Backport PR #47393 on branch 1.4.x (CI/TST: Don't require length for construct_1d_arraylike_from_scalar cast to float64) (#47460)

* Backport PR #47393: CI/TST: Don't require length for construct_1d_arraylike_from_scalar cast to float64

Co-authored-by: Matthew Roeschke <emailformattr@gmail.com>
Co-authored-by: Simon Hawkins <simonjayhawkins@gmail.com>

Author: 3 people

pandas/core/construction.py

@@ -533,7 +533,10 @@ def sanitize_array(
         if dtype is not None and is_float_dtype(data.dtype) and is_integer_dtype(dtype):
             # possibility of nan -> garbage
             try:
-                subarr = _try_cast(data, dtype, copy, True)
+                # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
+                # casting aligning with IntCastingNaNError below
+                with np.errstate(invalid="ignore"):
+                    subarr = _try_cast(data, dtype, copy, True)
             except IntCastingNaNError:
                 warnings.warn(
                     "In a future version, passing float-dtype values containing NaN "

pandas/core/dtypes/cast.py

@@ -1914,7 +1914,9 @@ def construct_1d_arraylike_from_scalar(
             value = maybe_unbox_datetimelike_tz_deprecation(value, dtype)
 
         subarr = np.empty(length, dtype=dtype)
-        subarr.fill(value)
+        if length:
+            # GH 47391: numpy > 1.24 will raise filling np.nan into int dtypes
+            subarr.fill(value)
 
     return subarr
 
@@ -2218,7 +2220,10 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
                 if isinstance(element, np.ndarray) and element.dtype.kind == "f":
                     # If all can be losslessly cast to integers, then we can hold them
                     #  We do something similar in putmask_smart
-                    casted = element.astype(dtype)
+
+                    # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
+                    with np.errstate(invalid="ignore"):
+                        casted = element.astype(dtype)
                     comp = casted == element
                     if comp.all():
                         return element

pandas/core/reshape/merge.py

@@ -1201,23 +1201,27 @@ def _maybe_coerce_merge_keys(self) -> None:
 
                 # check whether ints and floats
                 elif is_integer_dtype(rk.dtype) and is_float_dtype(lk.dtype):
-                    if not (lk == lk.astype(rk.dtype))[~np.isnan(lk)].all():
-                        warnings.warn(
-                            "You are merging on int and float "
-                            "columns where the float values "
-                            "are not equal to their int representation.",
-                            UserWarning,
-                        )
+                    # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
+                    with np.errstate(invalid="ignore"):
+                        if not (lk == lk.astype(rk.dtype))[~np.isnan(lk)].all():
+                            warnings.warn(
+                                "You are merging on int and float "
+                                "columns where the float values "
+                                "are not equal to their int representation.",
+                                UserWarning,
+                            )
                     continue
 
                 elif is_float_dtype(rk.dtype) and is_integer_dtype(lk.dtype):
-                    if not (rk == rk.astype(lk.dtype))[~np.isnan(rk)].all():
-                        warnings.warn(
-                            "You are merging on int and float "
-                            "columns where the float values "
-                            "are not equal to their int representation.",
-                            UserWarning,
-                        )
+                    # GH 47391 numpy > 1.24 will raise a RuntimeError for nan -> int
+                    with np.errstate(invalid="ignore"):
+                        if not (rk == rk.astype(lk.dtype))[~np.isnan(rk)].all():
+                            warnings.warn(
+                                "You are merging on int and float "
+                                "columns where the float values "
+                                "are not equal to their int representation.",
+                                UserWarning,
+                            )
                     continue
 
                 # let's infer and see if we are ok