array_2d=np.array([[1,3,5],[7,5,2]])max_indices=np.argmax(array_2d,axis=1)array_2d[np.arange(array_2d.shape[0]),max_indices]=99print(array_2d) Python Copy Output: 3. 结合其他numpy函数使用argmax argmax()可以与其他numpy函数结合使用,以实现更复杂的数据操作和分析。下面的示例展示了...
importnumpyasnp# 创建一个二维数组arr=np.array([[1,2,3],[4,5,6],[7,8,9]])# 找出数组中大于 5 的最大元素的索引index=np.argmax(arr[arr>5])print(index)# 输出: 3 Python Copy Output: 在这个例子中,我们首先创建了一个二维数组,然后使用argmax函数找出了数组中大于 5 的最大元素的索引。
maxIndex = np.argmax(array1, axis =1) print('\n Without keepdims: \n', maxIndex)print('Shape of array: ', maxIndex.shape) # set keepdims to True to retain the dimension of the input arraymaxIndex = np.argmax(array1, axis =1, keepdims =True) print('\n With keepdims: \n', ...
# 2D arraymy_array=np.array([[10,5,17],[2,16,3]])# Finding the indices of the maximum elements along each row (axis=1)print("Indices of maximum elements per row:", np.argmax(my_array, axis=1))print("Indices of maximum elements per column:", np.argmax(my_array, axis=0)) ...
np.argmax(my_2d_array) OUT: 5 Explanation Here the output is 5. Why? Here, we’re operating on a 2-dimensional array. By default, if we’re working with a 2D array and we donotspecify an axis, the Numpy argmax function will apply a 2-step process. ...
array_1 = numpy.array([[1,2,3], [4,5,6]])print(array_1.min())#Output: 1 案例 4：数组中最大元素所在的元素的位置/索引 array_1 = numpy.array([[1,2,3], [4,5,6]])print(array_1.argmax())#Output: 5 案例 5：数组中最小元素所在的元素的位置/索引 array_1 = numpy.array([[...
= np.argwhere(arr_3d == 2) print(value_two_indices) # 输出: # [[0 0 1]]23、argmax &...
>>> b =array( [ (1.5,2,3), (4,5,6) ] ) >>> barray([[1.5,2. ,3. ], [4. ,5. ,6. ]]) 数组类型可以在创建时显示指定 >>> c =array( [ [1,2], [3,4] ], dtype=complex ) >>> carray([[1.+0.j,2.+0.j], ...
用numpy建的列表类型都是ndarray,因此我们首先来看np.array的用法 np.array的参数列表如下: numpy.array(object, dtype = None, copy = True, order = None, subok = False, ndmin = 0) 首先来看一维列表的应用 # 构建一维列表 >>> a = np.array([1.0, 2.0, 3.0]) ...
arr_2.argmax() #This shows the index of the highest value in the array arr_2.argmin() #This shows the index of the lowest value in the array 假设存在大量数组，而你需要弄清楚数组的形态，你想知道这个数组是一维数组还是二维数组，只需要使用 shape 函数即可：arr.shape 从 NumPy 数组中索引／...