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50 changes: 29 additions & 21 deletions Exercise_1.py
Original file line number Diff line number Diff line change
@@ -1,22 +1,30 @@
# Python code to implement iterative Binary
# Search.

# It returns location of x in given array arr
# if present, else returns -1
def binarySearch(arr, l, r, x):

#write your code here



# Test array
arr = [ 2, 3, 4, 10, 40 ]
# Time complexity: O(log n)
# Space complexity: O(1)

# It returns location of x in given array arr
# if present, else returns -1
def binarySearch(arr, l, r, x):
while l <= r:
mid = (l + r) // 2

if arr[mid] == x:
return mid
elif arr[mid] < x:
l = mid + 1
else:
r = mid - 1

return -1


# Test array
arr = [2, 3, 4, 10, 40]
x = 10
# Function call
result = binarySearch(arr, 0, len(arr)-1, x)
if result != -1:
print "Element is present at index % d" % result
else:
print "Element is not present in array"

# Function call
result = binarySearch(arr, 0, len(arr) - 1, x)

if result != -1:
print("Element is present at index % d" % result)
else:
print("Element is not present in array")
63 changes: 40 additions & 23 deletions Exercise_2.py
Original file line number Diff line number Diff line change
@@ -1,23 +1,40 @@
# Python program for implementation of Quicksort Sort

# give you explanation for the approach
def partition(arr,low,high):


#write your code here


# Function to do Quick sort
def quickSort(arr,low,high):

#write your code here

# Driver code to test above
arr = [10, 7, 8, 9, 1, 5]
n = len(arr)
quickSort(arr,0,n-1)
print ("Sorted array is:")
for i in range(n):
print ("%d" %arr[i]),



# Time complexity: O(n log n)
# Space complexity: O(log n)

# The approach is to use the partition function to place the pivot in its correct position,
# and then recursively sort the sub-arrays on either side of the pivot.
def partition(arr, low, high):
# Select the rightmost element as pivot
pivot = arr[high]
# Index of smaller element (initially -1)
i = -1

for j in range(low, high):
# If current element is smaller than or equal to pivot
if arr[j] <= pivot:
i = i + 1
arr[i], arr[j] = arr[j], arr[i]

# Place pivot in its correct position
arr[i + 1], arr[high] = arr[high], arr[i + 1]
return i + 1

# Function to do Quick sort
def quickSort(arr, low, high):
if low < high:
# Partition the array
pi = partition(arr, low, high)

# Recursively sort elements before and after partition
quickSort(arr, low, pi - 1)
quickSort(arr, pi + 1, high)


# Driver code to test above
arr = [10, 7, 8, 9, 1, 5]
n = len(arr)
quickSort(arr, 0, n-1)
print("Sorted array is:")
for i in range(n):
print("%d" % arr[i]),
67 changes: 41 additions & 26 deletions Exercise_3.py
Original file line number Diff line number Diff line change
@@ -1,26 +1,41 @@
# Node class
class Node:

# Function to initialise the node object
def __init__(self, data):

class LinkedList:

def __init__(self):


def push(self, new_data):


# Function to get the middle of
# the linked list
def printMiddle(self):

# Driver code
list1 = LinkedList()
list1.push(5)
list1.push(4)
list1.push(2)
list1.push(3)
list1.push(1)
list1.printMiddle()
# Time complexity: O(n)
# Space complexity: O(1)

class Node:
# Function to initialise the node object
def __init__(self, data):
self.data = data
self.next = None

class LinkedList:

def __init__(self):
self.head = None

def push(self, new_data):
new_node = Node(new_data)
new_node.next = self.head
self.head = new_node

# Function to get the middle of
# the linked list
def printMiddle(self):
slow = self.head
fast = self.head

if self.head is not None:
while fast is not None and fast.next is not None:
fast = fast.next.next
slow = slow.next

print("The middle element is: ", slow.data)


# Driver code
list1 = LinkedList()
list1.push(5)
list1.push(4)
list1.push(2)
list1.push(3)
list1.push(1)
list1.printMiddle()
38 changes: 34 additions & 4 deletions Exercise_4.py
Original file line number Diff line number Diff line change
@@ -1,12 +1,42 @@
# Time complexity: O(n log n)
# Space complexity: O(n)

# Python program for implementation of MergeSort
def mergeSort(arr):

#write your code here
if len(arr) > 1:
mid = len(arr) // 2
left = arr[:mid]
right = arr[mid:]

mergeSort(left)
mergeSort(right)

i = j = k = 0

while i < len(left) and j < len(right):
if left[i] < right[j]:
arr[k] = left[i]
i += 1
else:
arr[k] = right[j]
j += 1
k += 1

while i < len(left):
arr[k] = left[i]
i += 1
k += 1

while j < len(right):
arr[k] = right[j]
j += 1
k += 1

# Code to print the list
def printList(arr):

#write your code here
for i in range(len(arr)):
print(arr[i], end=" ")
print()

# driver code to test the above code
if __name__ == '__main__':
Expand Down
37 changes: 32 additions & 5 deletions Exercise_5.py
Original file line number Diff line number Diff line change
@@ -1,10 +1,37 @@
# Time complexity: O(n log n)
# Space complexity: O(log n)

# Python program for implementation of Quicksort
def partition(arr, low, high):
# Select the rightmost element as pivot
pivot = arr[high]

# Index of the smaller element within the current subarray
i = low - 1

for j in range(low, high):
if arr[j] <= pivot:
i += 1
arr[i], arr[j] = arr[j], arr[i]

# Place the pivot in its correct position
arr[i + 1], arr[high] = arr[high], arr[i + 1]
return i + 1


def quick_sort_iterative(arr, low, high):
if low >= high:
return

# This function is same in both iterative and recursive
def partition(arr, l, h):
#write your code here
stack = [(low, high)]

while stack:
low, high = stack.pop()
pivot_index = partition(arr, low, high)

def quickSortIterative(arr, l, h):
#write your code here
# Push subarrays containing at least two elements
if low < pivot_index - 1:
stack.append((low, pivot_index - 1))

if pivot_index + 1 < high:
stack.append((pivot_index + 1, high))