EDA of news articles shared on PTT.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import jieba, jieba.analyse\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Re-correct news sources and tokenize news content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>author</th>\n",
       "      <th>datetime</th>\n",
       "      <th>upvotes</th>\n",
       "      <th>downvotes</th>\n",
       "      <th>forwards</th>\n",
       "      <th>source</th>\n",
       "      <th>headline</th>\n",
       "      <th>original_link</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mordecai (莫迪凱)</td>\n",
       "      <td>Mon Jan 23 13:45:31 2017</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>goo.gl</td>\n",
       "      <td>[新聞] 安徽老人組團攔花車 躺地耍賴逼付「喜錢</td>\n",
       "      <td>https://goo.gl/LccFQR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c8c8c8c8c8c8 (哈哈哈哈哈哈哈哈哈哈哈)</td>\n",
       "      <td>Mon Jan 23 13:45:49 2017</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>tinyurl.com</td>\n",
       "      <td>[新聞] 慎入!　胸腹中箭險傷心臟　男子奇蹟生還</td>\n",
       "      <td>http://tinyurl.com/zt52dyj</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>fun5566 (台大板高中數學考古題神)</td>\n",
       "      <td>Mon Jan 23 13:57:33 2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>14</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[新聞] 持刀威脅男子舔下體、性交　美17歲少女긱</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>cisbpmtw (cisbpmtw)</td>\n",
       "      <td>Mon Jan 23 14:01:38 2017</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>tinyurl.com</td>\n",
       "      <td>[新聞] 被狗追成精　他說這裡的狗比聯結車可怕</td>\n",
       "      <td>http://tinyurl.com/z9y57on</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kyo824 (Kyo)</td>\n",
       "      <td>Mon Jan 23 14:02:52 2017</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>goo.gl</td>\n",
       "      <td>[新聞] 基因好強大！大S首次公開兒子正面照</td>\n",
       "      <td>https://goo.gl/SYGpVF</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       author                  datetime upvotes  downvotes  \\\n",
       "0              Mordecai (莫迪凱)  Mon Jan 23 13:45:31 2017      37          0   \n",
       "1  c8c8c8c8c8c8 (哈哈哈哈哈哈哈哈哈哈哈)  Mon Jan 23 13:45:49 2017      21          0   \n",
       "2       fun5566 (台大板高中數學考古題神)  Mon Jan 23 13:57:33 2017      32          4   \n",
       "3         cisbpmtw (cisbpmtw)  Mon Jan 23 14:01:38 2017       9          7   \n",
       "4                Kyo824 (Kyo)  Mon Jan 23 14:02:52 2017       5          8   \n",
       "\n",
       "   forwards       source                   headline  \\\n",
       "0         9       goo.gl   [新聞] 安徽老人組團攔花車 躺地耍賴逼付「喜錢   \n",
       "1        11  tinyurl.com   [新聞] 慎入!　胸腹中箭險傷心臟　男子奇蹟生還   \n",
       "2        14          NaN  [新聞] 持刀威脅男子舔下體、性交　美17歲少女긱   \n",
       "3         6  tinyurl.com    [新聞] 被狗追成精　他說這裡的狗比聯結車可怕   \n",
       "4        10       goo.gl     [新聞] 基因好強大！大S首次公開兒子正面照   \n",
       "\n",
       "                original_link  \n",
       "0       https://goo.gl/LccFQR  \n",
       "1  http://tinyurl.com/zt52dyj  \n",
       "2                         NaN  \n",
       "3  http://tinyurl.com/z9y57on  \n",
       "4       https://goo.gl/SYGpVF  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"data/news_modified.csv\")\n",
    "del df[\"Unnamed: 0\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache /var/folders/n0/1wf1ts910pjdlzbtp2q3dp8m0000gn/T/jieba.cache\n",
