{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 一百以内质数之和"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 判断是否为质数\n",
    "判断一个整数是否为质数比较简单，即除了自身和1以外不可被别的数整除。不过根据数学理论证明，不用从2检查到n，到int(sqrt(n))+1即可，可以提高效率。注意返回值为True或False，方便后续的boolean索引。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def is_prime(num):\n",
    "\tif num <= 1:\n",
    "\t\treturn False\n",
    "\tfor i in range(2,int(np.sqrt(num))+1):\n",
    "\t\tif num % i == 0:\n",
    "\t\t\treturn False\n",
    "\treturn True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 利用循环\n",
    "简单粗暴的方式，从1循环到100，一次判断是否为质数，若是质数，则加到ans上，若不是直接跳过。因为%%timeit会执行1000，所以跑完代码就comment out了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1060\n"
     ]
    }
   ],
   "source": [
    "def prime_sum_iter(n=100):\n",
    "    ans = 0\n",
    "    for i in range(1,n+1):\n",
    "        if is_prime(i):\n",
    "            ans += i\n",
    "    return ans\n",
    "\n",
    "print (prime_sum_iter())\n",
    "# %%timeit\n",
    "# 1000 loops, best of 3: 253 µs per loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 利用np向量化方法\n",
    "利用numpy可以向量化，用更简洁的方式遍历所有的元素。向量化的理解，就本例子而言，循环的思想是每次取一个数，对其判断是否为质数；向量化是取这个数组为变量，直接对其所有元素判断是否为质数，然后返回一个同size的数组。由于is_prime()函数本身接受单个integer，如要接受向量、数组等变量，需要对函数进行向量话，is_prime_vec = np.vectorize(is_prime)。\n",
    "> np.vectorize: Define a vectorized function which takes a nested sequence of objects or numpy arrays as inputs and returns a numpy array as output，具体可参考[文档](http://docs.scipy.org/doc/numpy-1.10.1/reference/generated/numpy.vectorize.html)。\n",
    "\n",
    "is_prime_vec(np_arr)返回一个布尔型数组，比如np_arr = array([1,2,3,4]);那is_prime_vec(np_arr)返回array([False, True, True, False]),因为2,3是质数，1,4不是。np_arr[is_prime_vec(np_arr)]是布尔索引，简单讲就是返回对应True的元素，这里会返回array([2,3]),因为2,3对应的boolean值为True。之后再sum就实现了和循环一样的功能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1060\n"
     ]
    }
   ],
   "source": [
    "def prime_sum_vect(n=100):\n",
    "    np_arr = np.arange(1,n+1)\n",
    "    is_prime_vec = np.vectorize(is_prime)\n",
    "    return np.sum(np_arr[is_prime_vec(np_arr)])\n",
    "\n",
    "print (prime_sum_vect())\n",
    "# %%timeit\n",
    "# 1000 loops, best of 3: 286 µs per loop"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 模拟醉汉随机漫步"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 假设醉汉每一步的距离是1或2，方向也完全随机，360度不确定，然后模拟醉汉的行走路径.\n",
    "我们用坐标表示醉汉的位置,每次产生两个随机数，一个是步长，就是醉汉一步走多远，我们假设1或2，`r = np.random.randint(1,3,N)`,一个是方向，是一个度数，0-360,`theta = np.radians(np.random.randint(0,361,N))`,注意转化为弧度，每走一步，坐标值就是之前坐标值之和，用[cumsum](http://docs.scipy.org/doc/numpy-1.10.0/reference/generated/numpy.cumsum.html)函数可以很方便地实现，`x = np.cumsum(r*np.cos(theta))`,`y = np.cumsum(r*np.sin(theta))`。然后ok了，plot一下看看醉汉的步伐有多醉人。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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3SZ8eCAsDSpYEBg8GTp1ipc7Mmc1ttm1j8qny5YFPPwXOnxfnSSHtIhoIQRAEK157DVi6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i0cj1W3LseX/DpPi8RErZcv17p2bfZTv77Wc+ZQY+JKmPj2W/O4yV9KAkWGECD8tYgAIVjN+vXU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\nGOZFov5gOgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10aad54a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "N = 500 # steps\n",
    "\n",
    "r = np.random.randint(1,3,N) # move 1 or 2 units of distance each step\n",
    "theta = np.radians(np.random.randint(0,361,N))\n",
    "\n",
    "# np.cumsum Return the cumulative sum of the elements along a given axis.\n",
    "x = np.cumsum(r*np.cos(theta))\n",
    "y = np.cumsum(r*np.sin(theta))\n",
    "plt.plot(x,y);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. 使用梯形法计算一二次函数的数值积分"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "$$\n",
    "     \\int_{a}^{b}f(x)dx\n",
    "$$\n",
    "we can partition the integration interval $[a,b]$ into smaller subintervals,\n",
    "and approximate the area under the curve for each subinterval by the area of\n",
    "the trapezoid created by linearly interpolating between the two function values\n",
    "at each end of the subinterval:\n",
    "<img src=\"http://upload.wikimedia.org/wikipedia/commons/thumb/d/dd/Trapezoidal_rule_illustration.png/316px-Trapezoidal_rule_illustration.png\" /img>\n",
    "The blue line represents the function $f(x)$ and the red line\n",
    "is the linear interpolation.  By subdividing the interval $[a,b]$, the area under $f(x)$ can thus be approximated as the sum of the areas of all\n",
    "the resulting trapezoids. \n",
    "\n",
    "If we denote by $x_{i}$ ($i=0,\\ldots,n,$ with $x_{0}=a$ and\n",
    "$x_{n}=b$) the abscissas where the function is sampled, then\n",
    "\n",
    "$$\n",
    "   \\int_{a}^{b}f(x)dx\\approx\\frac{1}{2}\\sum_{i=1}^{n}\\left(x_{i}-x_{i-1}\\right)\\left(f(x_{i})+f(x_{i-1})\\right).\n",
    "$$\n",
    "\n",
    "The common case of using equally spaced abscissas with spacing $h=(b-a)/n$ reads simply\n",
    "\n",
    "$$\n",
    "   \\int_{a}^{b}f(x)dx\\approx\\frac{h}{2}\\sum_{i=1}^{n}\\left(f(x_{i})+f(x_{i-1})\\right).\n",
    "$$\n",
    "具体计算只需要这个公式![积分](https://upload.wikimedia.org/math/6/b/1/6b1d1e253d5ba589262271a030bcb8e4.png)\n",
    "道理很简单，就是把积分区间分割为很多小块，用梯形替代，其实还是局部用直线近似代替曲线的思想。这里对此一元二次函数积分，并与python模块积分制对比(精确值为4.5)用以验证。\n",
    "$$\n",
    "\\int_a^b (x^2 - 3x + 2) dx \n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from scipy import integrate\n",
    "import numpy as np\n",
    "def f(x):\n",
    "    return x*x - 3*x + 2\n",
    "\n",
    "def trape(f,a,b,n=100):\n",
    "    f = np.vectorize(f) # can apply on vector\n",
    "    h = float(b - a)/n\n",
    "    arr = f(np.linspace(a,b,n+1))\n",
    "    return (h/2.)*(2*arr.sum() - arr[0] - arr[-1])\n",
    "\n",
    "def quad(f,a,b):\n",
    "    return integrate.quad(f,a,b)[0] # compare with python library result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.50045\n",
      "4.499999999999999\n"
     ]
    }
   ],
   "source": [
    "a, b = -1, 2\n",
    "print (trape(f,a,b))\n",
    "print (quad(f,a,b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. 模拟生命"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[模拟生命](https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life)类似一个小游戏，可以假设有很多个小生命，或小细胞，可生可灭，具体k看这个细胞邻居的多少，规则如下,更多[参见](http://www.labri.fr/perso/nrougier/teaching/numpy/numpy.html)：\n",
    "\n",
    "> The universe of the Game of Life is an infinite two-dimensional orthogonal grid of square cells, each of which is in one of two possible states, live or dead. Every cell interacts with its eight neighbours, which are the cells that are directly horizontally, vertically, or diagonally adjacent. At each step in time, the following transitions occur:\n",
    ">\n",
    "- Any live cell with fewer than two live neighbours dies, as if by needs caused by underpopulation.\n",
    "- Any live cell with more than three live neighbours dies, as if by overcrowding.\n",
    "- Any live cell with two or three live neighbours lives, unchanged, to the next generation.\n",
    "- Any dead cell with exactly three live neighbours becomes a live cell.\n",
    ">\n",
    "The initial pattern constitutes the 'seed' of the system. The first generation is created by applying the above rules simultaneously to every cell in the seed – births and deaths happen simultaneously, and the discrete moment at which this happens is sometimes called a tick. (In other words, each generation is a pure function of the one before.) The rules continue to be applied repeatedly to create further generations.\n",
    "\n",
    "目标就是根据这些规则，确定经过若干次演变后，生命的形态，哪些细胞生，哪些细胞灭。例如：\n",
    "```python\n",
    "Z = np.array([[0,0,0,0,0,0],\n",
    "              [0,0,0,1,0,0],\n",
    "              [0,1,0,1,0,0],\n",
    "              [0,0,1,1,0,0],\n",
    "              [0,0,0,0,0,0],\n",
    "              [0,0,0,0,0,0]])\n",
    "```\n",
    "演变后就是\n",
    "```python\n",
    "array([[0, 0, 0, 0, 0, 0],\n",
    "       [0, 0, 0, 1, 0, 0],\n",
    "       [0, 0, 0, 0, 1, 0],\n",
    "       [0, 0, 1, 1, 1, 0],\n",
    "       [0, 0, 0, 0, 0, 0],\n",
    "       [0, 0, 0, 0, 0, 0]])\n",
    "```\n",
    "算法的关键是确定每个细胞的邻居多少，以决定生死，且假定边界均死，用0表示。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def evolve(Z):\n",
    "    N = (Z[0:-2,0:-2] + Z[0:-2,1:-1] + Z[0:-2,2:] +\n",
    "         Z[1:-1,0:-2]                + Z[1:-1,2:] +\n",
    "         Z[2:  ,0:-2] + Z[2:  ,1:-1] + Z[2:  ,2:])\n",
    "    # Apply rules\n",
    "    birth = (N==3) & (Z[1:-1,1:-1]==0) #若本来死，且邻居为3，则复活\n",
    "    survive = ((N==2) | (N==3)) & (Z[1:-1,1:-1]==1) # 若本来活，且邻居为2 or 3，则幸存\n",
    "    Z[...] = 0 # 重置为0\n",
    "    Z[1:-1,1:-1][birth | survive] = 1 # 复活或幸存的置为1\n",
    "    return Z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Z = np.random.randint(0,2,(256,512))\n",
    "for i in range(100): evolve(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cee9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot\n",
    "size = np.array(Z.shape)\n",
    "dpi = 72.0\n",
    "figsize= size[1]/float(dpi),size[0]/float(dpi)\n",
    "fig = plt.figure(figsize=figsize, dpi=dpi, facecolor=\"white\")\n",
    "fig.add_axes([0.0, 0.0, 1.0, 1.0], frameon=False)\n",
    "plt.imshow(Z,interpolation='nearest', cmap=plt.cm.hot)\n",
