8000 BUG: maybe_promote with dt64tz and mismatched NA by jbrockmendel · Pull Request #39743 · pandas-dev/pandas · GitHub
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BUG: maybe_promote with dt64tz and mismatched NA #39743

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Merged
merged 4 commits into from
Feb 12, 2021
Merged

BUG: maybe_promote with dt64tz and mismatched NA #39743

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aa166a4
BUG: maybe_promote with dt64tz and mismatched NA
jbrockmendel Feb 11, 2021
600a989
Merge branch 'master' into ref-maybe_promote-na
jbrockmendel Feb 11, 2021
4b20d4c
Merge branch 'master' into ref-maybe_promote-na
jbrockmendel Feb 12, 2021
7f15f5e
Fixup deleted wrong test
jbrockmendel Feb 12, 2021
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Merge branch 'master' into ref-maybe_promote-na
  • Loading branch information
@jbrockmendel
jbrockmendel committed Feb 12, 2021
commit 4b20d4c1a7945751987facc82613ddad76a2146d
22 changes: 4 additions & 18 deletions pandas/core/dtypes/cast.py
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Original file line number Diff line number Diff line change
Expand Up @@ -578,14 +578,6 @@ def maybe_promote(dtype: np.dtype, fill_value=np.nan):
fill_value = np.timedelta64("NaT", "ns")
else:
fill_value = fv.to_timedelta64()
elif isinstance(dtype, DatetimeTZDtype):
if not isinstance(fill_value, datetime):
dtype = np.dtype(np.object_)
elif fill_value.tzinfo is None:
dtype = np.dtype(np.object_)
elif not tz_compare(fill_value.tzinfo, dtype.tz):
# TODO: sure we want to cast here?
dtype = np.dtype(np.object_)

elif is_float(fill_value):
if issubclass(dtype.type, np.bool_):
Expand Down Expand Up @@ -638,20 +630,14 @@ def maybe_promote(dtype: np.dtype, fill_value=np.nan):
else:
dtype = np.dtype(np.object_)

dtype = sanitize_str_dtypes_to_object(dtype)
# in case we have a string that looked like a number
if issubclass(dtype.type, (bytes, str)):
dtype = np.dtype(np.object_)

fill_value = _ensure_dtype_type(fill_value, dtype)
return dtype, fill_value


def sanitize_str_dtypes_to_object(dtype: DtypeObj) -> DtypeObj:
"""
Convert any numpy str/bytes dtypes to object.
"""
if isinstance(dtype, np.dtype) and dtype.kind in ["S", "U"]:
dtype = np.dtype(object)
return dtype


def _ensure_dtype_type(value, dtype: DtypeObj):
"""
Ensure that the given value is an instance of the given dtype.
Expand Down
81 changes: 0 additions & 81 deletions pandas/tests/dtypes/cast/test_promote.py
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Expand Up @@ -20,7 +20,6 @@
is_scalar,
is_timedelta64_dtype,
)
from pandas.core.dtypes.dtypes import DatetimeTZDtype
from pandas.core.dtypes.missing import isna

import pandas as pd
Expand Down Expand Up @@ -406,86 +405,6 @@ def test_maybe_promote_any_with_datetime64(
_check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)


def test_maybe_promote_datetimetz_with_any_numpy_dtype(
tz_aware_fixture, any_numpy_dtype_reduced
):
dtype = DatetimeTZDtype(tz=tz_aware_fixture)
fill_dtype = np.dtype(any_numpy_dtype_reduced)

# create array of given dtype; casts "1" to correct dtype
fill_value = np.array([1], dtype=fill_dtype)[0]

# filling datetimetz with any numpy dtype casts to object
expected_dtype = np.dtype(object)
exp_val_for_scalar = fill_value

_check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)


def test_maybe_promote_datetimetz_with_datetimetz(tz_aware_fixture, tz_aware_fixture2):
dtype = DatetimeTZDtype(tz=tz_aware_fixture)
fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture2)

# create array of given dtype; casts "1" to correct dtype
fill_value = pd.Series([10 ** 9], dtype=fill_dtype)[0]

# filling datetimetz with datetimetz casts to object, unless tz matches
exp_val_for_scalar = fill_value
if tz_compare(dtype.tz, fill_dtype.tz):
expected_dtype = dtype
else:
expected_dtype = np.dtype(object)

_check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)


@pytest.mark.parametrize("fill_value", [None, np.nan, NaT])
def test_maybe_promote_datetimetz_with_na(tz_aware_fixture, fill_value):

dtype = DatetimeTZDtype(tz=tz_aware_fixture)

expected_dtype = dtype
exp_val_for_scalar = NaT

_check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)


def test_maybe_promote_datetimetz_with_mismatched_na(tz_aware_fixture):
fill_value = np.timedelta64("NaT")

dtype = DatetimeTZDtype(tz=tz_aware_fixture)

expected_dtype = np.dtype(object)
exp_val_for_scalar = fill_value

_check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)


@pytest.mark.parametrize(
"fill_value",
[
pd.Timestamp("now"),
np.datetime64("now"),
datetime.datetime.now(),
datetime.date.today(),
],
ids=["pd.Timestamp", "np.datetime64", "datetime.datetime", "datetime.date"],
)
def test_maybe_promote_any_numpy_dtype_with_datetimetz(
any_numpy_dtype_reduced, tz_aware_fixture, fill_value
):
dtype = np.dtype(any_numpy_dtype_reduced)
fill_dtype = DatetimeTZDtype(tz=tz_aware_fixture)

fill_value = pd.Series([fill_value], dtype=fill_dtype)[0]

# filling any numpy dtype with datetimetz casts to object
expected_dtype = np.dtype(object)
exp_val_for_scalar = fill_value

_check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)


def test_maybe_promote_timedelta64_with_any(timedelta64_dtype, any_numpy_dtype_reduced):
dtype = np.dtype(timedelta64_dtype)
fill_dtype = np.dtype(any_numpy_dtype_reduced)
Expand Down
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