labs/07_nlp_lab.ipynb

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      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    }
   ],
   "source": [
    "# read in tweets data into a dataframe\n",
    "import sys\n",
    "from textblob import TextBlob\n",
    "import pandas as pd\n",
    "%matplotlib inline"
   ]
  },
  {
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       "      <th>Text</th>\n",
       "      <th>Date</th>\n",
       "      <th>Status</th>\n",
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       "                                                Text                 Date  \\\n",
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     "execution_count": 3,
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   ],
   "source": [
    "# these tweets are from last May and are about the company Apple\n",
    "tweets = pd.read_csv('../data/so_many_tweets.csv', encoding='unicode-escape')\n",
    "tweets.head()"
   ]
  },
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   "execution_count": 4,
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   "source": [
    "###############\n",
    "#### TO DO ####\n",
    "###############\n",
    "\n",
    "# use the textblob module to make a function called stringToSentiment that returns a sentences sentiment\n",
    "def stringToSentiment(text):\n",
    "    return -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
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    {
     "data": {
      "text/plain": [
       "-0.8"
      ]
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     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "stringToSentiment('i hate you')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
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   "execution_count": 11,
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   "source": [
    "###############\n",
    "#### TO DO ####\n",
    "###############\n",
    "\n",
    "# create a new column in tweets called sentiment that maps stringToSentiment to the text column\n",
    "tweets['sentiment'] = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
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       "      <th>Date</th>\n",
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       "      <th>Retweet</th>\n",
       "      <th>sentiment</th>\n",
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      "text/plain": [
       "                                                                  Text  \\\n",
       "Date                                                                     \n",
       "2015-05-24 03:46:08  RT @j_o_h_n_danger: $TWTR now top holding for ...   \n",
       "2015-05-24 04:17:42  RT diggingplatinum RT WWalkerWW: iOS 9 vs. And...   \n",
       "2015-05-24 04:13:22  RT bosocial RT insidermonkey RT j_o_h_n_danger...   \n",
       "2015-05-24 04:08:34  RT @WWalkerWW: iOS 9 vs. Android M – The New F...   \n",
       "2015-05-24 04:04:42  RT @seeitmarket: Apple Chart Update: Big Test ...   \n",
       "\n",
       "                                   Date              Status       Retweet  \\\n",
       "Date                                                                        \n",
       "2015-05-24 03:46:08 2015-05-24 03:46:08  602319644234395648  6.022899e+17   \n",
       "2015-05-24 04:17:42 2015-05-24 04:17:42  602327586983796737           NaN   \n",
       "2015-05-24 04:13:22 2015-05-24 04:13:22  602326499534966784           NaN   \n",
       "2015-05-24 04:08:34 2015-05-24 04:08:34  602325288740114432  6.023104e+17   \n",
       "2015-05-24 04:04:42 2015-05-24 04:04:42  602324318903771136  6.023215e+17   \n",
       "\n",
       "                     sentiment  \n",
       "Date                            \n",
       "2015-05-24 03:46:08   0.500000  \n",
       "2015-05-24 04:17:42   0.136364  \n",
       "2015-05-24 04:13:22   0.500000  \n",
       "2015-05-24 04:08:34   0.136364  \n",
       "2015-05-24 04:04:42   0.000000  "
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     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2015-05-24 03:46:08   2015-05-24 03:46:08\n",
       "2015-05-24 04:17:42   2015-05-24 04:17:42\n",
       "2015-05-24 04:13:22   2015-05-24 04:13:22\n",
       "2015-05-24 04:08:34   2015-05-24 04:08:34\n",
       "2015-05-24 04:04:42   2015-05-24 04:04:42\n",
       "Name: Date, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
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   ],
   "source": [
