brunch-data-analysis/Brunch Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Brunch Data Analysis\n",
    "\n",
    "**Brunch** is one of the biggest blog platforms in South Korea. This study will investigate what factors made writers more famous and influential. \n",
    "\n",
    "- It is based on the data collected on 24 August 2017"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from data_management import read_related_data, get_list_df_by_writers, \\\n",
    "                            get_article_df_by_writers, get_follower_df_by_writers, \\\n",
    "                            get_following_df_by_writers, parse_datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = read_related_data()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The writers who have 5000+ followers\n",
    "\n",
    "- The number of writers who are followed by over 5000 members is 162\n",
    "- The average of the number of followers is 7589\n",
    "- The average of writer count and article count are 150, 166, but the standard deviation is 767, 224\n",
    "- The writer who has the least articles wrote only 8 to get over 5000 followers\n",
    "- The impact of article count on follower count is not relevant "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "profile_id        26\n",
       "count             26\n",
       "user_id           26\n",
       "username          26\n",
       "follower_count    26\n",
       "writer_count      26\n",
       "article_count     26\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_df = df[df[\"follower_count\"] > 5000].sort_values(\"follower_count\", ascending=False).iloc[1:,]\n",
    "top_df[top_df[\"follower_count\"] > 10000].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>follower_count</th>\n",
       "      <th>writer_count</th>\n",
       "      <th>article_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.00000</td>\n",
       "      <td>162.000000</td>\n",
       "      <td>162.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>456.475309</td>\n",
       "      <td>7589.04321</td>\n",
       "      <td>149.654321</td>\n",
       "      <td>165.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>411.724192</td>\n",
       "      <td>2527.17562</td>\n",
       "      <td>767.096521</td>\n",
       "      <td>223.593277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>71.000000</td>\n",
       "      <td>5010.00000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>297.250000</td>\n",
       "      <td>5752.00000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>55.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>404.500000</td>\n",
       "      <td>6818.00000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>113.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>525.000000</td>\n",
       "      <td>8573.25000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>189.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5009.000000</td>\n",
       "      <td>22154.00000</td>\n",
       "      <td>9084.000000</td>\n",
       "      <td>1747.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             count  follower_count  writer_count  article_count\n",
       "count   162.000000       162.00000    162.000000     162.000000\n",
       "mean    456.475309      7589.04321    149.654321     165.944444\n",
       "std     411.724192      2527.17562    767.096521     223.593277\n",
       "min      71.000000      5010.00000      0.000000       8.000000\n",
       "25%     297.250000      5752.00000      8.000000      55.250000\n",
       "50%     404.500000      6818.00000     25.000000     113.500000\n",
       "75%     525.000000      8573.25000     60.000000     189.750000\n",
       "max    5009.000000     22154.00000   9084.000000    1747.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile_id</th>\n",
       "      <th>count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>follower_count</th>\n",
       "      <th>writer_count</th>\n",
       "      <th>article_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>thespringtocome</td>\n",
       "      <td>71</td>\n",
       "      <td>RqM</td>\n",
       "      <td>오늘</td>\n",
       "      <td>5692</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1732</th>\n",
       "      <td>kimkwan</td>\n",
       "      <td>172</td>\n",
       "      <td>2zw</td>\n",
       "      <td>김관</td>\n",
       "      <td>6105</td>\n",
       "      <td>20</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1218</th>\n",
       "      <td>yoonhanna</td>\n",
       "      <td>245</td>\n",
       "      <td>3u4</td>\n",
       "      <td>hanna</td>\n",
       "      <td>5023</td>\n",
       "      <td>63</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>2kija</td>\n",
       "      <td>437</td>\n",
       "      <td>6i4</td>\n",
       "      <td>이기주작가</td>\n",
       "      <td>7300</td>\n",
       "      <td>8</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>248</th>\n",
       "      <td>gzerof</td>\n",
       "      <td>369</td>\n",
       "      <td>xYA</td>\n",
       "      <td>홍준호</td>\n",
       "      <td>6086</td>\n",
       "      <td>51</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           profile_id  count user_id username  follower_count  writer_count  \\\n",
       "552   thespringtocome     71     RqM       오늘            5692             1   \n",
       "1732          kimkwan    172     2zw       김관            6105            20   \n",
       "1218        yoonhanna    245     3u4    hanna            5023            63   \n",
       "544             2kija    437     6i4    이기주작가            7300             8   \n",
