answer:import string import re import difflib import textwrap from collections import Counter def process_text(input_text: str, comparison_text: str) -> dict: # Normalize the text to lower case input_text = input_text.lower() comparison_text = comparison_text.lower() # Remove punctuation and split into words translator = str.maketrans('', '', string.punctuation) words = re.findall(r'bw+b', input_text.translate(translator)) comparison_words = re.findall(r'bw+b', comparison_text.translate(translator)) # Get unique words list sorted unique_words = sorted(set(words)) # Frequency of each word word_frequencies = dict(Counter(words)) # Top 5 frequent words top_5_words = [word for word, freq in Counter(words).most_common(5)] # Wrap the text to 50 characters per line wrapped_text = textwrap.fill(input_text, width=50) # Words present in input_text but not in comparison_text word_differences = sorted(set(words) - set(comparison_words)) return { "words_list": unique_words, "word_frequencies": word_frequencies, "top_5_words": top_5_words, "wrapped_text": wrapped_text, "word_differences": word_differences }

answer:import pandas as pd import seaborn as sns import matplotlib.pyplot as plt def visualize_data(df): Generates a series of visualizations to explore the dataset using seaborn. Parameters: df (pd.DataFrame): The input data frame containing at least three numerical columns and two categorical columns. if df.empty: raise ValueError("The DataFrame should not be empty") required_numeric_cols = ['num_col1', 'num_col2', 'num_col3'] required_cat_cols = ['cat_col1', 'cat_col2'] for col in required_numeric_cols + required_cat_cols: if col not in df.columns: raise ValueError(f"Missing required column: {col}") # Scatter plot scatter_plot = sns.relplot( data=df, x='num_col1', y='num_col2', hue='cat_col1', style='cat_col2' ) scatter_plot.fig.suptitle('Relationship between Num_Col1 and Num_Col2') scatter_plot.set_axis_labels('Num_Col1', 'Num_Col2') # Distribution plot dist_plot = sns.displot( data=df, x='num_col3', hue='cat_col1', kde=True ) dist_plot.fig.suptitle('Distribution of Num_Col3') dist_plot.set_axis_labels('Num_Col3', 'Density') # Bar plot bar_plot = sns.catplot( data=df, kind='bar', x='cat_col1', y='num_col1', hue='cat_col2' ) bar_plot.fig.suptitle('Average Num_Col1 per Cat_Col1') bar_plot.set_axis_labels('Cat_Col1', 'Average Num_Col1') # Joint plot joint_plot = sns.jointplot( data=df, x='num_col1', y='num_col2', hue='cat_col1' ) joint_plot.fig.suptitle('Joint Distribution of Num_Col1 and Num_Col2') joint_plot.set_axis_labels('Num_Col1', 'Num_Col2') plt.show()

answer:import cmd import os import json class ShoppingCartCmd(cmd.Cmd): intro = 'Welcome to the shopping cart application. Type help or ? to list commands.n' prompt = '(cart) ' def __init__(self): super().__init__() self.cart = {} def do_add(self, arg): 'Add an item to the cart: add <item> <price> <quantity>' try: item, price, quantity = arg.split() price = float(price) quantity = int(quantity) if item in self.cart: self.cart[item][1] += quantity print(f"Updated {item} quantity to {self.cart[item][1]}") else: self.cart[item] = [price, quantity] print(f"Added {quantity} {item}(s) at {price} each.") except ValueError: print("Invalid arguments. Usage: add <item> <price> <quantity>") def do_remove(self, arg): 'Remove an item from the cart: remove <item>' if arg in self.cart: del self.cart[arg] print(f"Removed {arg} from the cart.") else: print(f"{arg} is not in the cart.") def do_view(self, arg): 'View the items in the cart' if not self.cart: print("Cart is empty.") else: total = 0.0 for item, (price, quantity) in self.cart.items(): print(f"Item: {item}, Price: {price:.2f}, Quantity: {quantity}") total += price * quantity print(f"Total Price: {total:.2f}") def do_clear(self, arg): 'Clear all items from the cart' self.cart.clear() print("Cart is now empty.") def do_checkout(self, arg): 'Display the total amount due, save the cart to checkout.txt, and exit the application' total = sum(price * quantity for price, quantity in self.cart.values()) with open('checkout.txt', 'w') as f: for item, (price, quantity) in self.cart.items(): f.write(f"Item: {item}, Price: {price:.2f}, Quantity: {quantity}n") f.write(f"Total Price: {total:.2f}n") print(f"Total Amount Due: {total:.2f}") print("Saved cart to 'checkout.txt'. Exiting application.") return True def do_load(self, arg): 'Load a shopping cart from a file: load <filename>' if not os.path.exists(arg): print(f"File {arg} does not exist.") return with open(arg, 'r') as f: self.cart = json.load(f) print(f"Loaded cart from {arg}.") def do_save(self, arg): 'Save the current shopping cart to a file: save <filename>' if not arg: print("Please specify a filename.") return with open(arg, 'w') as f: json.dump(self.cart, f) print(f"Saved cart to {arg}.") def do_exit(self, arg): 'Exit the shopping cart application' print("Exiting application.") return True

answer:import os def read_student_records(file_path): Reads student records from a file. Args: file_path (str): Path to the input text file. Returns: list: List of dictionaries containing student records. if not os.path.exists(file_path): raise FileNotFoundError(f"The file {file_path} does not exist.") student_records = [] with open(file_path, 'r') as file: for line in file: student_id, name, grade = line.strip().split(', ') student_records.append({ 'student_id': student_id, 'name': name, 'grade': int(grade) }) return student_records def grade_statistics(student_records): Calculates the average, highest, and lowest grades of the students. Args: student_records (list): List of dictionaries containing student records. Returns: dict: Dictionary containing average, highest, and lowest grades. grades = [student['grade'] for student in student_records] average_grade = sum(grades) / len(grades) highest_grade = max(grades) lowest_grade = min(grades) return { 'average_grade': average_grade, 'highest_grade': highest_grade, 'lowest_grade': lowest_grade } def write_statistics_to_file(statistics, output_file_path): Writes the grade statistics to a file. Args: statistics (dict): Dictionary containing the statistical summaries. output_file_path (str): Path to the output text file. with open(output_file_path, 'w') as file: file.write(f"Average Grade: {statistics['average_grade']:.2f}n") file.write(f"Highest Grade: {statistics['highest_grade']}n") file.write(f"Lowest Grade: {statistics['lowest_grade']}n") # Example usage if __name__ == "__main__": student_records = read_student_records('students.txt') statistics = grade_statistics(student_records) write_statistics_to_file(statistics, 'output.txt')