BLD: fix some build warnings (#29271)

Author: jbrockmendel

pandas/_libs/algos_common_helper.pxi.in

@@ -22,54 +22,55 @@ ctypedef fused out_t:
 def diff_2d(ndarray[diff_t, ndim=2] arr,
             ndarray[out_t, ndim=2] out,
             Py_ssize_t periods, int axis):
+    cdef:
+        Py_ssize_t i, j, sx, sy
 
     # Disable for unsupported dtype combinations,
     #  see https://github.com/cython/cython/issues/2646
-    if out_t is float32_t:
-        if not (diff_t is float32_t or diff_t is int8_t or diff_t is int16_t):
-            raise NotImplementedError
+    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
+    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
     else:
-        if (diff_t is float32_t or diff_t is int8_t or diff_t is int16_t):
-            raise NotImplementedError
-
-    cdef:
-        Py_ssize_t i, j, sx, sy
-
-    sx, sy = (<object>arr).shape
-    if arr.flags.f_contiguous:
-        if axis == 0:
-            if periods >= 0:
-                start, stop = periods, sx
+        # We put this inside an indented else block to avoid cython build
+        #  warnings about unreachable code
+        sx, sy = (<object>arr).shape
+        if arr.flags.f_contiguous:
+            if axis == 0:
+                if periods >= 0:
+                    start, stop = periods, sx
+                else:
+                    start, stop = 0, sx + periods
+                for j in range(sy):
+                    for i in range(start, stop):
+                        out[i, j] = arr[i, j] - arr[i - periods, j]
             else:
-                start, stop = 0, sx + periods
-            for j in range(sy):
-                for i in range(start, stop):
-                    out[i, j] = arr[i, j] - arr[i - periods, j]
+                if periods >= 0:
+                    start, stop = periods, sy
+                else:
+                    start, stop = 0, sy + periods
+                for j in range(start, stop):
+                    for i in range(sx):
+                        out[i, j] = arr[i, j] - arr[i, j - periods]
         else:
-            if periods >= 0:
-                start, stop = periods, sy
+            if axis == 0:
+                if periods >= 0:
+                    start, stop = periods, sx
+                else:
+                    start, stop = 0, sx + periods
+                for i in range(start, stop):
+                    for j in range(sy):
+                        out[i, j] = arr[i, j] - arr[i - periods, j]
             else:
-                start, stop = 0, sy + periods
-            for j in range(start, stop):
+                if periods >= 0:
+                    start, stop = periods, sy
+                else:
+                    start, stop = 0, sy + periods
                 for i in range(sx):
-                    out[i, j] = arr[i, j] - arr[i, j - periods]
-    else:
-        if axis == 0:
-            if periods >= 0:
-                start, stop = periods, sx
-            else:
-                start, stop = 0, sx + periods
-            for i in range(start, stop):
-                for j in range(sy):
-                    out[i, j] = arr[i, j] - arr[i - periods, j]
-        else:
-            if periods >= 0:
-                start, stop = periods, sy
-            else:
-                start, stop = 0, sy + periods
-            for i in range(sx):
-                for j in range(start, stop):
-                    out[i, j] = arr[i, j] - arr[i, j - periods]
+                    for j in range(start, stop):
+                        out[i, j] = arr[i, j] - arr[i, j - periods]
 
 
 # ----------------------------------------------------------------------

pandas/_libs/groupby.pyx

@@ -833,6 +833,9 @@ cdef inline bint _treat_as_na(rank_t val, bint is_datetimelike) nogil:
 
     elif rank_t is int64_t:
         return is_datetimelike and val == NPY_NAT
+    elif rank_t is uint64_t:
+        # There is no NA value for uint64
+        return False
     else:
         return val != val
 
@@ -1278,7 +1281,8 @@ def group_max(groupby_t[:, :] out,
                     if groupby_t is uint64_t:
                         runtime_error = True
                         break
-                    out[i, j] = nan_val
+                    else:
+                        out[i, j] = nan_val
                 else:
                     out[i, j] = maxx[i, j]
 
@@ -1349,7 +1353,8 @@ def group_min(groupby_t[:, :] out,
                     if groupby_t is uint64_t:
                         runtime_error = True
                         break
-                    out[i, j] = nan_val
+                    else:
+                        out[i, j] = nan_val
                 else:
                     out[i, j] = minx[i, j]
 

pandas/io/msgpack/_packer.pyx

@@ -235,7 +235,8 @@ cdef class Packer:
                 continue
             else:
                 raise TypeError("can't serialize {thing!r}".format(thing=o))
-            return ret
+            break
+        return ret
 
     cpdef pack(self, object obj):
         cdef int ret