      "Loading model cost 1.032 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    }
   ],
   "source": [
    "def tokenize(s):\n",
    "    if pd.isnull(s) or not \"[新聞]\" in s:\n",
    "        return \"\"\n",
    "    else:\n",
    "        return \" \".join(jieba.cut(s.split(\"[新聞]\")[1])).strip()\n",
    "\n",
    "df[\"tokens\"] = df[\"headline\"].apply(tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[df[\"source\"].str.contains(\"epoch|大紀\", na=False),\"source\"] = \"epochtimes\"\n",
    "df.loc[df[\"source\"].str.contains(\"imgur\", na=False),\"source\"] = \"imgur\"\n",
    "df.loc[df[\"source\"].str.contains(\"youtu\", na=False),\"source\"] = \"youtube\"\n",
    "df.loc[df[\"source\"].str.contains(\"cna|中央\", na=False),\"source\"] = \"cna\"\n",
    "df.loc[df[\"source\"].str.contains(\"hinet\", na=False),\"source\"] = \"hinet\"\n",
    "df.loc[df[\"source\"].str.contains(\"pts|公視\", na=False),\"source\"] = \"pts\"\n",
    "df.loc[df[\"source\"].str.contains(\"peoplenews\", na=False),\"source\"] = \"peoplenews\"\n",
    "df.loc[df[\"source\"].str.contains(\"ithome\", na=False),\"source\"] = \"ithome\"\n",
    "df.loc[df[\"source\"].str.contains(\"中國時報\", na=False),\"source\"] = \"china times\"\n",
    "df.loc[df[\"source\"].str.contains(\"cdnews\", na=False),\"source\"] = \"cdnews\"\n",
    "df.loc[df[\"source\"].str.contains(\"rti\", na=False),\"source\"] = \"rti\"\n",
    "df.loc[df[\"source\"].str.contains(\"ctee\", na=False),\"source\"] = \"ctee\"\n",
    "df.loc[df[\"source\"].str.contains(\"nextmag\", na=False),\"source\"] = \"nextmag\"\n",
    "df.loc[df[\"source\"].str.contains(\"科技新報\", na=False),\"source\"] = \"technews\"\n",
    "df.loc[df[\"source\"].str.contains(\"3立|三立|setn\", na=False),\"source\"] = \"setn\"\n",
    "df.loc[df[\"source\"].str.contains(\"lens|關鍵\", na=False),\"source\"] = \"thenewslens\"\n",
    "df.loc[df[\"source\"].str.contains(\"initium|端傳\", na=False),\"source\"] = \"the initium\"\n",
    "df.loc[df[\"source\"].str.contains(\"udn|經濟日報\", na=False),\"source\"] = \"udn\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df[\"datetime\"] = df[\"datetime\"].apply(lambda x: pd.to_datetime(x, errors='ignore')).astype(pd.Timestamp)\n",
    "df[\"upvotes\"] = pd.to_numeric(df[\"upvotes\"], errors='coerce')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['liberty times', 'apple', 'goo.gl', 'udn', 'ettoday', 'china times',\n",
       "       'setn', 'imgur', 'epochtimes', 'tinyurl.com', 'cna', 'tvbs',\n",
       "       'buzzorange', 'now news', 'storm media'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"source\"].value_counts().index[:15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Plot top 15 news sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x117d600b8>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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7nQG8gmpy72BOB8YBd9PUizFUmflt4PPAZ6gmHk8DjgSOKJt8FfgvqkB0L1UYOKz3kaWS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\nMLFpfgARsSqwVFl3d1k8oWn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ElgFmzczH67ItImIlYFNKC0Nj9fWN73YVuqKp592rjGfv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\n/g+qfZPQNZu0sgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108c3b4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "plt.style.use('seaborn-colorblind')\n",
    "plt.figure(figsize=(12, 10))\n",
