visualizations.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>upvote_ratio</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>removed_by_category</th>\n",
       "      <th>url</th>\n",
       "      <th>divisiveness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1588242127</td>\n",
       "      <td>gat3o9</td>\n",
       "      <td>TransGirl757</td>\n",
       "      <td>If we get approved by 100k people in 1 month, ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive</td>\n",
       "      <td>/r/progressive/comments/gat3o9/if_we_get_appro...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>moderator</td>\n",
       "      <td>https://petitions.whitehouse.gov/petition/give...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1588222576</td>\n",
       "      <td>gapbez</td>\n",
       "      <td>shallah</td>\n",
       "      <td>Elizabeth Warren and Alexandria Ocasio-Cortez ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive</td>\n",
       "      <td>/r/progressive/comments/gapbez/elizabeth_warre...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>moderator</td>\n",
       "      <td>https://theweek.com/speedreads/911377/elizabet...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1588216466</td>\n",
       "      <td>ganw7m</td>\n",
       "      <td>shallah</td>\n",
       "      <td>Gov Whitmer Announces \"Futures for Frontliners...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive</td>\n",
       "      <td>/r/progressive/comments/ganw7m/gov_whitmer_ann...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.michigan.gov/whitmer/0,9309,7-387-...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1588200464</td>\n",
       "      <td>gajmqr</td>\n",
       "      <td>thinkB4WeSpeak</td>\n",
       "      <td>Could pandemic rekindle labor movement?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive</td>\n",
       "      <td>/r/progressive/comments/gajmqr/could_pandemic_...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://finance-commerce.com/2020/04/could-pan...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1588197018</td>\n",
       "      <td>gaikq1</td>\n",
       "      <td>davyjoneses</td>\n",
       "      <td>Found on r/therightcantmeme. Should the govern...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive</td>\n",
       "      <td>/r/progressive/comments/gaikq1/found_on_rtheri...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>moderator</td>\n",
       "      <td>https://np.reddit.com/r/TheRightCantMeme/comme...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   created_utc      id          author  \\\n",
       "0   1588242127  gat3o9    TransGirl757   \n",
       "1   1588222576  gapbez         shallah   \n",
       "2   1588216466  ganw7m         shallah   \n",
       "3   1588200464  gajmqr  thinkB4WeSpeak   \n",
       "4   1588197018  gaikq1     davyjoneses   \n",
       "\n",
       "                                               title selftext    subreddit  \\\n",
       "0  If we get approved by 100k people in 1 month, ...      NaN  progressive   \n",
       "1  Elizabeth Warren and Alexandria Ocasio-Cortez ...      NaN  progressive   \n",
       "2  Gov Whitmer Announces \"Futures for Frontliners...      NaN  progressive   \n",
       "3            Could pandemic rekindle labor movement?      NaN  progressive   \n",
       "4  Found on r/therightcantmeme. Should the govern...      NaN  progressive   \n",
       "\n",
       "                                                link  score  upvote_ratio  \\\n",
       "0  /r/progressive/comments/gat3o9/if_we_get_appro...      1           1.0   \n",
       "1  /r/progressive/comments/gapbez/elizabeth_warre...      1           1.0   \n",
       "2  /r/progressive/comments/ganw7m/gov_whitmer_ann...      1           1.0   \n",
       "3  /r/progressive/comments/gajmqr/could_pandemic_...      1           1.0   \n",
       "4  /r/progressive/comments/gaikq1/found_on_rtheri...      1           1.0   \n",
       "\n",
       "  author_flair_text  num_comments removed_by_category  \\\n",
       "0               NaN             0           moderator   \n",
       "1               NaN             0           moderator   \n",
       "2               NaN             5                 NaN   \n",
       "3               NaN             2                 NaN   \n",
       "4               NaN             1           moderator   \n",
       "\n",
       "                                                 url  divisiveness  \n",
       "0  https://petitions.whitehouse.gov/petition/give...             1  \n",
       "1  https://theweek.com/speedreads/911377/elizabet...             1  \n",
       "2  https://www.michigan.gov/whitmer/0,9309,7-387-...             1  \n",
       "3  https://finance-commerce.com/2020/04/could-pan...             1  \n",
       "4  https://np.reddit.com/r/TheRightCantMeme/comme...             1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_path = './data'\n",
    "\n",
    "post_name = 'all_posts.pickle'\n",
    "post_dir = os.path.join(data_path, post_name)\n",
    "all_posts = pd.read_pickle(post_dir)\n",
    "all_posts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "39952586\n"
     ]
    }
   ],
   "source": [
    "comment_name = 'all_comments.pickle'\n",
    "comments_path = './data'\n",
    "comment_dir = os.path.join(data_path, comment_name)\n",
    "\n",
    "if os.path.isfile(comment_dir):\n",
    "    all_comments = pd.read_pickle(comment_dir)\n",
    "else: \n",
    "    all_data_sources = [comments_path + i for i in os.listdir(comments_path)][1:]\n",
    "    print(all_data_sources)\n",
    "\n",
    "    dfs = []\n",
    "\n",
    "    for name in all_data_sources[1:]:\n",
    "        print(name)\n",
    "        df = pd.read_pickle(name)\n",
    "        print(df.subreddit.memory_usage(deep=True))\n",
    "        df['subreddit'] = df.subreddit.astype('category')\n",
    "        df = df[['author', 'body', 'created_utc', 'id', 'parent_id', 'link_id','score', 'subreddit', 'vulgarity', 'civility', 'namecalling', 'stereotype', 'demeaning', 'author_flair_text']]\n",
    "        dfs.append(df)\n",
    "        print(df.subreddit.memory_usage(deep=True))\n",
    "\n",
    "    all_comments = pd.concat(dfs, axis=0)\n",
    "    del df, dfs\n",
    "    all_comments.to_pickle('../data/comments/all_comments.pickle')\n",
    "print(len(all_comments))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>vulgarity</th>\n",
       "      <th>personal_attacks</th>\n",
       "      <th>incivility</th>\n",
       "      <th>third_party_attacks</th>\n",
       "      <th>aspersion</th>\n",
       "      <th>comment_len</th>\n",
       "      <th>compound_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1612153203</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>glk8hec</td>\n",
       "      <td>runrain</td>\n",
       "      <td>Lol not even. Where I heard about it most was ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.160585</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.332831</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>96</td>\n",
       "      <td>-0.2357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1612152460</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk757a</td>\n",
       "      <td>SilverHerfer</td>\n",
       "      <td>To paraphrase one of the great conservatives o...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>3</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.115829</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.003651</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1612151108</td>\n",
       "      <td>t1_glit0la</td>\n",
       "      <td>t3_l9lonl</td>\n",
       "      <td>glk4jhg</td>\n",
       "      <td>Psychowitz</td>\n",
       "      <td>Like coding.</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9lonl/keystone_pipe...</td>\n",
       "      <td>0</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.203247</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1612150714</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3rkp</td>\n",
       "      <td>Salmankhan42069</td>\n",
       "      <td>Wait, this is a meme?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>-1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.148250</td>\n",
       "      <td>0.004309</td>\n",
       "      <td>0.323237</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1612150618</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3kvz</td>\n",
       "      <td>Pile_of_Walthers</td>\n",
       "      <td>Which one is more useful?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>2</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.094569</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.050714</td>\n",
       "      <td>0.003793</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1612149928</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk27ga</td>\n",
       "      <td>heartless_13</td>\n",
       "      <td>The new lady what's-her-face is horrible, she ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>8</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.592571</td>\n",
       "      <td>0.002211</td>\n",
       "      <td>0.936776</td>\n",
       "      <td>0.184635</td>\n",
       "      <td>0.000699</td>\n",
       "      <td>28</td>\n",
       "      <td>-0.5574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1612149549</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>glk1g71</td>\n",
       "      <td>Mr_Hyde_</td>\n",
       "      <td>If the lefts supporters can't see coup that's ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>3</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.082533</td>\n",
       "      <td>0.001017</td>\n",
       "      <td>0.040850</td>\n",
       "      <td>0.003554</td>\n",
       "      <td>0.000127</td>\n",
       "      <td>26</td>\n",
       "      <td>0.4404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1612148971</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk0a5i</td>\n",
       "      <td>ImportantNothings</td>\n",
       "      <td>This meme doesn’t work because when people thi...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>0</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.096316</td>\n",
       "      <td>0.002625</td>\n",
       "      <td>0.058954</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>0.000221</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>1612148254</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>gljytt3</td>\n",
       "      <td>revjoe918</td>\n",
       "      <td>She could never handle the pressure McEnany go...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>6</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.196140</td>\n",
       "      <td>0.001492</td>\n",
       "      <td>0.375939</td>\n",
       "      <td>0.026969</td>\n",
       "      <td>0.000275</td>\n",
       "      <td>14</td>\n",
       "      <td>0.5556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1612146616</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>gljvb38</td>\n",
       "      <td>queenie104</td>\n",
       "      <td>Facebook is boring and outdated!!  Twitter is ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>2</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.141871</td>\n",
       "      <td>0.002778</td>\n",
       "      <td>0.986934</td>\n",
       "      <td>0.006760</td>\n",
       "      <td>0.999797</td>\n",
       "      <td>8</td>\n",
       "      <td>-0.7633</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    created_utc   parent_id    link_id       id             author  \\\n",
       "1    1612153203   t3_l9gngx  t3_l9gngx  glk8hec            runrain   \n",
       "2    1612152460   t3_l9t1ha  t3_l9t1ha  glk757a       SilverHerfer   \n",
       "4    1612151108  t1_glit0la  t3_l9lonl  glk4jhg         Psychowitz   \n",
       "6    1612150714   t3_l9t1ha  t3_l9t1ha  glk3rkp    Salmankhan42069   \n",
       "9    1612150618   t3_l9t1ha  t3_l9t1ha  glk3kvz   Pile_of_Walthers   \n",
       "11   1612149928   t3_l9t1ha  t3_l9t1ha  glk27ga       heartless_13   \n",
       "12   1612149549   t3_l9gngx  t3_l9gngx  glk1g71           Mr_Hyde_   \n",
       "13   1612148971   t3_l9t1ha  t3_l9t1ha  glk0a5i  ImportantNothings   \n",
       "16   1612148254   t3_l9t1ha  t3_l9t1ha  gljytt3          revjoe918   \n",
       "18   1612146616   t3_l9gngx  t3_l9gngx  gljvb38         queenie104   \n",
       "\n",
       "                                                 body      subreddit  \\\n",
       "1   Lol not even. Where I heard about it most was ...  AskThe_Donald   \n",
       "2   To paraphrase one of the great conservatives o...  AskThe_Donald   \n",
       "4                                        Like coding.  AskThe_Donald   \n",
       "6                               Wait, this is a meme?  AskThe_Donald   \n",
       "9                           Which one is more useful?  AskThe_Donald   \n",
       "11  The new lady what's-her-face is horrible, she ...  AskThe_Donald   \n",
       "12  If the lefts supporters can't see coup that's ...  AskThe_Donald   \n",
       "13  This meme doesn’t work because when people thi...  AskThe_Donald   \n",
       "16  She could never handle the pressure McEnany go...  AskThe_Donald   \n",
       "18  Facebook is boring and outdated!!  Twitter is ...  AskThe_Donald   \n",
       "\n",
       "                                                 link  score  \\\n",
       "1   /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      1   \n",
       "2   /r/AskThe_Donald/comments/l9t1ha/we_definitely...      3   \n",
       "4   /r/AskThe_Donald/comments/l9lonl/keystone_pipe...      0   \n",
       "6   /r/AskThe_Donald/comments/l9t1ha/we_definitely...     -1   \n",
       "9   /r/AskThe_Donald/comments/l9t1ha/we_definitely...      2   \n",
       "11  /r/AskThe_Donald/comments/l9t1ha/we_definitely...      8   \n",
       "12  /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      3   \n",
       "13  /r/AskThe_Donald/comments/l9t1ha/we_definitely...      0   \n",
       "16  /r/AskThe_Donald/comments/l9t1ha/we_definitely...      6   \n",
       "18  /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      2   \n",
       "\n",
       "   author_flair_text  vulgarity  personal_attacks  incivility  \\\n",
       "1         :T: NOVICE   0.160585          0.001712    0.332831   \n",
       "2         :T: NOVICE   0.115829          0.002649    0.112667   \n",
       "4         :T: NOVICE   0.203247          0.002789    0.070185   \n",
       "6         :T: NOVICE   0.148250          0.004309    0.323237   \n",
       "9         :T: NOVICE   0.094569          0.003459    0.050714   \n",
       "11        :T: NOVICE   0.592571          0.002211    0.936776   \n",
       "12        :T: NOVICE   0.082533          0.001017    0.040850   \n",
       "13        :T: NOVICE   0.096316          0.002625    0.058954   \n",
       "16        :T: NOVICE   0.196140          0.001492    0.375939   \n",
       "18        :T: NOVICE   0.141871          0.002778    0.986934   \n",
       "\n",
       "    third_party_attacks  aspersion  comment_len  compound_sentiment  \n",
       "1              0.004760   0.000276           96             -0.2357  \n",
       "2              0.003651   0.000283           26              0.8428  \n",
       "4              0.003872   0.000680            2              0.3612  \n",
       "6              0.005040   0.000420            5              0.0000  \n",
       "9              0.003793   0.000210            5              0.4927  \n",
       "11             0.184635   0.000699           28             -0.5574  \n",
       "12             0.003554   0.000127           26              0.4404  \n",
       "13             0.003746   0.000221           16              0.0000  \n",
       "16             0.026969   0.000275           14              0.5556  \n",
       "18             0.006760   0.999797            8             -0.7633  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = '/Users/yujia/research/stance/data/comments/with_labels/all_comments_no_liwc.pickle'\n",
    "all_comments = pd.read_pickle(fname)\n",
    "all_comments.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_comments = all_comments[['created_utc', 'parent_id', 'link_id', 'id', 'author', 'body',\n",
    "       'subreddit', 'link', 'score', 'author_flair_text', 'vulgarity',\n",
    "       'personal_attacks', 'incivility', 'third_party_attacks', 'aspersion',\n",
    "       'comment_len', 'compound_sentiment']]\n",
    "all_comments.to_pickle('/Users/yujia/research/stance/data/comments/with_labels/all_comments_no_liwc.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1046958\n"
     ]
    },
    {
     "data": {
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       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>title</th>\n",
       "      <th>selftext</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>upvote_ratio</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>num_comments</th>\n",
       "      <th>removed_by_category</th>\n",
       "      <th>url</th>\n",
       "      <th>divisiveness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1588242127</td>\n",
       "      <td>gat3o9</td>\n",
       "      <td>TransGirl757</td>\n",
       "      <td>If we get approved by 100k people in 1 month, ...</td>\n",
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       "      <td>/r/progressive/comments/gat3o9/if_we_get_appro...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>https://petitions.whitehouse.gov/petition/give...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1588222576</td>\n",
       "      <td>gapbez</td>\n",
       "      <td>shallah</td>\n",
       "      <td>Elizabeth Warren and Alexandria Ocasio-Cortez ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive</td>\n",
       "      <td>/r/progressive/comments/gapbez/elizabeth_warre...</td>\n",
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       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>https://theweek.com/speedreads/911377/elizabet...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1588216466</td>\n",
       "      <td>ganw7m</td>\n",
       "      <td>shallah</td>\n",
       "      <td>Gov Whitmer Announces \"Futures for Frontliners...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive</td>\n",
       "      <td>/r/progressive/comments/ganw7m/gov_whitmer_ann...</td>\n",
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       "      <td>https://www.michigan.gov/whitmer/0,9309,7-387-...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1588200464</td>\n",
       "      <td>gajmqr</td>\n",
       "      <td>thinkB4WeSpeak</td>\n",
       "      <td>Could pandemic rekindle labor movement?</td>\n",
       "      <td>NaN</td>\n",
       "      <td>progressive</td>\n",
       "      <td>/r/progressive/comments/gajmqr/could_pandemic_...</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://finance-commerce.com/2020/04/could-pan...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1588197018</td>\n",
       "      <td>gaikq1</td>\n",
       "      <td>davyjoneses</td>\n",
       "      <td>Found on r/therightcantmeme. Should the govern...</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>moderator</td>\n",
       "      <td>https://np.reddit.com/r/TheRightCantMeme/comme...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   created_utc      id          author  \\\n",
       "0   1588242127  gat3o9    TransGirl757   \n",
       "1   1588222576  gapbez         shallah   \n",
       "2   1588216466  ganw7m         shallah   \n",
       "3   1588200464  gajmqr  thinkB4WeSpeak   \n",
       "4   1588197018  gaikq1     davyjoneses   \n",
       "\n",
       "                                               title selftext    subreddit  \\\n",
       "0  If we get approved by 100k people in 1 month, ...      NaN  progressive   \n",
       "1  Elizabeth Warren and Alexandria Ocasio-Cortez ...      NaN  progressive   \n",
       "2  Gov Whitmer Announces \"Futures for Frontliners...      NaN  progressive   \n",
       "3            Could pandemic rekindle labor movement?      NaN  progressive   \n",
       "4  Found on r/therightcantmeme. Should the govern...      NaN  progressive   \n",
       "\n",
       "                                                link  score  upvote_ratio  \\\n",
       "0  /r/progressive/comments/gat3o9/if_we_get_appro...      1           1.0   \n",
       "1  /r/progressive/comments/gapbez/elizabeth_warre...      1           1.0   \n",
       "2  /r/progressive/comments/ganw7m/gov_whitmer_ann...      1           1.0   \n",
       "3  /r/progressive/comments/gajmqr/could_pandemic_...      1           1.0   \n",
       "4  /r/progressive/comments/gaikq1/found_on_rtheri...      1           1.0   \n",
       "\n",
       "  author_flair_text  num_comments removed_by_category  \\\n",
       "0               NaN             0           moderator   \n",
       "1               NaN             0           moderator   \n",
       "2               NaN             5                 NaN   \n",
       "3               NaN             2                 NaN   \n",
       "4               NaN             1           moderator   \n",
       "\n",
       "                                                 url  divisiveness  \n",
       "0  https://petitions.whitehouse.gov/petition/give...             1  \n",
       "1  https://theweek.com/speedreads/911377/elizabet...             1  \n",
       "2  https://www.michigan.gov/whitmer/0,9309,7-387-...             1  \n",
       "3  https://finance-commerce.com/2020/04/could-pan...             1  \n",
       "4  https://np.reddit.com/r/TheRightCantMeme/comme...             1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_posts = pd.read_pickle('/Users/yujia/research/stance/data/all_posts.pickle')\n",
    "print(len(all_posts))\n",
    "all_posts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>vulgarity</th>\n",
       "      <th>personal_attacks</th>\n",
       "      <th>incivility</th>\n",
       "      <th>third_party_attacks</th>\n",
       "      <th>aspersion</th>\n",
       "      <th>comment_len</th>\n",
       "      <th>compound_sentiment</th>\n",
       "      <th>op_divisiveness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1612153203</td>\n",
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       "      <td>glk8hec</td>\n",
       "      <td>runrain</td>\n",
       "      <td>Lol not even. Where I heard about it most was ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.160585</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.332831</td>\n",
       "      <td>0.004760</td>\n",
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       "      <td>-0.2357</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1612152460</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk757a</td>\n",
       "      <td>SilverHerfer</td>\n",
       "      <td>To paraphrase one of the great conservatives o...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>3</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.115829</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.003651</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8428</td>\n",
       "      <td>1</td>\n",
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       "      <th>4</th>\n",
       "      <td>1612151108</td>\n",
       "      <td>t1_glit0la</td>\n",
       "      <td>t3_l9lonl</td>\n",
       "      <td>glk4jhg</td>\n",
       "      <td>Psychowitz</td>\n",
       "      <td>Like coding.</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9lonl/keystone_pipe...</td>\n",
       "      <td>0</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.203247</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1612150714</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3rkp</td>\n",
       "      <td>Salmankhan42069</td>\n",
       "      <td>Wait, this is a meme?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>-1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.148250</td>\n",
       "      <td>0.004309</td>\n",
       "      <td>0.323237</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1612150618</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3kvz</td>\n",
       "      <td>Pile_of_Walthers</td>\n",
       "      <td>Which one is more useful?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>2</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.094569</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.050714</td>\n",
       "      <td>0.003793</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4927</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2127588</th>\n",
       "      <td>1603994257</td>\n",
       "      <td>t1_gaidthx</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaiivoy</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>I remember when Fox, MSNBC and CNN [all broadc...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066037</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.016209</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>44</td>\n",
       "      <td>-0.2023</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2127589</th>\n",
       "      <td>1603993347</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaigl2w</td>\n",
       "      <td>etzel1200</td>\n",
       "      <td>That’s on Comey, not the NYT.</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256163</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>0.130545</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127590</th>\n",
       "      <td>1603992287</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaidthx</td>\n",
       "      <td>paywallpiker</td>\n",
       "      <td>People neglect that the media played a huge pa...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104737</td>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.037370</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.029973</td>\n",
       "      <td>63</td>\n",
       "      <td>0.8020</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127591</th>\n",
       "      <td>1603885499</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>gaddjx4</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>The Dakotas are getting slammed, especially co...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jj3nmi/oc_states_in...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209203</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.611730</td>\n",
       "      <td>0.837685</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127592</th>\n",
       "      <td>1603745345</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>ga7dx8h</td>\n",
       "      <td>ual33655</td>\n",
       "      <td>Thanks for telling it like it is you should ha...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jigjpx/hillary_clin...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.251816</td>\n",
       "      <td>0.989517</td>\n",
       "      <td>0.990107</td>\n",
       "      <td>0.956174</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>17</td>\n",
       "      <td>0.6597</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35567120 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_utc   parent_id    link_id       id            author  \\\n",
       "1         1612153203   t3_l9gngx  t3_l9gngx  glk8hec           runrain   \n",
       "2         1612152460   t3_l9t1ha  t3_l9t1ha  glk757a      SilverHerfer   \n",
       "4         1612151108  t1_glit0la  t3_l9lonl  glk4jhg        Psychowitz   \n",
       "6         1612150714   t3_l9t1ha  t3_l9t1ha  glk3rkp   Salmankhan42069   \n",
       "9         1612150618   t3_l9t1ha  t3_l9t1ha  glk3kvz  Pile_of_Walthers   \n",
       "...              ...         ...        ...      ...               ...   \n",
       "2127588   1603994257  t1_gaidthx  t3_jkbmw0  gaiivoy     GogglesPisano   \n",
       "2127589   1603993347   t3_jkbmw0  t3_jkbmw0  gaigl2w         etzel1200   \n",
       "2127590   1603992287   t3_jkbmw0  t3_jkbmw0  gaidthx      paywallpiker   \n",
       "2127591   1603885499   t3_jj3nmi  t3_jj3nmi  gaddjx4     GogglesPisano   \n",
       "2127592   1603745345   t3_jigjpx  t3_jigjpx  ga7dx8h          ual33655   \n",
       "\n",
       "                                                      body       subreddit  \\\n",
       "1        Lol not even. Where I heard about it most was ...   AskThe_Donald   \n",
       "2        To paraphrase one of the great conservatives o...   AskThe_Donald   \n",
       "4                                             Like coding.   AskThe_Donald   \n",
       "6                                    Wait, this is a meme?   AskThe_Donald   \n",
       "9                                Which one is more useful?   AskThe_Donald   \n",
       "...                                                    ...             ...   \n",
       "2127588  I remember when Fox, MSNBC and CNN [all broadc...  hillaryclinton   \n",
       "2127589                      That’s on Comey, not the NYT.  hillaryclinton   \n",
