BUG: Special-case setting nan into integer series

pandas/_libs/lib.pyi

@@ -195,6 +195,7 @@ def array_equivalent_object(
     right: npt.NDArray[np.object_],
 ) -> bool: ...
 def has_infs(arr: np.ndarray) -> bool: ...  # const floating[:]
+def has_only_ints_or_nan(arr: np.ndarray) -> bool: ...  # const floating[:]
 def get_reverse_indexer(
     indexer: np.ndarray,  # const intp_t[:]
     length: int,

pandas/_libs/lib.pyx

@@ -530,6 +530,22 @@ def has_infs(floating[:] arr) -> bool:
     return ret
 
 
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def has_only_ints_or_nan(floating[:] arr) -> bool:
+    cdef:
+        floating val
+        intp_t i
+
+    for i in range(len(arr)):
+        val = arr[i]
+        if (val != val) or (val == <int64_t>val):
+            continue
+        else:
+            return False
+    return True
+
+
 def maybe_indices_to_slice(ndarray[intp_t, ndim=1] indices, int max_len):
     cdef:
         Py_ssize_t i, n = len(indices)

pandas/core/internals/blocks.py

@@ -60,6 +60,7 @@
 from pandas.core.dtypes.common import (
     ensure_platform_int,
     is_1d_only_ea_dtype,
+    is_integer_dtype,
     is_list_like,
     is_string_dtype,
 )
@@ -453,6 +454,10 @@ def coerce_to_target_dtype(self, other, warn_on_upcast: bool = False) -> Block:
         we can also safely try to coerce to the same dtype
         and will receive the same block
         """
+        if isna(other) and is_integer_dtype(self.values.dtype):
+            # In a future version of pandas, the default will be that
+            # setting `nan` into an integer series won't raise.
+            warn_on_upcast = False
         new_dtype = find_result_type(self.values.dtype, other)
         if warn_on_upcast:
             warnings.warn(