    "# cast the date columd as a datetime\n",
    "tweets['Date'] = pd.to_datetime(tweets.Date)\n",
    "tweets['Date'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "tweets.index = tweets.Date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
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     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:7: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).mean()\n"
     ]
    }
   ],
   "source": [
    "###############\n",
    "#### TO DO ####\n",
    "###############\n",
    "\n",
    "# create a dataframe called daily_tweets which resamples tweets by D, averaging the columns\n",
    "# Hint: look up pandas dataframe resample function\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
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      "text/plain": [
       "                        Status       Retweet  sentiment\n",
       "Date                                                   \n",
       "2015-05-02  594606835155331840  5.942058e+17   0.083031\n",
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "daily_tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10dc31d90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###############\n",
    "#### TO DO ####\n",
    "###############\n",
    "\n",
    "# plot the sentiment as a line graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sinanozdemir/anaconda/envs/sfdat26-env/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: how in .resample() is deprecated\n",
      "the new syntax is .resample(...).count()\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1179f4bd0>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5+suBmyQdy7N/upwCPJEeG0atM/PKrNt4nem5UEZmm8qeo5+ULic6/GJERBwY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\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117c955d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the volume of tweets\n",
    "tweets.resample('D', how='count').Status.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Text</th>\n",
       "      <th>Date</th>\n",
       "      <th>Status</th>\n",
       "      <th>Retweet</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:46:08</th>\n",
       "      <td>RT @j_o_h_n_danger: $TWTR now top holding for ...</td>\n",
       "      <td>2015-05-24 03:46:08</td>\n",
       "      <td>602319644234395648</td>\n",
       "      <td>6.022899e+17</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:17:42</th>\n",
       "      <td>RT diggingplatinum RT WWalkerWW: iOS 9 vs. And...</td>\n",
       "      <td>2015-05-24 04:17:42</td>\n",
       "      <td>602327586983796737</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:13:22</th>\n",
       "      <td>RT bosocial RT insidermonkey RT j_o_h_n_danger...</td>\n",
       "      <td>2015-05-24 04:13:22</td>\n",
       "      <td>602326499534966784</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:08:34</th>\n",
       "      <td>RT @WWalkerWW: iOS 9 vs. Android M – The New F...</td>\n",
       "      <td>2015-05-24 04:08:34</td>\n",
       "      <td>602325288740114432</td>\n",
       "      <td>6.023104e+17</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:04:42</th>\n",
       "      <td>RT @seeitmarket: Apple Chart Update: Big Test ...</td>\n",
       "      <td>2015-05-24 04:04:42</td>\n",
       "      <td>602324318903771136</td>\n",
       "      <td>6.023215e+17</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:00:01</th>\n",
       "      <td>Free Video: One Day Range Leaders http://t.co/...</td>\n",
       "      <td>2015-05-24 04:00:01</td>\n",
       "      <td>602323139297705984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:54:07</th>\n",
       "      <td>Option Millionaires: golfnara: $AAPL 135.79 ht...</td>\n",
       "      <td>2015-05-24 03:54:07</td>\n",
       "      <td>602321653645709313</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:25:29</th>\n",
       "      <td>RT @AppleReporter: What to Do When Your #iPhon...</td>\n",
       "      <td>2015-05-24 04:25:29</td>\n",
       "      <td>602329548860993537</td>\n",
       "      <td>6.013210e+17</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:24:47</th>\n",
       "      <td>RT @AppleReporter: Apple Near All-Time High!\\n...</td>\n",
       "      <td>2015-05-24 04:24:47</td>\n",
       "      <td>602329372083650560</td>\n",
       "      <td>6.021077e+17</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:06:42</th>\n",
       "      <td>RT @AmericanBanking: Morgan Stanley Reiterates...</td>\n",
