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   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Homework 2: More Exploratory Data Analysis\n",
      "## Gene Expression Data and Election Polls \n",
      "\n",
      "Due: Thursday, October 2, 2014 11:59 PM\n",
      "\n",
      "<a href=https://raw.githubusercontent.com/cs109/2014/master/homework/HW2.ipynb download=HW2.ipynb> Download this assignment</a>\n",
      "\n",
      "#### Submission Instructions\n",
      "To submit your homework, create a folder named lastname_firstinitial_hw# and place your IPython notebooks, data files, and any other files in this folder. Your IPython Notebooks should be completely executed with the results visible in the notebook. We should not have to run any code. Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. If we cannot access your work because these directions are not followed correctly, we will not grade your work.\n",
      "\n",
      "\n",
      "---\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Introduction\n",
      "\n",
      "John Tukey wrote in [Exploratory Data Analysis, 1977](http://www.amazon.com/Exploratory-Data-Analysis-Wilder-Tukey/dp/0201076160/ref=pd_bbs_sr_2/103-4466654-5303007?ie=UTF8&s=books&qid=1189739816&sr=8-2): \"The greatest value of a picture is when it forces us to notice what we never expected to see.\" In this assignment we will continue using our exploratory data analysis tools, but apply it to new sets of data: [gene expression](http://en.wikipedia.org/wiki/Gene_expression) and polls from the [2012 Presidental Election](http://en.wikipedia.org/wiki/United_States_presidential_election,_2012) and from the [2014 Senate Midterm Elections](http://en.wikipedia.org/wiki/United_States_Senate_elections,_2014).   \n",
      "\n",
      "**First**: You will use exploratory data analysis and apply the [singular value decomposition](http://en.wikipedia.org/wiki/Singular_value_decomposition) (SVD) to a gene expression data matrix to determine if the the date that the gene expression samples are processed has large effect on the variability seen in the data.  \n",
      "\n",
      "**Second**: You will use the polls from the 2012 Presidential Elections to determine (1) Is there a pollster bias in presidential election polls? and (2) Is the average of polls better than just one poll?\n",
      "\n",
      "**Finally**: You will use the [HuffPost Pollster API](http://elections.huffingtonpost.com/pollster/api) to extract the polls for the current 2014 Senate Midterm Elections and provide a preliminary prediction of the result of each state.\n",
      "\n",
      "#### Data\n",
      "\n",
      "We will use the following data sets: \n",
      "\n",
      "1. A gene expression data set called `exprs_GSE5859.csv` and sample annotation table called `sampleinfo_GSE5859.csv` which are both available on Github in the 2014_data repository: [expression data set](https://github.com/cs109/2014_data/blob/master/exprs_GSE5859.csv) and [sample annotation table](https://github.com/cs109/2014_data/blob/master/sampleinfo_GSE5859.csv).  \n",
      "\n",
      "2. Polls from the [2012 Presidential Election: Barack Obama vs Mitt Romney](http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama). The polls we will use are from the [Huffington Post Pollster](http://elections.huffingtonpost.com/pollster).  \n",
      "\n",
      "3. Polls from the [2014 Senate Midterm Elections](http://elections.huffingtonpost.com/pollster) from the [HuffPost Pollster API](http://elections.huffingtonpost.com/pollster/api). \n",
      "\n",
      "---"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Load Python modules"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# special IPython command to prepare the notebook for matplotlib\n",
      "%matplotlib inline \n",
      "\n",
      "import requests \n",
      "from StringIO import StringIO\n",
      "import numpy as np\n",
      "import pandas as pd # pandas\n",
      "import matplotlib.pyplot as plt # module for plotting \n",
      "import datetime as dt # module for manipulating dates and times\n",
      "import numpy.linalg as lin # module for performing linear algebra operations"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# special matplotlib argument for improved plots\n",
      "from matplotlib import rcParams\n",
      "\n",
      "#colorbrewer2 Dark2 qualitative color table\n",
      "dark2_colors = [(0.10588235294117647, 0.6196078431372549, 0.4666666666666667),\n",
      "                (0.8509803921568627, 0.37254901960784315, 0.00784313725490196),\n",
      "                (0.4588235294117647, 0.4392156862745098, 0.7019607843137254),\n",
      "                (0.9058823529411765, 0.1607843137254902, 0.5411764705882353),\n",
      "                (0.4, 0.6509803921568628, 0.11764705882352941),\n",
      "                (0.9019607843137255, 0.6705882352941176, 0.00784313725490196),\n",
      "                (0.6509803921568628, 0.4627450980392157, 0.11372549019607843)]\n",
      "\n",
      "rcParams['figure.figsize'] = (10, 6)\n",
      "rcParams['figure.dpi'] = 150\n",
      "rcParams['axes.color_cycle'] = dark2_colors\n",
      "rcParams['lines.linewidth'] = 2\n",
      "rcParams['axes.facecolor'] = 'white'\n",
      "rcParams['font.size'] = 14\n",
      "rcParams['patch.edgecolor'] = 'white'\n",
      "rcParams['patch.facecolor'] = dark2_colors[0]\n",
      "rcParams['font.family'] = 'StixGeneral'"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Problem 1\n",
      "\n",
      "In this problem we will be using a [gene expression](http://en.wikipedia.org/wiki/Gene_expression) data set obtained from a [microarray](http://en.wikipedia.org/wiki/DNA_microarray) experiement [Read more about the specific experiment here](http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE5859).  There are two data sets we will use:  \n",
      "\n",
      "1. The gene expression intensities where the rows represent the features on the microarray (e.g. genes) and the columsns represent the different microarray samples.  \n",
      "\n",
      "2. A table that contains the information about each of the samples (columns in the gene expression data set) such as the sex, the age, the treatment status, the date the samples were processed.  Each row represents one sample. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(a) "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Read in the two files from Github: [exprs_GSE5859.csv](https://github.com/cs109/2014_data/blob/master/exprs_GSE5859.csv) and [sampleinfo_GSE5859.csv](https://github.com/cs109/2014_data/blob/master/sampleinfo_GSE5859.csv) as pandas DataFrames called `exprs` and `sampleinfo`. Use the gene names as the index of the `exprs` DataFrame."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "url_exprs = \"https://raw.githubusercontent.com/cs109/2014_data/master/exprs_GSE5859.csv\"\n",
      "exprs = pd.read_csv(url_exprs, index_col=0)\n",
      "\n",
      "url_sampleinfo = \"https://raw.githubusercontent.com/cs109/2014_data/master/sampleinfo_GSE5859.csv\"\n",
      "sampleinfo = pd.read_csv(url_sampleinfo)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Make sure the order of the columns in the gene expression DataFrame match the order of file names in the sample annotation DataFrame. If the order of the columns the `exprs` DataFrame do not match the order of the file names in the `sampleinfo` DataFrame, reorder the columns in the `exprs` DataFrame. \n",
      "\n",
      "**Note**: The column names of the gene expression DataFrame are the filenames of the orignal files from which these data were obtained.  \n",
      "\n",
      "**Hint**: The method `list.index(x)` [[read here](https://docs.python.org/2/tutorial/datastructures.html)] can be used to return the index in the list of the first item whose value is x. It is an error if there is no such item. To check if the order of the columns in `exprs` matches the order of the rows in `sampleinfo`, you can check using the method `.all()` on a Boolean or list of Booleans: \n",
      "\n",
      "Example code: `(exprs.columns == sampleinfo.filename).all()`"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's check if order of the columns in the `exprs` DataFrame match the order of the `filename` in the `sampleinfo` DataFrame using the `==` Boolean operator. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(exprs.columns == sampleinfo.filename).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "False"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we know the columns in the `exprs` DataFrame are out of order compared to the order of the rows in the `sampleinfo` DataFrame.  To check if there are any columns in the correct order, we can test for which columns are equal to the `filename` in the `sampleinfo` DataFrame.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampleinfo[exprs.columns == sampleinfo.filename]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ethnicity</th>\n",
        "      <th>date</th>\n",
        "      <th>filename</th>\n",
        "      <th>sex</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
        "      <td> CEU</td>\n",
        "      <td> 2002-11-21</td>\n",
        "      <td> GSM25482.CEL.gz</td>\n",
        "      <td> F</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "   ethnicity        date         filename sex\n",
        "23       CEU  2002-11-21  GSM25482.CEL.gz   F"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we want to re-order the columns in the `exprs` DataFrame to match the order of the file names in the `sampleinfo` DataFrame. One way of doing this is to use the `list.index()` method.  First, we convert the file names in `sampleinfo` and the column names of `exprs` and to two lists: `a` and `b`. Then, we use a list comprehension to iterate through each element in `a` and return the index in `b` that matches using the `list.index()` method.  Once we know all the indexes, we can re-order `exprs` so that the columns match the same order as the `filenames` in `sampleinfo`. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "a = list(sampleinfo.filename)\n",
      "b = list(exprs.columns)\n",
      "matchIndex = [b.index(x) for x in a]\n",
      "exprs = exprs[matchIndex]\n",
      "\n",
      "# check if all the column names match the file names in sampleinfo\n",
      "(exprs.columns == sampleinfo.filename).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 66,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Show the head of the two tables: `exprs` and `sampleinfo`. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "exprs.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>GSM25349.CEL.gz</th>\n",
        "      <th>GSM25350.CEL.gz</th>\n",
        "      <th>GSM25356.CEL.gz</th>\n",
        "      <th>GSM25357.CEL.gz</th>\n",
        "      <th>GSM25358.CEL.gz</th>\n",
        "      <th>GSM25359.CEL.gz</th>\n",
        "      <th>GSM25360.CEL.gz</th>\n",
        "      <th>GSM25361.CEL.gz</th>\n",
        "      <th>GSM25377.CEL.gz</th>\n",
        "      <th>GSM25378.CEL.gz</th>\n",
        "      <th>...</th>\n",
        "      <th>GSM136719.CEL.gz</th>\n",
        "      <th>GSM136720.CEL.gz</th>\n",
        "      <th>GSM136721.CEL.gz</th>\n",
        "      <th>GSM136722.CEL.gz</th>\n",
        "      <th>GSM136723.CEL.gz</th>\n",
        "      <th>GSM136724.CEL.gz</th>\n",
        "      <th>GSM136725.CEL.gz</th>\n",
        "      <th>GSM136726.CEL.gz</th>\n",
        "      <th>GSM136727.CEL.gz</th>\n",
        "      <th>GSM136729.CEL.gz</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1007_s_at</th>\n",
        "      <td> 6.627014</td>\n",
        "      <td> 6.249807</td>\n",
        "      <td> 5.934128</td>\n",
        "      <td> 6.145268</td>\n",
        "      <td> 6.091270</td>\n",
        "      <td> 6.041186</td>\n",
        "      <td> 6.050375</td>\n",
        "      <td> 6.063847</td>\n",
        "      <td> 6.226106</td>\n",
        "      <td> 6.358282</td>\n",
        "      <td>...</td>\n",
        "      <td> 6.466445</td>\n",
        "      <td> 6.533592</td>\n",
        "      <td> 6.631492</td>\n",
        "      <td> 6.513362</td>\n",
        "      <td> 6.440706</td>\n",
        "      <td> 6.704324</td>\n",
        "      <td> 6.488579</td>\n",
        "      <td> 6.809481</td>\n",
        "      <td> 6.133068</td>\n",
        "      <td> 6.155473</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1053_at</th>\n",
        "      <td> 6.939184</td>\n",
        "      <td> 6.818038</td>\n",
        "      <td> 7.047962</td>\n",
        "      <td> 7.422477</td>\n",
        "      <td> 7.272361</td>\n",
        "      <td> 7.128216</td>\n",
        "      <td> 6.750719</td>\n",
        "      <td> 6.836287</td>\n",
        "      <td> 6.738022</td>\n",
        "      <td> 7.367895</td>\n",
        "      <td>...</td>\n",
        "      <td> 7.032885</td>\n",
        "      <td> 7.156344</td>\n",
        "      <td> 7.018025</td>\n",
        "      <td> 6.973322</td>\n",
        "      <td> 6.884738</td>\n",
        "      <td> 7.120898</td>\n",
        "      <td> 7.517410</td>\n",
        "      <td> 7.200596</td>\n",
        "      <td> 7.280781</td>\n",
        "      <td> 7.111583</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117_at</th>\n",
        "      <td> 5.113570</td>\n",
        "      <td> 5.074096</td>\n",
        "      <td> 5.371201</td>\n",
        "      <td> 5.266550</td>\n",
        "      <td> 5.342047</td>\n",
        "      <td> 5.063876</td>\n",
        "      <td> 5.315898</td>\n",
        "      <td> 5.483652</td>\n",
        "      <td> 6.689444</td>\n",
        "      <td> 6.482782</td>\n",
        "      <td>...</td>\n",
        "      <td> 5.661112</td>\n",
        "      <td> 5.127260</td>\n",
        "      <td> 5.151840</td>\n",
        "      <td> 5.505602</td>\n",
        "      <td> 5.687689</td>\n",
        "      <td> 4.942651</td>\n",
        "      <td> 5.247190</td>\n",
        "      <td> 5.237239</td>\n",
        "      <td> 5.401876</td>\n",
        "      <td> 5.302628</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>121_at</th>\n",
        "      <td> 7.833862</td>\n",
        "      <td> 7.780682</td>\n",
        "      <td> 7.458197</td>\n",
        "      <td> 7.655948</td>\n",
        "      <td> 7.546555</td>\n",
        "      <td> 7.072670</td>\n",
        "      <td> 7.092984</td>\n",
        "      <td> 6.954225</td>\n",
        "      <td> 7.489785</td>\n",
        "      <td> 7.388539</td>\n",
        "      <td>...</td>\n",
        "      <td> 7.769734</td>\n",
        "      <td> 7.815864</td>\n",
        "      <td> 7.683279</td>\n",
        "      <td> 7.883231</td>\n",
        "      <td> 7.913621</td>\n",
        "      <td> 7.834196</td>\n",
        "      <td> 7.331864</td>\n",
        "      <td> 7.357102</td>\n",
        "      <td> 7.607461</td>\n",
        "      <td> 7.456453</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1255_g_at</th>\n",
        "      <td> 3.152269</td>\n",
        "      <td> 3.111747</td>\n",
        "      <td> 3.018932</td>\n",
        "      <td> 3.154545</td>\n",
        "      <td> 3.107954</td>\n",
        "      <td> 3.224284</td>\n",
        "      <td> 3.114241</td>\n",
        "      <td> 3.044975</td>\n",
        "      <td> 3.304038</td>\n",
        "      <td> 2.887919</td>\n",
        "      <td>...</td>\n",
        "      <td> 3.257484</td>\n",
        "      <td> 3.339234</td>\n",
        "      <td> 3.298384</td>\n",
        "      <td> 3.150654</td>\n",
        "      <td> 3.344501</td>\n",
        "      <td> 3.230285</td>\n",
        "      <td> 3.175846</td>\n",
        "      <td> 3.105092</td>\n",
        "      <td> 3.225123</td>\n",
        "      <td> 3.090149</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 208 columns</p>\n",
        "</div>"
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       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 67,
       "text": [
        "           GSM25349.CEL.gz  GSM25350.CEL.gz  GSM25356.CEL.gz  GSM25357.CEL.gz  \\\n",
        "1007_s_at         6.627014         6.249807         5.934128         6.145268   \n",
        "1053_at           6.939184         6.818038         7.047962         7.422477   \n",
        "117_at            5.113570         5.074096         5.371201         5.266550   \n",
        "121_at            7.833862         7.780682         7.458197         7.655948   \n",
        "1255_g_at         3.152269         3.111747         3.018932         3.154545   \n",
        "\n",
        "           GSM25358.CEL.gz  GSM25359.CEL.gz  GSM25360.CEL.gz  GSM25361.CEL.gz  \\\n",
        "1007_s_at         6.091270         6.041186         6.050375         6.063847   \n",
        "1053_at           7.272361         7.128216         6.750719         6.836287   \n",
        "117_at            5.342047         5.063876         5.315898         5.483652   \n",
        "121_at            7.546555         7.072670         7.092984         6.954225   \n",
        "1255_g_at         3.107954         3.224284         3.114241         3.044975   \n",
        "\n",
        "           GSM25377.CEL.gz  GSM25378.CEL.gz       ...         \\\n",
        "1007_s_at         6.226106         6.358282       ...          \n",
        "1053_at           6.738022         7.367895       ...          \n",
        "117_at            6.689444         6.482782       ...          \n",
        "121_at            7.489785         7.388539       ...          \n",
        "1255_g_at         3.304038         2.887919       ...          \n",
        "\n",
        "           GSM136719.CEL.gz  GSM136720.CEL.gz  GSM136721.CEL.gz  \\\n",
        "1007_s_at          6.466445          6.533592          6.631492   \n",
        "1053_at            7.032885          7.156344          7.018025   \n",
        "117_at             5.661112          5.127260          5.151840   \n",
        "121_at             7.769734          7.815864          7.683279   \n",
        "1255_g_at          3.257484          3.339234          3.298384   \n",
        "\n",
        "           GSM136722.CEL.gz  GSM136723.CEL.gz  GSM136724.CEL.gz  \\\n",
        "1007_s_at          6.513362          6.440706          6.704324   \n",
        "1053_at            6.973322          6.884738          7.120898   \n",
        "117_at             5.505602          5.687689          4.942651   \n",
        "121_at             7.883231          7.913621          7.834196   \n",
        "1255_g_at          3.150654          3.344501          3.230285   \n",
        "\n",
        "           GSM136725.CEL.gz  GSM136726.CEL.gz  GSM136727.CEL.gz  \\\n",
        "1007_s_at          6.488579          6.809481          6.133068   \n",
        "1053_at            7.517410          7.200596          7.280781   \n",
        "117_at             5.247190          5.237239          5.401876   \n",