       "248            gzerof    369     xYA      홍준호            6086            51   \n",
       "\n",
       "      article_count  \n",
       "552               8  \n",
       "1732             11  \n",
       "1218             12  \n",
       "544              13  \n",
       "248              14  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_df.sort_values(\"article_count\").head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile_id</th>\n",
       "      <th>count</th>\n",
       "      <th>user_id</th>\n",
       "      <th>username</th>\n",
       "      <th>follower_count</th>\n",
       "      <th>writer_count</th>\n",
       "      <th>article_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>wikitree</td>\n",
       "      <td>545</td>\n",
       "      <td>Jp6</td>\n",
       "      <td>위키트리 WIKITREE</td>\n",
       "      <td>8775</td>\n",
       "      <td>1</td>\n",
       "      <td>1747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>bookdb</td>\n",
       "      <td>522</td>\n",
       "      <td>ocv</td>\n",
       "      <td>인터파크 북DB</td>\n",
       "      <td>5947</td>\n",
       "      <td>1</td>\n",
       "      <td>1393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>dong02</td>\n",
       "      <td>410</td>\n",
       "      <td>iDz</td>\n",
       "      <td>이동영 작가의 글쓰기</td>\n",
       "      <td>5940</td>\n",
       "      <td>15</td>\n",
       "      <td>1028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>470</th>\n",
       "      <td>brunchflgu</td>\n",
       "      <td>806</td>\n",
       "      <td>oZ3</td>\n",
       "      <td>조우성 변호사</td>\n",
       "      <td>10606</td>\n",
       "      <td>46</td>\n",
       "      <td>992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>573</th>\n",
       "      <td>hitchwill</td>\n",
       "      <td>465</td>\n",
       "      <td>3kT</td>\n",
       "      <td>나는 누군가</td>\n",
       "      <td>7250</td>\n",
       "      <td>6</td>\n",
       "      <td>866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     profile_id  count user_id       username  follower_count  writer_count  \\\n",
       "74     wikitree    545     Jp6  위키트리 WIKITREE            8775             1   \n",
       "83       bookdb    522     ocv       인터파크 북DB            5947             1   \n",
       "217      dong02    410     iDz    이동영 작가의 글쓰기            5940            15   \n",
       "470  brunchflgu    806     oZ3        조우성 변호사           10606            46   \n",
       "573   hitchwill    465     3kT         나는 누군가            7250             6   \n",
       "\n",
       "     article_count  \n",
       "74            1747  \n",
       "83            1393  \n",
       "217           1028  \n",
       "470            992  \n",
       "573            866  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_df.sort_values(\"article_count\", ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.05407113062275383"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(top_df[\"follower_count\"], top_df[\"writer_count\"])[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.079968862893865525"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(top_df[\"follower_count\"], top_df[\"article_count\"])[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1289fbac8>"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dvrCllcKcc9kVOqTLzcAFZvYYgKSDgF8AnyhRvvqUbF+aMxub+Obv5rOxreN4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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128d47f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_df.plot(kind=\"scatter\", x=\"article_count\", y=\"follower_count\", \n",
    "            title=\"The relation between follower count and article count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The articles whose writer has 5000+ followers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The relation between SNS share count and like count\n",
    "\n",
    "- These two factors are strongly related\n",
    "- Sharing numbers are much bigger than like counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "top_articles = get_article_df_by_writers(top_df[\"profile_id\"].values)\n",
    "top_articles_cleaned = top_articles.drop([\n",
    "    \"articleCoverColorForHome\", \"magazineArticleId\", \"coverColor\", \"contentId\", \"magazineNo\", \n",
    "    \"magazineType\", \"readSeconds\", \"pick\", \"userId\", \"sharedFacebookComment\", \"status\"], 1)\n",
    "parse_datetime(top_articles_cleaned, fields=[\"createTime\", \"publishTime\", \"updateTime\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.51849899507926045"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.corrcoef(top_articles_cleaned[\"socialShareTotalCount\"], top_articles_cleaned[\"likeCount\"])[0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>magazineTitle</th>\n",
       "      <th>no</th>\n",
       "      <th>profileId</th>\n",
       "      <th>publishTime</th>\n",
       "      <th>socialShareTotalCount</th>\n",
       "      <th>subTitle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>NaN</td>\n",
       "      <td>73</td>\n",
       "      <td>stillalive31</td>\n",
       "      <td>2015-08-11 14:35:54</td>\n",
       "      <td>61234</td>\n",
       "      <td>반드시 피해야 할 지극히 나쁜 언행, 6천(賤)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>삶의 교훈</td>\n",
       "      <td>389</td>\n",
       "      <td>jade</td>\n",
       "      <td>2016-08-24 10:59:21</td>\n",
       "      <td>55222</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>NaN</td>\n",
       "      <td>35</td>\n",
       "      <td>sunsutu</td>\n",
