Election 2016 (Plotly).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
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   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "#import matplotlib.pyplot as plt\n",
    "from plotly import tools\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "pd.set_option('display.max_columns', 100) #overrides default to display up to 100 columns in dataframes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "py.sign_in('emmanduncan','Qvo1b9O5Nx1cuDGuTuUf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>cycle</th>\n",
       "      <th>branch</th>\n",
       "      <th>type</th>\n",
       "      <th>matchup</th>\n",
       "      <th>forecastdate</th>\n",
       "      <th>state</th>\n",
       "      <th>startdate</th>\n",
       "      <th>enddate</th>\n",
       "      <th>pollster</th>\n",
       "      <th>grade</th>\n",
       "      <th>samplesize</th>\n",
       "      <th>population</th>\n",
       "      <th>poll_wt</th>\n",
       "      <th>rawpoll_clinton</th>\n",
       "      <th>rawpoll_trump</th>\n",
       "      <th>rawpoll_johnson</th>\n",
       "      <th>rawpoll_mcmullin</th>\n",
       "      <th>adjpoll_clinton</th>\n",
       "      <th>adjpoll_trump</th>\n",
       "      <th>adjpoll_johnson</th>\n",
       "      <th>adjpoll_mcmullin</th>\n",
       "      <th>multiversions</th>\n",
       "      <th>url</th>\n",
       "      <th>poll_id</th>\n",
       "      <th>question_id</th>\n",
       "      <th>createddate</th>\n",
       "      <th>timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016</td>\n",
       "      <td>President</td>\n",
       "      <td>polls-plus</td>\n",
       "      <td>Clinton vs. Trump vs. Johnson</td>\n",
       "      <td>11/8/16</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/3/2016</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>ABC News/Washington Post</td>\n",
       "      <td>A+</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>8.720654</td>\n",
       "      <td>47.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>4.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.20163</td>\n",
       "      <td>41.72430</td>\n",
       "      <td>4.626221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://www.washingtonpost.com/news/the-fix/wp...</td>\n",
       "      <td>48630</td>\n",
       "      <td>76192</td>\n",
       "      <td>11/7/16</td>\n",
       "      <td>09:35:33  8 Nov 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>President</td>\n",
       "      <td>polls-plus</td>\n",
       "      <td>Clinton vs. Trump vs. Johnson</td>\n",
       "      <td>11/8/16</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/1/2016</td>\n",
       "      <td>11/7/2016</td>\n",
       "      <td>Google Consumer Surveys</td>\n",
       "      <td>B</td>\n",
       "      <td>26574.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>38.03</td>\n",
       "      <td>35.69</td>\n",
       "      <td>5.46</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43.34557</td>\n",
       "      <td>41.21439</td>\n",
       "      <td>5.175792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://datastudio.google.com/u/0/#/org//repor...</td>\n",
       "      <td>48847</td>\n",
       "      <td>76443</td>\n",
       "      <td>11/7/16</td>\n",
       "      <td>09:35:33  8 Nov 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>President</td>\n",
       "      <td>polls-plus</td>\n",
       "      <td>Clinton vs. Trump vs. Johnson</td>\n",
       "      <td>11/8/16</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/2/2016</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>A-</td>\n",
       "      <td>2195.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>6.424334</td>\n",
       "      <td>42.00</td>\n",
       "      <td>39.00</td>\n",
       "      <td>6.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42.02638</td>\n",
       "      <td>38.81620</td>\n",
       "      <td>6.844734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://projects.fivethirtyeight.com/polls/2016...</td>\n",
       "      <td>48922</td>\n",
       "      <td>76636</td>\n",
       "      <td>11/8/16</td>\n",
       "      <td>09:35:33  8 Nov 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>President</td>\n",
       "      <td>polls-plus</td>\n",
       "      <td>Clinton vs. Trump vs. Johnson</td>\n",
       "      <td>11/8/16</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/4/2016</td>\n",
       "      <td>11/7/2016</td>\n",
       "      <td>YouGov</td>\n",
       "      <td>B</td>\n",
       "      <td>3677.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>6.087135</td>\n",
       "      <td>45.00</td>\n",
       "      <td>41.00</td>\n",
       "      <td>5.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.65676</td>\n",
       "      <td>40.92004</td>\n",
       "      <td>6.069454</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>https://d25d2506sfb94s.cloudfront.net/cumulus_...</td>\n",
       "      <td>48687</td>\n",
       "      <td>76262</td>\n",
       "      <td>11/7/16</td>\n",
       "      <td>09:35:33  8 Nov 2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016</td>\n",
       "      <td>President</td>\n",
       "      <td>polls-plus</td>\n",
       "      <td>Clinton vs. Trump vs. Johnson</td>\n",
       "      <td>11/8/16</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/3/2016</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>B-</td>\n",
       "      <td>16639.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>5.316449</td>\n",
       "      <td>47.00</td>\n",
       "      <td>43.00</td>\n",
       "      <td>3.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.84089</td>\n",
       "      <td>42.33184</td>\n",
       "      <td>3.726098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://www.gravispolls.com/2016/11/final-natio...</td>\n",
       "      <td>48848</td>\n",
       "      <td>76444</td>\n",
       "      <td>11/7/16</td>\n",
       "      <td>09:35:33  8 Nov 2016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   cycle     branch        type                        matchup forecastdate  \\\n",
       "0   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/8/16   \n",
       "1   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/8/16   \n",
       "2   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/8/16   \n",
       "3   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/8/16   \n",
       "4   2016  President  polls-plus  Clinton vs. Trump vs. Johnson      11/8/16   \n",
       "\n",
       "  state  startdate    enddate                  pollster grade  samplesize  \\\n",
       "0  U.S.  11/3/2016  11/6/2016  ABC News/Washington Post    A+      2220.0   \n",
       "1  U.S.  11/1/2016  11/7/2016   Google Consumer Surveys     B     26574.0   \n",
       "2  U.S.  11/2/2016  11/6/2016                     Ipsos    A-      2195.0   \n",
       "3  U.S.  11/4/2016  11/7/2016                    YouGov     B      3677.0   \n",
       "4  U.S.  11/3/2016  11/6/2016          Gravis Marketing    B-     16639.0   \n",
