* add test for float to_datetime near overflow bounds

* fix float to_datetime near overflow bounds

* fix typo and formatting

* fix formatting

* fix test to not fail on rounding differences

* don't use approximate comparison on datetimes, it doesn't work

* also can't convert datetime to float

* match dtypes

* TST: don't try to use non-integer years (see #50301)

* TST: don't cross an integer

(tsmax_in_days happens to be close to an integer,
and this is a test of rounding)

* PERF: remove unnecessary copy

* add whatsnew

* add test for float to_datetime near overflow bounds

* fix float to_datetime near overflow bounds

* fix typo and formatting

* fix formatting

* fix test to not fail on rounding differences

* don't use approximate comparison on datetimes, it doesn't work

* also can't convert datetime to float

* match dtypes

* TST: don't try to use non-integer years (see pandas-dev#50301)

* TST: don't cross an integer

(tsmax_in_days happens to be close to an integer,
and this is a test of rounding)

* PERF: remove unnecessary copy

* add whatsnew

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 pandas-dev#50301, an 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.

Since `cast_from_unit` takes an `object`, not a more specific Cython
type, remove the explicit type from the temporary `fval` variable
entirely. This will cause it to be a (64-bit) Python float, and thus not
lose precision.

Fixes pandas-dev#57051

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 pandas-dev#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.

Since `cast_from_unit` takes an `object`, not a more specific Cython
type, remove the explicit type from the temporary `fval` variable
entirely. This will cause it to be a (64-bit) Python float, and thus not
lose precision.

Fixes pandas-dev#57051

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 pandas-dev#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 pandas-dev#57051

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 pandas-dev#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 pandas-dev#57051

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 pandas-dev#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 pandas-dev#57051

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

…as-dev#57548)

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 pandas-dev#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 pandas-dev#57051