p28_1_numpy01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "array([2, 7, 4, 3])"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([2, 7, 4, 3])  # 含有四个元素的一维向量\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[0., 0., 0.],\n       [0., 0., 0.]])"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros([2, 3])\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1., 1., 1.],\n       [1., 1., 1.]])"
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones([2, 3])\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "(2, 3)"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.array([[10, 11, 12], [13, 14, 15]])\n",
    "b\n",
    "b.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[11., 12., 13.],\n       [14., 15., 16.]])"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 算术运算\n",
    "a + b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[ -9., -10., -11.],\n       [-12., -13., -14.]])"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a - b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1., 1., 1.],\n       [1., 1., 1.]])"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a ** b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[False, False, False],\n       [False, False, False]])"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 逻辑运算\n",
    "a > b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[False, False, False],\n       [ True,  True,  True]])"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.logical_and(a < 2, b > 12) # 逻辑与，会检查每个对应位置逻辑运算的结果"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10, 11, 12],\n       [13, 14, 15]])"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "array([12, 15])"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(b, axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "array([13, 14, 15])"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.max(b, axis=0)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "array([130, 154, 180])"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.product(b, 0) # 连乘"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "['Lock',\n 'RandomState',\n '__RandomState_ctor',\n '__all__',\n '__builtins__',\n '__cached__',\n '__doc__',\n '__file__',\n '__loader__',\n '__name__',\n '__package__',\n '__path__',\n '__spec__',\n 'absolute_import',\n 'beta',\n 'binomial',\n 'bytes',\n 'chisquare',\n 'choice',\n 'dirichlet',\n 'division',\n 'exponential',\n 'f',\n 'gamma',\n 'geometric',\n 'get_state',\n 'gumbel',\n 'hypergeometric',\n 'laplace',\n 'logistic',\n 'lognormal',\n 'logseries',\n 'mtrand',\n 'multinomial',\n 'multivariate_normal',\n 'negative_binomial',\n 'noncentral_chisquare',\n 'noncentral_f',\n 'normal',\n 'np',\n 'operator',\n 'pareto',\n 'permutation',\n 'poisson',\n 'power',\n 'print_function',\n 'rand',\n 'randint',\n 'randn',\n 'random',\n 'random_integers',\n 'random_sample',\n 'ranf',\n 'rayleigh',\n 'sample',\n 'seed',\n 'set_state',\n 'shuffle',\n 'standard_cauchy',\n 'standard_exponential',\n 'standard_gamma',\n 'standard_normal',\n 'standard_t',\n 'test',\n 'triangular',\n 'uniform',\n 'vonmises',\n 'wald',\n 'warnings',\n 'weibull',\n 'zipf']"
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(np.random)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[[4.90663333, 5.26188969, 4.86061816, 5.37285917, 4.91367438,\n         5.23238551, 4.94746669],\n        [4.58437468, 4.68032145, 4.92580704, 4.80776999, 5.37718995,\n         4.15567337, 5.18051914],\n        [5.0937494 , 4.744687  , 5.15345641, 4.73824026, 4.54058864,\n         5.2146763 , 5.2139856 ]],\n\n       [[4.64061463, 4.81477397, 5.33144602, 4.81349404, 4.9123123 ,\n         4.41429647, 4.90845476],\n        [4.62860557, 5.14023874, 4.66309326, 4.77557675, 4.68921616,\n         5.43502969, 4.75402171],\n        [5.75419472, 5.44411834, 5.28920345, 5.08181946, 5.20092454,\n         4.5181677 , 4.83360819]]])"
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.random.normal(5, 0.3, [2, 3, 7])\n",
    "d"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "outputs": [],
   "source": [
    "d1 = np.sum(d, 0)  # 按照第0个维度相加，也就是2，结果就是（3， 7）"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "outputs": [
    {
     "data": {
      "text/plain": "(3, 7)"
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "outputs": [
    {
     "data": {
      "text/plain": "array([14161.57370479, 15743.87887834, 16224.70828609, 14297.88231936,\n       14372.77689958, 12291.19926492, 15073.12797007])"
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2 = np.product(d, (0, 1))\n",
    "d2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[11., 11., 11.],\n       [11., 11., 11.]])"
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.ones([2,3]) * 11\n",
    "a"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10, 11, 12],\n       [13, 14, 15]])"
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[11., 11., 12.],\n       [13., 14., 15.]])"