    "plt.title(\"Top news sources of articles shared on PTT\")\n",
    "plt.barh(np.arange(0,15,1), df[\"source\"].value_counts().tolist()[:15])\n",
    "ax = plt.gca()\n",
    "_ = ax.invert_yaxis()\n",
    "_ = ax.set_yticklabels(df[\"source\"].value_counts().index[:15], fontsize='15')\n",
    "_ = ax.set_xticklabels(np.arange(0,6000,500), fontsize='14')\n",
    "_ = ax.set_yticks(np.arange(0,15,1))\n",
    "ax.set_xlabel('Number of articles shared on PPT', fontsize='13')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Plot upvotes vs forwards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1187e1fd0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I+kdJPybpg5ZlPR5w24BQcAg4AKBV/Gzo+meSdkr6JUkpSW+S9PeSCGLoSBwCDgBoFT9D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\ng5gkSVIlBjFJkqRK/g0AB5YCnjjupQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117dd2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.scatter('upvotes','forwards',figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Filtering by top writers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(847, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "topauthors = df[\"author\"].value_counts().index.values\n",
    "df_top10authors = df[df[\"author\"].isin(topauthors[:10])]\n",
    "df_top10authors[\"id_author\"] = df_top10authors[\"author\"].apply(lambda x: np.where(topauthors[:10] == x)[0][0] + 1)\n",
    "df_top10authors.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(847, 3948)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer\n",
    "smat = TfidfVectorizer().fit_transform(df_top10authors[\"tokens\"].values)\n",
    "smat.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Perform tSNE on truncated data to save time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(847, 50)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.decomposition import TruncatedSVD\n",
    "\n",
    "smat_50 = TruncatedSVD(n_components = 50, random_state = 0).fit_transform(smat)\n",
    "smat_50.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[t-SNE] Computing pairwise distances...\n",
      "[t-SNE] Computing 91 nearest neighbors...\n",
      "[t-SNE] Computed conditional probabilities for sample 847 / 847\n",
      "[t-SNE] Mean sigma: 0.054361\n",
      "[t-SNE] Iteration 25: error = 1.0675659, gradient norm = 0.0162857\n",
      "[t-SNE] Iteration 50: error = 1.0498644, gradient norm = 0.0148060\n",
      "[t-SNE] Iteration 75: error = 0.6762921, gradient norm = 0.0076999\n",
      "[t-SNE] Iteration 100: error = 0.6620607, gradient norm = 0.0083872\n",
      "[t-SNE] KL divergence after 100 iterations with early exaggeration: 0.662061\n",
      "[t-SNE] Iteration 125: error = 0.5821584, gradient norm = 0.0038843\n",
      "[t-SNE] Iteration 150: error = 0.5823601, gradient norm = 0.0052152\n",
      "[t-SNE] Iteration 175: error = 0.5963241, gradient norm = 0.0053520\n",
      "[t-SNE] Iteration 175: did not make any progress during the last 30 episodes. Finished.\n",
      "[t-SNE] Error after 175 iterations: 0.662061\n"
     ]
    }
   ],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "tsne = TSNE(\n",
    "    n_components=2,\n",
    "    random_state=101,\n",
    "    #perplexity = 5,\n",
    "    method='barnes_hut',\n",
    "    n_iter=200,\n",
    "    verbose=2,\n",
    ").fit_transform(smat_50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from subprocess import check_output\n",
