import re
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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 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 (
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SparseArray,
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SparseDtype,
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)
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@pytest.fixture
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def arr_data():
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"""Fixture returning numpy array with valid and missing entries"""
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return np.array([np.nan, np.nan, 1, 2, 3, np.nan, 4, 5, np.nan, 6])
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@pytest.fixture
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def arr(arr_data):
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"""Fixture returning SparseArray from 'arr_data'"""
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return SparseArray(arr_data)
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@pytest.fixture
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def zarr():
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"""Fixture returning SparseArray with integer entries and 'fill_value=0'"""
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return SparseArray([0, 0, 1, 2, 3, 0, 4, 5, 0, 6], fill_value=0)
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class TestSparseArray:
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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_set_fill_value(self):
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arr = SparseArray([1.0, np.nan, 2.0], fill_value=np.nan)
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arr.fill_value = 2
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assert arr.fill_value == 2
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arr = SparseArray([1, 0, 2], fill_value=0, dtype=np.int64)
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arr.fill_value = 2
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assert arr.fill_value == 2
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# TODO: this seems fine? You can construct an integer
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# sparsearray with NaN fill value, why not update one?
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# coerces to int
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# msg = "unable to set fill_value 3\\.1 to int64 dtype"
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# with pytest.raises(ValueError, match=msg):
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arr.fill_value = 3.1
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assert arr.fill_value == 3.1
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# msg = "unable to set fill_value nan to int64 dtype"
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# with pytest.raises(ValueError, match=msg):
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arr.fill_value = np.nan
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assert np.isnan(arr.fill_value)
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arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
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arr.fill_value = True
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assert arr.fill_value
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# FIXME: don't leave commented-out
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# coerces to bool
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# TODO: we can construct an sparse array of bool
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# type and use as fill_value any value
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# msg = "fill_value must be True, False or nan"
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# with pytest.raises(ValueError, match=msg):
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# arr.fill_value = 0
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# msg = "unable to set fill_value nan to bool dtype"
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# with pytest.raises(ValueError, match=msg):
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arr.fill_value = np.nan
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assert np.isnan(arr.fill_value)
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@pytest.mark.parametrize("val", [[1, 2, 3], np.array([1, 2]), (1, 2, 3)])
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def test_set_fill_invalid_non_scalar(self, val):
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arr = SparseArray([True, False, True], fill_value=False, dtype=np.bool_)
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msg = "fill_value must be a scalar"
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with pytest.raises(ValueError, match=msg):
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arr.fill_value = val
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def test_copy(self, arr):
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arr2 = arr.copy()
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assert arr2.sp_values is not arr.sp_values
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assert arr2.sp_index is arr.sp_index
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def test_values_asarray(self, arr_data, arr):
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tm.assert_almost_equal(arr.to_dense(), arr_data)
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@pytest.mark.parametrize(
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"data,shape,dtype",
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[
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([0, 0, 0, 0, 0], (5,), None),
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([], (0,), None),
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([0], (1,), None),
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(["A", "A", np.nan, "B"], (4,), object),
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],
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)
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def test_shape(self, data, shape, dtype):
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# GH 21126
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out = SparseArray(data, dtype=dtype)
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assert out.shape == shape
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@pytest.mark.parametrize(
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"vals",
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[
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[np.nan, np.nan, np.nan, np.nan, np.nan],
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[1, np.nan, np.nan, 3, np.nan],
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[1, np.nan, 0, 3, 0],
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],
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)
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@pytest.mark.parametrize("fill_value", [None, 0])
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def test_dense_repr(self, vals, fill_value):
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vals = np.array(vals)
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arr = SparseArray(vals, fill_value=fill_value)
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res = arr.to_dense()
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tm.assert_numpy_array_equal(res, vals)
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@pytest.mark.parametrize("fix", ["arr", "zarr"])
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def test_pickle(self, fix, request):
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obj = request.getfixturevalue(fix)
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unpickled = tm.round_trip_pickle(obj)
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tm.assert_sp_array_equal(unpickled, obj)
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def test_generator_warnings(self):
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sp_arr = SparseArray([1, 2, 3])
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with tm.assert_produces_warning(None):
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for _ in sp_arr:
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pass
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def test_where_retain_fill_value(self):
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# GH#45691 don't lose fill_value on _where
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arr = SparseArray([np.nan, 1.0], fill_value=0)
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mask = np.array([True, False])
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res = arr._where(~mask, 1)