       "      <td>2016-01-26 16:24:01</td>\n",
       "      <td>31780</td>\n",
       "      <td>전원주택 공사 시공 편</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669</th>\n",
       "      <td>리더십 조직관리 Must Know</td>\n",
       "      <td>202</td>\n",
       "      <td>brunchflgu</td>\n",
       "      <td>2015-10-08 17:49:01</td>\n",
       "      <td>25334</td>\n",
       "      <td>조우성 변호사의 비즈니스 인사이트</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>퇴사의 추억</td>\n",
       "      <td>27</td>\n",
       "      <td>suhanjang</td>\n",
       "      <td>2015-09-25 11:06:47</td>\n",
       "      <td>25087</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          magazineTitle   no     profileId         publishTime  \\\n",
       "148                 NaN   73  stillalive31 2015-08-11 14:35:54   \n",
       "34                삶의 교훈  389          jade 2016-08-24 10:59:21   \n",
       "294                 NaN   35       sunsutu 2016-01-26 16:24:01   \n",
       "669  리더십 조직관리 Must Know  202    brunchflgu 2015-10-08 17:49:01   \n",
       "47               퇴사의 추억   27     suhanjang 2015-09-25 11:06:47   \n",
       "\n",
       "     socialShareTotalCount                    subTitle  \n",
       "148                  61234  반드시 피해야 할 지극히 나쁜 언행, 6천(賤)  \n",
       "34                   55222                         NaN  \n",
       "294                  31780                전원주택 공사 시공 편  \n",
       "669                  25334          조우성 변호사의 비즈니스 인사이트  \n",
       "47                   25087                         NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_articles_cleaned.sort_values(\"socialShareTotalCount\", ascending=False).iloc[:5, 6:12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>likeCount</th>\n",
       "      <th>no</th>\n",
       "      <th>profileId</th>\n",
       "      <th>publishTime</th>\n",
       "      <th>subTitle</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>734</td>\n",
       "      <td>389</td>\n",
       "      <td>jade</td>\n",
       "      <td>2016-08-24 10:59:21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>잠시 생각해 볼만한 그림들</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>699</td>\n",
       "      <td>44</td>\n",
       "      <td>lunarshore</td>\n",
       "      <td>2016-03-14 05:45:39</td>\n",
       "      <td>차별화 전략을 잃어버린 브랜드가 망가지는 수순</td>\n",
       "      <td>브랜드가 망가지는 9단계</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>696</td>\n",
       "      <td>12</td>\n",
       "      <td>haneulalice</td>\n",
       "      <td>2015-11-12 00:55:51</td>\n",
       "      <td>저녁이 없는 삶은 누가 만든걸까요?</td>\n",
       "      <td>당신의 이직을 바랍니다</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>668</td>\n",
       "      <td>68</td>\n",
       "      <td>dohyunkim</td>\n",
       "      <td>2016-01-12 17:54:08</td>\n",
       "      <td>TED 해설강의 by NYU 유학생</td>\n",
       "      <td>6개월만에 영어를 마스터 하는 것이 가능할까?</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>658</td>\n",
       "      <td>264</td>\n",
       "      <td>jade</td>\n",
       "      <td>2016-03-28 12:05:35</td>\n",
       "      <td>NaN</td>\n",
       "      <td>바쁜 사람과 유능한 사람의 12가지 차이</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     likeCount   no    profileId         publishTime  \\\n",
       "34         734  389         jade 2016-08-24 10:59:21   \n",
       "130        699   44   lunarshore 2016-03-14 05:45:39   \n",
       "60         696   12  haneulalice 2015-11-12 00:55:51   \n",
       "21         668   68    dohyunkim 2016-01-12 17:54:08   \n",
       "96         658  264         jade 2016-03-28 12:05:35   \n",
       "\n",
       "                      subTitle                      title  \n",
       "34                         NaN             잠시 생각해 볼만한 그림들  \n",
       "130  차별화 전략을 잃어버린 브랜드가 망가지는 수순              브랜드가 망가지는 9단계  \n",
       "60         저녁이 없는 삶은 누가 만든걸까요?               당신의 이직을 바랍니다  \n",
       "21         TED 해설강의 by NYU 유학생  6개월만에 영어를 마스터 하는 것이 가능할까?  \n",
       "96                         NaN     바쁜 사람과 유능한 사람의 12가지 차이  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_articles_cleaned \\\n",
    "    .drop([\"magazineAddress\", \"magazineTitle\", \"socialShareTotalCount\", \"ip\"], 1) \\\n",
    "    .sort_values(\"likeCount\", ascending=False).iloc[:5, 3:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>commentCount</th>\n",
       "      <th>no</th>\n",
       "      <th>profileId</th>\n",
       "      <th>publishTime</th>\n",
       "      <th>subTitle</th>\n",
       "      <th>title</th>\n",
       "      <th>updateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>310</td>\n",
       "      <td>42</td>\n",
       "      <td>dalda</td>\n",
       "      <td>2016-02-17 17:58:03</td>\n",
       "      <td>오늘은 달다</td>\n",
       "      <td>대한민국 남자들</td>\n",
       "      <td>2017-01-04 00:01:09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1672</th>\n",
       "      <td>273</td>\n",
       "      <td>80</td>\n",
       "      <td>wikitree</td>\n",
       "      <td>2015-12-31 14:50:37</td>\n",
       "      <td>NaN</td>\n",
       "      <td>남자가 결혼상대로 피해야 하는 '여자 유형' 8가지</td>\n",
       "      <td>2017-08-16 00:01:47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>228</td>\n",
       "      <td>152</td>\n",
       "      <td>brunch27uc</td>\n",
       "      <td>2016-06-11 05:48:02</td>\n",
       "      <td>결혼할 여자를 고르는  법</td>\n",
       "      <td>결혼해서는 안 될 여자</td>\n",
       "      <td>2017-06-17 10:20:54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>219</td>\n",
       "      <td>67</td>\n",
       "      <td>nitro2red</td>\n",
       "      <td>2016-05-18 05:21:30</td>\n",