        "121_at             7.331864          7.357102          7.607461   \n",
        "1255_g_at          3.175846          3.105092          3.225123   \n",
        "\n",
        "           GSM136729.CEL.gz  \n",
        "1007_s_at          6.155473  \n",
        "1053_at            7.111583  \n",
        "117_at             5.302628  \n",
        "121_at             7.456453  \n",
        "1255_g_at          3.090149  \n",
        "\n",
        "[5 rows x 208 columns]"
       ]
      }
     ],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampleinfo.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ethnicity</th>\n",
        "      <th>date</th>\n",
        "      <th>filename</th>\n",
        "      <th>sex</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> CEU</td>\n",
        "      <td> 2003-02-04</td>\n",
        "      <td> GSM25349.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> CEU</td>\n",
        "      <td> 2003-02-04</td>\n",
        "      <td> GSM25350.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> CEU</td>\n",
        "      <td> 2002-12-17</td>\n",
        "      <td> GSM25356.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> CEU</td>\n",
        "      <td> 2003-01-30</td>\n",
        "      <td> GSM25357.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> CEU</td>\n",
        "      <td> 2003-01-03</td>\n",
        "      <td> GSM25358.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "  ethnicity        date         filename sex\n",
        "0       CEU  2003-02-04  GSM25349.CEL.gz   M\n",
        "1       CEU  2003-02-04  GSM25350.CEL.gz   M\n",
        "2       CEU  2002-12-17  GSM25356.CEL.gz   M\n",
        "3       CEU  2003-01-30  GSM25357.CEL.gz   M\n",
        "4       CEU  2003-01-03  GSM25358.CEL.gz   M"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(b)\n",
      "\n",
      "Extract the year and month as integers from the `sampleinfo` table. \n",
      "\n",
      "**Hint**: To convert a Series or a column of a pandas DataFrame that contains a date-like object, you can use the `to_datetime` function [[read here](http://pandas.pydata.org/pandas-docs/stable/timeseries.html)].  This will create a `DatetimeIndex` which can be used to extract the month and year for each row in the DataFrame. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "sampleinfo[\"date\"] = pd.to_datetime(sampleinfo.date)\n",
      "sampleinfo[\"month\"] = map(lambda x: x.month, sampleinfo.date)\n",
      "sampleinfo[\"year\"] = map(lambda x: x.year, sampleinfo.date)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 69
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(c)\n",
      "\n",
      "Convert the dates in the `date` column from the `sampleinfo` table into days since October 31, 2002. Add a column to the `sampleinfo` DataFrame titled `elapsedInDays` containing the days since October 31, 2002.  Show the head of the `sampleinfo` DataFrame which includes the new column.  \n",
      "\n",
      "**Hint**: Use the `datetime` module to create a new `datetime` object for the specific date October 31, 2002. Then, subtract the October 31, 2002 date from each date from the `date` column in the `sampleinfo` DataFrame. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "oct31 = dt.datetime(2002,10,31,0,0)\n",
      "oct31"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 70,
       "text": [
        "datetime.datetime(2002, 10, 31, 0, 0)"
       ]
      }
     ],
     "prompt_number": 70
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sampleinfo[\"elapsedInDays\"] = map(lambda x: (x - oct31).days, sampleinfo.date)\n",
      "sampleinfo.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ethnicity</th>\n",
        "      <th>date</th>\n",
        "      <th>filename</th>\n",
        "      <th>sex</th>\n",
        "      <th>month</th>\n",
        "      <th>year</th>\n",
        "      <th>elapsedInDays</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2003-02-04</td>\n",
        "      <td> GSM25349.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td>  2</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2003-02-04</td>\n",
        "      <td> GSM25350.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td>  2</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2002-12-17</td>\n",
        "      <td> GSM25356.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td> 12</td>\n",
        "      <td> 2002</td>\n",
        "      <td> 47</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2003-01-30</td>\n",
        "      <td> GSM25357.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td>  1</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2003-01-03</td>\n",
        "      <td> GSM25358.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td>  1</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 64</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 71,
       "text": [
        "  ethnicity       date         filename sex  month  year  elapsedInDays\n",
        "0       CEU 2003-02-04  GSM25349.CEL.gz   M      2  2003             96\n",
        "1       CEU 2003-02-04  GSM25350.CEL.gz   M      2  2003             96\n",
        "2       CEU 2002-12-17  GSM25356.CEL.gz   M     12  2002             47\n",
        "3       CEU 2003-01-30  GSM25357.CEL.gz   M      1  2003             91\n",
        "4       CEU 2003-01-03  GSM25358.CEL.gz   M      1  2003             64"
       ]
      }
     ],
     "prompt_number": 71
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 1(d)\n",
      "\n",
      "Use exploratory analysis and the singular value decomposition (SVD) of the gene expression data matrix to determine if the date the samples were processed has large effect on the variability seen in the data or if it is just ethnicity (which is confounded with date).\n",
      "\n",
      "**Hint**: See the end of the [lecture from 9/23/2014 for help with SVD](http://nbviewer.ipython.org/github/cs109/2014/blob/master/lectures/lecture07/data_scraping_transcript.ipynb). \n",
      "\n",
      "First subset the the `sampleinfo` DataFrame to include only the CEU ethnicity.  Call this new subsetted DataFrame `sampleinfoCEU`.  Show the head of `sampleinfoCEU` DataFrame. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "sampleinfoCEU = sampleinfo[sampleinfo.ethnicity == \"CEU\"]\n",
      "sampleinfoCEU.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>ethnicity</th>\n",
        "      <th>date</th>\n",
        "      <th>filename</th>\n",
        "      <th>sex</th>\n",
        "      <th>month</th>\n",
        "      <th>year</th>\n",
        "      <th>elapsedInDays</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2003-02-04</td>\n",
        "      <td> GSM25349.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td>  2</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2003-02-04</td>\n",
        "      <td> GSM25350.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td>  2</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 96</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2002-12-17</td>\n",
        "      <td> GSM25356.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td> 12</td>\n",
        "      <td> 2002</td>\n",
        "      <td> 47</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2003-01-30</td>\n",
        "      <td> GSM25357.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td>  1</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 91</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> CEU</td>\n",
        "      <td>2003-01-03</td>\n",
        "      <td> GSM25358.CEL.gz</td>\n",
        "      <td> M</td>\n",
        "      <td>  1</td>\n",
        "      <td> 2003</td>\n",
        "      <td> 64</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 72,
       "text": [
        "  ethnicity       date         filename sex  month  year  elapsedInDays\n",
        "0       CEU 2003-02-04  GSM25349.CEL.gz   M      2  2003             96\n",
        "1       CEU 2003-02-04  GSM25350.CEL.gz   M      2  2003             96\n",
        "2       CEU 2002-12-17  GSM25356.CEL.gz   M     12  2002             47\n",
        "3       CEU 2003-01-30  GSM25357.CEL.gz   M      1  2003             91\n",
        "4       CEU 2003-01-03  GSM25358.CEL.gz   M      1  2003             64"
       ]
      }
     ],
     "prompt_number": 72
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Next, subset the `exprs` DataFrame to only include the samples with the CEU ethnicity. Name this new subsetted DataFrame `exprsCEU`. Show the head of the `exprsCEU` DataFrame. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "exprsCEU = exprs[sampleinfoCEU.filename]\n",
      "exprsCEU.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>GSM25349.CEL.gz</th>\n",
        "      <th>GSM25350.CEL.gz</th>\n",
        "      <th>GSM25356.CEL.gz</th>\n",
        "      <th>GSM25357.CEL.gz</th>\n",
        "      <th>GSM25358.CEL.gz</th>\n",
        "      <th>GSM25359.CEL.gz</th>\n",
        "      <th>GSM25360.CEL.gz</th>\n",
        "      <th>GSM25361.CEL.gz</th>\n",
        "      <th>GSM25377.CEL.gz</th>\n",
        "      <th>GSM25378.CEL.gz</th>\n",
        "      <th>...</th>\n",
        "      <th>GSM48658.CEL.gz</th>\n",
        "      <th>GSM48660.CEL.gz</th>\n",
        "      <th>GSM48661.CEL.gz</th>\n",
        "      <th>GSM48662.CEL.gz</th>\n",
        "      <th>GSM48663.CEL.gz</th>\n",
        "      <th>GSM48664.CEL.gz</th>\n",
        "      <th>GSM48665.CEL.gz</th>\n",
        "      <th>GSM136725.CEL.gz</th>\n",
        "      <th>GSM136726.CEL.gz</th>\n",
        "      <th>GSM136727.CEL.gz</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1007_s_at</th>\n",
        "      <td> 6.627014</td>\n",
        "      <td> 6.249807</td>\n",
        "      <td> 5.934128</td>\n",
        "      <td> 6.145268</td>\n",
        "      <td> 6.091270</td>\n",
        "      <td> 6.041186</td>\n",
        "      <td> 6.050375</td>\n",
        "      <td> 6.063847</td>\n",
        "      <td> 6.226106</td>\n",
        "      <td> 6.358282</td>\n",
        "      <td>...</td>\n",
        "      <td> 6.501510</td>\n",
        "      <td> 6.558100</td>\n",
        "      <td> 6.618286</td>\n",
        "      <td> 6.869995</td>\n",
        "      <td> 5.981000</td>\n",
        "      <td> 6.403285</td>\n",
        "      <td> 6.248702</td>\n",
        "      <td> 6.488579</td>\n",
        "      <td> 6.809481</td>\n",
        "      <td> 6.133068</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1053_at</th>\n",
        "      <td> 6.939184</td>\n",
        "      <td> 6.818038</td>\n",
        "      <td> 7.047962</td>\n",
        "      <td> 7.422477</td>\n",
        "      <td> 7.272361</td>\n",
        "      <td> 7.128216</td>\n",
        "      <td> 6.750719</td>\n",
        "      <td> 6.836287</td>\n",
        "      <td> 6.738022</td>\n",
        "      <td> 7.367895</td>\n",
        "      <td>...</td>\n",
        "      <td> 7.066686</td>\n",
        "      <td> 7.386702</td>\n",
        "      <td> 6.407958</td>\n",
        "      <td> 7.089180</td>\n",
        "      <td> 7.120923</td>\n",
        "      <td> 7.018998</td>\n",
        "      <td> 7.155419</td>\n",
        "      <td> 7.517410</td>\n",
        "      <td> 7.200596</td>\n",
        "      <td> 7.280781</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117_at</th>\n",
        "      <td> 5.113570</td>\n",
        "      <td> 5.074096</td>\n",
        "      <td> 5.371201</td>\n",
        "      <td> 5.266550</td>\n",
        "      <td> 5.342047</td>\n",
        "      <td> 5.063876</td>\n",
        "      <td> 5.315898</td>\n",
        "      <td> 5.483652</td>\n",
        "      <td> 6.689444</td>\n",
        "      <td> 6.482782</td>\n",
        "      <td>...</td>\n",
        "      <td> 5.600514</td>\n",
        "      <td> 5.232676</td>\n",
        "      <td> 5.630720</td>\n",
        "      <td> 4.944748</td>\n",
        "      <td> 5.275577</td>\n",
        "      <td> 5.770358</td>\n",
        "      <td> 5.616842</td>\n",
        "      <td> 5.247190</td>\n",
        "      <td> 5.237239</td>\n",
        "      <td> 5.401876</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>121_at</th>\n",
        "      <td> 7.833862</td>\n",
        "      <td> 7.780682</td>\n",
        "      <td> 7.458197</td>\n",
        "      <td> 7.655948</td>\n",
        "      <td> 7.546555</td>\n",
        "      <td> 7.072670</td>\n",
        "      <td> 7.092984</td>\n",
        "      <td> 6.954225</td>\n",
        "      <td> 7.489785</td>\n",
        "      <td> 7.388539</td>\n",
        "      <td>...</td>\n",
        "      <td> 7.437535</td>\n",
        "      <td> 7.714650</td>\n",
        "      <td> 7.416252</td>\n",
        "      <td> 7.746448</td>\n",
        "      <td> 8.001434</td>\n",
        "      <td> 7.626723</td>\n",
        "      <td> 7.452299</td>\n",
        "      <td> 7.331864</td>\n",
        "      <td> 7.357102</td>\n",
        "      <td> 7.607461</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1255_g_at</th>\n",
        "      <td> 3.152269</td>\n",
        "      <td> 3.111747</td>\n",
        "      <td> 3.018932</td>\n",
        "      <td> 3.154545</td>\n",
        "      <td> 3.107954</td>\n",
        "      <td> 3.224284</td>\n",
        "      <td> 3.114241</td>\n",
        "      <td> 3.044975</td>\n",
        "      <td> 3.304038</td>\n",
        "      <td> 2.887919</td>\n",
        "      <td>...</td>\n",
        "      <td> 3.009983</td>\n",
        "      <td> 3.151203</td>\n",
        "      <td> 3.199709</td>\n",
        "      <td> 3.159496</td>\n",
        "      <td> 3.149710</td>\n",
        "      <td> 3.242780</td>\n",
        "      <td> 3.433125</td>\n",
        "      <td> 3.175846</td>\n",
        "      <td> 3.105092</td>\n",
        "      <td> 3.225123</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 102 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 73,
       "text": [
        "           GSM25349.CEL.gz  GSM25350.CEL.gz  GSM25356.CEL.gz  GSM25357.CEL.gz  \\\n",
        "1007_s_at         6.627014         6.249807         5.934128         6.145268   \n",
        "1053_at           6.939184         6.818038         7.047962         7.422477   \n",
        "117_at            5.113570         5.074096         5.371201         5.266550   \n",
        "121_at            7.833862         7.780682         7.458197         7.655948   \n",
        "1255_g_at         3.152269         3.111747         3.018932         3.154545   \n",
        "\n",
        "           GSM25358.CEL.gz  GSM25359.CEL.gz  GSM25360.CEL.gz  GSM25361.CEL.gz  \\\n",
        "1007_s_at         6.091270         6.041186         6.050375         6.063847   \n",
        "1053_at           7.272361         7.128216         6.750719         6.836287   \n",
        "117_at            5.342047         5.063876         5.315898         5.483652   \n",
        "121_at            7.546555         7.072670         7.092984         6.954225   \n",
        "1255_g_at         3.107954         3.224284         3.114241         3.044975   \n",
        "\n",
        "           GSM25377.CEL.gz  GSM25378.CEL.gz       ...         GSM48658.CEL.gz  \\\n",
        "1007_s_at         6.226106         6.358282       ...                6.501510   \n",
        "1053_at           6.738022         7.367895       ...                7.066686   \n",
        "117_at            6.689444         6.482782       ...                5.600514   \n",
        "121_at            7.489785         7.388539       ...                7.437535   \n",
        "1255_g_at         3.304038         2.887919       ...                3.009983   \n",
        "\n",
        "           GSM48660.CEL.gz  GSM48661.CEL.gz  GSM48662.CEL.gz  GSM48663.CEL.gz  \\\n",
        "1007_s_at         6.558100         6.618286         6.869995         5.981000   \n",
        "1053_at           7.386702         6.407958         7.089180         7.120923   \n",
        "117_at            5.232676         5.630720         4.944748         5.275577   \n",
        "121_at            7.714650         7.416252         7.746448         8.001434   \n",
        "1255_g_at         3.151203         3.199709         3.159496         3.149710   \n",
        "\n",
        "           GSM48664.CEL.gz  GSM48665.CEL.gz  GSM136725.CEL.gz  \\\n",
        "1007_s_at         6.403285         6.248702          6.488579   \n",
        "1053_at           7.018998         7.155419          7.517410   \n",
        "117_at            5.770358         5.616842          5.247190   \n",
        "121_at            7.626723         7.452299          7.331864   \n",
        "1255_g_at         3.242780         3.433125          3.175846   \n",
        "\n",
        "           GSM136726.CEL.gz  GSM136727.CEL.gz  \n",
        "1007_s_at          6.809481          6.133068  \n",
        "1053_at            7.200596          7.280781  \n",
        "117_at             5.237239          5.401876  \n",
        "121_at             7.357102          7.607461  \n",
        "1255_g_at          3.105092          3.225123  \n",
        "\n",
        "[5 rows x 102 columns]"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Check to make sure the order of the columns in the `exprsCEU` DataFrame matches the rows in the `sampleinfoCEU` DataFrame.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "(exprsCEU.columns == sampleinfoCEU.filename).all()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 74,
       "text": [
        "True"
       ]
      }
     ],
     "prompt_number": 74
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Compute the average gene expression intensity in the `exprsCEU` DataFrame across all the samples. For each sample in the `exprsCEU` DataFrame, subtract the average gene expression intensity from each of the samples. Show the head of the mean normalized gene expression data.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "data = exprsCEU.apply(lambda x: x - exprsCEU.mean(axis=1), axis = 0)\n",
      "data.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>GSM25349.CEL.gz</th>\n",
        "      <th>GSM25350.CEL.gz</th>\n",
        "      <th>GSM25356.CEL.gz</th>\n",
        "      <th>GSM25357.CEL.gz</th>\n",
        "      <th>GSM25358.CEL.gz</th>\n",
        "      <th>GSM25359.CEL.gz</th>\n",
        "      <th>GSM25360.CEL.gz</th>\n",
        "      <th>GSM25361.CEL.gz</th>\n",
        "      <th>GSM25377.CEL.gz</th>\n",
        "      <th>GSM25378.CEL.gz</th>\n",
        "      <th>...</th>\n",
        "      <th>GSM48658.CEL.gz</th>\n",
        "      <th>GSM48660.CEL.gz</th>\n",
        "      <th>GSM48661.CEL.gz</th>\n",
        "      <th>GSM48662.CEL.gz</th>\n",
        "      <th>GSM48663.CEL.gz</th>\n",
        "      <th>GSM48664.CEL.gz</th>\n",
        "      <th>GSM48665.CEL.gz</th>\n",
        "      <th>GSM136725.CEL.gz</th>\n",
        "      <th>GSM136726.CEL.gz</th>\n",
