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import numpy as np
import pytest
import pandas.util._test_decorators as td
from pandas._libs.sparse import IntIndex
import pandas as pd
from pandas import (
SparseDtype,
isna,
)
import pandas._testing as tm
from pandas.core.arrays.sparse import SparseArray
class TestConstructors:
def test_constructor_dtype(self):
arr = SparseArray([np.nan, 1, 2, np.nan])
assert arr.dtype == SparseDtype(np.float64, np.nan)
assert arr.dtype.subtype == np.float64
assert np.isnan(arr.fill_value)
arr = SparseArray([np.nan, 1, 2, np.nan], fill_value=0)
assert arr.dtype == SparseDtype(np.float64, 0)
assert arr.fill_value == 0
arr = SparseArray([0, 1, 2, 4], dtype=np.float64)
assert arr.dtype == SparseDtype(np.float64, np.nan)
assert np.isnan(arr.fill_value)
arr = SparseArray([0, 1, 2, 4], dtype=np.int64)
assert arr.dtype == SparseDtype(np.int64, 0)
assert arr.fill_value == 0
arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=np.int64)
assert arr.dtype == SparseDtype(np.int64, 0)
assert arr.fill_value == 0
arr = SparseArray([0, 1, 2, 4], dtype=None)
assert arr.dtype == SparseDtype(np.int64, 0)
assert arr.fill_value == 0
arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=None)
assert arr.dtype == SparseDtype(np.int64, 0)
assert arr.fill_value == 0
def test_constructor_dtype_str(self):
result = SparseArray([1, 2, 3], dtype="int")
expected = SparseArray([1, 2, 3], dtype=int)
tm.assert_sp_array_equal(result, expected)
def test_constructor_sparse_dtype(self):
result = SparseArray([1, 0, 0, 1], dtype=SparseDtype("int64", -1))
expected = SparseArray([1, 0, 0, 1], fill_value=-1, dtype=np.int64)
tm.assert_sp_array_equal(result, expected)
assert result.sp_values.dtype == np.dtype("int64")
def test_constructor_sparse_dtype_str(self):
result = SparseArray([1, 0, 0, 1], dtype="Sparse[int32]")
expected = SparseArray([1, 0, 0, 1], dtype=np.int32)
tm.assert_sp_array_equal(result, expected)
assert result.sp_values.dtype == np.dtype("int32")
def test_constructor_object_dtype(self):
# GH#11856
arr = SparseArray(["A", "A", np.nan, "B"], dtype=object)
assert arr.dtype == SparseDtype(object)
assert np.isnan(arr.fill_value)
arr = SparseArray(["A", "A", np.nan, "B"], dtype=object, fill_value="A")
assert arr.dtype == SparseDtype(object, "A")
assert arr.fill_value == "A"
def test_constructor_object_dtype_bool_fill(self):
# GH#17574
data = [False, 0, 100.0, 0.0]
arr = SparseArray(data, dtype=object, fill_value=False)
assert arr.dtype == SparseDtype(object, False)
assert arr.fill_value is False
arr_expected = np.array(data, dtype=object)
it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected))
assert np.fromiter(it, dtype=np.bool_).all()
@pytest.mark.parametrize("dtype", [SparseDtype(int, 0), int])
def test_constructor_na_dtype(self, dtype):
with pytest.raises(ValueError, match="Cannot convert"):
SparseArray([0, 1, np.nan], dtype=dtype)
def test_constructor_warns_when_losing_timezone(self):
# GH#32501 warn when losing timezone information
dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
expected = SparseArray(np.asarray(dti, dtype="datetime64[ns]"))
with tm.assert_produces_warning(UserWarning):
result = SparseArray(dti)
tm.assert_sp_array_equal(result, expected)
with tm.assert_produces_warning(UserWarning):
result = SparseArray(pd.Series(dti))
tm.assert_sp_array_equal(result, expected)
def test_constructor_spindex_dtype(self):
arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]))
