{
 "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",
    "---"
   ]
  },
  {
   "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",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# special IPython command to prepare the notebook for matplotlib\n",
    "%matplotlib inline \n",
    "\n",
    "import requests \n",
    "# fix stringio for python 3 version\n",
    "#from StringIO import StringIO\n",
    "from io import StringIO\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd # pandas\n",
    "import matplotlib.pyplot as plt # module for plotting \n",
    "import seaborn as sns\n",
    "import datetime as dt # module for manipulating dates and times\n",
    "import numpy.linalg as lin # module for performing linear algebra operations\n",
    "import os"
   ]
  },
  {
   "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) \n",
    "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",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "file exprs_GSE5859.csv downloaded succesfully\n",
      "file sampleinfo_GSE5859.csv downloaded succesfully\n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "exprs__url = \"https://github.com/cs109/2014_data/raw/master/exprs_GSE5859.csv\"\n",
    "sampleinfo_url = \"https://github.com/cs109/2014_data/raw/master/sampleinfo_GSE5859.csv\"\n",
    "\n",
    "urls = [exprs__url, sampleinfo_url]\n",
    "filenames = [\"exprs_GSE5859.csv\", \"sampleinfo_GSE5859.csv\"]\n",
    "\n",
    "for url, filename in zip(urls, filenames):\n",
    "    r = requests.get(url, stream=True)\n",
    "    with open(\"data/\"+filename, 'wb') as fd:\n",
    "        for chunk in r.iter_content(chunk_size=256):\n",
    "            fd.write(chunk)\n",
    "    print(f\"file {filename} downloaded succesfully\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Double checking that our files are in the data dir:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['exprs_GSE5859.csv', 'sampleinfo_GSE5859.csv']"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[file for file in os.listdir(\"data\") if file in filenames]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>GSM25581.CEL.gz</th>\n",
       "      <th>GSM25681.CEL.gz</th>\n",
       "      <th>GSM136524.CEL.gz</th>\n",
       "      <th>GSM136707.CEL.gz</th>\n",
       "      <th>GSM25553.CEL.gz</th>\n",
       "      <th>GSM136676.CEL.gz</th>\n",
       "      <th>GSM136711.CEL.gz</th>\n",
       "      <th>GSM136542.CEL.gz</th>\n",
       "      <th>GSM136535.CEL.gz</th>\n",
       "      <th>GSM25399.CEL.gz</th>\n",
       "      <th>...</th>\n",
       "      <th>GSM48650.CEL.gz</th>\n",
       "      <th>GSM25687.CEL.gz</th>\n",
       "      <th>GSM25685.CEL.gz</th>\n",
       "      <th>GSM136549.CEL.gz</th>\n",
       "      <th>GSM25427.CEL.gz</th>\n",
       "      <th>GSM25525.CEL.gz</th>\n",
       "      <th>GSM25349.CEL.gz</th>\n",
       "      <th>GSM136727.CEL.gz</th>\n",
       "      <th>GSM25626.CEL.gz</th>\n",
       "      <th>GSM136725.CEL.gz</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1007_s_at</th>\n",
       "      <td>6.333951</td>\n",
       "      <td>5.736190</td>\n",
       "      <td>6.156754</td>\n",
       "      <td>6.513010</td>\n",
       "      <td>6.061013</td>\n",
       "      <td>6.506493</td>\n",
       "      <td>6.863426</td>\n",
       "      <td>6.369645</td>\n",
       "      <td>6.646321</td>\n",
       "      <td>5.810930</td>\n",
       "      <td>...</td>\n",
       "      <td>7.082636</td>\n",
       "      <td>6.315866</td>\n",
       "      <td>7.010165</td>\n",
       "      <td>6.460107</td>\n",
       "      <td>6.122355</td>\n",
       "      <td>6.330314</td>\n",
       "      <td>6.627014</td>\n",
       "      <td>6.133068</td>\n",
       "      <td>6.419444</td>\n",
       "      <td>6.488579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1053_at</th>\n",
       "      <td>7.255622</td>\n",
       "      <td>7.399993</td>\n",
       "      <td>7.491967</td>\n",
       "      <td>7.006123</td>\n",
       "      <td>7.250995</td>\n",
       "      <td>7.082581</td>\n",
       "      <td>6.842236</td>\n",
       "      <td>7.048487</td>\n",
       "      <td>7.042361</td>\n",
       "      <td>6.666625</td>\n",
       "      <td>...</td>\n",
       "      <td>6.599718</td>\n",
       "      <td>7.412731</td>\n",
       "      <td>7.274429</td>\n",
       "      <td>6.928642</td>\n",
       "      <td>7.168782</td>\n",
       "      <td>7.235648</td>\n",
       "      <td>6.939184</td>\n",
       "      <td>7.280781</td>\n",
       "      <td>6.351776</td>\n",
       "      <td>7.517410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117_at</th>\n",
       "      <td>5.760106</td>\n",
       "      <td>4.825169</td>\n",
       "      <td>5.039387</td>\n",
       "      <td>5.414160</td>\n",
       "      <td>5.205697</td>\n",
       "      <td>5.300078</td>\n",
       "      <td>5.099337</td>\n",
       "      <td>5.156459</td>\n",
       "      <td>5.736109</td>\n",
       "      <td>5.807496</td>\n",
       "      <td>...</td>\n",
       "      <td>6.231457</td>\n",
       "      <td>5.240717</td>\n",
       "      <td>5.275062</td>\n",
       "      <td>5.759827</td>\n",
       "      <td>5.409720</td>\n",
       "      <td>5.491938</td>\n",
       "      <td>5.113570</td>\n",
       "      <td>5.401876</td>\n",
       "      <td>5.537605</td>\n",
       "      <td>5.247190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121_at</th>\n",
       "      <td>6.935451</td>\n",
       "      <td>7.025210</td>\n",
       "      <td>7.543667</td>\n",
       "      <td>7.959781</td>\n",
       "      <td>7.223323</td>\n",
       "      <td>8.006816</td>\n",
       "      <td>8.102504</td>\n",
       "      <td>7.434617</td>\n",
       "      <td>7.532321</td>\n",
       "      <td>7.711461</td>\n",
       "      <td>...</td>\n",
       "      <td>8.215096</td>\n",
       "      <td>7.677593</td>\n",
       "      <td>7.923624</td>\n",
       "      <td>7.219401</td>\n",
       "      <td>7.432338</td>\n",
       "      <td>6.785174</td>\n",
       "      <td>7.833862</td>\n",
       "      <td>7.607461</td>\n",
       "      <td>7.302935</td>\n",
       "      <td>7.331864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1255_g_at</th>\n",
       "      <td>3.597872</td>\n",
       "      <td>3.108801</td>\n",
       "      <td>3.128269</td>\n",
       "      <td>3.364947</td>\n",
       "      <td>3.125909</td>\n",
       "      <td>3.186834</td>\n",
       "      <td>3.311022</td>\n",
       "      <td>3.092042</td>\n",
       "      <td>3.167737</td>\n",
       "      <td>2.969579</td>\n",
       "      <td>...</td>\n",
       "      <td>3.331424</td>\n",
       "      <td>3.043856</td>\n",
       "      <td>3.151900</td>\n",
       "      <td>3.132253</td>\n",
       "      <td>3.250600</td>\n",
       "      <td>2.984125</td>\n",
       "      <td>3.152269</td>\n",
       "      <td>3.225123</td>\n",
       "      <td>3.222731</td>\n",
       "      <td>3.175846</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 208 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           GSM25581.CEL.gz  GSM25681.CEL.gz  GSM136524.CEL.gz  \\\n",
       "1007_s_at         6.333951         5.736190          6.156754   \n",
       "1053_at           7.255622         7.399993          7.491967   \n",
       "117_at            5.760106         4.825169          5.039387   \n",
       "121_at            6.935451         7.025210          7.543667   \n",
       "1255_g_at         3.597872         3.108801          3.128269   \n",
       "\n",
       "           GSM136707.CEL.gz  GSM25553.CEL.gz  GSM136676.CEL.gz  \\\n",
       "1007_s_at          6.513010         6.061013          6.506493   \n",
       "1053_at            7.006123         7.250995          7.082581   \n",
       "117_at             5.414160         5.205697          5.300078   \n",
       "121_at             7.959781         7.223323          8.006816   \n",
       "1255_g_at          3.364947         3.125909          3.186834   \n",
       "\n",
