FinalProject_group078.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS108 - Final Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our work looks at data from the midterm and general elections and compares metrics from both of elections to see if one can predict another. The majority of our work consisted of sourcing and aggregating our data, since it came from multiple sources. Next, we looked at the proportion of votes cast to particular candidates and their parties, comparing these proportions between the midterm and general elections. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Sruthi Vedantham\n",
    "* Sreyas Narayanan\n",
    "* Karissa Jacobsen \n",
    "* Saveree Joshipura"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Can the results of the presidential election be predicted through the results of the previous midterm election? Is the decision of the electoral college a good indicator for prediction? \n",
    "\n",
    "* How well does the electoral college represent the spread of Republicans/Democrats using primaries? \n",
    "\n",
    "* Can we determine how the electoral college will vote based on the spread of votes from the midterm elections? \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Every four years, citizens of the United States vote to elect their president. Halfway through this four year presidency term, midterm elections are held in which people vote for politicians in a part of the government called Congress - made up of the Senate and the House of Representatives - also known as America’s lawmakers. Congress can have a massive impact on what the president is able to accomplish based on whether or not control is held by the same party as that of the President or not. Because of this dynamic of power, it is interesting to see if the results of the midterm elections can reliably predict the winning party in the following general election for presidency. \n",
    "\n",
    " Though there are many iterations of prediction models for US presidential elections, many of these models are based off of factors such as consecutive terms in office for a certain party, presidential approval rate, unemployment rate, etc. whereas we would like to take a deeper look at how well the electoral college represents the popular vote as well as well as how the latter can predict the former. A similar study is described in an article from Texas Tech University called “A Bayesian Model for the Prediction of United States Presidential Elections” wherein data from poll data accessed through the various websites was used as the chief source to predict election results and had success in predicting electoral college results. This study had an average 95% accuracy rate for 2008, 2012, and 2016 presidential elections. However, this model was unsuccessful in predicting 2016 election results according to the study because it failed to account for the date of polls and a lack of adapting to trends over time.\n",
    " \n",
    "In another study “Modeling and forecasting US presidential election using learning algorithms” stepwise regression testing was used to determine the significance of the following factors: the number of consecutive terms the incumbent party has been in office, personal income, electoral votes of the incumbent party in the previous election, votes of the incumbent party in the last senate election, votes of the incumbent party in the last house of representatives election, the president’s approval rate, unemployment rate and the number of times that the 3-month GDP is above 3.2 within the previous 4 years. Of these, all variables except the president’s job approval rate were omitted. Because many of the above factors did not pass significance testing, and the success of the previously mentioned study based on polling data, we think that our model has the potential to predict the 2020 presidential election.\n",
    "\n",
    "\n",
    "Study 1: https://www.siam.org/Portals/0/Publications/SIURO/Vol12/S01616.pdf?ver=2019-02-12-215230-620\n",
    "\n",
    "Study 2: https://link.springer.com/article/10.1007/s40092-017-0238-2#Sec14"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We should be able to see how the spread of superdelegates in the electoral college in the main election can predict the overall spread and winning party of the following midterm elections of the House of Representatives. We predict to see a correlation in the overall spread and ratio of the win, as well as also being able to predict the binary result of which party won in terms of both elections. For example, if the winning party in the main election won 62% of the superdelegates in the electoral college, we expect to see a similar percentage of House seats won in the midterm election two years later by the same party. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset Name: Party Divisions of the House of Representatives\n",
    "\n",
    "Link to the Dataset: https://history.house.gov/Institution/Party-Divisions/Party-Divisions/\n",
    "\n",
    "Number of Observations: 130 \n",
    "\n",
    "Description: Electoral college dataset: formed by scraping 18 years worth of data off of website and then concatenating them together, Removed data that was not relevant to our analysis (vice-president votes) \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dataset Name: Electoral College Results\n",
    "\n",
    "Link to the Dataset: https://www.archives.gov/electoral-college/results?fbclid=IwAR3xewRfvComTf-98iWRK9TceYXEyJdh0_kMTPehagBZRx_b6OHwbhXo4BQ\n",
    "\n",
    "Number of Observations: 956 \n",
    "\n",
    "Description: Data from the Electoral College historic votes webpage, formed by scraping a table, and creating several separate dataframes for the different elections"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup / Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our project, we analyzed two different datasets. One was data on the votes that were cast by the Electoral College and the other was data on votes that were cast by the House of Representatives. \n",
