EDACheckpoint_Group054_WI24.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# COGS 108 - EDA Checkpoint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Names\n",
    "\n",
    "- Ivan Chen \n",
    "- Leo Fleury\n",
    "- Daniel Yoon   \n",
    "- Fei He\n",
    "- HaoTing Huang"
   ]
  },
  {
   "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?\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "The United States was born out of the colonies' desire to 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\n"
   ]
  },
  {
   "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",
    "For each dataset include the following information\n",
    "- Dataset #1\n",
    "  - Dataset Name: Gross Domestic Product (GDP) by state\n",
    "  - Link to the dataset: https://usafacts.org/metrics/gross-domestic-product-gdp-by-state/?adjustment=None&timeGranularity=Yearly\n",
    "  - Number of observations: 45\n",
    "  - Number of variables: 51\n",
    "- Dataset #2 \n",
    "  - Dataset Name:\n",
    "  - Link to the dataset: https://www.bea.gov/data/gdp/gdp-state\n",
    "  - Number of observations:\n",
    "  - Number of variables:\n",
    "- Dataset #3\n",
    "  - Dataset Name: Emergency Management Performance Grants\n",
    "  - Link to dataset: https://www.fema.gov/openfema-data-page/emergency-management-performance-grants-v2\n",
    "  - Number of observations: 19615\n",
    "  - Number of variables: 9\n",
    "- Dataset #4\n",
    "  - Dataset Name: Public Assistance Funded Projects Detail\n",
    "  - Link to dataset: https://www.fema.gov/openfema-data-page/public-assistance-funded-projects-details-v1\n",
    "  - Number of observations: 805,572\n",
    "  - Number of variables: 22\n",
    "\n",
    "\n",
    "For our first 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",
    "The second dataset by BEA, it is a dataset that provides the GDP by each state, seperated by region from most to least. This dataset includes each states GDP by quarter and takes inflation into account (2017 Dollar value). This dataset has many tables with valuable information, but we are focused on Tables 1, 3, and 5. Table 2 includes data about the Personal Income per state in the same format as Table 1. A few important varaibles in this dataset would include States, Regions, Quarters, and Total. These are the main variables that will help us analyze the data. Since the data is already well organized, the only things we would need to do is find missing data, if there is any, and understand why it may be missing. \n",
    "\n",
    "Our third 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",
    "Our last dataset, Public Assistance Funded Projects Detail, is another very detailed dataset that has information about emergency funding. To be more exact, it has information about what kind of natural disasters, which county and it's county code, project amount, federal share obligated, total obligated, and more. For this project, knowing what specific natural disaster and how much a state got because of that isn't our main goal, so we would have to sort it by year, maintaining our goal of finding data from 2019-2022.\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 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\leofl\\AppData\\Local\\Temp\\ipykernel_14116\\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",
       "</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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       "    }\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": {
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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",
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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": [
    "### Merged Data Sets"
   ]
  },
  {
   "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:                Mon, 18 Mar 2024   Prob (F-statistic):             0.0158\n",
      "Time:                        12:51:33   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 - Disaster Funding over the years by Party"
   ]
  },
  {
   "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": 18,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "too many values to unpack (expected 3)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[18], line 21\u001b[0m\n\u001b[0;32m     17\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mYear\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     18\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mTotal Funding (USD)\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m---> 21\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m year, party, funding \u001b[38;5;129;01min\u001b[39;00m df_grouped:\n\u001b[0;32m     22\u001b[0m     \u001b[38;5;66;03m# Check if the year is already in the differences dictionary\u001b[39;00m\n\u001b[0;32m     23\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m year \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m differences:\n\u001b[0;32m     24\u001b[0m         differences[year] \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m\n",
      "\u001b[1;31mValueError\u001b[0m: too many values to unpack (expected 3)"
     ]
    },
    {
     "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"
   ]
  },
  {
   "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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      "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 = 'mean').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_14116\\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:                Mon, 18 Mar 2024   Prob (F-statistic):           1.57e-36\n",
      "Time:                        12:51:34   Log-Likelihood:                -760.17\n",
      "No. Observations:                  50   AIC:                             1524.\n",
      "Df Residuals:                      48   BIC:                             1528.\n",
      "Df Model:                           1                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept    3.59e+06   1.78e+05     20.191      0.000    3.23e+06    3.95e+06\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.                     9.37e+11\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, 9.37e+11. 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": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         disasters\n",
      "count    50.000000\n",
      "mean    277.380000\n",
      "std     242.214182\n",
      "min       9.000000\n",
      "25%     103.750000\n",
      "50%     214.000000\n",
      "75%     379.500000\n",
      "max    1178.000000\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 3500x2500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pivot3 = pd.pivot_table(master_df, values='disasters', index='state', aggfunc='sum').reset_index()\n",
    "pivot3 = pivot3.sort_values(by='disasters')\n",
    "fig, ax = plt.subplots(figsize=(35,25))\n",
    "sns.barplot(x='disasters', y='state', data=pivot3, ax=ax)\n",
    "print(pivot3.describe())\n",
    "ax.set_title('States With the Most Amount of Disasters from 2019-2020', fontsize=25)\n",
    "ax.set_xlabel('Number of Disasters', fontsize=30)\n",
    "ax.set_ylabel('State', fontsize=30)\n",
    "ax.tick_params(axis='x', labelsize=20)\n",
    "ax.tick_params(axis='y', labelsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "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. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Team Expectations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "* *Be Communicative* - Communicate any difficulties ahead of time! If assistance is needed, or maybe if a team member is unable to do/finish what they need to do, communicating it ahead of time will allow others to help expedite the process.\n",
    "\n",
    "* *Contribute Fairly* - Uphold the responsibilities assigned or taken up by each individual group member, but also keep in mind to make key decisions as a group. \n",
    "\n",
    "* *Communication Guideline* - Communicate primarily through Discord and attend scheduled weekly/bi-weekly calls. Most, if not all calls will be virtual, but be present and be involved in the project."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Project Timeline Proposal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Meeting Date | Meeting Time | Completed Before Meeting      | Discuss at Meeting                                                                 |\n",
    "|--------------|--------------|-------------------------------|------------------------------------------------------------------------------------|\n",
    "| 2/4          | 6 PM         | Read Previous COGS 108 Projects | Discussed the strengths and weaknesses of two COGS 108 projects made by previous groups and noted things to keep and leave in our projects. |\n",
    "| 2/8          | 3 PM         | Research Question              | Discuss hypothesis, research question, project timeline proposal                   |\n",
    "| 2/11         | 6 PM         | Work on Project Proposal       | Discuss hypothesis, research question, project timeline proposal                   |\n",
    "| 2/19         | 7 PM         | Find Relevant Data             | Choose which data we found best fits our research purposes                         |\n",
    "| 2/22         | 8:30 PM      | Categorize Relevant Data       | Discuss what kind of further questions we want to ask within our research question |\n",
    "| 3/4          | 7 PM         | Work on individual categories  | Review and discuss progress                                                        |\n",
    "| 3/7          | 7 PM         | Work on individual categories  | Review and discuss progress                                                        |\n",
    "| 3/14         | 7 PM         | Work on individual categories  | Review and discuss progress                                                        |\n",
    "| 3/17         | 7 PM         | Work on individual categories  | Review and discuss progress                                                        |\n",
    "| 3/20         | Before 11:59\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9.6 64-bit",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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