       "\n",
       "  population   poll_wt  rawpoll_clinton  rawpoll_trump  rawpoll_johnson  \\\n",
       "0         lv  8.720654            47.00          43.00             4.00   \n",
       "1         lv  7.628472            38.03          35.69             5.46   \n",
       "2         lv  6.424334            42.00          39.00             6.00   \n",
       "3         lv  6.087135            45.00          41.00             5.00   \n",
       "4         rv  5.316449            47.00          43.00             3.00   \n",
       "\n",
       "   rawpoll_mcmullin  adjpoll_clinton  adjpoll_trump  adjpoll_johnson  \\\n",
       "0               NaN         45.20163       41.72430         4.626221   \n",
       "1               NaN         43.34557       41.21439         5.175792   \n",
       "2               NaN         42.02638       38.81620         6.844734   \n",
       "3               NaN         45.65676       40.92004         6.069454   \n",
       "4               NaN         46.84089       42.33184         3.726098   \n",
       "\n",
       "   adjpoll_mcmullin multiversions  \\\n",
       "0               NaN           NaN   \n",
       "1               NaN           NaN   \n",
       "2               NaN           NaN   \n",
       "3               NaN           NaN   \n",
       "4               NaN           NaN   \n",
       "\n",
       "                                                 url  poll_id  question_id  \\\n",
       "0  https://www.washingtonpost.com/news/the-fix/wp...    48630        76192   \n",
       "1  https://datastudio.google.com/u/0/#/org//repor...    48847        76443   \n",
       "2  http://projects.fivethirtyeight.com/polls/2016...    48922        76636   \n",
       "3  https://d25d2506sfb94s.cloudfront.net/cumulus_...    48687        76262   \n",
       "4  http://www.gravispolls.com/2016/11/final-natio...    48848        76444   \n",
       "\n",
       "  createddate             timestamp  \n",
       "0     11/7/16  09:35:33  8 Nov 2016  \n",
       "1     11/7/16  09:35:33  8 Nov 2016  \n",
       "2     11/8/16  09:35:33  8 Nov 2016  \n",
       "3     11/7/16  09:35:33  8 Nov 2016  \n",
       "4     11/7/16  09:35:33  8 Nov 2016  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#import data as a Pandas dataframe\n",
    "df = pd.read_csv('http://projects.fivethirtyeight.com/general-model/president_general_polls_2016.csv')\n",
    "df.head() #display the first five rows of dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows (polls): 12624\n",
      "Number of columns (data categories): 27\n",
      "\n",
      "Number of empty values for each column:\n",
      "cycle                   0\n",
      "branch                  0\n",
      "type                    0\n",
      "matchup                 0\n",
      "forecastdate            0\n",
      "state                   0\n",
      "startdate               0\n",
      "enddate                 0\n",
      "pollster                0\n",
      "grade                1287\n",
      "samplesize              3\n",
      "population              0\n",
      "poll_wt                 0\n",
      "rawpoll_clinton         0\n",
      "rawpoll_trump           0\n",
      "rawpoll_johnson      4227\n",
      "rawpoll_mcmullin    12534\n",
      "adjpoll_clinton         0\n",
      "adjpoll_trump           0\n",
      "adjpoll_johnson      4227\n",
      "adjpoll_mcmullin    12534\n",
      "multiversions       12588\n",
      "url                     3\n",
      "poll_id                 0\n",
      "question_id             0\n",
      "createddate             0\n",
      "timestamp               0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of rows (polls): \" + str(df.shape[0]))\n",
    "print(\"Number of columns (data categories): \" + str(df.shape[1]))\n",
    "\n",
    "print(\"\\nNumber of empty values for each column:\")\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>type</th>\n",
       "      <th>state</th>\n",
       "      <th>enddate</th>\n",
       "      <th>pollster</th>\n",
       "      <th>grade</th>\n",
       "      <th>samplesize</th>\n",
       "      <th>population</th>\n",
       "      <th>poll_wt</th>\n",
       "      <th>adjpoll_clinton</th>\n",
       "      <th>adjpoll_trump</th>\n",
       "      <th>adjpoll_johnson</th>\n",
       "      <th>adjpoll_mcmullin</th>\n",
       "      <th>poll_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>polls-plus</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>ABC News/Washington Post</td>\n",
       "      <td>A+</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>8.720654</td>\n",
       "      <td>45.20163</td>\n",
       "      <td>41.72430</td>\n",
       "      <td>4.626221</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>polls-plus</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/7/2016</td>\n",
       "      <td>Google Consumer Surveys</td>\n",
       "      <td>B</td>\n",
       "      <td>26574.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>43.34557</td>\n",
       "      <td>41.21439</td>\n",
       "      <td>5.175792</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>polls-plus</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>A-</td>\n",
       "      <td>2195.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>6.424334</td>\n",
       "      <td>42.02638</td>\n",
       "      <td>38.81620</td>\n",
       "      <td>6.844734</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polls-plus</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/7/2016</td>\n",
       "      <td>YouGov</td>\n",
       "      <td>B</td>\n",
       "      <td>3677.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>6.087135</td>\n",
       "      <td>45.65676</td>\n",
       "      <td>40.92004</td>\n",
       "      <td>6.069454</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>polls-plus</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>B-</td>\n",
       "      <td>16639.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>5.316449</td>\n",
       "      <td>46.84089</td>\n",
       "      <td>42.33184</td>\n",
       "      <td>3.726098</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         type state    enddate                  pollster grade  samplesize  \\\n",
       "0  polls-plus  U.S.  11/6/2016  ABC News/Washington Post    A+      2220.0   \n",
       "1  polls-plus  U.S.  11/7/2016   Google Consumer Surveys     B     26574.0   \n",
       "2  polls-plus  U.S.  11/6/2016                     Ipsos    A-      2195.0   \n",
       "3  polls-plus  U.S.  11/7/2016                    YouGov     B      3677.0   \n",
       "4  polls-plus  U.S.  11/6/2016          Gravis Marketing    B-     16639.0   \n",
       "\n",
       "  population   poll_wt  adjpoll_clinton  adjpoll_trump  adjpoll_johnson  \\\n",
       "0         lv  8.720654         45.20163       41.72430         4.626221   \n",
       "1         lv  7.628472         43.34557       41.21439         5.175792   \n",
       "2         lv  6.424334         42.02638       38.81620         6.844734   \n",
       "3         lv  6.087135         45.65676       40.92004         6.069454   \n",
