importnumpyasnp arr=np.array([2,4,6,8,10])index=np.where(arr==6)print("Index of number 6:",index)index=np.argwhere(arr==6)print("Index of number 6:",index)index=np.where(arr==6)[0][0]print("Index of number 6:",index) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12...
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。
With two-dimensional arrays, the first index specifies the row of the array and the second index 对于二维数组,第一个索引指定数组的行,第二个索引指定行 specifies the column of the array. 指定数组的列。 This is exactly the way we would index elements of a matrix in linear algebra. 这正是我...
Generally speaking, what is returned when index arrays are used is an array with the same shape as the index array, but with the type and values of the array being indexed. 以二维数组为例: >>>importnumpyasnp>>>x = np.arange(12).reshape(3,4)>>>x array([[0,1,2,3], [4,5,6...
你可以通过调用NumPy的array()函数将一维数据从列表转换为数组。 代码语言:txt AI代码解释 # one dimensional example from numpy import array # list of data data = [11, 22, 33, 44, 55] # array of data data = array(data) print(data) ...
index with slice slice还可以作为index使用,作为index使用表示的就是一个index范围值。 作为index表示的slice可以有多种形式。 有头有尾的,表示index从1开始到6-1结束: arr[1:6] array([ 1, 2, 3, 4, 64]) 无头有尾的,表示index从0开始,到尾-1结束: arr2d[:2] array([[1, 2, 3], [4, 5...
# Removeindex2frompreviousarray print(np.delete(b,2)) >>> [12456789] 组合数组 举例: importnumpyasnp a = np.array([1,3,5]) b = np.array([2,4,6]) # Stack two arrays row-wise print(np.vstack((a,b))) >>>[[135] [246]] ...
shape (4, 3, 2) >>> s[3,2,1] # index i + j*4 + k*(3*4) 23 >>> s[0,1,0] 4 >>> s[0,0,1] # the first index advances serially 12 >>> t1 = t[1,2] # partial indices return slices >>> t1 array([20, 21, 22, 23], dtype=uint8) >>> t1.shape (4,) >...
In NumPy, each element in an array is associated with a number.In NumPy, each element in an array is associated with a number. The number is known as an array index. Let's see an example to demonstrate NumPy array indexing. Array Indexing in NumPy In the
array([1, 4, 2]) sum_a = np.sum(a[index]) print(sum_a) # 输出 11 Python Copy上面的代码中,我们首先创建了一个一维数组 a 和一个索引数组 index,然后使用 np.sum 方法对指定索引位置的元素进行求和。输出结果为 11。需要注意的是,使用索引数组求和时,索引数组的元素必须都是合法的数组下标。