       "      <td>2015-05-24 04:06:42</td>\n",
       "      <td>602324819342823424</td>\n",
       "      <td>6.023225e+17</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:58:09</th>\n",
       "      <td>RT insidermonkey RT j_o_h_n_danger: $TWTR now ...</td>\n",
       "      <td>2015-05-24 03:58:09</td>\n",
       "      <td>602322668642238464</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:45:12</th>\n",
       "      <td>Logging in now to live stock chat http://t.co/...</td>\n",
       "      <td>2015-05-24 03:45:12</td>\n",
       "      <td>602319408556544000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:38:18</th>\n",
       "      <td>Find active and helpful professional daytrader...</td>\n",
       "      <td>2015-05-24 03:38:18</td>\n",
       "      <td>602317674765484032</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-0.016667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:09:02</th>\n",
       "      <td>These Are The 50 Top Hedge Fund Long And Short...</td>\n",
       "      <td>2015-05-24 03:09:02</td>\n",
       "      <td>602310306489065472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:28:49</th>\n",
       "      <td>RT bosocial RT bosocial RT insidermonkey RT j_...</td>\n",
       "      <td>2015-05-24 04:28:49</td>\n",
       "      <td>602330384756510720</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:17:41</th>\n",
       "      <td>RT bosocial RT stocknugget Helpful video for s...</td>\n",
       "      <td>2015-05-24 04:17:41</td>\n",
       "      <td>602327585901645825</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:13:24</th>\n",
       "      <td>RT bosocial RT ferrarigtr RT j_o_h_n_danger: $...</td>\n",
       "      <td>2015-05-24 04:13:24</td>\n",
       "      <td>602326506027786241</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:13:23</th>\n",
       "      <td>RT bosocial RT Petrecson RT j_o_h_n_danger: $T...</td>\n",
       "      <td>2015-05-24 04:13:23</td>\n",
       "      <td>602326500625506306</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:11:06</th>\n",
       "      <td>Apple Chart Update: Big Test Looming http://t....</td>\n",
       "      <td>2015-05-24 04:11:06</td>\n",
       "      <td>602325927889117186</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:08:33</th>\n",
       "      <td>RT @WWalkerWW: iOS 9 vs. Android M – The New F...</td>\n",
       "      <td>2015-05-24 04:08:33</td>\n",
       "      <td>602325288068980736</td>\n",
       "      <td>6.023104e+17</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:08:33</th>\n",
       "      <td>RT @MarketsRecap: These Are The 50 Top Hedge F...</td>\n",
       "      <td>2015-05-24 04:08:33</td>\n",
       "      <td>602325287372722176</td>\n",
       "      <td>6.023103e+17</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:08:33</th>\n",
       "      <td>RT @AppleNewsAAPL: $AAPL Daily Deals: Long Wee...</td>\n",
       "      <td>2015-05-24 04:08:33</td>\n",
       "      <td>602325286714273792</td>\n",
       "      <td>6.023103e+17</td>\n",
       "      <td>-0.025000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:02:55</th>\n",
       "      <td>ChaytonFalke: Free Video: One Day Range Leader...</td>\n",
       "      <td>2015-05-24 04:02:55</td>\n",
       "      <td>602323867915427840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:53:26</th>\n",
       "      <td>Apple Chart Update: Big Test Looming http://t....</td>\n",
       "      <td>2015-05-24 03:53:26</td>\n",
       "      <td>602321480119099392</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:31:04</th>\n",
       "      <td>Be water, my friends. Be water. $MSFT $SPY $F ...</td>\n",
       "      <td>2015-05-24 03:31:04</td>\n",
       "      <td>602315854244683777</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:09:31</th>\n",
       "      <td>iOS 9 vs. Android M – The New Features By Appl...</td>\n",
       "      <td>2015-05-24 03:09:31</td>\n",
       "      <td>602310430023929856</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 04:24:53</th>\n",
       "      <td>RT @MatrixMarkets: Weekly Market Review\\n$SPX ...</td>\n",
       "      <td>2015-05-24 04:24:53</td>\n",
       "      <td>602329396658049024</td>\n",
       "      <td>6.021040e+17</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:41:04</th>\n",
       "      <td>Good morning, trading friends. Make it a great...</td>\n",
       "      <td>2015-05-24 03:41:04</td>\n",
