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941 lines
32 KiB
941 lines
32 KiB
from __future__ import annotations
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from datetime import (
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datetime,
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timedelta,
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)
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import itertools
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import numpy as np
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import pytest
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from pandas._config import using_pyarrow_string_dtype
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from pandas.compat import (
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IS64,
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is_platform_windows,
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)
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from pandas.compat.numpy import np_version_gt2
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import pandas as pd
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import pandas._testing as tm
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###############################################################
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# Index / Series common tests which may trigger dtype coercions
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###############################################################
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@pytest.fixture(autouse=True, scope="class")
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def check_comprehensiveness(request):
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# Iterate over combination of dtype, method and klass
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# and ensure that each are contained within a collected test
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cls = request.cls
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combos = itertools.product(cls.klasses, cls.dtypes, [cls.method])
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def has_test(combo):
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klass, dtype, method = combo
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cls_funcs = request.node.session.items
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return any(
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klass in x.name and dtype in x.name and method in x.name for x in cls_funcs
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)
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opts = request.config.option
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if opts.lf or opts.keyword:
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# If we are running with "last-failed" or -k foo, we expect to only
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# run a subset of tests.
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yield
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else:
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for combo in combos:
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if not has_test(combo):
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raise AssertionError(
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f"test method is not defined: {cls.__name__}, {combo}"
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)
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yield
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class CoercionBase:
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klasses = ["index", "series"]
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dtypes = [
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"object",
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"int64",
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"float64",
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"complex128",
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"bool",
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"datetime64",
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"datetime64tz",
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"timedelta64",
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"period",
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]
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@property
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def method(self):
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raise NotImplementedError(self)
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class TestSetitemCoercion(CoercionBase):
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method = "setitem"
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# disable comprehensiveness tests, as most of these have been moved to
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# tests.series.indexing.test_setitem in SetitemCastingEquivalents subclasses.
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klasses: list[str] = []
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def test_setitem_series_no_coercion_from_values_list(self):
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# GH35865 - int casted to str when internally calling np.array(ser.values)
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ser = pd.Series(["a", 1])
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ser[:] = list(ser.values)
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expected = pd.Series(["a", 1])
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tm.assert_series_equal(ser, expected)
