Appearance
question:**Question: Custom Exception Handling in Python** You are required to implement a custom exception handling system in Python. Your task is to define a set of custom exceptions and implement a function that processes a list of operations. Each operation consists of a dictionary with two keys: "operation" and "value". The function should perform error checking and handle any exceptions that may occur during the operations. # Definitions: 1. **Custom Exceptions**: - `InvalidOperationError`: Raised when an invalid operation is encountered. - `DivisionByZeroError`: Raised when a division by zero is attempted. - `OperationValueError`: Raised when the value provided for an operation is invalid (e.g., non-numeric value for arithmetic operations). 2. **Function**: ```python def process_operations(operations: List[Dict[str, Union[str, Any]]]) -> List[Union[int, float, str]]: # Function implementation ``` # Function Requirements: - The function `process_operations` takes a list of dictionaries as input. Each dictionary has two keys: - `operation`: A string that can be one of the following: "add", "subtract", "multiply", "divide". - `value`: An integer or float representing the value for the operation. - Implement error checking and use the custom exceptions to handle errors. You should catch these exceptions and append an appropriate error message to the result list. # Input/Output: - **Input**: `operations` is a list of dictionaries. Each dictionary has the following format: ```python [ {"operation": "add", "value": 10}, {"operation": "subtract", "value": 5}, {"operation": "multiply", "value": 3}, {"operation": "divide", "value": 0}, {"operation": "divide", "value": 2} ] ``` - **Output**: The function returns a list of results for each operation. If an error occurs, the corresponding result should be an error message. Example output for the provided input: ```python [10, 5, 15, "Division by zero error", 7.5] ``` # Constraints: - The operations list will always contain dictionaries with "operation" and "value" keys. - You can assume that "value" will either be an integer or a float, and "operation" will always be a string. - You do not need to handle unexpected dictionary formats or types. # Example: ```python class InvalidOperationError(Exception): pass class DivisionByZeroError(Exception): pass class OperationValueError(Exception): pass def process_operations(operations): result = [] accumulator = 0 for op in operations: try: operation = op['operation'] value = op['value'] if not isinstance(value, (int, float)): raise OperationValueError("Operation value must be numeric") if operation == 'add': accumulator += value elif operation == 'subtract': accumulator -= value elif operation == 'multiply': accumulator *= value elif operation == 'divide': if value == 0: raise DivisionByZeroError("Cannot divide by zero") accumulator /= value else: raise InvalidOperationError(f"Invalid operation: {operation}") result.append(accumulator) except (InvalidOperationError, DivisionByZeroError, OperationValueError) as e: result.append(str(e)) return result # Test case operations = [ {"operation": "add", "value": 10}, {"operation": "subtract", "value": 5}, {"operation": "multiply", "value": 3}, {"operation": "divide", "value": 0}, {"operation": "divide", "value": 2} ] print(process_operations(operations)) # Output: [10, 5, 15, "Cannot divide by zero", 7.5] ```
answer:class InvalidOperationError(Exception): Raised when an invalid operation is encountered. pass class DivisionByZeroError(Exception): Raised when a division by zero is attempted. pass class OperationValueError(Exception): Raised when the value provided for an operation is invalid (e.g., non-numeric value for arithmetic operations). pass def process_operations(operations): Processes a list of operations and returns the result of each operation. Parameters: operations (List[Dict[str, Union[str, Any]]]): A list of dictionaries representing operations. Returns: List[Union[int, float, str]]: A list of results for each operation. If an error occurs, the result will be an error message. result = [] accumulator = 0 for op in operations: try: operation = op['operation'] value = op['value'] if not isinstance(value, (int, float)): raise OperationValueError("Operation value must be numeric") if operation == 'add': accumulator += value elif operation == 'subtract': accumulator -= value elif operation == 'multiply': accumulator *= value elif operation == 'divide': if value == 0: raise DivisionByZeroError("Cannot divide by zero") accumulator /= value else: raise InvalidOperationError(f"Invalid operation: {operation}") result.append(accumulator) except (InvalidOperationError, DivisionByZeroError, OperationValueError) as e: result.append(str(e)) return result
question:**Coding Assessment Question** # Objective Demonstrate your understanding of seaborn's `pointplot` function and its various customizations. # Task You are given a dataset of penguin measurements and flight passenger numbers. Your task is to generate a pointplot that meets the following specifications: 1. **Data Preparation**: - Load the penguins dataset using `sns.load_dataset("penguins")`. - Load the flights dataset using `sns.load_dataset("flights")`. 2. **Penguin Data Plot**: - Create a pointplot of `body_mass_g` across different islands. - Differentiate the data by `sex` using different colors. - Use different markers (`"o"` for male and `"s"` for female) and linestyles (`"-"` for male and `"--"` for female). - Show error bars representing the standard deviation (`"sd"`). 