    "\n",
    "%matplotlib inline\n",
    "import plotly.offline as py\n",
    "py.init_notebook_mode(connected=True)\n",
    "import plotly.graph_objs as go\n",
    "import plotly.tools as tls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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9.334403064622943, 34.49598041227709, 2.4031050195051304, 44.183779491980815, -13.448708560493236, 10.56431971250582, -15.194840221889056, 4.7151191783231345, 24.264773792015927, -5.886445095742737, 12.969760859595585, 1.5027787816464602, 36.27562339280405, 25.528060894309398, 14.937782520979065, -2.658190044084848, -4.831825673042725, 12.297824518091645, 32.45645401556863, 43.176451539113586, 37.84137789554573, 15.168727479850322, 34.65978460545896, 38.87280134233465, 39.841428840848295, 34.08209745978629, -15.230791758814366, 12.549465230104289, 29.110804477109138, 9.06847182953747, -24.647332975782117, 31.478902469302334, -10.223897048408535, -21.50988933928708, 25.587352833583722, 35.8857196283832, 37.25824056700599, -4.6551717673070465, 40.59937868370355, 7.020024916802751, 18.19640112268859, 40.12835659946979, 38.58702709973809, -21.368292063591745, 20.836092093511056, 3.550762972480722, 38.103600452207615, 27.887608686534907, -15.420613289148175, -25.729296008214188, 23.52392197983663, 6.359652211491104, 14.518173136509612, -14.052853951654173, 29.95904273024084, -0.29729526658750677, 34.35809988043458, -21.907032428921003, -23.608281353487666, -4.700098090257728, 33.92151981166763, 38.24532201127709, -17.897607574005388, -4.703742428938418, 45.43338222418783, 33.98430200451669, -5.121469457393794, -0.6725941833309388, 6.071315148797774, -22.463574552819377, -24.155155141062462, 34.89960990569857, -6.09880045213576, 13.948650526001236, 7.759476493593157, 45.46414511417253, 41.9728865683937, 13.462105661049462], \"marker\": {\"opacity\": 0.75, \"line\": {\"color\": \"rgb(255, 255, 255)\"}, \"colorbar\": {\"title\": \"author\"}, \"color\": [2, 3, 7, 6, 9, 5, 8, 1, 1, 10, 2, 6, 3, 5, 7, 9, 8, 1, 10, 2, 4, 7, 9, 3, 6, 8, 5, 1, 4, 10, 9, 2, 6, 3, 7, 5, 8, 4, 10, 1, 2, 5, 3, 7, 6, 8, 4, 1, 10, 2, 3, 7, 6, 9, 5, 8, 10, 1, 4, 6, 7, 5, 3, 2, 9, 8, 1, 4, 10, 2, 6, 3, 7, 5, 9, 8, 1, 4, 10, 2, 9, 7, 3, 6, 5, 8, 1, 10, 6, 2, 3, 9, 7, 5, 8, 1, 4, 10, 6, 9, 2, 5, 7, 3, 8, 1, 10, 2, 3, 7, 6, 4, 5, 9, 8, 4, 1, 10, 9, 2, 3, 7, 6, 5, 8, 4, 1, 10, 2, 6, 3, 7, 5, 8, 9, 1, 10, 2, 3, 6, 7, 9, 5, 8, 1, 10, 2, 9, 8, 3, 6, 7, 4, 5, 1, 10, 9, 2, 3, 7, 6, 5, 8, 1, 4, 10, 8, 3, 2, 7, 6, 9, 5, 1, 10, 3, 2, 7, 6, 5, 8, 4, 9, 4, 1, 10, 2, 6, 3, 5, 7, 9, 8, 1, 4, 3, 2, 10, 7, 5, 9, 8, 6, 1, 4, 10, 9, 3, 7, 2, 6, 5, 8, 1, 4, 10, 3, 2, 7, 9, 6, 5, 8, 1, 10, 2, 3, 7, 6, 5, 8, 9, 1, 10, 4, 3, 7, 6, 5, 9, 8, 2, 1, 4, 10, 2, 6, 3, 9, 7, 8, 5, 1, 10, 6, 2, 9, 3, 7, 5, 4, 8, 4, 1, 10, 2, 3, 6, 7, 8, 9, 5, 1, 10, 9, 4, 3, 7, 2, 8, 5, 6, 4, 1, 10, 9, 3, 7, 2, 6, 5, 8, 9, 10, 1, 3, 2, 6, 5, 8, 1, 10, 9, 3, 7, 2, 4, 5, 6, 8, 10, 4, 1, 3, 7, 9, 2, 5, 6, 8, 4, 10, 9, 1, 2, 3, 7, 6, 8, 5, 4, 1, 10, 9, 2, 3, 7, 5, 6, 8, 1, 10, 9, 3, 7, 2, 8, 6, 5, 1, 5, 10, 2, 3, 9, 4, 7, 6, 8, 4, 10, 1, 9, 3, 7, 2, 6, 8, 5, 4, 1, 4, 10, 9, 3, 2, 7, 6, 5, 8, 4, 1, 10, 4, 9, 3, 7, 6, 5, 2, 8, 4, 10, 4, 9, 2, 3, 1, 5, 7, 6, 8, 4, 1, 10, 9, 2, 3, 7, 6, 5, 8, 1, 10, 9, 2, 3, 7, 6, 5, 8, 10, 1, 9, 4, 3, 2, 7, 8, 6, 5, 1, 10, 9, 7, 3, 2, 8, 6, 5, 4, 10, 9, 3, 7, 6, 5, 2, 1, 8, 10, 1, 9, 3, 4, 7, 2, 6, 8, 5, 1, 9, 1, 10, 2, 6, 3, 7, 5, 4, 8, 