345 lines
11 KiB
Python
345 lines
11 KiB
Python
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import collections
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import operator
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import pytest
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import pandas as pd
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import pandas._testing as tm
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from pandas.tests.extension import base
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from .array import JSONArray, JSONDtype, make_data
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@pytest.fixture
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def dtype():
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return JSONDtype()
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@pytest.fixture
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def data():
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"""Length-100 PeriodArray for semantics test."""
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data = make_data()
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# Why the while loop? NumPy is unable to construct an ndarray from
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# equal-length ndarrays. Many of our operations involve coercing the
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# EA to an ndarray of objects. To avoid random test failures, we ensure
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# that our data is coercible to an ndarray. Several tests deal with only
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# the first two elements, so that's what we'll check.
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while len(data[0]) == len(data[1]):
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data = make_data()
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return JSONArray(data)
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@pytest.fixture
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def data_missing():
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"""Length 2 array with [NA, Valid]"""
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return JSONArray([{}, {"a": 10}])
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@pytest.fixture
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def data_for_sorting():
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return JSONArray([{"b": 1}, {"c": 4}, {"a": 2, "c": 3}])
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@pytest.fixture
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def data_missing_for_sorting():
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return JSONArray([{"b": 1}, {}, {"a": 4}])
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@pytest.fixture
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def na_value(dtype):
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return dtype.na_value
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@pytest.fixture
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def na_cmp():
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return operator.eq
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@pytest.fixture
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def data_for_grouping():
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return JSONArray(
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[
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{"b": 1},
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{"b": 1},
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{},
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{},
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{"a": 0, "c": 2},
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{"a": 0, "c": 2},
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{"b": 1},
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{"c": 2},
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]
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)
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class BaseJSON:
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# NumPy doesn't handle an array of equal-length UserDicts.
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# The default assert_series_equal eventually does a
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# Series.values, which raises. We work around it by
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# converting the UserDicts to dicts.
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@classmethod
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def assert_series_equal(cls, left, right, *args, **kwargs):
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if left.dtype.name == "json":
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assert left.dtype == right.dtype
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left = pd.Series(
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JSONArray(left.values.astype(object)), index=left.index, name=left.name
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)
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right = pd.Series(
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JSONArray(right.values.astype(object)),
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index=right.index,
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name=right.name,
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)
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tm.assert_series_equal(left, right, *args, **kwargs)
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@classmethod
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def assert_frame_equal(cls, left, right, *args, **kwargs):
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obj_type = kwargs.get("obj", "DataFrame")
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tm.assert_index_equal(
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left.columns,
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right.columns,
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exact=kwargs.get("check_column_type", "equiv"),
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check_names=kwargs.get("check_names", True),
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check_exact=kwargs.get("check_exact", False),
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check_categorical=kwargs.get("check_categorical", True),
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obj=f"{obj_type}.columns",
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)
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jsons = (left.dtypes == "json").index
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for col in jsons:
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cls.assert_series_equal(left[col], right[col], *args, **kwargs)
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left = left.drop(columns=jsons)
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right = right.drop(columns=jsons)
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tm.assert_frame_equal(left, right, *args, **kwargs)
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class TestDtype(BaseJSON, base.BaseDtypeTests):
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pass
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class TestInterface(BaseJSON, base.BaseInterfaceTests):
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def test_custom_asserts(self):
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# This would always trigger the KeyError from trying to put
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# an array of equal-length UserDicts inside an ndarray.
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data = JSONArray(
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[
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collections.UserDict({"a": 1}),
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collections.UserDict({"b": 2}),
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collections.UserDict({"c": 3}),
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]
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)
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a = pd.Series(data)
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self.assert_series_equal(a, a)
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self.assert_frame_equal(a.to_frame(), a.to_frame())
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b = pd.Series(data.take([0, 0, 1]))
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msg = r"ExtensionArray are different"
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with pytest.raises(AssertionError, match=msg):
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self.assert_series_equal(a, b)
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with pytest.raises(AssertionError, match=msg):
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self.assert_frame_equal(a.to_frame(), b.to_frame())
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@pytest.mark.xfail(
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reason="comparison method not implemented for JSONArray (GH-37867)"
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)
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def test_contains(self, data):
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# GH-37867
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super().test_contains(data)
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class TestConstructors(BaseJSON, base.BaseConstructorsTests):
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@pytest.mark.skip(reason="not implemented constructor from dtype")
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def test_from_dtype(self, data):
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# construct from our dtype & string dtype
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pass
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@pytest.mark.xfail(reason="RecursionError, GH-33900")
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def test_series_constructor_no_data_with_index(self, dtype, na_value):
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# RecursionError: maximum recursion depth exceeded in comparison
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super().test_series_constructor_no_data_with_index(dtype, na_value)
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@pytest.mark.xfail(reason="RecursionError, GH-33900")
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def test_series_constructor_scalar_na_with_index(self, dtype, na_value):
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# RecursionError: maximum recursion depth exceeded in comparison
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super().test_series_constructor_scalar_na_with_index(dtype, na_value)
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@pytest.mark.xfail(reason="collection as scalar, GH-33901")
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def test_series_constructor_scalar_with_index(self, data, dtype):
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# TypeError: All values must be of type <class 'collections.abc.Mapping'>
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super().test_series_constructor_scalar_with_index(data, dtype)
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class TestReshaping(BaseJSON, base.BaseReshapingTests):
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@pytest.mark.skip(reason="Different definitions of NA")
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def test_stack(self):
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"""
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The test does .astype(object).stack(). If we happen to have
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any missing values in `data`, then we'll end up with different
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rows since we consider `{}` NA, but `.astype(object)` doesn't.