       "2127590  People neglect that the media played a huge pa...  hillaryclinton   \n",
       "2127591  The Dakotas are getting slammed, especially co...  hillaryclinton   \n",
       "2127592  Thanks for telling it like it is you should ha...  hillaryclinton   \n",
       "\n",
       "                                                      link  score  \\\n",
       "1        /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      1   \n",
       "2        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      3   \n",
       "4        /r/AskThe_Donald/comments/l9lonl/keystone_pipe...      0   \n",
       "6        /r/AskThe_Donald/comments/l9t1ha/we_definitely...     -1   \n",
       "9        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      2   \n",
       "...                                                    ...    ...   \n",
       "2127588  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127589  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      2   \n",
       "2127590  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127591  /r/hillaryclinton/comments/jj3nmi/oc_states_in...      1   \n",
       "2127592  /r/hillaryclinton/comments/jigjpx/hillary_clin...      1   \n",
       "\n",
       "        author_flair_text  vulgarity  personal_attacks  incivility  \\\n",
       "1              :T: NOVICE   0.160585          0.001712    0.332831   \n",
       "2              :T: NOVICE   0.115829          0.002649    0.112667   \n",
       "4              :T: NOVICE   0.203247          0.002789    0.070185   \n",
       "6              :T: NOVICE   0.148250          0.004309    0.323237   \n",
       "9              :T: NOVICE   0.094569          0.003459    0.050714   \n",
       "...                   ...        ...               ...         ...   \n",
       "2127588               NaN   0.066037          0.001627    0.016209   \n",
       "2127589               NaN   0.256163          0.002296    0.130545   \n",
       "2127590               NaN   0.104737          0.002317    0.037370   \n",
       "2127591               NaN   0.209203          0.002534    0.611730   \n",
       "2127592               NaN   0.251816          0.989517    0.990107   \n",
       "\n",
       "         third_party_attacks  aspersion  comment_len  compound_sentiment  \\\n",
       "1                   0.004760   0.000276           96             -0.2357   \n",
       "2                   0.003651   0.000283           26              0.8428   \n",
       "4                   0.003872   0.000680            2              0.3612   \n",
       "6                   0.005040   0.000420            5              0.0000   \n",
       "9                   0.003793   0.000210            5              0.4927   \n",
       "...                      ...        ...          ...                 ...   \n",
       "2127588             0.005488   0.000130           44             -0.2023   \n",
       "2127589             0.004628   0.000172            6              0.0000   \n",
       "2127590             0.004813   0.029973           63              0.8020   \n",
       "2127591             0.837685   0.000281           29              0.0000   \n",
       "2127592             0.956174   0.000320           17              0.6597   \n",
       "\n",
       "         op_divisiveness  \n",
       "1                      1  \n",
       "2                      1  \n",
       "4                      1  \n",
       "6                      1  \n",
       "9                      1  \n",
       "...                  ...  \n",
       "2127588                1  \n",
       "2127589                1  \n",
       "2127590                1  \n",
       "2127591                1  \n",
       "2127592                0  \n",
       "\n",
       "[35567120 rows x 18 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "divisive_mapping = dict(zip(all_posts.id, all_posts.divisiveness))\n",
    "all_comments['op_divisiveness'] = all_comments['link_id'].str[3:].apply(lambda x: divisive_mapping[x] if x in divisive_mapping else 0)\n",
    "all_comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = np.load('all_labels.npy')\n",
    "all_comments['stereotyppe_new'] =  labels\n",
    "# all_comments\n",
    "all_comments.to_pickle('../data/comments/all_comments.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>vulgarity</th>\n",
       "      <th>personal_attacks</th>\n",
       "      <th>incivility</th>\n",
       "      <th>third_party_attacks</th>\n",
       "      <th>aspersion</th>\n",
       "      <th>comment_len</th>\n",
       "      <th>compound_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1612153203</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>glk8hec</td>\n",
       "      <td>runrain</td>\n",
       "      <td>Lol not even. Where I heard about it most was ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.160585</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.332831</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>96</td>\n",
       "      <td>-0.2357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1612152460</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk757a</td>\n",
       "      <td>SilverHerfer</td>\n",
       "      <td>To paraphrase one of the great conservatives o...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>3</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.115829</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.003651</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1612151108</td>\n",
       "      <td>t1_glit0la</td>\n",
       "      <td>t3_l9lonl</td>\n",
       "      <td>glk4jhg</td>\n",
       "      <td>Psychowitz</td>\n",
       "      <td>Like coding.</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9lonl/keystone_pipe...</td>\n",
       "      <td>0</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.203247</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1612150714</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3rkp</td>\n",
       "      <td>Salmankhan42069</td>\n",
       "      <td>Wait, this is a meme?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>-1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.148250</td>\n",
       "      <td>0.004309</td>\n",
       "      <td>0.323237</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1612150618</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3kvz</td>\n",
       "      <td>Pile_of_Walthers</td>\n",
       "      <td>Which one is more useful?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>2</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.094569</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.050714</td>\n",
       "      <td>0.003793</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127588</th>\n",
       "      <td>1603994257</td>\n",
       "      <td>t1_gaidthx</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaiivoy</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>I remember when Fox, MSNBC and CNN [all broadc...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066037</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.016209</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>44</td>\n",
       "      <td>-0.2023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127589</th>\n",
       "      <td>1603993347</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaigl2w</td>\n",
       "      <td>etzel1200</td>\n",
       "      <td>That’s on Comey, not the NYT.</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256163</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>0.130545</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127590</th>\n",
       "      <td>1603992287</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaidthx</td>\n",
       "      <td>paywallpiker</td>\n",
       "      <td>People neglect that the media played a huge pa...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104737</td>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.037370</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.029973</td>\n",
       "      <td>63</td>\n",
       "      <td>0.8020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127591</th>\n",
       "      <td>1603885499</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>gaddjx4</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>The Dakotas are getting slammed, especially co...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jj3nmi/oc_states_in...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209203</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.611730</td>\n",
       "      <td>0.837685</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127592</th>\n",
       "      <td>1603745345</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>ga7dx8h</td>\n",
       "      <td>ual33655</td>\n",
       "      <td>Thanks for telling it like it is you should ha...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jigjpx/hillary_clin...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.251816</td>\n",
       "      <td>0.989517</td>\n",
       "      <td>0.990107</td>\n",
       "      <td>0.956174</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>17</td>\n",
       "      <td>0.6597</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35567120 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_utc   parent_id    link_id       id            author  \\\n",
       "1         1612153203   t3_l9gngx  t3_l9gngx  glk8hec           runrain   \n",
       "2         1612152460   t3_l9t1ha  t3_l9t1ha  glk757a      SilverHerfer   \n",
       "4         1612151108  t1_glit0la  t3_l9lonl  glk4jhg        Psychowitz   \n",
       "6         1612150714   t3_l9t1ha  t3_l9t1ha  glk3rkp   Salmankhan42069   \n",
       "9         1612150618   t3_l9t1ha  t3_l9t1ha  glk3kvz  Pile_of_Walthers   \n",
       "...              ...         ...        ...      ...               ...   \n",
       "2127588   1603994257  t1_gaidthx  t3_jkbmw0  gaiivoy     GogglesPisano   \n",
       "2127589   1603993347   t3_jkbmw0  t3_jkbmw0  gaigl2w         etzel1200   \n",
       "2127590   1603992287   t3_jkbmw0  t3_jkbmw0  gaidthx      paywallpiker   \n",
       "2127591   1603885499   t3_jj3nmi  t3_jj3nmi  gaddjx4     GogglesPisano   \n",
       "2127592   1603745345   t3_jigjpx  t3_jigjpx  ga7dx8h          ual33655   \n",
       "\n",
       "                                                      body       subreddit  \\\n",
       "1        Lol not even. Where I heard about it most was ...   AskThe_Donald   \n",
       "2        To paraphrase one of the great conservatives o...   AskThe_Donald   \n",
       "4                                             Like coding.   AskThe_Donald   \n",
       "6                                    Wait, this is a meme?   AskThe_Donald   \n",
       "9                                Which one is more useful?   AskThe_Donald   \n",
       "...                                                    ...             ...   \n",
       "2127588  I remember when Fox, MSNBC and CNN [all broadc...  hillaryclinton   \n",
       "2127589                      That’s on Comey, not the NYT.  hillaryclinton   \n",
       "2127590  People neglect that the media played a huge pa...  hillaryclinton   \n",
       "2127591  The Dakotas are getting slammed, especially co...  hillaryclinton   \n",
       "2127592  Thanks for telling it like it is you should ha...  hillaryclinton   \n",
       "\n",
       "                                                      link  score  \\\n",
       "1        /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      1   \n",
       "2        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      3   \n",
       "4        /r/AskThe_Donald/comments/l9lonl/keystone_pipe...      0   \n",
       "6        /r/AskThe_Donald/comments/l9t1ha/we_definitely...     -1   \n",
       "9        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      2   \n",
       "...                                                    ...    ...   \n",
       "2127588  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127589  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      2   \n",
       "2127590  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127591  /r/hillaryclinton/comments/jj3nmi/oc_states_in...      1   \n",
       "2127592  /r/hillaryclinton/comments/jigjpx/hillary_clin...      1   \n",
       "\n",
       "        author_flair_text  vulgarity  personal_attacks  incivility  \\\n",
       "1              :T: NOVICE   0.160585          0.001712    0.332831   \n",
       "2              :T: NOVICE   0.115829          0.002649    0.112667   \n",
       "4              :T: NOVICE   0.203247          0.002789    0.070185   \n",
       "6              :T: NOVICE   0.148250          0.004309    0.323237   \n",
       "9              :T: NOVICE   0.094569          0.003459    0.050714   \n",
       "...                   ...        ...               ...         ...   \n",
       "2127588               NaN   0.066037          0.001627    0.016209   \n",
       "2127589               NaN   0.256163          0.002296    0.130545   \n",
       "2127590               NaN   0.104737          0.002317    0.037370   \n",
       "2127591               NaN   0.209203          0.002534    0.611730   \n",
       "2127592               NaN   0.251816          0.989517    0.990107   \n",
       "\n",
       "         third_party_attacks  aspersion  comment_len  compound_sentiment  \n",
       "1                   0.004760   0.000276           96             -0.2357  \n",
       "2                   0.003651   0.000283           26              0.8428  \n",
       "4                   0.003872   0.000680            2              0.3612  \n",
       "6                   0.005040   0.000420            5              0.0000  \n",
       "9                   0.003793   0.000210            5              0.4927  \n",
       "...                      ...        ...          ...                 ...  \n",
       "2127588             0.005488   0.000130           44             -0.2023  \n",
       "2127589             0.004628   0.000172            6              0.0000  \n",
       "2127590             0.004813   0.029973           63              0.8020  \n",
       "2127591             0.837685   0.000281           29              0.0000  \n",
       "2127592             0.956174   0.000320           17              0.6597  \n",
       "\n",
       "[35567120 rows x 17 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comments = all_comments[all_comments.author != 'AutoModerator']\n",
    "all_comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['There is no bias in the media you stupid conspiracy theorists! /s\\n\\n🙄',\n",
       " 'You do realize that you’re on a libertarian subreddit, right? Where we believe in freedom of movement and that no individual illegal.\\n\\nYour fucking tag is a “Minarchist” and you don’t believe people have to right to move about freely? Give me a break. You’re an embarrassment to true supporters of minarchism.',\n",
       " \"Go ahead and link that fucking article all you want. Go ahead take the drug, one fewer idiot we'll need to worry about when it doesn't work or causes heart issues. I gave you my fucking argument, sorry but it's not based on political bias. The only thing that's troubling is your lack of reasoning and intelligence. Oh look, there you go again wasting my time. Bye!\",\n",
       " 'He didn\\'t use any insults. \"Are you insane\" is a common English phrase when you think someone\\'s opinion is outlandish. It\\'s not an insult and he didn\\'t use any pejoratives.\\n\\nTry responding to his point rather than deflecting to made up slights. How does the woman being Asian affect his point?\\n\\nI haven\\'t insulted you at all, so I\\'m very curious to hear your point.',\n",
       " 'Like you are',\n",
       " 'So you admit Kamala is a liar?',\n",
       " \"You can't support Antifa and call yourself anti-fascist.\\n\\nSee how this isn't an actual argument?\",\n",
       " \"Do you think you're arguing with an opinion piece? This is a poll that shows the exact opposite of what you're saying. Maybe you should read the story instead of just posting as a reaction to the headline.\",\n",
       " 'Well America clearly disagrees with you about the ideal age of presidential candidates. They _certainly_ don\\'t think the age she\\'d be in 2024 is disqualifying.\\n\\nGet outta here with your my-opinion-is-law \"shut up and dribble\"',\n",
       " 'You call *that* patriotism? Really?']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(all_comments[all_comments.personal_attacks > 0.9].sample(10).body)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['level_0', 'index', 'created_utc', 'score', 'vulgarity', 'civility',\n",
       "       'namecalling', 'stereotype', 'demeaning', 'aggregate', 'depth',\n",
       "       'stereotyppe_new'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comments.corr().columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>vulgarity</th>\n",
       "      <th>personal_attacks</th>\n",
       "      <th>incivility</th>\n",
       "      <th>third_party_attacks</th>\n",
       "      <th>aspersion</th>\n",
       "      <th>comment_len</th>\n",
       "      <th>compound_sentiment</th>\n",
       "      <th>stereotype</th>\n",
       "      <th>aggregate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1612153203</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>glk8hec</td>\n",
       "      <td>runrain</td>\n",
       "      <td>Lol not even. Where I heard about it most was ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.160585</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.332831</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>96</td>\n",
       "      <td>-0.2357</td>\n",
       "      <td>0.234234</td>\n",
       "      <td>0.064974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1612152460</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk757a</td>\n",
       "      <td>SilverHerfer</td>\n",
       "      <td>To paraphrase one of the great conservatives o...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>3</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.115829</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.003651</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8428</td>\n",
       "      <td>0.536770</td>\n",
       "      <td>0.125015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1612151108</td>\n",
       "      <td>t1_glit0la</td>\n",
       "      <td>t3_l9lonl</td>\n",
       "      <td>glk4jhg</td>\n",
       "      <td>Psychowitz</td>\n",
       "      <td>Like coding.</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9lonl/keystone_pipe...</td>\n",
       "      <td>0</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.203247</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>0.288935</td>\n",
       "      <td>0.087804</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1612150714</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3rkp</td>\n",
       "      <td>Salmankhan42069</td>\n",
       "      <td>Wait, this is a meme?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>-1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.148250</td>\n",
       "      <td>0.004309</td>\n",
       "      <td>0.323237</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.249884</td>\n",
       "      <td>0.066450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1612150618</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3kvz</td>\n",
       "      <td>Pile_of_Walthers</td>\n",
       "      <td>Which one is more useful?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>2</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.094569</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.050714</td>\n",
       "      <td>0.003793</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4927</td>\n",
       "      <td>0.308906</td>\n",
       "      <td>0.067157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127588</th>\n",
       "      <td>1603994257</td>\n",
       "      <td>t1_gaidthx</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaiivoy</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>I remember when Fox, MSNBC and CNN [all broadc...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066037</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.016209</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>44</td>\n",
       "      <td>-0.2023</td>\n",
       "      <td>0.238228</td>\n",
       "      <td>0.043984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127589</th>\n",
       "      <td>1603993347</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaigl2w</td>\n",
       "      <td>etzel1200</td>\n",
       "      <td>That’s on Comey, not the NYT.</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256163</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>0.130545</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.261439</td>\n",
       "      <td>0.093671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127590</th>\n",
       "      <td>1603992287</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaidthx</td>\n",
       "      <td>paywallpiker</td>\n",
       "      <td>People neglect that the media played a huge pa...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104737</td>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.037370</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.029973</td>\n",
       "      <td>63</td>\n",
       "      <td>0.8020</td>\n",
       "      <td>0.320039</td>\n",
       "      <td>0.079030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127591</th>\n",
       "      <td>1603885499</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>gaddjx4</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>The Dakotas are getting slammed, especially co...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jj3nmi/oc_states_in...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209203</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.611730</td>\n",
       "      <td>0.837685</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.232618</td>\n",
       "      <td>0.270247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127592</th>\n",
       "      <td>1603745345</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>ga7dx8h</td>\n",
       "      <td>ual33655</td>\n",
       "      <td>Thanks for telling it like it is you should ha...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jigjpx/hillary_clin...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.251816</td>\n",
       "      <td>0.989517</td>\n",
       "      <td>0.990107</td>\n",
       "      <td>0.956174</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>17</td>\n",
       "      <td>0.6597</td>\n",
       "      <td>0.317675</td>\n",
       "      <td>0.557658</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35567120 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_utc   parent_id    link_id       id            author  \\\n",
       "1         1612153203   t3_l9gngx  t3_l9gngx  glk8hec           runrain   \n",
       "2         1612152460   t3_l9t1ha  t3_l9t1ha  glk757a      SilverHerfer   \n",
       "4         1612151108  t1_glit0la  t3_l9lonl  glk4jhg        Psychowitz   \n",
       "6         1612150714   t3_l9t1ha  t3_l9t1ha  glk3rkp   Salmankhan42069   \n",
       "9         1612150618   t3_l9t1ha  t3_l9t1ha  glk3kvz  Pile_of_Walthers   \n",
       "...              ...         ...        ...      ...               ...   \n",
       "2127588   1603994257  t1_gaidthx  t3_jkbmw0  gaiivoy     GogglesPisano   \n",
       "2127589   1603993347   t3_jkbmw0  t3_jkbmw0  gaigl2w         etzel1200   \n",
       "2127590   1603992287   t3_jkbmw0  t3_jkbmw0  gaidthx      paywallpiker   \n",
       "2127591   1603885499   t3_jj3nmi  t3_jj3nmi  gaddjx4     GogglesPisano   \n",
       "2127592   1603745345   t3_jigjpx  t3_jigjpx  ga7dx8h          ual33655   \n",
       "\n",
       "                                                      body       subreddit  \\\n",
       "1        Lol not even. Where I heard about it most was ...   AskThe_Donald   \n",
       "2        To paraphrase one of the great conservatives o...   AskThe_Donald   \n",
       "4                                             Like coding.   AskThe_Donald   \n",
       "6                                    Wait, this is a meme?   AskThe_Donald   \n",
       "9                                Which one is more useful?   AskThe_Donald   \n",
       "...                                                    ...             ...   \n",
       "2127588  I remember when Fox, MSNBC and CNN [all broadc...  hillaryclinton   \n",
       "2127589                      That’s on Comey, not the NYT.  hillaryclinton   \n",
       "2127590  People neglect that the media played a huge pa...  hillaryclinton   \n",
       "2127591  The Dakotas are getting slammed, especially co...  hillaryclinton   \n",
       "2127592  Thanks for telling it like it is you should ha...  hillaryclinton   \n",
       "\n",
       "                                                      link  score  \\\n",
       "1        /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      1   \n",
       "2        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      3   \n",
       "4        /r/AskThe_Donald/comments/l9lonl/keystone_pipe...      0   \n",
       "6        /r/AskThe_Donald/comments/l9t1ha/we_definitely...     -1   \n",
       "9        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      2   \n",
       "...                                                    ...    ...   \n",
       "2127588  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127589  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      2   \n",
       "2127590  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127591  /r/hillaryclinton/comments/jj3nmi/oc_states_in...      1   \n",
       "2127592  /r/hillaryclinton/comments/jigjpx/hillary_clin...      1   \n",
       "\n",
       "        author_flair_text  vulgarity  personal_attacks  incivility  \\\n",
       "1              :T: NOVICE   0.160585          0.001712    0.332831   \n",
       "2              :T: NOVICE   0.115829          0.002649    0.112667   \n",
       "4              :T: NOVICE   0.203247          0.002789    0.070185   \n",
       "6              :T: NOVICE   0.148250          0.004309    0.323237   \n",
       "9              :T: NOVICE   0.094569          0.003459    0.050714   \n",
       "...                   ...        ...               ...         ...   \n",
       "2127588               NaN   0.066037          0.001627    0.016209   \n",
       "2127589               NaN   0.256163          0.002296    0.130545   \n",
       "2127590               NaN   0.104737          0.002317    0.037370   \n",
       "2127591               NaN   0.209203          0.002534    0.611730   \n",
       "2127592               NaN   0.251816          0.989517    0.990107   \n",
       "\n",
       "         third_party_attacks  aspersion  comment_len  compound_sentiment  \\\n",
       "1                   0.004760   0.000276           96             -0.2357   \n",
       "2                   0.003651   0.000283           26              0.8428   \n",
       "4                   0.003872   0.000680            2              0.3612   \n",
       "6                   0.005040   0.000420            5              0.0000   \n",
       "9                   0.003793   0.000210            5              0.4927   \n",
       "...                      ...        ...          ...                 ...   \n",
       "2127588             0.005488   0.000130           44             -0.2023   \n",
       "2127589             0.004628   0.000172            6              0.0000   \n",
       "2127590             0.004813   0.029973           63              0.8020   \n",
       "2127591             0.837685   0.000281           29              0.0000   \n",
       "2127592             0.956174   0.000320           17              0.6597   \n",
       "\n",
       "         stereotype  aggregate  \n",
       "1          0.234234   0.064974  \n",
       "2          0.536770   0.125015  \n",
       "4          0.288935   0.087804  \n",
       "6          0.249884   0.066450  \n",
       "9          0.308906   0.067157  \n",
       "...             ...        ...  \n",
       "2127588    0.238228   0.043984  \n",
       "2127589    0.261439   0.093671  \n",
       "2127590    0.320039   0.079030  \n",
       "2127591    0.232618   0.270247  \n",
       "2127592    0.317675   0.557658  \n",
       "\n",
       "[35567120 rows x 19 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comments['aggregate'] = all_comments['vulgarity']+all_comments['personal_attacks']+all_comments['stereotype']+all_comments['third_party_attacks']+all_comments['aspersion']\n",
    "all_comments['aggregate'] = (all_comments['aggregate'] - np.min(all_comments['aggregate'])) / (np.max(all_comments['aggregate']) - np.min(all_comments['aggregate']))\n",
