import collections
|
import operator
|
import sys
|
|
import numpy as np
|
import pytest
|
|
import pandas as pd
|
import pandas._testing as tm
|
from pandas.tests.extension import base
|
from pandas.tests.extension.json.array import (
|
JSONArray,
|
JSONDtype,
|
make_data,
|
)
|
|
# We intentionally don't run base.BaseSetitemTests because pandas'
|
# internals has trouble setting sequences of values into scalar positions.
|
unhashable = pytest.mark.xfail(reason="Unhashable")
|
|
|
@pytest.fixture
|
def dtype():
|
return JSONDtype()
|
|
|
@pytest.fixture
|
def data():
|
"""Length-100 PeriodArray for semantics test."""
|
data = make_data()
|
|
# Why the while loop? NumPy is unable to construct an ndarray from
|
# equal-length ndarrays. Many of our operations involve coercing the
|
# EA to an ndarray of objects. To avoid random test failures, we ensure
|
# that our data is coercible to an ndarray. Several tests deal with only
|
# the first two elements, so that's what we'll check.
|
|
while len(data[0]) == len(data[1]):
|
data = make_data()
|
|
return JSONArray(data)
|
|
|
@pytest.fixture
|
def data_missing():
|
"""Length 2 array with [NA, Valid]"""
|
return JSONArray([{}, {"a": 10}])
|
|
|
@pytest.fixture
|
def data_for_sorting():
|
return JSONArray([{"b": 1}, {"c": 4}, {"a": 2, "c": 3}])
|
|
|
@pytest.fixture
|
def data_missing_for_sorting():
|
return JSONArray([{"b": 1}, {}, {"a": 4}])
|
|
|
@pytest.fixture
|
def na_cmp():
|
return operator.eq
|
|
|
@pytest.fixture
|
def data_for_grouping():
|
return JSONArray(
|
[
|
{"b": 1},
|
{"b": 1},
|
{},
|
{},
|
{"a": 0, "c": 2},
|
{"a": 0, "c": 2},
|
{"b": 1},
|
{"c": 2},
|
]
|
)
|
|
|
class TestJSONArray(base.ExtensionTests):
|
@pytest.mark.xfail(
|
reason="comparison method not implemented for JSONArray (GH-37867)"
|
)
|
def test_contains(self, data):
|
# GH-37867
|
super().test_contains(data)
|
|
@pytest.mark.xfail(reason="not implemented constructor from dtype")
|
def test_from_dtype(self, data):
|
# construct from our dtype & string dtype
|
super().test_from_dtype(data)
|
|
@pytest.mark.xfail(reason="RecursionError, GH-33900")
|
def test_series_constructor_no_data_with_index(self, dtype, na_value):
|
# RecursionError: maximum recursion depth exceeded in comparison
|
rec_limit = sys.getrecursionlimit()
|
try:
|
# Limit to avoid stack overflow on Windows CI
|
sys.setrecursionlimit(100)
|
super().test_series_constructor_no_data_with_index(dtype, na_value)
|
finally:
|
sys.setrecursionlimit(rec_limit)
|
|
@pytest.mark.xfail(reason="RecursionError, GH-33900")
|
def test_series_constructor_scalar_na_with_index(self, dtype, na_value):
|
# RecursionError: maximum recursion depth exceeded in comparison
|
rec_limit = sys.getrecursionlimit()
|
try:
|
# Limit to avoid stack overflow on Windows CI
|
sys.setrecursionlimit(100)
|
super().test_series_constructor_scalar_na_with_index(dtype, na_value)
|
finally:
|
sys.setrecursionlimit(rec_limit)
|
|
@pytest.mark.xfail(reason="collection as scalar, GH-33901")
|
def test_series_constructor_scalar_with_index(self, data, dtype):
|
# TypeError: All values must be of type <class 'collections.abc.Mapping'>
|
rec_limit = sys.getrecursionlimit()
|
try:
|
# Limit to avoid stack overflow on Windows CI
|
sys.setrecursionlimit(100)
|
super().test_series_constructor_scalar_with_index(data, dtype)
|
finally:
|
sys.setrecursionlimit(rec_limit)
|
|
@pytest.mark.xfail(reason="Different definitions of NA")
|
def test_stack(self):
|
"""
|
The test does .astype(object).stack(future_stack=True). If we happen to have
|
any missing values in `data`, then we'll end up with different
|
rows since we consider `{}` NA, but `.astype(object)` doesn't.
