Completed PreCourse-2 exercises in Python

Exercise_1.py

@@ -1,13 +1,28 @@
-# Python code to implement iterative Binary  
-# Search. 
-
-# It returns location of x in given array arr  
-# if present, else returns -1 
+# Time Complexity: O(log n)
+# Space Complexity: O(1)
+
+# Did this code successfully run on LeetCode: Yes
+
+# Any problem you faced while coding this: No major issues
+
+# Approach:
+# Binary search works only on a sorted array
+# Compare the target with the middle element
+# If they match, return the middle index
+# If the target is greater, search the right half
+# If the target is smaller, search the left half
+
 def binarySearch(arr, l, r, x): 
-
-  #write your code here
-
-
+    while (l <= r):
+            m = (l + r) // 2
+            if (arr[m] == x):
+                return m
+            elif (arr[m] < x):
+                l = m + 1
+            else:
+                r = m -1
+
+    return -1
 
 # Test array 
 arr = [ 2, 3, 4, 10, 40 ] 
@@ -17,6 +32,6 @@ def binarySearch(arr, l, r, x):
 result = binarySearch(arr, 0, len(arr)-1, x) 
 
 if result != -1: 
-    print "Element is present at index % d" % result 
+    print("Element is present at index %d" % result) 
 else: 
-    print "Element is not present in array"
+    print("Element is not present in array")

Exercise_2.py

@@ -1,16 +1,42 @@
-# Python program for implementation of Quicksort Sort 
-
-# give you explanation for the approach
+# Time Complexity:
+# Average Case: O(n log n)
+# Worst Case: O(n^2)
+
+# Space Complexity:
+# Average Case: O(log n)
+# Worst Case: O(n)
+
+# Did this code successfully run on LeetCode: Not directly applicable because LeetCode uses a different function format
+
+# Any problem you faced while coding this: The main challenge was placing the pivot in its correct position and using the correct recursive boundaries
+
+# Approach:
+# 1. Select the final element as the pivot
+# 2. Move all elements smaller than or equal to the pivot to its left
+# 3. Move all elements greater than the pivot to its right
+# 4. Return the pivot's final index
+# 5. Recursively sort the left and right parts of the array
+
+
 def partition(arr,low,high):
-
-
-    #write your code here
+    pivot = arr[high]
+    i = low - 1
+
+    for j in range(low, high):
+        if arr[j] <= pivot:
+            i += 1
+            arr[i], arr[j] = arr[j], arr[i]
+
+    arr[i + 1], arr[high] = arr[high], arr[i + 1]
+    return i + 1
 
 
 # Function to do Quick sort 
 def quickSort(arr,low,high): 
-
-    #write your code here
+    if low < high:
+        pi = partition(arr, low, high)
+        quickSort(arr, low, pi - 1)
+        quickSort(arr, pi + 1, high)
 
 # Driver code to test above 
 arr = [10, 7, 8, 9, 1, 5] 

Exercise_3.py

@@ -1,20 +1,48 @@
-# Node class  
+# Time Complexity:
+# push: O(1)
+# printMiddle: O(n)
+
+# Space Complexity:
+# O(n) total space for storing n nodes
+# O(1) extra space for printMiddle
+
+# Did this code successfully run on LeetCode: Yes
+
+# Any problem you faced while coding this: no major issues
+
+# Approach:
+# Use two pointers: slow moves one node at a time, fast moves two nodes at a time
+# When fast reaches the end, slow will be at the middle node
+
+
 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_ptr = self.head 
+        fast_ptr = self.head 
+
+        if self.head is not None: 
+            while (fast_ptr is not None and fast_ptr.next is not None): 
+                fast_ptr = fast_ptr.next.next
+                slow_ptr = slow_ptr.next
+            print("The middle element is:", slow_ptr.data)
 
 # Driver code 
 list1 = LinkedList() 

Exercise_4.py

@@ -1,12 +1,53 @@
-# Python program for implementation of MergeSort 
+# Time Complexity: O(n log n)
+# Space Complexity: O(n)
+
+# Did this code successfully run on LeetCode: Not directly applicable because LeetCode uses a different function format
+
+# Any problem you faced while coding this: The main challenge was correctly merging the two sorted halves and copying any remaining elements
+
+# Approach:
+# 1. Divide the array into two halves
+# 2. Recursively sort the left half
+# 3. Recursively sort the right half
+# 4. Merge the two sorted halves back into the original array
+
 def mergeSort(arr):
+    if len(arr) > 1:
+        mid = len(arr) // 2
+        L = arr[:mid]
+        R = arr[mid:]
+
+        mergeSort(L)
+        mergeSort(R)
+
+        i = j = k = 0
+
+        while i < len(L) and j < len(R):
+            if L[i] <= R[j]:
+                arr[k] = L[i]
+                i += 1
+            else:
+                arr[k] = R[j]
+                j += 1
+            k += 1
+
+        while i < len(L):
+            arr[k] = L[i]
+            i += 1
+            k += 1
+
+        while j < len(R):
+            arr[k] = R[j]
+            j += 1
+            k += 1
 
-  #write your code here
 
 # 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__': 

Exercise_5.py

@@ -1,10 +1,62 @@
-# Python program for implementation of Quicksort
+# Time Complexity:
+# Average Case: O(n log n)
+# Worst Case: O(n^2)
+
+# Space Complexity:
+# Average Case: O(log n)
+# Worst Case: O(n)
+
+# Did this code successfully run on LeetCode: Not directly applicable because LeetCode uses a different function format
+
+# Any problem you faced while coding this: The main challenge was correctly storing and processing the left and right subarray boundaries using a stack instead of recursion
+
+# Approach:
+# 1. Store the initial low and high indices in a stack
+# 2. Remove one range from the stack
+# 3. Partition that range and place the pivot in its correct position
+# 4. Add the left and right unsorted ranges to the stack
+# 5. Continue until the stack is empty
 
 # This function is same in both iterative and recursive
 def partition(arr, l, h):
-  #write your code here
+    pivot = arr[h]
+    i = l - 1
+
+    for j in range(l, h):
+        if arr[j] <= pivot:
+            i += 1
+            arr[i], arr[j] = arr[j], arr[i]
+
+    arr[i + 1], arr[h] = arr[h], arr[i + 1]
+    return i + 1
 
 
 def quickSortIterative(arr, l, h):
-  #write your code here
+    stack = [(l, h)]
+
+    while stack:
+        low, high = stack.pop()
+
+        if low < high:
+            pivot_index = partition(arr, low, high)
+
+            stack.append((low, pivot_index - 1))
+            stack.append((pivot_index + 1, high))
+
+if __name__ == "__main__":
+    test_cases = [
+        [10, 7, 8, 9, 1, 5],
+        [1, 2, 3, 4, 5],
+        [5, 4, 3, 2, 1],
+        [4, 2, 4, 1, 2],
+        [7],
+        [],
+    ]
 
+    for arr in test_cases:
+        expected = sorted(arr)
+        quickSortIterative(arr, 0, len(arr) - 1)
+        print("Sorted:", arr)
+        print("Expected:", expected)
+        print("Passed:", arr == expected)
+        print()