    "plt.xticks([]), plt.yticks([])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. 随机漫步"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from ipywidgets import *\n",
    "from scipy.stats import norm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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T5325YXd58di+ppAO1zvWJOH+vUXLeFZLC9kdnjkjZideP9qFqsbBEDGbECtGRmIMugYs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esXJPuDV5OBp7cHr71Pjotg85JUFubGTsq0YJfbS3OnjaYOG00d/TQO3LZ02i5au9fm9ODxKugl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\nCCGEEELIkkaBLSGEEEIIIYSQJe3/ACElhxCZpST8AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189b9fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"darkgrid\") #利用seaborn的style更漂亮一些\n",
    "def random_walk(m,n,N=200):\n",
    "    fig = plt.figure(figsize=(12,5))\n",
    "    for i in range(1,m*n+1):\n",
    "        r = np.random.randint(1,3,N)\n",
    "        theta = np.radians(np.random.randint(0,361,N))\n",
    "        x = np.cumsum(r*np.cos(theta))\n",
    "        y = np.cumsum(r*np.sin(theta))\n",
    "        plt.subplot(m,n,i) #切换到特定的subplot\n",
    "        plt.plot(x,y)\n",
    "        plt.xticks([]), plt.yticks([]) # 去掉ticks\n",
    "    plt.show()\n",
    "    \n",
    "random_walk(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. 画一二次函数及梯形法积分时的各个梯形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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66GzT8RrKpk2d+vjHL9KMGTM1f/4CHX/8ifryl/+P/vSnP7CyAQAAMEa4R6NB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PAAAU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1190812b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def f(x):\n",
    "    return x*x - 3*x\n",
    "\n",
    "a,b = -1,3\n",
    "x = np.linspace(a,b,100,endpoint=True)\n",
    "# 基本绘图\n",
    "plt.figure(figsize=(10,8))\n",
    "plt.plot(x,f(x),color='black',alpha=.1)\n",
    "\n",
    "# 填充积分区域fill_between\n",
    "plt.axhline(0,color='gray')\n",
    "plt.fill_between(x,0,f(x),f(x)>0,color='blue',alpha=.15)\n",
    "plt.fill_between(x,0,f(x),f(x)<0,color='blue',alpha=.05)\n",
    "\n",
    "n = 7 #为凸显梯形和原来曲线的区别，把分割分数降低\n",
    "# 画梯形\n",
    "for i in range(n):\n",
    "    x = np.linspace(a,b,n,endpoint=True)\n",
    "    # 画垂直的线\n",
    "    plt.plot([x[i],x[i]],[0,f(x[i])],color='black',linestyle='--')\n",
    "    # 画特定的点\n",
    "    plt.scatter(x[i],f(x[i]),20,color='black')\n",
    "    if i==0: continue \n",
    "    # 画梯形最后的斜边\n",
    "    plt.plot([x[i-1],x[i]],[f(x[i-1]),f(x[i])],color='black',linestyle='--')\n",
    "\n",
    "\n",
    "ax = plt.gca()\n",
    "# 注释\n",
    "ax.annotate(r'$f(x)=x^2 - 3x$', xy=(2, -2), xytext=(2.5, -2),arrowprops=dict(arrowstyle=\"->\", connectionstyle=\"arc3,rad=-.2\"))\n",
    "\n",
    "# 坐标轴平移\n",
    "ax.spines['right'].set_color('none')\n",
    "ax.spines['top'].set_color('none')\n",
    "ax.spines['bottom'].set_position(('data',0))\n",
    "ax.spines['left'].set_position(('data',0))\n",
    "\n",
    "# 旋转label\n",
    "for label in ax.get_xticklabels() + ax.get_yticklabels():\n",
    "    label.set_rotation(45)\n",
    "\n",
    "# 设置坐标轴的limit\n",
    "plt.xlim([a-.1,b+.1]),plt.xticks([-1,0,1,2,3])\n",
    "plt.ylim([-2.3,4.1]),plt.yticks([-2,0,2,4])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## 3. 交互函数"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "基本想法很简单，首先写一个你感兴趣的函数，然后指定想要交互参数就行。直接利用interact函数调用相应的函数，然后就会有交互效果，比如一个slider之类的。更多[参考](http://ipywidgets.readthedocs.io/en/latest/index.html)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cfffdSR65SObpHwjQ3OYDYGZJ/APTc8dD1aVcRy4fnXZJ3M8vciYFJpEEsCpM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\nEREFwIaJiIiIiIgogP8C/6aSEiY84GcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118bda1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 画一个正态分布曲线，调节其两个参数，看概率密度函数和累计分布函数\n",
    "def f(mean,std):\n",
    "    x = np.arange(-10, 10, 0.1)\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(10, 5))\n",
    "    axes[0].plot(x,norm.pdf(x,0,1))\n",
    "    axes[0].plot(x,norm.pdf(x,mean,std))\n",
    "    axes[1].plot(x,norm.cdf(x,0,1))\n",
    "    axes[1].plot(x,norm.cdf(x,mean,std)) \n",
    "    \n",
    "#     fig, axes = plt.subplots(2, 2, figsize=(10, 5))\n",
    "#     axes[0][0].plot(x,norm.pdf(x,0,1))\n",
    "#     axes[0][1].plot(x,norm.cdf(x,0,1))\n",
    "#     axes[1][0].plot(x,norm.pdf(x,mean,std))\n",
    "#     axes[1][1].plot(x,norm.cdf(x,mean,std)) \n",
    "\n",
    "interact(f,mean=(-7.,7,0.5),std=(0.0,5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 雅虎财经\n",
    "利用Pandas模块直接获取雅虎财经数据，方便之极。注意把官方提示把`from pandas.io import data, wb`替换为`from pandas_datareader import data, wb`。  \n",
    "Pandas for finance [文档](http://pandas.pydata.org/pandas-docs/stable/remote_data.html#remote-data-yahoo)。  \n",
    "上证指数[000001.SS](http://finance.yahoo.com/q?s=000001.SS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas_datareader import data, wb # 需要安装 pip install pandas_datareader\n",
    "import datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 可以直接[下载数据](http://finance.yahoo.com/q/hp?s=000001.SS+Historical+Prices)\n",
    "网站提供了csv格式数据下载服务。下载，然后读取`sh_table = pd.read_csv('sh_table.csv')`。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 利用DataReader抓取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 定义获取数据的时间段\n",
    "start = datetime.datetime(2010, 1, 1)\n",
    "end = datetime.datetime(2016,5,20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "RemoteDataError",
     "evalue": "Unable to read URL: http://ichart.finance.yahoo.com/table.csv?g=d&ignore=.csv&e=20&c=2010&s=000001.SS&b=1&a=0&d=4&f=2016",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRemoteDataError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-39617ce71698>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msh\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"000001.SS\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'yahoo'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/lee/anaconda/lib/python3.5/site-packages/pandas_datareader/data.py\u001b[0m in \u001b[0;36mDataReader\u001b[0;34m(name, data_source, start, end, retry_count, pause, session, access_key)\u001b[0m\n\u001b[1;32m    114\u001b[0m                                 \u001b[0madjust_price\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchunksize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    115\u001b[0m                                 \u001b[0mretry_count\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mretry_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpause\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpause\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 116\u001b[0;31m                                 session=session).read()\n\u001b[0m\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    118\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mdata_source\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"yahoo-actions\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lee/anaconda/lib/python3.5/site-packages/pandas_datareader/yahoo/daily.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     74\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m         \u001b[0;34m\"\"\" read one data from specified URL \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m         \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mYahooDailyReader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mret_index\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m             \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Ret_Index'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_calc_return_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Adj Close'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lee/anaconda/lib/python3.5/site-packages/pandas_datareader/base.py\u001b[0m in \u001b[0;36mread\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    153\u001b[0m         \u001b[0;31m# If a single symbol, (e.g., 'GOOG')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    154\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 155\u001b[0;31m             \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_one_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_params\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    156\u001b[0m         \u001b[0;31m# Or multiple symbols, (e.g., ['GOOG', 'AAPL', 'MSFT'])\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    157\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msymbols\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lee/anaconda/lib/python3.5/site-packages/pandas_datareader/base.py\u001b[0m in \u001b[0;36m_read_one_data\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m     72\u001b[0m         \u001b[0;34m\"\"\" read one data from specified URL \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     73\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'string'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 74\u001b[0;31m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_read_url_as_StringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     75\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_format\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'json'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     76\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lee/anaconda/lib/python3.5/site-packages/pandas_datareader/base.py\u001b[0m in \u001b[0;36m_read_url_as_StringIO\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0mOpen\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;32mand\u001b[0m \u001b[0mretry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \"\"\"\n\u001b[0;32m---> 85\u001b[0;31m         \u001b[0mresponse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparams\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     86\u001b[0m         \u001b[0mtext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sanitize_response\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresponse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStringIO\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/lee/anaconda/lib/python3.5/site-packages/pandas_datareader/base.py\u001b[0m in \u001b[0;36m_get_response\u001b[0;34m(self, url, params)\u001b[0m\n\u001b[1;32m    118\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mparams\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    119\u001b[0m             \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0murl\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"?