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def _assert_setitem_index_conversion(
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self, original_series, loc_key, expected_index, expected_dtype
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):
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"""test index's coercion triggered by assign key"""
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temp = original_series.copy()
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# GH#33469 pre-2.0 with int loc_key and temp.index.dtype == np.float64
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# `temp[loc_key] = 5` treated loc_key as positional
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temp[loc_key] = 5
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exp = pd.Series([1, 2, 3, 4, 5], index=expected_index)
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tm.assert_series_equal(temp, exp)
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# check dtype explicitly for sure
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assert temp.index.dtype == expected_dtype
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temp = original_series.copy()
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temp.loc[loc_key] = 5
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exp = pd.Series([1, 2, 3, 4, 5], index=expected_index)
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tm.assert_series_equal(temp, exp)
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# check dtype explicitly for sure
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assert temp.index.dtype == expected_dtype
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@pytest.mark.parametrize(
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"val,exp_dtype", [("x", object), (5, IndexError), (1.1, object)]
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)
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def test_setitem_index_object(self, val, exp_dtype):
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obj = pd.Series([1, 2, 3, 4], index=pd.Index(list("abcd"), dtype=object))
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assert obj.index.dtype == object
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if exp_dtype is IndexError:
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temp = obj.copy()
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warn_msg = "Series.__setitem__ treating keys as positions is deprecated"
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msg = "index 5 is out of bounds for axis 0 with size 4"
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with pytest.raises(exp_dtype, match=msg):
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with tm.assert_produces_warning(FutureWarning, match=warn_msg):
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temp[5] = 5
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else:
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exp_index = pd.Index(list("abcd") + [val], dtype=object)
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self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype)
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@pytest.mark.parametrize(
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"val,exp_dtype", [(5, np.int64), (1.1, np.float64), ("x", object)]
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)
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def test_setitem_index_int64(self, val, exp_dtype):
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obj = pd.Series([1, 2, 3, 4])
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assert obj.index.dtype == np.int64
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exp_index = pd.Index([0, 1, 2, 3, val])
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self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype)
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@pytest.mark.parametrize(
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"val,exp_dtype", [(5, np.float64), (5.1, np.float64), ("x", object)]
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)
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def test_setitem_index_float64(self, val, exp_dtype, request):
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obj = pd.Series([1, 2, 3, 4], index=[1.1, 2.1, 3.1, 4.1])
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assert obj.index.dtype == np.float64
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exp_index = pd.Index([1.1, 2.1, 3.1, 4.1, val])
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self._assert_setitem_index_conversion(obj, val, exp_index, exp_dtype)
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@pytest.mark.xfail(reason="Test not implemented")
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def test_setitem_series_period(self):
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raise NotImplementedError
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@pytest.mark.xfail(reason="Test not implemented")
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def test_setitem_index_complex128(self):
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raise NotImplementedError
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@pytest.mark.xfail(reason="Test not implemented")
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def test_setitem_index_bool(self):
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raise NotImplementedError
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@pytest.mark.xfail(reason="Test not implemented")
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def test_setitem_index_datetime64(self):
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raise NotImplementedError
