panda  always MUST RESTART  建议用ipython   .ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14938.44701855365"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "i=np.random.normal(27000,15000,10000)#正态分布\n",
    "i.std()#标准差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223157199.32613441"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i.var()#方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26942.856097402608"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(i)#平均数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26756.883212083452"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(i)#中位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6a9v77gB2Jtk1nlAlSdPwZPsYTqmqJYCqOgyc0upPBQ4NtHuk1WmTeFqqpI3aMebxDfvG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\ni2lgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b46471320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(i,50)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([81, 38, 64, 46, 55, 39, 42, 33, 58, 42, 53, 35, 75, 60, 27, 57, 80,\n",
       "       31, 58, 69, 45, 45, 83, 72, 42, 44, 81, 34, 52, 74, 60, 77, 27, 79,\n",
       "       36, 22, 37, 43, 69, 83, 68, 22, 64, 63, 71, 23, 79, 76, 79, 72, 67,\n",
       "       25, 52, 46, 48, 33, 37, 54, 75, 71, 26, 67, 54, 43, 26, 21, 53, 45,\n",
       "       49, 71, 38, 36, 44, 26, 64, 84, 59, 50, 58, 58, 59, 38, 51, 71, 64,\n",
       "       43, 49, 84, 25, 72, 30, 75, 25, 56, 29, 73, 31, 32, 80, 39])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "age =np.random.randint(20,85,100)\n",
    "age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([58]), count=array([4]))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.mode(age) #统计最多的个数     语法正确   3.4的问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Tl9PG3vvH4zb2De3tva19Q3t7D8PmXRskoapmG1zvZ+jpnSasdyU5vimdCnwbuBF4V1N7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\nL0kdYuhLUocY+pLUIf8f3XpqCma4tQ0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49703dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "e=np.random.randint(1,7,10000)#均匀分布\n",
    "plt.hist(e,6)\n",
    "plt.show()#语法正确   3.4的问题\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b497e0588>]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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N6tq2/erWpo3/2qKF/7fdvLn/WvnWuLFf1NSgQbjzW1GLeVXxYt8gP+EEWL3a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\nkgAq5iIiCaBiLiKSACrmIiIJ8H+mXmJkm38XTQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49793630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import norm #正态分布\n",
    "data2 =np.linspace(1.5,1.9,10000)#语法正确   3.4的问题\n",
    "plt.plot(data2,norm.pdf(data2,loc=1.7,scale=0.05))#pdf概率密度 loc 中间值scale 标准差\n",
    "#平均 1.7 +-0.05      1.65-1.7占比 plot(x,y) 原数据x norm.pdf()求出来的数 为y绘制\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b49851908>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4978f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy .stats import binom#二项分布\n",
    "n,p =10,0.5 #抛10个硬币 ，正面 反面 ，成功概率 0.5\n",
    "data3=np.arange(1,11)#期望1-10个\n",
    "plt.plot(data3,binom.pmf(data3,n,p),'bo')#pmf针对离散 ，用bo绘制\n",
    "#抛10个硬币 5个正面 概率0.25  10 个正面 概率0\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b498bb1d0>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RYJ9nRaQEeEtVJ6XvKyKPAjep6rPB79OBh1X1faFjrQGqVbVJRCqA36vqiRHt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\nGBcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4985cd30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import poisson #泊松分布\n",
    "mu =20000#假设 非假期 日均流量 2万次\n",
    "data4 =np.arange(19000,21000)#出现19000-21000的概率\n",
    "plt.plot(data4,poisson.pmf(data4,mu),'bo')#pmf针对离散数据，用bo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x11b49915cc0>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49893780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.stats import expon#指数发布\n",
    "data5 =np.linspace(0,7,10000)#双11 第一天-第7天\n",
    "plt.plot(data5,expon.pdf(data5)*100)#媒体成交额1-100亿"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b4993c860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data6 =np.random.normal(70,10,10000)#1万个考试分数\n",
    "plt.hist(data6,40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70.121235440159751"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(data6,50)#中位数，Q2第二个4分位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6HqFHxB8A45m5NSJq+3YXX80O2Igca3q1eq1WcyQkSdPU6/WWl6Lo+KZoRPwNcDmwBzga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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b48f5a208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(data6,40)\n",
    "plt.show()#箱型图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "63.295659834882066"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(data6,25)#第25位数 q3第3个四分位数   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.69786400384956"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.percentile(data6,75)#第75位数 q3第1个四分位数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JwJaZhUCS1I2hLlSX5NXAXwDPA34EnAPsDnwJOBjYCJxeVVva6T8LrAGeBM6pqjtmmact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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b47f287b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data7 =np.random.normal(70,10,10000)#10000个考试分数\n",
    "data8 =np.random.normal(40,5,10000)#new no.\n",
    "data7 =np.append(data7,data8)#  append\n",
    "plt.hist(data7,40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55.040603298648648"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(data7)#一阶矩 平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "286.25853907210069"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(data7)#2阶矩 方差"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.342368481053855"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from scipy import stats\n",
    "stats.skew(data7)#3阶矩 偏度 正态分布  几乎对称"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.174466264641597"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.kurtosis(data7)#4阶矩 峰度 正态分布  几乎是0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.06610711],\n",
       "       [-0.06610711,  1.        ]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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w5UBhZlPM7HEz2xReD5rZI2a218zuM7OpIX26mW00s31m9s9mdkbsPW4K6bvN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\nRKSu/w/mU/8CVIgKVwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b499f8128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pylab import scatter#散点图\n",
    "np.random.seed(0)#随机种子，每次随机都一样\n",
    "age =np.random.uniform(30,101,100)#100个人 年龄随机分布 30-100\n",
    "income =np.random.uniform(3000,10000,100)#收入 分布3000-10000之间\n",
    "scatter(age,income)\n",
    "np.cov(age,income)#协方差\n",
    "np.corrcoef(age,income)#相关系数 +-相关  0相关 \n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        , -0.97533526],\n",
       "       [-0.97533526,  1.        ]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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++AOzJiCFWRO//e1vjy9sM1aoZCAio0KpJYOhx30FWMakSWfzxhu7Y1U11QOV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\nY2k0twAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49eae9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "age =np.random.uniform(30,101,100)\n",
    "fhl=(100-age)*1.42*20 +2500 +np.random.uniform(0,500,100)\n",
    "#增加随机数  分布2500-4500ml 肺活量\n",
    "scatter(age,fhl)\n",
    "np.corrcoef(age,fhl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'scipy.stats' has no attribute 'lineregree'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-37-4613f0abbd47>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mscipy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mstats\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstats\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlineregree\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfhl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[1;31m#result#语法正确   3.5的问题\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'scipy.stats' has no attribute 'lineregree'"
     ]
    }
   ],
   "source": [
    "from scipy import stats\n",
    "result=stats.lineregree(age,fhl)\n",
    "#result#语法正确   3.5的问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'result' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-42-36213d75aafa>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrvalue\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;31m#R-squared 可决系数越接近1，匹配得越好\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'result' is not defined"
     ]
    }
   ],
   "source": [
    "result.rvalue*2#R-squared 可决系数越接近1，匹配得越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'result' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-670375b8fc2d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mpredict_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mx\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0mb\u001b[0m \u001b[1;31m#\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mpredict_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m120\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslope\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintercept\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#预测120岁的肺活量\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'result' is not defined"
     ]
    }
   ],
   "source": [
    "def predict_line(x,a,b):\n",
    "    return a*x +b #\n",
    "predict_line(120,result.slope,result.intercept)#预测120岁的肺活量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'result' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-46-e12a6950f824>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mpredict_line\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslope\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintercept\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mc\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mfhl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#散点图\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'result' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(age,predict_line(result.slope,result.intercept),c='r')\n",
    "plt.scatter(age,fhl)#散点图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>str07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>1990-12-19</th>\n",
       "      <td>96</td>\n",
       "      <td>99.98</td>\n",
       "      <td>95.79</td>\n",
       "      <td>99.98</td>\n",
       "      <td>1260</td>\n",
       "      <td>494000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>104</td>\n",
       "      <td>104.39</td>\n",
       "      <td>99.98</td>\n",
       "      <td>104.39</td>\n",
       "      <td>197</td>\n",
       "      <td>84000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>109</td>\n",
       "      <td>109.13</td>\n",
       "      <td>103.73</td>\n",
       "      <td>109.13</td>\n",
       "      <td>28</td>\n",
       "      <td>16000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>113</td>\n",
       "      <td>114.55</td>\n",
       "      <td>109.13</td>\n",
       "      <td>114.55</td>\n",
       "      <td>32</td>\n",
       "      <td>31000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>120</td>\n",
       "      <td>120.25</td>\n",
       "      <td>114.55</td>\n",
       "      <td>120.25</td>\n",
       "      <td>15</td>\n",
       "      <td>6000.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            open    high     low   close   vol    amount  str07\n",
       "datetime                                                       \n",
       "1990-12-19    96   99.98   95.79   99.98  1260  494000.0    NaN\n",
       "1990-12-20   104  104.39   99.98  104.39   197   84000.0    NaN\n",
       "1990-12-21   109  109.13  103.73  109.13    28   16000.0    NaN\n",
       "1990-12-24   113  114.55  109.13  114.55    32   31000.0    NaN\n",
       "1990-12-25   120  120.25  114.55  120.25    15    6000.0    NaN"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh =pd.read_csv('sh000001.csv',index_col=0,parse_dates=['datetime'])\n",
    "sh.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1990"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sh.index[0].year   #parse_dates  to  timestamp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">open</th>\n",
       "      <th colspan=\"2\" halign=\"left\">high</th>\n",
       "      <th colspan=\"2\" halign=\"left\">low</th>\n",
       "      <th colspan=\"2\" halign=\"left\">close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">vol</th>\n",
       "      <th colspan=\"2\" halign=\"left\">amount</th>\n",
       "      <th colspan=\"2\" halign=\"left\">str07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>2232.204082</td>\n",
       "      <td>282.839726</td>\n",
       "      <td>2251.033633</td>\n",
       "      <td>295.392374</td>\n",
       "      <td>2218.166531</td>\n",
       "      <td>274.605798</td>\n",
       "      <td>2238.214939</td>\n",
       "      <td>288.783022</td>\n",
       "      <td>1.730075e+08</td>\n",
       "      <td>1.280086e+08</td>\n",
       "      <td>1.527627e+11</td>\n",
       "      <td>1.297361e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3712.069672</td>\n",
       "      <td>538.951993</td>\n",
       "      <td>3768.015492</td>\n",
       "      <td>542.945976</td>\n",
       "      <td>3657.852131</td>\n",
       "      <td>516.951386</td>\n",
       "      <td>3721.552090</td>\n",
       "      <td>534.069014</td>\n",
       "      <td>4.158621e+08</td>\n",
       "      <td>1.600050e+08</td>\n",
       "      <td>5.420882e+11</td>\n",
       "      <td>2.525199e+11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>3000.758197</td>\n",
       "      <td>138.352754</td>\n",
       "      <td>3023.206434</td>\n",
       "      <td>133.637271</td>\n",
       "      <td>2978.427582</td>\n",
       "      <td>139.861449</td>\n",
       "      <td>3003.684303</td>\n",
       "      <td>133.640466</td>\n",
       "      <td>1.829344e+08</td>\n",
       "      <td>4.997810e+07</td>\n",
       "      <td>2.029360e+11</td>\n",
       "      <td>5.334141e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>3244.807377</td>\n",
       "      <td>99.509330</td>\n",
       "      <td>3260.052746</td>\n",
       "      <td>98.026498</td>\n",
       "      <td>3232.696270</td>\n",
       "      <td>99.468767</td>\n",
       "      <td>3249.687049</td>\n",
       "      <td>97.965699</td>\n",
       "      <td>1.787203e+08</td>\n",
       "      <td>3.980584e+07</td>\n",
       "      <td>2.072487e+11</td>\n",
       "      <td>4.205288e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>3333.186441</td>\n",
       "      <td>120.325059</td>\n",
       "      <td>3356.116102</td>\n",
       "      <td>120.970295</td>\n",
       "      <td>3313.045763</td>\n",
       "      <td>123.586368</td>\n",
       "      <td>3338.138983</td>\n",
       "      <td>121.308992</td>\n",
       "      <td>1.930647e+08</td>\n",
       "      <td>4.143641e+07</td>\n",
       "      <td>2.276483e+11</td>\n",
       "      <td>4.751461e+10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open                     high                      low  \\\n",
       "             mean         std         mean         std         mean   \n",
       "2014  2232.204082  282.839726  2251.033633  295.392374  2218.166531   \n",
       "2015  3712.069672  538.951993  3768.015492  542.945976  3657.852131   \n",
       "2016  3000.758197  138.352754  3023.206434  133.637271  2978.427582   \n",
       "2017  3244.807377   99.509330  3260.052746   98.026498  3232.696270   \n",
       "2018  3333.186441  120.325059  3356.116102  120.970295  3313.045763   \n",
       "\n",
       "                        close                       vol                \\\n",
       "             std         mean         std          mean           std   \n",
       "2014  274.605798  2238.214939  288.783022  1.730075e+08  1.280086e+08   \n",
       "2015  516.951386  3721.552090  534.069014  4.158621e+08  1.600050e+08   \n",
       "2016  139.861449  3003.684303  133.640466  1.829344e+08  4.997810e+07   \n",
       "2017   99.468767  3249.687049   97.965699  1.787203e+08  3.980584e+07   \n",
       "2018  123.586368  3338.138983  121.308992  1.930647e+08  4.143641e+07   \n",
       "\n",
       "            amount               str07      \n",
       "              mean           std  mean std  \n",
       "2014  1.527627e+11  1.297361e+11   NaN NaN  \n",
       "2015  5.420882e+11  2.525199e+11   NaN NaN  \n",
       "2016  2.029360e+11  5.334141e+10   NaN NaN  \n",
       "2017  2.072487e+11  4.205288e+10   NaN NaN  \n",
       "2018  2.276483e+11  4.751461e+10   NaN NaN  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "key=lambda x:x.year #def key(x):return x.year\n",
