其中array_name是要删除的数组的名称,index-value是要删除的元素的索引。例如,如果我们有一个有5个元素的数组,索引从0到n-1开始。如果我们想删除2,那么2元素的索引是1。 所以,我们可以指定 如果我们想一次删除多个元素,即1,2,3,4,5,你可以在一个列表中指定所有索引元素。
arr1 = np.array([1, 2, 3, 4, 5]) arr2 = np.array([2, 4]) result = remove_elements(arr1, arr2) print(result) 输出结果为:[1 3 5] 在这个示例中,arr1是第一个numpy数组,arr2是第二个numpy数组。通过调用remove_elements函数,将arr1中存在于arr2中的元素删除,最后返回删除后的...
Write a NumPy program to remove specific elements from a NumPy array. Pictorial Presentation: Sample Solution: Python Code: # Importing the NumPy library and aliasing it as 'np'importnumpyasnp# Creating a NumPy array 'x' containing integersx=np.array([10,20,30,40,50,60,70,80,90,100])#...
array([1, 2, 6, 7, 8, 9])''' numpy.delete(arr,obj,axis=None) Return a new array with sub-arrays along an axis deleted. For a one dimensional array, this returns those entries not returned byarr[obj]. From https://docs.scipy.org/doc/numpy/reference/generated/numpy.delete.html...
arr=np.array([1,0,2,0,3,4,0,5])non_zero_indices=np.nonzero(arr)non_zero=arr[non_zero_indices]print("Non-zero elements from numpyarray.com:",non_zero) Python Copy Output: 这个方法首先找到所有非零元素的索引,然后使用这些索引来选择相应的元素。这种方法在需要同时获取非零元素索引的情况下...
问Numpy -从一维数组中删除最后一个元素的最好方法？EN是的，我之前写过你不能就地修改数组。但我这么...
Indicate which sub-arrays to remove. axis : int, optional The axis along which to delete the subarray defined by obj. If axis is None, obj is applied to the flattened array. Returns: out : ndarray A copy of arr with the elements specified by obj removed. Note that delete does not occ...
array([[0,1],[0,1]]) 要显示数组形状,请参见以下代码行: In: m.shape Out: (2,2) 我们使用arange()函数创建了一个2 x 2的数组。 没有任何警告,array()函数出现在舞台上。 array()函数从提供给它的对象创建一个数组。 该对象必须是类似数组的,例如 Python 列表。 在前面的示例中,我们传入了一个...
By reshaping we can add or remove dimensions or change number of elements in each dimension.Reshape From 1-D to 2-DExampleGet your own Python Server Convert the following 1-D array with 12 elements into a 2-D array. The outermost dimension will have 4 arrays, each with 3 elements: ...
# > array([1, 3, 5, 7, 9]) # 不改变原始位置替换 where arr = np.arange(10) out = np.where(arr % 2 == 1, -1, arr) print(arr) print(out) # [0 1 2 3 4 5 6 7 8 9] # [ 0, -1, 2, -1, 4, -1, 6, -1, 8, -1] ...