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491 lines
18 KiB
491 lines
18 KiB
import collections
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import operator
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import sys
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import numpy as np
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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 pandas.tests.extension.json.array import (
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JSONArray,
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JSONDtype,
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make_data,
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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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unhashable = pytest.mark.xfail(reason="Unhashable")
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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_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 TestJSONArray(base.ExtensionTests):
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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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@pytest.mark.xfail(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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super().test_from_dtype(data)
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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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rec_limit = sys.getrecursionlimit()
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try:
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# Limit to avoid stack overflow on Windows CI
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sys.setrecursionlimit(100)
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super().test_series_constructor_no_data_with_index(dtype, na_value)
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finally:
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sys.setrecursionlimit(rec_limit)
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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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rec_limit = sys.getrecursionlimit()
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try:
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# Limit to avoid stack overflow on Windows CI
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sys.setrecursionlimit(100)
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super().test_series_constructor_scalar_na_with_index(dtype, na_value)
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finally:
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sys.setrecursionlimit(rec_limit)
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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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rec_limit = sys.getrecursionlimit()
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try:
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# Limit to avoid stack overflow on Windows CI
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sys.setrecursionlimit(100)
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super().test_series_constructor_scalar_with_index(data, dtype)
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finally:
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sys.setrecursionlimit(rec_limit)
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@pytest.mark.xfail(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(future_stack=True). 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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super().test_stack()
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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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@pytest.mark.xfail(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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super().test_fillna_series()
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@pytest.mark.xfail(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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super().test_fillna_frame()
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@pytest.mark.parametrize(
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"limit_area, input_ilocs, expected_ilocs",
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[
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("outside", [1, 0, 0, 0, 1], [1, 0, 0, 0, 1]),
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("outside", [1, 0, 1, 0, 1], [1, 0, 1, 0, 1]),
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("outside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 1]),
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("outside", [0, 1, 0, 1, 0], [0, 1, 0, 1, 1]),
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("inside", [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),
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("inside", [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]),
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("inside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 0]),
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("inside", [0, 1, 0, 1, 0], [0, 1, 1, 1, 0]),
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],
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)
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def test_ffill_limit_area(
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self, data_missing, limit_area, input_ilocs, expected_ilocs
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):
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# GH#56616
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msg = "JSONArray does not implement limit_area"
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with pytest.raises(NotImplementedError, match=msg):
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super().test_ffill_limit_area(
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data_missing, limit_area, input_ilocs, expected_ilocs
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)
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@unhashable
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def test_value_counts(self, all_data, dropna):
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super().test_value_counts(all_data, dropna)
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@unhashable
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def test_value_counts_with_normalize(self, data):
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super().test_value_counts_with_normalize(data)
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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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super().test_sort_values_frame()
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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.xfail(reason="combine for JSONArray not supported")
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def test_combine_le(self, data_repeated):
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super().test_combine_le(data_repeated)
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@pytest.mark.xfail(
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reason="combine for JSONArray not supported - "
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"may pass depending on random data",
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strict=False,
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raises=AssertionError,
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)
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def test_combine_first(self, data):
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super().test_combine_first(data)
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@pytest.mark.xfail(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.xfail(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.xfail(reason="Can't compare dicts.")
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def test_equals(self, data, na_value, as_series):
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super().test_equals(data, na_value, as_series)
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@pytest.mark.skip("fill-value is interpreted as a dict of values")
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def test_fillna_copy_frame(self, data_missing):
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super().test_fillna_copy_frame(data_missing)
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def test_equals_same_data_different_object(
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self, data, using_copy_on_write, request
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):
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if using_copy_on_write:
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mark = pytest.mark.xfail(reason="Fails with CoW")
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request.applymarker(mark)
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super().test_equals_same_data_different_object(data)
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@pytest.mark.xfail(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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super().test_astype_str()
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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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super().test_groupby_extension_transform()
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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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super().test_groupby_extension_apply()
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@unhashable
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def test_groupby_extension_agg(self):
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"""
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This fails when we get to tm.assert_series_equal when left.index
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contains dictionaries, which are not hashable.
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"""
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super().test_groupby_extension_agg()
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@unhashable
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def test_groupby_extension_no_sort(self):
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"""
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This fails when we get to tm.assert_series_equal when left.index
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contains dictionaries, which are not hashable.