    "sh.groupby(key).agg([np.mean,np.std]).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>str07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>1990-12-19</th>\n",
       "      <td>-1.807016</td>\n",
       "      <td>-1.634879</td>\n",
       "      <td>-1.498206</td>\n",
       "      <td>-1.634879</td>\n",
       "      <td>2.640981</td>\n",
       "      <td>2.600217</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-20</th>\n",
       "      <td>-1.084209</td>\n",
       "      <td>-1.211021</td>\n",
       "      <td>-1.141418</td>\n",
       "      <td>-1.211021</td>\n",
       "      <td>-0.031001</td>\n",
       "      <td>-0.133344</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-21</th>\n",
       "      <td>-0.632456</td>\n",
       "      <td>-0.755447</td>\n",
       "      <td>-0.822098</td>\n",
       "      <td>-0.755447</td>\n",
       "      <td>-0.455804</td>\n",
       "      <td>-0.586716</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-24</th>\n",
       "      <td>-0.271052</td>\n",
       "      <td>-0.234515</td>\n",
       "      <td>-0.362275</td>\n",
       "      <td>-0.234515</td>\n",
       "      <td>-0.445749</td>\n",
       "      <td>-0.486707</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-12-25</th>\n",
       "      <td>0.361403</td>\n",
       "      <td>0.313328</td>\n",
       "      <td>0.099250</td>\n",
       "      <td>0.313328</td>\n",
       "      <td>-0.488481</td>\n",
       "      <td>-0.653388</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                open      high       low     close       vol    amount  str07\n",
       "datetime                                                                     \n",
       "1990-12-19 -1.807016 -1.634879 -1.498206 -1.634879  2.640981  2.600217    NaN\n",
       "1990-12-20 -1.084209 -1.211021 -1.141418 -1.211021 -0.031001 -0.133344    NaN\n",
       "1990-12-21 -0.632456 -0.755447 -0.822098 -0.755447 -0.455804 -0.586716    NaN\n",
       "1990-12-24 -0.271052 -0.234515 -0.362275 -0.234515 -0.445749 -0.486707    NaN\n",
       "1990-12-25  0.361403  0.313328  0.099250  0.313328 -0.488481 -0.653388    NaN"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zscore =lambda x:(x-x.mean())/x.std()\n",
    "transformed =sh.groupby(key).transform(zscore)\n",
    "transformed.head()#每个位置元素取值是data里“对应位置元素”按transform的“函数参数”运算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b4ba790f0>"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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c+NnPou8sn2y1ld33/vtVv/hCdd481VNOiX4H5cyKFdE4w5/ddlPdfffGfVev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\njuNUOS7oHcdxqpz/B8WtubwxEBbbAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49a080b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sh['close'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b4b1cc630>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wf7a0SH9KbFNus2pTmbuuLrGNoziMk5n7rFnFddcm5q6WFUdy32yz\ncHo+9JZWNKqGYO5Ll5abZ/li7tzwtX7qzAZm7lGbkAyTr2e2xNGZe7du9hBZplOvLlNIwH40W7UE\nMrlL8JXck6JLl7ANMtPLfam/sE0Mw6TTnjZNbsxxu3Tr5mZi3brJD7tuYJUQo7VVqrVU3H672Yc5\n06QGUJg0CfjjH83pABkBTD+gpI6Jq66SjMNlLKDDpYJjtUiPHvGZu0ktY2PuJhpMTNjG3PV9LNVv\n+b77BqsiH7WMii22sDNWk/WVCnXDVVd9qXNOHY+2OeTLpPk+W/64HPOpMVrrQi1DREcS0dtE9A4R\nRcjA5fj4Y/+Yozpsx9L1SEYmEMkB9oMfhK0TTI36xhvl+moeSO3t4cHmOhmrL1V1mJaINpXCd74T\nWLb4qmXi+EGfMCEsMevQXyaq61cgsGpiPPSQdDOhgulzOebq1s204V2w0qVvXpraJuosgs8qkWk3\npdVVF3/7W7mu1WbnDtgjkgHhTcY0JHfTJqLNdXV5m9l17vrLQZXcVfioZVS4TI2jDCN22w244gq5\nOjzoINl3Jp27et7GxmR9HJuZ5iA7aDPp3n3dUgNVYO5E1ALgZgBHANgNwClE9Cn3U2EsWWJfyjNs\nDWg78PHcc375tbRIJqSeCjSVNWBA+X/qQFft4KN82tig2wHzwLYx9wcfDHT/PsydT1v6Ikpitk1q\npkNnUC6/H2yFodLL+fTpE2YUNonbBCHMG8ZRp5d9cNVVcuVoYu7qyVxAHotXx5jpmWnTgrJdMUnV\nVUVcK6+77grH27XRwlKtbsFmajN13L3ySvBbP2lrixEaV3J39Z16nsGEtjZ52IgDpgBmgUod9za1\nzLe/Lb/LVVXl6VX1JbeDibmbzrzYUA3JfT8A04UQs4QQbQAeBHBsxDMhLF9ullT0ypkGodpoKtTO\nv+22cglfnfDdu4cnialRn3zS7VtGHcgu3TJb55xzjvk+Q1Wz+GwG+jB3onixR80ScwC25AGk+SOD\n6YjjSsJk+sjXm2wSXvHIZ4rRmSPoZxNzd+lnfa21bM2pH7oBylcyus79U58KwtiZjtzr2GSTZK4Y\nVFv0KOiB2suZajH0n6ry0V8M3Kb6OJ0502zWbDMTruTFrJuZEgGzZxcBhHXu6knXqDw7OqIDrj/+\neHCtM3dTgB4fQawazH0bAKq7nrml/7xhYu6+k+vGG83/q53/5z+HQ5IVi/IovrprrwbMsDFm03Kf\noUucNo+BV1whv6OixavM2qctfDdU4yBKclePnasSFtPLG2Y+MNHP1716yYkQ51g+Y9w4cxukIbkz\nolaJDP1Alwkcbu7733fn9YUvSFcBcZg718ul8tHhE+1MZZbqONCD0NjG82OPSTcUOkz7SIC776Ii\noM2ZU65rVyV33Z5eva9CFXrUWLQ6eA6qZXL9+Rm9jp2ddh9RKqrB3E3DNNb0sEnulUBlvDqznjBB\nfnMj60yDO1OVRgHg6qsLAMzOk9QB+957dp0/S4sdHeFj/nrZNncCNvhsqBKZde5nnGG2znAx98MO\nC2+Cq5OKvVz6uMT1kdyJpJTK6gT5TMGY3ze+ETbjvPdec7lp6NyZFlM4OhO20UQek879l7+U7e7y\nIwTIF+tGG8VTy7BRQL9+ciXqA53m9nbg6KOD6/vuK2CrrYJrZu7nnSfdUahQ+9MHtjngYu4uC5an\nnpKHC3VGu912BQD2E6omIU51weyjllHnJv/HBx1N81Yty4ZqMPe5ANRFxLYAjKxtxIgRGDlyJEaO\nHIlRo0atUxEsXw7Mm1cMqQza24sYMya4fvXVIj75pKjkVkR4aR6+7ugIrmXjFTF6tLzmZf1HH8nr\nHXcENt44KJ/vv/++OX/5xi1i2bLg/qpVwfNyYobrUyzKax6UixcX0dkZvv/BB8H1smXFdS5hg+P2\n9vq+8koRS5eG8+vsLCoDpIjVq8vpAeSEnT69nN6XXy6uY+5qes5PvZ45sxjqT7lcD+fH9M6eHeQX\nWJwUIUQ4f14uA0D37kX85z/yOlDLlNfnoIPkZNPp1a/fequIOXPM94UAFi5015fLF4J1r+b7bCGz\nxRZFvPpqcH/JkmJILaO2T1sbMHasm/5isYiJE+39Y7rm8nr3lvU3tZ96PWBAcZ11iTp+5WFD+Ty7\nU+D7zNwXLAjPn2KxiClT5DWrB13tWywWMXVqcR1zV9untRUYP97c3sxkTfV5/nl53bWr+f6CBUWF\nuQflsRm0mv6NN4L7M2cCEycG13p9hGC3wvK6pUXmN2FCUB81/2KxiGeeGYH77pP80gohRKYfAK0A\n3gWwPYBuACYD+LQhnbDhG98Q4q67wv9tuKEQn3wSXBeLQgwbFly3tPBWWfgjRPl/hYL8bm+X96++\nWl6fdJK8XrhQiI03DvJeuVLev+UWPa/RQgghli2T15/+dHBvl12C5+fPF2Kzzcx13XlnmX71aiEm\nThTitNOCe9//vhC/+Y38vcsuQrz1lvw9alSYjt69hRgwIFznZ5+V9VSx0UZCfPyxEG1tMt3AgUKM\nHj26jKavflWIP/+5nNZFi+RzkyeH/29pEWLOnOD6mWeEWLo0uL73Xvnc/vuHn2N6338/+O/99+V/\n774r6VXT/exnQbp99xVi3Dj5e/Bg2Rdq/Rl33SXHE+PMM8vTCBH0rQmPPCLEcceV061fA0Lcc48Q\nl11Wnhff32yz8ueFEOLAA4W46abR6647O4N0ra1munQsXSrEBhv4pRVCiLfflvlPmiTE6NHy97x5\n9vSDBwsxZUr4v6uuEuKnPw1oHTVqdOj+vHny/4svDvefEHKMAuGxIoS5fYQQ4oEH5NjU0+2+ezAm\n9bl+1VX2+nAatU7XXSfESSeNXnf/r38tz/eDD4L0t98u/7vyynCaKVOC39/6VlCf+fOF2HxzIYYO\nDe7fcou8t+++8nrQoHJav/tdIYYP53EBIQy8N3PJXQjRAeB7AJ4C8AaAB4UQhljmdixfHt/OPU7E\nF1UVAgTLHdXUTtWZ8dv/pJPc+anLJpUePT8VX/mKlBy6d5dmhrr9NOc5bZrdZn/t2nKdvUvnztKj\nbTmsWuaoYClG3xjbZJPw8vXww8P9x/Sqp0ZVqOaQnNbkbkClt7VVhjV8883gGdMRfX0zW287xj//\nGb5euzZIqz/zwAPBkX8dHR1u9Y5rU1ktR7XPdrkeUNGtm5w7f/+73z5Be7vctB0yJGhb12E7054N\nh49jW239vmqmaTvT4KtutOncXSq1uMFL9DnBh9HYy6QtT73eahpTPGaTmvill+S3qT2I5B7fGWfY\naa+KnbsQ4t9CiF2EEDsJIa6J+/zixeYj+64BO26cPDZvO2WoQg/XxR7kbIdkhJCbeOW29wUIEeSn\nbr6ohyP0/BgLF8q9BVtgZtUDJBC8IPTB1dYWZu5Tp9p17v36hQebSede7i9EgsvQGb8aH9IEDmKh\nvgCYHsAcCchkGaF72gTkgSJR0rnrvuv5GZ8Jrsck7d49fEpZnfT9+5ePBfbp0tbmZu62vSTpMqGw\n7lq1i9fbzQbus+OP9zsRvWJFvL0tkzDAff/gg/J6zz0LRpq6dSvvh85OaWHlY0XF6dUxcPvt8ttl\n6eTT97qJ5rbbFgBIunj/QG1PIeThr0GDgv0cvRy1rdTxwC9I08lWduvsmkv33We/1xAnVP/3v7A1\nC1A+sHRGv/fe0u/36tXRkg4zWm70224L3+/atVxyU8vfbbfgd0eHWXL/9a/D+Zkk9802k5Y6UScV\n9WdNA1ZlAHvsIZmVbs2jS16uTV6bPTFgXiW4BiQfCNP7jK/VlyL/Z1t5qDQCsi34mVNPLS/bV3Ln\n9tt//0Bq5omkP9O9e7DpzidP+dzDDTeYmTsHfnD1tVqOaurpGxdVba+oeL+A3XeKCzbJ3Xafaero\nMK9KTWZ/NujM/aCD5HfSDVXGp5RTOOoYW73abMUmhDzEqJo7upi7uslt2kTWx2/cuLOMhmDuS5ZE\nO9sySaY9e8oOifLhbIqDCYTtbtUBI0R4UAWrg2KIuavBL9RBwczdxFgWLDCbWzHUlQHTZxqweluY\nYkfqL0ibP3cbcwekuR1L4mzVFMXcbbv9bLb62c8G/3Ga9nY3c2cLp+CZIvbYo9y1sn6AzObmmOs7\ncaLZ54dado8ewYTVVxjTp5sZDdfLJoUTAZMmFddd77qrOZ0vVDWCDY8/HqxafZigSy3DUOsABO1a\nLJa3vT6vGM8+axbQ9Bf+brsFfCApc7/++vJob7NnF9HeLukzzYPOznK3Bno5Kj2jRoXPBwgRjsms\nuy0wzSUfi6KGYO7LlplDrakwMaAePWSjR+kb+U3tigzD0jtQ7itG16eZloRqeiKZnyldW1v0SkON\nUqOXz/B52/uanNl07kDYbfAnn8gl7YoVfuXradhJm8nVhEnn7qOb1U8k6pK7zeTPVF99Oc1QJffy\nYBXlLhWA8he0urLLAqNHR6e59trAF5NqvmiDjbmr9dfv9+wpVx7cdxxMHCifV4zttpP9b1qxmlSN\nSZn7dtsBJ55Ynh9QfriJ67X99vK3ztxN7cLYcsvAz5GpDXXm3tQBspcti95QrYS5q0xbhe7si9Pp\nkluQfwHt7dHMHQi/LFTYNonUPHTmbqqfXt7w4eUrGD1Nz552nbuNuat5qMvSKOY+fnz5OQFTpHuu\n25o1wSlidWKY6JH67oKx3Jdekj5s/vEPN33qC9amPmL06BEwd9PegMk1hb66MwkvQ4YU3ERmiN12\ni543Jsak13/33Qtlz/XpE6RRV1b6vGLw2NOt/mx27mPGAA8/bKbZpW6y2bFvu23BKuBwDALb5nBU\nuab68rPsVbZpmfvateEoPjaYGr9bt3K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VPXD12GPxpbk4adgTnS2MIA9+k7VPr17u5aEv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\nr4itfAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49eae048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "transformed['close'].plot(grid=True)#网格 ,figsizie=(9,9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b49ee97b8>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KiviUjdzBt/7E7TVhTMJcjH0ezGAczWAczQgWR+uPXVGRJGHdugEdOoS+Z0GB\n3SH/pptiLmJK4PMYO38x/Phje79nT0nqf/qp5sqUbJiEERG5zLnnShKWlxf+e6yaszp14lMmcgdn\nTeqUKbKGpJsxCXMxzoNjBuNoBuNoRrhxLC+PbLoJKwk7eDDyMqUiPo+x8xfD3Fx7v1atyJbNSkcu\n//aJiNzpgw+Ad94J/3q3JWEUH86a1Oxs6RPmZkzCXIx9HsxgHM1gHM0IN44nnwz87W/h3/ess2Rr\nJWPpjs9j7PzFUGvgyCNlv1YtJmFMwoiIXKiiAjj88PCv79YNuPde1oRRbCorZeFuQLZsjiTXYp8H\nMxhHMxhHM8KN48GDUhMRiVq13FMTxucxdv5iuGQJsGyZ7FtrRwKSnLlx+k8mYURELhRtEsaaMIrF\n9dd7H1sTt2ZlAa+9VvPlSTRjSZhSKkMp9ZNSaobnuK1Saq5SaplS6nWlVJbnfC2l1BtKqRVKqe+U\nUq0d97jdc/43pVQPU2Uj/9jnwQzG0QzG0Yxw4xhNEpad7Z4kjM9j7ILFcOZM2TqbI3/9Nb7lSUYm\na8JGAVjiOB4P4FGt9dEAdgKwuoD+DcB2rXV7AI8DeBgAlFLHAhgAoCOAngCeUirSVc2IiCgc33/P\n5kiqeVdcIdsenmoW5195Ny7kbSQJU0q1BNALwAuO0+cB+D/P/lQA/Tz7fT3HADDdcx0AXALgDa11\nhda6GMAKAKebKB/5xz4PZjCOZjCOZgSK4ymneB+vWRPZPGGAu2rC+DzGzl8M27cH7rnHPm7QoObK\nk4xM1YRNBPBPABoAlFINAOzQWlsrQq0H0MKz3wLAOgDQWlcC2KWUKnCe99jgeA8REcXAd2mY8nL2\nCaOad+AAULu2fdyihfex28SchCmlLgZQorVeCMCqWFSOfYt2vOZLBzlPccI+D2YwjmYwjmaEG8do\nkzC3NEfyeYydvxj++GP1RN7qF5aZCaxeHf9yJZMsA/c4C8AlSqleAOoAOBzS1ytfKZXhqQ1rCWCj\n5/r1AFoB2KiUygSQr7XeoZSyzluc76lm2LBhaNu2LQCgXr166Ny586GqT+sHz+Pgx5ZkKU+qHi9c\nuDCpypOqx5ZkKU+qHgd6HoFCz1aOy8sLUatWZPfPzgY2bChCUVHyfL98HlPreNasIsyaBdx7r/36\ngQMAUIgHHwQefLAIc+YkT3ljeX6KiopQXFyMYJQ2ODGHUuocALdorS9RSk0D8LbWeppS6mkAP2ut\nn1FKXQ/zLSa7AAAgAElEQVSgk9b6eqXUQAD9tNYDPR3zXwXQBdIMOQtAe+2ngEopf6eJiCgA32FO\nBQXAf/4DXH55+Pd4/33g2WdlySOiaCglTZDr13ufc0rHP+9KKWitq7X4magJC2Q0gDeUUv8GsADA\ni57zLwL4n1JqBYA/AAwEAK31EqXUm5ARluUArmemRUQUH5mZbI6kmteyJfDqq4kuRfLIMHkzrfUX\nWutLPPu/a627aK07aK0v01qXe86Xaa0HaK3ba627ekZCWu9/UGt9lNa6o9b6U5Nlo+p8q90pOoyj\nGYyjGeHEsUEDYOvWyO/tptGRfB5j5y+GublA48Y1X5ZkZTQJIyKi5PfHH7KVvjjhY00YxWrPnsjW\nLE13RvuE1RT2CSMiCp/WMg3AjBnARRfZ5ydPBoYPD/8+8+YBI0fKligaeXnAunVAfr59zs19wlgT\nRkSU5nbskCTssMO8z0daq+Wm5kiKj337pEkykIKCmitLMmAS5mLs82AG42gG42iGvzju3Cl/3DIz\nvc87ayPC4abmSD6PsfONYUWFbIOt1JDhsqzEZd8uEZH77N0L1K1rN/vUry/bAQMiu092NrBkCRBi\n6iMiv/bvD70+pNuSMPYJIyJKc5ddBrz5JvD118DZZwPHHw8sXhx535viYqBdO+DLL+U+WrvvjyZF\nb+pUYNiw6s+ds09Y06bApk01WqwawT5hREQuVVYm2z17ZBvt/2GtecWUAh55JPJ5xsjdwqlBdVtS\n77Jvl5zY58EMxtEMxtEMf3G86CLgmmtiH3Xm7Mvz5Zehm5ZSGZ/H2PnG8NRTQ7+HSRgREaUV3xFp\nvlMChMuq+aqqArZti71c5C4HDwJ9+wa/xm1JGPuEERGlubFjJRE7+2ygV6/o+4Tt2yfTXMyZIzVr\nK1ak55xOFB9W8h+sT1ibNuk58IN9woiIXMqqCWvfPrb7WDVh5eV2P7P9+2O7J5GTb4K2b5+M7q2q\n8l70O10wCXMx9nkwg3E0g3E0w18crSTsqKNiq7nKypL+ZeXlwNq1ci5dF2Pm8xg73xgWFgITJgR/\nT9263scnnijLHBUWAq1amSxdcmASRkSU5qZNsyfKBIBGjaK/V3Y28Npr9vEvv0R/L3KX7GygU6fg\n1/gO9li5UrZffRWfMiUa+4QREaU5pYDTTwe+/16Od+6U6SqiqVno3x949137uE8fWZOSKJSzzgIe\nfli2TkoBxx4LvPMO0Ls3sHy592sAkJMjTeCp+qeffcKIiFysSRN7v1696Jt2nAkY4F3DRhTM/v1A\nnTqBX8/ICDztidUHcdky8+Uybf9+YPv28K5lEuZi7PNgBuNoBuNoRqA4xmth5N2743PfROPzGDvf\nGB44IAvJB5KZWT0J69jR+/ijj8yULZ5yc4EGDcK7lkkYEVEaW7dOtnl58bn/N9/E576UfkLVhGVm\nyihIJ9/jSBedT4TTTgv/WiZhLlZYWJjoIqQFxtEMxtEM3zhaSxVFO0GrW/F5jJ1vDAPVhDVpApx8\nsv/mSN/jhg3NljEeGjcO/1omYUREaczqs5WqSdiKFem9PJKb+K7cYFm3DpgyxX9NmLPPYW6u3Tcs\nmW3dKts5c0JfyyTMxdjnwQzG0QzG0QzfOO7bF//PXLIkfvfu0AF4/fX43T8QPo+x8/cs+kvCsrMl\nAQtVE1arlix9lGx27fLuiH/ggGzDWdqLSRgRURqzZrT/29/M39vqZxbO//ijsXSpbH1rRyj1lJfL\n1rkIvC/fjvlvvGH3aQRk0tZkrAk76STpiK+11NwtWiTn69cP/V4mYS7GPg9mMI5mMI5m+MZx3z6g\nZ09ZL9K0KVNke9hh5u8NAJ9/LlvfWdRrAp/H2DljGKgWzMm3OXLQINkuWgTMny/PcbLVhD36KPD7\n77JfWgp88YXsn3BCeP95YBJGRJTGQo1Ii9SYMfZ+To5sM+L0l2TLFtmyT1hq0hr47jsZHBJOEhZo\nnrDmzYFTTrEnbE20TZuAm28G5s0Dbr3VPl9VJTV3RxwBNGsW3nPLJMzF2OfBDMbRDMbRjHD74UTL\nOfLLGukWr9oJq09NIhYJ5/MYu4yMIpx5pqwXuW9f6P8MOJsjnTPjZ2bKdutWe9WHRGreHJg4EejS\nxft8RQWwejUweLA0u4YzkTGTMCKiNGa6JszZxHLOObLAcrw6/1vJl78k7Ndfgd9+S91lbNykTh3g\np5/sZCoQZ3OkM4HJypLtG28kZpBGMPPm2bVzP/4oSVi7dvK9MAmjoNjnwQzG0QzG0QzfOO7da7bP\n1rHH2vuZmcCFF8YvCbNGmVlbp06dpCxWvzHT+DyaUHhob8AAezHuQJzNkVZHfut8MjnsMGmSvO46\nmZi1Vi0536MHsHixNEdmZQGXXipTwyxcGPheSfatERGRSSUl3utGxqqwELjvPvs4O9v7D6ZJVnIX\nrDly4MD4fDbFxllDGW6fPmdzpJV4P/ec2ZrccBQXy39efO3aBfzwg3TALygAnnrKfu2uu2S7aJHU\nhFm1d0DwBe6ZhLkY+zyYwTiawTia4RvH3bvNL/Xi/AMTzyRs9WrZWpNf+vPHH1LbsHmz2c/m8xgb\nSaKKAESWhFnNkR98INu//92eaNi3D1a09uyRe/obvfjaa5JEHX549dfGjAFOP132fafacFacNm3q\n3fR6772By8IkjIgoje3albpJ2OLFsn3ssdDXjh4dnzJQdHbutPetZLpeveDvsZKtDRv8X3v99dLp\nPVZWguecYPXdd+Xzr7hCjlu3rv6+iROrl9XSrRtw9tnAPffI74fzdyTYuq1MwlyMfR7MYBzNYBzN\n8I3jjh3SdGJSTSVhls6dvY/91axMnWr2M/k8xkaSsEIA9s/mzjvDe2/LljI3nO+PIFDtVaTeeUe2\no0bZ56zmRItvrdvu3fZ+z57V75mdDXz1lT2Fi9WU/vrrwefoYxJGRJTGtm83n4Q5m2JqIgnr2NH7\n2FqUnJKX73xeAwZ4z6kVSmlp9QElGRlmRsO+9ZZsX3vNPvfrr97XrF3rfeysTT711NCfYc3037p1\n8OZYJmEuxj4PZjCOZjCOZvjGMR5JWE3WhF19dfWh/hs3xu/zLHweY1NRIfOEWSJdNuuaa6onYQsX\neidO0XCOtG3RQpI6K7F7/315/bPPZECL5cUX7f1Vq4C77w79Odb7Q01VwSSMiCiN/fFHaidhJ59c\n/f6TJwd/z7ZtQPv2wMsvh/85kydzzjGTfvzRu+nQX0f3YDZvrp6EvfJK7OWyRjZ+8olMcZKRAZx/\nviRLF18ss/KfeKKMkPzjD2nOv/pqee/ll8v0E8HWv7RYzZGZmbLkUiBMwlyMfR7MYBzNYBzNcMax\nslKa7kx3zHeO/IpnEla3rnRq9q1J8J0+4KKLgLPOso8//FDmpLr55vA+Z+9eqal59137HJ/H2GzZ\nAnTqVHjoOJr1P32TMGfyH61775Xa4Xr1gFmz5Nzs2fK7YnW2b9hQtoWFwBNPyH779sCrr4b/OdZq\nD6GSTyZhRERpaudOScBCzVQeKWfSFa8k7Iwz7IlmnUlYeTnw5Zfe1w4d6t1xetgw2bZvH95nWU1H\nS5ZEXVzycc89wC+/2MfRJGHNmnkfX3hh5PdwTl+yfz/w5JOy7zti0erD5XzfL7/YHe3ffjuyz330\nUeCFF4Cjjgp+HZMwF2OfBzMYRzMYRzOccYxHfzCgZpKwuXNlm5vrff8nn5Tlilq2tM+1aiXTGmgt\nfzytWf3/9KfwPsvqJ+Ss/OLzaEIRpk+XvWhWbWjRwvv4xhtlpQRf/iZWBezm0GbNpPlxxQo5njoV\nOOYY72udzxMAfPut97G/zw3mppukdjUzM/jaqjEnYUqpHKXU90qpBUqpxUqpez3n2yql5iqlliml\nXldKZXnO11JKvaGUWqGU+k4p1dpxr9s9539TSvWItWxERG62fTtQv775+15zjf0HLV5JmFVTUauW\n1IRVVsof23/8Q84/9ZQ0eVnX7tlj12QtWQK0bet/uSN/rD+SvnM/+WKfschkZUmNJgA0ahT5+5s2\n9T7Oyak+6hKQJj/fnPmPP7yX05o4ERg/HmjTRuYaU0pqxjZt8v9zdc4TtnRp5GV3CtaHLOYkTGtd\nBuBcrfVJADoD6KmU6gJgPIBHtdZHA9gJwBob8TcA27XW7QE8DuBhAFBKHQtgAICOAHoCeEqpUL8S\nFAv2eTCDcTSDcTTDiuO6dfIVjyQsJ8duZolXEnbppTIqLStL7n///d79axo0sP+wFxTINc8/b79e\nVhZ8uSOnk0+WrbMjue/z+NNP0ok7XutkpqM6dQrRvLk9Q32kGjf2Pq5Vq3oS9tlnsvXt/N6woffz\nsnevjKxcs8YuS+3a1RM9i3V+wADg6KMjL3u4jDRHaq2txzIHQBYADeBcAP/nOT8VQD/Pfl/PMQBM\nB3CeZ/8SAG9orSu01sUAVgA43UT5iIjcpnVrmZcp1CzlsYpXElZWJslerVp2EgbYa0VafYyKi6XZ\nKjtbmi4tmzZJn5xIPzMQa+mZ336L7J5utGGDbGvXlm00/cGA6v2pcnK8m/YqK2XEIuC9jqPvhK7P\nPGM3Lz7zTHifnZEhfSqnTYuszJEykoQppTKUUgsAbAYwC8AqADu11lYo1gOwWndbAFgHAFrrSgC7\nlFIFzvMeGxzvoThgnwczGEczGEczioqKDi0Zk5+fuknYgQPyR7xWLe95waxaDKuPUZs2si0vBx58\n0L7Otz9RIM6JNK+6yt63nsctW2QR6ZwcOf/66+F/D25lNUFqXRT1PcaOrT6yMCdHOtlPmSLHTz9t\nryt62mkynQQAfPSR9/uczdLXXht+GUyPKvbHwIBPwJNsnaSUygPwDqRJsdplnq2/Skkd5Lxfw4YN\nQ9u2bQEA9erVQ+fOnQ9VH1u/PDwOfmxJlvKk6vHChQuTqjypemxJlvKk6vHs2Qshpwpx4ACwe3cR\niori93m//FLk+UNo9v6ffVaIK68EFi4s8tSsyOuvvy6v5+Z6X2+9bi0a3a1bIV5/PfTnvfee/f7s\n7OrP4+jRRXjpJfv+WVnxjWc6HK9fDwCFyMyM/P1AEUaNAv75z+qvSyJchLFjgWHDCtGggRwDwJtv\nFqJxY+DPfy7yjJ617yfNiYXo2rVm/z0rKipCcXExgtJaG/0CcA+AWwFsAZDhOdcVwMee/U8AdPHs\nZwLY4tkfDeA2x30OXefnMzQREVVXr541B7h8nX9+fD/v22+17tLF/H0BrUeN0nrlSu/vx/raubP6\n9YDWv/2m9axZWu/Z4/86X7fcItddeKHW48dXf/2ee+x79+6t9aRJ5r7HdGXF6913o3vvO+/4f+3g\nQXn9xBPl+H//834m7r5bzj/5pNbXXivn+veXc6tWaf3HH5GXxxRP3lItn8kInqKFppRqqJTK9+zX\nAXA+gCUA5gD4q+eyoQDe8+zP8BzD8/psx/mBntGT7QAcBWBerOUjInITqynSYnVcjpfsbOD774HV\nq83f+/bbpTnS6bjjgI8/DtxUdMwxMgN63boyNUFpafDPOPlkoE4d4KSTpGly+XL7tdJSYNw4+7hZ\nM+C//43ue3Gjvn2je1+gUajWZK0//yxNjlbfM+sZadwYOPNM4Pff5Wf1zjvSlAzITPfxmK4lVjEn\nYQCaAZijlFoI4HsAM7XWH0Fqtm5WSi0HUADAWn3pRQANlVIrANzkuQ5a6yUA3oQkcB8BuN6TPVKc\n+Fa7U3QYRzMYR1OKvI4uvTS+n2YNvz/ySOlIvX177Pc8cED+sDZuXD0J+/VXoEOH6u9ZtAj45hvv\nc3XqhB7NmJEB9Oljr/F39NHS+b6oqAg9enhPFLt4sXeSRsFF8zs9ZIj36gdOzhGWF18MjB4t+6++\nCvTqJc/ed98BEybI6MZ+/ezZ75NVzH3CtNaLAZzs5/zvALr4OV8GmYrC370eBPCgv9eIiChyjz4a\n3/s750BatUqSMauDdLRKSoAmTeSPrm8SBvhfCub446ufy80NPU1FZaUkYMXF9rI0J50kk3s6Z1Uf\nMQJYsED2Fy/2/3kUu6lTQ1/jdOONwF/+AsyZYy8VBHiPlE1mJmrCKEXZHSEpFoyjGYyjKYVeR57x\nS3FTp473sW9zaDQ2b7bnabJGJVqOPz78iT9/+SX44smATHlQq5b3uoBlZfI8du4sx8XF0gz55pty\nfMIJ4X2+28Xjd7pbN+9jaxqMww6zR0oC0U+LUdOYhBERUdTatfM+ttZtjIUzCfOtCYt0+RvntBP+\n7NlTfR1BQJIyazmcVq1kG+60FwQ0bx6f+95xB9DF0cZmJWGlpcAbb9jnTzwxPp9vGpMwF2MfHDMY\nRzMYx9h9+y3g2yespvmuwReNt96yZ0v3XXw82BIwvjp1Cn397t2ShMk0FLYrr5T51qZOlX5jFme/\ns6VLpV9SPOZJS2U9eshqB/H4nc7K8m5itmpinfPh3X67dMRPBUzCiIjSxIwZiS4B8MADsd/j1Vft\ndSB9l7vxbZ4MZsSI0DVhVhI2bJj3rOsA8Mor1WvJTj1VEgGtgY4dZT1Cq6wkyssjS5YjkZnpPfnq\nccfJduxY2d56q/eI1mRnZLJWSk3sg2MG42gG4xg7Wa6lMMGliM3ixbINtKZfJElYVpb36EZ/du+2\na02uu06mNujfH7Di6JxRH5Dm0exs71GXsQ5ESDdWEtatW6Hxe2dleY9Q7dPH3k/F+RRYE0ZElCac\nzWI9esi0DTUt1r44Vuf31q3tc1YtBxB+p3wg/CTMWdvlbP4sKJA4+lIKeOwx+9jEtBzpJN41YemE\nSZiLsQ+OGYyjGYxj7KR2pgiA1M7U1DQKgwbZ+z//HNu9rEWbr7yy+mvFxcB//hP+vcJJwnbs8J74\n1eroDRThvPP8T4exbx9wzz32Z1hTV5CwkrB4/E47p6FIB0zCiIjShDU7OAAMHRr4OtNuv93cvXbv\nBkaOlL5Xvtq0iWzqgcxM6V8mzbTe9u+Xz1q3zh79CMhs+1ZStWJF6M+oqDDTDy6dxLMmzBqx6qwp\nTWVMwlyMfXDMYBzNYBxNKQQQvykC/DE5OnDRoupLEkV7/4ULZeusPdFa5vzKzZXPWbNGkjuLUlaT\namHAWr233rL3rf5kZWXRlTHdaC39+rKz4/M7bdVUHnus8VsnBJMwIqI05DuqMJ6sDtFz5sR2n7Fj\ngRde8J5uAIh+fq7LL5dtkyb2ubPOAm64wT7OzKye9Fmxe/11//f9y1+ABg1k3xpIsG5ddGVMN9aA\nBd/540x75x1gy5b4fkZNYBLmYuyDYwbjaAbjaEa9ekUAarZmpmVL4LTTgMJCSWB8RxSG6667ZOub\nFP3tb6EX4vbHmmvM6bvvvI8DLW8zZ04RBg4MfG+rdi43F2jfPvrvOd2Ulsp6jXXqxOd3+k9/Aq65\nRmrEIhmkkayYhBERpRGrX5hzLqV4a9IEmDdP9rOzY2+e9O1PpFR0awE2axb6mk2bIr8vAJx9tj2N\nxooVwDHHRH+vdLJ3b+SrGkSiYUPg2Wfjd/+apnQKTqyhlNKpWG4ionjKyQF27ZJZ3i+7rHqzXk2o\nW1eSEX+jCkOxmgH79ZPmJhOse37xBXD66RIT31rCaP6clJXJupOHH25/xpAhkS9AnW4WL5bRsr/8\nkuiSJBelFLTW1ToJcLJWIqI0UVkpfZyuvTZxZTBREzZ+vJmyOJ1zjoxitBKw996TdSPPOiu6++Xk\nVJ841sTi5amutDS+NWHphs2RLsY+OGYwjmYwjrGpqpKvr78uSmg5ok3CrObGLVuADh3Mlsny22+y\n3bwZuOQS4IorgLZt/V8bzfPIJMy7OZK/06ExCSMiSgP790tn5ZocFenPjh3S9BeJ8nJ7UWbTTajO\nOcKWL5f5pZyjJU1yriF5wgmJ/1kkQmlpZHO5uR37hBERpYE//pBReoleQsdKPCL5J3r9envC1Hj8\n0963ryxu3rmzNEeaXnB7yRJ7IWlAvodo4pAOHnsM+OGHwNN7uFWgPmGsCSMiSgP798u0AKkomukn\nIvHuu9JhfuFCu0nSpGOPZUd0yy23AKtXJ7oUqYNJmIuxvd4MxtEMxjE2VhKWinG0JviMF6WAWrUi\ne0+kcXT2LQu23NHOnVIeazb/4mLg998jK1uyOnhQttZKA6n4LNY0JmFERGngwAHn4tOpZcyY+H9G\npElYpA47DBgwQPY7dAAyMiTZ8m2OvPpq2Z50kmwHDZI5x6KhNXDhhckzIGD5ctlOm5bYcqQS9gkj\nIkoD8+YBI0ZIf5xEiqYvlLMDe7z+af/gA6BPH2DYMOCll+LzGVu2VO/0/+CDwOjR9vGQIcD//if7\nu3bZqwNE831//z3QtassOj5rVnRlNum004D5893XDy4c7BNGRJTGkq1PWKDFry2bNwOTJ3ufO+WU\n+JUnyzMr5hNPxO8z/C2jc/vt3sfOn5Hztf/8J/LPW7dOpgRxjgBNlOefl5GnkyYluiSphUmYi7G9\n3gzG0QzGMTZjxgBffZX4OFpLBf30U/DrJk2qvibk/PnxK9fatbINdyLRaOKoFFC/vuxnBZgK/bnn\n7Gtq1QLy8mT/xhsjr0Havl2WS5o9W0bHJsqGDbKe49tve9cEJvpZTAVMwoiI0sCcOYkugbD6Xu3Z\nE/w6qzN+TU2pcfXVwDPPyIoC8dS3r2yzsoA//9n7NWt9zW+/le3jjwO7d9uvO/fDsX27PbVHw4aR\nl9WE8nJg1Cj7OJo1Pt2MSZiLFRYWJroIaYFxNINxjM2llwJXXpn4OFo1QKEWz7ZqwCZMkG1JSfzK\nBEhH+UiWc4o2js88I9vsbGDwYNlfvhxYtQro0kWOjzkGuOAC2R80COjeXfZvvTX8z1m6VJoznQMO\nbr7Z3n/gAWDBgqi+hYjUrQv83//J8wd4J2GJfhZTAZMwIqI0cPjhwHnnJboUwKefyqSoK1cCH38c\n+DprOgOrQ3njxvEvW03IyZEYfPSR1Iq1awds3Qocf7z3dQcOyPahh4DPPpP9F14If8knawRihuOv\n+MSJsi0uBu6+u3p/tHiwfo6nnirbeNc0phsmYS7G9nozGEczGMfY7NkjiVii43jEEbIo9rhxQK9e\nga+zko1kGkzgFEscL7jAnnbiuOMkCbOWZbJqrhYtkm2DBt7v3bQpvM+47z7Z3nhj9dfef1+2NdE0\neMwxsu3VC+jRQ37+lkQ/i6mASRgRoaoqukWXKXns3i1JWDI4/PDQNSLWsjYNGgD33x//MiVKw4bS\ncd1iDT7YtQto2tQeKLBnj8wd5rw2kG3b7P1zzgG++84+3rLFXrvT3+CAf/5Tasos5eXRz+u1ZYvM\nUVZcDJx4IjBzpt1HjcITYPwGuQHb681IhzhafzA//zxxTVrpEMdE2rNHRtqdcUZhoouCww+XJAOQ\nBD8jyH/3t25Nzs7cpp7HzEw7Kbr2WrtZ8uef7ZGRgPStysmRTu5WB/5AtmyRrTXxa9eu9mvO0YnW\nz8BpwgRZZP3FF2WppbIyYOBAmR7kqKPC/762b7c/y5rrzBd/p0NjTRiRy113nb0/dGjiykGxsZoj\nk4GzHFu3+r+mXj3ZLlyYujP9h+PFF4G33pJ95+jHE07wXuoIAObOlcl2KyuD3/OOO2T7/PP2Od+5\nwo4+2n8SZpUJkElerbnZli4N/pm+nM2o1s+SIsckzMXYXm9GqsZx926ZJsAazQUA69dLk0IipGoc\nk0Wy9AkDvJOwQM1rVl8iIDn7hcUjjo8+Gvz1q66S7S23BO8ecNZZQPPm3ueUAp56yj7+5BNg48bg\nn+ccWRluX7RIJMOzmOyYhBG5VH6+3R9l/Hj7/EUXJaY8FL0pU2T29GSsCQuUCOzda+8nYxJmypdf\n2vuhpu2waqieeCL4Wpf/+pf/2idrSozu3YEWLeSZsAYAAPZIRkCaEq0JbC+7LLy+aL6mTeMSRbFi\nEuZibK83Ix3i+K9/yR8L35FaNSkd4pgo338v28MPT444OpOwQDO5W1M0AOndJ+xPf5KRouGqW9fe\n37hR5hfT2u4nZjU7WrVmvu/VWqa8yM6Wc3ffbb++Y4cMFOjbF/jLX+zz3btLwmbp1g1Ys0b2P/+8\nelOn1pIk9ukT/HtJhmcx2TEJI0oDJSXV/0f68MPAjz/KP8hKeS8P41w/r3Nn2f7pT6E7BFNysvr+\nWH94E82ZhAVqVisrs/d959BKN7ffHn6NkbPfWIsW0lm+b1+Z6HXKFKCiQn7Ot9wS+l533WX3+aqq\nklUVtm0D3n0X6N3bvq55c+DVV2Vut4oKWf7q2WeltvL8871HXwJyPjs7vWswawqTMBdje70ZiY7j\nvn0y1P2cc+xzFRXAbbfJfEVffSXnNm+2X7/pJnvfqkUBZGJJpUJ3DI6HRMcxle3cae8nQxytJKx2\nbbsJrKIC+Ppr+xpnTZhzbqlkkag4KiWd6p2seb+GD5c4hvv7WaeOPT/Z8OEyO7/F2e0gJ0eS4uOO\nk0QPkCknrKbkZctkW1UlAy22bQtvmaRkeBaTXcxJmFKqpVJqtlJqiVJqsVLqRs/5+kqpT5VSy5RS\nM5VS+Y73TFJKrVBKLVRKdXacH6qUWu55z5BYy0bkBtYcP9Z6dICMsAKk+cGag2nFCtlaNRMjRtjN\nChalpGnI+oebUkOTJvYyOMnASsIOHLCb4t59V2pbLc6aMPK2dKn9nydf99xTvXkwkDp1ZCqM668H\nXn5Zzr3xhv36Tz/Jf9asJLh9e3u6iYEDgQ8/lP0xY2Q7frysbDB+PH9+xmitY/oC0BRAZ89+XQDL\nABwDYDyAf3nO3wbgIc9+TwAfeva7AJjr2a8PYBWAfAD1rP0An6mJSBx3nNaAbC0ffijnfL+01nrr\nVtlft87//Ro10nrz5viXm8wBtL7rrkSXwrZtW/XnbupUe19rrWvV0nrVKq0XL05MGZPdr7/a8Wvc\nWOtzzpH9bt284xjM009X/zeguNj/tbfc4v/fDOvr7LP9/3tC4fHkLdXymZhrwrTWm7XWCz37ewH8\nBmwH1nsAACAASURBVKAlgL4Apnoum+o5hmf7suf67wHkK6WaALgQwKda611a650APgXAcVpEQZSV\nAb/+Kgs3W30/AGlGOP98aaJ0TuS4bZv87xcAWrb0f8/DDpMmTkoNVrPeSSclthxOvqM0Z86056Ar\nLZWanIMHpfm7U6eaL18qOPZYe3/LFqCoSOL65ZfAP/4R3j18+2ytWAG0aeP/WmtkJeB/tQNnUzIA\nLFkSXhkoOKN9wpRSbQF0BjAXQBOtdQkgiRoAa3nWFgAc4zCw3nPO9/wGzzmKE7bXm5HIOFpNjUce\naTc3AJKEdeki/3B/953MITVwIDB9OnDhhcHvmZvr3Ym/pvB5jM6uXUCjRsCll8pxMsTRd3oFZ/+j\n3r1l6RxAmr+TVTLE0bcz/549sg13NKlvEhZsRvwjj7T3nYMpnP0NjzsOuOYaSfw7dgz9+ckQw2Rn\nbNkipVRdANMBjNJa71VKBRoL4vtrpwBoP+fhOe/XsGHD0NYz3XC9evXQuXPnQ8NhrR88j4MfW5Kl\nPKl6vHDhwoR8/umnF2LcOCAvr8gztUQhXnwRaNasCPfeCzz1lPf1p5xS6OkrVuT5n7T/+y9ZUoQ7\n7gBmzKjZ78eS6J9nqh3PnFmEnBzA+nkm6nms/vMsxHHHAb/+ah8DwPz5RZBLElu+VHkep08v8vTR\nLMTBg0CtWkVYvFiOQ71fViKwvp/Q119yCaB1Eb74AjjnnEJ88QWwYEERHngA6NGjECedBHz9dRG+\n+y7xP59kP7b2i50LdfqhtIGZ1pRSWQA+APCx1voJz7nfABRqrUuUUk0BzNFad1RKPePZn+a5bimA\ncwCc67n+/3nOe13n83naRLmJUtn330tTY2WljFhq2hS45BLZPvecLKrrbHq4/37g3ntlP9ivj1U7\nUVWV3DUVJObOlfUGnaNck4FSwOWXA6+95n2+SROZUmXtWi72HA2l5Pc6xN92ADIYon9/2X/ySemg\nT4mhlILWutq/qBmG7j8ZwBIrAfOYAWCYZ38YgPcc54d4CtUVwE5Ps+VMABcopfKVUvUBXOA5R0QO\nZWXyD3HXrjJ/UEYGPDUh0jT13HPSn8S378ePP0b2OaGWWKHksHMnUL9+okvhn9V85lRSItsW7GwS\nlW+/tft1hnLWWTKacdkyJmDJKuYkTCl1FoArAJynlFqglPpJKXURZHTkBUqpZQC6A3gIALTWHwH4\nXSm1EsCzAK73nN8B4N8A5gP4HsAYTwd9ihPfaneKTk3H8b777P2VK2Vr9RH54gvZOmfdttx5p2yd\nfT+CsRYJril8HqOzY4f3EjbJFMdg6xFmmKoCiJNkiqPTGWcABQXhXduokSS9HTrEt0yBJGsMk0nM\nfcK01t8A8DOWAgBwfoD3jAxwfgqAKbGWiSidPfSQvb96tWxr1QKefhq47jo59rdMyumny9p0vgv/\n+mreXDr2//WvZspL8XX55TLKMBn16wfMn5/oUhAlLyN9wmoa+4SRmyklIx+nTZMmnSzPf6UqK2V/\n377YlhOZNQvo0UP2X3kFuOKK2MtM8VFVZU8nkGz/JO7aJTWyF1wg0xmUlEjNzNat8nqylZconuLd\nJ4yI4uxf/wI++UT+sH3yifT5ynLUZWdmyh/lWNdzu+ACYPZs2b/yytjuRfFldca3fl7JJD9fnsna\ntWV+ukmTgE8/ldfOOCOxZSNKFkzCXIzt9WbURBzvvx945BGgZ09ZPDcvz/91pkYzWh39Ae8FheOJ\nz2PkDh4ETjgBOPdc+1yyxbF2baml7djRXiz+wQcTW6ZwJFscUxFjGBqTMKIkV1VlTy1hiXen5jPP\ntPdffTW+n0XRqayURZmTfeZymavKHizyzjsyao+ImIS5mjW5HMUm3nH8/Xd7vyaH9VdWynb5cvvc\n1q3xW9KoJp7H8eODj9hLJaecIs/GkCHe55Pt99qapuKww2Tbr593M3qySrY4piLGMDQmYURJbuNG\n2T78sJ0Y1YSMDOmYv2iR3Ym6cWPgmGNqrgwm/fe/wOjR1ScPTQWlpd7Jr9Z2cuxZOCRpffCBbP1N\nm0LkdkzCXIzt9WaEE8eJE2U2+0jdcAPQrRvQqxdw660yUWtN6tVLOn3/5z/2uXXrAl/vVF4ufdQ+\n/DC86+P1PFZWShJzww1y7JxTK1XUrWvXJFnH+/cDf/wB3H6797XJ+nsd64CRmpascUwljGFoKVAp\nTJT6pk4Ffv458vf997+yLShIzBJC1kzso0bZzaLt24f3XmsOs969ZXScrG9Z8y64AJgzxz5esCAx\n5YjVCSfY+1atWLiTdiYD1oQRVceaMBdje70Z4cSxvNz7WOvQTYuTJtn7jRrJ9uOP7eadmvb447Jd\nsUJqYJy++QZYvFhqnCzOUZWLFgH//Cdw4EDg+8fjeZw3z07AcnKk1ujJJ41/TNxMmCDL1AASw1Wr\n7KbhWbP8vyfZfq8rKmRwR6olYckWx1TEGIbGJIyoBuzYIduqKmlGGj48dOfkUaPsfasWqUsX4OKL\n41PGcFxzjWxnzLDP7dwJnH221NTUrSt91wCprTn7bNk/7zxJKGq6SWr9enu/rEyaV4Ga7VsXrfvv\nl8TVOZLwjjvsyXO7d09MuSKVmSmz+hNRdUzCXIzt9WaEE0drRN5jj8k6j1OnyvG2beF9RrLUIhx3\nnGyvuso+Z30vlttuA4qLJQmrW1f6w1mCLTTtL467d8c2s3qmz4JqZ58NHH6494jPZOU7LQkgnfBf\nf132AzVP8/faDMYxdoxhaEzCiGqQb38kq9bIn+xsez+RS7zcdpu976zJsgYJ+Gti/PFHScJyc4Gb\nbgJuvFFGVu7YIQMMwpWfLyM0o/H22zIdAiDrF77zjuwffbQ903yqGDZMFm6fOVOOrZnniSi1ce1I\nohoQrFO9v0e5vFz6MGkNNG0qCUTXrvErXzDff29/9pYtMtv5xInSN+yoo4CnnpKpH/bsAfr3lyk1\nzj4bOPFESRb+9z/7XlYcwv31VUqSQOei5eFyxtz5eeedJ/3EPv4YuOiiyO9bU5zlf/BBmW2+Z085\n5j9/RKmFa0cSJaFAM4evXg20awd89JE0ZSYqAQPsJZKmTZMBAo89JsvkFBfL+dJSu6/Y88/L+Ucf\ntWvCnBYtku2vv4b/+QcPSif0Tp2qDwgIxyefeB9bHd179gSmTIn8fjUlM1OeAUASx5YtZd9ZQ0pE\nqY1JmIuxvd6MSOOYnw8ccYR0sJ43z57KwVJSInNzHX20XfORSIcfLts//9k+17atPWXF3r0yh5XW\nMoCgpETO//xz9STs+OMlifvrX6Wm5+237dd842g1d27ZAvToIYlbw4Yy2q6qKrxy79gBXHih9/ld\nu+z9Rx4JfZ9Eyc4GHnhA9tu3t5OwhQuDv4+/12YwjrFjDENjEhZnXbrIKCdyN+cEoTt3ylQDr7wi\nzY5HHgk8/bT9eufOwPXXhz8fV7xZgwKc61W2aQOsWSOjD++/364VA+wZ6Z9+WhI0X8cfD/z2m+x/\n/XX11/fvB957D/juOzn2XbsyOzv0aLuyMumrlp9f/bWcHHuE4cknB79PolRUSA3geefJcZ068r08\n/7wshE1E6YF9wuIs0j4wFB6tgc8+k4k4U0Gg/knW+QsvtJvNrHNjxgD33FMz5Qumqgo45xzgq6/s\nc888A7z1ltTYAcB110nfMEtGhv19+j77u3fbydHdd9v/SdEaeOONwAnWihV2Ytq8uSSBgab5CPV7\nV1Ehydxf/wq8+ab/axJp505JdHftkhpHq1mSiFIT+4RRWnnmGWmi8lfTkmysaSiuv776a3Pnynbm\nTHvkmyVZlnnJyPBOwABpFrQSsPfeq17ba01l8dFH1e+XlyfNhEceCWzebJ9v1qx6AuZMso86yt7f\nuDG2vlFZWdI5f/bs8Jo2a9qGDTKaFGACRpTOmIS5WCq311sJwMGDiS0HEDqOBw/KCMe77pLO7U7O\nxbAvugjYutU+tvoAJSNrBv9mzWRNzIYNvV8/9ljZBurTVq8ecOed0rxm1VaVlBQBAG65xb5uwgRJ\n1FaulOOtW70nYHXOym+xOttbHfADqV9fOvo/+aT8XKxk7I47ErNElNPcudKVIRqp/HudTBjH2DGG\noTEJo5Rkdf6u6QWto3HwoIwUbNYMGDDA+7W8PKlJsli1H0D1DuXJxEq6rElofb34onc/MX+s6SEW\nLJAljwBJPCZMkPcOHy5JapMmUmtmfW6LFnbilp8PLFvmfd/hw2XbvHnwz7fWYrzxRmDgQLuf2oMP\nyjaRXQic04IQUfpin7AwjBwJ1K4tfxwixT5h8dG2rfQJ+v132TflkUeAf/1LakVM1YbceScwblzw\nZ+D55+1pHizJ/MyUlEjtHhBbOZ0xbttWpqJwNjsGM3Qo8PLL0k/sl1+AWrUkKa9dW2pKzz03ss+f\nOhUYMsQ+d955wOefh/2tGFNeLt/LBx8kdokqIjKHfcJi8OST0gcpUsn8RzTVWbUc+/aZve8LL8g2\nw+Bvxs8/h77m73+X0Xy1aslx//7mPj8erLUsTSourj6lRTAvvABceaV02M/JkeTJ6nd3zjnh3cM5\nh9jQobIm5s03y7HV5O1sIq4JK1bINh4xJqLkwiQsTLVrR/4eq4nFadw4GVWWDFK1vf7gQXv6gk6d\nzDVJLljgvabgqlXhvS9UHE8/Pbz75OTYCUC0S/XUlFCLj0en6FBfs3BkZ1efbHXyZNmGm0SffroM\nBjjzTDmeO1emyBg0COjQQX6HGzeu2bUmv/xSFueOtjkyVX+vkw3jGDvGMDQmYWGKZqTac89VP3fn\nndX7BVFkliyx97WWDu+x2ru3+pxRRx1lpjbzwAHg3/8O71oreYikRiiRRoyI7f1r19rTMQCRj3jM\nzARuuMH7XLNm4b+/fn2Zh8yq9XrwQekb1rmzJF5Wv7Fwf34mXHddYppBiajmMQkLUzT/83eOGKus\nNFcWUwoLCxNdhKi8+KL3sTUFRLQWLrRnhZ8yRZqkLEOHhn5/qDiWlEjn8nDcdpvUhKQCrYH//je2\ne7RqJZ3r16wBgMKo7jFpkpTFmqcsmhqknBx7IEFRkXT+d3rlFe/pNCorpZnZdJcD635W8heNVP29\nTjaMY+wYw9CYhIUpmiSqQwegdWvZLyuTuY0odqWl3kvoTJnivfxNpE46yd4fMgTo08euifrf//zX\nSkTys5w8WZb1CUdeHvCnP4V/73Txww/AF1/Edo/rrpPtkCHRvb9NG7vpuKDAPm+tZuCsYbvoIqkt\n8x2ZGa0PPpA+bdb8aqGWJiKi9MAkLEzRTOhYWmov+VJWJsu1APK/7mSQqu31L70kCa5zNnlnUhYp\na06rTZvskXGlpXbt1dKl3te/957UlHz6qRwHi6O1YHWyTLyarE49FaiqKor5PloD/fpF/35r8lyt\n5Xd+yhQZNNGpk5zfs0fmLPvsMzm2lmiKxSuvSOIPSFNoXl5sI3NT9fc62TCOsWMMQ2MSFqZoasJK\nS+1lVjp3lqVSgNSY2yrRduyQDtK+rNiddhrwzTf2+Q4d7P1582Q2fX+qquTrhx9kVnJA/uAtWGBP\nuWDZvBkYNkwGZWzcKH3Rnn3WHlF3552hvw+rpsRZ20bJy0p+srJkf+hQ6XdmJV15efbv9ODB0oct\nlhq8ffvkPk47d0Z/PyJKLXEZ45SOnP1BwlVaKrUs770nHZAtybIwczK31xcUAJddJmsJOr38smzP\nOst72pDVq+1950zjTz0lzUjWlA/Nm9sTvQKSGK9a5Z3EOTVoILOq+/YR6tMHeP99Sc6DxXH/flks\n2uRcZukqWZ7HkhJUG6Xp26fvrbek1sqqjV27Vvq3BaK1/9otq6n74EFg+nT5z0Gs89MlSxxTHeMY\nO8YwNNaERaCyUtabC9fevf77ApWXmytTOvM3G/vjj8u2cWNZePmOO+zjfv2Ajz/2vn7ECGDUKPvY\nSsCs2doLC6WpMNBoxNxc4Mcfq58fO1a2gfpvLVggf3h37JAReJQ6Gjf2nwhZNdmAdJw/4gj7eN26\nwPebPVtGvfqrTb/kEtlmZ8u0GI89Fl2ZiSg1MQmLwDnnyCza4SZRe/bYo+6ciouTYyLXZG+vz8+v\nfm7JEnsW8fx8YMwYaSrauFFqHHv1sq/9/+3deZxbdb3/8dcnyXSZ6UpZZWlZugHKThHRlrII+AME\nvYjiTxYVuFxAZXkAv6si4kUEBARBELkIKMtFBEFQFm2hLEIREEtXuIDsULov084kn98f33OSTGZJ\nZnKmyXTez8djHsk5OSfzzWdOMp9817j28s03w7xLxf8o4+TtiSdCotSZW28NyV6cpN12W5gSIe7f\n9/TTMG3a9DbnvPFGmO5i5kwlYd1R79djOg0//Wm4P2BAIZGHjgeGrFkTkrn99gvbpetcxv1MTz01\n2XLWexz7CsWxeopheUrCuiHug9TVt95iH3zQdi3AMWMK8ypVOqN3f1ZaGxEnrj/5SWFfJlNIiEod\nfXTh/l//WhipCqEGK56Z/FOf6rwMV1wRbletghkzQm3FVVeFfX/8Y7idOrVQMzZjRqHpcdIkJWHr\nmzPOCMn16NFtZ7SPk7NiP/tZ2+077ghfzGLnnx9ur746+XKKSN+gJKwC8VIysW23bds00Zni9fUg\nfBOOO4zPmJFc+Xqq3tvrSyfufO21cBuPOI111JS4xx6hj86YMYWFmSEkUfHs5/FkrE880XkZivsG\nlSZrheRvCt/9bmhu+sxn2h5TPEJWulbv12Ns990LXxBaWwvzk02fHrbjLwvFST+EJZWGDSts/+hH\nvVO+vhLHeqc4Vk8xLE9JWAX22CPc3n13YV/xN9rOvPde2w69y5drqoLuKJ0g9+WXw23pkjRxknP8\n8eF2441D8vub34Sm3623LhwbL/hcqeIEr7Rmbqut2vY3++EPw+2VVxZGRd54o/7m67N0OsxPtu22\nYZqUhoZwfc6cGeb6Ou20UHP+7W8Xzvntb+E73wn3NR+YSP+mJKwC6XS4/eCDwr7S/h2lsll4662Q\nhMVD0FtaQmLx1FNtR/DVSr2218cdmEuTrdmzQzNuZ6PQDjwQnnsu9Mm6//6w7ytfCfOy/elP4e/X\n3ZUP4iRs7dqOH7/kErj66ul87nNw6aVhIMZpp7VN9Hqy7mh/VK/XYyVOPRUWLSpsT5sWms3ffx+2\n2CI0a8c1ZF/9akjUv/Ut2Gmn5MvSl+NYTxTH6imG5SWShJnZjWb2vpm9VLRvpJk9bGbzzOwhMxte\n9NhVZrbAzF40s52L9h9rZvOjc3o473Xy4iTskEMK6xSWqwk7+eTQj2j48FD7cvjhYX9DQ/hH/cwz\nlS/s3N/E84OVDoC4/HL44hc7Puexx8Jju+3WNum56aZwe9BB7acdqETcJNrZmoYDBoSJPEeODOV+\n+OGQPJoVas5UE7b+K+1cH3dXiGtHOzJ+fO+VR0T6hqTmCbsJuBq4pWjfucCj7n6JmZ0DnAeca2YH\nA9u6+1gzmwRcB+xlZiOB7wO7Agb83cz+4O5LEypjj8VJ2JZbFmpGytWExbOsx+fG/4gzmcJzzJyZ\nbDm7q17b61euDLeltU8ffND5OpGlfbFGjgyd4kv783XXlluW7zg9ZcoU0umQfO29d2H/8uWhqbTa\neZ/6i3q9HiuRyYQa1zVrwrx011wTprEoTbRWrQqfBR99BCNG9E5Z+nIc64niWD3FsLxEasLc/Qmg\ndKD/4cDN0f2bo+14/y3Rec8Aw81sE+CzwMPuvtTdlwAPAwdRB8zggQfCbVzLUjz5akeKR05BYdLR\nTKbt3GHXXBNuFy4sLCBcqpr16W67Dc49t+fnr2uvvhr+UUHbJOyjj8LteedV9jznnQdnn119eRoa\nKptC4NOfhptvbruvqSlMQ1A8bYasv/7wh3D7H/8Rpkx56aX2x8RfxkaNKnxBE5H+qzf7hG3s7u8D\nuPt7QDxZw+ZA8SQPb0X7Sve/He2rOfdCX6J4RNz113d9TkdL7kChOTIW/4M/9NC2Hchjs2fDhAnd\nK2+xY45pO6VDsXprr1+4MIxYjCfBLE7CTjop3Fa67ubZZ4f+WutCV3FULVjl6u167K6do44Vp50W\nbuP57Na1vh7HeqE4Vk8xLK8WyxaV/lsywDvYT7S/Q8cddxxjogmZRowYwc4775yv+oz/8EltL1o0\nnZdeggMPnML++8Pxx0/n3nsBOj8/JGFtH4cpZDIwc2ZhG+Dhh6dHNWvtn2+HHQCm8/DD4ff3pPww\nnenT2z8eSzpePd1ubCyUF2DFirA9bdp0nn8eDj64tuXrbPvFaIhbvZSnr27H6qU8PdluaYEnnpjO\nXXfBEUfUpjy6HpPZjtVLebTdt7bj+6931sQVMU9o6nYzGw3c7+6fiLbnAFPc/X0z2xSY5u4Tzey6\n6P6d0XFzgcnAvtHxJ0f72xxX8rs8qXJXYr/9QvPW/vuH7SefhDPPhL/9rfNzTj019AeJvxXHNSLz\n5oV1CuOmzebmMGloPJqu9GXF5/3v/3ZcU1ZOfH5nSyjVk5tughNOaLvPHaZMCR3vt9++ME2FiIhI\nX2FmuHu7yqZUkr+DtrVZ9wHHRfePA/5QtP9rUaH2ApZEzZYPAQeY2fCok/4B0b6aK118Nx7d+Pjj\nnZ+zYkXbJYvi84uX4jnpJNhnH3j77cK+jvqJDB1a+Sz9nYk7u9ez0gQM4J57QgIGbSe6FBER6esS\nScLM7DbgKWCcmf3LzI4HLiYkVfOA/aJt3P1B4DUzewW4Hjgl2r8YuBB4DngGuCDqoF9zpUlYnChd\nd13n58Qj42JxDU48eeuhh8IBB8AGG4RRfFOnhv3xenIQOqgPGhQ6dlebhL3xRvt9pdXu9eTGG8Pt\nkUcW9tVr/6p6jmNfojgmQ3FMhuJYPcWwvET6hLn7Vzp5aP9Oju9wvJm7/xr4dRJlSlJpEhZ30u9q\n/qfSxbsnTmzb1HjffeH2rrvCJI/xYtMQfteMGWHKg+bmMNnjAw+ESR672wrb2BiSualTK5vlP3b/\n/WGC03hJlqR98EFoht1ss0JH/BEj4KGHwmsunpD1G9+AX/2qZ/N8iYiI1KvE+oStS+u6T9iUKWGx\n3X33DdsLFoR+XdB5UrT33nDZZW3njerI5z4HDz7Yfn9DQ5h89Pbbw++++26YNSvMJp+qsP7SPdTa\nxWXsTsg+85mQCPZWmIuTWvcwe/hVV4WawPix+LalJQz5Hz68bXOuiIhIX7Au+oStt0prwiqxZEll\nCUNxAvbUU2GR6vvuC4nHq6/CsceGpGvWrHBMXINWibVrC7V23/hG5edBKH9vKl6TMZcLCdj48W3j\nfMUVYdLbTCas06gETERE1idKwipQmoTFS5JA2z5cxRYtCv29yvnNbwr3P/lJGDMm9BcDePbZMOHj\n+ecXjjniiIqLzerVocn0Bz8IzX6lumqv782KRvfChKxQWF2gdGb6b3+7byzton4PyVAck6E4JkNx\nrJ5iWJ6SsAqUJmHFCcrSkkWVTjwx1G4tXlxZEnbMMeF2iy06fvyee8LyJ8VWrQrLJpUb8RgnYQ0N\n7ddhLKfSJs/uWr688NwNDSFGYS60whQgIiIi/YGSsAp01jE/k4FHH2177A03wFlnwaabVj67+/bb\nt6/h2mWXcDt6NEyaFO4/8ki4bWoKTXPllsNZvTqMrhw4MIyOLB6tCcWTubbX0VQZzz0Hb70F06Z1\n/Xu7UlyrN3t225UF6nX0YzldxVEqpzgmQ3FMhuJYPcWwvFrMmN/nlCZh48bBP/4BO+0ERx3Vvulu\nzpzuPX/c36vY/ffD+++H+1dfDRdf3H6erGhi7E69/nqoCRs2LBzbnbnCOqoJ22OPwv0FC+DWW2H3\n3QvNp5W44opwu9tuYYmi3/627TQUIiIi/YVqwirQUcf84ibC2bPbn/O971X+/Gbtn3/zzWHXXcP9\ngQNDs10mE0ZJxsnQsmWdP+ef/xxm+p87N5wfJ4aXXBJGWELX7fXlmiNPOAF++EM47DBYs6brY995\nJ0xKGzvqKPjLX8L9I44IAxCefrrr56hn6veQDMUxGYpjMhTH6imG5SkJq0C50ZF77tl+30EH9U5Z\njjyybWf+FSs6Pu7sswv3i8t+zjmVTfy6YEG43XnnkESVvv4ZMwr3X3ih6+d6/PGw1NNPfwoDBoTl\niYpHOm6zDey1V/kyiYiIrE+UhFWgXBK2cmX7psHRo3uvPPEcZRBqozpS3MRZPGkshDUo33wTJk+e\n0unviNZG5x//gOefL+x/990wGGHPPcO0ETvuCOee23V542bQs84K02Z0NcltX6R+D8lQHJOhOCZD\ncayeYliekrAKVDJP2DHHhAQjtvnmvV+mW26BSy/t+rhp0zpeuHurreDKK9s/p1lIuiZNgtNPD/vj\n45YuDQMOhg0La2e+8QacfHJhbceuylqsr3bAFxERSZKSsAp0lYTFIw6XLl33i2Qfckio5epqTq8p\nUwqjKwGuv76QlJ1xxvT8PGdr1xaaGHfbLcx/Fk8d8Ze/hD5iHS2gfcop4fbggzsvQ7k+Y32d+j0k\nQ3FMhuKYDMWxeopheUrCqvCzn8Hll8N554WEpXi6hXVh1KiQUF15Zed9w6Bt8nTiiYXpL6DQYX+z\nzWDy5ML+tWtD4vVv/xa2OxrBCSE53WmnMBDgww87Pubhh+ErXwnLMMULmIuIiPR3WjuyAjvuCLfd\n1n7S1Nizz4YaobvvDn2l5s6FkSPXTdn22CPM35VOhxqneH6vww4L5b7oorD92GNhVOTUqWHx7Hff\nhQsvDIncRRfBhhuG4669NnTe33FH+OY3w7JJ++4bmjU7GzHpHh475pjCoIEnnwwTzV52WXjsssvg\njDN6NxYiIiL1SGtHVuHDD2GjjTp/fPBgaG4Os9I3Na27BAxCAgYhwfr97wv7hw+HiRML25MnhwQM\nYOONQ+3VmDHwy1/CBRcUjjvssDCr/dNPh9eUSoUErqspK8zga18Lc37FjjoqjIY86KCQpB17GzsB\nBAAAGxlJREFUbNUvVUREZL2iJKyMXC6sAxnXFHUkm4WXXw5rSjY0rLuyFfvsZ0PiE2ttLczs35lJ\nk6YDhTUbL7kkDCi45JKwfdxxlf/++DnM4O23w7QWAA89FGoQR42q/Ln6GvV7SIbimAzFMRmKY/UU\nw/KUhJWxZEnofN9VchWvEbl69bpPwvbbD7773dDfCgq1Yc3NYcmirmy0UdvpK846K9yefXaoverO\nVBLF/c622CKsA9ncHDr1P/ts5c8jIiLSX6hPWBmzZoXO6eWWIjILtUE33lh+8tLeEo/gdA+jFU8/\nvetRixBq8WbMCJ31iydQ7YnVq6GxMdy/9Vb46lerez4REZH1QWd9wrR2ZBmvvgrbblvZsaed1nZ9\nxXVtzJiwXuSHH4aEqJKarHQ6TGORhMGDQ/Ot5gETEREpT82RZcycGWaYr1St+oRBGJW54YZw++2V\nNUf2Rnt9f0zA1O8hGYpjMhTHZCiO1VMMy1MS1gV3+K//CnNoVaqWSdjAgaFZ8VvfCpPHrm/LA4mI\niKxP1CesC7feGqZeqKSJbf58GD8e9tmn7eLW69p//zd8/evh/ty5oUwiIiJSO531CVMS1olcrjDx\naSW/atmyQsf2Wof0yCPDRKlvvBHWiBQREZHa0WStFVq4MIzqGz4cttkGFi+u7Lxhw+DBB0PiU2sL\nF4bbcs2Raq9PhuKYDMUxGYpjMhTH6imG5SkJKxHPtzV/fhgZOWJE5ecefHB91DzFywOV65gvIiIi\ntaPmyCJLl4bpKGbNgk03Tfzp15l4LceWlvKz5ouIiEjvUnNkF/71L/jSl2D06DAD/Sab1LpE1TEL\nfdqUgImIiNSvfp+EPfQQ7LxzGEX46qtw553rx1xXlbwGtdcnQ3FMhuKYDMUxGYpj9RTD8vp1Xcm9\n98Ipp4SRhJMn17o0IiIi0p/02z5hjz4KX/5yGNFYy6WGREREZP2mPmFFnnkmJGC/+50SMBEREamN\nfpOELVkC3/kOXHMNHHoo3HyzmiDVXp8MxTEZimMyFMdkKI7VUwzL6xd9whYuhEMOCROwzpoFv/xl\n2BYRERGplT7bJ+zdd50NN4RFi2Djjdsfk8uFZscPPww1YEcfDT/60fox8lFERET6jvVu7cgRI5yl\nS2HIkDC/18EHw3bbwUEHhcTszDPh9ddhww3h3HPhiCNqXWoRERHpj9a7jvmLF0M2G/p6XXttWLtx\nxgzYddeQiB15JMyZE2rDlIB1TO31yVAck6E4JkNxTIbiWD3FsLy66xNmZgcBVxISxBvd/SedHxt+\nPv3p8AMhMXPXbPEiIiJS3+qqOdLMUsB8YD/gHWAmcLS7zy05rlfWjhQRERFJWl9pjtwTWODub7h7\nC3AHcHiNyyQiIiKSuHprtNsceLNo+y1CYtbOi++9WPGT9qTWzOneOe6O4+Q81+bHPewDaGxoJJ1K\ntzvXaD9k00qGcWZSGdydrGfJ5rLkPEfWs/nfW1yO4vK3ZFtY2bKSxoZGBmUGYRgpS2FmvPD0C+y+\n9+6YRfuKHlvSvIRVLasYMmAImVT5yySTytCQaqAh3dAuJnEMcp7rMEbxTzaXpbm1mbXZtbTmWhk6\ncCibDtmUpoYm1mTX0NzanP9Zm10LwMD0QFJW/XeJOF7FMSi+Xxqf4tf43FPPsfveu7d5nvix0uev\n5LGGVAONDY00pBtozbW2+YmvlebWZrKezV9jcRnjcuZj6tn886YtTSaVwczy8Y6fI5sLx8Wvu9Lb\n+Jy12bX5391RXIvF5RiQHsCgzCDSqTRpS/P0E0+z814709zazJrWNW2uFyD/+op/GlINDEgPaPe+\n6ug931FZOjou61lasi2hrKl0/ncBvLP8nXZ/K8cZmB5IJpXJHxufV/x+7Oh+R+UqLVPxe6b4PRXv\nb8m20JJryd+umL+CSZ+a1O49V/o+BGjNtbI2uzb/96/k+o3fB8Wvs9xP8XVRXP7S22wu2+6az3qW\nAekBNKQKny1dKf3sbPNYB5+1QP59UlzeZ598ll0+uUv+vHLvgY4ey6QyOM6a1jWsza7N/8S/r/jY\nlKUYlBlEJpUhnUrnP1MzqQwtuZYQi6LP/tZcK6tbVtPc2hzeC+mG/GdwfF5DuiG8xyxN1rP5z9aW\nbAuOk7Z0u79DfH109jfq6Drs6PzWXCtzZs5ht713y79PG9IN5DzX7m88KDOIgemBNKQbGJge2OY6\nKS5PHOvGhkYMa3ONAAxID8g/f2evpTXX2uZvXfoeh/AZFe+PY9eSbWFNdk3+uePfm81lMbP8OcW3\ngxsGk/McaWv/fz9Wb0lYR++QDrOhqUdMZcCoAQCkB6cZvOVghowbAsCK+SsA2m0PHT8UgOXzlle0\nPWz8sG4dP3zCcMyMlfNXAjBy4khSlmLp3KUADNg2XCDF5zve6fPF5V8+fznZXJYh44aQTqVZNX8V\nqVSKERNG5J/fMIZPGA7AsnnLABgxYQSZVIbVC1azJruGwWMH4+4smbskfKg6NH3UlD++cWxjvjw5\nz7HxDhvTkG5g8ZzFbeIRHx9vL523lGwuy6DtBtGSa2HVglUYRuPYRswslNdSDB0/lJSlWDl/JSlL\nMXzCcFKWYvm85aQsxciJIxmUGcTyectJp9I0bNvAa4tfo/mVZhrSDYyaOIpBmUE0v9JMOpVmg4kb\n0NzazJK5S/KvF6hqO45PznMMGz8MJ2zj0DSuKfz95i/HsPzf58O/fMjgJwfn/34r5hWuNzPr9Hoa\nNqEQT8Pyjy+as4jm1mYatg0fpmteWUM6lWb4hOG4Oyvmr2BAegAjJ44kben833P4+OHkPMeSuUvy\n8YyvD8cZOm4oWc+GxwmPp1Npls1dlj/ecRbNXoTjIR44i+cszj+/4+H53Bk2YRjuztJ5S8mkMoXr\nIbre89fj3GX57fj5sh6ul9Utq1kxf0U+0drgXxvQ8moLDekGRkwYgZm1iU/8+nKeo2lcE6251vzz\nNY1tAmDVglVA+HsVbzeODR/cxdsdHb96wWoyqQxDxg3Jv1+znmXw2MF8bOjH8q9n5MSRrM2uZcnc\nJbTmWmka10Q2l2XZvGXkPMfgsYMxjJULwufB0HHhelgxfwWGtXm/t9u2ttvx+8UoxGPExPD+XzFv\nBZl0hk122ITm1mZevPdFBv81fB4axuoFq8HC9RcfHz9/Q6qBlQtWkrY0IyeODO+HOUvyr8/M8u//\nDSZukL8+4/dH1rMsmROuvzhe8fXWOLaRnOfynxdxfFfOXxneP+OH5OMTv/5MKsOqBatIW5pR248i\nk8qwdO5SWnIt+fdb6fVVvO3unT4ev986ejxl4fO0+PrKpDKMWDkivL9Krnd3z/99ls1dln/98een\n4zSNDddnfD2N2n4UAzMDWTF/RT7e7s5Hcz4CD+Vpbm3Ox3fw2MG0ZFtYPn85aQvv/7Slw+dnKsUG\nEzdgcGYwK+evDH+PCcNoybawcPZCWnOtNI5tpCXXkn8/N41rYkB6QPg8sTTDJgwjm8vmr8dhE4bl\nP4+Bwvtv7jIwGDkhfJ4smbsEw9hg+w0wjMVzF2MYo7YfFbbnLMbM2GTHTXhv7nv8bs7vyOayNI1r\nYm12LSvnr2zz9102bxmrW1fTNLYpX16AIeOH5P9fFH9+Lpu3jBVrV+SvlzWvrMn/f2nJtoTnT6Xz\n13v8eR2/f1bOX5l/P2Rz2fzft3FcY/795jiDtxtM1gvxGTFhBAMzA/PX76jtR+U/Px3Pvx8WzV5E\njvDFdvGcxbQsaumyIqjekrC3gK2Ktrcg9A1rZ9HTi9ZJgUTKOrnWBRAp8rVaF0AkcnStC1A/Oquh\nrbc+YTOB7cxstJkNIPwJ76txmUREREQSV1dJmLtngVOBh4GXgTvcfU5tS7X+0hwuyVAck6E4JkNx\nTIbiWD3FsLx6a47E3f8MjK91OURERER6U13NE1YpzRMmIiIifUVfmSdMREREpF9QEtaPqb0+GYpj\nMhTHZCiOyVAcq6cYlqckTERERKQG1CdMREREpBepT5iIiIhIHVES1o+pvT4ZimMyFMdkKI7JUByr\npxiWpyRMREREpAbUJ0xERESkF6lPmIiIiEgdURLWj6m9PhmKYzIUx2QojslQHKunGJanJExERESk\nBtQnTERERKQXqU+YiIiISB1REtaPqb0+GYpjMhTHZCiOyVAcq6cYlqckTERERKQG1CdMREREpBep\nT5iIiIhIHVES1o+pvT4ZimMyFMdkKI7JUByrpxiWpyRMREREpAbUJ0xERESkF6lPmIiIiEgdURLW\nj6m9PhmKYzIUx2QojslQHKunGJanJExERESkBtQnTERERKQXqU+YiIiISB1REtaPqb0+GYpjMhTH\nZCiOyVAcq6cYlqckTERERKQG1CdMREREpBepT5iIiIhIHVES1o+pvT4ZimMyFMdkKI7JUByrpxiW\npyRMREREpAbUJ0xERESkF6lPmIiIiEgdURLWj6m9PhmKYzIUx2QojslQHKunGJZXVRJmZl80s1lm\nljWzXUseO8/MFpjZHDM7sGj/QWY218zmm9k5RfvHmNnfzGyemd1uZplqyiblvfjii7UuwnpBcUyG\n4pgMxTEZimP1FMPyqq0J+ydwBPBY8U4zmwgcBUwEDgautSAF/Bz4LLAD8GUzmxCd9hPgp+4+HlgC\nfL3KskkZS5YsqXUR1guKYzIUx2QojslQHKunGJZXVRLm7vPcfQFQ2tnscOAOd29199eBBcCe0c8C\nd3/D3VuAO6JjAaYCd0f3byYkdyIiIiLrpd7qE7Y58GbR9tvRvtL9bwGbm9koYLG754r2f6yXyiaR\n119/vdZFWC8ojslQHJOhOCZDcayeYlhe2SkqzOwRYJPiXYAD/+nu90fHTAPOdPfno+2fA0+5+23R\n9q+AB4A0cKC7nxjt/yqwB3Ah8LS7j432bwE84O47dVImzU8hIiIifUZHU1SU7fzu7gf04He9BWxZ\ntL0F8A4hgduqdL+7LzSzEWaWimrD4uM7K1O7FyIiIiLSlyTZHFmcGN0HHG1mA8xsa2A74FlgJrCd\nmY02swHA0cAfonP+CvxbdP/Yov0iIiIi651qp6j4vJm9CewF/NHM/gTg7rOB/wFmAw8Cp3iQBU4F\nHgZeJnTenxs93bnAGWY2H9gAuLGasomIiIjUsz65bJGIiIhIX6cZ80VknTMz9esUkX5PSZhIhZQ4\n9JyZ7WFmvzSzb5nZEFcVfFXMbNdoah+pQjSBuFTBzDaNbvX52AN1dwGa2WbRbbrWZenLzGxPM7tI\nHzLVMbPtzewzAEocus/MMmZ2LfAL4BnCahlX1rZUfZeZ7WJmjxJiqaXdeiD6bDwdoGhuSumm6Fr8\nC2GKKX0+9lDd/IM2syFmdivwtpl93N2zSsS6z8yGmdk1hOWh3nL3nL6hdJ+ZNZjZ9cDtwGlmdraZ\n7RY9Vjfvmz7AgaeBKe5+I/BdIKf3dveY2UAzuw64AbgWeBz4XPSY3t8VMrNvA/cA3zWzg6N9uha7\nIVqC8ArgFuBmd/9mrcvUl9XTP5P/Q5hN/0rCt2ai0ZTSPf+PMFr1QHe/FvQNpYd2AIZHEwafDLQA\n3zGzRn177pqZHWVmZ5rZXu6edfdb3X2FmX2WsM7shsAFZja8xkXtSzYD/g7s4+6/J4wwH2Vmpvd3\nt7xC+F/z78B5EP7PKJGtXHS9DQFecPdbAMxsW3057ZmaBs3MtjazwdHmQ8CV7n4GsJWZHR0doyr3\nMqI4NkabtwAfAhub2RfN7DIzO9rMturiKYR8HAdFm03AzmaWdvePgGZge6KF5fWh3Z6Zpc3s+8A5\nhBqwG8yseA3YFGFuwC8TJnP+ejRfoHQgSmbPMrM93f11d7/B3Zujh4cAW7q7qyanc2a2l5mNK9r1\nAPBSdLsibpYkrOYineggjmcCk8zse2b2JHAp8Ou4tUAqV5MkzMzGRHOK/Qr4jZmNd/fF7v5BdMgZ\nwCUA7t5aizL2BSVxvNXMto/maJtBSGpPAeYRJsE9O1oOSkqUxPG3ZjYB+AfwBPALM9sG+CShGWNX\nM9tQtQ/tRTXX4wlLmF0OnE9oyp0YPf4nd3/A3dcAvwcOJ9QwSpGSZDYH3GhmR0aPxZ/Z9wKHRTWz\najEoYWEFlgeAR4CjzKwpfiiqnW0Gfkr4IrCh/s90rLM4uvsy4BrgC4QaxS8D7wJfMLONalXevmid\nJWElNQdnAc+4+37ANOBCM9shftDdfwe8ZWYXROcOQoCK4rgN4VvJD9x9qrvfAHyP8M1563Ve4DpV\nJo4/ICyd9X1CDdjPCE1B9xG+MS9ep4WtY2b2NTObbGYjol3vAyPNLBM1m80Gjurg1DGEVTRUA1Gi\nk2T2VDObWNQU/iFhlZEJNSpmvWsifBE9LbofD64p7kowHfhbdAxmtue6LWKf0GEcAdz9KmBfd388\n+mJ1L7A7sKoWBe2r1mVN2CBo07z4MoC7/xzYE/iKmW1cdPzngdPN7AfAz8yseBHx/qyrOO4GnAg0\nufvN8QlR7dimwL/WbVHrWldx3As4Dljp7qcDX4g+cBYAo4DB7Z6tH4k65m5mZtMIS4wdA1xjZkOA\nhcDHCUk/wNXAkdHxA83sIDP7G7A/cL1qIIJKk9mimrAVhOXgPDq/3zePF8VwmLu/DfySsHJLM6Hp\n7GPRcQb5ZPdHwDlmtpRQy604VhhHAHcv/kK6G2HdaNXMdkOvJ2FmdoCZPQJcamZHRR+6i4BdzGwn\nM9sJmAWMJixXFNsIGAZMAX7u7u/3dlnrWTfiuAWwSdF5h1kYRvwOsKi/f8h0M46bRadlzewwwoi0\n5+jH3/SiPnIODAXejmoPTwGWEmoMrwU+BXwiaiqbB8wFjoy+LQ8HLnb3Q939ldq8ivrQk2SW6L3t\n7ouAj4Cp0Xa/bB7vJIa/iJoYm919FfAoMJKiWJlZysy2A24DniQMeLhOcaw8jtF5A81sipk9R5h+\n5uKifotSCXfvtR/CN7VnCH0/diEM9z+F8AH+PeCPhH43uxPeDKdG520BXAd8qTfL11d+qojj3oRm\ntM/X+jXUw08VcRwP3E1IJGr+OmoUuwxwEfATYDJwKGF4evx4CviAMKr0WEIy9qXosd8Ck2r9Gurp\nB0hHt+OA3xTF+FrCurkjCM1AnwEao8fvBE4veo5htX4ddRrDq4Hflxz7HUKt1/CieG5MaE6r+Wvp\no3EcHO3bW/9jev6T+MjDuLrcQ9v7JODv7v6H6LFHCZ0h73L3C81sG3f/3+ixJ4E10blvEaYF6Leq\niONThGpj3P0pQhVxv5XQ9TiP0AG1XzKzycBVwFPA84TJGS8C9rUwcu9ZD/PRXQBc6u6HRM073zSz\nc4H5wD9rVf56EjV//xBIm9mDhNr+LIRBSGZ2KvAecDnhi8DRhBrZO4FWwpcIouOXrdvS14cKYng6\n8I6ZTXb3x6LTbiAkD48Ao81st+j/zAftf0P/UGUcHyXMYrBL9H9GeijR5kgzO57QJnxhtOufwJfN\nbEy0nQFeBa6Itl+LzjuRMPT/+STL01dVGccTUBwBXY8JygGXufu/exjoMYswyOP7RHP6Rcnu3cAq\nM9vS3e8lxPAL7v4lD80Z/VqUzP6d0KTzCuG6bCFKZiH/ZSFOZm8mzAf2NTN7gXC99utktsIYOiG5\n+EHRqZ8j1Hr/A/h4lID1WwnE8UVCHN9Zh8VeLyWWhEX9GA4nNFccbGYT3P0l4Gbgx1HNwqcJHZ5H\nmtkm7u4WZjD+JnCSu/f7f3qKYzIUx0T9HfgfK8xH9SSwlbv/mvAt+rQoedgCaHH3NwHc/b24ZlEA\nJbNJqDSG9wAfFn3hagb2d/dvemEqpP5McawXSbZtEj6YAS4G7ozupwkd7veJtrcEfg0MjLYba90m\nW28/iqPiWM8/UbzOiO7vQujH9EfC6NGza12+ev0BGoGBFPrgHAP8OLr/InBadH934PZal7cefxRD\nxXF9+0m0OdLd4ykQrgS2NrPPehgGvNTdn4geO5kwuqw1Oqe/f7NrR3FMhuKYLAuTiKYII/Tui3Yv\nJyyVdTFhfchLa1W+eufuq9x9jRcmVz2AMN8XwPHARDP7I2HAiGphO9CTGPb3EeEdURzrR68sCeTu\n75nZjYQP54c8rM21J/CfQANwgmuW57IUx2QojonJAQMI0yd8wsyuJEyTcFpRUitlRM26TsfJ7I7A\nax7mZ5JOdCeGHlXpSHuKY+1Zb8TVzFIeRkv9jrCUwRrCaIoF7v5q4r9wPaU4JkNxTI6Z7UUYJfkU\ncJO731jjIvU5UY3CAMIyWfcQBtPEyWy/HPHYXYphMhTH2uutmrCchQWlNyZMtvpDd/9zb/yu9Zni\nmAzFMVFvEWoQL/cw+ap0k7u7me1C6IezNUpmu00xTIbiWHu9UhMGYGZnEUZLnaMP655THJOhOEo9\nMbMtgP+LktkeUwyToTjWVm8mYSlvu1iq9IDimAzFUURE6k2vJWEiIiIi0rleX8BbRERERNpTEiYi\nIiJSA0rCRERERGpASZiIiIhIDSgJE5E+z8zON7Mzunj8cDObUMHztDnOzC4ws6lJlVNEpJiSMBHp\nDz4P7NDd49z9fHf/a6+VSkT6NSVhItInmdl/mtk8M3scGB/t+4aZPWtmL5jZXWY2yMw+CRwGXGJm\nz5vZ1ma2jZn9ycxmmtljZjauk+NuMrMjo+d+zcwuip77WTPbxcz+bGYLzOykonKdFT3+opmdX4PQ\niEgf0SvLFomI9CYz2xU4CvgEYe2754HngLvd/VfRMRcCX3f3a8zsPuB+d/999NijwEnu/mq0mPsv\n3H2/Do4r/dWvu/suZnY5cBOwN9AIvAxcb2YHAGPdfc9oXb77zGwfLXAuIh1REiYifdGngXuiZVbW\nRMkTwMfN7EfACKAJeKj0RDNrIiRPd1khy2qo8PfeH93+E2hy91XAKjNbbWbDgAOBA8zsecCiMowF\nlISJSDtKwkSkrypd7sOAXwOHufssMzsWmNzBeSlgsbvv2oPfGa+tlyu6H29nojL82N1v6MFzi0g/\noz5hItIXPQ4cYWYDzWwocGi0fwjwnpk1AMcUHb8cGAbg7suB18zsi/GDZvaJ0uO6Ka5Rewg4Iapt\nw8w+ZmYb9eD5RKQfUBImIn2Ou78A3Am8BDwAPEuoGftedH8GMKfolDuAs83s72a2NSFB+3rUeX4W\noUN+R8cV17Z1tdCuR+V6BLgNeNrMXgLuIiSGIiLtaAFvERERkRpQTZiIiIhIDSgJExEREakBJWEi\nIiIiNaAkTERERKQGlISJiIiI1ICSMBEREZEaUBImIiIiUgNKwkRERERq4P8D+katLCmStQ0AAAAA\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49ee5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "com=pd.DataFrame({\"sh close\":sh['close'],\n",
    "                 'transformed close':transformed['close']})\n",
    "com.plot(grid=True,figsize=(10,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>str07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>2018-03-26</th>\n",
       "      <td>446.0</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650.0</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>446.0</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650.0</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-28</th>\n",
       "      <td>446.0</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650.0</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <td>446.0</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650.0</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-30</th>\n",
       "      <td>446.0</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650.0</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             open    high     low   close          vol        amount  str07\n",
       "datetime                                                                   \n",
       "2018-03-26  446.0  452.75  471.46  437.18  180232650.0  2.042663e+11    NaN\n",
       "2018-03-27  446.0  452.75  471.46  437.18  180232650.0  2.042663e+11    NaN\n",
       "2018-03-28  446.0  452.75  471.46  437.18  180232650.0  2.042663e+11    NaN\n",
       "2018-03-29  446.0  452.75  471.46  437.18  180232650.0  2.042663e+11    NaN\n",
       "2018-03-30  446.0  452.75  471.46  437.18  180232650.0  2.042663e+11    NaN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price=lambda x:x.max()-x.min()\n",
    "sh.groupby(key).transform(price).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>str07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</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>2018-03-26</th>\n",
       "      <td>446</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-27</th>\n",
       "      <td>446</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-28</th>\n",
       "      <td>446</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-29</th>\n",
       "      <td>446</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-03-30</th>\n",
       "      <td>446</td>\n",
       "      <td>452.75</td>\n",
       "      <td>471.46</td>\n",
       "      <td>437.18</td>\n",
       "      <td>180232650</td>\n",
       "      <td>2.042663e+11</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            open    high     low   close        vol        amount  str07\n",
       "datetime                                                                \n",
       "2018-03-26   446  452.75  471.46  437.18  180232650  2.042663e+11    NaN\n",
       "2018-03-27   446  452.75  471.46  437.18  180232650  2.042663e+11    NaN\n",
       "2018-03-28   446  452.75  471.46  437.18  180232650  2.042663e+11    NaN\n",
       "2018-03-29   446  452.75  471.46  437.18  180232650  2.042663e+11    NaN\n",
       "2018-03-30   446  452.75  471.46  437.18  180232650  2.042663e+11    NaN"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(sh.groupby(key).transform(\"max\")-sh.groupby(key).transform(\"min\")).tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">open</th>\n",
       "      <th colspan=\"2\" halign=\"left\">high</th>\n",
       "      <th colspan=\"2\" halign=\"left\">low</th>\n",
       "      <th colspan=\"2\" halign=\"left\">close</th>\n",
       "      <th colspan=\"2\" halign=\"left\">vol</th>\n",
       "      <th colspan=\"2\" halign=\"left\">amount</th>\n",
       "      <th colspan=\"2\" halign=\"left\">str07</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990</th>\n",
       "      <th>12</th>\n",
       "      <td>116.000000</td>\n",
       "      <td>11.067972</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>10.404443</td>\n",
       "      <td>113.384444</td>\n",
       "      <td>11.743673</td>\n",
       "      <td>116.990000</td>\n",
       "      <td>10.404443</td>\n",
       "      <td>209.333333</td>\n",
       "      <td>397.831623</td>\n",
       "      <td>1.040000e+05</td>\n",
       "      <td>1.499875e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">1991</th>\n",
       "      <th>1</th>\n",
       "      <td>131.818182</td>\n",
       "      <td>1.991323</td>\n",
       "      <td>132.800909</td>\n",
       "      <td>1.786491</td>\n",
       "      <td>132.190909</td>\n",
       "      <td>1.895641</td>\n",
       "      <td>132.628182</td>\n",
       "      <td>1.764493</td>\n",
       "      <td>3054.409091</td>\n",
       "      <td>3967.611498</td>\n",
       "      <td>1.653227e+06</td>\n",
       "      <td>2.148181e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>131.333333</td>\n",
       "      <td>2.029199</td>\n",
       "      <td>132.067778</td>\n",
       "      <td>2.123747</td>\n",
       "      <td>131.546667</td>\n",
       "      <td>2.153014</td>\n",
       "      <td>131.887778</td>\n",
       "      <td>2.086344</td>\n",
       "      <td>2832.333333</td>\n",
       "      <td>2743.472799</td>\n",
       "      <td>1.681833e+06</td>\n",
       "      <td>1.405367e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>125.571429</td>\n",
       "      <td>4.056740</td>\n",
       "      <td>126.556190</td>\n",
       "      <td>4.053580</td>\n",
       "      <td>125.985238</td>\n",
       "      <td>4.101964</td>\n",
       "      <td>126.011429</td>\n",
       "      <td>4.109010</td>\n",
       "      <td>1168.000000</td>\n",
       "      <td>1480.783914</td>\n",
       "      <td>8.215714e+05</td>\n",
       "      <td>8.449842e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>118.000000</td>\n",
       "      <td>2.581989</td>\n",
       "      <td>118.873636</td>\n",
       "      <td>2.280870</td>\n",
       "      <td>118.216818</td>\n",
       "      <td>2.418924</td>\n",
       "      <td>118.426818</td>\n",
       "      <td>2.497512</td>\n",
       "      <td>3212.318182</td>\n",
       "      <td>4611.085664</td>\n",
       "      <td>1.657864e+06</td>\n",
       "      <td>2.114055e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               open                   high                    low             \\\n",
       "               mean        std        mean        std        mean        std   \n",
       "1990 12  116.000000  11.067972  116.990000  10.404443  113.384444  11.743673   \n",
       "1991 1   131.818182   1.991323  132.800909   1.786491  132.190909   1.895641   \n",
       "     2   131.333333   2.029199  132.067778   2.123747  131.546667   2.153014   \n",
       "     3   125.571429   4.056740  126.556190   4.053580  125.985238   4.101964   \n",
       "     4   118.000000   2.581989  118.873636   2.280870  118.216818   2.418924   \n",
       "\n",
       "              close                     vol                     amount  \\\n",
       "               mean        std         mean          std          mean   \n",
       "1990 12  116.990000  10.404443   209.333333   397.831623  1.040000e+05   \n",
       "1991 1   132.628182   1.764493  3054.409091  3967.611498  1.653227e+06   \n",
       "     2   131.887778   2.086344  2832.333333  2743.472799  1.681833e+06   \n",
       "     3   126.011429   4.109010  1168.000000  1480.783914  8.215714e+05   \n",
       "     4   118.426818   2.497512  3212.318182  4611.085664  1.657864e+06   \n",
       "\n",
       "                      str07      \n",
       "                  std  mean std  \n",
       "1990 12  1.499875e+05   NaN NaN  \n",
       "1991 1   2.148181e+06   NaN NaN  \n",
       "     2   1.405367e+06   NaN NaN  \n",
       "     3   8.449842e+05   NaN NaN  \n",
       "     4   2.114055e+06   NaN NaN  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year=lambda x:x.year\n",
    "mon=lambda y :y.month\n",
    "sh.groupby([year,mon]).agg([np.mean,np.std\n",
    "                           ]).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "year=lambda x:x.year\n",
    "mon=lambda y :y.month\n",
    "month_sh=sh.groupby([year,mon]).first()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['1990-12', '1991-01', '1991-02', '1991-03', '1991-04', '1991-05',\n",
       "             '1991-06', '1991-07', '1991-08', '1991-09',\n",
       "             ...\n",
       "             '2017-06', '2017-07', '2017-08', '2017-09', '2017-10', '2017-11',\n",
       "             '2017-12', '2018-01', '2018-02', '2018-03'],\n",
       "            dtype='period[M]', length=328, freq='M')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index=[str(i[0])+ '-'+str(i[1]) for i in month_sh.index.values]\n",
    "index=pd.PeriodIndex(index,freq='M')\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b499fcda0>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1t/vtctJJ2gG7eDE89pjKkIj991c30dix8PzzOmJ28+bc65mrfx7C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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b49ea9128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "month_sh['close'].plot(grid=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(2/3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(1//2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nhello world\\nhello world\\nhello word\\n'"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "hello world\n",
    "hello world\n",
    "hello word\n",
    "'''#\\n换行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "hello world\n",
      "hello world\n",