        "      <th>GSM136727.CEL.gz</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1007_s_at</th>\n",
        "      <td> 0.365059</td>\n",
        "      <td>-0.012149</td>\n",
        "      <td>-0.327827</td>\n",
        "      <td>-0.116687</td>\n",
        "      <td>-0.170685</td>\n",
        "      <td>-0.220769</td>\n",
        "      <td>-0.211580</td>\n",
        "      <td>-0.198109</td>\n",
        "      <td>-0.035849</td>\n",
        "      <td> 0.096327</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.239554</td>\n",
        "      <td> 0.296144</td>\n",
        "      <td> 0.356331</td>\n",
        "      <td> 0.608040</td>\n",
        "      <td>-0.280956</td>\n",
        "      <td> 0.141330</td>\n",
        "      <td>-0.013254</td>\n",
        "      <td> 0.226624</td>\n",
        "      <td> 0.547526</td>\n",
        "      <td>-0.128887</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1053_at</th>\n",
        "      <td>-0.136032</td>\n",
        "      <td>-0.257178</td>\n",
        "      <td>-0.027254</td>\n",
        "      <td> 0.347260</td>\n",
        "      <td> 0.197144</td>\n",
        "      <td> 0.053000</td>\n",
        "      <td>-0.324497</td>\n",
        "      <td>-0.238930</td>\n",
        "      <td>-0.337195</td>\n",
        "      <td> 0.292678</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.008531</td>\n",
        "      <td> 0.311485</td>\n",
        "      <td>-0.667259</td>\n",
        "      <td> 0.013964</td>\n",
        "      <td> 0.045706</td>\n",
        "      <td>-0.056219</td>\n",
        "      <td> 0.080203</td>\n",
        "      <td> 0.442193</td>\n",
        "      <td> 0.125379</td>\n",
        "      <td> 0.205564</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>117_at</th>\n",
        "      <td>-0.490556</td>\n",
        "      <td>-0.530031</td>\n",
        "      <td>-0.232926</td>\n",
        "      <td>-0.337577</td>\n",
        "      <td>-0.262080</td>\n",
        "      <td>-0.540250</td>\n",
        "      <td>-0.288228</td>\n",
        "      <td>-0.120475</td>\n",
        "      <td> 1.085317</td>\n",
        "      <td> 0.878655</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.003613</td>\n",
        "      <td>-0.371451</td>\n",
        "      <td> 0.026594</td>\n",
        "      <td>-0.659379</td>\n",
        "      <td>-0.328549</td>\n",
        "      <td> 0.166231</td>\n",
        "      <td> 0.012716</td>\n",
        "      <td>-0.356936</td>\n",
        "      <td>-0.366887</td>\n",
        "      <td>-0.202251</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>121_at</th>\n",
        "      <td> 0.418026</td>\n",
        "      <td> 0.364847</td>\n",
        "      <td> 0.042362</td>\n",
        "      <td> 0.240113</td>\n",
        "      <td> 0.130720</td>\n",
        "      <td>-0.343165</td>\n",
        "      <td>-0.322852</td>\n",
        "      <td>-0.461611</td>\n",
        "      <td> 0.073949</td>\n",
        "      <td>-0.027296</td>\n",
        "      <td>...</td>\n",
        "      <td> 0.021700</td>\n",
        "      <td> 0.298814</td>\n",
        "      <td> 0.000417</td>\n",
        "      <td> 0.330612</td>\n",
        "      <td> 0.585598</td>\n",
        "      <td> 0.210887</td>\n",
        "      <td> 0.036463</td>\n",
        "      <td>-0.083972</td>\n",
        "      <td>-0.058733</td>\n",
        "      <td> 0.191626</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1255_g_at</th>\n",
        "      <td> 0.018335</td>\n",
        "      <td>-0.022187</td>\n",
        "      <td>-0.115002</td>\n",
        "      <td> 0.020611</td>\n",
        "      <td>-0.025980</td>\n",
        "      <td> 0.090351</td>\n",
        "      <td>-0.019693</td>\n",
        "      <td>-0.088959</td>\n",
        "      <td> 0.170104</td>\n",
        "      <td>-0.246015</td>\n",
        "      <td>...</td>\n",
        "      <td>-0.123951</td>\n",
        "      <td> 0.017269</td>\n",
        "      <td> 0.065775</td>\n",
        "      <td> 0.025562</td>\n",
        "      <td> 0.015776</td>\n",
        "      <td> 0.108846</td>\n",
        "      <td> 0.299192</td>\n",
        "      <td> 0.041912</td>\n",
        "      <td>-0.028842</td>\n",
        "      <td> 0.091189</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 102 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 75,
       "text": [
        "           GSM25349.CEL.gz  GSM25350.CEL.gz  GSM25356.CEL.gz  GSM25357.CEL.gz  \\\n",
        "1007_s_at         0.365059        -0.012149        -0.327827        -0.116687   \n",
        "1053_at          -0.136032        -0.257178        -0.027254         0.347260   \n",
        "117_at           -0.490556        -0.530031        -0.232926        -0.337577   \n",
        "121_at            0.418026         0.364847         0.042362         0.240113   \n",
        "1255_g_at         0.018335        -0.022187        -0.115002         0.020611   \n",
        "\n",
        "           GSM25358.CEL.gz  GSM25359.CEL.gz  GSM25360.CEL.gz  GSM25361.CEL.gz  \\\n",
        "1007_s_at        -0.170685        -0.220769        -0.211580        -0.198109   \n",
        "1053_at           0.197144         0.053000        -0.324497        -0.238930   \n",
        "117_at           -0.262080        -0.540250        -0.288228        -0.120475   \n",
        "121_at            0.130720        -0.343165        -0.322852        -0.461611   \n",
        "1255_g_at        -0.025980         0.090351        -0.019693        -0.088959   \n",
        "\n",
        "           GSM25377.CEL.gz  GSM25378.CEL.gz       ...         GSM48658.CEL.gz  \\\n",
        "1007_s_at        -0.035849         0.096327       ...                0.239554   \n",
        "1053_at          -0.337195         0.292678       ...               -0.008531   \n",
        "117_at            1.085317         0.878655       ...               -0.003613   \n",
        "121_at            0.073949        -0.027296       ...                0.021700   \n",
        "1255_g_at         0.170104        -0.246015       ...               -0.123951   \n",
        "\n",
        "           GSM48660.CEL.gz  GSM48661.CEL.gz  GSM48662.CEL.gz  GSM48663.CEL.gz  \\\n",
        "1007_s_at         0.296144         0.356331         0.608040        -0.280956   \n",
        "1053_at           0.311485        -0.667259         0.013964         0.045706   \n",
        "117_at           -0.371451         0.026594        -0.659379        -0.328549   \n",
        "121_at            0.298814         0.000417         0.330612         0.585598   \n",
        "1255_g_at         0.017269         0.065775         0.025562         0.015776   \n",
        "\n",
        "           GSM48664.CEL.gz  GSM48665.CEL.gz  GSM136725.CEL.gz  \\\n",
        "1007_s_at         0.141330        -0.013254          0.226624   \n",
        "1053_at          -0.056219         0.080203          0.442193   \n",
        "117_at            0.166231         0.012716         -0.356936   \n",
        "121_at            0.210887         0.036463         -0.083972   \n",
        "1255_g_at         0.108846         0.299192          0.041912   \n",
        "\n",
        "           GSM136726.CEL.gz  GSM136727.CEL.gz  \n",
        "1007_s_at          0.547526         -0.128887  \n",
        "1053_at            0.125379          0.205564  \n",
        "117_at            -0.366887         -0.202251  \n",
        "121_at            -0.058733          0.191626  \n",
        "1255_g_at         -0.028842          0.091189  \n",
        "\n",
        "[5 rows x 102 columns]"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using this mean normalized gene expression data, compute the projection to the first Principal Component (PC1).  \n",
      "\n",
      "**Hint**: Use the `numpy.linalg.svd()` function in the `numpy.linalg` module (or the `scipy.linalg.svd()` function in the `scipy.linalg` module) to apply an [singular value decomposition](http://en.wikipedia.org/wiki/Singular_value_decomposition) to a matrix.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "U,s,Vh = lin.svd(data.values)\n",
      "V = Vh.T"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a histogram using the values from PC1.  Use a bin size of 25.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "plt.hist(V[:,0], bins = 25)\n",
      "plt.xlabel('PC1')\n",
      "plt.ylabel('Frequency')\n",
      "plt.title('Distribution of the values from PC1')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 77,
       "text": [
        "<matplotlib.text.Text at 0x108362a10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x1077be990>"
       ]
      }
     ],
     "prompt_number": 77
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a scatter plot with the days since October 31, 2002 on the x-axis and PC1 on the y-axis."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "plt.scatter(sampleinfoCEU.elapsedInDays, V[:,0])\n",
      "plt.xlabel('Date sample was processed (Number of days since Oct 31, 2012)')\n",
      "plt.ylabel('PC1')\n",
      "plt.title('Relationship between the PC1 and the date the samples were processed')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 78,
       "text": [
        "<matplotlib.text.Text at 0x10f007f10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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SQGlpgfXWi+c1NbDRRqVNjyRJg0059mGsAW4GjgPuBR4AbgPWBHJHY2sHPgKm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\nA6OB/ZPlVYnf4DHA3USp2eosev7JNifr+WrA7CQ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       "text": [
        "<matplotlib.figure.Figure at 0x10dec4e10>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Around what day do you notice a difference in the way the samples were processed?"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "plt.scatter(sampleinfoCEU.elapsedInDays, V[:,0])\n",
      "plt.xlim(0,160)\n",
      "plt.xlabel('Date sample was processed (Number of days since Oct 31, 2012)')\n",
      "plt.ylabel('PC1')\n",
      "plt.title('Relationship between the PC1 and the date the samples were processed')\n",
      "plt.axvline(x=100, color='r')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 79,
       "text": [
        "<matplotlib.lines.Line2D at 0x10a211110>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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rrfi/xhqe9DQw2QEKl10Wq21ss01lrWjS3h5/o7ykLT6nO5GKwnnsemdgJ0ml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\nsU0iAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10f1d2d10>"
       ]
      }
     ],
     "prompt_number": 79
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: There is a difference around day 100. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Discussion for Problem 1\n",
      "\n",
      "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
      "\n",
      "Using exploratory data analysis and the SVD of the gene expression data matrix, we see the date the samples were processed does have a large effect on the variability seen in the data. This can been seen in the scatter plots in Problem 1(d) which shows a difference around day 100.  \n",
      "\n",
      "---\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Problem 2: Is there a pollster bias in presidential election polls?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(a)\n",
      "\n",
      "The [HuffPost Pollster](http://elections.huffingtonpost.com/pollster) contains many political polls. You can access these polls from individual races as a CSV but you can also access polls through the [HuffPost Pollster API](http://elections.huffingtonpost.com/pollster/api) to access the data.  \n",
      "\n",
      "Read in the polls from the [2012 Presidential Election: Barack Obama vs Mitt Romney](http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama) into a pandas DataFrame called `election`. For this problem, you may read in the polls for this race directly using [the CSV file](http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv) available from the HuffPost Pollster page."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "url = \"http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv\"\n",
      "source = requests.get(url).text\n",
      "s = StringIO(source)\n",
      "election = pd.DataFrame.from_csv(s, index_col=None).convert_objects(\n",
      "            convert_dates=\"coerce\", convert_numeric=True) # Access polls as a CSV file"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Show the head of the `election` DataFrame. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "election.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Pollster</th>\n",
        "      <th>Start Date</th>\n",
        "      <th>End Date</th>\n",
        "      <th>Entry Date/Time (ET)</th>\n",
        "      <th>Number of Observations</th>\n",
        "      <th>Population</th>\n",
        "      <th>Mode</th>\n",
        "      <th>Obama</th>\n",
        "      <th>Romney</th>\n",
        "      <th>Undecided</th>\n",
        "      <th>Pollster URL</th>\n",
        "      <th>Source URL</th>\n",
        "      <th>Partisan</th>\n",
        "      <th>Affiliation</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Politico/GWU/Battleground</td>\n",
        "      <td>2012-11-04</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 08:40:26</td>\n",
        "      <td> 1000</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>      Live Phone</td>\n",
        "      <td> 47</td>\n",
        "      <td> 47</td>\n",
        "      <td>  6</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>                UPI/CVOTER</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-05 18:30:15</td>\n",
        "      <td> 3000</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>      Live Phone</td>\n",
        "      <td> 49</td>\n",
        "      <td> 48</td>\n",
        "      <td>NaN</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>          Gravis Marketing</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 09:22:02</td>\n",
        "      <td>  872</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td> Automated Phone</td>\n",
        "      <td> 48</td>\n",
        "      <td> 48</td>\n",
        "      <td>  4</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>      JZ Analytics/Newsmax</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 07:38:41</td>\n",
        "      <td> 1041</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>        Internet</td>\n",
        "      <td> 47</td>\n",
        "      <td> 47</td>\n",
        "      <td>  6</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>     Sponsor</td>\n",
        "      <td>  Rep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>                 Rasmussen</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 08:47:50</td>\n",
        "      <td> 1500</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td> Automated Phone</td>\n",
        "      <td> 48</td>\n",
        "      <td> 49</td>\n",
        "      <td>NaN</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 81,
       "text": [
        "                    Pollster Start Date   End Date Entry Date/Time (ET)  \\\n",
        "0  Politico/GWU/Battleground 2012-11-04 2012-11-05  2012-11-06 08:40:26   \n",
        "1                 UPI/CVOTER 2012-11-03 2012-11-05  2012-11-05 18:30:15   \n",
        "2           Gravis Marketing 2012-11-03 2012-11-05  2012-11-06 09:22:02   \n",
        "3       JZ Analytics/Newsmax 2012-11-03 2012-11-05  2012-11-06 07:38:41   \n",
        "4                  Rasmussen 2012-11-03 2012-11-05  2012-11-06 08:47:50   \n",
        "\n",
        "   Number of Observations     Population             Mode  Obama  Romney  \\\n",
        "0                    1000  Likely Voters       Live Phone     47      47   \n",
        "1                    3000  Likely Voters       Live Phone     49      48   \n",
        "2                     872  Likely Voters  Automated Phone     48      48   \n",
        "3                    1041  Likely Voters         Internet     47      47   \n",
        "4                    1500  Likely Voters  Automated Phone     48      49   \n",
        "\n",
        "   Undecided                                       Pollster URL  Source URL  \\\n",
        "0          6  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "1        NaN  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "2          4  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "3          6  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "4        NaN  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "\n",
        "      Partisan Affiliation  \n",
        "0  Nonpartisan        None  \n",
        "1  Nonpartisan        None  \n",
        "2  Nonpartisan        None  \n",
        "3      Sponsor         Rep  \n",
        "4  Nonpartisan        None  "
       ]
      }
     ],
     "prompt_number": 81
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "How many polls were conducted in November? Define this number as M.  \n",
      "\n",
      "**Hint**: Subset the `election` DataFrame for only dates in the `Start Date` column that are in November 2012.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "filtered = election[map(lambda x: (x.month == 11) and (x.year ==2012), election[\"Start Date\"])]\n",
      "filtered.drop_duplicates('Pollster', inplace = True) # Removes duplicate pollsters\n",
      "M = len(filtered)\n",
      "print \"Number of polls in November: %i\" % M "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Number of polls in November: 18\n"
       ]
      }
     ],
     "prompt_number": 82
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: There were 18 polls with a Start Date in November after accounting for multiple polls within a given pollster (or 19 polls total with a Start Date in November). "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What was the median of the number of observations in the November polls? Define this quantity as N. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "N = np.median(filtered[\"Number of Observations\"])\n",
      "print N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1200.0\n"
       ]
      }
     ],
     "prompt_number": 83
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: The median number of observations in the November polls was 1200. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(b)\n",
      "\n",
      "Using the median sample size $N$ from Problem 2(a), simulate the results from a single poll:  simulate the number of votes for Obama out of a sample size $N$ where $p$ = 0.53 is the percent of voters who are voting for Obama.  \n",
      "\n",
      "**Hint**: Use the binomial distribution with parameters $N$ and $p$ = 0.53. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 84
