Python
Big O notation Ascending order of complexity
O(1), O(log n), O(n), O(n log n), O(n^2), O(n^3), O(2^n), O(n!)
Binary Search Time Complexity
O(log n)
Time needed to search a sorted list with binary search?
O(log n)
efficiency of merge sort
O(n log n)
Big O notation with the worst time complexity?
O(n!)
Big O notation for adding new item in front of list?
O(n)
Linear Search Time Complexity
O(n)
List time complexity
O(n)
Queue time complexity
O(n)
Searching for an element in linked list time complexity
O(n)
Efficiency of Bubble Sort
O(n^2)
Efficiency of Insertion Sort
O(n^2)
efficiency of selection sort
O(n^2)
What is the set recursion depth by default and can we modify it?
1000, can be modified irrespective of system
Big O notation calculates the time of execution of an algorithm
False
O(n) is better as compared to O(log n) in terms of time complexity
False
What principle does a Stack data structure use
LIFO
Big O notation for inserting item in back of list?
O(1)
Linked list time complexity
O(1)
Searching for an element in list
O(1)
Stack Time Complexity
O(1)
Using big O notation, what indicates a constant time complexity?
O(1)
What is the error given by the system when the recursion limit is exceeded?
Recursion Error
What is stored in a "file handle" that is returned from a successful open() call?
The handle is a connection to the file's data
Big O notation indicates the efficiency of algorithms when the input size is big
True
Merge sort has a better time complexity compared to bubble sort
True
Normally, worst case situation is used to estimate the big O notation
True
What do we use the second parameter of the open() call to indicate?
Whether we want to read data from the file or write data to the file
Which python function to prompt user for a filename to open?
input()
Where are files stored?
secondary memory
Recursion calls are stored on the memory in which data structure?
stack
