499 lines
17 KiB
Python
499 lines
17 KiB
Python
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"""
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This file contains a minimal set of tests for compliance with the extension
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array interface test suite, and should contain no other tests.
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The test suite for the full functionality of the array is located in
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`pandas/tests/arrays/`.
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The tests in this file are inherited from the BaseExtensionTests, and only
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minimal tweaks should be applied to get the tests passing (by overwriting a
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parent method).
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Additional tests should either be added to one of the BaseExtensionTests
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classes (if they are relevant for the extension interface for all dtypes), or
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be added to the array-specific tests in `pandas/tests/arrays/`.
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"""
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import numpy as np
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import pytest
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from pandas.errors import PerformanceWarning
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import pandas as pd
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from pandas import SparseDtype
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import pandas._testing as tm
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from pandas.arrays import SparseArray
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from pandas.tests.extension import base
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def make_data(fill_value):
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rng = np.random.default_rng(2)
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if np.isnan(fill_value):
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data = rng.uniform(size=100)
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else:
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data = rng.integers(1, 100, size=100, dtype=int)
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if data[0] == data[1]:
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data[0] += 1
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data[2::3] = fill_value
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return data
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@pytest.fixture
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def dtype():
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return SparseDtype()
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@pytest.fixture(params=[0, np.nan])
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def data(request):
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"""Length-100 PeriodArray for semantics test."""
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res = SparseArray(make_data(request.param), fill_value=request.param)
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return res
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@pytest.fixture
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def data_for_twos():
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return SparseArray(np.ones(100) * 2)
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@pytest.fixture(params=[0, np.nan])
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def data_missing(request):
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"""Length 2 array with [NA, Valid]"""
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return SparseArray([np.nan, 1], fill_value=request.param)
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@pytest.fixture(params=[0, np.nan])
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def data_repeated(request):
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"""Return different versions of data for count times"""
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def gen(count):
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for _ in range(count):
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yield SparseArray(make_data(request.param), fill_value=request.param)
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yield gen
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@pytest.fixture(params=[0, np.nan])
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def data_for_sorting(request):
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return SparseArray([2, 3, 1], fill_value=request.param)
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@pytest.fixture(params=[0, np.nan])
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def data_missing_for_sorting(request):
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return SparseArray([2, np.nan, 1], fill_value=request.param)
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@pytest.fixture
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def na_cmp():
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return lambda left, right: pd.isna(left) and pd.isna(right)
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@pytest.fixture(params=[0, np.nan])
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def data_for_grouping(request):
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return SparseArray([1, 1, np.nan, np.nan, 2, 2, 1, 3], fill_value=request.param)
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@pytest.fixture(params=[0, np.nan])
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def data_for_compare(request):
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return SparseArray([0, 0, np.nan, -2, -1, 4, 2, 3, 0, 0], fill_value=request.param)
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class TestSparseArray(base.ExtensionTests):
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def _supports_reduction(self, obj, op_name: str) -> bool:
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return True
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@pytest.mark.parametrize("skipna", [True, False])
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def test_reduce_series_numeric(self, data, all_numeric_reductions, skipna, request):
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if all_numeric_reductions in [
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"prod",
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"median",
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"var",
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"std",
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"sem",
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"skew",
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"kurt",
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]:
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mark = pytest.mark.xfail(
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reason="This should be viable but is not implemented"
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)
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request.node.add_marker(mark)
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elif (
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all_numeric_reductions in ["sum", "max", "min", "mean"]
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and data.dtype.kind == "f"
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and not skipna
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):
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mark = pytest.mark.xfail(reason="getting a non-nan float")
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request.node.add_marker(mark)
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super().test_reduce_series_numeric(data, all_numeric_reductions, skipna)
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@pytest.mark.parametrize("skipna", [True, False])
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def test_reduce_frame(self, data, all_numeric_reductions, skipna, request):
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if all_numeric_reductions in [
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"prod",
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"median",
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"var",
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"std",
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"sem",
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"skew",
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"kurt",
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]:
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mark = pytest.mark.xfail(
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reason="This should be viable but is not implemented"
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)
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request.node.add_marker(mark)
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elif (
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all_numeric_reductions in ["sum", "max", "min", "mean"]
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and data.dtype.kind == "f"
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and not skipna
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):
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mark = pytest.mark.xfail(reason="ExtensionArray NA mask are different")
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request.node.add_marker(mark)
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super().test_reduce_frame(data, all_numeric_reductions, skipna)
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def _check_unsupported(self, data):
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if data.dtype == SparseDtype(int, 0):
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pytest.skip("Can't store nan in int array.")