       "      <td>곡성에 나타난 상징에 대한 분석</td>\n",
       "      <td>귀신이 곡할 영화 '곡성'</td>\n",
       "      <td>2017-08-22 17:06:36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>191</td>\n",
       "      <td>8</td>\n",
       "      <td>goodrain</td>\n",
       "      <td>2015-11-09 18:55:59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>07. 헤어진 사람에게</td>\n",
       "      <td>2017-08-13 19:05:10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      commentCount   no   profileId         publishTime           subTitle  \\\n",
       "68             310   42       dalda 2016-02-17 17:58:03             오늘은 달다   \n",
       "1672           273   80    wikitree 2015-12-31 14:50:37                NaN   \n",
       "77             228  152  brunch27uc 2016-06-11 05:48:02     결혼할 여자를 고르는  법   \n",
       "13             219   67   nitro2red 2016-05-18 05:21:30  곡성에 나타난 상징에 대한 분석   \n",
       "43             191    8    goodrain 2015-11-09 18:55:59                NaN   \n",
       "\n",
       "                             title          updateTime  \n",
       "68                        대한민국 남자들 2017-01-04 00:01:09  \n",
       "1672  남자가 결혼상대로 피해야 하는 '여자 유형' 8가지 2017-08-16 00:01:47  \n",
       "77                    결혼해서는 안 될 여자 2017-06-17 10:20:54  \n",
       "13                  귀신이 곡할 영화 '곡성' 2017-08-22 17:06:36  \n",
       "43                    07. 헤어진 사람에게 2017-08-13 19:05:10  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_articles_cleaned \\\n",
    "    .drop([\"createTime\", \"likeCount\", \"magazineAddress\", \"magazineTitle\", \"socialShareTotalCount\", \"ip\"], 1) \\\n",
    "    .sort_values(\"commentCount\", ascending=False).iloc[:5, 1:9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x12fdf9ef0>"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PMYUSF5FhdItp8lDVt8svUnXIvN1ffOcSOlLFQ6k7U1r0h5hvfgUQ2GmXq369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\nSzbZ0DAqR88Kotch++2+S5+eLx6DHZ0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4Nu9HgZXAn4Fd3bYCXO+u429Aa5VkPgJv+LwU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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12fe069e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_articles_cleaned[top_articles_cleaned[\"socialShareTotalCount\"] < 5000] \\\n",
    "    .plot(kind=\"scatter\", x=\"likeCount\", y=\"socialShareTotalCount\", \n",
    "          title=\"The relation between like count and SNS share count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How are top articles?\n",
    "\n",
    "- The average of the number of images is 5.45, and the standard deviation is 8.46\n",
    "- The mean of comment, like, SNS share is 3.61, 11.66, 60.12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>articleImageList</th>\n",
       "      <th>commentCount</th>\n",
       "      <th>likeCount</th>\n",
       "      <th>socialShareTotalCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>26891.000000</td>\n",
       "      <td>26891.000000</td>\n",
       "      <td>26891.000000</td>\n",
       "      <td>26891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.451006</td>\n",
       "      <td>3.610093</td>\n",
       "      <td>11.655238</td>\n",
       "      <td>60.121676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.457590</td>\n",
       "      <td>8.765005</td>\n",
       "      <td>25.753824</td>\n",
       "      <td>690.119018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>147.000000</td>\n",
       "      <td>310.000000</td>\n",
       "      <td>734.000000</td>\n",
       "      <td>61234.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       articleImageList  commentCount     likeCount  socialShareTotalCount\n",
       "count      26891.000000  26891.000000  26891.000000           26891.000000\n",
       "mean           5.451006      3.610093     11.655238              60.121676\n",
       "std            8.457590      8.765005     25.753824             690.119018\n",
       "min            0.000000      0.000000      0.000000               0.000000\n",
       "25%            1.000000      0.000000      2.000000               0.000000\n",
       "50%            3.000000      1.000000      5.000000               2.000000\n",
       "75%            6.000000      4.000000     12.000000              14.000000\n",
       "max          147.000000    310.000000    734.000000           61234.000000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_articles_cleaned.drop([\"no\"], 1).describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How they write?\n",
    "\n",
    "- The total number of articles of writers with 5000+ followers is gradually increasing\n",
    "- 98% of them started writing on Brunch before March 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x126f07978>, <matplotlib.text.Text at 0x126eea240>]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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x49aDVIoIY0j7aMYMiKVXbLHNm8YElDfVX7WAx4FBbtECYJyqppd0Zaq6z2O5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\nGOMz/w/fXVta9/0kNAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12f88ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_articles_cleaned = top_articles_cleaned.sort_values(\"createTime\", ascending=True)\n",
    "top_articles_cleaned[\"default\"] = 1\n",
    "top_articles_cleaned[\"accumCount\"] = top_articles_cleaned[\"default\"].cumsum()\n",
    "ax = top_articles_cleaned.plot(x=\"createTime\", y=\"accumCount\", title=\"The accumulative number of article\")\n",
