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From cbf5299a065e20a5b129ad5eed6953262ce54f37 Mon Sep 17 00:00:00 2001
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From: Elliott Sales de Andrade <quantum.analyst@gmail.com>
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Date: Wed, 21 Feb 2024 06:55:19 -0500
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Subject: [PATCH 6/6] Fix accidental loss-of-precision for to_datetime(str,
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unit=...)
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In Pandas 1.5.3, the `float(val)` cast was inline to the
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`cast_from_unit` call in `array_with_unit_to_datetime`. This caused the
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intermediate (unnamed) value to be a Python float.
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Since #50301, a temporary variable was added to avoid multiple casts,
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but with explicit type `cdef float`, which defines a _Cython_ float.
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This type is 32-bit, and causes a loss of precision, and a regression in
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parsing from 1.5.3.
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So widen the explicit type of the temporary `fval` variable to (64-bit)
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`double`, which will not lose precision.
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Fixes #57051
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Signed-off-by: Elliott Sales de Andrade <quantum.analyst@gmail.com>
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---
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pandas/_libs/tslib.pyx | 2 +-
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pandas/tests/tools/test_to_datetime.py | 8 ++++++++
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2 files changed, 9 insertions(+), 1 deletion(-)
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diff --git a/pandas/_libs/tslib.pyx b/pandas/_libs/tslib.pyx
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index 017fdc4bc8..dd23c2f27c 100644
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--- a/pandas/_libs/tslib.pyx
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+++ b/pandas/_libs/tslib.pyx
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@@ -277,7 +277,7 @@ def array_with_unit_to_datetime(
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bint is_raise = errors == "raise"
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ndarray[int64_t] iresult
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tzinfo tz = None
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- float fval
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+ double fval
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assert is_ignore or is_coerce or is_raise
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diff --git a/pandas/tests/tools/test_to_datetime.py b/pandas/tests/tools/test_to_datetime.py
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index 6791ac0340..a4194dcff2 100644
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--- a/pandas/tests/tools/test_to_datetime.py
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+++ b/pandas/tests/tools/test_to_datetime.py
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@@ -1912,6 +1912,14 @@ class TestToDatetimeUnit:
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with pytest.raises(ValueError, match=msg):
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to_datetime([1], unit="D", format="%Y%m%d", cache=cache)
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+ def test_unit_str(self, cache):
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+ # GH 57051
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+ # Test that strs aren't dropping precision to 32-bit accidentally.
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+ with tm.assert_produces_warning(FutureWarning):
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+ res = pd.to_datetime(["1704660000"], unit="s", origin="unix")
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+ expected = pd.to_datetime([1704660000], unit="s", origin="unix")
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+ tm.assert_index_equal(res, expected)
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+
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def test_unit_array_mixed_nans(self, cache):
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values = [11111111111111111, 1, 1.0, iNaT, NaT, np.nan, "NaT", ""]
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result = to_datetime(values, unit="D", errors="ignore", cache=cache)
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--
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2.43.0
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