Fix accidental loss-of-precision for to_datetime(str, unit=...)

In Pandas 1.5.3, the `float(val)` cast was inline to the
`cast_from_unit` call in `array_with_unit_to_datetime`. This caused the
intermediate (unnamed) value to be a Python float.

Since #50301, a temporary variable was added to avoid multiple casts,
but with explicit type `cdef float`, which defines a _Cython_ float.
This type is 32-bit, and causes a loss of precision, and a regression in
parsing from 1.5.3.

So widen the explicit type of the temporary `fval` variable to (64-bit)
`double`, which will not lose precision.

Fixes #57051

Author: QuLogic

doc/source/whatsnew/v2.2.1.rst

@@ -50,6 +50,7 @@ Fixed regressions
 - Fixed regression in :meth:`Series.pct_change` raising a ``ValueError`` for an empty :class:`Series` (:issue:`57056`)
 - Fixed regression in :meth:`Series.to_numpy` when dtype is given as float and the data contains NaNs (:issue:`57121`)
 - Fixed regression in addition or subtraction of :class:`DateOffset` objects with millisecond components to ``datetime64`` :class:`Index`, :class:`Series`, or :class:`DataFrame` (:issue:`57529`)
+- Fixed regression in precision of :func:`to_datetime` with string and ``unit`` input (:issue:`57051`)
 
 .. ---------------------------------------------------------------------------
 .. _whatsnew_221.bug_fixes:

pandas/_libs/tslib.pyx

@@ -275,7 +275,7 @@ def array_with_unit_to_datetime(
         bint is_raise = errors == "raise"
         ndarray[int64_t] iresult
         tzinfo tz = None
-        float fval
+        double fval
 
     assert is_coerce or is_raise
 

pandas/tests/tools/test_to_datetime.py

@@ -1735,6 +1735,14 @@ 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 = pd.to_datetime(["1704660000"], unit="s", origin="unix")
+        expected = pd.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", ""]