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To simulate the *number* of votes for Obama from a single poll, we can use the `random.binomial` function in `numpy` to simulate one binomial random variable with a sample size $N$ and $p$ which is the percent of voters who are voting for Obama. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = 0.53\n",
      "\"Simulated number of votes for Obama: %i\" % np.random.binomial(N, p, size=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 85,
       "text": [
        "'Simulated number of votes for Obama: 645'"
       ]
      }
     ],
     "prompt_number": 85
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, perform a Monte Carlo simulation to obtain the estimated percentage of Obama votes with a sample size $N$ where $N$ is the median sample size calculated in Problem 2(a). Let $p$=0.53 be the percent of voters are voting for Obama. \n",
      "\n",
      "**Hint**: You will repeat the simulation above 1,000 times and plot the distribution of the estimated *percent* of Obama votes from a single poll.  The results from the single poll you simulate is random variable and will be different every time you sample. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To simulate the the estimate *percentage* of Obama votes, we can repeat the above simulation using a Bernoulli distribution, but just divide by the total number of votes. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = 0.53\n",
      "B = 1000\n",
      "obs = np.random.binomial(N, p, size = B) / N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 87
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We can also write this in terms of a Bernoulli random variable $X$ where $X$ has a binary outcome with probability $p$ of success where \n",
      "$$E(X) = p$$ \n",
      "and \n",
      "$$Var(X) = p(1-p)$$\n",
      "\n",
      "In a single simulation, we simulate N binary outcomes of 1 or 0 representing a vote for Obama or not where N represents the poll sample size.  The average of the binary outcomes represents the *percent* of Obama votes from a single poll.  We repeat this process 1000 times. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = 0.53\n",
      "B = 1000\n",
      "obs = map(lambda x: np.mean(np.random.binomial(1, p, size = N)), xrange(B))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 88
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot the distribution of the estimated percentage of Obama votes from your single poll. What is the distribution of the estimated percentage of Obama votes? "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "plt.hist(obs)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 89,
       "text": [
        "(array([   1.,   21.,   39.,  100.,  216.,  213.,  235.,  115.,   50.,   10.]),\n",
        " array([ 0.48166667,  0.49041667,  0.49916667,  0.50791667,  0.51666667,\n",
        "         0.52541667,  0.53416667,  0.54291667,  0.55166667,  0.56041667,\n",
        "         0.56916667]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x10a241f90>"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At first glance the distribution looks normally distributed. We can use a qqplot to visually inspect if this distribution is normally distributed. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import scipy.stats as stats\n",
      "stats.probplot((obs - np.mean(obs)) / np.std(obs, ddof=1), dist=\"norm\", plot = plt)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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g82TH7r57kU8PWV/Ad1xFX6KiHjzNtsZZVFfqKRMREQmmggLvVZV5eRAfD04njBgBMTFl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\nREREREREREREREREREQax/8D6LJ6g1cCakwAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a227150>"
       ]
      }
     ],
     "prompt_number": 90
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: From the histogram and qqplot, we see the distribution looks normally distributed. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is the standard error (SE) of the estimated percentage from the poll. \n",
      "\n",
      "**Hint**: Remember the SE is the standard deviation (SD) of the distribution of a random variable. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In this example, the standard error (SE) is the standard deviation of the distribution of the percent of Obama votes from a single poll.  Therefore, we can compute the standard deviation as"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "np.std(obs, ddof=1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 91,
       "text": [
        "0.014217259754234046"
       ]
      }
     ],
     "prompt_number": 91
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "From the lecture we saw if we observe $N$ Bernoulli random variables $(X_1, \\ldots X_N)$, then \n",
      "$$ \\mbox{E}(\\bar{X}) = \\frac{1}{N} \\sum_{i=1}^N p = p$$\n",
      "and \n",
      "$$\\mbox{Var}(\\bar{X})= \\frac{1}{N^2} \\sum_{i=1}^N p(1-p) = \\frac{p(1-p)}{N}$$\n",
      "\n",
      "In our example, we assume each $X_i$ is a Bernoulli distribution with $p$ = 0.53. Therefore, if $N$ = 1200, we can analytically calculate the standard deviation of $\\bar{X}$ directly and compare to the standard error above"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sqrt((0.53 * 0.47) / 1200)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 92,
       "text": [
        "0.014407752542757436"
       ]
      }
     ],
     "prompt_number": 92
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(c)\n",
      "\n",
      "Now suppose we run M polls where M is the number of polls that happened in November (calculated in Problem 2(a)). Run 1,000 simulations and compute the mean of the M polls for each simulation. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First, let's recall what M and N were in Problem 2(a): "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"Number of polls in November: %i\" % M"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 93,
       "text": [
        "'Number of polls in November: 18'"
       ]
      }
     ],
     "prompt_number": 93
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"Median size of polls in November: %i\" % N"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 94,
       "text": [
        "'Median size of polls in November: 1200'"
       ]
      }
     ],
     "prompt_number": 94
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Within one iteration of the simulation, we want to simulate M polls each measuring the *percent* of Obama votes out of a sample size of N. We can use again the Bernoulli distribution with parameter $p$. We simulate the *percentage* of Obama votes from M polls and compute the mean across the M polls using `np.mean`.  \n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Represents the percentage of Obama votes from M polls\n",
      "def simulatePolls(p, N, M):\n",
      "    \"\"\" Function to simulate the results \n",
      "    of M polls each measuring the percent\n",
      "    of Obama votes out of a sample size of N\n",
      "    with probability p of voting for Obama\n",
      "    \n",
      "    M = Number of polls to simulate\n",
      "    N = Sample size of each poll\n",
      "    p = Probability of voting for Obama \"\"\"\n",
      "    \n",
      "    return map(lambda x: np.mean(np.random.binomial(1, p, size = N)), xrange(M))\n",
      "\n",
      "simulatePolls(p, N, M) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 95,
       "text": [
        "[0.56166666666666665,\n",
        " 0.54583333333333328,\n",
        " 0.51083333333333336,\n",
        " 0.55666666666666664,\n",
        " 0.51833333333333331,\n",
        " 0.54000000000000004,\n",
        " 0.51333333333333331,\n",
        " 0.52000000000000002,\n",
        " 0.53166666666666662,\n",
        " 0.52749999999999997,\n",
        " 0.53333333333333333,\n",
        " 0.52916666666666667,\n",
        " 0.53166666666666662,\n",
        " 0.52666666666666662,\n",
        " 0.53916666666666668,\n",
        " 0.53166666666666662,\n",
        " 0.53166666666666662,\n",
        " 0.53416666666666668]"
       ]
      }
     ],
     "prompt_number": 95
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now, we want to repeat this simulation 1000 times. For every iteration of the simulation, we will compute the average across the 19 polls (or average of averages).   "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = 0.53\n",
      "B = 1000\n",
      "mom = map(lambda y: np.mean(simulatePolls(p, N, M)), xrange(B))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 96
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is the distribution of the average of polls?\n",
      "\n",
      "**Hint**: Show a plot. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "plt.hist(mom)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 97,
       "text": [
        "(array([  10.,   48.,  109.,  216.,  253.,  220.,   84.,   48.,   10.,    2.]),\n",
        " array([ 0.52   ,  0.52225,  0.5245 ,  0.52675,  0.529  ,  0.53125,\n",
        "         0.5335 ,  0.53575,  0.538  ,  0.54025,  0.5425 ]),\n",
        " <a list of 10 Patch objects>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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9+lj1oep51aM7PB8AwK6y05GxN1Tfr75Xfb76VnVZdUG1MdvnjupQddUOzwUAsOvsJIzt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\nAAAAAAAAAAAAAAAAAABgl/n//aLkDnzSKL8AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1077b3ed0>"
       ]
      }
     ],
     "prompt_number": 97
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Using a qqplot, we can compare this distribuiton to a normal distribution. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.probplot((mom - np.mean(mom)) / np.std(mom, ddof=1), dist=\"norm\", plot = plt)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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37KQhxeQRxKN05DHOIo9cTKkyCmgBbMe9gT86+hjLlk1QA79IDfNkp+wrzBiM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\nSZIkSZIkSZIkSZIkSZIkSZIkSaod/w9KksQe1E4ThwAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10db0eb10>"
       ]
      }
     ],
     "prompt_number": 98
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: Using a histogram and qqplot, we can see this distribution is very similar to a normal distribution. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is the standard error (SE) of the average of polls? "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "np.std(mom, ddof = 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 99,
       "text": [
        "0.003470334629167922"
       ]
      }
     ],
     "prompt_number": 99
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"The SE of the average of polls is %g\" % np.round(np.std(mom, ddof = 1), 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 100,
       "text": [
        "'The SE of the average of polls is 0.00347'"
       ]
      }
     ],
     "prompt_number": 100
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Is the SE of the average of polls larger, the same, or smaller than that the SD of a single poll (calculated in Problem 2(b))? By how much?\n",
      "\n",
      "**Hint**: Compute a ratio of the two quantities.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "ratio = np.std(mom, ddof = 1) / np.std(obs, ddof = 1) \n",
      "\n",
      "\"The ratio of the SE of the average of polls to the SD of a single poll is %g\" % ratio"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 101,
       "text": [
        "'The ratio of the SE of the average of polls to the SD of a single poll is 0.244093'"
       ]
      }
     ],
     "prompt_number": 101
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: The SE of the average of polls is smaller than the SD of a single poll by approximately factor of 4. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(d) \n",
      "\n",
      "Repeat Problem 2(c) but now record the *across poll* standard deviation in each simulation. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "B = 1000\n",
      "p = 0.53\n",
      "sds = map(lambda y: np.std(simulatePolls(p, N, M), ddof = 0), xrange(B))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 102
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "What is the distribution of the *across M polls* standard deviation?\n",
      "\n",
      "**Hint**: Show a plot. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "plt.hist(sds)\n",
      "plt.xlabel('Standard deviations across %i polls' % M)\n",
      "plt.ylabel('Frequency')\n",
      "plt.title('Histogram of standard deviations across %i polls' % M)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 103,
       "text": [
        "<matplotlib.text.Text at 0x1120a3b10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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kSSVhYidJklQS3UrsJgMrd2nbkiRJpdTpxK4POAi4FdgmN38qcAZwGHAusGmD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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112110250>"
       ]
      }
     ],
     "prompt_number": 103
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "stats.probplot((sds - np.mean(sds)) / np.std(sds, ddof=1), dist=\"norm\", plot = plt)\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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3mbIlmDEY3YHJrmvNgY9d16qTMmUiImdgypUb+DVO5rCBzq4BsP+iMffRk+00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\nkRvjX8qOCBSXO971AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x10a211ad0>"
       ]
      }
     ],
     "prompt_number": 104
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: Using the histogram and qqplot, we see the distribution is normally distributed. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(e) \n",
      "\n",
      "What is the standard deviation of M polls in our real (not simulated) 2012 presidential election data ? "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "thesd = np.std(filtered[\"Obama\"] / 100, ddof = 0)\n",
      "thesd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 105,
       "text": [
        "0.010829771494228929"
       ]
      }
     ],
     "prompt_number": 105
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Is this larger, the same, or smaller than what we expeced if polls were not biased."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "thesd / np.mean(sds)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 106,
       "text": [
        "0.78702707298570951"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: The standard deviation in our real 2012 presidential election data is smaller than what we expect if the polls were not biased. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Another way of comparing the standard deviation of the M polls in the 2012 presidential eletion data is to compare it directly to the distribuiton of `sds`. We can calculate a *p*-value or the probability of seeing values as large as `thesd` or larger. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.mean(thesd > sds)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 107,
       "text": [
        "0.10000000000000001"
       ]
      }
     ],
     "prompt_number": 107
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We see `thesd` is consistent with the null distribution of `sds`.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 2(f)\n",
      "\n",
      "**For AC209 Students**: Learn about the normal approximation for the binomial distribution and derive the results of Problem 2(b) and 2(c) analytically (using this approximation). Compare the results obtained analytically to those obtained from simulations.\n",
      "\n",
      "**Solution:** A random variable $X$ that has a [Binomial distribution](http://en.wikipedia.org/wiki/Binomial_distribution) with $N$ independent trials of a binary outcome (e.g. yes/no) each with probability of success $p$ can be [approximated by a normal distribution](http://en.wikipedia.org/wiki/Binomial_distribution#Normal_approximation) if $n$ is large enough. Another way of writing this is if $X$ has a Binomial distribution with parameters $N$ and $p$, $X \\sim Bin(N,p)$, and $N$ is \"large enough\", then $X$ can be approximated by a normal distribution with mean $Np$ and variance $Np(1-p)$, or $X \\sim Normal(Np, Np(1-p))$. \n",
      "\n",
      "In Problem 2(b), we are asking about the *percentage* of Obama votes, therefore we are actually interested in $Y_i = \\frac{X_i}{N}$ where $X_i \\sim Bin(N, p)$. In this case, $\\mbox{Var}(Y_i)  = p(1-p)$. To estimate the standard deviation of $\\bar{Y}$, \n",
      "\n",
      "$$\\mbox{Var}(\\bar{Y}) =  \\frac{1}{N^2} \\sum_{i=1}^N p(1-p) = \\frac{p(1-p)}{N}$$\n",
      "\n",
      "Thus, we can analytically compute the standard deviation of $\\bar{Y}$ using $\\sqrt{\\frac{p(1-p)}{N}}$ and compare to the standard deviation obtained from simulations. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Standard deviation from simulations in 2(b) \n",
      "print \"SD from simulations: %g\" % np.std(obs, ddof=1)\n",
      "\n",
      "# Standard deviation computed analytically\n",
      "print \"SD using normal approximation %g\" % np.sqrt(p * (1-p)/ N)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SD from simulations: 0.0142173\n",
        "SD using normal approximation 0.0144078\n"
       ]
      }
     ],
     "prompt_number": 108
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In Problem 2(c), we are asking about the *percentage* of Obama votes from averaged across M polls.  Above we saw the variance for each poll is $\\frac{p(1-p)}{N}$. Therefore we can treat the variance at the poll level as $\\sigma_{poll}^2 = \\frac{p(1-p)}{N}$. Then, when we average across M polls the variance becomes $\\sigma_{poll}^2 / M = \\frac{p(1-p)}{N \\cdot M }$\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Standard deviation from simulations in 2(c) \n",
      "print \"SD from simulations: %g\" % np.std(mom, ddof=1)\n",
      "\n",
      "# Standard deviation computed analytically\n",
      "print \"SD using normal approximation %g\" % np.sqrt((p * (1-p)/ N) / M)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "SD from simulations: 0.00347033\n",
        "SD using normal approximation 0.00339594\n"
       ]
      }
     ],
     "prompt_number": 109
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: We see the SD from the simulations match the analytical results using the normal approximation.  "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Discussion for Problem 2\n",
      "\n",
      "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
      "\n",
      "In this problem we considered all the polls from pollsters in November 2012. After removing polls that came from the same pollster in Nov 2012, we did not see any pollster bias.  The standard deviation from the 2012 Presidential Election was smaller than what we expect by chance.  \n",
      "\n",
      "---\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Problem 3: Is the average of polls better than just one poll?"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(a)\n",
