FinalProject_Group054_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - Unveiling the Complexities of FEMA Disaster Relief Funding: The Surprising Role of State GDP Over Number of Disasters \n",
    "\n",
    "# Permissions\n",
    "\n",
    "Place an `X` in the appropriate bracket below to specify if you would like your group's project to be made available to the public. (Note that student names will be included (but PIDs will be scraped from any groups who include their PIDs).\n",
    "\n",
    "* [ X ] YES - make available\n",
    "* [  ] NO - keep private\n",
    "\n",
    "# Names\n",
    "\n",
    "- Ivan Chen \n",
    "- Leo Fleury\n",
    "- Daniel Yoon   \n",
    "- Fei He\n",
    "- HaoTing Huang\n",
    "\n",
    "# Abstract\n",
    "\n",
    "The surprising and sudden nature of the floods which brought disaster to a large portion of California, particularly in San Diego, inspired the group to consider the resources put into improving a state's infrastructure after a disaster occurred. As participants in a democracy, of the U.S. or otherwise, we felt it important to analyze the relationship between the states and funding from federal government, specifically looking at disaster relief. Utilizing the knowledge obtained from COGS 108, we were able to answer our research question in a manner that was efficient and effective, with the use of Python and libraries such as Pandas, Seaborn, and Matplot. We were able to graph four models (correlation between number of disasters and funding, disaster funding over the years by party, ratio of funding to a state's GDP, and correlation between a state's GDP and amount received in funding) to determine that independently small state size, high GDP, and Republican leaning positively influenced the amount a state received in disaster funding. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Research Question"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the association between the amount of federal money a state receives for disaster relief and the factors, state GDP , number of disasters, and political leaning per year from 2019 to 2022?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The United States was born out of the colonies' desire to be free from the tyranny of a government holding complete control over their commerce and rights. This wariness of a centralized governmental structure influences many aspects of how the government runs today, such as the separation of powers as labeled within the United States Constitution. The states can be considered, to an extent, nations within a nation. A study published in 2023 by smartasset<a name=\"cite_ref-1\"></a>[<sup>1</sup>](#cite_note-1) displays one aspect of this relationship - the depth to which states receive their funding from the federal government. But after decades of peace and prosperity as a singular united nation on the world stage - how many maintain this level of self-sufficiency in disasters?\n",
    "\n",
    "Another source we looked at was a data science project done by Money Geek that looked at which states were most reliant on federal aid. This project is relevant to ours because federal aid reduces the burden on a states budget and how much they can allocate towards disaster relief <a name=\"cite_ref-2\"></a>[<sup>2</sup>](#cite_note-2). To compute this, the team at Money Geek did not just look at total federal aid, but looked at federal funding as a percentage of state revenue and return on tax dollars which was a statistic showing how much the state got back for every dollar that they gave the government. What we learned from the metrics used (such as aid as a percentage of state revenue and return on tax dollars) is that often a data set will require a lot of cleaning and development of our own metrics before we can get the answer to our question. Our question being the relation between GDP and a state's amount spent on disaster relief. From the Money Geek project, we also saw how others incorporate graphs into their findings. Money geek did a very good job about making the main findings visible in the titles of their graphs and throughout their paragraphs. For example, one of the main findings from the data set was that states that get the most in federal aid are often republican states. Then, they created a illustrative bar chart to display this. \n",
    "\n",
    "Lastly, another project we found relevant from economy.com looking at how natural disasters affect national GDP, which we also found relevant to our project as it relates to natural disasters and uses GDP as a metric. However, our project takes a different angle by using GDP as a measure of a states self sufficiency and seeing how that correlates to how much disaster relief aid that state gets. Some relevant take-aways from the source were that natural disasters only have a minor impact on national GDP but can be very impactful for local GDP. For example, from economy.com calculations, real GDP growth for New Orleans fell 20% at an annual rate in the third quarter of 2005 after Katrina <a name=\"cite_ref-3\"></a>[<sup>3</sup>](#cite_note-3). \n",
    "\n",
    "1. <a name=\\\"cite_note-1\\\"></a> [^](#cite_ref-1) Jaclyn, D. (14 Apr. 2023)  Where Your Tax Dollars Go: States Most Dependent on the Federal Government – 2023 Study *smartasset* https://smartasset.com/data-studies/states-most-dependent-federal-government-2023 \n",
    "2. <a name=\\\"cite_note-2\\\"></a> [^](#cite_ref-2) MoneyGeek. (n.d.). States Most Reliant on the Federal Government., from https://www.moneygeek.com/living/states-most-reliant-federal-government/\n",
    "3. <a name=\\\"cite_note-3\\\"></a> [^](#cite_ref-3) Moody's Analytics. (n.d.). How Natural Disasters Affect US GDP. Economy.com., from https://www.economy.com/economicview/analysis/296804/How-Natural-Disasters-Affect-US-GDP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We predict that in the United States in 2022, states with lower Gross Domestic Product (GDP) and historical Republican inclinations will be associated with an increase in federal disaster relief funding (as a percentage of the state's GDP). Conversely, states with higher GDP and historical Democratic ties will receive a lower percentage of their GDP in federal disaster relief funding. This study aims to analyze data over the past two decades to investigate these relationships, employing statistical analysis methods to understand the impact of economic status and political affiliation on disaster relief funding."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data overview\n",
    "\n",
    "- Dataset #1\n",
    "  - Dataset Name: Emergency Management Performance Grants\n",
    "  - Link to the dataset: https://www.fema.gov/openfema-data-page/emergency-management-performance-grants-v2\n",
    "  - Number of observations: 20825\n",
    "  - Number of variables: 9\n",
    "- Dataset #2 \n",
    "  - Dataset Name: Disaster Declarations by State\n",
    "  - Link to the dataset: https://www.fema.gov/openfema-data-page/fema-web-disaster-declarations-v1\n",
    "  - Number of observations: 4854\n",
    "  - Number of variables: 20\n",
    "- Dataset #3\n",
    "  - Dataset Name: State Historical Political Leaning\n",
    "  - Link to dataset: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/IG0UN2\n",
    "  - Number of observations: 32452\n",
    "  - Number of variables: 19\n",
    "- Dataset #4\n",
    "  - Dataset Name: GDP by state\n",
    "  - Link to dataset: https://www.bea.gov/data/gdp/gdp-state\n",
    "  - Number of observations: 200\n",
    "  - Number of variables: 3\n",
    "\n",
    "Our first dataset, Emergency Managment Performance Grants, is a detailed dataset of emergency funding each state as received. This dataset inlcudes every agency that has provided a specific state emergency funding. The data variables includes states, id, agency name, closing and opening date, and more. We are mainly looking for state, amount and reporting period (2019-2022), so we can focus on extracting those variables out. With the large amount of agency's in each state, it would be best to combine it all under the state, where we have the total number of funding per state. \n",
    "\n",
    "The second dataset, Disaster Declarations by State, is a dataset from FEMA that provides detailed information on a disaster which occurred in a state from the years 1953 to 2023. Data variables include disaster type, which, for the purposes of our research, will be grouped under one category as disaster as we wish to view the influence the number of disasters have on funding, the state in which it occurred, another variable useful for our measurement to determine what kind of states receive more funding, and other variables such as incident declaration times and end. \n",
    "\n",
