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question:# Problem Statement Create a command-line application that simulates a basic text-based shopping cart system. Users should be able to add items to the cart, view the cart contents, remove items from the cart, and check out. # Requirements 1. **Add Item**: Allow users to add items to their shopping cart by specifying the item name and quantity. 2. **View Cart**: Display all items currently in the cart along with their quantities. 3. **Remove Item**: Allow users to remove an item or decrease the quantity of an item in the cart. 4. **Checkout**: Display the total number of items in the cart and the grand total price of all items. Implement a class `ShoppingCart` with the following methods: * `add_item(self, item_name: str, quantity: int, price: float) -> None`: Add specified quantity of the item with the given price to the cart. * `view_cart(self) -> List[Tuple[str, int, float]]`: Return a list of tuples containing item name, quantity, and price per unit for each item in the cart. * `remove_item(self, item_name: str, quantity: int) -> None`: Remove the specified quantity of the item from the cart. If the quantity to remove is greater than or equal to the current quantity, remove the item entirely. * `checkout(self) -> Tuple[int, float]`: Return the total number of items and the grand total price of all items in the cart. # Input and Output Formats 1. **add_item** * **Input**: `item_name` (string), `quantity` (integer), `price` (float). * **Output**: None. 2. **view_cart** * **Input**: None. * **Output**: A list of tuples, each containing `item_name` (string), `quantity` (integer), `price` (float). 3. **remove_item** * **Input**: `item_name` (string), `quantity` (integer). * **Output**: None. 4. **checkout** * **Input**: None. * **Output**: A tuple containing the total number of items (integer) and the grand total price (float). # Example ```python cart = ShoppingCart() # Test add_item cart.add_item("apple", 3, 0.5) cart.add_item("banana", 2, 0.3) # Test view_cart assert cart.view_cart() == [("apple", 3, 0.5), ("banana", 2, 0.3)] # Test remove_item cart.remove_item("apple", 1) assert cart.view_cart() == [("apple", 2, 0.5), ("banana", 2, 0.3)] # Test checkout assert cart.checkout() == (4, 1.6) # Test remove all quantity of an item cart.remove_item("banana", 2) assert cart.view_cart() == [("apple", 2, 0.5)] # Test add more item cart.add_item("orange", 1, 0.7) assert cart.view_cart() == [("apple", 2, 0.5), ("orange", 1, 0.7)] # Final checkout assert cart.checkout() == (3, 1.7) ``` # Constraints: * Item names will be non-empty strings. * Quantities and prices will be positive numbers. * The cart can hold any number of items but checkout should accurately reflect the current state of the cart.
answer:from typing import List, Tuple class ShoppingCart: def __init__(self): self.cart = {} def add_item(self, item_name: str, quantity: int, price: float) -> None: if item_name in self.cart: self.cart[item_name]['quantity'] += quantity else: self.cart[item_name] = {'quantity': quantity, 'price': price} def view_cart(self) -> List[Tuple[str, int, float]]: return [(item_name, details['quantity'], details['price']) for item_name, details in self.cart.items()] def remove_item(self, item_name: str, quantity: int) -> None: if item_name in self.cart: if self.cart[item_name]['quantity'] <= quantity: del self.cart[item_name] else: self.cart[item_name]['quantity'] -= quantity def checkout(self) -> Tuple[int, float]: total_quantity = sum(details['quantity'] for details in self.cart.values()) total_price = sum(details['quantity'] * details['price'] for details in self.cart.values()) return total_quantity, total_price
question:# Problem: Binary Tree Zigzag Level Order Traversal As a software developer, you are tasked with implementing a function to perform a zigzag level order traversal of a binary tree. The zigzag traversal implies that alternate levels are traversed from right to left and left to right, respectively. Instructions: 1. **Function Name**: `zigzag_level_order` 2. **Input**: * **root** (`TreeNode`): The root node of the binary tree. 3. **Output**: * A list of lists, where each inner list contains the values of the nodes at each level in zigzag order. 4. **Constraints**: * You may assume that `TreeNode` class is defined as follows: ```python class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right ``` * The binary tree node values will be integers. * The number of nodes in the tree is limited to 10^4. * The node values are within the range `-1000` to `1000`. Scenario: You are provided with the root node of a binary tree, and you must traverse it in zigzag order. Your function should alternate the traversal direction at every level of the tree to create a zigzag pattern. **Hint**: You may use a deque to efficiently implement the alternating level order traversal. Example: ```python # Example Binary Tree # 3 # / # 9 20 # / # 15 7 root = TreeNode(3) root.left = TreeNode(9) root.right = TreeNode(20) root.right.left = TreeNode(15) root.right.right = TreeNode(7) print(zigzag_level_order(root)) # Expected Output: [[3], [20, 9], [15, 7]] ``` **Deliverable**: Provide a complete implementation of `zigzag_level_order` function that efficiently performs the zigzag traversal of the binary tree and returns the resulting list of lists. ```python from collections import deque def zigzag_level_order(root: TreeNode) -> [[int]]: if not root: return [] results = [] current_level = deque([root]) left_to_right = True while current_level: level_size = len(current_level) level_nodes = [] for _ in range(level_size): if left_to_right: node = current_level.popleft() level_nodes.append(node.val) if node.left: current_level.append(node.left) if node.right: current_level.append(node.right) else: node = current_level.pop() level_nodes.append(node.val) if node.right: current_level.appendleft(node.right) if node.left: current_level.appendleft(node.left) results.append(level_nodes) left_to_right = not left_to_right return results # Example Usage if __name__ == "__main__": root = TreeNode(3) root.left = TreeNode(9) root.right = TreeNode(20) root.right.left = TreeNode(15) root.right.right = TreeNode(7) print(zigzag_level_order(root)) # Expected Output: [[3], [20, 9], [15, 7]] ```