      "hello word\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print('\\nhello world\\nhello world\\nhello word\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unexpected character after line continuation character (<ipython-input-66-e9facf912d08>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-66-e9facf912d08>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    \\n #换行  \\'  \\t\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected character after line continuation character\n"
     ]
    }
   ],
   "source": [
    "\\n #换行  \\'  \\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello \n",
      " world\n"
     ]
    }
   ],
   "source": [
    "print('hello \\n world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello \\n world\n"
     ]
    }
   ],
   "source": [
    "print('hello \\\\n world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\n",
      "ort\\hello  world\n"
     ]
    }
   ],
   "source": [
    "print('c:\\nort\\hello  world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\nort\\hello  world\n"
     ]
    }
   ],
   "source": [
    "print('c:\\\\nort\\\\hello  world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c:\\nort\\hello  world\n"
     ]
    }
   ],
   "source": [
    "print(r'c:\\nort\\hello  world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ss', 'dd', 'ss', 'dd', 'ss', 'dd']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "['ss','dd']*3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'list' and 'list'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-73-7a68b45288b7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;33m[\u001b[0m\u001b[1;34m'ss'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'dd'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'ss'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'list' and 'list'"
     ]
    }
   ],
   "source": [
    "['ss','dd']-['ss']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1,)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1,)#tuple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "True and True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not not True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'a' and 'b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'a' or 'b'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not 'a'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 and 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not '0'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not ''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a={1,2,3}\n",
    "b={2,1,3}\n",
    "a==b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c={1,2,3}\n",
    "d={2,1,3}\n",
    "a is b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1216683013736"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id(c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1216683012168"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "id (d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "a=[1,2,3]\n",
    "for x in a:\n",
    "    if x ==2:\n",
    "        continue\n",
    "    print(x) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "a=[1,2,3]\n",
    "for x in a:\n",
    "    if x ==2:\n",
    "        break\n",
    "    print(x)\n",
    "else:\n",
    "    print('eof')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n",
      "10\n",
      "11\n",
      "eof\n"
     ]
    }
   ],
   "source": [
    "counter=1\n",
    "while counter<=10:\n",
    "    counter+=1\n",
    "    print(counter)\n",
    "else:\n",
    "    print('eof')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "apple\n",
      "1\n",
      "2\n",
      "3\n",
      "go\n"
     ]
    }
   ],
   "source": [
    "a=[['apple','orange','banana'],(1,2,3)]\n",
    "for x in a :\n",
    "    for y in x:\n",
    "        if y =='orange':\n",
    "            break\n",
    "        print(y)\n",
    "else:\n",
    "    print('go')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "2\n",
      "4\n",
      "6\n",
      "8\n"
     ]
    }
   ],
   "source": [
    "for i in range(0,10,2):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0   |   1   |   2   |   3   |   4   |   5   |   6   |   7   |   8   |   9   |   "
     ]
    }
   ],
   "source": [
    "for i in range(0,10):#split\n",
    "    print(i,end='   |   ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0123456789"
     ]
    }
   ],
   "source": [
    "for i in range(0,10):\n",
    "    print(i,end='')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0,1,2,3,4,5,6,7,8,9,"
     ]
    }
   ],
   "source": [
    "for i in range(0,10):#split\n",
    "    print(i,end=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 7]\n"
     ]
    }
   ],
   "source": [
    "a=[1,2,4,56,7,8]\n",
    "b=a[0:len(a):2]\n",
    "print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "##import t.c7\n",
    "##print(t.c7.a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "__all__ =['a','c']#output something\n",
    "a=2 \n",
    "c =3 \n",
    "d =4\n",
    "#output a c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "unexpected character after line continuation character (<ipython-input-100-40903c4b5f58>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-100-40903c4b5f58>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    from c9 import a,b,\\  change line\u001b[0m\n\u001b[0m                                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected character after line continuation character\n"
     ]
    }
   ],
   "source": [
    "from c9 import a,b,\\  change line\n",
    "c\n",
    "\n",
    "from c9 import (a,b\n",
    "c)  best               "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-101-2bcb23a80313>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-101-2bcb23a80313>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    a.file  __init__.py\u001b[0m\n\u001b[0m                   ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    " a.file  __init__.py\n",
    "a='.py'\n",
    "print(a)\n",
    "import a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\python35.zip', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\DLLs', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\lib', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\lib\\\\site-packages', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\lib\\\\site-packages\\\\FontTools', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\lib\\\\site-packages\\\\win32', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\lib\\\\site-packages\\\\win32\\\\lib', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\lib\\\\site-packages\\\\Pythonwin', 'C:\\\\zwPython\\\\zwQuant\\\\Source', 'C:\\\\zwPython\\\\py35\\\\python-3.5.2.amd64\\\\lib\\\\site-packages\\\\IPython\\\\extensions', 'C:\\\\Users\\\\l\\\\.ipython']\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import datetime\n",
    "import io\n",
    "print(sys.path\n",
    "     )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "54\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(5, 6)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def add(x,y):\n",
    "    result=x+y,x*y\n",
    "    print(54)\n",
    "    return result\n",
    "    print(54)\n",
    "    print(54)\n",
    "    print(54)#break\n",
    "add(2,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=1 \n",
    "b=1\n",
    "c=3\n",
    "a,b,c=1,1,3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "name:\n",
      "age0\n"
     ]
    }
   ],
   "source": [
    "class Student():#封装\n",
    "    name=''\n",
    "    age=0#\n",
    "    def print_file(self):\n",
    "        print('name:' +self.name)\n",
    "        print('age' +str(self.age))\n",
    "student=Student()\n",
    "student.print_file()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "Can't convert 'int' object to str implicitly",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-106-9667e58a8c85>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_homework\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;31m#method\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'homework'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mstudent1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ss'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m \u001b[0mstudent2\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'bb'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m19\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-106-9667e58a8c85>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, name, age)\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m+=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#类变量\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'student 数量'\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__class__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m###str(\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mdo_homework\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;31m#method\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Can't convert 'int' object to str implicitly"
     ]
    }
   ],
   "source": [
    "class Student():\n",
    "    sum=0\n",
    "    name='qq'\n",
    "    age=0#类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.__class__.sum+=1\n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量' +self.__class__.sum)###str(\n",
    "        \n",
    "    def do_homework(self):#method \n",
    "        print('homework')\n",
    "student1=Student('ss',18)\n",
    "student2=Student('bb',19)\n",
    "print(student1.name)\n",
    "print(student2.name)\n",
    "print(Student.name)\n",
    "print(Student.sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'student1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-107-beb2e26cbfc7>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#dict\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'student1' is not defined"
     ]
    }
   ],
   "source": [
    "print(student1.__dict__)#dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "student 数量::1\n",
      "2\n",
      "3\n",
      "student 数量::3\n",
      "4\n",
      "student 数量::4\n",
      "ss\n",
      "bb\n",
      "qq\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "class Student():#class\n",
    "    sum=0\n",
    "    name='qq'\n",
    "    age=0#类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.__class__.sum+=1\n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量::' +str(self.__class__.sum))\n",
    "    def do_homework(self):##实例method \n",
    "        print('homework')\n",
    "        \n",
    "    @classmethod #装饰器   类方法\n",
    "    def plus_sum(cls):#can change cls\n",
    "        cls.sum+=1\n",
    "        print(cls.sum) \n",
    "        \n",
    "        \n",
    "        #print('555555 static method')#break\n",
    "\n",
    "student1=Student('ss',18)\n",
    "Student.plus_sum()#实例调用类方法  another language can not 实例调用类方法\n",
    "student2=Student('bb',19) \n",
    "student3=Student('cc',20)\n",
    "print(student1.name)\n",
    "print(student2.name)\n",
    "print(Student.name)\n",
    "print(Student.sum)\n",
    "Student.plus_sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "student 数量::1\n",
      "1\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'name' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-e353b490689f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'is static method'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mstudent1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ss'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#对象 调用j静态方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0mStudent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#类调 用j静态方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-1-e353b490689f>\u001b[0m in \u001b[0;36madd\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#静态方法  访问  类变量   inside\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'is static method'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'name' is not defined"
     ]
    }
   ],
   "source": [
    "class Student():#class\n",
    "    sum=0\n",
    "    name='qq'\n",
    "    age=0#类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.__class__.sum+=1\n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量::' +str(self.__class__.sum))\n",
    "    def do_homework(self):##实例method \n",
    "        print('homework')\n",
    "        \n",
    "    @classmethod #装饰器   类方法\n",
    "    def plus_sum(cls): #can change cls\n",
    "        cls.sum+=1\n",
    "        print(cls.sum) \n",
    "        print(self.name)\n",
    "\n",
    "       # c c# de @staticmethod like classmethod\n",
    "    @staticmethod  #与类  实例无太大关系 可以用 like 函数    #调用j静态方法\n",
    "    def add(x,y):\n",
    "        print(Student.sum)#静态方法  访问  类变量   inside\n",
    "        print(name)\n",
    "\n",
    "        print('is static method')\n",
    "student1=Student('ss',18)\n",
    "student1.add(1,2) #对象 调用j静态方法\n",
    "Student.add(1,2) #类调 用j静态方法\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "student 数量::1\n",
      "1\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'name' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-7b2af631ae16>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'is static method'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mstudent1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ss'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#对象 调用j静态方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0mStudent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#类调 用j静态方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[1;31m#实例调用类方法  another language can not 实例调用类方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-1-7b2af631ae16>\u001b[0m in \u001b[0;36madd\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#静态方法  访问  类变量   inside\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'is static method'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'name' is not defined"
     ]
    }
   ],
   "source": [
    "class Student():#class\n",
    "    sum=0\n",
    "    name='qq'\n",
    "    age=0#类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.__class__.sum+=1\n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量::' +str(self.__class__.sum))\n",
    "    def do_homework(self):##实例method \n",
    "        print('homework')\n",
    "        \n",
    "    @classmethod #装饰器   类方法\n",
    "    def plus_sum(cls): #can change cls\n",
    "        cls.sum+=1\n",
    "        print(cls.sum) \n",
    "        print(self.name)\n",
    "\n",
    "        \n",
    "    @staticmethod  #调用j静态方法\n",
    "    def add(x,y):\n",
    "        print(Student.sum)#静态方法  访问  类变量   inside\n",
    "        print(name)\n",
    "\n",
    "        print('is static method')\n",
    "student1=Student('ss',18)\n",
    "student1.add(1,2) #对象 调用j静态方法\n",
    "Student.add(1,2) #类调 用j静态方法\n",
    "#实例调用类方法  another language can not 实例调用类方法      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "student 数量::1\n",
      "1\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'self' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-a3719198c701>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'is static method'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[0mstudent1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ss'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m \u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#对象 调用j静态方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     29\u001b[0m \u001b[0mStudent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m#类调 用j静态方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m \u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplus_sum\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-2-a3719198c701>\u001b[0m in \u001b[0;36madd\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0madd\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0my\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#静态方法  访问  类变量   inside\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'is static method'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'self' is not defined"
     ]
    }
   ],
   "source": [
    "class Student():#class\n",
    "    sum=0\n",
    "    name='qq'\n",
    "    age=0#类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.__class__.sum+=1\n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量::' +str(self.__class__.sum))\n",
    "    def do_homework(self):##实例method \n",
    "        print('homework')\n",
    "        \n",
    "    @classmethod #装饰器   类方法\n",
    "    def plus_sum(cls): #can change cls\n",
    "        cls.sum+=1\n",
    "        print(cls.sum) \n",
    "        print(self.name)\n",
    "\n",
    "    #与类  实例无太大关系 可以用 like 函数    \n",
    "    @staticmethod  #调用j静态方法\n",
    "    def add(x,y):\n",
    "        print(Student.sum)#静态方法  访问  类变量   inside\n",
    "        print(self.name)\n",
    "\n",
    "        print('is static method')\n",
    "student1=Student('ss',18)\n",
    "student1.add(1,2) #对象 调用j静态方法\n",