    "all_comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "subreddit_groups = all_comments.groupby('subreddit')\n",
    "# subreddit_count = dict(subreddit_groups.count()['author'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5096961259841919"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "threshold = np.quantile(all_comments['aggregate'].values)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>...</th>\n",
       "      <th>personal_attacks</th>\n",
       "      <th>incivility</th>\n",
       "      <th>third_party_attacks</th>\n",
       "      <th>aspersion</th>\n",
       "      <th>comment_len</th>\n",
       "      <th>compound_sentiment</th>\n",
       "      <th>stereotype</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>user_activity_level</th>\n",
       "      <th>user_toxicity_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
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       "      <td>t1_glhsgh4</td>\n",
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       "      <td>gliq9gd</td>\n",
       "      <td>thxpk</td>\n",
       "      <td>&amp;gt; While the rules regarding standing **do n...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l8lub6/new_york_judg...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: PROFICIENT</td>\n",
       "      <td>...</td>\n",
       "      <td>0.994875</td>\n",
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       "      <td>579</td>\n",
       "      <td>50.0</td>\n",
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       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>1611933696</td>\n",
       "      <td>t3_l7h78y</td>\n",
       "      <td>t3_l7h78y</td>\n",
       "      <td>gl8uhbr</td>\n",
       "      <td>powowspells</td>\n",
       "      <td>It Is also happening with Cryptocurrencies.  C...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l7h78y/dave_portnoy_...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001677</td>\n",
       "      <td>0.994987</td>\n",
       "      <td>0.973129</td>\n",
       "      <td>0.999398</td>\n",
       "      <td>59</td>\n",
       "      <td>-0.9403</td>\n",
       "      <td>0.232135</td>\n",
       "      <td>0.527006</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>398</th>\n",
       "      <td>1611908505</td>\n",
       "      <td>t3_l7kw4k</td>\n",
       "      <td>t3_l7kw4k</td>\n",
       "      <td>gl7nkzx</td>\n",
       "      <td>cockpugwash1</td>\n",
       "      <td>What a terrible turn of events. This virtue si...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l7kw4k/beto_orourke_...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010535</td>\n",
       "      <td>0.991273</td>\n",
       "      <td>0.874915</td>\n",
       "      <td>0.999765</td>\n",
       "      <td>44</td>\n",
       "      <td>-0.0644</td>\n",
       "      <td>0.180205</td>\n",
       "      <td>0.525569</td>\n",
       "      <td>14</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>423</th>\n",
       "      <td>1611893895</td>\n",
       "      <td>t3_l1f597</td>\n",
       "      <td>t3_l1f597</td>\n",
       "      <td>gl6yqqs</td>\n",
       "      <td>Johnnythrash001</td>\n",
       "      <td>Haha you gullible idiots. Fell for the koolaid...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l1f597/rsb_president...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.994932</td>\n",
       "      <td>0.993873</td>\n",
       "      <td>0.979271</td>\n",
       "      <td>0.000220</td>\n",
       "      <td>22</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.211667</td>\n",
       "      <td>0.553608</td>\n",
       "      <td>30</td>\n",
       "      <td>7.0</td>\n",
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       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>1611813510</td>\n",
       "      <td>t3_l6mfmn</td>\n",
       "      <td>t3_l6mfmn</td>\n",
       "      <td>gl20z6d</td>\n",
       "      <td>peglegpowderskier</td>\n",
       "      <td>Just what this fucking pandemic lockdown neede...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l6mfmn/thanks_dement...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993272</td>\n",
       "      <td>0.993692</td>\n",
       "      <td>0.865569</td>\n",
       "      <td>0.076339</td>\n",
       "      <td>14</td>\n",
       "      <td>-0.5423</td>\n",
       "      <td>0.433662</td>\n",
       "      <td>0.671888</td>\n",
       "      <td>91</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2127208</th>\n",
       "      <td>1603916067</td>\n",
       "      <td>t1_gaew1ej</td>\n",
       "      <td>t3_jjmbfo</td>\n",
       "      <td>gaf17j3</td>\n",
       "      <td>SteakAndEggs2k</td>\n",
       "      <td>You know, condescending shitlibs like you are ...</td>\n",
       "      <td>Political_Revolution</td>\n",
       "      <td>/r/Political_Revolution/comments/jjmbfo/6_days...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993540</td>\n",
       "      <td>0.992808</td>\n",
       "      <td>0.781162</td>\n",
       "      <td>0.010381</td>\n",
       "      <td>21</td>\n",
       "      <td>0.9371</td>\n",
       "      <td>0.285760</td>\n",
       "      <td>0.591979</td>\n",
       "      <td>177</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127211</th>\n",
       "      <td>1603913529</td>\n",
       "      <td>t1_gaeve06</td>\n",
       "      <td>t3_jjmbfo</td>\n",
       "      <td>gaevu0j</td>\n",
       "      <td>King_Of_The_Cold</td>\n",
       "      <td>No a deep red state like fucking McConnell ken...</td>\n",
       "      <td>Political_Revolution</td>\n",
       "      <td>/r/Political_Revolution/comments/jjmbfo/6_days...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993857</td>\n",
       "      <td>0.993649</td>\n",
       "      <td>0.006966</td>\n",
       "      <td>0.999770</td>\n",
       "      <td>65</td>\n",
       "      <td>-0.8375</td>\n",
       "      <td>0.384456</td>\n",
       "      <td>0.699970</td>\n",
       "      <td>77</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127382</th>\n",
       "      <td>1603805693</td>\n",
       "      <td>t1_ga9ljpm</td>\n",
       "      <td>t3_jiu97t</td>\n",
       "      <td>ga9r2k9</td>\n",
       "      <td>is_there_pie</td>\n",
       "      <td>Yeah, when dark money and hyperparistans spear...</td>\n",
       "      <td>Political_Revolution</td>\n",
       "      <td>/r/Political_Revolution/comments/jiu97t/la_jef...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004190</td>\n",
       "      <td>0.992171</td>\n",
       "      <td>0.969286</td>\n",
       "      <td>0.979019</td>\n",
       "      <td>34</td>\n",
       "      <td>-0.5994</td>\n",
       "      <td>0.514569</td>\n",
       "      <td>0.666889</td>\n",
       "      <td>72</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127441</th>\n",
       "      <td>1603725302</td>\n",
       "      <td>t1_ga66nex</td>\n",
       "      <td>t3_jhw3ne</td>\n",
       "      <td>ga69289</td>\n",
       "      <td>mathiue777</td>\n",
       "      <td>That's exactly it. They think they are all bil...</td>\n",
       "      <td>Political_Revolution</td>\n",
       "      <td>/r/Political_Revolution/comments/jhw3ne/jeff_b...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.971401</td>\n",
       "      <td>0.995553</td>\n",
       "      <td>0.937359</td>\n",
       "      <td>0.203727</td>\n",
       "      <td>33</td>\n",
       "      <td>0.6124</td>\n",
       "      <td>0.272044</td>\n",
       "      <td>0.668176</td>\n",
       "      <td>42</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127592</th>\n",
       "      <td>1603745345</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>ga7dx8h</td>\n",
       "      <td>ual33655</td>\n",
       "      <td>Thanks for telling it like it is you should ha...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jigjpx/hillary_clin...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989517</td>\n",
       "      <td>0.990107</td>\n",
       "      <td>0.956174</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>17</td>\n",
       "      <td>0.6597</td>\n",
       "      <td>0.317675</td>\n",
       "      <td>0.557658</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>673770 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_utc   parent_id    link_id       id             author  \\\n",
       "38        1612127091  t1_glhsgh4  t3_l8lub6  gliq9gd              thxpk   \n",
       "352       1611933696   t3_l7h78y  t3_l7h78y  gl8uhbr        powowspells   \n",
       "398       1611908505   t3_l7kw4k  t3_l7kw4k  gl7nkzx       cockpugwash1   \n",
       "423       1611893895   t3_l1f597  t3_l1f597  gl6yqqs    Johnnythrash001   \n",
       "582       1611813510   t3_l6mfmn  t3_l6mfmn  gl20z6d  peglegpowderskier   \n",
       "...              ...         ...        ...      ...                ...   \n",
       "2127208   1603916067  t1_gaew1ej  t3_jjmbfo  gaf17j3     SteakAndEggs2k   \n",
       "2127211   1603913529  t1_gaeve06  t3_jjmbfo  gaevu0j   King_Of_The_Cold   \n",
       "2127382   1603805693  t1_ga9ljpm  t3_jiu97t  ga9r2k9       is_there_pie   \n",
       "2127441   1603725302  t1_ga66nex  t3_jhw3ne  ga69289         mathiue777   \n",
       "2127592   1603745345   t3_jigjpx  t3_jigjpx  ga7dx8h           ual33655   \n",
       "\n",
       "                                                      body  \\\n",
       "38       &gt; While the rules regarding standing **do n...   \n",
       "352      It Is also happening with Cryptocurrencies.  C...   \n",
       "398      What a terrible turn of events. This virtue si...   \n",
       "423      Haha you gullible idiots. Fell for the koolaid...   \n",
       "582      Just what this fucking pandemic lockdown neede...   \n",
       "...                                                    ...   \n",
       "2127208  You know, condescending shitlibs like you are ...   \n",
       "2127211  No a deep red state like fucking McConnell ken...   \n",
       "2127382  Yeah, when dark money and hyperparistans spear...   \n",
       "2127441  That's exactly it. They think they are all bil...   \n",
       "2127592  Thanks for telling it like it is you should ha...   \n",
       "\n",
       "                    subreddit  \\\n",
       "38              AskThe_Donald   \n",
       "352             AskThe_Donald   \n",
       "398             AskThe_Donald   \n",
       "423             AskThe_Donald   \n",
       "582             AskThe_Donald   \n",
       "...                       ...   \n",
       "2127208  Political_Revolution   \n",
       "2127211  Political_Revolution   \n",
       "2127382  Political_Revolution   \n",
       "2127441  Political_Revolution   \n",
       "2127592        hillaryclinton   \n",
       "\n",
       "                                                      link  score  \\\n",
       "38       /r/AskThe_Donald/comments/l8lub6/new_york_judg...      1   \n",
       "352      /r/AskThe_Donald/comments/l7h78y/dave_portnoy_...      1   \n",
       "398      /r/AskThe_Donald/comments/l7kw4k/beto_orourke_...      1   \n",
       "423      /r/AskThe_Donald/comments/l1f597/rsb_president...      1   \n",
       "582      /r/AskThe_Donald/comments/l6mfmn/thanks_dement...      1   \n",
       "...                                                    ...    ...   \n",
       "2127208  /r/Political_Revolution/comments/jjmbfo/6_days...      1   \n",
       "2127211  /r/Political_Revolution/comments/jjmbfo/6_days...      1   \n",
       "2127382  /r/Political_Revolution/comments/jiu97t/la_jef...      1   \n",
       "2127441  /r/Political_Revolution/comments/jhw3ne/jeff_b...      1   \n",
       "2127592  /r/hillaryclinton/comments/jigjpx/hillary_clin...      1   \n",
       "\n",
       "        author_flair_text  ...  personal_attacks  incivility  \\\n",
       "38         :T: PROFICIENT  ...          0.994875    0.993740   \n",
       "352                   NaN  ...          0.001677    0.994987   \n",
       "398            :T: NOVICE  ...          0.010535    0.991273   \n",
       "423                   NaN  ...          0.994932    0.993873   \n",
       "582            :T: NOVICE  ...          0.993272    0.993692   \n",
       "...                   ...  ...               ...         ...   \n",
       "2127208               NaN  ...          0.993540    0.992808   \n",
       "2127211               NaN  ...          0.993857    0.993649   \n",
       "2127382               NaN  ...          0.004190    0.992171   \n",
       "2127441               NaN  ...          0.971401    0.995553   \n",
       "2127592               NaN  ...          0.989517    0.990107   \n",
       "\n",
       "         third_party_attacks  aspersion  comment_len  compound_sentiment  \\\n",
       "38                  0.977948   0.000120           39              0.6124   \n",
       "352                 0.973129   0.999398           59             -0.9403   \n",
       "398                 0.874915   0.999765           44             -0.0644   \n",
       "423                 0.979271   0.000220           22              0.0000   \n",
       "582                 0.865569   0.076339           14             -0.5423   \n",
       "...                      ...        ...          ...                 ...   \n",
       "2127208             0.781162   0.010381           21              0.9371   \n",
       "2127211             0.006966   0.999770           65             -0.8375   \n",
       "2127382             0.969286   0.979019           34             -0.5994   \n",
       "2127441             0.937359   0.203727           33              0.6124   \n",
       "2127592             0.956174   0.000320           17              0.6597   \n",
       "\n",
       "         stereotype  aggregate  user_activity_level  user_toxicity_level  \n",
       "38         0.384394   0.528759                  579                 50.0  \n",
       "352        0.232135   0.527006                    1                  1.0  \n",
       "398        0.180205   0.525569                   14                  3.0  \n",
       "423        0.211667   0.553608                   30                  7.0  \n",
       "582        0.433662   0.671888                   91                 11.0  \n",
       "...             ...        ...                  ...                  ...  \n",
       "2127208    0.285760   0.591979                  177                 19.0  \n",
       "2127211    0.384456   0.699970                   77                 12.0  \n",
       "2127382    0.514569   0.666889                   72                 12.0  \n",
       "2127441    0.272044   0.668176                   42                  6.0  \n",
       "2127592    0.317675   0.557658                    2                  1.0  \n",
       "\n",
       "[673770 rows x 21 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncivil_comments = all_comments[all_comments['aggregate'] > 0.5]\n",
    "uncivil_comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([32070258,   477540,   255706,   191894,   167458,   163482,\n",
       "          177575,   223055,   354061,  1486091]),\n",
       " array([0.00186049, 0.10041662, 0.19897275, 0.29752886, 0.396085  ,\n",
       "        0.49464113, 0.5931972 , 0.6917534 , 0.7903095 , 0.88886565,\n",
       "        0.98742175], dtype=float32))"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.histogram(all_comments['third_party_attacks'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.02324476735666388"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# np.histogram(all_comments['third_party_attacks'].values)\n",
    "np.quantile(all_comments['third_party_attacks'].values, 0.85)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>...</th>\n",
       "      <th>personal_attacks</th>\n",
       "      <th>incivility</th>\n",
       "      <th>third_party_attacks</th>\n",
       "      <th>aspersion</th>\n",
       "      <th>comment_len</th>\n",
       "      <th>compound_sentiment</th>\n",
       "      <th>stereotype</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>user_activity_level</th>\n",
       "      <th>user_toxicity_level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1612153203</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>glk8hec</td>\n",
       "      <td>runrain</td>\n",
       "      <td>Lol not even. Where I heard about it most was ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.332831</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>96</td>\n",
       "      <td>-0.2357</td>\n",
       "      <td>0.234234</td>\n",
       "      <td>0.064974</td>\n",
       "      <td>46</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1612152460</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk757a</td>\n",
       "      <td>SilverHerfer</td>\n",
       "      <td>To paraphrase one of the great conservatives o...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>3</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.003651</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8428</td>\n",
       "      <td>0.536770</td>\n",
       "      <td>0.125015</td>\n",
       "      <td>665</td>\n",
       "      <td>197.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1612151108</td>\n",
       "      <td>t1_glit0la</td>\n",
       "      <td>t3_l9lonl</td>\n",
       "      <td>glk4jhg</td>\n",
       "      <td>Psychowitz</td>\n",
       "      <td>Like coding.</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9lonl/keystone_pipe...</td>\n",
       "      <td>0</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>0.288935</td>\n",
       "      <td>0.087804</td>\n",
       "      <td>13</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1612150714</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3rkp</td>\n",
       "      <td>Salmankhan42069</td>\n",
       "      <td>Wait, this is a meme?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>-1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.004309</td>\n",
       "      <td>0.323237</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.249884</td>\n",
       "      <td>0.066450</td>\n",
       "      <td>7</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1612150618</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3kvz</td>\n",
       "      <td>Pile_of_Walthers</td>\n",
       "      <td>Which one is more useful?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>2</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.050714</td>\n",
       "      <td>0.003793</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4927</td>\n",
       "      <td>0.308906</td>\n",
       "      <td>0.067157</td>\n",
       "      <td>161</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127588</th>\n",
       "      <td>1603994257</td>\n",
       "      <td>t1_gaidthx</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaiivoy</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>I remember when Fox, MSNBC and CNN [all broadc...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.016209</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>44</td>\n",
       "      <td>-0.2023</td>\n",
       "      <td>0.238228</td>\n",
       "      <td>0.043984</td>\n",
       "      <td>401</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127589</th>\n",
       "      <td>1603993347</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaigl2w</td>\n",
       "      <td>etzel1200</td>\n",
       "      <td>That’s on Comey, not the NYT.</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>0.130545</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.261439</td>\n",
       "      <td>0.093671</td>\n",
       "      <td>444</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127590</th>\n",
       "      <td>1603992287</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaidthx</td>\n",
       "      <td>paywallpiker</td>\n",
       "      <td>People neglect that the media played a huge pa...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.037370</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.029973</td>\n",
       "      <td>63</td>\n",
       "      <td>0.8020</td>\n",
       "      <td>0.320039</td>\n",
       "      <td>0.079030</td>\n",
       "      <td>21</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127591</th>\n",
       "      <td>1603885499</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>gaddjx4</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>The Dakotas are getting slammed, especially co...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jj3nmi/oc_states_in...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.611730</td>\n",
       "      <td>0.837685</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.232618</td>\n",
       "      <td>0.270247</td>\n",
       "      <td>401</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127592</th>\n",
       "      <td>1603745345</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>ga7dx8h</td>\n",
       "      <td>ual33655</td>\n",
       "      <td>Thanks for telling it like it is you should ha...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jigjpx/hillary_clin...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.989517</td>\n",
       "      <td>0.990107</td>\n",
       "      <td>0.956174</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>17</td>\n",
       "      <td>0.6597</td>\n",
       "      <td>0.317675</td>\n",
       "      <td>0.557658</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35567120 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_utc   parent_id    link_id       id            author  \\\n",
       "1         1612153203   t3_l9gngx  t3_l9gngx  glk8hec           runrain   \n",
       "2         1612152460   t3_l9t1ha  t3_l9t1ha  glk757a      SilverHerfer   \n",
       "4         1612151108  t1_glit0la  t3_l9lonl  glk4jhg        Psychowitz   \n",
       "6         1612150714   t3_l9t1ha  t3_l9t1ha  glk3rkp   Salmankhan42069   \n",
       "9         1612150618   t3_l9t1ha  t3_l9t1ha  glk3kvz  Pile_of_Walthers   \n",
       "...              ...         ...        ...      ...               ...   \n",
       "2127588   1603994257  t1_gaidthx  t3_jkbmw0  gaiivoy     GogglesPisano   \n",
       "2127589   1603993347   t3_jkbmw0  t3_jkbmw0  gaigl2w         etzel1200   \n",
       "2127590   1603992287   t3_jkbmw0  t3_jkbmw0  gaidthx      paywallpiker   \n",
       "2127591   1603885499   t3_jj3nmi  t3_jj3nmi  gaddjx4     GogglesPisano   \n",
       "2127592   1603745345   t3_jigjpx  t3_jigjpx  ga7dx8h          ual33655   \n",
       "\n",
       "                                                      body       subreddit  \\\n",
       "1        Lol not even. Where I heard about it most was ...   AskThe_Donald   \n",
       "2        To paraphrase one of the great conservatives o...   AskThe_Donald   \n",
       "4                                             Like coding.   AskThe_Donald   \n",
       "6                                    Wait, this is a meme?   AskThe_Donald   \n",
       "9                                Which one is more useful?   AskThe_Donald   \n",
       "...                                                    ...             ...   \n",
       "2127588  I remember when Fox, MSNBC and CNN [all broadc...  hillaryclinton   \n",
       "2127589                      That’s on Comey, not the NYT.  hillaryclinton   \n",
       "2127590  People neglect that the media played a huge pa...  hillaryclinton   \n",
       "2127591  The Dakotas are getting slammed, especially co...  hillaryclinton   \n",
       "2127592  Thanks for telling it like it is you should ha...  hillaryclinton   \n",
       "\n",
       "                                                      link  score  \\\n",
       "1        /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      1   \n",
       "2        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      3   \n",
       "4        /r/AskThe_Donald/comments/l9lonl/keystone_pipe...      0   \n",
       "6        /r/AskThe_Donald/comments/l9t1ha/we_definitely...     -1   \n",
       "9        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      2   \n",
       "...                                                    ...    ...   \n",
       "2127588  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127589  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      2   \n",
       "2127590  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127591  /r/hillaryclinton/comments/jj3nmi/oc_states_in...      1   \n",
       "2127592  /r/hillaryclinton/comments/jigjpx/hillary_clin...      1   \n",
       "\n",
       "        author_flair_text  ...  personal_attacks  incivility  \\\n",
       "1              :T: NOVICE  ...          0.001712    0.332831   \n",
       "2              :T: NOVICE  ...          0.002649    0.112667   \n",
       "4              :T: NOVICE  ...          0.002789    0.070185   \n",
       "6              :T: NOVICE  ...          0.004309    0.323237   \n",
       "9              :T: NOVICE  ...          0.003459    0.050714   \n",
       "...                   ...  ...               ...         ...   \n",
       "2127588               NaN  ...          0.001627    0.016209   \n",
       "2127589               NaN  ...          0.002296    0.130545   \n",
       "2127590               NaN  ...          0.002317    0.037370   \n",
       "2127591               NaN  ...          0.002534    0.611730   \n",
       "2127592               NaN  ...          0.989517    0.990107   \n",
       "\n",
       "         third_party_attacks  aspersion  comment_len  compound_sentiment  \\\n",
       "1                   0.004760   0.000276           96             -0.2357   \n",
       "2                   0.003651   0.000283           26              0.8428   \n",
       "4                   0.003872   0.000680            2              0.3612   \n",
       "6                   0.005040   0.000420            5              0.0000   \n",
       "9                   0.003793   0.000210            5              0.4927   \n",
       "...                      ...        ...          ...                 ...   \n",
       "2127588             0.005488   0.000130           44             -0.2023   \n",
       "2127589             0.004628   0.000172            6              0.0000   \n",
       "2127590             0.004813   0.029973           63              0.8020   \n",
       "2127591             0.837685   0.000281           29              0.0000   \n",
       "2127592             0.956174   0.000320           17              0.6597   \n",
       "\n",
       "         stereotype  aggregate  user_activity_level  user_toxicity_level  \n",
       "1          0.234234   0.064974                   46                 13.0  \n",
       "2          0.536770   0.125015                  665                197.0  \n",
       "4          0.288935   0.087804                   13                  2.0  \n",
       "6          0.249884   0.066450                    7                  1.0  \n",
       "9          0.308906   0.067157                  161                 35.0  \n",
       "...             ...        ...                  ...                  ...  \n",
       "2127588    0.238228   0.043984                  401                 99.0  \n",
       "2127589    0.261439   0.093671                  444                 47.0  \n",
       "2127590    0.320039   0.079030                   21                  4.0  \n",
       "2127591    0.232618   0.270247                  401                 99.0  \n",
       "2127592    0.317675   0.557658                    2                  1.0  \n",
       "\n",
       "[35567120 rows x 21 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_comment_count = dict(all_comments.groupby('author').count()['id'])\n",
    "all_comments['user_activity_level'] = all_comments['author'].map(author_comment_count)\n",
    "all_comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0      4450407\n",
       "2.0      2858708\n",
       "3.0      2130762\n",
       "4.0      1659905\n",
       "5.0      1277484\n",
       "          ...   \n",
       "191.0        835\n",
       "185.0        817\n",
       "364.0        777\n",
       "198.0        776\n",
       "165.0        732\n",
       "Name: user_toxicity_level, Length: 206, dtype: int64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "author_uncivil_comment_count = dict(uncivil_comments.groupby('author').count()['id'])\n",
    "all_comments['user_toxicity_level'] = all_comments['author'].map(author_uncivil_comment_count)\n",
    "all_comments.user_toxicity_level.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>created_utc</th>\n",
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       "      <th>stereotype</th>\n",
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       "      <td>Bywater</td>\n",
       "      <td>Agree.\\r\\nSincerely,\\r\\nAnother vet.</td>\n",
       "      <td>politics</td>\n",
       "      <td>/r/politics/comments/kx5eyc/upset_by_veterans_...</td>\n",
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       "      <td>g0mduf0</td>\n",
       "      <td>Bywater</td>\n",
