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。
x = np.array([1,2,3]) #2 dimensional y = np.array([(1,2,3),(4,5,6)]) x = np.arange(3) >>> array([0, 1, 2]) y = np.arange(3.0) >>> array([ 0., 1., 2.]) x = np.arange(3,7) >>> array([3, 4, 5, 6]) y ...
array([(1, 2, 3),(4, 5, 6)]) b = np.append(a, [(7, 8, 9)]) print(b) >>> [1 2 3 4 5 6 7 8 9] # Remove index 2 from previous array print(np.delete(b, 2)) >>> [1 2 4 5 6 7 8 9] 组合数组 操作 描述 文档 np.concatenate((a,b),axis=0) 连接2个数组,...
In [40]: a = np.array([[2,2], [2,3]]) In [41]: a.flatten() Out[41]: array([2, 2, 2, 3]) In [43]: a.reshape(-1) Out[43]: array([2, 2, 2, 3]) 但是像这种不规则维度的多维数组就不能转换成功了,还是本身 a = np.array([[[2,3]], [2,3]]) 转换成二维表示的...
print(index) 1. 2. 3. 4. 5. 6. 7. 9、数组排序 # Sort an array # Time complexiyt:O(NlogN) # From small to big a = [3,1,2] a.sort() # [1,2,3] print(a) # From big to small a.sort(reverse=True) # [3,2,1] ...
>>> array([0, 1, 2]) y = np.arange(3.0) >>> array([ 0., 1., 2.]) x = np.arange(3,7) >>> array([3, 4, 5, 6]) y = np.arange(3,7,2) >>> array([3, 5]) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.
import numpy as np # Append items to array a = np.array([(1, 2, 3),(4, 5, 6)]) b = np.append(a, [(7, 8, 9)]) print(b) >>> [1 2 3 4 5 6 7 8 9] # Remove index 2 from previous array print(np.delete(b, 2)) >>> [1 2 4 5 6 7 8 9] 组合数组 举例...
a = np.array([1,2,3,2,3,4,3,4,5,6]) b = np.array([7,2,10,2,7,4,9,4,9,8]) np.where(a == b) #> (array([1, 3, 5, 7]),) 如何从numpy数组中提取给定范围内的所有数字? a = np.arange(15) # Method 1 ( & intersect) ( | union) index = np.where((a >= 5...
Run from the command line as follows python vectorsum.py n where n is an integer that specifies the size of the vectors. The first vector to be added contains the squares of 0 up to n. The second vector contains the cubes of 0 up to n. ...
Array indexing Numpy offers several ways to index into arrays. slicing importnumpyasnp# Create the following rank 2 array with shape (3, 4)# [[ 1 2 3 4]# [ 5 6 7 8]# [ 9 10 11 12]]a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])# 利用切片取出下面模型的数...