answer:import pandas as pd def rental_duration_stats(df: pd.DataFrame, month: str, day_of_week: str) -> None: Computes and prints rental duration statistics based on the given DataFrame. Parameters: - df (pd.DataFrame): DataFrame containing 'start_time', 'end_time', and 'duration_min' columns. - month (str): The month filter for the data, or 'all' for no filter. - day_of_week (str): The day of the week filter for the data, or 'all' for no filter. # Convert 'start_time' and 'end_time' to datetime df['start_time'] = pd.to_datetime(df['start_time']) df['end_time'] = pd.to_datetime(df['end_time']) # Extract month and day of week from 'start_time' df['month'] = df['start_time'].dt.strftime('%B') df['day_of_week'] = df['start_time'].dt.strftime('%A') # Compute average rental duration for each month avg_duration_per_month = df.groupby('month')['duration_min'].mean() # Compute average rental duration for each day of the week avg_duration_per_day = df.groupby('day_of_week')['duration_min'].mean() # Filter the dataframe based on user input if month != 'all': df = df[df['month'] == month] if day_of_week != 'all': df = df[df['day_of_week'] == day_of_week] # Compute overall average after applying filters overall_avg_duration = df['duration_min'].mean() if not df.empty else 0 # Print the statistics print(f"Average rental duration for the specified month ({month}) and day of the week ({day_of_week}): {overall_avg_duration:.2f} minutes") print("Average rental duration per month:") print(avg_duration_per_month) print("Average rental duration per day of the week:") print(avg_duration_per_day)

answer:from collections import Counter def frequency_sort(arr): Sorts an array of integers based on the frequency of values. More frequent numbers appear earlier. If frequencies are equal, the numbers appear in ascending order. # Count frequency of each number in the array frequency = Counter(arr) # Sort the array first by frequency (descending), then by the number (ascending) sorted_arr = sorted(arr, key=lambda x: (-frequency[x], x)) return sorted_arr

answer:import heapq def path_finder(graph, start, target): Finds the shortest path from start to target using Dijkstra's algorithm. :param graph: Dict representing the graph where keys are nodes and values are lists of tuples (neighbor, distance) :param start: Starting node identifier (str) :param target: Target node identifier (str) :return: Tuple containing a list of node identifiers for the shortest path and the total distance # Validate input graph for node, neighbors in graph.items(): for neighbor, distance in neighbors: if distance <= 0: raise ValueError("Graph cannot have non-positive distances.") if start not in graph: return [], -1 if target not in graph: return [], -1 # Initialize Dijkstra's algorithm data structures distance = {node: float('inf') for node in graph} previous = {node: None for node in graph} distance[start] = 0 priority_queue = [(0, start)] heapq.heapify(priority_queue) while priority_queue: current_dist, current_node = heapq.heappop(priority_queue) if current_node == target: break if current_dist > distance[current_node]: continue for neighbor, weight in graph[current_node]: new_dist = current_dist + weight if new_dist < distance[neighbor]: distance[neighbor] = new_dist previous[neighbor] = current_node heapq.heappush(priority_queue, (new_dist, neighbor)) # Reconstruct the path from start to target path = [] total_distance = distance[target] if total_distance == float('inf'): return [], -1 current = target while current: path.append(current) current = previous[current] path.reverse() return path, total_distance

answer:def calculate_statistics(employees): if not employees: return {"average_salary": 0, "highest_salary_employee": None} total_salary = sum(employee['salary'] for employee in employees) average_salary = round(total_salary / len(employees), 2) highest_salary_employee = max(employees, key=lambda x: x['salary'])['name'] return {"average_salary": average_salary, "highest_salary_employee": highest_salary_employee}