       "      <td>602318368281075712</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24 03:09:31</th>\n",
       "      <td>iOS 9 vs. Android M – The New Features By Appl...</td>\n",
       "      <td>2015-05-24 03:09:31</td>\n",
       "      <td>602310427783991296</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.136364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  Text  \\\n",
       "Date                                                                     \n",
       "2015-05-24 03:46:08  RT @j_o_h_n_danger: $TWTR now top holding for ...   \n",
       "2015-05-24 04:17:42  RT diggingplatinum RT WWalkerWW: iOS 9 vs. And...   \n",
       "2015-05-24 04:13:22  RT bosocial RT insidermonkey RT j_o_h_n_danger...   \n",
       "2015-05-24 04:08:34  RT @WWalkerWW: iOS 9 vs. Android M – The New F...   \n",
       "2015-05-24 04:04:42  RT @seeitmarket: Apple Chart Update: Big Test ...   \n",
       "2015-05-24 04:00:01  Free Video: One Day Range Leaders http://t.co/...   \n",
       "2015-05-24 03:54:07  Option Millionaires: golfnara: $AAPL 135.79 ht...   \n",
       "2015-05-24 04:25:29  RT @AppleReporter: What to Do When Your #iPhon...   \n",
       "2015-05-24 04:24:47  RT @AppleReporter: Apple Near All-Time High!\\n...   \n",
       "2015-05-24 04:06:42  RT @AmericanBanking: Morgan Stanley Reiterates...   \n",
       "2015-05-24 03:58:09  RT insidermonkey RT j_o_h_n_danger: $TWTR now ...   \n",
       "2015-05-24 03:45:12  Logging in now to live stock chat http://t.co/...   \n",
       "2015-05-24 03:38:18  Find active and helpful professional daytrader...   \n",
       "2015-05-24 03:09:02  These Are The 50 Top Hedge Fund Long And Short...   \n",
       "2015-05-24 04:28:49  RT bosocial RT bosocial RT insidermonkey RT j_...   \n",
       "2015-05-24 04:17:41  RT bosocial RT stocknugget Helpful video for s...   \n",
       "2015-05-24 04:13:24  RT bosocial RT ferrarigtr RT j_o_h_n_danger: $...   \n",
       "2015-05-24 04:13:23  RT bosocial RT Petrecson RT j_o_h_n_danger: $T...   \n",
       "2015-05-24 04:11:06  Apple Chart Update: Big Test Looming http://t....   \n",
       "2015-05-24 04:08:33  RT @WWalkerWW: iOS 9 vs. Android M – The New F...   \n",
       "2015-05-24 04:08:33  RT @MarketsRecap: These Are The 50 Top Hedge F...   \n",
       "2015-05-24 04:08:33  RT @AppleNewsAAPL: $AAPL Daily Deals: Long Wee...   \n",
       "2015-05-24 04:02:55  ChaytonFalke: Free Video: One Day Range Leader...   \n",
       "2015-05-24 03:53:26  Apple Chart Update: Big Test Looming http://t....   \n",
       "2015-05-24 03:31:04  Be water, my friends. Be water. $MSFT $SPY $F ...   \n",
       "2015-05-24 03:09:31  iOS 9 vs. Android M – The New Features By Appl...   \n",
       "2015-05-24 04:24:53  RT @MatrixMarkets: Weekly Market Review\\n$SPX ...   \n",
       "2015-05-24 03:41:04  Good morning, trading friends. Make it a great...   \n",
       "2015-05-24 03:09:31  iOS 9 vs. Android M – The New Features By Appl...   \n",
       "\n",
       "                                   Date              Status       Retweet  \\\n",
       "Date                                                                        \n",
       "2015-05-24 03:46:08 2015-05-24 03:46:08  602319644234395648  6.022899e+17   \n",
       "2015-05-24 04:17:42 2015-05-24 04:17:42  602327586983796737           NaN   \n",
       "2015-05-24 04:13:22 2015-05-24 04:13:22  602326499534966784           NaN   \n",
       "2015-05-24 04:08:34 2015-05-24 04:08:34  602325288740114432  6.023104e+17   \n",
       "2015-05-24 04:04:42 2015-05-24 04:04:42  602324318903771136  6.023215e+17   \n",
       "2015-05-24 04:00:01 2015-05-24 04:00:01  602323139297705984           NaN   \n",
       "2015-05-24 03:54:07 2015-05-24 03:54:07  602321653645709313           NaN   \n",
       "2015-05-24 04:25:29 2015-05-24 04:25:29  602329548860993537  6.013210e+17   \n",
       "2015-05-24 04:24:47 2015-05-24 04:24:47  602329372083650560  6.021077e+17   \n",
       "2015-05-24 04:06:42 2015-05-24 04:06:42  602324819342823424  6.023225e+17   \n",
       "2015-05-24 03:58:09 2015-05-24 03:58:09  602322668642238464           NaN   \n",
       "2015-05-24 03:45:12 2015-05-24 03:45:12  602319408556544000           NaN   \n",
       "2015-05-24 03:38:18 2015-05-24 03:38:18  602317674765484032           NaN   \n",
       "2015-05-24 03:09:02 2015-05-24 03:09:02  602310306489065472           NaN   \n",
       "2015-05-24 04:28:49 2015-05-24 04:28:49  602330384756510720           NaN   \n",
       "2015-05-24 04:17:41 2015-05-24 04:17:41  602327585901645825           NaN   \n",
