test_array.py

import operator
import re
import warnings

import numpy as np
import pytest

from pandas._libs.sparse import IntIndex
from pandas.compat.numpy import _np_version_under1p16
import pandas.util._test_decorators as td

import pandas as pd
from pandas import isna
from pandas.core.sparse.api import SparseArray, SparseDtype, SparseSeries
import pandas.util.testing as tm
from pandas.util.testing import assert_almost_equal


@pytest.fixture(params=["integer", "block"])
def kind(request):
    return request.param


class TestSparseArray:

    def setup_method(self, method):
        self.arr_data = np.array([np.nan, np.nan, 1, 2, 3,
                                  np.nan, 4, 5, np.nan, 6])
        self.arr = SparseArray(self.arr_data)
        self.zarr = SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)

    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=np.object)
        assert arr.dtype == SparseDtype(np.object)
        assert np.isnan(arr.fill_value)

        arr = SparseArray(['A', 'A', np.nan, 'B'], dtype=np.object,
                          fill_value='A')
        assert arr.dtype == SparseDtype(np.object, 'A')
        assert arr.fill_value == 'A'

        # GH 17574
        data = [False, 0, 100.0, 0.0]
        arr = SparseArray(data, dtype=np.object, fill_value=False)
        assert arr.dtype == SparseDtype(np.object, False)
        assert arr.fill_value is False
        arr_expected = np.array(data, dtype=np.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_spindex_dtype(self):
        arr = SparseArray(data=[1, 2], sparse_index=IntIndex(4, [1, 2]))
        # 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
        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

    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 pd.isna(fill_value):
            assert pd.isna(result)
        else:
            assert result == fill_value

    @pytest.mark.parametrize('format', ['coo', 'csc', 'csr'])
    @pytest.mark.parametrize('size', [
        pytest.param(0,
                     marks=pytest.mark.skipif(_np_version_under1p16,
                                              reason='NumPy-11383')),
        10
    ])
    @td.skip_if_no_scipy
    def test_from_spmatrix(self, size, format):
        import scipy.sparse

        mat = scipy.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)

    @td.skip_if_no_scipy
    def test_from_spmatrix_raises(self):
        import scipy.sparse

        mat = scipy.sparse.eye(5, 4, format='csc')

        with pytest.raises(ValueError, match="not '4'"):
            SparseArray.from_spmatrix(mat)

    @pytest.mark.parametrize('scalar,dtype', [
        (False, SparseDtype(bool, False)),
        (0.0, SparseDtype('float64', 0)),
        (1, SparseDtype('int64', 1)),
        ('z', SparseDtype('object', 'z'))])
    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

    @pytest.mark.parametrize("fill", [1, np.nan, 0])
    @pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
    def test_sparse_series_round_trip(self, kind, fill):
        # see gh-13999
        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)

    @pytest.mark.parametrize("fill", [True, False, np.nan])
    @pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
    def test_sparse_series_round_trip2(self, kind, fill):
        # see gh-13999
        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)

    def test_get_item(self):

        assert np.isnan(self.arr[1])
        assert self.arr[2] == 1
        assert self.arr[7] == 5

        assert self.zarr[0] == 0
        assert self.zarr[2] == 1
        assert self.zarr[7] == 5

        errmsg = re.compile("bounds")

        with pytest.raises(IndexError, match=errmsg):
            self.arr[11]

        with pytest.raises(IndexError, match=errmsg):
            self.arr[-11]

        assert self.arr[-1] == self.arr[len(self.arr) - 1]

    def test_take_scalar_raises(self):
        msg = "'indices' must be an array, not a scalar '2'."
        with pytest.raises(ValueError, match=msg):
            self.arr.take(2)

    def test_take(self):
        exp = SparseArray(np.take(self.arr_data, [2, 3]))
        tm.assert_sp_array_equal(self.arr.take([2, 3]), exp)

        exp = SparseArray(np.take(self.arr_data, [0, 1, 2]))
        tm.assert_sp_array_equal(self.arr.take([0, 1, 2]), exp)

    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)

    def test_take_negative(self):
        exp = SparseArray(np.take(self.arr_data, [-1]))
        tm.assert_sp_array_equal(self.arr.take([-1]), exp)

        exp = SparseArray(np.take(self.arr_data, [-4, -3, -2]))
        tm.assert_sp_array_equal(self.arr.take([-4, -3, -2]), exp)