    "ax.set(xlabel=\"create time\", ylabel=\"total count\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x130cd3c18>]"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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T4IaB1Za2tunaJ+/jgCQXJ7n41ltv7VOeJKmHYYPj1CT/QncW8Dbgqwz5UKck6wOfBd5Z\nVXcPvjYYRiuqqo6qqh2qaoeFCxfOxiYlSVMY9q6qf2jPGr8beDrwf6vqrGWtl2RtutA4qar+vTXf\nnGTjqvpJ64q6pbXfCGw+sPpmre1GYJdJ7ecOU7ckafYt84wjyZpJzqmqs6rqf1fV/xoyNAIcA1w1\naSyrM4CJO6P2BT4/0P7WdnfVC4C7WpfWl4FXJnlyuyj+ytYmSRqDZZ5xVNXDSR5JsqCq7uqx7Z2B\ntwCXJfleazsEOJyu62t/4Ec8Ojz7l4DXAEuAnwH7tf3fnuQDwEVtufdX1e096pAkzaJhvzl+L10A\nnEW7swqgqv58uhWq6htApnl51ymWL+DAabZ1LHDskLVKkkZo2OD49/YjSVrNzRgcSbaoqh9XleNS\nSZKAZV8c/9zERJLPjrgWSdI8sKzgGLxG8bRRFiJJmh+WFRw1zbQkaTW1rIvjz01yN92Zx7ptmjZf\nVfWkkVYnSZpzZgyOqlpzZRUiSZof+jyPQ5Ikg0OS1I/BIUnqxeCQJPVicEiSejE4JEm9GBySpF4M\nDklSLwaHJKkXg0OS1IvBIUnqxeCQJPVicEiSejE4JEm9GBySpF4MDklSLwaHJKkXg0OS1IvBIUnq\nxeCQJPVicEiSejE4JEm9GBySpF4MDklSLwaHJKkXg0OS1IvBIUnqxeCQJPVicEiSejE4JEm9jCw4\nkhyb5JYklw+0PSXJWUmuaf8+ubUnyZFJliS5NMn2A+vs25a/Jsm+o6pXkjScUZ5xHAfsNqntIOBr\nVbUN8LU2D/BqYJv2cwDwCeiCBjgUeD6wE3DoRNhIksZjZMFRVV8Hbp/UvCdwfJs+HthroP2E6nwb\n2CDJxsCrgLOq6vaqugM4i8eGkSRpJVrZ1zg2qqqftOmfAhu16U2BGwaWW9rapmt/jCQHJLk4ycW3\n3nrr7FYtSfqlsV0cr6oCaha3d1RV7VBVOyxcuHC2NitJmmRlB8fNrQuK9u8trf1GYPOB5TZrbdO1\nS5LGZGUHxxnAxJ1R+wKfH2h/a7u76gXAXa1L68vAK5M8uV0Uf2VrkySNyVqj2nCSk4FdgA2TLKW7\nO+pw4NQk+wM/At7YFv8S8BpgCfAzYD+Aqro9yQeAi9py76+qyRfcJUkr0ciCo6rePM1Lu06xbAEH\nTrOdY4FjZ7E0SdIK8JvjkqReDA5JUi8GhySpF4NDktSLwSFJ6sXgkCT1YnBIknoxOCRJvRgckqRe\nDA5JUi8GhySpF4NDktSLwSFJ6sXgkCT1YnBIknoxOCRJvRgckqReDA5JUi8GhySpF4NDktSLwSFJ\n6sXgkCT1YnBIknoxOCRJvRgckqReDA5JUi8GhySpF4NDktSLwSFJ6sXgkCT1YnBIknoxOCRJvRgc\nkqReDA5JUi8GhySpF4NDktSLwSFJ6sXgkCT1Mm+CI8luSa5OsiTJQeOuR5JWV/MiOJKsCXwMeDWw\nLfDmJNuOtypJWj3Ni+AAdgKWVNW1VfUgcAqw55hrkqTV0lrjLmBImwI3DMwvBZ4/uECSA4AD2uy9\nSa5egf1tCPz3Cqw/V6wqxwEey1y0qhwHrELHkg+t0LFsOcxC8yU4lqmqjgKOmo1tJbm4qnaYjW2N\n06pyHOCxzEWrynGAx9LXfOmquhHYfGB+s9YmSVrJ5ktwXARsk2SrJI8D9gbOGHNNkrRamhddVVX1\nUJI/Bb4MrAkcW1VXjHCXs9LlNQesKscBHstctKocB3gsvaSqRr0PSdIqZL50VUmS5giDQ5LUi8Ex\nYFUZ1iTJsUluSXL5uGtZUUk2T3JOkiuTXJHkHeOuaXkkWSfJhUm+347jfeOuaUUlWTPJJUm+MO5a\nVkSS65NcluR7SS4edz3LK8kGSU5L8oMkVyV54cj25TWOThvW5L+A36X7guFFwJur6sqxFrYckrwU\nuBc4oaqeNe56VkSSjYGNq+q7SZ4ILAb2mm9/lyQBnlBV9yZZG/gG8I6q+vaYS1tuSd4F7AA8qar2\nGHc9yyvJ9cAOVTWvvwCY5Hjg/Ko6ut19ul5V3TmKfXnG8ahVZliTqvo6cPu465gNVfWTqvpum74H\nuIpuJIF5pTr3ttm128+8/dSWZDNgd+DocdciSLIAeClwDEBVPTiq0ACDY9BUw5rMuzeoVVmSRcDz\ngO+Mt5Ll07p2vgfcApxVVfPyOJoPA+8GHhl3IbOggK8kWdyGLpqPtgJuBf61dR8eneQJo9qZwaF5\nIcn6wGeBd1bV3eOuZ3lU1cNVtR3dyAc7JZmX3YhJ9gBuqarF465llry4qranG337wNbVO9+sBWwP\nfKKqngfcB4zsOq3B8SiHNZmj2jWBzwInVdW/j7ueFdW6EM4Bdht3LctpZ+B17drAKcArkvzbeEta\nflV1Y/v3FuB0um7r+WYpsHTgLPY0uiAZCYPjUQ5rMge1i8rHAFdV1T+Nu57llWRhkg3a9Lp0N2H8\nYLxVLZ+qOriqNquqRXT/n5xdVX845rKWS5IntJsuaF07rwTm3d2IVfVT4IYkT29NuwIju4FkXgw5\nsjKMYViTkUlyMrALsGGSpcChVXXMeKtabjsDbwEua9cHAA6pqi+NsablsTFwfLt7bw3g1Kqa17ex\nriI2Ak7vPp+wFvCpqvrP8Za03P4MOKl98L0W2G9UO/J2XElSL3ZVSZJ6MTgkSb0YHJKkXgwOSVIv\nBockqReDQyssycNtZNEr2uivf5lkVv7bSrLxTKOvthFB/2Q5t33IpPkLlmc702z75CSXJvmL2drm\nNPs5N8mP2/ddJto+l+Temdbrsf1dVmT02yR7Jfm/bfrxST7dRp/+ThtChiTPTnLcwDp7JHn/Cpau\nETI4NBvur6rtquqZdF9sezVw6Cxt+13AJ2d4fQOgV3CkswbwK8FRVS/qX96U2/91YMeqek5VHTHp\ntVF8d+pOuu+70L5kuPEI9rG83g18vE3vD9xRVVsDRwAfAqiqy4DNkmzRlvsi8Nok663sYjUcg0Oz\nqg3bcADwp+0NelGS85N8t/28CCDJCUn2mlgvyUlJphqN+A+A/2zLPLM90+J77dP8NsDhwG+0tr9P\nsn6Sr7V9XTaxzVbH1UlOoPtm8DHAum29k9oy97Z/d2mf5CeebXDSxCf6JK9pbYuTHDnNp/GvAJu2\nbb+kbevD6Z718I5Wy9ntGL428YaZ5Lgkn0jy7STXtjqOTfdsheNm+LWfQvcNboDfB345LEv7G/x9\nksvb7+NNQxzjbq3tu217E9vaKcm30g2id0Hat5STfD3JdgPLfSPJc5P8JvDAwHDlewLHt+nTgF0H\nzpTOnDiG6r5cdi4wb4dqX+VVlT/+rNAPcO8UbXfSfSt3PWCd1rYNcHGbfhnwuTa9ALgOWGvSNrYC\nFg/MfxTYp00/DlgXWARcPrDMWnTPhwDYEFgCpC33CPCC6eqemKf71v1ddOOVrQF8C3gxsA7dCMpb\nteVOBr4wxbFPrulc4OMD82cC+7bpPxr4PRxHFwKhe5O9G3h2q2ExsN0U+zoXeD5wKd2IB19p+584\nlj8AzmqvbQT8mO6MZFnHuE2r49SJYwSeNPE3An4H+Gyb3hf4cJv+zYG/8X7APw7Uejmw2cD8D4EN\n2/TOwJkDr+0DfHTc/237M/WPZxwatbWBTya5DPgMsC1AVZ1HNzbYQuDNdG9CD01ad2O6oaInfAs4\nJMl7gC2r6v4p9hfgb5NcCnyVbmj8jdprP6rhH5x0YVUtrapHgO/RvRk/A7i2qq5ry5w85LYAPj0w\n/ULgU236RLo37AlnVvfOeRlwc1Vd1mq4otUwlYfpHgy1N7BuVV0/8NqLgZOrG5n3ZuA8YMdlHON1\nVXVNq2Nw8MIFwGfSPVnyCOCZrf0zwB7pBqP8I7oAhMf+/WZyC7DJDPOaQwwOzbokT6N7M7sF+Avg\nZuC5dE+Le9zAoicAf0j3yfTYKTZ1P90nYACq6lPA61r7l5K8Yop19gEWAr9d3RDmNw9s474eh/HA\nwPTDrPi4bsPue2K/j0yq4ZFl1HAKcCTdGcKw+h7jB4Bzqnuq5Gtpv9eq+hndWc2ewBuBk9ryv/L3\nY2AE6natZwFwW3ttnbY808xrDjE4NKvaGcQ/A/+vfWJdAPykfap9C12XyYTjgHcC1NSPgv0vBj5l\nt0C6tqqOBD4PPAe4B3jiwDoL6J4V8YskLwe2nKHcX7RPycO6GnjaxN1AwJt6rDvoAh69JrEPcP5y\nbmfQ+cAHeexZ0PnAm9I9RGoh3VPiLpxhOz8AFiX5jTb/5oHXFvDoowb+56T1jqYLrouq6o7WdhWw\n9cAyZ9B1awG8nm5U3YnB8n6TXx2VdvK85hCDQ7Nh4iLzFXTdQ18B3tde+ziwb5Lv03WD/PKTd+s6\nuQr416k2WlX3AT9MMvHm80bg8nSj5D6L7pnqtwHfbBd//57u0+4OrWvsrcw8dPlRwKUTF8eXpXWN\n/Qnwn0kW04XWXcOsO8mfAfu17rS3AO9Yjm1Mrq2q6h/qsc/NPp3u+sf3gbOBd1c3BPd02/k53c0N\nX2wXx28ZePnvgA8muYRJZyfVPdTpbn71b/l14HkDF8CPAZ6aZAnd3XKDDxp6Od3dVNPNaw5xdFyN\nTbvd8jJg+6qa8g04ye/RdTv99UotbhpJ1q+qe9ub4ceAa2rSLberoySb0F2of0Y7u5xo/wjddZuv\nzrDu4+muvby4uscbbEQ3vPmuIy5by8kzDo1Fkt+hO9v46HShAVBVpwPXr6y6hvC2dsZzBV3Xzb+M\nuZ6xS/JWuufA/9VgaDR/S3dn3Uy2AA4auDliC+AvZ7dKzSbPOCRJvXjGIUnqxeCQJPVicEiSejE4\nJEm9GBySpF7+P20YAJ4CO9ChAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1289863c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_articles_cleaned[\"day\"] = top_articles_cleaned[\"createTime\"].apply(lambda x: x.weekday())\n",
    "ax = top_articles_cleaned[\"day\"].plot(kind=\"hist\", x=\"day\", bins=7, title=\"When writers write\")\n",