\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0murlencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 120\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mRemoteDataError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unable to read URL: {0}'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    122\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_read_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mRemoteDataError\u001b[0m: Unable to read URL: http://ichart.finance.yahoo.com/table.csv?g=d&ignore=.csv&e=20&c=2010&s=000001.SS&b=1&a=0&d=4&f=2016"
     ]
    }
   ],
   "source": [
    "sh = data.DataReader(\"000001.SS\", 'yahoo', start, end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>0</td>\n",
       "      <td>3243.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>0</td>\n",
       "      <td>3282.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>0</td>\n",
       "      <td>3254.22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Volume  Adj Close\n",
       "Date                                                             \n",
       "2010-01-04  3243.76  3243.76  3243.76  3243.76       0    3243.76\n",
       "2010-01-05  3282.18  3282.18  3282.18  3282.18       0    3282.18\n",
       "2010-01-06  3254.22  3254.22  3254.22  3254.22       0    3254.22"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(3) # 数据获取成功"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1558.000000</td>\n",
       "      <td>1558.000000</td>\n",
       "      <td>1558.000000</td>\n",
       "      <td>1558.000000</td>\n",
       "      <td>1558.0</td>\n",
       "      <td>1558.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2659.427452</td>\n",
       "      <td>2659.427452</td>\n",
       "      <td>2659.427452</td>\n",
       "      <td>2659.427452</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2659.427452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>595.170673</td>\n",
       "      <td>595.170673</td>\n",
       "      <td>595.170673</td>\n",
       "      <td>595.170673</td>\n",
       "      <td>0.0</td>\n",
       "      <td>595.170673</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1950.010000</td>\n",
       "      <td>1950.010000</td>\n",
       "      <td>1950.010000</td>\n",
       "      <td>1950.010000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1950.010000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2199.832500</td>\n",
       "      <td>2199.832500</td>\n",
       "      <td>2199.832500</td>\n",
       "      <td>2199.832500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2199.832500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2492.600000</td>\n",
       "      <td>2492.600000</td>\n",
       "      <td>2492.600000</td>\n",
       "      <td>2492.600000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2492.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2968.977500</td>\n",
       "      <td>2968.977500</td>\n",
       "      <td>2968.977500</td>\n",
       "      <td>2968.977500</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2968.977500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5166.350000</td>\n",
       "      <td>5166.350000</td>\n",
       "      <td>5166.350000</td>\n",
       "      <td>5166.350000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5166.350000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Open         High          Low        Close  Volume    Adj Close\n",
       "count  1558.000000  1558.000000  1558.000000  1558.000000  1558.0  1558.000000\n",
       "mean   2659.427452  2659.427452  2659.427452  2659.427452     0.0  2659.427452\n",
       "std     595.170673   595.170673   595.170673   595.170673     0.0   595.170673\n",
       "min    1950.010000  1950.010000  1950.010000  1950.010000     0.0  1950.010000\n",
       "25%    2199.832500  2199.832500  2199.832500  2199.832500     0.0  2199.832500\n",
       "50%    2492.600000  2492.600000  2492.600000  2492.600000     0.0  2492.600000\n",
       "75%    2968.977500  2968.977500  2968.977500  2968.977500     0.0  2968.977500\n",
       "max    5166.350000  5166.350000  5166.350000  5166.350000     0.0  5166.350000"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.describe() # 数据整体概览"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5hO82pHtInBuPxxGRGU3PS8XVMxJ4LvLxvwrMEpFn8Ab/AGvtx8B+wBUAkf9f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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f7a92d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sh['Close'].plot(); #看一下收盘趋势"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据读取和输出`pd.read_csv` and `to_csv`\n",
    "从文件读取数据是非常常见的操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sh.to_csv('sh.csv',header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "names = ['Date','Open','High','Low','Close','Volume','Adj Close']\n",
    "sh1 = pd.read_csv('sh.csv',names=names,index_col='Date')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-05-19</th>\n",
       "      <td>2806.91</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>0</td>\n",
       "      <td>2806.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-20</th>\n",
       "      <td>2825.48</td>\n",
       "      <td>2825.48</td>\n",
       "      <td>2825.48</td>\n",
       "      <td>2825.48</td>\n",
       "      <td>0</td>\n",
       "      <td>2825.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Volume  Adj Close\n",
       "Date                                                             \n",
       "2016-05-19  2806.91  2806.91  2806.91  2806.91       0    2806.91\n",
       "2016-05-20  2825.48  2825.48  2825.48  2825.48       0    2825.48"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh1.tail(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 清洗数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# drop Volume列，所以数据都为0\n",
    "sh = sh.drop('Volume',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close\n",
       "Date                                                     \n",
       "2010-01-04  3243.76  3243.76  3243.76  3243.76    3243.76\n",
       "2010-01-05  3282.18  3282.18  3282.18  3282.18    3282.18"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(2) # Volume列消失了"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 检查缺失值\n",
    "有些时候会有数据缺失，不完整，需要查看一下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close Adj Close\n",
       "Date                                            \n",
       "2010-01-04  False  False  False  False     False\n",
       "2010-01-05  False  False  False  False     False\n",
       "2010-01-06  False  False  False  False     False\n",
       "2010-01-07  False  False  False  False     False\n",
       "2010-01-08  False  False  False  False     False"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Detect missing values,用isnull函数\n",
    "pd.isnull(sh).head() # or sh.isnull()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "但返回的是一个boolean型的DataFrame，我只想先看一下是否有缺失值，**不用肉眼**。  \n",
    "利用any()函数，如果有可以用sum()函数查看有多少。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#参考 http://stackoverflow.com/questions/29530232/python-pandas-check-if-any-value-is-nan-in-dataframe\n",
    "sh.isnull().values.any() # 数据很完整"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.isnull().values.sum() # 没有值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "      <td>3243.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3192.78</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>3192.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close\n",
       "Date                                                     \n",
       "2010-01-04  3243.76  3243.76  3243.76  3243.76    3243.76\n",
       "2010-01-05  3282.18  3282.18  3282.18  3282.18    3282.18\n",
       "2010-01-06  3254.22  3254.22  3254.22  3254.22    3254.22\n",
       "2010-01-07  3192.78  3192.78  3192.78  3192.78    3192.78\n",