    "The first dataset, which contained data about the House of Representatives, was already formatted in a readable format, especially with the pandas read_csv package. There were a few problems with the pre-existing dataset. We converted all of the columns to integers, if they were encoded in strings. This would later enable us to perform visual analysis. Throughout the years, there has been a pro-administration, and an anti-administration. However, the names of these parties have changed (ex: Pro-Administration -> Federalists). We split the DataFrame into several smaller DataFrames, based on the names of the pro and anti administration parties. This results in 10 different DataFrames. \n",
    "\n",
    "The Electoral College (EC) dataset was created by scraping data from every year off of the Electoral College historic votes webpage. This data was not very clean. We scraped and cleaned a dataframe for every year, making sure to only take columns that were relevant to our work. This helped to make sure that every year’s dataframe had identical columns, which made it easy to concat the data-frames together. After scraping, we performed some feature engineering on the columns to aggregate winners per year and look at the proportion of Democrat/Republican wins. We also re-coded the categorical columns to include numerical values. For example, we turned values such as “Democrat” and “Republican” into 1’s and 0’s, which helped in aggregating values by year. In regards to preprocessing, we realized that each state gets a different number of delegates, which is based on the state’s population. To normalize our values, we gave each state one “vote”, which was whatever the majority of the delegates from that state had voted. We aggregated this vote by year to see who had won each year. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FIRST DATASET: Cleaning of Party Divisions of the House of Representatives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "ename": "URLError",
     "evalue": "<urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)>",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mSSLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/lib/python3.6/urllib/request.py\u001b[0m in \u001b[0;36mdo_open\u001b[0;34m(self, http_class, req, **http_conn_args)\u001b[0m\n\u001b[1;32m   1317\u001b[0m                 h.request(req.get_method(), req.selector, req.data, headers,\n\u001b[0;32m-> 1318\u001b[0;31m                           encode_chunked=req.has_header('Transfer-encoding'))\n\u001b[0m\u001b[1;32m   1319\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mOSError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# timeout error\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mrequest\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1238\u001b[0m         \u001b[0;34m\"\"\"Send a complete request to the server.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1239\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_request\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0murl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheaders\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\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   1240\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_request\u001b[0;34m(self, method, url, body, headers, encode_chunked)\u001b[0m\n\u001b[1;32m   1284\u001b[0m             \u001b[0mbody\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_encode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'body'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendheaders\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mbody\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\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   1286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mendheaders\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1233\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mCannotSendHeader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1234\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_send_output\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage_body\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mencode_chunked\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mencode_chunked\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   1235\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36m_send_output\u001b[0;34m(self, message_body, encode_chunked)\u001b[0m\n\u001b[1;32m   1025\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1026\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\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   1027\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36msend\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    963\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mauto_open\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 964\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconnect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    965\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/http/client.py\u001b[0m in \u001b[0;36mconnect\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1399\u001b[0m             self.sock = self._context.wrap_socket(self.sock,\n\u001b[0;32m-> 1400\u001b[0;31m                                                   server_hostname=server_hostname)\n\u001b[0m\u001b[1;32m   1401\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_hostname\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_hostname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mwrap_socket\u001b[0;34m(self, sock, server_side, do_handshake_on_connect, suppress_ragged_eofs, server_hostname, session)\u001b[0m\n\u001b[1;32m    406\u001b[0m                          \u001b[0mserver_hostname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mserver_hostname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 407\u001b[0;31m                          _context=self, _session=session)\n\u001b[0m\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, sock, keyfile, certfile, server_side, cert_reqs, ssl_version, ca_certs, do_handshake_on_connect, family, type, proto, fileno, suppress_ragged_eofs, npn_protocols, ciphers, server_hostname, _context, _session)\u001b[0m\n\u001b[1;32m    816\u001b[0m                         \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"do_handshake_on_connect should not be specified for non-blocking sockets\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 817\u001b[0;31m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    818\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[0;34m(self, block)\u001b[0m\n\u001b[1;32m   1076\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msettimeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1077\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1078\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/ssl.py\u001b[0m in \u001b[0;36mdo_handshake\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    688\u001b[0m         \u001b[0;34m\"\"\"Start the SSL/TLS handshake.