      "\n",
      "Most undecided voters vote for one of the two candidates at the election. Therefore, the reported percentages underestimate the final value of both candidates. However, if we assume the undecided will split evenly, then the observed difference should be an unbiased estimate of the final difference. \n",
      "\n",
      "Add a new column to the `election` DataFrame containg the difference between Obama and Romeny called `Diff`. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "election[\"Diff\"] = (election.Obama / 100) - (election.Romney / 100)\n",
      "election.head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Pollster</th>\n",
        "      <th>Start Date</th>\n",
        "      <th>End Date</th>\n",
        "      <th>Entry Date/Time (ET)</th>\n",
        "      <th>Number of Observations</th>\n",
        "      <th>Population</th>\n",
        "      <th>Mode</th>\n",
        "      <th>Obama</th>\n",
        "      <th>Romney</th>\n",
        "      <th>Undecided</th>\n",
        "      <th>Pollster URL</th>\n",
        "      <th>Source URL</th>\n",
        "      <th>Partisan</th>\n",
        "      <th>Affiliation</th>\n",
        "      <th>Diff</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Politico/GWU/Battleground</td>\n",
        "      <td>2012-11-04</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 08:40:26</td>\n",
        "      <td> 1000</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>      Live Phone</td>\n",
        "      <td> 47</td>\n",
        "      <td> 47</td>\n",
        "      <td>  6</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>                UPI/CVOTER</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-05 18:30:15</td>\n",
        "      <td> 3000</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>      Live Phone</td>\n",
        "      <td> 49</td>\n",
        "      <td> 48</td>\n",
        "      <td>NaN</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "      <td> 0.01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>          Gravis Marketing</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 09:22:02</td>\n",
        "      <td>  872</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td> Automated Phone</td>\n",
        "      <td> 48</td>\n",
        "      <td> 48</td>\n",
        "      <td>  4</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>      JZ Analytics/Newsmax</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 07:38:41</td>\n",
        "      <td> 1041</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>        Internet</td>\n",
        "      <td> 47</td>\n",
        "      <td> 47</td>\n",
        "      <td>  6</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td>     Sponsor</td>\n",
        "      <td>  Rep</td>\n",
        "      <td> 0.00</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>                 Rasmussen</td>\n",
        "      <td>2012-11-03</td>\n",
        "      <td>2012-11-05</td>\n",
        "      <td>2012-11-06 08:47:50</td>\n",
        "      <td> 1500</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td> Automated Phone</td>\n",
        "      <td> 48</td>\n",
        "      <td> 49</td>\n",
        "      <td>NaN</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "      <td>-0.01</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 110,
       "text": [
        "                    Pollster Start Date   End Date Entry Date/Time (ET)  \\\n",
        "0  Politico/GWU/Battleground 2012-11-04 2012-11-05  2012-11-06 08:40:26   \n",
        "1                 UPI/CVOTER 2012-11-03 2012-11-05  2012-11-05 18:30:15   \n",
        "2           Gravis Marketing 2012-11-03 2012-11-05  2012-11-06 09:22:02   \n",
        "3       JZ Analytics/Newsmax 2012-11-03 2012-11-05  2012-11-06 07:38:41   \n",
        "4                  Rasmussen 2012-11-03 2012-11-05  2012-11-06 08:47:50   \n",
        "\n",
        "   Number of Observations     Population             Mode  Obama  Romney  \\\n",
        "0                    1000  Likely Voters       Live Phone     47      47   \n",
        "1                    3000  Likely Voters       Live Phone     49      48   \n",
        "2                     872  Likely Voters  Automated Phone     48      48   \n",
        "3                    1041  Likely Voters         Internet     47      47   \n",
        "4                    1500  Likely Voters  Automated Phone     48      49   \n",
        "\n",
        "   Undecided                                       Pollster URL  Source URL  \\\n",
        "0          6  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "1        NaN  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "2          4  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "3          6  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "4        NaN  http://elections.huffingtonpost.com/pollster/p...         NaN   \n",
        "\n",
        "      Partisan Affiliation  Diff  \n",
        "0  Nonpartisan        None  0.00  \n",
        "1  Nonpartisan        None  0.01  \n",
        "2  Nonpartisan        None  0.00  \n",
        "3      Sponsor         Rep  0.00  \n",
        "4  Nonpartisan        None -0.01  "
       ]
      }
     ],
     "prompt_number": 110
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(b)\n",
      "\n",
      "Make a plot of the differences for the week before the election (e.g. 5 days) where the days are on the x-axis and the differences are on the y-axis.  Add a horizontal line showing 3.9%: the difference between Obama and Romney on election day.\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "last_day = max(election[\"Start Date\"])\n",
      "filtered = election[map(lambda x: (last_day - x).days <= 5, election[\"Start Date\"]) ]\n",
      "filtered = filtered.sort(columns=[\"Start Date\"])\n",
      "days= map(lambda x: (last_day - x).days , filtered[\"Start Date\"])\n",
      "\n",
      "color_map = {}\n",
      "for i, p in enumerate(set(filtered.Pollster)):\n",
      "    color_map[p] = np.random.rand();\n",
      "\n",
      "plt.scatter(days, filtered.Diff, c = map(lambda x: color_map[x], filtered.Pollster),  s=60 )\n",
      "plt.axhline(y=0.039, c = \"gray\")\n",
      "plt.axhline(y=np.mean(filtered.Diff), c = \"red\")\n",
      "plt.xlabel(\"Days\")\n",
      "plt.ylabel(\"Difference (Obama - Romney)\")\n",
      "plt.title(\"Plot of the difference between Obama and Romney colored by different pollsters in the last week\")\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 111,
       "text": [
        "<matplotlib.text.Text at 0x1126f7410>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BCrO2N9YuHEmSJI0F1fSOv4ShCWgrsEXtwpEkSdJYUE2R7nbA0cAq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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112222290>"
       ]
      }
     ],
     "prompt_number": 111
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(c) \n",
      "\n",
      "Make a plot showing the differences by pollster where the pollsters are on the x-axis and the differences on the y-axis. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "pollster_map = {}\n",
      "\n",
      "polls = list(set(filtered.Pollster))\n",
      "\n",
      "for i, p in enumerate(polls):\n",
      "    pollster_map[p] = i\n",
      "\n",
      "plt.scatter(map(lambda x: pollster_map[x],filtered.Pollster), filtered.Diff, \\\n",
      "            c = map(lambda x: color_map[x],filtered.Pollster),s=60)\n",
      "plt.xticks(range(len(polls)), polls, rotation = 90)\n",
      "plt.xlabel(\"Pollsters\")\n",
      "plt.ylabel(\"Difference (Obama - Romney)\")\n",
      "plt.title(\"Plot of the difference between Obama and Romney by different pollsters\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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r65DrVZLM/Znoo7ZpQdkGwEYVRTa4m4mksdCWwMUFyzmglRjhmjcPeAh4ocQ2\nXwY6y71gYTIn1cysXWDaRrD8if7la+8A0zYe9mamT4Nj/xeu+cPAx47+FEwc3QBZvQZlyfERpvGt\nojFvDcAnmMFq9pmTxkxxJdPJJ59ccr1KPoXLgN2S50wA3gr8HLhxpEGWcAfwBLBPsjyP6Kd3NTFy\ndduk/AJi8uK8A+ireduPSDIhml33okytnFQ3Gprh4Ftg9l5JQQY2fCu8+0ZomjTszWQy8MYd4aIf\nwqx1o2z11eEbJ8EH3hvTlEiVmEQDRzKDTzODqUkt3Po08SNmsmFF9QGSxkol9ePNwOeIptYNiRqv\n3xN905YO8rxKbQKcSIxO3ZmYy+5uYv65bwBXJOsdSzSvdhBTmRxP1NpdTCR6FxDNrn8AFpZ5LUez\nqn7ksjE9CTnINEAuF6NYM5U3Y3WsjA93WztMnQK92fgtjVQbWZrI0E6WyWTIkGGiTazSuKr2HSAK\nrQs8X4Xt1ILJnCRJSoVyyVwldeQziDsqzKb/6NG9gX1HGZ8kSZJGoJJkLt837kF4dWKhJqo7AEKS\nJEkVqCSZmwhsX6J88xJlkiRJGgeVjGb9HqXnk1u/RJkkSZLGQSUDIP4E7MDA223NBKZWLaLx5QAI\nSZKUCtUYAPFn4DT6T8DbQAyKkCRJUg1UUjM3ibhFVrFtgfuqE864s2ZOkiSlQjVq5tYCDgfmEneA\nyHs90fwqSZKkcVZJMvdH4B7gYWJqkhzRzDr8G0dKkiSpqipJ5p4F/rtEuaNZJUmSaqSSqUkuoXQt\nXKnpSiRJkjQOKqmZWwTcCnQVlGWAWcTgCEmSJI2zSpK584FTgEeAbEH526sakSRJkoatkmTuLuC8\nEuX3VikWSZIkVaiSeeYOJqYk+WvR8w8EzqpmUOPIeeYkSVIqlJtnrpJk7n5g6xLlOaBxZGHVnMmc\nJElKhXLJXCWjWb8PTE+ek/9pBD5WhfgkSZI0ApXUzEEkcLsDs4FHiX50aWbNnCRJSoVq3M5rLnA1\nsBXwEjAReAg4BHhm1BFKkiSpYpU0s54J/IBoal0XWB34NPDFMYhLkiRJw1BJMncnkcy1FZT9HXiq\nqhFJkiRp2CpJ5kp1LpsLvKk6oUiSJKlSlfSZWwj8maihmwpsDuwFvG8M4pIkSdIwVDqa9U3EVCRz\ngMeJO0Kk+Q4QjmaVJEmpUI1Jg8t5JzHKNY1M5iRJUiqMZtLgZmBbolm12EbAd0YVmSRJkkZsqGRu\nDvAgcA8GDJczAAAgAElEQVTwMHAlfRnhPsAC4JUxi06SJEmDGiqZ+zpxl4eDgI8C84D3A8cANwBX\nEYMgJEmSVANDjWZdC3hXwfKtwN+AaURCd84YxSVJkqRhGKpmblHR8r+B+4E305fIOc+cJElSjQxV\nM7cz8GH6JgzOACuAjZOfJuC/gLeNVYCSJEkqb6ipSbLD3E4ld5KoJ05NIkmSUmGkU5OcSExN0lDm\nZxLwtWoGKkmSpOEbqmZuOrBsiHWmAcurE864s2ZOkiSlwljeASLNTOYkSVIqjOYOEJIkSapTQ41m\nrYU5wJeBe4FdgdOAB0qsdyQwi8hQm4ATkvJJwHeB9wEdwDeBH4xtyJIkSbUx0pq5Y6saRZ8M8Hvg\nCuBc4FTiLhONResdSEyZcgpwMrAF8LHksc8DNxF3prgM+D6w+xjFK0mSVFMjTeZOqmoUffYDtgJa\nk+WHgG7idmKFjgOuK1i+krgjBcDzRBL3IPA54AlM5lYZuVyOnqVL6V6yhGxXV63DUS11L4euJdDT\nPqrNZOmliza6aCM77NmYNBo9PVmWLOliyZIustlR7vPONmhfAitXVCe4KljZCUuWw9L6CUmruHpr\nZt0deBToKShbCOwLXJ4sTwB2IppS8x4BtgHWBs4v2ubzwJNjEazGV29bG8v+9jeeOessepYuZe2D\nD2a9j32MxilTah2axlNPG7Q9Bfd/A9oeh5l7wdbHQtNUaGiubFN08gR/4z/8hQYa2Zw3M4ftaWLi\n2MQu2tt7+M1vnuZnP3ucpqYMH//4prztbbOYMqXCr6OeLmhfCld/Ex5bAOttCe/+CkyfCRMmj03w\nQ8jloK0DzvkVXHsLzFwTPn8EbL0prOZpSmNopKNZnyP6q1XbucB2wG4FZT8npj85MFmeRdxm7K3A\njUnZZkTStyPwz4LnTkqWX0/0nyvmaNaU6G1r49kLL+TfRx3Vr3zGnnuy3fXX0zi5NidvjbNsL7x0\nB/xxH8h295VP3RDecR9MmD7sTXWzkts5n8e4rV/5VhzA6/kvmvGYqra2th4+/vG7+OUv+19fH3PM\nFpxyyuuYNq2CZHzpC/ClbWD5S31lEybDyQtgztZVirgyHZ2w64fgnof7yjIZuORUOGhfmOQ1gkap\n2qNZxyKRg6iR6y4qK44xX2vXXWKd4jf4caKptVQipzRpaODxE04YULz0lltYdscdNQhINdHbAX//\nv/6JHEDbk/DwWdC7ctibWsnSAYkcwL/4A9l+jQOqluefXzkgkQM4++xH6OqqoLl15Qr43Sn9EzmA\nrg649AvQMdT0qNXX2wtX3tQ/kYOorfv8d8Y9HL3G1NvUJIuAGUVlqwPPFCy/TCRyM4rWoWi9bYnE\n79oqx6ga6Hr2WXqWLi352JKbbiI32n43Sofm1eClO0s/9uJfK0rmXuSRkuU5sizmiZFEpyHcfvvL\nJct7e3Pce++S4W8o2wv/+Vvpxx69ExrHvwdRZzf8eUHpx55+HtqHf2hKFau3PnM3A8cXlW0JXFyw\nnCMGSGxeUDaPGCzxQrI8G3gzcGbBOk0w8HJ7/vz5r/7d0tJCS0vLCMLWWJswcyaZCRPIlRj0MHW7\n7cg01Nt1icZETwdM2wyWl0jEpm9RUZ+5Gcwu+9g01h1JdBrCvHnTyj62ySarDX9DmQysu1n0lSs2\na3PoHf+a1QlNsPUmpR+bMQ2mTBrfeLRqaG1tpbW1dcj16u0OEBlifrmjiMRuXvJ7U+BLwKXAfcQc\ncp8B9k6e9yvgLuAMosbuVOB7yWONwLuBs4HisUX2mUuJnuXLefT441n0g/5TBk7aeGPe+MAD9pl7\nrejtgid+DX89rH9501R498MwZc6wN9VDJ9dzCi/x737lc3g9e3MMzfjtW23t7T3ss8/N3Hnn4n7l\nBxywHpdeuhurrVZB/cIzD8JXdoDeoib3z18P27wZGopntBp7y9thswPghf5vj/mfgmMPh6kOgtAo\njdXtvJqJSXmrOe/cJsCJwJ3AzkQSdjewAPgGMQcdyWuuTvSHm07U6GXom2Ou0CXAh0q8lslcivS2\nt/P0WWfx7Lnn0rN0KWu9611sesYZNK25Jg2N43/iVo30tMHTVyejWR+DdXaHHc+A1TaGpsqS+m46\nuJtf8jh/pYFGNmFPduB9jmYdQ8uXd/PFL97LpZc+RVNThsMOm8tJJ23D1KkVNhR1tsHTD8CvvwCP\n3R2jWQ8+BebtBROnjk3wQ+jqjkTus6fBDbfDumvB0R+EjxxkzZyqoxrJ3P5EjdcaBc+bBvRCatsk\nTOZSprejA3I5Mo2NZLu6aJpWvtlGq7BsN/R2QuOE6CfXPPxRrMW6WUlDMi95jqyJ3DhYsaKH5uYM\nuVz0l6s4kcvLZqFzBTRPgp5OaJ4CdXBht2xFjFzt6YXGBpg4odYRaVVRjWTup0QN185ErdlKYr63\nB0nvIAOTOUmSlArVSOY+BvyYqI37AH2T8/6Zvr5raWMyJ0mSUqEa88y9HrgaWAdYjRhh+nNgh9GH\nJ0mSpJGoJJn7EnHT+5eA7wB/J0aOfmIM4pIkSdIwVGNqkjcS04Kkkc2skiQpFco1s1YyhGg74NPA\nXOJm93mbARuMIjZJkiSNUCXJ3FXAL4G/Afl7J2WAt1c7KEmSJA1PJc2s1wDvKFG+NtGPLo1sZpUk\nSalQjdGsPwLeUKL83SOMSZIkSaNUSTK3NnGf1GzRz4/GIC5JkiQNQyV95k4kbnD/CH195gDeU9WI\nJEmSNGyV9Jm7FHh/ifI1gcXVCWfc2WdOkiSlQjVu5/VhYD3gr0XPfysxoXAamcxJkqRUqEYydyew\nEdBe9PxZwKTRBFdDJnOSJCkVqjFp8LeAK4Di7OctIw9LkiRJo1HJaNbLGZjINQJbVy8cSZIkVaKS\nZtYdgBOANQqeN4O4vdea1Q1r3NjMKkmSUqEafeZ+DdxE3KP1bqCXmET4T8CVow+xJkzmJElSKlQj\nmfsIcBExefD+xH1aM8Q9W985+hBrwmROkiSlQjUGQLyRqIn7OrAFcBLQDOxdhfgkSZI0ApUMgPga\nsJy4+8PXif5y7wbmVz8sSZIkDUclzax5TcA04JUqx1ILNrNKkqRUKNfMWknN3LrEQIeVwMvAs8BR\n1QhOkiRJI1NJzdz1xN0eTgUeBCYQt/LqScrSyJo5SZKUCtUYALELsDnwQkHZXcApo4pMkiRJI1ZJ\nM+uNZdafVqVYJEmSVKHBauZ2AI4uWO4lJg2+g74qvgai6VWSJEk1MFifudWJ2rhrBlknB1wB3FfN\noMaRfeYkSVIqjPQOEBsDj41FQHXCZE6SJKXCSKcmeQxoBP4XWEBMGryCuDfr55LHJEmSVCND1cxN\nJJpZ9yCSuaeIBHAOsCMxmvUtQOcYxjiWrJmTJEmpMNKpSb4IPA9sALxY9NjqwDeALwMnjj5ESZIk\nVWqoZtYdgcMZmMgBLAE+DWxZ7aAkSZI0PEMlc48TU5KUkyNu6yVJkqQaGCqZmzOMbaxXjUAkSZJU\nuaGSuWeBb5ZZr4m4ldfz1Q5KkiRJwzPUaNa1gb8St+z6E/BEUr4hsC+wDNgdWDxWAVbBupRPOB3N\nKkmSUmGkkwYDrAl8HfhvYEZSthj4JfAVYGl1QuxnDjFK9l5gV+A04IES6x1J3E4sQ9QUnlDw2Nwk\n7vWBvcu8jsmcJElKhdEkc3kNwDrEoIcXk99jIUPMafcF4I/AVsRcd5vTfzDGgcBxRM0gwKXADcCP\nk+UNgc8D2wN7lXktkzlJkpQKI7kDxJrAJwqWs0Rz5QuUTuTeABww8hBftR+RwLUmyw8B3cBBResd\nB1xXsHwlcEzB8pPAy1SWsOo1pHPZMnpWrqRrxQp6u7pqHY4qke2B7uXQ2wndy2odjVYBK3uhrSd+\nL++udTShqwuWLYeVnfDKWLSBjUAul2PZshydnTmWL8/R22uFSD0YLJlbTNSQ/QzYepD11iQmDX4X\ncG0VYtodeBToKShbSPTRy5sA7AT8q6DsEWAbop+fVFZvdzfLFy3iqiOP5Dtz5vCjN76Rf150Ed3t\n7bUOTcPRvQKe/h3cuDdcvh7c+kFY+jD0dNQ6MqVUew+c/Qhsfz1scBUc9Q94uRN6s7WLafkK+N31\n0HIIzH49HH4UPPQIrGirXUzt7Tn+8Y8sBx/cznrrLectb2nnppt6aWszoau14dRabQhcCGxBJE9L\niebOGcljE4FjiZqxajgX2A7YraDs58QgjAOT5VnAIuCtwI1J2WZE0rcj8M+kbD7wZmDPMq9lM+tr\nUNeKFZy9+easeO65fuX7fv3rvOnoo5kwdWqNItOQsl3w5OVw66H9y5tWgwMXwmRnSlJllnfDl++L\nZK7QpqvBfW+DyTW4A3nHSrjiWvjQZ/qXrzYVHr4FZs8a/5gAHn64l+22a6OwISOTgRtvnMI++zTS\n0GBD2FgbSTNr3pNE0+d+RM3bU8BLRDPoJ4m+bNVK5CBq5IoruYvjzNfadZdYx6NJZWV7evjnxRcP\nSOQA/nr66TQ01uDMreHL9sC9Jw8s71kBD5wGPdauqjKZDJz/n4Hl/1kB1y4a/3gAsln46pkDy1e0\nwek/jKbX8bZsWY5TTumkuEdKLgfz53eyYsX4x6Q+Q92btdDC5GesLQL2KCpbnbgbRd7LRCI3o2gd\ngGcqebH58+e/+ndLSwstLS2VPF0p09vVxQv331/ysZVLltDd3k7TpEnjHJWGrWkKLCtzGlr2cNTc\nMWVcQ1K6vbASOss0p/5zCbxnfRjvCqepU2BhiQQT4OH/QFcN+vTlcrBwYekdtXBhlqZKsgkNW2tr\nK62trUOuV4+7/2bg+KKyLYGLC5ZzRM3g5gVl84jBEi9U8mKFyZxWfY0TJ7Lhnnty93nnDXhs2pw5\nNE8xEahr3StgrZ3g5bsGPrb2LtBoIq7KrDcJpjfDshIJUsvM8U/kIPrL7bQ93PXPgY/tvANMnjj+\nMTU2whvf2MiCBQMTup12aqSnp8STNGrFlUwnn1yiZYLhNbOOtzuIyYn3SZbnEZfaVwNfA7ZNyi8g\nBl3kHUD07StUj+9PNdTQ2MhWBx/MzNe9bsBj+516ag0iUkUaJ8OO34ZM0XXolDkw72iTOVUsC5y0\nzcDy3daOn1qYNBFO+woDarvmrAefPRJq0a13tdUynHjiRNZcs392O2kSfOtbE5k+3R5OtVSve38T\nYoTsncDOwNnA3cTo2m8AVyTrHUs0r3YA04kavfyIhr2As4hJgz9OJIPF114OgHgNymWzdLe3c8eZ\nZ/Lv665j6syZ7HrssczaYQcHP6RBTxuseBzu/zqseAxm7gnbHA/N06ChudbRKYXaeuDWl+CshbC0\nG967Phy5KUytYdvVshXwxFPwze/DY0/CHjvD8Z+BqZMjgaqFzs4cS5bk+NrXOlmwIMuWWzZw4okT\nmTUrw5Qp9ZpOrFqqNWnwh4lRpWcRk/HuRN8kvWlkMvca1r1yJb0rV0Imw6QZM4Z+gupL1zIgCw0T\noi+dNArZXIxszREjWCfWyVioV5ZEbM1NMH1araMJK1bk6OmJptdp00zixlM1krnzgfcSfdoOScre\nC7yeuK1XGpnMSZKkVBjN1CR5c4D1gMKex7cS90eVJElSDVSSzP0DKL7n0SElyiRJkjROKuneuYAY\niLAeURu3D/A++t8PVZIkSeOo0p6LGwKHJr9fBn5P/2bXtLHPnCRJSoVyfeYqqZmbCGwF5Cfj2piY\nokeSJEk1UkmfuR8Rk/Kuliw/RvSZe3O1g5IkSdLwVFIz9yIxAW9hu+RvgWuIGjtJkiSNs0pq5hbT\nP5ED2BdYp3rhSJIkqRKV1MwtJCYOvppI6vYBPgn8cAzikiRJ0jBUOpr1bcBRxL1TXwAuA34A9FY5\nrvHiaFZJkpQK1bidVzkbE4Mh0shkTpIkpUI1piaZC3wUmE1fX7sMsBOw7ejCkyRJ0khUksxdD9wH\n3J8s55LnO5JVkiSpRippZr0d2LVE+WxgUXXCGXc2s0qSpFQo18xaydQk3wD2LFG+1whjkiRJ0ihV\nUjN3P9GkWvycHNBYtYjGlzVzkiQpFaoxAOIC4Dqgs6CsATh0VJFJkiRpxEYyNcnqwBJgC+BJYGVV\nIxpf1sxJkqRUqEafud2Bx4FfJ8tPAd/GaUkkSZJqppKaubuAS4F1gc8nZRsDvwB2q3Jc48WaOUmS\nlArVqJm7haiJe6mgbCrWzEmSJNVMJclcO7B+wfI84ELgjqpGJEmSpGGrpJl1GnAq8J7keWsDNwCf\nAJ6ufmjjwmZWSZKUCuWaWStJ5j5B3AXiPmAm8ArQVY3gashkTpIkpUI1+sx9BZhETBL8PH2J3Nqj\nDU6SJEkjU0kydzSwDbARsGHyMxf43+qHJUmSpOGopJn1L8AeJcq9nZckSdIYq0Yz6w+JJtWGgp9G\n4KgqxCdJkqQRqPR2XqsR05P8i5hf7mVgUbWDGkfWzEmSpFSoRs3ce4BngbOS5fuBzwIto4xNkiRJ\nI9RUwbrHAx8BtkuWc8CZwHUFZZIkSRpHldTM/Rn4DdBWULYBMbpVkiRJNVBJMrcM2C15zgTgrcDP\ngRvHIC5JkiQNw1ADIOYSNXEvAs3A54im1g2JwQ+/B74ELB27EMeUAyAkSVIqjPR2Xv8BTgF+QiRw\nT1Y9stoymZMkSakw0tGsFxGJHMBhZdYpNZGwJEmSxsFQo1k7gK8CPcBexCTB+Ywwlzz/LcAuVYxp\nDvBl4F5gV+A04IES6x0JzEriaQJOGOZjkiRJq4yhmlmbgCOAnYE3EAlW8eM7A1tWMZ4FwBeAPwJb\nAdcAmwO9BesdCBwH7J4sXwrcAPx4iMeK2cwqSZJSoVwz61A1c+8E7gEuIJpZf1Zind1GG1yB/YgE\nrjVZfgjoBg4CLi9Y7zhifru8K4mBGD8e4jHVQM/KlQD0rlxJ06RJZLNZmqdMqXFU1ZPLtkGmCXId\nkJkINJDJTKx1WNXTtQwaJ0BvF2QaoHEyNFR+O+ZsTw+9nZ3kenponDSJbFcXzdOmjUHAtdGbHOfZ\nlStpmDQJslkaR3icL1uWZcKEDF1dORoaYPLkDI2Nld6wpz7lcjlWrOghk8nQ0AA9PTmmT2+udVhS\nqg3VZ+5MIH82erzMOndWLZqoTXuUaNbNWwjsW7A8AdiJuKVY3iPANsA6gzy2dhXj1DB1t7Xx6G9+\nwyWbbcaFa6zBT+bM4YFzzqG7vb3WoVVFLtcOK78BS2bCkjVg6eug+0Zyubahn5wG3Svgjk/CL6bD\nJTPgD/vAsoeht7OizfS0t/PSggX8fpdd+Onqq/PT1Vfnb8ceS88qchz0tLXx3G9+w18224w/rrEG\nN8+ZwxPnnEPvCN7fihVZPvnJl5k+/QlmzHiSffZ5jocf7qazM/2tCD09WRYtWsmBB97KtGmXM23a\nFRx66O0sXtxJNpv+9yfVylDJ3CnEZMEQfeZKeWf1wmEWMZ9doaXE/WDz1iSmSSmcDmVJ8nuzQR4r\n3IbGQS6X48W77+ZPhx1G2zPPANC5eDG3H3ccj152Gb3d3TWOcHRyuQ5YeXYkc7nksM0+CisOguwT\ntQ2uGrqXwy2Hw6OXQDb5X728AK7do295mHra2rhm33155f77gajF+tf553P7McfQtXx5tSMfV7lc\njmV33829hx3GyuQ47168mIePO45nL7uMbAXH+fLlWQ4//CUuuaSN/NMWLOhijz2eo7s7/clOd3eO\nPff8Ezff/AIA2WyOa655lre85c+sXNk7xLMllTNUMpcjkrmbgY8lvwt//kJ1my97iGbVwWLM19p1\nl1ind5DHVo02ihTpWraMf55+esnH7vnud19tlkqvBlj5vRLlvdBxBrnsinGPqKqyPfDklQPLu16B\nx3417M30dnXx0Lnn0tvRMeCxf//0pzQ2p7uJrWfZMh4rc5w//t3vkq3gOO/pyXHllQNr8155Jcuv\nfpX+2t4773yZxx4b+D7uvvsVnnhi1aillWphqD5zFwG3EU2XBwNX0T8paqS6NXOLGDjVyer0b+J9\nmUjWZhStAzEPXrnHnin1gvPnz3/175aWFlpaWiqLWOXlcnQ8/3zJh9qfe46Gxsr7XdWXCZB7ofRD\nuefo31sghbpeIa7nSmh7EnLZ6EM3hGxPD+2LFpV8rLezk572dhonTRpFoDWWy9FZ5jjvfO45MhUc\n56+8kqXcmKwnn+whm83R0JDe69JnnhmY0Oc9//xKttpq+jhGI9W/1tZWWltbh1xvqGQOos/aQuBp\noiau2J9LlI3UzcDxRWVbAhcXLOeIARKbF5TNIwZLPDfIYyW/dQuTOVVX48SJbPDWt/LCXXcNeGyD\n/fcn25v2ZpUV0Pxm6L5h4EPN74TM1PEPqZqmzIbJ60HHswMf2+jgYSVyAE2TJ7PRQQfx0LnnDnhs\n9a22SnciBzRMnMjab30rS0sc52vvvz+5Co7z2bObWG+9Rp59duBzDj54aqoTOYA3v3ldGhoyA/rH\nTZrUyBvesEaNopLqV3El08knn1xyvUruzVoqkQOYXcE2hnIH8ASwT7I8jxiAcTXwNWDbpPwC4F0F\nzzsAuHAYj2kcNU2ezA7HHcfqW/afuWbKrFnsevrpTEj5SMZMZhpM+T5kir6EGt8IEz9MJpPu5kNy\nOdjtvBipW2iTw2D6FsPeTCaTYdZee7HBAQf0K2+cNIk9f/QjMilvZm2cPJlNjjuOqUXH+cRZs5h3\n+uk0VXCc53I5zjtvLZqKdvlhh01liy2Gc+1d36ZObeQrX9lqQPm3v709mXTnqVJNDfXxORv4DVH7\n9lmiqbVQA7AFMYq0WjYBTiRGye6cxHA3Mf/cN4ArkvWOJZpQO4DpRI1ebhiPFXKeuTGWzWbJdnby\n70sv5bnbbmPNbbdlq49+lIaJE1PfVwogl+uMKUk6fwTZhdC0P0x4F5nM5FqHVh3dbdC1GP51DnQt\ngY0PhbV3gqbKp9zo6ejg2T//mSd++1smz5rF1p/6FM3TptG0CkxTk0uO82cvvZRXbruNadtuy/rJ\ncd5Q4XHe1pZl8eIs55yzjCVLchx66FR22mkCU6ZUcu1dv9raeli4cDkXXvgYTU0ZPvGJTVl//Sms\ntlr6k1VprI303qzfI5KnPwP7A58jas/yz2sA9k5+0shkbpxke3vpXbmSxokTaSiudlgF5HLdQBcw\niUwm7X0BS+jpBHpHlMQVyuVy9LS309DcTOOECdWJrY5ke3tjnrkqHOednVl6e1llkrhiHR0x19yk\nSavg50UaIyNN5orX3QJ4uKh8a+DBEUdWWyZzkiQpFUabzM0C3kckc1liQMHlwItViq9WTOYkSVIq\njCaZO4QYTToFeIlI5tYGVgAfBn5XrSBrwGROkiSlQrlkbqjOGFsDPyEGIcwBZhK1dGsBXyRGjm5S\nzUAlSZI0fEPVzF1ADHi4oMzjbycmDf50NYMaR9bMSZKkVBhpzdwsyidyANcRza+SJEmqgaGSuaeG\nsY2lQ68iSZKksTBUMjeciaBWvcmiJEmSUmKoPnO9xM3rBzMbmFidcMadfeYkSVIqlOszN9QU5fcD\nvyeSulKagXeMKjJJkiSN2FDJ3NFA6xDr3FidUCRJklSpSm7ntSqymVWSJKXCSKcmkSRJUh0zmZMk\nSUoxkzlJkqQUM5mTJElKMZM5SZKkFDOZkyRJSjGTOUmSpBQzmZMkSUoxkzlJkqQUM5mTJElKMZM5\nSZKkFDOZkyRJSjGTOUmSpBQzmZMkSUoxkzlJkqQUM5mTJElKMZM5SZKkFDOZkyRJSjGTOUmSpBQz\nmZMkSUoxkzlJkqQUM5mTJElKMZM5SZKkFDOZkyRJSrHXQjLXCKxV6yAkSZLGQr0mcwcBpwLHAWcD\nzWXWmwGcBXwSuADYq+jxdwP3JduTJEla5TTVOoAS3gB8G9gCyALfAk4ETiix7kXAtUQityZwP/A6\nYDGR6N0OzARyYx61JElSDdRjzdzngFYikQO4EvgfYELRepsTNW5/SJYXE7VwH0mWlwIvAsvHMFZp\n1ZbthZ42WLoQXrobejriR+nQ1Qbd7fDMXfDKY9C1AnJe20qrmnqsmdsNOKdg+RGiz9t2wIKC8t2B\nDuDpgrKFwL7AGWMco7Tqy2ah80W48e2w+J9RNnFN2OMnMGsfaJ5a2/g0uK42ePByuPYz0Jlc0264\nOxx6NUxevbaxSaqqeqyZm0XUquUtSX6vP8R6JMvF60kaid52uOngvkQOoHMx3HwwZFfWLi4Nz4rn\n4Moj+hI5gCdvg98eDp3LahaWpOqrx2SuB+guWM7HmBlivfy6xetJGomeFfDi7QPLs93w75/YXFfP\nejphwXml/0cLr4FM4/jHJGnMjHcz6wbA3wd5/PfAs0BhG0D+72eK1n2WGORA0bpPU4H58+e/+ndL\nSwstLS2VPF1adfV2lX+sp3384lDlclnoKVN7mstCrnd845E0Iq2trbS2tg65Xj3WYp1H1Lh9Jlne\nC/gdMSq1sCZuDvBkUv5yUnYTMSDitIL1HgO+ClxY4rVyOWsXpNJ62uGqHWHpwwMfO+RxWG2jcQ9J\nFVh0N5y308DyDXaDw66HiauNf0ySRiWTyUCJ3K0em1l/DLyNvtgOAH5OJHLr0zc44hkicXt3srwG\nMUjikqLtNVKfSatU3zLN0HJZDHp4tawBdjodJjoPd91bex7s/vn+ZdNmw3t/Ac1TahOTpDFRr0nO\nYcCORJPpZsR0JR3ALsAvgK2BTmKU6+nAP4BtgcuB65NtrAZ8APghcB1wEgObeK2ZkwbT2xnNcs9c\nF4MfNng3NE2BZmt1UqFrBXS8Av++DqbNgY33hYYmaCw3D7ukelauZq5ek7nxYjInSZJSIU3NrJIk\nSRomkzlJkqQUM5mTJElKMZM5SZKkFDOZkyRJSjGTOUmSpBQzmZMkSUoxkzlJkqQUM5mTJElKMZM5\nSZKkFDOZkyRJSjGTOUmSpBQzmZMkSUoxkzlJkqQUM5mTJElKMZM5SZKkFDOZkyRJSjGTOUmSpBQz\nmUoItaEAACAASURBVJMkSUoxkzlJkqQUM5mTJElKMZM5SZKkFDOZkyRJSjGTOUmSpBQzmZMkSUox\nkzlJkqQUM5mTJElKMZM5SZKkFDOZkyRJSjGTOUmSpBQzmZMkSUoxkzlJkqQUM5mTJElKMZM5SZKk\nFDOZkyRJSjGTOUmSpBQzmZMkSUqxekzmDgJOBY4Dzgaay6w3AzgL+CRwAbBXwWPbAn8Flie/XzdW\nwUqSJNVSptYBFHkDcCmwBZAFvgV0ASeUWPcK4FoikVsTuB/YBmgHLgTOAKYSCd/UZJvFcrlcrrrv\nQJIkaQxkMhkokbvVW83c54BWIpEDuBL4H2BC0XqbEzV4f0iWFwP3AR8F9gS+AvwduAX4LLAZMHMM\n49Ywtba21tV2qrmteoypmtsypvHfljGN/7aMqTbb0ujUWzK3G/CvguVHgLWA7YrW2x3oAJ4uKFsI\n7Av8EXisoPx5oI1I+FRjq/LJrR5jqua2jGn8t2VM478tY6rNtjQ69ZbMzQKWFiwvSX6vP8R6JMvF\n6wHsCPwY6KlGgJIkSfWkqdYBFOkBuguW88lmcftw8Xr5dYvXawDeCRxZrQAlSZLqyXgOgNiA6MdW\nzu+J/m4/AM5MymYCzwG7AHcWrPtB4Bxg9YKyHwBzgQMKyo4i+tUtLPOa/wS2H1b0kiRJtXUPsEOt\ngxjKecD3C5b3Al5h4PQkc4Beoj9d3k3EdCZ5BwM7FyyXm+JEkiRJVbIz8G/6mldPJeaag+gPd07B\nutcAH0n+XgN4ib4+c28FjgbmJT9vAj4zZlFLkiTpVYcB3wX+D/ghMDkp3wX4DzAxWV6LmE/uf4Hz\niQQOovpxOf+fvfMOk5u83va9xQ13wCUYXCimYwjFQMAYSCghoYVOwLTQe0IHm2YIEEIJEEjoxbRQ\nQ8fYpiQE+FFDTWiGQIwBA7aBGOz198cjfaPRSjOvNJrRzO65r0vX7mgk7bsajXTeU54jeRN/WQD8\nuAZjNwzDMAzDqCn1JhpsGOVYBOiDcikNwzCM8iwPLIPunf9BIvtfltzDMAyjBM3AsIT7rIxyKWdQ\n8LZ+h4piDqayfMhfImHq7hUcI0yeRTXLRKwbCKxY64GEWIxs8la7AFsAhwDHo89vpZTHyiqJ+MiY\n9T9FkYM1yuxfrXzejVCqyoXAAagTTlak+R6Xotw5CrMW+t9uBx5GnYAORik3LmQ5dp9afPf6JNx+\nf2S4/Q/4FPgQyXh9C9wGLO1wjMWBbVAOuzmA6hT7YIwsWQHYGViIri2/V1rwOlvcW3+4w/GagRO9\n404DXgdmoxtTH6Q3uB56EByBwvDl+BAZAZOAs4FfAdcjY+4gh/2DrIn+jyFAi7euCc2Cf5DwWEeg\ntILvEu4X5kTgrNC6ZuBRYNMKj+2zM2q7Vwpf6LsJVZOvBuyAOrWAroHb0WfpQjdgAury8hXtr4Pp\n6Bw+7Xg8UFXYX1H+bZL9wvzXG98C9D8d4Y3rY5THuwbwOfB8zP5HodSSLBkPnAq8gsTXe6OWhrsC\nz5TZN+vvcZBdgFtC69YDbiTaGAqzPPqedAVeQ9fBPArXwerAn4HzyhznUtTyMUp/dDbpvFa1+O6d\nDJzpsN1iwO/QZz0Z5aIH6YVyyceha/avMccZCfyDgnLEsyilKazzahhGB2JxdNO4BrgWPbwe8V77\nyzRkPLlwLLCqw3aLoAf9AIdt9/J+jkY38i1C65MwHT0Q9vH238v7/a4Ux7oaOA04A3VCScpB6NzP\nQh1QgsuXyHBJyv7AB8hICeeglqMNFSUdgB6864aO0Qac5DiOEeja2YJCDm2YZdADfB/HY4KM8FZg\nK3Tuj0byRkmZhbxCqwIboAfkyhTOUxPtDZggC5B36ZqI5UFgKoVCMFc+B7YLreuPjKZyZP09DvIS\n8oSDJjzXIy+Ry/X5E+B0ynvftqO9URUmfC2Gl/e9v+dClt+9TdA9ZSLtvWaH4DbZ64q+W3HflTA7\nEe+lvg2dy1HovvQQcK7jcQ3DaGCCIcajIt5vBq5zOM5i6GGbhFUctjkN3ezfQ6EZkFet1MM2jr/H\nrHcxKuPogkIav0ez8KUS7Ls5cDEwNrSsTbpQ3gwkuj0CGTnDkdHk4hn4lmJDfDQK+/nend7EewOC\n9EXFUK5RhI2RQZWU0ei6XIA8Gkk4JvT6N8iYmxNYF+eVA12TYVqBc7zxXIsmLEl4Kmb9+MDvw0vs\nn9X3OMxgZBT8A3nA7kW9s/uW2W8x9L1wZUlkGMVxK/KWjY1ZNgVuSvD3svjubYEMyVnIaPsMna+u\nqIvR90R/FmF6krwhQJyBfFvotev31jCMDkTUQ6o/Cj8lYTDKPfoDcBGa2ac1mEaiHKIjkBE3BHn1\nrklxrG1oHz5pAvZLcSx/Fr4eeijMQDP6w1IcK0g30udK3Uy0EbVYxLowD4Ve96J9+8ArHI7TUub9\nqFxH1wfZMciD8SbSs/wjysVKyrnIa3IE0ru8CckmvY/+5/7AzBL7hx+kw5Gx8zUF+aWkHAycgvKc\n/OWnyAAbgwyNS+N2DpHV9xjktX4X+D90vs6mYLCkYWkUxk56jZeb+J1D6c/MhaTfvakUIgVd0f3u\nQnQtfEpp4zSOnwN3oLy5l9GkdfOSexRzecS6P4Ve/yLFuAzDaCBOQTeDX6AHydEofypJGHIHYC6a\nsc5ERs58FL5IMlOPI0lu2waUD8+4hiHDvIXCtt8hr9xqpTeP5WrUIaUJPbBnowfBgSX2ieNQ9Pnt\nGVjG4eaxCM/oo3AJ+YUZjAySq7zXI1EifDnPThRtwBOoiMI1JBVFbxSKegDlTTUhI3xfZCC+hD4X\nF3ZChuUbuHma45iMvi/hsF9wcc1XzOJ77DMLnRd/ktAHfX6TEx5nJPAihe/c9+h7k1UxyQ7Atgn3\nqfS7d0Po9SLoPvcChYKNJHnu56Nz8ybKi7sVeYjno3Puwgfo//KXa9C16b++AU1aDMPo4OyOkmZn\no0TxK4FFHfddCXknzqbY6OqL8lQ+xa0aK8gKwOPAfd7rAWgG7BLObEIP7eEllqXRwy8pT6EZ81j0\nUDqS4nZ1rkz0fvYFPvKOBQr9JeVRFEqeGliewM1j8SSlK+/6oryrpExBYZ4rA+vWws14DJMkvy4t\nPdCko5yx2AMl7rchI7fSytNtKO/t2jrB8Sr5HgeJ0/tM+lk8jfL41kPX0gBklE8stVOA/qjKdGMK\nnubuyFt4IcVtIV2p9LsX9niBcgqD186ejsfaAviEaC/cyqiYbIzDcWaj7/K00OLfD57CJE4Mo9My\n2HG7KykdstwS91CRz9PIeAsmlK+EEtCTEhX2GEg6j+GgwO8DUJhnDu4PJx8/LHczmpH7YcjjUoxp\n7Zj1LpIepyKDuXfEe0shT8yxEe+Vw/fIBf+fpdBDJw190APyOGBH0of7uqI8raGBxfWcr4IqM79B\n1dVRpMkDDNMDef6ywlUGpBwtxMu7xPEV0dfWBMf925CXahMUCm9FRkob8qR+TPKuQZV+92ZTMJKm\neD+ne79PQROpWY7HugsVHcWxLG55wrs4bLOj04iMqpI0SdIwktAXhWaWoJAv1YQS4V3yPwZT7IEJ\n8yDJH07PoxBY8AY7j2QVpEuiB9AWtC/5H4hCGPckHNfW6GG+O8rD+zuSfbg94XG6UKii2xp5HQ5G\nHodzEh7rOeQpHIRCNKNQjuEDDvuegx5GHyAPyjvI4FkOVQm+hnIDk/Jp6HUrMhw/SHGsUd7YFqKH\nZjfked3WG58rp6PrKRziW4jbOX/W+9sXozD7uND7fVBlsUtlt09bzPpncPNiDkceuHnI2BwYer8Z\nPcQPSDAm0ETgFGQI+iHDvt7fuzDBcW5G3vo5ofWDIraNYga6B/kSG6cjT9V+KHzYAxk7l0TuHU2l\n370vkKBvME1jeuD3rrhHIuajXLs4/OrbctyCvKlbeeO7CnkdgyS9RxmG0WA86y3XUpA0uAE3PThQ\nvlE5kjwAQCHbRSgYc4sinbFy2ltBfoZm7lH5cnNxS+wP4+e2nER1BE3TcAZ6sNwXWHcASvZ3YRH0\nEPuEwvn5Aj0gk4qf+qwE/AXlEd2MHnYzcQsZhbmP9uG0QSSXAfkIGTzBfKYW4r1sYZ5EE5y4ZRuU\nGpCEoygO/4/wxrOl4/4fUvCW/Zr4/NCk3IZyyC5DuXN7ofNdLj9tHYrD/Y8jgyy87r64A4S4OfD7\niih/8NrQNkm9/pXiEtp1/fxcjNCLHLbZjML39htkAK7gOAbDMDoIcfpKyznuf1X5TbjM8Vg+Q5Bg\n8Hso5DoHCdmunPA4S5FtFdeuGR5rNxSWeRMZqluU3jyWKcgjE/RiDkTegyQ0odDxYIorWpMKK/s0\noxDSzshrkFS2w+fXMeuPT3icP1HoGR1kScf918lomyBxOXfPOu4/jML/tATyGAdpRt7CpPihyMUp\nXPNNlDfCFkEe4b3KLK7nfBoKNa6I8sdm0d77+KLjscqRRaHWhui6HO24/QPoMxwaswxDVa7lmIwM\nOtAE5Qzci3kMw+gg7EN0VebGjvsvoHQ13nsoDJSGwejGuCzZd0JJq/a+NPLEgEKAa6Y4xuEo5HgO\nCvEchUK+SbtbQEFPLmjM7YI8bVlwfop9zkBevbCxsj8yzjZqt0c8p9H+sx+DtM+ScIq3z3iUs+X/\nfNRxfxftsKSMiViOJL3cRpQRnia/8DLkNfoBOkcT0HUWDpdGUUqOKOnEYBvvb7ahXLWgV6ybN04X\nz+O9FIy18cijGVw+wr2ryzyUI9sLff9X834u4b2/Ke4VyFlV3V8bet1M+yrmtF52I0MsZ86oJrsj\nVf5wcvpA3Kr1XkU3y7ibTheUy5GEJm+ZSeHB1oKMgaRevvci1vVALZQeS3isg9BD7jEULnoZyVws\nTbKcFN9ADT5ALiBaK6wc73pjGIFm5xsjY9G19dQ+KCT9Lfrf/JZZzcjYPJJ471gcR1HoHPEQ8s71\nRCH5RVEY9se4SV08hnLjXvOOsRwKsybR4QLpnM1F58mnBXcv0e+Q9zRKt20wSqR/lWSag4+gnLcg\nn5NOA3EFlDowB32e81Fo9FxksCThTPR/tCHD5Vz0eZ3qsG8wX7LSMd2DvOsjkSyQnzvXjK6xt3Cr\n9p1GQZrjUfR9nRZ4vxn30OgDFK7t4SiCcCTyroOu16mOx7odeYxL3Tv3dThO2BBtQ+HtILvjlhJj\nGEaDMp6C3Ia/bIJ7LsrYjLZ5kUJJ/zlkpw13DvIEjfWWjZHnKE1119+QxMaJgXWDaF9gUY44TStX\nXakwo5HG2AMo39H1f9sdeRpeQA+7r1H3iP7I0JgNbJ9iPO8jL0pvZCzti/LVgj02k+ifDUPn/DIk\nIpwm9Lt8zHrXZPVqdIBYL+H2pciyAjyOpM3oKx2TS0V2GoZGrHPVCwzfF6Mmca4TTpe8Npdt5qFJ\nXTAaMjvw+4dI48/IGfPMGdXkXKLDAv922HdRlHRbjmnez64oryiqjdFEpDgPBcHbBwPvN5Mu/+1c\n5O0I8jiaQSet8HocjTEYol2d5Fpew5DB/AwyAEYigyftd/0ZCsUhw3AX1z0QSZu84r0+Hnll1kEG\nyrooVykpF1Kopp2HQlALkWfMZ9kEx+uHtLyS5gEGeStm/aroQViOcBHPcFRFuCrypKXpTvJ0zPof\noYlDErKoAPcZgiZWw9F3diHylK9BMgOr0jFdh4z4hymeCFTK9+ja9kPQTcjD5+KBDod150Zs4xKy\nXRQZkG+W2c5/fxgyhB+M2OYNpOsYN9ltJX1OrpEhZswZ1eQ4Cjdr/2dPFDY6vMy+s1B+1nKoerEU\nyyBhzjjdsuD+r6Bw1mehbZJIUfj0pH24+Iek697wKSpcWAwZYL6XL6lReB4SnQ3eYO/ALaQS5mFk\n6ExCHrkb0Xm6m/Jh27cpGHKg/LiPkdzJriis1RN57JIwCIUlW1D4ajJ6WH7pHW9RlFwfx13oWvwQ\n5Wv1QA+sT9H12YY+h3LVzfeiAp17kAc6XLnajHK8XPLKgpOWnVD4cAbyir7qsD/ounPJQVyeQg6W\nK3Mo9gwuimRU0hjjk1EKwVvoc1iIztWIUjtVYUwvIO/uhegz/yuahKWp0PW5HMnKzAwcpwmFyl2M\nuR9Q8Ow1ody5oKevBbd7yyx0fn6PJlBxEiTNwB4oNzfufnwgpSVOwHq11gVmzBnVZDuKK8Ka0Ywx\nagYYxWVo9v0Uqqx8AxkBC5A21TDkafge5Zy5JFFfgfK3BqKZqX/TPQzNhF/HPQH+/Yh1syjkvbiw\nIspH+xx9H1dDxtLnyDBzkQ/w6UshmXsJFIZ8n/RJ75ORJ3MJdN5O8sbkUkwRfih+D9xJsS7ZL0ku\n43IKysVbHWmDTUIJ6Pt7Y9uG0m3CtkH5Pb9GHpmF6Np6yfu9t/deOf3CaWSXKwUyKi9GRvckdJ6S\nGLqvIwPg2hLbpPVAX4L0HtdDEiKroP/dRVA2zH9j9nPNL8xqTL9B37Fb0CRga+9436LK2skkDx9u\nggy3r0LrN4vYNortaZ96EP68Fjoe62E0YZ2K7gmvU7h39kNG4ioo/7XUxDpc4RtFqcmTYRgdgKhZ\n5CCSCXGCPG9nIcPwc2R0+f0Bk1QvQiFH7jMkzDsI3dja0E19ZdyFiLdL+LfDrI1CQ/6Y3kVhqKT0\nQwZvGwpr/67Ccfkc6h17Mpqd+xWNZ8buUeB72lcef0kh/+YD3Kv8XOiCjJlRFFdehvkX8nD4rED7\ntmnXp/j74Vyp3rjnzGXVAcJF8qcSDcNwBXia/ML9iPbCuWisRRVNBcdUKSugooE2ZPiclXD/PxBd\nGe96HdyO0izGxiw/xq1rQ5BmZExeiDz096HJzJ7I81eOV9CkaXzMciaSdjJyJmtJBsMoR08kGuza\n0itrXkZhvtdRuHcPlF/0IgUF//tQhZwLw71jLIEMlWtp36UgjvtRmO5WdF4moge6qyivz/nIMLzD\nO86ByKBL02EhyLYoFPk+8hR1QXInO1P+4fkiOo9xuTZdgZ+TrKuBzwhk9Aa7imyHW0uomymv6Veu\njVyYI2mf99YdfZ4u4bVvKHSAeCni/TQdIOLYikJ1ZKWcT/Jq5J+iHrRBQ94PRXaP3KPAZWiCUUko\n1Gd1dK77oethHMrn/Ax9HycRn3cYxzBkAL5FIbWkGaVMuEw6R6NJ6nfES5AsT3yOZjVYgNIj4vIK\nu6LPriXmfaNGWJjVqCZRZfTLEv3AqhVXU8ir6Y5mzX+nWD7FdZa/MTJYXkUGzxLI6NkbqfqX4wsK\nzbW/8vYN58i1Uj5B228Y7htOt6DwbKXG3N3eEuQk3MLIh1I+0f6bFGO6Bj18Z1AIObUgL5GLMefS\nT9TV47Q7ulbGIIMrODkegIxeF2Pn/1D4OI5+lG+9NBo3r80g3CtjqyEt8yfk6fk3xUaZS0h6UzRZ\nmomuy6hiJ1d+j87pVuhzuxd5mR4mfXXmHd6+8ynOmYvqIRvF0+g8X0F8Dm8tDTnQZ1yuI0rSyadR\nBcyYM6rJf1CILviQ+xzpg+VFL1QQ0YJU1S9CeUqzkLenH+4Pu6Non6TeDT3oXIy5cLXad7TXcNoV\nSYKUItzP8V3aF3isj4zWJGyEHkpPofNyHjp/Eyj/UCllyK2KjL1xuIVsg2yMjOawceNaUTcfeQTj\nkra3xz1X7R4UsupH+9Dh17jnb32Jcvnmos86XO19AMrxKsXzqLiklHSFn/Dugq8d9hoqLtgWpU3M\nQp6rdUknwfMc0XmSr0SsC7M3uob7oM9pb288d5O8QnesN5YT0Pd+KTSxW5T0otjfontKmJGO+/+N\n8oVh3UgvlJ6Gv6HP/TOidRBBXkzDMDow/ox0KDJ6Sim415LNUKWtn4e0Nsq1Ogd5i45xPM4RMetP\njFkfZhZKvp8a+PmR9/sUZBB+6XCcp1EOjL+MQ5V5/ut9ce9GEGQaesiBQnMvood60t6loHDMbhRy\n+/5LOjmQ82if4wbuD8z1UaHMBOTR7IoM1DWQztc3SO/PlSbc29PF0YY8snFK+puic1eOch7FFgqf\nZzmepDjn9XgUon4H5R2u5HicMNsjIzfcWqqcERPHAOTN+hhVk7oWG4yPWLcBMqYno9zFpLJAJxNd\nKbyG4/43ld/k/+tl1orvkfc1bes9wzA6AIuhsEVQnPcWkrV/+TGquBpPuuKAcoTzdPom2PcsikMo\nXdHN1rUa9gP08Lg2ZrmR6C4TYVxa96TJM/I9S+OQ18E3mPZJcIzhwNnI2/E/YDryjDXjltQf5gLU\nX/Sa0JIkdL8zhVZOweVb0j0seyKpFj9hfhSaLHSJ3aOY6UT3dg1yeYpxrYI8RX47r62RN8qFsLZd\nF5QL+gCF74hLF5cwr5JetDv43exHQTajDXn8r8BNRByK9RJb0STlDnQNtKHzlDSEfAOS5JkaWqY7\n7j+DwqQuvCSZ3JXjl+jclctRBLdJoEvqglFlLMxqVJM/oIKDIyk8sHz9tDivVpDNUEj2K2/fI5Fn\npZwYZikGIw/cfOSxGoqMk7O9vxOWFSjFzUiv6isUmh2KHiqu7aB+RXnFehdPw3g0/lLCnq7exiA/\nRNIP+6Fz/y+UI3YI5Zttb4UkTLZA3pzzkFDruRRCnGlynvogz4mfQL8QeZyWSXCMW9HDcUdUvdyC\n/rfbSOctvBGdl5e91y+jicwlFEuxxPEE5UNnLpWHQS5G/18LhbDx4ig07EK1pGUuQUZPMJTdhCYM\n5TgShZP3QIZpE8pZPRsZmUly3fZGVZi7oer1/sjQPANNOF3EnsPMRoUwwfSJJtzvB1+j+2RcYWIX\nNDlKyofIszoJnatfoYrtCygvMxRO+4hiF6ydl2F0aCbGrI8T9w0zmYIx04JutOWMiHJMQcbElYF1\na6EHeRr6oAfCseih0J3aT5KuRg+MUn83qTEAMqC3pRAm8tX7XR68+yLh3TspDlel6WYQJMo72xKz\nPi1rJtz+Vu9nsBvBErh1MAGdo3IkzQk7wfu5EYV8vuGUz73zqZW0TDfcPXy+F+8lZPQcT/JQaPhY\nb6GCjJVTHidInCabi1YbuOWepdEJ3Mv7ORpNYrcIrS/FHNq38wou1s6rTjDPnFFNohKJe+OeQ/If\n1McT5HWagEIhQfpQXIlajveQoRF88H5C+pY0XyMD0Vf6H4jCeOelPF4aHkQ35j8jL8+d3rpvA9tE\ntQYqxzyKq1k/QjN6F2PnKm9ZDRkWPVBeWKVySB+h8GH/wLH6oWrQtRMeK6rauie67pL0jX0nYt1B\nlK9A9VmE0gUqW5E8TN6GPHGPI0/0BG88h+KmQ/gqbtIySbkafV9uQobmveg6O4XyoeQpSNz7DWTA\nb4wmjL3QfeIu3K/z55GH+dlkwy/J5yhfdj9kzL+DohOuIfK4Hr9Bwvc/F0Yg6Z7fo/SNh9D524LS\nItOg++UdlPYWuso4GYbRoByIQiqHoDDfn9HsPk4YNcyfItaF3fku3QiC+A3nfWOuFRkdrm2TgpyE\nHh5p8n+qQTf04L8KeS7uQiGpqIIBF6Jyd57FzZMUpgcyOB9AXswlcfPwhbkYFU/MpOAdmEPprg9x\nXOeNYS9v2Rt5DpO2PtsQGSePoTDW0yg/cAfH/ceg78XZyEO0CPosV0Feo29I/sA8CF2HqyOPzCfo\nWnVtW/ejjLYJ43vr+yLD/PfeaxePYbjoohUZlHdRyHl0Ffo9ETfjKQlnIm/0QUhPbzt0vZaSnQny\nPjJ2k05KyjEShX+PoODFPhU3L7mL3M2m5TcxDKPR2QnNml9DFZFbJdh3Hu1d/LOpzMW/EpImeQHl\nvE1HhsGYhMcB3XxXoLjjgGv+T7XxPRd/QOfwkdKbR5KVsRNmFWSoxwmjlqLS8GGQqNBsN+SRSkov\nlDt0LDo/S5GsmGYHlHsZnhjMQ96oNGxEcYh8eyqvKE9znoPs7f28GeW++kn4x0VvXsQo7+dolHvn\n90D9F8lDpccgT+iryAj7YYJ947gyZv0JMetLsQoKqe5ANmLRzehetTrJUi7eoHRHFcMwOgE349am\nJ46XUJ7cqTHLmUhwNSnNSCdrZ1Qt25d0N6yrYtbXuoy/j7f0Jbqxu9/yKClZGjtRpKkcPY5CL8hz\nkDHQh+g+uWlYHYXLkhCn7p+kRy8odHwAMlQuB47GXUoEJB3zAtKa64eu8bDHOG06gY9L/+NS7I8m\nYi8ij9EQ5K1zCY++hSYmbejzPo/KjbAlUORgMgqLXoA8rWnSAfaPWZ9EymcUCgGHjfqXSJ7L6bMO\nBZHmNpTr+Efc9DT/jopcksj1GIbRwXia6IeRqybYeg7bJDVSjopY1x21JkrKcBQaC2q87UX6Yoq0\ntKGH7GlEn++JZGdgpjF2siQcPpxBsvBhkDiJjKRCxmEB4mYUWivXuSNr2ij2Ao1G2nl7o+vyMNp3\n9EhKpcZcJfwPGarrUZ1WlIuhc3Uv8thHpXmU4nRUGb8yCpXujvL8wq3e4lge5TXehVJRNkedMfZD\nE+PPkTZiUt5A+aq+V26wd0yX/+8DFGrdkXRSNIZhdAB8CYtx3uIbO3c57t+CHqwvoZtimoRrn91R\nEvhjSMpjAoVm0Zfg3k81yEMo1Ps4hZyyad66WjKf0mHi3rglvYfJytjJmqBG2JIoHJUmfHgUMsiD\nS5LQqM/pFCq0l0aVp6+STg6mEl4OvR5B+wdwpdXELlIVpbiA9BMLF+HkrOiNUkSS0AMZm99QmGCd\nR7S3PIpbKZ2GsgFuOWxhPiS62DH4PY7TROyCjMorkDf0r8jgTVtFbBhGA3ILCvsEE+gfx71dzsGo\njcxdKOfrO9x6OEbRCxVjPE97wdlLSJfE+xzRDaazyL9JQjmtOojP5ylFVsZO1nRB4Tm/g8BwJFOR\nhj6oSOQ45H1wffCGGYlCo1+iB3g30mmCVYKLtEVcakCtGI+6PfwWGWcuwrUg42HjBH+nH0qhiOML\nNIG7iOgOHueSTNw8SDPFciSux7nWYZukun6gSfUqoXVNFBtzOzscpxmFoC9AYdspqDq6GmLuo49k\ndgAAIABJREFUhmHUEesSnYsWl2MUZioKCfhsSbpKSp+41ktpdcrOJ9q4qbS9U1Kuc9jGVSzWhVIP\nyWpzKioKyKKCeBSaWMxAVYgvoYdUuUT6kRQ8u8Gfk9CkY4K31LoH8WMO26Rp6zYOFU80IeN5Muq8\nsH6KYwUZhSZSV6J7RTl2QwUY5SS1xqAQYqkOHG2U9rwNJLkH2i86+iXFrfVudty/VF9dn4sTjglk\ndH1FcSHZDDRRfg953NIUI62JUjjSpDgYGWM6c0Y1WRfd9MP0RPk7T6FE6DjeoTis8yDtG4UvQ7TO\nVxQL0cM6K52yZvTADnekWJXaJgwPRw+uuMre3ij8l5SfoIfncIo9VgNIJ0KcBfuhc/saBe21Zm99\nUiaikNEDgXWDUI/NUhWkX6DcqKnomvKZh+Q2hqNra8kUY6qEz1ASflwu1KGk63CxNTJQmlA+6Fwk\nO/RL4rXx4lgNtbzaBtgVGT9TcAu9TkKpFi8gw/sNZKQsQN/hocjAnIY+v1KV7k9ROrd1JroWknA/\nure8TeG6aMW9ynYYytuLy0ntQ7rv8VRkBJZqBVYupDwYea5Hou/dG0h/7iSSF/oYhtFg/Bcl9H6K\nZp1d0M1qHjIINqN0hdYdtG/IfUPg9TIoTJKELHXKbkQhtQnIYzQBFSFEidFWk7NQSDsqj6Uvqj5N\nM6P/D3pob4J6Xo5FD99L0gwyI+4g2tubJmcurvemS8g2WJyzOtHnvtYVgCuha/ka5MUeiYyL7ZCh\nMRf34qMgfo/eY5FB4DeTj6veLMXnKKdsHjICFktxjD7e374bpU28iiZ643EPbbt4s5MK9L5FdJje\ndZK4L2oxthXFvXq7euv+iVvXhjC3UV5VoFT6xA4U9DRnogn2fHQtbJNiPIZhNBizUN7bqih5dxya\npfohsSZKJ/S6NI9PGl7LUqcsbpY8LMWxKqEn8oDOQVVr56Iq278gz8VbpEtYjgtpp3kAp2UYxdXC\nx6GiGj+EtSfyrqUJv59G+6rIMaiaMQmfEd0xIiqfstpsiCoxw9+TmcjTmoaJKEz/NfLSNXvHcvWI\nB7kLVW2OQOf/DLIX73VhSpn3uwKvJDzm8URXkyfJoT2fgnzIR97ynbfutyX2K0UlqgIroc/9bIq9\np31RZfmnpPMWGhlTjfJuw/A5huK2Vr9BM+h/oNAfaGYd5527BeXTxEk8tKIHejj0WorjUBL4ZxTa\nHPk37uEJjuPTFeXXBL1Fu3rHriXdkadjXwp5hl+hCrkTcW8tFWRDdNMOPtSaUWVwXN/drFkSVWmW\nerC2IEFU1x6YPmNQ0cJryCBeDoXWNkcPQFf2QddxuJp0Zwp9W2tJN1TQszI6N2+hdIBvS+1UgiaU\n3zbDWwah8w0qaEpCLwqacmsgCY79kbf3yJTjS8OlKE/sONrfX1qRDlsrBZFjF25CqSUfhNYvR7KQ\n+2iUHzgShWvf9I79fIJjBDkE3WMfpxD+bUZete3K7Hslul/HFVBtibqTHJJybEZGmDFnVJNzkZeg\nFd04/ouSpyeg2Vxf9KCJewgPR8m5fdHsMMqoG4FCpa4chB4ca6KH3j3oATOd5M22T0cPg3Ci9ULy\n8cr4LI7O+ScU53Ql5VTkycz7/xuNChRKsRZuAtJbUFyYMAwZp0ui6+gGkstvPIqEWb+gcL6bkCej\nW9xOHYAfo+9zEiYio3I3dM7vQP1+a52asDgy2Lsgb/Z7yMu/HDJweqFrKonM0PmoG8W8wLpmZPDs\n6LD/Ugn/niu3IMPwq9C4VqB8XuB9lG8ldw3JjF7DMBqM3iif6wHkHWpCicn7opnvSyhkFkc/lKjc\nhmbRabTSwgykWCqgEp0yv+l7cFLUgnvv2WrSA3ktDyF9de2/UQgsnKPmImOQJRegHp6nE/2/HJDg\nWG+TTni1FL8GtqWQVzgW5RamkZGoR96LWGaQrkXcAmRM745bB4JqMgAZ734Y00/beIh0ocP+3s8W\nio0kV2mSx6iOnM16ROeZjnXYN9wLOwpXUWTDMDogPZC3rkeJbc4HnkDhlxNR+OLwCv/uTPQgyYI/\nEe15qXUV40CUAzQbPZx6o2Rp/wH1Ne5yMEGuJ9p7v3jEumrShrwncQ/FTXEXlL0BGVmnEu816x2z\nPo6etD9PLcjA6wicg66fsRQM1TNw8zaF2SCzUbU/btpWZb4Xbl0qywftge4JvnTOXJTnVkoiJcgU\nFM68HVUKl7o3JmV9NHF+EIWYXaMQLrqELpIqRpWxMKtRTZqRd6g3qqYchW6arsKlDyApAr/IYWlU\nvVpJJ4jx6IEeDs1uiW50SfDlTP4PfZcWej83IH2yeRquQJ6AyShENA8ZE6ciQ25PFAYs1SUiisNQ\nKOYZiv+/jVCeWK2YjsJE80psczmqvC3HIBR+XgfldN5A+4KHvUnWKaEfytvsh675hd7vu1Co/Gxk\nouQympEnKYmQr89Y9Dnciu4JS6Jq2yS8iDz1kyiIED+E0jKiWva50IS+I72Q5MoXCff/IzpX16Br\nths6P4MpdAgpxcoof7Mbmgj8Ak3QrkeT2rTsjIzEBwLj2gCFvMsVDi2gfQ5gmCXo2OkEDYHpzBnV\n5HJ0Q/J1jl4GlkVinCc77P8fiqtV30WFC0HWJ5nW1UikQ/UexflNK5M81LoGmn2PoGDstFB7z9xQ\nCp0xrkX/2zLoQQAqPEkjGrw9erCtFFjXTO2rD5+gtCEH7rp3fveRZ9FDbhIy8P1z1QUZGEmMuSuR\nptkS6BptQues1kUw1aIn7duC/RBpxiXlDORlfxAZcy8jj9ghyGPkyp9QUcDyyJA7ABk9+zruvwjK\nnV0LfTdO88bkd4L5FEknhYtaSvEd7fXaXsTNkIOC+O48dG5uRWLmv0SFZA+i//HdBGPCG9PSFLcs\nbELXZzlj7lU02YlTDehC6RZkRo0wY86oJkNQEnhwpvwUmsG6GHOrIq+Sj69A76/rgrwfSbxgbyNP\nWlBAswlJLiTlBFTAEWaZFMeqhODNfS6qWpsd2qbc7DqK04lOTB+d4liV4NLge0T5TQCFamej6+hC\n5AF5hELRQ1eSf34PA39GXswBwJMoRNZRconej1g3i3RisT9C94RgwvxdSAg4iTHXF02mrkIewuu9\n9T/EzfN/Orq/PIK82Uui9IHdkTd7D2QkJmkf+EbEOr/QIA2tyAu+NYoADEa5w0klSqbSvvf0QvR/\nluMINPktRZquIoZhNBB+O5zjAusOwV2F3kVnri1272gG0j69YCDpw2G7oVyXN1GoKG3eTiVMolhY\n+V6KxZaXJnkIOYofkc//9xClW0dthQwoF05H4fG5yIMZJaa6Z8S6UvwOCat2Rx6UjVClZxo5mHqk\nnHxFEqLuCbvg3q/ZZz1kBF6AvGxDUNjQtSp2MoVctgHIwA+2DmzCredxkBPQ9bUVyic8CVW3ukxc\noeDxHor+l0/Q/e0x9BlEFTG48FuKCzqGIG90UlFkwzA6KdsCf0DitfujHoXzUVshF06mtARGKwUR\nYFdOjFmfRnT2cOTxOgeJIx+FQjYHpThWJVRDXBkUItodPdiOQJIS/gO0loxBntSzUTh8EZSjswp6\neH5DefkEnzZ0DZ5PfDVl0lziMSjUvzwyCF7w/k5HeVjGGbdroLCmq1cUlGt5IvJkboY+069J3gf1\nQNKFeX3CVZp3R2yTxFMIulcdi7z1X6OwaRL9tYfRhHA+Mi4vJZvK66FIp/Ezb1xtKM3ApYUaaHJy\nFIWiib2Qt/ZTlGKQZaGGYRh1ylCkjH4ZypdJ0v/0aiTgWiodwDVX6kA0a3/X+xlc/okSg5NyE9Ht\ne05LcaxK+AvyBI2NWX5COmPVN0qXR4ac/1B3zUvKkh2QTlbYSJ1H6T6qYWYgD2O1qWWXjGrzL2Sg\nPkch/6sP+jyGotzKZRMcbzTKp30AFaCkqYr9EBWxhHGttL489Prm0OtmKhd8Ttq/uA3JAR1G8orq\ncrQg7/ZORJ+3OHZCE8FPkSG4PfrOPYzuKbPIRjLKMIwGZJTjdjuim+wHqA/q9lQ2C/wV6lM4Ds0u\n/WVHkncPgPjqybRtd9Li0i4ojbr+8cj78gISD/VJ6rHIiv4o0f0S9DA+mug2RaVw9eCVYy+HbcZl\n9Lfy5n9IAPznKBl/F+Sl8VMcWildMJK0itqF7VHe3TAK6QTDcZ9IVcObvScSRvcNsQHIC9k/do9i\nriB9KDVMVtqMUyncX7ZCHvLgd2hJ3MS6jSpj0iRGlqyKHrBx+NIWa5IsRNINeZ62936+gGaFf6W4\nkMH1WOUqI105GyX/PoNCdiOR16qV5HlX1WJVFNYeh3K6krAe8sS8j/J/+qNw8vqkk6ToSMxFYeio\nriQLkcd2FNl7WPLgFORV9zkUPeSfpVCc8g9UlRrFa2gCEJVw71eUt6EJmytPEK1Z59qdZBryrH8f\n834XVASRxBC9G00ygh1GRiGJIJe8Q784J47uyLB2wQ/z7xtzzE2RPMykMse5imJP/N9o79m+EtjP\ncVxGlbBqViNLpqNS/2CukG/A+b+3kLxt1jyUS3K/t/8YZNidhqpTNyuz/w/RjWsQCu3MRGGwk1G+\nzyOkE748Dz2AgkUB/g00T7qisKRveH1Ce0kXF56m+CH0DUqczrqDQiPyHTLeH0YenPDEuCvJcsnq\nmT7IK9OK8mAXReFAv2hhEUp3TJiJvOtzQuvXQB1iviK5kPcfvbEEi0ya0DXvwhSUt1eKqOrUUkyj\n2JADRRI2bb9pJNPR5GAS8j7/O/T+6Si3sJTB5/Mh8qLGTVwfQ4ZnOWNubuh1VFV82p6/RoaYZ87I\nmjWQxyKOFpSD9brDsVZHLb/iaEL5H+X6drahm+xhwDsol+UZFH54AIXqHkAeiHLshbTcgvwAhRve\nR7kl44DrHI6VNcNR+GQfJN3wCfKi3I+MuqcSHq8VGar9Kdwr+nnHHFn5cBuaHkin7qco5/JKFGIL\n8gs6RhHEEigvanV0DR2JPHVfI8/jiuiBHud9WpP2TeIPQ6Hbp4FdSV7NCvoeL4kqyVdFwsYfO+77\nMZqIlfIWfkOyfLAJyFPv616uhnICv8LNw9eGQti3xbw/EBVduVTH3oA8i6W4ERl8pXiXgjSJLxg+\nLfB+K5JvSdMO0TCMBiGq+fKiKBfLhSnoYblRYBmDwn9DEozjHYoLFa5AM+DNvddNqIWOC3ORDIZf\nPDEltDxFew9EtdkK5bTNR5V0v0E31yTCt1HchB62r1P4f6ej8HJeVFrFWA1WR6LYV9P5ws8DUKWz\na+HBoqjiez7yNKXNEdsOfc/8/rBNyFM+1nH/21E19LDQchAy4l6jWCzbhUVQju/nyGPYhvLJXHUL\nXbo8lPMm+rgUPP3NYZs56J7mf/+nUVw89jfc9OqMKmNhVqMa/AyFMbeifRLxQDSzdykS6I/CN2Hj\nqAXNBpdFXqJyel7PoLAYyBDcFwm6+jpSC3EPQ9ZjeG0weqjei3JXstI3+zsKf62F/u9X0Od6TEbH\nT8NJyOMVZnHShZKz4CXkMemNdAePQdfHtcgr01HZGVV8XuS4/YYorNcVfX8rEZs9Hk0WfcN+IfpO\nP4ibsX8yxYLfXdA96UjksfKNuiR8g7yMg9B963NUCexKuMVgFIs6HmsRSnfH2Qo3jc7zkNFdivGO\nYzIMo8EYgrxUUTIg9+FeHFCuOfwKtJcYiGIaKpzYEvgI5ZMEE9Nbcb/p9kCh1ttQWDZKqynK2KgF\nq6Gb7yXo3F1b4fHOQIZcMwo3DUMG9IxSO1WZSqsYq8lmyKD2dcLOzXc4mbE/ypVaQLpqz2YUgvwe\ndSeJEugelHBM/rkNig+vSzrjeRkku/I1yXsOj0P3sygZknVQmD2saRfHlDLvd0UTKhey0mY8i9Le\n01ZvG8MwOihdUa5MmD5kV34PErEtx49Q14k2lNe2VuC9QegBnLSTBNRveM03OB9A1ahLkk4m4xco\nt2hV5I38CJ2nFzIZZTqeIDtR5CzojfK/3vTG8QbV0QnLkxnowT8CGc7DkQHkKvQ7DX0+cYZBb5T6\nkISTkefpBHSv2RwVQ/0l4XF2QwbgG8jQScqHFDo3HIvkQM6koKfZB/c8vkuRmHVUxKwVhViTpE5k\noc1YLUFywzAaiKMi1nVHN6ysuN9xuybat/JqRjlY25DuRu7TGxUePIDyh/pWcKysWQU1JXeVNChF\nD2SguxSKVItdaR9qaiKZ0n4WrIgevrORJ+4e2vcIrnWP3mpxM9HFcq7CyLORERK33Elyj1oX5JV7\nE3mZPkSfh+t3bxHvb7eh3NCo/r8un1/QuOqOPPz9Qtvc5DimxVEF6/vIw34wuq/