1303 lines
45 KiB
Python
1303 lines
45 KiB
Python
import operator
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import re
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import warnings
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import numpy as np
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import pytest
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from pandas._libs.sparse import IntIndex
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import pandas.util._test_decorators as td
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import pandas as pd
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from pandas import isna
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import pandas._testing as tm
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from pandas.core.arrays.sparse import SparseArray, SparseDtype
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class TestSparseArray:
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def setup_method(self, method):
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self.arr_data = np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
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self.arr = SparseArray(self.arr_data)
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self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
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def test_constructor_dtype(self):
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arr = SparseArray([np.nan, 1, 2, np.nan])
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assert arr.dtype == SparseDtype(np.float64, np.nan)
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assert arr.dtype.subtype == np.float64
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assert np.isnan(arr.fill_value)
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arr = SparseArray([np.nan, 1, 2, np.nan], fill_value=0)
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assert arr.dtype == SparseDtype(np.float64, 0)
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assert arr.fill_value == 0
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arr = SparseArray([0, 1, 2, 4], dtype=np.float64)
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assert arr.dtype == SparseDtype(np.float64, np.nan)
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assert np.isnan(arr.fill_value)
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arr = SparseArray([0, 1, 2, 4], dtype=np.int64)
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assert arr.dtype == SparseDtype(np.int64, 0)
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assert arr.fill_value == 0
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arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=np.int64)
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assert arr.dtype == SparseDtype(np.int64, 0)
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assert arr.fill_value == 0
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arr = SparseArray([0, 1, 2, 4], dtype=None)
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assert arr.dtype == SparseDtype(np.int64, 0)
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assert arr.fill_value == 0
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arr = SparseArray([0, 1, 2, 4], fill_value=0, dtype=None)
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assert arr.dtype == SparseDtype(np.int64, 0)
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assert arr.fill_value == 0
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def test_constructor_dtype_str(self):
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result = SparseArray([1, 2, 3], dtype="int")
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expected = SparseArray([1, 2, 3], dtype=int)
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tm.assert_sp_array_equal(result, expected)
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def test_constructor_sparse_dtype(self):
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result = SparseArray([1, 0, 0, 1], dtype=SparseDtype("int64", -1))
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expected = SparseArray([1, 0, 0, 1], fill_value=-1, dtype=np.int64)
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tm.assert_sp_array_equal(result, expected)
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assert result.sp_values.dtype == np.dtype("int64")
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def test_constructor_sparse_dtype_str(self):
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result = SparseArray([1, 0, 0, 1], dtype="Sparse[int32]")
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expected = SparseArray([1, 0, 0, 1], dtype=np.int32)
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tm.assert_sp_array_equal(result, expected)
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assert result.sp_values.dtype == np.dtype("int32")
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def test_constructor_object_dtype(self):
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# GH 11856
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arr = SparseArray(["A", "A", np.nan, "B"], dtype=object)
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assert arr.dtype == SparseDtype(object)
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assert np.isnan(arr.fill_value)
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arr = SparseArray(["A", "A", np.nan, "B"], dtype=object, fill_value="A")
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assert arr.dtype == SparseDtype(object, "A")
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assert arr.fill_value == "A"
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# GH 17574
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data = [False, 0, 100.0, 0.0]
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arr = SparseArray(data, dtype=object, fill_value=False)
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assert arr.dtype == SparseDtype(object, False)
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assert arr.fill_value is False
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arr_expected = np.array(data, dtype=object)
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it = (type(x) == type(y) and x == y for x, y in zip(arr, arr_expected))
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assert np.fromiter(it, dtype=np.bool_).all()
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@pytest.mark.parametrize("dtype", [SparseDtype(int, 0), int])
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def test_constructor_na_dtype(self, dtype):
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with pytest.raises(ValueError, match="Cannot convert"):
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SparseArray([0, 1, np.nan], dtype=dtype)
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def test_constructor_warns_when_losing_timezone(self):
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# GH#32501 warn when losing timezone inforamtion
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dti = pd.date_range("2016-01-01", periods=3, tz="US/Pacific")
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expected = SparseArray(np.asarray(dti, dtype="datetime64[ns]"))
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with tm.assert_produces_warning(UserWarning):
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result = SparseArray(dti)
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tm.assert_sp_array_equal(result, expected)
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with tm.assert_produces_warning(UserWarning):
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result = SparseArray(pd.Series(dti))
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tm.assert_sp_array_equal(result, expected)
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def test_constructor_spindex_dtype(self):
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arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]))
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# XXX: Behavior change: specifying SparseIndex no longer changes the
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# fill_value
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expected = SparseArray([0, 1, 2, 0], kind="integer")
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tm.assert_sp_array_equal(arr, expected)
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assert arr.dtype == SparseDtype(np.int64)
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assert arr.fill_value == 0
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arr = SparseArray(
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data=[1, 2, 3],
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sparse_index=IntIndex(4, [1, 2, 3]),
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dtype=np.int64,
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fill_value=0,
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)
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exp = SparseArray([0, 1, 2, 3], dtype=np.int64, fill_value=0)
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tm.assert_sp_array_equal(arr, exp)
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assert arr.dtype == SparseDtype(np.int64)
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assert arr.fill_value == 0