       "           GSM136711.CEL.gz  GSM136542.CEL.gz  GSM136535.CEL.gz  \\\n",
       "1007_s_at          6.863426          6.369645          6.646321   \n",
       "1053_at            6.842236          7.048487          7.042361   \n",
       "117_at             5.099337          5.156459          5.736109   \n",
       "121_at             8.102504          7.434617          7.532321   \n",
       "1255_g_at          3.311022          3.092042          3.167737   \n",
       "\n",
       "           GSM25399.CEL.gz        ...         GSM48650.CEL.gz  \\\n",
       "1007_s_at         5.810930        ...                7.082636   \n",
       "1053_at           6.666625        ...                6.599718   \n",
       "117_at            5.807496        ...                6.231457   \n",
       "121_at            7.711461        ...                8.215096   \n",
       "1255_g_at         2.969579        ...                3.331424   \n",
       "\n",
       "           GSM25687.CEL.gz  GSM25685.CEL.gz  GSM136549.CEL.gz  \\\n",
       "1007_s_at         6.315866         7.010165          6.460107   \n",
       "1053_at           7.412731         7.274429          6.928642   \n",
       "117_at            5.240717         5.275062          5.759827   \n",
       "121_at            7.677593         7.923624          7.219401   \n",
       "1255_g_at         3.043856         3.151900          3.132253   \n",
       "\n",
       "           GSM25427.CEL.gz  GSM25525.CEL.gz  GSM25349.CEL.gz  \\\n",
       "1007_s_at         6.122355         6.330314         6.627014   \n",
       "1053_at           7.168782         7.235648         6.939184   \n",
       "117_at            5.409720         5.491938         5.113570   \n",
       "121_at            7.432338         6.785174         7.833862   \n",
       "1255_g_at         3.250600         2.984125         3.152269   \n",
       "\n",
       "           GSM136727.CEL.gz  GSM25626.CEL.gz  GSM136725.CEL.gz  \n",
       "1007_s_at          6.133068         6.419444          6.488579  \n",
       "1053_at            7.280781         6.351776          7.517410  \n",
       "117_at             5.401876         5.537605          5.247190  \n",
       "121_at             7.607461         7.302935          7.331864  \n",
       "1255_g_at          3.225123         3.222731          3.175846  \n",
       "\n",
       "[5 rows x 208 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "exprs = pd.read_csv(\"data/\"+filenames[0], index_col=0)\n",
    "exprs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\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>"
      ],
      "text/plain": [
       "  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"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleinfo = pd.read_csv(\"data/\"+filenames[1])\n",
    "sampleinfo.head()"
   ]
  },
  {
   "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": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "# change the ordering of columns to be the same as the sample filenames\n",
    "exprs = exprs[sampleinfo.filename]\n",
    "#check order\n",
    "(exprs.columns == sampleinfo.filename).all()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the head of the two tables: `exprs` and `sampleinfo`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\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 × 208 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           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]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "exprs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "</style>\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>"
      ],
      "text/plain": [
       "  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"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sampleinfo.head()"
   ]
  },
  {
   "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",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here\n",
    "dates = pd.to_datetime(sampleinfo[\"date\"])\n",
    "years = [t.year for t in dates]\n",
    "months = [t.month for t in dates]"
   ]
  },
  {
   "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",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
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       "</style>\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>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>96 days</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>96 days</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>47 days</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>91 days</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>64 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ethnicity        date         filename sex elapsedInDays\n",
       "0       CEU  2003-02-04  GSM25349.CEL.gz   M       96 days\n",
       "1       CEU  2003-02-04  GSM25350.CEL.gz   M       96 days\n",
       "2       CEU  2002-12-17  GSM25356.CEL.gz   M       47 days\n",
       "3       CEU  2003-01-30  GSM25357.CEL.gz   M       91 days\n",
       "4       CEU  2003-01-03  GSM25358.CEL.gz   M       64 days"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "basedate = pd.to_datetime(\"October 31, 2002\")\n",
    "basedate\n",
    "\n",
    "sampleinfo[\"elapsedInDays\"] = pd.to_datetime(sampleinfo.date) - basedate\n",
    "sampleinfo.head()"
   ]
  },
  {
   "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",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(102, 5) (208, 5)\n"
     ]
    },
    {
     "data": {
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       "  <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>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>96 days</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>96 days</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>47 days</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>91 days</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>64 days</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  ethnicity        date         filename sex elapsedInDays\n",
       "0       CEU  2003-02-04  GSM25349.CEL.gz   M       96 days\n",
       "1       CEU  2003-02-04  GSM25350.CEL.gz   M       96 days\n",
       "2       CEU  2002-12-17  GSM25356.CEL.gz   M       47 days\n",
       "3       CEU  2003-01-30  GSM25357.CEL.gz   M       91 days\n",
       "4       CEU  2003-01-03  GSM25358.CEL.gz   M       64 days"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "sampleinfoCEU = sampleinfo[sampleinfo[\"ethnicity\"]==\"CEU\"]\n",
    "print(sampleinfoCEU.shape, sampleinfo.shape)\n",
    "sampleinfoCEU.head()"
   ]
  },
  {
   "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",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(8793, 102) (8793, 208)\n"
     ]
    },
    {
     "data": {
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       "  <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",
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       "  <tbody>\n",
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       "      <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",
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       "    <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",
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       "    <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 × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           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",
       "           GSM48658.CEL.gz  GSM48660.CEL.gz  GSM48661.CEL.gz  GSM48662.CEL.gz  \\\n",
       "1007_s_at         6.501510         6.558100         6.618286         6.869995   \n",
       "1053_at           7.066686         7.386702         6.407958         7.089180   \n",
       "117_at            5.600514         5.232676         5.630720         4.944748   \n",
       "121_at            7.437535         7.714650         7.416252         7.746448   \n",
       "1255_g_at         3.009983         3.151203         3.199709         3.159496   \n",
       "\n",