8DuX9fkJyXcVK\ntRmnoLSUvWKWX6HKWMMwOiC7o7DKYyifYkJguYT2TZ/jCD8cwyxLfQhWNkJ4LYnu3SbI4J5Ie8mJ\nQyjkH+ZFH1SpdxwSfI7qwlFt2tB5uBWFtP0Eer8/7sqoorAjcCgyBPYMLONwN1JcpHr2TzimKENr\nEO6yOW9S8BaGiyCGIW+0SxHBH0Kvo6rYXXMKQbmvN6BrK+j5eojS8i/VolyqC0Tr/Rk1xqRJjGrQ\nC+WJrET7HI+vUWjUZTb3T2/bsDBrM7rp7YAMRJe8uazpjWamh6AQ5FtIq+5aal/NmiVbIA/jl+hz\nnI28e7PQZ7onCifVuj+rzyhUwbgQVdZ2Q16VbVEFYq14FYnExonO9kIyE0k9KfXIo+gcBzXLWlDO\nqkvnlDfQuboT5aZlwYm0z9VqRmN10XX7CHms4+iH7i/lPr8/UByyvIb2VfyX4uY53g0Joc9B188K\nKLz6NvkV9xiG0clpIrqNTAvusiLzUeL1e6HlTTRTTSo0mgUdPbw2lYIIclf0sLoQKfx/irx2eXIf\nkqsJMoj2HpJqEx5DFNtUfRS1Ia6fsmuLuEEo1PcQKoq6GOWYpsmdPQgZN7Nof1/4EnjZ8TguHRlc\nCpleRdEHPwLxYuD1eFSYEyW0G8Wl6Pzcj6rSXeVeDMMwqs4qSJJgjLdsjfvsvNZ5UC509PBaeOyL\noIfkC+j/g3w9+r+OWe+qXWhUziqoYCFNHmY/FCK/Cxk5V6HiiiSh8s2RQTg2tKyNculqSRvK1/MN\nyvcpNjD/Q7ICgSYkn3Qe8vY/gfJwh1YwRpMgMwyjIi5GqvgzKdzc5uDeg9HlBpY0jNUTzZb9EM0o\nlHvl+hB4FVWrTYhZzkM390YlKvR0PSp+8Mmz7+xptDcmx6CcRaN6dEVhwKeQAfNfZKhUwiLI+3UT\n2X1nls3oOK4c6rBNJZPSUeiafxnlB5+Y4hh/pbiC3zAMIxEneD83oiCkOxx1KHDhNMprHCXVF7sL\n5eIFDcpNcJdG6OjhtdkUd7fwuz74HS6eIDtR4jSMQR0pbke5ff9GY14vxzF1ZIYjrbJPkCduOvBz\n9L3MIvF9A6T/mNSjFlUVexuVaysmZQoKIZfSyXP1OparDl4O5asmZTdUdXo50pirtw4qRgZkqfdl\nGGHaUN7H40gCpDsyBFxmsyAJjPnE6xt9h1ufwiDfoUq1fwbWvQns5Lj/Aw7b3JNwTPXEF8jjMh2F\nwaYjg+4Db/kPyZXxK+VgVDEMMia3RLlJ7yNP4kjUdszIDr9F3NtIqPk85AWfgjw9bSTv9Qv63HZH\n3tUjUMHC/iSvAF8Cff7TvZ8foCpnV1HdrDgOGbx+a6ujad8BxrX6u1wO8L9JF9qehDTqDkSVtcei\nCdEE8qmQNQyjwTgI5YusDoxG4qNzca86rIbGkR9eDSrHn4F0nAw3z6PLNlnyBgo3QbxqfZQCf60Y\nhwyeJpQaMBkVjKyf45gqZV/UBu9OinX9Ku33e5D3c3ngWwohexf5kiBR4dQ+uMulVIOV0QT0eWS0\nnkIy/coFRBd8BfPvwpX9LgxF6SUHozSRT9F9cEM0Gb6eynLyDMPoBARzrZZEOTIDHPcd67BN0lDP\nhuiG/xgKHz2NZrs7JDyOUTuCLZZOitkmj8pmnzvQdd6MjLjJaALzuxzHlBVZt4g7HlgDFdTcF1h/\naYXHBbXWc9WwrDYjULHOU8jz78LLaGJ5aswyEfUBTsprKBXhn6hKtnvo/T3It6uLYRgNRCvZek82\nRA+G0Sn29TXAjkMegY6gBdaR2RZ5594jWpLiA/IVMt7F+3ksqvz1e48mFcKtZ7JqEbce0py7ABVA\nDEFGytSEx4nyXH1D7XPmgqwes36w4/4uvVK3dDxWkH+g3tRxnIS7fIphGJ2IPVFuhp/nNB4JjrYh\nr4VrG6B56EbfC4XZVvN++g/LTcmmTRWof6tRv/RFYcsrkYdobGDZlHyEo30mojzJr5H0TjPSHuyo\nofssW8Sl5TzaS5OsmttoxMuo0r2S8PqPkaE7Hnc9znKUE3ZuIt80BcMw6pCDkUv/Oe/nId7PK1AT\n6nfQA9mFuwK/b4Melr+iYMwBPFjmGD+kUJ1ZanFthm3ky6iY9UvErK8FTcgr43tgBiGD06UVUiOT\nRqJmC5Ta4BsPw1AHhaQt2fzq1/7ez5G0Dx/WmiEoArEVqrI/GhVHuLIZmvB+gbyMs1AXiErJ6pwb\nhtGJeIJCMu2a6KYUVJDvA/zd8VjhPJrbI7a5rMwxugPTiC+i2AvlZN3vOCbDcGWNvAdQhzyAqiqD\nrAtcnfA4P0JVrI94r3ugvL68vXM+o1Gf1gW4505OphDNaEH5c0nPSxRZnXPDMDoRYb22yRHblOqJ\nGCTcoimqku5Ch+O4CIkOK7+JYfx/fkNBr2sf1DnDX65HRTZv5TO0uuboiHWjgK8SHuc59BmcF1g3\nAveJYjU4BuWfvYm8a38kmVjvtaHXzRRHJ0CT4aRkdc6NOsZ05oys+Tb0Oqq6zLW8/gfIy+e3yuoV\neD0U3bxdBDDfDvy+CNJX+gfKcbkZzVKnO47JMEBV1L5G1ywkSzEfeWKCi1FMf4onTgNRjti7CY/z\nJPJ4BRvQ9yRfz9w5qNXYmSjsfxDq2uBKuIinDck5BUlTtZ3VOTcMoxPxGYXOAVORcrz/2u8g8LXj\nseLEgoNL0gfmbd4YL0Tixcd7Y0qqc2XUD60k0/PKmi5Eh1TXqfVAGoDFUdrDi0gO41ukn5a03dSZ\nqKLW14tcAXgWiRDnxT7lNynJPGRgBSt0Zwd+/xD4PsVxszrnRh2TZ8Nso2PyH3TjiPO+dQE2xi1h\n/XZUpRhnsLUi3aRdYt6P4itUFBGuNLwCteUx6ovVgKMCrxfS/r61KPKO7V2rQYXYHHg4p7/diDSh\nPNplUN/mv9Peo1+OPkgncjvveIuj/LkDqLxnbCX0QVI6P0CG2T24y+a8hLprlLrfbUE6Iyx8zp/x\n1s9NcSzDMDoBuzlss6vjsZbPaJsgk4iuejs18LvNWOuH3ujBMwF9Ro+hnMsJgXU3oPykvPg7CrHt\nSrFItuFO2n7GTah62K/MXC6b4aRiFIpEzEDX7EuoBdfKjvuPd9gmja4myMhcikKKyijy9WIaGWOe\nOSNrHkGK+HejG1uldEez3JkUwrPboHy5KSTvxTgR3ciepXD990QG3DSUR7oxHV9WopFYmkJ+z+HA\nxRHb3A7sWLMRFdMfJbyvhHTmuqDQ/eM5jafeuBe4CnmpxiN5oSDNqCtMFlIZ15NOMiUL7kPV9cH+\nzYNQy6zDHPb/AhWMBb1lC1Fo9UPgForzf105Dt33wjnyf0Pi60YHoDXvARgdjpNQn8qp6OZ0B6rI\nei/FsWZ7xzgN3cjwfu6ElPYnImmRKMmSOIahm2O4GfYH3romyotsGrUlmKgd1a1jOMnbumXJF97P\nd9B1Pg715bwIVTh2dqYhGRGQN2hpb51PM26dDVqQN/ZnKCx7AfAX771FUDXo1uRnzE2l2JADTWg/\nctz/RuAE2oc+m9G9aTyq6J+WcFyLouKxsShv7hMUcs1Cw84wjE5ApY2nv6BYBf0IVPRwGgWjK3zz\nLMfhlBfyjGvLY+TP/sBDqO/lwchL9xn5amZdCvwZXa9vIk/ID3IcT70Tburem0JlcCkOAz4H7kQG\nzTzkZR+BKtM/QpXpeeHfl4KMQZ5JF8p1fOhGwXhNwiHezyYK6SStwOspjmUYRicnTePp2wK/D0Ge\nuvtC24S16MoxE7VaCtMz4XGM/PgRujZeQcr2J5Ov+v/3KLyXp3ewUTgyYl134HyHfZ+guHBqT5TW\n8TkKGbr2QK0WY5CBdDuaZP4b3bPWy/Bv3Jlin1NRispqwE+R/uG7mByTYRglcHmgud50p6JZez9k\nBH5D+/Dok+5DAxTO2Thi/cEJj2Pki9/GaXnyLzrYPOe/3wjsjkKkj6Fw4YTAcgnRepRhrgq9bkLe\nuMsptPfKO0ViGHAiyp07hmQeWpcJ5bQUY2pB3xPfa7gmmlivmOJYhmF0El5ChtG4wLInqvT7EcmE\nqvcH5qCG3vNpLz1xPAq7JuEFstGrM/Kh3ts4+XTBvY1TZ6AXqjp+HuV9BZdLgE0djnFzxLpgB5hm\nlHOWJ6OQ/l0aShVJNKEUkYdSHPd9YI80AzIaB6tmNbJmMrrBzgmtb0FetZ+j3DdXF/8KKDzwIgpb\ngG7apyCP3TvoYeDKAaiS9YvAuiYk+HlKguMY+fAccCuqEvSLC0ag9lnr5zSmnwC/Rd5C/57aG00Q\nBuU0pnqkCemcpanIBLeJ20J0r6kVd3l/80PkZVwBFXl8iv7fNiTX9EzcAQIsQJ7G8MSyGV1b3yGj\n9+WEY/wz8ob+N7R+AxTxMDoAVs1qZM1xaPYdx4PAWagKtRx/QtWwd1IsQuwXQaThSqK9cLdFrDPq\nD7+N03GBdXm3cdoDVXGvgyYK/0NSN5ZgXsxCZICPJ51s0WPIkI8TJO+Km85llmyDNA5/jca1EEkm\nveT93tt7byeHY72McoLDRus85F27H+XgJWUOyuF7KbCuCRWLWEVrB8GMOSNrShlyAB/j3iz6UWT0\n/RklP9+JjMGkavFBroxY1xPl4+1VwXGN2vANxWGsFVAl6z/yGQ4gPbmHUBL+rmgSMs3J3xC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SxHYwdguPf7asA5SGzUXzbBEt87G28R/RBau9YDMZz5A/quvof05KJyXC+p6Yg6PgsoXzw2L7fR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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x112222b90>"
       ]
      }
     ],
     "prompt_number": 112
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Is the *across poll* difference larger than the *between pollster* difference? "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: For this question, we can compare the variability *within* each pollster (*across* a set of polls) compared to the variability *between* each pollster.  From these two visualization it is clear that the *between* pollster difference is larger. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(d)\n",
      "\n",
      "Take the average for each pollster and then compute the average of that. Given this difference how confident would you have been of an Obama victory?\n",
      "\n",
      "**Hint**: Compute an estimate of the SE of this average based exclusively on the observed data. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "aggr  = filtered.groupby(\"Pollster\").mean()\n",
      "print \"Average across pollsters: %g\" % np.round(np.mean(aggr.Diff),4)\n",
      "print \"Standard error: %g\" % np.std(aggr.Diff, ddof = 0)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Average across pollsters: 0.0124\n",
        "Standard error: 0.0129669\n"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: Given the large standard error, I would not have been very confident in the Obama victory. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 3(e)\n",
      "\n",
      "**For AC209 Students**: Show the difference against time and see if you can detect a trend towards the end. Use this trend to see if it improves the final estimate."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "three_months = dt.datetime(2012,8,15,0,0)\n",
      "\n",
      "new_data = election [map(lambda x: x >= three_months , election[\"Start Date\"]) ]\n",
      "new_data = new_data.sort(\"Start Date\")\n",
      "new_data[\"days\"]= map(lambda x: (x - three_months).days , new_data[\"Start Date\"])\n",
      "\n",
      "new_data[\"Diff\"] = (new_data.Obama/100) - (new_data.Romney/100)\n",
      "new_data = new_data.groupby([\"days\"], as_index=False).mean()\n",
      "\n",
      "plt.figure()\n",
      "plt.plot(new_data.days, new_data.Diff )\n",
      "plt.xlabel(\"Days from three month before the election\")\n",
      "plt.ylabel(\"Difference (Obama - Romney)\")\n",
      "plt.title(\"Difference between Obama and Romney across time\")\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TSyYA58bEbnJ4dgIDg5Ut7QR9fiDdyiRcRDpvJDzDfDxGKNiUNfJlF0FJI1nw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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x1126f7950>"
       ]
      }
     ],
     "prompt_number": 114
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Answer: Around fifty days before the election, there was a change resulting a positive difference between Obama and Romney in the polls. "
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Discussion for Problem 3\n",
      "\n",
      "*Write a brief discussion of your conclusions to the questions and tasks above in 100 words or less.*\n",
      "\n",
      "Yes, the average of polls is better than just one poll because there can be a large amount of variability between pollsters.  \n",
      "\n",
      "---\n"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Problem 4\n",
      "\n",
      "In this last problem, we will use the polls from the [2014 Senate Midterm Elections](http://elections.huffingtonpost.com/pollster) from the [HuffPost Pollster API](http://elections.huffingtonpost.com/pollster/api) to create a preliminary prediction of the result of each state. \n",
      "\n",
      "The HuffPost Pollster API allows you to access the data as a CSV or a JSON response by tacking \".csv\" or \".json\" at the end of the URLs. For example the 2012 Presidential Election could be accessed as a [.json](http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.json) instead of a [.csv](http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv)"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 4(a)\n",
      "\n",
      "Read in the polls for **all** of the 2014 Senate Elections using the HuffPost API. For example, we can consider the [2014 Senate race in Kentucky between Mitch McConnell and Alison Grimes](http://elections.huffingtonpost.com/pollster/2014-kentucky-senate-mcconnell-vs-grimes). \n",
      "\n",
      "To search for the 2014 Senate races, use the `topics` parameter in the API [[read more about topics here](http://elections.huffingtonpost.com/pollster/api)].  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "url_str = \"http://elections.huffingtonpost.com/pollster/api/charts/?topic=2014-senate\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 115
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To list all the URLs related to the 2014 Senate races using the pollster API, we can use a list comprehension:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "election_urls = [election['url'] + '.csv' for election in requests.get(url_str).json()]\n",
      "election_urls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 116,
       "text": [
        "[u'http://elections.huffingtonpost.com/pollster/2014-kentucky-senate-mcconnell-vs-grimes.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-arkansas-senate-cotton-vs-pryor.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-michigan-senate-land-vs-peters.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-louisiana-senate-cassidy-vs-landrieu.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-new-hampshire-senate-brown-vs-shaheen.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-west-virginia-senate-capito-vs-tennant.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-new-hampshire-senate-bass-vs-shaheen.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-north-carolina-senate-tillis-vs-hagan.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-virginia-senate-gillespie-vs-warner.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-colorado-senate-gardner-vs-udall.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-illinois-senate-oberweis-vs-durbin.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-alaska-senate-sullivan-vs-begich.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-iowa-senate-ernst-vs-braley.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-mississippi-senate-cochran-vs-childers.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-oregon-senate-wehby-vs-merkley.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-georgia-senate-perdue-vs-nunn.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-nebraska-senate-sasse-vs-domina.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-south-dakota-senate-rounds-vs-weiland.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-maine-senate-collins-vs-bellows.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-minnesota-senate-mcfadden-vs-franken.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-texas-senate-cornyn-vs-alameel.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-south-carolina-senate-graham-vs-hutto.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-south-carolina-senate-scott-vs-dickerson.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-oklahoma-senate-inhofe-vs-silverstein.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-new-mexico-senate-weh-vs-udall.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-new-jersey-senate-bell-vs-booker.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-idaho-senate-risch-vs-mitchell.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-tennessee-senate-alexander-vs-ball.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-wyoming-senate.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-kansas-senate-roberts-vs-orman-vs-taylor.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-hawaii-senate-cavasso-vs-schatz.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-oklahoma-senate-lankford-vs-johnson.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-montana-senate-daines-vs-curtis.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-rhode-island-senate-zaccaria-vs-reed.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-massachusetts-senate-herr-vs-markey.csv',\n",
        " u'http://elections.huffingtonpost.com/pollster/2014-delaware-senate-wade-vs-coons.csv']"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Because there so many Senate races, we can create a dictionary of pandas DataFrames that will be keyed by the name of the election (a string). "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def build_frame(url):\n",
      "    \"\"\"\n",
      "    Returns a pandas DataFrame object containing\n",
      "    the data returned from the given url\n",
      "    \"\"\"\n",
      "    source = requests.get(url).text\n",
      "    \n",
      "    # Use StringIO because pd.DataFrame.from_csv requires .read() method\n",
      "    s = StringIO(source)\n",
      "    \n",
      "    return pd.DataFrame.from_csv(s, index_col=None).convert_objects(\n",
      "            convert_dates=\"coerce\", convert_numeric=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 117
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Makes a dictionary of pandas DataFrames keyed on election string.\n",
      "dfs = dict((election.split(\"/\")[-1][:-4], build_frame(election)) for election in election_urls)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 118
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Show the head of the DataFrame containing the polls for the 2014 Senate race in Kentucky between McConnell and Grimes."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "dfs['2014-kentucky-senate-mcconnell-vs-grimes'].head()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Pollster</th>\n",
        "      <th>Start Date</th>\n",
        "      <th>End Date</th>\n",
        "      <th>Entry Date/Time (ET)</th>\n",
        "      <th>Number of Observations</th>\n",
        "      <th>Population</th>\n",
        "      <th>Mode</th>\n",
        "      <th>McConnell</th>\n",
        "      <th>Grimes</th>\n",
        "      <th>Undecided</th>\n",
        "      <th>Pollster URL</th>\n",
        "      <th>Source URL</th>\n",
        "      <th>Partisan</th>\n",
        "      <th>Affiliation</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> SurveyUSA/Courier-Journal/Herald-Leader/WHAS/WKYT</td>\n",
        "      <td>2014-09-29</td>\n",
        "      <td>2014-10-02</td>\n",
        "      <td>2014-10-06 15:54:36</td>\n",
        "      <td>  632</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>      IVR/Online</td>\n",
        "      <td> 44</td>\n",
        "      <td> 46</td>\n",
        "      <td>  7</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td> http://www.courier-journal.com/story/news/poli...</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>                                    CBS/NYT/YouGov</td>\n",
        "      <td>2014-09-20</td>\n",
        "      <td>2014-10-01</td>\n",
        "      <td>2014-10-05 15:42:23</td>\n",
        "      <td> 1689</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>        Internet</td>\n",
        "      <td> 47</td>\n",
        "      <td> 41</td>\n",
        "      <td>  9</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td> https://today.yougov.com/news/2014/09/07/battl...</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>                                Mellman (D-Grimes)</td>\n",
        "      <td>2014-09-19</td>\n",
        "      <td>2014-09-27</td>\n",
        "      <td>2014-10-01 15:13:47</td>\n",
        "      <td> 1800</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>      Live Phone</td>\n",
        "      <td> 40</td>\n",
        "      <td> 42</td>\n",
        "      <td> 16</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td> http://images.politico.com/global/2014/09/30/m...</td>\n",
        "      <td>     Sponsor</td>\n",
        "      <td>  Dem</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>                           Gravis/Human Events (R)</td>\n",
        "      <td>2014-09-13</td>\n",
        "      <td>2014-09-16</td>\n",
        "      <td>2014-09-19 14:57:03</td>\n",
        "      <td>  839</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td> Automated Phone</td>\n",
        "      <td> 51</td>\n",
        "      <td> 41</td>\n",
        "      <td>  8</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td> http://gravismarketing.com/polling-and-market-...</td>\n",
        "      <td>     Sponsor</td>\n",
        "      <td>  Rep</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>                                     Ipsos/Reuters</td>\n",
        "      <td>2014-09-08</td>\n",
        "      <td>2014-09-12</td>\n",
        "      <td>2014-09-22 15:23:23</td>\n",
        "      <td>  944</td>\n",
        "      <td> Likely Voters</td>\n",
        "      <td>        Internet</td>\n",
        "      <td> 46</td>\n",
        "      <td> 42</td>\n",
        "      <td>  6</td>\n",
        "      <td> http://elections.huffingtonpost.com/pollster/p...</td>\n",
        "      <td>     http://ipsos-na.com/download/pr.aspx?id=13904</td>\n",
        "      <td> Nonpartisan</td>\n",
        "      <td> None</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 119,
       "text": [
        "                                            Pollster Start Date   End Date  \\\n",
        "0  SurveyUSA/Courier-Journal/Herald-Leader/WHAS/WKYT 2014-09-29 2014-10-02   \n",
        "1                                     CBS/NYT/YouGov 2014-09-20 2014-10-01   \n",
        "2                                 Mellman (D-Grimes) 2014-09-19 2014-09-27   \n",
        "3                            Gravis/Human Events (R) 2014-09-13 2014-09-16   \n",
        "4                                      Ipsos/Reuters 2014-09-08 2014-09-12   \n",
        "\n",
        "  Entry Date/Time (ET)  Number of Observations     Population  \\\n",
