13985
arr = pd.SparseArray([1, 2, 3], kind=kind)
indexer = pd.SparseArray([True, False, True],
fill_value=fill,
dtype=bool)
expected = arr[indexer]
result = pd.SparseArray([1, 3], kind=kind)
tm.assert_sp_array_equal(result, expected)
s = pd.SparseSeries(arr, index=["a", "b", "c"], dtype=np.float64)
expected = pd.SparseSeries([1, 3], index=["a", "c"], kind=kind,
dtype=SparseDtype(np.float64, s.fill_value))
tm.assert_sp_series_equal(s[indexer], expected)
tm.assert_sp_series_equal(s.loc[indexer], expected)
tm.assert_sp_series_equal(s.iloc[indexer], expected)
indexer = pd.SparseSeries(indexer, index=["a", "b", "c"])
tm.assert_sp_series_equal(s[indexer], expected)
tm.assert_sp_series_equal(s.loc[indexer], expected)
msg = ("iLocation based boolean indexing cannot "
"use an indexable as a mask")
with pytest.raises(ValueError, match=msg):
s.iloc[indexer]
Example #19
| Source File: test_array.py From vnpy_crypto with MIT License | 5 votes |
|
def test_ufunc_args(self):
# GH 13853 make sure ufunc is applied to fill_value, including its arg
sparse = SparseArray([1, np.nan, 2, np.nan, -2])
result = SparseArray([2, np.nan, 3, np.nan, -1])
tm.assert_sp_array_equal(np.add(sparse, 1), result)
sparse = SparseArray([1, -1, 2, -2], fill_value=1)
result = SparseArray([2, 0, 3, -1], fill_value=2)
tm.assert_sp_array_equal(np.add(sparse, 1), result)
sparse = SparseArray([1, -1, 0, -2], fill_value=0)
result = SparseArray([2, 0, 1, -1], fill_value=1)
tm.assert_sp_array_equal(np.add(sparse, 1), result)
Example #20
| Source File: test_array.py From vnpy_crypto with MIT License | 5 votes |
|
def test_ufunc(self):
# GH 13853 make sure ufunc is applied to fill_value
sparse = SparseArray([1, np.nan, 2, np.nan, -2])
result = SparseArray([1, np.nan, 2, np.nan, 2])
tm.assert_sp_array_equal(abs(sparse), result)
tm.assert_sp_array_equal(np.abs(sparse), result)
sparse = SparseArray([1, -1, 2, -2], fill_value=1)
result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index,
fill_value=1)
tm.assert_sp_array_equal(abs(sparse), result)
tm.assert_sp_array_equal(np.abs(sparse), result)
sparse = SparseArray([1, -1, 2, -2], fill_value=-1)
result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index,
fill_value=1)
tm.assert_sp_array_equal(abs(sparse), result)
tm.assert_sp_array_equal(np.abs(sparse), result)
sparse = SparseArray([1, np.nan, 2, np.nan, -2])
result = SparseArray(np.sin([1, np.nan, 2, np.nan, -2]))
tm.assert_sp_array_equal(np.sin(sparse), result)
sparse = SparseArray([1, -1, 2, -2], fill_value=1)
result = SparseArray(np.sin([1, -1, 2, -2]), fill_value=np.sin(1))
tm.assert_sp_array_equal(np.sin(sparse), result)
sparse = SparseArray([1, -1, 0, -2], fill_value=0)
result = SparseArray(np.sin([1, -1, 0, -2]), fill_value=np.sin(0))
tm.assert_sp_array_equal(np.sin(sparse), result)
Example #21
| Source File: test_array.py From vnpy_crypto with MIT License | 5 votes |
|
def test_numpy_cumsum(self):
non_null_data = np.array([1, 2, 3, 4, 5], dtype=float)
non_null_expected = SparseArray(non_null_data.cumsum())
null_data = np.array([1, 2, np.nan, 4, 5], dtype=float)
null_expected = SparseArray(np.array([1.0, 3.0, np.nan, 7.0, 12.0]))
for data, expected in [
(null_data, null_expected),
(non_null_data, non_null_expected)
]:
out = np.cumsum(SparseArray(data))
tm.assert_sp_array_equal(out, expected)
out = np.cumsum(SparseArray(data, fill_value=np.nan))
tm.assert_sp_array_equal(out, expected)
out = np.cumsum(SparseArray(data, fill_value=2))
tm.assert_sp_array_equal(out, expected)
msg = "the 'dtype' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.cumsum,
SparseArray(data), dtype=np.int64)
msg = "the 'out' parameter is not supported"
tm.assert_raises_regex(ValueError, msg, np.cumsum,
SparseArray(data), out=out)
Example #22
| Source File: test_array.py From vnpy_crypto with MIT License | 5 votes |
|
def test_fillna_overlap(self):
s = SparseArray([1, np.nan, np.nan, 3, np.nan])
# filling with existing value doesn't replace existing value with
# fill_value, i.e. existing 3 remains in sp_values
res = s.fillna(3)
exp = np.array([1, 3, 3, 3, 3], dtype=np.float64)
tm.assert_numpy_array_equal(res.to_dense(), exp)
s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
res = s.fillna(3)
exp = SparseArray([1, 3, 3, 3, 3], fill_value=0, dtype=np.float64)
