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REF: share DTA/TDA.__init__ #47583

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Merged
merged 1 commit into from
Jul 5, 2022
Merged

REF: share DTA/TDA.__init__ #47583

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78 changes: 78 additions & 0 deletions pandas/core/arrays/datetimelike.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
algos,
lib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import (
BaseOffset,
IncompatibleFrequency,
Expand Down Expand Up @@ -1906,6 +1907,81 @@ class TimelikeOps(DatetimeLikeArrayMixin):
Common ops for TimedeltaIndex/DatetimeIndex, but not PeriodIndex.
"""

_default_dtype: np.dtype

def __init__(self, values, dtype=None, freq=lib.no_default, copy: bool = False):
values = extract_array(values, extract_numpy=True)
if isinstance(values, IntegerArray):
values = values.to_numpy("int64", na_value=iNaT)

inferred_freq = getattr(values, "_freq", None)
explicit_none = freq is None
freq = freq if freq is not lib.no_default else None

if isinstance(values, type(self)):
if explicit_none:
# don't inherit from values
pass
elif freq is None:
freq = values.freq
elif freq and values.freq:
freq = to_offset(freq)
freq, _ = validate_inferred_freq(freq, values.freq, False)

if dtype is not None:
dtype = pandas_dtype(dtype)
if not is_dtype_equal(dtype, values.dtype):
# TODO: we only have tests for this for DTA, not TDA (2022-07-01)
raise TypeError(
f"dtype={dtype} does not match data dtype {values.dtype}"
)

dtype = values.dtype
values = values._ndarray

elif dtype is None:
dtype = self._default_dtype

if not isinstance(values, np.ndarray):
raise ValueError(
f"Unexpected type '{type(values).__name__}'. 'values' must be a "
f"{type(self).__name__}, ndarray, or Series or Index "
"containing one of those."
)
if values.ndim not in [1, 2]:
raise ValueError("Only 1-dimensional input arrays are supported.")

if values.dtype == "i8":
# for compat with datetime/timedelta/period shared methods,
# we can sometimes get here with int64 values. These represent
# nanosecond UTC (or tz-naive) unix timestamps
values = values.view(self._default_dtype)

dtype = self._validate_dtype(values, dtype)

if freq == "infer":
raise ValueError(
f"Frequency inference not allowed in {type(self).__name__}.__init__. "
"Use 'pd.array()' instead."
)

if copy:
values = values.copy()
if freq:
freq = to_offset(freq)

NDArrayBacked.__init__(self, values=values, dtype=dtype)
self._freq = freq

if inferred_freq is None and freq is not None:
type(self)._validate_frequency(self, freq)

@classmethod
def _validate_dtype(cls, values, dtype):
raise AbstractMethodError(cls)

# --------------------------------------------------------------

@cache_readonly
def _reso(self) -> int:
return get_unit_from_dtype(self._ndarray.dtype)
Expand All @@ -1917,6 +1993,8 @@ def _unit(self) -> str:
# "ExtensionDtype"; expected "Union[DatetimeTZDtype, dtype[Any]]"
return dtype_to_unit(self.dtype) # type: ignore[arg-type]

# --------------------------------------------------------------

def __array_ufunc__(self, ufunc: np.ufunc, method: str, *inputs, **kwargs):
if (
ufunc in [np.isnan, np.isinf, np.isfinite]
Expand Down
75 changes: 5 additions & 70 deletions pandas/core/arrays/datetimes.py
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Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -19,7 +19,6 @@
lib,
tslib,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import (
BaseOffset,
NaT,
Expand All @@ -30,7 +29,6 @@
fields,
get_resolution,
get_unit_from_dtype,
iNaT,
ints_to_pydatetime,
is_date_array_normalized,
is_unitless,
Expand Down Expand Up @@ -72,9 +70,7 @@

from pandas.core.arrays import datetimelike as dtl
from pandas.core.arrays._ranges import generate_regular_range
from pandas.core.arrays.integer import IntegerArray
import pandas.core.common as com
from pandas.core.construction import extract_array

from pandas.tseries.frequencies import get_period_alias
from pandas.tseries.offsets import (
Expand Down Expand Up @@ -254,75 +250,14 @@ def _scalar_type(self) -> type[Timestamp]:

_dtype: np.dtype | DatetimeTZDtype
_freq = None
_default_dtype = DT64NS_DTYPE # used in TimeLikeOps.__init__

def __init__(
self, values, dtype=None, freq=lib.no_default, copy: bool = False
) -> None:
values = extract_array(values, extract_numpy=True)
if isinstance(values, IntegerArray):
values = values.to_numpy("int64", na_value=iNaT)

inferred_freq = getattr(values, "_freq", None)
explicit_none = freq is None
freq = freq if freq is not lib.no_default else None

if isinstance(values, type(self)):
if explicit_none:
# don't inherit from values
pass
elif freq is None:
freq = values.freq
elif freq and values.freq:
freq = to_offset(freq)
freq, _ = dtl.validate_inferred_freq(freq, values.freq, False)

if dtype is not None:
dtype = pandas_dtype(dtype)
if not is_dtype_equal(dtype, values.dtype):
raise TypeError(
f"dtype={dtype} does not match data dtype {values.dtype}"
)

dtype = values.dtype
values = values._ndarray

elif dtype is None:
dtype = DT64NS_DTYPE

if not isinstance(values, np.ndarray):
raise ValueError(
f"Unexpected type '{type(values).__name__}'. 'values' must be a "
f"{type(self).__name__}, ndarray, or Series or Index "
"containing one of those."
)
if values.ndim not in [1, 2]:
raise ValueError("Only 1-dimensional input arrays are supported.")

if values.dtype == "i8":
# for compat with datetime/timedelta/period shared methods,
# we can sometimes get here with int64 values. These represent
# nanosecond UTC (or tz-naive) unix timestamps
values = values.view(DT64NS_DTYPE)

@classmethod
def _validate_dtype(cls, values, dtype):
# used in TimeLikeOps.__init__
_validate_dt64_dtype(values.dtype)
dtype = _validate_dt64_dtype(dtype)

if freq == "infer":
raise ValueError(
f"Frequency inference not allowed in {type(self).__name__}.__init__. "
"Use 'pd.array()' instead."
)

if copy:
values = values.copy()
if freq:
freq = to_offset(freq)

NDArrayBacked.__init__(self, values=values, dtype=dtype)
self._freq = freq

if inferred_freq is None and freq is not None:
type(self)._validate_frequency(self, freq)
return dtype

# error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
@classmethod
Expand Down
67 changes: 6 additions & 61 deletions pandas/core/arrays/timedeltas.py
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Original file line number Diff line number Diff line change
Expand Up @@ -12,7 +12,6 @@
lib,
tslibs,
)
from pandas._libs.arrays import NDArrayBacked
from pandas._libs.tslibs import (
BaseOffset,
NaT,
Expand Down Expand Up @@ -54,13 +53,9 @@
from pandas.core.dtypes.missing import isna

from pandas.core import nanops
from pandas.core.arrays import (
IntegerArray,
datetimelike as dtl,
)
from pandas.core.arrays import datetimelike as dtl
from pandas.core.arrays._ranges import generate_regular_range
import pandas.core.common as com
from pandas.core.construction import extract_array
from pandas.core.ops.common import unpack_zerodim_and_defer

if TYPE_CHECKING:
Expand Down Expand Up @@ -171,64 +166,14 @@ def dtype(self) -> np.dtype: # type: ignore[override]
# Constructors

_freq = None
_default_dtype = TD64NS_DTYPE # used in TimeLikeOps.__init__

def __init__(
self, values, dtype=TD64NS_DTYPE, freq=lib.no_default, copy: bool = False
) -> None:
values = extract_array(values, extract_numpy=True)
if isinstance(values, IntegerArray):
values = values.to_numpy("int64", na_value=tslibs.iNaT)

inferred_freq = getattr(values, "_freq", None)
explicit_none = freq is None
freq = freq if freq is not lib.no_default else None

if isinstance(values, type(self)):
if explicit_none:
# don't inherit from values
pass
elif freq is None:
freq = values.freq
elif freq and values.freq:
freq = to_offset(freq)
freq, _ = dtl.validate_inferred_freq(freq, values.freq, False)

values = values._ndarray

if not isinstance(values, np.ndarray):
raise ValueError(
f"Unexpected type '{type(values).__name__}'. 'values' must be a "
f"{type(self).__name__}, ndarray, or Series or Index "
"containing one of those."
)
if values.ndim not in [1, 2]:
raise ValueError("Only 1-dimensional input arrays are supported.")

if values.dtype == "i8":
# for compat with datetime/timedelta/period shared methods,
# we can sometimes get here with int64 values. These represent
# nanosecond UTC (or tz-naive) unix timestamps
values = values.view(TD64NS_DTYPE)

@classmethod
def _validate_dtype(cls, values, dtype):
# used in TimeLikeOps.__init__
_validate_td64_dtype(values.dtype)
dtype = _validate_td64_dtype(dtype)

if freq == "infer":
raise ValueError(
f"Frequency inference not allowed in {type(self).__name__}.__init__. "
"Use 'pd.array()' instead."
)

if copy:
values = values.copy()
if freq:
freq = to_offset(freq)

NDArrayBacked.__init__(self, values=values, dtype=dtype)
self._freq = freq

if inferred_freq is None and freq is not None:
type(self)._validate_frequency(self, freq)
return dtype

# error: Signature of "_simple_new" incompatible with supertype "NDArrayBacked"
@classmethod
Expand Down