       "2010-01-08  3196.00  3196.00  3196.00  3196.00    3196.00"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 填补缺失值\n",
    "这里把一些位置设置为np.nan，然后填充，没有实际意义，这里只是拿来练手。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**设置nan如下：**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sh.iloc[0,:] = np.nan\n",
    "sh.iloc[[1,3],1] = np.nan\n",
    "sh.iloc[2,2] = np.nan\n",
    "sh.iloc[3,3] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3192.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3192.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close\n",
       "Date                                                     \n",
       "2010-01-04      NaN      NaN      NaN      NaN        NaN\n",
       "2010-01-05  3282.18      NaN  3282.18  3282.18    3282.18\n",
       "2010-01-06  3254.22  3254.22      NaN  3254.22    3254.22\n",
       "2010-01-07  3192.78      NaN  3192.78      NaN    3192.78"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Missing values count per column:\n",
      "Open         1\n",
      "High         3\n",
      "Low          2\n",
      "Close        2\n",
      "Adj Close    1\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# check NaN number per column\n",
    "def num_missing(x):\n",
    "    return x.isnull().sum()\n",
    "\n",
    "print \"Missing values count per column:\"\n",
    "print sh.apply(num_missing, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**dropna**  \n",
    "有几个参数\n",
    "- `how='all'`只有全部为NaN的行才drop，若axis=1则对列；\n",
    "- 若`how='any'`默认，则drop所有含NaN的行或列;\n",
    "- `inplacce=True`则inplace操作，不返回；\n",
    "- 默认`inplace=False`，返回一个drop后的，不改变原DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sh.dropna(how='all',inplace=True); "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3192.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3192.78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close\n",
       "Date                                                     \n",
       "2010-01-05  3282.18      NaN  3282.18  3282.18    3282.18\n",
       "2010-01-06  3254.22  3254.22      NaN  3254.22    3254.22\n",
       "2010-01-07  3192.78      NaN  3192.78      NaN    3192.78"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**填充fillna**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sh['High'].fillna(sh['High'].mean(),inplace=True) #fill in with mean\n",
    "sh['Low'].fillna(method='ffill',inplace=True) #fill in with value from previous row\n",
    "sh['Close'].fillna(method='bfill',inplace=True) # fill in with value from the row behind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>2658.308199</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.220000</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3192.78</td>\n",
       "      <td>2658.308199</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3192.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.000000</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>3212.75</td>\n",
       "      <td>3212.750000</td>\n",
       "      <td>3212.75</td>\n",
       "      <td>3212.75</td>\n",
       "      <td>3212.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open         High      Low    Close  Adj Close\n",
       "Date                                                         \n",
       "2010-01-05  3282.18  2658.308199  3282.18  3282.18    3282.18\n",
       "2010-01-06  3254.22  3254.220000  3282.18  3254.22    3254.22\n",
       "2010-01-07  3192.78  2658.308199  3192.78  3196.00    3192.78\n",
       "2010-01-08  3196.00  3196.000000  3196.00  3196.00    3196.00\n",
       "2010-01-11  3212.75  3212.750000  3212.75  3212.75    3212.75"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.isnull().values.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算涨跌额\n",
    "涨跌额是指当日股票价格与前一日收盘价格相比的涨跌数值。添加一列change，其为当日close价格与之前一天的差值。当然注意这里数据有缺失，有的日期没有记录。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "change = sh.Close.diff()\n",
    "change.fillna(change.mean(),inplace=True)\n",
    "sh['Change'] = change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>2658.308199</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>-0.293509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.220000</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>-27.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3192.78</td>\n",
       "      <td>2658.308199</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>-58.220000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open         High      Low    Close  Adj Close     Change\n",
       "Date                                                                    \n",
       "2010-01-05  3282.18  2658.308199  3282.18  3282.18    3282.18  -0.293509\n",
       "2010-01-06  3254.22  3254.220000  3282.18  3254.22    3254.22 -27.960000\n",
       "2010-01-07  3192.78  2658.308199  3192.78  3196.00    3192.78 -58.220000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-10-12</th>\n",
       "      <td>3287.66</td>\n",
       "      <td>3287.66</td>\n",
       "      <td>3287.66</td>\n",
       "      <td>3287.66</td>\n",
       "      <td>3287.66</td>\n",
       "      <td>234.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-30</th>\n",
       "      <td>4277.22</td>\n",
       "      <td>4277.22</td>\n",
       "      <td>4277.22</td>\n",
       "      <td>4277.22</td>\n",
       "      <td>4277.22</td>\n",
       "      <td>224.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>4828.74</td>\n",
       "      <td>4828.74</td>\n",
       "      <td>4828.74</td>\n",
       "      <td>4828.74</td>\n",
       "      <td>4828.74</td>\n",
       "      <td>217.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close  Change\n",
       "Date                                                             \n",
       "2015-10-12  3287.66  3287.66  3287.66  3287.66    3287.66  234.88\n",
       "2015-06-30  4277.22  4277.22  4277.22  4277.22    4277.22  224.19\n",
       "2015-06-01  4828.74  4828.74  4828.74  4828.74    4828.74  217.00"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 按涨跌额排序,未改变sh，若需要，可以`inplace=True`\n",
    "sh.sort_values(by='Change',ascending=False).head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 计算涨跌幅"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>2658.308199</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>-0.293509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.220000</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>-27.960000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-07</th>\n",
       "      <td>3192.78</td>\n",
       "      <td>2658.308199</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3192.78</td>\n",
       "      <td>-58.220000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-08</th>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.000000</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>3196.00</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-11</th>\n",
       "      <td>3212.75</td>\n",
       "      <td>3212.750000</td>\n",
       "      <td>3212.75</td>\n",
       "      <td>3212.75</td>\n",
       "      <td>3212.75</td>\n",
       "      <td>16.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open         High      Low    Close  Adj Close     Change\n",
       "Date                                                                    \n",
       "2010-01-05  3282.18  2658.308199  3282.18  3282.18    3282.18  -0.293509\n",
       "2010-01-06  3254.22  3254.220000  3282.18  3254.22    3254.22 -27.960000\n",
       "2010-01-07  3192.78  2658.308199  3192.78  3196.00    3192.78 -58.220000\n",
       "2010-01-08  3196.00  3196.000000  3196.00  3196.00    3196.00   0.000000\n",
       "2010-01-11  3212.75  3212.750000  3212.75  3212.75    3212.75  16.750000"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-12</th>\n",
       "      <td>3273.97</td>\n",
       "      <td>3273.97</td>\n",
       "      <td>3273.97</td>\n",
       "      <td>3273.97</td>\n",
       "      <td>3273.97</td>\n",
       "      <td>61.22</td>\n",
       "      <td>2.654925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-13</th>\n",
       "      <td>3172.66</td>\n",
       "      <td>3172.66</td>\n",
       "      <td>3172.66</td>\n",
       "      <td>3172.66</td>\n",
       "      <td>3172.66</td>\n",
       "      <td>-101.31</td>\n",
       "      <td>-2.654851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-14</th>\n",
       "      <td>3215.55</td>\n",
       "      <td>3215.55</td>\n",
       "      <td>3215.55</td>\n",
       "      <td>3215.55</td>\n",
       "      <td>3215.55</td>\n",
       "      <td>42.89</td>\n",
       "      <td>-1.423354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-15</th>\n",
       "      <td>3224.15</td>\n",
       "      <td>3224.15</td>\n",
       "      <td>3224.15</td>\n",
       "      <td>3224.15</td>\n",
       "      <td>3224.15</td>\n",
       "      <td>8.60</td>\n",
       "      <td>-0.799487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close  Change  pct_change\n",
       "Date                                                                         \n",
       "2010-01-12  3273.97  3273.97  3273.97  3273.97    3273.97   61.22    2.654925\n",