    "Student.add(1,2) #类调 用j静态方法\n",
    "student1.plus_sum()\n",
    "Student.plus_sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "student 数量::1\n",
      "homework\n",
      "not 负分\n"
     ]
    }
   ],
   "source": [
    "class Student():#class\n",
    "    sum=0\n",
    "    #类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.score=0\n",
    "        self.__class__.sum+=1\n",
    "        \n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量::' +str(self.__class__.sum))\n",
    "        \n",
    "    def making(self,score):\n",
    "        if score<0:\n",
    "            return 'not 负分'#score=0\n",
    "        self.score=score\n",
    "        print(self.name+'score::'+str(self.score))\n",
    "        \n",
    "    def do_homework(self):##实例method \n",
    "        print('homework')\n",
    "        \n",
    "    def do_english_homework(self):\n",
    "        print()   \n",
    "        \n",
    "    @classmethod #装饰器   类方法\n",
    "    def plus_sum(cls): #can change cls\n",
    "        cls.sum+=1\n",
    "        print(cls.sum) \n",
    "        print(self.name)\n",
    "\n",
    "    #与类  实例无太大关系 可以用 like 函数    \n",
    "    @staticmethod  #调用j静态方法\n",
    "    def add(x,y):\n",
    "        print(Student.sum)#静态方法  访问  类变量   inside\n",
    "        print(self.name)\n",
    "\n",
    "        print('is static method')\n",
    "\n",
    "student1=Student('ss',18)\n",
    "student1.do_homework()\n",
    "result=student1.making(-1)\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-4-174584d99013>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-4-174584d99013>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    public公有 private私有\u001b[0m\n\u001b[0m                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "public公有 private私有"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "student 数量::1\n",
      "homework\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'Student' object has no attribute '__making'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-8d8e8ca704d3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     39\u001b[0m \u001b[0mstudent1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'ss'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m18\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     40\u001b[0m \u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdo_homework\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 41\u001b[0;31m \u001b[0mresult\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__making\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m59\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#私有\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     42\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Student' object has no attribute '__making'"
     ]
    }
   ],
   "source": [
    "class Student():#class\n",
    "    sum=0\n",
    "    #类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.score=0\n",
    "        self.__class__.sum+=1\n",
    "        \n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量::' +str(self.__class__.sum))\n",
    "        \n",
    "    def __making(self,score):#私有方法\n",
    "        if score<0:\n",
    "            return 'not 负分'#score=0\n",
    "        self.score=score\n",
    "        print(self.name+'score::'+str(self.score))\n",
    "        \n",
    "    def do_homework(self):##实例method \n",
    "        print('homework')\n",
    "        \n",
    "    def do_english_homework(self):\n",
    "        print('english')   \n",
    "        \n",
    "    @classmethod #装饰器   类方法\n",
    "    def plus_sum(cls): #can change cls\n",
    "        cls.sum+=1\n",
    "        print(cls.sum) \n",
    "        print(self.name)\n",
    "\n",
    "    #与类  实例无太大关系 可以用 like 函数    \n",
    "    @staticmethod  #调用j静态方法\n",
    "    def add(x,y):\n",
    "        print(Student.sum)#静态方法  访问  类变量   inside\n",
    "        print(self.name)\n",
    "\n",
    "        print('is static method')\n",
    "\n",
    "student1=Student('ss',18)\n",
    "student1.do_homework()\n",
    "result=student1.__making(59)#私有\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "student 数量::1\n",
      "homework\n",
      "ssscore::59\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "class Student():#class\n",
    "    sum=0\n",
    "    #类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.score=0\n",
    "        self.__class__.sum+=1\n",
    "        \n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量::' +str(self.__class__.sum))\n",
    "        \n",
    "    def __making__(self,score):#私有方法\n",
    "        if score<0:\n",
    "            return 'not 负分'#score=0\n",
    "        self.score=score\n",
    "        print(self.name+'score::'+str(self.score))\n",
    "        \n",
    "    def do_homework(self):##实例method \n",
    "        print('homework')\n",
    "        \n",
    "    def do_english_homework(self):\n",
    "        print('english')   \n",
    "        \n",
    "    @classmethod #装饰器   类方法\n",
    "    def plus_sum(cls): #can change cls\n",
    "        cls.sum+=1\n",
    "        print(cls.sum) \n",
    "        print(self.name)\n",
    "\n",
    "    #与类  实例无太大关系 可以用 like 函数    \n",
    "    @staticmethod  #调用j静态方法\n",
    "    def add(x,y):\n",
    "        print(Student.sum)#静态方法  访问  类变量   inside\n",
    "        print(self.name)\n",
    "\n",
    "        print('is static method')\n",
    "\n",
    "student1=Student('ss',18)\n",
    "student1.do_homework()\n",
    "result=student1.__making__(59)#私有\n",
    "print(result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "student 数量::1\n",
      "2\n",
      "student 数量::2\n",
      "homework\n",
      "ssscore::59\n",
      "None\n",
      "-1\n",
      "{'name': 'ss', '__score': -1, '_Student__score': 0, 'age': 18, 'score': 59}\n",
      "{'age': 18, 'name': 's1s', '_Student__score': 0}\n"
     ]
    }
   ],
   "source": [
    "class Student():#class\n",
    "    sum=0\n",
    "    #类变量   \n",
    "    def __init__(self,name,age):\n",
    "        self.name=name #\n",
    "        self.age=age#实例变量 value\n",
    "        self.__score=0#私有变量\n",
    "        self.__class__.sum+=1\n",
    "        \n",
    "        print(Student.sum)#类变量   \n",
    "        print('student 数量::' +str(self.__class__.sum))\n",
    "        \n",
    "    def making(self,score):#私有方法\n",
    "        if score<0:\n",
    "            return 'not 负分'#score=0\n",
    "        self.score=score\n",
    "        print(self.name+'score::'+str(self.score))\n",
    "        \n",
    "    def do_homework(self):##实例method \n",
    "        print('homework')\n",
    "        \n",
    "    def do_english_homework(self):\n",
    "        print('english')   \n",
    "        \n",
    "    @classmethod #装饰器   类方法\n",
    "    def plus_sum(cls): #can change cls\n",
    "        cls.sum+=1\n",
    "        print(cls.sum) \n",
    "        print(self.name)\n",
    "\n",
    "    #与类  实例无太大关系 可以用 like 函数    \n",
    "    @staticmethod  #调用j静态方法\n",
    "    def add(x,y):\n",
    "        print(Student.sum)#静态方法  访问  类变量   inside\n",
    "        print(self.name)\n",
    "\n",
    "        print('is static method')\n",
    "\n",
    "student1=Student('ss',18)\n",
    "student2=Student('s1s',18)\n",
    "student1.do_homework()\n",
    "result=student1.making(59)#私有变量\n",
    "print(result)\n",
    "student1.__score=-1\n",
    "\n",
    "print(student1.__score)\n",
    "print(student1.__dict__)\n",
    "print(student2.__dict__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "print(student2._Student__score)#add _  可以访问"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ImportError",
     "evalue": "No module named 'c6'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-ef03b54f4d38>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mimport\u001b[0m \u001b[0mc6\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mc6\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpeople\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mc6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mPeople\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mPeople\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named 'c6'"
     ]
    }
   ],
   "source": [
    "import c6\n",
    "class Student(c6.people):\n",
    "    pass\n",
    "from c6 import People\n",
    "class Student(People):\n",
    "    pass\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "aa\n",
      "18\n",
      "-------------\n",
      "aa\n"
     ]
    }
   ],
   "source": [
    "class Human():\n",
    "    def __init__(self,name,age):\n",
    "        self.name=name\n",
    "        self.age =age\n",
    "    def get_name(self\n",
    "                 ):\n",
    "        print(self.name)\n",
    "class Student(Human):\n",
    "    sum = 0\n",
    "\n",
    "    def __init__(self,name, age):\n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        self.__score = 0\n",
    "        self.__class__.sum += 1\n",
    "    def do_homework(selfself):\n",
    "        print('english homework')\n",
    "\n",
    "student1=Student('aa','18')\n",
    "print(student1.sum)\n",
    "print(Student.sum)\n",
    "print(student1.name)\n",
    "print(student1.age)\n",
    "print('-------------')\n",
    "student1.get_name()#getname\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() missing 1 required positional argument: 'age'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-11-53b6ed735921>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'english homework'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 22\u001b[0;31m \u001b[0mstudent1\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'big school'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'aa'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m8\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     23\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstudent1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     24\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mStudent\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-11-53b6ed735921>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, school, name, age)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mschool\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mschool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mschool\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mHuman\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;31m#son call father\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mage\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() missing 1 required positional argument: 'age'"
     ]
    }
   ],
   "source": [
    "class Human():#father from c6 import Human\n",
    "    sum=0\n",
    "    def __init__(self,name,age):\n",
    "        self.name=name\n",
    "        self.age =age\n",
    "    def get_name(self\n",
    "                 ):\n",
    "        print(self.name)\n",
    "class Student(Human):\n",
    "    sum = 0\n",
    "\n",
    "    def __init__(self,school,name, age):\n",
    "        self.school = school\n",
    "        Human.__init__(name,age)#son call father\n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        self.__score = 0\n",
    "        self.__class__.sum += 1\n",
    "    def do_homework(selfself):\n",
    "        print('english homework')\n",
    "\n",
    "student1=Student('big school','aa',8)\n",
    "print(student1.sum)\n",
    "print(Student.sum)\n",
    "print(student1.name)\n",
    "print(student1.age)\n",
    "print('-------------')\n",
    "student1.get_name()#getname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "1\n",
      "aa\n",
      "118\n",
      "-------------\n",
      "aa\n"
     ]
    }
   ],
   "source": [
    "class Human():#father from c6 import Human\n",
    "    sum=0\n",
    "    def __init__(self,name,age):\n",
    "        self.name=name\n",
    "        self.age =age\n",
    "    def get_name(self\n",
    "                 ):\n",
    "        print(self.name)\n",
    "class Student(Human):\n",
    "    sum = 0\n",
    "\n",
    "    def __init__(self,school,name, age):\n",
    "        self.school = school\n",
    "        Human.__init__(self,name,age)#son call father  类call 调用实例方法method \n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        self.__score = 0\n",
    "        self.__class__.sum += 1\n",
    "    def do_homework(selfself):\n",
    "        print('english homework')\n",
    "\n",
    "student1=Student('big school','aa',118)\n",
    "print(student1.sum)\n",
    "print(Student.sum)\n",
    "print(student1.name)\n",
    "print(student1.age)\n",
    "print('-------------')\n",
    "student1.get_name()#getname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "english homework\n",
      "-------------\n",
      "1\n",
      "1\n",
      "aa\n",
      "118\n",
      "-------------\n",
      "aa\n"
     ]
    }
   ],
   "source": [
    "class Human():#father from c6 import Human\n",
    "    sum=0\n",
    "    def __init__(self,name,age):\n",
    "        self.name=name\n",
    "        self.age =age\n",
    "    def get_name(self):\n",
    "                 \n",
    "        print(self.name)\n",
    "    def do_homework(self):\n",
    "        print('english homew55ork')\n",
    "class Student(Human):\n",
    "    sum = 0\n",
    "\n",
    "    def __init__(self,school,name, age):\n",
    "        self.school = school\n",
    "        Human.__init__(self,name,age)#son call father #son call father \n",
    "        #类call 调用实例方法method \n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        self.__score = 0\n",
    "        self.__class__.sum += 1\n",
    "    def do_homework(self):\n",
    "        print('english homework')\n",
    "\n",
    "student1=Student('big school','aa',118)\n",
    "Student.do_homework('')#student1)#麻烦  任意参数\n",
    "print('-------------')\n",
    "student1.do_homework#简单 \n",
    "print(student1.sum)\n",
    "print(Student.sum)\n",
    "print(student1.name)\n",
    "print(student1.age)\n",
    "print('-------------')\n",
    "student1.get_name()#getname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "english homework\n",
      "-------------\n",
      "english homework\n",
      "1\n",
      "1\n",
      "aa\n",
      "118\n",
      "-------------\n",
      "aa\n"
     ]
    }
   ],
   "source": [
    "class Human():#father from c6 import Human\n",
    "    sum=0\n",
    "    def __init__(self,name,age):\n",
    "        self.name=name\n",
    "        self.age =age\n",
    "    def get_name(self):\n",
    "                 \n",
    "        print(self.name)\n",
    "        \n",
    "class Student(Human):\n",
    "    sum = 0\n",
    "\n",
    "    def __init__(self,school,name, age):\n",
    "        self.school = school\n",
    "        #Human.__init__(self,name,age)#son call father\n",
    "        super(Student,self).__init__(name,age)\n",
    "        \n",
    "        self.name = name\n",
    "        self.age = age\n",
    "        self.__score = 0\n",
    "        self.__class__.sum += 1\n",
    "    def do_homework(self):\n",
    "        \n",
    "        print('english homework')\n",
    "\n",
    "student1=Student('big school','aa',118)\n",
    "Student.do_homework('')#student1)#麻烦  任意参数   子父类方法  same      子类方法  first run\n",
    "print('-------------')\n",
    "student1.do_homework()#简单 \n",
    "print(student1.sum)\n",
    "print(Student.sum)\n",
    "print(student1.name)\n",
    "print(student1.age)\n",
    "print('-------------')\n",
    "student1.get_name()#getname"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------\n",
      "aa\n",
      "18\n",
      "english homework552222222222222222\n",
      "english homework\n"
     ]
    }
   ],
   "source": [
    "class Human():#father from c6 import Human\n",
    "    #sum=0\n",
    "    def __init__(self,name,age):\n",
    "        self.name=name\n",
    "        self.age =age\n",
    "        \n",
    "    def get_name(self):               \n",
    "        print(self.name)\n",
    "        \n",
    "    def do_homework(self):\n",
    "        print('english homework552222222222222222')  \n",
    "        \n",
    "class Student(Human):\n",
    "    #sum = 0\n",
    "    def __init__(self,school,name, age):\n",
    "        self.school = school\n",
    "        super(Student,self).__init__(name,age)#不用到处\n",
    "     \n",
    "    def do_homework(self):\n",
    "        super(Student,self).do_homework()#调用父类方法\n",
    "        print('english homework')\n",
    "\n",
    "student1=Student('big school','aa',18)\n",
    "#Student.do_homework('')#student1)#麻烦  任意参数   子父类方法  same      子类方法  first run\n",
    "print('-----------------------')\n",
    "print(student1.name)\n",
    "print(student1.age)\n",
    "student1.do_homework()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "B.__init__\n",
      "A1.__init__\n",
      "<function A1.__init__ at 0x0000014BF0A2E8C8>\n",
      "-----------\n",
