|
|
""" |
|
|
This file contains a minimal set of tests for compliance with the extension |
|
|
array interface test suite, and should contain no other tests. |
|
|
The test suite for the full functionality of the array is located in |
|
|
`pandas/tests/arrays/`. |
|
|
|
|
|
The tests in this file are inherited from the BaseExtensionTests, and only |
|
|
minimal tweaks should be applied to get the tests passing (by overwriting a |
|
|
parent method). |
|
|
|
|
|
Additional tests should either be added to one of the BaseExtensionTests |
|
|
classes (if they are relevant for the extension interface for all dtypes), or |
|
|
be added to the array-specific tests in `pandas/tests/arrays/`. |
|
|
|
|
|
""" |
|
|
import numpy as np |
|
|
import pytest |
|
|
|
|
|
from pandas.core.dtypes.dtypes import DatetimeTZDtype |
|
|
|
|
|
import pandas as pd |
|
|
import pandas._testing as tm |
|
|
from pandas.core.arrays import DatetimeArray |
|
|
from pandas.tests.extension import base |
|
|
|
|
|
|
|
|
@pytest.fixture(params=["US/Central"]) |
|
|
def dtype(request): |
|
|
return DatetimeTZDtype(unit="ns", tz=request.param) |
|
|
|
|
|
|
|
|
@pytest.fixture |
|
|
def data(dtype): |
|
|
data = DatetimeArray._from_sequence( |
|
|
pd.date_range("2000", periods=100, tz=dtype.tz), dtype=dtype |
|
|
) |
|
|
return data |
|
|
|
|
|
|
|
|
@pytest.fixture |
|
|
def data_missing(dtype): |
|
|
return DatetimeArray._from_sequence( |
|
|
np.array(["NaT", "2000-01-01"], dtype="datetime64[ns]"), dtype=dtype |
|
|
) |
|
|
|
|
|
|
|
|
@pytest.fixture |
|
|
def data_for_sorting(dtype): |
|
|
a = pd.Timestamp("2000-01-01") |
|
|
b = pd.Timestamp("2000-01-02") |
|
|
c = pd.Timestamp("2000-01-03") |
|
|
return DatetimeArray._from_sequence( |
|
|
np.array([b, c, a], dtype="datetime64[ns]"), dtype=dtype |
|
|
) |
|
|
|
|
|
|
|
|
@pytest.fixture |
|
|
def data_missing_for_sorting(dtype): |
|
|
a = pd.Timestamp("2000-01-01") |
|
|
b = pd.Timestamp("2000-01-02") |
|
|
return DatetimeArray._from_sequence( |
|
|
np.array([b, "NaT", a], dtype="datetime64[ns]"), dtype=dtype |
|
|
) |
|
|
|
|
|
|
|
|
@pytest.fixture |
|
|
def data_for_grouping(dtype): |
|
|
""" |
|
|
Expected to be like [B, B, NA, NA, A, A, B, C] |
|
|
|
|
|
Where A < B < C and NA is missing |
|
|
""" |
|
|
a = pd.Timestamp("2000-01-01") |
|
|
b = pd.Timestamp("2000-01-02") |
|
|
c = pd.Timestamp("2000-01-03") |
|
|
na = "NaT" |
|
|
return DatetimeArray._from_sequence( |
|
|
np.array([b, b, na, na, a, a, b, c], dtype="datetime64[ns]"), dtype=dtype |
|
|
) |
|
|
|
|
|
|
|
|
@pytest.fixture |
|
|
def na_cmp(): |
|
|
def cmp(a, b): |
|
|
return a is pd.NaT and a is b |
|
|
|
|
|
return cmp |
|
|
|
|
|
|
|
|
|
|
|
class TestDatetimeArray(base.ExtensionTests): |
|
|
def _get_expected_exception(self, op_name, obj, other): |
|
|
if op_name in ["__sub__", "__rsub__"]: |
|
|
return None |
|
|
return super()._get_expected_exception(op_name, obj, other) |
|
|
|
|
|
def _supports_accumulation(self, ser, op_name: str) -> bool: |
|
|
return op_name in ["cummin", "cummax"] |
|
|
|
|
|
def _supports_reduction(self, obj, op_name: str) -> bool: |
|
|
return op_name in ["min", "max", "median", "mean", "std", "any", "all"] |
|
|
|
|
|
@pytest.mark.parametrize("skipna", [True, False]) |
|
|
def test_reduce_series_boolean(self, data, all_boolean_reductions, skipna): |
|
|
meth = all_boolean_reductions |
|
|
msg = f"'{meth}' with datetime64 dtypes is deprecated and will raise in" |
|
|
with tm.assert_produces_warning( |
|
|
FutureWarning, match=msg, check_stacklevel=False |
|
|
): |
|
|
super().test_reduce_series_boolean(data, all_boolean_reductions, skipna) |
|
|
|
|
|
def test_series_constructor(self, data): |
|
|
|
|
|
data = data._with_freq(None) |
|
|
super().test_series_constructor(data) |
|
|
|
|
|
@pytest.mark.parametrize("na_action", [None, "ignore"]) |
|
|
def test_map(self, data, na_action): |
|
|
result = data.map(lambda x: x, na_action=na_action) |
|
|
tm.assert_extension_array_equal(result, data) |
|
|
|
|
|
def check_reduce(self, ser: pd.Series, op_name: str, skipna: bool): |
|
|
if op_name in ["median", "mean", "std"]: |
|
|
alt = ser.astype("int64") |
|
|
|
|
|
res_op = getattr(ser, op_name) |
|
|
exp_op = getattr(alt, op_name) |
|
|
result = res_op(skipna=skipna) |
|
|
expected = exp_op(skipna=skipna) |
|
|
if op_name in ["mean", "median"]: |
|
|
|
|
|
|
|
|
tz = ser.dtype.tz |
|
|
expected = pd.Timestamp(expected, tz=tz) |
|
|
else: |
|
|
expected = pd.Timedelta(expected) |
|
|
tm.assert_almost_equal(result, expected) |
|
|
|
|
|
else: |
|
|
return super().check_reduce(ser, op_name, skipna) |
|
|
|
|
|
|
|
|
class Test2DCompat(base.NDArrayBacked2DTests): |
|
|
pass |
|
|
|
