numpy_notebook.ipynb · jeffsui/jupyter_notebook_note

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numpy_notebook.ipynb 10.76 KB

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "# numpy创建数组\n",
    "## 1. 使用numpy内置函数创建"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "array = np.arange(20)\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "验证该数组维度,使用shape属性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "访问数组中的值,直接使用索引,索引从零开始。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与Python列表不同，Numpy数组的内容是同质的。 因此，如果你尝试将字符串值分配给数组中的元素，其数据类型为int，则会出现错误。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[3] =\"apple\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 创建二维数组\n",
    "\n",
    "如果只使用arange函数，它将输出一维数组。 要使其成为二维数组，请使用reshape函数链接其输出。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.arange(20).reshape(4,5)\n",
    "array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "使用`shape`查看这个数组的维数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "访问二维数组中的元素,需要为行和列指定对应的索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array[3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 创建三维或跟多维度数组\n",
    "和二维数组一样,通过reshape重塑数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.arange(27).reshape(3,3,3)\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "检查数组维度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用arange函数,可以创建一个起始值和特定值之间具备特定秩序的数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.arange(10,35,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "### 其他方式创建数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "print(np.zeros((5)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.zeros((2,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用`ones`函数创建用1填充的数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.ones((3,4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用`empty`函数创建一个初始内容是随机的数组,取决于内存的状态"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.empty((2,3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`full`函数创建一个填充给定值的n * n数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.full((2,2), 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`eye`函数可以创建一个n * n矩阵，对角线为1s，其他为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.eye(3,3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "##  从Python列表转换\n",
    "除了使用Numpy函数之外，你还可以直接从Python列表创建数组。将Python列表传递给数组函数以创建Numpy数组："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "array = np.array([4,5,6])\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "你还可以创建Python列表并传递其变量名以创建Numpy数组。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "list = [4,5,6]\n",
    "array = np.array(list)\n",
    "array"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## 使用特殊的库函数\n",
    "\n",
    "要创建一个填充0到1之间随机值的数组，请使用random函数。这对于需要随机状态才能开始的问题特别有用。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.random((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "a = np.array([[1,2,3],[4,5,6]])\n",
    "print(type(a))\n",
    "print(a)\n",
    "print(a.shape) #几行几列\n",
    "print(a.ndim)# 维度 \n",
    "print(a.dtype)#数据类型\n",
    "print(a.size)\n",
    "print(a[0],a[1])#访问列表元素\n",
    "a[0]=5\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc-hr-collapsed": true
   },
   "source": [
    "## 多维数组 ndarray \n",
    "创建一个随机多维数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.22162604,  0.72717293,  0.80381741],\n",
       "       [-1.08441623, -1.49322895,  0.72228904]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data = np.random.randn(2,3)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "给data添加一个数学操作:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 12.21626042,   7.2717293 ,   8.03817406],\n",
       "       [-10.8441623 , -14.93228954,   7.2228904 ]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data*10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.44325208,  1.45434586,  1.60763481],\n",
       "       [-2.16883246, -2.98645791,  1.44457808]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data+data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ndarray 专用创建数组方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.90389481e-316, 7.56603881e-307],\n",
       "       [0.00000000e+000, 8.90097448e-307]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1 = np.ndarray(shape=(2,2), dtype=float, order='F')\n",
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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提交于 2020-02-16 18:32 . add pyecharts

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