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.maximum(a, b)\n",
    "# maximum:a, b矩阵对应位置元素取最大值\n",
    "# max : 单个矩阵中每个元素取最大值"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10., 11., 11.],\n       [11., 11., 11.]])"
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.minimum(a, b)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10.5, 11. , 11.5],\n       [12. , 12.5, 13. ]])"
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(a + b) / 2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10, 11, 12],\n       [13, 14, 15]])"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "outputs": [
    {
     "data": {
      "text/plain": "(2, 3)"
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10, 11],\n       [12, 13],\n       [14, 15]])"
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(b, [3, 2]) # 注意reshape的构建的新的形状"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[[4.90663333, 5.26188969, 4.86061816, 5.37285917, 4.91367438,\n         5.23238551, 4.94746669],\n        [4.58437468, 4.68032145, 4.92580704, 4.80776999, 5.37718995,\n         4.15567337, 5.18051914],\n        [5.0937494 , 4.744687  , 5.15345641, 4.73824026, 4.54058864,\n         5.2146763 , 5.2139856 ]],\n\n       [[4.64061463, 4.81477397, 5.33144602, 4.81349404, 4.9123123 ,\n         4.41429647, 4.90845476],\n        [4.62860557, 5.14023874, 4.66309326, 4.77557675, 4.68921616,\n         5.43502969, 4.75402171],\n        [5.75419472, 5.44411834, 5.28920345, 5.08181946, 5.20092454,\n         4.5181677 , 4.83360819]]])"
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "(2, 3, 7)"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.shape"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[4.90663333, 5.26188969, 4.86061816, 5.37285917, 4.91367438,\n        5.23238551, 4.94746669],\n       [4.58437468, 4.68032145, 4.92580704, 4.80776999, 5.37718995,\n        4.15567337, 5.18051914],\n       [5.0937494 , 4.744687  , 5.15345641, 4.73824026, 4.54058864,\n        5.2146763 , 5.2139856 ],\n       [4.64061463, 4.81477397, 5.33144602, 4.81349404, 4.9123123 ,\n        4.41429647, 4.90845476],\n       [4.62860557, 5.14023874, 4.66309326, 4.77557675, 4.68921616,\n        5.43502969, 4.75402171],\n       [5.75419472, 5.44411834, 5.28920345, 5.08181946, 5.20092454,\n        4.5181677 , 4.83360819]])"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(d, [6, 7])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[4.90663333, 5.26188969, 4.86061816, 5.37285917, 4.91367438,\n        5.23238551, 4.94746669],\n       [4.58437468, 4.68032145, 4.92580704, 4.80776999, 5.37718995,\n        4.15567337, 5.18051914],\n       [5.0937494 , 4.744687  , 5.15345641, 4.73824026, 4.54058864,\n        5.2146763 , 5.2139856 ],\n       [4.64061463, 4.81477397, 5.33144602, 4.81349404, 4.9123123 ,\n        4.41429647, 4.90845476],\n       [4.62860557, 5.14023874, 4.66309326, 4.77557675, 4.68921616,\n        5.43502969, 4.75402171],\n       [5.75419472, 5.44411834, 5.28920345, 5.08181946, 5.20092454,\n        4.5181677 , 4.83360819]])"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(d, [-1, 7])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[[4.75762659, 9.93736474, 4.69753524, 5.83830446, 9.00392025,\n         4.12879437, 1.97322141],\n        [7.61292292, 4.06744488, 9.85393688, 8.78430755, 4.82795377,\n         7.44604294, 2.6822143 ],\n        [9.49741521, 8.81241517, 5.28519162, 5.25214835, 3.29038461,\n         5.10286258, 5.00347367]],\n\n       [[7.97028968, 7.21277459, 9.9988865 , 2.82702912, 9.52044671,\n         1.65067368, 1.96895268],\n        [1.13284964, 4.58266889, 0.2107302 , 3.64972321, 4.9942649 ,\n         9.00643035, 9.82986296],\n        [1.40993426, 6.38566965, 4.54823706, 6.77726313, 5.91381124,\n         9.72466906, 8.69992217]]])"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e = np.random.uniform(0, 10, [2, 3, 7])\n",
    "e"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10, 11, 12],\n       [13, 14, 15]])"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10, 11],\n       [12, 13],\n       [14, 15]])"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.reshape(b, (3,2)) # reshape不改变元素之间的次序"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[10, 13],\n       [11, 14],\n       [12, 15]])"
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.transpose(b, (1, 0)) # 注意np.transpose里的参数是axis，默认axis是（0,1）"
   ],
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     "name": "#%%\n"
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   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
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