3. **Flights Data Plot**: - Transform the flights data into a wide format using `pivot` function. - Create a pointplot of the number of passengers in June across different years. - Customize the x-tick labels to show only the last two digits of the year, e.g., `1950` should be shown as `'50`. # Expected Input and Output Formats - There are no input parameters for the function. All tasks should be performed within the specified function and parameters loaded using seaborn datasets. - Save the plots as `penguins_plot.png` and `flights_plot.png`. # Constraints - Ensure the plots are clear and well-labeled. - Follow seaborn best practices and settings to maintain visual consistency. # Performance Requirements - The function should execute within a reasonable time frame (a few seconds). # Write a function `create_plots` to implement the above specifications. ```python import seaborn as sns def create_plots(): # Set theme for seaborn sns.set_theme(style="whitegrid") # Load datasets penguins = sns.load_dataset("penguins") flights = sns.load_dataset("flights") # Create penguins plot penguins_plot = sns.pointplot(data=penguins, x="island", y="body_mass_g", hue="sex", markers=["o", "s"], linestyles=["-", "--"], errorbar="sd") penguins_plot.figure.savefig("penguins_plot.png") penguins_plot.figure.clf() # Clear the figure for reuse # Prepare flights data flights_wide = flights.pivot(index="year", columns="month", values="passengers") # Create flights plot flights_plot = sns.pointplot(flights_wide["Jun"], formatter=lambda x: f"'{x % 1900}") flights_plot.figure.savefig("flights_plot.png") ``` # Example When you run `create_plots()`, it should generate the following plots and save them as `.png` files: - A pointplot of body mass of penguins differentiated by island and sex with specified styles. - A pointplot of the number of flight passengers in June across different years with custom x-tick labels.
answer:import seaborn as sns import matplotlib.pyplot as plt def create_plots(): # Set theme for seaborn sns.set_theme(style="whitegrid") # Load datasets penguins = sns.load_dataset("penguins") flights = sns.load_dataset("flights") # Create penguins plot penguins_plot = sns.pointplot(data=penguins, x="island", y="body_mass_g", hue="sex", markers=["o", "s"], linestyles=["-", "--"], errorbar="sd") penguins_plot.set_title('Penguins: Body Mass by Island and Sex') penguins_plot.figure.savefig("penguins_plot.png") plt.clf() # Clear the current figure # Prepare flights data flights_wide = flights.pivot(index="year", columns="month", values="passengers") # Create flights plot flights_plot = sns.pointplot(x=flights_wide.index, y=flights_wide["Jun"]) flights_plot.set_xticklabels([f"'{str(year)[-2:]}" for year in flights_wide.index]) flights_plot.set_title('Flight Passengers in June over the Years') flights_plot.set(xlabel='Year', ylabel='Number of Passengers') flights_plot.figure.savefig("flights_plot.png") plt.clf() # Clear the current figure
question:Objective Implement a Python script that utilizes the `time` module to perform and measure various time-related tasks efficiently. Description You are required to implement a program that performs the following tasks: 1. **Get Current Time:** - Print the current local time in the format "YYYY-MM-DD HH:MM:SS". 2. **Elapsed Time Measurement:** - Implement a function `measure_function_runtime(func)` which takes another function `func` as an argument, measures and returns the time taken to execute that function (in seconds). 3. **Convert Time Formats:** - Implement a function `convert_to_gmt(struct_time_obj)` that takes a `struct_time` object representing a local time and returns a string of the equivalent GMT time in the format "YYYY-MM-DD HH:MM:SS". 4. **Performance Analysis:** - Implement a function `compare_performance()` which performs the following: - Measures the time taken to sleep for 2 seconds using `time.sleep(2)`. - Measures the time taken to execute an empty loop running 1,000,000 iterations. - Prints out the time taken for each task and determines which function was more efficient. Input and Output - The functions `measure_function_runtime(func)` and `convert_to_gmt(struct_time_obj)` should be properly tested within the `compare_performance()` function. Ensure the results are displayed clearly. - Handle any exceptions or errors gracefully and ensure that the output is well formatted. **Example:** ```python import time def measure_function_runtime(func): start_time = time.time() func() end_time = time.time() return end_time - start_time def convert_to_gmt(struct_time_obj): gmt_time = time.gmtime(time.mktime(struct_time_obj)) return time.strftime('%Y-%m-%d %H:%M:%S', gmt_time) def compare_performance(): # Measuring sleep time def sleep_func(): time.sleep(2) sleep_time = measure_function_runtime(sleep_func) print(f"Time taken to sleep: {sleep_time} seconds") # Measuring loop time def loop_func(): for _ in range(1000000): pass loop_time = measure_function_runtime(loop_func) print(f"Time taken to run a loop: {loop_time} seconds") # More efficient task if sleep_time < loop_time: print("Sleeping was more efficient.") else: print("Running a loop was more efficient.") # Current local time local_time = time.localtime() print("Current Local Time:", time.strftime('%Y-%m-%d %H:%M:%S', local_time)) # Test time conversion print("GMT Time:", convert_to_gmt(local_time)) # Performance comparison compare_performance() ``` Constraints - Ensure the solution is efficient in terms of both time and space complexity. - Use only the functions and methods provided by the `time` module. - The code should handle edge cases, such as the current time crossing from one second/minute/hour/day into the next. This task will test students' ability to interact with the `time` module, format times correctly, and measure execution time of functions accurately.
