DistPlots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./analysis/BTC.csv')\n",
    "df.drop(columns='Unnamed: 0', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.rename(index=str, columns={\"votes_disliked\": \"dislike\",\n",
    "                              \"votes_important\": \"important\",\n",
    "                              \"votes_liked\": \"liked\",\n",
    "                              \"votes_lol\": \"lol\",\n",
    "                              \"votes_negative\": \"bearish\",\n",
    "                              \"votes_positive\": \"bullish\",\n",
    "                              \"votes_saved\": \"saved\",\n",
    "                              \"votes_toxic\": \"toxic\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['user_sent_opinions'] = df['bearish'] + df['bullish']\n",
    "\n",
    "df['bullishness'] = ((df['bullish'] / df['user_sent_opinions']) * 100)\n",
    "\n",
    "df['bearishness'] = -((df['bearish'] / df['user_sent_opinions']) * 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>date_time</th>\n",
       "      <th>dislike</th>\n",
       "      <th>important</th>\n",
       "      <th>liked</th>\n",
       "      <th>lol</th>\n",
       "      <th>bearish</th>\n",
       "      <th>bullish</th>\n",
       "      <th>saved</th>\n",
       "      <th>toxic</th>\n",
       "      <th>title_smog_score</th>\n",
       "      <th>...</th>\n",
       "      <th>is_Monday</th>\n",
       "      <th>is_Saturday</th>\n",
       "      <th>is_Sunday</th>\n",
       "      <th>is_Thursday</th>\n",
       "      <th>is_Tuesday</th>\n",
       "      <th>is_Wednesday</th>\n",
       "      <th>is_nan</th>\n",
       "      <th>user_sent_opinions</th>\n",
       "      <th>bullishness</th>\n",
       "      <th>bearishness</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2018-04-08 14:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.208143</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>84.615385</td>\n",
       "      <td>-15.384615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2018-04-09 08:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>14.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.841846</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>92.307692</td>\n",
       "      <td>-7.692308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2018-04-10 10:00:00</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.129100</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>90.476190</td>\n",
       "      <td>-9.523810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018-04-10 11:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11.208143</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2018-04-10 12:00:00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.129100</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>-0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 4130 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date_time  dislike  important  liked  lol  bearish  bullish  \\\n",
       "0  2018-04-08 14:00:00      2.0       19.0   18.0  1.0      4.0     22.0   \n",
       "1  2018-04-09 08:00:00      0.0       15.5   14.5  1.0      2.0     24.0   \n",
       "2  2018-04-10 10:00:00      2.0       12.0   16.0  4.0      2.0     19.0   \n",
       "3  2018-04-10 11:00:00      0.0        5.0    6.0  0.0      0.0      7.0   \n",
