import numpy as np

# 创建一个随机数组，用于演示本操作
arr = np.random.randint(0, 2, size=(5, 5)) # 随机生成0和1组成的5*5数组
print("Original array:\n", arr)

# 将1变为0，将0变为1
new_arr = np.where(arr == 1, 0, 1)
print("New array:\n", new_arr)

Original array:
 [[1 0 0 1 1]
  [1 1 0 1 1]
  [1 1 1 0 1]
  [1 0 1 1 0]
  [1 1 1 0 0]]
New array:
 [[0 1 1 0 0]
  [0 0 1 0 0]
  [0 0 0 1 0]
  [0 1 0 0 1]
  [0 0 0 1 1]]

import numpy as np
import time

# 创建一个超大的随机数组，用于对比性能
LARGE_ARR_SIZE = 10000
arr = np.random.randint(0, 2, size=(LARGE_ARR_SIZE, LARGE_ARR_SIZE))

# 使用条件运算符
start_time = time.time()
new_arr = np.where(arr == 1, 0, 1)
end_time = time.time()
print("Time used using np.where():", end_time-start_time)

# 使用循环
start_time = time.time()
new_arr = np.zeros(arr.shape)
for i in range(arr.shape[0]):
    for j in range(arr.shape[1]):
        if arr[i][j] == 0:
            new_arr[i][j] = 1
        else:
            new_arr[i][j] = 0
end_time = time.time()
print("Time used using loop:", end_time-start_time)

Time used using np.where(): 0.13526630401611328
Time used using loop: 124.2841305732727