Linear Regression - Iowa Original.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Already up to date.\n"
     ]
    }
   ],
   "source": [
    "#https://plotly.com/python/reference/\n",
    "\n",
    "default_title = 'Animated Map - County Spread Rate'\n",
    "\n",
    "%matplotlib inline\n",
    "%config InlineBackend.print_figure_kwargs={'facecolor' : 'white'}\n",
    "\n",
    "import io\n",
    "import json\n",
    "import os\n",
    "import pandas as pd\n",
    "import mpu\n",
    "import numpy as np\n",
    "import random\n",
    "import requests\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.express as px\n",
    "from geojson_utils import centroid\n",
    "import plotly.graph_objects as go\n",
    "from datetime import datetime, timedelta\n",
    "from IPython.display import Markdown, display, Image, clear_output, Video\n",
    "from matplotlib.colors import ListedColormap, LinearSegmentedColormap\n",
    "from PIL import Image as PILImage\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler, PowerTransformer\n",
    "from scipy.signal import find_peaks, peak_prominences\n",
    "from sklearn.neural_network import MLPRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "import statsmodels.api as sm\n",
    "\n",
    "# LOCAL GIT REPOSITORY LOCATION FOR https://github.com/CSSEGISandData/COVID-19\n",
    "directory = 'C:\\\\Users\\\\izdr\\\\source\\\\repos\\\\COVID-19\\\\csse_covid_19_data\\\\csse_covid_19_daily_reports\\\\'\n",
    "\n",
    "!git -C $directory pull\n",
    "\n",
    "state_names = pd.read_csv('Data\\\\states.csv').set_index('State').to_dict()['Abbreviation']\n",
    "bgcolor = '#0d1238'\n",
    "\n",
    "excluded_ny_counties = [36047, # Kings County (Brooklyn)\n",
    "    36005, # Bronx County (Bronx)\n",
    "    36085, # Richmond County (Staten Island)\n",
    "    36081] # Queens County (Queens)\n",
    "\n",
    "voter_data_filename = 'Data\\\\countypres_2000-2016.csv'\n",
    "\n",
    "location_of_census_data = 'https://www2.census.gov/programs-surveys/popest/datasets/2010-2019/counties/asrh/'\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_census_info():\n",
    "    result = (\n",
    "        pd.read_csv(\"Data\\\\co-est2019-alldata.csv\", encoding = \"ISO-8859-1\") # Fetch the file\n",
    "        .query('SUMLEV == 50')                                         # 40 is states, 50 is counties\n",
    "        [['STATE', 'COUNTY', 'CTYNAME', 'POPESTIMATE2019', 'STNAME']]            # Keep Just population estimate\n",
    "        .assign(fips=lambda x: (x['STATE'] * 1000) + x['COUNTY'])      # FIPS = stateId*1000+countyId                                # \n",
    "        .rename(columns={'POPESTIMATE2019':'pop', 'CTYNAME':'county', 'STNAME': 'st'})                     # Shorten Population Estimate column\n",
    "        [['fips', 'pop']]                                              # Keep just FIPS and PopEstimate\n",
    "        .set_index('fips')                                             # Index by FIPS\n",
    "    )\n",
    "    return sum_ny_under_manhattan(result, 'pop')\n",
    "\n",
    "def get_county_names():\n",
    "    return (\n",
    "        pd.read_csv(\"Data\\\\co-est2019-alldata.csv\", encoding = \"ISO-8859-1\") # Fetch the file\n",
    "        .query('SUMLEV == 50 & COUNTY % 2 == 1')                                         # 40 is states, 50 is counties\n",
    "        [['STATE', 'COUNTY', 'CTYNAME', 'POPESTIMATE2019', 'STNAME']]            # Keep Just population estimate\n",
    "        .assign(fips=lambda x: (x['STATE'] * 1000) + x['COUNTY'])      # FIPS = stateId*1000+countyId                                # \n",
    "        .rename(columns={'POPESTIMATE2019':'pop', 'CTYNAME':'county', 'STNAME': 'st'})                     # Shorten Population Estimate column\n",
    "        [['fips', 'st', 'county']]                                              # Keep just FIPS and PopEstimate\n",
    "        .set_index('fips')                                             # Index by FIPS\n",
    "    )\n",
    "\n",
    "def get_daily_report_data():\n",
    "    states = list(state_names.keys())                                 # List of 51 states (including DC)\n",
    "    print(directory)\n",
    "    return (\n",
    "        pd.concat([ \\\n",
    "            pd.read_csv(directory+file) \\\n",
    "            .assign(date=datetime.strptime(file, '%m-%d-%Y.csv')) \\\n",
    "            for file \\\n",
    "            in os.listdir(directory) \\\n",
    "            if file.endswith(\".csv\") \\\n",
    "        ])                                                            # List the files, appending a date column to each file's data\n",
    "        .reset_index()                                                # Reset the index\n",
    "        .query('FIPS.notna()')                                        # Pick only items with FIPS values\n",
    "        .assign(FIPS=lambda x:x['FIPS'].astype(int))                  # And cast that FIPS value to integer\n",
    "        .query('Province_State.isin(@states)')                        # Only the states\n",
    "    )\n",
    "\n",
    "def get_county_percentages():\n",
    "    daily_report_data = get_daily_report_data()\n",
    "    \n",
    "    starter = (\n",
    "        daily_report_data\n",
    "        [['Confirmed', 'FIPS', 'date']]                           # Keep these three columns\n",
    "        .rename(columns={'Confirmed':'confirmed', 'FIPS':'fips'}) # Rename for simplicity\n",
    "        .drop_duplicates(['date','fips'])                         # There are a few duplicates. Drop them. \n",
    "        .pivot(values='confirmed', index='date', columns='fips')  # Create the pivot table. \n",
    "        .fillna(0)                                                # Fill NA values here.\n",
    "    )\n",
    "    \n",