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arr = SparseArray(
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data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=np.int64
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)
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exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=np.int64)
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tm.assert_sp_array_equal(arr, exp)
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assert arr.dtype == SparseDtype(np.int64)
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assert arr.fill_value == 0
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arr = SparseArray(
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data=[1, 2, 3],
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sparse_index=IntIndex(4, [1, 2, 3]),
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dtype=None,
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fill_value=0,
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)
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exp = SparseArray([0, 1, 2, 3], dtype=None)
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tm.assert_sp_array_equal(arr, exp)
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assert arr.dtype == SparseDtype(np.int64)
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assert arr.fill_value == 0
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@pytest.mark.parametrize("sparse_index", [None, IntIndex(1, [0])])
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def test_constructor_spindex_dtype_scalar(self, sparse_index):
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# scalar input
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arr = SparseArray(data=1, sparse_index=sparse_index, dtype=None)
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exp = SparseArray([1], dtype=None)
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tm.assert_sp_array_equal(arr, exp)
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assert arr.dtype == SparseDtype(np.int64)
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assert arr.fill_value == 0
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arr = SparseArray(data=1, sparse_index=IntIndex(1, [0]), dtype=None)
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exp = SparseArray([1], dtype=None)
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tm.assert_sp_array_equal(arr, exp)
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assert arr.dtype == SparseDtype(np.int64)
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assert arr.fill_value == 0
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def test_constructor_spindex_dtype_scalar_broadcasts(self):
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arr = SparseArray(
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data=[1, 2], sparse_index=IntIndex(4, [1, 2]), fill_value=0, dtype=None
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)
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exp = SparseArray([0, 1, 2, 0], fill_value=0, dtype=None)
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tm.assert_sp_array_equal(arr, exp)
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assert arr.dtype == SparseDtype(np.int64)
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assert arr.fill_value == 0
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@pytest.mark.parametrize(
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"data, fill_value",
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[
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(np.array([1, 2]), 0),
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(np.array([1.0, 2.0]), np.nan),
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([True, False], False),
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([pd.Timestamp("2017-01-01")], pd.NaT),
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],
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)
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def test_constructor_inferred_fill_value(self, data, fill_value):
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result = SparseArray(data).fill_value
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if pd.isna(fill_value):
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assert pd.isna(result)
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else:
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assert result == fill_value
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@pytest.mark.parametrize("format", ["coo", "csc", "csr"])
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@pytest.mark.parametrize("size", [0, 10])
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@td.skip_if_no_scipy
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def test_from_spmatrix(self, size, format):
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import scipy.sparse
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mat = scipy.sparse.random(size, 1, density=0.5, format=format)
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result = SparseArray.from_spmatrix(mat)
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result = np.asarray(result)
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expected = mat.toarray().ravel()
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tm.assert_numpy_array_equal(result, expected)
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@pytest.mark.parametrize("format", ["coo", "csc", "csr"])
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@td.skip_if_no_scipy
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def test_from_spmatrix_including_explicit_zero(self, format):
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import scipy.sparse
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mat = scipy.sparse.random(10, 1, density=0.5, format=format)
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mat.data[0] = 0
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result = SparseArray.from_spmatrix(mat)
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result = np.asarray(result)
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expected = mat.toarray().ravel()
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tm.assert_numpy_array_equal(result, expected)
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@td.skip_if_no_scipy
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def test_from_spmatrix_raises(self):
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import scipy.sparse
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mat = scipy.sparse.eye(5, 4, format="csc")
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with pytest.raises(ValueError, match="not '4'"):
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SparseArray.from_spmatrix(mat)
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@pytest.mark.parametrize(
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"scalar,dtype",
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[
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(False, SparseDtype(bool, False)),
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(0.0, SparseDtype("float64", 0)),
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(1, SparseDtype("int64", 1)),
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("z", SparseDtype("object", "z")),
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],
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)
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def test_scalar_with_index_infer_dtype(self, scalar, dtype):
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# GH 19163
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arr = SparseArray(scalar, index=[1, 2, 3], fill_value=scalar)
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exp = SparseArray([scalar, scalar, scalar], fill_value=scalar)
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tm.assert_sp_array_equal(arr, exp)
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assert arr.dtype == dtype
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assert exp.dtype == dtype
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def test_get_item(self):
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assert np.isnan(self.arr[1])
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assert self.arr[2] == 1
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assert self.arr[7] == 5
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assert self.zarr[0] == 0
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assert self.zarr[2] == 1
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assert self.zarr[7] == 5
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errmsg = re.compile("bounds")
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with pytest.raises(IndexError, match=errmsg):
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self.arr[11]
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with pytest.raises(IndexError, match=errmsg):
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self.arr[-11]
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assert self.arr[-1] == self.arr[len(self.arr) - 1]
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def test_take_scalar_raises(self):
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msg = "'indices' must be an array, not a scalar '2'."
