Fix Pandas 3.x compatibility, float-casting, and test discovery gap for USCensusPEP_Sex

[rohitkumarbhagat]

PR Description

Context & Problem

The scheduled execution for the USCensusPEP_Sex import failed in production due to the following reasons:

1. Pandas 3.x Deprecation Removal: The preprocessing script called pd.read_table(..., delim_whitespace=True). This keyword was completely removed in Pandas 3.x, causing the pipeline to crash in the production environment.
2. Silent Test Discovery Skipping: Unit tests for this import were silently skipped in CI builds because the directories scripts/us_census/pep/ and scripts/us_census/pep/us_pep_sex/ were missing package-initialization __init__.py files, masking errors.
3. Float-Casting Bug: Re-enabling the tests revealed a bug where a blank row in county intercensal files introduced NaN values, forcing year and FIPS codes to be cast to floats (resulting in malformed values like 1989.0 and geoId/1001.0 in the CSV outputs).

Solution & Changes

1. Pandas 3.x Compatibility:

   • Replaced all instances of delim_whitespace=True with sep=r'\s+' in scripts/us_census/pep/us_pep_sex/process.py.
   • Added checks to guard absl.flags definitions in process.py from duplicate definition crashes during test discovery.

2. Data Type and Format Correctness:

   • Updated the intercensal county parsing function to drop NaN/blank rows immediately and cast year/FIPS columns explicitly to int64. This fixes the malformed 1989.0 years and geoId/1001.0 county codes.
   • Cast the melted Observation column explicitly to int64 to prevent .0 suffixes in the final CSV file.
   • Regenerated the expected unit test CSV, MCF, and TMCF files to align with the correct integer-formatted output.

3. CI/CD Enablement & Code Cleanup:

   • Created empty package __init__.py files in both PEP directory levels to ensure unit tests are auto-discovered and executed by CI.
   • Refactored the adjacent annual_population unit test preprocess_test.py to write outputs to a temporary directory rather than writing directly into the local git workspace.

Verification

• All PEP tests auto-discovered and passed successfully (OK).
• Project linter check passed successfully with zero warnings/errors.

[gemini-code-assist]

Code Review

This pull request refactors the US Census PEP preprocessing scripts and tests. Key changes include using temporary directories for test outputs, avoiding flag redefinitions, replacing deprecated pandas parameters, and casting data columns to integer types to clean up the output formatting. The reviewer feedback highlights a potential runtime crash when casting the Observation column to int64 if nulls or empty strings are present, suggesting a safer conversion to Int64. Additionally, the reviewer recommends refactoring the test setup in preprocess_test.py from __init__ to setUpClass to prevent redundant execution of heavy setup tasks.

[rohitkumarbhagat]

/gemini review

[gemini-code-assist]

Code Review

This pull request refactors the PEP (Population Estimates Program) preprocessing scripts and tests. Key changes include refactoring test classes to use standard setUpClass and tearDownClass methods, preventing duplicate flag definitions, replacing deprecated pandas parameters, and ensuring proper integer casting for years, FIPS codes, and observations. The expected test data has been updated to reflect these formatting improvements. There are no review comments, so I have no feedback to provide.

[ajaits]

Is there a redefinition or cyclic dependency with this?

[rohitkumarbhagat]

Yes, this prevents a flag redefinition rather than a cyclic dependency. Abseil uses a process-global flag registry, and test discovery imports both annual_population.preprocess and us_pep_sex.process; both define the same mode string flag. Without the guard, whichever module is imported second raises DuplicateFlagError.

[rohitkumarbhagat]

i have refactored code to lazy load the flags, to avoid cases where redeclaration conflict.