Implementing automatic fallback to coarser units when OutOfBoundsDatetime occurs in array_strptime and array_to_datetime during inference mode only.

Summary

Successfully implemented automatic fallback to coarser datetime units when OutOfBoundsDatetime errors occur during inference mode, allowing pandas to gracefully handle dates outside the nanosecond range without changing behavior for explicit unit requests.

Changes Made

Core Implementation

• Added get_next_coarser_unit() helper function in both strptime.pyx and tslib.pyx
• Modified array_strptime to catch OutOfBoundsDatetime during inference and recursively retry with coarser units
• Modified array_to_datetime to catch OverflowError during inference and recursively retry with coarser units
• Implemented fallback sequence: ns → us → ms → s
• Used recursion pattern matching existing code style

Key Behavioral Changes

• Inference mode (creso=GENERIC): When OutOfBoundsDatetime occurs during inference, automatically falls back to coarser units (ns → us → ms → s) by recursively calling the function with the next coarser unit.
• Explicit resolution (creso=ns/us/ms/s): Raises OutOfBoundsDatetime as before - NO fallback. This preserves existing behavior when users explicitly request a specific resolution.
• Array-level fallback: When any element causes OutOfBoundsDatetime during inference, the entire array is re-parsed with a coarser unit using a recursive call.

Testing

• All 630 tslibs tests passing with no regressions
• Verified inference mode falls back correctly
• Verified explicit mode raises OutOfBoundsDatetime as expected

Examples

# Inference mode - automatic fallback
array_strptime(["2401-09-15"], "ISO8601", creso=GENERIC)
# Returns: M8[s] (falls back from ns to coarser units during inference)

# Explicit ns resolution - raises error (no fallback)
array_strptime(["2401-09-15"], "ISO8601", creso=NPY_FR_ns)
# Raises: OutOfBoundsDatetime (preserves existing behavior)

Files Modified

• pandas/_libs/tslibs/strptime.pyx - Added helper and fallback logic during inference
• pandas/_libs/tslib.pyx - Added helper and fallback logic during inference

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Problem Statement

Implement an API change to array_strptime and array_to_datetime to gracefully handle OutOfBoundsDatetime errors by automatically falling back to coarser units instead of raising an error.

Current Behavior

Both array_strptime (in pandas/_libs/tslibs/strptime.pyx) and array_to_datetime (in pandas/_libs/tslib.pyx) perform unit inference to decide what dtype to return. Historically, they always returned a nanosecond unit and raised OutOfBoundsDatetime when there was a value that couldn't be represented in nanoseconds.

New API Behavior

• Default to nanoseconds (similar to the old behavior for backward compatibility)
• When OutOfBoundsDatetime occurs, automatically fall back to the closest-supported unit instead of raising an error
• Unit precedence: ns -> us (microseconds) -> ms (milliseconds) -> s (seconds)
• This should be automatic - no new parameter needed

Implementation Details

In array_strptime (pandas/_libs/tslibs/strptime.pyx):

1. When OutOfBoundsDatetime or OverflowError is raised from npy_datetimestruct_to_datetime, catch it
2. Try with the next coarser unit in the sequence: ns -> us -> ms -> s
3. Re-parse the entire array with the coarser unit
4. This affects two locations in the code:
   • Around lines 472-478 (inside the string_to_dts_succeeded block)
   • Around lines 517-523 (after _parse_with_format)

In array_to_datetime (pandas/_libs/tslib.pyx):

1. Similar approach - when OverflowError is raised during datetime conversion, catch it
2. Fall back to coarser units following the same precedence: ns -> us -> ms -> s
3. Re-parse the entire array with the coarser unit
4. This primarily affects the calls to parse_pydatetime and pydate_to_dt64 which can raise OverflowError

Key Considerations

• The fallback should happen at the array level, not per-element. When one value is out of bounds for a given unit, retry parsing the entire array with a coarser unit.
• When doing unit inference (infer_reso=True or creso=NPY_FR_GENERIC), the fallback logic should work seamlessly with the existing resolution inference mechanism.
• The implementation should catch both OutOfBoundsDatetime and OverflowError exceptions as they can both indicate out-of-bounds conditions.
• Add helper logic to determine the next coarser unit in the sequence.

Testing

Add tests to verify:

1. Arrays with values out of nanosecond bounds automatically fall back to microseconds
2. Arrays with values out of microsecond bounds automatically fall back to milliseconds
3. Arrays with values out of millisecond bounds automatically fall back to seconds
4. The behavior is automatic and doesn't require any parameter changes
5. Error messages are appropriate if even seconds cannot represent the values

Files to Modify

1. pandas/_libs/tslibs/strptime.pyx - Modify array_strptime function
2. pandas/_libs/tslib.pyx - Modify array_to_datetime function
3. Add or update tests in pandas/tests/tslibs/test_strptime.py and pandas/tests/tslibs/test_array_to_datetime.py

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