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exp = SparseArray([1, 1.0], fill_value=0)
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tm.assert_sp_array_equal(res, exp)
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ser = pd.Series(arr)
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res = ser.where(~mask, 1)
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tm.assert_series_equal(res, pd.Series(exp))
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def test_fillna(self):
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s = SparseArray([1, np.nan, np.nan, 3, np.nan])
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res = s.fillna(-1)
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exp = SparseArray([1, -1, -1, 3, -1], fill_value=-1, dtype=np.float64)
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tm.assert_sp_array_equal(res, exp)
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s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
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res = s.fillna(-1)
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exp = SparseArray([1, -1, -1, 3, -1], fill_value=0, dtype=np.float64)
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tm.assert_sp_array_equal(res, exp)
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s = SparseArray([1, np.nan, 0, 3, 0])
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res = s.fillna(-1)
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exp = SparseArray([1, -1, 0, 3, 0], fill_value=-1, dtype=np.float64)
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tm.assert_sp_array_equal(res, exp)
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s = SparseArray([1, np.nan, 0, 3, 0], fill_value=0)
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res = s.fillna(-1)
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exp = SparseArray([1, -1, 0, 3, 0], fill_value=0, dtype=np.float64)
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tm.assert_sp_array_equal(res, exp)
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s = SparseArray([np.nan, np.nan, np.nan, np.nan])
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res = s.fillna(-1)
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exp = SparseArray([-1, -1, -1, -1], fill_value=-1, dtype=np.float64)
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tm.assert_sp_array_equal(res, exp)
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s = SparseArray([np.nan, np.nan, np.nan, np.nan], fill_value=0)
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res = s.fillna(-1)
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exp = SparseArray([-1, -1, -1, -1], fill_value=0, dtype=np.float64)
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tm.assert_sp_array_equal(res, exp)
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# float dtype's fill_value is np.nan, replaced by -1
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s = SparseArray([0.0, 0.0, 0.0, 0.0])
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res = s.fillna(-1)
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exp = SparseArray([0.0, 0.0, 0.0, 0.0], fill_value=-1)
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tm.assert_sp_array_equal(res, exp)
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# int dtype shouldn't have missing. No changes.
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s = SparseArray([0, 0, 0, 0])
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assert s.dtype == SparseDtype(np.int64)
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assert s.fill_value == 0
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res = s.fillna(-1)
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tm.assert_sp_array_equal(res, s)
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s = SparseArray([0, 0, 0, 0], fill_value=0)
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assert s.dtype == SparseDtype(np.int64)
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assert s.fill_value == 0
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res = s.fillna(-1)
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exp = SparseArray([0, 0, 0, 0], fill_value=0)
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tm.assert_sp_array_equal(res, exp)
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# fill_value can be nan if there is no missing hole.
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# only fill_value will be changed
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s = SparseArray([0, 0, 0, 0], fill_value=np.nan)
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assert s.dtype == SparseDtype(np.int64, fill_value=np.nan)
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assert np.isnan(s.fill_value)
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res = s.fillna(-1)
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exp = SparseArray([0, 0, 0, 0], fill_value=-1)
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tm.assert_sp_array_equal(res, exp)
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def test_fillna_overlap(self):
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s = SparseArray([1, np.nan, np.nan, 3, np.nan])
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# filling with existing value doesn't replace existing value with
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# fill_value, i.e. existing 3 remains in sp_values
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res = s.fillna(3)
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exp = np.array([1, 3, 3, 3, 3], dtype=np.float64)
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tm.assert_numpy_array_equal(res.to_dense(), exp)
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s = SparseArray([1, np.nan, np.nan, 3, np.nan], fill_value=0)
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res = s.fillna(3)
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exp = SparseArray([1, 3, 3, 3, 3], fill_value=0, dtype=np.float64)
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tm.assert_sp_array_equal(res, exp)
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def test_nonzero(self):
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# Tests regression #21172.
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sa = SparseArray([float("nan"), float("nan"), 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
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expected = np.array([2, 5, 9], dtype=np.int32)
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(result,) = sa.nonzero()
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tm.assert_numpy_array_equal(expected, result)
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sa = SparseArray([0, 0, 1, 0, 0, 2, 0, 0, 0, 3, 0, 0])
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(result,) = sa.nonzero()
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tm.assert_numpy_array_equal(expected, result)
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class TestSparseArrayAnalytics:
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@pytest.mark.parametrize(
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"data,expected",
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[
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(
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np.array([1, 2, 3, 4, 5], dtype=float), # non-null data
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SparseArray(np.array([1.0, 3.0, 6.0, 10.0, 15.0])),
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),
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(
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np.array([1, 2, np.nan, 4, 5], dtype=float), # null data
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SparseArray(np.array([1.0, 3.0, np.nan, 7.0, 12.0])),
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),
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],
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)
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@pytest.mark.parametrize("numpy", [True, False])
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def test_cumsum(self, data, expected, numpy):
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cumsum = np.cumsum if numpy else lambda s: s.cumsum()
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out = cumsum(SparseArray(data))
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tm.assert_sp_array_equal(out, expected)
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out = cumsum(SparseArray(data, fill_value=np.nan))
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tm.assert_sp_array_equal(out, expected)
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out = cumsum(SparseArray(data, fill_value=2))
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tm.assert_sp_array_equal(out, expected)
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if numpy: # numpy compatibility checks.