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 689\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sslobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdo_handshake\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    690\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcheck_hostname\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mSSLError\u001b[0m: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mURLError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-9d82d201af27>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#load first dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_html\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"https://history.house.gov/Institution/Party-Divisions/Party-Divisions/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\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      3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36mread_html\u001b[0;34m(io, match, flavor, header, index_col, skiprows, attrs, parse_dates, tupleize_cols, thousands, encoding, decimal, converters, na_values, keep_default_na, displayed_only)\u001b[0m\n\u001b[1;32m    985\u001b[0m                   \u001b[0mdecimal\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdecimal\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconverters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mconverters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mna_values\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mna_values\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    986\u001b[0m                   \u001b[0mkeep_default_na\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeep_default_na\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 987\u001b[0;31m                   displayed_only=displayed_only)\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/io/html.py\u001b[0m in \u001b[0;36m_parse\u001b[0;34m(flavor, io, match, attrs, encoding, displayed_only, **kwargs)\u001b[0m\n\u001b[1;32m    813\u001b[0m             \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    814\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 815\u001b[0;31m         \u001b[0mraise_with_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mretained\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    816\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    817\u001b[0m     \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/compat/__init__.py\u001b[0m in \u001b[0;36mraise_with_traceback\u001b[0;34m(exc, traceback)\u001b[0m\n\u001b[1;32m    401\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtraceback\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mEllipsis\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    402\u001b[0m             \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtraceback\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 403\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtraceback\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    404\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m     \u001b[0;31m# this version of raise is a syntax error in Python 3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mURLError\u001b[0m: <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed (_ssl.c:852)>"
     ]
    }
   ],
   "source": [
    "#load first dataset  \n",
    "df = pd.read_html(\"https://history.house.gov/Institution/Party-Divisions/Party-Divisions/\")[0]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#convert all column values to integers, to enable visualization \n",
    "def convert_int(entry): \n",
    "    try: \n",
    "        entry = int(entry)\n",
    "        return entry\n",
    "    except: \n",
    "        return entry \n",
    "df['# of House Seats'] = df['# of House Seats'].apply(convert_int)\n",
    "df['Pro-Administration'] = df['Pro-Administration'].apply(convert_int)\n",
    "df['Anti-Administration'] = df['Anti-Administration'].apply(convert_int)\n",
    "df['Other'] = df['Other'].apply(convert_int)\n",
    "df['Del./Res.'] = df['Del./Res.'].apply(convert_int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following 10 DataFrames contain the same information, but the name of the third and fourth column (the two parties) changes with time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_total = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.5692307692307692,\n",
       " 0.5652173913043478,\n",
       " 0.5142857142857142,\n",
       " 0.5692307692307692]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = df.loc[:2]\n",
    "for i in range(len(df1['# of House Seats'])): \n",
    "    house_total.append((max(df1['Pro-Administration'][i],df1['Anti-Administration'][i])/df1['# of House Seats'][i]))\n",
    "df2 = df[4:18].rename(columns={'Pro-Administration':'Federalists',\n",
    "                        'Anti-Administration': 'Democratic Republicans'}).reset_index()\n",
    "for i in range(len(df2['# of House Seats'])): \n",
    "    house_total.append(max(df2['Federalists'][i],df2['Democratic Republicans'][i])/df2['# of House Seats'][i])\n",
    "df3 = df[19:20].rename(columns={'Pro-Administration':'Adams-Clay Republicans',\n",
    "                        'Anti-Administration': 'Jackson Republicans'}).reset_index()\n",
    "for i in range(len(df3['# of House Seats'])): \n",
    "    house_total.append(max(df3['Adams-Clay Republicans'][i],df3['Jackson Republicans'][i])/df3['# of House Seats'][i])\n",
    "df4 = df.loc[21:22].rename(columns={'Pro-Administration':'Adams',\n",