    "ax.set(xlabel=\"Day (starting from Monday(0))\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x126df93c8>]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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GffH/8MD0I8Aq4IhNXo0kadYb9qqqV4+6EEmair262WfYoao/WNfjVfUXm6YcSdJs1+eq\nqpcAZ7f5Xwa+CVw7iqIkSbPXsMGxG7BvVd0HkOR44Jyq+q1RFSZJmp2GveXIzsCPBuZ/1NokSfPM\nsD2OTwDfTHJWm389cPpoSpIkzWbDXlV1QpJ/AV7Zmo6sqm+NrixJ0mw17FAVwFOBe6vqr4Abk+y5\nroWTnJrktiRXDbTtmGR5kmvb7x1ae5J8NMl1Sa4Y/MBhkqVt+WuTLO35/CRJm9iwXx17HPBu4D2t\naSvgU+tZ7TTg4Elty4ALqmoRcEGbBzgEWNR+jgJObvvdETgOeCmwH3DcRNhIksZj2B7HG4BfAR4A\nqKqbgO3WtUJVfQW4c1Lz4aw9N3I63bmSifZPVOdiYPskuwCvA5ZX1Z1VdRewnCeGkSRpBg0bHD+q\nqqLdWj3J0zZwfztX1c1t+hbWXpm1K3DDwHI3trbp2p8gyVFJViZZuWbNmg0sT5K0PsMGx5lJ/pau\nJ/C7wJfYyC91GgyiTaGqTqmqJVW1ZMGCBZtqs5KkSYa9qupD7bvG7wX2Av6kqpZvwP5uTbJLVd3c\nhqJua+2rgd0Hltutta0GDpzU/uUN2K8kaRNZb48jyRZJLqyq5VX136rqjzYwNKC7ZcnElVFLgS8M\ntL+tXV31MuCeNqR1HnBQkh3aSfGDWpskaUzW2+OoqkeTPJbkGVV1z7AbTvJZut7CTklupLs66kS6\nYa93ANez9tbs5wKHAtcBDwJHtn3fmeQDwCVtufdX1eQT7pKkGTTsJ8fvB65Mspx2ZRVAVf3edCtU\n1Vumeeg1UyxbwNHTbOdU4NQh65QkjdiwwfH59iNJmufWGRxJnltV/15V3pdKkgSs/+T4P01MJPnc\niGuRJM0B6wuODEw/b5SFSJLmhvUFR00zLUmap9Z3cvxFSe6l63k8pU3T5quqnj7S6iRJs846g6Oq\ntpipQiRJc0Of7+OQJGnoz3FIm8zCZeeMuwRJG8EehySpF4NDktSLwSFJ6sXgkCT1YnBIknoxOCRJ\nvRgckqReDA5JUi9+AHAKfT+gturEw0ZUiSTNPvY4JEm9GBySpF4MDklSLwaHJKkXg0OS1IvBIUnq\nxeCQJPVicEiSejE4JEm9GBySpF4MDklSLwaHJKkXg0OS1IvBIUnqxeCQJPVicEiSejE4JEm9GByS\npF4MDklSL2MJjiSrklyZ5PIkK1vbjkmWJ7m2/d6htSfJR5Ncl+SKJPuOo2ZJUmecPY5XV9XiqlrS\n5pcBF1TVIuCCNg9wCLCo/RwFnDzjlUqSfmI2DVUdDpzepk8HXj/Q/onqXAxsn2SXcRQoSRpfcBRw\nfpJLkxzV2nauqpvb9C3Azm16V+CGgXVvbG2Pk+SoJCuTrFyzZs2o6pakeW/LMe33FVW1OsmzgOVJ\nvjv4YFVVkuqzwao6BTgFYMmSJb3WlSQNbyw9jqpa3X7fBpwF7AfcOjEE1X7f1hZfDew+sPpurU2S\nNAYzHhxJnpZku4lp4CDgKuBsYGlbbCnwhTZ9NvC2dnXVy4B7Boa0JEkzbBxDVTsDZyWZ2P9nquqL\nSS4BzkzyDuB64Ii2/LnAocB1wIPAkTNfsiRpwowHR1X9AHjRFO13AK+Zor2Ao2egNG2ghcvOGXcJ\nkmbQbLocV5I0BxgckqRexnU57mZlQ4ZqVp142AgqkaTRs8chSerF4JAk9WJwSJJ6MTgkSb0YHJKk\nXgwOSVIvBockqReDQ5LUi8EhSerF4JAk9WJwSJJ6MTgkSb0YHJKkXgwOSVIvBockqReDQ5LUi8Eh\nSerF4JAk9WJwSJJ68TvHx8TvKZc0V9njkCT1Yo9Dj7MhPSFJ84s9DklSLwaHJKkXg0OS1IvBIUnq\nxZPjc4gnriXNBvY4JEm9GBySpF4MDklSLwaHJKkXg0OS1IvBIUnqxeCQJPVicEiSepkzwZHk4CTf\nS3JdkmXjrkeS5qs5ERxJtgBOAg4B9gbekmTv8VYlSfPTnAgOYD/guqr6QVX9CDgDOHzMNUnSvDRX\n7lW1K3DDwPyNwEsHF0hyFHBUm70/yfc2Yn87AbdvxPqbC49Dx+PQ8Th0ZvVxyAc3avU9hllorgTH\nelXVKcApm2JbSVZW1ZJNsa25zOPQ8Th0PA4dj8PcGapaDew+ML9ba5MkzbC5EhyXAIuS7JnkycCv\nA2ePuSZJmpfmxFBVVT2S5BjgPGAL4NSqunqEu9wkQ16bAY9Dx+PQ8Th05v1xSFWNuwZJ0hwyV4aq\nJEmzhMEhSerF4BjgbU06SVYluTLJ5UlWjruemZTk1CS3JblqoG3HJMuTXNt+7zDOGmfCNMfh+CSr\n29/F5UkOHWeNMyHJ7kkuTPKdJFcn+f3WPu/+JgYZHI23NXmCV1fV4nl4vfppwMGT2pYBF1TVIuCC\nNr+5O40nHgeAj7S/i8VVde4M1zQOjwB/WFV7Ay8Djm6vC/Pxb+InDI61vK2JqKqvAHdOaj4cOL1N\nnw68fkaLGoNpjsO8U1U3V9Vlbfo+4Bq6O1nMu7+JQQbHWlPd1mTXMdUybgWcn+TSdiuX+W7nqrq5\nTd8C7DzOYsbsmCRXtKGseTU8k2Qh8GLg35jnfxMGh6byiqral27Y7ugkrxp3QbNFddevz9dr2E8G\nfgpYDNwMfHi85cycJNsCnwOOrap7Bx+bj38TBsda3takqarV7fdtwFl0w3jz2a1JdgFov28bcz1j\nUVW3VtWjVfUY8HHmyd9Fkq3oQuPTVfX51jyv/yYMjrW8rQmQ5GlJtpuYBg4Crlr3Wpu9s4GlbXop\n8IUx1jI2Ey+UzRuYB38XSQL8HXBNVf3FwEPz+m/CT44PaJcX/iVrb2tywphLmnFJnkfXy4DuljSf\nmU/HIclngQPpbp19K3Ac8E/AmcBzgeuBI6pqsz5xPM1xOJBumKqAVcA7B8b5N0tJXgF8FbgSeKw1\nv5fuPMe8+psYZHBIknpxqEqS1IvBIUnqxeCQJPVicEiSejE4JEm9GBwaqyTva3cdvaLdcfWlrf3Y\nJE/dgO29Pclzpmh/UZLLB+bfkuSh9uEukrwwyRXTbPP9SV47VV1Jzk2yfd86p9jHwsE70ba245P8\n0cZue5r9PSXJinZzT5IsbXd6vTbJ0mnW+cV2G5or2+9fGHjsi0m+3f4tPzaw3Q8NLqfNg8GhsUny\n88AvAftW1T7Aa1l7v7BjgV7B0V6s3g48ITjorsN/7sSHG4H96W5Y9+KB+a9Ptc2q+pOq+tJUdVXV\noVV1d586Z1qSqb4i+reBz1fVo0l2pPucxkvpPg1+3DT3obod+OWqeiHdh94+OfDYEVX1IuBngQXA\nm1v7XzPP7hw7HxgcGqddgNur6mGAqrq9qm5K8nt0L/4XJrkQIMnJSVa2d7R/OrGB9t0hH0xyGfAW\nYAnw6dZ7ecrEcu02GSvpXhwBfo7uNvr7t/n9gYum2Oabk5yW5E3T1LUqyU6tx3BNko+3Gs+f2H+S\nlwz0qP58cs9iGEkWJ7m4beesiRf2JF9OsqRN75RkVZt+e5Kzk/wr3W2/J/tN1n7a+XXA8qq6s6ru\nApYzxS3Vq+pbVXVTm70aeEqSrdtjE/dv2hJ4Mu3eTVV1PfDMJM/u+5w1exkcGqfzgd2T/L8kf5Pk\nAICq+ihwE913gry6Lfu+9t0g+wAHJNlnYDt3VNW+VfUpunD4zfZ9EQ9N2t9FwP7tViqPAV/m8cEx\n2OOY2OYZEw3T1DVoEXBSVb0AuBt4Y2v/P3Sfsl4MPLqO4/FTWfslSZcD7xp47BPAu1vP7Eq6HsL6\n7Au8qaoOGGxst9R5XlWtak0bcmfoNwKXTYR+2+55dPdsug/4x4FlLwNePkS9miMMDo1NVd1P987/\nKGAN8PdJ3j7N4ke0HsC3gBfQfdnWhL8fcpdfpwuI/YBLqur7wPOTLAC2bfN9tznoh1U1cR7lUmBh\nO/+xXVV9o7V/Zh3rf3/gS5IWAx8DSPIMYPuqWtGWOx0Y5o7Fy6e5DcZOdMG2QZK8APgg8M7B9qp6\nHV0vcmtg8LzGbUw9fKg5yuDQWLW7rX65qo4DjmHtu/SfSLIn8EfAa9o77nOAbQYWeWDI3V0MvITu\n3e/EC/mNdDe0/MakZYfd5qCHB6YfpRu2GbVHWPv/eJtJj033HB6atOyUd4ZO8oaBHtDEcNhudPcy\ne9ukoAWgqv6Dbghs8EvQtmn71GbC4NDYJNkryaKBpsV0N4yDbrhj4kT20+leBO9JsjPd94RMZ3C9\nx2nf4HYDcCRrg+IbdCe8Lxqy7Gm3P80+7wbum7hajC6keqmqe4C7kryyNb0VmOh9rKLrtQG8acjt\n3QVskWQiPM4DDkqyQzt3chBwXlWdNdADWtl6T+cAy6rqJ8crybZZe4vxLYHDgO8O7PI/MQ/upDuf\nGBwap22B05N8p10KuzdwfHvsFOCLSS6sqm/TDVF9l26oZ10v8qcBH5t8cnzARcDWVTUxpv8N4HlM\ncUXVNH5S15DLA7wD+Hg7b/E04J4e605YCvx5O06Lgfe39g8B/znJt+iGoIZ1PvAKgDac9QG6rxa4\nBHj/NENcxwDPB/5koCfyrPaczm61XU43NDUxzLZVW2dlnyer2c2740ojlmTbdj6HJMuAXarq98dc\n077Af62qt454P2+gu9z6j0e5H82smRiDlea7w5K8h+7/2/V0nzUZq6q6LMmF7XMq67rSa2NtyTz6\nitn5wh6HJKkXz3FIknoxOCRJvRgckqReDA5JUi8GhySpl/8P1je+1W2Zs2AAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122ef39b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_articles_cleaned[\"writingHour\"] = top_articles_cleaned[\"createTime\"].apply(lambda x: x.hour)\n",
    "ax = top_articles_cleaned[\"writingHour\"].plot(kind=\"hist\", x=\"writingHour\", bins=24, \n",
    "                                              title=\"When writers start writing\")\n",
    "ax.set(xlabel=\"Start Writing Hour (0-23)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x12d848630>]"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fts/uYHwRETEMtTur7v8Fvg2cavul3kLbT0j6XEcii4iIYavdbquDgYt7E4ekV0naCMD2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d851160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_articles_cleaned[\"publishingHour\"] = top_articles_cleaned[\"publishTime\"].apply(lambda x: x.hour)\n",
