- True/False questions:
- Linear search requires a number of steps proportional to the size of the list being searched (T/F)
- The python
**in** operator performs a binary search (T/F)
- Binary search is an
`NlogN` algorithm (T/F)
- The number of times N can be divided by 2 is log base-two of N (T/F)

- Approximately how many iterations will binary search need to find a value in a list of 1 million items?
- Place these algorithm classes in order from best to worst: N, logN, N*N, NlogN
- What is log(1024,2) in python? What about log(2048,2)?
- How many steps are required for each of the following: N, logN, N*N, NlogN?
#############################################
for i in range(n):
for j in range(n):
print i, j
#############################################
while n > 1:
print n
n = n/2
#############################################
for i in range(n):
for j in range(10):
print i, j
#############################################

- Show the index values for low, high, and mid
for each step in searching the list L for the value x using binary search.
x = 99
L = [-20, -12, -4, 1, 7, 44, 45, 46, 58, 67, 99, 145] low mid high
0 1 2 3 4 5 6 7 8 9 10 11 ----------------

- Given the following assignments:
ls = ["fish", "squirrel", "cat", "dog", "monkey", "pony"]
s = "a,b,c,d,e"

What are the value and type of the following expressions:
(1) ls[3]
(2) ls[3] > "apple"
(3) ls[3][1]
(4) 'b' < 'f'
(5) len(ls[5])
(6) range(len(ls))
(7) range(len(ls[0]))
(8) s.split(",")