feat: Support Multi-Year Spans in pandas.Period Parsing

[AhmedESamy]

Feature Type

• Adding new functionality to pandas

• Changing existing functionality in pandas

• Removing existing functionality in pandas

Problem Description

Currently, pandas.Period() does not support multi-year spans defined in a single string input. This is a limitation in use cases that require defining and working with multi-year periods for financial reports, fiscal years, and research studies.

Proposed Feature: Extend pandas.Period() and/or pandas.period_range() to support multi-year spans using a standardized format. Possible formats:

• "2019-2021" → Represents a multi-year period (Jan 1, 2019 - Dec 31, 2021)
• "2020Q2-2023Q1" → Represents a quarter-based multi-year period

Why Is This Needed?

• pandas.Period("2019", freq="Y") exists, but pandas.Period("2019-2021", freq="Y") throws an error.
• Business reports and research commonly span multiple years.
• This would add consistency to how pandas handles date-based period parsing.

Feature Description

• Modify pandas/_libs/tslibs/parsing.pyx to recognize "YYYY-YYYY" format.
• Adjust pandas.Period() parsing logic to correctly interpret multi-year ranges.
• Implement unit tests for expected behavior.

Alternative Solutions

• Manual Conversion – Users manually parse ordinal dates (YYYY-DDD) or multi-year spans (YYYY-YYYY) and convert them using pd.Timestamp or pd.period_range().
  Limitation: Requires extra effort and lacks built-in support.

• Using dateutil – dateutil.parser.isoparse() supports ordinal dates but does not return Period. Multi-year spans require additional handling.
  Limitation: Not integrated with pandas.Period.

• Extending pd.to_datetime() – Modify pandas timestamp parsing instead of Period.
  Limitation: to_datetime() is focused on timestamp conversion rather than handling periods, making it a less natural fit.

Additional Context

No response