answer:def is_valid_parentheses(s): Determine if the input string consisting of '(', ')', '{', '}', '[' and ']' is valid. Args: s (str): the input string with parentheses Returns: bool: True if the string is valid, False otherwise stack = [] matching_bracket = {')': '(', '}': '{', ']': '['} for char in s: if char in matching_bracket: top_element = stack.pop() if stack else '#' if matching_bracket[char] != top_element: return False else: stack.append(char) return not stack # Time complexity: O(n) where n is the length of the string, as we process each character once. # Space complexity: O(n) in the worst case, if all opening brackets are stored in the stack. # A stack is used here because the last opened bracket needs to be the first one to be closed, which follows the LIFO (Last In, First Out) principle.

answer:from typing import Union def withdraw_balance(current_balance: float, withdrawal_amount: float) -> Union[str, float]: Simulates a bank account withdrawal. Parameters: - current_balance (float): The current account balance. - withdrawal_amount (float): The amount to withdraw. Returns: - (float): The new balance if the withdrawal is successful. - (str): An error message if there are insufficient funds or the withdrawal amount is invalid. if withdrawal_amount <= 0: return "Invalid withdrawal amount" elif withdrawal_amount > current_balance: return "Insufficient funds" else: return current_balance - withdrawal_amount

answer:def length_of_longest_substring(s): Returns the length of the longest substring with all unique characters. :param s: String containing only lowercase letters :return: Length of the longest substring with all unique characters if not s: return 0 n = len(s) max_length = 0 start = 0 char_index_map = {} for end in range(n): if s[end] in char_index_map: start = max(start, char_index_map[s[end]] + 1) char_index_map[s[end]] = end max_length = max(max_length, end - start + 1) return max_length

answer:import pandas as pd import numpy as np def process_sales_data(input_file, output_file): Processes the sales data from the given input file and generates a summary file. Parameters: input_file (str): The path to the input CSV file containing sales records. output_file (str): The path to the output CSV file to save the summary. try: # Load sales data sales_data = pd.read_csv(input_file) # Check if the required columns are present if 'Category' not in sales_data.columns or 'Sales' not in sales_data.columns: raise ValueError("Input file must contain 'Category' and 'Sales' columns.") # Calculate total sales for each product category total_sales_by_category = sales_data.groupby('Category')['Sales'].sum() # Find the overall total sales total_sales = sales_data['Sales'].sum() # Calculate the average sales per transaction average_sales = sales_data['Sales'].mean() # Generate a summary summary = { 'Total Sales by Category': total_sales_by_category, 'Overall Total Sales': total_sales, 'Average Sales per Transaction': average_sales } # Convert the summary to a DataFrame summary_df = pd.DataFrame({ 'Description': ['Total Sales by Category', 'Overall Total Sales', 'Average Sales per Transaction'], 'Value': [total_sales_by_category.to_dict(), total_sales, average_sales] }) # Save the summary to a CSV file summary_df.to_csv(output_file, index=False) except FileNotFoundError: print(f"Error: The file '{input_file}' was not found.") except pd.errors.EmptyDataError: print("Error: The input file is empty.") except ValueError as e: print(f"Error: {e}") except Exception as e: print(f"An unexpected error occurred: {e}") # Example usage: # process_sales_data('sales_records.csv', 'sales_summary.csv')