机器学习 番外篇 02 numpy.array基本操作

numpy.array基本操作

import numpy as np

x=np.arange(10)

x

>>>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

x=np.arange(15).reshape((3,5))

x

>>>array([[ 0,  1,  2,  3,  4],
       [ 5,  6,  7,  8,  9],
       [10, 11, 12, 13, 14]])

# 基本属性

x.ndim# 维数

>>>2

x.shape

>>>(3, 5)

xx=np.arange(10)

xx

>>>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

xx[::-1]

>>>array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])

x[:2,:3]

>>>array([[0, 1, 2],
       [5, 6, 7]])

x[:,0] # 获取第一列

>>>array([ 0,  5, 10])

subx=x[:2,:3].copy() # 没有copy，则原来的数组的改变会影响现在数组

subx

>>>array([[0, 1, 2],
       [5, 6, 7]])

xx.shape

>>>(10,)

xx.reshape(2,5)

>>>array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

xx

>>>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

xx.reshape(10,-1)

>>>array([[0],
       [1],
       [2],
       [3],
       [4],
       [5],
       [6],
       [7],
       [8],
       [9]])

xx.reshape(2,-1)

>>>array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])