        "0  2014-10-06 15:54:36                     632  Likely Voters   \n",
        "1  2014-10-05 15:42:23                    1689  Likely Voters   \n",
        "2  2014-10-01 15:13:47                    1800  Likely Voters   \n",
        "3  2014-09-19 14:57:03                     839  Likely Voters   \n",
        "4  2014-09-22 15:23:23                     944  Likely Voters   \n",
        "\n",
        "              Mode  McConnell  Grimes  Undecided  \\\n",
        "0       IVR/Online         44      46          7   \n",
        "1         Internet         47      41          9   \n",
        "2       Live Phone         40      42         16   \n",
        "3  Automated Phone         51      41          8   \n",
        "4         Internet         46      42          6   \n",
        "\n",
        "                                        Pollster URL  \\\n",
        "0  http://elections.huffingtonpost.com/pollster/p...   \n",
        "1  http://elections.huffingtonpost.com/pollster/p...   \n",
        "2  http://elections.huffingtonpost.com/pollster/p...   \n",
        "3  http://elections.huffingtonpost.com/pollster/p...   \n",
        "4  http://elections.huffingtonpost.com/pollster/p...   \n",
        "\n",
        "                                          Source URL     Partisan Affiliation  \n",
        "0  http://www.courier-journal.com/story/news/poli...  Nonpartisan        None  \n",
        "1  https://today.yougov.com/news/2014/09/07/battl...  Nonpartisan        None  \n",
        "2  http://images.politico.com/global/2014/09/30/m...      Sponsor         Dem  \n",
        "3  http://gravismarketing.com/polling-and-market-...      Sponsor         Rep  \n",
        "4      http://ipsos-na.com/download/pr.aspx?id=13904  Nonpartisan        None  "
       ]
      }
     ],
     "prompt_number": 119
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "#### Problem 4(b)\n",
      "\n",
      "For each 2014 Senate race, create a preliminary prediction of the result for that state.\n",
      "\n",
      "This is a very crude way of making a preliminary prediction: compute the difference between each candidate using all the polls in each race and predict the winner based on if the differences is positive or negative.  We want you to be creative and use the tools you are learning in class to make more accurate predictions. "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#your code here\n",
      "x = {}\n",
      "for keys in dfs:\n",
      "    dat = dfs[keys]\n",
      "    candidate1 = dat.columns[7] \n",
      "    candidate2 = dat.columns[8] \n",
      "    dat.Diff = (dat[candidate1]/100) - (dat[candidate2]/100)\n",
      "    x[keys] = [candidate1, candidate2, np.round(np.mean(dat.Diff), 3)]\n",
      "\n",
      "predictions = pd.DataFrame(x).T  \n",
      "predictions.columns = ['Candidate1', 'Candidate2', 'Difference']\n",
      "predictions['Winner'] = np.where(predictions.Difference >=0, \n",
      "                                 predictions.Candidate1, predictions.Candidate2)\n",
      "predictions"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>Candidate1</th>\n",
        "      <th>Candidate2</th>\n",
        "      <th>Difference</th>\n",
        "      <th>Winner</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2014-alaska-senate-sullivan-vs-begich</th>\n",
        "      <td>  Sullivan</td>\n",
        "      <td>      Begich</td>\n",
        "      <td>-0.013</td>\n",
        "      <td>    Begich</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-arkansas-senate-cotton-vs-pryor</th>\n",
        "      <td>    Cotton</td>\n",
        "      <td>       Pryor</td>\n",
        "      <td> 0.008</td>\n",
        "      <td>    Cotton</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-colorado-senate-gardner-vs-udall</th>\n",
        "      <td>   Gardner</td>\n",
        "      <td>       Udall</td>\n",
        "      <td>-0.011</td>\n",
        "      <td>     Udall</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-delaware-senate-wade-vs-coons</th>\n",
        "      <td>     Coons</td>\n",
        "      <td>        Wade</td>\n",
        "      <td> 0.173</td>\n",
        "      <td>     Coons</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-georgia-senate-perdue-vs-nunn</th>\n",
        "      <td>    Perdue</td>\n",
        "      <td>        Nunn</td>\n",
        "      <td> 0.022</td>\n",
        "      <td>    Perdue</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-hawaii-senate-cavasso-vs-schatz</th>\n",
        "      <td>    Schatz</td>\n",
        "      <td>     Cavasso</td>\n",
        "      <td> 0.388</td>\n",
        "      <td>    Schatz</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-idaho-senate-risch-vs-mitchell</th>\n",
        "      <td>     Risch</td>\n",
        "      <td>    Mitchell</td>\n",
        "      <td> 0.325</td>\n",
        "      <td>     Risch</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-illinois-senate-oberweis-vs-durbin</th>\n",
        "      <td>    Durbin</td>\n",
        "      <td>    Oberweis</td>\n",
        "      <td> 0.121</td>\n",
        "      <td>    Durbin</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-iowa-senate-ernst-vs-braley</th>\n",
        "      <td>     Ernst</td>\n",
        "      <td>      Braley</td>\n",
        "      <td>-0.011</td>\n",
        "      <td>    Braley</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-kansas-senate-roberts-vs-orman-vs-taylor</th>\n",
        "      <td>     Orman</td>\n",
        "      <td>     Roberts</td>\n",
        "      <td>-0.044</td>\n",
        "      <td>   Roberts</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-kentucky-senate-mcconnell-vs-grimes</th>\n",
        "      <td> McConnell</td>\n",
        "      <td>      Grimes</td>\n",
        "      <td> 0.024</td>\n",
        "      <td> McConnell</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-louisiana-senate-cassidy-vs-landrieu</th>\n",
        "      <td>   Cassidy</td>\n",
        "      <td>    Landrieu</td>\n",
        "      <td> 0.001</td>\n",
        "      <td>   Cassidy</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-maine-senate-collins-vs-bellows</th>\n",
        "      <td>   Collins</td>\n",
        "      <td>     Bellows</td>\n",
        "      <td> 0.342</td>\n",
        "      <td>   Collins</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-massachusetts-senate-herr-vs-markey</th>\n",
        "      <td>    Markey</td>\n",
        "      <td>        Herr</td>\n",
        "      <td>  0.24</td>\n",
        "      <td>    Markey</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-michigan-senate-land-vs-peters</th>\n",
        "      <td>    Peters</td>\n",
        "      <td>        Land</td>\n",
        "      <td> 0.036</td>\n",
        "      <td>    Peters</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-minnesota-senate-mcfadden-vs-franken</th>\n",
        "      <td>   Franken</td>\n",
        "      <td>    McFadden</td>\n",
        "      <td> 0.105</td>\n",
        "      <td>   Franken</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-mississippi-senate-cochran-vs-childers</th>\n",
        "      <td>   Cochran</td>\n",
        "      <td>    Childers</td>\n",
        "      <td> 0.146</td>\n",
        "      <td>   Cochran</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-montana-senate-daines-vs-curtis</th>\n",
        "      <td>    Daines</td>\n",
        "      <td>      Curtis</td>\n",
        "      <td> 0.197</td>\n",
        "      <td>    Daines</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-nebraska-senate-sasse-vs-domina</th>\n",
        "      <td>     Sasse</td>\n",
        "      <td>      Domina</td>\n",
        "      <td> 0.236</td>\n",
        "      <td>     Sasse</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-new-hampshire-senate-bass-vs-shaheen</th>\n",
        "      <td>   Shaheen</td>\n",
        "      <td>        Bass</td>\n",
        "      <td> 0.153</td>\n",
        "      <td>   Shaheen</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-new-hampshire-senate-brown-vs-shaheen</th>\n",
        "      <td>   Shaheen</td>\n",
        "      <td>       Brown</td>\n",
        "      <td>  0.07</td>\n",
        "      <td>   Shaheen</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-new-jersey-senate-bell-vs-booker</th>\n",
        "      <td>    Booker</td>\n",
        "      <td>        Bell</td>\n",
        "      <td> 0.131</td>\n",
        "      <td>    Booker</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-new-mexico-senate-weh-vs-udall</th>\n",
        "      <td>     Udall</td>\n",
        "      <td>         Weh</td>\n",
        "      <td> 0.161</td>\n",
        "      <td>     Udall</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-north-carolina-senate-tillis-vs-hagan</th>\n",
        "      <td>     Hagan</td>\n",
        "      <td>      Tillis</td>\n",
        "      <td> 0.034</td>\n",
        "      <td>     Hagan</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-oklahoma-senate-inhofe-vs-silverstein</th>\n",
        "      <td>    Inhofe</td>\n",
        "      <td> Silverstein</td>\n",
        "      <td> 0.322</td>\n",
        "      <td>    Inhofe</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-oklahoma-senate-lankford-vs-johnson</th>\n",
        "      <td>  Lankford</td>\n",
        "      <td>     Johnson</td>\n",
        "      <td> 0.315</td>\n",
        "      <td>  Lankford</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-oregon-senate-wehby-vs-merkley</th>\n",
        "      <td>   Merkley</td>\n",
        "      <td>       Wehby</td>\n",
        "      <td>  0.12</td>\n",
        "      <td>   Merkley</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-rhode-island-senate-zaccaria-vs-reed</th>\n",
        "      <td>      Reed</td>\n",
        "      <td>    Zaccaria</td>\n",
        "      <td> 0.323</td>\n",
        "      <td>      Reed</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-south-carolina-senate-graham-vs-hutto</th>\n",
        "      <td>    Graham</td>\n",
        "      <td>       Hutto</td>\n",
        "      <td>  0.15</td>\n",
        "      <td>    Graham</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-south-carolina-senate-scott-vs-dickerson</th>\n",
        "      <td>     Scott</td>\n",
        "      <td>   Dickerson</td>\n",
        "      <td>   0.2</td>\n",
        "      <td>     Scott</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-south-dakota-senate-rounds-vs-weiland</th>\n",
        "      <td>    Rounds</td>\n",
        "      <td>     Weiland</td>\n",
        "      <td> 0.134</td>\n",
        "      <td>    Rounds</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-tennessee-senate-alexander-vs-ball</th>\n",
        "      <td> Alexander</td>\n",
        "      <td>        Ball</td>\n",
        "      <td> 0.184</td>\n",
        "      <td> Alexander</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-texas-senate-cornyn-vs-alameel</th>\n",
        "      <td>    Cornyn</td>\n",
        "      <td>     Alameel</td>\n",
        "      <td> 0.173</td>\n",
        "      <td>    Cornyn</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-virginia-senate-gillespie-vs-warner</th>\n",
        "      <td>    Warner</td>\n",
        "      <td>   Gillespie</td>\n",
        "      <td>  0.17</td>\n",
        "      <td>    Warner</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-west-virginia-senate-capito-vs-tennant</th>\n",
        "      <td>    Capito</td>\n",
        "      <td>     Tennant</td>\n",
        "      <td> 0.135</td>\n",
        "      <td>    Capito</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2014-wyoming-senate</th>\n",
        "      <td>      Enzi</td>\n",
        "      <td>       Hardy</td>\n",
        "      <td>  0.46</td>\n",
        "      <td>      Enzi</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 120,
       "text": [
        "                                              Candidate1   Candidate2  \\\n",
        "2014-alaska-senate-sullivan-vs-begich           Sullivan       Begich   \n",
        "2014-arkansas-senate-cotton-vs-pryor              Cotton        Pryor   \n",
        "2014-colorado-senate-gardner-vs-udall            Gardner        Udall   \n",
        "2014-delaware-senate-wade-vs-coons                 Coons         Wade   \n",
        "2014-georgia-senate-perdue-vs-nunn                Perdue         Nunn   \n",
        "2014-hawaii-senate-cavasso-vs-schatz              Schatz      Cavasso   \n",
        "2014-idaho-senate-risch-vs-mitchell                Risch     Mitchell   \n",
        "2014-illinois-senate-oberweis-vs-durbin           Durbin     Oberweis   \n",
        "2014-iowa-senate-ernst-vs-braley                   Ernst       Braley   \n",
        "2014-kansas-senate-roberts-vs-orman-vs-taylor      Orman      Roberts   \n",
        "2014-kentucky-senate-mcconnell-vs-grimes       McConnell       Grimes   \n",
        "2014-louisiana-senate-cassidy-vs-landrieu        Cassidy     Landrieu   \n",
        "2014-maine-senate-collins-vs-bellows             Collins      Bellows   \n",
        "2014-massachusetts-senate-herr-vs-markey          Markey         Herr   \n",
        "2014-michigan-senate-land-vs-peters               Peters         Land   \n",
        "2014-minnesota-senate-mcfadden-vs-franken        Franken     McFadden   \n",
        "2014-mississippi-senate-cochran-vs-childers      Cochran     Childers   \n",
        "2014-montana-senate-daines-vs-curtis              Daines       Curtis   \n",
        "2014-nebraska-senate-sasse-vs-domina               Sasse       Domina   \n",
        "2014-new-hampshire-senate-bass-vs-shaheen        Shaheen         Bass   \n",
        "2014-new-hampshire-senate-brown-vs-shaheen       Shaheen        Brown   \n",
        "2014-new-jersey-senate-bell-vs-booker             Booker         Bell   \n",
        "2014-new-mexico-senate-weh-vs-udall                Udall          Weh   \n",
        "2014-north-carolina-senate-tillis-vs-hagan         Hagan       Tillis   \n",
        "2014-oklahoma-senate-inhofe-vs-silverstein        Inhofe  Silverstein   \n",
        "2014-oklahoma-senate-lankford-vs-johnson        Lankford      Johnson   \n",
        "2014-oregon-senate-wehby-vs-merkley              Merkley        Wehby   \n",
        "2014-rhode-island-senate-zaccaria-vs-reed           Reed     Zaccaria   \n",
        "2014-south-carolina-senate-graham-vs-hutto        Graham        Hutto   \n",
        "2014-south-carolina-senate-scott-vs-dickerson      Scott    Dickerson   \n",
        "2014-south-dakota-senate-rounds-vs-weiland        Rounds      Weiland   \n",
        "2014-tennessee-senate-alexander-vs-ball        Alexander         Ball   \n",
        "2014-texas-senate-cornyn-vs-alameel               Cornyn      Alameel   \n",
        "2014-virginia-senate-gillespie-vs-warner          Warner    Gillespie   \n",
        "2014-west-virginia-senate-capito-vs-tennant       Capito      Tennant   \n",
        "2014-wyoming-senate                                 Enzi        Hardy   \n",
        "\n",
        "                                              Difference     Winner  \n",
        "2014-alaska-senate-sullivan-vs-begich             -0.013     Begich  \n",
        "2014-arkansas-senate-cotton-vs-pryor               0.008     Cotton  \n",
        "2014-colorado-senate-gardner-vs-udall             -0.011      Udall  \n",
        "2014-delaware-senate-wade-vs-coons                 0.173      Coons  \n",
        "2014-georgia-senate-perdue-vs-nunn                 0.022     Perdue  \n",
        "2014-hawaii-senate-cavasso-vs-schatz               0.388     Schatz  \n",
        "2014-idaho-senate-risch-vs-mitchell                0.325      Risch  \n",
        "2014-illinois-senate-oberweis-vs-durbin            0.121     Durbin  \n",
        "2014-iowa-senate-ernst-vs-braley                  -0.011     Braley  \n",
        "2014-kansas-senate-roberts-vs-orman-vs-taylor     -0.044    Roberts  \n",
        "2014-kentucky-senate-mcconnell-vs-grimes           0.024  McConnell  \n",
        "2014-louisiana-senate-cassidy-vs-landrieu          0.001    Cassidy  \n",
        "2014-maine-senate-collins-vs-bellows               0.342    Collins  \n",
        "2014-massachusetts-senate-herr-vs-markey            0.24     Markey  \n",
        "2014-michigan-senate-land-vs-peters                0.036     Peters  \n",
        "2014-minnesota-senate-mcfadden-vs-franken          0.105    Franken  \n",
        "2014-mississippi-senate-cochran-vs-childers        0.146    Cochran  \n",
        "2014-montana-senate-daines-vs-curtis               0.197     Daines  \n",
        "2014-nebraska-senate-sasse-vs-domina               0.236      Sasse  \n",
        "2014-new-hampshire-senate-bass-vs-shaheen          0.153    Shaheen  \n",
        "2014-new-hampshire-senate-brown-vs-shaheen          0.07    Shaheen  \n",
        "2014-new-jersey-senate-bell-vs-booker              0.131     Booker  \n",
        "2014-new-mexico-senate-weh-vs-udall                0.161      Udall  \n",
        "2014-north-carolina-senate-tillis-vs-hagan         0.034      Hagan  \n",
        "2014-oklahoma-senate-inhofe-vs-silverstein         0.322     Inhofe  \n",
        "2014-oklahoma-senate-lankford-vs-johnson           0.315   Lankford  \n",
        "2014-oregon-senate-wehby-vs-merkley                 0.12    Merkley  \n",
        "2014-rhode-island-senate-zaccaria-vs-reed          0.323       Reed  \n",
        "2014-south-carolina-senate-graham-vs-hutto          0.15     Graham  \n",
        "2014-south-carolina-senate-scott-vs-dickerson        0.2      Scott  \n",
        "2014-south-dakota-senate-rounds-vs-weiland         0.134     Rounds  \n",
        "2014-tennessee-senate-alexander-vs-ball            0.184  Alexander  \n",
        "2014-texas-senate-cornyn-vs-alameel                0.173     Cornyn  \n",
        "2014-virginia-senate-gillespie-vs-warner            0.17     Warner  \n",
        "2014-west-virginia-senate-capito-vs-tennant        0.135     Capito  \n",
        "2014-wyoming-senate                                 0.46       Enzi  "
       ]
      }
     ],
     "prompt_number": 120
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Submission Instructions\n",
      "\n",
      "To submit your homework, create a folder named **lastname_firstinitial_hw#** and place your IPython notebooks, data files, and any other files in this folder. Your IPython Notebooks should be completely executed with the results visible in the notebook. We should not have to run any code.  Compress the folder (please use .zip compression) and submit to the CS109 dropbox in the appropriate folder. *If we cannot access your work because these directions are not followed correctly, we will not grade your work.*\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 120
    }
   ],
   "metadata": {}
  }
 ]
}