       "      <td>If you can't draw the correlation on your own,...</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>/r/Libertarian/comments/i51q1a/judge_rules_vot...</td>\n",
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       "      <td>0.011480</td>\n",
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       "      <td>t3_i51xt3</td>\n",
       "      <td>g0mdrsl</td>\n",
       "      <td>Bywater</td>\n",
       "      <td>I was going to do Pan-Anarchist at one point, ...</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>/r/Libertarian/comments/i51xt3/out_of_interest...</td>\n",
       "      <td>1</td>\n",
       "      <td>Meat Popsicle</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002520</td>\n",
       "      <td>0.121753</td>\n",
       "      <td>0.006244</td>\n",
       "      <td>0.000269</td>\n",
       "      <td>21</td>\n",
       "      <td>-0.5927</td>\n",
       "      <td>0.266183</td>\n",
       "      <td>0.077816</td>\n",
       "      <td>6764</td>\n",
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       "      <th>3019941</th>\n",
       "      <td>1596756522</td>\n",
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       "      <td>t3_i2gluv</td>\n",
       "      <td>g0mb1bu</td>\n",
       "      <td>Bywater</td>\n",
       "      <td>Have you considered how many current libertari...</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>/r/Libertarian/comments/i2gluv/update_from_tex...</td>\n",
       "      <td>1</td>\n",
       "      <td>Meat Popsicle</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005171</td>\n",
       "      <td>0.041520</td>\n",
       "      <td>0.002967</td>\n",
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       "      <td>Historically I guess it would have been  mostl...</td>\n",
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       "      <td>t1_fsd68lg</td>\n",
       "      <td>t3_gtipns</td>\n",
       "      <td>fsd9fbk</td>\n",
       "      <td>Bywater</td>\n",
       "      <td>You blowing smoke up your ass if you think the...</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>/r/Libertarian/comments/gtipns/make_life_decen...</td>\n",
       "      <td>1</td>\n",
       "      <td>Dissident</td>\n",
       "      <td>...</td>\n",
       "      <td>0.070079</td>\n",
       "      <td>0.979540</td>\n",
       "      <td>0.009250</td>\n",
       "      <td>0.001737</td>\n",
       "      <td>139</td>\n",
       "      <td>-0.6006</td>\n",
       "      <td>0.211589</td>\n",
       "      <td>0.132337</td>\n",
       "      <td>6764</td>\n",
       "      <td>869.0</td>\n",
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       "      <th>4485963</th>\n",
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       "      <td>t3_gtipns</td>\n",
       "      <td>fscldj9</td>\n",
       "      <td>Bywater</td>\n",
       "      <td>Our standard of living is very high compared t...</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>/r/Libertarian/comments/gtipns/make_life_decen...</td>\n",
       "      <td>1</td>\n",
       "      <td>Dissident</td>\n",
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       "      <td>0.000412</td>\n",
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       "      <td>869.0</td>\n",
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       "      <td>fsck6rz</td>\n",
       "      <td>Bywater</td>\n",
       "      <td>I think he was referring to reality, so no, ob...</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>/r/Libertarian/comments/gtipns/make_life_decen...</td>\n",
       "      <td>1</td>\n",
       "      <td>Dissident</td>\n",
       "      <td>...</td>\n",
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       "      <td>12</td>\n",
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       "      <td>869.0</td>\n",
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       "    <tr>\n",
       "      <th>4486004</th>\n",
       "      <td>1590870293</td>\n",
       "      <td>t1_fsc0z53</td>\n",
       "      <td>t3_gtipns</td>\n",
       "      <td>fsck16k</td>\n",
       "      <td>Bywater</td>\n",
       "      <td>They kinda should be.  They help keep everyone...</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>/r/Libertarian/comments/gtipns/make_life_decen...</td>\n",
       "      <td>1</td>\n",
       "      <td>Dissident</td>\n",
       "      <td>...</td>\n",
       "      <td>0.001643</td>\n",
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       "      <td>30</td>\n",
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       "      <td>6764</td>\n",
       "      <td>869.0</td>\n",
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       "    <tr>\n",
       "      <th>4486078</th>\n",
       "      <td>1590869309</td>\n",
       "      <td>t3_gtipns</td>\n",
       "      <td>t3_gtipns</td>\n",
       "      <td>fscha69</td>\n",
       "      <td>Bywater</td>\n",
       "      <td>We are in a post-scarcity environment right no...</td>\n",
       "      <td>Libertarian</td>\n",
       "      <td>/r/Libertarian/comments/gtipns/make_life_decen...</td>\n",
       "      <td>1</td>\n",
       "      <td>Dissident</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006177</td>\n",
       "      <td>0.360193</td>\n",
       "      <td>0.005339</td>\n",
       "      <td>0.499543</td>\n",
       "      <td>257</td>\n",
       "      <td>-0.7795</td>\n",
       "      <td>0.270779</td>\n",
       "      <td>0.277111</td>\n",
       "      <td>6764</td>\n",
       "      <td>869.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>6764 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_utc   parent_id    link_id       id   author  \\\n",
       "3272500   1610643650  t1_gj8soeh  t3_kx5eyc  gj8twjt  Bywater   \n",
       "3019813   1596758146  t1_g0m8idu  t3_i51q1a  g0mduf0  Bywater   \n",
       "3019818   1596758102  t1_g0m7yud  t3_i51xt3  g0mdrsl  Bywater   \n",
       "3019941   1596756522  t1_g0m8qeb  t3_i2gluv  g0mb1bu  Bywater   \n",
       "3020008   1596755746   t3_i5169w  t3_i5169w  g0m9o9b  Bywater   \n",
       "...              ...         ...        ...      ...      ...   \n",
       "4485062   1590880997  t1_fsd68lg  t3_gtipns  fsd9fbk  Bywater   \n",
       "4485963   1590870769  t1_fsbzcra  t3_gtipns  fscldj9  Bywater   \n",
       "4485999   1590870351  t1_fsbxflq  t3_gtipns  fsck6rz  Bywater   \n",
       "4486004   1590870293  t1_fsc0z53  t3_gtipns  fsck16k  Bywater   \n",
       "4486078   1590869309   t3_gtipns  t3_gtipns  fscha69  Bywater   \n",
       "\n",
       "                                                      body    subreddit  \\\n",
       "3272500               Agree.\\r\\nSincerely,\\r\\nAnother vet.     politics   \n",
       "3019813  If you can't draw the correlation on your own,...  Libertarian   \n",
       "3019818  I was going to do Pan-Anarchist at one point, ...  Libertarian   \n",
       "3019941  Have you considered how many current libertari...  Libertarian   \n",
       "3020008  Historically I guess it would have been  mostl...  Libertarian   \n",
       "...                                                    ...          ...   \n",
       "4485062  You blowing smoke up your ass if you think the...  Libertarian   \n",
       "4485963  Our standard of living is very high compared t...  Libertarian   \n",
       "4485999  I think he was referring to reality, so no, ob...  Libertarian   \n",
       "4486004  They kinda should be.  They help keep everyone...  Libertarian   \n",
       "4486078  We are in a post-scarcity environment right no...  Libertarian   \n",
       "\n",
       "                                                      link  score  \\\n",
       "3272500  /r/politics/comments/kx5eyc/upset_by_veterans_...     37   \n",
       "3019813  /r/Libertarian/comments/i51q1a/judge_rules_vot...      1   \n",
       "3019818  /r/Libertarian/comments/i51xt3/out_of_interest...      1   \n",
       "3019941  /r/Libertarian/comments/i2gluv/update_from_tex...      1   \n",
       "3020008  /r/Libertarian/comments/i5169w/libertarian_rol...      1   \n",
       "...                                                    ...    ...   \n",
       "4485062  /r/Libertarian/comments/gtipns/make_life_decen...      1   \n",
       "4485963  /r/Libertarian/comments/gtipns/make_life_decen...      1   \n",
       "4485999  /r/Libertarian/comments/gtipns/make_life_decen...      1   \n",
       "4486004  /r/Libertarian/comments/gtipns/make_life_decen...      1   \n",
       "4486078  /r/Libertarian/comments/gtipns/make_life_decen...      1   \n",
       "\n",
       "        author_flair_text  ...  personal_attacks  incivility  \\\n",
       "3272500               NaN  ...          0.001827    0.017729   \n",
       "3019813     Meat Popsicle  ...          0.011480    0.015596   \n",
       "3019818     Meat Popsicle  ...          0.002520    0.121753   \n",
       "3019941     Meat Popsicle  ...          0.005171    0.041520   \n",
       "3020008     Meat Popsicle  ...          0.002798    0.268107   \n",
       "...                   ...  ...               ...         ...   \n",
       "4485062         Dissident  ...          0.070079    0.979540   \n",
       "4485963         Dissident  ...          0.002749    0.036536   \n",
       "4485999         Dissident  ...          0.001976    0.023576   \n",
       "4486004         Dissident  ...          0.001643    0.966544   \n",
       "4486078         Dissident  ...          0.006177    0.360193   \n",
       "\n",
       "         third_party_attacks  aspersion  comment_len  compound_sentiment  \\\n",
       "3272500             0.004002   0.000153            4              0.6808   \n",
       "3019813             0.004736   0.000255           25              0.6705   \n",
       "3019818             0.006244   0.000269           21             -0.5927   \n",
       "3019941             0.002967   0.000139           65              0.3690   \n",
       "3020008             0.004475   0.998488          143             -0.8102   \n",
       "...                      ...        ...          ...                 ...   \n",
       "4485062             0.009250   0.001737          139             -0.6006   \n",
       "4485963             0.007333   0.000412          168             -0.9187   \n",
       "4485999             0.003747   0.000215           12             -0.0762   \n",
       "4486004             0.005054   0.000129           30             -0.2960   \n",
       "4486078             0.005339   0.499543          257             -0.7795   \n",
       "\n",
       "         stereotype  aggregate  user_activity_level  user_toxicity_level  \n",
       "3272500    0.204099   0.038842                 6764                869.0  \n",
       "3019813    0.202953   0.049704                 6764                869.0  \n",
       "3019818    0.266183   0.077816                 6764                869.0  \n",
       "3019941    0.515700   0.110608                 6764                869.0  \n",
       "3020008    0.354816   0.418412                 6764                869.0  \n",
       "...             ...        ...                  ...                  ...  \n",
       "4485062    0.211589   0.132337                 6764                869.0  \n",
       "4485963    0.277769   0.061264                 6764                869.0  \n",
       "4485999    0.297357   0.075781                 6764                869.0  \n",
       "4486004    0.231579   0.168267                 6764                869.0  \n",
       "4486078    0.270779   0.277111                 6764                869.0  \n",
       "\n",
       "[6764 rows x 21 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comments[all_comments.author == 'Bywater']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Selethorme': 1,\n",
       " 'AlternativeSuccotash': 2,\n",
       " 'Havetologintovote': 3,\n",
       " 'Raunchy_Potato': 4,\n",
       " 'Earthly_Knight': 5,\n",
       " '3720-To-One': 6,\n",
       " 'jim351': 7,\n",
       " 'GreyInkling': 8,\n",
       " 'sunny_in_MN': 9,\n",
       " 'th_brown_bag': 10,\n",
       " 'BrighamYoung': 11,\n",
       " 'brewbr0': 12,\n",
       " 'Bywater': 13,\n",
       " 'Rightquercusalba': 14,\n",
       " 'GrandpaHardcore': 15,\n",
       " 'Libertarian4All': 16,\n",
       " 'MeatsimN64': 17,\n",
       " 'cooneyes': 18,\n",
       " 'carnagebestvillain': 19,\n",
       " 'Sean951': 20,\n",
       " 'Shaman_Bond': 21,\n",
       " 'Vladimus44': 22,\n",
       " 'ComradeBerns2ndGulag': 23,\n",
       " 'HohlraumHe3': 24,\n",
       " 'TheBirminghamBear': 25,\n",
       " 'ChristopherPoontang': 26,\n",
       " 'yasmin1334': 27,\n",
       " 'NemosGhost': 28,\n",
       " 'HarryBergeron927': 29,\n",
       " 'sacrastic_monitor': 30,\n",
       " 'The_LSD_Fairy': 31,\n",
       " 'Bruce_G': 32,\n",
       " 'Twerck': 33,\n",
       " 'Enjolras55': 34,\n",
       " 'moak0': 35,\n",
       " '5th_Law_of_Robotics': 36,\n",
       " 'HorseWhisperer32': 37,\n",
       " 'TFU_Merk': 38,\n",
       " 'Carbonatite': 39,\n",
       " 'spoobles': 40,\n",
       " 'DeliciouslyCynical': 41,\n",
       " 'IPredictAReddit': 42,\n",
       " 'AIDS-Sundae': 43,\n",
       " 'degeneracypromoter': 44,\n",
       " 'dextersgold': 45,\n",
       " 'goddamnzilla': 46,\n",
       " 'CarsomyrPlusSix': 47,\n",
       " 'TristedOpinions': 48,\n",
       " 'Naptownfellow': 49,\n",
       " 'NarwhalsAndBacon': 50,\n",
       " 'optionhome': 51,\n",
       " 'SaltHash': 52,\n",
       " 'penguished': 53,\n",
       " 'BidenIsTooSleepy': 54,\n",
       " 'padizzledonk': 55,\n",
       " 'whileoceaniasleeps': 56,\n",
       " 'hunt4redglocktober': 57,\n",
       " 'Herr__Nilpferd': 58,\n",
       " 'MoscowMitchMcKiller': 59,\n",
       " 'You_Dont_Party': 60,\n",
       " 'Riaayo': 61,\n",
       " 'Rat_Salat': 62,\n",
       " 'Vyuvarax': 63,\n",
       " 'bmanCO': 64,\n",
       " 'hahainternet': 65,\n",
       " 'Malaix': 66,\n",
       " 'sup3riorw0n': 67,\n",
       " 'Knicks1986': 68,\n",
       " 'zacaloni': 69,\n",
       " 'LawVol99': 70,\n",
       " 'Velshtein': 71,\n",
       " 'slacboy101': 72,\n",
       " 'MarTweFah': 73,\n",
       " 'jonyappleseedd': 74,\n",
       " 'NinjaNerd99': 75,\n",
       " 'vote_ffs': 76,\n",
       " 'alaskansteve': 77,\n",
       " 'TheRealPariah': 78,\n",
       " 'Keetony': 79,\n",
       " 'Xboarder84': 80,\n",
       " 'oTHEWHITERABBIT': 81,\n",
       " 'tfresh111': 82,\n",
       " 'ultimis': 83,\n",
       " 'Lastaccountcensored': 84,\n",
       " 'Not_A_Democrat_': 85,\n",
       " 'hairybeasty': 86,\n",
       " 'zapffe21': 87,\n",
       " 'NakedMiguel': 88,\n",
       " 'basscorruption': 89,\n",
       " 'HijacksMissiles': 90,\n",
       " 'Retardican_President': 91,\n",
       " 'benning_17': 92,\n",
       " 'crudedrawer': 93,\n",
       " 'Samurai_gaijin': 94,\n",
       " 'sunset117': 95,\n",
       " 'whatisliquidity': 96,\n",
       " 'iushciuweiush': 97,\n",
       " 'WalesIsForTheWhales': 98,\n",
       " 'Jizzlobber42': 99,\n",
       " 'Shift84': 100,\n",
       " 'myrddyna': 101,\n",
       " 'Rivsmama': 102,\n",
       " 'bhknb': 103,\n",
       " 'ScaryScarabBM': 104,\n",
       " 'macacu': 105,\n",
       " 'whenimmadrinkin': 106,\n",
       " 'Hot-Pretzel': 107,\n",
       " 'BitterFuture': 108,\n",
       " 'CarmenFandango': 109,\n",
       " 'Kecir': 110,\n",
       " 'fahqjokah': 111,\n",
       " 'reeeeeeee42069': 112,\n",
       " 'FrenchLlamas': 113,\n",
       " 'DW6565': 114,\n",
       " 'TheDirtyFuture': 115,\n",
       " 'walkinman19': 116,\n",
       " 'VoiceOfTheMillenium': 117,\n",
       " 'feral_lib': 118,\n",
       " 'EMONEYOG': 119,\n",
       " 'YoitsSean610': 120,\n",
       " 'Gooch222': 121,\n",
       " 'NightmareNeomys': 122,\n",
       " 'jubbergun': 123,\n",
       " 'Aphroditaeum': 124,\n",
       " 'Colonel_Chestbridge1': 125,\n",
       " 'clancy200': 126,\n",
       " 'ilivedownyourroad': 127,\n",
       " 'le672': 128,\n",
       " 'CherryCokeNixon': 129,\n",
       " 'Indiana_Curmudgeon': 130,\n",
       " 'adminsare55IQ': 131,\n",
       " 'deathakissaway': 132,\n",
       " 'snowbirdnerd': 133,\n",
       " 'ruiner8850': 134,\n",
       " 'Drunken_Priest': 135,\n",
       " 'PoeWasRight': 136,\n",
       " 'makldiz': 137,\n",
       " 'paulsmt': 138,\n",
       " 'Stigmetal110': 139,\n",
       " 'altmaltacc': 140,\n",
       " 'barneyrubbble': 141,\n",
       " 'johnny_birds': 142,\n",
       " 'Dingus-ate-your-baby': 143,\n",
       " 'The_Best_Yak_Ever': 144,\n",
       " 'nimdhiran': 145,\n",
       " 'ApollosCrow': 146,\n",
       " 'timar48': 147,\n",
       " 'BranfordJeff2': 148,\n",
       " 'ClownholeContingency': 149,\n",
       " 'HammyMacc': 150,\n",
       " 'Morat20': 151,\n",
       " 'MoreLibertarianThanU': 152,\n",
       " 'Spaceman1stClass': 153,\n",
       " 'hello_japan': 154,\n",
       " 'nslinkns24': 155,\n",
       " 'CorDra2011': 156,\n",
       " 'lilrabbitfoofoo': 157,\n",
       " 'Billy-Mays-Ghost': 158,\n",
       " 'lotta_love': 159,\n",
       " 'sherman_fairchild_II': 160,\n",
       " 'trumpsiranwar': 161,\n",
       " 'DubsNFuugens': 162,\n",
       " 'StarFuryG7': 163,\n",
       " 'StrokingBadger': 164,\n",
       " 'Zepp87': 165,\n",
       " 'corey_m_snow': 166,\n",
       " 'workforyourstuff': 167,\n",
       " '1SmokingBandit01': 168,\n",
       " 'catgirl_apocalypse': 169,\n",
       " 'tmone': 170,\n",
       " 'Fred_Evil': 171,\n",
       " 'Khaldara': 172,\n",
       " 'dominantspecies': 173,\n",
       " 'propshaft': 174,\n",
       " 'sfgunner': 175,\n",
       " 'Darkmortal10': 176,\n",
       " 'Electric_Pudding': 177,\n",
       " '_______-_-__________': 178,\n",
       " 'hildebrand_rarity': 179,\n",
       " 'johnny_soultrane': 180,\n",
       " 'worldwidewillie': 181,\n",
       " 'Christhephotographer': 182,\n",
       " 'Stockpile_Tom_Remake': 183,\n",
       " 'brkello': 184,\n",
       " 'dokikod': 185,\n",
       " 'armchairmegalomaniac': 186,\n",
       " 'sanjsrik': 187,\n",
       " 'veridique': 188,\n",
       " 'Bartimaeus222': 189,\n",
       " 'BohdiZafa': 190,\n",
       " 'Initial-Tangerine': 191,\n",
       " 'Jason_OT': 192,\n",
       " 'MM7299': 193,\n",
       " 'skellener': 194,\n",
       " 'Franticalmond2': 195,\n",
       " 'Muted-Bee': 196,\n",
       " 'The-Tet-Corporation': 197,\n",
       " 'darena': 198,\n",
       " 'AbsentGlare': 199,\n",
       " 'Little_Wooden_Boy': 200,\n",
       " 'Monkmode300': 201,\n",
       " '2057Champs__': 202,\n",
       " '2_Spicy_2_Impeach': 203,\n",
       " 'Loose_with_the_truth': 204,\n",
       " 'doctor_piranha': 205,\n",
       " 'ninjaluvr': 206,\n",
       " 'ultimatefighting': 207,\n",
       " 'Ledcookey': 208,\n",
       " 'Reddit_beard': 209,\n",
       " 'fowlraul': 210,\n",
       " 'no-stop911': 211,\n",
       " 'NovaDeez': 212,\n",
       " 'RedditAdminsHateCons': 213,\n",
       " 'The_Abyss_Blinked': 214,\n",
       " 'bluehonoluluballs': 215,\n",
       " 'gometrule': 216,\n",
       " 'newsreadhjw': 217,\n",
       " 'uiy_b7_s4': 218,\n",
       " 'whatever658': 219,\n",
       " 'zaparans': 220,\n",
       " 'TwilitSky': 221,\n",
       " 'a_fuller': 222,\n",
       " 'ifuc---pipeline': 223,\n",
       " 'CaptainNoBoat': 224,\n",
       " 'surfteacher1962': 225,\n",
       " 'the_BIGHEAD': 226,\n",
       " 'ALeaf_OnThe_Wind': 227,\n",
       " 'NtStkrWM': 228,\n",
       " 'ReturnOfDaSnack420': 229,\n",
       " 'Skeptical_Detroiter': 230,\n",
       " 'WhyAreYouSoMadAtMe': 231,\n",
       " 'dtabbaad': 232,\n",
       " 'streetvoyager': 233,\n",
       " 'professorshillphd': 234,\n",
       " 'BenedictD0nald': 235,\n",
       " 'BillyBludgeon': 236,\n",
       " 'CharmCityKid09': 237,\n",
       " 'Frying_Dutchman': 238,\n",
       " 'KillahKorona': 239,\n",
       " 'Reddit_guard': 240,\n",
       " 'dafunkmunk': 241,\n",
       " 'dazedANDconfused2020': 242,\n",
       " 'monsantobreath': 243,\n",
       " 'BernieStanders2020': 244,\n",
       " 'Disposable-001': 245,\n",
       " 'Fat_Mustard_Ovaries': 246,\n",
       " 'GoldenKaze': 247,\n",
       " 'Paradise_Found_': 248,\n",
       " 'Son-of-Neleus': 249,\n",
       " 'bandit69': 250,\n",
       " 'IBiteYou': 251,\n",
       " 'LuvNMuny': 252,\n",
       " 'Monkcoon': 253,\n",
       " 'InclementImmigrant': 254,\n",
       " 'Knight_of_the_Lepus': 255,\n",
       " 'aesirtap101': 256,\n",
       " 'dukeofmadnessmotors': 257,\n",
       " 's3509662mctom2': 258,\n",
       " 'toomanytabsopen': 259,\n",
       " 'Garuda_4': 260,\n",
       " 'LucianWaifu': 261,\n",
       " 'SpaceAdventureCobraX': 262,\n",
       " 'arcticlizard7': 263,\n",
       " 'buttstick69': 264,\n",
       " 'mknsky': 265,\n",
       " 'sardia1': 266,\n",
       " 'JupiterandMars1': 267,\n",
       " 'LittleSister_9982': 268,\n",
       " 'MoreHandsome-ThanYou': 269,\n",
       " 'NoAbsense': 270,\n",
       " 'Sweatytubesock': 271,\n",
       " 'spidersinterweb': 272,\n",
       " 'unclefire': 273,\n",
       " '-Fireball': 274,\n",
       " 'Alpinegoatherd': 275,\n",
       " 'Cherssssss': 276,\n",
       " 'SheriffComey': 277,\n",
       " 'concretebeats': 278,\n",
       " 'universalChamp1on': 279,\n",
       " 'AnnoyedHappyMan': 280,\n",
       " 'TheHomersapien': 281,\n",
       " 'dragonfliesloveme': 282,\n",
       " 'higher_and_higher_': 283,\n",
       " 'pluckflopboy': 284,\n",
       " 'pplrstypid': 285,\n",
       " 'watersnark': 286,\n",
       " 'GODGK2': 287,\n",
       " 'MostAssuredlyHuman': 288,\n",
       " 'NoFascist': 289,\n",
       " 'Zandt88': 290,\n",
       " 'dLo187': 291,\n",
       " 'mzpip': 292,\n",
       " 'oapster79': 293,\n",
       " 'thinkingdoing': 294,\n",
       " 'Daisy_Doll85': 295,\n",
       " 'Herbivore4Life': 296,\n",
       " 'IndiaCompany-': 297,\n",
       " 'Mayor_Minderbinder': 298,\n",
       " 'PM_PICS_OF_MANATEES': 299,\n",
       " 'Timbershoe': 300,\n",
       " 'agentup': 301,\n",
       " 'HandsomeSpider': 302,\n",
       " 'ILoveRegenHealth': 303,\n",
       " 'Shadowman-The-Ghost': 304,\n",
       " 'Shittalkinbswalkin': 305,\n",
       " 'SpearNmagicHelmet': 306,\n",
       " 'Zefram_C_Warp_Drive': 307,\n",
       " 'allendrio': 308,\n",
       " 'extremely_unlikely': 309,\n",
       " 'mces97': 310,\n",
       " 'milo7even': 311,\n",
       " 'Chuckox50': 312,\n",
       " 'King_Mierdas': 313,\n",
       " 'Osiris_Of_This_Shit_': 314,\n",
       " 'RedWindArt': 315,\n",
       " 'TankerD18': 316,\n",
       " 'WuddaWoodchuck': 317,\n",
       " 'lefteryet': 318,\n",
       " 'DemWitty': 319,\n",
       " 'Extension_Bathroom_2': 320,\n",
       " 'FinancialTea4': 321,\n",
       " 'MagaMagaChooChoo': 322,\n",
       " 'Mildad': 323,\n",
       " 'Personal_Bottle': 324,\n",
       " 'Sagebrush-1138': 325,\n",
       " 'AbbadonCox': 326,\n",
       " 'External_Scheme8855': 327,\n",
       " 'SweatingSoy': 328,\n",
       " 'Will_Muny': 329,\n",
       " 'bearrosaurus': 330,\n",
       " 'elephantphallus': 331,\n",
       " 'goforthandconquer': 332,\n",
       " 'DublinCheezie': 333,\n",
       " 'FruedanSlip': 334,\n",
       " 'HWGA_Gallifrey': 335,\n",
       " 'IHeartGeorgeConway': 336,\n",
       " 'TruthTold89': 337,\n",
       " 'Tufkidd': 338,\n",
       " 'gishbot1': 339,\n",
       " 'kevonicus': 340,\n",
       " 'stoniegreen': 341,\n",
       " 'AnalSoapOpera': 342,\n",
       " 'GloboGymPurpleCobras': 343,\n",
       " 'Kostya_M': 344,\n",
       " 'NewAccount10Thousand': 345,\n",
       " 'SmashBusters': 346,\n",
       " 'pmmeyourneardeathexp': 347,\n",
       " 'sanitysepilogue': 348,\n",
       " '--Zman--': 349,\n",
       " 'Gallijl3': 350,\n",
       " 'PoopMobile9000': 351,\n",
       " 'SeeBadd': 352,\n",
       " 'frostbitfeet': 353,\n",
       " 'BeachCruisin22': 354,\n",
       " 'HectorHuntsNazis': 355,\n",
       " 'Ode_to_bees': 356,\n",
       " 'PresidentBunkerBitch': 357,\n",
       " 'PrincessToadTool': 358,\n",
       " 'ShoutingMatch': 359,\n",
       " 'gatechthrowaway1873': 360,\n",
       " 'mpyles10': 361,\n",
       " 'paenultimus': 362,\n",
       " 'stay-can-cheese': 363,\n",
       " 'stinky-weaselteats': 364,\n",
       " '-misanthroptimist': 365,\n",
       " '200_Proof_Brain': 366,\n",
       " '40for60': 367,\n",
       " 'IrrationalExpression': 368,\n",
       " 'OptimoussePrime': 369,\n",
       " 'TemplarDane': 370,\n",
       " 'TreyMysterioJr': 371,\n",
       " 'bradley_j': 372,\n",
       " 'justacupofcofee': 373,\n",
       " 'purplebrown_updown': 374,\n",
       " 'srbesq61': 375,\n",
       " 'unRealityEngineer': 376,\n",
       " 'voidxleech': 377,\n",
       " 'CaptWildBillKelsoP40': 378,\n",
       " 'GhettoChemist': 379,\n",
       " 'GreenRaspberry9': 380,\n",
       " 'KVWebs': 381,\n",
       " 'LRonPaul2012': 382,\n",
       " 'SLCW718': 383,\n",
       " 'bry2k200': 384,\n",
       " 'rentoq': 385,\n",
       " 'Chaotic-Catastrophe': 386,\n",
       " 'ChewieWookie': 387,\n",
       " 'MBAMBA3': 388,\n",
       " 'Pink3y3': 389,\n",
       " 'SavvyEquestrian': 390,\n",
       " 'allonzeeLV': 391,\n",
       " 'borkborkbork99': 392,\n",
       " 'fortknox7012': 393,\n",
       " 'ghostsofpigs': 394,\n",
       " 'hearsecloth': 395,\n",
       " 'pegothejerk': 396,\n",
       " 'GamePlayer88': 397,\n",
       " 'ImOnLeapFrog': 398,\n",
       " 'Nikovelli': 399,\n",
       " 'Ocerion': 400,\n",
       " 'OrderofMagnitude_': 401,\n",
       " 'ScienceBreathingDrgn': 402,\n",
       " 'THExLASTxDON': 403,\n",
       " 'diatomicsoda': 404,\n",
       " 'hubbardcustarded': 405,\n",
       " 'misterlakatos': 406,\n",
       " 'mojanis': 407,\n",
       " 'schoocher': 408,\n",
       " 'sickofthisshit': 409,\n",
       " 'Archivist_of_Lewds': 410,\n",
       " 'Beoftw': 411,\n",
       " 'CMDR_KingErvin': 412,\n",
       " 'DontTouchTheCancer': 413,\n",
       " 'GiantSquidd': 414,\n",
       " 'HillaryWon1': 415,\n",
       " 'Magic8BallResponse': 416,\n",
       " 'Smirking_Puppet': 417,\n",
       " 'Triassic_Bark': 418,\n",
       " 'Warm-Risk-3352': 419,\n",
       " 'Whisper2000': 420,\n",
       " 'felinefelons': 421,\n",
       " 'fuck_im_dead': 422,\n",
       " 'fuck_the_fuckin_mods': 423,\n",
       " 'madcaesar': 424,\n",
       " 'roosterinflight': 425,\n",
       " 'Devil-sAdvocate': 426,\n",
       " 'ElPlywood': 427,\n",
       " 'Nest-egg': 428,\n",
       " 'PopeKevin45': 429,\n",
       " 'RoyceAli': 430,\n",
       " 'SenorBurns': 431,\n",
       " 'Summebride': 432,\n",
       " 'Terminal-Psychosis': 433,\n",
       " 'TheOwlAndOak': 434,\n",
       " 'Tokoyami8711': 435,\n",
       " 'borfmantality': 436,\n",
       " 'cybersifter': 437,\n",
       " 'guiltyas-sin': 438,\n",
       " 'BillowBrie': 439,\n",
       " 'Diarygirl': 440,\n",
       " 'Don_Cheech': 441,\n",
       " 'Malachorn': 442,\n",
       " 'Omahunek': 443,\n",
       " 'PhilosoGuido': 444,\n",
       " 'PoliticalPleionosis': 445,\n",
       " 'SgtFraggleRock': 446,\n",
       " 'Slapbox': 447,\n",
       " 'ToeJamFootballs': 448,\n",
       " 'UrRedCapIsOnTooTight': 449,\n",
       " '_oddballwoofwoof_': 450,\n",
       " 'cornbreadbiscuit': 451,\n",
       " 'drvondoctor': 452,\n",
       " 'gizamo': 453,\n",
       " 'monkeywithgun': 454,\n",
       " 'sagan_drinks_cosmos': 455,\n",
       " 'tunaburn': 456,\n",
       " 'Conker1985': 457,\n",
       " 'FrigginTommyNoble': 458,\n",
       " 'HBPilot': 459,\n",
       " 'McNuttyNutz': 460,\n",
       " 'More-Like-a-Nonja': 461,\n",
       " 'Warm_Tax': 462,\n",
       " 'jello3d': 463,\n",
       " 'multiple4': 464,\n",
       " 'redditbsbsbs': 465,\n",
       " 'ronm4c': 466,\n",
       " 'shapeshifter83': 467,\n",
       " 'GlobalTravelR': 468,\n",
       " 'KGBcommunist': 469,\n",
       " 'SG1youhaveago': 470,\n",
       " 'garethbullsey': 471,\n",
       " 'iamTHESunDevil': 472,\n",
       " 'miskoschiff': 473,\n",
       " 'rock61': 474,\n",
       " 'thenetwrkguy': 475,\n",
       " 'ureecka': 476,\n",
       " 'CamSeabass': 477,\n",
       " 'DadJokesFTW': 478,\n",
       " 'Duck_It': 479,\n",
       " 'JJDude': 480,\n",
       " 'OutInLF25': 481,\n",
       " 'ReptilicansWH': 482,\n",
       " 'StarWarsMonopoly': 483,\n",
       " 'Th3Seconds1st': 484,\n",
       " 'TheLastBestHope': 485,\n",
       " 'Wattsferatu': 486,\n",
       " 'absolutegov': 487,\n",
       " 'appleparkfive': 488,\n",
       " 'colbycheese2316': 489,\n",
       " 'icomeforthereaper': 490,\n",
       " 'k2on0s': 491,\n",
       " 'lawnessd': 492,\n",
       " 'liberules': 493,\n",
       " 'notoyrobots': 494,\n",
       " 'outerworldLV': 495,\n",
       " 'stevefrench707': 496,\n",
       " 'vankorgan': 497,\n",
       " 'Better_illini_2008': 498,\n",
       " 'CertifiedWarlock': 499,\n",
       " 'Drex_Can': 500,\n",
       " 'GreyLordQueekual': 501,\n",
       " 'Hardest_Fart': 502,\n",
       " 'Hillarys_Brown_Eye': 503,\n",
       " 'MadmansScalpel': 504,\n",
       " 'Phallconn': 505,\n",
       " 'Rookwood': 506,\n",
       " 'Socketz11': 507,\n",
       " 'Sr_Mothballs': 508,\n",
       " 'Taman_Should': 509,\n",
       " 'Who_Wouldnt_': 510,\n",
       " 'cos_tan_za': 511,\n",
       " 'demingo398': 512,\n",