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@pytest.mark.xfail(reason="Test not implemented")
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def test_setitem_index_datetime64tz(self):
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raise NotImplementedError
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@pytest.mark.xfail(reason="Test not implemented")
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def test_setitem_index_timedelta64(self):
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raise NotImplementedError
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@pytest.mark.xfail(reason="Test not implemented")
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def test_setitem_index_period(self):
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raise NotImplementedError
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class TestInsertIndexCoercion(CoercionBase):
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klasses = ["index"]
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method = "insert"
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def _assert_insert_conversion(self, original, value, expected, expected_dtype):
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"""test coercion triggered by insert"""
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target = original.copy()
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res = target.insert(1, value)
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tm.assert_index_equal(res, expected)
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assert res.dtype == expected_dtype
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@pytest.mark.parametrize(
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"insert, coerced_val, coerced_dtype",
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[
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(1, 1, object),
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(1.1, 1.1, object),
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(False, False, object),
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("x", "x", object),
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],
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)
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def test_insert_index_object(self, insert, coerced_val, coerced_dtype):
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obj = pd.Index(list("abcd"), dtype=object)
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assert obj.dtype == object
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exp = pd.Index(["a", coerced_val, "b", "c", "d"], dtype=object)
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self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
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@pytest.mark.parametrize(
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"insert, coerced_val, coerced_dtype",
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[
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(1, 1, None),
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(1.1, 1.1, np.float64),
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(False, False, object), # GH#36319
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("x", "x", object),
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],
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)
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def test_insert_int_index(
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self, any_int_numpy_dtype, insert, coerced_val, coerced_dtype
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):
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dtype = any_int_numpy_dtype
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obj = pd.Index([1, 2, 3, 4], dtype=dtype)
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coerced_dtype = coerced_dtype if coerced_dtype is not None else dtype
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exp = pd.Index([1, coerced_val, 2, 3, 4], dtype=coerced_dtype)
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self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
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@pytest.mark.parametrize(
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"insert, coerced_val, coerced_dtype",
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[
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(1, 1.0, None),
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# When float_numpy_dtype=float32, this is not the case
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# see the correction below
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(1.1, 1.1, np.float64),
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(False, False, object), # GH#36319
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("x", "x", object),
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],
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)
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def test_insert_float_index(
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self, float_numpy_dtype, insert, coerced_val, coerced_dtype
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):
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dtype = float_numpy_dtype
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obj = pd.Index([1.0, 2.0, 3.0, 4.0], dtype=dtype)
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coerced_dtype = coerced_dtype if coerced_dtype is not None else dtype
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if np_version_gt2 and dtype == "float32" and coerced_val == 1.1:
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# Hack, in the 2nd test case, since 1.1 can be losslessly cast to float32
