data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "pd.options.display.float_format = '{:20.4f}'.format\n",
    "\n",
    "import _pickle as cPickle\n",
    "import boto3\n",
    "import pandas as pd\n",
    "import boto3.session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from exploration import exploration_functions as ef"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     /home/ec2-user/nltk_data...\n",
      "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
     ]
    }
   ],
   "source": [
    "import feature_generation_functions as fgf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv(\"s3://advancedml-koch-mathur-hinkson/train.csv\")\n",
    "test = pd.read_csv(\"s3://advancedml-koch-mathur-hinkson/test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((97320, 2), (1804874, 45))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.shape, train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['id', 'target', 'comment_text', 'severe_toxicity', 'obscene',\n",
       "       'identity_attack', 'insult', 'threat', 'asian', 'atheist', 'bisexual',\n",
       "       'black', 'buddhist', 'christian', 'female', 'heterosexual', 'hindu',\n",
       "       'homosexual_gay_or_lesbian', 'intellectual_or_learning_disability',\n",
       "       'jewish', 'latino', 'male', 'muslim', 'other_disability',\n",
       "       'other_gender', 'other_race_or_ethnicity', 'other_religion',\n",
       "       'other_sexual_orientation', 'physical_disability',\n",
       "       'psychiatric_or_mental_illness', 'transgender', 'white', 'created_date',\n",
       "       'publication_id', 'parent_id', 'article_id', 'rating', 'funny', 'wow',\n",
       "       'sad', 'likes', 'disagree', 'sexual_explicit',\n",
       "       'identity_annotator_count', 'toxicity_annotator_count'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Average Toxicity of Comments with Specific Identities or Reactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10400058616844869"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.funny > 0].target.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.18159273674318185"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.male > 0.5].target.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.1726707927689452"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train[train.female > 0.5].target.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >id</th>        <th class=\"col_heading level0 col1\" >target</th>        <th class=\"col_heading level0 col2\" >comment_text</th>        <th class=\"col_heading level0 col3\" >severe_toxicity</th>        <th class=\"col_heading level0 col4\" >obscene</th>        <th class=\"col_heading level0 col5\" >identity_attack</th>        <th class=\"col_heading level0 col6\" >insult</th>        <th class=\"col_heading level0 col7\" >threat</th>        <th class=\"col_heading level0 col8\" >asian</th>        <th class=\"col_heading level0 col9\" >atheist</th>        <th class=\"col_heading level0 col10\" >bisexual</th>        <th class=\"col_heading level0 col11\" >black</th>        <th class=\"col_heading level0 col12\" >buddhist</th>        <th class=\"col_heading level0 col13\" >christian</th>        <th class=\"col_heading level0 col14\" >female</th>        <th class=\"col_heading level0 col15\" >heterosexual</th>        <th class=\"col_heading level0 col16\" >hindu</th>        <th class=\"col_heading level0 col17\" >homosexual_gay_or_lesbian</th>        <th class=\"col_heading level0 col18\" >intellectual_or_learning_disability</th>        <th class=\"col_heading level0 col19\" >jewish</th>        <th class=\"col_heading level0 col20\" >latino</th>        <th class=\"col_heading level0 col21\" >male</th>        <th class=\"col_heading level0 col22\" >muslim</th>        <th class=\"col_heading level0 col23\" >other_disability</th>        <th class=\"col_heading level0 col24\" >other_gender</th>        <th class=\"col_heading level0 col25\" >other_race_or_ethnicity</th>        <th class=\"col_heading level0 col26\" >other_religion</th>        <th class=\"col_heading level0 col27\" >other_sexual_orientation</th>        <th class=\"col_heading level0 col28\" >physical_disability</th>        <th class=\"col_heading level0 col29\" >psychiatric_or_mental_illness</th>        <th class=\"col_heading level0 col30\" >transgender</th>        <th class=\"col_heading level0 col31\" >white</th>        <th class=\"col_heading level0 col32\" >created_date</th>        <th class=\"col_heading level0 col33\" >publication_id</th>        <th class=\"col_heading level0 col34\" >parent_id</th>        <th class=\"col_heading level0 col35\" >article_id</th>        <th class=\"col_heading level0 col36\" >rating</th>        <th class=\"col_heading level0 col37\" >funny</th>        <th class=\"col_heading level0 col38\" >wow</th>        <th class=\"col_heading level0 col39\" >sad</th>        <th class=\"col_heading level0 col40\" >likes</th>        <th class=\"col_heading level0 col41\" >disagree</th>        <th class=\"col_heading level0 col42\" >sexual_explicit</th>        <th class=\"col_heading level0 col43\" >identity_annotator_count</th>        <th class=\"col_heading level0 col44\" >toxicity_annotator_count</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col0\" class=\"data row0 col0\" >59848</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col1\" class=\"data row0 col1\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col2\" class=\"data row0 col2\" >This is so cool. It's like, 'would you want your mother to read this??' Really great idea, well done!</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col3\" class=\"data row0 col3\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col4\" class=\"data row0 col4\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col5\" class=\"data row0 col5\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col6\" class=\"data row0 col6\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col7\" class=\"data row0 col7\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col8\" class=\"data row0 col8\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col9\" class=\"data row0 col9\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col10\" class=\"data row0 col10\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col11\" class=\"data row0 col11\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col12\" class=\"data row0 col12\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col13\" class=\"data row0 col13\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col14\" class=\"data row0 col14\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col15\" class=\"data row0 col15\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col16\" class=\"data row0 col16\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col17\" class=\"data row0 col17\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col18\" class=\"data row0 col18\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col19\" class=\"data row0 col19\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col20\" class=\"data row0 col20\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col21\" class=\"data row0 col21\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col22\" class=\"data row0 col22\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col23\" class=\"data row0 col23\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col24\" class=\"data row0 col24\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col25\" class=\"data row0 col25\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col26\" class=\"data row0 col26\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col27\" class=\"data row0 col27\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col28\" class=\"data row0 col28\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col29\" class=\"data row0 col29\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col30\" class=\"data row0 col30\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col31\" class=\"data row0 col31\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col32\" class=\"data row0 col32\" >2015-09-29 10:50:41.987077+00</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col33\" class=\"data row0 col33\" >2</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col34\" class=\"data row0 col34\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col35\" class=\"data row0 col35\" >2006</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col36\" class=\"data row0 col36\" >rejected</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col37\" class=\"data row0 col37\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col38\" class=\"data row0 col38\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col39\" class=\"data row0 col39\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col40\" class=\"data row0 col40\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col41\" class=\"data row0 col41\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col42\" class=\"data row0 col42\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col43\" class=\"data row0 col43\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row0_col44\" class=\"data row0 col44\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col0\" class=\"data row1 col0\" >59849</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col1\" class=\"data row1 col1\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col2\" class=\"data row1 col2\" >Thank you!! This would make my life a lot less anxiety-inducing. Keep it up, and don't let anyone get in your way!</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col3\" class=\"data row1 col3\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col4\" class=\"data row1 col4\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col5\" class=\"data row1 col5\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col7\" class=\"data row1 col7\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col8\" class=\"data row1 col8\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col9\" class=\"data row1 col9\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col10\" class=\"data row1 col10\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col11\" class=\"data row1 col11\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col12\" class=\"data row1 col12\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col13\" class=\"data row1 col13\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col15\" class=\"data row1 col15\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col16\" class=\"data row1 col16\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col17\" class=\"data row1 col17\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col18\" class=\"data row1 col18\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col19\" class=\"data row1 col19\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col20\" class=\"data row1 col20\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col21\" class=\"data row1 col21\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col22\" class=\"data row1 col22\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col23\" class=\"data row1 col23\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col24\" class=\"data row1 col24\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col25\" class=\"data row1 col25\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col26\" class=\"data row1 col26\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col27\" class=\"data row1 col27\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col28\" class=\"data row1 col28\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col29\" class=\"data row1 col29\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col30\" class=\"data row1 col30\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col31\" class=\"data row1 col31\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col32\" class=\"data row1 col32\" >2015-09-29 10:50:42.870083+00</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col33\" class=\"data row1 col33\" >2</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col34\" class=\"data row1 col34\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col35\" class=\"data row1 col35\" >2006</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col36\" class=\"data row1 col36\" >rejected</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col37\" class=\"data row1 col37\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col38\" class=\"data row1 col38\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col39\" class=\"data row1 col39\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col40\" class=\"data row1 col40\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col41\" class=\"data row1 col41\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col42\" class=\"data row1 col42\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col43\" class=\"data row1 col43\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row1_col44\" class=\"data row1 col44\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col0\" class=\"data row2 col0\" >59852</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col2\" class=\"data row2 col2\" >This is such an urgent design problem; kudos to you for taking it on. Very impressive!