Numpy/Array/Numpy_Array_02.ipynb

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   "source": [
    "## 通过 Numpy 创建各式各样的矩阵\n",
    "\n",
    "参考：http://blog.51cto.com/qianqiansun/1964097"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 二、用指定的数据填充\n",
    "\n",
    "### 1. array(object[, dtype, copy, order, subok, ndmin])\n",
    "\n",
    "* 用对象直接填充数组\n",
    "* object 中括号括起来的数组\n",
    "* ndmin 表示维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3])    # 一维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1, 2], [3, 4]])    # 二维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1, 2, 3], ndmin=2)    # 只有一个元素的二维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(np.mat('1 2; 3 4'))    # 从子类（矩阵）创建"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. numpy.mat(data,dtype=None) \n",
    "\n",
    "* 输入转化为矩阵,创建一个新变量,指向旧的数据\n",
    "* numpy中数组和矩阵的区别 - jiangsujiangjiang的博客 - CSDN博客  https://blog.csdn.net/jiangsujiangjiang/article/details/83714475\n",
    "* matrix是array的分支，matrix和array在很多时候都是通用的，用哪一个都一样；\n",
    "* 如果两个可以通用，那就选择array，因为array更灵活，速度更快，很多人把二维的array也翻译成矩阵。\n",
    "* 相同点：索引方式相同，都可以使用a[i][j]，a[i,j]进行索引\n",
    "* numpy.mat(), numpy.array() 可以保证两者的互相转换\n",
    "\n",
    "matrix（矩阵）：\n",
    "\n",
    "* 具有相对简单的运算符号，比如两个matrix相乘（求内积），就是用符号*\n",
    "\n",
    "array（数组）：\n",
    "* 两个一维数组相乘用\\*，对应元素相乘\n",
    "* 多维数组也可以看成矩阵，如果进行矩阵相乘（求内积），则需要用到np.dot(），如np.dot(arr.T,arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1, 2],\n",
       "        [3, 4]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mat('1 2; 3 4')    # 矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1, 2, 3, 4]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.mat('1 2 3 4')    # 一维矩阵\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[1],\n",
       "        [2],\n",
       "        [3],\n",
       "        [4]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = a.T\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[30]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 1,  2,  3,  4],\n",
       "        [ 2,  4,  6,  8],\n",
       "        [ 3,  6,  9, 12],\n",
       "        [ 4,  8, 12, 16]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b*a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. asarray(a[, dtype, order])\n",
    "\n",
    "* 把lists, lists of tuples, tuples, tuples of tuples, tuples of lists and ndarrays转化为array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = [1, 2, 3]\n",
    "np.asarray(a)"
   ]
  }
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