# TODO: actionable?
# XXX: Behavior change: specifying SparseIndex no longer changes the
# fill_value
expected = SparseArray([0, 1, 2, 0], kind="integer")
tm.assert_sp_array_equal(arr, expected)
assert arr.dtype == SparseDtype(np.int64)
assert arr.fill_value == 0
arr = SparseArray(
data=[1, 2, 3],
sparse_index=IntIndex(4, [1, 2, 3]),
dtype=np.int64,
fill_value=0,
)
exp = SparseArray([0, 1, 2, 3], dtype=np.int64, fill_value=0)
tm.assert_sp_array_equal(arr, exp)
assert arr.dtype == SparseDtype(np.int64)
assert arr.fill_value == 0
arr = SparseArray(
data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=np.int64
)
exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=np.int64)
tm.assert_sp_array_equal(arr, exp)
assert arr.dtype == SparseDtype(np.int64)
assert arr.fill_value == 0
arr = SparseArray(
data=[1, 2, 3],
sparse_index=IntIndex(4, [1, 2, 3]),
dtype=None,
fill_value=0,
)
exp = SparseArray([0, 1, 2, 3], dtype=None)
tm.assert_sp_array_equal(arr, exp)
assert arr.dtype == SparseDtype(np.int64)
assert arr.fill_value == 0
@pytest.mark.parametrize("sparse_index", [None, IntIndex(1, [0])])
def test_constructor_spindex_dtype_scalar(self, sparse_index):
# scalar input
msg = "Constructing SparseArray with scalar data is deprecated"
with tm.assert_produces_warning(FutureWarning, match=msg):
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
with tm.assert_produces_warning(FutureWarning, match=msg):
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
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
@pytest.mark.parametrize(
"data, fill_value",
[
(np.array([1, 2]), 0),
(np.array([1.0, 2.0]), np.nan),
([True, False], False),
([pd.Timestamp("2017-01-01")], pd.NaT),
],
)
def test_constructor_inferred_fill_value(self, data, fill_value):
result = SparseArray(data).fill_value
if isna(fill_value):
assert isna(result)
else:
assert result == fill_value
@pytest.mark.parametrize("format", ["coo", "csc", "csr"])
@pytest.mark.parametrize("size", [0, 10])
def test_from_spmatrix(self, size, format):
sp_sparse = td.versioned_importorskip("scipy.sparse")
mat = sp_sparse.random(size, 1, density=0.5, format=format)
result = SparseArray.from_spmatrix(mat)
result = np.asarray(result)
expected = mat.toarray().ravel()
tm.assert_numpy_array_equal(result, expected)
@pytest.mark.parametrize("format", ["coo", "csc", "csr"])
def test_from_spmatrix_including_explicit_zero(self, format):
sp_sparse = td.versioned_importorskip("scipy.sparse")
mat = sp_sparse.random(10, 1, density=0.5, format=format)
mat.data[0] = 0
result = SparseArray.from_spmatrix(mat)
result = np.asarray(result)
expected = mat.toarray().ravel()
tm.assert_numpy_array_equal(result, expected)
def test_from_spmatrix_raises(self):
sp_sparse = td.versioned_importorskip("scipy.sparse")
mat = sp_sparse.eye(5, 4, format="csc")
with pytest.raises(ValueError, match="not '4'"):
SparseArray.from_spmatrix(mat)
def test_constructor_from_too_large_array(self):
with pytest.raises(TypeError, match="expected dimension <= 1 data"):
SparseArray(np.arange(10).reshape((2, 5)))
def test_constructor_from_sparse(self):
zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
res = SparseArray(zarr)
assert res.fill_value == 0
tm.assert_almost_equal(res.sp_values, zarr.sp_values)
def test_constructor_copy(self):
arr_data = np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
arr = SparseArray(arr_data)
cp = SparseArray(arr, copy=True)
cp.sp_values[:3] = 0
assert not (arr.sp_values[:3] == 0).any()
not_copy = SparseArray(arr)
not_copy.sp_values[:3] = 0
assert (arr.sp_values[:3] == 0).all()
def test_constructor_bool(self):
# GH#10648
data = np.array([False, False, True, True, False, False])
arr = SparseArray(data, fill_value=False, dtype=bool)
assert arr.dtype == SparseDtype(bool)
tm.assert_numpy_array_equal(arr.sp_values, np.array([True, True]))
# Behavior change: np.asarray densifies.
# tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr))
tm.assert_numpy_array_equal(arr.sp_index.indices, np.array([2, 3], np.int32))
dense = arr.to_dense()
assert dense.dtype == bool
tm.assert_numpy_array_equal(dense, data)
def test_constructor_bool_fill_value(self):
arr = SparseArray([True, False, True], dtype=None)
assert arr.dtype == SparseDtype(np.bool_)
assert not arr.fill_value
arr = SparseArray([True, False, True], dtype=np.bool_)
assert arr.dtype == SparseDtype(np.bool_)
assert not arr.fill_value
arr = SparseArray([True, False, True], dtype=np.bool_, fill_value=True)
assert arr.dtype == SparseDtype(np.bool_, True)
assert arr.fill_value
def test_constructor_float32(self):
# GH#10648
data = np.array([1.0, np.nan, 3], dtype=np.float32)
arr = SparseArray(data, dtype=np.float32)
assert arr.dtype == SparseDtype(np.float32)
tm.assert_numpy_array_equal(arr.sp_values, np.array([1, 3], dtype=np.float32))
# Behavior change: np.asarray densifies.
# tm.assert_numpy_array_equal(arr.sp_values, np.asarray(arr))
tm.assert_numpy_array_equal(
arr.sp_index.indices, np.array([0, 2], dtype=np.int32)
)
dense = arr.to_dense()
assert dense.dtype == np.float32
tm.assert_numpy_array_equal(dense, data)
|
