02_data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Cleaning part II"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After gathering and combining the tweets, it was important to remove null values in order to create a VADER-based sentiment analysis for data exploration."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd  \n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anneb/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (0,1,2,3,4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
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       "      <th>cashtags</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>tweet</th>\n",
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       "      <td>1.15135E+18</td>\n",
       "      <td>21:25:13</td>\n",
       "      <td>Wow, my dad yday: “you don’t take those stupid...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:25:07</td>\n",
       "      <td>what part of this was really harmfult of a lot...</td>\n",
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       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>['#depression', '#uncoveringthenewu', '#change...</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:25:06</td>\n",
       "      <td>one of the ways I got through my #depression i...</td>\n",
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       "      <td>3</td>\n",
       "      <td>3</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:55</td>\n",
       "      <td>see i wanna do one of them but they all say th...</td>\n",
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       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:51</td>\n",
       "      <td>IS IT clinical depression or is it the palpabl...</td>\n",
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       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:46</td>\n",
       "      <td>My new mantra for dealing with my anxiety/depr...</td>\n",
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       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:41</td>\n",
       "      <td>Can I get a woot woot for 3 months depression ...</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:37</td>\n",
       "      <td>Hope it's working for you.  I was on sertralin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:22</td>\n",
       "      <td>When my depression and anxiety strike at the s...</td>\n",
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       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:20</td>\n",
       "      <td>I want to leave, but I'm still waiting for you...</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>10</td>\n",
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       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:07</td>\n",
       "      <td>lrt i have FUCKING DEPRESSION</td>\n",
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       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:07</td>\n",
       "      <td>If you’re a new mother who is tired from postp...</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:04</td>\n",
       "      <td>Currently 5:23 and I’ve got depression  Ranger...</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:01</td>\n",
       "      <td>omg wish I could swim cause I'm gonna drown in...</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:00</td>\n",
       "      <td>I don’t talk about it much at all unless you’r...</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:55</td>\n",
       "      <td>🚨🚨🚨 If someone can help me by suggesting me ho...</td>\n",
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       "    <tr>\n",
       "      <th>16</th>\n",
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       "      <td>16</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:39</td>\n",
       "      <td>luv 2 just start crying bc you're thinking of ...</td>\n",
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       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:33</td>\n",
       "      <td>yOu cAnT hUrT mE DePrEsSiOn GoT mE AlReAdY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:32</td>\n",
       "      <td>The free market cab cure your depression, just...</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:31</td>\n",
       "      <td>I’m not steeped enough in the research to have...</td>\n",
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       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>[]</td>\n",
       "      <td>['#triggerwarning']</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:28</td>\n",
       "      <td>I feel like I can’t work for more than two to ...</td>\n",
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       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:28</td>\n",
       "      <td>dvdRadio 102 Still looking for a title.  (That...</td>\n",
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       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:27</td>\n",
       "      <td>Do you recommend this for every kind of depres...</td>\n",
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       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:26</td>\n",
       "      <td>I was having a bad day and was all depressed. ...</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:25</td>\n",
       "      <td>but it completely makes sense tho it a surviva...</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:23</td>\n",
       "      <td>For example a top has to know how to deal with...</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:17</td>\n",
       "      <td>So. Here is the short and skinny about me here...</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:12</td>\n",
       "      <td>me when someone points out my crippling anxiet...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:23:05</td>\n",
       "      <td>No one:  Literally no one:  Clair Boucher/ム尺ﾉﾶ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:22:52</td>\n",
       "      <td>As a platoon leader I have to deal with that m...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:25</td>\n",
       "      <td>Suicide by oyster is a noble way to go  https:...</td>\n",
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       "    <tr>\n",
       "      <th>2783547</th>\n",
       "      <td>225148</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:24</td>\n",
       "      <td>floors are covered in water, so obviously a cl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783548</th>\n",
       "      <td>225149</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:17</td>\n",
       "      <td>That's a depressingly relatable perspective.  ...</td>\n",
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       "    <tr>\n",
       "      <th>2783549</th>\n",
       "      <td>225150</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:15</td>\n",
       "      <td>@glitterndior https://twitter.com/DankNeme/sta...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783550</th>\n",
       "      <td>225151</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:13</td>\n",
       "      <td>TW for suicide and fatphobia.  My mom has seve...</td>\n",
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       "    <tr>\n",
       "      <th>2783551</th>\n",
       "      <td>225152</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:03</td>\n",
       "      <td>« Le professeur meurt et Tokyo se suicide » jl...</td>\n",
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       "    <tr>\n",
       "      <th>2783552</th>\n",
       "      <td>225153</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:02</td>\n",
       "      <td>Telling a gay youth is choices are suicide, ex...</td>\n",
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       "    <tr>\n",
       "      <th>2783553</th>\n",