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with pytest.raises(ValueError, match=msg):
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self.arr.take(2)
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def test_take(self):
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exp = SparseArray(np.take(self.arr_data, [2, 3]))
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tm.assert_sp_array_equal(self.arr.take([2, 3]), exp)
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exp = SparseArray(np.take(self.arr_data, [0, 1, 2]))
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tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), exp)
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def test_take_all_empty(self):
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a = pd.array([0, 0], dtype=SparseDtype("int64"))
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result = a.take([0, 1], allow_fill=True, fill_value=np.nan)
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tm.assert_sp_array_equal(a, result)
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def test_take_fill_value(self):
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data = np.array([1, np.nan, 0, 3, 0])
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sparse = SparseArray(data, fill_value=0)
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exp = SparseArray(np.take(data, [0]), fill_value=0)
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tm.assert_sp_array_equal(sparse.take([0]), exp)
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exp = SparseArray(np.take(data, [1, 3, 4]), fill_value=0)
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tm.assert_sp_array_equal(sparse.take([1, 3, 4]), exp)
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def test_take_negative(self):
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exp = SparseArray(np.take(self.arr_data, [-1]))
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tm.assert_sp_array_equal(self.arr.take([-1]), exp)
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exp = SparseArray(np.take(self.arr_data, [-4, -3, -2]))
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tm.assert_sp_array_equal(self.arr.take([-4, -3, -2]), exp)
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@pytest.mark.parametrize("fill_value", [0, None, np.nan])
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def test_shift_fill_value(self, fill_value):
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# GH #24128
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sparse = SparseArray(np.array([1, 0, 0, 3, 0]), fill_value=8.0)
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res = sparse.shift(1, fill_value=fill_value)
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if isna(fill_value):
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fill_value = res.dtype.na_value
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exp = SparseArray(np.array([fill_value, 1, 0, 0, 3]), fill_value=8.0)
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tm.assert_sp_array_equal(res, exp)
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def test_bad_take(self):
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with pytest.raises(IndexError, match="bounds"):
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self.arr.take([11])
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def test_take_filling(self):
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# similar tests as GH 12631
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sparse = SparseArray([np.nan, np.nan, 1, np.nan, 4])
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result = sparse.take(np.array([1, 0, -1]))
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expected = SparseArray([np.nan, np.nan, 4])
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tm.assert_sp_array_equal(result, expected)
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# XXX: test change: fill_value=True -> allow_fill=True
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result = sparse.take(np.array([1, 0, -1]), allow_fill=True)
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expected = SparseArray([np.nan, np.nan, np.nan])
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tm.assert_sp_array_equal(result, expected)
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# allow_fill=False
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result = sparse.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
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expected = SparseArray([np.nan, np.nan, 4])
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tm.assert_sp_array_equal(result, expected)
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msg = "Invalid value in 'indices'"
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with pytest.raises(ValueError, match=msg):
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sparse.take(np.array([1, 0, -2]), allow_fill=True)
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with pytest.raises(ValueError, match=msg):
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sparse.take(np.array([1, 0, -5]), allow_fill=True)
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msg = "out of bounds value in 'indices'"
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, -6]))
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, 5]))
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, 5]), allow_fill=True)
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def test_take_filling_fill_value(self):
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# same tests as GH 12631
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sparse = SparseArray([np.nan, 0, 1, 0, 4], fill_value=0)
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result = sparse.take(np.array([1, 0, -1]))
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expected = SparseArray([0, np.nan, 4], fill_value=0)
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tm.assert_sp_array_equal(result, expected)
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# fill_value
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result = sparse.take(np.array([1, 0, -1]), allow_fill=True)
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# XXX: behavior change.
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# the old way of filling self.fill_value doesn't follow EA rules.
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# It's supposed to be self.dtype.na_value (nan in this case)
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expected = SparseArray([0, np.nan, np.nan], fill_value=0)
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tm.assert_sp_array_equal(result, expected)
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# allow_fill=False