       "           GSM48663.CEL.gz  GSM48664.CEL.gz  GSM48665.CEL.gz  \\\n",
       "1007_s_at         5.981000         6.403285         6.248702   \n",
       "1053_at           7.120923         7.018998         7.155419   \n",
       "117_at            5.275577         5.770358         5.616842   \n",
       "121_at            8.001434         7.626723         7.452299   \n",
       "1255_g_at         3.149710         3.242780         3.433125   \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",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "exprsCEU = exprs[sampleinfoCEU.filename]\n",
    "print(exprsCEU.shape, exprs.shape)\n",
    "exprsCEU.head()"
   ]
  },
  {
   "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",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "(exprsCEU.columns == sampleinfoCEU.filename).any()"
   ]
  },
  {
   "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",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1007_s_at    6.261955\n",
       "1053_at      7.075217\n",
       "117_at       5.604127\n",
       "121_at       7.415835\n",
       "dtype: float64"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "avg = exprsCEU.mean(axis=1)\n",
    "avg[:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1007_s_at</th>\n",
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       "      <td>-0.327827</td>\n",
       "      <td>-0.116687</td>\n",
       "      <td>-0.170685</td>\n",
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       "      <td>-0.128887</td>\n",
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       "    <tr>\n",
       "      <th>1053_at</th>\n",
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       "      <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",
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       "    <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",
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       "      <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",
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       "      <td>0.012716</td>\n",
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       "      <td>-0.202251</td>\n",
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       "      <th>121_at</th>\n",
       "      <td>0.418026</td>\n",
       "      <td>0.364847</td>\n",
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       "      <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 × 102 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           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        ...         \\\n",
       "1007_s_at        -0.035849         0.096327        ...          \n",
       "1053_at          -0.337195         0.292678        ...          \n",
       "117_at            1.085317         0.878655        ...          \n",
       "121_at            0.073949        -0.027296        ...          \n",
       "1255_g_at         0.170104        -0.246015        ...          \n",
       "\n",
       "           GSM48658.CEL.gz  GSM48660.CEL.gz  GSM48661.CEL.gz  GSM48662.CEL.gz  \\\n",
       "1007_s_at         0.239554         0.296144         0.356331         0.608040   \n",
       "1053_at          -0.008531         0.311485        -0.667259         0.013964   \n",
       "117_at           -0.003613        -0.371451         0.026594        -0.659379   \n",
       "121_at            0.021700         0.298814         0.000417         0.330612   \n",
       "1255_g_at        -0.123951         0.017269         0.065775         0.025562   \n",
       "\n",
       "           GSM48663.CEL.gz  GSM48664.CEL.gz  GSM48665.CEL.gz  \\\n",
       "1007_s_at        -0.280956         0.141330        -0.013254   \n",
       "1053_at           0.045706        -0.056219         0.080203   \n",
       "117_at           -0.328549         0.166231         0.012716   \n",
       "121_at            0.585598         0.210887         0.036463   \n",
       "1255_g_at         0.015776         0.108846         0.299192   \n",
       "\n",
       "           GSM136725.CEL.gz  GSM136726.CEL.gz  GSM136727.CEL.gz  \n",
       "1007_s_at          0.226624          0.547526         -0.128887  \n",
       "1053_at            0.442193          0.125379          0.205564  \n",
       "117_at            -0.356936         -0.366887         -0.202251  \n",
       "121_at            -0.083972         -0.058733          0.191626  \n",
       "1255_g_at          0.041912         -0.028842          0.091189  \n",
       "\n",
       "[5 rows x 102 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = exprsCEU.sub(avg, axis=\"index\")\n",
    "df.head()"
   ]
  },
  {
   "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",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here\n",
    "U, s, Vh = np.linalg.svd(df.values, full_matrices=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((8793, 102), (8793, 102), (102,), (102, 102), (102, 102))"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape, u.shape, s.shape, Vh.shape, Vh.T.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a histogram using the values from PC1.  Use a bin size of 25.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vJvl8kruT/O0kapwGffTTUd3f0NeSXJTkwEnUudDUBDpwPHBBVe0HXNDNL3Qf8Nqq2h94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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1244c4eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "plt.title(\"Vh.T[:0,] histogram\")\n",
    "plt.hist(Vh.T[:,0], bins=25);"
   ]
  },
  {
   "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",
   "execution_count": 236,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x126945b70>"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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4QdI7gK8CSyLijxmvuRB4LiI+W2Xfq4HnIyIkvQm4OiIOSfe9FXgO+FZEvLGB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\nFzMzy52Di5mZ5c7BxczMcufgYmZmufv/8MtxXZq6BjMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x12548f278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "x = sampleinfoCEU.elapsedInDays.dt.days\n",
    "y = Vh.T[:,0]\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel(\"Days since Oct 31, 2002\")\n",
    "plt.ylabel(\"PC1\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Around what day do you notice a difference in the way the samples were processed?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12427cb70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "for X,Y in zip(x,y):\n",
    "    if X <=100:\n",
    "        plt.scatter(X,Y, color=\"b\")\n",
    "    else:\n",
    "        plt.scatter(X,Y, color=\"r\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more efficent version of the above, using a mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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D3cBDe3bS0J6dmh8QAADwFbNfm6Cxor+R9tdvoXNOev99r3RJc4UnZ6Hu4LIy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\nkNQBAABkgf8P5So5e1u2ZsAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1240cc0f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mask = x<=100\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.scatter(x[mask], y[mask], color=\"b\", label=\"100 or less days\", s=20)\n",
    "plt.scatter(x[~mask], y[~mask], color=\"r\", label=\"over a 100 days\", s=20)\n",
    "plt.axvline(100, label=\"100 days\", linestyle=\"dashed\")\n",
    "plt.xlabel(\"Days since Oct 31, 2002\")\n",
    "plt.ylabel(\"PC1\")\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: At about **100 days** the PC1 of the samples is markedly different. Something seems to be going on there."
   ]
  },
  {
   "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",
    "---\n",
    "First, I need to better understand the PC1 here, but given that numpy must have done the right SVD and projected the high D gene data into a smaller D principal componenets, by plotting PC1 against days we can see that there is a clear difference in the samples after day 100.\n",
    "\n",
    "The original gene data was very hard to visualize, doing a PCA analysis makes it possible to do so."