    "    # Here we're going to ensure cumulative confirmed values are always increasing. \n",
    "    # We'll do this by comparing the existing values against the cumulative maximum \n",
    "    # for that time series, removing the numbers that don't conform to this, and then \n",
    "    # interpolating those missing values. For an example of a time series that does \n",
    "    # this, look at the Brazoria County values (48049), between June 8 and 9. The \n",
    "    # cumulative maximum drops from 990 to 770, which, since we're creating a differential\n",
    "    # here, would inject a massive negative value into our rolling average. This clearly\n",
    "    # isn't the way it's supposed to be, because when you look at the data between\n",
    "    # June 15 and June 16, the values jump again from 864 to 1495. We'll smooth those\n",
    "    # values out here, in the absence of any accessible SME who can tell us what \n",
    "    # happened, and in the interest of time. \n",
    "    daily_report_data = (\n",
    "        starter[starter >= starter.cummax()] # Remove values that are lower than the time series cumulative maximum\n",
    "        .interpolate()                       # Interpolate the \"actual\" values for those items.\n",
    "        .diff()                              # _Now_ we get our dialy differential. \n",
    "        .rolling(window=7)                   # Roll this value 7 days for smoothness. \n",
    "        .mean()                              # Take the mean of the rolling window. \n",
    "        .dropna()                            # Drop the first 6 rows, an artifact of rolling.\n",
    "        .T                                   # Transpose the whole DataFrame\n",
    "    )\n",
    "\n",
    "    # Grab the county populations\n",
    "    county_populations = get_census_info()\n",
    "    \n",
    "    # Divide the values of new confirmed cases by the county population,\n",
    "    # and then multiply by 100,000 to get the \"per 100,000\" people metric.\n",
    "    return (daily_report_data.div(county_populations.iloc[:,0], axis=0).dropna()) * 100000\n",
    "\n",
    "def sum_ny_under_manhattan(frame, field):\n",
    "    df = frame.copy()\n",
    "    # New York is a special case. All the data is aggregated under Manhattan. \n",
    "    manhattan = 36061 # New York County (Manhattan) -- INCLUDED\n",
    "\n",
    "    # EXCLUDED\n",
    "    excluded_ny_counties = [36047, # Kings County (Brooklyn)\n",
    "    36005, # Bronx County (Bronx)\n",
    "    36085, # Richmond County (Staten Island)\n",
    "    36081] # Queens County (Queens)\n",
    "\n",
    "    nyc_population = df.loc[excluded_ny_counties + [manhattan]].sum().sum()\n",
    "\n",
    "    df.loc[manhattan, field] = nyc_population\n",
    "\n",
    "    #county_populations.index.isin(excluded_ny_counties)\n",
    "    return df[~df.index.isin(excluded_ny_counties)]\n",
    "\n",
    "def get_new_cases_info_raw(daily_report_data=None):\n",
    "    if (daily_report_data is None):\n",
    "        daily_report_data=get_daily_report_data()\n",
    "        \n",
    "    starter = (\n",
    "        daily_report_data\n",
    "        [['Confirmed', 'FIPS', 'date']]                           # Keep these three columns\n",
    "        .rename(columns={'Confirmed':'confirmed', 'FIPS':'fips'}) # Rename for simplicity\n",
    "        .drop_duplicates(['date','fips'])                         # There are a few duplicates. Drop them. \n",
    "        .pivot(values='confirmed', index='date', columns='fips')  # Create the pivot table. \n",
    "        .fillna(0)                                                # Fill NA values here.\n",
    "    )\n",
    "    \n",
    "    return starter\n",
    "\n",
    "def get_new_cases_info(daily_report_data=None):\n",
    "    starter = get_new_cases_info_raw(daily_report_data)\n",
    "\n",
    "    rep_a = (\n",
    "        starter[starter >= starter.cummax()] # Remove values that are lower than the time series cumulative maximum\n",
    "        .interpolate()                       # Interpolate the \"actual\" values for those items.\n",
    "        .rolling(window=7)                   # Roll this value 7 days for smoothness. \n",
    "        .mean()                              # Take the mean of the rolling window. \n",
    "        .dropna()                            # Drop the first 6 rows, an artifact of rolling.\n",
    "        .T                                   # Transpose the whole DataFrame\n",
    "    )\n",
    "\n",
    "    return (rep_a.sum().diff().dropna())\n",
    "\n",
    "def get_land_mass_data():\n",
    "    counties = get_allcounties()\n",
    "    land_mass_data = [[int(x['properties']['STATE']) * 1000 + int(x['properties']['COUNTY']), x['properties']['CENSUSAREA']] for x in counties['features']]\n",
    "    land_mass = pd.DataFrame(land_mass_data, columns=['fips', 'area']).set_index('fips')\n",
    "    land_mass = sum_ny_under_manhattan(land_mass, 'area')\n",
    "    land_mass['pop'] = get_census_info()\n",
    "    land_mass = land_mass.dropna()\n",
    "    land_mass['density'] = land_mass['pop'].div(land_mass['area'])\n",
    "    return land_mass\n",
    "\n",
    "def get_allcounties():\n",
    "    # Get the GEOJson file for the counties.\n",
    "    with open(os.getcwd() + '/Data/geojson-counties-fips.json') as response:\n",
    "        allcounties = json.load(response)\n",
    "    return allcounties\n",
    "\n",
    "def get_centroid(geo):\n",
    "    fips = int(geo['properties']['STATE'] + geo['properties']['COUNTY'])\n",
    "    coords = geo['geometry']\n",
    "\n",
    "    if (coords['type'] == 'MultiPolygon'):\n",
    "        return [fips] + np.mean([o for ol in [item for sublist in coords['coordinates'] for item in sublist] for o in ol], axis=0).tolist()\n",
    "        \n",
    "    return [fips] + centroid(coords)['coordinates']\n",
