format(np.round(x,2),np.round(x,4))

这段代码中的format函数是用来格式化输出的，其中np.round(x,2)表示将x保留两位小数，np.round(x,4)表示将x保留四位小数。具体用法可以参考下面的例子：

import numpy as np

x = 3.1415926
print("保留两位小数：", format(np.round(x,2)))
print("保留四位小数：", format(np.round(x,4)))

输出结果为：

保留两位小数： 3.14
保留四位小数： 3.1416

相关问题

优化这段代码import numpy as np fp=np.genfromtxt(C:/Exam/2274135219/Python编程题/2/成绩单数字.csvdelimiter=’,’, encoding='urf-8’) a=np.round(np.mean(fp,axis=1),decimals=1) b=np.round(np.median(fp,axis=1),decimals=1) c=np.round(np.std(fp,axis=1),decimals=1) for i in range(len(a)): print(’{:.1f}，{:.1f},{:.1f}'.format(a[i],b[i],c[1]))

可以尝试以下优化：

1. 将文件路径改为相对路径，避免因为文件路径错误导致的异常。

2. 使用 with open() as f 语句来打开文件，避免忘记关闭文件导致资源泄漏。

3. 将代码块封装成函数，增加代码可读性和复用性。

4. 将循环中的字符串格式化改为列表推导式。

5. 将 print() 函数改为 join() 方法，减少 IO 操作。

6. 可以使用 numpy.around() 方法代替 np.round()，简化代码。

改进后的代码如下所示：

import numpy as np 

def process_data(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        fp = np.genfromtxt(f, delimiter=',')
    a = np.around(np.mean(fp, axis=1), decimals=1)
    b = np.around(np.median(fp, axis=1), decimals=1)
    c = np.around(np.std(fp, axis=1), decimals=1)
    result = ['{:.1f},{:.1f},{:.1f}'.format(a[i], b[i], c[i]) for i in range(len(a))]
    return result

file_path = './成绩单数字.csv'
result = process_data(file_path)
print('\n'.join(result))

import numpy as np import matplotlib.pyplot as plt import pywt from skimage import io, color # 读取灰度图像并转换为RGB图像 img_gray = io.imread('lena.png', as_gray=True) img = color.gray2rgb(img_gray) # 对图像的三个通道进行DWT变换 coeffs_r = pywt.dwt2(img[:, :, 0], 'haar') coeffs_g = pywt.dwt2(img[:, :, 1], 'haar') coeffs_b = pywt.dwt2(img[:, :, 2], 'haar') # 设置压缩比率 compress_ratio = 0.5 # 计算阈值 threshold_r = np.sort(np.abs(coeffs_r[1].ravel()))[::-1][int(compress_ratio * len(coeffs_r[1].ravel()))] threshold_g = np.sort(np.abs(coeffs_g[1].ravel()))[::-1][int(compress_ratio * len(coeffs_g[1].ravel()))] threshold_b = np.sort(np.abs(coeffs_b[1].ravel()))[::-1][int(compress_ratio * len(coeffs_b[1].ravel()))] # 对小于阈值的系数进行置零 coeffs_r = list(coeffs_r) coeffs_r[0] = np.round(coeffs_r[0]) coeffs_r[1] = np.where(np.abs(coeffs_r[1]) < threshold_r, 0, coeffs_r[1]) coeffs_r[2] = np.where(np.abs(coeffs_r[2]) < threshold_r, 0, coeffs_r[2]) coeffs_g = list(coeffs_g) coeffs_g[0] = np.round(coeffs_g[0]) coeffs_g[1] = np.where(np.abs(coeffs_g[1]) < threshold_g, 0, coeffs_g[1]) coeffs_g[2] = np.where(np.abs(coeffs_g[2]) < threshold_g, 0, coeffs_g[2]) coeffs_b = list(coeffs_b) coeffs_b[0] = np.round(coeffs_b[0]) coeffs_b[1] = np.where(np.abs(coeffs_b[1]) < threshold_b, 0, coeffs_b[1]) coeffs_b[2] = np.where(np.abs(coeffs_b[2]) < threshold_b, 0, coeffs_b[2]) # 合并三个通道的系数 coeffs = [np.stack([coeffs_r[i], coeffs_g[i], coeffs_b[i]], axis=-1) for i in range(len(coeffs_r))] # 对图像进行IDWT反变换 img_dwt = pywt.idwt2(coeffs, 'haar') # 显示原始图像和压缩后的图像 fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(8, 4)) ax = axes.ravel() ax[0].imshow(img) ax[0].set_title("Original image") ax[1].imshow(img_dwt) ax[1].set_title("Compressed image (DWT)") ax[1].set_xlabel("Compression ratio: {:.2f}".format(compress_ratio)) plt.tight_layout() plt.show()

这段代码缺少了两行，分别是导入numpy和matplotlib.pyplot两个模块的语句。请在代码的开头添加以下两行语句：

import numpy as np
import matplotlib.pyplot as plt

这样就可以解决代码中的报错。