       "      <td>225154</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:01</td>\n",
       "      <td>Tokyo tue professor et se suicide</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783554</th>\n",
       "      <td>225155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:02:00</td>\n",
       "      <td>suicide's time :))</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783555</th>\n",
       "      <td>225156</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:58</td>\n",
       "      <td>For Those Considering Suicide  https://youtu.b...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783556</th>\n",
       "      <td>225157</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:54</td>\n",
       "      <td>Tf suicide isn’t no joke  https://twitter.com/...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783557</th>\n",
       "      <td>225158</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:53</td>\n",
       "      <td>It always brings to mind the “body in the bag ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783558</th>\n",
       "      <td>225159</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>['#suicidesilence', '#youonlyliveonce', '#lege...</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:45</td>\n",
       "      <td>You only live once so just go fucking nuts!Go!...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783559</th>\n",
       "      <td>225160</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:40</td>\n",
       "      <td>i cant wait to entrap a woman into a web of vi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783560</th>\n",
       "      <td>225161</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:21</td>\n",
       "      <td>고마웠었어 #€</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783561</th>\n",
       "      <td>225162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:14</td>\n",
       "      <td>The “Clinton Suicide Squad “ is gearing up as ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783562</th>\n",
       "      <td>225163</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:14</td>\n",
       "      <td>Last Monday a I had to tell a client his benef...</td>\n",
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       "    <tr>\n",
       "      <th>2783563</th>\n",
       "      <td>225164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:08</td>\n",
       "      <td>http://bit.ly/MurderOfTalent  Why is the publ...</td>\n",
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       "    <tr>\n",
       "      <th>2783564</th>\n",
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       "      <td>1.15237e+18</td>\n",
       "      <td>17:01:02</td>\n",
       "      <td>戸塚：電車の車内でケータイが鳴った一八木さんが「いま電話の中だから電車切るぞ」って言ってケー...</td>\n",
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       "      <th>2783565</th>\n",
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       "      <td>17:00:54</td>\n",
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       "      <td>17:00:22</td>\n",
       "      <td>pic.twitter.com/sm9tNmrEQJ</td>\n",
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       "    <tr>\n",
       "      <th>2783570</th>\n",
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       "      <td>1.15237e+18</td>\n",
       "      <td>17:00:18</td>\n",
       "      <td>https://youtu.be/WJ0OkkIDZug   Deathnote is r...</td>\n",
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       "    <tr>\n",
       "      <th>2783571</th>\n",
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       "      <td>[]</td>\n",
       "      <td>1.15237e+18</td>\n",
       "      <td>17:00:17</td>\n",
       "      <td>えっ？！オニィ結構なお歳……（今知った） 変な声でちゃった(笑)</td>\n",
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       "    <tr>\n",
       "      <th>2783572</th>\n",
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       "      <td>1.15237e+18</td>\n",
       "      <td>17:00:12</td>\n",
       "      <td>#PhysicianFriday \"Let's empower doctors to tak...</td>\n",
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       "      <td>17:00:00</td>\n",
       "      <td>Need some support? Check out the following res...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2783575</th>\n",
       "      <td>225176</td>\n",
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       "      <td>17:00:00</td>\n",
       "      <td>You can improve the quality of life for a frie...</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2783576 rows × 7 columns</p>\n",
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       "                                                  hashtags           id  \\\n",
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       "1                                                       []  1.15135E+18   \n",
       "2        ['#depression', '#uncoveringthenewu', '#change...  1.15135E+18   \n",
       "3                                                       []  1.15135E+18   \n",
       "4                                                       []  1.15135E+18   \n",
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       "10                                                      []  1.15135E+18   \n",
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       "18                                                      []  1.15135E+18   \n",
       "19                                                      []  1.15135E+18   \n",
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       "22                                                      []  1.15135E+18   \n",
       "23                                                      []  1.15135E+18   \n",
       "24                                                      []  1.15135E+18   \n",
       "25                                                      []  1.15135E+18   \n",
       "26                                                      []  1.15135E+18   \n",
       "27                                                      []  1.15135E+18   \n",
       "28                                                      []  1.15135E+18   \n",
       "29                                                      []  1.15135E+18   \n",
       "...                                                    ...          ...   \n",
       "2783546                                                 []  1.15237e+18   \n",
       "2783547                                                 []  1.15237e+18   \n",
       "2783548                                                 []  1.15237e+18   \n",
       "2783549                                                 []  1.15237e+18   \n",
       "2783550                                                 []  1.15237e+18   \n",
       "2783551                                                 []  1.15237e+18   \n",
       "2783552                                                 []  1.15237e+18   \n",
       "2783553                                                 []  1.15237e+18   \n",
       "2783554                                                 []  1.15237e+18   \n",
       "2783555                                                 []  1.15237e+18   \n",
       "2783556                                                 []  1.15237e+18   \n",
       "2783557                                                 []  1.15237e+18   \n",
       "2783558  ['#suicidesilence', '#youonlyliveonce', '#lege...  1.15237e+18   \n",
       "2783559                                                 []  1.15237e+18   \n",
       "2783560                                                 []  1.15237e+18   \n",
       "2783561                                                 []  1.15237e+18   \n",
       "2783562                                                 []  1.15237e+18   \n",
       "2783563                                                 []  1.15237e+18   \n",