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"""
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super().test_groupby_extension_no_sort()
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def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
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if len(data[0]) != 1:
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mark = pytest.mark.xfail(reason="raises in coercing to Series")
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request.applymarker(mark)
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super().test_arith_frame_with_scalar(data, all_arithmetic_operators)
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def test_compare_array(self, data, comparison_op, request):
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if comparison_op.__name__ in ["eq", "ne"]:
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mark = pytest.mark.xfail(reason="Comparison methods not implemented")
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request.applymarker(mark)
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super().test_compare_array(data, comparison_op)
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@pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
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def test_setitem_loc_scalar_mixed(self, data):
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super().test_setitem_loc_scalar_mixed(data)
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@pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
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def test_setitem_loc_scalar_multiple_homogoneous(self, data):
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super().test_setitem_loc_scalar_multiple_homogoneous(data)
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@pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
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def test_setitem_iloc_scalar_mixed(self, data):
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super().test_setitem_iloc_scalar_mixed(data)
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@pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
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def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
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super().test_setitem_iloc_scalar_multiple_homogoneous(data)
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@pytest.mark.parametrize(
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"mask",
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[
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np.array([True, True, True, False, False]),
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pd.array([True, True, True, False, False], dtype="boolean"),
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pd.array([True, True, True, pd.NA, pd.NA], dtype="boolean"),
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],
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ids=["numpy-array", "boolean-array", "boolean-array-na"],
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)
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def test_setitem_mask(self, data, mask, box_in_series, request):
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if box_in_series:
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mark = pytest.mark.xfail(
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reason="cannot set using a list-like indexer with a different length"
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)
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request.applymarker(mark)
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elif not isinstance(mask, np.ndarray):
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mark = pytest.mark.xfail(reason="Issues unwanted DeprecationWarning")
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request.applymarker(mark)
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super().test_setitem_mask(data, mask, box_in_series)
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def test_setitem_mask_raises(self, data, box_in_series, request):
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if not box_in_series:
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mark = pytest.mark.xfail(reason="Fails to raise")
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request.applymarker(mark)
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super().test_setitem_mask_raises(data, box_in_series)
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@pytest.mark.xfail(
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reason="cannot set using a list-like indexer with a different length"
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)
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def test_setitem_mask_boolean_array_with_na(self, data, box_in_series):
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super().test_setitem_mask_boolean_array_with_na(data, box_in_series)
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@pytest.mark.parametrize(
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"idx",
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[[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
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ids=["list", "integer-array", "numpy-array"],
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)
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def test_setitem_integer_array(self, data, idx, box_in_series, request):
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if box_in_series:
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mark = pytest.mark.xfail(
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reason="cannot set using a list-like indexer with a different length"
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)
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request.applymarker(mark)
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super().test_setitem_integer_array(data, idx, box_in_series)
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@pytest.mark.xfail(reason="list indices must be integers or slices, not NAType")
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@pytest.mark.parametrize(
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"idx, box_in_series",
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[
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([0, 1, 2, pd.NA], False),
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pytest.param(
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[0, 1, 2, pd.NA], True, marks=pytest.mark.xfail(reason="GH-31948")
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),
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(pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
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(pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
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],
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ids=["list-False", "list-True", "integer-array-False", "integer-array-True"],
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)
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def test_setitem_integer_with_missing_raises(self, data, idx, box_in_series):
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super().test_setitem_integer_with_missing_raises(data, idx, box_in_series)
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@pytest.mark.xfail(reason="Fails to raise")
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def test_setitem_scalar_key_sequence_raise(self, data):
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super().test_setitem_scalar_key_sequence_raise(data)
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def test_setitem_with_expansion_dataframe_column(self, data, full_indexer, request):
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if "full_slice" in request.node.name:
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mark = pytest.mark.xfail(reason="slice is not iterable")
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request.applymarker(mark)
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super().test_setitem_with_expansion_dataframe_column(data, full_indexer)
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@pytest.mark.xfail(reason="slice is not iterable")
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def test_setitem_frame_2d_values(self, data):
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super().test_setitem_frame_2d_values(data)
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@pytest.mark.xfail(
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reason="cannot set using a list-like indexer with a different length"
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)
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@pytest.mark.parametrize("setter", ["loc", None])
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def test_setitem_mask_broadcast(self, data, setter):
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super().test_setitem_mask_broadcast(data, setter)
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@pytest.mark.xfail(
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reason="cannot set using a slice indexer with a different length"
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)
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def test_setitem_slice(self, data, box_in_series):
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super().test_setitem_slice(data, box_in_series)
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@pytest.mark.xfail(reason="slice object is not iterable")
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def test_setitem_loc_iloc_slice(self, data):
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super().test_setitem_loc_iloc_slice(data)
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@pytest.mark.xfail(reason="slice object is not iterable")
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def test_setitem_slice_mismatch_length_raises(self, data):
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super().test_setitem_slice_mismatch_length_raises(data)
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@pytest.mark.xfail(reason="slice object is not iterable")
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def test_setitem_slice_array(self, data):
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super().test_setitem_slice_array(data)
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@pytest.mark.xfail(reason="Fail to raise")
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def test_setitem_invalid(self, data, invalid_scalar):
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super().test_setitem_invalid(data, invalid_scalar)
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@pytest.mark.xfail(reason="only integer scalar arrays can be converted")
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def test_setitem_2d_values(self, data):
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super().test_setitem_2d_values(data)
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@pytest.mark.xfail(reason="data type 'json' not understood")
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@pytest.mark.parametrize("engine", ["c", "python"])
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def test_EA_types(self, engine, data, request):
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super().test_EA_types(engine, data, request)
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|
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def custom_assert_series_equal(left, right, *args, **kwargs):
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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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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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def custom_assert_frame_equal(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
|
|
|
|
for col in jsons:
|
|
custom_assert_series_equal(left[col], right[col], *args, **kwargs)
|
|
|
|
left = left.drop(columns=jsons)
|
|
right = right.drop(columns=jsons)
|
|
tm.assert_frame_equal(left, right, *args, **kwargs)
|
|
|
|
|
|
def test_custom_asserts():
|
|
# This would always trigger the KeyError from trying to put
|
|
# an array of equal-length UserDicts inside an ndarray.
|
|
data = JSONArray(
|
|
[
|
|
collections.UserDict({"a": 1}),
|
|
collections.UserDict({"b": 2}),
|
|
collections.UserDict({"c": 3}),
|
|
]
|
|
)
|
|
a = pd.Series(data)
|
|
custom_assert_series_equal(a, a)
|
|
custom_assert_frame_equal(a.to_frame(), a.to_frame())
|
|
|
|
b = pd.Series(data.take([0, 0, 1]))
|
|
msg = r"Series are different"
|
|
with pytest.raises(AssertionError, match=msg):
|
|
custom_assert_series_equal(a, b)
|
|
|
|
with pytest.raises(AssertionError, match=msg):
|
|
custom_assert_frame_equal(a.to_frame(), b.to_frame())
|