    "The third dataset, State Historical Political Leaning, is a dataset from MIT Election Lab providing detailed information on the House results of elections from the year 1976 to 2022. It provides information such as number of votes received, the county for which the representative in question represents, the state, and other information such as number of writeins for that election. For our purposes, we will be utilizing information such as political party of the state for the years 2019 to 2022, the years of which are our primary concern. \n",
    "\n",
    "Our last dataset, GDP by state, is a dataset that gives us each states, GDP by year. The dataset includes data from 1977 - 2022, with the GDP of each state per year. The states are organized by alphabetical order and the year from 2022 down to 1977. As for data, majority of it are integers, which simplifies it a little. As for cleaning the dataset, we want to extract data from 2019 to 2022 only, which means we need to filter more than half the dataset.\n",
    "\n",
    "With our datasets, it would be useful to look at them and understand if a state with a higher GDP gets more emergency funding overall. We want to see the relationships between the two and with the information we have, it would help prove our research question."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset #1 Emergency Management Performance Grants\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\leofl\\AppData\\Local\\Temp\\ipykernel_30884\\558211804.py:42: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_grouped = df.groupby(by = ['state', 'reportingPeriod']).sum()\n"
     ]
    },
    {
     "data": {
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       "    }\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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>2019</td>\n",
       "      <td>5728503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>2020</td>\n",
       "      <td>5810021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>2021</td>\n",
       "      <td>7446681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>2022</td>\n",
       "      <td>6681181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>2019</td>\n",
       "      <td>3093229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>WI</td>\n",
       "      <td>2022</td>\n",
       "      <td>7311711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>WY</td>\n",
       "      <td>2019</td>\n",
       "      <td>2991828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>WY</td>\n",
       "      <td>2020</td>\n",
       "      <td>3033266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>WY</td>\n",
       "      <td>2021</td>\n",
       "      <td>3889834</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>WY</td>\n",
       "      <td>2022</td>\n",
       "      <td>3455841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  funding\n",
       "0      AL  2019  5728503\n",
       "1      AL  2020  5810021\n",
       "2      AL  2021  7446681\n",
       "3      AL  2022  6681181\n",
       "4      AK  2019  3093229\n",
       "..    ...   ...      ...\n",
       "195    WI  2022  7311711\n",
       "196    WY  2019  2991828\n",
       "197    WY  2020  3033266\n",
       "198    WY  2021  3889834\n",
       "199    WY  2022  3455841\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "pd.set_option('display.max_rows', 10)\n",
    "\n",
    "url = 'Data/EmergencyManagementPerformanceGrants.csv'\n",
    "df = pd.read_csv(url)\n",
    "df['state'].unique()\n",
    "df_not_state = df['state'].str.contains('Puerto Rico|American Samoa|District of Columbia|Northern Mariana Islands|Guam|Virgin Islands')\n",
    "df = df[~df_not_state] #filter to only include US states\n",
    "df['state'].unique().size #check to see that our state column only has 50 values\n",
    "\n",
    "def standardize_reportingPeriod(string):\n",
    "    \n",
    "    string = string.lower().strip()\n",
    "    \n",
    "    if '2014' in string:\n",
    "        output = 2014\n",
    "    elif '2015' in string:\n",
    "        output = 2015\n",
    "    elif '2016' in string:\n",
    "        output = 2016\n",
    "    elif '2017' in string:\n",
    "        output = 2017\n",
    "    elif '2018' in string:\n",
    "        output = 2018\n",
    "    elif '2019' in string:\n",
    "        output = 2019\n",
    "    elif '2020' in string:\n",
    "        output = 2020\n",
    "    elif '2021' in string:\n",
    "        output = 2021\n",
    "    elif '2022' in string:\n",
    "        output = 2022\n",
    "    # Otherwise, if uncaught - keep as is\n",
    "    else:\n",
    "        output = string\n",
    "    \n",
    "    return output\n",
    "\n",
    "df['reportingPeriod'] = df['reportingPeriod'].apply(standardize_reportingPeriod)\n",
    "df = df.query('reportingPeriod >= 2019') #filter so lowest year is 2017 to match other data\n",
    "\n",
    "df_grouped = df.groupby(by = ['state', 'reportingPeriod']).sum()\n",
    "df_grouped = df_grouped.reset_index()\n",
    "df_grouped = df_grouped.rename(columns = {'reportingPeriod': 'year', 'fundingAmount' : 'funding'})\n",
    "df_grouped['funding'] = df_grouped['funding'].astype(int)\n",
    "\n",
    "us_state_to_abbrev = {\n",
    "    \"Alabama\": \"AL\", \"Alaska\": \"AK\", \"Arizona\": \"AZ\", \"Arkansas\": \"AR\", \"California\": \"CA\",\n",
    "    \"Colorado\": \"CO\", \"Connecticut\": \"CT\", \"Delaware\": \"DE\", \"Florida\": \"FL\", \"Georgia\": \"GA\",\n",
    "    \"Hawaii\": \"HI\", \"Idaho\": \"ID\", \"Illinois\": \"IL\", \"Indiana\": \"IN\", \"Iowa\": \"IA\",\n",
    "    \"Kansas\": \"KS\", \"Kentucky\": \"KY\", \"Louisiana\": \"LA\", \"Maine\": \"ME\", \"Maryland\": \"MD\",\n",
    "    \"Massachusetts\": \"MA\", \"Michigan\": \"MI\", \"Minnesota\": \"MN\", \"Mississippi\": \"MS\", \"Missouri\": \"MO\",\n",
    "    \"Montana\": \"MT\", \"Nebraska\": \"NE\", \"Nevada\": \"NV\", \"New hampshire\": \"NH\", \"New jersey\": \"NJ\",\n",
    "    \"New mexico\": \"NM\", \"New york\": \"NY\", \"North carolina\": \"NC\", \"North dakota\": \"ND\", \"Ohio\": \"OH\",\n",
    "    \"Oklahoma\": \"OK\", \"Oregon\": \"OR\", \"Pennsylvania\": \"PA\", \"Rhode island\": \"RI\", \"South carolina\": \"SC\",\n",
    "    \"South dakota\": \"SD\", \"Tennessee\": \"TN\", \"Texas\": \"TX\", \"Utah\": \"UT\", \"Vermont\": \"VT\",\n",
    "    \"Virginia\": \"VA\", \"Washington\": \"WA\", \"West virginia\": \"WV\", \"Wisconsin\": \"WI\", \"Wyoming\": \"WY\"\n",
    "}\n",
    "\n",
    "df_grouped['state'] = df_grouped['state'].str.lower().str.capitalize().map(us_state_to_abbrev)\n",
    "EMPG = df_grouped #naming to something we will remember\n",
    "EMPG['year'] = EMPG['year'].astype(int) # change to integers\n",
    "EMPG"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset #2 Disaster Declarations by State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    }\n",
       "\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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>disasters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>2019</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>2020</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>2021</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>WI</td>\n",
       "      <td>2022</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>WY</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>WY</td>\n",
       "      <td>2020</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>WY</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>WY</td>\n",
       "      <td>2022</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  disasters\n",
       "0      AL  2019         36\n",
       "1      AL  2020        257\n",
       "2      AL  2021         30\n",
       "3      AL  2022          2\n",
       "4      AK  2019          9\n",
       "..    ...   ...        ...\n",
       "195    WI  2022          0\n",
       "196    WY  2019          0\n",
       "197    WY  2020         51\n",
       "198    WY  2021          0\n",
       "199    WY  2022          0\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url2 = 'Data/DisasterDeclarationsSummaries.csv'\n",
    "df2= pd.read_csv(url2)\n",
    "\n",
    "df2 = df2.query('2023 > fyDeclared >= 2019 ') # make sure to include equals so 2017 included\n",
    "df2.sort_values(by = 'fyDeclared', ascending = True) # set to true so make sure have correct info\n",
    "df2 = df2.groupby(by = ['state', 'fyDeclared']).size().reset_index(name= 'disasters') # use size for num of disasters instead of sum & give column name\n",
    "df2 = df2.rename(columns={'fyDeclared': 'year'})\n",
    "valid_states = ['AL', 'AK', 'AZ', 'AR', 'CA', 'CO', 'CT', 'DE', 'FL', 'GA', \n",
    "                'HI', 'ID', 'IL', 'IN', 'IA', 'KS', 'KY', 'LA', 'ME', 'MD', \n",