       "2783564                                                 []  1.15237e+18   \n",
       "2783565                                                 []  1.15237e+18   \n",
       "2783566            ['#mentalhealth', '#suicideprevention']  1.15237e+18   \n",
       "2783567                                                 []  1.15237e+18   \n",
       "2783568                                                 []  1.15237e+18   \n",
       "2783569                                                 []  1.15237e+18   \n",
       "2783570                                                 []  1.15237e+18   \n",
       "2783571                                                 []  1.15237e+18   \n",
       "2783572  ['#physicianfriday', '#suicide', '#physicians'...  1.15237e+18   \n",
       "2783573  ['#aztrauma', '#traumatraining', '#suicide', '...  1.15237e+18   \n",
       "2783574                                                 []  1.15237e+18   \n",
       "2783575                                                 []  1.15237e+18   \n",
       "\n",
       "             time                                              tweet  \n",
       "0        21:25:13  Wow, my dad yday: “you don’t take those stupid...  \n",
       "1        21:25:07  what part of this was really harmfult of a lot...  \n",
       "2        21:25:06  one of the ways I got through my #depression i...  \n",
       "3        21:24:55  see i wanna do one of them but they all say th...  \n",
       "4        21:24:51  IS IT clinical depression or is it the palpabl...  \n",
       "5        21:24:46  My new mantra for dealing with my anxiety/depr...  \n",
       "6        21:24:41  Can I get a woot woot for 3 months depression ...  \n",
       "7        21:24:37  Hope it's working for you.  I was on sertralin...  \n",
       "8        21:24:22  When my depression and anxiety strike at the s...  \n",
       "9        21:24:20  I want to leave, but I'm still waiting for you...  \n",
       "10       21:24:07                      lrt i have FUCKING DEPRESSION  \n",
       "11       21:24:07  If you’re a new mother who is tired from postp...  \n",
       "12       21:24:04  Currently 5:23 and I’ve got depression  Ranger...  \n",
       "13       21:24:01  omg wish I could swim cause I'm gonna drown in...  \n",
       "14       21:24:00  I don’t talk about it much at all unless you’r...  \n",
       "15       21:23:55  🚨🚨🚨 If someone can help me by suggesting me ho...  \n",
       "16       21:23:39  luv 2 just start crying bc you're thinking of ...  \n",
       "17       21:23:33         yOu cAnT hUrT mE DePrEsSiOn GoT mE AlReAdY  \n",
       "18       21:23:32  The free market cab cure your depression, just...  \n",
       "19       21:23:31  I’m not steeped enough in the research to have...  \n",
       "20       21:23:28  I feel like I can’t work for more than two to ...  \n",
       "21       21:23:28  dvdRadio 102 Still looking for a title.  (That...  \n",
       "22       21:23:27  Do you recommend this for every kind of depres...  \n",
       "23       21:23:26  I was having a bad day and was all depressed. ...  \n",
       "24       21:23:25  but it completely makes sense tho it a surviva...  \n",
       "25       21:23:23  For example a top has to know how to deal with...  \n",
       "26       21:23:17  So. Here is the short and skinny about me here...  \n",
       "27       21:23:12  me when someone points out my crippling anxiet...  \n",
       "28       21:23:05  No one:  Literally no one:  Clair Boucher/ム尺ﾉﾶ...  \n",
       "29       21:22:52  As a platoon leader I have to deal with that m...  \n",
       "...           ...                                                ...  \n",
       "2783546  17:02:25  Suicide by oyster is a noble way to go  https:...  \n",
       "2783547  17:02:24  floors are covered in water, so obviously a cl...  \n",
       "2783548  17:02:17  That's a depressingly relatable perspective.  ...  \n",
       "2783549  17:02:15  @glitterndior https://twitter.com/DankNeme/sta...  \n",
       "2783550  17:02:13  TW for suicide and fatphobia.  My mom has seve...  \n",
       "2783551  17:02:03  « Le professeur meurt et Tokyo se suicide » jl...  \n",
       "2783552  17:02:02  Telling a gay youth is choices are suicide, ex...  \n",
       "2783553  17:02:01                  Tokyo tue professor et se suicide  \n",
       "2783554  17:02:00                                 suicide's time :))  \n",
       "2783555  17:01:58  For Those Considering Suicide  https://youtu.b...  \n",
       "2783556  17:01:54  Tf suicide isn’t no joke  https://twitter.com/...  \n",
       "2783557  17:01:53  It always brings to mind the “body in the bag ...  \n",
       "2783558  17:01:45  You only live once so just go fucking nuts!Go!...  \n",
       "2783559  17:01:40  i cant wait to entrap a woman into a web of vi...  \n",
       "2783560  17:01:21                                           고마웠었어 #€  \n",
       "2783561  17:01:14  The “Clinton Suicide Squad “ is gearing up as ...  \n",
       "2783562  17:01:14  Last Monday a I had to tell a client his benef...  \n",
       "2783563  17:01:08   http://bit.ly/MurderOfTalent  Why is the publ...  \n",
       "2783564  17:01:02  戸塚：電車の車内でケータイが鳴った一八木さんが「いま電話の中だから電車切るぞ」って言ってケー...  \n",
       "2783565  17:00:54                 y'a pas d'incruste ti é la famille  \n",
       "2783566  17:00:52  A study will use CCTV from certain locations t...  \n",
       "2783567  17:00:41  Since I was reminded of her today, everyone pl...  \n",
       "2783568  17:00:35  Mariko was a quick favorite for me. I'm glad s...  \n",
       "2783569  17:00:22                         pic.twitter.com/sm9tNmrEQJ  \n",
       "2783570  17:00:18   https://youtu.be/WJ0OkkIDZug   Deathnote is r...  \n",
       "2783571  17:00:17                   えっ？！オニィ結構なお歳……（今知った） 変な声でちゃった(笑)  \n",
       "2783572  17:00:12  #PhysicianFriday \"Let's empower doctors to tak...  \n",
       "2783573  17:00:01  A spike in suicides among teenage boys in the ...  \n",
       "2783574  17:00:00  Need some support? Check out the following res...  \n",
       "2783575  17:00:00  You can improve the quality of life for a frie...  \n",
       "\n",
       "[2783576 rows x 7 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('depressive_unigram_tweets_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to /Users/anneb/nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to /Users/anneb/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import nltk\n",
    "nltk.download(['punkt','stopwords'])\n",
    "from nltk.corpus import stopwords\n",
    "stopwords = stopwords.words('english')\n",
    "\n",
    "from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\n",
    "analyzer = SentimentIntensityAnalyzer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anneb/anaconda3/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3049: DtypeWarning: Columns (0,1,2,3,4,5,6) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.read_csv('depressive_unigram_tweets_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 5,
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    }
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   "source": [
    "df2.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df2.isnull().any().any()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
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      "time            1650612 non-null object\n",
      "tweet           1650599 non-null object\n",
      "dtypes: object(7)\n",
      "memory usage: 148.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df2.info(null_counts=True) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df2[df2['tweet'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1650599 entries, 0 to 2783575\n",
      "Data columns (total 7 columns):\n",
      "Unnamed: 0      1650599 non-null object\n",