</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col4\" class=\"data row2 col4\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col5\" class=\"data row2 col5\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col7\" class=\"data row2 col7\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col8\" class=\"data row2 col8\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col9\" class=\"data row2 col9\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col10\" class=\"data row2 col10\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col11\" class=\"data row2 col11\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col12\" class=\"data row2 col12\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col13\" class=\"data row2 col13\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col15\" class=\"data row2 col15\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col16\" class=\"data row2 col16\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col17\" class=\"data row2 col17\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col18\" class=\"data row2 col18\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col19\" class=\"data row2 col19\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col20\" class=\"data row2 col20\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col21\" class=\"data row2 col21\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col22\" class=\"data row2 col22\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col23\" class=\"data row2 col23\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col24\" class=\"data row2 col24\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col25\" class=\"data row2 col25\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col26\" class=\"data row2 col26\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col27\" class=\"data row2 col27\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col28\" class=\"data row2 col28\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col29\" class=\"data row2 col29\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col30\" class=\"data row2 col30\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col31\" class=\"data row2 col31\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col32\" class=\"data row2 col32\" >2015-09-29 10:50:45.222647+00</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col33\" class=\"data row2 col33\" >2</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col34\" class=\"data row2 col34\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col35\" class=\"data row2 col35\" >2006</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col36\" class=\"data row2 col36\" >rejected</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col37\" class=\"data row2 col37\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col38\" class=\"data row2 col38\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col39\" class=\"data row2 col39\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col40\" class=\"data row2 col40\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col41\" class=\"data row2 col41\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col42\" class=\"data row2 col42\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col43\" class=\"data row2 col43\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row2_col44\" class=\"data row2 col44\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col0\" class=\"data row3 col0\" >59855</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col1\" class=\"data row3 col1\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col2\" class=\"data row3 col2\" >Is this something I'll be able to install on my site? When will you be releasing it?</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col3\" class=\"data row3 col3\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col4\" class=\"data row3 col4\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col5\" class=\"data row3 col5\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col7\" class=\"data row3 col7\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col8\" class=\"data row3 col8\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col9\" class=\"data row3 col9\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col10\" class=\"data row3 col10\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col11\" class=\"data row3 col11\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col12\" class=\"data row3 col12\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col13\" class=\"data row3 col13\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col14\" class=\"data row3 col14\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col15\" class=\"data row3 col15\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col16\" class=\"data row3 col16\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col17\" class=\"data row3 col17\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col18\" class=\"data row3 col18\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col19\" class=\"data row3 col19\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col20\" class=\"data row3 col20\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col21\" class=\"data row3 col21\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col22\" class=\"data row3 col22\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col23\" class=\"data row3 col23\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col24\" class=\"data row3 col24\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col25\" class=\"data row3 col25\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col26\" class=\"data row3 col26\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col27\" class=\"data row3 col27\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col28\" class=\"data row3 col28\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col29\" class=\"data row3 col29\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col30\" class=\"data row3 col30\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col31\" class=\"data row3 col31\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col32\" class=\"data row3 col32\" >2015-09-29 10:50:47.601894+00</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col33\" class=\"data row3 col33\" >2</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col34\" class=\"data row3 col34\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col35\" class=\"data row3 col35\" >2006</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col36\" class=\"data row3 col36\" >rejected</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col37\" class=\"data row3 col37\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col38\" class=\"data row3 col38\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col39\" class=\"data row3 col39\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col40\" class=\"data row3 col40\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col41\" class=\"data row3 col41\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col42\" class=\"data row3 col42\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col43\" class=\"data row3 col43\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row3_col44\" class=\"data row3 col44\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col0\" class=\"data row4 col0\" >59856</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col1\" class=\"data row4 col1\" >0.893617</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col2\" class=\"data row4 col2\" >haha you guys are a bunch of losers.</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col3\" class=\"data row4 col3\" >0.0212766</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col4\" class=\"data row4 col4\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col5\" class=\"data row4 col5\" >0.0212766</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col6\" class=\"data row4 col6\" >0.87234</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col7\" class=\"data row4 col7\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col8\" class=\"data row4 col8\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col9\" class=\"data row4 col9\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col10\" class=\"data row4 col10\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col11\" class=\"data row4 col11\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col12\" class=\"data row4 col12\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col13\" class=\"data row4 col13\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col14\" class=\"data row4 col14\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col15\" class=\"data row4 col15\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col16\" class=\"data row4 col16\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col17\" class=\"data row4 col17\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col18\" class=\"data row4 col18\" >0.25</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col19\" class=\"data row4 col19\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col20\" class=\"data row4 col20\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col21\" class=\"data row4 col21\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col22\" class=\"data row4 col22\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col23\" class=\"data row4 col23\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col24\" class=\"data row4 col24\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col25\" class=\"data row4 col25\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col26\" class=\"data row4 col26\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col27\" class=\"data row4 col27\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col28\" class=\"data row4 col28\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col29\" class=\"data row4 col29\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col30\" class=\"data row4 col30\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col31\" class=\"data row4 col31\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col32\" class=\"data row4 col32\" >2015-09-29 10:50:48.488476+00</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col33\" class=\"data row4 col33\" >2</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col34\" class=\"data row4 col34\" >nan</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col35\" class=\"data row4 col35\" >2006</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col36\" class=\"data row4 col36\" >rejected</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col37\" class=\"data row4 col37\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col38\" class=\"data row4 col38\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col39\" class=\"data row4 col39\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col40\" class=\"data row4 col40\" >1</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col41\" class=\"data row4 col41\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col42\" class=\"data row4 col42\" >0</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col43\" class=\"data row4 col43\" >4</td>\n",
