CLN: remove unused in tools.datetimes

pandas/_libs/tslibs/parsing.pyi

@@ -23,11 +23,6 @@ def try_parse_dates(
     values: npt.NDArray[np.object_],  # object[:]
     parser,
 ) -> npt.NDArray[np.object_]: ...
-def try_parse_year_month_day(
-    years: npt.NDArray[np.object_],  # object[:]
-    months: npt.NDArray[np.object_],  # object[:]
-    days: npt.NDArray[np.object_],  # object[:]
-) -> npt.NDArray[np.object_]: ...
 def guess_datetime_format(
     dt_str,
     dayfirst: bool | None = ...,

pandas/_libs/tslibs/parsing.pyx

@@ -766,25 +766,6 @@ def try_parse_dates(object[:] values, parser) -> np.ndarray:
     return result.base  # .base to access underlying ndarray
 
 
-def try_parse_year_month_day(
-    object[:] years, object[:] months, object[:] days
-) -> np.ndarray:
-    cdef:
-        Py_ssize_t i, n
-        object[::1] result
-
-    n = len(years)
-    # TODO(cython3): Use len instead of `shape[0]`
-    if months.shape[0] != n or days.shape[0] != n:
-        raise ValueError("Length of years/months/days must all be equal")
-    result = np.empty(n, dtype="O")
-
-    for i in range(n):
-        result[i] = datetime(int(years[i]), int(months[i]), int(days[i]))
-
-    return result.base  # .base to access underlying ndarray
-
-
 # ----------------------------------------------------------------------
 # Miscellaneous
 

pandas/core/tools/datetimes.py

@@ -28,8 +28,6 @@
     get_unit_from_dtype,
     iNaT,
     is_supported_unit,
-    nat_strings,
-    parsing,
     timezones as libtimezones,
 )
 from pandas._libs.tslibs.conversion import precision_from_unit
@@ -42,7 +40,6 @@
     AnyArrayLike,
     ArrayLike,
     DateTimeErrorChoices,
-    npt,
 )
 from pandas.util._exceptions import find_stack_level
 
@@ -62,14 +59,12 @@
     ABCDataFrame,
     ABCSeries,
 )
-from pandas.core.dtypes.missing import notna
 
 from pandas.arrays import (
     DatetimeArray,
     IntegerArray,
     NumpyExtensionArray,
 )
-from pandas.core import algorithms
 from pandas.core.algorithms import unique
 from pandas.core.arrays import ArrowExtensionArray
 from pandas.core.arrays.base import ExtensionArray
@@ -1273,58 +1268,6 @@ def coerce(values):
     return values
 
 
-def _attempt_YYYYMMDD(arg: npt.NDArray[np.object_], errors: str) -> np.ndarray | None:
-    """
-    try to parse the YYYYMMDD/%Y%m%d format, try to deal with NaT-like,
-    arg is a passed in as an object dtype, but could really be ints/strings
-    with nan-like/or floats (e.g. with nan)
-
-    Parameters
-    ----------
-    arg : np.ndarray[object]
-    errors : {'raise','ignore','coerce'}
-    """
-
-    def calc(carg):
-        # calculate the actual result
-        carg = carg.astype(object, copy=False)
-        parsed = parsing.try_parse_year_month_day(
-            carg / 10000, carg / 100 % 100, carg % 100
-        )
-        return tslib.array_to_datetime(parsed, errors=errors)[0]
-
-    def calc_with_mask(carg, mask):
-        result = np.empty(carg.shape, dtype="M8[ns]")
-        iresult = result.view("i8")
-        iresult[~mask] = iNaT
-
-        masked_result = calc(carg[mask].astype(np.float64).astype(np.int64))
-        result[mask] = masked_result.astype("M8[ns]")
-        return result
-
-    # try intlike / strings that are ints
-    try:
-        return calc(arg.astype(np.int64))
-    except (ValueError, OverflowError, TypeError):
-        pass
-
-    # a float with actual np.nan
-    try:
-        carg = arg.astype(np.float64)
-        return calc_with_mask(carg, notna(carg))
-    except (ValueError, OverflowError, TypeError):
-        pass
-
-    # string with NaN-like
-    try:
-        mask = ~algorithms.isin(arg, list(nat_strings))
-        return calc_with_mask(arg, mask)
-    except (ValueError, OverflowError, TypeError):
-        pass
-
-    return None
-
-
 __all__ = [
     "DateParseError",
     "should_cache",