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def test_concat_mixed_dtypes(self, data):
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# https://github.com/pandas-dev/pandas/issues/20762
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# This should be the same, aside from concat([sparse, float])
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df1 = pd.DataFrame({"A": data[:3]})
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df2 = pd.DataFrame({"A": [1, 2, 3]})
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df3 = pd.DataFrame({"A": ["a", "b", "c"]}).astype("category")
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dfs = [df1, df2, df3]
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# dataframes
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result = pd.concat(dfs)
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expected = pd.concat(
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[x.apply(lambda s: np.asarray(s).astype(object)) for x in dfs]
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)
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tm.assert_frame_equal(result, expected)
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@pytest.mark.filterwarnings(
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"ignore:The previous implementation of stack is deprecated"
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)
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@pytest.mark.parametrize(
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"columns",
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[
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["A", "B"],
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pd.MultiIndex.from_tuples(
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[("A", "a"), ("A", "b")], names=["outer", "inner"]
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),
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],
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)
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@pytest.mark.parametrize("future_stack", [True, False])
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def test_stack(self, data, columns, future_stack):
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super().test_stack(data, columns, future_stack)
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def test_concat_columns(self, data, na_value):
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self._check_unsupported(data)
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super().test_concat_columns(data, na_value)
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def test_concat_extension_arrays_copy_false(self, data, na_value):
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self._check_unsupported(data)
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super().test_concat_extension_arrays_copy_false(data, na_value)
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def test_align(self, data, na_value):
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self._check_unsupported(data)
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super().test_align(data, na_value)
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def test_align_frame(self, data, na_value):
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self._check_unsupported(data)
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super().test_align_frame(data, na_value)
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def test_align_series_frame(self, data, na_value):
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self._check_unsupported(data)
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super().test_align_series_frame(data, na_value)
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def test_merge(self, data, na_value):
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self._check_unsupported(data)
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super().test_merge(data, na_value)
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def test_get(self, data):
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ser = pd.Series(data, index=[2 * i for i in range(len(data))])
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if np.isnan(ser.values.fill_value):
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assert np.isnan(ser.get(4)) and np.isnan(ser.iloc[2])
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else:
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assert ser.get(4) == ser.iloc[2]
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assert ser.get(2) == ser.iloc[1]
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def test_reindex(self, data, na_value):
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self._check_unsupported(data)
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super().test_reindex(data, na_value)
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def test_isna(self, data_missing):
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sarr = SparseArray(data_missing)
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expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value))
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expected = SparseArray([True, False], dtype=expected_dtype)
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result = sarr.isna()
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tm.assert_sp_array_equal(result, expected)
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# test isna for arr without na
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sarr = sarr.fillna(0)
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expected_dtype = SparseDtype(bool, pd.isna(data_missing.dtype.fill_value))
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expected = SparseArray([False, False], fill_value=False, dtype=expected_dtype)
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tm.assert_equal(sarr.isna(), expected)
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def test_fillna_limit_backfill(self, data_missing):
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warns = (PerformanceWarning, FutureWarning)
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with tm.assert_produces_warning(warns, check_stacklevel=False):
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super().test_fillna_limit_backfill(data_missing)
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def test_fillna_no_op_returns_copy(self, data, request):
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if np.isnan(data.fill_value):
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request.applymarker(
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pytest.mark.xfail(reason="returns array with different fill value")
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)
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super().test_fillna_no_op_returns_copy(data)
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@pytest.mark.xfail(reason="Unsupported")
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def test_fillna_series(self, data_missing):
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# this one looks doable.