    @pytest.mark.parametrize('fill_value', [0, None, np.nan])
    def test_shift_fill_value(self, fill_value):
        # GH #24128
        sparse = SparseArray(np.array([1, 0, 0, 3, 0]),
                             fill_value=8.0)
        res = sparse.shift(1, fill_value=fill_value)
        if isna(fill_value):
            fill_value = res.dtype.na_value
        exp = SparseArray(np.array([fill_value, 1, 0, 0, 3]),
                          fill_value=8.0)
        tm.assert_sp_array_equal(res, exp)

    def test_bad_take(self):
        with pytest.raises(IndexError, match="bounds"):
            self.arr.take([11])

    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)

        # XXX: test change: fill_value=True -> allow_fill=True
        result = sparse.take(np.array([1, 0, -1]), allow_fill=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 = "Invalid value in 'indices'"
        with pytest.raises(ValueError, match=msg):
            sparse.take(np.array([1, 0, -2]), allow_fill=True)

        with pytest.raises(ValueError, match=msg):
            sparse.take(np.array([1, 0, -5]), allow_fill=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]), allow_fill=True)

    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]), allow_fill=True)
        # XXX: behavior change.
        # the old way of filling self.fill_value doesn't follow EA rules.
        # It's supposed to be self.dtype.na_value (nan in this case)
        expected = SparseArray([0, np.nan, np.nan], 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 = ("Invalid value in 'indices'.")
        with pytest.raises(ValueError, match=msg):
            sparse.take(np.array([1, 0, -2]), allow_fill=True)
        with pytest.raises(ValueError, match=msg):
            sparse.take(np.array([1, 0, -5]), allow_fill=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)

    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)

    def test_set_item(self):
        def setitem():
            self.arr[5] = 3

        def setslice():
            self.arr[1:5] = 2

        with pytest.raises(TypeError, match="assignment via setitem"):
            setitem()

        with pytest.raises(TypeError, match="assignment via setitem"):
            setslice()

    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):
        res = SparseArray(self.zarr)
        assert res.fill_value == 0
        assert_almost_equal(res.sp_values, self.zarr.sp_values)

    def test_constructor_copy(self):
        cp = SparseArray(self.arr, copy=True)
        cp.sp_values[:3] = 0
        assert not (self.arr.sp_values[:3] == 0).any()

        not_copy = SparseArray(self.arr)
        not_copy.sp_values[:3] = 0
        assert (self.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))

        for dense in [arr.to_dense(), arr.values]:
            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., 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))

        for dense in [arr.to_dense(), arr.values]:
            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., 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]')

    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)

    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.values),
                                    vals.astype(typ))

    @pytest.mark.parametrize('array, dtype, expected', [
        (SparseArray([0, 1]), 'float',
         SparseArray([0., 1.], 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., np.nan, 2.], 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_shallow(self):
        arr2 = self.arr.copy(deep=False)
        assert arr2.sp_values is self.arr.sp_values
        assert arr2.sp_index is self.arr.sp_index

    def test_values_asarray(self):
        assert_almost_equal(self.arr.values, self.arr_data)
        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,), np.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("method", ["to_dense", "get_values"])
    @pytest.mark.parametrize("fill_value", [None, 0])
    def test_dense_repr(self, vals, fill_value, method):
        vals = np.array(vals)
        arr = SparseArray(vals, fill_value=fill_value)
        dense_func = getattr(arr, method)

        res = dense_func()
        tm.assert_numpy_array_equal(res, vals)

    def test_getitem(self):
        def _checkit(i):
            assert_almost_equal(self.arr[i], self.arr.values[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.values[:-3])
        tm.assert_sp_array_equal(result, exp)

        result = self.arr[-4:]
        exp = SparseArray(self.arr.values[-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.values[: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[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:, :]

    def test_boolean_slice_empty(self):
        arr = pd.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.values, second.values),
                              fill_value=first.fill_value)
            assert isinstance(res, SparseArray)
            assert_almost_equal(res.values, exp.values)

            res2 = op(first, second.values)
            assert isinstance(res2, SparseArray)
            tm.assert_sp_array_equal(res, res2)

            res3 = op(first.values, 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.values, 4)
                exp_fv = op(first.fill_value, 4)
            except ValueError:
                pass
            else:
                assert_almost_equal(res4.fill_value, exp_fv)
                assert_almost_equal(res4.values, 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.])
        res = s.fillna(-1)
        exp = SparseArray([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 = pd.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 = pd.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)
    ])
    @td.skip_if_np_lt_115  # prior didn't dispatch
    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)
    ])
    @td.skip_if_np_lt_115  # prior didn't dispatch
    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

    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 = "axis\\(={axis}\\) out of bounds".format(axis=axis)
            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)

    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, blenghts
        assert result == 24

    def test_asarray_datetime64(self):
        s = pd.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


@pytest.mark.filterwarnings("ignore:Sparse:FutureWarning")
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]
        sp_array = scipy.sparse.coo_matrix((data, (row, col)))
        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., 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 = pd.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)