    "\n",
    "\n",
    "def get_centroids():\n",
    "    counties = get_allcounties()\n",
    "    centroids = [get_centroid(x) for x in counties['features']]\n",
    "    return pd.DataFrame(centroids, columns=['fips', 'longitude', 'latitude']).dropna().assign(fips=lambda x:x['fips'].astype(int)).set_index('fips')\n",
    "\n",
    "def get_conservatism():\n",
    "    return (\n",
    "        pd.read_csv(voter_data_filename)\n",
    "        .query('year == 2016 & FIPS.notna() & party.isin([\"democrat\", \"republican\"])')\n",
    "        .assign(\n",
    "            fips=lambda x:x['FIPS'].astype(int),\n",
    "            d=lambda x:(x['party'] == 'democrat').astype(int) * x['candidatevotes'], \n",
    "            r=lambda x:(x['party'] == 'republican').astype(int) * x['candidatevotes'])\n",
    "        .groupby('fips')\n",
    "        .sum()\n",
    "        [['d', 'r']]\n",
    "        .query('d != r')\n",
    "        .assign(\n",
    "            p_winner=lambda x:x.idxmax(axis=1),\n",
    "            total=lambda x:x['d'] + x['r'],\n",
    "            conservative=lambda x:x['r'] / x['total']\n",
    "        )[['conservative']]\n",
    "    )\n",
    "\n",
    "def get_mean_ages():\n",
    "    return (\n",
    "        pd.read_csv(\"Data/cc-est2019-alldata.csv\", encoding = \"ISO-8859-1\")\n",
    "        .dropna()\n",
    "        .query('YEAR==12 & AGEGRP > 0')\n",
    "        .assign(fips=lambda x:x['STATE'].astype(int)*1000+x['COUNTY'].astype(int))\n",
    "        [['fips', 'AGEGRP', 'TOT_POP']]\n",
    "        .assign(\n",
    "            AGEGRP=lambda x:x['AGEGRP'] * 5 - 2.5, \n",
    "            SUMAGE=lambda x:x['AGEGRP'] * x['TOT_POP']\n",
    "        )\n",
    "        .groupby('fips')\n",
    "        .sum()\n",
    "        .assign(mean_age=lambda x:x['SUMAGE']/x['TOT_POP'])\n",
    "        [['mean_age']]\n",
    "    )\n",
    "\n",
    "def download_census_data(fips_code):\n",
    "    print(fips_code)\n",
    "    file = 'cc-est2019-agesex-{0}.csv'.format(fips_code);\n",
    "    url = '{0}{1}'.format(location_of_census_data, file)\n",
    "    print(url)\n",
    "    r = requests.get(url, allow_redirects=True)\n",
    "    if (r.status_code != 200):\n",
    "        raise 'Error!'\n",
    "    write_location = './gitignore-census-data/'\n",
    "    if (not os.path.exists(write_location)):\n",
    "        os.mkdir(write_location)\n",
    "    open(write_location + file, 'wb').write(r.content)\n",
    "        \n",
    "def get_all_census_data():\n",
    "    county_demographics = pd.read_csv(\"Data/cc-est2019-alldata.csv\", encoding = \"ISO-8859-1\")\n",
    "    fips = county_demographics[['STATE', 'STNAME']].groupby(['STATE', 'STNAME']).sum().reset_index().set_index('STNAME').loc[list(state_names.keys())]['STATE'].apply(lambda x:str(x).zfill(2)).tolist()\n",
    "    df = pd.DataFrame()\n",
    "    for fips_code in fips:\n",
    "        file = 'cc-est2019-agesex-{0}.csv'.format(fips_code);\n",
    "        fullpath = './gitignore-census-data/' + file\n",
    "        if (not os.path.exists(fullpath)):\n",
    "            download_census_data(fips_code)\n",
    "        df = pd.concat([df, pd.read_csv(fullpath, encoding = \"ISO-8859-1\")])\n",
    "        \n",
    "    return df\n",
    "\n",
    "def get_median_ages():\n",
    "    return (\n",
    "        get_all_census_data()\n",
    "        .query('YEAR == 12')\n",
    "        .assign(\n",
    "            fips=lambda x:(x['STATE'].astype(int) * 1000 + x['COUNTY'].astype(int)), \n",
    "            tots=lambda x:(x['UNDER5_TOT']).div(x['POPESTIMATE']),\n",
    "            kids=lambda x:(x['AGE513_TOT']).div(x['POPESTIMATE']),\n",
    "            teens=lambda x:(x['AGE1417_TOT']).div(x['POPESTIMATE'])\n",
    "        )\n",
    "        .rename(columns={'MEDIAN_AGE_TOT': 'median_age' })\n",
    "        .set_index('fips')\n",
    "        [['median_age', 'tots', 'kids', 'teens']]\n",
    "    )\n",
    "\n",
    "def get_demographic_data():\n",
    "    county_demographics = pd.read_csv(\"Data/cc-est2019-alldata.csv\", encoding = \"ISO-8859-1\")\n",
    "    return (county_demographics\n",
    "         .query('(YEAR == 12) & (AGEGRP == 0)')\n",
    "         .assign(fips=lambda x:x['STATE'].astype(int) * 1000 + x['COUNTY'].astype(int))\n",
    "         .set_index('fips')\n",
    "         .assign(hispanic=lambda x:(x['H_MALE'] + x['H_FEMALE']).div(x['TOT_POP']))\n",
    "         .assign(black=lambda x:(x['BA_MALE'] + x['BA_FEMALE']).div(x['TOT_POP']))\n",
    "         .assign(asian=lambda x:(x['AA_MALE'] + x['AA_FEMALE']).div(x['TOT_POP']))\n",
    "         .assign(white=lambda x:(x['WA_MALE'] + x['WA_FEMALE']).div(x['TOT_POP']))\n",
    "    )[['hispanic', 'black', 'asian', 'white']]\n",
    "\n",
    "def adjusted_density(ds, loc):\n",
    "    row = ds.loc[loc][['latitude', 'longitude']].dropna()\n",
    "    if (len(row) == 0):\n",
    "        return ds.loc[loc].density\n",
    "    latlong = ds[['latitude', 'longitude']].dropna()\n",
    "    latlong = latlong[(abs(latlong['latitude'] - row['latitude']) < 2) & (abs(latlong['longitude'] - row['longitude']) < 2)]\n",
    "    origin = (row['latitude'], row['longitude'])\n",
    "    distances = latlong.apply(lambda x:mpu.haversine_distance(origin, (x['latitude'], x['longitude'])), axis=1)\n",
    "    distances = 100 - distances[distances < 100]\n",
    "    return (ds.loc[distances.index]['density'] * distances).sum() / distances.sum()\n",
    "    \n",
    "def get_median_income():\n",
    "    return (\n",
    "        pd.read_excel('Data/est18all.xls', header=3)\n",
    "        .assign(fips=lambda x:x['State FIPS Code'] * 1000 + x['County FIPS Code'])\n",
    "        .rename(columns={\n",
    "            'Median Household Income': 'median_income',\n",
    "            'Poverty Percent, All Ages': 'poverty_percent'\n",
