CLN: follow-ups

pandas/_libs/reduction.pyx

@@ -82,11 +82,9 @@ cdef class Reducer:
         else:
 
             # we passed a series-like
-            if hasattr(dummy, 'values'):
-
-                typ = type(dummy)
-                index = getattr(dummy, 'index', None)
-                dummy = dummy.values
+            typ = type(dummy)
+            index = dummy.index
+            dummy = dummy.values
 
             if dummy.dtype != self.arr.dtype:
                 raise ValueError('Dummy array must be same dtype')
@@ -99,10 +97,10 @@ cdef class Reducer:
         cdef:
             char* dummy_buf
             ndarray arr, result, chunk
-            Py_ssize_t i, incr
+            Py_ssize_t i
             flatiter it
             bint has_labels
-            object res, name, labels, index
+            object res, name, labels
             object cached_typ = None
 
         arr = self.arr
@@ -112,7 +110,6 @@ cdef class Reducer:
         labels = self.labels
         has_labels = labels is not None
         has_index = self.index is not None
-        incr = self.increment
 
         result = np.empty(self.nresults, dtype='O')
         it = <flatiter>PyArray_IterNew(result)
@@ -193,10 +190,10 @@ cdef class _BaseGrouper:
         return values, index
 
     cdef inline _update_cached_objs(self, object cached_typ, object cached_ityp,
-                                    Slider islider, Slider vslider, object name):
+                                    Slider islider, Slider vslider):
         if cached_typ is None:
             cached_ityp = self.ityp(islider.buf)
-            cached_typ = self.typ(vslider.buf, index=cached_ityp, name=name)
+            cached_typ = self.typ(vslider.buf, index=cached_ityp, name=self.name)
         else:
             # See the comment in indexes/base.py about _index_data.
             # We need this for EA-backed indexes that have a reference
@@ -205,7 +202,7 @@ cdef class _BaseGrouper:
             cached_ityp._engine.clear_mapping()
             object.__setattr__(cached_typ._data._block, 'values', vslider.buf)
             object.__setattr__(cached_typ, '_index', cached_ityp)
-            object.__setattr__(cached_typ, 'name', name)
+            object.__setattr__(cached_typ, 'name', self.name)
 
         return cached_typ, cached_ityp
 
@@ -254,7 +251,7 @@ cdef class SeriesBinGrouper(_BaseGrouper):
             object res
             bint initialized = 0
             Slider vslider, islider
-            object name, cached_typ = None, cached_ityp = None
+            object cached_typ = None, cached_ityp = None
 
         counts = np.zeros(self.ngroups, dtype=np.int64)
 
@@ -268,7 +265,6 @@ cdef class SeriesBinGrouper(_BaseGrouper):
 
         group_size = 0
         n = len(self.arr)
-        name = self.name
 
         vslider = Slider(self.arr, self.dummy_arr)
         islider = Slider(self.index, self.dummy_index)
@@ -283,7 +279,7 @@ cdef class SeriesBinGrouper(_BaseGrouper):
                 vslider.set_length(group_size)
 
                 cached_typ, cached_ityp = self._update_cached_objs(
-                    cached_typ, cached_ityp, islider, vslider, name)
+                    cached_typ, cached_ityp, islider, vslider)
 
                 cached_ityp._engine.clear_mapping()
                 res = self.f(cached_typ)
@@ -356,13 +352,12 @@ cdef class SeriesGrouper(_BaseGrouper):
             object res
             bint initialized = 0
             Slider vslider, islider
-            object name, cached_typ = None, cached_ityp = None
+            object cached_typ = None, cached_ityp = None
 
         labels = self.labels
         counts = np.zeros(self.ngroups, dtype=np.int64)
         group_size = 0
         n = len(self.arr)
-        name = self.name
 
         vslider = Slider(self.arr, self.dummy_arr)
         islider = Slider(self.index, self.dummy_index)
@@ -386,7 +381,7 @@ cdef class SeriesGrouper(_BaseGrouper):
                     vslider.set_length(group_size)
 
                     cached_typ, cached_ityp = self._update_cached_objs(
-                        cached_typ, cached_ityp, islider, vslider, name)
+                        cached_typ, cached_ityp, islider, vslider)
 
                     cached_ityp._engine.clear_mapping()
                     res = self.f(cached_typ)

pandas/_libs/tslibs/c_timestamp.pyx

@@ -201,7 +201,7 @@ cdef class _Timestamp(datetime):
         """
         return np.datetime64(self.value, 'ns')
 
-    def to_numpy(self, dtype=None, copy=False):
+    def to_numpy(self, dtype=None, copy=False) -> np.datetime64:
         """
         Convert the Timestamp to a NumPy datetime64.
 