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      "time            1650599 non-null object\n",
      "tweet           1650599 non-null object\n",
      "dtypes: object(7)\n",
      "memory usage: 100.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_new.info(null_counts=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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   "source": [
    "df_new.isnull().any().any() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anneb/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_new['clean_tweet'] = df_new['tweet'].apply(lambda x: ' '.join([item for item in x.split() if item not in stopwords])) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 12,
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   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anneb/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df_new['vader_score'] = df_new['clean_tweet'].apply(lambda x: analyzer.polarity_scores(x)['compound'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_num = len(df_new[df_new['vader_score'] >=0.05])\n",
    "#neutral_num = len(df_new[(df_new['vaderReviewScore'] >-0.05) & (df_new['vaderReviewScore']<0.05)])\n",
    "negative_num = len(df_new[df_new['vader_score']<0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42745, 1607854)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "#positive_num,neutral_num, negative_num\n",
    "positive_num, negative_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anneb/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
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   ],
   "source": [
    "df_new['vader_sentiment_label']= df_new['vader_score'].map(lambda x:int(1) if x>=0.05 else int(0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
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       "      <td>[]</td>\n",
       "      <td>['#depression', '#uncoveringthenewu', '#change...</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:25:06</td>\n",
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       "\n",
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       "\n",
       "   vader_sentiment_label  \n",
       "0                      0  \n",
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_new.loc[df_new['vaderReviewScore'] >=0.00,\"vaderSentimentLabel\"] = 1\n",
    "#df_new.loc[df_new['vaderReviewScore']<=-0.00,\"vaderSentimentLabel\"] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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     "execution_count": 20,
     "metadata": {},
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   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/anneb/anaconda3/lib/python3.7/site-packages/pandas/core/frame.py:3940: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  errors=errors)\n"
     ]
    }
   ],
   "source": [
    "df_new.drop(['Unnamed: 0.1', 'id', 'time', 'tweet', ], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new = df_new[['Unnamed: 0', 'vader_sentiment_label', 'vader_score', 'clean_tweet']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
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       "  Unnamed: 0  vader_sentiment_label  vader_score  \\\n",
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       "                                         clean_tweet  \n",
       "0  Wow, dad yday: “you don’t take stupid depressi...  \n",
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       "3  see wanna one say PTSD, depression, and/or anx...  \n",
       "4  IS IT clinical depression palpable hopelessnes...  "
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     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "positive_num = len(df_new[df_new['vader_score'] >=0.05])\n",
    "neutral_num = len(df_new[(df_new['vader_score'] >-0.05) & (df_new['vader_score']<0.05)])\n",
    "negative_num = len(df_new[df_new['vader_score']<=-0.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(42745, 1493208, 114646)"
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     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positive_num,neutral_num, negative_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new.to_csv('vader_processed_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import itertools\n",
    "import collections\n",
    "\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import re\n",
    "from nltk import bigrams\n",
    "import networkx as nx\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "sns.set(font_scale=1.5)\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_new['text'] = df_new['clean_tweet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          Wow, dad yday: “you don’t take stupid depressi...\n",
       "1          part really harmfult lot people went every gui...\n",
       "2          one ways I got #depression learning dance rain...\n",
       "3          see wanna one say PTSD, depression, and/or anx...\n",
       "4          IS IT clinical depression palpable hopelessnes...\n",
       "5          My new mantra dealing anxiety/depression overa...\n",
       "6          Can I get woot woot 3 months depression medica...\n",
       "7          Hope working you. I sertraline year helped dep...\n",
       "8          When depression anxiety strike time I can’t an...\n",
       "9          I want leave, I'm still waiting legalize Assis...\n",
       "10                                    lrt FUCKING DEPRESSION\n",
       "11         If you’re new mother tired postpartum depressi...\n",
       "12         Currently 5:23 I’ve got depression Rangers cou...\n",
       "13         omg wish I could swim cause I'm gonna drown de...\n",
       "14         I don’t talk much unless you’re close FUCK anx...\n",
       "15         🚨🚨🚨 If someone help suggesting I respond/say/w...\n",
       "16         luv 2 start crying bc thinking ur ex amazing i...\n",
       "17                yOu cAnT hUrT mE DePrEsSiOn GoT mE AlReAdY\n",
       "18             The free market cab cure depression, cut eye!\n",
       "19         I’m steeped enough research strong professiona...\n",
       "20         I feel like I can’t work two three days withou...\n",
       "21         dvdRadio 102 Still looking title. (That title....\n",
       "22         Do recommend every kind depression like heartb...\n",
       "23         I bad day depressed. My 4 year old came me, pu...\n",
       "24         completely makes sense tho survival mechanism ...\n",
       "25         For example top know deal submissive depressed...\n",
       "26         So. Here short skinny lately. I know know I've...\n",
       "27         someone points crippling anxiety depression ah...\n",
       "28         No one: Literally one: Clair Boucher/ム尺ﾉﾶ乇丂: M...\n",
       "29         As platoon leader I deal often I want to. One ...\n",
       "                                 ...                        \n",
       "2783546    Suicide oyster noble way go https://twitter.co...\n",
       "2783547    floors covered water, obviously clean store cl...\n",
       "2783548    That's depressingly relatable perspective. As ...\n",
       "2783549    @glitterndior https://twitter.com/DankNeme/sta...\n",
       "2783550    TW suicide fatphobia. My mom severe pain one h...\n",
       "2783551    « Le professeur meurt et Tokyo se suicide » jl...\n",
       "2783552    Telling gay youth choices suicide, exile commu...\n",
       "2783553                    Tokyo tue professor et se suicide\n",
       "2783554                                   suicide's time :))\n",
       "2783555    For Those Considering Suicide https://youtu.be...\n",
       "2783556    Tf suicide isn’t joke https://twitter.com/lenr...\n",
       "2783557    It always brings mind “body bag suicide” case ...\n",
       "2783558    You live go fucking nuts!Go! #SuicideSilence #...\n",
       "2783559           cant wait entrap woman web violence deceit\n",