    "        })\n",
    "        .query('fips % 2 == 1')\n",
    "        .set_index('fips')\n",
    "        .assign(\n",
    "            median_income=lambda x:x['median_income'].astype(str).apply(lambda x:float(x) if x.replace('.', '').isnumeric() else np.nan).astype(float),\n",
    "            poverty_percent=lambda x:x['poverty_percent'].astype(str).apply(lambda x:float(x) if x.replace('.', '').isnumeric() else np.nan).astype(float)\n",
    "\n",
    "        )\n",
    "        [['median_income', 'poverty_percent']]\n",
    "    )\n",
    "\n",
    "def get_risk_factors():\n",
    "    return (\n",
    "        pd.ExcelFile('Data/CHSI DataSet.xls')\n",
    "        .parse(10)\n",
    "        .assign(fips=lambda x:x['State_FIPS_Code'] * 1000 + x['County_FIPS_Code'])\n",
    "        .query('fips % 2 == 1')\n",
    "        .set_index('fips')\n",
    "        .rename(columns={'Obesity':'obesity', 'Diabetes':'diabetes'})\n",
    "        [['State_FIPS_Code', 'obesity', 'diabetes']]\n",
    "        .assign(\n",
    "            obesity=lambda x:x[x['obesity'] > 0]['obesity'],\n",
    "            diabetes=lambda x:x[x['diabetes'] > 0]['diabetes']\n",
    "        )\n",
    "        .groupby('State_FIPS_Code')\n",
    "        .apply(lambda x:x.fillna(x.mean()))\n",
    "        .drop(columns=['State_FIPS_Code'])\n",
    "        .reset_index()\n",
    "        .drop(columns=['State_FIPS_Code'])\n",
    "        .set_index('fips')\n",
    "    )\n",
    "\n",
    "def get_county_details():\n",
    "    county_details = pd.concat([\n",
    "        get_county_names(),\n",
    "        get_land_mass_data(),\n",
    "        get_centroids(),\n",
    "        get_conservatism(),\n",
    "        get_median_ages(),\n",
    "        get_mean_ages(), \n",
    "        get_demographic_data(), \n",
    "        get_median_income(), \n",
    "        get_risk_factors()\n",
    "    ], axis=1)\n",
    "    county_details['adjusted_density'] = county_details.apply(lambda x:adjusted_density(county_details, x.name), axis=1)\n",
    "    return county_details\n",
    "\n",
    "def get_details_and_percentages():\n",
    "    county_details_raw = get_county_details()\n",
    "    county_percentages_raw = get_county_percentages()\n",
    "    county_details = county_details_raw[county_details_raw.index.isin(county_percentages_raw.index)].copy()\n",
    "    county_percentages = county_percentages_raw[county_percentages_raw.index.isin(county_details_raw.index)].copy()\n",
    "\n",
    "    max_index = len(county_percentages.columns) - 10\n",
    "\n",
    "    county_details = (\n",
    "        county_details\n",
    "        .assign(\n",
    "            max_height=lambda x:county_percentages.max(axis=1),\n",
    "            peak_info_raw=lambda x:x.index.to_series()\n",
    "            .apply(lambda y:find_peaks(county_percentages.loc[y], height=10, distance=30)),\n",
    "            peak_info=lambda x:x['peak_info_raw'].apply(lambda y:list(zip(y[0], y[1]['peak_heights'])))\n",
    "\n",
    "            #\n",
    "        )\n",
    "        .assign(\n",
    "            peak_info=lambda x:(x['peak_info'].apply(lambda y:list(filter(lambda z:(z[0] < max_index), y))))\n",
    "        )\n",
    "        .assign(\n",
    "            peak_info=lambda z:z.index.to_series().apply(lambda i:list(filter(lambda x:x[1]>=county_percentages.loc[i][-10:].max(), z.loc[i]['peak_info']))), \n",
    "            peak_count=lambda x:x['peak_info'].apply(lambda y:len(y)),\n",
    "            peak_date=lambda x:x[x['peak_count'] > 0]['peak_info'].apply(lambda x:county_percentages.columns[x[-1][0]])\n",
    "        )\n",
    "    )\n",
    "\n",
    "    county_details['median_infection'] = county_percentages.median(axis=1)\n",
    "    county_details['mean_infection'] = county_percentages.mean(axis=1)\n",
    "    county_details['max_infection'] = county_percentages.max(axis=1)\n",
    "    return (county_details, county_percentages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fetching original data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\izdr\\source\\repos\\COVID-19\\csse_covid_19_data\\csse_covid_19_daily_reports\\\n"
     ]
    }
   ],
   "source": [
    "#get_median_ages()\n",
    "county_details_raw, county_percentages_raw = get_details_and_percentages()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "st                                                              Texas\n",
       "county                                                Brazoria County\n",
       "area                                                           1357.7\n",
       "pop                                                            374264\n",
       "density                                                        275.66\n",
       "longitude                                                    -95.4458\n",
       "latitude                                                      29.1884\n",
       "conservative                                                 0.627557\n",
       "median_age                                                       36.1\n",
       "tots                                                        0.0671692\n",
       "kids                                                         0.134672\n",
       "teens                                                       0.0592576\n",
       "mean_age                                                      36.8205\n",
       "hispanic                                                     0.315689\n",
       "black                                                        0.151262\n",
       "asian                                                        0.069467\n",
       "white                                                        0.749017\n",
       "median_income                                                   74225\n",