|
"""
|
super().test_stack()
|
|
@pytest.mark.xfail(reason="dict for NA")
|
def test_unstack(self, data, index):
|
# The base test has NaN for the expected NA value.
|
# this matches otherwise
|
return super().test_unstack(data, index)
|
|
@pytest.mark.xfail(reason="Setting a dict as a scalar")
|
def test_fillna_series(self):
|
"""We treat dictionaries as a mapping in fillna, not a scalar."""
|
super().test_fillna_series()
|
|
@pytest.mark.xfail(reason="Setting a dict as a scalar")
|
def test_fillna_frame(self):
|
"""We treat dictionaries as a mapping in fillna, not a scalar."""
|
super().test_fillna_frame()
|
|
@pytest.mark.parametrize(
|
"limit_area, input_ilocs, expected_ilocs",
|
[
|
("outside", [1, 0, 0, 0, 1], [1, 0, 0, 0, 1]),
|
("outside", [1, 0, 1, 0, 1], [1, 0, 1, 0, 1]),
|
("outside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 1]),
|
("outside", [0, 1, 0, 1, 0], [0, 1, 0, 1, 1]),
|
("inside", [1, 0, 0, 0, 1], [1, 1, 1, 1, 1]),
|
("inside", [1, 0, 1, 0, 1], [1, 1, 1, 1, 1]),
|
("inside", [0, 1, 1, 1, 0], [0, 1, 1, 1, 0]),
|
("inside", [0, 1, 0, 1, 0], [0, 1, 1, 1, 0]),
|
],
|
)
|
def test_ffill_limit_area(
|
self, data_missing, limit_area, input_ilocs, expected_ilocs
|
):
|
# GH#56616
|
msg = "JSONArray does not implement limit_area"
|
with pytest.raises(NotImplementedError, match=msg):
|
super().test_ffill_limit_area(
|
data_missing, limit_area, input_ilocs, expected_ilocs
|
)
|
|
@unhashable
|
def test_value_counts(self, all_data, dropna):
|
super().test_value_counts(all_data, dropna)
|
|
@unhashable
|
def test_value_counts_with_normalize(self, data):
|
super().test_value_counts_with_normalize(data)
|
|
@unhashable
|
def test_sort_values_frame(self):
|
# TODO (EA.factorize): see if _values_for_factorize allows this.
|
super().test_sort_values_frame()
|
|
@pytest.mark.parametrize("ascending", [True, False])
|
def test_sort_values(self, data_for_sorting, ascending, sort_by_key):
|
super().test_sort_values(data_for_sorting, ascending, sort_by_key)
|
|
@pytest.mark.parametrize("ascending", [True, False])
|
def test_sort_values_missing(
|
self, data_missing_for_sorting, ascending, sort_by_key
|
):
|
super().test_sort_values_missing(
|
data_missing_for_sorting, ascending, sort_by_key
|
)
|
|
@pytest.mark.xfail(reason="combine for JSONArray not supported")
|
def test_combine_le(self, data_repeated):
|
super().test_combine_le(data_repeated)
|
|
@pytest.mark.xfail(
|
reason="combine for JSONArray not supported - "
|
"may pass depending on random data",
|
strict=False,
|
raises=AssertionError,
|
)
|
def test_combine_first(self, data):
|
super().test_combine_first(data)
|
|
@pytest.mark.xfail(reason="broadcasting error")
|
def test_where_series(self, data, na_value):
|
# Fails with
|
# *** ValueError: operands could not be broadcast together
|
# with shapes (4,) (4,) (0,)
|
super().test_where_series(data, na_value)
|
|
@pytest.mark.xfail(reason="Can't compare dicts.")
|
def test_searchsorted(self, data_for_sorting):
|
super().test_searchsorted(data_for_sorting)
|
|
@pytest.mark.xfail(reason="Can't compare dicts.")
|
def test_equals(self, data, na_value, as_series):
|
super().test_equals(data, na_value, as_series)
|
|
@pytest.mark.skip("fill-value is interpreted as a dict of values")
|
def test_fillna_copy_frame(self, data_missing):
|
super().test_fillna_copy_frame(data_missing)
|
|
def test_equals_same_data_different_object(
|
self, data, using_copy_on_write, request
|
):
|
if using_copy_on_write:
|
mark = pytest.mark.xfail(reason="Fails with CoW")
|
request.applymarker(mark)
|
super().test_equals_same_data_different_object(data)
|
|
@pytest.mark.xfail(reason="failing on np.array(self, dtype=str)")
|
def test_astype_str(self):
|
"""This currently fails in NumPy on np.array(self, dtype=str) with
|
|
*** ValueError: setting an array element with a sequence
|
"""
|
super().test_astype_str()
|
|
@unhashable
|
def test_groupby_extension_transform(self):
|
"""
|
This currently fails in Series.name.setter, since the
|
name must be hashable, but the value is a dictionary.