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# the expected dtype will be float32 if the original dtype was float32
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coerced_dtype = np.float32
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exp = pd.Index([1.0, coerced_val, 2.0, 3.0, 4.0], dtype=coerced_dtype)
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self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
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@pytest.mark.parametrize(
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"fill_val,exp_dtype",
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[
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(pd.Timestamp("2012-01-01"), "datetime64[ns]"),
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(pd.Timestamp("2012-01-01", tz="US/Eastern"), "datetime64[ns, US/Eastern]"),
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],
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ids=["datetime64", "datetime64tz"],
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)
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@pytest.mark.parametrize(
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"insert_value",
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[pd.Timestamp("2012-01-01"), pd.Timestamp("2012-01-01", tz="Asia/Tokyo"), 1],
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)
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def test_insert_index_datetimes(self, fill_val, exp_dtype, insert_value):
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obj = pd.DatetimeIndex(
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["2011-01-01", "2011-01-02", "2011-01-03", "2011-01-04"], tz=fill_val.tz
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).as_unit("ns")
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assert obj.dtype == exp_dtype
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exp = pd.DatetimeIndex(
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["2011-01-01", fill_val.date(), "2011-01-02", "2011-01-03", "2011-01-04"],
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tz=fill_val.tz,
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).as_unit("ns")
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self._assert_insert_conversion(obj, fill_val, exp, exp_dtype)
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if fill_val.tz:
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# mismatched tzawareness
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ts = pd.Timestamp("2012-01-01")
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result = obj.insert(1, ts)
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expected = obj.astype(object).insert(1, ts)
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assert expected.dtype == object
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tm.assert_index_equal(result, expected)
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ts = pd.Timestamp("2012-01-01", tz="Asia/Tokyo")
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result = obj.insert(1, ts)
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# once deprecation is enforced:
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expected = obj.insert(1, ts.tz_convert(obj.dtype.tz))
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assert expected.dtype == obj.dtype
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tm.assert_index_equal(result, expected)
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else:
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# mismatched tzawareness
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ts = pd.Timestamp("2012-01-01", tz="Asia/Tokyo")
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result = obj.insert(1, ts)
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expected = obj.astype(object).insert(1, ts)
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assert expected.dtype == object
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tm.assert_index_equal(result, expected)
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item = 1
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result = obj.insert(1, item)
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expected = obj.astype(object).insert(1, item)
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assert expected[1] == item
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assert expected.dtype == object
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tm.assert_index_equal(result, expected)
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def test_insert_index_timedelta64(self):
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obj = pd.TimedeltaIndex(["1 day", "2 day", "3 day", "4 day"])
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assert obj.dtype == "timedelta64[ns]"
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# timedelta64 + timedelta64 => timedelta64
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exp = pd.TimedeltaIndex(["1 day", "10 day", "2 day", "3 day", "4 day"])
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self._assert_insert_conversion(
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obj, pd.Timedelta("10 day"), exp, "timedelta64[ns]"
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)
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for item in [pd.Timestamp("2012-01-01"), 1]:
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result = obj.insert(1, item)
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expected = obj.astype(object).insert(1, item)
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assert expected.dtype == object