   ]
  },
  {
   "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",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here\n",
    "election = pd.read_csv(\"http://elections.huffingtonpost.com/pollster/2012-general-election-romney-vs-obama.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the head of the `election` DataFrame. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df shape: (586, 17)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</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>2012-11-06T08:40:26Z</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>47.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.politico.com/news/stories/1112/8338...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>YouGov/Economist</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-26T15:31:23Z</td>\n",
       "      <td>740.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>49.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://cdn.yougov.com/cumulus_uploads/document...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</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>2012-11-06T09:22:02Z</td>\n",
       "      <td>872.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>48.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.gravispolls.com/2012/11/gravis-mark...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>IBD/TIPP</td>\n",
       "      <td>2012-11-03</td>\n",
       "      <td>2012-11-05</td>\n",
       "      <td>2012-11-06T08:51:48Z</td>\n",
       "      <td>712.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://news.investors.com/special-report/50841...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</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>2012-11-06T08:47:50Z</td>\n",
       "      <td>1500.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.rasmussenreports.com/public_content...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Pollster  Start Date    End Date  Entry Date/Time (ET)  \\\n",
       "0  Politico/GWU/Battleground  2012-11-04  2012-11-05  2012-11-06T08:40:26Z   \n",
       "1           YouGov/Economist  2012-11-03  2012-11-05  2012-11-26T15:31:23Z   \n",
       "2           Gravis Marketing  2012-11-03  2012-11-05  2012-11-06T09:22:02Z   \n",
       "3                   IBD/TIPP  2012-11-03  2012-11-05  2012-11-06T08:51:48Z   \n",
       "4                  Rasmussen  2012-11-03  2012-11-05  2012-11-06T08:47:50Z   \n",
       "\n",
       "   Number of Observations     Population             Mode  Obama  Romney  \\\n",
       "0                  1000.0  Likely Voters       Live Phone   47.0    47.0   \n",
       "1                   740.0  Likely Voters         Internet   49.0    47.0   \n",
       "2                   872.0  Likely Voters  Automated Phone   48.0    48.0   \n",
       "3                   712.0  Likely Voters       Live Phone   50.0    49.0   \n",
       "4                  1500.0  Likely Voters  Automated Phone   48.0    49.0   \n",
       "\n",
       "   Undecided  Other                                       Pollster URL  \\\n",
       "0        6.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1        3.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "2        4.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "3        NaN    1.0  http://elections.huffingtonpost.com/pollster/p...   \n",
       "4        NaN    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.politico.com/news/stories/1112/8338...  Nonpartisan        None   \n",
       "1  http://cdn.yougov.com/cumulus_uploads/document...  Nonpartisan        None   \n",
       "2  http://www.gravispolls.com/2012/11/gravis-mark...  Nonpartisan        None   \n",
       "3  http://news.investors.com/special-report/50841...  Nonpartisan        None   \n",
       "4  http://www.rasmussenreports.com/public_content...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration  \n",
       "0            NaN                   1  \n",
       "1            NaN                   1  \n",
       "2            NaN                   1  \n",
       "3            NaN                   1  \n",
       "4            NaN                   1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\"df shape:\", election.shape)\n",
    "election.head()"
   ]
  },
  {
   "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",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1523.368421</td>\n",
       "      <td>48.789474</td>\n",
       "      <td>47.736842</td>\n",
       "      <td>3.083333</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>999.457754</td>\n",
       "      <td>1.084176</td>\n",
       "      <td>1.097578</td>\n",
       "      <td>1.676486</td>\n",
       "      <td>0.517549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>693.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1009.500000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1200.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1487.500000</td>\n",
       "      <td>49.500000</td>\n",
       "      <td>48.500000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4725.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Observations      Obama     Romney  Undecided     Other  \\\n",
       "count               19.000000  19.000000  19.000000  12.000000  8.000000   \n",
       "mean              1523.368421  48.789474  47.736842   3.083333  1.375000   \n",
       "std                999.457754   1.084176   1.097578   1.676486  0.517549   \n",
       "min                693.000000  47.000000  45.000000   1.000000  1.000000   \n",
       "25%               1009.500000  48.000000  47.000000   2.000000  1.000000   \n",
       "50%               1200.000000  49.000000  48.000000   3.000000  1.000000   \n",
       "75%               1487.500000  49.500000  48.500000   4.000000  2.000000   \n",
       "max               4725.000000  51.000000  49.000000   6.000000  2.000000   \n",
       "\n",
       "       Question Text  Question Iteration  \n",
       "count            0.0                19.0  \n",
       "mean             NaN                 1.0  \n",
       "std              NaN                 0.0  \n",
       "min              NaN                 1.0  \n",
       "25%              NaN                 1.0  \n",
       "50%              NaN                 1.0  \n",
       "75%              NaN                 1.0  \n",
       "max              NaN                 1.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# converting string date columns into datetime\n",
    "election[\"Start Date\"] = pd.to_datetime(election[\"Start Date\"])\n",
    "election[\"End Date\"] = pd.to_datetime(election[\"End Date\"])\n",
    "election[\"Entry Date/Time (ET)\"] = pd.to_datetime(election[\"Entry Date/Time (ET)\"])\n",
    "\n",
    "pollsNov12 = election[(election[\"Start Date\"] >= pd.datetime(2012,11,1)) &\n",
    "                      (election[\"Start Date\"] < pd.datetime(2012,12,1))]\n",
    "M = pollsNov12.shape[0]\n",
    "pollsNov12.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: 19 polls were conducted in Nov"
   ]
  },
  {
   "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",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1200.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "N = np.median(pollsNov12[\"Number of Observations\"])\n",
    "N"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: Median for polls in Nov is 1200"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Problem 2(b)\n",
    "\n",
    "Using the median sample size $N$ from Problem 1(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",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.52666666666666662, 0.52500000000000002)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "poll = np.mean(np.random.binomial(1, p=0.53, size=int(N)))\n",
    "poll2 = np.random.binomial(N, 0.53) / N\n",
    "poll, poll2"
   ]
  },
  {
   "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. \n",
    "\n",
    "Two days, first to draw a number from N, or to generate N votes and take the mean:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0.54416667,  0.50916667,  0.55083333,  0.5375    ,  0.5175    ]),\n",
       " [0.54000000000000004,\n",
       "  0.52749999999999997,\n",
       "  0.51916666666666667,\n",
       "  0.53333333333333333,\n",
       "  0.54249999999999998])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "sim = np.random.binomial(N, 0.53, 1000)/N\n",
    "sim2 = [np.mean(np.random.binomial(1, p=0.53, size=int(N))) for _ in range(1000)]\n",
    "sim[:5], sim2[:5]"
   ]
  },
  {
   "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",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eFGt1nI5INXccIUQtZ9StfWojvV5Pfn75LYNsbZXk5eWZKVHDU1CgpaSEavW5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\nPhDDAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11cb5e588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "#plt.hist(sim, bins=20);\n",
    "simP = sim2 # to easily choose b/w sim and sim2\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.distplot(simP, label=\"Voting distribution\")\n",
    "plt.axvline(np.mean(simP), color=\"g\", label=f\"Mean: {np.mean(simP):.2f}\", linestyle=\"dashed\")\n",