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msg = "the 'dtype' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.cumsum(SparseArray(data), dtype=np.int64)
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msg = "the 'out' parameter is not supported"
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with pytest.raises(ValueError, match=msg):
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np.cumsum(SparseArray(data), out=out)
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else:
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axis = 1 # SparseArray currently 1-D, so only axis = 0 is valid.
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msg = re.escape(f"axis(={axis}) out of bounds")
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with pytest.raises(ValueError, match=msg):
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SparseArray(data).cumsum(axis=axis)
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def test_ufunc(self):
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# GH 13853 make sure ufunc is applied to fill_value
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sparse = SparseArray([1, np.nan, 2, np.nan, -2])
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result = SparseArray([1, np.nan, 2, np.nan, 2])
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tm.assert_sp_array_equal(abs(sparse), result)
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tm.assert_sp_array_equal(np.abs(sparse), result)
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sparse = SparseArray([1, -1, 2, -2], fill_value=1)
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result = SparseArray([1, 2, 2], sparse_index=sparse.sp_index, fill_value=1)
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tm.assert_sp_array_equal(abs(sparse), result)
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tm.assert_sp_array_equal(np.abs(sparse), result)
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sparse = SparseArray([1, -1, 2, -2], fill_value=-1)
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exp = SparseArray([1, 1, 2, 2], fill_value=1)
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tm.assert_sp_array_equal(abs(sparse), exp)
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tm.assert_sp_array_equal(np.abs(sparse), exp)
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sparse = SparseArray([1, np.nan, 2, np.nan, -2])
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result = SparseArray(np.sin([1, np.nan, 2, np.nan, -2]))
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tm.assert_sp_array_equal(np.sin(sparse), result)
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sparse = SparseArray([1, -1, 2, -2], fill_value=1)
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result = SparseArray(np.sin([1, -1, 2, -2]), fill_value=np.sin(1))
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tm.assert_sp_array_equal(np.sin(sparse), result)
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sparse = SparseArray([1, -1, 0, -2], fill_value=0)
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result = SparseArray(np.sin([1, -1, 0, -2]), fill_value=np.sin(0))
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tm.assert_sp_array_equal(np.sin(sparse), result)
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def test_ufunc_args(self):
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# GH 13853 make sure ufunc is applied to fill_value, including its arg
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sparse = SparseArray([1, np.nan, 2, np.nan, -2])
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result = SparseArray([2, np.nan, 3, np.nan, -1])
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tm.assert_sp_array_equal(np.add(sparse, 1), result)
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sparse = SparseArray([1, -1, 2, -2], fill_value=1)
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result = SparseArray([2, 0, 3, -1], fill_value=2)
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tm.assert_sp_array_equal(np.add(sparse, 1), result)
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sparse = SparseArray([1, -1, 0, -2], fill_value=0)
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result = SparseArray([2, 0, 1, -1], fill_value=1)
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tm.assert_sp_array_equal(np.add(sparse, 1), result)
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@pytest.mark.parametrize("fill_value", [0.0, np.nan])
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def test_modf(self, fill_value):
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# https://github.com/pandas-dev/pandas/issues/26946
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sparse = SparseArray([fill_value] * 10 + [1.1, 2.2], fill_value=fill_value)
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r1, r2 = np.modf(sparse)
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e1, e2 = np.modf(np.asarray(sparse))
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tm.assert_sp_array_equal(r1, SparseArray(e1, fill_value=fill_value))
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tm.assert_sp_array_equal(r2, SparseArray(e2, fill_value=fill_value))
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def test_nbytes_integer(self):
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arr = SparseArray([1, 0, 0, 0, 2], kind="integer")
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result = arr.nbytes
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# (2 * 8) + 2 * 4
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assert result == 24
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def test_nbytes_block(self):
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arr = SparseArray([1, 2, 0, 0, 0], kind="block")
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result = arr.nbytes
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# (2 * 8) + 4 + 4
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# sp_values, blocs, blengths
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assert result == 24
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def test_asarray_datetime64(self):
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s = SparseArray(pd.to_datetime(["2012", None, None, "2013"]))
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np.asarray(s)
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def test_density(self):
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arr = SparseArray([0, 1])
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assert arr.density == 0.5
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def test_npoints(self):
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arr = SparseArray([0, 1])
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assert arr.npoints == 1
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def test_setting_fill_value_fillna_still_works():
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# This is why letting users update fill_value / dtype is bad
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# astype has the same problem.