       "2010-01-13  3172.66  3172.66  3172.66  3172.66    3172.66 -101.31   -2.654851\n",
       "2010-01-14  3215.55  3215.55  3215.55  3215.55    3215.55   42.89   -1.423354\n",
       "2010-01-15  3224.15  3224.15  3224.15  3224.15    3224.15    8.60   -0.799487"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 用shift方法错位\n",
    "# sh['pct_change'] = ((sh['Change'] - sh['Change'].shift(1)) / sh['Change'])\n",
    "# 或用pct_Change函数\n",
    "sh['pct_change'] = sh.Change.pct_change()\n",
    "sh.iloc[5:9]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply and Applymap"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用applymap格式化数据，均保留两位小数。这是对其进行element-wise操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>2658.308199</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>-0.293509</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.220000</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>-27.960000</td>\n",
       "      <td>94.261134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open         High      Low    Close  Adj Close     Change  \\\n",
       "Date                                                                       \n",
       "2010-01-05  3282.18  2658.308199  3282.18  3282.18    3282.18  -0.293509   \n",
       "2010-01-06  3254.22  3254.220000  3282.18  3254.22    3254.22 -27.960000   \n",
       "\n",
       "            pct_change  \n",
       "Date                    \n",
       "2010-01-05         NaN  \n",
       "2010-01-06   94.261134  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "format = lambda x: '%.2f' % x\n",
    "sh = sh.applymap(format)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>2658.31</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>nan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>-27.96</td>\n",
       "      <td>94.26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close Adj Close  Change pct_change\n",
       "Date                                                                       \n",
       "2010-01-05  3282.18  2658.31  3282.18  3282.18   3282.18   -0.29        nan\n",
       "2010-01-06  3254.22  3254.22  3282.18  3254.22   3254.22  -27.96      94.26"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sh = sh.applymap(float) # 可以重新把数据类型转换为float类型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Open          3254.22\n",
       "High          3254.22\n",
       "Low           3282.18\n",
       "Close         3254.22\n",
       "Adj Close     3254.22\n",
       "Change         -27.96\n",
       "pct_change      94.26\n",
       "Name: 2010-01-06 00:00:00, dtype: float64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.ix[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "利用apply找出每列最值及变化范围"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>pct_change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1950.01</td>\n",
       "      <td>1950.01</td>\n",
       "      <td>1950.01</td>\n",
       "      <td>1950.01</td>\n",
       "      <td>1950.01</td>\n",
       "      <td>-345.35</td>\n",
       "      <td>-inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5166.35</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>5166.35</td>\n",
       "      <td>234.88</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>range</th>\n",
       "      <td>3216.34</td>\n",
       "      <td>3216.34</td>\n",
       "      <td>3216.34</td>\n",
       "      <td>3216.34</td>\n",
       "      <td>3216.34</td>\n",
       "      <td>580.23</td>\n",
       "      <td>inf</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Open     High      Low    Close  Adj Close  Change  pct_change\n",
       "min    1950.01  1950.01  1950.01  1950.01    1950.01 -345.35        -inf\n",
       "max    5166.35  5166.35  5166.35  5166.35    5166.35  234.88         inf\n",
       "range  3216.34  3216.34  3216.34  3216.34    3216.34  580.23         inf"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def f(x):\n",
    "    return pd.Series([x.min(),x.max(),x.max()-x.min()],index=['min','max','range'])\n",
    "sh.apply(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group by Month"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 手工"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 按月分组\n",
    "sh['group_index'] = sh.index.map(lambda x: 100*x.year + x.month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Change</th>\n",
       "      <th>pct_change</th>\n",
       "      <th>group_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-05</th>\n",
       "      <td>3282.18</td>\n",
       "      <td>2658.31</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>-0.29</td>\n",
       "      <td>NaN</td>\n",
       "      <td>201001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-01-06</th>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3282.18</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>3254.22</td>\n",
       "      <td>-27.96</td>\n",
       "      <td>94.26</td>\n",
       "      <td>201001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-19</th>\n",
       "      <td>2806.91</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>-0.60</td>\n",
       "      <td>-0.98</td>\n",
       "      <td>201605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-20</th>\n",
       "      <td>2825.48</td>\n",
       "      <td>2825.48</td>\n",
       "      <td>2825.48</td>\n",
       "      <td>2825.48</td>\n",
       "      <td>2825.48</td>\n",
       "      <td>18.57</td>\n",
       "      <td>-31.95</td>\n",
       "      <td>201605</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Open     High      Low    Close  Adj Close  Change  pct_change  \\\n",
       "Date                                                                            \n",
       "2010-01-05  3282.18  2658.31  3282.18  3282.18    3282.18   -0.29         NaN   \n",
       "2010-01-06  3254.22  3254.22  3282.18  3254.22    3254.22  -27.96       94.26   \n",
       "2016-05-19  2806.91  2806.91  2806.91  2806.91    2806.91   -0.60       -0.98   \n",
       "2016-05-20  2825.48  2825.48  2825.48  2825.48    2825.48   18.57      -31.95   \n",
       "\n",
       "            group_index  \n",
       "Date                     \n",
       "2010-01-05       201001  \n",
       "2010-01-06       201001  \n",
       "2016-05-19       201605  \n",
       "2016-05-20       201605  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.head(2).append(sh.tail(2)) # 查看group_index, as expected"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<pandas.core.groupby.DataFrameGroupBy object at 0x10f7a9110>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.groupby('group_index') # 返回一个groupby对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>group_index</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>201001</th>\n",
       "      <td>3273.97</td>\n",
       "      <td>295.57</td>\n",
       "      <td>2986.61</td>\n",
       "      <td>-15.430526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201002</th>\n",
       "      <td>3060.62</td>\n",
       "      <td>125.91</td>\n",
       "      <td>2934.71</td>\n",
       "      <td>3.132500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201003</th>\n",
       "      <td>3128.47</td>\n",
       "      <td>151.53</td>\n",
       "      <td>2976.94</td>\n",
       "      <td>2.485217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201603</th>\n",
       "      <td>3018.80</td>\n",
       "      <td>285.63</td>\n",
       "      <td>2733.17</td>\n",
       "      <td>13.736522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201604</th>\n",
       "      <td>3082.36</td>\n",
       "      <td>144.04</td>\n",
       "      <td>2938.32</td>\n",
       "      <td>-3.452632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201605</th>\n",
       "      <td>2997.84</td>\n",
       "      <td>190.93</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>-8.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                High    Open      Low     Change\n",
       "group_index                                     \n",
       "201001       3273.97  295.57  2986.61 -15.430526\n",
       "201002       3060.62  125.91  2934.71   3.132500\n",
       "201003       3128.47  151.53  2976.94   2.485217\n",
       "201603       3018.80  285.63  2733.17  13.736522\n",
       "201604       3082.36  144.04  2938.32  -3.452632\n",
       "201605       2997.84  190.93  2806.91  -8.060000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对不同的列运用不同的函数聚合\n",
    "def peak_to_peak(arr):\n",
    "    return arr.max() - arr.min()\n",
    "\n",
    "grouped = sh.groupby('group_index').agg({\n",
    "        'Open' : peak_to_peak,\n",
    "        'High' : 'max',\n",
    "        'Low' : 'min',\n",
    "        'Change' : 'mean'\n",
    "    })\n",
    "grouped.head(3).append(grouped.tail(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "##### 用pandas时间序列功能更便捷"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2010-01-05', '2010-01-06', '2010-01-07', '2010-01-08',\n",
       "               '2010-01-11', '2010-01-12', '2010-01-13', '2010-01-14',\n",
       "               '2010-01-15', '2010-01-18',\n",
       "               ...\n",
