"The solution produces the same result as the 'prescribed solution' yet it does not get accepted In the test results section
transcript['year'] = transcript['year'].astype(str)
df = pd.pivottable(data = transcript, index = 'studentid', columns = 'year', values = 'yearlygpa', aggfunc = 'mean').resetindex()
df = df[(df['2021'] < df['2022']) & (df['2022'] < df['2023'])]
df['average_gpa'] = df[['2021', '2022', '2023']].mean(axis=1).round(2)
return df
"
Prachi G. - "The solution produces the same result as the 'prescribed solution' yet it does not get accepted In the test results section
transcript['year'] = transcript['year'].astype(str)
df = pd.pivottable(data = transcript, index = 'studentid', columns = 'year', values = 'yearlygpa', aggfunc = 'mean').resetindex()
df = df[(df['2021'] < df['2022']) & (df['2022'] < df['2023'])]
df['average_gpa'] = df[['2021', '2022', '2023']].mean(axis=1).round(2)
return df
"See full answer
"The best and average of both the algorithms is same which is O(nlog(n)), but the worst time complexity of QuickSort is O(n^2){a case where all the elements are sorted in opposite to the fashion/order you want} while the worst TC for merge sort remains the same O(nlog(n)).
and The SC for QS=O(logn) and MS=O(n)."
The_ A. - "The best and average of both the algorithms is same which is O(nlog(n)), but the worst time complexity of QuickSort is O(n^2){a case where all the elements are sorted in opposite to the fashion/order you want} while the worst TC for merge sort remains the same O(nlog(n)).
and The SC for QS=O(logn) and MS=O(n)."See full answer
"As we can pass info to only one child at a time, I told that from any given node, we have to pass the info to that child(of this node) which has the largest subtree rooted at it. To calculate the subtree sizes, I used DFS. And then to calculate the minimum time to pass info to all the nodes, I used BFS picking the largest subtree child first at every node. I couldn't write the complete code in the given time and also made a mistake in telling the overall time complexity of my approach. I think t"
Lakshman B. - "As we can pass info to only one child at a time, I told that from any given node, we have to pass the info to that child(of this node) which has the largest subtree rooted at it. To calculate the subtree sizes, I used DFS. And then to calculate the minimum time to pass info to all the nodes, I used BFS picking the largest subtree child first at every node. I couldn't write the complete code in the given time and also made a mistake in telling the overall time complexity of my approach. I think t"See full answer
"
read_dir(path: str) -> list[str] returns the full path of all files and sub- directories of a given directory.
is_file(path: str) -> bool: returns true if the path points to a regular file.
is_dir(path: str) -> bool: returns true if the path points to a directory.
read_file(path: str) -> str: reads and returns the content of the file.
The algorithm: notice that storing all the file contents' is too space intensive, so we can't read all the files' contents to store and compare with each"
Idan R. - "
read_dir(path: str) -> list[str] returns the full path of all files and sub- directories of a given directory.
is_file(path: str) -> bool: returns true if the path points to a regular file.
is_dir(path: str) -> bool: returns true if the path points to a directory.
read_file(path: str) -> str: reads and returns the content of the file.
The algorithm: notice that storing all the file contents' is too space intensive, so we can't read all the files' contents to store and compare with each"See full answer
"if decreasing arr, start from end and keep checking if next element increases by 1 or not. wherever not, put that value there."
Rishabh R. - "if decreasing arr, start from end and keep checking if next element increases by 1 or not. wherever not, put that value there."See full answer