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result = sparse.take(np.array([1, 0, -1]), allow_fill=False, fill_value=True)
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expected = SparseArray([0, np.nan, 4], fill_value=0)
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tm.assert_sp_array_equal(result, expected)
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msg = "Invalid value in 'indices'."
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with pytest.raises(ValueError, match=msg):
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sparse.take(np.array([1, 0, -2]), allow_fill=True)
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with pytest.raises(ValueError, match=msg):
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sparse.take(np.array([1, 0, -5]), allow_fill=True)
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msg = "out of bounds value in 'indices'"
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, -6]))
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, 5]))
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, 5]), fill_value=True)
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def test_take_filling_all_nan(self):
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sparse = SparseArray([np.nan, np.nan, np.nan, np.nan, np.nan])
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# XXX: did the default kind from take change?
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result = sparse.take(np.array([1, 0, -1]))
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expected = SparseArray([np.nan, np.nan, np.nan], kind="block")
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tm.assert_sp_array_equal(result, expected)
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result = sparse.take(np.array([1, 0, -1]), fill_value=True)
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expected = SparseArray([np.nan, np.nan, np.nan], kind="block")
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tm.assert_sp_array_equal(result, expected)
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msg = "out of bounds value in 'indices'"
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, -6]))
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, 5]))
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with pytest.raises(IndexError, match=msg):
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sparse.take(np.array([1, 5]), fill_value=True)
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def test_set_item(self):
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def setitem():
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self.arr[5] = 3
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def setslice():
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self.arr[1:5] = 2
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with pytest.raises(TypeError, match="assignment via setitem"):
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setitem()
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with pytest.raises(TypeError, match="assignment via setitem"):
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setslice()
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def test_constructor_from_too_large_array(self):
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with pytest.raises(TypeError, match="expected dimension <= 1 data"):
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SparseArray(np.arange(10).reshape((2, 5)))
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def test_constructor_from_sparse(self):
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res = SparseArray(self.zarr)
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assert res.fill_value == 0
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tm.assert_almost_equal(res.sp_values, self.zarr.sp_values)
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def test_constructor_copy(self):
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cp = SparseArray(self.arr, copy=True)
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cp.sp_values[:3] = 0
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assert not (self.arr.sp_values[:3] == 0).any()
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not_copy = SparseArray(self.arr)
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not_copy.sp_values[:3] = 0
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assert (self.arr.sp_values[:3] == 0).all()
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def test_constructor_bool(self):
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# GH 10648
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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)
|
|
|
|
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([0.0, 2.0], 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]")
|
|
|
|
def test_astype_bool(self):
|
|
a = 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)
|
|
|
|
def test_astype_all(self, any_real_dtype):
|
|
vals = np.array([1, 2, 3])
|
|
arr = SparseArray(vals, fill_value=1)
|
|
typ = np.dtype(any_real_dtype)
|
|
res = arr.astype(typ)
|
|
assert res.dtype == SparseDtype(typ, 1)
|
|
assert res.sp_values.dtype == typ
|
|
|
|
tm.assert_numpy_array_equal(np.asarray(res.to_dense()), vals.astype(typ))
|
|
|
|
@pytest.mark.parametrize(
|
|
"array, dtype, expected",
|
|
[
|
|
(
|
|
SparseArray([0, 1]),
|
|
"float",
|
|
SparseArray([0.0, 1.0], dtype=SparseDtype(float, 0.0)),
|
|
),
|
|
(SparseArray([0, 1]), bool, SparseArray([False, True])),
|
|
(
|
|
SparseArray([0, 1], fill_value=1),
|
|
bool,
|
|
SparseArray([False, True], dtype=SparseDtype(bool, True)),
|
|
),
|
|
pytest.param(
|
|
SparseArray([0, 1]),
|
|
"datetime64[ns]",
|
|
SparseArray(
|
|
np.array([0, 1], dtype="datetime64[ns]"),
|
|
dtype=SparseDtype("datetime64[ns]", pd.Timestamp("1970")),
|
|
),
|
|
marks=[pytest.mark.xfail(reason="NumPy-7619")],
|
|
),
|
|
(
|
|
SparseArray([0, 1, 10]),
|
|
str,
|
|
SparseArray(["0", "1", "10"], dtype=SparseDtype(str, "0")),
|
|
),
|
|
(SparseArray(["10", "20"]), float, SparseArray([10.0, 20.0])),
|
|
(
|
|
SparseArray([0, 1, 0]),
|
|
object,
|
|
SparseArray([0, 1, 0], dtype=SparseDtype(object, 0)),
|
|
),
|
|
],
|
|
)
|
|
def test_astype_more(self, array, dtype, expected):
|
|
result = array.astype(dtype)
|
|
tm.assert_sp_array_equal(result, expected)
|
|
|
|
def test_astype_nan_raises(self):
|
|
arr = SparseArray([1.0, np.nan])
|
|
with pytest.raises(ValueError, match="Cannot convert non-finite"):
|
|
arr.astype(int)
|
|
|
|
def test_set_fill_value(self):
|
|
arr = SparseArray([1.0, np.nan, 2.0], fill_value=np.nan)
|
|
arr.fill_value = 2
|
|
assert arr.fill_value == 2
|
|
|
|
arr = SparseArray([1, 0, 2], fill_value=0, dtype=np.int64)
|
|
arr.fill_value = 2
|
|
assert arr.fill_value == 2
|
|
|
|
# XXX: this seems fine? You can construct an integer
|
|
# sparsearray with NaN fill value, why not update one?
|