       " 'huxtiblejones': 513,\n",
       " 'link_ganon': 514,\n",
       " 'negativenewton': 515,\n",
       " 'nikolajdancing': 516,\n",
       " 'tr851': 517,\n",
       " 'Gonkar': 518,\n",
       " 'LibertySomeday': 519,\n",
       " 'MaximumEffort433': 520,\n",
       " 'NigerianPrince76': 521,\n",
       " 'Progman12093': 522,\n",
       " 'Sim888': 523,\n",
       " 'Verrence': 524,\n",
       " 'WabbitSweason': 525,\n",
       " 'all4reddit': 526,\n",
       " 'metaltimmy': 527,\n",
       " 'seatbeltfilms': 528,\n",
       " 'suprahelix': 529,\n",
       " 'x_cLOUDDEAD_x': 530,\n",
       " 'xxCOVIDfan420xx': 531,\n",
       " 'Bardali': 532,\n",
       " 'Bearstein_bear': 533,\n",
       " 'BuddhaFacepalmed': 534,\n",
       " 'Bulkmanhunk': 535,\n",
       " 'Cantholdmymud': 536,\n",
       " 'Darkstarviserys': 537,\n",
       " 'Durdyboy': 538,\n",
       " 'Sovereign19117': 539,\n",
       " 'Spin_Quarkette': 540,\n",
       " 'bbjenn': 541,\n",
       " 'elconquistador1985': 542,\n",
       " 'fleentrain89': 543,\n",
       " 'jimmygee2': 544,\n",
       " 'lovestheasianladies': 545,\n",
       " 'robertcole23': 546,\n",
       " 'versusgorilla': 547,\n",
       " 'yaitstone': 548,\n",
       " 'AFlockOfTySegalls': 549,\n",
       " 'Bluika': 550,\n",
       " 'Canadapoli': 551,\n",
       " 'GuardYourPrivates': 552,\n",
       " 'LolAtAllOfThis': 553,\n",
       " 'MC_Fap_Commander': 554,\n",
       " 'RacistsAreFacts': 555,\n",
       " 'T0mThomas': 556,\n",
       " 'Wisex': 557,\n",
       " 'boredoutofmymind20': 558,\n",
       " 'cosmos_jm': 559,\n",
       " 'jest4fun': 560,\n",
       " 'politicalGuitarist': 561,\n",
       " 'ryuujinusa': 562,\n",
       " 'spaceburner99': 563,\n",
       " 'viverator': 564,\n",
       " 'B10kh3d': 565,\n",
       " 'EnemyAsmodeus': 566,\n",
       " 'EnticingInsouciance': 567,\n",
       " 'Facerealityalready': 568,\n",
       " 'FetcherLeVache': 569,\n",
       " 'LegendaryWarriorPoet': 570,\n",
       " 'Longhairdontcare1291': 571,\n",
       " 'MAMark1': 572,\n",
       " 'MisfireHurricane': 573,\n",
       " 'MynaSpirit789': 574,\n",
       " 'OptimisticRealist__': 575,\n",
       " 'Rekky1992': 576,\n",
       " 'TATP1982': 577,\n",
       " '_Captain_Canuck_': 578,\n",
       " '_waxy_': 579,\n",
       " 'cciv': 580,\n",
       " 'frostfall010': 581,\n",
       " 'garry_shandling_': 582,\n",
       " 'gorbo12': 583,\n",
       " 'hurrdurraj': 584,\n",
       " 'k393': 585,\n",
       " 'lakxmaj': 586,\n",
       " 'mcg42ray': 587,\n",
       " 'stanisvict': 588,\n",
       " 'typing1-handed': 589,\n",
       " 'voice-of-hermes': 590,\n",
       " '7daykatie': 591,\n",
       " 'AnarchistBorganism': 592,\n",
       " 'BenevolentBlackbird': 593,\n",
       " 'Carp8DM': 594,\n",
       " 'Coyote65': 595,\n",
       " 'GadreelsSword': 596,\n",
       " 'HallucinatesSJWs': 597,\n",
       " 'TheSweetestKill': 598,\n",
       " 'Trump_is_My_Father': 599,\n",
       " 'Wacocaine': 600,\n",
       " 'celerydonut': 601,\n",
       " 'entebbe07': 602,\n",
       " 'hasa_deega_eebowai': 603,\n",
       " 'jva5th': 604,\n",
       " 'mattyoclock': 605,\n",
       " 'negroflaco': 606,\n",
       " 'slim_scsi': 607,\n",
       " 'swag-and-tag4545': 608,\n",
       " 'zach0011': 609,\n",
       " 'zvwmbxkjqlrcgfyp': 610,\n",
       " 'Etna_No_Pyroclast': 611,\n",
       " 'GiggityDPT': 612,\n",
       " 'Kingemfj3201': 613,\n",
       " 'Mortambulist': 614,\n",
       " 'Ohfuckofftrumpnuts': 615,\n",
       " 'Prime157': 616,\n",
       " 'RealUncleBuck': 617,\n",
       " 'Satanfan': 618,\n",
       " 'SearchForTheSecret': 619,\n",
       " 'SupremeDearLeader': 620,\n",
       " 'Technical-Citron-750': 621,\n",
       " 'Tundrok221': 622,\n",
       " 'Vinny_Cerrato': 623,\n",
       " '_wok_lobster_': 624,\n",
       " 'allenahansen': 625,\n",
       " 'golokoyo': 626,\n",
       " 'mike2lane': 627,\n",
       " 'nx85': 628,\n",
       " 'synyaks': 629,\n",
       " 'uping1965': 630,\n",
       " 'BanjoSmamjo': 631,\n",
       " 'Belowaverage_Joe': 632,\n",
       " 'BraveSignal': 633,\n",
       " 'Cheese_Pancakes': 634,\n",
       " 'ErshinHavok': 635,\n",
       " 'Inkberrow': 636,\n",
       " 'JobieWanKenobi': 637,\n",
       " 'Jon-Shannow': 638,\n",
       " 'LanceBarney': 639,\n",
       " 'LoveArguingPolitics': 640,\n",
       " 'MrQuarterFootPenis': 641,\n",
       " 'Phonemonkey2500': 642,\n",
       " 'RayMosch': 643,\n",
       " 'RemarkableRegret7': 644,\n",
       " 'Scribe08': 645,\n",
       " 'SeegsonSynthetics': 646,\n",
       " 'Simple_Barry': 647,\n",
       " 'Tasgall': 648,\n",
       " 'Validus812': 649,\n",
       " 'X_Bob_Sacamano_X': 650,\n",
       " 'YachtShopping': 651,\n",
       " 'dragonphysics': 652,\n",
       " 'ghintziest': 653,\n",
       " 'lazyboyzob12': 654,\n",
       " 'oldbastardbob': 655,\n",
       " '3doglateafternoon': 656,\n",
       " 'Almostsuicide1234': 657,\n",
       " 'AndySmalls': 658,\n",
       " 'BashfulDaschund': 659,\n",
       " 'Bullmoosefuture': 660,\n",
       " 'CawoodsRadio': 661,\n",
       " 'Crappyfanchandelier': 662,\n",
       " 'Disgod': 663,\n",
       " 'DoubleJumps': 664,\n",
       " 'Goyteamsix': 665,\n",
       " 'Johnny_Mister': 666,\n",
       " 'N3xrad': 667,\n",
       " 'RIP_GOP_4EVER': 668,\n",
       " 'Randomabcd1234': 669,\n",
       " 'SpaceJesusIsHere': 670,\n",
       " 'The_Mighty_Rex': 671,\n",
       " 'TripppingRoses': 672,\n",
       " 'Zeroch123': 673,\n",
       " 'backpackwayne': 674,\n",
       " 'charlieblue666': 675,\n",
       " 'ducksfan2020': 676,\n",
       " 'hhubble': 677,\n",
       " 'pudintame33': 678,\n",
       " 'supercali45': 679,\n",
       " 'v9Pv': 680,\n",
       " '16Paws': 681,\n",
       " 'BadStupidCrow': 682,\n",
       " 'BrownSugarBare': 683,\n",
       " 'CCPCanuck': 684,\n",
       " 'Dirkamuhammedjihad': 685,\n",
       " 'EuphioMachine': 686,\n",
       " 'JohnnyGFX': 687,\n",
       " 'Moonthrower': 688,\n",
       " 'NarwhalStreet': 689,\n",
       " 'Quicklyquigly': 690,\n",
       " 'Raspberries-Are-Evil': 691,\n",
       " 'Skiro89p': 692,\n",
       " 'Snoo47858': 693,\n",
       " 'TastefulThiccness': 694,\n",
       " 'Vladius28': 695,\n",
       " 'WittsandGrit': 696,\n",
       " 'You-said-it-man': 697,\n",
       " 'YourMomAteMyDad': 698,\n",
       " 'boxer78': 699,\n",
       " 'evollie': 700,\n",
       " 'foolmanchoo': 701,\n",
       " 'kvossera': 702,\n",
       " 'lHelpWithTheLogic': 703,\n",
       " 'mafulazula': 704,\n",
       " 'mutemutiny': 705,\n",
       " 'neverbetray': 706,\n",
       " 'phantomreader42': 707,\n",
       " 'suddenlypandabear': 708,\n",
       " '-This-Machine-': 709,\n",
       " 'AntiSinoFascism': 710,\n",
       " 'BadMuthaFunka': 711,\n",
       " 'CleverEmber': 712,\n",
       " 'Custergrant': 713,\n",
       " 'DavidEdgrrr': 714,\n",
       " 'Domini384': 715,\n",
       " 'FreezieKO': 716,\n",
       " 'Holding_Cauliflora': 717,\n",
       " 'ILikeMyGrassBlue': 718,\n",
       " 'LibtardsWhine': 719,\n",
       " 'MopBucketMcJangles': 720,\n",
       " 'Nords': 721,\n",
       " 'Rawrsomesausage': 722,\n",
       " 'TheJokerandTheKief': 723,\n",
       " 'UnclaEnzo': 724,\n",
       " 'Virgil_Tennyson': 725,\n",
       " 'Vthyarilops': 726,\n",
       " '_im_helping': 727,\n",
       " 'beatyatoit': 728,\n",
       " 'ironmanwatcher2': 729,\n",
       " 'jess_albas_twin': 730,\n",
       " 'joshTheGoods': 731,\n",
       " 'kristamhu2121': 732,\n",
       " 'moonbeanie': 733,\n",
       " 'ngoni': 734,\n",
       " 'perspective2020': 735,\n",
       " 'rectanguloid666': 736,\n",
       " 'sandwooder': 737,\n",
       " 'tampamike69': 738,\n",
       " 'target_locked': 739,\n",
       " 'Bla_bla_boobs': 740,\n",
       " 'BlackMamba1008': 741,\n",
       " 'Boomslangalang': 742,\n",
       " 'DingDongDooDoo': 743,\n",
       " 'Gary007Seven': 744,\n",
       " 'HotpieTargaryen': 745,\n",
       " 'ShowerCurtainRings': 746,\n",
       " 'XDarkstarX1138': 747,\n",
       " 'dakandy': 748,\n",
       " 'druid06': 749,\n",
       " 'fujiman': 750,\n",
       " 'hall_residence': 751,\n",
       " 'mafco': 752,\n",
       " 'middlefingerpuppet': 753,\n",
       " 'modsarefascists42': 754,\n",
       " 'pointsouturhypocrisy': 755,\n",
       " 'prallundfeucht': 756,\n",
       " 'profhelios': 757,\n",
       " 'tyrotio': 758,\n",
       " 'zigazigazah': 759,\n",
       " 'A_RealHuman_Bean': 760,\n",
       " 'AnimaniacSpirits': 761,\n",
       " 'AssCalloway': 762,\n",
       " 'Deconratthink': 763,\n",
       " 'DisownedByMother': 764,\n",
       " 'DraxRieper': 765,\n",
       " 'ExpensiveReporter': 766,\n",
       " 'Grunchlk': 767,\n",
       " 'HereForAnArgument': 768,\n",
       " 'Kironvb': 769,\n",
       " 'Northwinds56649': 770,\n",
       " 'PostingIcarus': 771,\n",
       " 'RamboGoesMeow': 772,\n",
       " 'RustNeverSleeps77': 773,\n",
       " 'Studfarm86': 774,\n",
       " 'Thedirtyjersey': 775,\n",
       " 'Vroom_Broom': 776,\n",
       " 'bang_the_drums': 777,\n",
       " 'bittertruth61': 778,\n",
       " 'irlyseevridge': 779,\n",
       " 'knight029': 780,\n",
       " 'lemmywinks11': 781,\n",
       " 'lifeisforkiamsoup': 782,\n",
       " 'nonamenolastname': 783,\n",
       " 'noxxadamous': 784,\n",
       " 'redwoods_orthodox': 785,\n",
       " 's_0_s_z': 786,\n",
       " 'thepartypantser': 787,\n",
       " 'yoyoJ': 788,\n",
       " 'ASlingshot123': 789,\n",
       " 'AlphaTangoFoxtrt': 790,\n",
       " 'BigNickDipples': 791,\n",
       " 'ChickenNuggetDeity': 792,\n",
       " 'ChuckFeathers': 793,\n",
       " 'Curb5Enthusiasm': 794,\n",
       " 'DeviantGraviton': 795,\n",
       " 'Eli_Truax': 796,\n",
       " 'FumblingBumbler': 797,\n",
       " 'HandMeMyThinkingPipe': 798,\n",
       " 'HelloJoeyJoeJoe': 799,\n",
       " 'JTambo86': 800,\n",
       " 'Leg__Day': 801,\n",
       " 'Merreck1983': 802,\n",
       " 'Ransom__Stoddard': 803,\n",
       " 'RyFisch': 804,\n",
       " 'Sm0kescreeen': 805,\n",
       " 'SoggyFlakes4US': 806,\n",
       " 'TinyNuggins92': 807,\n",
       " 'Tommy-1111': 808,\n",
       " 'WallingFoodie': 809,\n",
       " 'Zenning2': 810,\n",
       " 'ZigsL0theon': 811,\n",
       " 'bartoksic': 812,\n",
       " 'crunchy_staccato-017': 813,\n",
       " 'daryltry': 814,\n",
       " 'gdshaffe': 815,\n",
       " 'goblue21': 816,\n",
       " 'legitmadman82': 817,\n",
       " 'mildlydisturbedtway': 818,\n",
       " 'naslam74': 819,\n",
       " 'preciousillusion': 820,\n",
       " 'pugnaciousthefirth': 821,\n",
       " 'roboninja': 822,\n",
       " 'thiscouldbemassive': 823,\n",
       " '-StupidFace-': 824,\n",
       " 'AngryAsian26': 825,\n",
       " 'Ashontez': 826,\n",
       " 'Aviternus': 827,\n",
       " 'BicycleOfLife': 828,\n",
       " 'Chinacat2020': 829,\n",
       " 'DunkingOnInfants': 830,\n",
       " 'EUJourney': 831,\n",
       " 'Exviper': 832,\n",
       " 'I_make_things': 833,\n",
       " 'JustSomeGhost': 834,\n",
       " 'Kattomatte': 835,\n",
       " 'Rooster1981': 836,\n",
       " 'Rowdy_Tardigrade': 837,\n",
       " 'Stupid_Bearded_Idiot': 838,\n",
       " 'TheNightBench': 839,\n",
       " 'TuckerMcInnes': 840,\n",
       " 'WAHgop': 841,\n",
       " 'Whompa': 842,\n",
       " 'alternatepseudonym': 843,\n",
       " 'ausernamez001': 844,\n",
       " 'brdwatchr': 845,\n",
       " 'dbandit1': 846,\n",
       " 'devindertheaverage': 847,\n",
       " 'gtnclz15': 848,\n",
       " 'kickstarterscience': 849,\n",
       " 'real_sadboi': 850,\n",
       " 'tbiedenkapp': 851,\n",
       " 'tit_fucker_': 852,\n",
       " 'viva_la_vinyl': 853,\n",
       " 'wrongmoviequotes': 854,\n",
       " '5IHearYou': 855,\n",
       " 'Balls_of_Adamanthium': 856,\n",
       " 'BrofessorFarnsworth': 857,\n",
       " 'Butwhatisitho': 858,\n",
       " 'Cthaat': 859,\n",
       " 'Hiccup': 860,\n",
       " 'IowaForWarren': 861,\n",
       " 'OfHumanBondage': 862,\n",
       " 'Ozwaldo': 863,\n",
       " 'PunisherASM129': 864,\n",
       " 'Starky_McStarkface': 865,\n",
       " 'TRUMP_IN_PRISON': 866,\n",
       " 'TheHasturRule': 867,\n",
       " 'allnamesaretaken45': 868,\n",
       " 'bizounce': 869,\n",
       " 'bubblebosses': 870,\n",
       " 'captmorgan0210': 871,\n",
       " 'hasordealsw1thclams': 872,\n",
       " 'hopstar': 873,\n",
       " 'johnnydrama00': 874,\n",
       " 'kmschaef1': 875,\n",
       " 'noteveryagain': 876,\n",
       " 'parkerjames29': 877,\n",
       " 'patriot2024': 878,\n",
       " 'phunnypharm': 879,\n",
       " 're1078': 880,\n",
       " 'reddit211221122112': 881,\n",
       " 'rounder55': 882,\n",
       " 'sclsmdsntwrk': 883,\n",
       " 'workshardanddies': 884,\n",
       " '3lRey': 885,\n",
       " 'A_Kindly_Man': 886,\n",
       " 'Arzie5676': 887,\n",
       " 'BillyNutBuster': 888,\n",
       " 'ChrisCrossbow': 889,\n",
       " 'Chuck_Foolery': 890,\n",
       " 'Daddy_Schmeegs': 891,\n",
       " 'DapperDanManCan': 892,\n",
       " 'DweEbLez0': 893,\n",
       " 'Firepower1994': 894,\n",
       " 'G_o_o_d_n_a_s_t_y': 895,\n",
       " 'Globalist_Nationlist': 896,\n",
       " 'HopeIsAHellOfADrug': 897,\n",
       " 'Imnewhere948': 898,\n",
       " 'KillaB33z': 899,\n",
       " 'Odani_cullah': 900,\n",
       " 'Pats_fan_seeking_fi': 901,\n",
       " 'PopcornInMyTeeth': 902,\n",
       " 'Snouters': 903,\n",
       " 'Spiralyst': 904,\n",
       " 'WorkplaceWatcher': 905,\n",
       " 'accountabilitycounts': 906,\n",
       " 'barlow_straker': 907,\n",
       " 'bobndougmckenzie': 908,\n",
       " 'boomerghost': 909,\n",
       " 'dnm314': 910,\n",
       " 'frogandbanjo': 911,\n",
       " 'geneeva71': 912,\n",
       " 'grixorbatz': 913,\n",
       " 'jessflyc': 914,\n",
       " 'jme365': 915,\n",
       " 'justathot_': 916,\n",
       " 'kneeco28': 917,\n",
       " 'sarcastroll': 918,\n",
       " 'satiricnewt': 919,\n",
       " 'skibityboo': 920,\n",
       " 'xanthine_junkie': 921,\n",
       " '13speed': 922,\n",
       " '42_youre_welcome': 923,\n",
       " 'Aptnatural': 924,\n",
       " 'BaldEagleFreedomMAGA': 925,\n",
       " 'Brad_tilf': 926,\n",
       " 'Brokeasscars': 927,\n",
       " 'Broken_Face7': 928,\n",
       " 'DaisyHotCakes': 929,\n",
       " 'Derp_State_Agent': 930,\n",
       " 'Dth817': 931,\n",
       " 'GallusAA': 932,\n",
       " 'GhostFish': 933,\n",
       " 'KeepFaithOutPolitics': 934,\n",
       " 'Khrimz000': 935,\n",
       " 'MauPow': 936,\n",
       " 'Methadras': 937,\n",
       " 'NatWilo': 938,\n",
       " 'Rated_PG-Squirteen': 939,\n",
       " 'RollTheB0nes': 940,\n",
       " 'SNStains': 941,\n",
       " 'SockPuppet-57': 942,\n",
       " 'ZarosGuardian': 943,\n",
       " 'bantargetedads': 944,\n",
       " 'blueyedoe': 945,\n",
       " 'dick_whitham': 946,\n",
       " 'jdickman78': 947,\n",
       " 'nekomancey': 948,\n",
       " 'o_O-JBL': 949,\n",
       " 'pj7140': 950,\n",
       " 'sapper_spiegel': 951,\n",
       " 'somegridplayer': 952,\n",
       " 'xarnzul': 953,\n",
       " 'yardrunt': 954,\n",
       " 'ALessonInLust': 955,\n",
       " 'BroadAsparagus': 956,\n",
       " 'DXGypsy': 957,\n",
       " 'Damean1': 958,\n",
       " 'Dehydrated-Horse': 959,\n",
       " 'FelixFuckfurter': 960,\n",
       " 'Manfred-V-Carstein': 961,\n",
       " 'Melicor': 962,\n",
       " 'Polar--Vortex': 963,\n",
       " 'Stennick': 964,\n",
       " 'Stiggy_771': 965,\n",
       " 'Swiggy': 966,\n",
       " 'The-Fanta-Menace': 967,\n",
       " 'TraitorCom3y': 968,\n",
       " 'UWCG': 969,\n",
       " 'appmanga': 970,\n",
       " 'asyoulikeit1': 971,\n",
       " 'bobbyrickets': 972,\n",
       " 'gingerhasyoursoul': 973,\n",
       " 'highermonkey': 974,\n",
       " 'johnnymaryseed': 975,\n",
       " 'metronomemike': 976,\n",
       " 'mirrth': 977,\n",
       " 'murphadam420': 978,\n",
       " 'plynthy': 979,\n",
       " 'scope_creep': 980,\n",
       " 'stingublue': 981,\n",
       " 'velveteenelahrairah': 982,\n",
       " 'ziggyscoob': 983,\n",
       " '1A1-1': 984,\n",
       " '3q5wy8j9ew': 985,\n",
       " 'Amazing-Pepper': 986,\n",
       " 'ArTiyme': 987,\n",
       " 'Archangel1313': 988,\n",
       " 'CannedMango': 989,\n",
       " 'ConsciousExtreme': 990,\n",
       " 'CreativeFreefall': 991,\n",
       " 'Dhaerrow': 992,\n",
       " 'FishSpeaker_Sheeana': 993,\n",
       " 'Goodrug42069': 994,\n",
       " 'HollyDiver': 995,\n",
       " 'ILoveItEspecially': 996,\n",
       " 'Maxwell_RN': 997,\n",
       " 'No-Me-Diga': 998,\n",
       " 'OlderTheBetter': 999,\n",
       " 'PresidentWordSalad': 1000,\n",
       " ...}"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# sorted(uncivil_comment_count, key=uncivil_comment_count.get, reverse=True)\n",
    "{key: rank for rank, key in enumerate(sorted(author_uncivil_comment_count, key=author_uncivil_comment_count.get, reverse=True), 1)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1.0, 6.0, 30.0, 1037]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_quantile_thresholds(array, proportions):\n",
    "    return [np.quantile(array, i) for i in proportions]\n",
    "\n",
    "get_quantile_thresholds(list(author_uncivil_comment_count.values()), [0, 0.5, 0.9, 0.99, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aggregate\n",
      "673770 35567120\n",
      "(1.0, 6.0]        71264\n",
      "(6.0, 30.0]       17374\n",
      "(30.0, 97.0]       1835\n",
      "(97.0, 1037.0]      212\n",
      "dtype: int64\n",
      "(1.0, 6.0]        0.334648\n",
      "(6.0, 30.0]       0.081586\n",
      "(30.0, 97.0]      0.008617\n",
      "(97.0, 1037.0]    0.000996\n",
      "dtype: float64\n",
      "(-0.001, 10.0]     409157\n",
      "(10.0, 20.0]        94050\n",
      "(20.0, 50.0]        93125\n",
      "(50.0, 200.0]       63176\n",
      "(200.0, 1037.0]     14262\n",
      "Name: user_aggregate_level, dtype: int64\n",
      "(-0.001, 10.0]     0.607265\n",
      "(10.0, 20.0]       0.139588\n",
      "(20.0, 50.0]       0.138215\n",
      "(50.0, 200.0]      0.093765\n",
      "(200.0, 1037.0]    0.021167\n",
      "Name: user_aggregate_level, dtype: float64\n",
      "1.0\n",
      "[[0.6072650904611366, 0.139587693129703, 0.13821482108137792, 0.09376493462160677, 0.021167460706175697]]\n",
      "[array([0.33464818, 0.08158646, 0.00861697, 0.00099553])]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/1d/mj1m58c50b790y544kd1bflm0000gn/T/ipykernel_6046/767309201.py:29: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  uncivil_comments['user_{}_level'.format(i)] = uncivil_comments['author'].map(uncivil_comment_count)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1600x1440 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "import numpy as np  \n",
    "from matplotlib.pyplot import figure\n",
    "\n",
    "# a = np.array(list(author_comment_count.values()))\n",
    "# print(a) \n",
    "# plt.hist(a, bins = [0, 2, 5, 50, 100]) \n",
    "# plt.title(\"histogram\") \n",
    "# plt.show()\n",
    "# print(np.mean(a), max(a), min(a))\n",
    "figure(figsize=(20, 18), dpi=80)\n",
    "results = {}\n",
    "table = []\n",
    "level_distribution = []\n",
    "\n",
    "# 'namecalling', 'demeaning', 'stereotype', 'aggregate'\n",
    "for i in ['aggregate']:\n",
    "    print(i)\n",
    "    uncivil_comments = all_comments[all_comments[i] > 0.5]\n",
    "    print(len(uncivil_comments), len(all_comments))\n",
    "    uncivil_comment_count = dict(uncivil_comments.groupby('author').count()['id'])\n",
    "    user_count = list(uncivil_comment_count.values())\n",
    "    bins = get_quantile_thresholds(user_count, [0, 0.9, 0.99, 0.999, 1])\n",
    "    out = pd.cut(pd.Series(user_count), bins=bins, include_lowest=False)\n",
    "    print(out.value_counts(sort=False) )\n",
    "    values = (out.value_counts(sort=False) / len(uncivil_comment_count.values()))\n",
    "    print(values)\n",
    "    level_distribution.append(values.values)\n",
    "    uncivil_comments['user_{}_level'.format(i)] = uncivil_comments['author'].map(uncivil_comment_count)\n",
    "    out = pd.cut(uncivil_comments['user_{}_level'.format(i)], bins=[0, 10, 20, 50, 200, max(uncivil_comments['user_{}_level'.format(i)])], include_lowest=True)\n",
    "    print(out.value_counts(sort=False) )\n",
    "    values = (out.value_counts(sort=False) / len(uncivil_comments))\n",
    "    results[i] = values\n",
    "    print(values)\n",
    "    print(sum(values))\n",
    "    table.append(list(values))\n",
    "print(table)\n",
    "print(level_distribution)\n",
    "# import seaborn as sns\n",
    "# sns.set_theme()\n",
    "# ax = values.plot.bar(rot=0, color=\"b\", figsize=(6,4))\n",
    "# # ax.set_xticklabels([c[1:-1].replace(\",\",\" to\") for c in out.cat.categories])\n",
    "# ax.set_xticklabels(['1', '2-10', '10-50', '50-100', '100+'])\n",
    "# ax.set_xlabel(\"Incivility level of a user\")\n",
    "# ax.set_ylabel(\"Percentage of contributed uncivil comments\")\n",
    "# # ax.set_xticklabels(['a', 'b', 'c', 'd', 'e'])\n",
    "# plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.236759</td>\n",
       "      <td>0.108108</td>\n",
       "      <td>0.176113</td>\n",
       "      <td>0.144061</td>\n",
       "      <td>0.300674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.225947</td>\n",
       "      <td>0.105885</td>\n",
       "      <td>0.175023</td>\n",
       "      <td>0.144835</td>\n",
       "      <td>0.306740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.235534</td>\n",
       "      <td>0.108888</td>\n",
       "      <td>0.173305</td>\n",
       "      <td>0.140456</td>\n",
       "      <td>0.314589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.243010</td>\n",
       "      <td>0.107509</td>\n",
       "      <td>0.171396</td>\n",
       "      <td>0.137222</td>\n",
       "      <td>0.275987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.236760</td>\n",
       "      <td>0.107625</td>\n",
       "      <td>0.178647</td>\n",
       "      <td>0.149913</td>\n",
       "      <td>0.327054</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c         d         e\n",
       "0  0.236759  0.108108  0.176113  0.144061  0.300674\n",
       "1  0.225947  0.105885  0.175023  0.144835  0.306740\n",
       "2  0.235534  0.108888  0.173305  0.140456  0.314589\n",
       "3  0.243010  0.107509  0.171396  0.137222  0.275987\n",
       "4  0.236760  0.107625  0.178647  0.149913  0.327054"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.DataFrame(table, columns=['a', 'b', 'c', 'd', 'e'])\n",
    "axis = df[[\"a\", \"b\", \"c\", \"d\", \"e\"]].plot(kind=\"bar\", stacked=True)\n",
    "fig = axis.get_figure()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 5]            1989526\n",
      "(5, 20]           3170285\n",
      "(20, 100]         7373827\n",
      "(100, 119105]    23033482\n",
      "Name: user_activity_level, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt \n",
    "import numpy as np  \n",
    "   \n",
    "# a = np.array(list(author_comment_count.values()))\n",
    "# print(a) \n",
    "# plt.hist(a, bins = [0, 2, 5, 50, 100]) \n",
    "# plt.title(\"histogram\") \n",
    "# plt.show()\n",
    "# print(np.mean(a), max(a), min(a))\n",
    "figure(figsize=(20, 18), dpi=80)\n",
    "out = pd.cut(all_comments.user_activity_level, bins=[0, 5, 20, 100, max(all_comments.user_activity_level)], include_lowest=False)\n",
    "print(out.value_counts(sort=False) )\n",
    "values = (out.value_counts(sort=False) / len(all_comments))\n",
    "# print(values)\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "ax = values.plot.bar(rot=0, color=\"b\", figsize=(6,4))\n",
    "# ax.set_xticklabels([c[1:-1].replace(\",\",\" to\") for c in out.cat.categories])\n",
    "ax.set_xticklabels(['1', '2-10', '10-100', '100+'])\n",
    "ax.set_xlabel(\"The number of comments users sent\")\n",
    "ax.set_ylabel(\"Percentage of contributed uncivil comments\")\n",
    "# ax.set_xticklabels(['a', 'b', 'c', 'd', 'e'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'figure' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-186fd31af136>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m18\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdpi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m80\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      2\u001b[0m \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcut\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muncivil_comment_count\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m20\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muncivil_comment_count\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_lowest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\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[1;32m      4\u001b[0m \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalue_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0muncivil_comment_count\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\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[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'figure' is not defined"
     ]
    }
   ],
   "source": [
    "figure(figsize=(20, 18), dpi=80)\n",
    "\n",
    "print(values)\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "ax = values.plot.bar(rot=0, color=\"b\", figsize=(6,4))\n",
    "# ax.set_xticklabels([c[1:-1].replace(\",\",\" to\") for c in out.cat.categories])\n",
    "ax.set_xticklabels(['1', '2-10', '10-100', '100+'])\n",
    "ax.set_xlabel(\"Toxicity level of a user\")\n",
    "ax.set_ylabel(\"Number of users\")\n",
    "# ax.set_xticklabels(['a', 'b', 'c', 'd', 'e'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 1]         0.574153\n",
      "(1, 20]        0.407444\n",
      "(20, 100]      0.017459\n",
      "(100, 1037]    0.000944\n",
      "dtype: float64\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "   \n",
    "a = np.array(list(uncivil_comment_count.values()))\n",
    "figure(figsize=(20, 18), dpi=80)\n",
    "out = pd.cut(pd.Series(a), bins=[0, 1, 20, 100, max(a)], include_lowest=False)\n",
    "values = (out.value_counts(sort=False) / len(a))\n",
    "print(values)\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "ax = values.plot.bar(rot=0, color=\"b\", figsize=(6,4))\n",
    "# ax.set_xticklabels([c[1:-1].replace(\",\",\" to\") for c in out.cat.categories])\n",
    "ax.set_xticklabels(['1', '2-10', '10-100', '100+'])\n",
    "ax.set_xlabel(\"The number of comments users sent\")\n",
    "ax.set_ylabel(\"Percentage of all comments\")\n",
    "# ax.set_xticklabels(['a', 'b', 'c', 'd', 'e'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "212952"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(uncivil_comments.author))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pdf</th>\n",
       "      <th>x</th>\n",
       "      <th>cdf</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.044334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.044334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.035360</td>\n",
       "      <td>1.971352</td>\n",
       "      <td>0.079694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.030472</td>\n",
       "      <td>2.942705</td>\n",
       "      <td>0.110166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.027335</td>\n",
       "      <td>3.914057</td>\n",
       "      <td>0.137500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.025217</td>\n",
       "      <td>4.885409</td>\n",
       "      <td>0.162717</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19473</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18916.143239</td>\n",
       "      <td>0.999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19474</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18917.114591</td>\n",
       "      <td>0.999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19475</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18918.085943</td>\n",
       "      <td>0.999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19476</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>18919.057295</td>\n",
       "      <td>0.999995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19477</th>\n",
       "      <td>0.000005</td>\n",
       "      <td>18920.028648</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>19478 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            pdf             x       cdf\n",
       "0      0.044334      1.000000  0.044334\n",
       "1      0.035360      1.971352  0.079694\n",
       "2      0.030472      2.942705  0.110166\n",
       "3      0.027335      3.914057  0.137500\n",
       "4      0.025217      4.885409  0.162717\n",
       "...         ...           ...       ...\n",
       "19473  0.000000  18916.143239  0.999995\n",
       "19474  0.000000  18917.114591  0.999995\n",
       "19475  0.000000  18918.085943  0.999995\n",
       "19476  0.000000  18919.057295  0.999995\n",
       "19477  0.000005  18920.028648  1.000000\n",
       "\n",
       "[19478 rows x 3 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uncivil = {k: author_comment_count[k] for k in uncivil_comments.author}\n",
    "frequency = (list(np.histogram(list(uncivil.values()), bins=19478)[0]))\n",