       "               '2016-05-09', '2016-05-10', '2016-05-11', '2016-05-12',\n",
       "               '2016-05-13', '2016-05-16', '2016-05-17', '2016-05-18',\n",
       "               '2016-05-19', '2016-05-20'],\n",
       "              dtype='datetime64[ns]', name=u'Date', length=1557, freq=None)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.index # DatetimeIndex 提供很多便捷操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>3273.97</td>\n",
       "      <td>295.57</td>\n",
       "      <td>2986.61</td>\n",
       "      <td>-15.430526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>3060.62</td>\n",
       "      <td>125.91</td>\n",
       "      <td>2934.71</td>\n",
       "      <td>3.132500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>3128.47</td>\n",
       "      <td>151.53</td>\n",
       "      <td>2976.94</td>\n",
       "      <td>2.485217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>3018.80</td>\n",
       "      <td>285.63</td>\n",
       "      <td>2733.17</td>\n",
       "      <td>13.736522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-30</th>\n",
       "      <td>3082.36</td>\n",
       "      <td>144.04</td>\n",
       "      <td>2938.32</td>\n",
       "      <td>-3.452632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>2997.84</td>\n",
       "      <td>190.93</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>-8.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               High    Open      Low     Change\n",
       "Date                                           \n",
       "2010-01-31  3273.97  295.57  2986.61 -15.430526\n",
       "2010-02-28  3060.62  125.91  2934.71   3.132500\n",
       "2010-03-31  3128.47  151.53  2976.94   2.485217\n",
       "2016-03-31  3018.80  285.63  2733.17  13.736522\n",
       "2016-04-30  3082.36  144.04  2938.32  -3.452632\n",
       "2016-05-31  2997.84  190.93  2806.91  -8.060000"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_1 = sh.groupby(pd.TimeGrouper(\"M\")).agg({\n",
    "        'Open' : peak_to_peak,\n",
    "        'High' : 'max',\n",
    "        'Low' : 'min',\n",
    "        'Change' : 'mean'\n",
    "    })\n",
    "grouped_1.head(3).append(grouped_1.tail(3))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#####  或者用resample实现重采样\n",
    "可以downsampling，也可以upsampling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Open</th>\n",
       "      <th>Low</th>\n",
       "      <th>Change</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-01-31</th>\n",
       "      <td>3273.97</td>\n",
       "      <td>295.57</td>\n",
       "      <td>2986.61</td>\n",
       "      <td>-15.430526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-02-28</th>\n",
       "      <td>3060.62</td>\n",
       "      <td>125.91</td>\n",
       "      <td>2934.71</td>\n",
       "      <td>3.132500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-03-31</th>\n",
       "      <td>3128.47</td>\n",
       "      <td>151.53</td>\n",
       "      <td>2976.94</td>\n",
       "      <td>2.485217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31</th>\n",
       "      <td>3018.80</td>\n",
       "      <td>285.63</td>\n",
       "      <td>2733.17</td>\n",
       "      <td>13.736522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-30</th>\n",
       "      <td>3082.36</td>\n",
       "      <td>144.04</td>\n",
       "      <td>2938.32</td>\n",
       "      <td>-3.452632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-31</th>\n",
       "      <td>2997.84</td>\n",
       "      <td>190.93</td>\n",
       "      <td>2806.91</td>\n",
       "      <td>-8.060000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               High    Open      Low     Change\n",
       "Date                                           \n",
       "2010-01-31  3273.97  295.57  2986.61 -15.430526\n",
       "2010-02-28  3060.62  125.91  2934.71   3.132500\n",
       "2010-03-31  3128.47  151.53  2976.94   2.485217\n",
       "2016-03-31  3018.80  285.63  2733.17  13.736522\n",
       "2016-04-30  3082.36  144.04  2938.32  -3.452632\n",
       "2016-05-31  2997.84  190.93  2806.91  -8.060000"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# resample('M',how='mean')这样的语法将来不支持，推荐.mean()以及.apply()\n",
    "# 可以传入词典给apply\n",
    "grouped_2 = sh[['Open','High','Low','Change']].resample('M').apply({\n",
    "        'Open' : peak_to_peak,\n",
    "        'High' : 'max',\n",
    "        'Low' : 'min',\n",
    "        'Change' : 'mean'\n",
    "    })\n",
    "grouped_2.head(3).append(grouped_2.tail(3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jIjUisgxYCozVWg8AwiIyP+j3ZMw5iZFX/5cScfEAdjIllvyuUFaCSSQVahLw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\n6oyFX5I68w+ev/FpXSs5iQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112bec590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sh[['Open']].plot();\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 资源\n",
    "- [地震数据](http://data.earthquake.cn/datashare/globeEarthquake_csn.html)\n",
    "- [read_html](http://pandas.pydata.org/pandas-docs/stable/io.html#io-read-html)\n",
    "- [read_html参数详解](http://pandas.pydata.org/pandas-docs/version/0.12.0/generated/pandas.io.html.read_html.html)\n",
    "- mpl_toolkits[安装参考](https://peak5390.wordpress.com/2012/12/08/matplotlib-basemap-tutorial-installing-matplotlib-and-basemap/),官方安装说的不是很清楚。\n",
    "- [Basemap很nice的教程](http://introtopython.org/visualization_earthquakes.html#Inline-output)\n",
    "- [Bug: 'NoneType' object has no attribute 'next_element'](https://bugs.launchpad.net/beautifulsoup/+bug/1270611)\n",
    "- [Request乱码问题](http://sh3ll.me/2014/06/18/python-requests-encoding/)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import requests\n",
    "import numpy as np\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>編號</th>\n",
       "      <th>行政區</th>\n",
       "      <th>簡稱</th>\n",
       "      <th>省會或首府</th>\n",
       "      <th>地區</th>\n",
       "      <th>Unnamed: 5</th>\n",
       "      <th>編號.1</th>\n",
       "      <th>行政區.1</th>\n",
       "      <th>簡稱.1</th>\n",
       "      <th>省會或首府.1</th>\n",
       "      <th>地區.1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>江蘇省</td>\n",
       "      <td>蘇</td>\n",
       "      <td>鎮江</td>\n",
       "      <td>華中</td>\n",
       "      <td>20</td>\n",
       "      <td>甘肅省</td>\n",
       "      <td>隴</td>\n",
       "      <td>蘭州</td>\n",
       "      <td>華北</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>浙江省</td>\n",
       "      <td>浙</td>\n",
       "      <td>杭州</td>\n",
       "      <td>華中</td>\n",
       "      <td>21</td>\n",
       "      <td>寧夏省</td>\n",
       "      <td>寧</td>\n",
       "      <td>銀川</td>\n",
       "      <td>塞北</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>安徽省</td>\n",
       "      <td>皖</td>\n",
       "      <td>合肥</td>\n",
       "      <td>華中</td>\n",
       "      <td>22</td>\n",
       "      <td>青海省</td>\n",
       "      <td>青</td>\n",
       "      <td>西寧</td>\n",
       "      <td>西部</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>江西省</td>\n",
       "      <td>贛</td>\n",
       "      <td>南昌</td>\n",
       "      <td>華中</td>\n",
       "      <td>23</td>\n",
       "      <td>綏遠省</td>\n",
       "      <td>綏</td>\n",
       "      <td>歸綏（今呼和浩特）</td>\n",
       "      <td>塞北</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>湖北省</td>\n",
       "      <td>鄂</td>\n",
       "      <td>武昌（今武漢）</td>\n",
       "      <td>華中</td>\n",
       "      <td>24</td>\n",
       "      <td>察哈爾省</td>\n",
       "      <td>察</td>\n",
       "      <td>張垣（今張家口）</td>\n",
       "      <td>塞北</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   編號  行政區 簡稱    省會或首府  地區  Unnamed: 5  編號.1 行政區.1       簡稱.1 省會或首府.1  地區.1\n",
       "0   1  江蘇省  蘇       鎮江  華中          20   甘肅省     隴         蘭州      華北   NaN\n",
       "1   2  浙江省  浙       杭州  華中          21   寧夏省     寧         銀川      塞北   NaN\n",
       "2   3  安徽省  皖       合肥  華中          22   青海省     青         西寧      西部   NaN\n",
       "3   4  江西省  贛       南昌  華中          23   綏遠省     綏  歸綏（今呼和浩特）      塞北   NaN\n",
       "4   5  湖北省  鄂  武昌（今武漢）  華中          24  察哈爾省     察   張垣（今張家口）      塞北   NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html_1 = requests.get('http://zh.wikipedia.org/wiki/%E7%9C%81%E4%BC%9A')\n",
    "html_1_text = html_1.text\n",
    "dfs_1 = pd.read_html(html_1_text,header=0,attrs={'class': 'wikitable'})\n",
    "dfs_1[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取表格read_html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 安装\n",
    "read_html依赖一些库，比如`html5lib`,`lxml`,`beautiful soup`等，如果没有安装会报错。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 吐槽\n",
    "本来就想安安静静地写一行解决问题，谁知道还有坑有坑  \n",
    "`dsf = pd.read_html('http://data.earthquake.cn/datashare/globeEarthquake_csn.html',header=0)[4]`  \n",
    "执行后文字部分都是乱码，应该是编码问题,下面给出了解决方案。  \n",
    "\n",
    "不过我忍不住要吐槽一句，为什么[这个网站](http://data.earthquake.cn/datashare/globeEarthquake_csn.html)把所有的内容都放在table里，如果这样，能不能给个`id`或者`class`，导致利用attrs精确获得表格的微操失败，心中也是万马奔腾。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 获取数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "url = 'http://data.earthquake.cn/datashare/globeEarthquake_csn.html'\n",
