CLN: remove ABCTimedelta (#34559)

Author: jbrockmendel

pandas/_libs/index.pyx

@@ -22,9 +22,9 @@ cnp.import_array()
 from pandas._libs cimport util
 
 from pandas._libs.tslibs.nattype cimport c_NaT as NaT
-from pandas._libs.tslibs.base cimport ABCTimedelta
 from pandas._libs.tslibs.period cimport is_period_object
 from pandas._libs.tslibs.timestamps cimport _Timestamp
+from pandas._libs.tslibs.timedeltas cimport _Timedelta
 
 from pandas._libs.hashtable cimport HashTable
 
@@ -471,7 +471,7 @@ cdef class TimedeltaEngine(DatetimeEngine):
         return 'm8[ns]'
 
     cdef int64_t _unbox_scalar(self, scalar) except? -1:
-        if not (isinstance(scalar, ABCTimedelta) or scalar is NaT):
+        if not (isinstance(scalar, _Timedelta) or scalar is NaT):
             raise TypeError(scalar)
         return scalar.value
 

pandas/_libs/interval.pyx

@@ -42,9 +42,9 @@ from pandas._libs.tslibs.util cimport (
     is_timedelta64_object,
 )
 
-from pandas._libs.tslibs.base cimport ABCTimedelta
 from pandas._libs.tslibs.timezones cimport tz_compare
 from pandas._libs.tslibs.timestamps cimport _Timestamp
+from pandas._libs.tslibs.timedeltas cimport _Timedelta
 
 _VALID_CLOSED = frozenset(['left', 'right', 'both', 'neither'])
 
@@ -340,7 +340,7 @@ cdef class Interval(IntervalMixin):
     def _validate_endpoint(self, endpoint):
         # GH 23013
         if not (is_integer_object(endpoint) or is_float_object(endpoint) or
-                isinstance(endpoint, (_Timestamp, ABCTimedelta))):
+                isinstance(endpoint, (_Timestamp, _Timedelta))):
             raise ValueError("Only numeric, Timestamp and Timedelta endpoints "
                              "are allowed when constructing an Interval.")
 

pandas/_libs/tslibs/base.pxd

@@ -1,7 +1,4 @@
-from cpython.datetime cimport datetime, timedelta
-
-cdef class ABCTimedelta(timedelta):
-    pass
+from cpython.datetime cimport datetime
 
 
 cdef class ABCTimestamp(datetime):

pandas/_libs/tslibs/base.pyx

@@ -5,11 +5,7 @@ in order to allow for fast isinstance checks without circular dependency issues.
 This is analogous to core.dtypes.generic.
 """
 
-from cpython.datetime cimport datetime, timedelta
-
-
-cdef class ABCTimedelta(timedelta):
-    pass
+from cpython.datetime cimport datetime
 
 
 cdef class ABCTimestamp(datetime):

pandas/_libs/tslibs/timedeltas.pxd

@@ -1,6 +1,18 @@
+from cpython.datetime cimport timedelta
 from numpy cimport int64_t
 
 # Exposed for tslib, not intended for outside use.
 cpdef int64_t delta_to_nanoseconds(delta) except? -1
 cdef convert_to_timedelta64(object ts, object unit)
 cdef bint is_any_td_scalar(object obj)
+
+
+cdef class _Timedelta(timedelta):
+    cdef readonly:
+        int64_t value      # nanoseconds
+        object freq        # frequency reference
+        bint is_populated  # are my components populated
+        int64_t _d, _h, _m, _s, _ms, _us, _ns
+
+    cpdef timedelta to_pytimedelta(_Timedelta self)
+    cpdef bint _has_ns(self)

pandas/_libs/tslibs/timedeltas.pyx

@@ -21,7 +21,7 @@ from pandas._libs.tslibs.util cimport (
     is_float_object, is_array
 )
 
-from pandas._libs.tslibs.base cimport ABCTimedelta, ABCTimestamp
+from pandas._libs.tslibs.base cimport ABCTimestamp
 
 from pandas._libs.tslibs.conversion cimport cast_from_unit
 
@@ -675,12 +675,12 @@ cdef _to_py_int_float(v):
 # timedeltas that we need to do object instantiation in python. This will
 # serve as a C extension type that shadows the Python class, where we do any
 # heavy lifting.
-cdef class _Timedelta(ABCTimedelta):
-    cdef readonly:
-        int64_t value      # nanoseconds
-        object freq        # frequency reference
-        bint is_populated  # are my components populated
-        int64_t _d, _h, _m, _s, _ms, _us, _ns
+cdef class _Timedelta(timedelta):
+    # cdef readonly:
+    #    int64_t value      # nanoseconds
+    #    object freq        # frequency reference
+    #    bint is_populated  # are my components populated
+    #    int64_t _d, _h, _m, _s, _ms, _us, _ns
 
     # higher than np.ndarray and np.matrix
     __array_priority__ = 100