Completed Pandas5

[Paneripatel]

No description provided.

[super30admin]

1. Correctness:

   • For Problem 1 (Department Highest Salary), the solution correctly identifies the highest salary in each department using dense ranking and merges with the department table. However, it doesn't handle cases where multiple employees might share the same highest salary in a department (though dense ranking would include them).
   • For Problem 2 (Rank Scores), the solution correctly ranks scores using dense ranking and sorts them in descending order. This appears to be correct.

2. Time Complexity:

   • For both problems, the time complexity is O(n log n) due to the ranking operations and sorting (in Problem 2). This is efficient for typical dataset sizes in pandas.

3. Space Complexity:

   • Both solutions have O(n) space complexity as they create new columns for ranking but don't create additional data structures proportional to input size.

4. Code Quality:

   • The code is generally clean and follows pandas best practices.
   • Variable names could be more descriptive (e.g., 'rnk' could be 'rank' for consistency).
   • The merge in Problem 1 could be more explicit about the join type (though inner join is default and correct here).
   • The column renaming in Problem 1 is clear but could be done earlier in the pipeline for better readability.

5. Efficiency:

   • Both solutions are efficient for their respective problems.
   • For Problem 1, an alternative approach could be to use groupby with transform to find max salaries, which might be slightly more intuitive.
   • No major optimizations needed, but the code could be made slightly more readable with minor adjustments.

[super30admin]

1. Correctness:

   • For Problem 1 (Department Highest Salary), the solution correctly identifies the highest salary in each department using dense ranking and merges with the department table. However, it doesn't handle cases where multiple employees might share the highest salary in a department (though dense ranking would capture this).
   • For Problem 2 (Rank Scores), the solution correctly ranks scores using dense ranking and sorts them in descending order.

2. Time Complexity:

   • For both problems, the time complexity is O(n log n) due to the ranking operation and sorting (in Problem 2). This is efficient for typical input sizes in these problems.

3. Space Complexity:

   • The space complexity is O(n) for both solutions as they create new columns in the DataFrames but don't use additional significant space beyond the input size.

4. Code Quality:

   • The code is generally clean and follows good practices.
   • For Problem 1, the column renaming at the end is clear but could be improved by using more descriptive variable names earlier (e.g., 'dept_id' instead of 'id' in the merge).
   • Both solutions could benefit from brief docstrings explaining the approach.
   • The solutions handle the core requirements well but could include comments explaining the ranking method choice.

5. Efficiency:

   • The solutions are efficient for their problem sizes.
   • For Problem 1, an alternative approach could be to use groupby with transform to find max salaries, which might be more intuitive for some readers.
   • No major optimizations are needed as the current approaches are already efficient.