基本 Square操作在 Python 中有几种方法可以计算平方：
# Using the ** operatornumber = 5square = number ** 2print(square) # Output: 25# Using multiplicationsquare = number * numberprint(square) # Output: 25# Using the pow() functionsquare = pow(number, 2)print(square) # Output: 25# Using math.pow() (returns float)import mathsquare = math.pow(number, 2)print(square) # Output: 25.0使用数字列表以下是使用列表处理正方形的方法：
numbers = [1, 2, 3, 4, 5]# List comprehensionsquares = [num ** 2 for num in numbers]print(squares) # Output: [1, 4, 9, 16, 25]# Using mapsquares = list(map(lambda x: x**2, numbers))print(squares) # Output: [1, 4, 9, 16, 25]# Generator expression (memory efficient)squares_generator = (num ** 2 for num in numbers)for square in squares_generator: print(square)实际应用计算面积class Rectangle: def __init__(self, width, height): self.width = width self.height = height def area(self): return self.width * self.height def is_square(self): return self.width == self.height @classmethod def create_square(cls, side_length): return cls(side_length, side_length)# Usagesquare = Rectangle.create_square(5)print(square.area()) # Output: 25print(square.is_square()) # Output: Truerectangle = Rectangle(4, 6)print(rectangle.area()) # Output: 24print(rectangle.is_square()) # Output: False距离计算from math import sqrtclass Point: def __init__(self, x, y): self.x = x self.y = y def distance_to(self, other_point): # Using squares to calculate Euclidean distance x_diff = (self.x - other_point.x) ** 2 y_diff = (self.y - other_point.y) ** 2 return sqrt(x_diff + y_diff) def distance_from_origin(self): return self.distance_to(Point(0, 0))# Usagepoint1 = Point(3, 4)point2 = Point(6, 8)print(point1.distance_from_origin()) # Output: 5.0print(point1.distance_to(point2)) # Output: 5.0使用 Squares 进行数据分析import statisticsdef analyze_data_spread(numbers): """Calculate statistical measures using squares.""" mean = sum(numbers) / len(numbers) # Calculate variance using squares squared_diff_sum = sum((x - mean) ** 2 for x in numbers) variance = squared_diff_sum / len(numbers) # Standard deviation is square root of variance std_dev = variance ** 0.5 return { 'mean': mean, 'variance': variance, 'std_dev': std_dev }# Example usagedata = [2, 4, 4, 4, 5, 5, 7, 9]stats = analyze_data_spread(data)print(f"Mean: {stats['mean']:.2f}")print(f"Variance: {stats['variance']:.2f}")print(f"Standard Deviation: {stats['std_dev']:.2f}")图像处理class ImageProcessor: def __init__(self, width, height): self.width = width self.height = height self.pixels = [[0 for _ in range(width)] for _ in range(height)] def apply_gaussian_blur(self, kernel_size): """Simplified Gaussian blur using square kernels.""" if kernel_size % 2 == 0: raise ValueError("Kernel size must be odd") result = [[0 for _ in range(self.width)] for _ in range(self.height)] radius = kernel_size // 2 for y in range(radius, self.height - radius): for x in range(radius, self.width - radius): # Calculate average of square neighborhood sum_values = 0 for dy in range(-radius, radius + 1): for dx in range(-radius, radius + 1): sum_values += self.pixels[y + dy][x + dx] result[y][x] = sum_values / (kernel_size ** 2) return result# Usageimg = ImageProcessor(100, 100)blurred = img.apply_gaussian_blur(3)性能优化使用正方形时，请考虑以下有效方法：
def optimize_square_calculations(numbers): # Pre-compute squares for frequently used numbers square_lookup = {i: i**2 for i in range(10)} def get_square(n): # Use lookup table for small numbers if n in square_lookup: return square_lookup[n] return n ** 2 return [get_square(n) for n in numbers]# Memory-efficient square calculation for large rangesdef square_generator(start, end): for n in range(start, end): yield n ** 2# Usagenumbers = [2, 4, 6, 8, 3, 5, 7, 9]result = optimize_square_calculations(numbers)print(result)# For large rangesfor square in square_generator(1, 5): print(square)平方根近似有时你需要在不使用内置函数的情况下找到平方根：
def newton_square_root(n, precision=0.0001): """Calculate square root using Newton's method.""" if n < 0: raise ValueError("Cannot calculate square root of negative number") if n == 0: return 0 x = n # Initial guess while True: # Calculate next approximation next_x = (x + n/x) / 2 # Check if we've reached desired precision if abs(x - next_x) < precision: return next_x x = next_x# Usageprint(newton_square_root(16)) # Output: ~4.0print(newton_square_root(2)) # Output: ~1.4142135623730951测试 Square作以下是测试 square 相关函数的方法：
import unittestclass TestSquareOperations(unittest.TestCase): def test_basic_square(self): self.assertEqual(5 ** 2, 25) self.assertEqual(pow(5, 2), 25) def test_square_root(self): # Test our custom Newton method self.assertAlmostEqual(newton_square_root(16), 4.0, places=6) self.assertAlmostEqual(newton_square_root(2), 1.4142135623730951, places=6) def test_negative_numbers(self): with self.assertRaises(ValueError): newton_square_root(-1) def test_zero(self): self.assertEqual(newton_square_root(0), 0)if __name__ == '__main__': unittest.main()Python 中的 Square 运算很简单，但可以以许多复杂的方式使用。无论您是计算面积、分析数据还是处理图像，了解如何有效地处理正方形都可以使您的代码更清晰、更有效。