       "4         rv  5.316449         46.84089       42.33184         3.726098   \n",
       "\n",
       "   adjpoll_mcmullin  poll_id  \n",
       "0               NaN    48630  \n",
       "1               NaN    48847  \n",
       "2               NaN    48922  \n",
       "3               NaN    48687  \n",
       "4               NaN    48848  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = df.loc[:, ['type', 'state', 'enddate', 'pollster', 'grade', 'samplesize', 'population',\n",
    "             'adjpoll_clinton', 'adjpoll_trump', 'adjpoll_johnson', 'adjpoll_mcmullin', 'poll_id']]\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['polls-plus' 'now-cast' 'polls-only']\n"
     ]
    }
   ],
   "source": [
    "print(df2.loc[:,'type'].unique()) #display unique values of the 'type' factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>type</th>\n",
       "      <th>state</th>\n",
       "      <th>enddate</th>\n",
       "      <th>pollster</th>\n",
       "      <th>grade</th>\n",
       "      <th>samplesize</th>\n",
       "      <th>population</th>\n",
       "      <th>poll_wt</th>\n",
       "      <th>adjpoll_clinton</th>\n",
       "      <th>adjpoll_trump</th>\n",
       "      <th>adjpoll_johnson</th>\n",
       "      <th>adjpoll_mcmullin</th>\n",
       "      <th>poll_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>polls-only</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>ABC News/Washington Post</td>\n",
       "      <td>A+</td>\n",
       "      <td>2220.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>8.720654</td>\n",
       "      <td>45.21947</td>\n",
       "      <td>41.70754</td>\n",
       "      <td>4.606925</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>polls-only</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/7/2016</td>\n",
       "      <td>Google Consumer Surveys</td>\n",
       "      <td>B</td>\n",
       "      <td>26574.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>7.628472</td>\n",
       "      <td>43.40083</td>\n",
       "      <td>41.14659</td>\n",
       "      <td>5.164047</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>polls-only</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>Ipsos</td>\n",
       "      <td>A-</td>\n",
       "      <td>2195.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>6.424334</td>\n",
       "      <td>42.01984</td>\n",
       "      <td>38.74365</td>\n",
       "      <td>6.816055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48922</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>polls-only</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/7/2016</td>\n",
       "      <td>YouGov</td>\n",
       "      <td>B</td>\n",
       "      <td>3677.0</td>\n",
       "      <td>lv</td>\n",
       "      <td>6.087135</td>\n",
       "      <td>45.68214</td>\n",
       "      <td>40.90047</td>\n",
       "      <td>6.118311</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>polls-only</td>\n",
       "      <td>U.S.</td>\n",
       "      <td>11/6/2016</td>\n",
       "      <td>Gravis Marketing</td>\n",
       "      <td>B-</td>\n",
       "      <td>16639.0</td>\n",
       "      <td>rv</td>\n",
       "      <td>5.316449</td>\n",
       "      <td>46.83107</td>\n",
       "      <td>42.27754</td>\n",
       "      <td>3.749071</td>\n",
       "      <td>NaN</td>\n",
       "      <td>48848</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         type state    enddate                  pollster grade  samplesize  \\\n",
       "0  polls-only  U.S.  11/6/2016  ABC News/Washington Post    A+      2220.0   \n",
       "1  polls-only  U.S.  11/7/2016   Google Consumer Surveys     B     26574.0   \n",
       "2  polls-only  U.S.  11/6/2016                     Ipsos    A-      2195.0   \n",
       "3  polls-only  U.S.  11/7/2016                    YouGov     B      3677.0   \n",
       "4  polls-only  U.S.  11/6/2016          Gravis Marketing    B-     16639.0   \n",
       "\n",
       "  population   poll_wt  adjpoll_clinton  adjpoll_trump  adjpoll_johnson  \\\n",
       "0         lv  8.720654         45.21947       41.70754         4.606925   \n",
       "1         lv  7.628472         43.40083       41.14659         5.164047   \n",
       "2         lv  6.424334         42.01984       38.74365         6.816055   \n",
       "3         lv  6.087135         45.68214       40.90047         6.118311   \n",
       "4         rv  5.316449         46.83107       42.27754         3.749071   \n",
       "\n",
       "   adjpoll_mcmullin  poll_id  \n",
       "0               NaN    48630  \n",
       "1               NaN    48847  \n",
       "2               NaN    48922  \n",
       "3               NaN    48687  \n",
       "4               NaN    48848  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_po = df2[df2.loc[:,'type']=='polls-only'] #select only the values for which 'type' is 'polls-only'\n",
    "df_po = df_po.reset_index(drop=True) #reset the indexes from original values to 0, and drop the original indexes from memory\n",
    "df_po.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>samplesize</th>\n",
       "      <th>poll_wt</th>\n",
       "      <th>adjpoll_clinton</th>\n",
       "      <th>adjpoll_trump</th>\n",
       "      <th>adjpoll_johnson</th>\n",
       "      <th>adjpoll_mcmullin</th>\n",
       "      <th>poll_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4207.000000</td>\n",
       "      <td>4208.000000</td>\n",
       "      <td>4208.000000</td>\n",
       "      <td>4208.000000</td>\n",
       "      <td>2799.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>4208.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1148.216068</td>\n",
       "      <td>0.255352</td>\n",
       "      <td>43.322517</td>\n",
       "      <td>42.654425</td>\n",
       "      <td>4.651088</td>\n",
       "      <td>24.508827</td>\n",
       "      <td>45910.899477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2630.856265</td>\n",
       "      <td>0.669404</td>\n",
       "      <td>7.097772</td>\n",
       "      <td>6.948612</td>\n",
       "      <td>2.472390</td>\n",
       "      <td>5.235812</td>\n",
       "      <td>2864.763228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17.115890</td>\n",
       "      <td>4.488276</td>\n",
       "      <td>-3.677883</td>\n",
       "      <td>11.028320</td>\n",
       "      <td>35362.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>447.500000</td>\n",
       "      <td>0.000354</td>\n",
       "      <td>40.220230</td>\n",
       "      <td>38.449348</td>\n",
       "      <td>3.130344</td>\n",
       "      <td>23.108497</td>\n",
       "      <td>45151.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>772.000000</td>\n",
       "      <td>0.008029</td>\n",
       "      <td>44.142125</td>\n",
       "      <td>42.704720</td>\n",
       "      <td>4.366810</td>\n",
       "      <td>25.135225</td>\n",
       "      <td>46384.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1236.500000</td>\n",
       "      <td>0.086035</td>\n",
       "      <td>46.901398</td>\n",