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# TODO: this fails bc we do not pass through data_missing. If we did,
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# the 0-fill case would xpass
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super().test_fillna_series()
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def test_fillna_frame(self, data_missing):
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# Have to override to specify that fill_value will change.
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fill_value = data_missing[1]
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result = pd.DataFrame({"A": data_missing, "B": [1, 2]}).fillna(fill_value)
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if pd.isna(data_missing.fill_value):
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dtype = SparseDtype(data_missing.dtype, fill_value)
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else:
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dtype = data_missing.dtype
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expected = pd.DataFrame(
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{
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"A": data_missing._from_sequence([fill_value, fill_value], dtype=dtype),
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"B": [1, 2],
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}
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)
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tm.assert_frame_equal(result, expected)
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_combine_le_expected_dtype = "Sparse[bool]"
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def test_fillna_copy_frame(self, data_missing, using_copy_on_write):
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arr = data_missing.take([1, 1])
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df = pd.DataFrame({"A": arr}, copy=False)
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filled_val = df.iloc[0, 0]
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result = df.fillna(filled_val)
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if hasattr(df._mgr, "blocks"):
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if using_copy_on_write:
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assert df.values.base is result.values.base
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else:
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assert df.values.base is not result.values.base
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assert df.A._values.to_dense() is arr.to_dense()
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def test_fillna_copy_series(self, data_missing, using_copy_on_write):
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arr = data_missing.take([1, 1])
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ser = pd.Series(arr, copy=False)
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filled_val = ser[0]
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result = ser.fillna(filled_val)
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if using_copy_on_write:
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assert ser._values is result._values
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else:
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assert ser._values is not result._values
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assert ser._values.to_dense() is arr.to_dense()
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@pytest.mark.xfail(reason="Not Applicable")
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def test_fillna_length_mismatch(self, data_missing):
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super().test_fillna_length_mismatch(data_missing)
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def test_where_series(self, data, na_value):
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assert data[0] != data[1]
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cls = type(data)
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a, b = data[:2]
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ser = pd.Series(cls._from_sequence([a, a, b, b], dtype=data.dtype))
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cond = np.array([True, True, False, False])
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result = ser.where(cond)
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new_dtype = SparseDtype("float", 0.0)
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expected = pd.Series(
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cls._from_sequence([a, a, na_value, na_value], dtype=new_dtype)
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)
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tm.assert_series_equal(result, expected)
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other = cls._from_sequence([a, b, a, b], dtype=data.dtype)
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cond = np.array([True, False, True, True])
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result = ser.where(cond, other)
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expected = pd.Series(cls._from_sequence([a, b, b, b], dtype=data.dtype))
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tm.assert_series_equal(result, expected)
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def test_searchsorted(self, data_for_sorting, as_series):
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with tm.assert_produces_warning(PerformanceWarning, check_stacklevel=False):
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super().test_searchsorted(data_for_sorting, as_series)
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def test_shift_0_periods(self, data):
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# GH#33856 shifting with periods=0 should return a copy, not same obj
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result = data.shift(0)
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data._sparse_values[0] = data._sparse_values[1]
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assert result._sparse_values[0] != result._sparse_values[1]
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@pytest.mark.parametrize("method", ["argmax", "argmin"])
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def test_argmin_argmax_all_na(self, method, data, na_value):
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# overriding because Sparse[int64, 0] cannot handle na_value
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self._check_unsupported(data)
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super().test_argmin_argmax_all_na(method, data, na_value)
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@pytest.mark.parametrize("box", [pd.array, pd.Series, pd.DataFrame])
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def test_equals(self, data, na_value, as_series, box):
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self._check_unsupported(data)
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super().test_equals(data, na_value, as_series, box)
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@pytest.mark.parametrize(
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"func, na_action, expected",
|
||
|
|
[
|
||
|
|
(lambda x: x, None, SparseArray([1.0, np.nan])),
|
||
|
|
(lambda x: x, "ignore", SparseArray([1.0, np.nan])),
|
||
|
|
(str, None, SparseArray(["1.0", "nan"], fill_value="nan")),
|
||
|
|
(str, "ignore", SparseArray(["1.0", np.nan])),
|
||
|
|
],
|
||
|
|
)
|
||
|
|
def test_map(self, func, na_action, expected):
|
||
|
|
# GH52096
|
||
|
|
data = SparseArray([1, np.nan])
|
||
|
|
result = data.map(func, na_action=na_action)
|
||
|
|
tm.assert_extension_array_equal(result, expected)
|
||
|
|
|
||
|
|
@pytest.mark.parametrize("na_action", [None, "ignore"])
|
||
|
|
def test_map_raises(self, data, na_action):
|
||
|
|
# GH52096
|
||
|
|
msg = "fill value in the sparse values not supported"
|
||
|
|
with pytest.raises(ValueError, match=msg):
|
||
|
|
data.map(lambda x: np.nan, na_action=na_action)
|
||
|
|
|
||
|
|
@pytest.mark.xfail(raises=TypeError, reason="no sparse StringDtype")
|
||
|
|
def test_astype_string(self, data, nullable_string_dtype):
|
||
|
|
# TODO: this fails bc we do not pass through nullable_string_dtype;
|
||
|
|
# If we did, the 0-cases would xpass
|
||
|
|
super().test_astype_string(data)
|
||
|
|
|
||
|
|
series_scalar_exc = None
|
||
|
|
frame_scalar_exc = None
|
||
|
|
divmod_exc = None
|
||
|
|
series_array_exc = None
|
||
|
|
|
||
|
|
def _skip_if_different_combine(self, data):
|
||
|
|
if data.fill_value == 0:
|
||
|
|
# arith ops call on dtype.fill_value so that the sparsity
|
||
|
|
# is maintained. Combine can't be called on a dtype in
|