    "                        'Anti-Administration': 'Jacksons'}).reset_index() \n",
    "for i in range(len(df4['# of House Seats'])): \n",
    "    house_total.append(max(df4['Adams'][i],df4['Jacksons'][i])/df4['# of House Seats'][i])\n",
    "df5 = df.loc[24:27].rename(columns={'Pro-Administration':'Anti-Jacksons',\n",
    "                        'Anti-Administration': 'Jacksons'}).reset_index()\n",
    "for i in range(len(df5['# of House Seats'])): \n",
    "    house_total.append(max(df5['Anti-Jacksons'][i],df5['Jacksons'][i])/df5['# of House Seats'][i])\n",
    "df6 = df.loc[29:37].rename(columns={'Pro-Administration':'Democrats',\n",
    "                        'Anti-Administration': 'Whigs'}).reset_index()\n",
    "for i in range(len(df6['# of House Seats'])): \n",
    "    house_total.append(max(df6['Democrats'][i],df6['Whigs'][i])/df6['# of House Seats'][i])\n",
    "df7 = df.loc[29:37].rename(columns={'Pro-Administration':'Democrats',\n",
    "                        'Anti-Administration': 'Whigs'}).reset_index()\n",
    "for i in range(len(df7['# of House Seats'])): \n",
    "    house_total.append(max(df7['Democrats'][i],df7['Whigs'][i])/df7['# of House Seats'][i])\n",
    "df8 = df.loc[39:39].rename(columns={'Pro-Administration':'Democrats',\n",
    "                        'Anti-Administration': 'Oppositions'}).reset_index()\n",
    "for i in range(len(df8['# of House Seats'])): \n",
    "    house_total.append(max(df8['Democrats'][i],df8['Oppositions'][i])/df8['# of House Seats'][i])\n",
    "df9 = df.loc[41:50].rename(columns={'Pro-Administration':'Democrats',\n",
    "                        'Anti-Administration': 'Republicans'}).reset_index()\n",
    "for i in range(len(df9['# of House Seats'])): \n",
    "    house_total.append(max(df9['Democrats'][i],df9['Republicans'][i])/df9['# of House Seats'][i])\n",
    "df10 = df.loc[41:].rename(columns={'Pro-Administration':'Democrats',\n",
    "                        'Anti-Administration': 'Republicans'})\n",
    "dropindex = df10[df10['Congress (Years)'].str.contains('Congress')].index\n",
    "df10 = df10.drop(dropindex).reset_index()\n",
    "for i in range(len(df10['# of House Seats'])): \n",
    "    house_total.append(max(df10['Democrats'][i],df10['Republicans'][i])/df10['# of House Seats'][i])\n",
    "house_total[:4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a263c8278>"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1[['Pro-Administration','Anti-Administration']].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a26638dd8>"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df10[['Democrats','Republicans']].loc[52:].plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first plot shows the rise of Anti-Administration in the first few years (1789-1795). The second plot shows the alternating nature of house wins between both parties. There has been multiple majority changes within the house."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following visualization shows the proportion of the winning parties in clinching house seats from the years 1789-2021. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a23f06518>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = pd.DataFrame({'year': np.arange(len(house_total)),\n",
    "                    'Proportions of Win: Majority House Seats/Total House Seats':house_total})\n",
    "plot.hist('Proportions of Win: Majority House Seats/Total House Seats')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Distribution: There is a large right-skew, as the majority party was able to get bigger wins at a certain point because the American political system started to become one of two main parties. Later, however, we can see the decline in this nature, as there starts to form an anti-adminstration sentiment. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>Proportions of Win: Majority House Seats/Total House Seats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>0.802817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>0.816901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>0.789189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>0.860215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>0.828877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>0.338028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>48</td>\n",
       "      <td>0.076515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>58</td>\n",
       "      <td>0.076515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>93</td>\n",
       "      <td>0.767816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>104</td>\n",
       "      <td>0.064634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>134</td>\n",
       "      <td>0.053973</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  Proportions of Win: Majority House Seats/Total House Seats\n",
       "8       8                                           0.802817         \n",
       "9       9                                           0.816901         \n",
       "14     14                                           0.789189         \n",
       "15     15                                           0.860215         \n",
       "16     16                                           0.828877         \n",
       "17     17                                           0.338028         \n",
       "48     48                                           0.076515         \n",
       "58     58                                           0.076515         \n",
       "93     93                                           0.767816         \n",
       "104   104                                           0.064634         \n",
       "134   134                                           0.053973         "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot[(plot['Proportions of Win: Majority House Seats/Total House Seats'] > 0.75)|\n",
