stats.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# CPP知乎专栏 数据分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "def read_json(fname):\n",
    "    with open(fname) as f:\n",
    "        data = f.read()\n",
    "        data = json.loads(data)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 用户关注和点赞数据\n",
    "followers = read_json('data/followerList.json')\n",
    "posts = read_json('data/postsList.json')\n",
    "articles = read_json('data/articleLikeList.json')\n",
    "iGuo_followers = read_json('data/iGuoFollowerList.json')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "104"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总文章数量\n",
    "len(posts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 专栏点赞和关注用户分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9308"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总点赞数量\n",
    "articles_likes = []\n",
    "for i in articles.values():\n",
    "    articles_likes.extend(i)\n",
    "len(articles_likes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6851"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 不重复总点赞数\n",
    "articles_likes_distinct = set(articles_likes)\n",
    "len(articles_likes_distinct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 总关注数量\n",
    "followers = set(followers)\n",
    "len(followers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1647"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关注并点赞\n",
    "len(followers&articles_likes_distinct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'jiang-shan-73-5', 78),\n (u'zhang_guo', 61),\n (u'er-dong-hao', 38),\n (u'zha-cheng-67', 36),\n (u'liu-li-94-49', 30),\n (u'liu-xiao-man-3-2', 29),\n (u'dementia-otoma', 21),\n (u'felix-zhu-23', 21),\n (u'du-xue-yang-Stella', 21),\n (u'zcp-30-32', 18),\n (u'kevin-hill', 18),\n (u'yue-zhong-xin-63', 17),\n (u'chen-qian-87-81', 16),\n (u'gao-han-12-9', 15),\n (u'JasonZhangyj', 15),\n (u'ma-xian-sheng-54', 15),\n (u'qiu-kun', 14),\n (u'vercase', 14),\n (u'christing-25', 14),\n (u'david-qian-51', 14),\n (u'ben-hembert', 14),\n (u'yuen-wong-17', 13),\n (u'xiao-xie-xie-14', 12),\n (u'zhilong-zhang-69', 12),\n (u'cpp-45-10', 12),\n (u'xiao-yao-wen-wen', 12),\n (u'jiakelong', 11),\n (u'ceng-xiao-yu-68', 11),\n (u'jung-freud', 11),\n (u'hua-tian-qing', 10),\n (u'zhang-wei-xian-35', 10),\n (u'Gorbachev', 10),\n (u'ann-huang', 10),\n (u'potter-here', 10),\n (u'sushengbuhuo', 9),\n (u'zhao-xing-fei-12', 9),\n (u'figo-von', 9),\n (u'wang-kun-48-12', 9),\n (u'xiang.ming', 9),\n (u'jin-xuan-yi-97', 9)]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 高频点赞（10及以上）用户\n",
    "from collections import Counter\n",
    "articles_likes_stats = Counter(articles_likes)\n",
    "articles_likes_stats.most_common(40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "非CPP团队成员、认识的：\n",
    "- https://www.zhihu.com/people/zha-cheng-67/answers\n",
    "- https://www.zhihu.com/people/gao-han-12-9/answers\n",
    "- https://www.zhihu.com/people/qiu-kun/answers\n",
    "- https://www.zhihu.com/people/hua-tian-qing/answers\n",
    "- \n",
    "- https://www.zhihu.com/people/jin-xuan-yi-97/answers\n",
    "\n",
    "非CPP团队成员、不认识的：\n",
    "- https://www.zhihu.com/people/er-dong-hao\n",
    "- https://www.zhihu.com/people/zcp-30-32/answers\n",
    "- https://www.zhihu.com/people/kevin-hill/answers\n",
    "- https://www.zhihu.com/people/ma-xian-sheng-54/answers\n",
    "- https://www.zhihu.com/people/vercase/answers\n",
    "- https://www.zhihu.com/people/ben-hembert/activities\n",
    "- https://www.zhihu.com/people/yuen-wong-17/activities\n",
    "- https://www.zhihu.com/people/zhilong-zhang-69/activities\n",
    "- https://www.zhihu.com/people/jiakelong/answers\n",
    "- https://www.zhihu.com/people/ceng-xiao-yu-68/answers\n",
    "- https://www.zhihu.com/people/jung-freud/answers\n",
    "- https://www.zhihu.com/people/Gorbachev/answers\n",
    "- https://www.zhihu.com/people/ann-huang/activities\n",
    "- https://www.zhihu.com/people/potter-here/answers\n",
    "- \n",
    "- https://www.zhihu.com/people/sushengbuhuo/answers\n",
    "- https://www.zhihu.com/people/zhao-xing-fei-12/activities\n",
    "- https://www.zhihu.com/people/figo-von/answers\n",
    "- https://www.zhihu.com/people/wang-kun-48-12/answers\n",
    "- https://www.zhihu.com/people/xiang.ming/answers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 689.,    0.,    0.,    0.,  182.,    0.,    0.,    0.,   75.,\n           0.,    0.,    0.,   41.,    0.,    0.,    0.,    0.,   23.,\n           0.,    0.,    0.,   23.,    0.,    0.,    0.,   11.,    0.,\n           0.,    0.,    7.]),\n array([ 2.        ,  2.23333333,  2.46666667,  2.7       ,  2.93333333,\n         3.16666667,  3.4       ,  3.63333333,  3.86666667,  4.1       ,\n         4.33333333,  4.56666667,  4.8       ,  5.03333333,  5.26666667,\n         5.5       ,  5.73333333,  5.96666667,  6.2       ,  6.43333333,\n         6.66666667,  6.9       ,  7.13333333,  7.36666667,  7.6       ,\n         7.83333333,  8.06666667,  8.3       ,  8.53333333,  8.76666667,  9.        ]),\n <a list of 30 Patch objects>)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8M8mfLcZzSJJmt+DhnmQF8D+AXwMuAD6c5J0L/TyTN5x0AfM0nHQB8zScdAHzMJx0AfM0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\nGe6S1CDDXZIa9K9tk7+EGfqR5QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10e2b3690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 点赞2-9次的用户\n",
    "% matplotlib inline\n",
    "tmp = np.array(dict(articles_likes_stats).values())\n",
    "plt.hist(tmp[(tmp>1)&(tmp<10)], bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5766"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 点赞一次的用户\n",
    "len(tmp[tmp==1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 点赞和关注汇总数据\n",
    "df1 = pd.Series(dict(articles_likes_stats),name='likes')\n",
    "df2 = pd.Series(1, name='follow', index = followers)\n",
    "df3 = pd.Series(1, name='iGuo', index = iGuo_followers)\n",
    "df = pd.merge(df1.reset_index(), df2.reset_index(), on='index', how='outer')\n",
    "df = pd.merge(df, df3.reset_index(), on='index', how='outer')\n",