       "      <td>46.315503</td>\n",
       "      <td>5.763004</td>\n",
       "      <td>27.976062</td>\n",
       "      <td>47741.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>84292.000000</td>\n",
       "      <td>8.720654</td>\n",
       "      <td>86.713200</td>\n",
       "      <td>72.376610</td>\n",
       "      <td>20.357000</td>\n",
       "      <td>31.574690</td>\n",
       "      <td>48922.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         samplesize      poll_wt  adjpoll_clinton  adjpoll_trump  \\\n",
       "count   4207.000000  4208.000000      4208.000000    4208.000000   \n",
       "mean    1148.216068     0.255352        43.322517      42.654425   \n",
       "std     2630.856265     0.669404         7.097772       6.948612   \n",
       "min       35.000000     0.000000        17.115890       4.488276   \n",
       "25%      447.500000     0.000354        40.220230      38.449348   \n",
       "50%      772.000000     0.008029        44.142125      42.704720   \n",
       "75%     1236.500000     0.086035        46.901398      46.315503   \n",
       "max    84292.000000     8.720654        86.713200      72.376610   \n",
       "\n",
       "       adjpoll_johnson  adjpoll_mcmullin       poll_id  \n",
       "count      2799.000000         30.000000   4208.000000  \n",
       "mean          4.651088         24.508827  45910.899477  \n",
       "std           2.472390          5.235812   2864.763228  \n",
       "min          -3.677883         11.028320  35362.000000  \n",
       "25%           3.130344         23.108497  45151.750000  \n",
       "50%           4.366810         25.135225  46384.500000  \n",
       "75%           5.763004         27.976062  47741.250000  \n",
       "max          20.357000         31.574690  48922.000000  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_po.describe() #display summary statistics for numerical variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    11/6/2016\n",
       "1    11/7/2016\n",
       "2    11/6/2016\n",
       "3    11/7/2016\n",
       "4    11/6/2016\n",
       "Name: enddate, dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_po.loc[:,'enddate'].head() #view first 5 'enddate' values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2016-11-06\n",
       "1   2016-11-07\n",
       "2   2016-11-06\n",
       "3   2016-11-07\n",
       "4   2016-11-06\n",
       "Name: enddate, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_po.loc[:,'enddate'] = pd.to_datetime(df_po.loc[:,'enddate']) #convert 'enddate' into 'datetime' format\n",
    "df_po.loc[:, 'enddate'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>enddate</th>\n",
       "      <th>adjpoll_clinton</th>\n",
       "      <th>adjpoll_trump</th>\n",
       "      <th>adjpoll_johnson</th>\n",
       "      <th>adjpoll_mcmullin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>45.21947</td>\n",
       "      <td>41.70754</td>\n",
       "      <td>4.606925</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>43.40083</td>\n",
       "      <td>41.14659</td>\n",
       "      <td>5.164047</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>42.01984</td>\n",
       "      <td>38.74365</td>\n",
       "      <td>6.816055</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>45.68214</td>\n",
       "      <td>40.90047</td>\n",
       "      <td>6.118311</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>46.83107</td>\n",
       "      <td>42.27754</td>\n",
       "      <td>3.749071</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>49.05626</td>\n",
       "      <td>43.87898</td>\n",
       "      <td>3.018706</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>45.31196</td>\n",
       "      <td>40.80614</td>\n",
       "      <td>4.230162</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2016-11-05</td>\n",
       "      <td>43.68695</td>\n",
       "      <td>40.80897</td>\n",
       "      <td>5.381917</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2016-11-06</td>\n",
       "      <td>45.03026</td>\n",
       "      <td>41.83415</td>\n",
       "      <td>8.034579</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2016-11-07</td>\n",
       "      <td>42.88452</td>\n",
       "      <td>42.18602</td>\n",
       "      <td>6.367243</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     enddate  adjpoll_clinton  adjpoll_trump  adjpoll_johnson  \\\n",
       "0 2016-11-06         45.21947       41.70754         4.606925   \n",
       "1 2016-11-07         43.40083       41.14659         5.164047   \n",
       "2 2016-11-06         42.01984       38.74365         6.816055   \n",
       "3 2016-11-07         45.68214       40.90047         6.118311   \n",
       "4 2016-11-06         46.83107       42.27754         3.749071   \n",
       "5 2016-11-06         49.05626       43.87898         3.018706   \n",
       "6 2016-11-06         45.31196       40.80614         4.230162   \n",
       "7 2016-11-05         43.68695       40.80897         5.381917   \n",
       "8 2016-11-06         45.03026       41.83415         8.034579   \n",
       "9 2016-11-07         42.88452       42.18602         6.367243   \n",
       "\n",
       "   adjpoll_mcmullin  \n",
       "0               NaN  \n",
       "1               NaN  \n",
       "2               NaN  \n",
       "3               NaN  \n",
       "4               NaN  \n",
       "5               NaN  \n",
       "6               NaN  \n",
       "7               NaN  \n",
       "8               NaN  \n",
       "9               NaN  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_po.loc[:, ['enddate', 'adjpoll_clinton', 'adjpoll_trump', 'adjpoll_johnson', 'adjpoll_mcmullin']].head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfuly sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~emmanduncan/0 or inside your plot.ly account where it is named 'Election Poll Data'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~emmanduncan/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PLOT ALL DATA USING PLOTLY\n",
    "\n",
    "fig = {\n",
    "    'data':[\n",
    "        #Clinton data\n",
    "        {\n",
    "            'name':\"Clinton\", \n",
    "            'x':df_po.loc[:,'enddate'], \n",
    "            'y':df_po.loc[:,'adjpoll_clinton'],\n",
    "            'mode':'markers', 'marker':{'color':'blue', 'opacity':0.1}},\n",
    "        #Trump data\n",
    "        {   \n",
    "            'name':\"Trump\",\n",
    "            'x':df_po.loc[:,'enddate'], \n",
    "            'y':df_po.loc[:,'adjpoll_trump'],\n",
    "            'mode':'markers', 'marker':{'color':'red', 'opacity':0.1}},\n",
    "        #Johnson data\n",
    "        {\n",
    "            'name':\"Johnson\", \n",
    "            'x':df_po.loc[:,'enddate'], \n",
    "            'y':df_po.loc[:,'adjpoll_johnson'],\n",
    "            'mode':'markers', 'marker':{'color':'gold', 'opacity':0.1}},\n",
    "        #McMullin data\n",
    "        {\n",
    "            'name':\"McMullin\", \n",
    "            'x':df_po.loc[:, 'enddate'], \n",
    "            'y':df_po.loc[:,'adjpoll_mcmullin'],\n",
