task1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 这100道练习，带你玩转Numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.14.3'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.__version__ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 1., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "v1 = np.zeros(10)\n",
    "v1[4] = 1\n",
    "v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
       "       27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
       "       44, 45, 46, 47, 48, 49])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(10,50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(10)\n",
    "arr[::-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(9).reshape(3,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 1, 4]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.array([1,2,0,0,4,0])\n",
    "[i for i in range(len(arr)) if int(arr[i]) != 0 ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0, 1, 4]),)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nz = np.nonzero([1,2,0,0,4,0])\n",
    "nz"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "nonzero 更直接"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1.2016197 , -0.04438386, -0.84162221],\n",
       "        [ 0.47699674, -0.86262864, -1.22125379],\n",
       "        [-1.21804633,  0.58053736, -1.82707452]],\n",
       "\n",
       "       [[ 0.54823395, -0.71139911, -0.80329413],\n",
       "        [-0.62852226,  0.26274385,  0.46310845],\n",
       "        [-0.29305424,  0.03107417, -0.31430184]],\n",
       "\n",
       "       [[-1.24729196,  1.21842736, -0.38346615],\n",
       "        [-0.17038662,  1.06815745, -0.52041542],\n",
       "        [-0.23128766,  0.21238888,  0.56639941]]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(27).reshape(3,3,3) #更简洁的写法 np.random.randn((3,3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.005616564448622685, 0.9942622210686155)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.random((10,10))\n",
    "arr.min(),arr.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6028645573874816"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.random(30)\n",
    "arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 0., 0., 0., 0., 0., 1.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.ones((10,10))\n",
    "arr[1:-1,1:-1] = 0\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 1., 1., 1., 1., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.ones((5,5))\n",
    "arr = np.pad(arr, pad_width=1, mode='constant', constant_values=0)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(nan, False, False, nan, False)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0 * np.nan,np.nan == np.nan,np.inf > np.nan,np.nan - np.nan,0.3 == 3 * 0.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, 0, 0],\n",
       "       [1, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0],\n",
       "       [0, 0, 0, 4, 0]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.diag(1+np.arange(4),k=-1)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.zeros((8,8),dtype=int)\n",
    "arr[1::2,::2] = 1\n",
    "arr[::2,1::2] = 1\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 5, 4)\n"
     ]
    }
   ],
   "source": [
    "print(np.unravel_index(100,(6,7,8)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0, 0, 0), (1, 0, 0), (1, 2, 3))"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(24).reshape(2,3,4)\n",
    "np.unravel_index(0,(2,3,4)),np.unravel_index(12,(2,3,4)),np.unravel_index(23,(2,3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "print(arr[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 13, 14, 15],\n",
       "       [16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [12, 13, 14, 15]],\n",
       "\n",
       "       [[ 4,  5,  6,  7],\n",
       "        [16, 17, 18, 19]],\n",
       "\n",
       "       [[ 8,  9, 10, 11],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.transpose(1,0,2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "感觉array的空间图形出来了..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0],\n",
       "       [0, 1, 0, 1, 0, 1, 0, 1],\n",
       "       [1, 0, 1, 0, 1, 0, 1, 0]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.tile( np.array([[0,1],[1,0]]), (4,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.08318772, 0.        , 0.38957134, 0.23409766, 0.80796341],\n",
       "       [0.55151902, 0.78331514, 0.45082384, 0.89290427, 0.18799472],\n",
       "       [0.80153455, 0.14361055, 0.49414381, 0.07933882, 0.14569523],\n",
       "       [0.09259934, 0.33680761, 1.        , 0.93416928, 0.59825302],\n",
       "       [0.97606681, 0.30332348, 0.65048944, 0.07814397, 0.56156783]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.random.random((5,5))\n",
    "arrMax, arrMin = arr.max(), arr.min()\n",
    "z = (arr - arrMin)/(arrMax - arrMin)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype([('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1')])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "color = np.dtype([(\"r\", np.ubyte, 1),\n",
    "                  (\"g\", np.ubyte, 1),\n",
    "                  (\"b\", np.ubyte, 1),\n",
    "                  (\"a\", np.ubyte, 1)])\n",
    "color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 3.],\n",
       "       [3., 3.],\n",
       "       [3., 3.],\n",
       "       [3., 3.],\n",
       "       [3., 3.]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(np.ones((5,3)),np.ones((3,2)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3, -4, -5, -6, -7, -8,  9, 10])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(11)\n",
    "arr[(3 < arr) & (arr <= 8)] *= -1\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "print(sum(range(5),-1))\n",
    "from numpy import *\n",
    "print(sum(range(5),-1)) #这里涉及到的是python自带的sum和numpy中的sum不同的用法，也算是sum方法的重载了。\n",
    "#在py本地库里面sum中接受的第一个值是可迭代的对象，第二个值是最后再相加的值或者迭代器；sum(iterable, start)=可迭代的所有相加和+start值\n",
    "#而numpy中的sum的第二个元素-1则是代表着最高维度下的相加"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3 12]\n"
     ]
    }
   ],
   "source": [
    "print(sum(np.arange(6).reshape((2,3)),-1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 5 7]\n"
     ]
    }
   ],
   "source": [
    "print(sum(np.arange(6).reshape((2,3)),0)) #按照列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 3 12]\n"
     ]
    }
   ],
   "source": [
    "print(sum(np.arange(6).reshape((2,3)),1)) #按照行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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