import numpy as np # 创建一个一维NumPy数组 arr = np.array([1, 2, 3, 4, 5]) # 获取数组最后一个元素的地址 last_element_address = id(arr[-1]) print(f"内存地址: {last_element_address}") 优势 性能:NumPy数组在数值计算方面比Python列表更快,因为它们是在连续的内存块中存储的,这使得CPU缓...
A = array([1,2,3,4,5]) A[-1] # the last element in the array=> 5A[-3:] # the last three elements=> array([3, 4, 5]) 索引切片在多维数组的应用也是一样的: 代码语言:javascript 复制 A = array([[n+m*10 for n in range(5)] for m in range(5)]) A => array([[ 0,...
>>> def my_func(a): ... """Average first and last element of a 1-D array""" ... return (a[0] + a[-1]) * 0.5 >>> b = np.array([[1,2,3], [4,5,6], [7,8,9]]) >>> np.apply_along_axis(my_func, 0, b) array([4., 5., 6.]) 三,栅格数据 对于mgrid()...
import numpy as np arr = np.array([[1,2,3,4,5], [6,7,8,9,10]]) print('Last element from 2nd dim: ', arr[1, -1]) 1. 2. 3. 4. 5. Last element from 2nd dim: 10 五、裁切数组 python中裁切的意思是将元素从一个给定的索引获取到另一个给定的索引。 我们像这样传递切片而不...
argsort和 lexsort 返回的是数组里的索引，例如 >>> np.argsort([100, 50, 75]) array([1, 2, ...
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # 获取第一行 print(arr[0, :]) # 输出:[1 2 3] # 获取第一列 print(arr[:, 0]) # 输出:[1 4 7] # 获取子数组(2x2,从arr[1:, 1:]开始) print(arr[1:, 1:]) # 输出: # [[5 6] # [8 9]] 布尔索引 ...
[0, 0, 1]], dtype=int16) >>> i + i # add element to element array([[2, 0, 0], [0, 2, 0], [0, 0, 2]], dtype=int16) >>> i + 4 # add a scalar to every entry array([[5, 4, 4], [4, 5, 4], [4, 4, 5]], dtype=int16) >>> a = array( range(1,...
b = np.array([7, 2, 10, 2, 7, 4, 9, 4, 9, 8]) print(np.where(a == b)[0]) # [1 3 5 7] 1. 2. 3. 4. 5. 10、创建一个python函数可以在numpy数组上运行 # 转换适用于两个标量的函数maxx,以处理两个数组。 def maxx(x, y): ...
>>> A = np.array([[1, 1], ... [0, 1]]) >>> B = np.array([[2, 0], ... [3, 4]]) >>> A * B # elementwise product array([[2, 0], [0, 4]]) >>> A @ B # matrix product array([[5, 4], [3, 4]]) >>> A.dot(B) # another matrix product array([[...
Any arithmetic operations between equal-size arrays applies the operation element-wise: In [51]: arr = np.array([[1., 2., 3.], [4., 5., 6.]]) In [52]: arr Out[52]: array([[1., 2., 3.], [4., 5., 6.]]) In [53]: arr * arr Out[53]: array([[ 1., 4., ...