       "2783560                                             고마웠었어 #€\n",
       "2783561          The “Clinton Suicide Squad “ gearing speak.\n",
       "2783562    Last Monday I tell client benefits delayed, AG...\n",
       "2783563    http://bit.ly/MurderOfTalent Why public willin...\n",
       "2783564    戸塚：電車の車内でケータイが鳴った一八木さんが「いま電話の中だから電車切るぞ」って言ってケー...\n",
       "2783565                   y'a pas d'incruste ti é la famille\n",
       "2783566    A study use CCTV certain locations see behavio...\n",
       "2783567    Since I reminded today, everyone please block ...\n",
       "2783568    Mariko quick favorite me. I'm glad recovered t...\n",
       "2783569                           pic.twitter.com/sm9tNmrEQJ\n",
       "2783570    https://youtu.be/WJ0OkkIDZug Deathnote real FB...\n",
       "2783571                     えっ？！オニィ結構なお歳……（今知った） 変な声でちゃった(笑)\n",
       "2783572    #PhysicianFriday \"Let's empower doctors take t...\n",
       "2783573    A spike suicides among teenage boys United Sta...\n",
       "2783574    Need support? Check following resources: Couns...\n",
       "2783575    You improve quality life friend need. Learn Me...\n",
       "Name: text, Length: 1650599, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_url(txt):\n",
    "    \"\"\"Replace URLs found in a text string with nothing \n",
    "    (i.e. it will remove the URL from the string).\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    txt : string\n",
    "        A text string that you want to parse and remove urls.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    The same txt string with url's removed.\n",
    "    \"\"\"\n",
    "\n",
    "    return \" \".join(re.sub(\"([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \"\", txt).split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Wow dad yday you dont take stupid depression drugs anymore though you Because theyre absolute worst thing and never need them Aint great family supportive My moms sisters stance similar btw',\n",
       " 'part really harmfult lot people went every guideline understand 13rw horror show supposed insight depression mental illness overall helpful public narrative topic',\n",
       " 'one ways I got depression learning dance rain It source strength stronger circumstances UncoveringthenewU ChangeYourMindChangeYourCircumstances pictwittercom3vxKuQ0DvD',\n",
       " 'see wanna one say PTSD depression andor anxiety play LIKEE',\n",
       " 'IS IT clinical depression palpable hopelessness generation']"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tweets_no_urls = [remove_url(tweet) for tweet in df_new['text']]\n",
    "all_tweets_no_urls[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lower_case = [word.lower() for word in df_new['text']]\n",
    "sentences = df_new['text']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Wow',\n",
       " 'dad',\n",
       " 'yday',\n",
       " 'you',\n",
       " 'dont',\n",
       " 'take',\n",
       " 'stupid',\n",
       " 'depression',\n",
       " 'drugs',\n",
       " 'anymore',\n",
       " 'though',\n",
       " 'you',\n",
       " 'Because',\n",
       " 'theyre',\n",
       " 'absolute',\n",
       " 'worst',\n",
       " 'thing',\n",
       " 'and',\n",
       " 'never',\n",
       " 'need',\n",
       " 'them',\n",
       " 'Aint',\n",
       " 'great',\n",
       " 'family',\n",
       " 'supportive',\n",
       " 'My',\n",
       " 'moms',\n",
       " 'sisters',\n",
       " 'stance',\n",
       " 'similar',\n",
       " 'btw']"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tweets_no_urls[0].split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wow',\n",
       " 'dad',\n",
       " 'yday',\n",
       " 'you',\n",
       " 'dont',\n",
       " 'take',\n",
       " 'stupid',\n",
       " 'depression',\n",
       " 'drugs',\n",
       " 'anymore',\n",
       " 'though',\n",
       " 'you',\n",
       " 'because',\n",
       " 'theyre',\n",
       " 'absolute',\n",
       " 'worst',\n",
       " 'thing',\n",
       " 'and',\n",
       " 'never',\n",
       " 'need',\n",
       " 'them',\n",
       " 'aint',\n",
       " 'great',\n",
       " 'family',\n",
       " 'supportive',\n",
       " 'my',\n",
       " 'moms',\n",
       " 'sisters',\n",
       " 'stance',\n",
       " 'similar',\n",
       " 'btw']"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_tweets_no_urls[0].lower().split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['wow',\n",
       "  'dad',\n",
       "  'yday',\n",
       "  'you',\n",
       "  'dont',\n",
       "  'take',\n",
       "  'stupid',\n",
       "  'depression',\n",
       "  'drugs',\n",
       "  'anymore',\n",
       "  'though',\n",
       "  'you',\n",
       "  'because',\n",
       "  'theyre',\n",
       "  'absolute',\n",
       "  'worst',\n",
       "  'thing',\n",
       "  'and',\n",
       "  'never',\n",
       "  'need',\n",
       "  'them',\n",
       "  'aint',\n",
       "  'great',\n",
       "  'family',\n",
       "  'supportive',\n",
       "  'my',\n",
       "  'moms',\n",
       "  'sisters',\n",
       "  'stance',\n",
       "  'similar',\n",
       "  'btw'],\n",
       " ['part',\n",
       "  'really',\n",
       "  'harmfult',\n",
       "  'lot',\n",
       "  'people',\n",
       "  'went',\n",
       "  'every',\n",
       "  'guideline',\n",
       "  'understand',\n",
       "  '13rw',\n",
       "  'horror',\n",
       "  'show',\n",
       "  'supposed',\n",
       "  'insight',\n",
       "  'depression',\n",
       "  'mental',\n",
       "  'illness',\n",
       "  'overall',\n",
       "  'helpful',\n",
       "  'public',\n",
       "  'narrative',\n",
       "  'topic']]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_in_tweet = [tweet.lower().split() for tweet in all_tweets_no_urls]\n",
    "words_in_tweet[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('i', 65848),\n",
       " ('lonely', 55378),\n",
       " ('smile', 42634),\n",
       " ('wide', 42271),\n",
       " ('suicide', 39566),\n",
       " ('im', 29153),\n",
       " ('depression', 26013),\n",
       " ('like', 14866),\n",
       " ('depressed', 14064),\n",
       " ('people', 11913),\n",
       " ('hopeless', 11236),\n",
       " ('the', 10641),\n",
       " ('you', 10512),\n",
       " ('feel', 10500),\n",
       " ('its', 9617)]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List of all words across tweets\n",
    "all_words_no_urls = list(itertools.chain(*words_in_tweet))\n",
    "\n",
    "# Create counter\n",
    "counts_no_urls = collections.Counter(all_words_no_urls)\n",
    "\n",
    "counts_no_urls.most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>i</td>\n",
       "      <td>65848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lonely</td>\n",
       "      <td>55378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>smile</td>\n",
       "      <td>42634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>wide</td>\n",
       "      <td>42271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>suicide</td>\n",
       "      <td>39566</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     words  count\n",
       "0        i  65848\n",
       "1   lonely  55378\n",
       "2    smile  42634\n",
       "3     wide  42271\n",
       "4  suicide  39566"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tweets_no_urls = pd.DataFrame(counts_no_urls.most_common(15),\n",
    "                             columns=['words', 'count'])\n",
    "\n",
    "clean_tweets_no_urls.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 493,
       "width": 629
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Plot horizontal bar graph\n",
    "clean_tweets_no_urls.sort_values(by='count').plot.barh(x='words',\n",
    "                      y='count',\n",
    "                      ax=ax,\n",
    "                      color=\"purple\")\n",
    "\n",