    "                'MA', 'MI', 'MN', 'MS', 'MO', 'MT', 'NE', 'NV', 'NH', 'NJ', \n",
    "                'NM', 'NY', 'NC', 'ND', 'OH', 'OK', 'OR', 'PA', 'RI', 'SC', \n",
    "                'SD', 'TN', 'TX', 'UT', 'VT', 'VA', 'WA', 'WV', 'WI', 'WY']\n",
    "\n",
    "# Filter the DataFrame to only include rows where the state code is in the list of valid states\n",
    "df2 = df2[df2['state'].isin(valid_states)]\n",
    "df2\n",
    "\n",
    "# states didnt have disasters every year so we need to create columns with 0 for years no disasters were declared\n",
    "import itertools\n",
    "\n",
    "years = [2019, 2020, 2021, 2022]\n",
    "combinations = list(itertools.product(valid_states, years)) # Use itertools.product to get all combinations of states and years -- this works like a double sum where the valid states is fixed and its looking at all the states to match\n",
    "combinations\n",
    "df2_combinations = pd.DataFrame(combinations, columns=['state', 'year'])\n",
    "df2_combinations\n",
    "df2_merged = pd.merge(df2_combinations, df2, on = ['state', 'year'], how = 'left')# how on left so that df2_combinations has all rows saved and df2 added -- state and year pairs will get na values\n",
    "df2_merged['disasters'].fillna(0, inplace= True) #in place so a new data frame is not created and old one is kept\n",
    "df2_merged['disasters'] = df2_merged['disasters'].astype(int) # astype converts to a integer\n",
    "disasters =  df2_merged #naming to something we will remember\n",
    "disasters['year'] = disasters['year'].astype(int) # change to integers\n",
    "disasters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset #3 State Historical Political Leaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>party</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>2019</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>2020</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>2021</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>2022</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>2019</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>WI</td>\n",
       "      <td>2022</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>WY</td>\n",
       "      <td>2019</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>WY</td>\n",
       "      <td>2020</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>WY</td>\n",
       "      <td>2021</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>WY</td>\n",
       "      <td>2022</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year       party\n",
       "0      AL  2019  REPUBLICAN\n",
       "1      AL  2020  REPUBLICAN\n",
       "2      AL  2021  REPUBLICAN\n",
       "3      AL  2022  REPUBLICAN\n",
       "4      AK  2019  REPUBLICAN\n",
       "..    ...   ...         ...\n",
       "199    WI  2022  REPUBLICAN\n",
       "200    WY  2019  REPUBLICAN\n",
       "201    WY  2020  REPUBLICAN\n",
       "202    WY  2021  REPUBLICAN\n",
       "203    WY  2022  REPUBLICAN\n",
       "\n",
       "[204 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url3 = 'Data/1976-2022-house.csv'\n",
    "election_data= pd.read_csv(url3)\n",
    "\n",
    "elections_post_2017 = election_data[election_data['year'] > 2019]\n",
    "relevant_elections = elections_post_2017[elections_post_2017['party'].isin(['DEMOCRAT', 'REPUBLICAN'])]\n",
    "grouped_elections = relevant_elections.groupby(['state', 'year', 'party'])['candidatevotes'].sum().reset_index()\n",
    "winning_party = grouped_elections.sort_values('candidatevotes', ascending=False).drop_duplicates(['state', 'year'])\n",
    "winning_party = winning_party.sort_values(by = 'state')\n",
    "winning_party = winning_party.drop(columns= 'candidatevotes')\n",
    "\n",
    "\n",
    "#df.reindex -- confrom DF to a new index w. optional filling -- places NA in locastions having no value in teh previous index\n",
    "# we reindex the winning_party data frame to do this\n",
    "# we use pd.MultiIndex.from_product instead of iterools.prodcut(state, year) bcs it returns a list instead of a multiindex\n",
    "winning_party_indexed = winning_party.set_index(['state', 'year'])\n",
    "state = winning_party_indexed.index.levels[0]\n",
    "year = [2019,2020,2021,2022]\n",
    "multi_index = pd.MultiIndex.from_product([state, year])\n",
    "winning_party_indexed = winning_party_indexed.reindex(multi_index)\n",
    "winning_party_indexed_filled = winning_party_indexed.groupby(level=0).fillna(method='ffill').fillna(method='bfill').reset_index()\n",
    "winning_party_indexed_filled = winning_party_indexed_filled.rename(columns = {'level_1' : 'year'})\n",
    "\n",
    "#the rest of our data tables use state postal codes instead of the full names\n",
    "#using a dictionary I found on GitHub we can map these states to their postal codes\n",
    "us_state_to_abbrev = {\n",
    "    \"Alabama\": \"AL\", \"Alaska\": \"AK\", \"Arizona\": \"AZ\", \"Arkansas\": \"AR\", \"California\": \"CA\",\n",
    "    \"Colorado\": \"CO\", \"Connecticut\": \"CT\", \"Delaware\": \"DE\", \"Florida\": \"FL\", \"Georgia\": \"GA\",\n",
    "    \"Hawaii\": \"HI\", \"Idaho\": \"ID\", \"Illinois\": \"IL\", \"Indiana\": \"IN\", \"Iowa\": \"IA\",\n",
    "    \"Kansas\": \"KS\", \"Kentucky\": \"KY\", \"Louisiana\": \"LA\", \"Maine\": \"ME\", \"Maryland\": \"MD\",\n",
    "    \"Massachusetts\": \"MA\", \"Michigan\": \"MI\", \"Minnesota\": \"MN\", \"Mississippi\": \"MS\", \"Missouri\": \"MO\",\n",
    "    \"Montana\": \"MT\", \"Nebraska\": \"NE\", \"Nevada\": \"NV\", \"New hampshire\": \"NH\", \"New jersey\": \"NJ\",\n",
    "    \"New mexico\": \"NM\", \"New york\": \"NY\", \"North carolina\": \"NC\", \"North dakota\": \"ND\", \"Ohio\": \"OH\",\n",
    "    \"Oklahoma\": \"OK\", \"Oregon\": \"OR\", \"Pennsylvania\": \"PA\", \"Rhode island\": \"RI\", \"South carolina\": \"SC\",\n",
    "    \"South dakota\": \"SD\", \"Tennessee\": \"TN\", \"Texas\": \"TX\", \"Utah\": \"UT\", \"Vermont\": \"VT\",\n",
    "    \"Virginia\": \"VA\", \"Washington\": \"WA\", \"West virginia\": \"WV\", \"Wisconsin\": \"WI\", \"Wyoming\": \"WY\"\n",
    "}\n",
    "\n",
    "winning_party_indexed_filled['state'] = winning_party_indexed_filled['state'].str.lower().str.capitalize().map(us_state_to_abbrev)\n",
    "state_political_leaning = winning_party_indexed_filled #naming to something will actually remember\n",
    "state_political_leaning['year'] = state_political_leaning['year'].astype('int32')\n",
    "state_political_leaning\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataset #4 GDP by State"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>2019</td>\n",
       "      <td>234526.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>AL</td>\n",
       "      <td>2020</td>\n",
       "      <td>235118.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>AL</td>\n",
       "      <td>2022</td>\n",
       "      <td>281569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>AL</td>\n",
       "      <td>2021</td>\n",
       "      <td>257986.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>AK</td>\n",
       "      <td>2022</td>\n",
       "      <td>65698.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WI</td>\n",
       "      <td>2019</td>\n",
       "      <td>347398.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>WY</td>\n",
       "      <td>2020</td>\n",
       "      <td>36675.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WY</td>\n",
       "      <td>2019</td>\n",
       "      <td>39971.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>WY</td>\n",
       "      <td>2021</td>\n",
       "      <td>42176.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>WY</td>\n",
       "      <td>2022</td>\n",
       "      <td>49080.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year       GDP\n",
       "0      AL  2019  234526.4\n",
       "50     AL  2020  235118.3\n",
       "150    AL  2022  281569.0\n",
       "100    AL  2021  257986.5\n",
       "151    AK  2022   65698.8\n",
       "..    ...   ...       ...\n",
       "48     WI  2019  347398.6\n",
       "99     WY  2020   36675.5\n",
       "49     WY  2019   39971.4\n",
       "149    WY  2021   42176.2\n",
       "199    WY  2022   49080.6\n",
       "\n",
       "[200 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Log onto the BEA website (Bureau of Economic Analysis), the official site for government data.\n",
    "# Navigate to the \"Data\" section, select \"GDP,\" and then choose \"GDP by State.\"\n",
    "# Go to \"Interactive Data\" and select \"Interactive Tables: GDP by State.\"\n",
    "# Choose \"ANNUAL GROSS DOMESTIC PRODUCT (GDP) BY STATE,\" then \"SAGDP2 GDP in current dollars.\"\n",
    "# Select the United States, and view all statistics in the table.\n",
    "# In the resulting table, select the first row's line code where the description is \"all industry total.\"\n",
