bool_arr = np.array([True, False, True]) # Using Boolean NumPy array mask = np.ones(3, dtype=bool) mask[1] = False # Comparison operators num_arr = np.array([1, 2, 3]) mask = num_arr > 1 print(bool_arr) # [ True False True] print(mask) # [False True True] 我们可以...
x = np.zeros((2,5))# create 2D array of zeroes x[0][1:3] =5# replace some values along 1st dimension with 5 mask = (x[0] >0)# create a mask to only deal with the non negative values x[0][mask][1] =10# change one of the values that is non negative printx[0][mask]...
arr=np.array([1,2,3,4,5]) indices=np.array([0,2,4]) print(arr[indices])# 输出: [1, 3, 5] 布尔数组索引 布尔数组索引可以根据条件筛选数组元素。 arr=np.array([1,2,3,4,5]) mask=arr>3 print(arr[mask])# 输出: [4, 5] 四、Numpy数组布尔索引 布尔索引是一种强大的筛选工具,可...
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 创建一个布尔掩码,这里我们选择所有大于5的元素 mask = arr > 5 print("原始数组:") print(arr) print("布尔掩码:") print(mask) # 使用掩码来获取满足条件的元素 # 注意:这里会返回一个一维数组 filtered_elements = arr[mask] ...
importnumpyasnpimportnumpy.maasma# 创建和连接masked arraysarr1=ma.array([1,2,3],mask=[0,0,1])arr2=ma.array([4,5,6],mask=[1,0,0])result=np.concatenate((arr1,arr2))print("numpyarray.com - Concatenated masked arrays:")print(result)print("Mask of the result:",result.mask)...
fromtxt', 'mask_indices', 'mat', 'math', 'matmul', 'matrix', 'matrixlib', 'max', 'maximum', 'maximum_sctype', 'may_share_memory', 'mean', 'median', 'memmap', 'meshgrid', 'mgrid', 'min', 'min_scalar_type', 'minimum', 'mintypecode', 'mirr', 'mod', 'modf', '...
#NumPy: Apply a Mask from one 2D Array to another 2D Array This approach also works if you need to apply a mask from one 2D array to another 2D array. main.py importnumpyasnp x =np.array([[1,3],[5,7],[9,12]])y =np.array([[2,4],[6,8],[10,14]])masked_y_array=np...
arr1=ma.array([1,2,None,3],mask=[0,0,1,0])arr2=np.array([4,5,6])result=ma.concatenate([arr1,arr2])print("Concatenated masked array from numpyarray.com:",result) Python Copy Output: 在这个例子中,我们创建了一个masked arrayarr1,其中None值被标记为masked。然后我们可以使用ma.concatena...
import numpy as np # Create a 2D NumPy array of shape (5, 5) with random integers array_2d = np.random.randint(0, 100, size=(5, 5)) # Define a mask array to select elements that are greater than 50 mask = array_2d > 50 # Use the mask array for indexing to select elements ...
In [33]: mask = np.ones(arr.shape, bool)In [34]: mask[np.arange(10), idxs] = FalseIn [35]: arr[mask]Out[35]: array([ 1, 2, 3, 5, 6, 8, 9, 11, 12, 13, 16, 17, 18, 19, 21, 22, 24, 26, 28, 29]) boolean索引生成一个平面数组,因此我们需要重新整形以获得2d: ...