    "ax.set_title(\"Common Words Found in Tweets (Including All Words)\")\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['some',\n",
       " 'once',\n",
       " 'needn',\n",
       " 'an',\n",
       " \"shouldn't\",\n",
       " 'any',\n",
       " 'then',\n",
       " 'very',\n",
       " 'it',\n",
       " 'yourselves']"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# View a few words from the set\n",
    "list(stop_words)[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wow',\n",
       " 'dad',\n",
       " 'yday',\n",
       " 'you',\n",
       " 'dont',\n",
       " 'take',\n",
       " 'stupid',\n",
       " 'depression',\n",
       " 'drugs',\n",
       " 'anymore',\n",
       " 'though',\n",
       " 'you',\n",
       " 'because',\n",
       " 'theyre',\n",
       " 'absolute',\n",
       " 'worst',\n",
       " 'thing',\n",
       " 'and',\n",
       " 'never',\n",
       " 'need',\n",
       " 'them',\n",
       " 'aint',\n",
       " 'great',\n",
       " 'family',\n",
       " 'supportive',\n",
       " 'my',\n",
       " 'moms',\n",
       " 'sisters',\n",
       " 'stance',\n",
       " 'similar',\n",
       " 'btw']"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_in_tweet[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wow',\n",
       " 'dad',\n",
       " 'yday',\n",
       " 'dont',\n",
       " 'take',\n",
       " 'stupid',\n",
       " 'depression',\n",
       " 'drugs',\n",
       " 'anymore',\n",
       " 'though',\n",
       " 'theyre',\n",
       " 'absolute',\n",
       " 'worst',\n",
       " 'thing',\n",
       " 'never',\n",
       " 'need',\n",
       " 'aint',\n",
       " 'great',\n",
       " 'family',\n",
       " 'supportive',\n",
       " 'moms',\n",
       " 'sisters',\n",
       " 'stance',\n",
       " 'similar',\n",
       " 'btw']"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove stop words from each tweet list of words\n",
    "tweets_nsw = [[word for word in tweet_words if not word in stop_words]\n",
    "              for tweet_words in words_in_tweet]\n",
    "\n",
    "tweets_nsw[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('lonely', 55378),\n",
       " ('smile', 42634),\n",
       " ('wide', 42271),\n",
       " ('suicide', 39566),\n",
       " ('im', 29153),\n",
       " ('depression', 26013),\n",
       " ('like', 14866),\n",
       " ('depressed', 14064),\n",
       " ('people', 11913),\n",
       " ('hopeless', 11236),\n",
       " ('feel', 10500),\n",
       " ('get', 8998),\n",
       " ('one', 8971),\n",
       " ('de', 8204),\n",
       " ('know', 8041)]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_words_nsw = list(itertools.chain(*tweets_nsw))\n",
    "\n",
    "counts_nsw = collections.Counter(all_words_nsw)\n",
    "\n",
    "counts_nsw.most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 493,
       "width": 629
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "clean_tweets_nsw = pd.DataFrame(counts_nsw.most_common(15),\n",
    "                             columns=['words', 'count'])\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Plot horizontal bar graph\n",
    "clean_tweets_nsw.sort_values(by='count').plot.barh(x='words',\n",
    "                      y='count',\n",
    "                      ax=ax,\n",
    "                      color=\"purple\")\n",
    "\n",
    "ax.set_title(\"Common Words Found in Tweets (Without Stop Words)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "collection_words = ['im', 'de', 'like', 'one']\n",
    "tweets_nsw_nc = [[w for w in word if not w in collection_words]\n",
    "                 for word in tweets_nsw]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wow',\n",
       " 'dad',\n",
       " 'yday',\n",
       " 'dont',\n",
       " 'take',\n",
       " 'stupid',\n",
       " 'depression',\n",
       " 'drugs',\n",
       " 'anymore',\n",
       " 'though',\n",
       " 'theyre',\n",
       " 'absolute',\n",
       " 'worst',\n",
       " 'thing',\n",
       " 'never',\n",
       " 'need',\n",
       " 'aint',\n",
       " 'great',\n",
       " 'family',\n",
       " 'supportive',\n",
       " 'moms',\n",
       " 'sisters',\n",
       " 'stance',\n",
       " 'similar',\n",
       " 'btw']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_nsw[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['wow',\n",
       " 'dad',\n",
       " 'yday',\n",
       " 'dont',\n",
       " 'take',\n",
       " 'stupid',\n",
       " 'depression',\n",
       " 'drugs',\n",
       " 'anymore',\n",
       " 'though',\n",
       " 'theyre',\n",
       " 'absolute',\n",
       " 'worst',\n",
       " 'thing',\n",
       " 'never',\n",
       " 'need',\n",
       " 'aint',\n",
       " 'great',\n",
       " 'family',\n",
       " 'supportive',\n",
       " 'moms',\n",
       " 'sisters',\n",
       " 'stance',\n",
       " 'similar',\n",
       " 'btw']"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tweets_nsw_nc[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('lonely', 55378),\n",
       " ('smile', 42634),\n",
       " ('wide', 42271),\n",
       " ('suicide', 39566),\n",
       " ('depression', 26013),\n",
       " ('depressed', 14064),\n",
       " ('people', 11913),\n",
       " ('hopeless', 11236),\n",
       " ('feel', 10500),\n",
       " ('get', 8998),\n",
       " ('know', 8041),\n",
       " ('dont', 7855),\n",
       " ('time', 7433),\n",
       " ('life', 6812),\n",
       " ('love', 6585)]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Flatten list of words in clean tweets\n",
    "all_words_nsw_nc = list(itertools.chain(*tweets_nsw_nc))\n",
    "\n",
    "# Create counter of words in clean tweets\n",
    "counts_nsw_nc = collections.Counter(all_words_nsw_nc)\n",
    "\n",
    "counts_nsw_nc.most_common(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125585"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(counts_nsw_nc)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean(counts_nsw_nc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>words</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>lonely</td>\n",
       "      <td>55378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>smile</td>\n",
       "      <td>42634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>wide</td>\n",
       "      <td>42271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>suicide</td>\n",
       "      <td>39566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>depression</td>\n",
       "      <td>26013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        words  count\n",
       "0      lonely  55378\n",
       "1       smile  42634\n",
       "2        wide  42271\n",
       "3     suicide  39566\n",
       "4  depression  26013"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clean_tweets_ncw = pd.DataFrame(counts_nsw_nc.most_common(15),\n",
    "                             columns=['words', 'count'])\n",
    "clean_tweets_ncw.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 493,
       "width": 650
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 8))\n",
    "\n",
    "# Plot horizontal bar graph\n",
    "clean_tweets_ncw.sort_values(by='count').plot.barh(x='words',\n",
    "                      y='count',\n",
    "                      ax=ax,\n",
    "                      color=\"purple\")\n",
    "\n",
    "ax.set_title(\"Common Words Found in Tweets (Without Stop or Collection Words)\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('wow', 'dad'),\n",
       " ('dad', 'yday'),\n",
       " ('yday', 'dont'),\n",
       " ('dont', 'take'),\n",
       " ('take', 'stupid'),\n",
       " ('stupid', 'depression'),\n",
       " ('depression', 'drugs'),\n",
       " ('drugs', 'anymore'),\n",
       " ('anymore', 'though'),\n",
       " ('though', 'theyre'),\n",
       " ('theyre', 'absolute'),\n",
       " ('absolute', 'worst'),\n",
       " ('worst', 'thing'),\n",