       "poverty_percent                                                  10.1\n",
       "obesity                                                            32\n",
       "diabetes                                                          3.8\n",
       "adjusted_density                                               659.58\n",
       "max_height                                                    47.1401\n",
       "peak_info_raw       ([78, 125, 172], {'peak_heights': [16.86643652...\n",
       "peak_info           [(78, 16.86643652608854), (125, 47.14013942793...\n",
       "peak_count                                                          3\n",
       "peak_date                                         2020-09-17 00:00:00\n",
       "median_infection                                              11.9974\n",
       "mean_infection                                                15.2558\n",
       "max_infection                                                 47.1401\n",
       "Name: 48039, dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_details_raw.loc[48039]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation Heatmap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>conservative</th>\n",
       "      <th>median_age</th>\n",
       "      <th>tots</th>\n",
       "      <th>kids</th>\n",
       "      <th>teens</th>\n",
       "      <th>...</th>\n",
       "      <th>median_income</th>\n",
       "      <th>poverty_percent</th>\n",
       "      <th>obesity</th>\n",
       "      <th>diabetes</th>\n",
       "      <th>adjusted_density</th>\n",
       "      <th>max_height</th>\n",
       "      <th>peak_count</th>\n",
       "      <th>median_infection</th>\n",
       "      <th>mean_infection</th>\n",
       "      <th>max_infection</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3.136000e+03</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3107.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>3069.000000</td>\n",
       "      <td>3069.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3064.000000</td>\n",
       "      <td>3136.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "      <td>3138.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1120.128576</td>\n",
       "      <td>1.046610e+05</td>\n",
       "      <td>227.750723</td>\n",
       "      <td>-92.260283</td>\n",
       "      <td>38.440815</td>\n",
       "      <td>0.667340</td>\n",
       "      <td>41.754302</td>\n",
       "      <td>0.057564</td>\n",
       "      <td>0.110986</td>\n",
       "      <td>0.050716</td>\n",
       "      <td>...</td>\n",
       "      <td>52669.496579</td>\n",
       "      <td>15.121310</td>\n",
       "      <td>24.119118</td>\n",
       "      <td>7.789315</td>\n",
       "      <td>228.246603</td>\n",
       "      <td>65.618865</td>\n",
       "      <td>1.259720</td>\n",
       "      <td>7.008808</td>\n",
       "      <td>11.801870</td>\n",
       "      <td>65.618865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3603.877894</td>\n",
       "      <td>3.581701e+05</td>\n",
       "      <td>948.084838</td>\n",
       "      <td>12.810235</td>\n",
       "      <td>5.288740</td>\n",
       "      <td>0.160570</td>\n",
       "      <td>5.408456</td>\n",
       "      <td>0.011163</td>\n",
       "      <td>0.018491</td>\n",
       "      <td>0.007723</td>\n",
       "      <td>...</td>\n",
       "      <td>13647.991517</td>\n",
       "      <td>6.052155</td>\n",
       "      <td>4.303952</td>\n",
       "      <td>2.614966</td>\n",
       "      <td>495.261631</td>\n",
       "      <td>78.396375</td>\n",
       "      <td>1.260889</td>\n",
       "      <td>5.766046</td>\n",
       "      <td>7.753101</td>\n",
       "      <td>78.396375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.999000</td>\n",
       "      <td>8.600000e+01</td>\n",
       "      <td>0.035944</td>\n",
       "      <td>-165.821260</td>\n",
       "      <td>19.601235</td>\n",
       "      <td>0.043048</td>\n",
       "      <td>22.200000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>25385.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.035944</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>431.170750</td>\n",
       "      <td>1.090000e+04</td>\n",
       "      <td>16.559432</td>\n",
       "      <td>-98.218605</td>\n",
       "      <td>34.689867</td>\n",
       "      <td>0.577916</td>\n",
       "      <td>38.600000</td>\n",
       "      <td>0.050931</td>\n",
       "      <td>0.100073</td>\n",
       "      <td>0.046224</td>\n",
       "      <td>...</td>\n",
       "      <td>43717.000000</td>\n",
       "      <td>10.800000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>6.100000</td>\n",
       "      <td>30.547495</td>\n",
       "      <td>29.210158</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.755494</td>\n",
       "      <td>6.434310</td>\n",
       "      <td>29.210158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>616.475500</td>\n",
       "      <td>2.567300e+04</td>\n",
       "      <td>44.837082</td>\n",
       "      <td>-90.401169</td>\n",
       "      <td>38.358738</td>\n",
       "      <td>0.700845</td>\n",
       "      <td>41.600000</td>\n",
       "      <td>0.057165</td>\n",
       "      <td>0.110985</td>\n",
       "      <td>0.050798</td>\n",
       "      <td>...</td>\n",
       "      <td>50554.000000</td>\n",
       "      <td>14.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>7.700000</td>\n",
       "      <td>81.658414</td>\n",
       "      <td>48.612865</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.622543</td>\n",
       "      <td>10.480947</td>\n",
       "      <td>48.612865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>923.916000</td>\n",
       "      <td>6.770050e+04</td>\n",
       "      <td>118.177204</td>\n",
       "      <td>-83.454691</td>\n",