      "B.__init__\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'A' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-16-14cafd457b34>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[1;31m#则还是调用了A的__init__方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 24\u001b[0;31m \u001b[0mb\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mB\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     25\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mB\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-16-14cafd457b34>\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m         \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'B.__init__'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 21\u001b[0;31m         \u001b[0mA\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__init__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[1;31m#则还是调用了A的__init__方法\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'A' is not defined"
     ]
    }
   ],
   "source": [
    "#定义新的父类A1\n",
    "class A1(object):\n",
    "    def __init__(self):\n",
    "        print('A1.__init__')\n",
    "\n",
    "#更改继承关系B->A改成B->A1\n",
    "class B(A1):\n",
    "    def __init__(self):\n",
    "        print('B.__init__')\n",
    "        A1.__init__(self)\n",
    "\n",
    "#能正确调用新的父类A1的__init__方法\n",
    "b = B()\n",
    "B.__init__\n",
    "print(A1.__init__)\n",
    "print('-----------')\n",
    "#假设忘了修改A.__init__(self)\n",
    "class B(A1):\n",
    "    def __init__(self):\n",
    "        print('B.__init__')\n",
    "        A.__init__(self)\n",
    "      \n",
    "#则还是调用了A的__init__方法\n",
    "b = B()\n",
    "print(B.__init__)\n",
    "print(A.__init__)\n",
    "#errorrrrrrrrrrrrrrrrrrrr\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='c|c++\\java|python|javascript'\n",
    "a.index('python')>-1 #0,1#是否包含"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'python'in a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['python']"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='c|c++\\java|python|javascript'\n",
    "re.findall('python',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "包含pthon\n"
     ]
    }
   ],
   "source": [
    "r=re.findall('python',a)\n",
    "if len(r)!=0:\n",
    "    print('包含pthon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no\n"
     ]
    }
   ],
   "source": [
    "r=re.findall('phon',a)\n",
    "if len(r)!=0:\n",
    "    print('包含pthon')\n",
    "else:\n",
    "    print('no')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['on']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='c|c++\\java|python|javascript'\n",
    "re.findall('on',a)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['n']"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall('n',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['1', '3', '7']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='c|c++1\\java|pytho3n|javasc7ript'\n",
    "re.findall('\\d',a)#no.字符序列\n",
    "#’pythh' 普通字符'\\d'元字符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['c',\n",
       " '|',\n",
       " 'c',\n",
       " '+',\n",
       " '+',\n",
       " '\\\\',\n",
       " 'j',\n",
       " 'a',\n",
       " 'v',\n",
       " 'a',\n",
       " '|',\n",
       " 'p',\n",
       " 'y',\n",
       " 't',\n",
       " 'h',\n",
       " 'o',\n",
       " 'n',\n",
       " '|',\n",
       " 'j',\n",
       " 'a',\n",
       " 'v',\n",
       " 'a',\n",
       " 's',\n",
       " 'c',\n",
       " 'r',\n",
       " 'i',\n",
       " 'p',\n",
       " 't']"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "re.findall('\\D',a)#not no."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['acc']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='abc,acc,advc,ahc,afc'\n",
    "re.findall('acc',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['acc', 'afc']"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='abc,acc,advc,ahc,afc'\n",
    "re.findall('a[cf]c',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['acc', 'adc', 'afc']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='abc,acc,adc,ahc,afc'\n",
    "re.findall('a[cfd]c',a)#middle c .d. f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abc', 'ahc']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re #字符ji集\n",
    "a='abc,acc,adc,ahc,afc'\n",
    "re.findall('a[^cfd]c',a)#not c f d "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['2', '3', '5']"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#概括字符ji集\n",
    "import re \n",
    "a='ab2c,acc,a3dc,ah5c,afc'\n",
    "re.findall('[0-9]',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'b',\n",
       " 'c',\n",
       " ',',\n",
       " 'a',\n",
       " 'c',\n",
       " 'c',\n",
       " ',',\n",
       " 'a',\n",
       " 'd',\n",
       " 'c',\n",
       " ',',\n",
       " 'a',\n",
       " 'h',\n",
       " 'c',\n",
       " ',',\n",
       " 'a',\n",
       " 'f',\n",
       " 'c']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='ab2c,acc,a3dc,ah5c,afc'\n",
    "re.findall('[^0-9]',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'b',\n",
       " '2',\n",
       " 'c',\n",
       " 'a',\n",
       " 'c',\n",
       " 'c',\n",
       " 'a',\n",
       " '3',\n",
       " 'd',\n",
       " 'c',\n",
       " 'a',\n",
       " 'h',\n",
       " '5',\n",
       " 'c',\n",
       " 'a',\n",
       " 'f',\n",
       " 'c',\n",
       " 'h']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='ab2c,acc,a3dc,ah5c,a&fch'\n",
    "re.findall('\\w',a)#no output &   \\w单词字符"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a',\n",
       " 'b',\n",
       " '2',\n",
       " 'c',\n",
       " 'a',\n",
       " 'c',\n",
       " 'c',\n",
       " 'a',\n",
       " '3',\n",
       " 'd',\n",
       " 'c',\n",
       " 'a',\n",
       " 'h',\n",
       " '5',\n",
       " 'c',\n",
       " 'a',\n",
       " 'f',\n",
       " 'c']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='ab2c,acc,a3dc,ah5c,a&fc'\n",
    "re.findall('[A-Za-z0-9]',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[',', ',', ',', ',', '&', '\\n']"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='ab2c,acc,a3dc,ah5c,a&fc\\nh'\n",
    "re.findall('\\W',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['acc']"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='ab2c,acc,a3dc,ah5c,a&fc\\nh'\n",
    "re.findall('[a-z][a-z][a-z]',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cac', 'cca', 'dca']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='ab2caccca3dcah5ca&fc\\nh'\n",
    "re.findall('[a-z]{3}',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['caccca', 'dcah']"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='ab2caccca3dcah5ca&fc\\nh'\n",
    "re.findall('[a-z]{3,6}',a)#3-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['abc', 'ccc', 'dca', 'fcp']"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#默认贪婪 匹配more更多    加？是非贪婪\n",
    "a='abca ccca3dcah5ca&fcpnh'\n",
    "re.findall('[a-z]{3,6}?',a)#3-6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['python', 'pythonn']"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#              *   is 0--n times\n",
    "import re \n",
    "a='pythonc|c++\\java|pythonn|javascript'\n",
    "re.findall('python*',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['python', 'pythonn']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#                    + is 1--n times\n",
    "a='pythonc|c++\\java|pythonn|javascript'\n",
    "re.findall('python+',a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['python', 'python']"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#         ? is # 0--1 times\n",
    "a='pythonc|c++\\java|pythonn|javascript'\n",
    "re.findall('python?',a)# 0--1 times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['python', 'pythonn']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='pythonc|c++\\java|pythonn|javascript'\n",
    "re.findall('python{1,2}',a)# 1--2 times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['10000000', '00001']"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#边界匹配\n",
    "qq='1000000000001'\n",
    "r=re.findall('\\d{4,8}',qq)#4-8   ^ is start   $ is end\n",
    "r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#边界匹配\n",
    "qq='1000000000001'\n",
    "r=re.findall('^000',qq)#4-8   ^ is start   $ is end\n",
    "r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "qq='1000000000001'\n",
    "r=re.findall('000$',qq)#4-8   ^ is start   $ is end\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['python']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#组  ()is and    {} is or \n",
    "a='pythonpythonpythonpython'\n",
    "r=re.findall('(python){3}',a)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a='pythonpythonpythonpython'\n",
    "r=re.findall('(python){3}(js)',a)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['C#\\n']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lanuage='PythonC#\\nJavaPHP'\n",
    "r =re.findall('c#.{1}',lanuage,re.I|re.S)#忽略小写  | is and \n",
    "#    .匹配除\\n的其他所有字符\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PythonGO\\nJavaPHPGO'"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lanuage='PythonC#\\nJavaPHPC#'\n",
    "r =re.sub('C#','GO',lanuage)#替换\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PythonGO\\nJavaPHPGO'"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lanuage='PythonC#\\nJavaPHPC#'\n",
    "r =re.sub('C#','GO',lanuage,0)##all替换\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PythonGO\\nJavaPHPC#'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lanuage='PythonC#\\nJavaPHPC#'\n",
    "r =re.sub('C#','GO',lanuage,1)##替换1 次\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PythonGO\\nJavaPHPGO'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lanuage='PythonC#\\nJavaPHPC#'\n",
    "lanuage = lanuage.replace('C#','GO')#替换1\n",
    "lanuage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pythonc|c++\\java|pythonn|javascript\n"
     ]
    }
   ],
   "source": [
    "import re \n",
    "a='pythonc|c++\\java|pythonn|javascript'\n",
    "def convert(vallue):\n",
    "    pass\n",
    "r=re.sub('C#',convert,a)\n",
    "print(r)    #C# 消失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pythonc|c++\\\\java|pythonn|javascript'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='pythonc|c++\\java|pythonn|javascript'\n",
    "def convert(vallue):\n",
    "    return '!!'+value +'!!'\n",
    "r=re.sub('C#',convert,a)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_sre.SRE_Match object; span=(7, 9), match='C#'>\n",
      "<_sre.SRE_Match object; span=(26, 28), match='C#'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'pythonc|c++\\\\java|pythonn|javascript'"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='pythoncC#|c++\\java|pythonnC#|javascript'\n",
    "def convert(value):\n",
    "    print(value)\n",
    "r=re.sub('C#',convert,a)\n",
    "r                #object  对象   span  匹配位置7，9 is 8-9  c#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pythonc    !!C#!!   |c++\\\\java|pythonn    !!C#!!   |javascript'"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "a='pythoncC#|c++\\java|pythonnC#|javascript'\n",
    "def convert(value):\n",
    "    matched = value.group()\n",
    "    #print(value)\n",
    "    return '    !!' +  matched +   '!!   '\n",
    "r=re.sub('C#',convert,a)#  函数当作参数  \n",
    "r          "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-57-09d7519bd264>, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-57-09d7519bd264>\"\u001b[0;36m, line \u001b[0;32m8\u001b[0m\n\u001b[0;31m    reurn 0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "import re \n",
    "a='aasaassa15454321321635578778777777777'#is  str \n",
    "def convert(value):\n",
    "    matched = value.group()\n",
    "    if matched >=6:\n",
    "        return    9\n",
    "    else:\n",
    "        reurn 0\n",
    "r=re.sub('\\d',convert,a)#  函数当作参数  \n",
    "r       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-58-13cc96a647cb>, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-58-13cc96a647cb>\"\u001b[0;36m, line \u001b[0;32m8\u001b[0m\n\u001b[0;31m    reurn 0\u001b[0m\n\u001b[0m          ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "import re \n",
    "a='aasaassa15454321321635578778777777777'#is  str \n",
    "def convert(value):\n",
    "    matched = value.group()\n",
    "    if int(matched) >=6:\n",
    "        return    9\n",
    "    else:\n",
    "        reurn 0\n",
    "r=re.sub('\\d',convert,a)#  函数当作参数  \n",
    "r  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'00000000000900099999999999999'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "s='15454321321635578778777777777'#is  str \n",
    "def convert(value):\n",
    "    matched = value.group()\n",
    "    if int(matched) >=6:\n",
    "        return'9'\n",
    "    else:\n",
    "        return'0'\n",
    "r=re.sub('\\d',convert,s)#  函数当作参数  \n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "<_sre.SRE_Match object; span=(1, 2), match='8'>\n"
     ]
    }
   ],
   "source": [
    "import re \n",
    "s='a8355441655efsd55'\n",
    "r=re.match('\\d',s)\n",
    "print(r)#meet a    not   return none    匹配success  then stop\n",
    "\n",
    "r1 =re.search('\\d',s)#meet 8    not   return span \n",
    "print(r1)\n",
    "\n",
    "r2= re.findall('\\d',s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_sre.SRE_Match object; span=(0, 1), match='8'>\n",
      "<_sre.SRE_Match object; span=(0, 1), match='8'>\n"
     ]
    }
   ],
   "source": [
    "import re \n",
    "s='8355441655efsd55'\n",
    "r=re.match('\\d',s)#匹配success  then stop\n",
    "print(r)#meet a    not   return none\n",
    "\n",
    "r1 =re.search('\\d',s)#meet 8    not   return span \n",
    "print(r1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<_sre.SRE_Match object; span=(0, 1), match='8'>\n",
      "8\n",
      "['8', '3', '5', '5', '4', '4', '1', '6', '5', '5', '5', '5']\n"
     ]
    }
   ],
   "source": [
    "import re \n",
    "s='8355441655efsd55'\n",
    "r=re.match('\\d',s)\n",
    "print(r)#meet a    not   return none\n",
    "\n",
    "r1 =re.search('\\d',s)#meet 8    not   return span \n",
    "print(r1.group())#return 匹配结果8  匹配success  then stop\n",
    "\n",
    "r2= re.findall('\\d',s)\n",
    "print(r2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute 'group'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-65-ba1679470622>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'life is short ,i use python'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mre\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msearch\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'life\\w*python'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroup\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute 'group'"
     ]
    }
   ],
   "source": [
    "import re \n",
    "s='life is short ,i use python'\n",
    "r=re.search('life\\w*python',s)#  \\w  不能搞空格\n",
    "r.group()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'life is short ,i use python'"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "s='life is short ,i use python'\n",
    "r=re.search('life.*python',s)\n",
    "r.group()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'life is short ,i use python'"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "s='life is short ,i use python'\n",
    "r=re.search('(life.*python)',s)\n",
    "r.group(0)#0 iS 默认 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' is short ,i use '"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "s='life is short ,i use python'\n",
    "r=re.search('life(.*)python',s)\n",
    "r.group(1)#0 iS 默认 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['life is short ,i use python']"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "s='life is short ,i use python'\n",
    "r=re.findall('(life.*python)',s)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[' is short ,i use ']"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "s='life is short ,i use python'\n",
    "r=re.findall('life(.*)python',s)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import re \n",
    "s='life is short ,i use python'\n",
    "r=re.findall('life(.*)python(.*)python',s)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(' is short ,i use ', ',i love ')\n",
      " is short ,i use \n",
      ",i love \n",
      "life is short ,i use python,i love python\n",
      "('life is short ,i use python,i love python', ' is short ,i use ', ',i love ')\n"
     ]
    }
   ],
   "source": [
    "import re \n",
    "s='life is short ,i use python,i love python '\n",
    "r=re.search('life(.*)python(.*)python',s)\n",
    "print(r.groups())\n",
    "print(r.group(1))\n",
    "print(r.group(2))\n",
    "print(r.group(0))\n",
    "print(r.group(0,1,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "json  数据格式{\"name\":\"qiyue\"}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json \n",
    "json_str = '{\"name\":\"qiyue\",\"age\":\"9\"}'\n",
