Done Pandas3

[sakshiasati17]

No description provided.

[super30admin]

Let me evaluate each solution file separately:

1. CalculateSpecialBonus.py:

• Correctness: The logic appears correct for calculating bonus based on employee_id being odd and name not starting with 'M'.
• Time Complexity: O(n) where n is number of employees, which is optimal.
• Space Complexity: O(n) for storing results, which is necessary.
• Code Quality: The solution could be improved by using pandas vectorized operations instead of iterating through rows. For example, using np.where() or boolean masking would be more idiomatic pandas.
• Efficiency: The current implementation is less efficient than pandas vectorized operations.

2. FixNamesInATable.py:

• Correctness: The solution correctly capitalizes first letter and makes rest lowercase.
• Time Complexity: O(n) which is optimal.
• Space Complexity: O(1) as it modifies in-place.
• Code Quality: Clean and concise, good use of pandas string methods.
• Efficiency: Very efficient pandas vectorized approach.

3. PatientsWithACondition.py:

• Correctness: The logic for finding DIAB1 conditions appears correct, but may miss some edge cases where DIAB1 appears in the middle of a word.
• Time Complexity: O(n*m) where n is patients and m is conditions per patient - could be optimized.
• Space Complexity: O(n) for storing results.
• Code Quality: Again using iteration, which is less pandas-idiomatic. Could use str.contains() with regex.
• Efficiency: The nested loop makes this less efficient than possible with pandas string operations.

General improvements needed:

• Avoid iterating through DataFrames with for loops when pandas vectorized operations are available
• Consider edge cases more thoroughly (like DIAB1 in middle of word)
• Consistent formatting (some extra whitespace in files)
• More pandas-idiomatic solutions would be preferable