add more conditional nogil

pandas/_libs/algos.pyx

@@ -818,33 +818,7 @@ def is_monotonic(const numeric_object_t[:] arr, bint timelike):
     if timelike and <int64_t>arr[0] == NPY_NAT:
         return False, False, False
 
-    if numeric_object_t is not object:
-        with nogil:
-            prev = arr[0]
-            for i in range(1, n):
-                cur = arr[i]
-                if timelike and <int64_t>cur == NPY_NAT:
-                    is_monotonic_inc = 0
-                    is_monotonic_dec = 0
-                    break
-                if cur < prev:
-                    is_monotonic_inc = 0
-                elif cur > prev:
-                    is_monotonic_dec = 0
-                elif cur == prev:
-                    is_unique = 0
-                else:
-                    # cur or prev is NaN
-                    is_monotonic_inc = 0
-                    is_monotonic_dec = 0
-                    break
-                if not is_monotonic_inc and not is_monotonic_dec:
-                    is_monotonic_inc = 0
-                    is_monotonic_dec = 0
-                    break
-                prev = cur
-    else:
-        # object-dtype, identical to above except we cannot use `with nogil`
+    with nogil(numeric_object_t is not object):
         prev = arr[0]
         for i in range(1, n):
             cur = arr[i]

pandas/_libs/hashtable_func_helper.pxi.in

@@ -415,20 +415,7 @@ def mode(ndarray[htfunc_t] values, bint dropna, const uint8_t[:] mask=None):
 
     modes = np.empty(nkeys, dtype=values.dtype)
 
-    if htfunc_t is not object:
-        with nogil:
-            for k in range(nkeys):
-                count = counts[k]
-                if count == max_count:
-                    j += 1
-                elif count > max_count:
-                    max_count = count
-                    j = 0
-                else:
-                    continue
-
-                modes[j] = keys[k]
-    else:
+    with nogil(htfunc_t is not object):
         for k in range(nkeys):
             count = counts[k]
             if count == max_count:

pandas/_libs/reshape.pyx

@@ -40,27 +40,7 @@ def unstack(const numeric_object_t[:, :] values, const uint8_t[:] mask,
     cdef:
         Py_ssize_t i, j, w, nulls, s, offset
 
-    if numeric_object_t is not object:
-        # evaluated at compile-time
-        with nogil:
-            for i in range(stride):
-
-                nulls = 0
-                for j in range(length):
-
-                    for w in range(width):
-
-                        offset = j * width + w
-
-                        if mask[offset]:
-                            s = i * width + w
-                            new_values[j, s] = values[offset - nulls, i]
-                            new_mask[j, s] = 1
-                        else:
-                            nulls += 1
-
-    else:
-        # object-dtype, identical to above but we cannot use nogil
+    with nogil(numeric_object_t is not object):
         for i in range(stride):
 
             nulls = 0