@@ -369,18 +369,18 @@ cdef class _Timestamp(datetime):
         return out[0]
 
     @property
-    def _repr_base(self):
+    def _repr_base(self) -> str:
         return '{date} {time}'.format(date=self._date_repr,
                                       time=self._time_repr)
 
     @property
-    def _date_repr(self):
+    def _date_repr(self) -> str:
         # Ideal here would be self.strftime("%Y-%m-%d"), but
         # the datetime strftime() methods require year >= 1900
         return '%d-%.2d-%.2d' % (self.year, self.month, self.day)
 
     @property
-    def _time_repr(self):
+    def _time_repr(self) -> str:
         result = '%.2d:%.2d:%.2d' % (self.hour, self.minute, self.second)
 
         if self.nanosecond != 0:
@@ -391,7 +391,7 @@ cdef class _Timestamp(datetime):
         return result
 
     @property
-    def _short_repr(self):
+    def _short_repr(self) -> str:
         # format a Timestamp with only _date_repr if possible
         # otherwise _repr_base
         if (self.hour == 0 and
@@ -403,7 +403,7 @@ cdef class _Timestamp(datetime):
         return self._repr_base
 
     @property
-    def asm8(self):
+    def asm8(self) -> np.datetime64:
         """
         Return numpy datetime64 format in nanoseconds.
         """

pandas/_libs/tslibs/nattype.pyx

@@ -230,16 +230,16 @@ cdef class _NaT(datetime):
         return NotImplemented
 
     @property
-    def asm8(self):
+    def asm8(self) -> np.datetime64:
         return np.datetime64(NPY_NAT, 'ns')
 
-    def to_datetime64(self):
+    def to_datetime64(self) -> np.datetime64:
         """
         Return a numpy.datetime64 object with 'ns' precision.
         """
         return np.datetime64('NaT', 'ns')
 
-    def to_numpy(self, dtype=None, copy=False):
+    def to_numpy(self, dtype=None, copy=False) -> np.datetime64:
         """
         Convert the Timestamp to a NumPy datetime64.
 
@@ -265,7 +265,7 @@ cdef class _NaT(datetime):
     def __str__(self) -> str:
         return 'NaT'
 
-    def isoformat(self, sep='T'):
+    def isoformat(self, sep='T') -> str:
         # This allows Timestamp(ts.isoformat()) to always correctly roundtrip.
         return 'NaT'
 

pandas/_libs/tslibs/timedeltas.pyx

@@ -841,15 +841,15 @@ cdef class _Timedelta(timedelta):
         """
         return timedelta(microseconds=int(self.value) / 1000)
 
-    def to_timedelta64(self):
+    def to_timedelta64(self) -> np.timedelta64:
         """
         Return a numpy.timedelta64 object with 'ns' precision.
         """
         return np.timedelta64(self.value, 'ns')
 
-    def to_numpy(self, dtype=None, copy=False):
+    def to_numpy(self, dtype=None, copy=False) -> np.timedelta64:
         """
-        Convert the Timestamp to a NumPy timedelta64.
+        Convert the Timedelta to a NumPy timedelta64.
 
         .. versionadded:: 0.25.0
 
@@ -920,7 +920,7 @@ cdef class _Timedelta(timedelta):
         return self.value
 
     @property
-    def asm8(self):
+    def asm8(self) -> np.timedelta64:
         """
         Return a numpy timedelta64 array scalar view.
 
@@ -955,7 +955,7 @@ cdef class _Timedelta(timedelta):
         return np.int64(self.value).view('m8[ns]')
 
     @property
-    def resolution_string(self):
+    def resolution_string(self) -> str:
         """
         Return a string representing the lowest timedelta resolution.
 
@@ -1095,7 +1095,7 @@ cdef class _Timedelta(timedelta):
         self._ensure_components()
         return self._ns
 
-    def _repr_base(self, format=None):
+    def _repr_base(self, format=None) -> str:
         """
 
         Parameters
@@ -1148,10 +1148,10 @@ cdef class _Timedelta(timedelta):
     def __str__(self) -> str:
         return self._repr_base(format='long')
 
-    def __bool__(self):
+    def __bool__(self) -> bool:
         return self.value != 0
 
-    def isoformat(self):
+    def isoformat(self) -> str:
         """
         Format Timedelta as ISO 8601 Duration like
         ``P[n]Y[n]M[n]DT[n]H[n]M[n]S``, where the ``[n]`` s are replaced by the

pandas/core/groupby/ops.py

@@ -830,8 +830,7 @@ def agg_series(self, obj: Series, func):
         assert self.ngroups != 0
 
         if is_extension_array_dtype(obj.dtype):
-            # pre-empty SeriesBinGrouper from raising TypeError
-            # TODO: watch out, this can return None
+            # pre-empt SeriesBinGrouper from raising TypeError
             return self._aggregate_series_pure_python(obj, func)
 
         dummy = obj[:0]

pandas/core/reshape/merge.py

@@ -565,8 +565,8 @@ def __init__(
     ):
         _left = _validate_operand(left)
         _right = _validate_operand(right)
-        self.left = self.orig_left = _validate_operand(_left)  # type: "DataFrame"
-        self.right = self.orig_right = _validate_operand(_right)  # type: "DataFrame"
+        self.left = self.orig_left = _left
+        self.right = self.orig_right = _right
         self.how = how
         self.axis = axis