    "plt.axvline(0.5, label=\"50 percent line\", color=\"y\", alpha=0.8)\n",
    "plt.xlabel(\"percentage for votes for Obama\", fontsize=12)\n",
    "plt.ylabel(\"Probablity of it happening\", fontsize=12)\n",
    "plt.annotate(f\"The mean is {np.mean(simP):.2%}\", (np.mean(simP),0.0), (np.mean(simP)+0.01,3), \n",
    "             arrowprops={\"arrowstyle\":'-|>'}, bbox = {\"boxstyle\":\"round\", \"fc\":\"0.8\"})\n",
    "plt.text(0.48,20, \"So there is a small\\nchance of Obama\\nloosing\", bbox=dict(facecolor='red', alpha=0.3))\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: "
   ]
  },
  {
   "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": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.014020573152613193"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "np.std(simP)"
   ]
  },
  {
   "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": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([623, 667, 612, 630, 631, 630, 619, 610, 603, 640, 610, 633, 664,\n",
       "       668, 638, 646, 640, 632, 651])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.binomial(N, 0.53, M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[634.84210526315792,\n",
       " 639.42105263157896,\n",
       " 638.42105263157896,\n",
       " 643.47368421052636,\n",
       " 631.9473684210526,\n",
       " 631.68421052631584,\n",
       " 638.0,\n",
       " 634.89473684210532,\n",
       " 643.10526315789468,\n",
       " 642.26315789473688]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "polls1k = [np.mean(np.random.binomial(N, 0.53, M)) for _ in range(1000)]\n",
    "polls1k[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the distribution of the average of polls?\n",
    "\n",
    "**Hint**: Show a plot. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Ora0QRUQkAbg8R07a/vlbaaUtlc5ljocqbXsKtQepHB1qLWlbtmwZPXr0AKBz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\nIYQQvcD/B5kgPQO9Ip7kAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d552978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.xlim(500,700)\n",
    "plt.ylim(0,0.11)\n",
    "sns.distplot(polls1k, label=\"Voting distribution\")\n",
    "sns.distplot(np.array(simP)*1200, label=\"non averaged poll\")\n",
    "plt.axvline(np.mean(polls1k), color=\"g\", label=f\"Mean: {np.mean(polls1k):.2f}\", linestyle=\"dashed\")\n",
    "plt.axvline(N*0.5, label=\"50 percent line\", color=\"y\", alpha=0.8)\n",
    "plt.xlabel(\"votes for Obama\", fontsize=12)\n",
    "plt.ylabel(\"Probablity of it happening\", fontsize=12)\n",
    "plt.legend();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: The average poll has a much tighter outcome, as is clear on the chart above. The non-average poll has a much wider spread."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the standard error (SE) of the average of polls? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.0203287292723635, 16.824687783135836)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "np.std(polls1k), np.std(np.array(simP)*1200)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: "
   ]
  },
  {
   "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",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23895413579692845"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "np.std(polls1k) / np.std(np.array(simP)*1200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.184903503197094"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(np.array(simP)*1200) / np.std(polls1k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: "
   ]
  },
  {
   "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",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[14.847516743974527,\n",
       " 21.288936952443095,\n",
       " 17.348952405966404,\n",
       " 16.956192537653056,\n",
       " 20.507548775675353]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "polls1kstd = [np.std(np.random.binomial(N, 0.53, M)) for _ in range(1000)]\n",
    "polls1kstd[:5]"
   ]
  },
  {
   "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",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8vjn4GMM0mBhjQkt7N5rbeFoV0WAxnCkouD0+1DU7ERmug9mglbocGoKpSRYA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\nExERycz/A3TPEUvFS7xpAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d5eaf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "sns.distplot(polls1kstd, label=\"std across M polls\")\n",
    "plt.xlabel(\"Standard Dev\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: "
   ]
  },
  {
   "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",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.024199901720378662"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "np.std(election[\"Obama\"]/100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is this larger, the same, or smaller than what we expeced if polls were not biased."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.024199901720378662, 0.16824687783135833, 0.14383566597078459)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "np.std(election[\"Obama\"]/100), np.std(np.array(simP)*1200/100), np.std(election[\"Obama\"]/100)/np.std(np.array(simP)*1200/100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: "
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: "
   ]
  },
  {
   "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",
    "---\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",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-03-13</td>\n",
       "      <td>2009-03-15</td>\n",
       "      <td>2010-11-08 10:36:31</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/PPP_Rel...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-04-17</td>\n",
       "      <td>2009-04-19</td>\n",
       "      <td>2010-11-08 10:45:18</td>\n",
       "      <td>686.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>12.359551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>2009-05-18</td>\n",
       "      <td>2010-11-08 10:49:08</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>20.454545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-06-12</td>\n",
       "      <td>2009-06-16</td>\n",
       "      <td>2010-11-08 10:58:00</td>\n",
       "      <td>638.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>9.090909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-07-15</td>\n",
       "      <td>2009-07-16</td>\n",
       "      <td>2010-11-08 11:02:37</td>\n",
       "      <td>577.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>49.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10.112360</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pollster Start Date   End Date Entry Date/Time (ET)  \\\n",
       "585  PPP (D) 2009-03-13 2009-03-15  2010-11-08 10:36:31   \n",
       "584  PPP (D) 2009-04-17 2009-04-19  2010-11-08 10:45:18   \n",
       "583  PPP (D) 2009-05-14 2009-05-18  2010-11-08 10:49:08   \n",
       "582  PPP (D) 2009-06-12 2009-06-16  2010-11-08 10:58:00   \n",
       "581  PPP (D) 2009-07-15 2009-07-16  2010-11-08 11:02:37   \n",
       "\n",
       "     Number of Observations         Population             Mode  Obama  \\\n",
       "585                  1403.0  Registered Voters  Automated Phone   44.0   \n",
       "584                   686.0  Registered Voters  Automated Phone   50.0   \n",
       "583                  1000.0  Registered Voters  Automated Phone   53.0   \n",
       "582                   638.0  Registered Voters  Automated Phone   48.0   \n",
       "581                   577.0  Registered Voters  Automated Phone   49.0   \n",
       "\n",
       "     Romney  Undecided  Other  \\\n",
       "585    44.0       12.0    NaN   \n",
       "584    39.0       11.0    NaN   \n",
       "583    35.0       12.0    NaN   \n",
       "582    40.0       12.0    NaN   \n",
       "581    40.0       11.0    NaN   \n",
       "\n",
       "                                          Pollster URL  \\\n",
       "585  http://elections.huffingtonpost.com/pollster/p...   \n",
       "584  http://elections.huffingtonpost.com/pollster/p...   \n",
       "583  http://elections.huffingtonpost.com/pollster/p...   \n",
       "582  http://elections.huffingtonpost.com/pollster/p...   \n",
       "581  http://elections.huffingtonpost.com/pollster/p...   \n",
       "\n",
       "                                            Source URL  Partisan Affiliation  \\\n",
       "585  http://www.publicpolicypolling.com/pdf/PPP_Rel...  Pollster         Dem   \n",
       "584  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "583  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "582  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "581  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "\n",
       "     Question Text  Question Iteration       Diff  \n",
       "585            NaN                   1   0.000000  \n",
       "584            NaN                   1  12.359551  \n",
       "583            NaN                   1  20.454545  \n",
       "582            NaN                   1   9.090909  \n",
       "581            NaN                   1  10.112360  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "election[\"Diff\"] = 100*(election[\"Obama\"] - election[\"Romney\"]) / (election[\"Obama\"] + election[\"Romney\"])\n",