    "student=json.loads(json_str)#序列\n",
    "type(student)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'age': '9', 'name': 'qiyue'}"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'qiyue'"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'9'"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student['age']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "student=[{'name':'qiyue','age':18,'flag':False},\n",
    "         {'name':'aiyue','age':19}]\n",
    "json_str =json.dumps(student)#反序列\n",
    "type(json_str)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(student)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[{\"age\": 18, \"name\": \"qiyue\", \"flag\": false}, {\"age\": 19, \"name\": \"aiyue\"}]'"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_str"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VIP.YELLOW: 1>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from enum import Enum#枚举 de class 类型   枚举 de name  枚举 de value\n",
    "class VIP(Enum):\n",
    "    YELLOW=1\n",
    "    green=2\n",
    "    black=3\n",
    "    red=4\n",
    "VIP.YELLOW\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<enum 'VIP'>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(VIP.green)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(VIP.green.name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VIP.YELLOW: 1>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIP['YELLOW']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VIP.YELLOW\n",
      "VIP.green\n",
      "VIP.black\n",
      "VIP.red\n"
     ]
    }
   ],
   "source": [
    "for v in VIP:\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<VIP.green: 2>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIP.green"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VIP.green==VIP.red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "unorderable types: VIP() > VIP()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-9-4061aa375e51>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mVIP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgreen\u001b[0m\u001b[1;33m>\u001b[0m\u001b[0mVIP\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mred\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unorderable types: VIP() > VIP()"
     ]
    }
   ],
   "source": [
    "VIP.green>VIP.red"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class VIP1(Enum):\n",
    "    YELLOW=10\n",
    "    YELLOW_r=10  #value is same  is 别名\n",
    "    green=2 \n",
    "    black=3\n",
    "    red=4\n",
    "VIP.YELLOW==VIP1.YELLOW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'tuple'>\n",
      "<class 'tuple'>\n",
      "<class 'tuple'>\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "for v in VIP1.__members__.items():\n",
    "    print(type(v))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "YELLOW\n",
      "green\n",
      "black\n",
      "red\n"
     ]
    }
   ],
   "source": [
    "for v in VIP1.__members__:\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('YELLOW', <VIP1.YELLOW: 10>)\n",
      "('green', <VIP1.green: 2>)\n",
      "('black', <VIP1.black: 3>)\n",
      "('red', <VIP1.red: 4>)\n"
     ]
    }
   ],
   "source": [
    "for v in VIP1.__members__.items():\n",
    "    print(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VIP.YELLOW\n"
     ]
    }
   ],
   "source": [
    "a=1 \n",
    "print(VIP(a))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from enum import Enum,IntEnum\n",
    "class VIP(Enum):\n",
    "    YELLOW=10\n",
    "    YELLOW_r=10  #value is same  is 别名\n",
    "    green=2 \n",
    "    black='str'\n",
    "    red=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'sy'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-18-cb1f092f8868>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[1;32mclass\u001b[0m \u001b[0mVIP1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIntEnum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mYELLOW\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mYELLOW_r\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m  \u001b[1;31m#value is same  is 别名\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mgreen\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mblack\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\zwPython\\py35\\python-3.5.2.amd64\\lib\\enum.py\u001b[0m in \u001b[0;36m__new__\u001b[0;34m(metacls, cls, bases, classdict)\u001b[0m\n\u001b[1;32m    158\u001b[0m                     \u001b[0menum_member\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    159\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 160\u001b[0;31m                 \u001b[0menum_member\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m__new__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menum_class\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    161\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mhasattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0menum_member\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_value_'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    162\u001b[0m                     \u001b[0menum_member\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_value_\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmember_type\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: invalid literal for int() with base 10: 'sy'"
     ]
    }
   ],
   "source": [
    "class VIP1(IntEnum):\n",
    "    YELLOW=10\n",
    "    YELLOW_r=10  #value is same  is 别名\n",
    "    green=2 \n",
    "    black=3\n",
    "    red='sy'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "duplicate values found in <enum 'VIP2'>: YELLOW_r -> YELLOW",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-8a65dbc63ba8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0menum\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0munique\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[1;32mclass\u001b[0m \u001b[0mVIP2\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mIntEnum\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mYELLOW\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mYELLOW_r\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m10\u001b[0m  \u001b[1;31m#value is same  is 别名\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\zwPython\\py35\\python-3.5.2.amd64\\lib\\enum.py\u001b[0m in \u001b[0;36munique\u001b[0;34m(enumeration)\u001b[0m\n\u001b[1;32m    571\u001b[0m                 [\"%s -> %s\" % (alias, name) for (alias, name) in duplicates])\n\u001b[1;32m    572\u001b[0m         raise ValueError('duplicate values found in %r: %s' %\n\u001b[0;32m--> 573\u001b[0;31m                 (enumeration, alias_details))\n\u001b[0m\u001b[1;32m    574\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0menumeration\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: duplicate values found in <enum 'VIP2'>: YELLOW_r -> YELLOW"
     ]
    }
   ],
   "source": [
    "from enum import unique\n",
    "@unique   #value 10 is sme error\n",
    "class VIP2(IntEnum):\n",
    "    YELLOW=10\n",
    "    YELLOW_r=10  #value is same  is 别名\n",
    "    green=2 \n",
    "    black=3\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a=def or func#一切皆对象"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thiis \n"
     ]
    }
   ],
   "source": [
    "def cure_pre():\n",
    "    def cure():\n",
    "        print('thiis ')\n",
    "        pass\n",
    "    return cure\n",
    "f = cure_pre() \n",
    "f()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "thiis \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cure_pre():\n",
    "    a=25\n",
    "    def cure(x):\n",
    "        print('thiis ')\n",
    "        return a*x*x   #25*4\n",
    "        \n",
    "    return cure  #ruturn 函数\n",
    "f = cure_pre()\n",
    "f(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(<cell at 0x0000018E206117C8: int object at 0x000000006E7E04D0>,)\n",
      "25\n",
      "thiis \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cure_pre():\n",
    "    a=25 #闭包  = 函数  +  环境变量\n",
    "    def cure(x):\n",
    "        print('thiis ')\n",
    "        return a*x*x   #25*4\n",
    "        \n",
    "    return cure  #ruturn 函数\n",
    "a=10\n",
    "f = cure_pre()\n",
    "#f(2)\n",
    "print(f.__closure__)\n",
    "print(f.__closure__[0].cell_contents)#闭包\n",
    "f(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "None\n",
      "thiis \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def cure_pre():\n",
    "    #a=25\n",
    "    def cure(x):\n",
    "        print('thiis ')\n",
    "        return a*x*x   #25*4\n",
    "        \n",
    "    return cure  #ruturn 函数\n",
    "a=10  #\n",
    "f = cure_pre()\n",
    "\n",
    "#f(2)\n",
    "f(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "20\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "def f1():\n",
    "    a=10\n",
    "    def f2():\n",
    "        a=20\n",
    "        print(a)\n",
    "    print(a)\n",
    "    f2()\n",
    "    print(a)\n",
    "f1()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'NoneType' object has no attribute '__closure__'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-af8e57aafc32>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mf1\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__closure__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m: 'NoneType' object has no attribute '__closure__'"
     ]
    }
   ],
   "source": [
    "def f1():\n",
    "    a=10\n",
    "    def f2():\n",
    "        a=20\n",
    "       \n",
    "  \n",
    "    f2()\n",
    "   \n",
    "f=f1()\n",
    "print(f.__closure__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function f1.<locals>.f2 at 0x0000018E206FA6A8>\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "def f1():\n",
    "    a=10\n",
    "    def f2():\n",
    "        a=20\n",
    "    return f2  \n",
    "f=f1()\n",
    "print(f)\n",
    "print(f.__closure__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function f1.<locals>.f2 at 0x0000018E206FA378>\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "def f1():\n",
    "    a=10\n",
    "    def f2():\n",
    "        \n",
    "        a=20  #a 不引用a=10 is局部变量        闭包不存在\n",
    "        return a\n",
    "    return f2  \n",
    "f=f1()\n",
    "print(f)\n",
    "print(f.__closure__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function f1.<locals>.f2 at 0x0000018E206FA950>\n",
      "(<cell at 0x0000018E20611E28: int object at 0x000000006E7E02F0>,)\n"
     ]
    }
   ],
   "source": [
    "def f1():\n",
    "    a=10\n",
    "    def f2():\n",
    "        \n",
    "        \n",
    "        return a\n",
    "    return f2  \n",
    "f=f1()\n",
    "print(f)\n",
    "print(f.__closure__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function f1.<locals>.f2 at 0x0000018E206FA8C8>\n",
      "(<cell at 0x0000018E20611C78: int object at 0x000000006E7E02F0>,)\n"
     ]
    }
   ],
   "source": [
    "def f1():\n",
    "    a=10\n",
    "    def f2():        \n",
    "        return a\n",
    "    \n",
    "    return f2  \n",
    "f=f1()\n",
    "print(f)\n",
    "print(f.__closure__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<function f1.<locals>.f2 at 0x0000018E206FAB70>\n",
      "(<cell at 0x0000018E206116A8: int object at 0x000000006E7E02F0>,)\n"
     ]
    }
   ],
   "source": [
    "def f1():\n",
    "    a=10\n",
    "    def f2():\n",
    "        c= 20*a#闭包 可以引用  不能变量赋值    a=12\n",
    "        \n",
    "        \n",
    "    return f2  \n",
    "f=f1()\n",
    "print(f)\n",
    "print(f.__closure__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'origin' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-32-babe4e953342>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0morign\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-32-babe4e953342>\u001b[0m in \u001b[0;36mgo\u001b[0;34m(step)\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0mnew_pos\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morigin\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0morign\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'origin' referenced before assignment"
     ]
    }
   ],
   "source": [
    "#闭包   \n",
    "origin=0\n",
    "def go(step):\n",
    "    new_pos=origin +step#局部变量没有define\n",
    "    origin= new_pos\n",
    "    return orign\n",
    "print(go(2))\n",
    "print(go(3))\n",
    "print(go(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "origin=0\n",
    "def go(step):\n",
    "    new_pos=origin +step#局部变量没有define\n",
    "    #origin= new_pos\n",
    "    return origin\n",
    "print(go(2))\n",
    "print(go(3))\n",
    "print(go(6))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'origin' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-35-d3c4f998623a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-35-d3c4f998623a>\u001b[0m in \u001b[0;36mgo\u001b[0;34m(step)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mnew_pos\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morigin\u001b[0m \u001b[1;33m+\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;31m#局部变量没有define\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0morigin\u001b[0m\u001b[1;33m=\u001b[0m \u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'origin' referenced before assignment"
     ]
    }
   ],
   "source": [
    "origin=0\n",
    "def go(step):\n",
    "    new_pos=origin +step#new-pos 局部变量没有define\n",
    "    origin= new_pos#  等号左边的值 是局部变量  不去外面找\n",
    "    return new_pos\n",
    "print(go(2))\n",
    "print(go(3))\n",
    "print(go(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "5\n",
      "11\n"
     ]
    }
   ],
   "source": [
    "origin=0\n",
    "def go(step):\n",
    "    global origin\n",
    "    new_pos=origin +step\n",
    "    origin= new_pos#  \n",
    "    return new_pos\n",
    "print(go(2))\n",
    "print(go(3))\n",
    "print(go(6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'NoneType' object is not callable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-38-ff66aa3fe246>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtourist\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mfactory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtourist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtourist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtourist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not callable"
     ]
    }
   ],
   "source": [
    "origin=0\n",
    "def factory(pos):\n",
    "    def go(step):\n",
    "        new_pos=pos+step\n",
    "        pos =new_pos\n",
    "        return new_pos\n",
    "tourist =factory(origin)\n",
    "print(tourist(2))\n",
    "print(tourist(12))\n",
    "print(tourist(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "UnboundLocalError",
     "evalue": "local variable 'pos' referenced before assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mUnboundLocalError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-39-4e57bcb54e75>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mtourist\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mfactory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morigin\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtourist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtourist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m12\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtourist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m20\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m<ipython-input-39-4e57bcb54e75>\u001b[0m in \u001b[0;36mgo\u001b[0;34m(step)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mfactory\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m         \u001b[0mnew_pos\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpos\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m         \u001b[0mpos\u001b[0m \u001b[1;33m=\u001b[0m\u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mnew_pos\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mUnboundLocalError\u001b[0m: local variable 'pos' referenced before assignment"
     ]
    }
   ],
   "source": [
    "origin=0\n",
    "def factory(pos):\n",
    "    def go(step):\n",
    "        new_pos=pos+step\n",
    "        pos =new_pos\n",
    "        return new_pos\n",
    "    return go\n",
    "tourist =factory(origin)\n",
    "print(tourist(2))\n",
    "print(tourist(12))\n",
    "print(tourist(20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "5\n",
      "10\n"
     ]
    }
   ],
   "source": [
    "origin=0\n",
    "def factory1(pos):\n",
    "    def go(step):\n",
    "        nonlocal pos   #not local\n",
    "        new_pos=pos+step\n",
    "        pos =new_pos\n",
    "        return new_pos\n",
    "    return go\n",
    "tourist1 =factory1(origin)\n",
    "print(tourist1(2))\n",
    "print(tourist1(3))\n",
    "print(tourist1(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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