4, 4, 1, 10, 9, 2, 7, 3, 6, 8, 5, 4, 10, 1, 4, 2, 9, 7, 3, 5, 8, 6, 4, 1, 4, 10, 9, 3, 7, 2, 6, 5, 8, 4, 1, 10, 4, 9, 3, 7, 2, 8, 6, 5, 4, 1, 4, 10, 9, 3, 7, 8, 6, 5, 2, 1, 4, 9, 10, 3, 7, 2, 6, 8, 5, 1, 10, 2, 4, 9, 3, 7, 5, 6, 8, 1, 4, 9, 10, 3, 7, 2, 8, 6, 5, 1, 10, 9, 4, 3, 7, 2, 8, 6, 5, 1, 4, 10, 9, 3, 4, 2, 7, 8, 6, 5, 1, 4, 9, 10, 3, 7, 5, 8, 2, 6, 1, 3, 4, 10, 9, 2, 7, 8, 5, 6, 1, 4, 9, 10, 3, 4, 2, 7, 8, 5, 6, 1, 4, 10, 9, 3, 7, 2, 8, 4, 5, 6, 4, 1, 9, 10, 3, 2, 7, 8, 5, 6, 4, 10, 1, 9, 4, 3, 7, 5, 6, 8, 2, 1, 4, 10, 9, 3, 2, 7, 6, 5, 8, 4, 9, 10, 1, 3, 7, 2, 6, 5, 4, 3, 10, 1, 9, 7, 2, 8, 5, 6, 1, 10, 9, 4, 3, 7, 2, 6, 8, 5, 4, 10, 3, 9, 7, 1, 2, 8, 6, 5, 4, 1, 10, 3, 9, 7, 2, 8, 6, 5, 1, 10, 9, 2, 3, 7, 6, 8, 5, 1, 10, 3, 7, 9, 6, 2, 5, 8, 1, 4, 10, 3, 7, 2, 9, 5, 6, 8, 1, 10, 3, 7, 2, 4, 9, 8, 6, 5, 10, 1, 9, 3, 7, 2, 4, 5, 6, 8, 4, 1, 10, 9, 3, 7, 2, 6, 5, 8, 4, 1, 10, 9, 2, 3, 7, 8, 5, 6, 1, 9, 3, 10, 7, 2, 5, 6, 8, 10, 1, 9, 3, 7, 4, 2, 8, 6, 5, 1, 4, 10, 4, 9, 3, 2, 7, 8, 6, 5, 1, 4, 10, 9, 3, 7, 2, 8, 6, 5, 4, 3, 1, 10, 9, 7, 2, 4, 8, 6, 5, 1, 10, 9, 3, 7, 4, 2, 6, 8, 5, 4, 1, 9, 10, 3, 2, 7, 5, 6, 8, 10, 3, 1, 7, 9, 2, 6, 5, 8], \"colorscale\": \"Portland\", \"sizemode\": \"diameter\"}}], {\"width\": 800, \"height\": 800, \"title\": \"test\"}, {\"linkText\": \"Export to plot.ly\", \"showLink\": true})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace1 = go.Scatter(\n",
    "    x=tsne[:,0],\n",
    "    y=tsne[:,1],\n",
    "    #z=tsne[:,2],\n",
    "    mode='markers',\n",
    "    marker=dict(\n",
    "        sizemode='diameter',\n",
    "        color = df_top10authors[\"id_author\"].values,\n",
    "        colorscale = 'Portland',\n",
    "        colorbar = dict(title = 'author'),\n",
    "        line=dict(color='rgb(255, 255, 255)'),\n",
    "        opacity=0.75\n",
    "    )\n",
    ")\n",
    "\n",
    "data=[trace1]\n",
    "layout=dict(height=800, width=800, title='test')\n",
    "fig=dict(data=data, layout=layout)\n",
    "py.iplot(fig, filename='NewsScatter2D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Like Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "author           object\n",
       "datetime         object\n",
       "upvotes           int64\n",
       "downvotes         int64\n",
       "forwards          int64\n",
       "source           object\n",
       "headline         object\n",
       "original_link    object\n",
       "tokens           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"upvotes\"] = df[\"upvotes\"].fillna(0).astype(int)\n",
    "df[\"downvotes\"] = df[\"downvotes\"].fillna(0).astype(int)\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Liked = 1 if number of upvotes is at least 60% of total votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = df[\"upvotes\"]/(df[\"upvotes\"]+df[\"downvotes\"])\n",
    "df[\"liked\"] = a.apply(lambda x: int(x > 0.6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Let's take a look at authors whose posts are disliked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "badauthors = df['author'][df[\"liked\"]==0].value_counts()\n",
    "badauthors = badauthors[badauthors>20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Analysis of why some news articles are disliked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2584, 8183)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer\n",
    "vectorizer = TfidfVectorizer()\n",