       "4  2018-04-10 12:00:00      0.0        3.0    3.0  0.0      0.0      3.0   \n",
       "\n",
       "   saved  toxic  title_smog_score     ...       is_Monday  is_Saturday  \\\n",
       "0    3.0    0.0         11.208143     ...               0            0   \n",
       "1    3.0    0.0          8.841846     ...               1            0   \n",
       "2    4.0    0.0          3.129100     ...               0            0   \n",
       "3    3.0    0.0         11.208143     ...               0            0   \n",
       "4    3.0    0.0          3.129100     ...               0            0   \n",
       "\n",
       "   is_Sunday  is_Thursday  is_Tuesday  is_Wednesday  is_nan  \\\n",
       "0          1            0           0             0       0   \n",
       "1          0            0           0             0       0   \n",
       "2          0            0           1             0       0   \n",
       "3          0            0           1             0       0   \n",
       "4          0            0           1             0       0   \n",
       "\n",
       "   user_sent_opinions  bullishness  bearishness  \n",
       "0                26.0    84.615385   -15.384615  \n",
       "1                26.0    92.307692    -7.692308  \n",
       "2                21.0    90.476190    -9.523810  \n",
       "3                 7.0   100.000000    -0.000000  \n",
       "4                 3.0   100.000000    -0.000000  \n",
       "\n",
       "[5 rows x 4130 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Bullish Vs Bearish Votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df[df['bullishness'] > 0] ['bullish'], bins=20, alpha=0.5, density=True, label='Bullish')\n",
    "pyplot.hist(-df[df['bearishness'] < 0] ['bearish'], bins=20, alpha=0.5, density=True, label='Bearish')\n",
    "#pyplot.hist(df['important'], bins=20, alpha=0.5, density=True, label='Important')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Title Polarity and Subjectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df['title_polarity'], bins=20, alpha=0.5, density=True, label='Title Polarity')\n",
    "pyplot.hist(df['title_subjectivity'], bins=20, alpha=0.5, density=True, label='Title Subjectivity')\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distribution of Description Polarity and Subjectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df['description_polarity'], bins=20, alpha=0.5, density=True, label='Description Polarity')\n",
    "pyplot.hist(df['description_subjectivity'], bins=20, alpha=0.5, density=True, label='Description Subjectivity')\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Title Subjectivity Vs Description Subjectivity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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27NksXbr0jD8SCQkJTJw4keXLl9fYtqSkJIYNG8aAAQMYP348Dz74IN26dePSSy/l+uuvD/y9aAgtMiVymrN9kanariwfBuXl5QwaNIiPPvqopUvhiy++4IorrohOc6xOi0yJyFmvpKSEoUOHtoo/OG+88Qbf/va3+dnPflZjaMeChkpEJCrIjI7WqEePHuzb1/h367F0zTXXnHGB5lhTj1ukmngMH4qcEoufryAXC+5vZoWn/fsfM5vb5COLtEIdOnSgvLxc4S1x4e6Ul5dHr9fZWEEuXfYekA5gZgnAP4EX69xJJKR69uxJcXFxk9bLEKlLhw4d6NmzZ5Oeo6Fj3KOB/e7e8h/bisRB+/bto2cairRWDR3jng48G49CREQkmMDBbWbnAJOAP9TyeLaZFZhZgd5miojET0N63OOBHe7+WU0Punuuu2e6e2ZKSkpsqhMRkTM0JLh/hIZJRERaXKDgNrNOwBjghfiWIyIi9Qk0q8TdvwCS4lyLiIgEoDMnRURCRsEtIhIyCm4RkZBRcIuIhIyCW0QkZBTcIiIho+AWEQkZBbeISMgouEVEQkbBLSISMgpuEZGQUXCLiISMgltEJGQU3CIiIaPgFhEJGQW3iEjIKLhFREIm6