    "ax = top_articles_cleaned[\"publishingHour\"].plot(kind=\"hist\", x=\"publishingHour\", bins=24, \n",
    "                                            title=\"When writers start publishing\")\n",
    "ax.set(xlabel=\"Publishing Hour (0-23)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x126e049b0>]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4pqTXgTeBkyOi1Ml/CtmVZO8g64Mp9cNMAa6TdCLwDHBMrfbFzMyqU7OiEhETyoR/0cm8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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x129e4d6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_articles_cleaned[\"writingDuration\"] = top_articles_cleaned[\"publishTime\"] - top_articles_cleaned[\"createTime\"]\n",
    "top_articles_cleaned[\"writingDuration\"] = top_articles_cleaned[\"writingDuration\"].apply(lambda x: x.seconds / 60)\n",
    "ax = top_articles_cleaned[\"writingDuration\"].plot(kind=\"hist\", x=\"writingDuration\", bins=24, \n",
    "                                                 title=\"How long writers write by minute\")\n",
    "ax.set(xlabel=\"Minutes\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Articles written within 15 minutes\n",
    "- It means writers usually don't use the built-in writing system in brunch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61.585660629950546"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_articles_cleaned[top_articles_cleaned[\"writingDuration\"] < 15][\"writingDuration\"].count() \\\n",
    "    / top_articles_cleaned[\"writingDuration\"].count() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x12e865518>]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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mZqU4cZiZWSlOHGZmVooTh5mZleLEYU0n3y304sJ4H0ktWnuH3oM35k6mko7L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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12eac0320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = top_articles_cleaned[top_articles_cleaned[\"writingDuration\"] > 15][\"writingDuration\"] \\\n",
    "        .plot(kind=\"hist\", x=\"writingDuration\", bins=24, title=\"How long writers write above 15 minutes\")\n",
    "ax.set(xlabel=\"Minutes (over 15m)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16.250790227213567"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_articles_cleaned.query(\"writingDuration > 15 & writingDuration < 300\")[\"writingDuration\"].count() \\\n",
    "/ top_articles_cleaned[\"writingDuration\"].count() * 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x12a0f52b0>]"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dW1qRN8ZUXyBdN6cBm1Q1R1WLgfeAnwDN3K4cgPZAVoAZo1b+4RL+s3w7E05O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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12d994278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_top_writers = top_df[\"profile_id\"].unique()\n",
    "top_first_article_time = pd.DataFrame(\n",
    "    [top_articles_cleaned[top_articles_cleaned[\"profileId\"] == writer].iloc[0][\"createTime\"]\n",
    "     for writer in unique_top_writers],\n",
    "    columns=[\"time\"]\n",
    ")\n",
    "top_first_article_time = top_first_article_time.sort_values(\"time\")\n",
    "top_first_article_time[\"default\"] = 1\n",
    "top_first_article_time[\"accumCount\"] = top_first_article_time[\"default\"].cumsum()\n",
    "ax = top_first_article_time.plot(x=\"time\", y=\"accumCount\", title=\"When writers started writing\")\n",
    "ax.set(xlabel=\"starting date\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x12d7ea978>, <matplotlib.text.Text at 0x12df120f0>]"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+WLiqqohoUd4jIrcDtwM0a9bMHzGMKTGys5Wf1yfzj/+t5OjxTIZ3a8wd/WOt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\nGGOMV1YgjDHGeGUFwhhjjFf/H15j0ZM4VBlLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12df09898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def find_writer_count_of_lastest_time(time:pd.datetime) -> int:\n",
    "    result_values = top_first_article_time[top_first_article_time[\"time\"] < time][\"accumCount\"].values\n",
    "    if len(result_values) == 0:\n",
    "        return 1\n",
    "    return result_values[-1]\n",
    "top_articles_cleaned[\"accumCountPerWriter\"] = \\\n",
    "    top_articles_cleaned[\"accumCount\"] / top_articles_cleaned[\"createTime\"].apply(find_writer_count_of_lastest_time)\n",
    "ax = top_articles_cleaned.plot(x=\"createTime\", y=\"accumCountPerWriter\", title=\"The number of articles per writer\")\n",
    "ax.set(xlabel=\"date\", ylabel=\"the number of articles\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>createTime</th>\n",
       "      <th>description</th>\n",
       "      <th>followerCount</th>\n",
       "      <th>profileId</th>\n",
       "      <th>userName</th>\n",
       "      <th>writerCount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-08-24 08:04:50</td>\n",
       "      <td>MAGARETT의 브런치입니다.</td>\n",
       "      <td>0</td>\n",
       "      <td>megan-yoon</td>\n",
       "      <td>MAGARETT</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-08-24 07:29:31</td>\n",
       "      <td>구구의 브런치입니다.</td>\n",
       "      <td>0</td>\n",
       "      <td>jungulism</td>\n",
       "      <td>구구</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-08-24 07:28:40</td>\n",
       "      <td>김보람의 브런치입니다.</td>\n",
       "      <td>0</td>\n",
       "      <td>kimboroam</td>\n",
       "      <td>김보람</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-08-24 06:59:06</td>\n",
       "      <td>한수연의 브런치입니다.</td>\n",
       "      <td>1</td>\n",
