{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### 网络科学理论简介\n",
    "***\n",
    "***\n",
    "# 天涯论坛的回帖网络分析\n",
    "***\n",
    "***\n",
    "\n",
    "王成军 \n",
    "\n",
    "wangchengjun@nju.edu.cn\n",
    "\n",
    "计算传播网 http://computational-communication.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8079"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtt = []\n",
    "with open('/Users/chengjun/github/cjc2016/data/tianya_bbs_threads_network.txt', 'r') as f:\n",
    "    for line in f:\n",
    "        pnum, link, time, author_id, author, content = line.replace('\\n', '').split('\\t')\n",
    "        dtt.append([pnum, link, time, author_id, author, content])\n",
    "len(dtt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>page_num</th>\n",
       "      <th>link</th>\n",
       "      <th>time</th>\n",
       "      <th>author</th>\n",
       "      <th>author_name</th>\n",
       "      <th>reply</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 07:59:00</td>\n",
       "      <td>50499450</td>\n",
       "      <td>贾也</td>\n",
       "      <td>先生是一位真爷们！第161期导语：人人宁波，面朝大海，春暖花开!　　宁波的事，怎谈？无从谈，...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 08:13:54</td>\n",
       "      <td>22122799</td>\n",
       "      <td>三平67</td>\n",
       "      <td>我们中国人都在一条船，颠簸已久，我们都想做宁波人，希望有一个风平浪静的港湾，面朝大海，春暖花...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 08:27:02</td>\n",
       "      <td>39027950</td>\n",
       "      <td>赶浪头</td>\n",
       "      <td>默默围观~</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 08:43:15</td>\n",
       "      <td>53986501</td>\n",
       "      <td>m408833176</td>\n",
       "      <td>不能收藏？</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 08:55:52</td>\n",
       "      <td>39073643</td>\n",
       "      <td>兰质薰心</td>\n",
       "      <td>楼主好文！　　相信政府一定有能力解决好这些问题.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  page_num                        link                 time    author  \\\n",
       "0        1  /post-free-2849477-1.shtml  2012-10-29 07:59:00  50499450   \n",
       "1        1  /post-free-2849477-1.shtml  2012-10-29 08:13:54  22122799   \n",
       "2        1  /post-free-2849477-1.shtml  2012-10-29 08:27:02  39027950   \n",
       "3        1  /post-free-2849477-1.shtml  2012-10-29 08:43:15  53986501   \n",
       "4        1  /post-free-2849477-1.shtml  2012-10-29 08:55:52  39073643   \n",
       "\n",
       "  author_name                                              reply  \n",
       "0          贾也  先生是一位真爷们！第161期导语：人人宁波，面朝大海，春暖花开!　　宁波的事，怎谈？无从谈，...  \n",
       "1        三平67  我们中国人都在一条船，颠簸已久，我们都想做宁波人，希望有一个风平浪静的港湾，面朝大海，春暖花...  \n",
       "2         赶浪头                                              默默围观~  \n",
       "3  m408833176                                              不能收藏？  \n",
       "4        兰质薰心                           楼主好文！　　相信政府一定有能力解决好这些问题.  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "dt = pd.DataFrame(dtt)\n",
    "dt=dt.rename(columns = {0:'page_num', 1:'link', 2:'time', 3:'author',4:'author_name', 5:'reply'})\n",
    "dt[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "# extract date from datetime\n",
    "date = map(lambda x: x[:10], dt.time)\n",
    "dt['date'] = pd.to_datetime(date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
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       "      <th>reply</th>\n",
       "      <th>date</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
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       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 07:59:00</td>\n",
       "      <td>50499450</td>\n",
       "      <td>贾也</td>\n",
       "      <td>先生是一位真爷们！第161期导语：人人宁波，面朝大海，春暖花开!　　宁波的事，怎谈？无从谈，...</td>\n",
       "      <td>2012-10-29</td>\n",
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       "      <td>2012</td>\n",
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       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 08:13:54</td>\n",
       "      <td>22122799</td>\n",
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       "      <td>我们中国人都在一条船，颠簸已久，我们都想做宁波人，希望有一个风平浪静的港湾，面朝大海，春暖花...</td>\n",
       "      <td>2012-10-29</td>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 08:27:02</td>\n",
       "      <td>39027950</td>\n",
       "      <td>赶浪头</td>\n",