    "values = (list(np.histogram(list(uncivil.values()), bins=19478)[1]))\n",
    "# print(max(uncivil.values()))\n",
    "sum(frequency)\n",
    "frequency /= sum(frequency)\n",
    "# pd.Se(np.cumsum(frequency))\n",
    "df = pd.DataFrame([ [frequency[i], values[i]]for i in range(len(values)-1)], columns=['pdf','x'])\n",
    "df['cdf'] = np.cumsum(frequency)\n",
    "df\n",
    "# df.plot(x = 'x', y = ['cdf'], grid = True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40460.88"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set(uncivil_comments.author))*0.19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "673770"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1*len(uncivil_comments)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6670799053307788"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(frequency)[70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07195518238851972"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(frequency)[99] - np.cumsum(frequency)[70]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10 0.2621341898643826\n",
      "50 0.591438446222623\n",
      "0 1.000000000000033\n",
      "[0.04433393440775386, 0.21780025545662873, 0.3293042563582404, 0.4085615537774101]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 480x320 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "cumsum_freq = list(df.cdf)\n",
    "values = [cumsum_freq[0]]\n",
    "prev = [cumsum_freq[0]]\n",
    "for i in [9, 49, -1]:\n",
    "    print(i+1, np.cumsum(frequency)[i])\n",
    "    values.append(cumsum_freq[i]-prev[-1])\n",
    "    prev.append(cumsum_freq[i])\n",
    "print(values)\n",
    "figure(figsize=(20, 18), dpi=80)\n",
    "values = pd.Series(values)\n",
    "import seaborn as sns\n",
    "sns.set_theme()\n",
    "ax = values.plot.bar(rot=0, color=\"b\", figsize=(6,4))\n",
    "# ax.set_xticklabels([c[1:-1].replace(\",\",\" to\") for c in out.cat.categories])\n",
    "ax.set_xticklabels(['1', '2-10', '10-100', '100+'])\n",
    "# ax.set_title(\"EFO\")\n",
    "ax.set_xlabel(\"The number of comments users sent\")\n",
    "ax.set_ylabel(\"Percentage of uncivil comments\")\n",
    "# plt.grid(b=True, which='major', color='#666666', linestyle='-')\n",
    "# plt.minorticks_on()\n",
    "# plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)\n",
    "# plt.style.use('seaborn-paper')\n",
    "# ax.set_facecolor(color=\"#EAEAF2\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 0.04433393440775386\n",
      "5 0.16271741988804989\n",
      "20 0.3945208309853864\n",
      "50 0.591438446222623\n",
      "10 0.2621341898643826\n",
      "0 1.000000000000033\n"
     ]
    }
   ],
   "source": [
    "# np.cumsum(frequency)[5] - np.cumsum(frequency)[0]\n",
    "for i in [0, 4, 19, 49, 9, -1]:\n",
    "    print(i+1, np.cumsum(frequency)[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6633527169032953\n"
     ]
    }
   ],
   "source": [
    "# print(list(df['cdf'])[70])\n",
    "print(list(df['cdf'])[20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'zzzzzzzzzz55'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max(author_comment_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42853681982198744"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.quantile(all_comments['aggregate'], 0.75)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>score</th>\n",
       "      <th>vulgarity</th>\n",
       "      <th>personal_attacks</th>\n",
       "      <th>incivility</th>\n",
       "      <th>third_party_attacks</th>\n",
       "      <th>aspersion</th>\n",
       "      <th>comment_len</th>\n",
       "      <th>compound_sentiment</th>\n",
       "      <th>stereotype</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>user_activity_level</th>\n",
       "      <th>user_toxicity_level</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subreddit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>AskThe_Donald</th>\n",
       "      <td>1.592758e+09</td>\n",
       "      <td>1.225668</td>\n",
       "      <td>0.163840</td>\n",
       "      <td>0.061556</td>\n",
       "      <td>0.288317</td>\n",
       "      <td>0.074425</td>\n",
       "      <td>0.065467</td>\n",
       "      <td>39.752846</td>\n",
       "      <td>-0.030237</td>\n",
       "      <td>0.323410</td>\n",
       "      <td>0.131894</td>\n",
       "      <td>376.154375</td>\n",
       "      <td>13.245999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ask_Politics</th>\n",
       "      <td>1.594207e+09</td>\n",
       "      <td>1.566801</td>\n",
       "      <td>0.115004</td>\n",
       "      <td>0.049309</td>\n",
       "      <td>0.151530</td>\n",
       "      <td>0.031580</td>\n",
       "      <td>0.067308</td>\n",
       "      <td>72.359687</td>\n",
       "      <td>0.095941</td>\n",
       "      <td>0.323191</td>\n",
       "      <td>0.108050</td>\n",
       "      <td>465.767744</td>\n",
       "      <td>8.960899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BlueMidterm2018</th>\n",
       "      <td>1.604770e+09</td>\n",
       "      <td>1.781155</td>\n",
       "      <td>0.125265</td>\n",
       "      <td>0.009252</td>\n",
       "      <td>0.163989</td>\n",
       "      <td>0.030119</td>\n",
       "      <td>0.010334</td>\n",
       "      <td>20.574468</td>\n",
       "      <td>0.242099</td>\n",
       "      <td>0.336049</td>\n",
       "      <td>0.090483</td>\n",
       "      <td>1718.924012</td>\n",
       "      <td>6.828221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Conservative</th>\n",
       "      <td>1.598411e+09</td>\n",
       "      <td>3.940370</td>\n",
       "      <td>0.172240</td>\n",
       "      <td>0.071183</td>\n",
       "      <td>0.303230</td>\n",
       "      <td>0.071623</td>\n",
       "      <td>0.067017</td>\n",
       "      <td>34.116245</td>\n",
       "      <td>-0.023630</td>\n",
       "      <td>0.323976</td>\n",
       "      <td>0.135935</td>\n",
       "      <td>612.112946</td>\n",
       "      <td>20.075144</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ConservativesOnly</th>\n",
       "      <td>1.600351e+09</td>\n",
       "      <td>3.972199</td>\n",
       "      <td>0.175358</td>\n",
       "      <td>0.031557</td>\n",
       "      <td>0.355951</td>\n",
       "      <td>0.121404</td>\n",
       "      <td>0.062698</td>\n",
       "      <td>27.246764</td>\n",
       "      <td>-0.053810</td>\n",
       "      <td>0.319290</td>\n",
       "      <td>0.136930</td>\n",
       "      <td>825.773398</td>\n",
       "      <td>33.242364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JoeBiden</th>\n",
       "      <td>1.598001e+09</td>\n",
       "      <td>1.223702</td>\n",
       "      <td>0.148949</td>\n",
       "      <td>0.029362</td>\n",
       "      <td>0.207984</td>\n",
       "      <td>0.050072</td>\n",
       "      <td>0.034569</td>\n",
       "      <td>30.169823</td>\n",
       "      <td>0.142632</td>\n",
       "      <td>0.326553</td>\n",
       "      <td>0.108775</td>\n",
       "      <td>731.327979</td>\n",
       "      <td>10.927757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Libertarian</th>\n",
       "      <td>1.595813e+09</td>\n",
       "      <td>1.415440</td>\n",
       "      <td>0.173457</td>\n",
       "      <td>0.115245</td>\n",
       "      <td>0.294839</td>\n",
       "      <td>0.063585</td>\n",
       "      <td>0.075396</td>\n",
       "      <td>41.376454</td>\n",
       "      <td>-0.018246</td>\n",
       "      <td>0.323300</td>\n",
       "      <td>0.146410</td>\n",
       "      <td>1247.546143</td>\n",
       "      <td>58.260894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NeutralPolitics</th>\n",
       "      <td>1.596312e+09</td>\n",
       "      <td>1.237235</td>\n",
       "      <td>0.102247</td>\n",
       "      <td>0.029053</td>\n",
       "      <td>0.070072</td>\n",
       "      <td>0.011881</td>\n",
       "      <td>0.034140</td>\n",
       "      <td>71.730633</td>\n",
       "      <td>0.109678</td>\n",
       "      <td>0.324551</td>\n",
       "      <td>0.088351</td>\n",
       "      <td>571.780462</td>\n",
       "      <td>6.267137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OurPresident</th>\n",
       "      <td>1.587522e+09</td>\n",
       "      <td>1.746014</td>\n",
       "      <td>0.184892</td>\n",
       "      <td>0.085321</td>\n",
       "      <td>0.308934</td>\n",
       "      <td>0.077507</td>\n",
       "      <td>0.063612</td>\n",
       "      <td>33.644820</td>\n",
       "      <td>0.044278</td>\n",
       "      <td>0.322315</td>\n",
       "      <td>0.142370</td>\n",
       "      <td>189.705619</td>\n",
       "      <td>7.728202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoliticalDiscussion</th>\n",
       "      <td>1.595031e+09</td>\n",
       "      <td>1.187699</td>\n",
       "      <td>0.128444</td>\n",
       "      <td>0.037900</td>\n",
       "      <td>0.156473</td>\n",
       "      <td>0.034069</td>\n",
       "      <td>0.061020</td>\n",
       "      <td>58.710194</td>\n",
       "      <td>0.107491</td>\n",
       "      <td>0.311246</td>\n",
       "      <td>0.104854</td>\n",
       "      <td>693.150463</td>\n",
       "      <td>7.883513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Political_Revolution</th>\n",
       "      <td>1.592043e+09</td>\n",
       "      <td>1.727419</td>\n",
       "      <td>0.176343</td>\n",
       "      <td>0.082208</td>\n",
       "      <td>0.301844</td>\n",
       "      <td>0.070045</td>\n",
       "      <td>0.068114</td>\n",
       "      <td>40.894073</td>\n",
       "      <td>0.026744</td>\n",
       "      <td>0.319071</td>\n",
       "      <td>0.138206</td>\n",
       "      <td>297.459646</td>\n",
       "      <td>9.771741</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SandersForPresident</th>\n",
       "      <td>1.585310e+09</td>\n",
       "      <td>1.299409</td>\n",
       "      <td>0.161857</td>\n",
       "      <td>0.047335</td>\n",
       "      <td>0.233338</td>\n",
       "      <td>0.047034</td>\n",
       "      <td>0.045408</td>\n",
       "      <td>33.106562</td>\n",
       "      <td>0.130142</td>\n",
       "      <td>0.331526</td>\n",
       "      <td>0.118950</td>\n",
       "      <td>322.723468</td>\n",
       "      <td>6.555808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>The_Mueller</th>\n",
       "      <td>1.594001e+09</td>\n",
       "      <td>2.093730</td>\n",
       "      <td>0.188176</td>\n",
       "      <td>0.055488</td>\n",
       "      <td>0.327789</td>\n",
       "      <td>0.111686</td>\n",
       "      <td>0.054669</td>\n",
       "      <td>31.248415</td>\n",
       "      <td>-0.034610</td>\n",
       "      <td>0.326062</td>\n",
       "      <td>0.142937</td>\n",
       "      <td>346.133847</td>\n",
       "      <td>11.781658</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VoteBlue</th>\n",
       "      <td>1.589551e+09</td>\n",
       "      <td>3.126410</td>\n",
       "      <td>0.140958</td>\n",
       "      <td>0.025344</td>\n",
       "      <td>0.170982</td>\n",
       "      <td>0.039928</td>\n",
       "      <td>0.039849</td>\n",
       "      <td>36.493179</td>\n",
       "      <td>0.151790</td>\n",
       "      <td>0.324136</td>\n",
       "      <td>0.104280</td>\n",
       "      <td>1139.223456</td>\n",
       "      <td>12.100535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VoteDEM</th>\n",
       "      <td>1.600870e+09</td>\n",
       "      <td>5.871007</td>\n",
       "      <td>0.135725</td>\n",
       "      <td>0.015707</td>\n",
       "      <td>0.144456</td>\n",
       "      <td>0.033401</td>\n",
       "      <td>0.028594</td>\n",
       "      <td>30.184594</td>\n",
       "      <td>0.155248</td>\n",
       "      <td>0.324026</td>\n",
       "      <td>0.096644</td>\n",
       "      <td>1849.101304</td>\n",
       "      <td>12.129273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>donaldtrump</th>\n",
       "      <td>1.602217e+09</td>\n",
       "      <td>1.857786</td>\n",
       "      <td>0.182179</td>\n",
       "      <td>0.121092</td>\n",
       "      <td>0.360686</td>\n",
       "      <td>0.094663</td>\n",
       "      <td>0.059146</td>\n",
       "      <td>29.050143</td>\n",
       "      <td>-0.014095</td>\n",
       "      <td>0.326034</td>\n",
       "      <td>0.153899</td>\n",
       "      <td>444.569894</td>\n",
       "      <td>22.179291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hillaryclinton</th>\n",
       "      <td>1.589099e+09</td>\n",
       "      <td>1.746047</td>\n",
       "      <td>0.159682</td>\n",
       "      <td>0.071588</td>\n",
       "      <td>0.285744</td>\n",
       "      <td>0.076540</td>\n",
       "      <td>0.050444</td>\n",
       "      <td>36.294288</td>\n",
       "      <td>0.100403</td>\n",
       "      <td>0.315149</td>\n",
       "      <td>0.128329</td>\n",
       "      <td>338.937722</td>\n",
       "      <td>7.797327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>politics</th>\n",
       "      <td>1.596047e+09</td>\n",
       "      <td>3.098813</td>\n",
       "      <td>0.174883</td>\n",
       "      <td>0.042084</td>\n",
       "      <td>0.278329</td>\n",
       "      <td>0.077063</td>\n",
       "      <td>0.055668</td>\n",
       "      <td>31.090450</td>\n",
       "      <td>0.005940</td>\n",
       "      <td>0.322705</td>\n",
       "      <td>0.128096</td>\n",
       "      <td>1229.490546</td>\n",
       "      <td>14.685897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>progressive</th>\n",
       "      <td>1.597260e+09</td>\n",
       "      <td>2.250468</td>\n",
       "      <td>0.165352</td>\n",
       "      <td>0.053649</td>\n",
       "      <td>0.287618</td>\n",
       "      <td>0.083153</td>\n",
       "      <td>0.070939</td>\n",
       "      <td>38.740310</td>\n",
       "      <td>-0.016019</td>\n",
       "      <td>0.326697</td>\n",
       "      <td>0.134479</td>\n",
       "      <td>319.597567</td>\n",
       "      <td>9.384688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uspolitics</th>\n",
       "      <td>1.596070e+09</td>\n",
       "      <td>1.392157</td>\n",
       "      <td>0.167671</td>\n",
       "      <td>0.083195</td>\n",
       "      <td>0.321263</td>\n",
       "      <td>0.098179</td>\n",
       "      <td>0.060130</td>\n",
       "      <td>38.510675</td>\n",
       "      <td>-0.040759</td>\n",
       "      <td>0.324877</td>\n",
       "      <td>0.142465</td>\n",
       "      <td>722.138877</td>\n",
       "      <td>16.444482</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       created_utc     score  vulgarity  personal_attacks  \\\n",
       "subreddit                                                                   \n",
       "AskThe_Donald         1.592758e+09  1.225668   0.163840          0.061556   \n",
       "Ask_Politics          1.594207e+09  1.566801   0.115004          0.049309   \n",
       "BlueMidterm2018       1.604770e+09  1.781155   0.125265          0.009252   \n",
       "Conservative          1.598411e+09  3.940370   0.172240          0.071183   \n",
       "ConservativesOnly     1.600351e+09  3.972199   0.175358          0.031557   \n",
       "JoeBiden              1.598001e+09  1.223702   0.148949          0.029362   \n",
       "Libertarian           1.595813e+09  1.415440   0.173457          0.115245   \n",
       "NeutralPolitics       1.596312e+09  1.237235   0.102247          0.029053   \n",
       "OurPresident          1.587522e+09  1.746014   0.184892          0.085321   \n",
       "PoliticalDiscussion   1.595031e+09  1.187699   0.128444          0.037900   \n",
       "Political_Revolution  1.592043e+09  1.727419   0.176343          0.082208   \n",
       "SandersForPresident   1.585310e+09  1.299409   0.161857          0.047335   \n",
       "The_Mueller           1.594001e+09  2.093730   0.188176          0.055488   \n",
       "VoteBlue              1.589551e+09  3.126410   0.140958          0.025344   \n",
       "VoteDEM               1.600870e+09  5.871007   0.135725          0.015707   \n",
       "donaldtrump           1.602217e+09  1.857786   0.182179          0.121092   \n",
       "hillaryclinton        1.589099e+09  1.746047   0.159682          0.071588   \n",
       "politics              1.596047e+09  3.098813   0.174883          0.042084   \n",
       "progressive           1.597260e+09  2.250468   0.165352          0.053649   \n",
       "uspolitics            1.596070e+09  1.392157   0.167671          0.083195   \n",
       "\n",
       "                      incivility  third_party_attacks  aspersion  comment_len  \\\n",
       "subreddit                                                                       \n",
       "AskThe_Donald           0.288317             0.074425   0.065467    39.752846   \n",
       "Ask_Politics            0.151530             0.031580   0.067308    72.359687   \n",
       "BlueMidterm2018         0.163989             0.030119   0.010334    20.574468   \n",
       "Conservative            0.303230             0.071623   0.067017    34.116245   \n",
       "ConservativesOnly       0.355951             0.121404   0.062698    27.246764   \n",
       "JoeBiden                0.207984             0.050072   0.034569    30.169823   \n",
       "Libertarian             0.294839             0.063585   0.075396    41.376454   \n",
       "NeutralPolitics         0.070072             0.011881   0.034140    71.730633   \n",
       "OurPresident            0.308934             0.077507   0.063612    33.644820   \n",
       "PoliticalDiscussion     0.156473             0.034069   0.061020    58.710194   \n",
       "Political_Revolution    0.301844             0.070045   0.068114    40.894073   \n",
       "SandersForPresident     0.233338             0.047034   0.045408    33.106562   \n",
       "The_Mueller             0.327789             0.111686   0.054669    31.248415   \n",
       "VoteBlue                0.170982             0.039928   0.039849    36.493179   \n",
       "VoteDEM                 0.144456             0.033401   0.028594    30.184594   \n",
       "donaldtrump             0.360686             0.094663   0.059146    29.050143   \n",
       "hillaryclinton          0.285744             0.076540   0.050444    36.294288   \n",
       "politics                0.278329             0.077063   0.055668    31.090450   \n",
       "progressive             0.287618             0.083153   0.070939    38.740310   \n",
       "uspolitics              0.321263             0.098179   0.060130    38.510675   \n",
       "\n",
       "                      compound_sentiment  stereotype  aggregate  \\\n",
       "subreddit                                                         \n",
       "AskThe_Donald                  -0.030237    0.323410   0.131894   \n",
       "Ask_Politics                    0.095941    0.323191   0.108050   \n",
       "BlueMidterm2018                 0.242099    0.336049   0.090483   \n",
       "Conservative                   -0.023630    0.323976   0.135935   \n",
       "ConservativesOnly              -0.053810    0.319290   0.136930   \n",
       "JoeBiden                        0.142632    0.326553   0.108775   \n",
       "Libertarian                    -0.018246    0.323300   0.146410   \n",
       "NeutralPolitics                 0.109678    0.324551   0.088351   \n",
       "OurPresident                    0.044278    0.322315   0.142370   \n",
       "PoliticalDiscussion             0.107491    0.311246   0.104854   \n",
       "Political_Revolution            0.026744    0.319071   0.138206   \n",
       "SandersForPresident             0.130142    0.331526   0.118950   \n",
       "The_Mueller                    -0.034610    0.326062   0.142937   \n",
       "VoteBlue                        0.151790    0.324136   0.104280   \n",
       "VoteDEM                         0.155248    0.324026   0.096644   \n",
       "donaldtrump                    -0.014095    0.326034   0.153899   \n",
       "hillaryclinton                  0.100403    0.315149   0.128329   \n",
       "politics                        0.005940    0.322705   0.128096   \n",
       "progressive                    -0.016019    0.326697   0.134479   \n",
       "uspolitics                     -0.040759    0.324877   0.142465   \n",
       "\n",
       "                      user_activity_level  user_toxicity_level  \n",
       "subreddit                                                       \n",
       "AskThe_Donald                  376.154375            13.245999  \n",
       "Ask_Politics                   465.767744             8.960899  \n",
       "BlueMidterm2018               1718.924012             6.828221  \n",
       "Conservative                   612.112946            20.075144  \n",
       "ConservativesOnly              825.773398            33.242364  \n",
       "JoeBiden                       731.327979            10.927757  \n",
       "Libertarian                   1247.546143            58.260894  \n",
       "NeutralPolitics                571.780462             6.267137  \n",
       "OurPresident                   189.705619             7.728202  \n",
       "PoliticalDiscussion            693.150463             7.883513  \n",
       "Political_Revolution           297.459646             9.771741  \n",
       "SandersForPresident            322.723468             6.555808  \n",
       "The_Mueller                    346.133847            11.781658  \n",
       "VoteBlue                      1139.223456            12.100535  \n",
       "VoteDEM                       1849.101304            12.129273  \n",
       "donaldtrump                    444.569894            22.179291  \n",
       "hillaryclinton                 338.937722             7.797327  \n",
       "politics                      1229.490546            14.685897  \n",
       "progressive                    319.597567             9.384688  \n",
       "uspolitics                     722.138877            16.444482  "
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_values = subreddit_groups.mean()\n",
    "mean_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1580551200.0, 1581760800.0, 1582970400.0, 1584176400.0, 1585386000.0, 1586595600.0, 1587805200.0, 1589014800.0, 1590224400.0, 1591434000.0, 1592643600.0, 1593853200.0, 1595062800.0, 1596272400.0, 1597482000.0, 1598691600.0, 1599901200.0, 1601110800.0, 1602320400.0, 1603530000.0, 1604743200.0, 1605952800.0, 1607162400.0, 1608372000.0, 1609581600.0, 1610791200.0, 1612000800.0, 1613210400.0, 1614420000.0, 1615629600.0, 1616835600.0, 1618045200.0, 1619254800.0, 1620464400.0, 1621674000.0, 1622883600.0, 1624093200.0, 1625302800.0, 1626512400.0, 1627722000.0, 1628931600.0, 1630141200.0, 1631350800.0, 1632560400.0, 1633770000.0, 1634979600.0, 1636189200.0, 1637402400.0, 1638612000.0, 1639821600.0, 1641031200.0, 1642240800.0, 1643450400.0]\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime, timedelta\n",
    "from scipy import stats\n",
    "\n",
    "feb_2020_timestamp = 1580533200\n",
    "feb_2021_timestamp = 1612155600\n",
    "\n",
    "timestamps_datetime = [datetime.utcfromtimestamp(feb_2020_timestamp)]\n",
    "for i in range(1, 53):\n",
    "  timestamps_datetime.append( timestamps_datetime[i-1] + timedelta(days=14))\n",
    "\n",
    "timestamps = [i.timestamp() for i in timestamps_datetime]\n",
    "print(timestamps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = ['BlueMidterm2018', 'politics', 'JoeBiden', 'Libertarian', 'OurPresident', 'PoliticalDiscussion', 'Political_Revolution', 'SandersForPresident', 'VoteBlue', 'hillaryclinton', 'politics', 'progressive']\n",
    "right = ['AskThe_Donald', 'Conservative', 'ConservativesOnly', 'The_Mueller', 'donaldtrump']\n",
    "neutral = ['Ask_Politics', 'NeutralPolitics', 'uspolitics']\n",
    "categories = ['vulgarity', 'personal_attacks', 'third_party_attacks', 'stereotype', 'aspersion', 'aggregate']\n",
    "dfs = [left, right, neutral]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "res = []\n",
    "for i in dfs:\n",
    "    temp = all_comments[all_comments.subreddit.isin(i)]\n",
    "    res.append(stats.binned_statistic(temp['created_utc'], [temp[i] for i in categories], 'mean', bins=timestamps)[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "         0.17334028, 0.17360486, 0.17452786, 0.18063201, 0.17534216,\n",
       "         0.17508446, 0.17581978, 0.17774106, 0.17475017, 0.17634972,\n",
       "         0.17668645, 0.17459456, 0.17553082, 0.17348491, 0.16295203,\n",
       "         0.16793602, 0.17393197, 0.17722008, 0.17962151, 0.17895798,\n",
       "         0.17677139, 0.17555314,        nan,        nan,        nan,\n",
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       "         0.31474176, 0.3211013 , 0.32380908, 0.33051953, 0.32329047,\n",
       "         0.32811485, 0.30378033, 0.31608847, 0.32691847, 0.30533405,\n",
       "         0.32288288, 0.31834451, 0.32936006, 0.32949519, 0.31198255,\n",
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       "        [0.12369685, 0.12312524, 0.12020211, 0.12899333, 0.13235021,\n",
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       "         0.1347886 , 0.12626821, 0.12485804, 0.12849211, 0.11584593,\n",
       "         0.12621251, 0.12912221, 0.13258355, 0.13348924, 0.12550464,\n",
       "         0.12712333, 0.12597642,        nan,        nan,        nan,\n",
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       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan]]),\n",
       " array([[0.16933564, 0.16731547, 0.17026702, 0.16895718, 0.16836228,\n",
       "         0.16600744, 0.17032629, 0.16893854, 0.17528971, 0.17637429,\n",
       "         0.17784699, 0.17714523, 0.17609143, 0.1758846 , 0.17713833,\n",
       "         0.18119281, 0.17542596, 0.1757673 , 0.17480927, 0.16521724,\n",
       "         0.16917545, 0.17066031, 0.17293713, 0.17842874, 0.17538724,\n",
       "         0.17238422, 0.17187104,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan],\n",
       "        [0.05275051, 0.05304222, 0.0615124 , 0.0603713 , 0.0625376 ,\n",
       "         0.06381168, 0.06467483, 0.0671193 , 0.0723515 , 0.0711936 ,\n",
       "         0.0815985 , 0.0812492 , 0.07883535, 0.08041623, 0.08101565,\n",
       "         0.08536215, 0.07911624, 0.08129975, 0.07498191, 0.07528891,\n",
       "         0.10228086, 0.09425454, 0.08271348, 0.08444692, 0.06670787,\n",
       "         0.07556425, 0.07761458,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan],\n",
       "        [0.07957123, 0.07111606, 0.07197126, 0.06839599, 0.07213004,\n",
       "         0.06718207, 0.07596538, 0.06943018, 0.07484964, 0.07917579,\n",
       "         0.08215776, 0.08211601, 0.07722534, 0.07887568, 0.08373049,\n",
       "         0.08909649, 0.08025596, 0.07968334, 0.07740965, 0.06103319,\n",
       "         0.06979591, 0.0683979 , 0.0728816 , 0.07241864, 0.08168331,\n",
       "         0.07339245, 0.07532865,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan],\n",
       "        [0.31030159, 0.33243281, 0.35362775, 0.35224166, 0.34627328,\n",
       "         0.34291092, 0.34094381, 0.34237168, 0.31488874, 0.30193488,\n",
       "         0.30977382, 0.32065822, 0.3238465 , 0.32270234, 0.33193582,\n",
       "         0.32969221, 0.32564571, 0.32589646, 0.32230238, 0.298026  ,\n",
       "         0.33913195, 0.33838603, 0.32598149, 0.3227572 , 0.32716905,\n",
       "         0.29461804, 0.34089581,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan],\n",
       "        [0.12844126, 0.13198075, 0.13993164, 0.1393032 , 0.13763012,\n",
       "         0.13532427, 0.13860704, 0.13746982, 0.13659603, 0.13384599,\n",
       "         0.13909477, 0.14210239, 0.13991549, 0.14073329, 0.1436603 ,\n",
       "         0.14689758, 0.14045587, 0.14037118, 0.13753563, 0.12473631,\n",
       "         0.14565061, 0.14343909, 0.13820667, 0.14005853, 0.13697378,\n",
       "         0.12972485, 0.14173243,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan]]),\n",
       " array([[0.12458837, 0.12167359, 0.12063678, 0.1288977 , 0.12640083,\n",
       "         0.1216726 , 0.12514339, 0.12785948, 0.12660389, 0.12887104,\n",
       "         0.12657946, 0.12467331, 0.12176431, 0.12131847, 0.12648784,\n",
       "         0.12746922, 0.12933741, 0.12210606, 0.11861788, 0.12262725,\n",
       "         0.11692138, 0.12286207, 0.12653947, 0.12711685, 0.12812577,\n",
       "         0.13055315, 0.13433537,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan],\n",
       "        [0.05929745, 0.04776663, 0.05209049, 0.05996921, 0.05234785,\n",
       "         0.05565214, 0.05765957, 0.06582304, 0.06423164, 0.04978727,\n",
       "         0.06452071, 0.05839094, 0.05189907, 0.04877859, 0.05785687,\n",
       "         0.05930105, 0.05189582, 0.04027575, 0.04865874, 0.06177549,\n",
       "         0.03917351, 0.04866932, 0.0376781 , 0.05054154, 0.04323161,\n",
       "         0.04444416, 0.04062642,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan],\n",