||
|
|
# general, so we can't make the expected. This is tested elsewhere
|
||
|
|
pytest.skip("Incorrected expected from Series.combine and tested elsewhere")
|
||
|
|
|
||
|
|
def test_arith_series_with_scalar(self, data, all_arithmetic_operators):
|
||
|
|
self._skip_if_different_combine(data)
|
||
|
|
super().test_arith_series_with_scalar(data, all_arithmetic_operators)
|
||
|
|
|
||
|
|
def test_arith_series_with_array(self, data, all_arithmetic_operators):
|
||
|
|
self._skip_if_different_combine(data)
|
||
|
|
super().test_arith_series_with_array(data, all_arithmetic_operators)
|
||
|
|
|
||
|
|
def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
|
||
|
|
if data.dtype.fill_value != 0:
|
||
|
|
pass
|
||
|
|
elif all_arithmetic_operators.strip("_") not in [
|
||
|
|
"mul",
|
||
|
|
"rmul",
|
||
|
|
"floordiv",
|
||
|
|
"rfloordiv",
|
||
|
|
"pow",
|
||
|
|
"mod",
|
||
|
|
"rmod",
|
||
|
|
]:
|
||
|
|
mark = pytest.mark.xfail(reason="result dtype.fill_value mismatch")
|
||
|
|
request.applymarker(mark)
|
||
|
|
super().test_arith_frame_with_scalar(data, all_arithmetic_operators)
|
||
|
|
|
||
|
|
def _compare_other(
|
||
|
|
self, ser: pd.Series, data_for_compare: SparseArray, comparison_op, other
|
||
|
|
):
|
||
|
|
op = comparison_op
|
||
|
|
|
||
|
|
result = op(data_for_compare, other)
|
||
|
|
if isinstance(other, pd.Series):
|
||
|
|
assert isinstance(result, pd.Series)
|
||
|
|
assert isinstance(result.dtype, SparseDtype)
|
||
|
|
else:
|
||
|
|
assert isinstance(result, SparseArray)
|
||
|
|
assert result.dtype.subtype == np.bool_
|
||
|
|
|
||
|
|
if isinstance(other, pd.Series):
|
||
|
|
fill_value = op(data_for_compare.fill_value, other._values.fill_value)
|
||
|
|
expected = SparseArray(
|
||
|
|
op(data_for_compare.to_dense(), np.asarray(other)),
|
||
|
|
fill_value=fill_value,
|
||
|
|
dtype=np.bool_,
|
||
|
|
)
|
||
|
|
|
||
|
|
else:
|
||
|
|
fill_value = np.all(
|
||
|
|
op(np.asarray(data_for_compare.fill_value), np.asarray(other))
|
||
|
|
)
|
||
|
|
|
||
|
|
expected = SparseArray(
|
||
|
|
op(data_for_compare.to_dense(), np.asarray(other)),
|
||
|
|
fill_value=fill_value,
|
||
|
|
dtype=np.bool_,
|
||
|
|
)
|
||
|
|
if isinstance(other, pd.Series):
|
||
|
|
# error: Incompatible types in assignment
|
||
|
|
expected = pd.Series(expected) # type: ignore[assignment]
|
||
|
|
tm.assert_equal(result, expected)
|
||
|
|
|
||
|
|
def test_scalar(self, data_for_compare: SparseArray, comparison_op):
|
||
|
|
ser = pd.Series(data_for_compare)
|
||
|
|
self._compare_other(ser, data_for_compare, comparison_op, 0)
|
||
|
|
self._compare_other(ser, data_for_compare, comparison_op, 1)
|
||
|
|
self._compare_other(ser, data_for_compare, comparison_op, -1)
|
||
|
|
self._compare_other(ser, data_for_compare, comparison_op, np.nan)
|
||
|
|
|
||
|
|
def test_array(self, data_for_compare: SparseArray, comparison_op, request):
|
||
|
|
if data_for_compare.dtype.fill_value == 0 and comparison_op.__name__ in [
|
||
|
|
"eq",
|
||
|
|
"ge",
|
||
|
|
"le",
|
||
|
|
]:
|
||
|
|
mark = pytest.mark.xfail(reason="Wrong fill_value")
|
||
|
|
request.applymarker(mark)
|
||
|
|
|
||
|
|
arr = np.linspace(-4, 5, 10)
|
||
|
|
ser = pd.Series(data_for_compare)
|
||
|
|
self._compare_other(ser, data_for_compare, comparison_op, arr)
|
||
|
|
|
||
|
|
def test_sparse_array(self, data_for_compare: SparseArray, comparison_op, request):
|
||
|
|
if data_for_compare.dtype.fill_value == 0 and comparison_op.__name__ != "gt":
|
||
|
|
mark = pytest.mark.xfail(reason="Wrong fill_value")
|
||
|
|
request.applymarker(mark)
|
||
|
|
|
||
|
|
ser = pd.Series(data_for_compare)
|
||
|
|
arr = data_for_compare + 1
|
||
|
|
self._compare_other(ser, data_for_compare, comparison_op, arr)
|
||
|
|
arr = data_for_compare * 2
|
||
|
|
self._compare_other(ser, data_for_compare, comparison_op, arr)
|
||
|
|
|
||
|
|
@pytest.mark.xfail(reason="Different repr")
|
||
|
|
def test_array_repr(self, data, size):
|
||
|
|
super().test_array_repr(data, size)
|
||
|
|
|
||
|
|
@pytest.mark.xfail(reason="result does not match expected")
|
||
|
|
@pytest.mark.parametrize("as_index", [True, False])
|
||
|
|
def test_groupby_extension_agg(self, as_index, data_for_grouping):
|
||
|
|
super().test_groupby_extension_agg(as_index, data_for_grouping)
|
||
|
|
|
||
|
|
|
||
|
|
def test_array_type_with_arg(dtype):
|
||
|
|
assert dtype.construct_array_type() is SparseArray
|