03-numpy/03.01-numpy-overview.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Numpy 简介"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Numpy**是**Python**的一个很重要的第三方库，很多其他科学计算的第三方库都是以**Numpy**为基础建立的。\n",
    "\n",
    "**Numpy**的一个重要特性是它的数组计算。\n",
    "\n",
    "在使用**Numpy**之前，我们需要导入`numpy`包："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from numpy import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "使用前一定要先导入 Numpy 包，导入的方法有以下几种：\n",
    "\n",
    "```python    \n",
    "    import numpy\n",
    "    import numpy as np\n",
    "    from numpy import *\n",
    "    from numpy import array, sin\n",
    "```\n",
    "\n",
    "事实上，在**ipython**中可以使用magic命令来快速导入**Numpy**的内容。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using matplotlib backend: Qt4Agg\n",
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 数组上的数学操作"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假如我们想将列表中的每个元素增加`1`，但列表不支持这样的操作（报错）："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate list (not \"int\") to list",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-068856d2a224>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ma\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list"
     ]
    }
   ],
   "source": [
    "a = [1, 2, 3, 4]\n",
    "a + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "转成 `array` ："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = array(a)\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`array` 数组支持每个元素加 `1` 这样的操作："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 3, 4, 5])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "与另一个 `array` 相加，得到对应元素相加的结果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 5, 7, 9])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = array([2, 3, 4, 5])\n",
    "a + b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对应元素相乘："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  6, 12, 20])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "对应元素乘方："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    8,   81, 1024])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a ** b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 提取数组中的元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提取第一个元素："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "提取前两个元素："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "最后两个元素："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 4])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将它们相加："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 6])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:2] + a[-2:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 修改数组形状"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "查看 `array` 的形状："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4L,)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "修改 `array` 的形状："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape = 2,2\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多维数组"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`a` 现在变成了一个二维的数组，可以进行加法："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 4],\n",
       "       [6, 8]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "乘法仍然是对应元素的乘积，并不是按照矩阵乘法来计算："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4],\n",
       "       [ 9, 16]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a * a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 画图"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "linspace 用来生成一组等间隔的数据："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.   ,  0.314,  0.628,  0.942,  1.257,  1.571,  1.885,  2.199,\n",
       "        2.513,  2.827,  3.142,  3.456,  3.77 ,  4.084,  4.398,  4.712,\n",
       "        5.027,  5.341,  5.655,  5.969,  6.283])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = linspace(0, 2*pi, 21)\n",
    "%precision 3\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "三角函数："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.000e+00,   3.090e-01,   5.878e-01,   8.090e-01,   9.511e-01,\n",
       "         1.000e+00,   9.511e-01,   8.090e-01,   5.878e-01,   3.090e-01,\n",
       "         1.225e-16,  -3.090e-01,  -5.878e-01,  -8.090e-01,  -9.511e-01,\n",
       "        -1.000e+00,  -9.511e-01,  -8.090e-01,  -5.878e-01,  -3.090e-01,\n",
       "        -2.449e-16])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = sin(a)\n",
    "b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画出图像："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa128ba8>]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VtDNnhk7zU6tW+Vcdffv6HSklPBV6kRhq3hyeesp3y++/HzrNj/75T1/kr7oK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\nHzC+mVy4trE1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa0daf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plot(a, b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 从数组中选择元素"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假设我们想选取数组b中所有非负的部分，首先可以利用 `b` 产生一组布尔值："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True, False, False, False, False, False, False, False,\n",
       "       False, False, False], dtype=bool)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b >= 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mask = b >= 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "画出所有对应的非负值对应的点："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xa177be0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa109828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(a[mask], b[mask], 'ro')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}