Backport PR pandas-dev#57548: Fix accidental loss-of-precision for to…

…_datetime(str, unit=...)

doc/source/whatsnew/v2.2.2.rst

@@ -15,7 +15,7 @@ Fixed regressions
 ~~~~~~~~~~~~~~~~~
 - :meth:`DataFrame.__dataframe__` was producing incorrect data buffers when the a column's type was a pandas nullable on with missing values (:issue:`56702`)
 - :meth:`DataFrame.__dataframe__` was producing incorrect data buffers when the a column's type was a pyarrow nullable on with missing values (:issue:`57664`)
--
+- Fixed regression in precision of :func:`to_datetime` with string and ``unit`` input (:issue:`57051`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_222.bug_fixes:

pandas/_libs/tslib.pyx

@@ -277,7 +277,7 @@ def array_with_unit_to_datetime(
         bint is_raise = errors == "raise"
         ndarray[int64_t] iresult
         tzinfo tz = None
-        float fval
+        double fval
 
     assert is_ignore or is_coerce or is_raise
 

pandas/tests/tools/test_to_datetime.py

@@ -1912,6 +1912,14 @@ def test_unit(self, cache):
         with pytest.raises(ValueError, match=msg):
             to_datetime([1], unit="D", format="%Y%m%d", cache=cache)
 
+    def test_unit_str(self, cache):
+        # GH 57051
+        # Test that strs aren't dropping precision to 32-bit accidentally.
+        with tm.assert_produces_warning(FutureWarning):
+            res = to_datetime(["1704660000"], unit="s", origin="unix")
+        expected = to_datetime([1704660000], unit="s", origin="unix")
+        tm.assert_index_equal(res, expected)
+
     def test_unit_array_mixed_nans(self, cache):
         values = [11111111111111111, 1, 1.0, iNaT, NaT, np.nan, "NaT", ""]
         result = to_datetime(values, unit="D", errors="ignore", cache=cache)