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tm.assert_index_equal(result, expected)
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@pytest.mark.parametrize(
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"insert, coerced_val, coerced_dtype",
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[
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(pd.Period("2012-01", freq="M"), "2012-01", "period[M]"),
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(pd.Timestamp("2012-01-01"), pd.Timestamp("2012-01-01"), object),
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(1, 1, object),
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("x", "x", object),
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],
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)
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def test_insert_index_period(self, insert, coerced_val, coerced_dtype):
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obj = pd.PeriodIndex(["2011-01", "2011-02", "2011-03", "2011-04"], freq="M")
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assert obj.dtype == "period[M]"
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data = [
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pd.Period("2011-01", freq="M"),
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coerced_val,
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pd.Period("2011-02", freq="M"),
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pd.Period("2011-03", freq="M"),
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pd.Period("2011-04", freq="M"),
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]
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if isinstance(insert, pd.Period):
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exp = pd.PeriodIndex(data, freq="M")
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self._assert_insert_conversion(obj, insert, exp, coerced_dtype)
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# string that can be parsed to appropriate PeriodDtype
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self._assert_insert_conversion(obj, str(insert), exp, coerced_dtype)
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else:
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result = obj.insert(0, insert)
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expected = obj.astype(object).insert(0, insert)
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tm.assert_index_equal(result, expected)
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# TODO: ATM inserting '2012-01-01 00:00:00' when we have obj.freq=="M"
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# casts that string to Period[M], not clear that is desirable
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if not isinstance(insert, pd.Timestamp):
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# non-castable string
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result = obj.insert(0, str(insert))
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expected = obj.astype(object).insert(0, str(insert))
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tm.assert_index_equal(result, expected)
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@pytest.mark.xfail(reason="Test not implemented")
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def test_insert_index_complex128(self):
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raise NotImplementedError
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|
@pytest.mark.xfail(reason="Test not implemented")
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|
def test_insert_index_bool(self):
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raise NotImplementedError
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|
|
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class TestWhereCoercion(CoercionBase):
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method = "where"
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_cond = np.array([True, False, True, False])
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def _assert_where_conversion(
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self, original, cond, values, expected, expected_dtype
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):
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"""test coercion triggered by where"""
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target = original.copy()
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res = target.where(cond, values)
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tm.assert_equal(res, expected)
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assert res.dtype == expected_dtype
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|
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|
def _construct_exp(self, obj, klass, fill_val, exp_dtype):
|
|
if fill_val is True:
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|
values = klass([True, False, True, True])
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|
elif isinstance(fill_val, (datetime, np.datetime64)):
|
|
values = pd.date_range(fill_val, periods=4)
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|
else:
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|
values = klass(x * fill_val for x in [5, 6, 7, 8])
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|
|
|
exp = klass([obj[0], values[1], obj[2], values[3]], dtype=exp_dtype)
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return values, exp
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|
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|
def _run_test(self, obj, fill_val, klass, exp_dtype):
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|