    "smat_disliked = vectorizer.fit_transform(df[(df[\"author\"].isin(badauthors.index)) & (df[\"liked\"] == 0)][\"tokens\"].values)\n",
    "features = vectorizer.get_feature_names()\n",
    "smat_disliked.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "### Let's take a look at keywords in news articles that are disliked"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['龜速',\n",
       " '性大勝',\n",
       " '性玩具',\n",
       " '性病',\n",
       " '性質',\n",
       " '性連上',\n",
       " '怪東',\n",
       " '怪民進',\n",
       " '怪物',\n",
       " '怪當',\n",
       " '怪西',\n",
       " '恐使',\n",
       " '恐免',\n",
       " '恐全盤',\n",
       " '恐共',\n",
       " '恐嚇',\n",
       " '恐孫症',\n",
       " '恐影響',\n",
       " '恐得',\n",
       " '恐怕',\n",
       " '恐懼',\n",
       " '恐成',\n",
       " '恩人',\n",
       " '恩情',\n",
       " '悄悄',\n",
       " '性愛權',\n",
       " '性別',\n",
       " '悲傷',\n",
       " '急載',\n",
       " '忠信',\n",
       " '快刀',\n",
       " '快掛',\n",
       " '快樂',\n",
       " '快百萬',\n",
       " '快艇',\n",
       " '快訊',\n",
       " '快評',\n",
       " '快遞',\n",
       " '忽視',\n",
       " '怎走',\n",
       " '怒喊告',\n",
       " '怒噴',\n",
       " '怒槓',\n",
       " '思思',\n",
       " '思純',\n",
       " '思考',\n",
       " '怡潔批',\n",
       " '急凍',\n",
       " '急切',\n",
       " '急徵',\n",
       " '急拉',\n",
       " '急診',\n",
       " '患者',\n",
       " '悲劇',\n",
       " '志祥力',\n",
       " '愚人',\n",
       " '愛人',\n",
       " '愛兒',\n",
       " '愛嗎',\n",
       " '愛國',\n",
       " '愛國行',\n",
       " '愛女護爸',\n",
       " '愛心',\n",
       " '愛愛',\n",
       " '愛有',\n",
       " '愛滋賊',\n",
       " '愛玩女',\n",
       " '愛珍',\n",
       " '愛的',\n",
       " '愛盧',\n",
       " '愛罵',\n",
       " '愛美',\n",
       " '愛能',\n",
       " '愛腦公',\n",
       " '愛計',\n",
       " '愛講話',\n",
       " '愛車',\n",
       " '感人',\n",
       " '感到',\n",
       " '愛中國',\n",
       " '意志',\n",
       " '悲哀',\n",
       " '愈紀念',\n",
       " '悲歌',\n",
       " '情信',\n",
       " '情書',\n",
       " '情深',\n",
       " '情趣',\n",
       " '情願',\n",
       " '惡作劇',\n",
       " '惡少',\n",
       " '惡性',\n",
       " '惡意',\n",
       " '惡業者',\n",
       " '惡行',\n",
       " '惡閨密',\n",
       " '惡靈',\n",
       " '惡鬥',\n",
       " '想不出',\n",
       " '想像']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "indices = np.argsort(vectorizer.idf_)[::-1]\n",
    "[features[i] for i in indices[:100]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/news.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getSourceFromLink(link):\n",
    "    if pd.isnull(link):\n",
    "        return ''\n",
    "    source = ''\n",
    "    if r'udn.com' in link:\n",
    "        source = 'udn'\n",
    "    elif r'epochtimes' in link or r'大紀元' in link:\n",
    "        source = 'epochtimes'\n",
    "    elif r'imgur' in link:\n",
    "        source = 'imgur'\n",
    "    elif r'youtu' in link:\n",
    "        source = 'youtube'\n",
    "    elif r'cna' in link or r'中央' in link:\n",
    "        source = 'cna'\n",
    "    elif r'hinet' in link:\n",
    "        source = 'hinet'\n",
    "    elif r'cnyes' in link:\n",
    "        source = 'cnyes'\n",
    "    elif r'鉅亨' in link:\n",
    "        source = 'cnyes'\n",
    "    elif r'公視' in link or r'pts' in link:\n",
    "        source = 'pts'\n",
    "    elif r'peoplenews' in link:\n",
    "        source = 'peoplenews'\n",