    "    (plot['Proportions of Win: Majority House Seats/Total House Seats'] < 0.40)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outliers: We have defined the outliers above to be proportions that are either above 75% or below 40%. This indicates that there is either a super-majority in the house, or the presence of a third party that allowed the winning party to have a smaller win. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relationship Between Variables: Year indicates the two year term in Congress, starting with 1789, and ending at 2021. Proportions of Win indicates the quotient of the majority house seats and the total house seats."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SECOND DATASET "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>State</th>\n",
       "      <th>Electoral Vote of Each State</th>\n",
       "      <th>Democrat</th>\n",
       "      <th>Republican</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>California</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>1944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>1944</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0       State  Electoral Vote of Each State  Democrat  Republican  \\\n",
       "0           0     Alabama                            11        11           0   \n",
       "1           1     Arizona                             4         4           0   \n",
       "2           2    Arkansas                             9         9           0   \n",
       "3           3  California                            25        25           0   \n",
       "4           4    Colorado                             6         0           6   \n",
       "\n",
       "   Year  \n",
       "0  1944  \n",
       "1  1944  \n",
       "2  1944  \n",
       "3  1944  \n",
       "4  1944  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.read_csv('ElectoralCollegeVotesbyStatesandYears.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a178c03c8>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def is_dem(row):\n",
    "    if row['Democrat'] > row['Republican']:\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "    \n",
    "def is_repub(row):\n",
    "    if row['Democrat'] > row['Republican']:\n",
    "        return 0\n",
    "    else:\n",
    "        return 1\n",
    "\n",
    "df2['Is Democrat'] = df2.apply(lambda x: is_dem(x), axis=1)\n",
    "df2['Is Republican'] = df2.apply(lambda x: is_repub(x), axis=1)\n",
    "winners1 = ['Democrat', 'Democrat', 'Republican', 'Republican', 'Democrat', 'Democrat', 'Democrat', 'Republican', 'Republican', 'Republican', 'Democrat', 'Republican', 'Republican', 'Democrat', 'Democrat', 'Republican', 'Republican', 'Democrat', 'Democrat', 'Republican']\n",
    "count = 0\n",
    "winners2 = []\n",
    "for i in range(len(df2['Year'])):\n",
    "    idx = list(df2['Year'])[i] - 1944\n",
    "    idx = int(idx/4)\n",
    "    winners2.append(winners1[idx])\n",
    "df2['Winners'] = winners2\n",
    "denom = df2.groupby('Year').sum()['Electoral Vote of Each State']\n",
    "numerator = df2.groupby('Year').sum()\n",
    "numerator = numerator.apply(lambda x: x['Democrat'] if x['Democrat'] > x['Republican'] else x['Republican'], axis = 1)\n",
    "electoral_college_spread = numerator/denom\n",
    "electoral_college_spread.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bar_plot1 = pd.DataFrame(df2.groupby('State')['Is Democrat'].sum()).plot(kind='bar',figsize=(20,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17b19a90>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(df2.groupby('State')['Is Republican'].sum()).plot(kind='bar',figsize=(20,10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our first graph illustrates the percentage of electoral votes won by the winning party for that year. We decided to graph this metric in order to compare it to the votes that the winning party recieved during the midterm elections. From the graph we can see that while there were some years with a very clear majority, there were other years (such as the 2000 election) that barely recieved the requisite 50% of electoral college votes. \n",
    "\n",
    "Our two other graphs illustrate the distribution of each state voting for the Democrat or Republican candidate. Through these graphs we can see how certain states clearly vote for Republicans or Democrats. We can also isolate swing states by looking at whether at state has similar proportions in both graphs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "electoral_years = list(electoral_college_spread.keys())\n",
    "house_years = []\n",
    "for i in electoral_years: \n",
    "    house_years.append(i-3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "electoral_college_winners = []\n",
    "for i in range(len(df2['Winners'])): \n",
    "    if df2['Winners'][i] == \"Democrat\": \n",
    "        electoral_college_winners.append(1)\n",
    "    else:\n",
    "        electoral_college_winners.append(0)\n",
    "df2['Winners Binary'] = electoral_college_winners\n",
    "electoral_winners = df2.groupby('Year').agg(lambda x: x.value_counts().index[0])['Winners Binary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Congress (Years)'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3062\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3063\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   3064\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Congress (Years)'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-7b48d9b9a34c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mwinners_house\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhouse_years\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     curr_df = df2[(df2['Congress (Years)'].str.contains(str(i))) & \n\u001b[0m\u001b[1;32m      5\u001b[0m                 (df2['Congress (Years)'].str.contains(str(i+2)))].reset_index()\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2683\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2684\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2685\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2686\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2687\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2690\u001b[0m         \u001b[0;31m# get column\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2691\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2692\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\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   