3 New SV addition to eurostatdata education enrollment

[niveditasing]

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[gemini-code-assist]

Summary of Changes

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This pull request enhances the Eurostat education enrollment data pipeline by integrating new statistical variables for the 18-64 age demographic. The changes involve expanding the output schema, refining the data preprocessing logic to accommodate and consolidate data for both the new and existing age groups, and adding a filtering step to ensure data quality and relevance.

Highlights

• New Statistical Variables (SVs): Introduced three new Statistical Variables (SVs) to track education enrollment for the 18-64 age range, specifically for female, male, and total populations as a fraction of their respective age groups.
• Data Preprocessing Logic: Updated the 'preprocess' function to handle and write data for both the existing 25-64 age group and the newly added 18-64 age group, ensuring data points for the same geographical ID and year are consolidated into a single row.
• Data Filtering: Implemented a filtering step in the main processing flow to ensure that only data points with 'PC' (percentage) units and relevant age groups ('Y18-64', 'Y25-64') are processed, preventing irrelevant data from being included.

Changelog

• scripts/eurostat/regional_statistics_by_nuts/education_enrollment/education_enrollment_preprocess_gen_tmcf.py
  • Added three new SVs to the '_OUTPUT_COLUMNS' list for the 18-64 age range.
  • Modified the 'preprocess' function to iterate through grouped data ('geo', 'time') and dynamically populate output rows with data for both 'Y25-64' and 'Y18-64' age groups.
  • Removed the docstring from the 'preprocess' function.
  • Introduced a filtering step in the 'main' function to select rows where 'unit' is 'PC' and 'age' is either 'Y18-64' or 'Y25-64'.

Activity

• No specific human activity (comments, reviews) has been recorded for this pull request yet.

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[gemini-code-assist]

Code Review

This pull request adds three new statistical variables for the 18-64 age range to the Eurostat education enrollment dataset. The preprocessing logic has been updated to handle both the new and existing age ranges, combining them into a single output row per location and year.

My review includes a suggestion to refactor the preprocess function for better performance and maintainability using idiomatic pandas operations, as well as restoring its docstring. More importantly, the unit tests have not been updated to reflect these changes, which will likely cause them to fail. The test data and expected output files need to be updated to account for the new data structure. I've added a high-severity comment regarding the tests.