answer:import time def measure_function_runtime(func): Measures the runtime of a given function. start_time = time.time() func() end_time = time.time() return end_time - start_time def convert_to_gmt(struct_time_obj): Converts a local struct_time object to a GMT time string. gmt_time = time.gmtime(time.mktime(struct_time_obj)) return time.strftime('%Y-%m-%d %H:%M:%S', gmt_time) def compare_performance(): Compares performance of sleeping for 2 seconds vs running a loop with 1,000,000 iterations. # Measuring sleep time def sleep_func(): time.sleep(2) sleep_time = measure_function_runtime(sleep_func) print(f"Time taken to sleep: {sleep_time} seconds") # Measuring loop time def loop_func(): for _ in range(1000000): pass loop_time = measure_function_runtime(loop_func) print(f"Time taken to run a loop: {loop_time} seconds") # More efficient task if sleep_time < loop_time: print("Sleeping was more efficient.") else: print("Running a loop was more efficient.") # Get and print current local time local_time = time.localtime() print("Current Local Time:", time.strftime('%Y-%m-%d %H:%M:%S', local_time)) # Test time conversion print("GMT Time:", convert_to_gmt(local_time)) # Performance comparison compare_performance()
question:Objective Design and implement a Python function that demonstrates proficiency in handling lists using various methods and comprehensions. Problem Write a Python function named `matrix_sorter` that: 1. Accepts a list of lists (a 2D list) `matrix` as input, where each sublist represents a row of a matrix. 2. Sorts each row of the matrix in ascending order. 3. Sorts the entire matrix by the sum of the elements in each row in ascending order. 4. Flattens the sorted 2D list into a single list of tuples, where each tuple contains the original row index and the sorted elements of that row. **Function Signature:** ```python def matrix_sorter(matrix: list) -> list: pass ``` **Input:** - `matrix` (list of lists of integers): A 2D list where each sublist contains integers and represents a row of the matrix. The length of rows may vary. **Output:** - A list of tuples, where each tuple consists of an integer (the original row index) and list (sorted elements of the row). **Constraints:** - Each sublist will contain at least one integer. - The matrix will contain at least one row. - The matrix will contain at most 100 rows, and each row will contain at most 100 elements. Requirements 1. **Sort individual rows:** Each row should be sorted in ascending order. 2. **Sort by row sum:** The entire matrix should be sorted based on the sum of the elements of each row. 3. **Flatten the matrix:** The output should be a single list of tuples, where each tuple contains the original row index and the sorted row elements. Example Given the following `matrix`: ```python matrix = [ [3, 2, 5], [1, 4, 4], [6, 0, 0] ] ``` **Step-by-step**: 1. Sort individual rows: ```python sorted_rows = [ [2, 3, 5], [1, 4, 4], [0, 0, 6] ] ``` 2. Sort the matrix by the sum of the elements in each row: ```python sorted_matrix = [ [0, 0, 6], # sum is 6 [1, 4, 4], # sum is 9 [2, 3, 5] # sum is 10 ] ``` 3. Flatten the sorted matrix into a list of tuples: ```python result = [ (2, [0, 0, 6]), (1, [1, 4, 4]), (0, [2, 3, 5]) ] ``` Therefore, the expected output is: ```python [(2, [0, 0, 6]), (1, [1, 4, 4]), (0, [2, 3, 5])] ``` Usage You can test your function with different inputs to ensure its correctness and efficiency. ```python result = matrix_sorter([[3, 2, 5], [1, 4, 4], [6, 0, 0]]) print(result) # Output: [(2, [0, 0, 6]), (1, [1, 4, 4]), (0, [2, 3, 5])] ``` Good luck and happy coding!
answer:def matrix_sorter(matrix: list) -> list: This function sorts each row of the matrix in ascending order, then sorts the entire matrix by the sum of the elements in each row, and finally returns a list of tuples where each tuple contains the original row index and the sorted elements of that row. # Create a list of tuples where each tuple contains the original row index and sorted row indexed_sorted_rows = [(i, sorted(row)) for i, row in enumerate(matrix)] # Sort the list of tuples by the sum of the elements in each row indexed_sorted_rows.sort(key=lambda x: sum(x[1])) return indexed_sorted_rows