    "election.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    586.000000\n",
       "mean       2.433892\n",
       "std        4.211205\n",
       "min      -17.777778\n",
       "25%        0.000000\n",
       "50%        2.127660\n",
       "75%        4.651163\n",
       "max       20.454545\n",
       "Name: Diff, dtype: float64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "election[\"Diff\"].describe()"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "election.sort_values(\"End Date\", ascending=True, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Adding a column to easily see the days from end of elections:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "</style>\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>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Days from End</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-03-13</td>\n",
       "      <td>2009-03-15</td>\n",
       "      <td>2010-11-08 10:36:31</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/PPP_Rel...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-04-17</td>\n",
       "      <td>2009-04-19</td>\n",
       "      <td>2010-11-08 10:45:18</td>\n",
       "      <td>686.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>12.359551</td>\n",
       "      <td>1296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>583</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-05-14</td>\n",
       "      <td>2009-05-18</td>\n",
       "      <td>2010-11-08 10:49:08</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>53.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>20.454545</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>582</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-06-12</td>\n",
       "      <td>2009-06-16</td>\n",
       "      <td>2010-11-08 10:58:00</td>\n",
       "      <td>638.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>48.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>9.090909</td>\n",
       "      <td>1238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>581</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-07-15</td>\n",
       "      <td>2009-07-16</td>\n",
       "      <td>2010-11-08 11:02:37</td>\n",
       "      <td>577.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>49.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10.112360</td>\n",
       "      <td>1208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pollster Start Date   End Date Entry Date/Time (ET)  \\\n",
       "585  PPP (D) 2009-03-13 2009-03-15  2010-11-08 10:36:31   \n",
       "584  PPP (D) 2009-04-17 2009-04-19  2010-11-08 10:45:18   \n",
       "583  PPP (D) 2009-05-14 2009-05-18  2010-11-08 10:49:08   \n",
       "582  PPP (D) 2009-06-12 2009-06-16  2010-11-08 10:58:00   \n",
       "581  PPP (D) 2009-07-15 2009-07-16  2010-11-08 11:02:37   \n",
       "\n",
       "     Number of Observations         Population             Mode  Obama  \\\n",
       "585                  1403.0  Registered Voters  Automated Phone   44.0   \n",
       "584                   686.0  Registered Voters  Automated Phone   50.0   \n",
       "583                  1000.0  Registered Voters  Automated Phone   53.0   \n",
       "582                   638.0  Registered Voters  Automated Phone   48.0   \n",
       "581                   577.0  Registered Voters  Automated Phone   49.0   \n",
       "\n",
       "     Romney  Undecided  Other  \\\n",
       "585    44.0       12.0    NaN   \n",
       "584    39.0       11.0    NaN   \n",
       "583    35.0       12.0    NaN   \n",
       "582    40.0       12.0    NaN   \n",
       "581    40.0       11.0    NaN   \n",
       "\n",
       "                                          Pollster URL  \\\n",
       "585  http://elections.huffingtonpost.com/pollster/p...   \n",
       "584  http://elections.huffingtonpost.com/pollster/p...   \n",
       "583  http://elections.huffingtonpost.com/pollster/p...   \n",
       "582  http://elections.huffingtonpost.com/pollster/p...   \n",
       "581  http://elections.huffingtonpost.com/pollster/p...   \n",
       "\n",
       "                                            Source URL  Partisan Affiliation  \\\n",
       "585  http://www.publicpolicypolling.com/pdf/PPP_Rel...  Pollster         Dem   \n",
       "584  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "583  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "582  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "581  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "\n",
       "     Question Text  Question Iteration       Diff  Days from End  \n",
       "585            NaN                   1   0.000000           1331  \n",
       "584            NaN                   1  12.359551           1296  \n",
       "583            NaN                   1  20.454545           1267  \n",
       "582            NaN                   1   9.090909           1238  \n",
       "581            NaN                   1  10.112360           1208  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "election[\"Days from End\"] = -(election[\"End Date\"] - election[\"End Date\"].max()).dt.days.values\n",
    "election.sort_values(\"Days from End\", ascending=False, inplace=True)\n",
    "election.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, since we're looking only at the last 7 days, I make a mask to access only that data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Number of Observations</th>\n",
       "      <th>Obama</th>\n",
       "      <th>Romney</th>\n",
       "      <th>Undecided</th>\n",
       "      <th>Other</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Days from End</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2518.617647</td>\n",
       "      <td>48.294118</td>\n",
       "      <td>47.382353</td>\n",
       "      <td>4.045455</td>\n",
       "      <td>1.538462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.958681</td>\n",
       "      <td>2.411765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6070.819644</td>\n",
       "      <td>1.219275</td>\n",
       "      <td>1.392941</td>\n",
       "      <td>2.419706</td>\n",
       "      <td>0.776250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.453875</td>\n",
       "      <td>2.175868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>693.000000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-2.083333</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1004.750000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1164.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.030928</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1500.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.072704</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>36472.000000</td>\n",
       "      <td>51.000000</td>\n",
       "      <td>49.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.255319</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Number of Observations      Obama     Romney  Undecided      Other  \\\n",
       "count               34.000000  34.000000  34.000000  22.000000  13.000000   \n",
       "mean              2518.617647  48.294118  47.382353   4.045455   1.538462   \n",
       "std               6070.819644   1.219275   1.392941   2.419706   0.776250   \n",
       "min                693.000000  46.000000  43.000000   1.000000   1.000000   \n",
       "25%               1004.750000  48.000000  47.000000   2.250000   1.000000   \n",
       "50%               1164.000000  48.000000  48.000000   3.000000   1.000000   \n",
       "75%               1500.000000  49.000000  48.000000   6.000000   2.000000   \n",
       "max              36472.000000  51.000000  49.000000  11.000000   3.000000   \n",
       "\n",
       "       Question Text  Question Iteration       Diff  Days from End  \n",
       "count            0.0                34.0  34.000000      34.000000  \n",
       "mean             NaN                 1.0   0.958681       2.411765  \n",
       "std              NaN                 0.0   1.453875       2.175868  \n",
       "min              NaN                 1.0  -2.083333       0.000000  \n",
       "25%              NaN                 1.0   0.000000       1.000000  \n",
       "50%              NaN                 1.0   1.030928       2.000000  \n",
       "75%              NaN                 1.0   2.072704       4.000000  \n",
       "max              NaN                 1.0   4.255319       7.000000  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ma = election[\"Days from End\"] <= 7\n",
    "f\"there are {np.sum(ma)} polls in the 7 days before election\"\n",
    "election[ma].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qVGHlypWUKlUqx2vkUaZhklwMU2HKBQpXPoUlF3mUKcTrla93zLZs2aL/71qtlpkzZ/5j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nbu3Enbtm354IMPePnll7ly5eYdHLy8vGjXrh3/+te/yM7OJjs727ZJenZ2NlWrVuWDDz5g9erV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\nJiIiIuImlJiJiIiIuAklZiIiIiJu4v8BkI3Da40uYTMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d8b0160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(\"Days from End\", \"Diff\", data=election[ma], hue=\"Pollster\", fit_reg=False, legend=True, size=6)\n",