    "html = requests.get(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 乱码问题\n",
    "request默认是`ISO-8859-1`编码，这里会导致乱码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ISO-8859-1'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html.encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>·¢ÕðÈÕÆÚ</th>\n",
       "      <th>·¢ÕðÊ±¿Ì</th>\n",
       "      <th>Î³¶È(¡ã)</th>\n",
       "      <th>¾­¶È(¡ã)</th>\n",
       "      <th>Éî¶È(km)</th>\n",
       "      <th>Õð¼¶</th>\n",
       "      <th>ÊÂ¼þÀàÐÍ</th>\n",
       "      <th>²Î¿¼µØµã</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>20:12:09.6</td>\n",
       "      <td>40.9</td>\n",
       "      <td>79.3</td>\n",
       "      <td>6</td>\n",
       "      <td>Ms3.2</td>\n",
       "      <td>ÌìÈ»µØÕð</td>\n",
       "      <td>ÐÂ½®°¢¿ËËÕµØÇøÎÚÊ²ÏØ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>18:34:34.2</td>\n",
       "      <td>40.2</td>\n",
       "      <td>77.2</td>\n",
       "      <td>8</td>\n",
       "      <td>Ms3.0</td>\n",
       "      <td>ÌìÈ»µØÕð</td>\n",
       "      <td>ÐÂ½®¿Ë×ÎÀÕËÕÖÝ°¢Í¼Ê²ÊÐ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>08:56:14.7</td>\n",
       "      <td>37.4</td>\n",
       "      <td>92.9</td>\n",
       "      <td>10</td>\n",
       "      <td>Ms3.0</td>\n",
       "      <td>ÌìÈ»µØÕð</td>\n",
       "      <td>Çàº£º£Î÷ÖÝ¸ñ¶ûÄ¾ÊÐ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>03:09:56.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>85.3</td>\n",
       "      <td>8</td>\n",
       "      <td>Ms3.8</td>\n",
       "      <td>ÌìÈ»µØÕð</td>\n",
       "      <td>Äá²´¶û</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>02:09:01.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>87.5</td>\n",
       "      <td>7</td>\n",
       "      <td>Ms3.9</td>\n",
       "      <td>ÌìÈ»µØÕð</td>\n",
       "      <td>Î÷²ØÈÕ¿¦ÔòÊÐ¶¨ÈÕÏØ</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ·¢ÕðÈÕÆÚ    ·¢ÕðÊ±¿Ì  Î³¶È(¡ã)  ¾­¶È(¡ã)  Éî¶È(km)   Õð¼¶  ÊÂ¼þÀàÐÍ  \\\n",
       "0  2016-05-24  20:12:09.6      40.9      79.3         6  Ms3.2  ÌìÈ»µØÕð   \n",
       "1  2016-05-24  18:34:34.2      40.2      77.2         8  Ms3.0  ÌìÈ»µØÕð   \n",
       "2  2016-05-24  08:56:14.7      37.4      92.9        10  Ms3.0  ÌìÈ»µØÕð   \n",
       "3  2016-05-24  03:09:56.5      28.0      85.3         8  Ms3.8  ÌìÈ»µØÕð   \n",
       "4  2016-05-24  02:09:01.5      28.6      87.5         7  Ms3.9  ÌìÈ»µØÕð   \n",
       "\n",
       "                 ²Î¿¼µØµã  \n",
       "0    ÐÂ½®°¢¿ËËÕµØÇøÎÚÊ²ÏØ  \n",
       "1  ÐÂ½®¿Ë×ÎÀÕËÕÖÝ°¢Í¼Ê²ÊÐ  \n",
       "2      Çàº£º£Î÷ÖÝ¸ñ¶ûÄ¾ÊÐ  \n",
       "3                  Äá²´¶û  \n",
       "4      Î÷²ØÈÕ¿¦ÔòÊÐ¶¨ÈÕÏØ  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html_text = html.text\n",
    "dfs = pd.read_html(html_text,header=0)\n",
    "dfs[4].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 改变默认编码"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GB2312'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html.encoding =  html.apparent_encoding\n",
    "html.encoding #竟然是GB2312,一开始窃以为是UTF-8..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>发震日期</th>\n",
       "      <th>发震时刻</th>\n",
       "      <th>纬度(°)</th>\n",
       "      <th>经度(°)</th>\n",
       "      <th>深度(km)</th>\n",
       "      <th>震级</th>\n",
       "      <th>事件类型</th>\n",
       "      <th>参考地点</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>20:12:09.6</td>\n",
       "      <td>40.9</td>\n",
       "      <td>79.3</td>\n",
       "      <td>6</td>\n",
       "      <td>Ms3.2</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>新疆阿克苏地区乌什县</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>18:34:34.2</td>\n",
       "      <td>40.2</td>\n",
       "      <td>77.2</td>\n",
       "      <td>8</td>\n",
       "      <td>Ms3.0</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>新疆克孜勒苏州阿图什市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>08:56:14.7</td>\n",
       "      <td>37.4</td>\n",
       "      <td>92.9</td>\n",
       "      <td>10</td>\n",
       "      <td>Ms3.0</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>青海海西州格尔木市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>03:09:56.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>85.3</td>\n",
       "      <td>8</td>\n",
       "      <td>Ms3.8</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>尼泊尔</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>02:09:01.5</td>\n",
       "      <td>28.6</td>\n",
       "      <td>87.5</td>\n",
       "      <td>7</td>\n",
       "      <td>Ms3.9</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>西藏日喀则市定日县</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         发震日期        发震时刻  纬度(°)  经度(°)  深度(km)     震级  事件类型         参考地点\n",
       "0  2016-05-24  20:12:09.6   40.9   79.3       6  Ms3.2  天然地震   新疆阿克苏地区乌什县\n",
       "1  2016-05-24  18:34:34.2   40.2   77.2       8  Ms3.0  天然地震  新疆克孜勒苏州阿图什市\n",
       "2  2016-05-24  08:56:14.7   37.4   92.9      10  Ms3.0  天然地震    青海海西州格尔木市\n",
       "3  2016-05-24  03:09:56.5   28.0   85.3       8  Ms3.8  天然地震          尼泊尔\n",
       "4  2016-05-24  02:09:01.5   28.6   87.5       7  Ms3.9  天然地震    西藏日喀则市定日县"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "html_text = html.text\n",
    "dfs = pd.read_html(html_text,header=0) # 返回的是一个list,list里是表格\n",
    "dfs[4].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = dfs[4] \n",
    "df.columns = ['date','time','lats','lons','dept','mag','class','place']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>lats</th>\n",
       "      <th>lons</th>\n",
       "      <th>dept</th>\n",
       "      <th>mag</th>\n",
       "      <th>class</th>\n",
       "      <th>place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>20:12:09.6</td>\n",
       "      <td>40.9</td>\n",
       "      <td>79.3</td>\n",
       "      <td>6</td>\n",
       "      <td>Ms3.2</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>新疆阿克苏地区乌什县</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>18:34:34.2</td>\n",
       "      <td>40.2</td>\n",
       "      <td>77.2</td>\n",
       "      <td>8</td>\n",
       "      <td>Ms3.0</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>新疆克孜勒苏州阿图什市</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-05-24</td>\n",
       "      <td>08:56:14.7</td>\n",
       "      <td>37.4</td>\n",
       "      <td>92.9</td>\n",
       "      <td>10</td>\n",
       "      <td>Ms3.0</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>青海海西州格尔木市</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date        time  lats  lons  dept    mag class        place\n",
       "0  2016-05-24  20:12:09.6  40.9  79.3     6  Ms3.2  天然地震   新疆阿克苏地区乌什县\n",
       "1  2016-05-24  18:34:34.2  40.2  77.2     8  Ms3.0  天然地震  新疆克孜勒苏州阿图什市\n",
       "2  2016-05-24  08:56:14.7  37.4  92.9    10  Ms3.0  天然地震    青海海西州格尔木市"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 画地震分布图\n",
    "不太懂地震，除了了解一下分布，不知道还分析什么，就先画个分布图吧。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把以Ms震级开头的行去掉(共7个)，只保留ML开头的，便于分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "Ms = df.mag.map(lambda x: not x.startswith('Ms')) # boolean Series\n",
    "df = df[Ms]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "把震级转换为数字"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>lats</th>\n",
       "      <th>lons</th>\n",
       "      <th>dept</th>\n",
       "      <th>mag</th>\n",
       "      <th>class</th>\n",
       "      <th>place</th>\n",
       "      <th>mag_num</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>23:43:52.5</td>\n",
       "      <td>23.30</td>\n",
       "      <td>103.21</td>\n",
       "      <td>6</td>\n",
       "      <td>ML1.7</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>云南个旧</td>\n",
       "      <td>1.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>23:43:33.9</td>\n",
       "      <td>39.82</td>\n",
       "      <td>118.76</td>\n",
       "      <td>8</td>\n",
       "      <td>ML0.5</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>河北滦县</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-05-23</td>\n",
       "      <td>23:36:25.9</td>\n",
       "      <td>39.60</td>\n",
       "      <td>76.93</td>\n",
       "      <td>6</td>\n",
       "      <td>ML1.4</td>\n",
       "      <td>天然地震</td>\n",
       "      <td>新疆伽师</td>\n",
       "      <td>1.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date        time   lats    lons  dept    mag class place  mag_num\n",
       "5  2016-05-23  23:43:52.5  23.30  103.21     6  ML1.7  天然地震  云南个旧      1.7\n",
       "6  2016-05-23  23:43:33.9  39.82  118.76     8  ML0.5  天然地震  河北滦县      0.5\n",
       "7  2016-05-23  23:36:25.9  39.60   76.93     6  ML1.4  天然地震  新疆伽师      1.4"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re\n",
    "get_num = lambda x: float(re.findall('\\d+\\.\\d+', x)[0])\n",
    "# a = 'ML0.5'\n",
    "# n = re.findall('\\d+\\.\\d+', a)\n",
    "# float(n[0])\n",
    "# df['mag_num'] = df['mag'].map(get_num) 会报错\n",
    "# http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
    "temp =  df['mag'].map(get_num)\n",