       "        [0.04040116, 0.0345736 , 0.03559581, 0.04576328, 0.04794077,\n",
       "         0.04140222, 0.0437958 , 0.05022221, 0.04412774, 0.05024459,\n",
       "         0.04146631, 0.04434245, 0.0367069 , 0.0392855 , 0.04188783,\n",
       "         0.05108074, 0.05179607, 0.04142509, 0.03632555, 0.04273582,\n",
       "         0.03423402, 0.04318052, 0.05065859, 0.04685701, 0.04975094,\n",
       "         0.04695888, 0.06045556,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan],\n",
       "        [0.31548415, 0.31252955, 0.31394726, 0.31569013, 0.32095001,\n",
       "         0.32306232, 0.32036146, 0.32354173, 0.31896047, 0.31820363,\n",
       "         0.32067287, 0.32601694, 0.32784502, 0.33134955, 0.32685014,\n",
       "         0.33063907, 0.33029412, 0.33370802, 0.32556235, 0.35466271,\n",
       "         0.3502602 , 0.33628898, 0.31816575, 0.32306916, 0.31629893,\n",
       "         0.32386611, 0.31193587,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan],\n",
       "        [0.10892655, 0.10591164, 0.10687058, 0.11433646, 0.11443005,\n",
       "         0.1112368 , 0.11290608, 0.11775623, 0.11461012, 0.11402829,\n",
       "         0.11618783, 0.11551318, 0.11007684, 0.11217159, 0.11465055,\n",
       "         0.11860993, 0.11911083, 0.10685613, 0.1068078 , 0.1226364 ,\n",
       "         0.10918462, 0.1150692 , 0.10992323, 0.11299862, 0.1063285 ,\n",
       "         0.11271585, 0.11362493,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan,        nan,        nan,        nan,\n",
       "                nan,        nan]])]"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.28648678, 0.28379862, 0.28654636, 0.29947288, 0.30560722,\n",
       "       0.30081626, 0.30106302, 0.30170705, 0.30994273, 0.29956969,\n",
       "       0.30085892, 0.30224347, 0.30630702, 0.297938  , 0.29963316,\n",
       "       0.30482372, 0.30261138, 0.29540822, 0.29114626, 0.27287584,\n",
       "       0.29901547, 0.30399063, 0.30662747, 0.30832741, 0.30689826,\n",
       "       0.30325506, 0.31051539,        nan,        nan,        nan,\n",
       "              nan,        nan,        nan,        nan,        nan,\n",
       "              nan,        nan,        nan,        nan,        nan,\n",
       "              nan,        nan,        nan,        nan,        nan,\n",
       "              nan,        nan,        nan,        nan,        nan,\n",
       "              nan,        nan])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_res = stats.binned_statistic(all_comments['created_utc'], all_comments['aggregate'], 'mean', bins=timestamps)[0]\n",
    "all_res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-147-ba80b3785b30>:10: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = fig.add_subplot(2, 3, index+1)\n",
      "<ipython-input-147-ba80b3785b30>:10: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = fig.add_subplot(2, 3, index+1)\n",
      "<ipython-input-147-ba80b3785b30>:10: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = fig.add_subplot(2, 3, index+1)\n",
      "<ipython-input-147-ba80b3785b30>:10: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = fig.add_subplot(2, 3, index+1)\n",
      "<ipython-input-147-ba80b3785b30>:10: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.\n",
      "  ax = fig.add_subplot(2, 3, index+1)\n",
      "<ipython-input-147-ba80b3785b30>:19: UserWarning: You have mixed positional and keyword arguments, some input may be discarded.\n",
      "  fig.legend(graphs, labels=[\"left\", \"right\", \"neutral\"],\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x360 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "graphs = []\n",
    "fig = plt.figure()\n",
    "for index, c in enumerate(categories):\n",
    "    print(index)\n",
    "    ax = fig.add_subplot(2, 3, index+1)\n",
    "    time_data = pd.DataFrame(np.transpose(np.array(res)[:,index:index+1].squeeze()), columns=['left', 'right', 'neutral'], index=timestamps_datetime[:len(timestamps_datetime)-1])\n",
    "    graphs.append(time_data.plot.line(stacked=False, alpha=0.9, figsize=(15,5), color={'left':'blue', 'right':'red', 'neutral':'black'}, title=\"{} level vs. political leanings\".format(c), ax=ax, legend=False))\n",
    "\n",
    "    ax = fig.add_subplot(2, 3, index+1)\n",
    "    time_data = pd.DataFrame(np.transpose(np.array(res)[:,index:index+1].squeeze()), columns=['left', 'right', 'neutral'], index=timestamps_datetime[:len(timestamps_datetime)-1])\n",
    "    graphs.append(time_data.plot.line(stacked=False, alpha=0.9, figsize=(15,5), color={'left':'blue', 'right':'red', 'neutral':'black'}, title=\"{} level vs. political leanings\".format(c), ax=ax, legend=False))\n",
    "\n",
    "\n",
    "ax = fig.add_subplot(2, 3, 6)\n",
    "time_data = pd.DataFrame(np.transpose(all_res), index=timestamps_datetime[:len(timestamps_datetime)-1])\n",
    "graphs.append(time_data.plot.line(stacked=False, alpha=0.9, figsize=(15,5), title=\"overall incivility level vs. time\".format(c), ax=ax, legend=False))\n",
    "\n",
    "fig.legend(graphs, labels=[\"left\", \"right\", \"neutral\"],\n",
    "           loc=\"lower right\")\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>level_0</th>\n",
       "      <th>index</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>created_utc</th>\n",
       "      <th>id</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>score</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>vulgarity</th>\n",
       "      <th>civility</th>\n",
       "      <th>namecalling</th>\n",
       "      <th>stereotype</th>\n",
       "      <th>demeaning</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>aggregate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Koalemos78</td>\n",
       "      <td>Interesting take from someone who's education ...</td>\n",
       "      <td>1594611009</td>\n",
       "      <td>fxw8pc6</td>\n",
       "      <td>t3_hq7drs</td>\n",
       "      <td>t3_hq7drs</td>\n",
       "      <td>3</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.744458</td>\n",
       "      <td>0.152181</td>\n",
       "      <td>0.082147</td>\n",
       "      <td>0.026065</td>\n",
       "      <td>0.035793</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.271284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Thatsprettydank</td>\n",
       "      <td>Kinda lol they stopped researching out of bigotry</td>\n",
       "      <td>1594610955</td>\n",
       "      <td>fxw8mi5</td>\n",
       "      <td>t1_fxl5isk</td>\n",
       "      <td>t3_hovua5</td>\n",
       "      <td>1</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.595237</td>\n",
       "      <td>0.754733</td>\n",
       "      <td>0.239289</td>\n",
       "      <td>0.115970</td>\n",
       "      <td>0.038808</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.305323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>Peripatetic_deviant</td>\n",
       "      <td>So you’re just guessing information? Even if t...</td>\n",
       "      <td>1594608113</td>\n",
       "      <td>fxw4axz</td>\n",
       "      <td>t1_fxw2l39</td>\n",
       "      <td>t3_hq16qw</td>\n",
       "      <td>1</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.275143</td>\n",
       "      <td>0.980211</td>\n",
       "      <td>0.584546</td>\n",
       "      <td>0.032369</td>\n",
       "      <td>0.105278</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.308034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>buffalo_chum</td>\n",
       "      <td>They were out past curfew no? I bet there was ...</td>\n",
       "      <td>1594607019</td>\n",
       "      <td>fxw2l39</td>\n",
       "      <td>t1_fxvrvdq</td>\n",
       "      <td>t3_hq16qw</td>\n",
       "      <td>1</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.322457</td>\n",
       "      <td>0.078789</td>\n",
       "      <td>0.127013</td>\n",
       "      <td>0.081637</td>\n",
       "      <td>0.060492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.171077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>metaltimmy</td>\n",
       "      <td>Trump always likes these photo ops where he th...</td>\n",
       "      <td>1594606124</td>\n",
       "      <td>fxw153j</td>\n",
       "      <td>t3_hq4pq6</td>\n",
       "      <td>t3_hq4pq6</td>\n",
       "      <td>5</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.821457</td>\n",
       "      <td>0.990872</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>0.184373</td>\n",
       "      <td>0.031645</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.649641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>ouroboros-panacea</td>\n",
       "      <td>And he isn't even good at it. He's too transpa...</td>\n",
       "      <td>1594605259</td>\n",
       "      <td>fxvzr01</td>\n",
       "      <td>t3_hpzkft</td>\n",
       "      <td>t3_hpzkft</td>\n",
       "      <td>1</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.369666</td>\n",
       "      <td>0.971312</td>\n",
       "      <td>0.924112</td>\n",
       "      <td>0.029915</td>\n",
       "      <td>0.080515</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.445373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>ouroboros-panacea</td>\n",
       "      <td>Of course two of the states with the highest C...</td>\n",
       "      <td>1594604882</td>\n",
       "      <td>fxvz4um</td>\n",
       "      <td>t3_hq0ipe</td>\n",
       "      <td>t3_hq0ipe</td>\n",
       "      <td>3</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.109969</td>\n",
       "      <td>0.105576</td>\n",
       "      <td>0.086737</td>\n",
       "      <td>0.047303</td>\n",
       "      <td>0.044219</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.068674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>Mister_Met</td>\n",
       "      <td>He is a traitor. He’s just like Hitler</td>\n",
       "      <td>1594602179</td>\n",
       "      <td>fxvuq5q</td>\n",
       "      <td>t3_hq4pq6</td>\n",
       "      <td>t3_hq4pq6</td>\n",
       "      <td>7</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.421639</td>\n",
       "      <td>0.983711</td>\n",
       "      <td>0.943397</td>\n",
       "      <td>0.084075</td>\n",
       "      <td>0.058660</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.480331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "      <td>autotldr</td>\n",
       "      <td>This is the best tl;dr I could make, [original...</td>\n",
       "      <td>1594602084</td>\n",
       "      <td>fxvuki4</td>\n",
       "      <td>t3_hpof2a</td>\n",
       "      <td>t3_hpof2a</td>\n",
       "      <td>1</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.087793</td>\n",
       "      <td>0.158732</td>\n",
       "      <td>0.079129</td>\n",
       "      <td>0.010053</td>\n",
       "      <td>0.030873</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.041541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>greenbabyshit</td>\n",
       "      <td>Some gave all.\\n\\nAll gave some. \\n\\nOne had b...</td>\n",
       "      <td>1594601990</td>\n",
       "      <td>fxvuf00</td>\n",
       "      <td>t3_hq4pq6</td>\n",
       "      <td>t3_hq4pq6</td>\n",
       "      <td>14</td>\n",
       "      <td>uspolitics</td>\n",
       "      <td>0.232875</td>\n",
       "      <td>0.898647</td>\n",
       "      <td>0.758360</td>\n",
       "      <td>0.258102</td>\n",
       "      <td>0.053185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.411049</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   level_0  index               author  \\\n",
       "0        0      0           Koalemos78   \n",
       "1        1      1      Thatsprettydank   \n",
       "2        2      3  Peripatetic_deviant   \n",
       "3        3      4         buffalo_chum   \n",
       "4        4      5           metaltimmy   \n",
       "5        5      6    ouroboros-panacea   \n",
       "6        6      7    ouroboros-panacea   \n",
       "7        7      8           Mister_Met   \n",
       "8        8      9             autotldr   \n",
       "9        9     10        greenbabyshit   \n",
       "\n",
       "                                                body  created_utc       id  \\\n",
       "0  Interesting take from someone who's education ...   1594611009  fxw8pc6   \n",
       "1  Kinda lol they stopped researching out of bigotry   1594610955  fxw8mi5   \n",
       "2  So you’re just guessing information? Even if t...   1594608113  fxw4axz   \n",
       "3  They were out past curfew no? I bet there was ...   1594607019  fxw2l39   \n",
       "4  Trump always likes these photo ops where he th...   1594606124  fxw153j   \n",
       "5  And he isn't even good at it. He's too transpa...   1594605259  fxvzr01   \n",
       "6  Of course two of the states with the highest C...   1594604882  fxvz4um   \n",
       "7             He is a traitor. He’s just like Hitler   1594602179  fxvuq5q   \n",
       "8  This is the best tl;dr I could make, [original...   1594602084  fxvuki4   \n",
       "9  Some gave all.\\n\\nAll gave some. \\n\\nOne had b...   1594601990  fxvuf00   \n",
       "\n",
       "    parent_id    link_id  score   subreddit  vulgarity  civility  namecalling  \\\n",
       "0   t3_hq7drs  t3_hq7drs      3  uspolitics   0.744458  0.152181     0.082147   \n",
       "1  t1_fxl5isk  t3_hovua5      1  uspolitics   0.595237  0.754733     0.239289   \n",
       "2  t1_fxw2l39  t3_hq16qw      1  uspolitics   0.275143  0.980211     0.584546   \n",
       "3  t1_fxvrvdq  t3_hq16qw      1  uspolitics   0.322457  0.078789     0.127013   \n",
       "4   t3_hq4pq6  t3_hq4pq6      5  uspolitics   0.821457  0.990872     0.971880   \n",
       "5   t3_hpzkft  t3_hpzkft      1  uspolitics   0.369666  0.971312     0.924112   \n",
       "6   t3_hq0ipe  t3_hq0ipe      3  uspolitics   0.109969  0.105576     0.086737   \n",
       "7   t3_hq4pq6  t3_hq4pq6      7  uspolitics   0.421639  0.983711     0.943397   \n",
       "8   t3_hpof2a  t3_hpof2a      1  uspolitics   0.087793  0.158732     0.079129   \n",
       "9   t3_hq4pq6  t3_hq4pq6     14  uspolitics   0.232875  0.898647     0.758360   \n",
       "\n",
       "   stereotype  demeaning author_flair_text  aggregate  \n",
       "0    0.026065   0.035793               NaN   0.271284  \n",
       "1    0.115970   0.038808               NaN   0.305323  \n",
       "2    0.032369   0.105278               NaN   0.308034  \n",
       "3    0.081637   0.060492               NaN   0.171077  \n",
       "4    0.184373   0.031645               NaN   0.649641  \n",
       "5    0.029915   0.080515               NaN   0.445373  \n",
       "6    0.047303   0.044219               NaN   0.068674  \n",
       "7    0.084075   0.058660               NaN   0.480331  \n",
       "8    0.010053   0.030873               NaN   0.041541  \n",
       "9    0.258102   0.053185               NaN   0.411049  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comments = all_comments.reset_index()\n",
    "all_comments = all_comments.reset_index()\n",
    "all_comments.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15287313"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "incivility = all_comments[(all_comments['aggregate'] > 0.75) | (all_comments['aggregate'] < 0.25)]\n",
    "len(incivility)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /Users/yujia/nltk_data...\n",
      "[nltk_data]   Unzipping tokenizers/punkt.zip.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download('punkt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "from collections import Counter\n",
    "\n",
    "## Trie with list of possible categories at the end\n",
    "def build_category_trie(liwc, categories):\n",
    "    trie = {}\n",
    "    for category in categories:\n",
    "        # revert to wildcard format from strings starting with ‘^’\n",
    "        patterns = [s if s[0] != '^' else s[1:] + '*' for s in liwc.item().get(category)]\n",
    "        for pattern in patterns:\n",
    "            cursor = trie\n",
    "            for char in pattern:\n",
    "                if char == \"*\":\n",
    "                    # Leaf found. Check if leaf exists and append.\n",
    "                    if char in cursor:\n",
    "                        cursor['*'].append(category)\n",
    "                    else:\n",
    "                        cursor['*'] = [category]\n",
    "                    break\n",
    "                if char not in cursor:\n",
    "                    # Create new sub-trie\n",
    "                    cursor[char] = {}\n",
    "                cursor = cursor[char]\n",
    "            # End of word. Use $ to denote word end.\n",
    "            if '$' in cursor:\n",
    "                cursor['$'].append(category)\n",
    "            else:\n",
    "                cursor['$'] = [category]\n",
    "    return trie\n",
    "def search_trie(trie, token, token_i=0):\n",
    "    '''\n",
    "    Search the given character-trie for paths that match the `token` string.\n",
    "    '''\n",
    "    if '*' in trie:\n",
    "        # Wildstar ending\n",
    "        return trie['*']\n",
    "    if '$' in trie and token_i == len(token):\n",
    "        # Met end of word.\n",
    "        return trie['$']\n",
    "    if token_i < len(token):\n",
    "        char = token[token_i]\n",
    "        if char in trie:\n",
    "            return search_trie(trie[char], token, token_i + 1)\n",
    "    return []\n",
    "def search_trie_for_list(trie, l):\n",
    "    full_answer = []\n",
    "    for token in l:\n",
    "        full_answer += search_trie(trie, token)\n",
    "    return Counter(full_answer)\n",
    "incivility['tokenized'] = incivility['body'].apply(lambda s : [i for i in word_tokenize(s) if i.isalpha()])\n",
    "liwc = np.load('liwc.npy', allow_pickle=True)\n",
    "categories = list(liwc.item().keys())\n",
    "trie = build_category_trie(liwc, categories)\n",
    "incivility['categories'] = incivility.tokenized.apply(lambda l : search_trie_for_list(trie, l))\n",
    "# liwc_scores = incivility.categories.apply(pd.Series).fillna(0).astype(int)\n",
    "# politics = pd.concat([politics, liwc_scores], axis=1)\n",
    "# politics = politics.drop([‘tokenized’], axis=1)\n",
    "incivility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'fxuq6qg'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parent_ids = dict(zip(all_comments.id, all_comments.parent_id.str[3:]))\n",
    "parent_ids['fxuu216']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Has in file\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1,\n",
       " 5,\n",
       " 6,\n",
       " 5,\n",
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       " 1,\n",
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       " 1,\n",
       " 1,\n",
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       " 1,\n",
       " 4,\n",
       " 1,\n",
       " 3,\n",
       " 3,\n",
       " 1,\n",
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       " 2,\n",
       " 2,\n",
       " 1,\n",
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       " 4,\n",
       " 2,\n",
       " 1,\n",
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       " 3,\n",
       " 2,\n",
       " 4,\n",
       " 2,\n",
       " 4,\n",
       " 3,\n",
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       " 1,\n",
       " 2,\n",
       " 10,\n",
       " 1,\n",
       " 9,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 8,\n",
       " 7,\n",
       " 6,\n",
       " 1,\n",
       " 5,\n",
       " 4,\n",
       " 1,\n",
       " 3,\n",
       " 1,\n",
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       " 1,\n",
       " 4,\n",
       " 3,\n",
       " 3,\n",
       " 2,\n",
       " 1,\n",
       " 14,\n",
       " 2,\n",
       " 1,\n",
       " 13,\n",
       " 2,\n",
       " 3,\n",
       " 1,\n",
       " 12,\n",
       " 1,\n",
       " 11,\n",
       " 3,\n",
       " 2,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 2,\n",
       " 5,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 1,\n",
       " 4,\n",
       " 3,\n",
       " 4,\n",
       " 2,\n",
       " 1,\n",
       " 1,\n",
       " 2]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fname = 'depths_per_comments'\n",
    "if os.path.isfile(fname):\n",
    "    print(\"Has in file\")\n",
    "    depths = list(np.load(fname))\n",
    "else:\n",
    "    indices = list(all_comments[all_comments.link_id.str[3:] == all_comments.parent_id.str[3:]].index)\n",
    "    depths = [0]*len(all_comments)\n",
    "    for i in indices:\n",
    "        depths[i] = 1\n",
    "depths[:100]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_to_index = dict(zip(all_comments.id, all_comments.level_0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_depth(id, depth):\n",
    "    index = id_to_index[id]\n",
    "    # print(\"current search index is \" + str(index))\n",
    "    if depth[index] != 0:\n",
    "        return\n",
    "    \n",
    "    if id in parent_ids.keys() and parent_ids[id] in id_to_index and depth[id_to_index[parent_ids[id]]] == 1:\n",
    "            fill_depth(parent_ids[id] , depth)\n",
    "    elif parent_ids[id] not in id_to_index:\n",
    "        depth[index] = 1\n",
    "        return\n",
    "    depth[index] = depth[id_to_index[parent_ids[id]]]+1\n",
    "for index, row in all_comments.loc[10000000:10000000].iterrows():\n",
    "    fill_depth(row['id'], depths)\n",
    "with open('depths_per_comments', 'wb') as f:\n",
    "    np.save(f, depths)\n",
    "\n",
    "all_comments['depth'] = depths\n",
    "all_comments.to_pickle('../data/comments/all_comments.pickle')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4167423019411557"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>...</th>\n",
       "      <th>stereotype</th>\n",
       "      <th>aggregate</th>\n",
       "      <th>user_activity_level</th>\n",
       "      <th>user_toxicity_level</th>\n",
       "      <th>personal_attacks_b</th>\n",
       "      <th>third_party_attacks_b</th>\n",
       "      <th>vulgarity_b</th>\n",
       "      <th>aspersion_b</th>\n",
       "      <th>stereotype_b</th>\n",
       "      <th>aggregate_b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1612153203</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>glk8hec</td>\n",
       "      <td>runrain</td>\n",
       "      <td>Lol not even. Where I heard about it most was ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.234234</td>\n",
       "      <td>0.064974</td>\n",
       "      <td>46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1612152460</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk757a</td>\n",
       "      <td>SilverHerfer</td>\n",
       "      <td>To paraphrase one of the great conservatives o...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>3</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.536770</td>\n",
       "      <td>0.125015</td>\n",
       "      <td>665</td>\n",
       "      <td>17.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1612151108</td>\n",
       "      <td>t1_glit0la</td>\n",
       "      <td>t3_l9lonl</td>\n",
       "      <td>glk4jhg</td>\n",
       "      <td>Psychowitz</td>\n",
       "      <td>Like coding.</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9lonl/keystone_pipe...</td>\n",
       "      <td>0</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.288935</td>\n",
       "      <td>0.087804</td>\n",
       "      <td>13</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1612150714</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3rkp</td>\n",
       "      <td>Salmankhan42069</td>\n",
       "      <td>Wait, this is a meme?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>-1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.249884</td>\n",
       "      <td>0.066450</td>\n",
       "      <td>7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1612150618</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3kvz</td>\n",
       "      <td>Pile_of_Walthers</td>\n",
       "      <td>Which one is more useful?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>2</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>...</td>\n",
       "      <td>0.308906</td>\n",
       "      <td>0.067157</td>\n",
       "      <td>161</td>\n",
       "      <td>1.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127588</th>\n",
       "      <td>1603994257</td>\n",
       "      <td>t1_gaidthx</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaiivoy</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>I remember when Fox, MSNBC and CNN [all broadc...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238228</td>\n",
       "      <td>0.043984</td>\n",
       "      <td>401</td>\n",
       "      <td>10.0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127589</th>\n",
       "      <td>1603993347</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaigl2w</td>\n",
       "      <td>etzel1200</td>\n",
       "      <td>That’s on Comey, not the NYT.</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.261439</td>\n",
       "      <td>0.093671</td>\n",
       "      <td>444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127590</th>\n",
       "      <td>1603992287</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaidthx</td>\n",
       "      <td>paywallpiker</td>\n",
       "      <td>People neglect that the media played a huge pa...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.320039</td>\n",
       "      <td>0.079030</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127591</th>\n",
       "      <td>1603885499</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>gaddjx4</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>The Dakotas are getting slammed, especially co...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jj3nmi/oc_states_in...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.232618</td>\n",
       "      <td>0.270247</td>\n",
       "      <td>401</td>\n",
       "      <td>10.0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127592</th>\n",
       "      <td>1603745345</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>ga7dx8h</td>\n",
       "      <td>ual33655</td>\n",
       "      <td>Thanks for telling it like it is you should ha...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jigjpx/hillary_clin...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>0.317675</td>\n",
       "      <td>0.557658</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35567120 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_utc   parent_id    link_id       id            author  \\\n",
       "1         1612153203   t3_l9gngx  t3_l9gngx  glk8hec           runrain   \n",
       "2         1612152460   t3_l9t1ha  t3_l9t1ha  glk757a      SilverHerfer   \n",
       "4         1612151108  t1_glit0la  t3_l9lonl  glk4jhg        Psychowitz   \n",
       "6         1612150714   t3_l9t1ha  t3_l9t1ha  glk3rkp   Salmankhan42069   \n",
       "9         1612150618   t3_l9t1ha  t3_l9t1ha  glk3kvz  Pile_of_Walthers   \n",
       "...              ...         ...        ...      ...               ...   \n",
       "2127588   1603994257  t1_gaidthx  t3_jkbmw0  gaiivoy     GogglesPisano   \n",
       "2127589   1603993347   t3_jkbmw0  t3_jkbmw0  gaigl2w         etzel1200   \n",
       "2127590   1603992287   t3_jkbmw0  t3_jkbmw0  gaidthx      paywallpiker   \n",
       "2127591   1603885499   t3_jj3nmi  t3_jj3nmi  gaddjx4     GogglesPisano   \n",
       "2127592   1603745345   t3_jigjpx  t3_jigjpx  ga7dx8h          ual33655   \n",
       "\n",
       "                                                      body       subreddit  \\\n",
       "1        Lol not even. Where I heard about it most was ...   AskThe_Donald   \n",
       "2        To paraphrase one of the great conservatives o...   AskThe_Donald   \n",
       "4                                             Like coding.   AskThe_Donald   \n",
       "6                                    Wait, this is a meme?   AskThe_Donald   \n",
       "9                                Which one is more useful?   AskThe_Donald   \n",
       "...                                                    ...             ...   \n",
       "2127588  I remember when Fox, MSNBC and CNN [all broadc...  hillaryclinton   \n",
       "2127589                      That’s on Comey, not the NYT.  hillaryclinton   \n",
       "2127590  People neglect that the media played a huge pa...  hillaryclinton   \n",
       "2127591  The Dakotas are getting slammed, especially co...  hillaryclinton   \n",
       "2127592  Thanks for telling it like it is you should ha...  hillaryclinton   \n",
       "\n",
       "                                                      link  score  \\\n",
       "1        /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      1   \n",
       "2        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      3   \n",
       "4        /r/AskThe_Donald/comments/l9lonl/keystone_pipe...      0   \n",
       "6        /r/AskThe_Donald/comments/l9t1ha/we_definitely...     -1   \n",