KXLLjCzPDPba2Z7zGxovAsTEZGaBbp0GfB/gNfdfYqZnQN0imNNIiJSh3qD28y+AQwHZgK4+9fA1/EtS0REahNkqKQPUAb8xsx2mtl/mNl51Tcys2wzKzCzgrKyspgXKiIiEUGCOxEYBDzp7hnAUeCu6hu5e667Z7p7ZkpKSozLFBGRU4IEdzFQ7O5vV97OIxLkIiLSAuoNbnf/FPiHmfWvvGs08Pe4ViUiIrUKOqvkZ8AzlTNKDgCz4leSiIjUJVBwu3shkBnnWkREJACdOSkiEjIKbhGRkFFwi4iEjIJbRCRkFNwiIiGj4BYRCRkFt4hIyCi4RURCRsEtIhIyCm4RkZBRcIuIhIyCW0QkZBTcIiIho+AWEQkZBbeISMgouEVEQibQhRTMrAg4ApwETri7LqogItJCgl66DGCkux+MWyUiIhKIhkpEREImaHA7sM7MtptZdjwLEhGRugUdKhnm7iVm1hVYb2Z73X3T6RtUBno2QK9evWJcpoiInBKox+3uJZX/lwIvAkNq2CbX3TPdPTMlJSW2VYqISFS9wW1m55lZl1NfA2OB3fEuTEREahZkqKQb8KKZndp+pbu/HteqRESkVvUGt7sfAAY2Qy0iIhKApgOKiISMgltEJGQU3CIiIaPgFhEJGQW3iEjIKLhFREJGwS0iEjIKbhGRkFFwi4iEjIJbRCRkFNwiIiGj4BYRCRkFt4hIyCi4RURCRsEtIhIyCm4RkZAJHNxmlmBmO83slXgWJCIidWtIj/tfgD3xKkRERIIJFNxm1hOYAPxHfMsREZH6BO1xPwL8K1ARx1pERCSAeoPbzCYCpe6+vZ7tss2swMwKysrKYlagiIhUFaTHPQyYZGZFwHPAKDP7ffWN3D3X3TPdPTMlJSXGZYqIyCn1Bre73+3uPd09FZgObHD3GXGvTEREaqR53CIiIZPYkI3dPR/Ij0slIiISiHrcIiIho+AWEQkZBbeISMgouEVEQkbBLSISMgpuEZGQUXCLiISMgltEJGQU3CIiIaPgFhEJGQW3iEjIKLhFREJGwS0iEjIKbhGRkFFwi4iEjIJbRCRkglwsuIOZbTOzv5rZ38zsV81RmIiI1CzIFXC+Aka5++dm1h7YbGZ/dPetca5NRERqUG9wu7sDn1febF/5z+NZlIiI1C7QGLeZJZhZIVAKrHf3t+NbloiI1CZQcLv7SXdPB3oCQ8xsQPVtzCzbzArMrKCsrCzWdYqISKUGzSpx9/8mcpX3a2t4LNfdM909MyUlJUbliYhIdUFmlaSY2QWVX3cErgH2xrswERGpWZBZJd2B35pZApGgX+3ur8SroCs/zm3C3g/FrA4RkdYqyKySXUBGM9QiIiIB6MxJEZGQUXCLiISMgltEJGQU3CIiIaPgFhEJGQW3iEjIKLhFREJGwS0iEjIKbhGRkFFwi4iEjIJbRCRkFNwiIiGj4BYRCRkFt4hIyCi4RURCJsiFFETajo0PNH7fkXfHrg6RJlBwh52CSOSsE+Sak5eY2UYz22NmfzOzf2mOwkREpGZBetwngF+4+w4z6wJsN7P17v73ONcmIiI1qLfH7e6fuPuOyq+PAHuAi+NdmIiI1KxBY9xmlkrkwsFv1/BYNpAN0KtXrxiUJm2axuZFGi3wdEAz6ww8D8x19/+p/ri757p7prtnpqSkxLJGERE5TaDgNrP2REL7GXd/Ib4liYhIXYLMKjHgP4E97v7v8S9JRETqEmSMexiQBbxrZoWV9/0vd38tfmVJUH85UN7ofYeOjGEhItJs6g1ud98MWDPUIiIiAejMSQmfpsxIEWkDtMiUiEjIqMd9NlPPVSSU1OMWEQkZBbeISMhoqETOKpo+KW2BetwiIiGj4BYRCRkFt4hIyCi4RURCRsEtIhIyCm4RkZBRcIuIhIzmcbcGOvVcRBpAPW4RkZBRj1skKF3gWFqJIJcuW2ZmpWa2uzkKEhGRugXpcS8HHgNWxLeUlrVk/b5G7ztvTL8YViIiUrd6e9zuvgk41Ay1iIhIABrjbgWasmKdiJx9YjarxMyyzazAzArKyspi9bQiIlJNzILb3XPdPdPdM1NSUmL1tCIiUo3mcYuIhEyQ6YDPAn8B+ptZsZndGv+yRESkNvV+OOnuP2qOQkREJBjNKokFrTXSrDQLR852GuMWEQkZ9bhjQD1AEWlOCm5pEfpjJ9J4Cu6zWFPCc2ifpBhWIiINoeAWCahJf+ho4gfYWhZWTtOmgrspK/yJiEA4VgrVrBIRkZBpUz1ukTZLV9+R0yi4pVE0K0Sk5Si4RUKgSR+MjoxhIdIqKLgrXflxbkuXIG2Y3qFILLWp4Fb4isjZQLNKRERCpk31uEXkTH/5z/mN3nforQ/FsBKJFQW3iNQqDCejnI0CDZWY2bVm9p6ZfWBmd8W7KBERqV29PW4zSwAeB8YAxcA7ZrbW3f8e7+JEpGU17QP/lhtmaco7hTC0OchQyRDgA3c/AGBmzwGTAQW3iNSqKWPrAFt7ZTd637Y+wyzIUMnFwD9Ou11ceZ+IiLSAID1uq+E+P2Mjs2zg1J/Iz83svUbWlAwcbOS+YaU2t31nW3uhyW1+OGaFNJvbHm5Km78ZdMMgwV0MXHLa7Z5ASfWN3D0XaPL7EzMrcPfMpj5PmKjNbd/Z1l5Qm+MpyFDJO0BfM+ttZucA04G18S1LRERqU2+P291PmNkdwP8FEoBl7v63uFcmIiI1CnQCjru/BrwW51pOadsfB9dMbW77zrb2gtocN+Z+xueMIiLSimmRKRGRkGmx4K7vNHozO9fMVlU+/raZpTZ/lbEToL0/N7O/m9kuM/uTmQWeGtRaBV0qwcymmJmbWehnIARps5lNrXyt/2ZmK5u7xlgL8LPdy8w2mtnOyp/v61qizlgxs2VmVmpmu2t53MxsaeX3Y5eZDYp5Ee7e7P+IfMi5H+gDnAP8Fbis2jY/BZ6q/Ho6sKolam3G9o4EOlV+/ZMwtzdomyu36wJsArYCmS1ddzO8zn2BncCFlbe7tnTdzdDmXOAnlV9fBhS1dN1NbPNwYBCwu5bHrwP+SOQcmCuBt2NdQ0v1uKOn0bv718Cp0+hPNxn4beXXecBoM6vpZKAwqLe97r7R3b+ovLmVyHz5MAvyGgP8b+DfgGPNWVycBGnzbOBxd/8vAHcvbeYaYy1Imx34RuXX51PDeSBh4u6bgEN1bDIZWOERW4ELzKx7LGtoqeAOchp9dBt3PwEcBpKapbrYa+iyAbcS+YsdZvW22cwygEvc/ZXmLCyOgrzO/YB+ZvZnM9tqZtc2W3XxEaTNOcAMMysmMjvtZ81TWouJ+zIhLbUed5DT6AOdah8SgdtiZjOATODquFYUf3W22czaAUuAmc1VUDMI8jonEhkuGUHkXdVbZjbA3f87zrXFS5A2/whY7u4Pm9lQ4HeVba6If3ktIu7Z1VI97iCn0Ue3MbNEIm+x6np70poFWjbAzK4BFgKT3P2rZqotXuprcxdgAJBvZkVExgLXhvwDyqA/1y+5+3F3/xB4j0iQh1WQNt8KrAZw978AHYisY9JWBfp9b4qWCu4gp9GvBX5c+fUUYINXjvyHUL3trRw2+DWR0A77uCfU02Z3P+zuye6e6u6pRMb1J7l7QcuUGxNBfq7XEPkgGjNLJjJ0cqBZq4ytIG3+GBgNYGaXEgnusmatsnmtBW6unF1yJXDY3T+J6RFa8JPZ64B9RD6RXlh53/1Efnkh8uL+AfgA2Ab0aelPk+Pc3jeAz4DCyn9rW7rmeLe52rb5hHxWScDX2YB/J7Ke/bvA9JauuRnafBnwZyIzTgqBsS1dcxPb+yzwCXCcSO/6VmAOMOe01/jxyu/Hu/H4udaZkyIiIaMzJ0VEQkbBLSISMgpuEZGQUXCLiISMgltEJGQU3CIiIaPgFhEJGQW3iEjI/D8+EPJaxkbqWAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df['title_subjectivity'], bins=20, alpha=0.5, density=True, label='Title Subjectivity')\n",
    "pyplot.hist(df['description_subjectivity'], bins=20, alpha=0.5, density=True, label='Description Subjectivity')\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Title Polarity Vs Description Polarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df['title_polarity'], bins=20, alpha=0.5, density=True, label='Title Polarity')\n",
    "pyplot.hist(df['description_polarity'], bins=20, alpha=0.5, density=True, label='Description Polarity')\n",
    "\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Title Subjectivity on Bullish and Bearish Votes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df[df['bullishness'] > 0] ['title_subjectivity'], bins=20, alpha=0.5, density=True, label='Title Subjectivity on Bullish Votes')\n",
    "pyplot.hist(df[df['bearishness'] < 0] ['title_subjectivity'], bins=20, alpha=0.5, density=True, label='Title Subjectivity on Bearish Votes')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Title Polarity on Bullish and Bearish Votes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df[df['bullishness'] > 0] ['title_polarity'], bins=20, alpha=0.5, density=True, label='Title Polarity on Bullish Votes')\n",
    "pyplot.hist(-df[df['bearishness'] < 0] ['title_polarity'], bins=20, alpha=0.5, density=True, label='Title Polarity on Bearish Votes')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description Polarity on Bullish and Bearish Votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df[df['bullishness'] > 0] ['description_polarity'], bins=20, alpha=0.5, density=True, label='Description Polarity on Bullish Votes')\n",
    "pyplot.hist(-df[df['bearishness'] < 0] ['description_polarity'], bins=20, alpha=0.5, density=True, label='Description Polarity on Bearish Votes')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description Subjectivity on Bullish and Bearish Votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df[df['bullishness'] > 0] ['description_subjectivity'], bins=20, alpha=0.5, density=True, label='Description Subjectivity on Bullish Votes')\n",
    "pyplot.hist(-df[df['bearishness'] < 0] ['description_subjectivity'], bins=20, alpha=0.5, density=True, label='Description Subjectivity on Bearish Votes')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## User Generated Bullish Vs Bearish Votes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.hist(df[df['bullishness'] > 0] ['user_sent_opinions'], bins=20, alpha=0.5, density=True, label='Bullish Votes')\n",
    "pyplot.hist(-df[df['bearishness'] < 0] ['user_sent_opinions'], bins=20, alpha=0.5, density=True, label='Bearish Votes')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [conda env:anaconda3]",
   "language": "python",
   "name": "conda-env-anaconda3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}