BUG: fixed to_numeric loss in precision when converting decimal type …

…to integer pandas-dev#57213

doc/source/whatsnew/v3.0.0.rst

@@ -329,6 +329,7 @@ Performance improvements
 Bug fixes
 ~~~~~~~~~
 - Fixed bug in :class:`SparseDtype` for equal comparison with na fill value. (:issue:`54770`)
+- Fixed bug in :func:`to_numeric` that resulted in precision loss when converting decimal type to integer. (:issue:`57213`)
 - Fixed bug in :meth:`.DataFrameGroupBy.median` where nat values gave an incorrect result. (:issue:`57926`)
 - Fixed bug in :meth:`DataFrame.cumsum` which was raising ``IndexError`` if dtype is ``timedelta64[ns]`` (:issue:`57956`)
 - Fixed bug in :meth:`DataFrame.join` inconsistently setting result index name (:issue:`55815`)

pandas/core/tools/numeric.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from decimal import Decimal
 from typing import (
     TYPE_CHECKING,
     Literal,
@@ -209,6 +210,7 @@ def to_numeric(
         values = values.view(np.int64)
     else:
         values = ensure_object(values)
+        old_values = values
         coerce_numeric = errors != "raise"
         values, new_mask = lib.maybe_convert_numeric(  # type: ignore[call-overload]
             values,
@@ -255,7 +257,16 @@ def to_numeric(
             for typecode in typecodes:
                 dtype = np.dtype(typecode)
                 if dtype.itemsize <= values.dtype.itemsize:
-                    values = maybe_downcast_numeric(values, dtype)
+                    try:
+                        if (len(old_values) != 0) and isinstance(
+                            old_values[0], Decimal
+                        ):
+                            if dtype.itemsize == values.dtype.itemsize:
+                                values = np.array(old_values, dtype=dtype)
+                        else:
+                            values = maybe_downcast_numeric(values, dtype)
+                    except NameError:
+                        values = maybe_downcast_numeric(values, dtype)
 
                     # successful conversion
                     if values.dtype == dtype:

pandas/tests/tools/test_to_numeric.py

@@ -904,3 +904,10 @@ def test_coerce_pyarrow_backend():
     result = to_numeric(ser, errors="coerce", dtype_backend="pyarrow")
     expected = Series([1, 2, None], dtype=ArrowDtype(pa.int64()))
     tm.assert_series_equal(result, expected)
+
+
+def test_decimal_precision():
+    df = DataFrame({"column1": [decimal.Decimal("1" * 19)]})
+    result = to_numeric(df["column1"], downcast="integer")
+    expected = df["column1"].astype("int64")
+    tm.assert_series_equal(result, expected)