       "      <td>41.805300</td>\n",
       "      <td>0.786874</td>\n",
       "      <td>44.800000</td>\n",
       "      <td>0.063099</td>\n",
       "      <td>0.120415</td>\n",
       "      <td>0.054824</td>\n",
       "      <td>...</td>\n",
       "      <td>58693.000000</td>\n",
       "      <td>18.200000</td>\n",
       "      <td>26.100000</td>\n",
       "      <td>9.125000</td>\n",
       "      <td>207.422663</td>\n",
       "      <td>76.545962</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>10.143854</td>\n",
       "      <td>15.552518</td>\n",
       "      <td>76.545962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>145504.789000</td>\n",
       "      <td>1.003911e+07</td>\n",
       "      <td>27546.703542</td>\n",
       "      <td>-67.462330</td>\n",
       "      <td>69.311670</td>\n",
       "      <td>0.967532</td>\n",
       "      <td>68.100000</td>\n",
       "      <td>0.131706</td>\n",
       "      <td>0.215289</td>\n",
       "      <td>0.105114</td>\n",
       "      <td>...</td>\n",
       "      <td>140382.000000</td>\n",
       "      <td>48.400000</td>\n",
       "      <td>42.600000</td>\n",
       "      <td>20.800000</td>\n",
       "      <td>5998.565088</td>\n",
       "      <td>1648.351648</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>45.520358</td>\n",
       "      <td>82.420926</td>\n",
       "      <td>1648.351648</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                area           pop       density    longitude     latitude  \\\n",
       "count    3136.000000  3.136000e+03   3136.000000  3136.000000  3136.000000   \n",
       "mean     1120.128576  1.046610e+05    227.750723   -92.260283    38.440815   \n",
       "std      3603.877894  3.581701e+05    948.084838    12.810235     5.288740   \n",
       "min         1.999000  8.600000e+01      0.035944  -165.821260    19.601235   \n",
       "25%       431.170750  1.090000e+04     16.559432   -98.218605    34.689867   \n",
       "50%       616.475500  2.567300e+04     44.837082   -90.401169    38.358738   \n",
       "75%       923.916000  6.770050e+04    118.177204   -83.454691    41.805300   \n",
       "max    145504.789000  1.003911e+07  27546.703542   -67.462330    69.311670   \n",
       "\n",
       "       conservative   median_age         tots         kids        teens  ...  \\\n",
       "count   3107.000000  3138.000000  3138.000000  3138.000000  3138.000000  ...   \n",
       "mean       0.667340    41.754302     0.057564     0.110986     0.050716  ...   \n",
       "std        0.160570     5.408456     0.011163     0.018491     0.007723  ...   \n",
       "min        0.043048    22.200000     0.000000     0.000000     0.000000  ...   \n",
       "25%        0.577916    38.600000     0.050931     0.100073     0.046224  ...   \n",
       "50%        0.700845    41.600000     0.057165     0.110985     0.050798  ...   \n",
       "75%        0.786874    44.800000     0.063099     0.120415     0.054824  ...   \n",
       "max        0.967532    68.100000     0.131706     0.215289     0.105114  ...   \n",
       "\n",
       "       median_income  poverty_percent      obesity     diabetes  \\\n",
       "count    3069.000000      3069.000000  3064.000000  3064.000000   \n",
       "mean    52669.496579        15.121310    24.119118     7.789315   \n",
       "std     13647.991517         6.052155     4.303952     2.614966   \n",
       "min     25385.000000         2.600000     4.200000     0.500000   \n",
       "25%     43717.000000        10.800000    22.000000     6.100000   \n",
       "50%     50554.000000        14.100000    24.600000     7.700000   \n",
       "75%     58693.000000        18.200000    26.100000     9.125000   \n",
       "max    140382.000000        48.400000    42.600000    20.800000   \n",
       "\n",
       "       adjusted_density   max_height   peak_count  median_infection  \\\n",
       "count       3136.000000  3138.000000  3138.000000       3138.000000   \n",
       "mean         228.246603    65.618865     1.259720          7.008808   \n",
       "std          495.261631    78.396375     1.260889          5.766046   \n",
       "min            0.035944     0.000000     0.000000          0.000000   \n",
       "25%           30.547495    29.210158     0.000000          2.755494   \n",
       "50%           81.658414    48.612865     1.000000          5.622543   \n",
       "75%          207.422663    76.545962     2.000000         10.143854   \n",
       "max         5998.565088  1648.351648     6.000000         45.520358   \n",
       "\n",
       "       mean_infection  max_infection  \n",
       "count     3138.000000    3138.000000  \n",
       "mean        11.801870      65.618865  \n",
       "std          7.753101      78.396375  \n",
       "min          0.000000       0.000000  \n",
       "25%          6.434310      29.210158  \n",
       "50%         10.480947      48.612865  \n",
       "75%         15.552518      76.545962  \n",
       "max         82.420926    1648.351648  \n",
       "\n",
       "[8 rows x 25 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerics = ['int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
    "cols = county_details_raw.select_dtypes(include=numerics).columns;\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 10))\n",
    "ax.matshow(county_details_raw.corr())\n",
    "plt.xticks(range(len(cols)), cols, rotation='vertical');\n",
    "plt.yticks(range(len(cols)), cols);\n",
    "county_details_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared Adj: 0.41843746259875025\n",
      "                  risk_factor_coefficient\n",
      "const                            9.797625\n",
      "black                            0.109744\n",
      "tots                             0.075758\n",
      "teens                            0.041528\n",