       " ('thing', 'never'),\n",
       " ('never', 'need'),\n",
       " ('need', 'aint'),\n",
       " ('aint', 'great'),\n",
       " ('great', 'family'),\n",
       " ('family', 'supportive'),\n",
       " ('supportive', 'moms'),\n",
       " ('moms', 'sisters'),\n",
       " ('sisters', 'stance'),\n",
       " ('stance', 'similar'),\n",
       " ('similar', 'btw')]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nltk import bigrams\n",
    "\n",
    "# Create list of lists containing bigrams in tweets\n",
    "terms_bigram = [list(bigrams(tweet)) for tweet in tweets_nsw_nc]\n",
    "\n",
    "# View bigrams for the first tweet\n",
    "terms_bigram[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(('smile', 'wide'), 42185),\n",
       " (('afraid', 'loneliness'), 4641),\n",
       " (('feel', 'lonely'), 3541),\n",
       " (('suicide', 'squad'), 2769),\n",
       " (('commit', 'suicide'), 2624),\n",
       " (('committed', 'suicide'), 1685),\n",
       " (('feeling', 'lonely'), 1645),\n",
       " (('mental', 'health'), 1398),\n",
       " (('hopeless', 'romantic'), 1347),\n",
       " (('anxiety', 'depression'), 1337),\n",
       " (('suicide', 'bomber'), 1320),\n",
       " (('depression', 'anxiety'), 1278),\n",
       " (('sad', 'lonely'), 1181),\n",
       " (('social', 'media'), 958),\n",
       " (('get', 'lonely'), 922),\n",
       " (('suicide', 'prevention'), 841),\n",
       " (('committing', 'suicide'), 838),\n",
       " (('seasonal', 'depression'), 806),\n",
       " (('female', 'suicide'), 760),\n",
       " (('dont', 'know'), 741)]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Flatten list of bigrams in clean tweets\n",
    "bigrams = list(itertools.chain(*terms_bigram))\n",
    "\n",
    "# Create counter of words in clean bigrams\n",
    "bigram_counts = collections.Counter(bigrams)\n",
    "\n",
    "bigram_counts.most_common(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "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>bigram</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(smile, wide)</td>\n",
       "      <td>42185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(afraid, loneliness)</td>\n",
       "      <td>4641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(feel, lonely)</td>\n",
       "      <td>3541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(suicide, squad)</td>\n",
       "      <td>2769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(commit, suicide)</td>\n",
       "      <td>2624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>(committed, suicide)</td>\n",
       "      <td>1685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>(feeling, lonely)</td>\n",
       "      <td>1645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>(mental, health)</td>\n",
       "      <td>1398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>(hopeless, romantic)</td>\n",
       "      <td>1347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>(anxiety, depression)</td>\n",
       "      <td>1337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>(suicide, bomber)</td>\n",
       "      <td>1320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>(depression, anxiety)</td>\n",
       "      <td>1278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>(sad, lonely)</td>\n",
       "      <td>1181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>(social, media)</td>\n",
       "      <td>958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>(get, lonely)</td>\n",
       "      <td>922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>(suicide, prevention)</td>\n",
       "      <td>841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>(committing, suicide)</td>\n",
       "      <td>838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>(seasonal, depression)</td>\n",
       "      <td>806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>(female, suicide)</td>\n",
       "      <td>760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>(dont, know)</td>\n",
       "      <td>741</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    bigram  count\n",
       "0            (smile, wide)  42185\n",
       "1     (afraid, loneliness)   4641\n",
       "2           (feel, lonely)   3541\n",
       "3         (suicide, squad)   2769\n",
       "4        (commit, suicide)   2624\n",
       "5     (committed, suicide)   1685\n",
       "6        (feeling, lonely)   1645\n",
       "7         (mental, health)   1398\n",
       "8     (hopeless, romantic)   1347\n",
       "9    (anxiety, depression)   1337\n",
       "10       (suicide, bomber)   1320\n",
       "11   (depression, anxiety)   1278\n",
       "12           (sad, lonely)   1181\n",
       "13         (social, media)    958\n",
       "14           (get, lonely)    922\n",
       "15   (suicide, prevention)    841\n",
       "16   (committing, suicide)    838\n",
       "17  (seasonal, depression)    806\n",
       "18       (female, suicide)    760\n",
       "19            (dont, know)    741"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigram_df = pd.DataFrame(bigram_counts.most_common(20),\n",
    "                             columns=['bigram', 'count'])\n",
    "\n",
    "bigram_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 694,
       "width": 998
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create dictionary of bigrams and their counts\n",
    "d = bigram_df.set_index('bigram').T.to_dict('records')\n",
    "# Create network plot \n",
    "G = nx.Graph()\n",
    "\n",
    "# Create connections between nodes\n",
    "for k, v in d[0].items():\n",
    "    G.add_edge(k[0], k[1], weight=(v * 10))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(14, 12))\n",
    "\n",
    "pos = nx.spring_layout(G, k=1)\n",
    "\n",
    "# Plot networks\n",
    "nx.draw_networkx(G, pos,\n",
    "                 font_size=16,\n",
    "                 width=3,\n",
    "                 edge_color='grey',\n",
    "                 node_color='purple',\n",
    "                 with_labels = False,\n",
    "                 ax=ax)\n",
    "\n",
    "# Create offset labels\n",
    "for key, value in pos.items():\n",
    "    x, y = value[0]+.135, value[1]+.045\n",
    "    ax.text(x, y,\n",
    "            s=key,\n",
    "            bbox=dict(facecolor='red', alpha=0.25),\n",
    "            horizontalalignment='center', fontsize=13)\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('depressive_unigram_tweets_final.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>cashtags</th>\n",
       "      <th>hashtags</th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>tweet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:25:13</td>\n",
       "      <td>Wow, my dad yday: “you don’t take those stupid...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:25:07</td>\n",
       "      <td>what part of this was really harmfult of a lot...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>[]</td>\n",
       "      <td>['#depression', '#uncoveringthenewu', '#change...</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:25:06</td>\n",
       "      <td>one of the ways I got through my #depression i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:55</td>\n",
       "      <td>see i wanna do one of them but they all say th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>1.15135E+18</td>\n",
       "      <td>21:24:51</td>\n",
       "      <td>IS IT clinical depression or is it the palpabl...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Unnamed: 0 Unnamed: 0.1 cashtags  \\\n",
       "0          0            0       []   \n",
       "1          1            1       []   \n",
       "2          2            2       []   \n",
       "3          3            3       []   \n",
       "4          4            4       []   \n",
       "\n",