|
# coerces to int
|
|
# msg = "unable to set fill_value 3\\.1 to int64 dtype"
|
|
# with pytest.raises(ValueError, match=msg):
|
|
arr.fill_value = 3.1
|
|
assert arr.fill_value == 3.1
|
|
|
|
# msg = "unable to set fill_value nan to int64 dtype"
|
|
# with pytest.raises(ValueError, match=msg):
|
|
arr.fill_value = np.nan
|
|
assert np.isnan(arr.fill_value)
|
|
|
|
arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
|
|
arr.fill_value = True
|
|
assert arr.fill_value
|
|
|
|
# coerces to bool
|
|
# msg = "unable to set fill_value 0 to bool dtype"
|
|
# with pytest.raises(ValueError, match=msg):
|
|
arr.fill_value = 0
|
|
assert arr.fill_value == 0
|
|
|
|
# msg = "unable to set fill_value nan to bool dtype"
|
|
# with pytest.raises(ValueError, match=msg):
|
|
arr.fill_value = np.nan
|
|
assert np.isnan(arr.fill_value)
|
|
|
|
@pytest.mark.parametrize("val", [[1, 2, 3], np.array([1, 2]), (1, 2, 3)])
|
|
def test_set_fill_invalid_non_scalar(self, val):
|
|
arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
|
|
msg = "fill_value must be a scalar"
|
|
|
|
with pytest.raises(ValueError, match=msg):
|
|
arr.fill_value = val
|
|
|
|
def test_copy(self):
|
|
arr2 = self.arr.copy()
|
|
assert arr2.sp_values is not self.arr.sp_values
|
|
assert arr2.sp_index is self.arr.sp_index
|
|
|
|
def test_values_asarray(self):
|
|
tm.assert_almost_equal(self.arr.to_dense(), self.arr_data)
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,shape,dtype",
|
|
[
|
|
([0, 0, 0, 0, 0], (5,), None),
|
|
([], (0,), None),
|
|
([0], (1,), None),
|
|
(["A", "A", np.nan, "B"], (4,), object),
|
|
],
|
|
)
|
|
def test_shape(self, data, shape, dtype):
|
|
# GH 21126
|
|
out = SparseArray(data, dtype=dtype)
|
|
assert out.shape == shape
|
|
|
|
@pytest.mark.parametrize(
|
|
"vals",
|
|
[
|
|
[np.nan, np.nan, np.nan, np.nan, np.nan],
|
|
[1, np.nan, np.nan, 3, np.nan],
|
|
[1, np.nan, 0, 3, 0],
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("fill_value", [None, 0])
|
|
def test_dense_repr(self, vals, fill_value):
|
|
vals = np.array(vals)
|
|
arr = SparseArray(vals, fill_value=fill_value)
|
|
|
|
res = arr.to_dense()
|
|
tm.assert_numpy_array_equal(res, vals)
|
|
|
|
res2 = arr._internal_get_values()
|
|
|
|
tm.assert_numpy_array_equal(res2, vals)
|
|
|
|
def test_getitem(self):
|
|
def _checkit(i):
|
|
tm.assert_almost_equal(self.arr[i], self.arr.to_dense()[i])
|
|
|
|
for i in range(len(self.arr)):
|
|
_checkit(i)
|
|
_checkit(-i)
|
|
|
|
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)
|
|
|
|
def test_getslice(self):
|
|
result = self.arr[:-3]
|
|
exp = SparseArray(self.arr.to_dense()[:-3])
|
|
tm.assert_sp_array_equal(result, exp)
|
|
|
|
result = self.arr[-4:]
|
|
exp = SparseArray(self.arr.to_dense()[-4:])
|
|
tm.assert_sp_array_equal(result, exp)
|
|
|
|
# two corner cases from Series
|
|
result = self.arr[-12:]
|
|
exp = SparseArray(self.arr)
|
|
tm.assert_sp_array_equal(result, exp)
|
|
|
|
result = self.arr[:-12]
|
|
exp = SparseArray(self.arr.to_dense()[:0])
|
|
tm.assert_sp_array_equal(result, exp)
|
|
|
|
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[(slice(4, None),)]
|
|
exp = SparseArray(dense[4:])
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
sparse = SparseArray(dense, fill_value=0)
|
|
res = sparse[(slice(4, None),)]
|
|
exp = SparseArray(dense[4:], fill_value=0)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
msg = "too many indices for array"
|
|
with pytest.raises(IndexError, match=msg):
|
|
sparse[4:, :]
|
|
|
|
with pytest.raises(IndexError, match=msg):
|
|
# check numpy compat
|
|
dense[4:, :]
|
|
|
|
def test_boolean_slice_empty(self):
|
|
arr = SparseArray([0, 1, 2])
|
|
res = arr[[False, False, False]]
|
|
assert res.dtype == arr.dtype
|
|
|
|
@pytest.mark.parametrize("op", ["add", "sub", "mul", "truediv", "floordiv", "pow"])
|
|
def test_binary_operators(self, op):
|
|
op = getattr(operator, op)
|
|
data1 = np.random.randn(20)
|
|
data2 = np.random.randn(20)
|
|
|
|
data1[::2] = np.nan
|
|
data2[::3] = np.nan
|
|
|
|
arr1 = SparseArray(data1)
|
|
arr2 = SparseArray(data2)
|
|
|
|
data1[::2] = 3
|
|
data2[::3] = 3
|
|
farr1 = SparseArray(data1, fill_value=3)
|
|
farr2 = SparseArray(data2, fill_value=3)
|
|
|
|
def _check_op(op, first, second):
|
|
res = op(first, second)
|
|
exp = SparseArray(
|
|
op(first.to_dense(), second.to_dense()), fill_value=first.fill_value
|
|
)
|
|
assert isinstance(res, SparseArray)
|
|
tm.assert_almost_equal(res.to_dense(), exp.to_dense())
|
|
|
|
res2 = op(first, second.to_dense())
|
|
assert isinstance(res2, SparseArray)
|
|
tm.assert_sp_array_equal(res, res2)
|
|
|
|
res3 = op(first.to_dense(), second)
|
|
assert isinstance(res3, SparseArray)
|
|
tm.assert_sp_array_equal(res, res3)
|
|
|
|
res4 = op(first, 4)
|
|
assert isinstance(res4, SparseArray)
|
|
|
|
# Ignore this if the actual op raises (e.g. pow).
|
|
try:
|
|
exp = op(first.to_dense(), 4)
|
|
exp_fv = op(first.fill_value, 4)
|
|
except ValueError:
|
|
pass
|
|
else:
|
|
tm.assert_almost_equal(res4.fill_value, exp_fv)
|
|
tm.assert_almost_equal(res4.to_dense(), exp)
|
|
|
|
with np.errstate(all="ignore"):
|
|
for first_arr, second_arr in [(arr1, arr2), (farr1, farr2)]:
|
|
_check_op(op, first_arr, second_arr)
|
|
|
|
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)
|
|
|
|
def test_generator_warnings(self):
|
|
sp_arr = SparseArray([1, 2, 3])
|
|
with warnings.catch_warnings(record=True) as w:
|
|
warnings.filterwarnings(action="always", category=DeprecationWarning)
|
|
warnings.filterwarnings(action="always", category=PendingDeprecationWarning)
|
|
for _ in sp_arr:
|
|
pass
|
|
assert len(w) == 0
|
|
|
|
def test_fillna(self):
|
|
s = SparseArray([1, np.nan, np.nan, 3, np.nan])
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1, dtype=np.float64)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([1, -1, -1, 3, -1], fill_value=0, dtype=np.float64)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
s = SparseArray([1, np.nan, 0, 3, 0])
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1, dtype=np.float64)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0)
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([1, -1, 0, 3, 0], fill_value=0, dtype=np.float64)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
s = SparseArray([np.nan, np.nan, np.nan, np.nan])
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([-1, -1, -1, -1], fill_value=-1, dtype=np.float64)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0)
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([-1, -1, -1, -1], fill_value=0, dtype=np.float64)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
# float dtype's fill_value is np.nan, replaced by -1
|
|
s = SparseArray([0.0, 0.0, 0.0, 0.0])
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([0.0, 0.0, 0.0, 0.0], fill_value=-1)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
