Clean scores and return top 5 students

Q: Clean scores and return top 5 students

This question evaluates proficiency in data cleaning and transformation for Data Science, focusing on handling missing values, median imputation, and ranking/sorting logic using SQL or Python (Pandas).

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Question

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Implement a Python function to clean and rank student scores.

You are given a table (or DataFrame) students with schema:

column	type	notes
student_id	int	unique identifier
math_score	float	may be null
english_score	float	may be null
physics_score	float	may be null

Task

Write a function (e.g., select_top_students(students_df) -> pd.DataFrame) that:

Removes students with at least 2 missing scores across the three subjects.
For remaining students, fill missing scores with the subject median (median computed per subject using the remaining students’ non-missing values).
Sort the remaining students by:
- math_score descending, then
- physics_score descending
- (optional tie-breaker) student_id ascending.
Return the top 5 students as a table with the same columns.

Notes / edge cases to handle

Missing values may be represented as None / NaN .
If fewer than 5 students remain, return all remaining.
If a subject median is undefined (e.g., all remaining values are null for that subject), specify and implement a reasonable behavior (e.g., leave as null or raise an error)—state your assumption in comments.

Clean scores and return top 5 students

Quick Overview

Task

Notes / edge cases to handle

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