    "# The resulting table displays the GDP by state in millions of current dollars, with data collected from 2017 to 2022.\n",
    "\n",
    "url4 =  \"Data/GDP by state (2017-2022).csv\"\n",
    "\n",
    "df4 = pd.read_csv(url4, skiprows = 3) #skip first 3 rows because they are a description\n",
    "df4 = df4.drop(columns = ['GeoFips', '2017', '2018'])\n",
    "df4 = df4.rename(columns= {'GeoName': 'state'})\n",
    "df4_bad_GeoName = df4['state'].str.contains('District of Columbia|New England|nited States *|Mideast|Great Lakes|Plains|Southeast|Southwest|nan|Rocky Mountain|Far West').fillna(False) #dealing with NA values\n",
    "df4 = df4[~ df4_bad_GeoName] #this removes all the regions and territories we don't care about \n",
    "indexes_to_drop = df4.iloc[-4:].index #get the last 4 rows index using iloc\n",
    "df4 = df4.drop(indexes_to_drop)\n",
    "melted_df = pd.melt(df4, id_vars=['state'], value_vars=['2019', '2020', '2021', '2022'], var_name='year', value_name='amount')\n",
    "melted_df = melted_df.sort_values('state')\n",
    "melted_df\n",
    "\n",
    "#using the same code I used on the state leaning dat set we change the years to their postal codes\n",
    "\n",
    "us_state_to_abbrev = {\n",
    "    \"Alabama\": \"AL\", \"Alaska\": \"AK\", \"Arizona\": \"AZ\", \"Arkansas\": \"AR\", \"California\": \"CA\",\n",
    "    \"Colorado\": \"CO\", \"Connecticut\": \"CT\", \"Delaware\": \"DE\", \"Florida\": \"FL\", \"Georgia\": \"GA\",\n",
    "    \"Hawaii\": \"HI\", \"Idaho\": \"ID\", \"Illinois\": \"IL\", \"Indiana\": \"IN\", \"Iowa\": \"IA\",\n",
    "    \"Kansas\": \"KS\", \"Kentucky\": \"KY\", \"Louisiana\": \"LA\", \"Maine\": \"ME\", \"Maryland\": \"MD\",\n",
    "    \"Massachusetts\": \"MA\", \"Michigan\": \"MI\", \"Minnesota\": \"MN\", \"Mississippi\": \"MS\", \"Missouri\": \"MO\",\n",
    "    \"Montana\": \"MT\", \"Nebraska\": \"NE\", \"Nevada\": \"NV\", \"New Hampshire\": \"NH\", \"New Jersey\": \"NJ\",\n",
    "    \"New Mexico\": \"NM\", \"New York\": \"NY\", \"North Carolina\": \"NC\", \"North Dakota\": \"ND\", \"Ohio\": \"OH\",\n",
    "    \"Oklahoma\": \"OK\", \"Oregon\": \"OR\", \"Pennsylvania\": \"PA\", \"Rhode Island\": \"RI\", \"South Carolina\": \"SC\",\n",
    "    \"South Dakota\": \"SD\", \"Tennessee\": \"TN\", \"Texas\": \"TX\", \"Utah\": \"UT\", \"Vermont\": \"VT\",\n",
    "    \"Virginia\": \"VA\", \"Washington\": \"WA\", \"West Virginia\": \"WV\", \"Wisconsin\": \"WI\", \"Wyoming\": \"WY\",\n",
    "}\n",
    "\n",
    "melted_df['state'] = melted_df['state'].map(us_state_to_abbrev)\n",
    "gdp_by_state = melted_df.rename(columns = {'amount': 'GDP'})\n",
    "gdp_by_state #naming to something we will remember\n",
    "gdp_by_state['year'] = gdp_by_state['year'].astype(int) # change to integers\n",
    "gdp_by_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Merge Data Sets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>state</th>\n",
       "      <th>year</th>\n",
       "      <th>disasters</th>\n",
       "      <th>funding</th>\n",
       "      <th>party</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>2019</td>\n",
       "      <td>36</td>\n",
       "      <td>5728503</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>234526.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>2020</td>\n",
       "      <td>257</td>\n",
       "      <td>5810021</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>235118.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AL</td>\n",
       "      <td>2021</td>\n",
       "      <td>30</td>\n",
       "      <td>7446681</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>257986.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AL</td>\n",
       "      <td>2022</td>\n",
       "      <td>2</td>\n",
       "      <td>6681181</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>281569.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>2019</td>\n",
       "      <td>9</td>\n",
       "      <td>3093229</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>54469.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>WI</td>\n",
       "      <td>2022</td>\n",
       "      <td>0</td>\n",
       "      <td>7311711</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>396209.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>WY</td>\n",
       "      <td>2019</td>\n",
       "      <td>0</td>\n",
       "      <td>2991828</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>39971.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>WY</td>\n",
       "      <td>2020</td>\n",
       "      <td>51</td>\n",
       "      <td>3033266</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>36675.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>WY</td>\n",
       "      <td>2021</td>\n",
       "      <td>0</td>\n",
       "      <td>3889834</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>42176.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>WY</td>\n",
       "      <td>2022</td>\n",
       "      <td>0</td>\n",
       "      <td>3455841</td>\n",
       "      <td>REPUBLICAN</td>\n",
       "      <td>49080.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  disasters  funding       party       GDP\n",
       "0      AL  2019         36  5728503  REPUBLICAN  234526.4\n",
       "1      AL  2020        257  5810021  REPUBLICAN  235118.3\n",
       "2      AL  2021         30  7446681  REPUBLICAN  257986.5\n",
       "3      AL  2022          2  6681181  REPUBLICAN  281569.0\n",
       "4      AK  2019          9  3093229  REPUBLICAN   54469.9\n",
       "..    ...   ...        ...      ...         ...       ...\n",
       "195    WI  2022          0  7311711  REPUBLICAN  396209.3\n",
       "196    WY  2019          0  2991828  REPUBLICAN   39971.4\n",
       "197    WY  2020         51  3033266  REPUBLICAN   36675.5\n",
       "198    WY  2021          0  3889834  REPUBLICAN   42176.2\n",
       "199    WY  2022          0  3455841  REPUBLICAN   49080.6\n",
       "\n",
       "[200 rows x 6 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#the data sets we have now are EMPG, disasters, state_political_leaning, & gdp_by_state \n",
    "#we have individually pre procesesed every data frame so that merging will be a smooth process\n",
    "pd.set_option('display.max_rows', 10)\n",
    "master_df = disasters.merge(EMPG)\n",
    "master_df = master_df.merge(state_political_leaning)\n",
    "master_df = master_df.merge(gdp_by_state)\n",
    "master_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Section 1 - Correlation between Num. of disasters and Funding Allocated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n",
    "First, we create a pivot table using values from the ‘disasters’ and ‘funding’ columns, summing these values for each state. This data is plotted using a seaborn scatterplot, with the horizontal axis representing the number of disasters and the vertical axis indicating the funding received from FEMA. A for loop is used to plot each of the 50 states on the graph, identified by their two-character abbreviations. We then calculate and plot a line of best fit. The latter part of the code is dedicated to labeling the graph. For the OLS analysis, funding is the dependent variable, and the number of disasters is the independent variable.\n",
    "#### Intepretation\n",
    "The data reveals the correlation between the number of disasters a state experiences and the funding it receives from FEMA through its Emergency Management Performance Grants, encompassing the years 2019 to 2022. Although a positive linear correlation exists, it is relatively weak. The null hypothesis is rejected since the P-value is 0.0158, indicating a less than 2% chance of obtaining the same result if the null hypothesis were true. As this value is below 0.05, we reject the null hypothesis. The OLS results show an R-squared value of 0.115, suggesting that only 11.5% of the variance in funding can be predicted by the number of disasters. This confirms that the correlation is not as significant as hypothesized. Examining the coefficients, the intercept is approximately $22,150,000, indicating the baseline federal funding a state would receive without any disasters. The disaster coefficient is roughly $29,270, meaning that each disaster correlates to an increase of about $30,000 in federal funding. Overall, the data suggests that disaster funding is not solely dependent on the number of disasters. This is further supported by the fact that many states fall below the line of best fit. States like Maine and Mississippi, with more federally declared disasters, received less funding than Florida, which had fewer disasters. Therefore, we must explore other correlations in the data, such as between GDP, FEMA funding, the number of disasters, and political leaning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot = pd.pivot_table(master_df, values= ['disasters', 'funding'], index= 'state', aggfunc = 'sum')\n",