    "    elif r'ithome' in link:\n",
    "        source = 'ithome'\n",
    "    elif r'cdnews' in link:\n",
    "        source = 'cdnews'\n",
    "    elif r'rti' in link:\n",
    "        source = 'rti'\n",
    "    elif r'ctee' in link:\n",
    "        source = 'ctee'\n",
    "    elif r'nextmag' in link:\n",
    "        source = 'nextmag'\n",
    "    elif r'lens' in link:\n",
    "        source = 'thenewslens'\n",
    "    elif r'initium' in link:\n",
    "        source = 'theinitium'\n",
    "    elif r'apple' in link:\n",
    "        source = 'apple'\n",
    "    elif r'bbc.' in link:\n",
    "        source = 'bbc'\n",
    "    elif r'buzzfeed' in link:\n",
    "        source = 'buzzfeed'\n",
    "    elif r'buzzorange.com' in link:\n",
    "        source = 'buzzorange'\n",
    "    elif r'ettoday' in link:\n",
    "        source = 'ettoday'\n",
    "    elif r'ntdtv.com' in link:\n",
    "        source = 'ntdtv'\n",
    "    elif r'ltn.com' in link:\n",
    "        source = 'liberty times'\n",
    "    elif r'technews' in link or r'科技新報' in link:\n",
    "        source = 'technews'\n",
    "    elif r'tvbs.com' in link:\n",
    "        source = 'tvbs'\n",
    "    elif r'wsj.net' in link or r'wsj.com' in link:\n",
    "        source = 'wsj'\n",
    "    elif r'yahoo' in link:\n",
    "        source = 'yahoo'\n",
    "    elif r'yam.com' in link:\n",
    "        source = 'yam'\n",
    "    elif r'setn.com' in link:\n",
    "        source = '三立'\n",
    "    elif r'worldjournal.com' in link:\n",
    "        source = 'world journal'\n",
    "    elif r'upmedia.mg' in link:\n",
    "        source = 'up media'\n",
    "    elif r'ctitv.com' in link:\n",
    "        source = 'ctitv'\n",
    "    elif r'bcc.com' in link:\n",
    "        source = 'bcc'\n",
    "    elif r'chinatimes.com' in link or r'中國時報' in link:\n",
    "        source = 'china times'\n",
    "    elif r'nextmedia' in link:\n",
    "        source = 'next media'\n",
    "    elif r'ftv.com' in link:\n",
    "        source = 'formosa tv'\n",
    "    elif r'cts.com' in link:\n",
    "        source = 'cts'\n",
    "    elif r'mirrormedia.' in link:\n",
    "        source = 'mirror media'\n",
    "    elif r'storm.mg' in link:\n",
    "        source = 'storm media'\n",
    "    elif r'nownews.' in link:\n",
    "        source = 'now news'\n",
    "    elif r'pchome.' in link:\n",
    "        source = 'pchome'\n",
    "    elif r'newtalk.tw' in link:\n",
    "        source = 'newtalk'\n",
    "    else:\n",
    "        a = link.split('//')\n",
    "        if len(a) > 1:\n",
    "            source = link.split('//')[1].split('/')[0]\n",
    "    return source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"'author'\", \" 'datetime'\", \" 'upvotes'\", \" 'downvotes'\",\n",
       "       \" 'forwards'\", \" 'source'\", \" 'headline'\", \" 'original_link'+'\"], dtype=object)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'source'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2174\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2175\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mtslib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value_box\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ms\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2176\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/tslib.pyx\u001b[0m in \u001b[0;36mpandas.tslib.get_value_box (pandas/tslib.c:19053)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/tslib.pyx\u001b[0m in \u001b[0;36mpandas.tslib.get_value_box (pandas/tslib.c:18687)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: 'str' object cannot be interpreted as an integer",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-45-ff996d5423e3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrow\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterrows\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;32mif\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'source'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m         \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'source'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgetSourceFromLink\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'original_link'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m    601\u001b[0m         \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_apply_if_callable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    602\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 603\u001b[0;31m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    605\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2181\u001b[0m                     \u001b[0;32mraise\u001b[0m \u001b[0mInvalidIndexError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2182\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2183\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2184\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2185\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Library/Frameworks/Python.framework/Versions/3.4/lib/python3.4/site-packages/pandas/indexes/base.py\u001b[0m in \u001b[0;36mget_value\u001b[0;34m(self, series, key)\u001b[0m\n\u001b[1;32m   2167\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2168\u001b[0m             return self._engine.get_value(s, k,\n\u001b[0;32m-> 2169\u001b[0;31m                                           tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[1;32m   2170\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2171\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minferred_type\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'integer'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'boolean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3557)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_value (pandas/index.c:3240)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/index.pyx\u001b[0m in \u001b[0;36mpandas.index.IndexEngine.get_loc (pandas/index.c:4279)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13742)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/src/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas.hashtable.PyObjectHashTable.get_item (pandas/hashtable.c:13696)\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'source'"
     ]
    }
   ],
   "source": [
    "for i, row in df.iterrows():\n",
    "    if pd.isnull(row['source']):\n",
    "        df.set_value(index=i, col='source', value=getSourceFromLink(row['original_link']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'hitosh723 (楓葉狂刀),Thu Jun  1 10:25:28 2017,2,0,5,liberty times,[新聞] 今北高溫南有雨 吳德榮：明後天全台防雷,Unnamed: 7'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(','.join(df.columns.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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