fix: memory enhancement in import courses

[reybahl]

more efficient data processing / loading, specifically during the importing courses step during sync

[sentry]

🔍 Existing Issues For Review

Your pull request is modifying functions with the following pre-existing issues:

📄 File: ferry/transform/import_courses.py

Function	Unhandled Issue
import_courses	KeyError: 11423 ferry.transform.import_courses in... Event Count: 3
resolve_cross_listings	IndexError: single positional indexer is out-of-bounds ferry.transform.import_courses in resolve_cross_li... Event Count: 1

[Copilot]

Pull Request Overview

This PR implements memory optimizations to reduce memory usage during data transformation processes. The changes focus on reducing intermediate data structures, explicit garbage collection, and more efficient data processing patterns.

Key changes:

• Implemented explicit garbage collection at strategic points in the transformation pipeline
• Optimized professor and course rating computations to avoid large intermediate DataFrames
• Added immediate cleanup of temporary data structures with explicit del statements

Reviewed Changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 3 comments.

File	Description
ferry/transform/init.py	Added garbage collection calls after major transformation steps
ferry/transform/transform_compute.py	Refactored rating computations to use dictionaries and direct aggregation instead of lambda functions and intermediate lists
ferry/transform/import_courses.py	Added cleanup of source data immediately after concatenation

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[Copilot]

The rating_dict parameter should have a more specific type annotation. Consider using dict[int, float] or dict[int, float | None] to better document the expected key-value types.

Suggested change

	def compute_aggregate_rating(course_ids: list[int], rating_dict: dict) -> tuple[float | None, int]:
	def compute_aggregate_rating(course_ids: list[int], rating_dict: dict[int, float | None]) -> tuple[float | None, int]:

[bearsyankees]

lfg