       "2015-05-24 04:13:24 2015-05-24 04:13:24  602326506027786241           NaN   \n",
       "2015-05-24 04:13:23 2015-05-24 04:13:23  602326500625506306           NaN   \n",
       "2015-05-24 04:11:06 2015-05-24 04:11:06  602325927889117186           NaN   \n",
       "2015-05-24 04:08:33 2015-05-24 04:08:33  602325288068980736  6.023104e+17   \n",
       "2015-05-24 04:08:33 2015-05-24 04:08:33  602325287372722176  6.023103e+17   \n",
       "2015-05-24 04:08:33 2015-05-24 04:08:33  602325286714273792  6.023103e+17   \n",
       "2015-05-24 04:02:55 2015-05-24 04:02:55  602323867915427840           NaN   \n",
       "2015-05-24 03:53:26 2015-05-24 03:53:26  602321480119099392           NaN   \n",
       "2015-05-24 03:31:04 2015-05-24 03:31:04  602315854244683777           NaN   \n",
       "2015-05-24 03:09:31 2015-05-24 03:09:31  602310430023929856           NaN   \n",
       "2015-05-24 04:24:53 2015-05-24 04:24:53  602329396658049024  6.021040e+17   \n",
       "2015-05-24 03:41:04 2015-05-24 03:41:04  602318368281075712           NaN   \n",
       "2015-05-24 03:09:31 2015-05-24 03:09:31  602310427783991296           NaN   \n",
       "\n",
       "                     sentiment  \n",
       "Date                            \n",
       "2015-05-24 03:46:08   0.500000  \n",
       "2015-05-24 04:17:42   0.136364  \n",
       "2015-05-24 04:13:22   0.500000  \n",
       "2015-05-24 04:08:34   0.136364  \n",
       "2015-05-24 04:04:42   0.000000  \n",
       "2015-05-24 04:00:01   0.400000  \n",
       "2015-05-24 03:54:07   0.000000  \n",
       "2015-05-24 04:25:29   0.000000  \n",
       "2015-05-24 04:24:47   0.150000  \n",
       "2015-05-24 04:06:42   0.000000  \n",
       "2015-05-24 03:58:09   0.500000  \n",
       "2015-05-24 03:45:12   0.136364  \n",
       "2015-05-24 03:38:18  -0.016667  \n",
       "2015-05-24 03:09:02   0.150000  \n",
       "2015-05-24 04:28:49   0.500000  \n",
       "2015-05-24 04:17:41   0.000000  \n",
       "2015-05-24 04:13:24   0.500000  \n",
       "2015-05-24 04:13:23   0.500000  \n",
       "2015-05-24 04:11:06   0.000000  \n",
       "2015-05-24 04:08:33   0.136364  \n",
       "2015-05-24 04:08:33   0.150000  \n",
       "2015-05-24 04:08:33  -0.025000  \n",
       "2015-05-24 04:02:55   0.400000  \n",
       "2015-05-24 03:53:26   0.000000  \n",
       "2015-05-24 03:31:04   0.000000  \n",
       "2015-05-24 03:09:31   0.136364  \n",
       "2015-05-24 04:24:53   0.000000  \n",
       "2015-05-24 03:41:04   0.850000  \n",
       "2015-05-24 03:09:31   0.136364  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# look at tweets on the 24th of May\n",
    "tweets['2015-5-24']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get historical prices\n",
    "# pip install yahoo_finance\n",
    "from yahoo_finance import Share\n",
    "yahoo = Share('AAPL')\n",
    "historical_prices = yahoo.get_historical('2015-05-2', '2015-05-25')\n",
    "prices = pd.DataFrame(historical_prices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj_Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>129.180748</td>\n",
       "      <td>132.539993</td>\n",
       "      <td>2015-05-22</td>\n",
       "      <td>132.970001</td>\n",
       "      <td>131.399994</td>\n",
       "      <td>131.600006</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>45596000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>128.059901</td>\n",
       "      <td>131.389999</td>\n",
       "      <td>2015-05-21</td>\n",
       "      <td>131.630005</td>\n",
       "      <td>129.830002</td>\n",
       "      <td>130.070007</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>39730400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>126.763608</td>\n",
       "      <td>130.059998</td>\n",
       "      <td>2015-05-20</td>\n",
       "      <td>130.979996</td>\n",
       "      <td>129.339996</td>\n",
       "      <td>130.00</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>36454900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>126.773364</td>\n",
       "      <td>130.070007</td>\n",
       "      <td>2015-05-19</td>\n",
       "      <td>130.880005</td>\n",
       "      <td>129.639999</td>\n",
       "      <td>130.690002</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>44633200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>126.890318</td>\n",
       "      <td>130.190002</td>\n",
       "      <td>2015-05-18</td>\n",
       "      <td>130.720001</td>\n",
       "      <td>128.360001</td>\n",
