CLN: pragma no cover, post-array_ufunc (pandas-dev#44006)

Author: jbrockmendel

pandas/_libs/algos.pyx

@@ -1428,13 +1428,13 @@ def diff_2d(
     #  see https://github.com/cython/cython/issues/2646
     if (out_t is float32_t
             and not (diff_t is float32_t or diff_t is int8_t or diff_t is int16_t)):
-        raise NotImplementedError
+        raise NotImplementedError  # pragma: no cover
     elif (out_t is float64_t
           and (diff_t is float32_t or diff_t is int8_t or diff_t is int16_t)):
-        raise NotImplementedError
+        raise NotImplementedError  # pragma: no cover
     elif out_t is int64_t and diff_t is not int64_t:
         # We only have out_t of int64_t if we have datetimelike
-        raise NotImplementedError
+        raise NotImplementedError  # pragma: no cover
     else:
         # We put this inside an indented else block to avoid cython build
         #  warnings about unreachable code

pandas/_libs/arrays.pyx

@@ -84,7 +84,7 @@ cdef class NDArrayBacked:
             elif "_ndarray" in state:
                 data = state.pop("_ndarray")
             else:
-                raise ValueError
+                raise ValueError  # pragma: no cover
             self._ndarray = data
             self._dtype = state.pop("_dtype")
 
@@ -95,7 +95,7 @@ cdef class NDArrayBacked:
                 if len(state) == 1 and isinstance(state[0], dict):
                     self.__setstate__(state[0])
                     return
-                raise NotImplementedError(state)
+                raise NotImplementedError(state)  # pragma: no cover
 
             data, dtype = state[:2]
             if isinstance(dtype, np.ndarray):
@@ -107,9 +107,9 @@ cdef class NDArrayBacked:
                 for key, val in state[2].items():
                     setattr(self, key, val)
             else:
-                raise NotImplementedError(state)
+                raise NotImplementedError(state)  # pragma: no cover
         else:
-            raise NotImplementedError(state)
+            raise NotImplementedError(state)  # pragma: no cover
 
     def __len__(self) -> int:
         return len(self._ndarray)

pandas/_libs/index.pyx

@@ -264,7 +264,7 @@ cdef class IndexEngine:
         return algos.is_monotonic(values, timelike=False)
 
     cdef _make_hash_table(self, Py_ssize_t n):
-        raise NotImplementedError
+        raise NotImplementedError  # pragma: no cover
 
     cdef _check_type(self, object val):
         hash(val)
@@ -607,7 +607,7 @@ cdef class BaseMultiIndexCodesEngine:
         self._base.__init__(self, lab_ints)
 
     def _codes_to_ints(self, ndarray[uint64_t] codes) -> np.ndarray:
-        raise NotImplementedError("Implemented by subclass")
+        raise NotImplementedError("Implemented by subclass")  # pragma: no cover
 
     def _extract_level_codes(self, target) -> np.ndarray:
         """

pandas/_libs/indexing.pyx

@@ -19,7 +19,7 @@ cdef class NDFrameIndexerBase:
         if ndim is None:
             ndim = self._ndim = self.obj.ndim
             if ndim > 2:
-                raise ValueError(
+                raise ValueError(  # pragma: no cover
                     "NDFrameIndexer does not support NDFrame objects with ndim > 2"
                 )
         return ndim

pandas/_libs/internals.pyx

@@ -338,7 +338,7 @@ cpdef Py_ssize_t slice_len(slice slc, Py_ssize_t objlen=PY_SSIZE_T_MAX) except -
         Py_ssize_t start, stop, step, length
 
     if slc is None:
-        raise TypeError("slc must be slice")
+        raise TypeError("slc must be slice")  # pragma: no cover
 
     PySlice_GetIndicesEx(slc, objlen, &start, &stop, &step, &length)
 
@@ -358,7 +358,7 @@ cdef (Py_ssize_t, Py_ssize_t, Py_ssize_t, Py_ssize_t) slice_get_indices_ex(
         Py_ssize_t start, stop, step, length
 
     if slc is None:
-        raise TypeError("slc should be a slice")
+        raise TypeError("slc should be a slice")  # pragma: no cover
 
     PySlice_GetIndicesEx(slc, objlen, &start, &stop, &step, &length)
 

pandas/_libs/tslibs/dtypes.pyx

@@ -205,7 +205,7 @@ class Resolution(Enum):
         elif self == Resolution.RESO_YR:
             return FreqGroup.FR_ANN
         else:
-            raise ValueError(self)
+            raise ValueError(self)  # pragma: no cover
 