    "df.loc[:,('mag_num')] = temp\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 获取地图分布范围，经纬度表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# lower left\n",
    "llcrnrlon, llcrnrlat = df['lons'].min(), df['lats'].min()\n",
    "# upper right\n",
    "urcrnrlon, urcrnrlat = df['lons'].max(), df['lats'].max()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 获取地震地点经纬度及强度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lons, lats = list(df['lons']), list(df['lats'])\n",
    "mags = list(df['mag_num'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 辅助函数\n",
    "不同的等级用不同的颜色"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 按等级配色\n",
    "def get_marker_color(magnitude):\n",
    "    # Returns green for small earthquakes, yellow for moderate\n",
    "    #  earthquakes, and red for significant earthquakes.\n",
    "    if magnitude < 1.5:\n",
    "        return ('go')\n",
    "    elif magnitude < 3.0:\n",
    "        return ('yo')\n",
    "    else:\n",
    "        return ('ro')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 作图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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48ePqLkklUW4rGyqVinXr1tG7d2+cnZ3zPE+dOnWoWbNmtvsxZDIZU6ZMoUWLFgwcOJC7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BA7u/xlJj8VTU1NOnXqRHBwMA8ePMDc3Jz27dtz584dzp49S82aNenevTteXl6MGzeuTGz4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\nBiGEEEIIIcQpKNgghBBCCCGEOMX/AKxdlfHmO8LXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11370ccd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14,10))\n",
    "ax = plt.subplot(1,1,1)\n",
    "eq_map = Basemap(projection='merc', resolution = 'l', area_thresh = 1000.0,\n",
    "              lat_0=0, lon_0=120,\n",
    "              llcrnrlon=llcrnrlon-5, llcrnrlat=llcrnrlat-8,\n",
    "              urcrnrlon=urcrnrlon+10, urcrnrlat=urcrnrlat+3)\n",
    "\n",
    "eq_map.drawcoastlines()\n",
    "eq_map.drawcountries()\n",
    "eq_map.fillcontinents(color = 'lightgray')\n",
    "eq_map.drawmapboundary()\n",
    "eq_map.drawmeridians(np.arange(0, 360, 30))\n",
    "eq_map.drawparallels(np.arange(-90, 90, 30))\n",
    "\n",
    "min_marker_size = 4\n",
    "for lon, lat, mag in zip(lons,lats,mags):\n",
    "    x,y = eq_map(lon, lat)\n",
    "    msize = mag*min_marker_size\n",
    "    marker_string = get_marker_color(mag)\n",
    "    eq_map.plot(x, y, marker_string, markersize=msize)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到最近一段时间地震在全国范围内的分布。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 挖掘QQ聊天记录\n",
    "主要联系pandas的基本操作"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "matplotlib.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id,time\r\n",
      "8cha0,2011/7/8 12:11:13\r\n",
      "2cha061,2011/7/8 12:11:49\r\n",
      "6cha437,2011/7/8 12:13:36\r\n"
     ]
    }
   ],
   "source": [
    "# 数据初探\n",
    "!head -n 4 qqdata.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   11563 qqdata.csv\r\n"
     ]
    }
   ],
   "source": [
    "#统计行数\n",
    "!wc -l qqdata.csv #数据很小，才一万多行，直接读"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 解析时间\n",
    "直接读取的时间列是str类型，如果解析成时间类型，分析更方便。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 默认的parse_dates = True不能有效地解析\n",
    "# http://stackoverflow.com/questions/17465045/can-pandas-automatically-recognize-dates\n",
    "dateparse = lambda x: pd.datetime.strptime(x, '%Y/%m/%d %H:%M:%S')\n",
    "qq = pd.read_csv('qqdata.csv', parse_dates=['time'], date_parser=dateparse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2011-07-08 12:11:13')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qq['time'][0] # 时间戳类型，而不是str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8cha0</td>\n",
       "      <td>2011-07-08 12:11:13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2cha061</td>\n",
       "      <td>2011-07-08 12:11:49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6cha437</td>\n",
       "      <td>2011-07-08 12:13:36</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                time\n",
       "0    8cha0 2011-07-08 12:11:13\n",
       "1  2cha061 2011-07-08 12:11:49\n",
       "2  6cha437 2011-07-08 12:13:36"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qq.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 基本信息获取"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "群聊人数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "144"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(qq['id'].unique()) #群人数144人"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "时间戳是否唯一？有重复，暂时先不把其设为index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time = qq['time']\n",
    "len(time) == len(time.unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 找话唠\n",
    "把话唠定义为发言次数最多的人"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "qq['count'] = 1 #添加一列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 因为qq['count']设置为1,所以count()也可以替换为sum()\n",
    "gp_by_id = qq['count'].groupby(qq['id']).count().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gp_by_id) #返回一个Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id\n",
       "7cha1      1511\n",
       "6cha437    1238\n",
       "4cha387    1100\n",
       "8cha08      695\n",
       "4cha69      533\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_by_id[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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WKOdoRbtxVCGGqsTRiRiWZ8KECaN+rnqlCnFUIYaqxFGFGDoZR0TMaNqcI2lOY2PM39JS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\nzMxq6v8H+xV9Fte7DMAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113733590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "ax = gp_by_id[:10].plot.bar()\n",
    "ax.set_xticklabels(gp_by_id.index,rotation='45')\n",
    "ax.set_xlabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "发现一个很怪的id： `)chailed (104: Connection reset by pee`，确认一下是不是在."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(qq['id'] == ')chailed (104: Connection reset by pee').any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "而且万年潜水党，只说过一句话，名字还这么难记，内个群主，踢人了可以。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gp_by_id.ix[')chailed (104: Connection reset by pee']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 聊天密度周分布\n",
    "看看大家聊天主要集中在周几"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 添加一列 weekday, derived from time\n",
    "qq['weekday'] = qq['time'].map(lambda x : x.weekday())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gp_by_weekday = qq['count'].groupby(qq['weekday']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e44ec90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gp_by_weekday.plot.bar(); # 不好好上班，平时话真多"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 聊天密度小时分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Xb7lVZvdbbpXZ/ZbbTjd+93qZW2X2dMmt1WpIWtnc3vEcfz6d83REzMubTgP+DrgduDBv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\nKQNeoQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11373c090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 添加一列 hour, derived from time\n",
    "qq['hour'] = qq['time'].map(lambda x : x.hour)\n",
    "gp_by_hour = qq['count'].groupby(qq['hour']).count()\n",
    "gp_by_hour.plot.bar();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 聊天密度历史分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# 添加一列 day, derived from time\n",
    "qq['day'] = qq['time'].map(lambda x : x.date())\n",
    "gp_by_day = qq['count'].groupby(qq['day']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x113c4ba10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = gp_by_day.plot.bar();\n",
    "ax.set_xticks([])\n",
    "ax.set_xticklabels([]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 活跃天数最多的用户？\n",
    "如果某天说话了，则定义为这一天活跃。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# qq.groupby('id') group by id \n",
    "# .day we only interest in active day now\n",
    "# .nunique() the number of unique active day\n",
    "# 等价于 apply(lambda x: len(x.unique()))\n",
    "gp_by_act_day = qq.groupby('id').day.nunique().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V11YZqsWWgbahsYo3k+WOZcDZkr6/vLJMefxQbfN68nT6W+Xv8Gzy77M5OfDa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\nZlZT/x/mYPYwIB6nwQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11487ecd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6,4))\n",
    "ax = gp_by_act_day[:10].plot.bar()\n",
    "ax.set_xticklabels(gp_by_act_day.index,rotation='45')\n",
    "ax.set_xlabel('');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 活跃用户数与发言量的关系\n",
    "观察是否发言人数多，相应的发言量也增加了"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# 活跃用户数\n",
    "people = qq['id'].groupby(qq['day']).nunique()\n",
    "# 发言量\n",
    "speech = qq['count'].groupby(qq['day']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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/z3F333Yx8bVCz+FiNvdrGeF/pF8HPg/0AF80s6sb26pZ+xZBPJXuA1529y3A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\niIjE/H9hQUhA/jJAHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11497f850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可以看出正相关\n",
    "plt.figure(figsize=(6,4))\n",
    "ax = plt.scatter(people,speech)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