       "      <td>128.380005</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>50882900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Adj_Close       Close        Date        High         Low        Open  \\\n",
       "0  129.180748  132.539993  2015-05-22  132.970001  131.399994  131.600006   \n",
       "1  128.059901  131.389999  2015-05-21  131.630005  129.830002  130.070007   \n",
       "2  126.763608  130.059998  2015-05-20  130.979996  129.339996      130.00   \n",
       "3  126.773364  130.070007  2015-05-19  130.880005  129.639999  130.690002   \n",
       "4  126.890318  130.190002  2015-05-18  130.720001  128.360001  128.380005   \n",
       "\n",
       "  Symbol    Volume  \n",
       "0   AAPL  45596000  \n",
       "1   AAPL  39730400  \n",
       "2   AAPL  36454900  \n",
       "3   AAPL  44633200  \n",
       "4   AAPL  50882900  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prices.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "prices.index = pd.to_datetime(prices['Date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "not_null_close = prices[prices.Close.notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Adj_Close</th>\n",
       "      <th>Close</th>\n",
       "      <th>Date</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Symbol</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-22</th>\n",
       "      <td>129.180748</td>\n",
       "      <td>132.539993</td>\n",
       "      <td>2015-05-22</td>\n",
       "      <td>132.970001</td>\n",
       "      <td>131.399994</td>\n",
       "      <td>131.600006</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>45596000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-21</th>\n",
       "      <td>128.059901</td>\n",
       "      <td>131.389999</td>\n",
       "      <td>2015-05-21</td>\n",
       "      <td>131.630005</td>\n",
       "      <td>129.830002</td>\n",
       "      <td>130.070007</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>39730400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-20</th>\n",
       "      <td>126.763608</td>\n",
       "      <td>130.059998</td>\n",
       "      <td>2015-05-20</td>\n",
       "      <td>130.979996</td>\n",
       "      <td>129.339996</td>\n",
       "      <td>130.00</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>36454900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-19</th>\n",
       "      <td>126.773364</td>\n",
       "      <td>130.070007</td>\n",
       "      <td>2015-05-19</td>\n",
       "      <td>130.880005</td>\n",
       "      <td>129.639999</td>\n",
       "      <td>130.690002</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>44633200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-18</th>\n",
       "      <td>126.890318</td>\n",
       "      <td>130.190002</td>\n",
       "      <td>2015-05-18</td>\n",
       "      <td>130.720001</td>\n",
       "      <td>128.360001</td>\n",
       "      <td>128.380005</td>\n",
       "      <td>AAPL</td>\n",
       "      <td>50882900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Adj_Close       Close        Date        High         Low  \\\n",
       "Date                                                                     \n",
       "2015-05-22  129.180748  132.539993  2015-05-22  132.970001  131.399994   \n",
       "2015-05-21  128.059901  131.389999  2015-05-21  131.630005  129.830002   \n",
       "2015-05-20  126.763608  130.059998  2015-05-20  130.979996  129.339996   \n",
       "2015-05-19  126.773364  130.070007  2015-05-19  130.880005  129.639999   \n",
       "2015-05-18  126.890318  130.190002  2015-05-18  130.720001  128.360001   \n",
       "\n",
       "                  Open Symbol    Volume  \n",
       "Date                                     \n",
       "2015-05-22  131.600006   AAPL  45596000  \n",
       "2015-05-21  130.070007   AAPL  39730400  \n",
       "2015-05-20      130.00   AAPL  36454900  \n",
       "2015-05-19  130.690002   AAPL  44633200  \n",
       "2015-05-18  128.380005   AAPL  50882900  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not_null_close.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 15 entries, 2015-05-22 to 2015-05-04\n",
      "Data columns (total 8 columns):\n",
      "Adj_Close    15 non-null object\n",
      "Close        15 non-null object\n",
      "Date         15 non-null object\n",
      "High         15 non-null object\n",
      "Low          15 non-null object\n",
      "Open         15 non-null object\n",