       "                        <td id=\"T_c4e0b034_86e5_11e9_9b0a_a18ef1bd3ab4row4_col44\" class=\"data row4 col44\" >47</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f47c1011048>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head(5).style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  nan, 0.   , 0.6  , 0.167, 1.   , 0.833, 0.2  , 0.8  , 0.1  ,\n",
       "       0.5  , 0.333, 0.3  , 0.9  , 0.25 , 0.4  , 0.75 , 0.7  , 0.01 ,\n",
       "       0.002, 0.667, 0.009, 0.125, 0.111, 0.011, 0.819, 0.018, 0.721,\n",
       "       0.973, 0.759, 0.012, 0.856, 0.857, 0.889, 0.222, 0.803, 0.162,\n",
       "       0.004, 0.001, 0.026, 0.455, 0.014, 0.01 , 0.928, 0.029, 0.034,\n",
       "       0.003, 0.001, 0.617, 0.003, 0.001, 0.143, 0.011, 0.001, 0.05 ,\n",
       "       0.364, 0.881, 0.295, 0.156, 0.007, 0.599, 0.001, 0.286, 0.004,\n",
       "       0.778, 0.714, 0.818, 0.012, 0.047, 0.091, 0.009, 0.001, 0.636,\n",
       "       0.046, 0.014, 0.571, 0.919, 0.012, 0.182, 0.959, 0.005, 0.429,\n",
       "       0.875, 0.037, 0.005, 0.003, 0.023, 0.132, 0.864, 0.002, 0.03 ,\n",
       "       0.001, 0.815, 0.002, 0.001, 0.018, 0.98 , 0.727, 0.008, 0.003,\n",
       "       0.981, 0.919, 0.816, 0.909, 0.987, 0.01 , 0.004, 0.005, 0.001,\n",
       "       0.927, 0.545, 0.978, 0.015, 0.004, 0.007, 0.417, 0.02 , 0.001,\n",
       "       0.006, 0.97 , 0.99 , 0.003, 0.125, 0.009, 0.556, 0.97 , 0.011,\n",
       "       0.482, 0.001, 0.736, 0.002, 0.002, 0.035, 0.04 , 0.002, 0.004,\n",
       "       0.001, 0.027, 0.839, 0.757, 0.273, 0.001, 0.008, 0.006, 0.032,\n",
       "       0.005, 0.001, 0.004, 0.127, 0.001, 0.005, 0.144, 0.961, 0.004,\n",
       "       0.003, 0.93 , 0.989, 0.015, 0.157, 0.006, 0.79 , 0.001, 0.911,\n",
       "       0.014, 0.005, 0.056, 0.008, 0.986, 0.002, 0.542, 0.004, 0.005,\n",
       "       0.001, 0.095, 0.001, 0.004, 0.022, 0.955, 0.001, 0.033, 0.991,\n",
       "       0.007, 0.96 , 0.871, 0.001, 0.003, 0.01 , 0.008, 0.005, 0.005,\n",
       "       0.006, 0.002, 0.115, 0.071, 0.002, 0.85 , 0.828, 0.001, 0.002,\n",
       "       0.092, 0.135, 0.909, 0.001, 0.007, 0.001, 0.001, 0.004, 0.061,\n",
       "       0.975, 0.002, 0.036, 0.002, 0.003, 0.001, 0.001, 0.001, 0.005,\n",
       "       0.145, 0.92 , 0.006, 0.002, 0.001, 0.02 , 0.003, 0.002, 0.009,\n",
       "       0.002, 0.001, 0.001, 0.001, 0.002, 0.962, 0.01 , 0.005, 0.043,\n",
       "       0.188, 0.947, 0.054, 0.041, 0.017, 0.003, 0.001, 0.001, 0.286])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.male.unique().round(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_summary, train_types = ef.summarize_df(train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>other_religion</th>\n",
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       "      <th>white</th>\n",
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       "      <td>float64</td>\n",
       "      <td>numeric</td>\n",
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       "      <td>92.6078</td>\n",
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       "      <td>2015-09-29 16:20:53.059406+00</td>\n",
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       "      <td>711545</td>\n",
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       "      <td>1804874</td>\n",
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       "    <tr>\n",
       "      <th>funny</th>\n",
       "      <td>1804874</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>int64</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "      <td>85.8719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>wow</th>\n",
       "      <td>1804874</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>int64</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "      <td>96.2086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sad</th>\n",
       "      <td>1804874</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "      <td>int64</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "      <td>92.1104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>likes</th>\n",
       "      <td>1804874</td>\n",
       "      <td>0</td>\n",
       "      <td>170</td>\n",
       "      <td>int64</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>disagree</th>\n",
       "      <td>1804874</td>\n",
       "      <td>0</td>\n",
       "      <td>100</td>\n",
       "      <td>int64</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0</td>\n",
       "      <td>76.8035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sexual_explicit</th>\n",
       "      <td>1804874</td>\n",
       "      <td>0</td>\n",
       "      <td>1864</td>\n",
       "      <td>float64</td>\n",
       "      <td>numeric</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>96.1811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>identity_annotator_count</th>\n",
       "      <td>1804874</td>\n",
       "      <td>0</td>\n",
       "      <td>158</td>\n",
       "      <td>int64</td>\n",
       "      <td>ID</td>\n",
       "      <td>0</td>\n",
       "      <td>77.5536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>toxicity_annotator_count</th>\n",
       "      <td>1804874</td>\n",
       "      <td>0</td>\n",
       "      <td>775</td>\n",
       "      <td>int64</td>\n",
       "      <td>geo</td>\n",
       "      <td>4</td>\n",
       "      <td>65.6784</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    num_values num_nulls unique_values  \\\n",
       "col_name                                                                 \n",
       "id                                     1804874         0       1804874   \n",
       "target                                 1804874         0          2913   \n",
       "comment_text                           1804874         0       1780823   \n",
       "severe_toxicity                        1804874         0          1460   \n",
       "obscene                                1804874         0          2417   \n",
       "identity_attack                        1804874         0          2294   \n",
       "insult                                 1804874         0          3042   \n",
       "threat                                 1804874         0          2053   \n",
       "asian                                   405130   1399744           126   \n",
       "atheist                                 405130   1399744            85   \n",
       "bisexual                                405130   1399744            86   \n",
       "black                                   405130   1399744           128   \n",
       "buddhist                                405130   1399744            65   \n",
       "christian                               405130   1399744           161   \n",
       "female                                  405130   1399744           205   \n",
       "heterosexual                            405130   1399744           126   \n",
       "hindu                                   405130   1399744            78   \n",
       "homosexual_gay_or_lesbian               405130   1399744           125   \n",
       "intellectual_or_learning_disability     405130   1399744            77   \n",
       "jewish                                  405130   1399744           121   \n",
       "latino                                  405130   1399744           105   \n",
       "male                                    405130   1399744           243   \n",
       "muslim                                  405130   1399744           139   \n",
       "other_disability                        405130   1399744           117   \n",
       "other_gender                            405130   1399744           175   \n",
       "other_race_or_ethnicity                 405130   1399744           194   \n",
       "other_religion                          405130   1399744           192   \n",
       "other_sexual_orientation                405130   1399744           186   \n",
       "physical_disability                     405130   1399744            78   \n",
       "psychiatric_or_mental_illness           405130   1399744            95   \n",
       "transgender                             405130   1399744           125   \n",
       "white                                   405130   1399744           152   \n",
       "created_date                           1804874         0       1804362   \n",
       "publication_id                         1804874         0            53   \n",
       "parent_id                              1026228    778646        711545   \n",
       "article_id                             1804874         0        105380   \n",
       "rating                                 1804874         0             2   \n",
       "funny                                  1804874         0            61   \n",
       "wow                                    1804874         0            15   \n",
       "sad                                    1804874         0            25   \n",
       "likes                                  1804874         0           170   \n",
       "disagree                               1804874         0           100   \n",
       "sexual_explicit                        1804874         0          1864   \n",
       "identity_annotator_count               1804874         0           158   \n",
       "toxicity_annotator_count               1804874         0           775   \n",
       "\n",
       "                                    data_type col_type  \\\n",
       "col_name                                                 \n",
       "id                                      int64       ID   \n",
       "target                                float64  numeric   \n",
       "comment_text                           object     tops   \n",
       "severe_toxicity                       float64      geo   \n",
       "obscene                               float64  numeric   \n",
       "identity_attack                       float64       ID   \n",
       "insult                                float64  numeric   \n",
       "threat                                float64  numeric   \n",
       "asian                                 float64  numeric   \n",
       "atheist                               float64  numeric   \n",
       "bisexual                              float64  numeric   \n",
       "black                                 float64  numeric   \n",
       "buddhist                              float64  numeric   \n",
       "christian                             float64  numeric   \n",
       "female                                float64  numeric   \n",
       "heterosexual                          float64  numeric   \n",
       "hindu                                 float64  numeric   \n",
       "homosexual_gay_or_lesbian             float64  numeric   \n",
       "intellectual_or_learning_disability   float64  numeric   \n",
       "jewish                                float64  numeric   \n",
       "latino                                float64  numeric   \n",
       "male                                  float64  numeric   \n",
       "muslim                                float64  numeric   \n",
       "other_disability                      float64  numeric   \n",
       "other_gender                          float64  numeric   \n",
       "other_race_or_ethnicity               float64      geo   \n",
       "other_religion                        float64  numeric   \n",
       "other_sexual_orientation              float64  numeric   \n",
       "physical_disability                   float64  numeric   \n",
       "psychiatric_or_mental_illness         float64  numeric   \n",
       "transgender                           float64  numeric   \n",
       "white                                 float64  numeric   \n",
       "created_date                           object     tops   \n",
       "publication_id                          int64       ID   \n",
       "parent_id                             float64       ID   \n",
       "article_id                              int64       ID   \n",
       "rating                                 object   binary   \n",
       "funny                                   int64  numeric   \n",
       "wow                                     int64  numeric   \n",
       "sad                                     int64  numeric   \n",
       "likes                                   int64  numeric   \n",
