Pandas2 submitted

[PranathiDeepak]

No description provided.

[super30admin]

Let me evaluate each file separately.

ArticleViews1.py:

1. Correctness: The solution correctly identifies authors who viewed their own articles by comparing author_id and viewer_id, removes duplicates, sorts the result, and returns it with the correct column name. This appears correct.
2. Time Complexity: The operations are O(n) for filtering, O(n log n) for sorting, and O(n) for dropping duplicates - overall O(n log n).
3. Space Complexity: The solution creates intermediate DataFrames (df and df1), so space complexity is O(n).
4. Code Quality: The code is readable but could be improved by chaining operations instead of creating intermediate variables.
5. Efficiency: Could be optimized by chaining operations: views[views['author_id']==views['viewer_id']].drop_duplicates('author_id').sort_values('author_id')[['author_id']].rename(columns={'author_id':'id'})

InvalidTweets.py:

1. Correctness: The solution correctly filters tweets with content length > 15 characters and returns their tweet_ids. This appears correct.
2. Time Complexity: O(n) for the string length operation and filtering.
3. Space Complexity: O(n) as it creates a new DataFrame.
4. Code Quality: Simple and readable, though could use more descriptive variable names than 'df'.
5. Efficiency: The solution is already quite efficient for this simple operation.

General suggestions:

1. Consider adding docstrings to explain the function purpose and return values.
2. For simple operations like in InvalidTweets.py, chaining might make the code more concise.
3. Variable names could be more descriptive (e.g., 'self_viewed_articles' instead of 'df').