    "            'mode':'markers', 'marker':{'color':'green', 'opacity':0.1}}\n",
    "    ],\n",
    "    #set graph layout\n",
    "    'layout':{\n",
    "        'title':\"Adjusted Poll Data\",\n",
    "        #set x-axis default range from one month before the first observation, until one month after the last observation\n",
    "        'xaxis':{'title':\"Date\", \n",
    "                 'range':[min(df_po.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                                max(df_po.loc[:, 'enddate']) + pd.DateOffset(months=1)]},\n",
    "        #set y-axis default range from 0 to 100, with tick marks every 10 percentage points\n",
    "        'yaxis':{'title':\"Percentage\", \n",
    "                 'range':[0, 100], 'tick0':0, 'dtick':10},\n",
    "        #set background color\n",
    "        'plot_bgcolor':'ghostwhite', 'paper_bgcolor':'ghostwhite'}\n",
    "}\n",
    "\n",
    "#plot the data\n",
    "py.iplot(fig, filename='Election Poll Data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A+', 'B', 'A-', 'B-', 'A', nan, 'B+', 'C+', 'C-', 'C', 'D'], dtype=object)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_po.loc[:,'grade'].unique() #display unique values of the 'grade' factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/envs/venv/lib/python3.6/site-packages/ipykernel/__main__.py:10: SettingWithCopyWarning:\n",
      "\n",
      "\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",
<<<<<<< HEAD
      "\n",
      "//anaconda/envs/venv/lib/python3.6/site-packages/ipykernel/__main__.py:16: FutureWarning:\n",
      "\n",
      "sort(columns=....) is deprecated, use sort_values(by=.....)\n",
=======
>>>>>>> master
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['A+', 'B', 'A', 'NA', 'C', 'D'], dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_grade = df_po\n",
    "for index in range(len(df_grade)):\n",
    "    grade = df_grade.loc[index, 'grade']\n",
    "    if (grade=='A-'):                     #change A- grades to A\n",
    "        df_grade.loc[index, 'grade']='A'\n",
    "    elif (grade=='B+' or  grade=='B-'):   #change B+ and B- grades to B\n",
    "        df_grade.loc[index, 'grade']='B'\n",
    "    elif (grade=='C+' or grade=='C-'):\n",
    "        df_grade.loc[index, 'grade']='C'  #change C+ and C- grades to C\n",
    "df_grade.loc[:, 'grade'][df_grade.loc[:, 'grade'].isnull()] = 'NA'     #change empty grades ('nan') to string 'NA'\n",
    "\n",
    "df_grade.grade.unique() #display unique values of 'grade'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is the format of your plot grid:\n",
      "[ (1,1) x1,y1 ]  [ (1,2) x2,y2 ]\n",
      "[ (2,1) x3,y3 ]  [ (2,2) x4,y4 ]\n",
      "[ (3,1) x5,y5 ]  [ (3,2) x6,y6 ]\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~emmanduncan/2.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PLOTTING CLINTON, TRUMP BY GRADE\n",
    "\n",
    "#create traces for each grade, for each candidate\n",
    "trace_CAp = go.Scatter(\n",
    "    name=\"Clinton A+\", \n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='A+'], \n",
    "    y=df_grade.loc[:, 'adjpoll_clinton'][df_grade.loc[:, 'grade']=='A'],\n",
    "    mode='markers', marker=dict(color='blue', opacity=0.1))\n",
    "trace_CA = go.Scatter(\n",
    "    name=\"Clinton A\", \n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='A'], \n",
    "    y=df_grade.loc[:, 'adjpoll_clinton'][df_grade.loc[:, 'grade']=='A'],\n",
    "    mode='markers', marker=dict(color='blue', opacity=0.1))\n",
    "trace_CB = go.Scatter(\n",
    "    name=\"Clinton B\", \n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='B'], \n",
    "    y=df_grade.loc[:, 'adjpoll_clinton'][df_grade.loc[:, 'grade']=='B'],\n",
    "    mode='markers', marker=dict(color='blue', opacity=0.1))\n",
    "trace_CC = go.Scatter(\n",
    "    name=\"Clinton C\", \n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='C'], \n",
    "    y=df_grade.loc[:, 'adjpoll_clinton'][df_grade.loc[:, 'grade']=='C'],\n",
    "    mode='markers', marker=dict(color='blue', opacity=0.1))\n",
    "trace_CD = go.Scatter(\n",
    "    name=\"Clinton D\", \n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='D'], \n",
    "    y=df_grade.loc[:, 'adjpoll_clinton'][df_grade.loc[:, 'grade']=='C'],\n",
    "    mode='markers', marker=dict(color='blue', opacity=0.1))\n",
    "trace_CNA = go.Scatter(\n",
    "    name=\"Clinton NA\", \n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='NA'], \n",
    "    y=df_grade.loc[:, 'adjpoll_clinton'][df_grade.loc[:, 'grade']=='NA'],\n",
    "    mode='markers', marker=dict(color='blue', opacity=0.1))\n",
    "\n",
    "trace_TAp = go.Scatter( \n",
    "    name=\"Trump A+\",\n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='A+'], \n",
    "    y=df_grade.loc[:, 'adjpoll_trump'][df_grade.loc[:, 'grade']=='A+'], \n",
    "    mode='markers', marker=dict(color='red', opacity=0.1))\n",
    "trace_TA = go.Scatter( \n",
    "    name=\"Trump A\",\n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='A'], \n",
    "    y=df_grade.loc[:, 'adjpoll_trump'][df_grade.loc[:, 'grade']=='A'], \n",
    "    mode='markers', marker=dict(color='red', opacity=0.1))\n",
    "trace_TB = go.Scatter( \n",
    "    name=\"Trump B\",\n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='B'], \n",
    "    y=df_grade.loc[:, 'adjpoll_trump'][df_grade.loc[:, 'grade']=='B'], \n",
    "    mode='markers', marker=dict(color='red', opacity=0.1))\n",
    "trace_TC = go.Scatter( \n",
    "    name=\"Trump C\",\n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='C'], \n",
    "    y=df_grade.loc[:, 'adjpoll_trump'][df_grade.loc[:, 'grade']=='C'], \n",
    "    mode='markers', marker=dict(color='red', opacity=0.1))\n",
    "trace_TD = go.Scatter( \n",
    "    name=\"Trump D\",\n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='D'], \n",
    "    y=df_grade.loc[:, 'adjpoll_trump'][df_grade.loc[:, 'grade']=='D'], \n",
    "    mode='markers', marker=dict(color='red', opacity=0.1))\n",
    "trace_TNA = go.Scatter( \n",
    "    name=\"Trump NA\",\n",
    "    x=df_grade.loc[:, 'enddate'][df_grade.loc[:, 'grade']=='NA'], \n",
    "    y=df_grade.loc[:, 'adjpoll_trump'][df_grade.loc[:, 'grade']=='NA'], \n",
    "    mode='markers', marker=dict(color='red', opacity=0.1))\n",
    "\n",
    "#create a figure with 3 rows and 2 columns\n",
    "fig = tools.make_subplots(rows=3, cols=2, subplot_titles=('A+', 'A', 'B',\n",
    "                                                          'C', 'D', 'NA'))\n",
    "#apply traces to matching figure in subplot\n",
    "fig.append_trace(trace_CAp, 1, 1)\n",
    "fig.append_trace(trace_TAp, 1, 1)\n",
    "fig.append_trace(trace_CA, 1, 2)\n",
    "fig.append_trace(trace_TA, 1, 2)\n",
    "fig.append_trace(trace_CB, 2, 1)\n",
    "fig.append_trace(trace_TB, 2, 1)\n",
    "fig.append_trace(trace_CC, 2, 2)\n",
    "fig.append_trace(trace_TC, 2, 2)\n",
    "fig.append_trace(trace_CD, 3, 1)\n",
    "fig.append_trace(trace_TD, 3, 1)\n",
    "fig.append_trace(trace_CNA,  3, 2)\n",
    "fig.append_trace(trace_TNA, 3, 2)\n",
    "\n",