cond = klass(self._cond)
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exp = klass([obj[0], fill_val, obj[2], fill_val], dtype=exp_dtype)
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self._assert_where_conversion(obj, cond, fill_val, exp, exp_dtype)
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|
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|
values, exp = self._construct_exp(obj, klass, fill_val, exp_dtype)
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self._assert_where_conversion(obj, cond, values, exp, exp_dtype)
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|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,exp_dtype",
|
|
[(1, object), (1.1, object), (1 + 1j, object), (True, object)],
|
|
)
|
|
def test_where_object(self, index_or_series, fill_val, exp_dtype):
|
|
klass = index_or_series
|
|
obj = klass(list("abcd"), dtype=object)
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|
assert obj.dtype == object
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|
self._run_test(obj, fill_val, klass, exp_dtype)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,exp_dtype",
|
|
[(1, np.int64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)],
|
|
)
|
|
def test_where_int64(self, index_or_series, fill_val, exp_dtype, request):
|
|
klass = index_or_series
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|
|
|
obj = klass([1, 2, 3, 4])
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|
assert obj.dtype == np.int64
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|
self._run_test(obj, fill_val, klass, exp_dtype)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val, exp_dtype",
|
|
[(1, np.float64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)],
|
|
)
|
|
def test_where_float64(self, index_or_series, fill_val, exp_dtype, request):
|
|
klass = index_or_series
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|
|
|
obj = klass([1.1, 2.2, 3.3, 4.4])
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assert obj.dtype == np.float64
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|
self._run_test(obj, fill_val, klass, exp_dtype)
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|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,exp_dtype",
|
|
[
|
|
(1, np.complex128),
|
|
(1.1, np.complex128),
|
|
(1 + 1j, np.complex128),
|
|
(True, object),
|
|
],
|
|
)
|
|
def test_where_complex128(self, index_or_series, fill_val, exp_dtype):
|
|
klass = index_or_series
|
|
obj = klass([1 + 1j, 2 + 2j, 3 + 3j, 4 + 4j], dtype=np.complex128)
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assert obj.dtype == np.complex128
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self._run_test(obj, fill_val, klass, exp_dtype)
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|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,exp_dtype",
|
|
[(1, object), (1.1, object), (1 + 1j, object), (True, np.bool_)],
|
|
)
|
|
def test_where_series_bool(self, index_or_series, fill_val, exp_dtype):
|
|
klass = index_or_series
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|
|
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obj = klass([True, False, True, False])
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|
assert obj.dtype == np.bool_
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|
self._run_test(obj, fill_val, klass, exp_dtype)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,exp_dtype",
|
|
[
|
|
(pd.Timestamp("2012-01-01"), "datetime64[ns]"),
|
|
(pd.Timestamp("2012-01-01", tz="US/Eastern"), object),
|
|
],
|
|
ids=["datetime64", "datetime64tz"],
|
|
)
|
|
def test_where_datetime64(self, index_or_series, fill_val, exp_dtype):
|
|
klass = index_or_series
|
|
|
|
obj = klass(pd.date_range("2011-01-01", periods=4, freq="D")._with_freq(None))
|
|
assert obj.dtype == "datetime64[ns]"
|
|
|
|
fv = fill_val
|
|
# do the check with each of the available datetime scalars
|
|
if exp_dtype == "datetime64[ns]":
|
|
for scalar in [fv, fv.to_pydatetime(), fv.to_datetime64()]:
|
|
self._run_test(obj, scalar, klass, exp_dtype)
|
|
else:
|
|
for scalar in [fv, fv.to_pydatetime()]:
|
|
self._run_test(obj, fill_val, klass, exp_dtype)
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_where_index_complex128(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_where_index_bool(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_where_series_timedelta64(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_where_series_period(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.parametrize(
|
|
"value", [pd.Timedelta(days=9), timedelta(days=9), np.timedelta64(9, "D")]
|
|
)
|
|
def test_where_index_timedelta64(self, value):
|
|
tdi = pd.timedelta_range("1 Day", periods=4)
|
|
cond = np.array([True, False, False, True])
|
|
|
|
expected = pd.TimedeltaIndex(["1 Day", value, value, "4 Days"])
|
|
result = tdi.where(cond, value)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
# wrong-dtyped NaT
|
|
dtnat = np.datetime64("NaT", "ns")
|
|
expected = pd.Index([tdi[0], dtnat, dtnat, tdi[3]], dtype=object)
|
|
assert expected[1] is dtnat
|
|
|
|
result = tdi.where(cond, dtnat)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
def test_where_index_period(self):
|
|
dti = pd.date_range("2016-01-01", periods=3, freq="QS")
|
|
pi = dti.to_period("Q")
|
|
|
|
cond = np.array([False, True, False])
|
|
|
|
# Passing a valid scalar
|
|
value = pi[-1] + pi.freq * 10
|
|
expected = pd.PeriodIndex([value, pi[1], value])
|
|
result = pi.where(cond, value)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
# Case passing ndarray[object] of Periods
|
|
other = np.asarray(pi + pi.freq * 10, dtype=object)
|
|
result = pi.where(cond, other)
|
|
expected = pd.PeriodIndex([other[0], pi[1], other[2]])