       "      <td>nownhere23</td>\n",
       "      <td>한수연</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-08-24 06:52:07</td>\n",
       "      <td>풍꼬의 브런치입니다.</td>\n",
       "      <td>0</td>\n",
       "      <td>ljk4936</td>\n",
       "      <td>풍꼬</td>\n",
       "      <td>159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           createTime        description  followerCount   profileId  userName  \\\n",
       "0 2017-08-24 08:04:50  MAGARETT의 브런치입니다.              0  megan-yoon  MAGARETT   \n",
       "1 2017-08-24 07:29:31        구구의 브런치입니다.              0   jungulism        구구   \n",
       "2 2017-08-24 07:28:40       김보람의 브런치입니다.              0   kimboroam       김보람   \n",
       "3 2017-08-24 06:59:06       한수연의 브런치입니다.              1  nownhere23       한수연   \n",
       "4 2017-08-24 06:52:07        풍꼬의 브런치입니다.              0     ljk4936        풍꼬   \n",
       "\n",
       "   writerCount  \n",
       "0           48  \n",
       "1            8  \n",
       "2            2  \n",
       "3           50  \n",
       "4          159  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_followers = get_follower_df_by_writers(top_df[\"profile_id\"].values)\n",
    "top_followers_cleaned = top_followers.drop([\"articleCount\", \"userId\", \"myFollower\", \"myWriter\", \"subscribeNo\"], 1)\n",
    "parse_datetime(top_followers_cleaned, fields=[\"createTime\"])\n",
    "top_followers_cleaned.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x12df03860>]"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WuR77ke21ZXcRMGmwNiRNALa2vcjVK0XnAoeWwzOAOWV7Tr/4XFcWAduWdiIioks6TTa/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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x133bc6668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_followers_cleaned[\"day\"] = top_followers_cleaned[\"createTime\"].apply(lambda x: x.weekday())\n",
    "ax = top_followers_cleaned[\"day\"].plot(kind=\"hist\", x=\"day\", bins=7, title=\"When people follow 1\")\n",
    "ax.set(xlabel=\"Day (starting from Mon(0))\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.text.Text at 0x13085d3c8>]"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JSyTdXX7uVeKSdJGkbkm3STqkYT8zSvm7Jc1oiB9a9t9d6qrVE4+IiPq1evnrD5IOsX0L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\npFKiAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x134a2e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_followers_cleaned[\"followingHour\"] = top_followers_cleaned[\"createTime\"].apply(lambda x: x.hour)\n",
    "ax = top_followers_cleaned[\"followingHour\"].plot(kind=\"hist\", x=\"followingHour\", bins=24,\n",
    "                                                title=\"When people follow 2\")\n",
    "ax.set(xlabel=\"Hour (0-23)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tricky growth of Brunch\n",
    "\n",
    "- What did happen in May 2016?\n",
    "- The figure below includes duplicate followers of writers\n",
    "- The number of fans on 30 April 2016 is 4.28 times more than the one on 31 March 2016\n",
    "- This steep growth was affected by a marketing campaign, in which Kakao, the service provider, gave free promotional Kakaotalk emoticons"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1393437f0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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KwcYEyYU4z+Q7wDdFpAhvfONJV/4kkObKvwncA6CqO4A/AIXAX4C7VLXFjYnc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\njDEmYP8f9cNwQ9LjR5sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x139350b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "top_followers_cleaned = top_followers_cleaned.sort_values(\"createTime\", ascending=True)\n",
    "top_followers_cleaned[\"default\"] = 1\n",
    "top_followers_cleaned[\"accumCount\"] = top_followers_cleaned[\"default\"].cumsum()\n",
    "ax = top_followers_cleaned.plot(x=\"createTime\", y=\"accumCount\", title=\"When people follow 3\")\n",
    "ax.set(xlabel=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For fun : The authors who the writers with 5000+ followers like most"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "hyejinchoi      45\n",
       "daljasee        44\n",
       "kooning11       40\n",
       "suhanjang       37\n",
       "blade31         33\n",
       "2kija           32\n",
       "romanticgrey    32\n",
       "eastgo          31\n",
       "onec            31\n",
       "dalda           31\n",
       "jimmyrim        30\n",
       "sustainlife     30\n",
       "Name: profileId, dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_followings = get_following_df_by_writers(top_df[\"profile_id\"].values)\n",
    "top_followings_cleaned = top_followings.drop([\"articleCount\", \"userId\", \"myFollower\", \"myWriter\", \"subscribeNo\"], 1)\n",
    "parse_datetime(top_followings_cleaned, fields=[\"createTime\"])\n",
    "top_followings_cleaned[\"profileId\"].value_counts()[1:13]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "\n",
    "- The number of writers with over 5000 followers is 162 \n",
    "- The correlation between article count and follower count is low, which does not encourage authors to write more articles\n",
    "- The user engagement of Brunch is focused on the share of SNS\n",
    "- There was a strong marketing campaign on April 2016\n",
    "\n",
    "## Limitation of this study\n",
    "\n",
    "- It is not examined if the Kakaotalk marketing campaign helped the growth of Brunch\n",
    "- To figure it out, the active user data should be backed up "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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