       "disagree                                int64  numeric   \n",
       "sexual_explicit                       float64  numeric   \n",
       "identity_annotator_count                int64       ID   \n",
       "toxicity_annotator_count                int64      geo   \n",
       "\n",
       "                                                       most_common  \\\n",
       "col_name                                                             \n",
       "id                                                           59848   \n",
       "target                                                      0.0000   \n",
       "comment_text                                            Well said.   \n",
       "severe_toxicity                                             0.0000   \n",
       "obscene                                                     0.0000   \n",
       "identity_attack                                             0.0000   \n",
       "insult                                                      0.0000   \n",
       "threat                                                      0.0000   \n",
       "asian                                                       0.0000   \n",
       "atheist                                                     0.0000   \n",
       "bisexual                                                    0.0000   \n",
       "black                                                       0.0000   \n",
       "buddhist                                                    0.0000   \n",
       "christian                                                   0.0000   \n",
       "female                                                      0.0000   \n",
       "heterosexual                                                0.0000   \n",
       "hindu                                                       0.0000   \n",
       "homosexual_gay_or_lesbian                                   0.0000   \n",
       "intellectual_or_learning_disability                         0.0000   \n",
       "jewish                                                      0.0000   \n",
       "latino                                                      0.0000   \n",
       "male                                                        0.0000   \n",
       "muslim                                                      0.0000   \n",
       "other_disability                                            0.0000   \n",
       "other_gender                                                0.0000   \n",
       "other_race_or_ethnicity                                     0.0000   \n",
       "other_religion                                              0.0000   \n",
       "other_sexual_orientation                                    0.0000   \n",
       "physical_disability                                         0.0000   \n",
       "psychiatric_or_mental_illness                               0.0000   \n",
       "transgender                                                 0.0000   \n",
       "white                                                       0.0000   \n",
       "created_date                         2015-09-29 16:20:53.059406+00   \n",
       "publication_id                                                  54   \n",
       "parent_id                                             6044064.0000   \n",
       "article_id                                                  352700   \n",
       "rating                                                    approved   \n",
       "funny                                                            0   \n",
       "wow                                                              0   \n",
       "sad                                                              0   \n",
       "likes                                                            0   \n",
       "disagree                                                         0   \n",
       "sexual_explicit                                             0.0000   \n",
       "identity_annotator_count                                         0   \n",
       "toxicity_annotator_count                                         4   \n",
       "\n",
       "                                              prevalence  \n",
       "col_name                                                  \n",
       "id                                                0.0001  \n",
       "target                                           70.0749  \n",
       "comment_text                                      0.0102  \n",
       "severe_toxicity                                  94.2026  \n",
       "obscene                                          91.8457  \n",
       "identity_attack                                  87.8910  \n",
       "insult                                           74.8160  \n",
       "threat                                           94.0755  \n",
       "asian                                            97.2910  \n",
       "atheist                                          99.4439  \n",
       "bisexual                                         99.1780  \n",
       "black                                            95.1712  \n",
       "buddhist                                         99.6628  \n",
       "christian                                        84.8542  \n",
       "female                                           81.8108  \n",
       "heterosexual                                     99.1477  \n",
       "hindu                                            99.6157  \n",
       "homosexual_gay_or_lesbian                        96.2217  \n",
       "intellectual_or_learning_disability              99.3464  \n",
       "jewish                                           97.3083  \n",
       "latino                                           98.2880  \n",
       "male                                             80.2091  \n",
       "muslim                                           93.4219  \n",
       "other_disability                                 99.1250  \n",
       "other_gender                                     99.3279  \n",
       "other_race_or_ethnicity                          95.3430  \n",
       "other_religion                                   95.8700  \n",
       "other_sexual_orientation                         98.8873  \n",
       "physical_disability                              99.2035  \n",
       "psychiatric_or_mental_illness                    97.3675  \n",
       "transgender                                      98.4894  \n",
       "white                                            92.6078  \n",
       "created_date                                      0.0002  \n",
       "publication_id                                   34.7547  \n",
       "parent_id                                         0.0043  \n",
       "article_id                                        0.0763  \n",
       "rating                                           93.3449  \n",
       "funny                                            85.8719  \n",
       "wow                                              96.2086  \n",
       "sad                                              92.1104  \n",
       "likes                                            41.0816  \n",
       "disagree                                         76.8035  \n",
       "sexual_explicit                                  96.1811  \n",
       "identity_annotator_count                         77.5536  \n",
       "toxicity_annotator_count                         65.6784  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_summary.round(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_types = ef.recateogrize_col(col='toxicity_annotator_count', new_category='numeric', col_dict = train_types)\n",
    "train_types = ef.recateogrize_col(col='identity_annotator_count', new_category='numeric', col_dict = train_types)\n",
    "train_types = ef.recateogrize_col(col='identity_attack', new_category='numeric', col_dict = train_types)\n",
    "train_types = ef.recateogrize_col(col='rating', new_category='multi', col_dict = train_types)\n",
    "train_types = ef.recateogrize_col(col='severe_toxicity', new_category='numeric', col_dict = train_types)\n",
    "train_types = ef.recateogrize_col(col='other_race_or_ethnicity', new_category='numeric', col_dict = train_types)\n",
    "train_types = ef.recateogrize_col(col='comment_text', new_category='multi', col_dict = train_types)\n",
    "train_types = ef.recateogrize_col(col='created_date', new_category='multi', col_dict = train_types)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "defaultdict(list,\n",
       "            {'ID': ['id', 'publication_id', 'parent_id', 'article_id'],\n",
       "             'numeric': ['target',\n",
       "              'obscene',\n",
       "              'insult',\n",
       "              'threat',\n",
       "              'asian',\n",
       "              'atheist',\n",
       "              'bisexual',\n",
       "              'black',\n",
       "              'buddhist',\n",
       "              'christian',\n",
       "              'female',\n",
       "              'heterosexual',\n",
       "              'hindu',\n",
       "              'homosexual_gay_or_lesbian',\n",
       "              'intellectual_or_learning_disability',\n",
       "              'jewish',\n",
       "              'latino',\n",
       "              'male',\n",
       "              'muslim',\n",
       "              'other_disability',\n",
       "              'other_gender',\n",
       "              'other_religion',\n",
       "              'other_sexual_orientation',\n",
       "              'physical_disability',\n",
       "              'psychiatric_or_mental_illness',\n",
       "              'transgender',\n",
       "              'white',\n",
       "              'funny',\n",
       "              'wow',\n",
       "              'sad',\n",
       "              'likes',\n",
       "              'disagree',\n",
       "              'sexual_explicit',\n",
       "              'toxicity_annotator_count',\n",
       "              'identity_annotator_count',\n",
       "              'identity_attack',\n",
       "              'severe_toxicity',\n",
       "              'other_race_or_ethnicity'],\n",
       "             'tops': [],\n",
       "             'geo': [],\n",
       "             'binary': [],\n",
       "             'multi': ['rating', 'comment_text', 'created_date']})"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style><table id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >id</th>        <th class=\"col_heading level0 col1\" >target</th>        <th class=\"col_heading level0 col2\" >comment_text</th>        <th class=\"col_heading level0 col3\" >severe_toxicity</th>        <th class=\"col_heading level0 col4\" >obscene</th>        <th class=\"col_heading level0 col5\" >identity_attack</th>        <th class=\"col_heading level0 col6\" >insult</th>        <th class=\"col_heading level0 col7\" >threat</th>        <th class=\"col_heading level0 col8\" >asian</th>        <th class=\"col_heading level0 col9\" >atheist</th>        <th class=\"col_heading level0 col10\" >bisexual</th>        <th class=\"col_heading level0 col11\" >black</th>        <th class=\"col_heading level0 col12\" >buddhist</th>        <th class=\"col_heading level0 col13\" >christian</th>        <th class=\"col_heading level0 col14\" >female</th>        <th class=\"col_heading level0 col15\" >heterosexual</th>        <th class=\"col_heading level0 col16\" >hindu</th>        <th class=\"col_heading level0 col17\" >homosexual_gay_or_lesbian</th>        <th class=\"col_heading level0 col18\" >intellectual_or_learning_disability</th>        <th class=\"col_heading level0 col19\" >jewish</th>        <th class=\"col_heading level0 col20\" >latino</th>        <th class=\"col_heading level0 col21\" >male</th>        <th class=\"col_heading level0 col22\" >muslim</th>        <th class=\"col_heading level0 col23\" >other_disability</th>        <th class=\"col_heading level0 col24\" >other_gender</th>        <th class=\"col_heading level0 col25\" >other_race_or_ethnicity</th>        <th class=\"col_heading level0 col26\" >other_religion</th>        <th class=\"col_heading level0 col27\" >other_sexual_orientation</th>        <th class=\"col_heading level0 col28\" >physical_disability</th>        <th class=\"col_heading level0 col29\" >psychiatric_or_mental_illness</th>        <th class=\"col_heading level0 col30\" >transgender</th>        <th class=\"col_heading level0 col31\" >white</th>        <th class=\"col_heading level0 col32\" >created_date</th>        <th class=\"col_heading level0 col33\" >publication_id</th>        <th class=\"col_heading level0 col34\" >parent_id</th>        <th class=\"col_heading level0 col35\" >article_id</th>        <th class=\"col_heading level0 col36\" >rating</th>        <th class=\"col_heading level0 col37\" >funny</th>        <th class=\"col_heading level0 col38\" >wow</th>        <th class=\"col_heading level0 col39\" >sad</th>        <th class=\"col_heading level0 col40\" >likes</th>        <th class=\"col_heading level0 col41\" >disagree</th>        <th class=\"col_heading level0 col42\" >sexual_explicit</th>        <th class=\"col_heading level0 col43\" >identity_annotator_count</th>        <th class=\"col_heading level0 col44\" >toxicity_annotator_count</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4level0_row0\" class=\"row_heading level0 row0\" >1169305</th>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col0\" class=\"data row0 col0\" >5545064</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col1\" class=\"data row0 col1\" >0.6</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col2\" class=\"data row0 col2\" >Some things should be mercilessly mocked and shamed.