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
# Passing a mismatched scalar -> casts to object
|
|
td = pd.Timedelta(days=4)
|
|
expected = pd.Index([td, pi[1], td], dtype=object)
|
|
result = pi.where(cond, td)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
per = pd.Period("2020-04-21", "D")
|
|
expected = pd.Index([per, pi[1], per], dtype=object)
|
|
result = pi.where(cond, per)
|
|
tm.assert_index_equal(result, expected)
|
|
|
|
|
|
class TestFillnaSeriesCoercion(CoercionBase):
|
|
# not indexing, but place here for consistency
|
|
|
|
method = "fillna"
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_has_comprehensive_tests(self):
|
|
raise NotImplementedError
|
|
|
|
def _assert_fillna_conversion(self, original, value, expected, expected_dtype):
|
|
"""test coercion triggered by fillna"""
|
|
target = original.copy()
|
|
res = target.fillna(value)
|
|
tm.assert_equal(res, expected)
|
|
assert res.dtype == expected_dtype
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val, fill_dtype",
|
|
[(1, object), (1.1, object), (1 + 1j, object), (True, object)],
|
|
)
|
|
def test_fillna_object(self, index_or_series, fill_val, fill_dtype):
|
|
klass = index_or_series
|
|
obj = klass(["a", np.nan, "c", "d"], dtype=object)
|
|
assert obj.dtype == object
|
|
|
|
exp = klass(["a", fill_val, "c", "d"], dtype=object)
|
|
self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,fill_dtype",
|
|
[(1, np.float64), (1.1, np.float64), (1 + 1j, np.complex128), (True, object)],
|
|
)
|
|
def test_fillna_float64(self, index_or_series, fill_val, fill_dtype):
|
|
klass = index_or_series
|
|
obj = klass([1.1, np.nan, 3.3, 4.4])
|
|
assert obj.dtype == np.float64
|
|
|
|
exp = klass([1.1, fill_val, 3.3, 4.4])
|
|
self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,fill_dtype",
|
|
[
|
|
(1, np.complex128),
|
|
(1.1, np.complex128),
|
|
(1 + 1j, np.complex128),
|
|
(True, object),
|
|
],
|
|
)
|
|
def test_fillna_complex128(self, index_or_series, fill_val, fill_dtype):
|
|
klass = index_or_series
|
|
obj = klass([1 + 1j, np.nan, 3 + 3j, 4 + 4j], dtype=np.complex128)
|
|
assert obj.dtype == np.complex128
|
|
|
|
exp = klass([1 + 1j, fill_val, 3 + 3j, 4 + 4j])
|
|
self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,fill_dtype",
|
|
[
|
|
(pd.Timestamp("2012-01-01"), "datetime64[ns]"),
|
|
(pd.Timestamp("2012-01-01", tz="US/Eastern"), object),
|
|
(1, object),
|
|
("x", object),
|
|
],
|
|
ids=["datetime64", "datetime64tz", "object", "object"],
|
|
)
|
|
def test_fillna_datetime(self, index_or_series, fill_val, fill_dtype):
|
|
klass = index_or_series
|
|
obj = klass(
|
|
[
|
|
pd.Timestamp("2011-01-01"),
|
|
pd.NaT,
|
|
pd.Timestamp("2011-01-03"),
|
|
pd.Timestamp("2011-01-04"),
|
|
]
|
|
)
|
|
assert obj.dtype == "datetime64[ns]"
|
|
|
|
exp = klass(
|
|
[
|
|
pd.Timestamp("2011-01-01"),
|
|
fill_val,
|
|
pd.Timestamp("2011-01-03"),
|
|
pd.Timestamp("2011-01-04"),
|
|
]
|
|
)
|
|
self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val,fill_dtype",
|
|
[
|
|
(pd.Timestamp("2012-01-01", tz="US/Eastern"), "datetime64[ns, US/Eastern]"),
|
|
(pd.Timestamp("2012-01-01"), object),
|
|
# pre-2.0 with a mismatched tz we would get object result
|
|
(pd.Timestamp("2012-01-01", tz="Asia/Tokyo"), "datetime64[ns, US/Eastern]"),
|
|
(1, object),
|
|
("x", object),
|
|
],
|
|
)
|
|
def test_fillna_datetime64tz(self, index_or_series, fill_val, fill_dtype):
|
|
klass = index_or_series
|
|
tz = "US/Eastern"
|
|
|
|
obj = klass(
|
|
[
|
|
pd.Timestamp("2011-01-01", tz=tz),
|
|
pd.NaT,
|
|
pd.Timestamp("2011-01-03", tz=tz),
|
|
pd.Timestamp("2011-01-04", tz=tz),
|
|
]
|
|
)
|
|
assert obj.dtype == "datetime64[ns, US/Eastern]"
|
|
|
|
if getattr(fill_val, "tz", None) is None:
|
|
fv = fill_val
|
|
else:
|
|
fv = fill_val.tz_convert(tz)
|
|
exp = klass(
|
|
[
|
|
pd.Timestamp("2011-01-01", tz=tz),
|
|
fv,
|
|
pd.Timestamp("2011-01-03", tz=tz),
|
|
pd.Timestamp("2011-01-04", tz=tz),
|
|
]
|
|
)
|
|
self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val",
|
|
[
|
|
1,
|
|
1.1,
|
|
1 + 1j,
|
|
True,
|
|
pd.Interval(1, 2, closed="left"),
|
|
pd.Timestamp("2012-01-01", tz="US/Eastern"),
|
|
pd.Timestamp("2012-01-01"),
|
|
pd.Timedelta(days=1),
|
|
pd.Period("2016-01-01", "D"),
|
|
],
|
|
)
|
|
def test_fillna_interval(self, index_or_series, fill_val):
|
|
ii = pd.interval_range(1.0, 5.0, closed="right").insert(1, np.nan)
|
|
assert isinstance(ii.dtype, pd.IntervalDtype)
|
|
obj = index_or_series(ii)
|
|
|
|
exp = index_or_series([ii[0], fill_val, ii[2], ii[3], ii[4]], dtype=object)
|
|
|
|
fill_dtype = object
|
|
self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_fillna_series_int64(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_fillna_index_int64(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_fillna_series_bool(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_fillna_index_bool(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_fillna_series_timedelta64(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.parametrize(
|
|
"fill_val",
|
|
[
|
|
1,
|
|
1.1,
|
|
1 + 1j,
|
|
True,
|
|
pd.Interval(1, 2, closed="left"),
|
|
pd.Timestamp("2012-01-01", tz="US/Eastern"),
|
|
pd.Timestamp("2012-01-01"),
|
|
pd.Timedelta(days=1),
|
|
pd.Period("2016-01-01", "W"),
|
|
],
|
|
)
|
|
def test_fillna_series_period(self, index_or_series, fill_val):
|
|
pi = pd.period_range("2016-01-01", periods=4, freq="D").insert(1, pd.NaT)
|
|
assert isinstance(pi.dtype, pd.PeriodDtype)
|
|
obj = index_or_series(pi)
|
|
|
|
exp = index_or_series([pi[0], fill_val, pi[2], pi[3], pi[4]], dtype=object)
|
|
|
|
fill_dtype = object
|
|
self._assert_fillna_conversion(obj, fill_val, exp, fill_dtype)
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_fillna_index_timedelta64(self):