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arr = SparseArray([1.0, np.nan, 1.0], fill_value=0.0)
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arr.fill_value = np.nan
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result = arr.isna()
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# Can't do direct comparison, since the sp_index will be different
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# So let's convert to ndarray and check there.
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result = np.asarray(result)
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expected = np.array([False, True, False])
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tm.assert_numpy_array_equal(result, expected)
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def test_setting_fill_value_updates():
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arr = SparseArray([0.0, np.nan], fill_value=0)
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arr.fill_value = np.nan
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# use private constructor to get the index right
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# otherwise both nans would be un-stored.
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expected = SparseArray._simple_new(
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sparse_array=np.array([np.nan]),
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sparse_index=IntIndex(2, [1]),
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dtype=SparseDtype(float, np.nan),
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)
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tm.assert_sp_array_equal(arr, expected)
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@pytest.mark.parametrize(
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"arr,fill_value,loc",
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[
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([None, 1, 2], None, 0),
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([0, None, 2], None, 1),
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([0, 1, None], None, 2),
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([0, 1, 1, None, None], None, 3),
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([1, 1, 1, 2], None, -1),
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([], None, -1),
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([None, 1, 0, 0, None, 2], None, 0),
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([None, 1, 0, 0, None, 2], 1, 1),
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([None, 1, 0, 0, None, 2], 2, 5),
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([None, 1, 0, 0, None, 2], 3, -1),
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([None, 0, 0, 1, 2, 1], 0, 1),
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([None, 0, 0, 1, 2, 1], 1, 3),
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],
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)
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def test_first_fill_value_loc(arr, fill_value, loc):
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result = SparseArray(arr, fill_value=fill_value)._first_fill_value_loc()
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assert result == loc
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@pytest.mark.parametrize(
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"arr",
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[
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[1, 2, np.nan, np.nan],
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[1, np.nan, 2, np.nan],
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[1, 2, np.nan],
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[np.nan, 1, 0, 0, np.nan, 2],
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[np.nan, 0, 0, 1, 2, 1],
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],
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)
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@pytest.mark.parametrize("fill_value", [np.nan, 0, 1])
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def test_unique_na_fill(arr, fill_value):
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a = SparseArray(arr, fill_value=fill_value).unique()
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b = pd.Series(arr).unique()
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assert isinstance(a, SparseArray)
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a = np.asarray(a)
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tm.assert_numpy_array_equal(a, b)
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def test_unique_all_sparse():
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# https://github.com/pandas-dev/pandas/issues/23168
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arr = SparseArray([0, 0])
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result = arr.unique()
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expected = SparseArray([0])
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tm.assert_sp_array_equal(result, expected)
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|
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def test_map():
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arr = SparseArray([0, 1, 2])
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expected = SparseArray([10, 11, 12], fill_value=10)
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# dict
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result = arr.map({0: 10, 1: 11, 2: 12})
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tm.assert_sp_array_equal(result, expected)
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# series
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result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
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tm.assert_sp_array_equal(result, expected)
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# function
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result = arr.map(pd.Series({0: 10, 1: 11, 2: 12}))
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expected = SparseArray([10, 11, 12], fill_value=10)
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tm.assert_sp_array_equal(result, expected)
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|
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def test_map_missing():
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arr = SparseArray([0, 1, 2])
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expected = SparseArray([10, 11, None], fill_value=10)
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result = arr.map({0: 10, 1: 11})
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tm.assert_sp_array_equal(result, expected)
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|
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@pytest.mark.parametrize("fill_value", [np.nan, 1])
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def test_dropna(fill_value):
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# GH-28287
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arr = SparseArray([np.nan, 1], fill_value=fill_value)
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exp = SparseArray([1.0], fill_value=fill_value)
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tm.assert_sp_array_equal(arr.dropna(), exp)
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df = pd.DataFrame({"a": [0, 1], "b": arr})
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expected_df = pd.DataFrame({"a": [1], "b": exp}, index=pd.Index([1]))
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tm.assert_equal(df.dropna(), expected_df)
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|
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def test_drop_duplicates_fill_value():
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# GH 11726
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df = pd.DataFrame(np.zeros((5, 5))).apply(lambda x: SparseArray(x, fill_value=0))
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result = df.drop_duplicates()
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expected = pd.DataFrame({i: SparseArray([0.0], fill_value=0) for i in range(5)})
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tm.assert_frame_equal(result, expected)
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