</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col3\" class=\"data row0 col3\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col4\" class=\"data row0 col4\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col5\" class=\"data row0 col5\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col6\" class=\"data row0 col6\" >0.3</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col7\" class=\"data row0 col7\" >0.1</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col8\" class=\"data row0 col8\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col9\" class=\"data row0 col9\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col10\" class=\"data row0 col10\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col11\" class=\"data row0 col11\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col12\" class=\"data row0 col12\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col13\" class=\"data row0 col13\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col14\" class=\"data row0 col14\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col15\" class=\"data row0 col15\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col16\" class=\"data row0 col16\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col17\" class=\"data row0 col17\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col18\" class=\"data row0 col18\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col19\" class=\"data row0 col19\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col20\" class=\"data row0 col20\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col21\" class=\"data row0 col21\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col22\" class=\"data row0 col22\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col23\" class=\"data row0 col23\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col24\" class=\"data row0 col24\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col25\" class=\"data row0 col25\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col26\" class=\"data row0 col26\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col27\" class=\"data row0 col27\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col28\" class=\"data row0 col28\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col29\" class=\"data row0 col29\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col30\" class=\"data row0 col30\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col31\" class=\"data row0 col31\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col32\" class=\"data row0 col32\" >2017-07-07 12:12:33.593960+00</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col33\" class=\"data row0 col33\" >102</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col34\" class=\"data row0 col34\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col35\" class=\"data row0 col35\" >352700</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col36\" class=\"data row0 col36\" >approved</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col37\" class=\"data row0 col37\" >32</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col38\" class=\"data row0 col38\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col39\" class=\"data row0 col39\" >3</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col40\" class=\"data row0 col40\" >128</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col41\" class=\"data row0 col41\" >8</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col42\" class=\"data row0 col42\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col43\" class=\"data row0 col43\" >4</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row0_col44\" class=\"data row0 col44\" >10</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4level0_row1\" class=\"row_heading level0 row1\" >1169315</th>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col0\" class=\"data row1 col0\" >5545075</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col1\" class=\"data row1 col1\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col2\" class=\"data row1 col2\" >One would have to believe that millions and millions of people are involved in a conspiracy to cover up a flat earth! Deny flights that have gone around the planet, satellites, etc. All those images of the globe would have to be considered fakes and conspiratorial. Just wow!</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col3\" class=\"data row1 col3\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col4\" class=\"data row1 col4\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col5\" class=\"data row1 col5\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col6\" class=\"data row1 col6\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col7\" class=\"data row1 col7\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col8\" class=\"data row1 col8\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col9\" class=\"data row1 col9\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col10\" class=\"data row1 col10\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col11\" class=\"data row1 col11\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col12\" class=\"data row1 col12\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col13\" class=\"data row1 col13\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col14\" class=\"data row1 col14\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col15\" class=\"data row1 col15\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col16\" class=\"data row1 col16\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col17\" class=\"data row1 col17\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col18\" class=\"data row1 col18\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col19\" class=\"data row1 col19\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col20\" class=\"data row1 col20\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col21\" class=\"data row1 col21\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col22\" class=\"data row1 col22\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col23\" class=\"data row1 col23\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col24\" class=\"data row1 col24\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col25\" class=\"data row1 col25\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col26\" class=\"data row1 col26\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col27\" class=\"data row1 col27\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col28\" class=\"data row1 col28\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col29\" class=\"data row1 col29\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col30\" class=\"data row1 col30\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col31\" class=\"data row1 col31\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col32\" class=\"data row1 col32\" >2017-07-07 12:15:09.081441+00</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col33\" class=\"data row1 col33\" >102</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col34\" class=\"data row1 col34\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col35\" class=\"data row1 col35\" >352700</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col36\" class=\"data row1 col36\" >approved</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col37\" class=\"data row1 col37\" >2</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col38\" class=\"data row1 col38\" >1</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col39\" class=\"data row1 col39\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col40\" class=\"data row1 col40\" >18</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col41\" class=\"data row1 col41\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col42\" class=\"data row1 col42\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col43\" class=\"data row1 col43\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row1_col44\" class=\"data row1 col44\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4level0_row2\" class=\"row_heading level0 row2\" >1169322</th>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col0\" class=\"data row2 col0\" >5545084</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col1\" class=\"data row2 col1\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col2\" class=\"data row2 col2\" >\"They want to create two classes: the ultra rich and servants. At that point they would’ve taken over the world, and enslaved the population, and controlled everything.”\n",
       "\n",
       "Well, that explains everything</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col3\" class=\"data row2 col3\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col4\" class=\"data row2 col4\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col5\" class=\"data row2 col5\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col6\" class=\"data row2 col6\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col7\" class=\"data row2 col7\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col8\" class=\"data row2 col8\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col9\" class=\"data row2 col9\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col10\" class=\"data row2 col10\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col11\" class=\"data row2 col11\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col12\" class=\"data row2 col12\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col13\" class=\"data row2 col13\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col14\" class=\"data row2 col14\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col15\" class=\"data row2 col15\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col16\" class=\"data row2 col16\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col17\" class=\"data row2 col17\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col18\" class=\"data row2 col18\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col19\" class=\"data row2 col19\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col20\" class=\"data row2 col20\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col21\" class=\"data row2 col21\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col22\" class=\"data row2 col22\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col23\" class=\"data row2 col23\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col24\" class=\"data row2 col24\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col25\" class=\"data row2 col25\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col26\" class=\"data row2 col26\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col27\" class=\"data row2 col27\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col28\" class=\"data row2 col28\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col29\" class=\"data