       "      <td>默默围观~</td>\n",
       "      <td>2012-10-29</td>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 08:43:15</td>\n",
       "      <td>53986501</td>\n",
       "      <td>m408833176</td>\n",
       "      <td>不能收藏？</td>\n",
       "      <td>2012-10-29</td>\n",
       "      <td>10</td>\n",
       "      <td>2012</td>\n",
       "      <td>29</td>\n",
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       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>2012-10-29 08:55:52</td>\n",
       "      <td>39073643</td>\n",
       "      <td>兰质薰心</td>\n",
       "      <td>楼主好文！　　相信政府一定有能力解决好这些问题.</td>\n",
       "      <td>2012-10-29</td>\n",
       "      <td>10</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "  page_num                        link                time    author  \\\n",
       "0        1  /post-free-2849477-1.shtml 2012-10-29 07:59:00  50499450   \n",
       "1        1  /post-free-2849477-1.shtml 2012-10-29 08:13:54  22122799   \n",
       "2        1  /post-free-2849477-1.shtml 2012-10-29 08:27:02  39027950   \n",
       "3        1  /post-free-2849477-1.shtml 2012-10-29 08:43:15  53986501   \n",
       "4        1  /post-free-2849477-1.shtml 2012-10-29 08:55:52  39073643   \n",
       "\n",
       "  author_name                                              reply       date  \\\n",
       "0          贾也  先生是一位真爷们！第161期导语：人人宁波，面朝大海，春暖花开!　　宁波的事，怎谈？无从谈，... 2012-10-29   \n",
       "1        三平67  我们中国人都在一条船，颠簸已久，我们都想做宁波人，希望有一个风平浪静的港湾，面朝大海，春暖花... 2012-10-29   \n",
       "2         赶浪头                                              默默围观~ 2012-10-29   \n",
       "3  m408833176                                              不能收藏？ 2012-10-29   \n",
       "4        兰质薰心                           楼主好文！　　相信政府一定有能力解决好这些问题. 2012-10-29   \n",
       "\n",
       "   month  year  day  \n",
       "0     10  2012   29  \n",
       "1     10  2012   29  \n",
       "2     10  2012   29  \n",
       "3     10  2012   29  \n",
       "4     10  2012   29  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>【民间语文第161期】宁波px启示:船进港湾人应上岸</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>贾也</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>194675</td>\n",
       "      <td>2703</td>\n",
       "      <td>2012-10-29 07:59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>宁波镇海PX项目引发群体上访 当地政府发布说明(转载)</td>\n",
       "      <td>/post-free-2839539-1.shtml</td>\n",
       "      <td>无上卫士ABC</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>88244</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title                        link   author  \\\n",
       "0   【民间语文第161期】宁波px启示:船进港湾人应上岸  /post-free-2849477-1.shtml       贾也   \n",
       "1  宁波镇海PX项目引发群体上访 当地政府发布说明(转载)  /post-free-2839539-1.shtml  无上卫士ABC   \n",
       "\n",
       "                     author_page   click  reply              time  \n",
       "0  http://www.tianya.cn/50499450  194675   2703  2012-10-29 07:59  \n",
       "1  http://www.tianya.cn/74341835   88244   1041  2012-10-24 12:41  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('/Users/chengjun/github/cjc2016/data/tianya_bbs_threads_list.txt', sep = \"\\t\", header=None)\n",
    "df=df.rename(columns = {0:'title', 1:'link', 2:'author',3:'author_page', 4:'click', 5:'reply', 6:'time'})\n",
    "df[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "link_user_dict = defaultdict(list)\n",
    "for i in range(len(dt)):\n",
    "    link_user_dict[dt.link[i]].append(dt.author[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>title</th>\n",
       "      <th>link</th>\n",
       "      <th>author</th>\n",
       "      <th>author_page</th>\n",
       "      <th>click</th>\n",
       "      <th>reply</th>\n",
       "      <th>time</th>\n",
       "      <th>user</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>【民间语文第161期】宁波px启示:船进港湾人应上岸</td>\n",
       "      <td>/post-free-2849477-1.shtml</td>\n",
       "      <td>贾也</td>\n",
       "      <td>http://www.tianya.cn/50499450</td>\n",
       "      <td>194675</td>\n",
       "      <td>2703</td>\n",
       "      <td>2012-10-29 07:59</td>\n",
       "      <td>2478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>宁波镇海PX项目引发群体上访 当地政府发布说明(转载)</td>\n",
       "      <td>/post-free-2839539-1.shtml</td>\n",
       "      <td>无上卫士ABC</td>\n",