    "#create layout: x-axis range over course of data, y-axis from 0 to 100\n",
    "layout = go.Layout(\n",
    "    title=\"Clinton and Trump, By Pollster Grade\",\n",
    "    xaxis1=dict(range=(min(df_grade.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                       max(df_grade.loc[:, 'enddate']) + pd.DateOffset(months=1))),\n",
    "    xaxis2=dict(range=(min(df_grade.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                       max(df_grade.loc[:, 'enddate']) + pd.DateOffset(months=1))),\n",
    "    xaxis3=dict(range=(min(df_grade.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                       max(df_grade.loc[:, 'enddate']) + pd.DateOffset(months=1))),\n",
    "    xaxis4=dict(range=(min(df_grade.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                       max(df_grade.loc[:, 'enddate']) + pd.DateOffset(months=1))),\n",
    "    xaxis5=dict(range=(min(df_grade.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                       max(df_grade.loc[:, 'enddate']) + pd.DateOffset(months=1))),\n",
    "    xaxis6=dict(range=(min(df_grade.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                       max(df_grade.loc[:, 'enddate']) + pd.DateOffset(months=1))),\n",
    "    yaxis1=dict(range=[0,100], tick0=0, dtick=25), \n",
    "    yaxis2=dict(range=[0,100], tick0=0, dtick=25), \n",
    "    yaxis3=dict(range=[0,100], tick0=0, dtick=25), \n",
    "    yaxis4=dict(range=[0,100], tick0=0, dtick=25),\n",
    "    yaxis5=dict(range=[0,100], tick0=0, dtick=25),\n",
    "    yaxis6=dict(range=[0,100], tick0=0, dtick=25),\n",
    "    plot_bgcolor='ghostwhite', paper_bgcolor='ghostwhite')\n",
    "fig['layout'].update(layout)\n",
    "\n",
    "py.iplot(fig, filename='Election Poll Data, By Grade')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~emmanduncan/4.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#BOXPLOTS BY GRADE\n",
    "\n",
    "trace_Clinton = go.Box(\n",
    "    name=\"Clinton\",\n",
    "    x=df_grade.loc[:,'grade'], \n",
    "    y=df_grade.loc[:, 'adjpoll_clinton'],\n",
    "    marker=dict(color='blue'))\n",
    "trace_Trump = go.Box(\n",
    "    name=\"Trump\",\n",
    "    x=df_grade.loc[:, 'grade'], \n",
    "    y=df_grade.loc[:, 'adjpoll_trump'], \n",
    "    marker=dict(color='red'))\n",
    "\n",
    "data = [trace_Clinton, trace_Trump]\n",
    "layout = go.Layout(\n",
    "    title=\"Boxplots of Election Polls, By Pollster Grade\",\n",
    "    yaxis=dict(title=\"Percentage\", range=[0, 100], tick0=0, dtick=10, zeroline=False), \n",
    "    boxmode='group', #place boxes in groups by grade\n",
    "    plot_bgcolor='ghostwhite', paper_bgcolor='ghostwhite')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"Election Poll Boxplots, by Grade\")"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
=======
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~junseopark/28.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#reordering data in order of grade\n",
    "grades = {'A+':1, 'A':2, 'B':3, 'C':4, 'D':5, 'NA':6}\n",
    "df_grade.insert(loc=len(df_grade.iloc[0]), column='graderank', value=0)\n",
    "df_grade.loc[:,'graderank'] = df_grade.loc[:, 'grade'].map(grades)\n",
    "df_grade.sort_values(by='graderank', inplace=True)\n",
    "df_grade = df_grade.drop(labels='graderank', axis=1)\n",
    "\n",
    "trace_Clinton = go.Box(\n",
    "    name=\"Clinton\",\n",
    "    x=df_grade.loc[:,'grade'], \n",
    "    y=df_grade.loc[:, 'adjpoll_clinton'],\n",
    "    marker=dict(color='blue'))\n",
    "trace_Trump = go.Box(\n",
    "    name=\"Trump\",\n",
    "    x=df_grade.loc[:, 'grade'], \n",
    "    y=df_grade.loc[:, 'adjpoll_trump'], \n",
    "    marker=dict(color='red'))\n",
    "\n",
    "data = [trace_Clinton, trace_Trump]\n",
    "layout = go.Layout(\n",
    "    title=\"Boxplots of Election Polls, By Ordered Pollster Grade\",\n",
    "    yaxis=dict(title=\"Percentage\", range=[0, 100], tick0=0, dtick=10, zeroline=False), \n",
    "    boxmode='group', #place boxes in groups by grade\n",
    "    plot_bgcolor='ghostwhite', paper_bgcolor='ghostwhite')\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"Election Poll Boxplots, by Ordered Pollster Grade\")"
   ]
  },
  {
   "cell_type": "code",
>>>>>>> master
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas SD: 7.097772011313118\n",
      "Numpy SD : 7.096928594756007\n"
     ]
    }
   ],
   "source": [
    "print(\"Pandas SD: \" + str(df_po.loc[:,'adjpoll_clinton'].std()))\n",
    "print(\"Numpy SD : \" + str(np.std(df_po.loc[:, 'adjpoll_clinton'])))"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": 18,
=======
   "execution_count": 29,
>>>>>>> master
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
<<<<<<< HEAD
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~emmanduncan/6.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
=======
     "ename": "PlotlyError",
     "evalue": "Hey there! You've hit one of our API request limits. \n\nTo get unlimited API calls(10,000/day), please upgrade to a paid plan. \n\nUPGRADE HERE: https://goo.gl/i7glmM \n\nThanks for using Plotly! Happy Plotting!",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mPlotlyError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-29-8090ce88b080>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     58\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m     59\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m---> 60\u001b[0;31m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Election Poll Data, Continuous Error Chart'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32mC:\\Users\\Jun Seo\\Anaconda3\\envs\\venv\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36miplot\u001b[0;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m         \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m     \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\Jun Seo\\Anaconda3\\envs\\venv\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[1;32m    239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    240\u001b[0m     \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m--> 241\u001b[0;31m     \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    242\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m    243\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mC:\\Users\\Jun Seo\\Anaconda3\\envs\\venv\\lib\\site-packages\\plotly\\plotly\\plotly.py\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[0;34m(figure, **plot_options)\u001b[0m\n\u001b[1;32m   1429\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1430\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[1;34m'error'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1431\u001b[0;31m         \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1432\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m   1433\u001b[0m     \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mPlotlyError\u001b[0m: Hey there! You've hit one of our API request limits. \n\nTo get unlimited API calls(10,000/day), please upgrade to a paid plan. \n\nUPGRADE HERE: https://goo.gl/i7glmM \n\nThanks for using Plotly! Happy Plotting!"