      "Symbol       15 non-null object\n",
      "Volume       15 non-null object\n",
      "dtypes: object(8)\n",
      "memory usage: 1.1+ KB\n"
     ]
    }
   ],
   "source": [
    "not_null_close.info() #the columns aren't numbers!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# cast as numbers\n",
    "not_null_close.Volume = not_null_close.Volume.astype('int')\n",
    "not_null_close.Close = not_null_close.Close.astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b7bc1d0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SS9C+fc3LPf00XHpp2Pf017+GPn/JzBdfwEEHhZbF4MHRPMd//xv2Cd5xR5iU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\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1178a2f90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###############\n",
    "#### TO DO ####\n",
    "############### \n",
    "\n",
    "# plot both volume and close as line graphs in the same graph, what do you notice is the problem?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "###############\n",
    "#### TO DO ####\n",
    "############### \n",
    "\n",
    "# concatinate not_null_close.Close, and daily_tweets.sentiment\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Close</th>\n",
       "      <th>sentiment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-05-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.083031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-03</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.107045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-04</th>\n",
       "      <td>128.699997</td>\n",
       "      <td>0.084062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-05</th>\n",
       "      <td>125.800003</td>\n",
       "      <td>0.063895</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-06</th>\n",
       "      <td>125.010002</td>\n",
       "      <td>0.067004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-07</th>\n",
       "      <td>125.260002</td>\n",
       "      <td>0.078972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-08</th>\n",
       "      <td>127.620003</td>\n",
       "      <td>0.102869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-09</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.118071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.094882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-11</th>\n",
       "      <td>126.320000</td>\n",
       "      <td>0.067141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-12</th>\n",
       "      <td>125.870003</td>\n",
       "      <td>0.053873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-13</th>\n",
       "      <td>126.010002</td>\n",
       "      <td>0.063917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-14</th>\n",
       "      <td>128.949997</td>\n",
       "      <td>0.080332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-15</th>\n",
       "      <td>128.770004</td>\n",
       "      <td>0.059145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.103616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-18</th>\n",
       "      <td>130.190002</td>\n",
       "      <td>0.102250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-19</th>\n",
       "      <td>130.070007</td>\n",
       "      <td>0.070539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-20</th>\n",
       "      <td>130.059998</td>\n",
       "      <td>0.089767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-21</th>\n",
       "      <td>131.389999</td>\n",
       "      <td>0.078282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-22</th>\n",
       "      <td>132.539993</td>\n",
       "      <td>0.091865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.077321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-24</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.202638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Close  sentiment\n",
       "Date                             \n",
       "2015-05-02         NaN   0.083031\n",
       "2015-05-03         NaN   0.107045\n",
       "2015-05-04  128.699997   0.084062\n",
       "2015-05-05  125.800003   0.063895\n",
       "2015-05-06  125.010002   0.067004\n",
       "2015-05-07  125.260002   0.078972\n",
       "2015-05-08  127.620003   0.102869\n",
       "2015-05-09         NaN   0.118071\n",
       "2015-05-10         NaN   0.094882\n",
       "2015-05-11  126.320000   0.067141\n",
       "2015-05-12  125.870003   0.053873\n",
       "2015-05-13  126.010002   0.063917\n",
       "2015-05-14  128.949997   0.080332\n",