tm.assert_sp_array_equal(res, exp)
Example #23
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_astype_bool(self):
a = pd.SparseArray([1, 0, 0, 1], dtype=SparseDtype(int, 0))
result = a.astype(bool)
expected = SparseArray([True, 0, 0, True],
dtype=SparseDtype(bool, 0))
tm.assert_sp_array_equal(result, expected)
# update fill value
result = a.astype(SparseDtype(bool, False))
expected = SparseArray([True, False, False, True],
dtype=SparseDtype(bool, False))
tm.assert_sp_array_equal(result, expected)
Example #24
| Source File: test_array.py From vnpy_crypto with MIT License | 5 votes |
|
def test_pickle(self):
def _check_roundtrip(obj):
unpickled = tm.round_trip_pickle(obj)
tm.assert_sp_array_equal(unpickled, obj)
_check_roundtrip(self.arr)
_check_roundtrip(self.zarr)
Example #25
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_astype_more(self, array, dtype, expected):
result = array.astype(dtype)
tm.assert_sp_array_equal(result, expected)
Example #26
| Source File: test_array.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_getitem_arraylike_mask(self):
arr = SparseArray([0, 1, 2])
result = arr[[True, False, True]]
expected = SparseArray([0, 2])
tm.assert_sp_array_equal(result, expected)
Example #27
| Source File: test_array.py From vnpy_crypto with MIT License | 5 votes |
|
def test_take_fill_value(self):
data = np.array([1, np.nan, 0, 3, 0])
sparse = SparseArray(data, fill_value=0)
exp = SparseArray(np.take(data, [0]), fill_value=0)
tm.assert_sp_array_equal(sparse.take([0]), exp)
exp = SparseArray(np.take(data, [1, 3, 4]), fill_value=0)
tm.assert_sp_array_equal(sparse.take([1, 3, 4]), exp)
Example #28
| Source File: test_array.py From vnpy_crypto with MIT License | 5 votes |
|
def test_take_filling_fill_value(self):
# same tests as GH 12631
sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0)
result = sparse.take(np.array([1, 0, -1]))
expected = SparseArray([0, np.nan, 4], fill_value=0)
tm.assert_sp_array_equal(result, expected)
# fill_value
result = sparse.take(np.array([1, 0, -1]), fill_value=True)
expected = SparseArray([0, np.nan, 0], fill_value=0)
tm.assert_sp_array_equal(result, expected)
# allow_fill=False
result = sparse.take(np.array([1, 0, -1]),
allow_fill=False, fill_value=True)
expected = SparseArray([0, np.nan, 4], fill_value=0)
tm.assert_sp_array_equal(result, expected)
msg = ('When allow_fill=True and fill_value is not None, '
'all indices must be >= -1')
with tm.assert_raises_regex(ValueError, msg):
sparse.take(np.array([1, 0, -2]), fill_value=True)
with tm.assert_raises_regex(ValueError, msg):
sparse.take(np.array([1, 0, -5]), fill_value=True)
with pytest.raises(IndexError):
sparse.take(np.array([1, -6]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]), fill_value=True)
Example #29
| Source File: test_array.py From vnpy_crypto with MIT License | 5 votes |
|
def test_take_filling(self):
# similar tests as GH 12631
sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4])
result = sparse.take(np.array([1, 0, -1]))
expected = SparseArray([np.nan, np.nan, 4])
tm.assert_sp_array_equal(result, expected)
# fill_value
result = sparse.take(np.array([1, 0, -1]), fill_value=True)
expected = SparseArray([np.nan, np.nan, np.nan])
tm.assert_sp_array_equal(result, expected)
# allow_fill=False
result = sparse.take(np.array([1, 0, -1]),
allow_fill=False, fill_value=True)
expected = SparseArray([np.nan, np.nan, 4])
tm.assert_sp_array_equal(result, expected)
msg = ('When allow_fill=True and fill_value is not None, '
'all indices must be >= -1')
with tm.assert_raises_regex(ValueError, msg):
sparse.take(np.array([1, 0, -2]), fill_value=True)
with tm.assert_raises_regex(ValueError, msg):
sparse.take(np.array([1, 0, -5]), fill_value=True)
with pytest.raises(IndexError):
sparse.take(np.array([1, -6]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]))
with pytest.raises(IndexError):
sparse.take(np.array([1, 5]), fill_value=True)
Example #30
| Source File: test_arithmetics.py From predictive-maintenance-using-machine-learning with Apache License 2.0 | 5 votes |
|
def test_with_list(op):
arr = pd.SparseArray([0, 1], fill_value=0)
result = op(arr, [0, 1])
expected = op(arr, pd.SparseArray([0, 1]))
tm.assert_sp_array_equal(result, expected)
