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[b0100]<深入> 模板例子_numpy学习 v1.0_20200401

last update date： 2020-04-01　　 modify nums : 1　　last read date: 2020-04-01

关键词 5星 numpy 代码

摘要

　　通过造出的5种不同维度的数据，学习numpy多维数组(对多维空间的一种实现）基本概念、切片切块、其他常用函数

目的

　　掌握核心的numpy操作技能

环境

python3
Anaconda3

说明

0 资料摘录

1 数据准备

# 0维
arr0 =  np.array (
    0.05126289
)

# 1维
arr1 =  np.array (
   [ 0.05126289,  0.66402449,  0.22970131,  0.73774777]
)

# 2维
arr2 =  np.array (
   [
    [ 0.05126289,  0.66402449,  0.22970131,  0.73774777],
    [ 0.72501932,  0.20642975,  0.38318838,  0.70826703],
    [ 0.86349343,  0.34179916,  0.32829582,  0.55624637]
   ]
)


# 3维
arr3=  np.array(
    [
       [
        [ 0.05126289,  0.66402449,  0.22970131,  0.73774777],
        [ 0.72501932,  0.20642975,  0.38318838,  0.70826703],
        [ 0.86349343,  0.34179916,  0.32829582,  0.55624637]
       ],

       [[ 0.59645461,  0.83145358,  0.85956141,  0.81924494],
        [ 0.01116166,  0.71089623,  0.91432385,  0.66226528],
        [ 0.5791923 ,  0.42764113,  0.56575513,  0.54864404]
       ]
    ]
)


# 4维
arr4=  np.array(
  [
    [
       [
        [ 0.05126289,  0.66402449,  0.22970131,  0.73774777],
        [ 0.72501932,  0.20642975,  0.38318838,  0.70826703],
        [ 0.86349343,  0.34179916,  0.32829582,  0.55624637]
       ],

       [[ 0.59645461,  0.83145358,  0.85956141,  0.81924494],
        [ 0.01116166,  0.71089623,  0.91432385,  0.66226528],
        [ 0.5791923 ,  0.42764113,  0.56575513,  0.54864404]
       ]
    ],
    [
       [
        [ 1.05126289,  1.66402449,  1.22970131,  1.73774777],
        [ 1.72501932,  1.20642975,  1.38318838,  1.70826703],
        [ 1.86349343,  1.34179916,  1.32829582,  1.55624637]
       ],

       [[ 1.59645461,  1.83145358,  1.85956141,  1.81924494],
        [ 1.01116166,  1.71089623,  1.91432385,  1.66226528],
        [ 1.5791923 ,  1.42764113,  1.56575513,  1.54864404]
       ]
    ]
  ]
)


# 5维
arr5=  np.array(
[
  [
    [
       [
        [ 0.05126289,  0.66402449,  0.22970131,  0.73774777],
        [ 0.72501932,  0.20642975,  0.38318838,  0.70826703],
        [ 0.86349343,  0.34179916,  0.32829582,  0.55624637]
       ],

       [[ 0.59645461,  0.83145358,  0.85956141,  0.81924494],
        [ 0.01116166,  0.71089623,  0.91432385,  0.66226528],
        [ 0.5791923 ,  0.42764113,  0.56575513,  0.54864404]
       ]
    ],
    [
       [
        [ 1.05126289,  1.66402449,  1.22970131,  1.73774777],
        [ 1.72501932,  1.20642975,  1.38318838,  1.70826703],
        [ 1.86349343,  1.34179916,  1.32829582,  1.55624637]
       ],

       [[ 1.59645461,  1.83145358,  1.85956141,  1.81924494],
        [ 1.01116166,  1.71089623,  1.91432385,  1.66226528],
        [ 1.5791923 ,  1.42764113,  1.56575513,  1.54864404]
       ]
    ]
  ],
  [
    [
       [
        [ 5.05126289,  5.66402449,  5.22970131,  5.73774777],
        [ 5.72501932,  5.20642975,  5.38318838,  5.70826703],
        [ 5.86349343,  5.34179916,  5.32829582,  5.55624637]
       ],

       [[ 5.59645461,  5.83145358,  5.85956141,  5.81924494],
        [ 5.01116166,  5.71089623,  5.91432385,  5.66226528],
        [ 5.5791923 ,  5.42764113,  5.56575513,  5.54864404]
       ]
    ],
    [
       [
        [ 6.05126289,  6.66402449,  6.22970131,  6.73774777],
        [ 6.72501932,  6.20642975,  6.38318838,  6.70826703],
        [ 6.86349343,  6.34179916,  6.32829582,  6.55624637]
       ],

       [[ 6.59645461,  6.83145358,  6.85956141,  6.81924494],
        [ 6.01116166,  6.71089623,  6.91432385,  6.66226528],
        [ 6.5791923 ,  6.42764113,  6.56575513,  6.54864404]
       ]
    ]
  ]  
]
)