    "\n",
    "plt.figure(figsize =(14,10))\n",
    "scatter = sns.scatterplot(\n",
    "    data = pivot,\n",
    "    x = 'disasters',\n",
    "    y = 'funding',\n",
    ")\n",
    "\n",
    "for state, (x, y) in pivot.iterrows():\n",
    "    plt.text(x, y, state, horizontalalignment='center', verticalalignment = 'top', size='small', color='black', weight='semibold')\n",
    "\n",
    "# calculate line of bet fit\n",
    "x = pivot['disasters']\n",
    "y = pivot['funding']\n",
    "m, b = np.polyfit(x, y, 1)\n",
    "\n",
    "# add to plot \n",
    "plt.plot(x, m*x + b, color='red')\n",
    "\n",
    "plt.ticklabel_format(style='plain', axis='y')      \n",
    "plt.ticklabel_format(style='plain', axis='x')\n",
    "scatter.set_title('Disasters vs Funding by State')\n",
    "scatter.set_xlabel('Number of Disasters')\n",
    "scatter.set_ylabel('Amount of Funding')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import patsy\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                funding   R-squared:                       0.115\n",
      "Model:                            OLS   Adj. R-squared:                  0.097\n",
      "Method:                 Least Squares   F-statistic:                     6.262\n",
      "Date:                Tue, 19 Mar 2024   Prob (F-statistic):             0.0158\n",
      "Time:                        11:17:38   Log-Likelihood:                -910.06\n",
      "No. Observations:                  50   AIC:                             1824.\n",
      "Df Residuals:                      48   BIC:                             1828.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   2.215e+07   4.29e+06      5.166      0.000    1.35e+07    3.08e+07\n",
      "disasters   2.927e+04   1.17e+04      2.502      0.016    5751.369    5.28e+04\n",
      "==============================================================================\n",
      "Omnibus:                       47.118   Durbin-Watson:                   1.879\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              207.666\n",
      "Skew:                           2.457   Prob(JB):                     8.05e-46\n",
      "Kurtosis:                      11.691   Cond. No.                         561.\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "outcome_1, predictors_1 = patsy.dmatrices('funding ~ disasters', data= pivot, return_type='matrix')\n",
    "mod_1 = sm.OLS(outcome_1, predictors_1)\n",
    "res_1 = mod_1.fit()\n",
    "\n",
    "# Check the summary for results\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Section 2 - Correlation between Num. of disasters and Funding Allocated"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n",
    "In Section 2, we focus on analyzing FEMA disaster funding across different years and comparing it based on political parties (Democrat vs. Republican). We use Seaborn to generate a categorical bar chart from this aggregated data, plotting the total amount of disaster funding for each year, represented in different colors by political party. The chart displays funding on the Y-axis, while the X-axis showcases the 2019 to 2022 period, complete with descriptive titles and labels for both axes using `plt.ticklabel_format`, `plt.xlabel`, and `plt.ylabel`. The bar chart visualizes trends in disaster funding over the years, facilitating comparisons between the amount of FEMA funding in Democratic and Republican states, and is intended to reveal any patterns or differences that may exist between the two political parties.\n",
    "\n",
    "#### Interpretation\n",
    "Based on the results shown in the data, it appears that disaster relief funding for Democratic and Republican states changes over the years (2019-2022). In 2019 and 2022, Republican states received slightly more funding than Democratic states. In 2021, states of both parties received significant increases in funding, with Republican states consistently receiving more total funding than Democratic states. In 2022, Republican states maintained similar levels to 2021, while Democratic states experienced a substantial reduction in funding, returning to levels comparable to 2020. As can be seen, political preferences are considered relevant to the distribution of disaster relief funds, as evidenced by the differences and trends in the amounts received by states of different political affiliations over the years. This suggests that while the occurrence of a disaster may affect the distribution of funds, political factors may also play an important role in the distribution of federal disaster relief funds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(44.300306712962964, 0.5, 'Total Funding (USD)')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 738.25x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_grouped = master_df.groupby(by=['year', 'party', ])['funding'].sum().reset_index()\n",
    "\n",
    "bar_plot= sns.catplot(\n",
    "    data=df_grouped, \n",
    "    kind=\"bar\", \n",
    "    x=\"year\",\n",
    "    y = \"funding\",\n",
    "    hue=\"party\",\n",
    "    hue_order = [\"DEMOCRAT\", \"REPUBLICAN\"], #hue order to specify the way you want vars to show\n",
    "    palette=\"dark\", \n",
    "    alpha=.6, \n",
    "    height=6\n",
    ")\n",
    "\n",
    "plt.ticklabel_format(style='plain', axis='y')\n",
    "plt.title('Disaster Funding Over the Years by Party')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Funding (USD)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 3 - Ratio of funding divided by GDP (bar plot)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n",
    "In this code, we delve into examining the ratio of funding to GDP using a bar plot, while utilizing a Pandas Pivot Table. On the x-axis, we display the funding as a percentage of GDP, and on the y-axis, we list the states. We adjust the GDP values in the pivot table to show actual dollar amounts instead of millions by multiplying by 1,000,000. This adjustment enables us to calculate the percent of funding relative to each state's GDP. After sorting our data, we use Seaborn to create a bar plot, visualizing the emergency funding as a percentage of GDP for each state.\n",
    "#### Intepretation\n",
    "Looking at our data, it shows the correlation between the sum of the emergency funding a state receives and the state's GDP from 2019 to 2022, arranged from lowest to highest. From this, we can see that New York receives the least amount of emergency funding relative to its GDP, while Vermont has the highest percentage of emergency funding relative to its GDP. However, many factors contribute to why a state like Vermont has the highest percentage of emergency funding. These factors include the number of disasters, population, emergency funding, and more. We can observe that states with a higher GDP and more disasters typically have the lowest percentage of emergency funding relative to their GDP compared to those states with a lower GDP and fewer disasters.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAAAlqLy1CwAAAAAAlq5vfetbueaaa4rtN998M2ussUadfUeOHJlBgwYV28OGDcs555yzhCsE+GKYNWtWbrrpptxxxx15+umnM2XKlHzyySfF6wMHDszIkSOXeB1VVVX5z3/+k7feeitTpkzJzJkz07Fjx1RWVmbFFVfMBhtskA022CDl5X5sBAAAAFBqfEcIAAAAgGXKGmuskbfffrtZY5x00kn51a9+1TIFAY1SVla2yD7l5eXF4GL//v2z9dZb54ADDshaa621FCqEpevll1/OPvvsk5deeqlV5p8wYUL+9Kc/5bbbbsszzzyTQqHQYP8OHTpks802yz777JOhQ4dmnXXWadQ8V199dY488sgG+7Rt2zYdOnRIly5d0qNHj/Tt2zfrr79+Nt988+y0005Zc801G/1cC1rwF17q06FDh1RWVqZHjx7ZbLPNssMOO+SAAw5It27dFntuAAAAgC+CNq1dAAAAAAAApaGmpiZTp07Na6+9lttvvz0/+MEPss4662SfffbJu+++29rlsRStscYaKSsrS1lZWb07oi/Ppk+fnq9//eutEiKeNm1aTjjhhKyxxhr5yU9+kv/85z+LDBEnn++ePGbMmJx11llZd911M3jw4IwePbpFapozZ05mzJiRDz74IM8991z++c9/5pJLLskRRxyRtdZaK5tsskkuvvjiVFVVtch8dZk1a1Zx/hEjRuS73/1u+vTpk9NPPz2ffvrpEpsXAAAAYFknSAwAAAAAQKspFAq54447svHGG2fMmDGtXQ60iMsvvzyvv/56sb3++utnxIgRee655/Lmm28Wj5tuuqlF53300UezySab5Le//W0+++yzha6vt9562WWXXTJ06NAccMAB2WWXXbLRRhulvHzhP2B53333Zaeddsrdd9/dojXWZdy4cTnjjDOy2mqr5Ze//GXmzp27xOdMkhkzZuTnP/95ttpqq0yZMmWpzAkAAACwrFn4O0MAAAAAsAy58cYbs+222zbpnoqKiiVUDdBYo0aNSt++fWud++yzzzJ58uQ88cQTueKKK/L8888Xr3300UfZY4898tRTT2X11Vdf2uVCi/rLX/5SfN2+ffs88MAD6dOnzxKd85577sk+++yz0O66W221VU4++eR85StfSY8ePeq8t7q6Og8++GDuvvvuXH/99bXGaOpuvdtss02dAenp06fno48+yocffpj//Oc/efzxxzNq1Kh8/PHHteo49dRT8/e//z1/+ctf0q1btybNnSR9+vSpcyflmTNn5r333stDDz2Uyy+/PJMnTy5ee+6557LHHnvkkUceSdu2bZs8JwAAAMDyTJAYAAAAgGVar169vpB/9n55scsuu6RQKLR2GSyH+vbtW+fn7rrrrpvtt98+J554Yn7wgx9k+PDhxWtTp07Nj370o4wYMWIpVgota86cORk7dmyxve222y7xEPFrr72WAw44oFbot7KyMldeeWX23XffRd5fUVGRffbZJ/vss09++tOf5le/+lUuueSSzJw5s8m1dOzYcZH/b++9995JkqqqqvzpT3/KxRdfnIkTJxav33///Rk8eHAeeuihdO7cuUnzl5eX1zv/hhtumK985Ss544wzsv/+++e+++4rXnv88cdz9dVX56ijjmrSfAAAAADLuzatXQAAAAAAAKWnTZs2ufDCCzN06NBa52+44YZMmTKllaqC5quqqsqsWbOK7QV35m5pM2fOzNChQ1NVVVU8t+qqq2b06NGNChEvqGfPnrngggvy1FNPZbPNNmvJUhdSWVmZU089Nc8880yGDBlS69qTTz6Z4447bonM261bt9x6660LBY5//etfL5H5AAAAAJZlgsQAAAAAALSac845p1Z77ty5eeCBB1qnGGgBH3/8ca12u3btluh8f/zjH/Pss88W22VlZbnuuuvSv3//Zo274YYb5vHHH8+OO+7Y3BIXqXv37vnb3/6WQYMG1To/YsSIPPjgg0tkzq5du+a0006rde7ZZ5/NpEmTlsh8AAAAAMuq8tYuAAAAAABKySuvvJJnnnkm7777bmpqatK9e/dsscUW2XjjjZs99nvvvZdHHnkkEyZMSE1NTfr06ZP1118/m2++eQtU3jKmTJmSRx99NO+++26qqqqy8sorZ4MNNsh2223X7LBdVVVVHn744bz77ruprq5O7969s9pqq2XHHXdc4kG+RXn//ffzwgsv5PXXX89HH32U2bNnp1u3bllllVWyxRZbZO21126ROf7zn//kjTfeSHV1dWpqatKpU6esvPLKWX311fOlL30p3bt3b4GnaVn9+/dP7969M3HixOK5F154oVH3fvzxx8WP+cmTJ6dt27bp0aNHNtxww2y++eZp06Zl99J4+eWX89xzz2XixImpqqrKKquskgMPPDCVlZUN3jd9+vQ89thjmTBhQqZMmZLPPvssFRUVWXPNNbPxxhunX79+i13TxIkT8/jjj+eDDz7I1KlT06VLl/To0SNbbbVV1lprrcUety6FQiFPPPFEXn311UyYMCHt27dPr169suOOOy7xXXeXpAkTJmTMmDF5//33M23atFRWVqZ79+7ZaqutsuaaazZ5vLlz5y6BKuv22Wef5ec//3mtc9/97ncXCuQurnbt2qVXr14tMtaitG3bNjfddFP69++fyZMnF8+fe+652XXXXZfInIMHD17o3IsvvrjUnhkAAABgmVAAAAAAgGXI6quvXkhSPB566KFmjzlw4MBaYzbFVVddVeveq666qt6+b775Zq2+RxxxRPHaXXfdVdh2221rXZ//WGuttQrXXXfdYj3fk08+Wdhll10KZWVldY69/vrrF37zm98UampqCoVCoXDEEUfUuv7mm2/WO/ZDDz1Uq++wYcMarGX+99/qq69ePP/CCy8U9tlnn0K7du3qrLGioqJw7rnnFmbMmNHk53/99dcL++67b71jr7LKKoVTTz218PHHHxcKhaa9TxdXTU1N4YEHHigcd9xxhXXXXbfe9/u8o2/fvoXzzz+/8NFHHzV5rltuuaWwww471Pv+n/9Ye+21CyeeeGLh9ddfb/FnXnCuhj6uFrT11lvXuvd73/teg/3/+c9/FgYNGlTv+3ze+/3MM88sfPjhh42qoaHP3yuuuKKwySab1DnP2LFj6xxvzpw5hZtvvrmw/fbbF8rLyxt8v6y77rqFM888s9Hvl9mzZxcuu+yywsYbb7zIcf/whz8UPvvss0aNW9/nxpw5cwo///nPC2uuuWa9c+20006FMWPGNDj+gl97GnvM/75oKXPmzClcd911hQEDBjQ493rrrVf47W9/W5g9e3aD4y34/0xjjoEDBzb7OW644YZaY5aVlRVefvnlZo/bFAt+3DT3uc4999yF3lYvvfRSvf0X/H9q/v97FmXGjBkLzXXzzTc3q34AAACA5U3LbscAAAAAANQyZ86cnHDCCdlzzz0zZsyYevu98cYbOfTQQ3P88cenUCg0evzhw4dnm222yciRI+u97+WXX84JJ5yQr371q/n444+b/AzNddVVV2XLLbfM7bffns8++6zOPtXV1Rk2bFh22223fPTRR40e+6abbsqXvvSl3HbbbfWOPWXKlPziF7/IVlttlVdeeWVxHqHJLrnkknz5y1/OZZddlldffXWR/d9999384Ac/yKabbpqnn366UXPMmjUr++23X/bbb7888sgjjfq4ef311/PrX/86Dz74YKPmWFoa+zE/ZcqU7LbbbvnqV7+ahx56qN73+by+w4cPz7rrrpt//etfi1XX9OnTs+eee+Y73/lOnn322Ubf98orr2TzzTfP/vvvn0cffTQ1NTUN9n/11VczfPjw/M///M8ix37qqaeywQYb5Ljjjsu4ceMWOe6xxx6brbbaKu+9916j65/fBx98kF133TWnnXZa3nzzzXr7jRo1KjvttFNuuOGGxZpnaZo4cWK22267HHrooXnmmWca7PvKK6/k+OOPz8Ybb9yoz+Wl7d57763V3mmnnbLeeuu1UjUt43vf+95Cu8jfeeedrVQNAAAAwBdfeWsXAAAAAABfZCeddFJ+97vfJUnKysqyySabZK211kqHDh3y9ttv58knn6wVMvzd736XL33pSznuuOMWOfbw4cNz1llnLXR+wIABWXvttZMkr732WjEAef/99+eggw7Kyiuv3BKP1ih/+ctfctRRRxWDoquvvnr69++fysrKTJ48OWPGjMn06dOL/R955JEce+yx+fOf/7zIsW+++eYceuihmTNnTq3za621VjbaaKN07tw5EyZMyBNPPJFZs2blxRdfzF577ZXvf//7LfuQdZg7d26tdvv27bPBBhukb9++qayszJw5czJ58uQ888wz+fDDD4v93nrrrey666556qmniu/D+hx//PG59dZba53r2LFjBgwYkL59+6ZTp0755JNPMnXq1Lz44ouZPHlyyz1gC3v33XdrtVdZZZWF+rz22msZMmRI3njjjVrnu3btmi222CI9e/bMnDlz8tZbb+Xpp58uvg+mTp2a3XbbLXfeeWeGDBnS6JoKhUKOOOKI3H333Un++/m75pprpn379nn33Xfz5JNPLnTfyJEjs88++6SqqqrW+fLy8my22Wbp06dPVlhhhXz00Ud5+eWX88YbbzQ6SH333XfngAMOyIwZM2qd7927dwYMGJCVVlopn3zySV544YVaodf//Oc/2WabbTJmzJj07du30W+DmTNnZo899ig+Z4cOHbLlllumT58+mTNnTl588cW88MILxf6fffZZvvWtb6V///7ZZJNNGj3P0vT2229n4MCBefvtt2ud79q1a7beeuv06NEjH374Yf79739n6tSpxesvv/xytt9++9x///0ZMGDA0i67Xg8//HCt9sCBA1upkpazyiqrZNNNN631+fXII4/kjDPOaPG5xo8fX+f8AAAAAKVEkBgAAAAAlpC//e1vmTJlSpLkO9/5ToYNG7ZQiO+9997LMccck7///e/Fc2eddVYOP/zwdO7cud6xH3/88fzwhz+sdW7IkCH5zW9+k3XXXbfW+Zdeeinf//738+CDD+buu+/Oiiuu2NxHa5QpU6bkiCOOSKFQyM4775yLL744W221Va0+M2fOzP/8z//kwgsvLJ67+eabc/zxx2ennXaqd+zx48fn6KOPrhUiHjBgQC699NJsv/32tfpOnz49w4cPz/Dhw/Pyyy83atfXltCrV68cccQR2WOPPbLtttumvHzhb8fOnTs3999/f84666yMHTs2SVJVVZVDDjmkwR2sX3rppVxxxRXF9gorrJALL7wwRx11VFZYYYU673nzzTfz97//PVdddVUzn6xlPf/885k4cWKtcxtuuGGt9owZM/KNb3yjVoh4/fXXz09/+tPss88+adu2ba3+EydOzLBhw3L55ZcnSWbPnp1DDz00//nPf9KnT59G1fXXv/61GHI/4ogjct5556Vfv361+kyZMiUdOnQott94443su+++tULEK6+8cn74wx/mqKOOSkVFxULzTJkyJX/9619z5ZVXNljPCy+8kAMPPLBWiPirX/1qzj333Gy99dYL9R87dmxOOumkjBo1KsnnX2sOOuigjBw5cqG3V31+8pOfZMqUKenUqVPOPffcfO9731vo69ITTzyRQw45JK+99lqSz8PEp5xySh544IGFxrv44otzzjnnJEl23HHH4i7Jffr0yejRo+uto0uXLo2qd1Fqampy0EEH1QoRd+nSJT/96U9zzDHHpGPHjrX63nTTTTnllFOKX8enTJmS/fffP0899dRCNd1000359NNPk3wejJ//69fQoUNz8cUXL1TP/PMtjkmTJi20S3RdHwvLo+22265WkHhRO0cvrvvuu2+hcxtssMESmQsAAABgWdWmtQsAAAAAgIYMGjQoZWVljT5GjhzZ2iUXzQufXXrppbn88svr3Am0T58+ueOOO7LFFlsUz1VXV+emm25qcOxjjjmmVoj2oIMOyt///veFQsTJ56Goe++9N/vuu2+SZNq0aYv1PE31ySefZObMmTn44IPzwAMPLBQiTpJOnTrlggsuyEknnVTr/B//+McGxz7jjDNSXV1dbG+33XYZPXr0QiHi5POdRv/3f/831113XcrKyorvlyXp4IMPzjvvvJMLL7wwO+64Y50h4iRp06ZNBg8enMcee6zWbrmPP/54gx/Ld955Z6327373u5xwwgn1hoiTZM0118z3v//9/Pvf/86hhx7atAdags4777xa7bKysnz5y1+ude6MM87Ic889V2x/7Wtfy9ixYzN06NA6Q7G9e/fOH//4x/z85z8vnpsyZUp+/OMfN7queSHi888/P1dfffVCIeLk851Lu3btWmwfdthhtT6/Nthggzz11FM55ZRT6gwRzxvj6KOPzmOPPVYrUD+/uXPn5oADDsgnn3xSPHfOOefkH//4R73B0c022ywPPvhg8fM+SUaPHp3rr7++gaeubcqUKencuXMefvjhnHHGGXX+csPWW2+dBx54oNbb4aGHHioGixd81jXWWCNrrLFGrc+J8vLy4vm6jpbaIfbSSy/NY489Vmx37tw59957b0488cSFQr3l5eU59NBD869//avW/K+88krOPffchcbu1atXsd4Fv9Z36dKlzufq1atXs55nwoQJC51bb731mjXmsmLBMO+777670E7vzfXJJ5/kF7/4Ra1z/fv3T+/evVt0HgAAAIBlnSAxAAAAACxBBx98cI477rgG+5SXly8UTHvwwQfr7f/ggw/m2WefLbZXW221XHHFFWnTpv5v97Vt2zZXX311Vl111UZW3jLWWWedXHHFFfUGaef5yU9+kvbt2xfbDT3/hAkTcuuttxbbK6ywQm644YZF7lp6wAEH5Dvf+U4jK2+eVVddNe3atWt0/w4dOuSqq66qdU9Dgc/5d1RNkv32269J9TV3J9SWMHfu3PzoRz/Kn//851rnDzzwwHTv3r3YnjBhQq3dl9dYY43ccsst6dSp0yLnOPXUU7P77rsX29dff30mTZrU6Bq//OUv5+yzz25U3/vuuy+PPvposd2lS5fcddddWX311Rs9X33B0ttuu61WkHr//ffPsGHDFjleeXl5rrnmmvTo0aN4rq6dcRvyy1/+ss5fApjfaqutlmOPPbbYLhQKeeihh5o0z5I2d+7c/OpXv6p17sILL8x2223X4H0bbrhhfv/739c698c//rEYNG9NH3744ULnunXrtvQLWQIW3Dm/pqYmH330UYuNX1VVlf3226/WLudJcsIJJ7TYHAAAAADLC0FiAAAAAFiCGrsD6uDBg2sFaceOHVtv32uvvbZW+8wzz2xwJ9p5unbtmv/3//5fo+ppKaeddlqjAp8rrbRSrd2EJ0yYkA8++KDOvrfccktqamqK7UMOOSRrrLFGo+r50Y9+1GDgujX17t271ttg/lDqotT3tmpN7777bt56661ax2uvvZbHHnssl1xySTbddNP89Kc/rXXPiiuuuNC53//+95k9e3axPWzYsEZ9vM9z2mmnFV/Pnj07//znPxt971lnndXovpdddtlC866zzjqNvr8hv/71r4uvy8rK6t25uC5dunSpFfIdN25c3nrrrUbd26dPnxx55JGN6rvHHnvUajf0Naw1/Otf/8qbb75ZbPft2zff+973GnXv0KFDs+WWWxbb1dXV+etf/9riNTbV1KlTFzpXWVnZ6Pv322+/Ru/239iPmZayYJA4SWbOnNmoe2tqahb62vPWW2/lpZdeygMPPJAf//jHWX/99Rf6WrDlllvm29/+dovUDwAAALA8aXgbEAAAAABoZTfeeGO23XbbRvdv7p+Kb0lrrbXWQn+evT7t2rXL2muvnRdffDFJw8HQ0aNHF1+XlZXlgAMOaHRNBx98cE455ZQUCoVG39McX//61xvdd8MNN8zIkSOL7Q8++KDWTqrzLBiw3X///Rs9x2qrrZZtttkmjz32WKPvaWkzZszI9OnT8+mnny70fpg/PPfSSy+lUCikrKxsoTEW/Lg6/fTT8+c//7lJuyAvaTvttFOT+ldUVOSuu+7KmmuuWev8fffdV3zdtm3bJu++vOOOO6a8vLwYPh81alS+9a1vLfK+rl27Ztddd23UHHPnzq31sZukVni3OT755JOMGTOm2N5qq60WehstyqBBg3LeeecV26NGjWpU+H7IkCGL3E18ng033LBWe1kLt8//dTNJDjrooCb9UsHhhx+ef//737XGO/zww1usPmqbO3fuQufq+lpYl/fee6/JnyMbbrhh/va3vzX64x0AAADgi8R3RAAAAABYpvXq1avRu80uazbaaKMm9Z8/RFpVVVVnn48++iivv/56sb3OOutk5ZVXbvQc3bt3z5prrrnQn3NfErp06ZJ+/fo1uv+CO1DW9zZ45plnarXn3ym0MbbaaqulFiSeFzC95ZZb8uSTT+aFF17IjBkzGn1vdXV1nTuM7rvvvjnjjDMya9asJMlf//rXbLDBBvnOd76Tvffeu8kfe62prKwsu+++ey699NKsttpqta59+umneeqpp4rtfv36ZcqUKZkyZUqT5ujWrVvxnvk/fxoyYMCARgdNX3zxxUybNq3YXn/99dO7d+8m1VifMWPG5LPPPiu211prrSbvDrtgKLOxb4OmfBw19vO3tcwfAk5Sa/fvxliw/5NPPtnsmpprpZVWWuhcVVVVnb+Asbz56KOPFjrXmN3tm6pjx4459thjc/755zdpp3MAAACALxJBYgAAAABYQur60+wNmX832Xm7py7o/fffr9Ved911m1zXeuutt1SCxM15/iS1wpPzmz9EWlFRkW7dujVpngXDqkvKqFGjcvzxx+fZZ59d7DGqqqrqDBL36dMnw4cPz8knn1w898Ybb+QHP/hBfvCDH6RHjx7Zfvvts/3222ennXbKlltuuUzstNm2bdtUVFRkxRVXTP/+/bP11lvngAMOyDrrrFNn/0mTJtX6OHjrrbeavNPogj788MNG9WtKGHPixIm12l/60peaVFNDxo8fX6t900035aabbmrWmI19GzTlc7ixn7+tZcEdktdbb70m3b/gLuDLwo7Ldf0SSVOCxJdddlkuvvjiOq819/OsueYP5ieff3w19Wv9gtq3b5/Kysp07949m222WXbYYYcceOCBTf6/CgAAAOCLpvW/cwwAAAAAX1CN3c20KRYMV1VUVDR5jLqCqUvCknj+pPZOlV27dm3y/YvzNmuqW265JQcffHCzw5QL7iQ7v5NOOik9evTIaaedtlCQ9YMPPsjtt9+e22+/PcnngcOhQ4fm1FNPzfrrr9+smhrrzTffbPZu4lOnTm2ZYuYzffr0RvVrysfWgnW2ZDCxNd8GS+pzuDUs+LWzqV8HO3funPLy8uIveTQ2jL0k1bXr9csvv9zoXzDp3r17unfv3tJltYgXXnihVrtfv34pKytr1L2rr756k3ftBgAAAChlX5zvAgIAAABACWpssOqLpEOHDsXXs2fPbvL9i3NPU7z99ts54ogjaoWIe/TokVNOOSW33XZbnn322UyZMiUzZszI3LlzUygUiscRRxzRpLkOOuigvP7667n22muz11571RtgnTp1av74xz9mo402yllnndVgQHlZsiTeV4VCocXHXFBLfl4ur2+DZc2Cz9zc99Gy8LW3d+/eC4X1n3zyydYppoWNGTOmVnvAgAGtVAkAAADAF58diQEAAACgAXPmzGntEmpZMChaVVXV5DEW555lyYorrphPPvkkyefPMnfu3CbtnLqkdxIdPnx4ZsyYUWzvueeeufHGG9O5c+dF3ltdXd3k+Tp16pTDDjsshx12WObOnZsXX3wxjz32WEaPHp1777231m7Fc+fOzfDhwzN79uz84he/aPJcS9sqq6xSqz148ODcc889rVRN/RassyU/xhYc+/zzz8/ZZ5/dYuOXipVWWqlWu6qqKquuumqj7//kk0+KuxEnLbvrdHPssssuufrqq4vthx9+uPWKaSEffPBBnn322Vrndtxxx1aqBgAAAOCLz47EAAAAAHzhlZfX/n36+cNgizJt2rSWLqdZevToUav96quvNnmMV155paXKaRWrr7568fXs2bOb/Dzjxo1r6ZJqueOOO4qvu3Tpkuuuu65RIeIkmTBhQrPmbtOmTb70pS/lO9/5Tq6++uq89957+de//pXBgwfX6nfJJZfk5ZdfbtZcS0PPnj1rtZfVj93evXvXar/wwgstNvby8jZY1i34tbOpb8cFP18WHK+1LPi5/a9//Wux/l9Yllx66aW1dnRPkr322quVqgEAAAD44hMkBgAAAOALr6Kiolb7o48+avS9zz//fAtX0zwrrrhi1l577WL7tddey9SpUxt9/+TJk/Pmm28uidKWmm233bZW+6GHHmr0vXPmzMmoUaNauqSiGTNm1AoD77zzzgt9/NXn008/zdixY1u0nrKysuy000755z//mT322KN4fu7cubnzzjtbdK4loaKiIl/60peK7bfeemuZDEluuOGGtXa8ffnllzNp0qQWGXu77bZLWVlZsX3fffelUCi0yNitaf5nWhq23HLLWu1HH320Sfcv2H+rrbZqdk0tYb/99ku/fv2K7UKhkF/96letV1AzffDBB7n00ktrnRs0aFDWWWedVqoIAAAA4ItPkBgAAACAL7wFd45s7G6hn332We67774lUVKzzP8n3guFQm666aZG33vDDTcs9yHEr3zlK7XaV1xxRaOf6c4772yxgGddFgypV1ZWNvreG264IbNnz27hij5XVlaWI488sta55SVQPmTIkFrtyy+/vJUqqV9ZWVl23XXXYrtQKOSPf/xji4zdvXv3bLbZZsX2e++9l3/84x8tMnZr6tChQ/H1rFmzlvh883/dTJIbb7wxc+fObfT9I0aMaHC81tKuXbucfvrptc79/ve/z8iRI1unoGaYM2dODjzwwEyePLnW+WHDhrVSRQAAAAClQZAYAAAAgC+8zTffvFb7rrvuatR9V155Zd57770lUVKzHH744bXaF110UWbMmLHI+6ZPn56LLrpoSZW11AwePDhrrrlmsf300083Klz68ccf57TTTluSpWXFFVes1X7ppZcadV9VVVXOO++8JVFSUXl5ea32/EHOZdlxxx1Xq/bf/OY3y9xO4Uny/e9/v1b75z//ed54440WGfv444+v1T799NPz8ccft8jYraVbt27F11OmTMlnn322ROfbeeeda33dGD9+fP7whz806t6//vWveeKJJ4rtioqK7LPPPi1d4mI75phjMmDAgGJ77ty5OeSQQ5bJz5P6TJkyJXvsscdCO8x/61vfysCBA1upKgAAAIDSIEgMAAAAwBfe/DuFJskf/vCHjB8/vsF7xowZk1NPPXVJlrXYdt1112y88cbF9jvvvJOjjz66wd0158yZkyOPPDITJkxYGiUuUW3atMkPf/jDWueOP/74BndmnjJlSnbfffclvgtvp06dsu666xbbY8eOzb333tvgPTNmzMiBBx6Yt956q9Hz/OEPf8g777zTpNquvfbaWu0NN9ywSfe3lnXWWafWbsqffvppdt9990bvLD7PrFmzcvXVV7dwdf+1yy67ZOeddy62q6urs9deey3ya8386tst+/DDD88GG2xQbL/44ov5xje+kWnTpjWpxsmTJ+e2225r0j1LyvwffzU1NQsFSFtamzZtctJJJ9U6d+aZZ9YKCNfl5Zdfzne/+91a544++uhUVFS0eI2Lq2PHjrnttttqhbMnTJiQHXfcMXfccUeTx2vqx1VzVFVV5Ve/+lUGDBiQf/7zn7WubbPNNvnd73631GoBAAAAKFWCxAAAAAB84a2zzjoZNGhQsT19+vR8+ctfzpNPPrlQ348//jgXXXRRBg0alBkzZiy0w+yy4o9//GPatPnvt/duuOGGfP3rX8+rr766UN+XXnopQ4YMya233ppk4V1zl0dHHXVUhgwZUmx/9tlnOeiggzJkyJBcd911GTt2bF5++eWMHDkyP/zhD7P++utn1KhRKSsry/77779EazvggANqtb/5zW/m+uuvXyjoXSgUcv/992e77bYrBui6d+/eqDkuu+yyrLXWWtlzzz1z1VVXZeLEifX2feedd3L44YcX3/9J0rlz5wwdOrSxj9TqfvGLX2STTTYptt95551sueWW+eEPf9hgUHfmzJm5//77c+KJJ6Zfv361AslLwrXXXpuVV1652H7++eezxRZb5Ne//nWmT59e5z1TpkzJ/2/v3mOqrv84jr8O4A4nuWMqBBrsaJwQMQYJq4ScotVWbFbTMKqZq8mgi2zFylo02fojW7hprZs2nAplW7DMcKwNTUCcB0eevB0PDUIL8XCpKDmc3x/OUycv8EPwUDwf2/mDz3l/P5/3OYfLP6/vmw8//FAZGRl6+eWXr1jj7++vzz77zCu8unfvXs2dO1ebN2++6t6S1NXVpZ07d2rFihWKjY1VWVnZCF/d6Pr772RJeuqpp7Rp0yYdOnRIdrtdDofD8+js7ByVM/Pz8zV//nzP1729vVq8eLE2bdqkP/74w6t2YGBA27Zt0z333KOff/7Zs242m/X666+PSj+jKT4+XhUVFTKZTJ41p9OpnJwcZWRkaOfOnUO+j4cPH1ZxcbHX5OaR6O/v9/r8Lj1aWlq0f/9+ffnllyopKdEDDzygmJgYvfDCC5fd5JKdna09e/bopptuuq5eAAAAAAAAMLSAoUsAAAAAAACAf7933nlHaWlpunDhgiTpxIkTuvPOOzVnzhwlJCTI399f7e3tOnjwoCdQZrFY9Mwzz+j555/3YedXlp6ertLSUq/g4ddff63Zs2dr3rx5MpvNcrvdOnnypJqbmz01999/v6ZMmXLZdNp/ox07dmjp0qVqaGjwrH3zzTfXnAC8bt06xcXFqaKiwrNmMBhGta+1a9fq448/9gTjenp6tHLlShUVFSk1NVWhoaHq6uqS1Wr1CgDn5uYqICBAW7duHdY5LpdL1dXVqq6uliRFRUUpISFBERERMplM6uvr0/Hjx2Wz2eR2u72ufffdd70Cr+NdUFCQqqurlZ2drR9++EHSxZBwaWmpSktLFR8fr4SEBIWFhWlgYEDd3d1yOBw6efKkXC7XDetz5syZ2rVrlx566CE5nU5JF6cAP/fcc1q7dq1SUlJ0yy23yGQyyel06vjx4zp16pTn87ntttuuundiYqI+//xzPfzww+ru7pYktbW1ac2aNSooKFBSUpJmzJihkJAQ/fbbb57929raxvx1j8QjjzyiV155xdPfTz/9pPz8/CvWPvHEE6MyTTogIEDbt29XZmamJ4De09Oj/Px8FRcXa/78+ZoyZYrOnz+vpqamy4K3ERERqqioUHBw8HX3MhYWL16s2tpaPfroo14B+/r6ei1fvlwGg0EJCQmKiopSZGSkJk2apL6+Pp09e1Y2m009PT1X3HfZsmXDvslBkhoaGkYcRg4NDdUbb7yhgoICr5tlAAAAAAAAMHYIEgMAAAAAAGBCSE5O1pYtW/Tkk096wsSS1NLSopaWlsvqU1JSVFVVdc1Qqq+99NJLGhwc1Kuvvuo17dZqtcpqtV5Wn5WVpR07dqigoOAGdjl2wsLCVFNToxdffFEfffTRZWHZvzOZTNq4caNWrVqlzZs3ez032qHAsLAwVVdXa+nSpV6TTM+cOeMJ/f5Tbm6uPvnkE61evXrE53Z0dFxzMrF08X0oKyvTqlWrRnyOr8TGxurgwYN69tlntW3bNq/n7Ha77Hb7kHuEhYWNUXd/WbBggerr67Vs2TJ9//33nvWBgQE1NjZe196LFi1SU1OTVqxYoaamJs+6y+W66s/9P42XieQmk0lffPGFcnJy1N7efsPOjYuLU319vR588EEdOnTIs97T06OampqrXjdr1ixVVVVdM+w9HqSnp6u5uVmvvfaa3n//fa+/d263WzabTTabbch9DAaDFi5cqDfffFMZGRlj2bIkKSkpSXl5eVq9erVCQ0PH/DwAAAAAAAD8hdu5AQAAAAAAMGE89thj2rdvnzIzM69aM2PGDK1fv17fffedoqOjb2B3I1NcXKz6+nplZmZedbKu2WzWhg0bVFNTM24naY5UcHCwPvjgAx0+fFhFRUVKTk5WZGSkjEajZs6cqQULFujtt9/W6dOnPeHZS5NiLxmL0Nodd9whq9WqvLw8TZo06Yo1BoNBd999tyorK1VeXn7VuivZvn273nrrLS1cuFBBQUFD1k+dOlX5+fk6duyYnn766WGfM94EBQWpvLxczc3NWrly5bBCsdHR0crNzVVlZaXOnDlzA7q8OFm4ublZn376qVJTU4ecem2xWLRu3TqVlJQMubfZbFZjY6Oqqqq0aNEiGY3GIa+xWCwqKChQXV2ddu3aNezXMdZSU1N19OhRvffee8rJyZHZbFZISIj8/f3H9Nzo6Gg1NjZq69atmjt37jVrZ82apbKyMrW0tIz7EPEl4eHh2rhxo1pbW1VSUqJ58+YNa/J6YGCg0tPTtX79ejkcDu3du3fUQsR+fn4KDAzUzTffrNtvv11LlixRYWGhtmzZIrvdriNHjqioqIgQMQAAAAAAgA8Y3Nca0wEAAAAAAAD8R7W3t6uurk4dHR36888/FRUVJbPZrIyMjGEFrsajtrY27d+/X+3t7RocHFR0dLRmz56t1NRUX7c2rjz++OMqLy/3fN3S0qLExMQxO8/pdGrfvn2y2+3q6+tTRESEpk+frtTUVMXExFz3/i6XS8eOHdOJEyfU1tam3t5euVwuBQUFadq0aZozZ44sFsuYhzN9YXBwUEeOHNHRo0fV1dUlp9OpwMBAhYSE6NZbb5XFYlFsbKyv21RnZ6cOHDigjo4OnTt3TgaDQaGhoYqLi1NycrKioqJGvHd/f78aGhrU2tqqc+fO6ddff9XkyZMVHh4us9ksi8WiyMjIUXw1/z3t7e2qr6/X2bNn5XQ6FRwcrKlTpyotLU3x8fG+bm9UOJ1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      "text/plain": [
       "<Figure size 3500x2500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot2 = pd.pivot_table(master_df, values= ['funding', 'GDP'], index= 'state', aggfunc = 'sum').reset_index()\n",
    "pivot2['GDP'] = pivot2['GDP']*1000000 #get into dollar amount instead of millions\n",
    "percentage_of_gdp = pivot2['funding']/pivot2['GDP'] *100\n",
    "pivot2['funding_as_percent_of_GDP'] = percentage_of_gdp\n",
    "pivot2 = pivot2.sort_values(by ='funding_as_percent_of_GDP')\n",
    "\n",
    "ig, ax = plt.subplots(figsize=(35,25))\n",
    "sns.barplot(\n",
    "    x='funding_as_percent_of_GDP',\n",
    "    y='state', \n",
    "    data= pivot2, \n",
    "    ax=ax\n",
    ")\n",
    "\n",
    "ax.set_title('Emergency Funding as a Percentage of a States GDP from lowest to Highest', fontsize = 25)\n",
    "ax.set_xlabel('Funding as Percent of GDP', fontsize=30) # set_xlabel  better to put name and choose font size (we need bigger since its a big plot)\n",
    "ax.set_ylabel('State', fontsize=30)\n",
    "\n",
    "\n",
    "ax.tick_params(axis='x', labelsize=20) #this is to make our state values bigger too using tick_params\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Section 4: Correlation between GDP and Funding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Code\n",
    "Our fourth and final section consists of three parts, two of which involve code and graphs we have previously generated, but this time focusing on the correlation between GDP and the amount of funding each state receives. We utilize `pivot2`, derived from `master_df`, to calculate disaster relief funding and GDP, indexed by state. Initially, we create a scatter plot using seaborn's `.catplot`, where each point represents GDP on the x-axis and funding on the y-axis, drawing data from the ‘GDP’ and ‘funding’ columns of pivot2, respectively. We then iterate through each row in the DataFrame, adding text annotations to the scatter plot points to label the states they represent. Using np.polyfit, we perform linear regression on the data points in the scatter plot, employing polyfit to establish a line of best fit that numerically illustrates the correlation between GDP and Disaster Relief Funding amount. We then overlay this line onto the existing figure, showcasing each scatter plot point.\n",
    "\n",
    "Following this, as we are dealing with linear regression again, we apply OLS regression analysis to obtain summary statistics, testing the validity and reliability of our data. In this instance, we name our OLS regression analysis `res_2` to avoid confusion with `res1`. Finally, we generate a bar chart using seaborn’s `.barplot()` to identify which states have the highest number of disasters. This allows us to compare these findings with the states receiving the most funding.\n",
    "\n",
    "#### Intepretation\n",
    "Regarding the correlation between the two variables, the amount of funding a state receives and a country's GDP is the strongest relationship in our data and is likely the most interesting. From the regression line on the table, we can see that there is a strong, positive linear relationship between the funding a state receives and its GDP. The OLS regression analysis reveals that the r-squared value between GDP and funding is 96.5%, indicating that 96.5% of the variation in funding can be explained by changes in GDP. Additionally, the OLS shows that the p-value is less than 0.0001, recorded as 0.00, suggesting it’s virtually impossible that the strong correlation between emergency funding and GDP in this dataset is due to chance alone.\n",
    "\n",
    "Comparing a bar chart that shows the total number of disasters a state experiences with the scatter plot of the states receiving the most emergency funding, it becomes clear that a state's wealth is more significant than the number of disasters it experiences. A prime example is Louisiana, which reported over 1,000 disasters, making it the state with the second-highest rate of disasters for the 2019 to 2020 period and a major outlier in the dataset. To illustrate, the average number of disasters for a state in those years was 277. However, despite its high number of disasters, Louisiana is not even in the top 10 states for the highest amount of funding, which includes wealthy states like NY, FL, TX, and CA. This indicates that state wealth and population are probably the best predictors of how much emergency disaster funding a state will receive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\leofl\\AppData\\Local\\Temp\\ipykernel_30884\\153570227.py:2: UserWarning: Ignoring `palette` because no `hue` variable has been assigned.\n",