|
I think this is what we want, i.e. `.name` should be the original
|
values, and not the values for factorization.
|
"""
|
super().test_groupby_extension_transform()
|
|
@unhashable
|
def test_groupby_extension_apply(self):
|
"""
|
This fails in Index._do_unique_check with
|
|
> hash(val)
|
E TypeError: unhashable type: 'UserDict' with
|
|
I suspect that once we support Index[ExtensionArray],
|
we'll be able to dispatch unique.
|
"""
|
super().test_groupby_extension_apply()
|
|
@unhashable
|
def test_groupby_extension_agg(self):
|
"""
|
This fails when we get to tm.assert_series_equal when left.index
|
contains dictionaries, which are not hashable.
|
"""
|
super().test_groupby_extension_agg()
|
|
@unhashable
|
def test_groupby_extension_no_sort(self):
|
"""
|
This fails when we get to tm.assert_series_equal when left.index
|
contains dictionaries, which are not hashable.
|
"""
|
super().test_groupby_extension_no_sort()
|
|
def test_arith_frame_with_scalar(self, data, all_arithmetic_operators, request):
|
if len(data[0]) != 1:
|
mark = pytest.mark.xfail(reason="raises in coercing to Series")
|
request.applymarker(mark)
|
super().test_arith_frame_with_scalar(data, all_arithmetic_operators)
|
|
def test_compare_array(self, data, comparison_op, request):
|
if comparison_op.__name__ in ["eq", "ne"]:
|
mark = pytest.mark.xfail(reason="Comparison methods not implemented")
|
request.applymarker(mark)
|
super().test_compare_array(data, comparison_op)
|
|
@pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
|
def test_setitem_loc_scalar_mixed(self, data):
|
super().test_setitem_loc_scalar_mixed(data)
|
|
@pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
|
def test_setitem_loc_scalar_multiple_homogoneous(self, data):
|
super().test_setitem_loc_scalar_multiple_homogoneous(data)
|
|
@pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
|
def test_setitem_iloc_scalar_mixed(self, data):
|
super().test_setitem_iloc_scalar_mixed(data)
|
|
@pytest.mark.xfail(reason="ValueError: Must have equal len keys and value")
|
def test_setitem_iloc_scalar_multiple_homogoneous(self, data):
|
super().test_setitem_iloc_scalar_multiple_homogoneous(data)
|
|
@pytest.mark.parametrize(
|
"mask",
|
[
|
np.array([True, True, True, False, False]),
|
pd.array([True, True, True, False, False], dtype="boolean"),
|
pd.array([True, True, True, pd.NA, pd.NA], dtype="boolean"),
|
],
|
ids=["numpy-array", "boolean-array", "boolean-array-na"],
|
)
|
def test_setitem_mask(self, data, mask, box_in_series, request):
|
if box_in_series:
|
mark = pytest.mark.xfail(
|
reason="cannot set using a list-like indexer with a different length"
|
)
|
request.applymarker(mark)
|
elif not isinstance(mask, np.ndarray):
|
mark = pytest.mark.xfail(reason="Issues unwanted DeprecationWarning")
|
request.applymarker(mark)
|
super().test_setitem_mask(data, mask, box_in_series)
|
|
def test_setitem_mask_raises(self, data, box_in_series, request):
|
if not box_in_series:
|
mark = pytest.mark.xfail(reason="Fails to raise")
|
request.applymarker(mark)
|
|
super().test_setitem_mask_raises(data, box_in_series)
|
|
@pytest.mark.xfail(
|
reason="cannot set using a list-like indexer with a different length"
|
)
|
def test_setitem_mask_boolean_array_with_na(self, data, box_in_series):
|
super().test_setitem_mask_boolean_array_with_na(data, box_in_series)
|
|
@pytest.mark.parametrize(
|
"idx",
|
[[0, 1, 2], pd.array([0, 1, 2], dtype="Int64"), np.array([0, 1, 2])],
|
ids=["list", "integer-array", "numpy-array"],
|
)
|
def test_setitem_integer_array(self, data, idx, box_in_series, request):
|
if box_in_series:
|
mark = pytest.mark.xfail(
|
reason="cannot set using a list-like indexer with a different length"
|
)
|
request.applymarker(mark)
|
super().test_setitem_integer_array(data, idx, box_in_series)