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"""
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@pytest.mark.xfail(reason="dict for NA")
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def test_unstack(self, data, index):
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# The base test has NaN for the expected NA value.
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# this matches otherwise
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return super().test_unstack(data, index)
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class TestGetitem(BaseJSON, base.BaseGetitemTests):
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pass
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class TestMissing(BaseJSON, base.BaseMissingTests):
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@pytest.mark.skip(reason="Setting a dict as a scalar")
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def test_fillna_series(self):
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"""We treat dictionaries as a mapping in fillna, not a scalar."""
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@pytest.mark.skip(reason="Setting a dict as a scalar")
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def test_fillna_frame(self):
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"""We treat dictionaries as a mapping in fillna, not a scalar."""
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unhashable = pytest.mark.skip(reason="Unhashable")
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class TestReduce(base.BaseNoReduceTests):
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pass
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class TestMethods(BaseJSON, base.BaseMethodsTests):
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@unhashable
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def test_value_counts(self, all_data, dropna):
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pass
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@unhashable
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def test_value_counts_with_normalize(self, data):
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pass
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@unhashable
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def test_sort_values_frame(self):
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# TODO (EA.factorize): see if _values_for_factorize allows this.
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pass
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def test_argsort(self, data_for_sorting):
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super().test_argsort(data_for_sorting)
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def test_argsort_missing(self, data_missing_for_sorting):
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super().test_argsort_missing(data_missing_for_sorting)
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@pytest.mark.parametrize("ascending", [True, False])
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def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
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super().test_sort_values(data_for_sorting, ascending, sort_by_key)
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@pytest.mark.parametrize("ascending", [True, False])
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def test_sort_values_missing(
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self, data_missing_for_sorting, ascending, sort_by_key
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):
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super().test_sort_values_missing(
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data_missing_for_sorting, ascending, sort_by_key
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)
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@pytest.mark.skip(reason="combine for JSONArray not supported")
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def test_combine_le(self, data_repeated):
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pass
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@pytest.mark.skip(reason="combine for JSONArray not supported")
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def test_combine_add(self, data_repeated):
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pass
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@pytest.mark.skip(reason="combine for JSONArray not supported")
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def test_combine_first(self, data):
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pass
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@unhashable
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def test_hash_pandas_object_works(self, data, kind):
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super().test_hash_pandas_object_works(data, kind)
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@pytest.mark.skip(reason="broadcasting error")
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def test_where_series(self, data, na_value):
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# Fails with
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# *** ValueError: operands could not be broadcast together
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# with shapes (4,) (4,) (0,)
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super().test_where_series(data, na_value)
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@pytest.mark.skip(reason="Can't compare dicts.")
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def test_searchsorted(self, data_for_sorting):
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super().test_searchsorted(data_for_sorting)
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@pytest.mark.skip(reason="Can't compare dicts.")
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def test_equals(self, data, na_value, as_series):
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pass
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class TestCasting(BaseJSON, base.BaseCastingTests):
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@pytest.mark.skip(reason="failing on np.array(self, dtype=str)")
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def test_astype_str(self):
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"""This currently fails in NumPy on np.array(self, dtype=str) with
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*** ValueError: setting an array element with a sequence
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"""
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# We intentionally don't run base.BaseSetitemTests because pandas'
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# internals has trouble setting sequences of values into scalar positions.
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class TestGroupby(BaseJSON, base.BaseGroupbyTests):
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@unhashable
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def test_groupby_extension_transform(self):
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"""
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This currently fails in Series.name.setter, since the
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name must be hashable, but the value is a dictionary.
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I think this is what we want, i.e. `.name` should be the original
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values, and not the values for factorization.
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"""
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@unhashable
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def test_groupby_extension_apply(self):
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"""
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This fails in Index._do_unique_check with
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> hash(val)
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E TypeError: unhashable type: 'UserDict' with
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I suspect that once we support Index[ExtensionArray],
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we'll be able to dispatch unique.
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"""
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@pytest.mark.parametrize("as_index", [True, False])
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def test_groupby_extension_agg(self, as_index, data_for_grouping):
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super().test_groupby_extension_agg(as_index, data_for_grouping)
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@pytest.mark.xfail(reason="GH#39098: Converts agg result to object")
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def test_groupby_agg_extension(self, data_for_grouping):
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super().test_groupby_agg_extension(data_for_grouping)
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class TestArithmeticOps(BaseJSON, base.BaseArithmeticOpsTests):
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def test_error(self, data, all_arithmetic_operators):
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pass
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def test_add_series_with_extension_array(self, data):
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ser = pd.Series(data)
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with pytest.raises(TypeError, match="unsupported"):
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ser + data
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def test_divmod_series_array(self):
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# GH 23287
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# skipping because it is not implemented
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pass
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def _check_divmod_op(self, s, op, other, exc=NotImplementedError):
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return super()._check_divmod_op(s, op, other, exc=TypeError)
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class TestComparisonOps(BaseJSON, base.BaseComparisonOpsTests):
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pass
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class TestPrinting(BaseJSON, base.BasePrintingTests):
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pass
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