# int dtype shouldn't have missing. No changes.
|
|
s = SparseArray([0, 0, 0, 0])
|
|
assert s.dtype == SparseDtype(np.int64)
|
|
assert s.fill_value == 0
|
|
res = s.fillna(-1)
|
|
tm.assert_sp_array_equal(res, s)
|
|
|
|
s = SparseArray([0, 0, 0, 0], fill_value=0)
|
|
assert s.dtype == SparseDtype(np.int64)
|
|
assert s.fill_value == 0
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([0, 0, 0, 0], fill_value=0)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
# fill_value can be nan if there is no missing hole.
|
|
# only fill_value will be changed
|
|
s = SparseArray([0, 0, 0, 0], fill_value=np.nan)
|
|
assert s.dtype == SparseDtype(np.int64, fill_value=np.nan)
|
|
assert np.isnan(s.fill_value)
|
|
res = s.fillna(-1)
|
|
exp = SparseArray([0, 0, 0, 0], fill_value=-1)
|
|
tm.assert_sp_array_equal(res, exp)
|
|
|
|
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)
|
|
|
|
def test_nonzero(self):
|
|
# Tests regression #21172.
|
|
sa = SparseArray([float("nan"), float("nan"), 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
|
|
expected = np.array([2, 5, 9], dtype=np.int32)
|
|
(result,) = sa.nonzero()
|
|
tm.assert_numpy_array_equal(expected, result)
|
|
|
|
sa = SparseArray([0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
|
|
(result,) = sa.nonzero()
|
|
tm.assert_numpy_array_equal(expected, result)
|
|
|
|
|
|
class TestSparseArrayAnalytics:
|
|
@pytest.mark.parametrize(
|
|
"data,pos,neg",
|
|
[
|
|
([True, True, True], True, False),
|
|
([1, 2, 1], 1, 0),
|
|
([1.0, 2.0, 1.0], 1.0, 0.0),
|
|
],
|
|
)
|
|
def test_all(self, data, pos, neg):
|
|
# GH 17570
|
|
out = SparseArray(data).all()
|
|
assert out
|
|
|
|
out = SparseArray(data, fill_value=pos).all()
|
|
assert out
|
|
|
|
data[1] = neg
|
|
out = SparseArray(data).all()
|
|
assert not out
|
|
|
|
out = SparseArray(data, fill_value=pos).all()
|
|
assert not out
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,pos,neg",
|
|
[
|
|
([True, True, True], True, False),
|
|
([1, 2, 1], 1, 0),
|
|
([1.0, 2.0, 1.0], 1.0, 0.0),
|
|
],
|
|
)
|
|
def test_numpy_all(self, data, pos, neg):
|
|
# GH 17570
|
|
out = np.all(SparseArray(data))
|
|
assert out
|
|
|
|
out = np.all(SparseArray(data, fill_value=pos))
|
|
assert out
|
|
|
|
data[1] = neg
|
|
out = np.all(SparseArray(data))
|
|
assert not out
|
|
|
|
out = np.all(SparseArray(data, fill_value=pos))
|
|
assert not out
|
|
|
|
# raises with a different message on py2.
|
|
msg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.all(SparseArray(data), out=np.array([]))
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,pos,neg",
|
|
[
|
|
([False, True, False], True, False),
|
|
([0, 2, 0], 2, 0),
|
|
([0.0, 2.0, 0.0], 2.0, 0.0),
|
|
],
|
|
)
|
|
def test_any(self, data, pos, neg):
|
|
# GH 17570
|
|
out = SparseArray(data).any()
|
|
assert out
|
|
|
|
out = SparseArray(data, fill_value=pos).any()
|
|
assert out
|
|
|
|
data[1] = neg
|
|
out = SparseArray(data).any()
|
|
assert not out
|
|
|
|
out = SparseArray(data, fill_value=pos).any()
|
|
assert not out
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,pos,neg",
|
|
[
|
|
([False, True, False], True, False),
|
|
([0, 2, 0], 2, 0),
|
|
([0.0, 2.0, 0.0], 2.0, 0.0),
|
|
],
|
|
)
|
|
def test_numpy_any(self, data, pos, neg):
|
|
# GH 17570
|
|
out = np.any(SparseArray(data))
|
|
assert out
|
|
|
|
out = np.any(SparseArray(data, fill_value=pos))
|
|
assert out
|
|
|
|
data[1] = neg
|
|
out = np.any(SparseArray(data))
|
|
assert not out
|
|
|
|
out = np.any(SparseArray(data, fill_value=pos))
|
|
assert not out
|
|
|
|
msg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.any(SparseArray(data), out=out)
|
|
|
|
def test_sum(self):
|
|
data = np.arange(10).astype(float)
|
|
out = SparseArray(data).sum()
|
|
assert out == 45.0
|
|
|
|
data[5] = np.nan
|
|
out = SparseArray(data, fill_value=2).sum()
|
|
assert out == 40.0
|
|
|
|
out = SparseArray(data, fill_value=np.nan).sum()
|
|
assert out == 40.0
|
|
|
|
@pytest.mark.parametrize(
|
|
"arr",
|
|
[
|
|
np.array([0, 1, np.nan, 1]),
|
|
np.array([0, 1, 1]),
|
|