2693\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2694\u001b[0m         \u001b[0;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[0;34m(self, item)\u001b[0m\n\u001b[1;32m   2484\u001b[0m         \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2485\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2486\u001b[0;31m             \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\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   2487\u001b[0m             \u001b[0mres\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2488\u001b[0m             \u001b[0mcache\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mres\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/internals.py\u001b[0m in \u001b[0;36mget\u001b[0;34m(self, item, fastpath)\u001b[0m\n\u001b[1;32m   4113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4114\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4115\u001b[0;31m                 \u001b[0mloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\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   4116\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0misna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3063\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3064\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3065\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3066\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3067\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'Congress (Years)'"
     ]
    }
   ],
   "source": [
    "proportions = []\n",
    "winners_house = []\n",
    "for i in house_years: \n",
    "    curr_df = df[(df['Congress (Years)'].str.contains(str(i))) & \n",
    "                (df['Congress (Years)'].str.contains(str(i+2)))].reset_index()\n",
    "    \n",
    "    for j in range(len(curr_df['# of House Seats'])): \n",
    "        proportions.append((max(curr_df['Pro-Administration'][j],curr_df['Anti-Administration'][j])/curr_df['# of House Seats'][j]))\n",
    "        if curr_df['Pro-Administration'][j] > curr_df['Anti-Administration'][j]:\n",
    "            winners_house.append(1)\n",
    "        else: \n",
    "            winners_house.append(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [],
   "source": [
    "house_spread = pd.Series(proportions, house_years)\n",
    "house_winners = pd.Series(winners_house, house_years)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'house_spread' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-a95bc6e7ed98>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mhouse_spread\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'bar'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'house_spread' is not defined"
     ]
    }
   ],
   "source": [
    "house_spread.plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a26f9e550>"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "electoral_college_spread.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a27163d30>"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "electoral_winners.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a272afda0>"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "house_winners.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the midterm election predict general election"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5789473684210527"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = 0\n",
    "for i in range(len(electoral_winners.values)): \n",
    "    if electoral_winners.values[i] == house_winners.values[i]:\n",
    "        acc+=1\n",
    "acc/len(electoral_winners.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Does the midterm election predict the next general eelection "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.42105263157894735"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc = 0\n",
    "for i in range(len(electoral_winners.values)-1): \n",
    "    if electoral_winners.values[i]== house_winners.values[i+1]:\n",
    "        acc+=1\n",
    "acc/len(electoral_winners.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Analysis & Results "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* What distributions do your variables take? \n",
    "    * There is a lot of variance in terms of general election results, while the midterm elections were more prone to reelections. There is a similar overall shape of the graphs of the results of the midterm elections and general elections since 1944, with more fluctuations visually seen in between in terms of House results. Overall, we first compared the spread of the superdelegate vote with the House midterm spread throughout the country based on the winning party. Afterwards, we compared the binary result of the winning party in both elections and found more of a trend in the graphs in terms of historical progression of the party in power but not too close of a correlational relationship.\n",
    "    \n",
    "* Are there any outliers? \n",
    "    * Our dataset including results from midterm elections were dated from 1789 and so had political parties including Whig, Federalist etc. Since these could not necessarily be directly compared to Democratic vs Republican distributions, we are considering these as outliers and are not using election results from these earlier years.\n",
    "\n",
    "\n",
    "* Relationship between variables: \n",
    "    * We saw a very low correlation between midterm and general election results with a prediction rate of only 57%. We can not effectively predict the midterm election results or the spread of House Seats based on the superdelegate count, however there was more of  a pattern of which party was more successful in the following midterm election based on the main election results. By looking for the relationship between the superdelegate count and the following House results, we could not really see the inverse relationship of how the main election could have been influenced by the midterm election.\n",
    "\n",
    "* Approaches \n",