row2 col29\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col30\" class=\"data row2 col30\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col31\" class=\"data row2 col31\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col32\" class=\"data row2 col32\" >2017-07-07 12:19:29.788566+00</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col33\" class=\"data row2 col33\" >102</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col34\" class=\"data row2 col34\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col35\" class=\"data row2 col35\" >352700</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col36\" class=\"data row2 col36\" >approved</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col37\" class=\"data row2 col37\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col38\" class=\"data row2 col38\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col39\" class=\"data row2 col39\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col40\" class=\"data row2 col40\" >2</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col41\" class=\"data row2 col41\" >1</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col42\" class=\"data row2 col42\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col43\" class=\"data row2 col43\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row2_col44\" class=\"data row2 col44\" >4</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4level0_row3\" class=\"row_heading level0 row3\" >1169323</th>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col0\" class=\"data row3 col0\" >5545085</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col1\" class=\"data row3 col1\" >0.166667</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col2\" class=\"data row3 col2\" >Let me guess, these people don't think human carbon emissions contribute to climate change either...</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col3\" class=\"data row3 col3\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col4\" class=\"data row3 col4\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col5\" class=\"data row3 col5\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col6\" class=\"data row3 col6\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col7\" class=\"data row3 col7\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col8\" class=\"data row3 col8\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col9\" class=\"data row3 col9\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col10\" class=\"data row3 col10\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col11\" class=\"data row3 col11\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col12\" class=\"data row3 col12\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col13\" class=\"data row3 col13\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col14\" class=\"data row3 col14\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col15\" class=\"data row3 col15\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col16\" class=\"data row3 col16\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col17\" class=\"data row3 col17\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col18\" class=\"data row3 col18\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col19\" class=\"data row3 col19\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col20\" class=\"data row3 col20\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col21\" class=\"data row3 col21\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col22\" class=\"data row3 col22\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col23\" class=\"data row3 col23\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col24\" class=\"data row3 col24\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col25\" class=\"data row3 col25\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col26\" class=\"data row3 col26\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col27\" class=\"data row3 col27\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col28\" class=\"data row3 col28\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col29\" class=\"data row3 col29\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col30\" class=\"data row3 col30\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col31\" class=\"data row3 col31\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col32\" class=\"data row3 col32\" >2017-07-07 12:19:26.684442+00</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col33\" class=\"data row3 col33\" >102</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col34\" class=\"data row3 col34\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col35\" class=\"data row3 col35\" >352700</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col36\" class=\"data row3 col36\" >approved</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col37\" class=\"data row3 col37\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col38\" class=\"data row3 col38\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col39\" class=\"data row3 col39\" >2</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col40\" class=\"data row3 col40\" >10</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col41\" class=\"data row3 col41\" >4</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col42\" class=\"data row3 col42\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col43\" class=\"data row3 col43\" >4</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row3_col44\" class=\"data row3 col44\" >6</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4level0_row4\" class=\"row_heading level0 row4\" >1169327</th>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col0\" class=\"data row4 col0\" >5545090</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col1\" class=\"data row4 col1\" >0.166667</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col2\" class=\"data row4 col2\" >The climate change debate is about the quantity of contribution, impact, and ability of people to control the environment\n",
       "\n",
       "Nice troll though</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col3\" class=\"data row4 col3\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col4\" class=\"data row4 col4\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col5\" class=\"data row4 col5\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col6\" class=\"data row4 col6\" >0.166667</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col7\" class=\"data row4 col7\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col8\" class=\"data row4 col8\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col9\" class=\"data row4 col9\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col10\" class=\"data row4 col10\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col11\" class=\"data row4 col11\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col12\" class=\"data row4 col12\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col13\" class=\"data row4 col13\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col14\" class=\"data row4 col14\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col15\" class=\"data row4 col15\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col16\" class=\"data row4 col16\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col17\" class=\"data row4 col17\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col18\" class=\"data row4 col18\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col19\" class=\"data row4 col19\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col20\" class=\"data row4 col20\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col21\" class=\"data row4 col21\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col22\" class=\"data row4 col22\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col23\" class=\"data row4 col23\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col24\" class=\"data row4 col24\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col25\" class=\"data row4 col25\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col26\" class=\"data row4 col26\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col27\" class=\"data row4 col27\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col28\" class=\"data row4 col28\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col29\" class=\"data row4 col29\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col30\" class=\"data row4 col30\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col31\" class=\"data row4 col31\" >nan</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col32\" class=\"data row4 col32\" >2017-07-07 12:21:05.880053+00</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col33\" class=\"data row4 col33\" >102</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col34\" class=\"data row4 col34\" >5.54508e+06</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col35\" class=\"data row4 col35\" >352700</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col36\" class=\"data row4 col36\" >approved</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col37\" class=\"data row4 col37\" >1</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col38\" class=\"data row4 col38\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col39\" class=\"data row4 col39\" >3</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col40\" class=\"data row4 col40\" >8</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col41\" class=\"data row4 col41\" >2</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col42\" class=\"data row4 col42\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col43\" class=\"data row4 col43\" >0</td>\n",
       "                        <td id=\"T_fb0816d4_86e5_11e9_9b0a_a18ef1bd3ab4row4_col44\" class=\"data row4 col44\" >6</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f47c0206e48>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['article_id']==352700].head(5).style"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "annotator_dist = sns.distplot(train['toxicity_annotator_count'], hist=False)\n",
    "renamed_x = annotator_dist.get_xlabel()\n",
    "renamed_x = renamed_x.replace('_', ' ').upper()\n",
    "annotator_dist.set_xlabel(renamed_x)\n",
    "annotator_dist.set_title(\"Annotator Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "severe_toxicity_dist = sns.distplot(train['severe_toxicity'], hist=False)\n",
    "renamed_x = severe_toxicity_dist.get_xlabel()\n",
    "renamed_x = renamed_x.replace('_', ' ').upper()\n",
    "severe_toxicity_dist.set_xlabel(renamed_x)\n",
    "severe_toxicity_dist.set_title(\"Severe Toxicity Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "insult_dist = sns.distplot(train['insult'], hist=False)\n",
    "renamed_x = insult_dist.get_xlabel()\n",
    "renamed_x = renamed_x.replace('_', ' ').upper()\n",
    "insult_dist.set_xlabel(renamed_x)\n",
    "insult_dist.set_title(\"Insult Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "identity_dist = sns.distplot(train['identity_attack'], hist=False)\n",
    "renamed_x = identity_dist.get_xlabel()\n",
    "renamed_x = renamed_x.replace('_', ' ').upper()\n",
    "identity_dist.set_xlabel(renamed_x)\n",
    "identity_dist.set_title(\"Identity Attack Distribution\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           1804874.0000\n",
       "mean                  8.7847\n",
       "std                  43.5009\n",
       "min                   3.0000\n",
       "25%                   4.0000\n",
       "50%                   4.0000\n",
       "75%                   6.0000\n",
       "max                4936.0000\n",