      "  scatter = sns.scatterplot(\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize =(14,10))\n",
    "scatter = sns.scatterplot(\n",
    "    data = pivot2,\n",
    "    x = 'GDP',\n",
    "    y = 'funding',\n",
    "    palette= 'viridis_r'\n",
    ")\n",
    "\n",
    "\n",
    "for index, row in pivot2.iterrows():\n",
    "    state = row['state'] # have to specify what state , x, and y are this time or raises a value error\n",
    "    x = row['GDP']\n",
    "    y = row['funding']\n",
    "    plt.text(x, y, state, horizontalalignment='center', verticalalignment='bottom', size='small', color='black', weight='semibold')\n",
    "\n",
    "x = pivot2['GDP']\n",
    "y = pivot2['funding']\n",
    "m, b = np.polyfit(x, y, 1)\n",
    "\n",
    "# Add to plot\n",
    "plt.plot(x, m*x + b, color='red')\n",
    "\n",
    "plt.ticklabel_format(style='plain', axis='y')      \n",
    "plt.ticklabel_format(style='plain', axis='x')\n",
    "scatter.set_title('Correlation between a states GDP and its Emergency Disaster Relief Funding')\n",
    "scatter.set_xlabel('GDP')\n",
    "scatter.set_ylabel('Amount of Funding')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                funding   R-squared:                       0.965\n",
      "Model:                            OLS   Adj. R-squared:                  0.964\n",
      "Method:                 Least Squares   F-statistic:                     1314.\n",
      "Date:                Tue, 19 Mar 2024   Prob (F-statistic):           1.57e-36\n",
      "Time:                        11:17:40   Log-Likelihood:                -829.48\n",
      "No. Observations:                  50   AIC:                             1663.\n",
      "Df Residuals:                      48   BIC:                             1667.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept   1.436e+07   7.11e+05     20.191      0.000    1.29e+07    1.58e+07\n",
      "GDP         8.738e-06   2.41e-07     36.252      0.000    8.25e-06    9.22e-06\n",
      "==============================================================================\n",
      "Omnibus:                       13.479   Durbin-Watson:                   1.369\n",
      "Prob(Omnibus):                  0.001   Jarque-Bera (JB):               38.148\n",
      "Skew:                           0.450   Prob(JB):                     5.20e-09\n",
      "Kurtosis:                       7.183   Cond. No.                     3.75e+12\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
      "[2] The condition number is large, 3.75e+12. This might indicate that there are\n",
      "strong multicollinearity or other numerical problems.\n"
     ]
    }
   ],
   "source": [
    "outcome_2, predictors_2 = patsy.dmatrices('funding ~ GDP', data= pivot2, return_type='matrix')\n",
    "mod_2 = sm.OLS(outcome_2, predictors_2)\n",
    "res_2 = mod_2.fit()\n",
    "\n",
    "# Check the summary for results\n",
    "print(res_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our data is sourced from governmental agencies and nonprofit organizations such as USA Facts, FEMA, and BEA.gov. These sources aim to provide a strictly non-partisan outlook on the actions of the federal government in an effort to promote a pillar of government so necessary to any government's foundation - transparency. These sources do not contain any private data, as any data determined to be accessible to the public by the Freedom of Information act forgoes data which would be deemed a breach of privacy. Our research does not aim to harm any particular population within a county or state, as it is focused on compiling and analyzing the financial aspect of the relationship between state and federal governments. Moreover, decisions on federal funding are guided by uniform, pre-established standards, and commitments, all found within the constitution of the United States. Our compilation of statistical information lacks sufficient means to overturn the nature of this centuries old relation. Electoral data analyzed is on a strictly state-level basis, meaning that we do not utilize a voter's personal information. "
   ]
  },
  {
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   "source": [
    "# Discusison and Conclusion\n",
    "\n",
    "Our research into the complexities of U.S. government funding, specifically looking at FEMA disaster relief funding at the state level, unveiled many findings that proved our hypotheses wrong and also addressed some correlations outside of the variables we originally intended to examine. States with Republican governments and smaller GDPs do not necessarily receive more FEMA funding than Democratic states with larger GDPs. The states that receive the most FEMA funding are both Republican and Democratic, with population actually being the more important factor in determining funding.\n",
    "\n",
    "Our hypothesis was correct in assuming that smaller states would receive more FEMA funding as a ratio of their funding divided by their GDP, but there wasn't a dual correlation with smaller states and political leaning as we had originally thought. Four out of ten of the states that receive the most funding as a percentage of their state's GDP were Democratic, so there is no correlation there. Notably, our examination revealed that Republican states, on the whole, receive more disaster relief funding than Democratic states. We found this by summing the funding across years and visualizing the data in a bar chart. Looking at the bar chart highlights 2019 as the year with the smallest difference in funding between the parties — yet the difference was still a staggering $40 billion.\n",
    "\n",
    "The influence of a state's GDP on its FEMA funding was the most robust finding from our study, with an impressive 97%  r^2 value indicating a strong, positive correlation. This suggests that wealthier states like Texas, California, and New York receive more aid, likely because disasters in these areas affect a larger number of people and more costly infrastructure. This led us to question the allocation priorities of FEMA, considering that state population was a better predictor of funding than the number of disasters a state had suffered that year. The number of disasters, for example, only explained about 11% of the variance in funding, but the correlation was significant as the p-value was less than .05, indicating the correlation could not have come by chance alone.\n",
    "\n",
    "The observation that Republican states garner more federal aid as a whole, as supported by the Money Geek analysis, added an interesting dimension to our study. States typically known for lower taxation rates may depend more heavily on federal support, including disaster relief funding, as found through our study.\n",
    "\n",
    "Our initial inspiration for this project stemmed from the devastating floods in San Diego in early January 2024. This event spurred us to read between the lines by using data science to uncover any underlying patterns and principles guiding the government's response to natural disasters. Throughout our research, we encountered shifts in our focus and objectives. Originally, we didn't even consider the effect of the number of disasters or political leaning on state FEMA funding. Reflecting on our prior work and background, we realized the challenges inherent in navigating and wrangling our datasets, much like the Money Geek analysis encountered. Despite this, our study was able to reaffirm and build upon the findings regarding the political disparities in disaster relief funding, offering new insights into the large effect of population and GDP on how much FEMA funding a state secures.\n",
    "\n",
    "# Team Contributions\n",
    "\n",
    "Leo -- Most of the coding and data visualizations as he is the most experienced working with Pandas, Matplot, and Seaborn. Also wrote the conclusion summarizing the results.\n",
    "\n",
    "Daniel -- Conducted research on the background of our research question, responsible for project formatting, and writing up the abstract.\n",
    "\n",
    "Eyevan -- Helped develop a detailed research question and\n",
    "found datasets that connected back to the research question, giving each dataset a detailed description on how it may be useful.\n",
    "\n",
    "Fei -- Conducted Ethic & Privacy , providing datasets detailed  interpretation.\n",
    "\n",
    "Haoting -- Helped with writeups and interpretation of EDA results"
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