       "9        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      2   \n",
       "...                                                    ...    ...   \n",
       "2127588  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127589  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      2   \n",
       "2127590  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127591  /r/hillaryclinton/comments/jj3nmi/oc_states_in...      1   \n",
       "2127592  /r/hillaryclinton/comments/jigjpx/hillary_clin...      1   \n",
       "\n",
       "        author_flair_text  ...  stereotype  aggregate  user_activity_level  \\\n",
       "1              :T: NOVICE  ...    0.234234   0.064974                   46   \n",
       "2              :T: NOVICE  ...    0.536770   0.125015                  665   \n",
       "4              :T: NOVICE  ...    0.288935   0.087804                   13   \n",
       "6              :T: NOVICE  ...    0.249884   0.066450                    7   \n",
       "9              :T: NOVICE  ...    0.308906   0.067157                  161   \n",
       "...                   ...  ...         ...        ...                  ...   \n",
       "2127588               NaN  ...    0.238228   0.043984                  401   \n",
       "2127589               NaN  ...    0.261439   0.093671                  444   \n",
       "2127590               NaN  ...    0.320039   0.079030                   21   \n",
       "2127591               NaN  ...    0.232618   0.270247                  401   \n",
       "2127592               NaN  ...    0.317675   0.557658                    2   \n",
       "\n",
       "         user_toxicity_level  personal_attacks_b  third_party_attacks_b  \\\n",
       "1                        NaN               False                  False   \n",
       "2                       17.0               False                  False   \n",
       "4                        NaN               False                  False   \n",
       "6                        NaN               False                  False   \n",
       "9                        1.0               False                  False   \n",
       "...                      ...                 ...                    ...   \n",
       "2127588                 10.0               False                  False   \n",
       "2127589                  NaN               False                  False   \n",
       "2127590                  NaN               False                  False   \n",
       "2127591                 10.0               False                   True   \n",
       "2127592                  1.0                True                   True   \n",
       "\n",
       "         vulgarity_b  aspersion_b  stereotype_b  aggregate_b  \n",
       "1              False        False         False        False  \n",
       "2              False        False          True        False  \n",
       "4              False        False         False        False  \n",
       "6              False        False         False        False  \n",
       "9              False        False         False        False  \n",
       "...              ...          ...           ...          ...  \n",
       "2127588        False        False         False        False  \n",
       "2127589        False        False         False        False  \n",
       "2127590        False        False         False        False  \n",
       "2127591        False        False         False         True  \n",
       "2127592        False        False         False         True  \n",
       "\n",
       "[35567120 rows x 27 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comments['personal_attacks_b'] = all_comments.personal_attacks > 0.5\n",
    "all_comments['third_party_attacks_b'] = all_comments.third_party_attacks > 0.5\n",
    "all_comments['vulgarity_b'] = all_comments.vulgarity > 0.5\n",
    "all_comments['aspersion_b'] = all_comments.aspersion > 0.5\n",
    "all_comments['stereotype_b'] = all_comments.stereotype > 0.5\n",
    "quantile_75 = np.quantile(all_comments['aggregate'], 0.75)\n",
    "all_comments['aggregate_b'] = all_comments['aggregate'] > quantile_75\n",
    "all_comments"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Level of different incivility across subreddits and political disposition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "defaultdict(<class 'list'>, {'vulgarity': [[1795355, 29316651], [217021, 3775471], [2779, 175549]], 'personal_attacks': [[1270010, 29841996], [284838, 3707654], [8721, 169607]], 'third_party_attacks': [[2100837, 29011169], [280245, 3712247], [6771, 171557]], 'stereotype': [[3935999, 27176007], [535637, 3456855], [23234, 155094]], 'aspersion': [[1712228, 29399778], [256242, 3736250], [10402, 167926]], 'aggregate': [[7715576, 23396430], [1103806, 2888686], [33459, 144869]]})\n",
      "defaultdict(<class 'list'>, {'vulgarity': [0.05770617940868229, 0.05435727861195464, 0.015583643622986856], 'personal_attacks': [0.04082057582529394, 0.07134341158354231, 0.04890426629581445], 'third_party_attacks': [0.06752496126414992, 0.07019300226525188, 0.03796935983132206], 'stereotype': [0.1265106145839648, 0.13416107032900754, 0.1302880086133417], 'aspersion': [0.055034316977182376, 0.06418096767632847, 0.058330716432640976], 'aggregate': [0.24799352378628367, 0.2764704350065072, 0.1876261719976672]})\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "overall_pro = collections.defaultdict(list)\n",
    "dfs = [left, right, neutral]\n",
    "res = collections.defaultdict(list)\n",
    "for index, i in enumerate(dfs):\n",
    "    temp = all_comments[all_comments.subreddit.isin(i)]\n",
    "    for c in categories:\n",
    "        pos = len(temp[temp[c+\"_b\"] == True])\n",
    "        overall_pro[c].append(pos / len(temp))\n",
    "        res[c].append([pos, len(temp) - pos])\n",
    "print(res)\n",
    "print(overall_pro)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vulgarity 8688.772522763426 0.0 2\n",
      "personal_attacks 67819.51012372822 0.0 2\n",
      "third_party_attacks 3343.0607013661574 0.0 2\n",
      "stereotype 1852.640592451113 0.0 2\n",
      "aspersion 5403.735615697343 0.0 2\n",
      "aggregate 19042.85507538704 0.0 2\n"
     ]
    }
   ],
   "source": [
    "for k in res.keys():\n",
    "    g, p, dof, expctd = stats.chi2_contingency(list(res[k]), lambda_=\"log-likelihood\")\n",
    "    print(k,g,p,dof)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['BlueMidterm2018',\n",
       "  'politics',\n",
       "  'JoeBiden',\n",
       "  'Libertarian',\n",
       "  'OurPresident',\n",
       "  'PoliticalDiscussion',\n",
       "  'Political_Revolution',\n",
       "  'SandersForPresident',\n",
       "  'VoteBlue',\n",
       "  'hillaryclinton',\n",
       "  'politics',\n",
       "  'progressive'],\n",
       " ['AskThe_Donald',\n",
       "  'Conservative',\n",
       "  'ConservativesOnly',\n",
       "  'The_Mueller',\n",
       "  'donaldtrump'],\n",
       " ['Ask_Politics', 'NeutralPolitics', 'uspolitics']]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import mannwhitneyu\n",
    "# _, pnorm = mannwhitneyu(all_comments[all_comments.subreddit.isin(dfs[1])].namecalling, all_comments[all_comments.subreddit.isin(dfs[0])].namecalling, alternative=\"greater\")\n",
    "# print(pnorm)\n",
    "# _, pnorm = mannwhitneyu(all_comments[all_comments.subreddit.isin(dfs[1])].demeaning, all_comments[all_comments.subreddit.isin(dfs[0])].demeaning, alternative=\"greater\")\n",
    "# print(pnorm)\n",
    "# _, pnorm = mannwhitneyu(all_comments[all_comments.subreddit.isin(dfs[1])].vulgarity, all_comments[all_comments.subreddit.isin(dfs[0])].vulgarity, alternative=\"greater\")\n",
    "# print(pnorm)\n",
    "# _, pnorm = mannwhitneyu(all_comments[all_comments.subreddit.isin(dfs[1])].stereotype, all_comments[all_comments.subreddit.isin(dfs[0])].stereotype, alternative=\"greater\")\n",
    "# print(pnorm)\n",
    "_, pnorm = mannwhitneyu(all_comments[all_comments.subreddit.isin(dfs[1])]['aggregate'], all_comments[all_comments.subreddit.isin(dfs[0])]['aggregate'], alternative=\"less\")\n",
    "print(pnorm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KstestResult(statistic=0.009276829277628643, pvalue=2.9514966382571336e-302)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Kolmogorov-Smirnov test\n",
    "\n",
    "from scipy import stats\n",
    "stats.kstest(all_comments[all_comments.subreddit.isin(dfs[0])].vulgarity, all_comments[all_comments.subreddit.isin(dfs[1])].vulgarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vulgarity\n"
     ]
    },
    {
     "ename": "NameError",
     "evalue": "name 'stats' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/1d/mj1m58c50b790y544kd1bflm0000gn/T/ipykernel_6046/973717008.py\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[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcategories\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mt_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_val\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstats\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mttest_ind\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall_comments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mall_comments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubreddit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mall_comments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mall_comments\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubreddit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdfs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mequal_var\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malternative\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'greater'\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      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"t_stat: {}, p_val: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_stat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mp_val\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;31mNameError\u001b[0m: name 'stats' is not defined"
     ]
    }
   ],
   "source": [
    "for c in categories:\n",
    "    print(c)\n",
    "    t_stat, p_val = stats.ttest_ind(all_comments[all_comments.subreddit.isin(dfs[0])][c], all_comments[all_comments.subreddit.isin(dfs[1])][c], equal_var=False, alternative='greater')\n",
    "    print(\"t_stat: {}, p_val: {}\".format(t_stat, p_val))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = all_comments[all_comments.parent_id.str[3:] == all_comments.link_id.str[3:]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Level of incivility across topics (divisive vs. non-divisive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Unemployment Far Worse in Lockdown States, Data Show | AIER',\n",
       " 'Trump is winning the voter registration battle against Biden in key states',\n",
       " 'Hand recount finds 3k votes in Georgia county. “It appears that our elections office did not misplace any ballots, rather it appears as if this is a computer software mishap with the Dominion system.”',\n",
       " \"Serious question! Why can't we let the major cities be Democrat and the rest of the country Republican?\",\n",
       " \"61 percent believe America's response to COVID crisis is unsuccessful, poll says\",\n",
       " \"'Their blood is on your hands': veteran confronts Biden on war record\",\n",
       " 'Fake News: A Political Weapon',\n",
       " 'Republican Voters Agree: Fill That Seat',\n",
       " 'Biden is continuing his proven history of plagiarism by copying the Trump campaign platform. He’s been selling out and offshoring our manufacturers for 47 years!',\n",
       " \"Rep. Omar: We Have to Prosecute Trump for 'Criminal Neglect' in Coronavirus Pandemic\"]"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(all_posts[all_posts.divisiveness == 1].sample(10).title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "stereotypes = np.load('/Users/yujia/research/civility/data/stereotypes_new_all.npy', allow_pickle=True)\n",
    "all_comments['stereotype'] = all_comments['id'].map(stereotypes.item())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>created_utc</th>\n",
       "      <th>parent_id</th>\n",
       "      <th>link_id</th>\n",
       "      <th>id</th>\n",
       "      <th>author</th>\n",
       "      <th>body</th>\n",
       "      <th>subreddit</th>\n",
       "      <th>link</th>\n",
       "      <th>score</th>\n",
       "      <th>author_flair_text</th>\n",
       "      <th>vulgarity</th>\n",
       "      <th>personal_attacks</th>\n",
       "      <th>incivility</th>\n",
       "      <th>third_party_attacks</th>\n",
       "      <th>aspersion</th>\n",
       "      <th>comment_len</th>\n",
       "      <th>compound_sentiment</th>\n",
       "      <th>op_divisiveness</th>\n",
       "      <th>stereotype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1612153203</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>t3_l9gngx</td>\n",
       "      <td>glk8hec</td>\n",
       "      <td>runrain</td>\n",
       "      <td>Lol not even. Where I heard about it most was ...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9gngx/doj_arrest_re...</td>\n",
       "      <td>1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.160585</td>\n",
       "      <td>0.001712</td>\n",
       "      <td>0.332831</td>\n",
       "      <td>0.004760</td>\n",
       "      <td>0.000276</td>\n",
       "      <td>96</td>\n",
       "      <td>-0.2357</td>\n",
       "      <td>1</td>\n",
       "      <td>0.234234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1612152460</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk757a</td>\n",
       "      <td>SilverHerfer</td>\n",
       "      <td>To paraphrase one of the great conservatives o...</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>3</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.115829</td>\n",
       "      <td>0.002649</td>\n",
       "      <td>0.112667</td>\n",
       "      <td>0.003651</td>\n",
       "      <td>0.000283</td>\n",
       "      <td>26</td>\n",
       "      <td>0.8428</td>\n",
       "      <td>1</td>\n",
       "      <td>0.536770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1612151108</td>\n",
       "      <td>t1_glit0la</td>\n",
       "      <td>t3_l9lonl</td>\n",
       "      <td>glk4jhg</td>\n",
       "      <td>Psychowitz</td>\n",
       "      <td>Like coding.</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9lonl/keystone_pipe...</td>\n",
       "      <td>0</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.203247</td>\n",
       "      <td>0.002789</td>\n",
       "      <td>0.070185</td>\n",
       "      <td>0.003872</td>\n",
       "      <td>0.000680</td>\n",
       "      <td>2</td>\n",
       "      <td>0.3612</td>\n",
       "      <td>1</td>\n",
       "      <td>0.288935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1612150714</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3rkp</td>\n",
       "      <td>Salmankhan42069</td>\n",
       "      <td>Wait, this is a meme?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>-1</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.148250</td>\n",
       "      <td>0.004309</td>\n",
       "      <td>0.323237</td>\n",
       "      <td>0.005040</td>\n",
       "      <td>0.000420</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.249884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1612150618</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>t3_l9t1ha</td>\n",
       "      <td>glk3kvz</td>\n",
       "      <td>Pile_of_Walthers</td>\n",
       "      <td>Which one is more useful?</td>\n",
       "      <td>AskThe_Donald</td>\n",
       "      <td>/r/AskThe_Donald/comments/l9t1ha/we_definitely...</td>\n",
       "      <td>2</td>\n",
       "      <td>:T: NOVICE</td>\n",
       "      <td>0.094569</td>\n",
       "      <td>0.003459</td>\n",
       "      <td>0.050714</td>\n",
       "      <td>0.003793</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>5</td>\n",
       "      <td>0.4927</td>\n",
       "      <td>1</td>\n",
       "      <td>0.308906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127588</th>\n",
       "      <td>1603994257</td>\n",
       "      <td>t1_gaidthx</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaiivoy</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>I remember when Fox, MSNBC and CNN [all broadc...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.066037</td>\n",
       "      <td>0.001627</td>\n",
       "      <td>0.016209</td>\n",
       "      <td>0.005488</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>44</td>\n",
       "      <td>-0.2023</td>\n",
       "      <td>1</td>\n",
       "      <td>0.238228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127589</th>\n",
       "      <td>1603993347</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaigl2w</td>\n",
       "      <td>etzel1200</td>\n",
       "      <td>That’s on Comey, not the NYT.</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.256163</td>\n",
       "      <td>0.002296</td>\n",
       "      <td>0.130545</td>\n",
       "      <td>0.004628</td>\n",
       "      <td>0.000172</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.261439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127590</th>\n",
       "      <td>1603992287</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>t3_jkbmw0</td>\n",
       "      <td>gaidthx</td>\n",
       "      <td>paywallpiker</td>\n",
       "      <td>People neglect that the media played a huge pa...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.104737</td>\n",
       "      <td>0.002317</td>\n",
       "      <td>0.037370</td>\n",
       "      <td>0.004813</td>\n",
       "      <td>0.029973</td>\n",
       "      <td>63</td>\n",
       "      <td>0.8020</td>\n",
       "      <td>1</td>\n",
       "      <td>0.320039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127591</th>\n",
       "      <td>1603885499</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>t3_jj3nmi</td>\n",
       "      <td>gaddjx4</td>\n",
       "      <td>GogglesPisano</td>\n",
       "      <td>The Dakotas are getting slammed, especially co...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jj3nmi/oc_states_in...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.209203</td>\n",
       "      <td>0.002534</td>\n",
       "      <td>0.611730</td>\n",
       "      <td>0.837685</td>\n",
       "      <td>0.000281</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.232618</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2127592</th>\n",
       "      <td>1603745345</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>t3_jigjpx</td>\n",
       "      <td>ga7dx8h</td>\n",
       "      <td>ual33655</td>\n",
       "      <td>Thanks for telling it like it is you should ha...</td>\n",
       "      <td>hillaryclinton</td>\n",
       "      <td>/r/hillaryclinton/comments/jigjpx/hillary_clin...</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.251816</td>\n",
       "      <td>0.989517</td>\n",
       "      <td>0.990107</td>\n",
       "      <td>0.956174</td>\n",
       "      <td>0.000320</td>\n",
       "      <td>17</td>\n",
       "      <td>0.6597</td>\n",
       "      <td>0</td>\n",
       "      <td>0.317675</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35567120 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         created_utc   parent_id    link_id       id            author  \\\n",
       "1         1612153203   t3_l9gngx  t3_l9gngx  glk8hec           runrain   \n",
       "2         1612152460   t3_l9t1ha  t3_l9t1ha  glk757a      SilverHerfer   \n",
       "4         1612151108  t1_glit0la  t3_l9lonl  glk4jhg        Psychowitz   \n",
       "6         1612150714   t3_l9t1ha  t3_l9t1ha  glk3rkp   Salmankhan42069   \n",
       "9         1612150618   t3_l9t1ha  t3_l9t1ha  glk3kvz  Pile_of_Walthers   \n",
       "...              ...         ...        ...      ...               ...   \n",
       "2127588   1603994257  t1_gaidthx  t3_jkbmw0  gaiivoy     GogglesPisano   \n",
       "2127589   1603993347   t3_jkbmw0  t3_jkbmw0  gaigl2w         etzel1200   \n",
       "2127590   1603992287   t3_jkbmw0  t3_jkbmw0  gaidthx      paywallpiker   \n",
       "2127591   1603885499   t3_jj3nmi  t3_jj3nmi  gaddjx4     GogglesPisano   \n",
       "2127592   1603745345   t3_jigjpx  t3_jigjpx  ga7dx8h          ual33655   \n",
       "\n",
       "                                                      body       subreddit  \\\n",
       "1        Lol not even. Where I heard about it most was ...   AskThe_Donald   \n",
       "2        To paraphrase one of the great conservatives o...   AskThe_Donald   \n",
       "4                                             Like coding.   AskThe_Donald   \n",
       "6                                    Wait, this is a meme?   AskThe_Donald   \n",
       "9                                Which one is more useful?   AskThe_Donald   \n",
       "...                                                    ...             ...   \n",
       "2127588  I remember when Fox, MSNBC and CNN [all broadc...  hillaryclinton   \n",
       "2127589                      That’s on Comey, not the NYT.  hillaryclinton   \n",
       "2127590  People neglect that the media played a huge pa...  hillaryclinton   \n",
       "2127591  The Dakotas are getting slammed, especially co...  hillaryclinton   \n",
       "2127592  Thanks for telling it like it is you should ha...  hillaryclinton   \n",
       "\n",
       "                                                      link  score  \\\n",
       "1        /r/AskThe_Donald/comments/l9gngx/doj_arrest_re...      1   \n",
       "2        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      3   \n",
       "4        /r/AskThe_Donald/comments/l9lonl/keystone_pipe...      0   \n",
       "6        /r/AskThe_Donald/comments/l9t1ha/we_definitely...     -1   \n",
       "9        /r/AskThe_Donald/comments/l9t1ha/we_definitely...      2   \n",
       "...                                                    ...    ...   \n",
       "2127588  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127589  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      2   \n",
       "2127590  /r/hillaryclinton/comments/jkbmw0/4_yrs_ago_to...      4   \n",
       "2127591  /r/hillaryclinton/comments/jj3nmi/oc_states_in...      1   \n",
       "2127592  /r/hillaryclinton/comments/jigjpx/hillary_clin...      1   \n",
       "\n",
       "        author_flair_text  vulgarity  personal_attacks  incivility  \\\n",
       "1              :T: NOVICE   0.160585          0.001712    0.332831   \n",
       "2              :T: NOVICE   0.115829          0.002649    0.112667   \n",
       "4              :T: NOVICE   0.203247          0.002789    0.070185   \n",
       "6              :T: NOVICE   0.148250          0.004309    0.323237   \n",
       "9              :T: NOVICE   0.094569          0.003459    0.050714   \n",
       "...                   ...        ...               ...         ...   \n",
       "2127588               NaN   0.066037          0.001627    0.016209   \n",
       "2127589               NaN   0.256163          0.002296    0.130545   \n",
       "2127590               NaN   0.104737          0.002317    0.037370   \n",
       "2127591               NaN   0.209203          0.002534    0.611730   \n",
       "2127592               NaN   0.251816          0.989517    0.990107   \n",
       "\n",
       "         third_party_attacks  aspersion  comment_len  compound_sentiment  \\\n",
       "1                   0.004760   0.000276           96             -0.2357   \n",
       "2                   0.003651   0.000283           26              0.8428   \n",
       "4                   0.003872   0.000680            2              0.3612   \n",
       "6                   0.005040   0.000420            5              0.0000   \n",
       "9                   0.003793   0.000210            5              0.4927   \n",
       "...                      ...        ...          ...                 ...   \n",
       "2127588             0.005488   0.000130           44             -0.2023   \n",
       "2127589             0.004628   0.000172            6              0.0000   \n",
       "2127590             0.004813   0.029973           63              0.8020   \n",
       "2127591             0.837685   0.000281           29              0.0000   \n",
       "2127592             0.956174   0.000320           17              0.6597   \n",
       "\n",
       "         op_divisiveness  stereotype  \n",
       "1                      1    0.234234  \n",
       "2                      1    0.536770  \n",
       "4                      1    0.288935  \n",
       "6                      1    0.249884  \n",
       "9                      1    0.308906  \n",
       "...                  ...         ...  \n",
       "2127588                1    0.238228  \n",
       "2127589                1    0.261439  \n",
       "2127590                1    0.320039  \n",
       "2127591                1    0.232618  \n",
       "2127592                0    0.317675  \n",
       "\n",
       "[35567120 rows x 19 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_comments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vulgarity\n",
      "t_stat: 110.59113022120884, p_val: 0.0\n",
      "personal_attacks\n",
      "t_stat: 52.252095789696156, p_val: 0.0\n",
      "third_party_attacks\n",
      "t_stat: 226.98601339845402, p_val: 0.0\n",
      "stereotype\n",
      "t_stat: 28.678507204769673, p_val: 3.583762850525615e-181\n",
      "aspersion\n",
      "t_stat: 158.61843814819966, p_val: 0.0\n",
      "aggregate\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'aggregate'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3620\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3621\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\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   3622\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'aggregate'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/1d/mj1m58c50b790y544kd1bflm0000gn/T/ipykernel_10039/3520136273.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mc\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcategories\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m   \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m   \u001b[0mdivisive_means\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivisive_comments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\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     15\u001b[0m   \u001b[0mdivisive_std\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstd\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdivisive_comments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\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[1;32m     16\u001b[0m   \u001b[0mnon_divisive_means\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnon_divisive_comments\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m]\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~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3503\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3504\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3505\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3506\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\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[1;32m   3507\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.9/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3621\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3622\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3623\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3624\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3625\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'aggregate'"
     ]
    }
   ],
   "source": [
    "\n",
    "divisive_comments = all_comments[all_comments.link_id.str[3:].isin(all_posts[all_posts.divisiveness == 1].id)]\n",
    "non_divisive_comments = all_comments[all_comments.link_id.str[3:].isin(all_posts[all_posts.divisiveness == 0].id)]\n",
    "len(divisive_comments), len(non_divisive_comments)\n",
    "\n",
    "categories = ['vulgarity', 'personal_attacks', 'third_party_attacks', 'stereotype', 'aspersion', 'aggregate']\n",
    "\n",
    "divisive_means = []\n",
    "divisive_std = []\n",
    "non_divisive_means = []\n",
    "non_divisive_std = []\n",
    "import scipy.stats as stats\n",
    "for c in categories:\n",
    "  print(c)\n",
    "  divisive_means.append(np.mean(divisive_comments[c]))\n",
    "  divisive_std.append(np.std(divisive_comments[c]))\n",
    "  non_divisive_means.append(np.mean(non_divisive_comments[c]))\n",
    "  non_divisive_std.append(np.std(non_divisive_comments[c]))\n",
    "  t_stat, p_val = stats.ttest_ind(divisive_comments[c], non_divisive_comments[c], equal_var=False, alternative='greater')\n",
    "  print(\"t_stat: {}, p_val: {}\".format(t_stat, p_val))\n"
   ]
  }
 ],
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