       "Name: toxicity_annotator_count, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['toxicity_annotator_count'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(77183, 45)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['toxicity_annotator_count'] > 10].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1235, 45)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['toxicity_annotator_count'] > 1000].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66, 45)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.loc[train['toxicity_annotator_count'] > 2000].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f47c01600f0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ef.individual_distribution(df=train, col_name='female', dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lgbt_dist = ef.individual_distribution(df=train, col_name='homosexual_gay_or_lesbian', dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latino_dist = ef.individual_distribution(df=train, col_name='latino', dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "black_dist = ef.individual_distribution(df=train, col_name='black', dropna=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "threat_dist = ef.individual_distribution(df=train, col_name='threat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ec2-user/anaconda3/envs/python3/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "target_dist = ef.individual_distribution(df=train, col_name='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>id</th>\n",
       "      <th>target</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>severe_toxicity</th>\n",
       "      <th>obscene</th>\n",
       "      <th>identity_attack</th>\n",
       "      <th>insult</th>\n",
       "      <th>threat</th>\n",
       "      <th>asian</th>\n",
       "      <th>atheist</th>\n",
       "      <th>...</th>\n",
       "      <th>article_id</th>\n",
       "      <th>rating</th>\n",
       "      <th>funny</th>\n",
       "      <th>wow</th>\n",
       "      <th>sad</th>\n",
       "      <th>likes</th>\n",
       "      <th>disagree</th>\n",
       "      <th>sexual_explicit</th>\n",
       "      <th>identity_annotator_count</th>\n",
       "      <th>toxicity_annotator_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>59848</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>This is so cool. It's like, 'would you want yo...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>2006</td>\n",
       "      <td>rejected</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>59849</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Thank you!! This would make my life a lot less...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>2006</td>\n",
       "      <td>rejected</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>59852</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>This is such an urgent design problem; kudos t...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>2006</td>\n",
       "      <td>rejected</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>59855</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Is this something I'll be able to install on m...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>2006</td>\n",
       "      <td>rejected</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>59856</td>\n",
       "      <td>0.8936</td>\n",
       "      <td>haha you guys are a bunch of losers.</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0213</td>\n",
       "      <td>0.8723</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>...</td>\n",
       "      <td>2006</td>\n",
       "      <td>rejected</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>4</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 45 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      id               target  \\\n",
       "0  59848               0.0000   \n",
       "1  59849               0.0000   \n",
       "2  59852               0.0000   \n",
       "3  59855               0.0000   \n",
       "4  59856               0.8936   \n",
       "\n",
       "                                        comment_text      severe_toxicity  \\\n",
       "0  This is so cool. It's like, 'would you want yo...               0.0000   \n",
       "1  Thank you!! This would make my life a lot less...               0.0000   \n",
       "2  This is such an urgent design problem; kudos t...               0.0000   \n",
       "3  Is this something I'll be able to install on m...               0.0000   \n",
       "4               haha you guys are a bunch of losers.               0.0213   \n",
       "\n",
       "               obscene      identity_attack               insult  \\\n",
       "0               0.0000               0.0000               0.0000   \n",
       "1               0.0000               0.0000               0.0000   \n",
       "2               0.0000               0.0000               0.0000   \n",
       "3               0.0000               0.0000               0.0000   \n",
       "4               0.0000               0.0213               0.8723   \n",
       "\n",
       "                threat                asian              atheist  ...  \\\n",
       "0               0.0000                  nan                  nan  ...   \n",
       "1               0.0000                  nan                  nan  ...   \n",
       "2               0.0000                  nan                  nan  ...   \n",
       "3               0.0000                  nan                  nan  ...   \n",
       "4               0.0000               0.0000               0.0000  ...   \n",
       "\n",
       "   article_id    rating  funny  wow  sad  likes  disagree  \\\n",
       "0        2006  rejected      0    0    0      0         0   \n",
       "1        2006  rejected      0    0    0      0         0   \n",
       "2        2006  rejected      0    0    0      0         0   \n",
       "3        2006  rejected      0    0    0      0         0   \n",
       "4        2006  rejected      0    0    0      1         0   \n",
       "\n",
       "       sexual_explicit  identity_annotator_count  toxicity_annotator_count  \n",
       "0               0.0000                         0                         4  \n",
       "1               0.0000                         0                         4  \n",
       "2               0.0000                         0                         4  \n",
       "3               0.0000                         0                         4  \n",
       "4               0.0000                         4                        47  \n",
       "\n",
       "[5 rows x 45 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploration on some processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['toxicity_category'] = train.target.apply(lambda x: 1 if x > 0.5 else 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count           1804874.0000\n",
       "mean                  0.0590\n",
       "std                   0.2356\n",
       "min                   0.0000\n",
       "25%                   0.0000\n",
       "50%                   0.0000\n",
       "75%                   0.0000\n",
       "max                   1.0000\n",
       "Name: toxicity_category, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['toxicity_category'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Toxic and nontoxic counts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1698436\n",
       "1     106438\n",
       "Name: toxicity_category, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['toxicity_category'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Percent toxic comments in whole train data set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06266824301887149"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "106438/1698436"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_sample = train.sample(frac=0.25, replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(451218, 46)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Split comments...Elapsed Time:  0.054 minutes\n",
      "Cleaned with stopwords...Elapsed Time:  0.255 minutes\n",
      "Cleaned without stopwords...Elapsed Time:  0.355 minutes\n",
      "Stemmed (Porter)...Elapsed Time:  6.969 minutes\n",
      "Stemmed (Lancaster)...Elapsed Time:  5.763 minutes\n",
      "Created bigrams...Elapsed Time:  0.139 minutes\n",
      "Calculated uppercase pct...Elapsed Time:  0.037 minutes\n",
      "Count punctuation...Elapsed Time:  0.009 minutes\n",
      "Count words...Elapsed Time:  0.003 minutes\n",
      "Count stopwords pct...Elapsed Time:  0.125 minutes\n",
      "Count uppercase words...Elapsed Time:  0.03 minutes\n",
      "\n",
      "DONE GENERATING FEATURES\n"
     ]
    }
   ],
   "source": [
    "processed_data = fgf.generate_all_features(train_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "toxic = processed_data[processed_data.toxicity_category == 1]\n",
    "nontoxic = processed_data[processed_data.toxicity_category == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_data['comment_length'] = processed_data[\"comment_text\"].apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>id</th>\n",
       "      <th>target</th>\n",
       "      <th>comment_text</th>\n",
       "      <th>severe_toxicity</th>\n",
       "      <th>obscene</th>\n",
       "      <th>identity_attack</th>\n",
       "      <th>insult</th>\n",
       "      <th>threat</th>\n",
       "      <th>asian</th>\n",
       "      <th>atheist</th>\n",
       "      <th>...</th>\n",
       "      <th>cleaned_porter</th>\n",
       "      <th>cleaned_lancaster_str</th>\n",
       "      <th>cleaned_lancaster</th>\n",
       "      <th>bigrams_unstemmed</th>\n",
       "      <th>perc_upper</th>\n",
       "      <th>num_exclam</th>\n",
       "      <th>num_words</th>\n",
       "      <th>perc_stopwords</th>\n",
       "      <th>num_upper_words</th>\n",
       "      <th>comment_length</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>696469</th>\n",
       "      <td>4974305</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Hey, haven't we all had to take cuts in some f...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>[hey, haven't, take, cut, form, anoth, past, 8...</td>\n",
       "      <td>hey haven't tak cut form anoth past 8 year so ...</td>\n",
       "      <td>[hey, haven't, tak, cut, form, anoth, past, 8,...</td>\n",
       "      <td>[hey haven't, haven't take, take cuts, cuts fo...</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0</td>\n",
       "      <td>87</td>\n",
       "      <td>0.4940</td>\n",
       "      <td>0</td>\n",
       "      <td>460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540424</th>\n",
       "      <td>6007351</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>Baloney.... it was rigged. Clinton's book expl...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>[baloney, rig, clinton', book, explain]</td>\n",
       "      <td>baloney rig clinton's book explain</td>\n",
       "      <td>[baloney, rig, clinton's, book, explain]</td>\n",
       "      <td>[baloney rigged, rigged clinton's, clinton's b...</td>\n",
       "      <td>0.0340</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>0.4440</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1787566</th>\n",
       "      <td>6312341</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>Totally delusional junior,  your commenting on...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>[total, delusion, junior, comment, issu, noth,...</td>\n",
       "      <td>tot delud juny com issu noth discuss involv</td>\n",
       "      <td>[tot, delud, juny, com, issu, noth, discuss, i...</td>\n",
       "      <td>[totally delusional, delusional junior, junior...</td>\n",
       "      <td>0.0080</td>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>0.6190</td>\n",
       "      <td>0</td>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1406907</th>\n",
       "      <td>5838039</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>\"Ad Hominem attack\" is a synonym for Ad Homine...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>[ad, hominem, attack, synonym, ad, hominem, in...</td>\n",
       "      <td>ad hominem attack synonym ad hominem in cas me...</td>\n",
       "      <td>[ad, hominem, attack, synonym, ad, hominem, in...</td>\n",
       "      <td>[ad hominem, hominem attack, attack synonym, s...</td>\n",
       "      <td>0.0440</td>\n",
       "      <td>0</td>\n",
       "      <td>67</td>\n",
       "      <td>0.4480</td>\n",
       "      <td>2</td>\n",
       "      <td>366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>852872</th>\n",
       "      <td>5163942</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>It's hilarious how a bunch of home owners know...</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>nan</td>\n",