     @property
     def attrname(self) -> str:

pandas/_libs/tslibs/offsets.pyx

@@ -574,7 +574,7 @@ cdef class BaseOffset:
             When the specific offset subclass does not have a vectorized
             implementation.
         """
-        raise NotImplementedError(
+        raise NotImplementedError(  # pragma: no cover
             f"DateOffset subclass {type(self).__name__} "
             "does not have a vectorized implementation"
         )

pandas/core/array_algos/take.py

@@ -373,6 +373,9 @@ def wrapper(
     ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
         libalgos.take_1d_int64_int64, np.int64, np.int64, np.int64
     ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_1d_int64_int64, np.int64, np.int64, np.int64
+    ),
 }
 
 _take_2d_axis0_dict = {
@@ -402,6 +405,9 @@ def wrapper(
     ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
         libalgos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64
     ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis0_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
 }
 
 _take_2d_axis1_dict = {
@@ -431,6 +437,9 @@ def wrapper(
     ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
         libalgos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64
     ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_axis1_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
 }
 
 _take_2d_multi_dict = {
@@ -460,6 +469,9 @@ def wrapper(
     ("datetime64[ns]", "datetime64[ns]"): _view_wrapper(
         libalgos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64
     ),
+    ("timedelta64[ns]", "timedelta64[ns]"): _view_wrapper(
+        libalgos.take_2d_multi_int64_int64, np.int64, np.int64, fill_wrap=np.int64
+    ),
 }
 
 

pandas/core/generic.py

@@ -2057,54 +2057,12 @@ def empty(self) -> bool_t:
     def __array__(self, dtype: npt.DTypeLike | None = None) -> np.ndarray:
         return np.asarray(self._values, dtype=dtype)
 
-    def __array_wrap__(
-        self,
-        result: np.ndarray,
-        context: tuple[Callable, tuple[Any, ...], int] | None = None,
-    ):
-        """
-        Gets called after a ufunc and other functions.
-
-        Parameters
-        ----------
-        result: np.ndarray
-            The result of the ufunc or other function called on the NumPy array
-            returned by __array__
-        context: tuple of (func, tuple, int)
-            This parameter is returned by ufuncs as a 3-element tuple: (name of the
-            ufunc, arguments of the ufunc, domain of the ufunc), but is not set by
-            other numpy functions.q
-
-        Notes
-        -----
-        Series implements __array_ufunc_ so this not called for ufunc on Series.
-        """
-        res = lib.item_from_zerodim(result)
-        if is_scalar(res):
-            # e.g. we get here with np.ptp(series)
-            # ptp also requires the item_from_zerodim
-            return res
-        d = self._construct_axes_dict(self._AXIS_ORDERS, copy=False)
-        # error: Argument 1 to "NDFrame" has incompatible type "ndarray";
-        # expected "BlockManager"
-        return self._constructor(res, **d).__finalize__(  # type: ignore[arg-type]
-            self, method="__array_wrap__"
-        )
-
     @final
     def __array_ufunc__(
         self, ufunc: np.ufunc, method: str, *inputs: Any, **kwargs: Any
     ):
         return arraylike.array_ufunc(self, ufunc, method, *inputs, **kwargs)
 
-    # ideally we would define this to avoid the getattr checks, but
-    # is slower
-    # @property
-    # def __array_interface__(self):
-    #    """ provide numpy array interface method """
-    #    values = self.values
-    #    return dict(typestr=values.dtype.str,shape=values.shape,data=values)
-
     # ----------------------------------------------------------------------
     # Picklability
 

pandas/core/indexes/base.py

@@ -883,7 +883,7 @@ def __array_ufunc__(self, ufunc: np.ufunc, method: str_t, *inputs, **kwargs):
 
     def __array_wrap__(self, result, context=None):
         """
-        Gets called after a ufunc and other functions.
+        Gets called after a ufunc and other functions e.g. np.split.
         """
         result = lib.item_from_zerodim(result)
         if is_bool_dtype(result) or lib.is_scalar(result) or np.ndim(result) > 1:
@@ -6182,14 +6182,10 @@ def get_slice_bound(
                 raise err
 
         if isinstance(slc, np.ndarray):
-            # get_loc may return a boolean array or an array of indices, which
+            # get_loc may return a boolean array, which
             # is OK as long as they are representable by a slice.
-            if is_bool_dtype(slc):
-                slc = lib.maybe_booleans_to_slice(slc.view("u1"))
-            else:
-                slc = lib.maybe_indices_to_slice(
-                    slc.astype(np.intp, copy=False), len(self)
-                )
+            assert is_bool_dtype(slc.dtype)
+            slc = lib.maybe_booleans_to_slice(slc.view("u1"))
             if isinstance(slc, np.ndarray):
                 raise KeyError(
                     f"Cannot get {side} slice bound for non-unique "