    "plt.axhline(3.9, label=\"Actual Diff at Election\")\n",
    "plt.xlim(7, -0)\n",
    "ymin, ymax = plt.ylim()\n",
    "plt.fill_between(\"Days from End\", ymax, data=election[ma], alpha=0.051, color=\"b\")\n",
    "plt.fill_between(\"Days from End\", ymin, data=election[ma], alpha=0.051, color=\"r\")\n",
    "plt.ylim(ymin, ymax)\n",
    "plt.title(\"Polls 7 Days from elections\");"
   ]
  },
  {
   "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",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Days from End</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33</td>\n",
       "      <td>JZ Analytics/Newsmax</td>\n",
       "      <td>2012-10-27</td>\n",
       "      <td>2012-10-29</td>\n",
       "      <td>2012-10-30 17:16:32</td>\n",
       "      <td>1073.0</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Internet</td>\n",
       "      <td>46.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.jzanalytics.com/</td>\n",
       "      <td>Sponsor</td>\n",
       "      <td>Rep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.075269</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>32</td>\n",
       "      <td>High Point University</td>\n",
       "      <td>2012-10-22</td>\n",
       "      <td>2012-10-30</td>\n",
       "      <td>2012-11-01 13:19:44</td>\n",
       "      <td>805.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>46.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://acme.highpoint.edu/~mkifer/src/19releas...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3.370787</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index               Pollster Start Date   End Date Entry Date/Time (ET)  \\\n",
       "0     33   JZ Analytics/Newsmax 2012-10-27 2012-10-29  2012-10-30 17:16:32   \n",
       "1     32  High Point University 2012-10-22 2012-10-30  2012-11-01 13:19:44   \n",
       "\n",
       "   Number of Observations         Population        Mode  Obama  Romney  \\\n",
       "0                  1073.0      Likely Voters    Internet   46.0    47.0   \n",
       "1                   805.0  Registered Voters  Live Phone   46.0    43.0   \n",
       "\n",
       "   Undecided  Other                                       Pollster URL  \\\n",
       "0        7.0    NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1       11.0    1.0  http://elections.huffingtonpost.com/pollster/p...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0                        http://www.jzanalytics.com/      Sponsor         Rep   \n",
       "1  http://acme.highpoint.edu/~mkifer/src/19releas...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration      Diff  Days from End  \n",
       "0            NaN                   1 -1.075269              7  \n",
       "1            NaN                   1  3.370787              6  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfpoll = election[ma].reset_index()\n",
    "dfpoll.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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j37599OvXDwAfHx+io6Px8/PDx8eH5557zphE53F3d2fy5MkEBARw7Ngx3njjjXznW7Vq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xMJDAzE2toaf39/oqOj0Wg09OvXDw8PD7y8vPjxxx+pUqUKISEhPH/+nFKlSvHs2TNGjBhBnz59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\nRf8HcjDJsWUPSXMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11decaf60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=\"Pollster\", y=\"Diff\", hue=\"Pollster\", data=dfpoll, size=10)\n",
    "#sns.factorplot(x=\"Pollster\", y=\"Diff\", hue=\"Pollster\", data=dfpoll, size=10)\n",
    "plt.axhline(0, alpha=0.2, color=\"black\")\n",
    "plt.text(0,0.6,\"Obama Wins\", bbox={'facecolor':'blue', 'alpha':0.1})\n",
    "plt.text(0,-0.8,\"Romney Wins\", bbox={'facecolor':'red', 'alpha':0.1})\n",
    "plt.xticks(rotation=90, fontsize=11)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Is the *across poll* difference larger than the *between pollster* difference? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: There is a lot of variation across polls b/w the same pollster."
   ]
  },
  {
   "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",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "avg of polls predict an Obama win by 1.14%\n",
      "The avg error is 1.38\n"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "# first getting the avg of each pollsters poll\n",
    "dfpollavg = election[ma].groupby(\"Pollster\").mean().reset_index()\n",
    "win_perctange = dfpollavg['Diff'].mean() \n",
    "print(f\"avg of polls predict an Obama win by {win_perctange:.2f}%\")\n",
    "print(f\"The avg error is {np.std(dfpollavg.Diff):.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: The win % is small, and the standard error is large, so the polls make the Obama victory pretty uncertain. It's well within the margin of error."
   ]
  },
  {
   "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",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>Other</th>\n",
       "      <th>Pollster URL</th>\n",
       "      <th>Source URL</th>\n",
       "      <th>Partisan</th>\n",
       "      <th>Affiliation</th>\n",
       "      <th>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "      <th>Diff</th>\n",
       "      <th>Days from End</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>585</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-03-13</td>\n",
       "      <td>2009-03-15</td>\n",
       "      <td>2010-11-08 10:36:31</td>\n",
       "      <td>1403.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>44.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/PPP_Rel...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2009-04-17</td>\n",
       "      <td>2009-04-19</td>\n",
       "      <td>2010-11-08 10:45:18</td>\n",
       "      <td>686.0</td>\n",
       "      <td>Registered Voters</td>\n",
       "      <td>Automated Phone</td>\n",
       "      <td>50.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/surveys...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>12.359551</td>\n",
       "      <td>1296</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Pollster Start Date   End Date Entry Date/Time (ET)  \\\n",
       "585  PPP (D) 2009-03-13 2009-03-15  2010-11-08 10:36:31   \n",
       "584  PPP (D) 2009-04-17 2009-04-19  2010-11-08 10:45:18   \n",
       "\n",
       "     Number of Observations         Population             Mode  Obama  \\\n",
       "585                  1403.0  Registered Voters  Automated Phone   44.0   \n",
       "584                   686.0  Registered Voters  Automated Phone   50.0   \n",
       "\n",
       "     Romney  Undecided  Other  \\\n",
       "585    44.0       12.0    NaN   \n",
       "584    39.0       11.0    NaN   \n",
       "\n",
       "                                          Pollster URL  \\\n",
       "585  http://elections.huffingtonpost.com/pollster/p...   \n",
       "584  http://elections.huffingtonpost.com/pollster/p...   \n",
       "\n",
       "                                            Source URL  Partisan Affiliation  \\\n",
       "585  http://www.publicpolicypolling.com/pdf/PPP_Rel...  Pollster         Dem   \n",
       "584  http://www.publicpolicypolling.com/pdf/surveys...  Pollster         Dem   \n",
       "\n",
       "     Question Text  Question Iteration       Diff  Days from End  \n",
       "585            NaN                   1   0.000000           1331  \n",
       "584            NaN                   1  12.359551           1296  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "election.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
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KLUCNCS0fJ5crgRACXlyGUPVHIm7nulAuIlfUx5bRliFFB9eXPGrr1ws9ncnx8XGkak/6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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d467b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#your code here\n",
    "#sns.lmplot(\"Days from End\", \"Diff\", data=election, fit_reg=True, legend=False, size=6)\n",
    "sns.lmplot(\"Days from End\", \"Diff\", data=election[election[\"Days from End\"] <= 30], scatter=False)\n",
    "plt.plot(\"Days from End\", \"Diff\", data=election[election[\"Days from End\"] <= 100], color=\"g\", alpha=0.2,\n",
    "        label=\"All polls\")\n",
    "plt.axhline(3.9, label=\"Actual Diff at Election\", linestyle=\"dashed\", linewidth=1)\n",
    "plt.axhline(0, label=\"0 line\", linestyle=\"-\", linewidth=1)\n",
    "plt.xlim(100, 0)\n",
    "plt.title(\"All polls before election\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Answer: It's interesting how the polls markedly swing from Obama down to Romney, and then there is a marked upward trend in the last 30 days, though it doesn't reach close to the 3.9 mark."
   ]
  },
  {
   "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",
    "---\n",
    "\n",
    "I would look closely at the methods Nate Silver used to deal with polls, since there is so much variability both b/w the same poll and different polls. Also I'd compute confidence intervals etc."