       "2015-05-15  128.770004   0.059145\n",
       "2015-05-16         NaN   0.061381\n",
       "2015-05-17         NaN   0.103616\n",
       "2015-05-18  130.190002   0.102250\n",
       "2015-05-19  130.070007   0.070539\n",
       "2015-05-20  130.059998   0.089767\n",
       "2015-05-21  131.389999   0.078282\n",
       "2015-05-22  132.539993   0.091865\n",
       "2015-05-23         NaN   0.077321\n",
       "2015-05-24         NaN   0.202638"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11b7d6210>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tisfI/Odeptq3gKHAq2b293DeK2aWvhWQsV133+jhDjLgXuDwiO3+g2Br+b8A\n3P0loN7M+kSsb1hf5cC5wO8z1OWqvcTdnwzbfoIMp6TIVuvuy9z9FHc/nGB3TiFbexvMrCrs+wDg\nnQJqSxJhbEWRbWxlsz/RxldGEcdXNlHHV0YRxlYul0YYXxkVMr7MrIIg8B5x96nh7EhjLEttJNlq\no4yxCO3mHGMZ6gseY20V9C8Bw82s2swqgXFAjQWnMv4BcJa7byuwdnjKMmcT7LuKUvtf7j7A3fdz\n92EEAfwFd08fHNnaHZCyzLkE39hH6jPwJHAigJkdCHRx9/cKqAc4GVji7usy1GWrnQqsM7PRYdsn\nERxVE6ldM+sX1pUBPwL+b5a2IdiySN26qCH4gg3g0rAvUWvTH8ulSW3EsZWtNsrYyljv7osijq9s\nbUcZXxlriT6+MtVC/rGVq35thPGVsbbA8fUA8Ia7/yplXtQxlqk2vV+R2y1gjGWqLWSMNakvYoy1\n+eGVS4HlwPhw3nJgNbsO67q7gNongNcIjkiZCuwTtTbt8bfIfXhlersPp7T7JMH+wai1XYBHCA6T\nehkYXcj6Cuc/CPxrEev6mLDNBQSHwX2hgNprwnlvArfmaPcxgtNSbwPeBi4nOPRtZlg/A9i7gNqz\nCbZStxL8wvrpAmqjjq1MtU+E71HOsZWtvoDxlantqOMrU21FlPGVrc9RxlaOto+OOL4y1UYdX8cQ\n7LZYGLYzPxyvvfONsRy1ecdYltrTooyxHO1GGmPZ6qOOsYabfjAlIhJzu8WXsSIi0noU9CIiMaeg\nFxGJuVYJejOrN7OHU+6Xm9lGM6vJVSciIi2vtbboPwYONrM9wvsn0/QXlyIi0kZac9fNn4Ezwukm\nv7Yzs8PN7Hkze8XM5prZAeH82WY2KmW5OWb2+Vbso4hI7LVW0DvBL9wuCLfqR9H03BBLCM5W9yWC\nn/H+NJx/H8FxtYThv4cXf74QERGhFbfo3X0Rwc90LwCeoumvzvYGnjCz1wnOwHZQOP8J4Izwp9jf\nAB5qrf6JiHQWrX3UTQ3wv2l+kqT/AGa5++eBM4GuAO6+FfgLwa/Vvk5wsh8RESlBq1wzll1b7w8A\n77v74obzYIR6suvEZJen1d4PTANmu/uHrdQ/EZFOozX30ePua939PzM8Phm4zcxeSe+Du88HNhOc\nd0NEREq0253rxsz2Jdit89n27ouISBzsVr+MNbOLCc58d1N790VEJC52uy16ERFpWbvVFr2IiLS8\nFgt6MxsXMS/cAAADIElEQVRkZrPMbLGZvW5m14Tze5nZDDNbambTGy52bGa9w+U/MrM7056r1sze\nNLMFZjbfzPq2VD9FRDqbFtt1E14CbYC7LwwvZPsKMJbg8Mn33H2ymd1AcPHi8Wa2J8EVzQ8GDnb3\na1Keqxa4zt0XtEjnREQ6sRbbonf39e6+MJzeQnCag0EEYT8lXGwKwY+hcPdP3P15gkuKtWrfREQ6\ns9Y6TfFQgq31FwmuebkBgg8DIOuVytM8FO62+VFr9FFEpLNo8aAPd9s8AVwbbtmn7xuKsq/owvD0\nCMcBx5nZv7RwN0VEOo0WDXozqyAI+UfcfWo4e4OZVYWPDwDeyfc87v7P8N+PCa4af0RL9lNEpDNp\n6S36B4A33P1XKfNqgMvC6UuBqelFpJzZMrwaVZ9wugvwFWBRC/dTRKTTaMmjbo4BngVeJ9g94wS/\ncJ0HPA4MBlYD57n7B2HN34HuQCXwATAGeDt8ngqgHJhJcASOftklIlIE/TJWRCTmdAijiEjMKehF\nRGJOQS8iEnMKehGRmFPQi4jEnIJeRCTmFPTSaZjZzvD8SYvCU2BfZ2aWp6bazC5oqz6KtAYFvXQm\nH7v7F939YOBk4DRgQp6aYcCFrd4zkVakoJdOyd3fBf4VuBoat9yfNbOXw9uR4aI/BY4N/xK41szK\nzGyymf3NzBaa2RXt9RpEotIvY6XTMLPN7t4jbd4mYATwEVDv7tvNbDjwW3c/3MxGA99397PC5a8A\n+rn7rWZWCTwHfM3dV7ftqxGJrqK9OyDSzhr20VcC/2lmhwI7gQOyLD8G+LyZfT283yNcVkEvuy0F\nvXRaZrYfUOfuG81sArDe3UeZWTmwNVsZ8B13/0ubdVSkRNpHL51J6umw+wG/Bv5POKsn8M9w+hKC\nM6dCsEune8pzTAeuDK+9gJkdYGbdWrPTIqXSFr10Jl3NbD7BbpodwMPu/ovwsbuBP5rZJcAzwMfh\n/NeAejNbADzk7r8KL5U5Pzw08x3C6yCL7K70ZayISMxp142ISMwp6EVEYk5BLyIScwp6EZGYU9CL\niMScgl5EJOYU9CIiMfffVibpOGhlO4kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11b492310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merged.plot()\n",
    "# wow that looks awful"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "###############\n",
    "#### TO DO ####\n",
    "############### \n",
    "\n",
    "# First delete any rows with missing values in any column\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.22959679,  0.48903823],\n",
       "       [-1.02827619, -0.89329167],\n",
       "       [-1.37093928, -0.680184  ],\n",
       "       [-1.26250173,  0.14011607],\n",
       "       [-0.23885082,  1.778188  ]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# scale the columns by z scores using StandardScaler\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "s = StandardScaler()\n",
    "merged_scaled = s.fit_transform(merged)\n",
    "merged_scaled[:5,]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11bf39ad0>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Y7cy/nmc9OjfqzF8H/zJail1hT/5+E2rRt+osXAh9+kCGTCQrL8vi/urUgeuu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\naE1iEi0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11bf54dd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(merged_scaled, columns=merged.columns).plot()\n",
    "# notice how sentiment seems to follow the closing price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:sfdat28]",
   "language": "python",
   "name": "conda-env-sfdat28-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}