       "      <td>http://www.tianya.cn/74341835</td>\n",
       "      <td>88244</td>\n",
       "      <td>1041</td>\n",
       "      <td>2012-10-24 12:41</td>\n",
       "      <td>830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         title                        link   author  \\\n",
       "0   【民间语文第161期】宁波px启示:船进港湾人应上岸  /post-free-2849477-1.shtml       贾也   \n",
       "1  宁波镇海PX项目引发群体上访 当地政府发布说明(转载)  /post-free-2839539-1.shtml  无上卫士ABC   \n",
       "\n",
       "                     author_page   click  reply              time  user  \n",
       "0  http://www.tianya.cn/50499450  194675   2703  2012-10-29 07:59  2478  \n",
       "1  http://www.tianya.cn/74341835   88244   1041  2012-10-24 12:41   830  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['user'] = [len(link_user_dict[l]) for l in df.link]\n",
    "df[:2] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+AzOZTOmWKOSmspolRqNR3f0idiYiAkaOhMREmDULvuzehfmJ1zKc1zc+Pttr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d8ae90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "import numpy as np\n",
    "\n",
    "x = np.log(df.user+1)\n",
    "y = np.log(df.reply+1)\n",
    "xx = sm.add_constant(x, prepend=True)\n",
    "res = sm.OLS(y,xx).fit()\n",
    "constant,beta = res.params\n",
    "r2 = res.rsquared\n",
    "fig = plt.figure(figsize=(8, 4),facecolor='white')\n",
    "plt.plot(df.user, df.reply, 'rs', label= 'Data')\n",
    "plt.plot(np.exp(x), np.exp(constant + x*beta),\"-\", label = 'Fit')\n",
    "plt.yscale('log');plt.xscale('log')\n",
    "plt.xlabel(r'$Users$', fontsize = 20)\n",
    "plt.ylabel(r'$Replies$', fontsize = 20)\n",
    "plt.text(max(df.user)/300,max(df.reply)/20,\n",
    "         r'$\\beta$ = ' + str(round(beta,2)) +'\\n' + r'$R^2$ = ' + str(round(r2, 2)))\n",
    "plt.legend(loc=2,fontsize=10, numpoints=1)\n",
    "plt.axis('tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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0E1P8OjgGc9PZqtXD0EqBWQCCUalJvjwBj9bA+ytS6cbFmZ9KVwmrCBrC7jIi\naVgU/Js3b673s7u7u67vf9WqVXj55ZexZs0aSQpIpHS2elCy9LzWptIF5FtF0BxKX+mRSMksCv69\nevVCcnIyBg0aVO212NhYzJw50+qCmapicaEbN25g6NChmDx5st2uTfJg0291giAm5JMila6jYOsU\nkeUsCv79+vXDsGHDMGLECAwZMgR9+vRBvXr1dK+XlJRIVsDaVCwuJAgCoqKijAb/6GbNAABF3t5I\nPHnSbuUjy7BWZ5pi1EUiohB/+GVsmmZ+Kl0+SBGpk0XB/8knn0S9evWwevVqvPHGG2jQoAH69OmD\n/v3749dff8Wzzz6rd/zixYsxf/58k849efJk7Nq1C15eXjh27Jhuf1JSEubMmYOysjJMnjxZ73w7\nd+7EqlWr8Mwzzxg9b0Vq1IMFBeZ8VIfkDCl9lVyrq/pgcvyHHxAN4C6A0ErH2XLdgWtogdWYjpV4\nFqE4jofbr8WnRxebnEqXD1dE6mZR8O/SpQvi4uIAAL/99htSUlKQlpaGDz/8EHl5eUhKSkKvXr0w\nYMAA9O3bF+vXrzc5+MfExGDWrFl6+QHKysowc+ZMJCcno23btggLC8OoUaMQHBwMABgxYgRGjBiB\nUaNGYfTo0QbPqy3fRpWVWfKRHYoUX94n8/IQ3bRptf1FBqbYSXVNR1HTg4m20s/RhYWSX/s3hGBj\n3RewWHgcIS3TMMrnf+DV+AIaBAWZHPgBZT9cEZHtWRT8hwwZgn//+9/o378/HnvsMXTu3FlX2z9z\n5gxSU1ORmpqK9evXY9GiRXAxIxNI//79kZ2drbcvIyMDgYGBuuyCUVFR2L59O4KDg5Gamopt27ah\nqKgIAwcONHpebfn2ugqCvxSCvb2hNVBzrToCnIxzdXe36v0VrSmCAJz7MwwHc8fhj7sd0afrARz4\n2gOtWw8HIE0K7YophBVTByuXQU0PdkRqYVHwHzlyJCIjI5Gamoo///xTb1pfYGAgAgMDMWXKFADi\nw8DQoUOtKuSlS5fg5+en+9nX1xcZGRkAoEsuVJuU8m2OICAlJQWaKlnBiKRm7aJDs5fFY8OGv1Lp\nvvweMH48UL/+KIlK+JcilD8gl08drKCV/ErS4XgFIn0VeXdMYVHwv3v3Lr777juEh4ejZcuWuv0f\nfPABJk6cCPdKNZ7AwEDMnTvXkstIKqV8Ow5g4HdiztCXXZFKNz4e6N3b/ql0HYWj/H8S2YtGo9GL\nbwsWLDBetOUkAAAdsElEQVR6rEXBf8aMGQgNDUV8fDwSEhLQuHFjAGJ3wIwZM/DRRx/pHf/cc89Z\nchkdHx8f5OTk6H7Ozc2Fj5lzmDQA2gO4YVVJyF4srdU5cl/2kSPiVL2dO4EJE4D9+wFWYonIXKa0\nAFgU/Fu0aIF58+ZBo9EgMTFR18QfEBCAmJgYrFu3DjExMZacGgAgCAIEQdD9HBYWhrNnzyI7Oxtt\n2rRBYmIiEhISzDpnSvn2CYtLRSSq+mCSc+oUygoLcdfdHdpKTf2mND+XlgK7dolB/9w5oEubrZja\n+WO4H7+DjVP1ryllTbfqZ8jKzBSb/InI4VW0AEhe86+Y0x8WFobExES91x566CFs27bNktMCACZM\nmICUlBRcv34d7dq1w4IFCxATE4O4uDgMHTpUN9UvJCTEovO7mrK2Kcnenyp1Df74qVN6A9kqWBJU\nzTneWDeES4eu8OwZp5dKd+xY4I0hcXZpuaj6GbQajV5fPxE5N4uC/40bN5CXlwdvb2+DWfwqL/hj\nro0bNxrcHxkZicjISIvPq4XY9G/WfCgVc7b+1EaFhSYFVanHDBzauROhlaZH3oIf0jELh9ImY2Q+\n8MknQN++tunPr+mzANB7LefUKUQ3bYq77u4INbP1goiUxWbN/rGxsYiMjMTKlSsNBv/K/fNKkQXg\nYwCXOdXPJM4wcM4SUrc4NCoshBbAQfwdSzAXezEY0fgYbRtrsHnzsdrebpXaPovB12pZ01+t9wWR\nI7FZs3+PHj0we/ZsREREwNPTE3fu3EFAQABKSkqQlJSEsLAwiwttKx+Xb6PkLIQDcdSBc8YWJ/qj\nuNjuZSkpAbKK/4FwTMMf8MJsLMMaPIMmuINo1+pldARy3xd8+CCShkXBHwCio6PRqVMnaLVarFmz\nBqWlpWjSpAmmTZuGRYsWSVlGSWjBZn9bUsqXsrHFiaKbNgVMWHHv+KlTBgOZOUv1vjZpNn48EIyM\nS6NR795kDMTbGIIdKEQZVkKcU1+Un8/FdCwg98MHkSOwWbN/hfDwcOzevRslJSW4fv06PD09Ubdu\nXWtOaVMpAG7KXQgnJfWXslwDDiua6asyZanes2fFVLofbnwdj5fsQDKG40Ec1r2urbwtLTW4mI7c\nAy2JyPFJ1uyflpaGZcuWoXnz5njhhRcQVOWLqE6dOvDy8kJubi5ee+01vPDCC+hk5epmUtOWb9Ww\ntr8zkLoW/EdxsVm5CkxVOZXuDz+IqXRjez2N9w5uNXj8cRh+IKpoWWDtn4jsodbg/8svv2DIkCG4\nf/8+AGDbtm04cOAAOnfuXO1YX19fxMfHY8aMGVi9erX0pbWCBuIiP1zb3zFI3Y0glJSgvYHa+/Eq\n6++7ursbnO9edZ3+4mIgMVFceregQEylm5AgptLVaq4ZLENW06Zwy8+HtrS02mu2SAJUWytCTa8R\nkeOSpNl/1apViI2NxfTp05GVlYXly5cjJiYG6enpAIDz58/j2rVraN26Nby8vODi4oJiGQZX1UZT\nvr1YafEgMk7u5ueauhFqm8JmSLOyMpOa89t16gRUaQ14C0BZYSG0Gg0K7jfFz7+PxE+/j4J3qyt4\nf01nPPKIaUNJ2vfoYdfFdGzRimCr+0IpY0aInIEkzf5HjhzBDz/8ABcXFwQHB+PRRx/FtGnTsHfv\nXnz44YfYvHlztff079/fupLbgLZ8G6WQRX74ZWe5mh4MjAUn/PCD2M9uyfUAvHCrLZamTsBmjMPj\n2Ir3ocGWB1oiMjLFonM6Klvdm6aOGZH7oZTIWdQa/OvXr18tJe8bb7yBnj17IiAgAHFxccjJycF3\n332H48ePo0OHDli2bJnNCmwpbflWKc3+Sh+1bG75lPKlbCw4RTdrZlKNu/LnqEilu+/EI/igJBSx\nWIVT6ITWuAoA2ALD2SRr+rdwPXXKpG4FOSn5wVTu6xM5i1qDv7uBL6WWLVvigQcewDfffIP69evb\npGC2oAHwG5v9bcKeX8pSTMcz5sX4eBQWQi+VbljHt7Dp1HDUh2ndWTX9W2g1mmrdCoD16X+lpPQH\nUyKqmSR9/mVGasr9+vVzqMAPiFP9/lRIXlRbBjBnZ8l0PFMG8l2+DDw57CukH+8LX48T6OO7Ge2b\nHUH20UwsQTFelKDsSmkhqWColp+VmYm3AEk+LxHZnyR9/gcOHMD8+fMxZMgQPPTQQ7qAr+T5/IZk\nlW+LFFLzt2Y+uRrUGCQzM42+z1iTdUHTpnoZ9yoEBQXppdJ9oGExjtz/O4JunNHlf34LwElUr/la\n8qCmtGZr1vKJ1KnW4F9YWIj/+7//wzvvvIP69eujX79+GDRoEHJzc1FWVmZwbf/ly5dj5syZNimw\npT4u346TsxBkspqC5NM7dxqtxRsNZhERemvWV06lu2IkMHOm2Mz//phlCPr9jN57DwEINVCOfBPG\nECi5/1xJlNYiQuTsag3+3bt3x5YtW5CWlobU1FTs378fycnJAIDPPvsMvXr1Qt++fREeHo7w8HC0\natUKW7duVVzw15Zv/5SzEJX8UVxs8MtOjjXoDVHyl/EdGK6Z3jHhvfn5wLp14kp8LVqIqXQffxyo\nqSGrkZHrRZuQvZI1a9PwQYjIvmoN/t26dUPHjh3RsWNHxMTEAAAuXbqE1NRUpKSkYP/+/Vi8eLFu\nRkD79u1x6dIl25baAikQF/lRRqM/4FWvHrQGmvhNCSj2YO6XsT1ruJZ0mdwqao1588TAP3AgsH49\nEB5um1S6ziCraVNoe/TQ26eEBz8iqp0kA/4++eSTavt8fHwwYcIETJgwAQBw5coVXcvAd999p1sN\nUEk05duLchbCiZlTw7X2QaGmwXtVB1Lm4u84iLk4kzEEM/sBP/8MtG9f6yVUr30tqX2JSLlsltK3\nqtatW2Ps2LEYO3YsAKBr165SnFZSWeXbe3IWohKlN/vbsiZvbVO4oVX4KvZnZWbiZbhhG8ZgCebq\nUummezyPd9+t/dHPUHdH0YEDFi8QpHRK7t4hItuRJPhX1aZNG1uc1irty7dKqfkLJSVm7bc3W/ZV\n22qaY1FJYxwv+zcCEI32yMILeBsjsQNuKMPTDb1NOoehB5un27Qx+LChpIV5LMW+diJ1sknw/+yz\nz2xxWqtklW+VUvM3tta8o2YdNCegSz3N8Sw6Yhlm48P0SQj0TMcXd0brpdIFrFtEJ2jECGgNtIJU\nZLesqZWENWsiUiKbBP9WrVrZ4rRO5bqR4K+U5YfNZU5AzysoMHhsXkGBSddqEBSE1wQg+1YPHMwd\ni4u3u6Cn9y7MeWgh6l/KwINXDtd+EjPUVjuuqZWE/eZEpEQ2Cf5K1L58q5Rm/xaurgZTu0aZkh6u\nFlL015vbNG9qKlzAulaP4mKgbf94LPkJKLoHzH0DeOopoGHDfwEoXz6XiIhqpJrgvwVAa4gZ2pyd\nFP315jbN1zQIrypLWj2uXQNWrwZWrgQauf6G3s0/xgOtf8LlBAFvJ4jHsJmdiEiiqX7OYmz5dr+s\npajE1dXwCHIJav5SMKcmby5TWj0qWi+u3vXHwUtj8dtVDUJapuGJfj/h0vcfIPDX6g8ah06dwubL\nl60uHxGRI7PbVD8yX8MWLQzWlBu2aCFDaaozpyZvtloefAQB+PVgM1z95b84hm6IxSp8g8lonXcV\n2msRuMm8CEREVlFN8NeWb5Wytr8tg+v+jAxEG9h/MSPD6nMbY05ze4OGDQ22KtRx90R8vLjG/u9n\nJkGD/8NkjEQZirGy/Jjjp06hkQXls+W6BexqICJHo5rgrzS2DBitiot1iYwqizJjASFzB/yZE0Cr\ndilchjdWYAa25k/HrZ3A8uXAJ6P74ZPb1R8QLK3d23LdAs6VJyJHw+AvE1sGDCmmEXrAcGB82rIi\n6alo9TiCHliCudiJEZiAjZjy4BzE7dwAAFjPNfeZEZCIbEZ1wV8piX1s+cUuxTRCazLn1aS0FDhf\nbxzaN12KP4vaonfbbZjaZiLc694xudXDloMRlYQZAYnIVlQT/LXl27sKCRBK/2K/c/26Wftro59K\ndwbeWl2RSnc6gOnVjq8pwNe24h4REdVMNcFfaWy1vr1UpFp+OCcHiIszP5VuTS0PbPImIrKOaoJ/\nCsRV/pSSOEfq9e0rK6tXDzBwnrJ69aw+t6l+/BFYsgRITgaio/VT6ZrS5SH1mAOOyCciteAiP5Vo\nyreXFBL8balz796AgS6Fzr17m3wOSwYNlpQAW7eKQf/KFWD2bGDtWsDDQ/+4Qzt3ItTANMdDlVo9\npJ4KydYCIlILLvJTycny7V1BGUP+Lty+bXAu/oXbt60+98m8PEQ3bVptf5GBYGqMOYMGb94E1qwR\nm/fbtwfmzwdGjgTc3Ayf25atHs6ErRVEZCuqCf6J5VulLPJTVxB0yYYqy5Xg4STY2xtaA2MHtD16\nmH4SE5YfPntWHMD32WfA8OHAF18ADz5oSYnJELZWEJGtqCb4R5dvlbLyewH+ao2oul8JbtWpA+39\n+9X233Srg5QUsWn/xx+BZ54Bfv0VaNvW9HNbm9KXiIiso5rgXxFWDNRlZeGDv1ojKlNKy4RXvXrQ\nVmqGL0ZdJCIKccXPIzYWmDMHSEgAGjY0/9ymzCRgkzcRke2oJvhXNFYrZeG4GzA8mv2GnctRm2to\ngdWYjpV4Fl3wK/7W4H/x7fEt1ZIPmrNokSmDCdnkTURkO6oJ/g3Kt8pImKt8t+p0xzP4F7ZgLB7H\nVnyLoeiC43i6ibfBrMPmLFrUwkiXQlQd1dyORESyUs23bVH51rwlamzHE4YDoxTN/paO9hcEYPdu\nsT//hzvbMA/LcAqd0BpXdcdIkXXQWFa/Bpb0IRARkdlUE/wrgr4yJvrZlrmj/QsLgU8/FVPp1q0L\nzJ0LhBVG4dX9e21dVCIikoFqgr/S+vyNPYTY8+Hk8mVgxQogPh7o00dMpTtwoLj07ls/dIDWNaLa\ne6QYcGdKYh5mtCMish2nCP7bt2/HV199hTt37uDpp5/GkCFD5C5SrRo3bWowADY20FwvtSNHxKb9\nXbuACROA778HAgP1j7FlgDVl9b6axhDwwYCIyDpOEfxHjRqFUaNG4ebNm5g3b57B4K+0Zn97p6Ut\nhStOXusHjQY4dw6YNUtcoKd5c2nOb87UPGun8Un1YMCHCCJSK0UG/8mTJ2PXrl3w8vLCsWPHdPuT\nkpIwZ84clJWVYfLkyZg/f77e+xYuXIgZM2YYPGfFUDIjK87aXUHTpgaXsy2SuOafj0ZYhxgsw2wU\n5RTjnZcrUulKehmzgmVmWhoaGKj5m7P8sDHmzDpQelplIiJbUWTwj4mJwaxZszBx4kTdvrKyMsyc\nORPJyclo27YtwsLCMGrUKAQHBwMAXnzxRQwbNgw9jAxqU9oiP1dzcuBnIPhfLS62+twn8/Iw1qML\nTtybinP3n4S3Wxo6138WHm1yEBVlaF1B+2qQl4ePDbR6RNu/KEREqqTI4N+/f39kZ2fr7cvIyEBg\nYCD8/f0BAFFRUdi+fTuCg4MRFxeH5ORk3L59G2fPnsXUqVOrnbNz+fa6rQtvolbFxfjYwP4oK4P/\njz8C+86+gVulGnTEx3gMPdG4JBsoAS5eNL1LgU3iRETOS5HB35BLly7Bz89P97Ovry8yMjIAALNm\nzcKsWbNqfH9K+TYbYq5jjUZjk3Ka6lJpqcGa7iVDyXRqUTWVrk/ZAZxFNDyQr3ecOQ8WpqTdtaWa\nxgUYeighIlK7lJQUpKSkmHSswwR/a7Uq3xYBsgd+QJqpfpVT6Xbo8Fcq3Sfrx8HDgoeIyu5cN9xG\nYmy/1GpqXXhr6lQ+GBARVaHRaPTi24IFC4we6zDB38fHBzk5Obqfc3Nz4ePjI2OJrFMfMJjS96IJ\n760tla4UrQqmJN+xlLVZ/Sx5MPg+MxPRzZrp7SsqKMAgd3c81Lu33n4mDyIiZ6fY4C8IAoRKue3D\nwsJw9uxZZGdno02bNkhMTERCQoLJ5zuOv2r/jkgQgNRU01LpegMGxxMoJWNgEyNr+z8hwdr+xh4M\nops1MzzIsGFDaE1sJiMicgSmNP8rMvhPmDABKSkpuH79Otq1a4cFCxYgJiYGcXFxGDp0qG6qX0hI\niMnnDC3fmlKzVpJ794DERHHp3aIi01LpKj1joNC0qV664Mr7iYjIOhXN/w7X7L9x40aD+yMjIxEZ\nGWnROc9ArBEr5QPXltjn6lVg9Wpg1SqgSxdg0SLgkUdgMKOeuec2iasrYKibwJQC1KJRYaHB8hla\n94CIiMxjSs1fNRluMwEkAWgjd0FqcQud8cwzQFAQkJ0NfPut+Ccy0vS46+ZmeCkjY/sNadiihVn7\niYhIGTQaDbRabY3HKKUibHNR5dtLspbiL5fx16I2AoDf8Qh+w1xcRTe87AecOgW0bi1f+YJGjIDW\nwMj5IA6GIyJyeKoJ/scgDvizfqy6tC5jINKxHK64j85YAn+MxKuv3rPqnNdguNn/mhnnsOVCPvbO\nawAARd7eBmdAFHl72+yaRERycNgBf7agtAF/FVP9GuEyvDED7ZECFwD7JTh3HSP99XXM6K83d4U/\nc443Jauf1BJPyr+sMRGRPTjsgD81KAQghqOTAE7iVKX91moGwzX/KAP7jDE36Y05KwJam9WPiIis\no5rg71JlKzcfAIkG9ksxF79Bw4YGm9Ub1DQ/0ErmjOBnbgAiInmpJvj/CrHP35zlc21J6XPxzWXt\nqn1ERCQN9vlXorQ+f0nm4iuILZcDJiIi05nS56+aef5qYmzUvC1H0xMRkeNQTc1faWzZ7C/FHH2z\nB+XZcEVAIiKSlmqCv9IS+9wDkGVkv7WkGFBn7jkatmhhcPoeVwQkIrIv9vlXorQ+f2OzDqSYjWDu\nHH0pziHH3H0iIqqO8/wrUdpUv3oQF/mpSoqHE3Pn6EtxDs7dJyJyHKoJ/kKVLUmLc/eJiByHaoK/\n0jjbVD8iInIcqgn+JwC0Buc2EhGRc+OAv0pCyrdKGfBny6l+x0+dMnju4wbW2SciIufCAX+VVIz2\nV8ryubZs9vcwcu6nzTgHB/ARETkv1QT/ioSuUmTNk4Jb3brA/fuG91tJiml3HMBHROS8VNMFXlr+\nRykrzRtb/IaL4hARka2pJvgrjbFaOBfFISIiW1NNs7/S+vyJiIjkoprgvwXiVL8iuQtSzpYD6jhY\nj4hIvUyZ6uciCILTL3rn4uKCseV/vwjgoPN/ZCIiUjkXFxcYC/Hs8yciIlIZ1TT7Ky2xDxERkVxU\nE/wblm/dZC0FERGR/FQT/CsG+illnj8REZFc2OdPRESkMgz+REREKqOaZv/E8q0UiXOIiIgcmWqC\nvxaARuYyEBER2RoX+SnHRX6IiEhtalrkRzU1/83lWzb7ExGR2qkm+GvLt0zsQ0REaqeaZv+KDzkO\nwGbn/8hERKRybPYHa/5EREQVWPMnIiJyQszqVwmX9yUiIrVTXfD3aNpU7iIQERHJSnXB39XdXe4i\nkMxqW/yC1IX3A1VQ072gmuCvLf9zR95ikAKo6Recasf7gSqo6V5QXfD3kLcYBtnyhpPi3Oaew5zj\nTTm2pmMsfU3JlHw/WPJ+U99j7b1Q2+uOeD8o+V6w5Bz8brCOlOVWTfCv0K5TJ7mLUA1/wS0/hr/g\n9j03g799KflesOQc/G6wjpTlVs1UPyIiIrVR9SI/Kni+ISIiMpnqmv2JiIjUjsGfiIhIZRj8iYiI\nVIbBn4iISGUY/ImIiFRGtcG/oKAA0dHRmDZtGjZu3Ch3cUhGFy5cwJQpU/DEE0/IXRRSgO3bt2Pq\n1KkYP3489uzZI3dxSEYnT55EbGws/vnPf2Lt2rVyF0dSqpjnb8iGDRvQvHlzDB8+HFFRUUhMTJS7\nSCSzJ554Ap9//rncxSCFuHnzJubNm4c1a9bIXRSSmSAIiIqKwqZNm+QuimScpuY/efJkeHl5oVu3\nbnr7k5KSEBwcjKCgICxevFi3Pzc3F35+fgAANzc3u5aVbMvce4Gcm6X3w8KFCzFjxgx7FZPswJJ7\nYefOnbpKolMRnMT+/fuFI0eOCF27dtXtKy0tFTp27ChkZWUJxcXFQvfu3YUTJ04IgiAIGzZsEL76\n6itBEARh/PjxspSZbMPce6HC2LFj7V1UsgNL7of58+cLycnJchSXbMjS7wZBEISRI0fas6g25zQ1\n//79+6N58+Z6+zIyMhAYGAh/f3/UrVsXUVFR2L59OwBg9OjR2LJlC2bMmIERI0bIUWSyEXPvhRs3\nbiA2NhaZmZlsEXBC5t4PcXFxSE5OxpYtWxAfHy9HkclGzL0XUlNTMXv2bEybNg0DBw6Uo8g249TL\n+166dEnXtA8Avr6+yMjIAAA0bNgQH330kVxFIzur6V7w9PTEqlWr5CoayaCm+2HWrFmYNWuWXEUj\nO6vpXoiIiEBERIRcRbMpp6n5ExERkWmcOvj7+PggJydH93Nubi58fHxkLBHJhfcCVcb7gSqo9V5w\nquAvCIJeBr+wsDCcPXsW2dnZKC4uRmJiIkaOHCljCcleeC9QZbwfqALvBZHTBP8JEyagb9++OH36\nNNq1a4d169bBzc0NcXFxGDp0KEJDQxEVFYWQkBC5i0o2xnuBKuP9QBV4L/xFtYv8EBERqZXT1PyJ\niIjINAz+REREKsPgT0REpDIM/kRERCrD4E9ERKQyDP5EREQqw+BPRESkMgz+REREKsPgT6Ry6enp\nGDx4MFq3bg1XV1d4eXnh4YcfRkJCgu6YjRs3Ijg4GK6urmjQoAH69++P48ePy1hqIrIGV/gjIgDA\n9OnTsWbNGmRmZqJr164Gj+nSpQt27NiBgIAAO5eOiKTEmj8RAQD27duHVq1aGQ38RUVFCA0NZeAn\ncgIM/kSEvLw8nDlzBhqNxugx33//PcLDw+1XKCKyGQZ/IsK+ffsAABERETUeU9PDARE5jjpyF4CI\n5Ldv3z64uLhg4MCBRo/JzMzEwoUL9fZdvHgRr7/+Oho1aoQGDRqgQYMGeOGFF+Du7q73vlWrVsHT\n0xPFxcW4fv06VqxYgUaNGgEAtm3bhn379uHixYtISEjA5s2b8dtvvyE3Nxdz5sxBr169zLoeEZlA\nICLVCwwMFLy9vY2+np+fL4wdO1ZvX2FhoRAUFCScPn1aEARBOHfunNCyZUth+/btumPWrVsn9OjR\nQ7h8+bJu39tvvy3s2LFDEARBuHfvnjB//nxdGR577DHhwIEDwvXr1wV/f39h1qxZZl2PiEzDZn8i\nlfv9999x9uzZGmv9Bw4cQL9+/fT27dmzB1evXoWfnx8AoFmzZpg7dy6GDBkCAPjxxx8xdepUrFy5\nEt7e3gCAn376CWlpabqxAykpKQgPD8f9+/dx8eJF9OzZE/369UNBQQE8PT0xduxYk69HRKZjsz+R\nypna3x8VFaW3r1mzZrh58ya6dOmCYcOG4YknnsBLL72ke12r1cLT0xNffvkltm3bhrKyMoSEhCAh\nIQGNGzfWnSM8PBw//vgjiouLMW7cOACAr68vDh8+bNb1iMgMcjc9EJG85s2bJ7i6ugq//vqr0WOG\nDh1qcP/bb78t+Pn5Ca6uroKLi4uwcuVKQRAE4f79+0KdOnWE2NhYk8qg1WqFVq1a1XqcsesRkXnY\n7E+kcgUFBQCAoKAgg6/v2LHD6Cj/efPmIScnB7/++it69+6NFStWAACuX7+O0tJSk9cE2LdvX40t\nD4au9/e//113PSIyD4M/kco9+OCDAIBr165Ve+38+fNYvnw5nn/+eb39Y8aMQY8ePXQ/h4SE4Kmn\nntIF+5YtW6JJkyYoKSkxeM4PPvhA93NRUREOHjxY4zRCQ9f717/+xQWHiCzE4E+kcuPHj0f37t3x\nyiuvQKi02vc333yD//znP/jss89Qt25dvfccOnQIgwcP1v185coVbNy4Ea+++ioAwM3NDdOmTUNS\nUpLe+/bv34+FCxdi4sSJun3ff/897t+/X2Pwr+16RGQeru1PRLh9+zZeeeUV/Pzzz2jWrBlcXFww\naNAgzJw5s1rgB8QgnpqaisLCQty+fRsFBQWIjY3Vm5NfWlqKF198Ebm5uWjXrh2Ki4vRrVs3xMTE\n6J1rw4YNWL58OQ4ePGi0fKZcj4hMx+BPRESkMmz2JyIiUhkGfyIiIpVh8CciIlIZBn8iIiKVYfAn\nIiJSGQZ/IiIilWHwJyIiUhkGfyIiIpVh8CciIlKZ/wd50skdgmzo/QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119a2c2d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.log(df.user+1)\n",
    "y = np.log(df.click+1)\n",
    "xx = sm.add_constant(x, prepend=True)\n",
    "res = sm.OLS(y,xx).fit()\n",
    "constant,beta = res.params\n",
    "r2 = res.rsquared\n",
    "fig = plt.figure(figsize=(8, 4),facecolor='white')\n",
    "plt.plot(df.user, df.click, 'rs', label= 'Data')\n",
    "plt.plot(np.exp(x), np.exp(constant + x*beta),\"-\", label = 'Fit')\n",
    "plt.yscale('log');plt.xscale('log')\n",
    "plt.xlabel(r'$Users$', fontsize = 20)\n",
    "plt.ylabel(r'$Replies$', fontsize = 20)\n",
    "plt.text(max(df.user)/300,max(df.click)/20,\n",
    "         r'$\\beta$ = ' + str(round(beta,2)) +'\\n' + r'$R^2$ = ' + str(round(r2, 2)))\n",
    "plt.legend(loc=2,fontsize=10, numpoints=1)\n",
    "plt.axis('tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.tslib.Timestamp"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert str to datetime format\n",
    "dt.time = pd.to_datetime(dt.time)\n",
    "dt['month'] = dt.time.dt.month\n",
    "dt['year'] = dt.time.dt.year\n",
    "dt['day'] = dt.time.dt.day\n",
    "type(dt.time[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "d = dt.year.value_counts()\n",
    "dd = pd.DataFrame(d)\n",
    "dd = dd.sort_index(axis=0, ascending=True)\n",
    "ds = dd.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "def getDate(dat):\n",
    "    dat_date_str = map(lambda x: str(x) +'-01-01', dat.index)\n",
    "    dat_date = pd.to_datetime(dat_date_str)\n",
    "    return dat_date\n",
    "\n",
    "ds.date = getDate(ds)\n",
    "dd.date = getDate(dd)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mzZvH+fPnCQoKYufOnbRs2RJjDLGxsa5R6JvrPt9Uu3Zt9uzZQ/369THG0KZN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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1170be950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,5))\n",
    "plt.plot(ds.date, ds.year, 'g-s', label = '$Cumulative\\: Number\\:of\\: Threads$')\n",
    "plt.plot(dd.date, dd.year, 'r-o', label = '$Yearly\\:Number\\:of\\:Threads$')\n",
    "#plt.yscale('log')\n",
    "plt.legend(loc=2,numpoints=1,fontsize=13)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Extract @ "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     先生是一位真爷们！第161期导语：人人宁波，面朝大海，春暖花开!　　宁波的事，怎谈？无从谈，...\n",
       "1     我们中国人都在一条船，颠簸已久，我们都想做宁波人，希望有一个风平浪静的港湾，面朝大海，春暖花...\n",
       "2                                                 默默围观~\n",
       "3                                                 不能收藏？\n",
       "4                              楼主好文！　　相信政府一定有能力解决好这些问题.\n",
       "5                                                人民在觉醒。\n",
       "6                                          理性的文字，向楼主致敬！\n",
       "7                                     呼唤变革,人民需要的是服务型政府！\n",
       "8                                      顶贾兄！让我们携手努力保卫家园！\n",
       "9                                        围观就是力量,顶起就有希望.\n",
       "10                                       文章写得太有力量了，支持你！\n",
       "11    @贾也 2012-10-29 7:59:00　　导语：人人宁波，面朝大海，春暖花开　　......\n",
       "12                                   中国人从文盲走向民粹，实在是太快了。\n",
       "13                               杀死娘胎里的，毒死已出生的，这个社会怎么了？\n",
       "14                                                    3\n",
       "15    环境比什么都可贵，每一次呼吸，每一顿粮食，都息息相关，若任其恶化，而无从改观，那遑谈国家之未...\n",
       "16                                                 写的很好\n",
       "17    未来这里将是全球最大的垃圾场，而他们早已放浪西方。苟活的将面临数不清的癌症，无助的死亡。悲哀...\n",
       "18    媒体失声，高压维稳，只保留微博和论坛可以说这件事！因为那些人知道，网上的人和事就只能热几天，...\n",
       "19                                       说的太好了，看的我泪流满面！\n",
       "20          “我相信官场中，许多官员应该葆有社会正能量”　　通篇好文，顶！唯这句，不说也罢....\n",