   ]
  },
  {
   "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",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "url_str = \"http://elections.huffingtonpost.com/pollster/api/charts/?topic=2014-senate\""
   ]
  },
  {
   "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",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['http://elections.huffingtonpost.com/pollster/2014-delaware-senate-wade-vs-coons.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-massachusetts-senate-herr-vs-markey.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-rhode-island-senate-zaccaria-vs-reed.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-montana-senate-daines-vs-curtis.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-oklahoma-senate-lankford-vs-johnson.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-hawaii-senate-cavasso-vs-schatz.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-kansas-senate-roberts-vs-orman-vs-taylor.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-wyoming-senate.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-tennessee-senate-alexander-vs-ball.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-idaho-senate-risch-vs-mitchell.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-new-jersey-senate-bell-vs-booker.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-new-mexico-senate-weh-vs-udall.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-oklahoma-senate-inhofe-vs-silverstein.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-south-carolina-senate-scott-vs-dickerson.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-south-carolina-senate-graham-vs-hutto.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-texas-senate-cornyn-vs-alameel.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-minnesota-senate-mcfadden-vs-franken.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-maine-senate-collins-vs-bellows.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-south-dakota-senate-rounds-vs-weiland.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-nebraska-senate-sasse-vs-domina.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-georgia-senate-perdue-vs-nunn.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-oregon-senate-wehby-vs-merkley.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-mississippi-senate-cochran-vs-childers.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-iowa-senate-ernst-vs-braley.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-alaska-senate-sullivan-vs-begich.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-illinois-senate-oberweis-vs-durbin.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-colorado-senate-gardner-vs-udall.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-virginia-senate-gillespie-vs-warner.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-north-carolina-senate-tillis-vs-hagan.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-new-hampshire-senate-bass-vs-shaheen.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-west-virginia-senate-capito-vs-tennant.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-new-hampshire-senate-brown-vs-shaheen.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-louisiana-senate-cassidy-vs-landrieu.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-michigan-senate-land-vs-peters.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-arkansas-senate-cotton-vs-pryor.csv',\n",
       " 'http://elections.huffingtonpost.com/pollster/2014-kentucky-senate-mcconnell-vs-grimes.csv']"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "election_urls = [election['url'] + '.csv' for election in requests.get(url_str).json()]\n",
    "election_urls"
   ]
  },
  {
   "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",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ko/anaconda/lib/python3.6/site-packages/ipykernel_launcher.py:12: FutureWarning: convert_objects is deprecated.  Use the data-type specific converters pd.to_datetime, pd.to_timedelta and pd.to_numeric.\n",
      "  if sys.path[0] == '':\n"
     ]
    }
   ],
   "source": [
    "# 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)"
   ]
  },
  {
   "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",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>Grimes</th>\n",
       "      <th>McConnell</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>Question Text</th>\n",
       "      <th>Question Iteration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PPP (D)</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2014-11-01</td>\n",
       "      <td>2014-11-02 01:44:58</td>\n",
       "      <td>1503</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>IVR/Online</td>\n",
       "      <td>42.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://www.publicpolicypolling.com/pdf/2014/PP...</td>\n",
       "      <td>Pollster</td>\n",
       "      <td>Dem</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NBC/Marist</td>\n",
       "      <td>2014-10-27</td>\n",
       "      <td>2014-10-30</td>\n",
       "      <td>2014-11-02 14:37:22</td>\n",
       "      <td>556</td>\n",
       "      <td>Likely Voters</td>\n",
       "      <td>Live Phone</td>\n",
       "      <td>41.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>http://elections.huffingtonpost.com/pollster/p...</td>\n",
       "      <td>http://newscms.nbcnews.com/sites/newscms/files...</td>\n",
       "      <td>Nonpartisan</td>\n",
       "      <td>None</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Pollster Start Date   End Date Entry Date/Time (ET)  \\\n",
       "0     PPP (D) 2014-10-30 2014-11-01  2014-11-02 01:44:58   \n",
       "1  NBC/Marist 2014-10-27 2014-10-30  2014-11-02 14:37:22   \n",
       "\n",
       "   Number of Observations     Population        Mode  Grimes  McConnell  \\\n",
       "0                    1503  Likely Voters  IVR/Online    42.0       50.0   \n",
       "1                     556  Likely Voters  Live Phone    41.0       50.0   \n",
       "\n",
       "   Undecided                                       Pollster URL  \\\n",
       "0        NaN  http://elections.huffingtonpost.com/pollster/p...   \n",
       "1        3.0  http://elections.huffingtonpost.com/pollster/p...   \n",
       "\n",
       "                                          Source URL     Partisan Affiliation  \\\n",
       "0  http://www.publicpolicypolling.com/pdf/2014/PP...     Pollster         Dem   \n",
       "1  http://newscms.nbcnews.com/sites/newscms/files...  Nonpartisan        None   \n",
       "\n",
       "   Question Text  Question Iteration  \n",
       "0            NaN                   1  \n",
       "1            NaN                   1  "
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#your code here\n",
    "dfs['2014-kentucky-senate-mcconnell-vs-grimes'].head(2)"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, I get the names of the contenders in each polls:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Grimes', 'McConnell']"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all the columns besides the contenders\n",
    "drop_cols = ['Pollster','Start Date','End Date','Entry Date/Time (ET)','Number of Observations',\n",
    "             'Population','Mode','Undecided','Pollster URL', 'Source URL',\n",
    "             'Partisan','Affiliation','Question Text','Question Iteration']\n",
    "\n",
    "# lets see if that works to get the names\n",
    "[col for col in dfs['2014-kentucky-senate-mcconnell-vs-grimes'].columns if col not in drop_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() got an unexpected keyword argument 'cols'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-268-fa479debe2b4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m     \u001b[0;31m#print(predictions)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0mwinners\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpredictions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"Race\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Winner\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Poll\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;31m#winners.set_index(\"Race\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: __init__() got an unexpected keyword argument 'cols'"
     ]
    }
   ],
   "source": [
    "#your code here\n",
    "predictions = []\n",
    "for election, df in dfs.items():\n",
    "    names = [col for col in df.columns if col not in drop_cols]\n",
    "\n",
    "    win_name = None\n",
    "    high = False\n",
    "\n",
    "    for name in names:\n",
    "        m = df[name].mean()\n",
    "        if m > high:\n",
    "            high = m\n",
    "            win_name = name\n",
    "    \n",
    "    predictions.append({\"Winner\": win_name, \"Poll\": high, \"Race\": election})\n",
    "    #print(predictions)\n",
    "\n",
    "winners = pd.DataFrame(predictions, cols=[\"Race\",\"Winner\",\"Poll\"])\n",
    "#winners.set_index(\"Race\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "an attempt to show the winners visually, though this could be done better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ccw3r1q1j4sSJ6HQ69Ho9AwcOJC0tjeuvv54dO3ZUH6W8HNhO7ai5/FcXSwW2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\nhhWHw+FwOByORHDDisPhcDgcDkci/h8cpSltxqQEUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x123451470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.swarmplot(x=\"Race\", y=\"Poll\", data=winners, size=10, hue=\"Winner\")\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.);\n",
    "plt.xticks(rotation=90, fontsize=11);"
   ]
  },
  {
   "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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}