      "longitude                        0.027966\n",
      "obesity                          0.021796\n",
      "median_income                    0.009702\n",
      "diabetes                         0.007274\n",
      "hispanic                        -0.001859\n",
      "asian                           -0.006072\n",
      "kids                            -0.010050\n",
      "poverty_percent                 -0.011163\n",
      "white                           -0.015813\n",
      "conservative                    -0.020346\n",
      "adjusted_density                -0.049380\n",
      "latitude                        -0.070933\n",
      "median_age                      -0.095409\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "county_details = county_details_raw.copy()\n",
    "#whole_data = county_details.query('median_infection > 0')[]\n",
    "x_columns = ['adjusted_density', 'longitude', 'latitude', 'conservative', 'median_age', 'white', 'hispanic', 'black', 'asian', 'median_income', 'poverty_percent', 'obesity', 'diabetes', 'tots', 'kids', 'teens']\n",
    "y_columns = ['median_infection']\n",
    "\n",
    "whole_data = county_details.query('mean_infection > 0')[x_columns + y_columns].dropna()\n",
    "#print(county_details['median_infection'].min())\n",
    "X = whole_data[x_columns].copy()\n",
    "Y = whole_data[y_columns].copy()\n",
    "\n",
    "# Group 2: Multiplying for clarity of interpretation\n",
    "X['hispanic'] = X['hispanic'] * 100\n",
    "X['black'] = X['black'] * 100\n",
    "X['asian'] = X['asian'] * 100\n",
    "X['white'] = X['white'] * 100\n",
    "X['tots'] = X['tots'] * 100\n",
    "X['teens'] = X['teens'] * 100\n",
    "X['kids'] = X['kids'] * 100\n",
    "X['conservative'] = X['conservative'] * 100\n",
    "\n",
    "\n",
    "X_imputed = MinMaxScaler().fit_transform(X) * 100\n",
    "X = pd.DataFrame(X_imputed, columns=X.columns, index=X.index)\n",
    "X = sm.add_constant(X)\n",
    "\n",
    "model = sm.OLS(Y, X).fit()\n",
    "predictions = model.predict(X) \n",
    "print_model = model.summary()\n",
    "print('R-Squared Adj: ' + str(model.rsquared_adj))\n",
    "print(pd.DataFrame(model.params, columns=['risk_factor_coefficient']).sort_values(by='risk_factor_coefficient', ascending=False))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared Adj: 0.10587192710258153\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "county_details = county_details_raw.copy()\n",
    "#whole_data = county_details.query('median_infection > 0')[]\n",
    "x_columns = ['adjusted_density', 'longitude', 'latitude', 'conservative', 'median_age', 'white', 'hispanic', 'black', 'asian', 'median_income', 'poverty_percent', 'obesity', 'diabetes', 'tots', 'kids', 'teens']\n",
    "y_columns = ['max_infection']\n",
    "\n",
    "whole_data = county_details.query('mean_infection > 0')[x_columns + y_columns].dropna()\n",
    "#print(county_details['median_infection'].min())\n",
    "X = whole_data[x_columns].copy()\n",
    "Y = whole_data[y_columns].copy()\n",
    "\n",
    "# Group 2: Multiplying for clarity of interpretation\n",
    "X['hispanic'] = X['hispanic'] * 100\n",
    "X['black'] = X['black'] * 100\n",
    "X['asian'] = X['asian'] * 100\n",
    "X['white'] = X['white'] * 100\n",
    "X['tots'] = X['tots'] * 100\n",
    "X['teens'] = X['teens'] * 100\n",
    "X['kids'] = X['kids'] * 100\n",
    "X['conservative'] = X['conservative'] * 100\n",
    "\n",
    "X_imputed = MinMaxScaler().fit_transform(X) * 100\n",
    "\n",
    "polynomial_features = PolynomialFeatures(degree=3)\n",
    "X_poly = X # polynomial_features.fit_transform(X)\n",
    "X_poly = sm.add_constant(X_poly)\n",
    "\n",
    "model = sm.OLS(Y, X_poly).fit()\n",
    "predictions = model.predict(X_poly) \n",
    "\n",
    "Y_pred = Y.copy()\n",
    "Y_pred['predicted'] = predictions\n",
    "Y_pred['predicted'] = Y_pred['predicted'].apply(lambda x:x if x >=0 else 0).fillna(0)\n",
    "Y_pred.plot.scatter(x=y_columns[0], y='predicted', figsize=(16,16), ylim=(0,40), xlim=(0,40));\n",
    "print('R-Squared Adj: ' + str(model.rsquared_adj));\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "max_infection     53.039090\n",
       "predicted        117.020539\n",
       "Name: 48041, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_pred.loc[48041]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-Squared Adj: 0.10587192710258153\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x1152 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "county_details = county_details_raw.copy()\n",
    "#whole_data = county_details.query('median_infection > 0')[]\n",
    "x_columns = ['adjusted_density', 'longitude', 'latitude', 'conservative', 'median_age', 'white', 'hispanic', 'black', 'asian', 'median_income', 'poverty_percent', 'obesity', 'diabetes', 'tots', 'kids', 'teens']\n",
    "y_columns = ['max_infection']\n",
    "\n",
    "whole_data = county_details.query('mean_infection > 0')[x_columns + y_columns].dropna()\n",
    "#print(county_details['median_infection'].min())\n",
    "X = whole_data[x_columns].copy()\n",
    "Y = whole_data[y_columns].copy()\n",
    "\n",
    "# Group 2: Multiplying for clarity of interpretation\n",
    "X['hispanic'] = X['hispanic'] * 100\n",
    "X['black'] = X['black'] * 100\n",
    "X['asian'] = X['asian'] * 100\n",
    "X['white'] = X['white'] * 100\n",
    "X['tots'] = X['tots'] * 100\n",
    "X['teens'] = X['teens'] * 100\n",
    "X['kids'] = X['kids'] * 100\n",
    "X['conservative'] = X['conservative'] * 100\n",
    "\n",
    "X_imputed = MinMaxScaler().fit_transform(X) * 100\n",
    "\n",
    "polynomial_features = PolynomialFeatures(degree=1)\n",
    "X_poly = polynomial_features.fit_transform(X)\n",
    "X_poly = sm.add_constant(X_poly)\n",
    "\n",
    "#X_train, X_test, y_train, y_test = train_test_split(X_poly, Y, test_size=0.33, random_state=42)\n",
    "\n",