np.array([True, True, False]),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("fill_value", [0, 1, np.nan, True, False])
|
|
@pytest.mark.parametrize("min_count, expected", [(3, 2), (4, np.nan)])
|
|
def test_sum_min_count(self, arr, fill_value, min_count, expected):
|
|
# https://github.com/pandas-dev/pandas/issues/25777
|
|
sparray = SparseArray(arr, fill_value=fill_value)
|
|
result = sparray.sum(min_count=min_count)
|
|
if np.isnan(expected):
|
|
assert np.isnan(result)
|
|
else:
|
|
assert result == expected
|
|
|
|
def test_numpy_sum(self):
|
|
data = np.arange(10).astype(float)
|
|
out = np.sum(SparseArray(data))
|
|
assert out == 45.0
|
|
|
|
data[5] = np.nan
|
|
out = np.sum(SparseArray(data, fill_value=2))
|
|
assert out == 40.0
|
|
|
|
out = np.sum(SparseArray(data, fill_value=np.nan))
|
|
assert out == 40.0
|
|
|
|
msg = "the 'dtype' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.sum(SparseArray(data), dtype=np.int64)
|
|
|
|
msg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.sum(SparseArray(data), out=out)
|
|
|
|
@pytest.mark.parametrize(
|
|
"data,expected",
|
|
[
|
|
(
|
|
np.array([1, 2, 3, 4, 5], dtype=float), # non-null data
|
|
SparseArray(np.array([1.0, 3.0, 6.0, 10.0, 15.0])),
|
|
),
|
|
(
|
|
np.array([1, 2, np.nan, 4, 5], dtype=float), # null data
|
|
SparseArray(np.array([1.0, 3.0, np.nan, 7.0, 12.0])),
|
|
),
|
|
],
|
|
)
|
|
@pytest.mark.parametrize("numpy", [True, False])
|
|
def test_cumsum(self, data, expected, numpy):
|
|
cumsum = np.cumsum if numpy else lambda s: s.cumsum()
|
|
|
|
out = cumsum(SparseArray(data))
|
|
tm.assert_sp_array_equal(out, expected)
|
|
|
|
out = cumsum(SparseArray(data, fill_value=np.nan))
|
|
tm.assert_sp_array_equal(out, expected)
|
|
|
|
out = cumsum(SparseArray(data, fill_value=2))
|
|
tm.assert_sp_array_equal(out, expected)
|
|
|
|
if numpy: # numpy compatibility checks.
|
|
msg = "the 'dtype' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.cumsum(SparseArray(data), dtype=np.int64)
|
|
|
|
msg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.cumsum(SparseArray(data), out=out)
|
|
else:
|
|
axis = 1 # SparseArray currently 1-D, so only axis = 0 is valid.
|
|
msg = re.escape(f"axis(={axis}) out of bounds")
|
|
with pytest.raises(ValueError, match=msg):
|
|
SparseArray(data).cumsum(axis=axis)
|
|
|
|
def test_mean(self):
|
|
data = np.arange(10).astype(float)
|
|
out = SparseArray(data).mean()
|
|
assert out == 4.5
|
|
|
|
data[5] = np.nan
|
|
out = SparseArray(data).mean()
|
|
assert out == 40.0 / 9
|
|
|
|
def test_numpy_mean(self):
|
|
data = np.arange(10).astype(float)
|
|
out = np.mean(SparseArray(data))
|
|
assert out == 4.5
|
|
|
|
data[5] = np.nan
|
|
out = np.mean(SparseArray(data))
|
|
assert out == 40.0 / 9
|
|
|
|
msg = "the 'dtype' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.mean(SparseArray(data), dtype=np.int64)
|
|
|
|
msg = "the 'out' parameter is not supported"
|
|
with pytest.raises(ValueError, match=msg):
|
|
np.mean(SparseArray(data), out=out)
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
@pytest.mark.parametrize("fill_value", [0.0, np.nan])
|
|
def test_modf(self, fill_value):
|
|
# https://github.com/pandas-dev/pandas/issues/26946
|
|
sparse = SparseArray([fill_value] * 10 + [1.1, 2.2], fill_value=fill_value)
|
|
r1, r2 = np.modf(sparse)
|
|
e1, e2 = np.modf(np.asarray(sparse))
|
|
tm.assert_sp_array_equal(r1, SparseArray(e1, fill_value=fill_value))
|
|
tm.assert_sp_array_equal(r2, SparseArray(e2, fill_value=fill_value))
|
|
|
|
def test_nbytes_integer(self):
|
|
arr = SparseArray([1, 0, 0, 0, 2], kind="integer")
|
|
result = arr.nbytes
|
|
# (2 * 8) + 2 * 4
|
|
assert result == 24
|
|
|
|
def test_nbytes_block(self):
|
|
arr = SparseArray([1, 2, 0, 0, 0], kind="block")
|
|
result = arr.nbytes
|
|
# (2 * 8) + 4 + 4
|
|
# sp_values, blocs, blengths
|
|
assert result == 24
|
|
|
|
def test_asarray_datetime64(self):
|
|
s = SparseArray(pd.to_datetime(["2012", None, None, "2013"]))
|
|
np.asarray(s)
|
|
|
|
def test_density(self):
|
|
arr = SparseArray([0, 1])
|
|
assert arr.density == 0.5
|
|
|
|
def test_npoints(self):
|