answer:from collections import deque class TreeNode: def __init__(self, val=0, left=None, right=None): self.val = val self.left = left self.right = right def zigzag_level_order(root: TreeNode): if not root: return [] results = [] current_level = deque([root]) left_to_right = True while current_level: level_size = len(current_level) level_nodes = [] for _ in range(level_size): if left_to_right: node = current_level.popleft() level_nodes.append(node.val) if node.left: current_level.append(node.left) if node.right: current_level.append(node.right) else: node = current_level.pop() level_nodes.append(node.val) if node.right: current_level.appendleft(node.right) if node.left: current_level.appendleft(node.left) results.append(level_nodes) left_to_right = not left_to_right return results
question:# Context You are provided with a class `BinarySearchTree` in Python, and you are required to enhance its functionality. One important method, `insert`, facilitates the insertion of nodes while maintaining the properties of a binary search tree. # Task Implement a function `find_lowest_common_ancestor(self, node1: int, node2: int) -> int` within the `BinarySearchTree` class that finds the lowest common ancestor (LCA) of two given nodes in the tree. The LCA of two nodes `node1` and `node2` in a binary search tree is defined as the lowest node in the tree that has both `node1` and `node2` as descendants. # Function Signature ```python class BinarySearchTree: def find_lowest_common_ancestor(self, node1: int, node2: int) -> int: # Your code here ``` # Input * Two integers `node1` and `node2` representing the values of the nodes to find the LCA for. # Output * Returns an integer representing the value of the lowest common ancestor. # Constraints * The binary search tree is guaranteed to contain both `node1` and `node2`. * -10^4 <= `node1`, `node2` <= 10^4 # Example ```python >>> bst = BinarySearchTree() >>> bst.insert(20) >>> bst.insert(10) >>> bst.insert(30) >>> bst.insert(5) >>> bst.insert(15) >>> bst.find_lowest_common_ancestor(5, 15) 10 >>> bst = BinarySearchTree() >>> bst.insert(20) >>> bst.insert(10) >>> bst.insert(30) >>> bst.insert(25) >>> bst.insert(35) >>> bst.find_lowest_common_ancestor(25, 35) 30 ``` # Notes * If `node1` is equal to `node2`, then the LCA is either of the nodes themselves. * The binary search tree is assumed to contain unique values for simplicity.
answer:class Node: def __init__(self, key): self.data = key self.left = None self.right = None class BinarySearchTree: def __init__(self): self.root = None def insert(self, key): if self.root is None: self.root = Node(key) else: self._insert(self.root, key) def _insert(self, root, key): if key < root.data: if root.left is None: root.left = Node(key) else: self._insert(root.left, key) else: if root.right is None: root.right = Node(key) else: self._insert(root.right, key) def find_lowest_common_ancestor(self, node1: int, node2: int) -> int: return self._find_lca(self.root, node1, node2).data def _find_lca(self, root, node1, node2): # Base case if root is None: return None # If both node1 and node2 are smaller than root, then LCA lies in left if root.data > node1 and root.data > node2: return self._find_lca(root.left, node1, node2) # If both node1 and node2 are greater than root, then LCA lies in right if root.data < node1 and root.data < node2: return self._find_lca(root.right, node1, node2) # If one node is on the left and the other is on the right, root is the LCA return root
question:# Coding Assessment Question You are given an implementation of an algorithm that calculates the running average of a list of numbers. The running average is the mean of all numbers up to that point in the list. # Task Write a Python function that does the following: 1. Reads in a list of integers or floating-point numbers. 2. Computes the running average for each element in the list using a single pass from left to right. 3. Returns a new list containing the running averages. # Requirements 1. **Input**: A list of integers or floating-point numbers. 2. **Output**: A list of running averages as floating-point numbers. 3. **Constraints**: * The list input can have up to 1,000,000 elements. * The input list can be empty. In such a case, the function should return an empty list. # Performance: * The function must handle large lists efficiently with a time complexity of O(n). Examples: ```python # Example 1: input_list = [1, 2, 3, 4, 5] running_average(input_list) # Output: [1.0, 1.5, 2.0, 2.5, 3.0] # Example 2: input_list = [4, -1, 2, 10] running_average(input_list) # Output: [4.0, 1.5, 1.6666666666666667, 3.75] # Example 3: input_list = [] running_average(input_list) # Output: [] ```
answer:def running_average(input_list): Computes the running average of a list of numbers. :param input_list: List of integers or floating-point numbers :return: List of floating-point numbers representing the running averages if not input_list: return [] running_sums = [] current_sum = 0 for i, value in enumerate(input_list): current_sum += value running_sums.append(current_sum / (i + 1)) return running_sums