    "    * We originally wanted to use a Pearson’s correlation value to determine “how correlated” the winning proportions. This value showed that it was not very correlated. Looking at this number, we thought about what it meant to be correlated in this sense and if we could look at these numbers in a different way to better answer the question we are asking. We then looked at “how much” the winning party won by in the House and in the Electoral College. We created a proportion of the number of votes the winner received divided by total votes cast, which showed us how much the winner won by. We also looked at which party won in the midterm elections and which party won in the general elections (determined by the electoral college) . We looked at whether the midterm election could predict the general election (is the party the same). We also looked at whether the general election could predict the next midterm election, which produced an even less reliable statistic than the opposite. \n",
    "\n",
    "* Results\n",
    "    * The trend was roughly similar when looking at the proportion of the winning party between midterm and general elections. This indicates that the level of “certainty” about a party is similar between congress and the electoral college. However, the midterm elections outcome is not a good indicator of the electoral college (general election) outcome. The general election outcome is an even worse indicator of the next midterm election outcome\n",
    "\n",
    "* Interpretation of Findings\n",
    "    * These findings were disappointing and surprising. We expected midterm elections to give more of an indication of general elections. The fact that they do not shows that delegates might not be voting in the way the general population wants them to vote. It is a little concerning that they don’t! \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ethics and Privacy "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Yes, we do have permission to use our data. Since election data is freely and publicly available, we do have permission to use it. While no one explicitly granted us permission to use the data for this purpose, we feel that this would be a good use of the data because it aims to assess fairness in voting, which is something that is beneficial to voters in general. \n",
    "\n",
    "\n",
    "As of now, we cannot think of any privacy concerns regarding our dataset. We are using only publically available data and election data (data on which candidate won and what every state’s partisan breakdown is) which does not infringe on the privacy of individual voters. \n",
    "\n",
    "\n",
    "Our data is election data, which is inherently biased. Not every citizen votes and so therefore, voting does not completely signify the political viewpoints of the entire country. In addition, there are certain aspects of voting that further prevent minority voters from the polls. This bias is definitely reflected in the outcome of elections. Our analysis cannot affect this bias and so it will be apparent in our analysis as well. These problems definitely impact the significance of our analysis, since we are attempting to see whether the electoral college’s voting patterns represent the voting patterns of midterm voters. However, since midterm voters are those who are more heavily involved in politics (and therefore most likely heavily represented in politics at large), the significance of our results will be affected. There is no way to completely handle this issue other than working to procure better voting regulations and have more people vote to begin with. However, we feel that noting the disparity amongst those who vote and those do not is an important step in analyzing our results (or our expected results). \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, we worked with two datasets. The electoral college data originally included how super delegates from every state voted. The house data originally included the total number of house seats and the number of house seats each party won in the midterm election. Our datasets differed in their years, so we had to slice the data to ensure we were comparing an appropriate number of years. Originally, the electoral college data included a lot of information about both the presidential and vice-presidential candidates. \n",
    "\n",
    "**Our findings included three integral components:**\n",
    "\n",
    "1) How much greater the winning party won by in the general election. \n",
    "\n",
    "2) How much greater the winning party won by in the midterm election. \n",
    "\n",
    "3) Comparison of winners in the midterm election and the general election. \n",
    "\n",
    "We concluded that a correlation between midterm elections and general elections does not exist. In other words, the superdelegates’ votes in the electoral college do not necessarily reflect the votes of the American population during the midterm elections. Additionally, general elections also have an even lower amount of correlation with the midterm elections that occur after it. There is a lot of fluctuation with the midterm elections. \n",
    "We were limited by the relatively small dataset that we formed our analysis on. We used only elections with modern political parties (no Federalists, Whigs, etc), and therefore only used 19 elections. This would have had an impact on the quality or “sturdiness” or our results. \n",
    "\n",
    "From the results we found through our analysis, it is possible to become a little disillusioned with American democracy as it stands. Our results seem to say that the Electoral College does not pay attention to the popular vote (as perceived through the House of Representative), which is an alarming notion. However, we believe that if further analysis is done (with wide-spread data and rigorous statistical analysis), the results of a project like this could change how American voters view the fundamental idea of democracy and the people’s choice. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Team Contributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Karissa and Sruthi -> First Dataset Scraping, Cleaning, Analysis, EDA\n",
    "\n",
    "Sreyas and Saveree -> Second Dataset Scraping, Cleaning, Analysis, EDA\n",
    "\n",
    "Writeup -> All \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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}