     ]
>>>>>>> master
    }
   ],
   "source": [
    "df_po.sort_values(by='enddate', inplace=True)\n",
    "clinton_upper = go.Scatter(\n",
    "    name=\"Clinton Upper Bound\", \n",
    "    x=df_po.loc[:,'enddate'], \n",
    "    y=df_po.loc[:,'adjpoll_clinton'] + np.std(df_po.loc[:,'adjpoll_clinton']), \n",
    "    mode='lines', marker=dict(color='blue'),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(0, 0, 255, 0.2)',\n",
    "    fill='tonexty')\n",
    "clinton = go.Scatter(\n",
    "    name=\"Clinton Mean\", \n",
    "    x=df_po.loc[:,'enddate'], \n",
    "    y=np.mean(df_po.loc[:,'adjpoll_clinton']), \n",
    "    mode='lines', marker=dict(color='blue'),\n",
    "    line=dict(color='blue'),\n",
    "    fillcolor='rgba(0,0,255,0.2)',\n",
    "    fill='tonexty')\n",
    "clinton_lower = go.Scatter(\n",
    "    name=\"Clinton Lower Bound\",\n",
    "    x=df_po.loc[:,'enddate'],\n",
    "    y=df_po.loc[:,'adjpoll_clinton'] - np.std(df_po.loc[:,'adjpoll_clinton']),\n",
    "    mode='lines', marker=dict(color='blue'),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(0,0,255,0.2)',\n",
    "    fill='tonexty')\n",
    "\n",
    "trump_upper = go.Scatter(\n",
    "    name=\"Trump Upper Bound\", \n",
    "    x=df_po.loc[:,'enddate'], \n",
    "    y=df_po.loc[:,'adjpoll_trump'] + np.std(df_po.loc[:,'adjpoll_trump']), \n",
    "    mode='lines', marker=dict(color='red'),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(255, 0, 0, 0.2)',\n",
    "    fill='tonexty')\n",
    "trump = go.Scatter(\n",
    "    name=\"Trump Mean\", \n",
    "    x=df_po.loc[:,'enddate'], \n",
    "    y=np.mean(df_po.loc[:,'adjpoll_trump']), \n",
    "    mode='lines', marker=dict(color='red'),\n",
    "    line=dict(color='red'),\n",
    "    fillcolor='rgba(255, 0, 0, 0.2)',\n",
    "    fill='tonexty')\n",
    "trump_lower = go.Scatter(\n",
    "    name=\"Trump Lower Bound\",\n",
    "    x=df_po.loc[:,'enddate'],\n",
    "    y=df_po.loc[:,'adjpoll_trump'] - np.std(df_po.loc[:,'adjpoll_trump']),\n",
    "    mode='lines', marker=dict(color='red'),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(255, 0, 0, 0.2)')\n",
    "\n",
    "data = [clinton_lower, clinton, clinton_upper]\n",
    "layout = go.Layout(\n",
    "    title=\"Election Poll Means with Standard Deviation\",\n",
    "    xaxis=dict(range=(min(df_grade.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                            max(df_grade.loc[:, 'enddate']) + pd.DateOffset(months=1))),\n",
    "    yaxis=dict(title=\"Percentage\", range=[0,100], tick0=0, dtick=10),\n",
    "    plot_bgcolor='ghostwhite', paper_bgcolor='ghostwhite')\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Election Poll Data, Continuous Error Chart')"
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
=======
   "execution_count": 19,
>>>>>>> master
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     4207.000000\n",
       "mean      1148.216068\n",
       "std       2630.856265\n",
       "min         35.000000\n",
       "25%        447.500000\n",
       "50%        772.000000\n",
       "75%       1236.500000\n",
       "max      84292.000000\n",
       "Name: samplesize, dtype: float64"
      ]
     },
<<<<<<< HEAD
     "execution_count": 30,
=======
     "execution_count": 19,
>>>>>>> master
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
<<<<<<< HEAD
    "df_po['samplesize'].describe(include = ['number'])"
=======
    "#EXAMPLE CODE FOR STANDARD DEVIATION GRAPH\n",
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "dftest = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/wind_speed_laurel_nebraska.csv')\n",
    "\n",
    "upper_bound = go.Scatter(\n",
    "    name='Upper Bound',\n",
    "    x=dftest['Time'],\n",
    "    y=dftest['10 Min Sampled Avg']+dftest['10 Min Std Dev'],\n",
    "    mode='lines',\n",
    "    marker=dict(color=\"444\"),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(68, 68, 68, 0.3)',\n",
    "    fill='tonexty')\n",
    "\n",
    "trace = go.Scatter(\n",
    "    name='Measurement',\n",
    "    x=dftest['Time'],\n",
    "    y=dftest['10 Min Sampled Avg'],\n",
    "    mode='lines',\n",
    "    line=dict(color='rgb(31, 119, 180)'),\n",
    "    fillcolor='rgba(68, 68, 68, 0.3)',\n",
    "    fill='tonexty')\n",
    "\n",
    "lower_bound = go.Scatter(\n",
    "    name='Lower Bound',\n",
    "    x=dftest['Time'],\n",
    "    y=dftest['10 Min Sampled Avg']-dftest['10 Min Std Dev'],\n",
    "    marker=dict(color=\"444\"),\n",
    "    line=dict(width=0),\n",
    "    mode='lines')\n",
    "\n",
    "# Trace order can be important\n",
    "# with continuous error bars\n",
    "data = [lower_bound, trace, upper_bound]\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(title='Wind speed (m/s)'),\n",
    "    title='Continuous, variable value error bars.<br>Notice the hover text!')\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='pandas-continuous-error-bars')"
>>>>>>> master
   ]
  },
  {
   "cell_type": "code",
<<<<<<< HEAD
   "execution_count": null,
=======
   "execution_count": 20,
>>>>>>> master
   "metadata": {
    "collapsed": true
   },
<<<<<<< HEAD
   "outputs": [],
   "source": []
=======
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>10 Min Std Dev</th>\n",
       "      <th>Time</th>\n",
       "      <th>10 Min Sampled Avg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.73</td>\n",
       "      <td>2001-06-11 11:00</td>\n",
       "      <td>22.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.98</td>\n",
       "      <td>2001-06-11 11:10</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.87</td>\n",
       "      <td>2001-06-11 11:20</td>\n",
       "      <td>23.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.03</td>\n",
       "      <td>2001-06-11 11:30</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.10</td>\n",
       "      <td>2001-06-11 11:40</td>\n",
       "      <td>20.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   10 Min Std Dev              Time  10 Min Sampled Avg\n",
       "0            2.73  2001-06-11 11:00                22.3\n",
       "1            1.98  2001-06-11 11:10                23.0\n",
       "2            1.87  2001-06-11 11:20                23.3\n",
       "3            2.03  2001-06-11 11:30                22.0\n",