       "                                            hashtags           id      time  \\\n",
       "0                                                 []  1.15135E+18  21:25:13   \n",
       "1                                                 []  1.15135E+18  21:25:07   \n",
       "2  ['#depression', '#uncoveringthenewu', '#change...  1.15135E+18  21:25:06   \n",
       "3                                                 []  1.15135E+18  21:24:55   \n",
       "4                                                 []  1.15135E+18  21:24:51   \n",
       "\n",
       "                                               tweet  \n",
       "0  Wow, my dad yday: “you don’t take those stupid...  \n",
       "1  what part of this was really harmfult of a lot...  \n",
       "2  one of the ways I got through my #depression i...  \n",
       "3  see i wanna do one of them but they all say th...  \n",
       "4  IS IT clinical depression or is it the palpabl...  "
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>Wow, my dad yday: “you don’t take those stupid...</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>what part of this was really harmfult of a lot...</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one of the ways I got through my #depression i...</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>see i wanna do one of them but they all say th...</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IS IT clinical depression or is it the palpabl...</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  word_count\n",
       "0  Wow, my dad yday: “you don’t take those stupid...          50\n",
       "1  what part of this was really harmfult of a lot...          51\n",
       "2  one of the ways I got through my #depression i...          33\n",
       "3  see i wanna do one of them but they all say th...          24\n",
       "4  IS IT clinical depression or is it the palpabl...          13"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['word_count'] = train['tweet'].apply(lambda x: int(len(str(x).split(\" \"))))\n",
    "train[['tweet','word_count']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "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>tweet</th>\n",
       "      <th>char_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wow, my dad yday: “you don’t take those stupid...</td>\n",
       "      <td>278.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>what part of this was really harmfult of a lot...</td>\n",
       "      <td>274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>one of the ways I got through my #depression i...</td>\n",
       "      <td>235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>see i wanna do one of them but they all say th...</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>IS IT clinical depression or is it the palpabl...</td>\n",
       "      <td>78.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               tweet  char_count\n",
       "0  Wow, my dad yday: “you don’t take those stupid...       278.0\n",
       "1  what part of this was really harmfult of a lot...       274.0\n",
       "2  one of the ways I got through my #depression i...       235.0\n",
       "3  see i wanna do one of them but they all say th...       114.0\n",
       "4  IS IT clinical depression or is it the palpabl...        78.0"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['char_count'] = train['tweet'].str.len() ## this also includes spaces\n",
    "train[['tweet','char_count']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    278.0\n",
       "1    274.0\n",
       "2    235.0\n",
       "3    114.0\n",
       "4     78.0\n",
       "Name: tweet, dtype: float64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_count = train['tweet'].str.len() ## this also includes spaces\n",
    "char_count.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39.08271239713583"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_count.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_count.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    27.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char_count.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "word_counts = train['tweet'].apply(lambda x: int(len(str(x).split(\" \"))))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    50\n",
       "1    51\n",
       "2    33\n",
       "3    24\n",
       "4    13\n",
       "Name: tweet, dtype: int64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.8869669806033675"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.0"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "dtype: int64"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word_counts.mode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "Unknown property palette",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-169-6e92a7ede831>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchar_count\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbins\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Characters'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mylabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Frequency'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'characters'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'viridis'\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      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m300\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mset\u001b[0;34m(self, **kwargs)\u001b[0m\n\u001b[1;32m   1013\u001b[0m                    key=lambda x: (self._prop_order.get(x[0], 0), x[0])))\n\u001b[1;32m   1014\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1015\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprops\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   1016\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1017\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mfindobj\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmatch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minclude_self\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mupdate\u001b[0;34m(self, props)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    915\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setattr_cm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0meventson\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 916\u001b[0;31m             \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0m_update_property\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    917\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    918\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mret\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36m_update_property\u001b[0;34m(self, k, v)\u001b[0m\n\u001b[1;32m    910\u001b[0m                 \u001b[0mfunc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'set_'\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcallable\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Unknown property %s'\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0mk\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    913\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: Unknown property palette"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 296,
       "width": 478
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Histogram on x\n",
    "x = char_count\n",
    "plt.hist(x, bins=50)\n",
    "plt.gca().set(title='Characters', ylabel='Frequency', xlabel='characters');\n",
    "plt.xlim(0, 300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 100)"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 296,
       "width": 498
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Histogram on x\n",
    "x = word_counts\n",
    "plt.hist(x, bins=50)\n",
    "plt.gca().set(title='Words', ylabel='Frequency', xlabel='words');\n",
    "plt.xlim(0, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}