REF: implement LossySetitemError

pandas/core/arrays/interval.py

@@ -39,6 +39,7 @@
 from pandas.errors import IntCastingNaNError
 from pandas.util._decorators import Appender
 
+from pandas.core.dtypes.cast import LossySetitemError
 from pandas.core.dtypes.common import (
     is_categorical_dtype,
     is_dtype_equal,
@@ -1081,7 +1082,7 @@ def _validate_listlike(self, value):
 
         try:
             self.left._validate_fill_value(value_left)
-        except (ValueError, TypeError) as err:
+        except (LossySetitemError, TypeError) as err:
             msg = (
                 "'value' should be a compatible interval type, "
                 f"got {type(value)} instead."

pandas/core/dtypes/cast.py

@@ -1924,6 +1924,8 @@ def can_hold_element(arr: ArrayLike, element: Any) -> bool:
                 arr._validate_setitem_value(element)
                 return True
             except (ValueError, TypeError):
+                # TODO(2.0): stop catching ValueError for tzaware, see
+                #  _catch_deprecated_value_error
                 return False
 
         # This is technically incorrect, but maintains the behavior of
@@ -1933,7 +1935,7 @@ def can_hold_element(arr: ArrayLike, element: Any) -> bool:
     try:
         np_can_hold_element(dtype, element)
         return True
-    except (TypeError, ValueError):
+    except (TypeError, LossySetitemError):
         return False
 
 
@@ -1963,7 +1965,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
         if isinstance(element, range):
             if _dtype_can_hold_range(element, dtype):
                 return element
-            raise ValueError
+            raise LossySetitemError
 
         elif is_integer(element) or (is_float(element) and element.is_integer()):
             # e.g. test_setitem_series_int8 if we have a python int 1
@@ -1972,7 +1974,7 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
             info = np.iinfo(dtype)
             if info.min <= element <= info.max:
                 return dtype.type(element)
-            raise ValueError
+            raise LossySetitemError
 
         if tipo is not None:
             if tipo.kind not in ["i", "u"]:
@@ -1986,10 +1988,10 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
                         #  np.putmask, whereas the raw values cannot.
                         #  see TestSetitemFloatNDarrayIntoIntegerSeries
                         return casted
-                    raise ValueError
+                    raise LossySetitemError
 
                 # Anything other than integer we cannot hold
-                raise ValueError
+                raise LossySetitemError
             elif (
                 dtype.kind == "u"
                 and isinstance(element, np.ndarray)
@@ -2001,31 +2003,31 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
                     # TODO: faster to check (element >=0).all()?  potential
                     #  itemsize issues there?
                     return casted
-                raise ValueError
+                raise LossySetitemError
             elif dtype.itemsize < tipo.itemsize:
-                raise ValueError
+                raise LossySetitemError
             elif not isinstance(tipo, np.dtype):
                 # i.e. nullable IntegerDtype; we can put this into an ndarray
                 #  losslessly iff it has no NAs
                 if element._hasna:
-                    raise ValueError
+                    raise LossySetitemError
                 return element
 
             return element
 
-        raise ValueError
+        raise LossySetitemError
 
     elif dtype.kind == "f":
         if tipo is not None:
             # TODO: itemsize check?
             if tipo.kind not in ["f", "i", "u"]:
                 # Anything other than float/integer we cannot hold
-                raise ValueError
+                raise LossySetitemError
             elif not isinstance(tipo, np.dtype):
                 # i.e. nullable IntegerDtype or FloatingDtype;
                 #  we can put this into an ndarray losslessly iff it has no NAs
                 if element._hasna:
-                    raise ValueError
+                    raise LossySetitemError
                 return element
             elif tipo.itemsize > dtype.itemsize:
                 if isinstance(element, np.ndarray):
@@ -2034,13 +2036,13 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
                     # TODO(np>=1.20): we can just use np.array_equal with equal_nan
                     if array_equivalent(casted, element):
                         return casted
-                    raise ValueError
+                    raise LossySetitemError
 
             return element
 
         if lib.is_integer(element) or lib.is_float(element):
             return element
-        raise ValueError
+        raise LossySetitemError
 
     elif dtype.kind == "c":
         if lib.is_integer(element) or lib.is_complex(element) or lib.is_float(element):
@@ -2052,13 +2054,13 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
             if casted == element:
                 return casted
             # otherwise e.g. overflow see test_32878_complex_itemsize
-            raise ValueError
+            raise LossySetitemError
 
         if tipo is not None:
             if tipo.kind in ["c", "f", "i", "u"]:
                 return element
-            raise ValueError
-        raise ValueError
+            raise LossySetitemError
+        raise LossySetitemError
 
     elif dtype.kind == "b":
         if tipo is not None:
@@ -2067,23 +2069,23 @@ def np_can_hold_element(dtype: np.dtype, element: Any) -> Any:
                     # i.e. we have a BooleanArray
                     if element._hasna:
                         # i.e. there are pd.NA elements
-                        raise ValueError
+                        raise LossySetitemError
                 return element
-            raise ValueError
+            raise LossySetitemError
         if lib.is_bool(element):
             return element
-        raise ValueError
+        raise LossySetitemError
 
     elif dtype.kind == "S":
         # TODO: test tests.frame.methods.test_replace tests get here,
         #  need more targeted tests.  xref phofl has a PR about this
         if tipo is not None:
             if tipo.kind == "S" and tipo.itemsize <= dtype.itemsize:
                 return element
-            raise ValueError
+            raise LossySetitemError
         if isinstance(element, bytes) and len(element) <= dtype.itemsize:
             return element
-        raise ValueError
+        raise LossySetitemError
 
     raise NotImplementedError(dtype)
 