       "4            3.10  2001-06-11 11:40                20.5"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dftest.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>date</th>\n",
       "      <th>mean_c</th>\n",
       "      <th>mean_t</th>\n",
       "      <th>sd_c</th>\n",
       "      <th>sd_t</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-11-06</td>\n",
       "      <td>40.866410</td>\n",
       "      <td>45.800820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-11-11</td>\n",
       "      <td>44.608547</td>\n",
       "      <td>41.087447</td>\n",
       "      <td>5.778527</td>\n",
       "      <td>3.897990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-11-16</td>\n",
       "      <td>43.626064</td>\n",
       "      <td>43.885286</td>\n",
       "      <td>1.885810</td>\n",
       "      <td>3.093642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-11-21</td>\n",
       "      <td>29.194150</td>\n",
       "      <td>49.732650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-11-26</td>\n",
       "      <td>47.270880</td>\n",
       "      <td>40.884840</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date     mean_c     mean_t      sd_c      sd_t\n",
       "0 2015-11-06  40.866410  45.800820  0.000000  0.000000\n",
       "1 2015-11-11  44.608547  41.087447  5.778527  3.897990\n",
       "2 2015-11-16  43.626064  43.885286  1.885810  3.093642\n",
       "3 2015-11-21  29.194150  49.732650  0.000000  0.000000\n",
       "4 2015-11-26  47.270880  40.884840  0.000000  0.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#creating bins for dates\n",
    "df_date = df_po.loc[:,['enddate','adjpoll_clinton','adjpoll_trump']]\n",
    "\n",
    "for index in range(len(df_date)):     #change all dates to be on the 1st, 11th, or 21st\n",
    "    d = df_date.loc[index, 'enddate']\n",
    "    if (d.strftime(\"%d\") <= '05'):\n",
    "        df_date.loc[index, 'enddate']=d.replace(day=1)\n",
    "    elif (d.strftime(\"%d\") <= '10'):\n",
    "        df_date.loc[index, 'enddate']=d.replace(day=6)\n",
    "    elif (d.strftime(\"%d\") <= '15'):\n",
    "        df_date.loc[index, 'enddate']=d.replace(day=11)\n",
    "    elif (d.strftime(\"%d\") <= '20'):\n",
    "        df_date.loc[index, 'enddate']=d.replace(day=16)\n",
    "    elif (d.strftime(\"%d\") <= '25'):\n",
    "        df_date.loc[index, 'enddate']=d.replace(day=21)\n",
    "    elif (d.strftime(\"%d\") <= '31'):\n",
    "        df_date.loc[index, 'enddate']=d.replace(day=26)\n",
    "\n",
    "#adding rows to new dataframe df_bins\n",
    "df_bins = pd.DataFrame(columns=('date', 'mean_c', 'mean_t', 'sd_c', 'sd_t'))\n",
    "while (len(df_date) != 0):\n",
    "    row0 = df_date.iloc[0,:]\n",
    "    d = row0.loc['enddate']\n",
    "    mean_c = np.mean(df_date.loc[:,'adjpoll_clinton'][df_date.loc[:,'enddate']==d])\n",
    "    mean_t = np.mean(df_date.loc[:,'adjpoll_trump'][df_date.loc[:,'enddate']==d])\n",
    "    sd_c = np.std(df_date.loc[:,'adjpoll_clinton'][df_date.loc[:,'enddate']==d])\n",
    "    sd_t = np.std(df_date.loc[:,'adjpoll_trump'][df_date.loc[:,'enddate']==d])\n",
    "    df_bins.loc[df_bins.shape[0]] = [d, mean_c, mean_t, sd_c, sd_t]\n",
    "    df_date = df_date[df_date.loc[:,'enddate']!=d]\n",
    "    df_po = df_po.reset_index(drop=True)\n",
    "    \n",
    "#reordering rows by date\n",
    "df_bins.sort_values(by='date', inplace=True)\n",
    "df_bins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~junseopark/26.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#replotting continuous error charts\n",
    "clinton_upper = go.Scatter(\n",
    "    name=\"Clinton Upper Bound\", \n",
    "    x=df_bins.loc[:,'date'], \n",
    "    y=df_bins.loc[:,'mean_c'] + df_bins.loc[:,'sd_c'], \n",
    "    mode='lines', marker=dict(color='blue'),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(0, 0, 255, 0.2)',\n",
    "    fill='tonexty')\n",
    "clinton = go.Scatter(\n",
    "    name=\"Clinton Mean\", \n",
    "    x=df_bins.loc[:,'date'], \n",
    "    y=df_bins.loc[:,'mean_c'], \n",
    "    mode='lines', marker=dict(color='blue'),\n",
    "    line=dict(color='blue'),\n",
    "    fillcolor='rgba(0,0,255,0.2)',\n",
    "    fill='tonexty')\n",
    "clinton_lower = go.Scatter(\n",
    "    name=\"Clinton Lower Bound\",\n",
    "    x=df_bins.loc[:,'date'],\n",
    "    y=df_bins.loc[:,'mean_c'] - df_bins.loc[:,'sd_c'],\n",
    "    mode='lines', marker=dict(color='blue'),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(0,0,255,0.2)')\n",
    "\n",
    "trump_upper = go.Scatter(\n",
    "    name=\"Trump Upper Bound\", \n",
    "    x=df_bins.loc[:,'date'], \n",
    "    y=df_bins.loc[:,'mean_t'] + df_bins.loc[:,'sd_t'], \n",
    "    mode='lines', marker=dict(color='red'),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(255, 0, 0, 0.2)',\n",
    "    fill='tonexty')\n",
    "trump = go.Scatter(\n",
    "    name=\"Trump Mean\", \n",
    "    x=df_bins.loc[:,'date'], \n",
    "    y=df_bins.loc[:,'mean_t'], \n",
    "    mode='lines', marker=dict(color='red'),\n",
    "    line=dict(color='red'),\n",
    "    fillcolor='rgba(255, 0, 0, 0.2)',\n",
    "    fill='tonexty')\n",
    "trump_lower = go.Scatter(\n",
    "    name=\"Trump Lower Bound\",\n",
    "    x=df_bins.loc[:,'date'],\n",
    "    y=df_bins.loc[:,'mean_t'] - df_bins.loc[:,'sd_t'],\n",
    "    mode='lines', marker=dict(color='red'),\n",
    "    line=dict(width=0),\n",
    "    fillcolor='rgba(255, 0, 0, 0.2)')\n",
    "\n",
    "data = [clinton_lower, clinton, clinton_upper, trump_lower, trump, trump_upper]\n",
    "layout = go.Layout(\n",
    "    title=\"Election Poll Means with Standard Deviation (Adjusted)\",\n",
    "    xaxis=dict(range=(min(df_grade.loc[:, 'enddate']) - pd.DateOffset(months=1), \n",
    "                            max(df_grade.loc[:, 'enddate']) + pd.DateOffset(months=1))),\n",
    "    yaxis=dict(title=\"Percentage\", range=[0,100], tick0=0, dtick=10),\n",
    "    plot_bgcolor='ghostwhite', paper_bgcolor='ghostwhite')\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "py.iplot(fig, filename='Election Poll Data, Continuous Error Chart (Adjusted)')"
   ]
>>>>>>> master
  }
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