       "21                                            先占一环，然后看帖\n",
       "22                                                说的太好了\n",
       "23    我上的小学，隔壁就是一家水泥厂，到处飞扬的水泥灰是我最熟悉的颜色;坐一站地车，就是一家造纸厂...\n",
       "24     我们中国人都在一条船，颠簸已久，我们都想做宁波人，希望有一个风平浪静的港湾，面朝大海，春暖花开！\n",
       "25                                               前排占座~~\n",
       "26                                          贾也先生是一位真爷们！\n",
       "27                                                     \n",
       "28                           为什么我的眼里常含着泪水?因为我对这片土地爱得深沉!\n",
       "29    又是因为环保的群体事件，影响面大，危害严重，理由叫的响，取得阶段性胜利。　　那些拆迁的、城管...\n",
       "30    回复第20楼，@rual_f　　“我相信官场中，许多官员应该葆有社会正能量” 　　通篇好文，...\n",
       "31                                                  好文.\n",
       "32                         一环　　天涯现在太滞后了，看消息还是得靠微博。太失望了。\n",
       "33                                                 刘明……\n",
       "34                                杂谈的低智商屌丝男们确定自己懂什么是px？\n",
       "35                                                屁民的胜利\n",
       "36                                                说的太好了\n",
       "37                                          ．．．．．　　。。。。\n",
       "38                                               。。。。。。\n",
       "39                                             宁波人民胜利了！\n",
       "40                                                   好文\n",
       "41                                                  不显示\n",
       "42                                         含泪顶起~~~~~~~~\n",
       "43     盲目决策，　　然后群众反对，　　再抓人，　　政府出面承诺项目搁置，　　再偷偷上马，　　在各地轮回\n",
       "44    好文頂一個先！希望我們的政府不要再置人民的利益于不顧！不要再行滅絕漢族子孫的政策！不要逼民眾...\n",
       "45    每次看到这样的消息，都很痛心，很堵很堵。为什么在经济发展的同时，我的祖国却越来越残忍，越来越...\n",
       "46    @兰质薰心 2012-10-29 08:55:52　　楼主好文！　　相信政府一定有能力解决好...\n",
       "47                   记得以前有人说过 厦门 必将会成为一座里程碑 烛照万里 看来真的成了\n",
       "48                       贾也，真爷们，　　一直爱在天涯逛逛，是因为这还多少能让人说话\n",
       "49                                          帖子咋了，啥都看不到。\n",
       "50                                                    顶\n",
       "51                                                顶,好文章\n",
       "52                                        在宁波呆过几年，感觉还不错\n",
       "53    @昊爷7 2012-10-29 11:19:29　　杂谈的低智商屌丝男们确定自己懂什么是px...\n",
       "54                                            什么时候变成了奴隶\n",
       "Name: reply, dtype: object"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.reply[:55]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "@贾也2012-10-297:59:00　　导语：人人宁波，面朝大海，春暖花开　　........\n",
    "\n",
    "        @兰质薰心2012-10-2908:55:52　　楼主好文！　　相信政府一定有能力解决好这些...\n",
    "        \n",
    "                回复第20楼，@rual_f　　“我相信官场中，许多官员应该葆有社会正能量”　　通篇好文，顶..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lilei\n",
      "Bob\n",
      "Girl\n",
      "魏武\n",
      "MarkGreene\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "tweet = u\"//@lilei: dd //@Bob: cc//@Girl: dd//@魏武: \\\n",
    "    利益所致 自然念念不忘// @诺什: 吸引优质  客户，摆脱屌丝男！！！//@MarkGreene: 转发微博\"\n",
    "RTpattern = r'''//?@(\\w+)'''\n",
    "for word in re.findall(RTpattern, tweet, re.UNICODE):\n",
    "    print word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'\\u8d3e\\u4e5f']"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RTpattern = r'''@(\\w+)\\s'''\n",
    "re.findall(RTpattern, dt.reply[11].decode('utf8'), re.UNICODE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "if re.findall(RTpattern, dt.reply[0].decode('utf8'), re.UNICODE):\n",
    "    print True\n",
    "else:\n",
    "    print False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 \t木兰清风_ \t贾也 \t\t@贾也 2012-10-29 7:59:00　　导语：人人宁\n",
      "30 \t此东西非南北 \trual_f \t\t回复第20楼，@rual_f　　“我相信官场中，许多官员应该\n",
      "46 \t弱水三千chen \t兰质薰心 \t\t@兰质薰心 2012-10-29 08:55:52　　楼主好\n",
      "53 \t彼得与梦想 \t昊爷7 \t\t@昊爷7 2012-10-29 11:19:29　　杂谈的低\n",
      "66 \tlenglengjing \t蒙上眼睛的牛 \t\t@蒙上眼睛的牛 2012-10-29 10:15:56　　未\n",
      "67 \tAGy戰女 \t天时怼兮威灵怒 \t\t@天时怼兮威灵怒 66楼 　　http://baike.ba\n",
      "69 \t我真的要说吗 \t香湖人 \t\t@香湖人 27楼 　　　　-----------------\n",
      "71 \t2011我为卿狂 \tshdsb \t\t@shdsb 2012-10-29 10:17:43　　媒体\n",
      "72 \t无色天空FRANK \tpals2009 \t\t@pals2009 48楼 　　每次看到这样的消息，都很痛心\n",
      "76 \tgaolihshq \t兰质薰心 \t\t作者：弱水三千chen　回复日期：2012-10-29 11\n",
      "80 \tLBJStream \t曾开贵 \t\t@曾开贵 2012-10-29 11:40:09　　没有ZF\n",
      "93 \t大头菜烤年糕 \t横冲节度使 \t\t@横冲节度使 2012-10-29 12:11:50　　楼主\n",