|
arr = SparseArray([0, 1])
|
|
assert arr.npoints == 1
|
|
|
|
|
|
class TestAccessor:
|
|
@pytest.mark.parametrize("attr", ["npoints", "density", "fill_value", "sp_values"])
|
|
def test_get_attributes(self, attr):
|
|
arr = SparseArray([0, 1])
|
|
ser = pd.Series(arr)
|
|
|
|
result = getattr(ser.sparse, attr)
|
|
expected = getattr(arr, attr)
|
|
assert result == expected
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_from_coo(self):
|
|
import scipy.sparse
|
|
|
|
row = [0, 3, 1, 0]
|
|
col = [0, 3, 1, 2]
|
|
data = [4, 5, 7, 9]
|
|
# TODO: Remove dtype when scipy is fixed
|
|
# https://github.com/scipy/scipy/issues/13585
|
|
sp_array = scipy.sparse.coo_matrix((data, (row, col)), dtype="int")
|
|
result = pd.Series.sparse.from_coo(sp_array)
|
|
|
|
index = pd.MultiIndex.from_arrays([[0, 0, 1, 3], [0, 2, 1, 3]])
|
|
expected = pd.Series([4, 9, 7, 5], index=index, dtype="Sparse[int]")
|
|
tm.assert_series_equal(result, expected)
|
|
|
|
@td.skip_if_no_scipy
|
|
def test_to_coo(self):
|
|
import scipy.sparse
|
|
|
|
ser = pd.Series(
|
|
[1, 2, 3],
|
|
index=pd.MultiIndex.from_product([[0], [1, 2, 3]], names=["a", "b"]),
|
|
dtype="Sparse[int]",
|
|
)
|
|
A, _, _ = ser.sparse.to_coo()
|
|
assert isinstance(A, scipy.sparse.coo.coo_matrix)
|
|
|
|
def test_non_sparse_raises(self):
|
|
ser = pd.Series([1, 2, 3])
|
|
with pytest.raises(AttributeError, match=".sparse"):
|
|
ser.sparse.density
|
|
|
|
|
|
def test_setting_fill_value_fillna_still_works():
|
|
# This is why letting users update fill_value / dtype is bad
|
|
# astype has the same problem.
|
|
arr = SparseArray([1.0, np.nan, 1.0], fill_value=0.0)
|
|
arr.fill_value = np.nan
|
|
result = arr.isna()
|
|
# Can't do direct comparison, since the sp_index will be different
|
|
# So let's convert to ndarray and check there.
|
|
result = np.asarray(result)
|
|
|
|
expected = np.array([False, True, False])
|
|
tm.assert_numpy_array_equal(result, expected)
|
|
|
|
|
|
def test_setting_fill_value_updates():
|
|
arr = SparseArray([0.0, np.nan], fill_value=0)
|
|
arr.fill_value = np.nan
|
|
# use private constructor to get the index right
|
|
# otherwise both nans would be un-stored.
|
|
expected = SparseArray._simple_new(
|
|
sparse_array=np.array([np.nan]),
|
|
sparse_index=IntIndex(2, [1]),
|
|
dtype=SparseDtype(float, np.nan),
|
|
)
|
|
tm.assert_sp_array_equal(arr, expected)
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"arr, loc",
|
|
[
|
|
([None, 1, 2], 0),
|
|
([0, None, 2], 1),
|
|
([0, 1, None], 2),
|
|
([0, 1, 1, None, None], 3),
|
|
([1, 1, 1, 2], -1),
|
|
([], -1),
|
|
],
|
|
)
|
|
def test_first_fill_value_loc(arr, loc):
|
|
result = SparseArray(arr)._first_fill_value_loc()
|
|
assert result == loc
|
|
|
|
|
|
@pytest.mark.parametrize(
|
|
"arr", [[1, 2, np.nan, np.nan], [1, np.nan, 2, np.nan], [1, 2, np.nan]]
|
|
)
|
|
@pytest.mark.parametrize("fill_value", [np.nan, 0, 1])
|
|
def test_unique_na_fill(arr, fill_value):
|
|
a = SparseArray(arr, fill_value=fill_value).unique()
|
|
b = pd.Series(arr).unique()
|
|
assert isinstance(a, SparseArray)
|
|
a = np.asarray(a)
|
|
tm.assert_numpy_array_equal(a, b)
|
|
|
|
|
|
def test_unique_all_sparse():
|
|
# https://github.com/pandas-dev/pandas/issues/23168
|
|
arr = SparseArray([0, 0])
|
|
result = arr.unique()
|
|
expected = SparseArray([0])
|
|
tm.assert_sp_array_equal(result, expected)
|
|
|
|
|
|
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)
|
|
|
|
|
|
def test_map_missing():
|
|
arr = SparseArray([0, 1, 2])
|
|
expected = SparseArray([10, 11, None], fill_value=10)
|
|
|
|
result = arr.map({0: 10, 1: 11})
|
|
tm.assert_sp_array_equal(result, expected)
|
|
|
|
|
|
@pytest.mark.parametrize("fill_value", [np.nan, 1])
|
|
def test_dropna(fill_value):
|
|
# GH-28287
|
|
arr = SparseArray([np.nan, 1], fill_value=fill_value)
|
|
exp = SparseArray([1.0], fill_value=fill_value)
|
|
tm.assert_sp_array_equal(arr.dropna(), exp)
|
|
|
|
df = pd.DataFrame({"a": [0, 1], "b": arr})
|
|
expected_df = pd.DataFrame({"a": [1], "b": exp}, index=pd.Int64Index([1]))
|
|
tm.assert_equal(df.dropna(), expected_df)
|