    "df['follow']=df['follow'].fillna(0)\n",
    "df['likes']=df['likes'].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>likes</th>\n",
       "      <th>follow</th>\n",
       "      <th>iGuo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>1235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.463857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.523679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>likes</th>\n",
       "      <th>follow</th>\n",
       "      <th>iGuo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7000.000000</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>1235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.463857</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.523679</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关注汇总数据\n",
    "df_follow = df[df['follow']==1]\n",
    "df_follow.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>likes</th>\n",
       "      <th>follow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1647.000000</td>\n",
       "      <td>1647.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.971463</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.626352</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>likes</th>\n",
       "      <th>follow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1647.000000</td>\n",
       "      <td>1647.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.971463</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.626352</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关注者的点赞\n",
    "df_follow[df_follow['likes']>0].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x117ab7d10>"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kqUkvgZFkdZJrktya5OYkZ3Xtz05yVZI7knw1ycqRYz6ZZFOSm5Ic00fdkrSc9TXCeBQ4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\nQRYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ab7bd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 关注者的点赞分布（点赞数1-9）\n",
    "df_follow[(df_follow['likes']>0)&(df_follow['likes']<10)]['likes'].plot.hist(bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_iGuo = df[df['iGuo']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>likes</th>\n",
       "      <th>follow</th>\n",
       "      <th>iGuo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.353442</td>\n",
       "      <td>0.238786</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.536755</td>\n",
       "      <td>0.426383</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>likes</th>\n",
       "      <th>follow</th>\n",
       "      <th>iGuo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.000000</td>\n",
       "      <td>5172.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.353442</td>\n",
       "      <td>0.238786</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.536755</td>\n",
       "      <td>0.426383</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>38.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_iGuo.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1235"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关注专栏和关注iGuo的重合部分\n",
    "len(df[(df['follow']==1)&(df['iGuo']==1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "443"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关注专栏、关注iGuo并点赞\n",
    "len(df[(df['likes']>0)&(df['follow']==1)&(df['iGuo']==1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "859"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 关注iGuo并点赞\n",
    "len(df[(df['likes']>0)&(df['iGuo']==1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 专栏文章分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 专栏文章点赞数统计\n",
    "articles_stats = {}\n",
    "for key,value in articles.items():\n",
    "    articles_stats[key] = len(value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 53.,  26.,   9.,   7.,   4.,   0.,   1.,   0.,   0.,   1.,   0.,\n          2.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,\n          0.,   0.,   0.,   0.,   0.,   0.,   0.,   1.]),\n array([    3.        ,    48.56666667,    94.13333333,   139.7       ,\n          185.26666667,   230.83333333,   276.4       ,   321.96666667,\n          367.53333333,   413.1       ,   458.66666667,   504.23333333,\n          549.8       ,   595.36666667,   640.93333333,   686.5       ,\n          732.06666667,   777.63333333,   823.2       ,   868.76666667,\n          914.33333333,   959.9       ,  1005.46666667,  1051.03333333,\n         1096.6       ,  1142.16666667,  1187.73333333,  1233.3       ,\n         1278.86666667,  1324.43333333,  1370.        ]),\n <a list of 30 Patch objects>)"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4brXZz+t/mPzz+gjwPrrfMCR5NvBQVR3obh/s+8fqr6pfADODXxUn4ETgB0k+0009fSrJ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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115944110>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 点赞数分布\n",
    "% matplotlib inline\n",
    "tmp = np.array(articles_stats.values())\n",
    "plt.hist(tmp, bins=30)  # 图中一格是40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  7.,  14.,  12.,  13.,  10.,   5.,   9.,   3.,   4.,   4.,   2.,\n          4.,   1.,   0.,   2.,   0.,   1.,   2.,   2.,   0.,   1.,   1.,\n          2.,   0.,   0.,   0.,   0.,   0.,   0.,   1.]),\n array([   3. ,   12.6,   22.2,   31.8,   41.4,   51. ,   60.6,   70.2,\n          79.8,   89.4,   99. ,  108.6,  118.2,  127.8,  137.4,  147. ,\n         156.6,  166.2,  175.8,  185.4,  195. ,  204.6,  214.2,  223.8,\n         233.4,  243. ,  252.6,  262.2,  271.8,  281.4,  291. ]),\n <a list of 30 Patch objects>)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pn1p1/XjCkv48odu+9Jp+m817Td/YTU18eNG0xabtX5KdDP9CuZGjH4+b/vot6t9N3aYm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\nl6QG/T9Y4LtY4DVwPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115c69690>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 小于400点赞的点赞数分布\n",
    "plt.hist(tmp[tmp<400],bins=30)  # 每一格是10个"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2.0
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}