       "      <td>nan</td>\n",
       "      <td>...</td>\n",
       "      <td>[it', hilari, bunch, home, owner, know, peopl,...</td>\n",
       "      <td>it's hil bunch hom own know peopl design trans...</td>\n",
       "      <td>[it's, hil, bunch, hom, own, know, peopl, desi...</td>\n",
       "      <td>[it's hilarious, hilarious bunch, bunch home, ...</td>\n",
       "      <td>0.0060</td>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>0.5220</td>\n",
       "      <td>0</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              id               target  \\\n",
       "696469   4974305               0.0000   \n",
       "1540424  6007351               0.0000   \n",
       "1787566  6312341               0.4000   \n",
       "1406907  5838039               0.0000   \n",
       "852872   5163942               0.0000   \n",
       "\n",
       "                                              comment_text  \\\n",
       "696469   Hey, haven't we all had to take cuts in some f...   \n",
       "1540424  Baloney.... it was rigged. Clinton's book expl...   \n",
       "1787566  Totally delusional junior,  your commenting on...   \n",
       "1406907  \"Ad Hominem attack\" is a synonym for Ad Homine...   \n",
       "852872   It's hilarious how a bunch of home owners know...   \n",
       "\n",
       "             severe_toxicity              obscene      identity_attack  \\\n",
       "696469                0.0000               0.0000               0.0000   \n",
       "1540424               0.0000               0.0000               0.0000   \n",
       "1787566               0.0000               0.0000               0.0000   \n",
       "1406907               0.0000               0.0000               0.0000   \n",
       "852872                0.0000               0.0000               0.0000   \n",
       "\n",
       "                      insult               threat                asian  \\\n",
       "696469                0.0000               0.0000                  nan   \n",
       "1540424               0.0000               0.0000                  nan   \n",
       "1787566               0.4000               0.0000                  nan   \n",
       "1406907               0.0000               0.0000                  nan   \n",
       "852872                0.0000               0.0000                  nan   \n",
       "\n",
       "                     atheist  ...  \\\n",
       "696469                   nan  ...   \n",
       "1540424                  nan  ...   \n",
       "1787566                  nan  ...   \n",
       "1406907                  nan  ...   \n",
       "852872                   nan  ...   \n",
       "\n",
       "                                            cleaned_porter  \\\n",
       "696469   [hey, haven't, take, cut, form, anoth, past, 8...   \n",
       "1540424            [baloney, rig, clinton', book, explain]   \n",
       "1787566  [total, delusion, junior, comment, issu, noth,...   \n",
       "1406907  [ad, hominem, attack, synonym, ad, hominem, in...   \n",
       "852872   [it', hilari, bunch, home, owner, know, peopl,...   \n",
       "\n",
       "                                     cleaned_lancaster_str  \\\n",
       "696469   hey haven't tak cut form anoth past 8 year so ...   \n",
       "1540424                 baloney rig clinton's book explain   \n",
       "1787566        tot delud juny com issu noth discuss involv   \n",
       "1406907  ad hominem attack synonym ad hominem in cas me...   \n",
       "852872   it's hil bunch hom own know peopl design trans...   \n",
       "\n",
       "                                         cleaned_lancaster  \\\n",
       "696469   [hey, haven't, tak, cut, form, anoth, past, 8,...   \n",
       "1540424           [baloney, rig, clinton's, book, explain]   \n",
       "1787566  [tot, delud, juny, com, issu, noth, discuss, i...   \n",
       "1406907  [ad, hominem, attack, synonym, ad, hominem, in...   \n",
       "852872   [it's, hil, bunch, hom, own, know, peopl, desi...   \n",
       "\n",
       "                                         bigrams_unstemmed  \\\n",
       "696469   [hey haven't, haven't take, take cuts, cuts fo...   \n",
       "1540424  [baloney rigged, rigged clinton's, clinton's b...   \n",
       "1787566  [totally delusional, delusional junior, junior...   \n",
       "1406907  [ad hominem, hominem attack, attack synonym, s...   \n",
       "852872   [it's hilarious, hilarious bunch, bunch home, ...   \n",
       "\n",
       "                  perc_upper  num_exclam  num_words       perc_stopwords  \\\n",
       "696469                0.0150           0         87               0.4940   \n",
       "1540424               0.0340           0          9               0.4440   \n",
       "1787566               0.0080           1         21               0.6190   \n",
       "1406907               0.0440           0         67               0.4480   \n",
       "852872                0.0060           0         69               0.5220   \n",
       "\n",
       "         num_upper_words  comment_length  \n",
       "696469                 0             460  \n",
       "1540424                0              58  \n",
       "1787566                0             125  \n",
       "1406907                2             366  \n",
       "852872                 0             355  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processed_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "continuous_features = ['perc_upper', 'num_exclam', 'num_words', 'perc_stopwords',\n",
    "       'num_upper_words', 'comment_length']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "perc_upper - TOXIC\n",
      "count             26582.0000\n",
      "mean                  0.0394\n",
      "std                   0.0606\n",
      "min                   0.0000\n",
      "25%                   0.0180\n",
      "50%                   0.0280\n",
      "75%                   0.0430\n",
      "max                   1.0000\n",
      "Name: perc_upper, dtype: float64\n",
      "perc_upper - NONTOXIC\n",
      "count            424636.0000\n",
      "mean                  0.0370\n",
      "std                   0.0475\n",
      "min                   0.0000\n",
      "25%                   0.0180\n",
      "50%                   0.0270\n",
      "75%                   0.0420\n",
      "max                   1.0000\n",
      "Name: perc_upper, dtype: float64\n",
      "\n",
      "num_exclam - TOXIC\n",
      "count             26582.0000\n",
      "mean                  0.3964\n",
      "std                   1.9072\n",
      "min                   0.0000\n",
      "25%                   0.0000\n",
      "50%                   0.0000\n",
      "75%                   0.0000\n",
      "max                 105.0000\n",
      "Name: num_exclam, dtype: float64\n",
      "num_exclam - NONTOXIC\n",
      "count            424636.0000\n",
      "mean                  0.2148\n",
      "std                   0.9757\n",
      "min                   0.0000\n",
      "25%                   0.0000\n",
      "50%                   0.0000\n",
      "75%                   0.0000\n",
      "max                 139.0000\n",
      "Name: num_exclam, dtype: float64\n",
      "\n",
      "num_words - TOXIC\n",
      "count             26582.0000\n",
      "mean                 44.2393\n",
      "std                  40.0690\n",
      "min                   1.0000\n",
      "25%                  16.0000\n",
      "50%                  31.0000\n",
      "75%                  59.0000\n",
      "max                 310.0000\n",
      "Name: num_words, dtype: float64\n",
      "num_words - NONTOXIC\n",
      "count            424636.0000\n",
      "mean                 52.3998\n",
      "std                  47.2441\n",
      "min                   1.0000\n",
      "25%                  17.0000\n",
      "50%                  36.0000\n",
      "75%                  74.0000\n",
      "max                 841.0000\n",
      "Name: num_words, dtype: float64\n",
      "\n",
      "perc_stopwords - TOXIC\n",
      "count             26582.0000\n",
      "mean                  0.3962\n",
      "std                   0.1142\n",
      "min                  -1.3750\n",
      "25%                   0.3480\n",
      "50%                   0.4130\n",
      "75%                   0.4660\n",
      "max                   0.8000\n",
      "Name: perc_stopwords, dtype: float64\n",
      "perc_stopwords - NONTOXIC\n",
      "count            424636.0000\n",
      "mean                  0.3898\n",
      "std                   0.2633\n",
      "min                 -36.0000\n",
      "25%                   0.3530\n",
      "50%                   0.4170\n",
      "75%                   0.4670\n",
      "max                   1.0000\n",
      "Name: perc_stopwords, dtype: float64\n",
      "\n",
      "num_upper_words - TOXIC\n",
      "count             26582.0000\n",
      "mean                  1.0804\n",
      "std                   3.0576\n",
      "min                   0.0000\n",
      "25%                   0.0000\n",
      "50%                   0.0000\n",
      "75%                   1.0000\n",
      "max                 140.0000\n",
      "Name: num_upper_words, dtype: float64\n",
      "num_upper_words - NONTOXIC\n",
      "count            424636.0000\n",
      "mean                  1.1666\n",
      "std                   2.4118\n",
      "min                   0.0000\n",
      "25%                   0.0000\n",
      "50%                   0.0000\n",
      "75%                   2.0000\n",
      "max                 186.0000\n",
      "Name: num_upper_words, dtype: float64\n",
      "\n",
      "comment_length - TOXIC\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'comment_length'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/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   2656\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2657\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[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_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: 'comment_length'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-59-cf4a155b97e3>\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[0mfeature\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcontinuous_features\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[0mfeature\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" - TOXIC\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtoxic\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfeature\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribe\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      4\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[0mfeature\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" - NONTOXIC\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2925\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[0m\n\u001b[1;32m   2926\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[0m\n\u001b[0;32m-> 2927\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[0m\n\u001b[0m\u001b[1;32m   2928\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[0m\n\u001b[1;32m   2929\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[0m\n",
      "\u001b[0;32m~/anaconda3/envs/python3/lib/python3.6/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   2657\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[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2658\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2659\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[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\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   2660\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2661\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_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: 'comment_length'"
     ]
    }
   ],
   "source": [
    "for feature in continuous_features:\n",
    "    print(feature + \" - TOXIC\")\n",
    "    print(toxic[feature].describe())\n",
    "\n",
    "    print(feature + \" - NONTOXIC\")\n",
    "    print(nontoxic[feature].describe())\n",
    "    \n",
    "    print()"
   ]
  }
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