|
|
raise NotImplementedError
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_fillna_index_period(self):
|
|
raise NotImplementedError
|
|
|
|
|
|
class TestReplaceSeriesCoercion(CoercionBase):
|
|
klasses = ["series"]
|
|
method = "replace"
|
|
|
|
rep: dict[str, list] = {}
|
|
rep["object"] = ["a", "b"]
|
|
rep["int64"] = [4, 5]
|
|
rep["float64"] = [1.1, 2.2]
|
|
rep["complex128"] = [1 + 1j, 2 + 2j]
|
|
rep["bool"] = [True, False]
|
|
rep["datetime64[ns]"] = [pd.Timestamp("2011-01-01"), pd.Timestamp("2011-01-03")]
|
|
|
|
for tz in ["UTC", "US/Eastern"]:
|
|
# to test tz => different tz replacement
|
|
key = f"datetime64[ns, {tz}]"
|
|
rep[key] = [
|
|
pd.Timestamp("2011-01-01", tz=tz),
|
|
pd.Timestamp("2011-01-03", tz=tz),
|
|
]
|
|
|
|
rep["timedelta64[ns]"] = [pd.Timedelta("1 day"), pd.Timedelta("2 day")]
|
|
|
|
@pytest.fixture(params=["dict", "series"])
|
|
def how(self, request):
|
|
return request.param
|
|
|
|
@pytest.fixture(
|
|
params=[
|
|
"object",
|
|
"int64",
|
|
"float64",
|
|
"complex128",
|
|
"bool",
|
|
"datetime64[ns]",
|
|
"datetime64[ns, UTC]",
|
|
"datetime64[ns, US/Eastern]",
|
|
"timedelta64[ns]",
|
|
]
|
|
)
|
|
def from_key(self, request):
|
|
return request.param
|
|
|
|
@pytest.fixture(
|
|
params=[
|
|
"object",
|
|
"int64",
|
|
"float64",
|
|
"complex128",
|
|
"bool",
|
|
"datetime64[ns]",
|
|
"datetime64[ns, UTC]",
|
|
"datetime64[ns, US/Eastern]",
|
|
"timedelta64[ns]",
|
|
],
|
|
ids=[
|
|
"object",
|
|
"int64",
|
|
"float64",
|
|
"complex128",
|
|
"bool",
|
|
"datetime64",
|
|
"datetime64tz",
|
|
"datetime64tz",
|
|
"timedelta64",
|
|
],
|
|
)
|
|
def to_key(self, request):
|
|
return request.param
|
|
|
|
@pytest.fixture
|
|
def replacer(self, how, from_key, to_key):
|
|
"""
|
|
Object we will pass to `Series.replace`
|
|
"""
|
|
if how == "dict":
|
|
replacer = dict(zip(self.rep[from_key], self.rep[to_key]))
|
|
elif how == "series":
|
|
replacer = pd.Series(self.rep[to_key], index=self.rep[from_key])
|
|
else:
|
|
raise ValueError
|
|
return replacer
|
|
|
|
# Expected needs adjustment for the infer string option, seems to work as expecetd
|
|
@pytest.mark.skipif(using_pyarrow_string_dtype(), reason="TODO: test is to complex")
|
|
def test_replace_series(self, how, to_key, from_key, replacer):
|
|
index = pd.Index([3, 4], name="xxx")
|
|
obj = pd.Series(self.rep[from_key], index=index, name="yyy")
|
|
assert obj.dtype == from_key
|
|
|
|
if from_key.startswith("datetime") and to_key.startswith("datetime"):
|
|
# tested below
|
|
return
|
|
elif from_key in ["datetime64[ns, US/Eastern]", "datetime64[ns, UTC]"]:
|
|
# tested below
|
|
return
|
|
|
|
if (from_key == "float64" and to_key in ("int64")) or (
|
|
from_key == "complex128" and to_key in ("int64", "float64")
|
|
):
|
|
if not IS64 or is_platform_windows():
|
|
pytest.skip(f"32-bit platform buggy: {from_key} -> {to_key}")
|
|
|
|
# Expected: do not downcast by replacement
|
|
exp = pd.Series(self.rep[to_key], index=index, name="yyy", dtype=from_key)
|
|
|
|
else:
|
|
exp = pd.Series(self.rep[to_key], index=index, name="yyy")
|
|
assert exp.dtype == to_key
|
|
|
|
msg = "Downcasting behavior in `replace`"
|
|
warn = FutureWarning
|
|
if (
|
|
exp.dtype == obj.dtype
|
|
or exp.dtype == object
|
|
or (exp.dtype.kind in "iufc" and obj.dtype.kind in "iufc")
|
|
):
|
|
warn = None
|
|
with tm.assert_produces_warning(warn, match=msg):
|
|
result = obj.replace(replacer)
|
|
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
@pytest.mark.parametrize(
|
|
"to_key",
|
|
["timedelta64[ns]", "bool", "object", "complex128", "float64", "int64"],
|
|
indirect=True,
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"from_key", ["datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"], indirect=True
|
|
)
|
|
def test_replace_series_datetime_tz(
|
|
self, how, to_key, from_key, replacer, using_infer_string
|
|
):
|
|
index = pd.Index([3, 4], name="xyz")
|
|
obj = pd.Series(self.rep[from_key], index=index, name="yyy")
|
|
assert obj.dtype == from_key
|
|
|
|
exp = pd.Series(self.rep[to_key], index=index, name="yyy")
|
|
if using_infer_string and to_key == "object":
|
|
assert exp.dtype == "string"
|
|
else:
|
|
assert exp.dtype == to_key
|
|
|
|
msg = "Downcasting behavior in `replace`"
|
|
warn = FutureWarning if exp.dtype != object else None
|
|
with tm.assert_produces_warning(warn, match=msg):
|
|
result = obj.replace(replacer)
|
|
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
@pytest.mark.parametrize(
|
|
"to_key",
|
|
["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"],
|
|
indirect=True,
|
|
)
|
|
@pytest.mark.parametrize(
|
|
"from_key",
|
|
["datetime64[ns]", "datetime64[ns, UTC]", "datetime64[ns, US/Eastern]"],
|
|
indirect=True,
|
|
)
|
|
def test_replace_series_datetime_datetime(self, how, to_key, from_key, replacer):
|
|
index = pd.Index([3, 4], name="xyz")
|
|
obj = pd.Series(self.rep[from_key], index=index, name="yyy")
|
|
assert obj.dtype == from_key
|
|
|
|
exp = pd.Series(self.rep[to_key], index=index, name="yyy")
|
|
warn = FutureWarning
|
|
if isinstance(obj.dtype, pd.DatetimeTZDtype) and isinstance(
|
|
exp.dtype, pd.DatetimeTZDtype
|
|
):
|
|
# with mismatched tzs, we retain the original dtype as of 2.0
|
|
exp = exp.astype(obj.dtype)
|
|
warn = None
|
|
else:
|
|
assert exp.dtype == to_key
|
|
if to_key == from_key:
|
|
warn = None
|
|
|
|
msg = "Downcasting behavior in `replace`"
|
|
with tm.assert_produces_warning(warn, match=msg):
|
|
result = obj.replace(replacer)
|
|
|
|
tm.assert_series_equal(result, exp)
|
|
|
|
@pytest.mark.xfail(reason="Test not implemented")
|
|
def test_replace_series_period(self):
|
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raise NotImplementedError
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