@@ -2097,3 +2099,11 @@ def _dtype_can_hold_range(rng: range, dtype: np.dtype) -> bool:
     if not len(rng):
         return True
     return np.can_cast(rng[0], dtype) and np.can_cast(rng[-1], dtype)
+
+
+class LossySetitemError(Exception):
+    """
+    Raised when trying to do a __setitem__ on an np.ndarray that is not lossless.
+    """
+
+    pass

pandas/core/frame.py

@@ -93,6 +93,7 @@
 )
 
 from pandas.core.dtypes.cast import (
+    LossySetitemError,
     can_hold_element,
     construct_1d_arraylike_from_scalar,
     construct_2d_arraylike_from_scalar,
@@ -3882,11 +3883,11 @@ def _set_value(
                 series = self._get_item_cache(col)
                 loc = self.index.get_loc(index)
 
-            # setitem_inplace will do validation that may raise TypeError
-            #  or ValueError
+            # setitem_inplace will do validation that may raise TypeError,
+            #  ValueError, or LossySetitemError
             series._mgr.setitem_inplace(loc, value)
 
-        except (KeyError, TypeError, ValueError):
+        except (KeyError, TypeError, ValueError, LossySetitemError):
             # set using a non-recursive method & reset the cache
             if takeable:
                 self.iloc[index, col] = value

pandas/core/indexes/base.py

@@ -68,6 +68,7 @@
 
 from pandas.core.dtypes.astype import astype_nansafe
 from pandas.core.dtypes.cast import (
+    LossySetitemError,
     can_hold_element,
     common_dtype_categorical_compat,
     ensure_dtype_can_hold_na,
@@ -5071,12 +5072,13 @@ def _validate_fill_value(self, value):
         """
         dtype = self.dtype
         if isinstance(dtype, np.dtype) and dtype.kind not in ["m", "M"]:
+            # return np_can_hold_element(dtype, value)
             try:
                 return np_can_hold_element(dtype, value)
-            except ValueError as err:
+            except LossySetitemError as err:
                 # re-raise as TypeError for consistency
                 raise TypeError from err
-        if not can_hold_element(self._values, value):
+        elif not can_hold_element(self._values, value):
             raise TypeError
         return value
 
@@ -5294,7 +5296,7 @@ def putmask(self, mask, value) -> Index:
             value = self._na_value
         try:
             converted = self._validate_fill_value(value)
-        except (ValueError, TypeError) as err:
+        except (LossySetitemError, ValueError, TypeError) as err:
             if is_object_dtype(self):  # pragma: no cover
                 raise err
 
@@ -6719,7 +6721,7 @@ def insert(self, loc: int, item) -> Index:
                 return type(self)._simple_new(res_values, name=self.name)
             else:
                 item = self._validate_fill_value(item)
-        except (TypeError, ValueError):
+        except (TypeError, ValueError, LossySetitemError):
             # e.g. trying to insert an integer into a DatetimeIndex
             #  We cannot keep the same dtype, so cast to the (often object)
             #  minimal shared dtype before doing the insert.

pandas/core/internals/blocks.py

@@ -36,6 +36,7 @@
 
 from pandas.core.dtypes.astype import astype_array_safe
 from pandas.core.dtypes.cast import (
+    LossySetitemError,
     can_hold_element,
     find_result_type,
     maybe_downcast_to_dtype,
@@ -1191,7 +1192,7 @@ def where(self, other, cond) -> list[Block]:
             #  but this gets us back 'casted' which we will re-use below;
             #  without using 'casted', expressions.where may do unwanted upcasts.
             casted = np_can_hold_element(values.dtype, other)
-        except (ValueError, TypeError):
+        except (ValueError, TypeError, LossySetitemError):
             # we cannot coerce, return a compat dtype
             block = self.coerce_to_target_dtype(other)
             blocks = block.where(orig_other, cond)

pandas/core/series.py

@@ -62,6 +62,7 @@
 )
 
 from pandas.core.dtypes.cast import (
+    LossySetitemError,
     convert_dtypes,
     maybe_box_native,
     maybe_cast_pointwise_result,
@@ -1102,7 +1103,7 @@ def __setitem__(self, key, value) -> None:
                 # GH#12862 adding a new key to the Series
                 self.loc[key] = value
 
-        except (TypeError, ValueError):
+        except (TypeError, ValueError, LossySetitemError):
             # The key was OK, but we cannot set the value losslessly
             indexer = self.index.get_loc(key)
             self._set_values(indexer, value)