|
|
@pytest.mark.xfail(reason="list indices must be integers or slices, not NAType")
|
@pytest.mark.parametrize(
|
"idx, box_in_series",
|
[
|
([0, 1, 2, pd.NA], False),
|
pytest.param(
|
[0, 1, 2, pd.NA], True, marks=pytest.mark.xfail(reason="GH-31948")
|
),
|
(pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
|
(pd.array([0, 1, 2, pd.NA], dtype="Int64"), False),
|
],
|
ids=["list-False", "list-True", "integer-array-False", "integer-array-True"],
|
)
|
def test_setitem_integer_with_missing_raises(self, data, idx, box_in_series):
|
super().test_setitem_integer_with_missing_raises(data, idx, box_in_series)
|
|
@pytest.mark.xfail(reason="Fails to raise")
|
def test_setitem_scalar_key_sequence_raise(self, data):
|
super().test_setitem_scalar_key_sequence_raise(data)
|
|
def test_setitem_with_expansion_dataframe_column(self, data, full_indexer, request):
|
if "full_slice" in request.node.name:
|
mark = pytest.mark.xfail(reason="slice is not iterable")
|
request.applymarker(mark)
|
super().test_setitem_with_expansion_dataframe_column(data, full_indexer)
|
|
@pytest.mark.xfail(reason="slice is not iterable")
|
def test_setitem_frame_2d_values(self, data):
|
super().test_setitem_frame_2d_values(data)
|
|
@pytest.mark.xfail(
|
reason="cannot set using a list-like indexer with a different length"
|
)
|
@pytest.mark.parametrize("setter", ["loc", None])
|
def test_setitem_mask_broadcast(self, data, setter):
|
super().test_setitem_mask_broadcast(data, setter)
|
|
@pytest.mark.xfail(
|
reason="cannot set using a slice indexer with a different length"
|
)
|
def test_setitem_slice(self, data, box_in_series):
|
super().test_setitem_slice(data, box_in_series)
|
|
@pytest.mark.xfail(reason="slice object is not iterable")
|
def test_setitem_loc_iloc_slice(self, data):
|
super().test_setitem_loc_iloc_slice(data)
|
|
@pytest.mark.xfail(reason="slice object is not iterable")
|
def test_setitem_slice_mismatch_length_raises(self, data):
|
super().test_setitem_slice_mismatch_length_raises(data)
|
|
@pytest.mark.xfail(reason="slice object is not iterable")
|
def test_setitem_slice_array(self, data):
|
super().test_setitem_slice_array(data)
|
|
@pytest.mark.xfail(reason="Fail to raise")
|
def test_setitem_invalid(self, data, invalid_scalar):
|
super().test_setitem_invalid(data, invalid_scalar)
|
|
@pytest.mark.xfail(reason="only integer scalar arrays can be converted")
|
def test_setitem_2d_values(self, data):
|
super().test_setitem_2d_values(data)
|
|
@pytest.mark.xfail(reason="data type 'json' not understood")
|
@pytest.mark.parametrize("engine", ["c", "python"])
|
def test_EA_types(self, engine, data, request):
|
super().test_EA_types(engine, data, request)
|
|
|
def custom_assert_series_equal(left, right, *args, **kwargs):
|
# NumPy doesn't handle an array of equal-length UserDicts.
|
# The default assert_series_equal eventually does a
|
# Series.values, which raises. We work around it by
|
# converting the UserDicts to dicts.
|
if left.dtype.name == "json":
|
assert left.dtype == right.dtype
|
left = pd.Series(
|
JSONArray(left.values.astype(object)), index=left.index, name=left.name
|
)
|
right = pd.Series(
|
JSONArray(right.values.astype(object)),
|
index=right.index,
|
name=right.name,
|
)
|
tm.assert_series_equal(left, right, *args, **kwargs)
|
|
|
def custom_assert_frame_equal(left, right, *args, **kwargs):
|
obj_type = kwargs.get("obj", "DataFrame")
|
tm.assert_index_equal(
|
left.columns,
|
right.columns,
|
exact=kwargs.get("check_column_type", "equiv"),
|
check_names=kwargs.get("check_names", True),
|
check_exact=kwargs.get("check_exact", False),
|
check_categorical=kwargs.get("check_categorical", True),
|
obj=f"{obj_type}.columns",
|
)
|
|
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())
|