      "94 \txinggzyyc \t赶浪头 \t\t@赶浪头 2楼 　　默默围观~　　　　-----------\n"
     ]
    }
   ],
   "source": [
    "for k, tweet in enumerate(dt.reply[:100]):\n",
    "    tweet = tweet.decode('utf8')\n",
    "    RTpattern = r'''@(\\w+)\\s'''\n",
    "    for person in re.findall(RTpattern, tweet, re.UNICODE):\n",
    "        print k,'\\t',dt.author_name[k],'\\t', person,'\\t\\t', tweet[:30]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "@曾开贵 2012-10-29 11:40:09　　没有ZF，哪来新ZG，没有新ZG，你们吃什么？为什么你们总是要攻击ZF呢，就像之前那样骂骂小日本多好啊……看看台湾，一碗牛肉面竟然卖到100台币一碗，大L都才卖10元一碗，你还不知足！啥，工资不一样？没错，台湾平均工资5万台币能买500碗牛肉面，大L平均工资2000能买200碗牛肉面。但是，小同志，事情没这么简单啊，你能一下吃完500碗面吗？在倡导节约、环保低碳的今天，你不觉得铺张浪费是件可耻...........　　-----------------------------　　叼\n"
     ]
    }
   ],
   "source": [
    "print dt.reply[80]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "link_author_dict = {}\n",
    "for i in range(len(df)):\n",
    "    link_author_dict[df.link[i]] =df.author[i] \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "graph = []\n",
    "for k, tweet in enumerate(dt.reply):\n",
    "    tweet = tweet.decode('utf8')\n",
    "    url = dt.link[k]\n",
    "    RTpattern = r'''@(\\w+)\\s'''\n",
    "    persons = re.findall(RTpattern, tweet, re.UNICODE)\n",
    "    if persons:\n",
    "        for person in persons:\n",
    "            graph.append([dt.author_name[k].decode('utf8'), person])\n",
    "    else:\n",
    "        graph.append( [dt.author_name[k].decode('utf8'), link_author_dict[url].decode('utf8')]  )\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8769"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "贾也 贾也\n",
      "三平67 贾也\n",
      "赶浪头 贾也\n"
     ]
    }
   ],
   "source": [
    "for x, y in graph[:3]:\n",
    "    print x, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "G = nx.DiGraph()\n",
    "for x,y in graph:\n",
    "    if x != y:\n",
    "        G.add_edge(x,y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Name: \\nType: DiGraph\\nNumber of nodes: 4289\\nNumber of edges: 5995\\nAverage in degree:   1.3978\\nAverage out degree:   1.3978'"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.info(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "GU=G.to_undirected(reciprocal=True)\n",
    "graphs = list(nx.connected_component_subgraphs(GU))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4067, 120)"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "size = []\n",
    "for i in graphs:\n",
    "    size.append(len(i.nodes()))\n",
    "len(size), np.max(size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gs = []\n",
    "for i in graphs:\n",
    "    if len(i.nodes()) >5:\n",
    "        gs.append(i)\n",
    "len(gs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "120\n",
      "44\n",
      "9\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "for g in gs:\n",
    "    print len(g.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g_max = gs[0]\n",
    "len(g_max.nodes())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vret3c4bnTgs8fvyYVkZG8mxywVpalOjoKFmpsbGxLFOmjIKrPJeCtlPMpah5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EAAAg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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1199bf890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pos = nx.spring_layout(g_max)          \n",
    "#定义一个布局，此处采用了spectral布局方式，后变还会介绍其它布局方式，注意图形上的区别\n",
    "nx.draw(g_max,pos,with_labels=False,node_size = 30)  \n",
    "#绘制规则图的图形，with_labels决定节点是非带标签（编号）,node_size是节点的直径\n",
    "plt.show()  #显示图形"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "with open('/Users/chengjun/github/cjc2016/data/tianya_network_120.csv', 'a') as f:\n",
    "    for x, y in g_max.edges():\n",
    "        f.write(x.encode('utf8') + ',' + y.encode('utf8') + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![](./img/tianyaGephi.png)\n",
    "\n",
    "Gephi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# 1\t/post-free-2849477-1.shtml\t2012-10-29 11:11:32\t51150428\t生生0326\t一环　　天涯现在太滞后了，看消息还是得靠微博。太失望了。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# 作业：\n",
    "- 1. 计算节点的度，并绘制其散点图。\n",
    "- 2. 计算节点的聚类系数\n",
    "- 3. 计算节点的pagerank"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "celltoolbar": "Slideshow",
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  },
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