    "model = sm.OLS(Y, X_poly).fit()\n",
    "predictions = model.predict(X_poly) \n",
    "\n",
    "Y_pred = Y.copy()\n",
    "Y_pred['predicted'] = predictions\n",
    "Y_pred['predicted'] = Y_pred['predicted'].apply(lambda x:x if x >=0 else 0).fillna(0)\n",
    "Y_pred.plot.scatter(x=y_columns[0], y='predicted', figsize=(16,16), ylim=(0,40), xlim=(0,40));\n",
    "print('R-Squared Adj: ' + str(model.rsquared_adj));\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'county_percentages' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-31-bef7c5643b88>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     10\u001b[0m     \u001b[0mstate_name\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0minterested_county_details\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'st'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 12\u001b[1;33m     \u001b[0minterested_county\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcounty_percentages\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfips\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     13\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     14\u001b[0m     \u001b[0mpeak_info\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcounty_details\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mfips\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'peak_info'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'county_percentages' is not defined"
     ]
    }
   ],
   "source": [
    "county_name = 'Suffolk County'\n",
    "state_name = 'New York'\n",
    "\n",
    "specific_fips = [48039, 48201, 31153, 36103, 18169 ,42055, 12003, 40093, 1001, 26065]\n",
    "random_fips = [county_details.iloc[random.randrange(0, len(county_details.index))].name for i in range(20)]\n",
    "\n",
    "for fips in (specific_fips + random_fips):\n",
    "    interested_county_details = county_details.loc[fips];\n",
    "    county_name = interested_county_details['county']\n",
    "    state_name = interested_county_details['st']\n",
    "\n",
    "    interested_county = county_percentages.loc[fips]\n",
    "\n",
    "    peak_info = county_details.loc[fips]['peak_info']\n",
    "    raw_peak_info = county_details.loc[fips]['peak_info_raw']\n",
    "    #print(peak_info)\n",
    "    peaks = [a[0] for a in peak_info]\n",
    "\n",
    "    ax = interested_county.plot.line(figsize=(16,10))\n",
    "\n",
    "    interested_county.iloc[peaks].rename('values').reset_index().plot.scatter(\n",
    "        x='date', \n",
    "        y='values', \n",
    "        ax = ax, \n",
    "        color='red', \n",
    "        s=50, \n",
    "        title='{0}, {1} ({2})'.format(county_name, state_name, fips), \n",
    "    )\n",
    "    \n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    county_details\n",
    "    .loc[Y_pred[(Y_pred['predicted'] > Y_pred[y_columns[0]]) & ((Y_pred.index.astype(float) / 1000).astype(int) != 15)].index].plot.scatter(x='longitude', y='latitude')\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='longitude', ylabel='latitude'>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "(\n",
    "    county_details\n",
    "    .loc[Y_pred[(Y_pred['predicted'] < Y_pred[y_columns[0]]) & ((Y_pred.index.astype(float) / 1000).astype(int) != 15)].index].plot.scatter(x='longitude', y='latitude')\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13a678e10>"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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oiCFDhsS0adNizpw5MWHChJgzZ87nlvvggw9i1KhR0blz541SKAAAAFuHaoPos88+G82aNYumTZvGdtttFyeeeGJMnjz5c8tdfvnlcckll8QOO+ywUQoFAABg61CnugUWL14ce+21V+5x48aN45lnnqmyzPPPPx8LFy6Mvn37xi9+8YsvbGvs2LExduzYiIhYsmRJoTXDRtFk+NRql5k/sm+CSgAAYOtW68mK1q5dGz/84Q/juuuuq3bZwYMHx6xZs2LWrFmx22671fatAQAA2AJVG0QbNWoUCxcuzD1etGhRNGrUKPf4gw8+iJdffjl69OgRTZo0iaeffjr69+9vwiIAAAA2qNog2qlTpygvL4958+bF6tWrY+LEidG/f//c9+vVqxdLly6N+fPnx/z586NLly4xZcqU6Nix40YtHAAAgC1TtUG0Tp06MWbMmOjdu3e0bNkyBgwYEK1bt46ysrKYMmVKihoBAADYilQ7WVFERJ8+faJPnz5Vnrvqqqs2uOyMGTNqXRQAAABbr1pPVgQAAAD5EEQBAABIShAFAAAgKUEUAACApARRAAAAkhJEAQAASEoQBQAAIClBFAAAgKQEUQAAAJISRAEAAEhKEAUAACApQRQAAICkBFEAAACSEkQBAABIqs6mLgBqq8nwqdUuM39k3wSVAAAANeGKKAAAAEkJogAAACSlay6bVHXdanWpBQCArY8rogAAACQliAIAAJCUIAoAAEBSxohSELdMAQAACuWKKAAAAEkJogAAACQliAIAAJCUIAoAAEBSgigAAABJCaIAAAAkJYgCAACQlCAKAABAUoIoAAAASQmiAAAAJCWIAgAAkJQgCgAAQFKCKAAAAEkJogAAACQliAIAAJCUIAoAAEBSgigAAABJCaIAAAAkJYgCAACQlCAKAABAUoIoAAAASQmiAAAAJCWIAgAAkJQgCgAAQFKCKAAAAEkJogAAACRVZ1MXQM00GT612mXmj+ybrB0AAIBCuSIKAABAUoIoAAAASQmiAAAAJCWIAgAAkJQgCgAAQFKCKAAAAEkJogAAACQliAIAAJCUIAoAAEBSgigAAABJCaIAAAAkJYgCAACQlCAKAABAUoIoAAAASQmiAAAAJCWIAgAAkJQgCgAAQFKCKAAAAEkJogAAACQliAIAAJCUIAoAAEBSgigAAABJCaIAAAAkJYgCAACQlCAKAABAUoIoAAAASdXZ1AVs7ZoMn1rtMvNH9k1QCQAAwObBFVEAAACSEkQBAABIShAFAAAgKUEUAACApARRAAAAkjJr7hcw2y0AAMDG4YooAAAASQmiAAAAJFWjIDp9+vRo3rx5NGvWLEaOHPm5719//fXRqlWraNeuXRxxxBGxYMGCohcKAADA1qHaIFpRURFDhgyJadOmxZw5c2LChAkxZ86cKsscdNBBMWvWrHjxxRfj29/+dgwbNmyjFQwAAMCWrdog+uyzz0azZs2iadOmsd1228WJJ54YkydPrrJMz549Y8cdd4yIiC5dusSiRYs2TrUAAABs8aoNoosXL4699tor97hx48axePHiL1x+3LhxccwxxxSnOgAAALY6Rb19y+9///uYNWtWPP744xv8/tixY2Ps2LEREbFkyZJivjUAAABbiGqviDZq1CgWLlyYe7xo0aJo1KjR55Z7+OGH46c//WlMmTIltt9++w22NXjw4Jg1a1bMmjUrdtttt1qUDQAAwJaq2iDaqVOnKC8vj3nz5sXq1atj4sSJ0b9//yrLvPDCC3H22WfHlClTomHDhhutWAAAALZ81QbROnXqxJgxY6J3797RsmXLGDBgQLRu3TrKyspiypQpERFx8cUXx4cffhgnnHBClJaWfi6oAgAAQKUajRHt06dP9OnTp8pzV111Ve7/Dz/8cHGrAgAAYKtV7RVRAAAAKCZBFAAAgKQEUQAAAJISRAEAAEhKEAUAACApQRQAAICkBFEAAACSEkQBAABIShAFAAAgKUEUAACApARRAAAAkhJEAQAASEoQBQAAIClBFAAAgKQEUQAAAJISRAEAAEhKEAUAACApQRQAAICkBFEAAACSEkQBAABIShAFAAAgKUEUAACApARRAAAAkhJEAQAASEoQBQAAIClBFAAAgKQEUQAAAJISRAEAAEhKEAUAACApQRQAAICkBFEAAACSEkQBAABIShAFAAAgKUEUAACApARRAAAAkhJEAQAASKrOpi6g2JoMn1rtMvNH9k1QCQAAABviiigAAABJCaIAAAAkJYgCAACQlCAKAABAUoIoAAAASQmiAAAAJCWIAgAAkJQgCgAAQFKCKAAAAEkJogAAACQliAIAAJCUIAoAAEBSgigAAABJCaIAAAAkJYgCAACQlCAKAABAUoIoAAAASQmiAAAAJCWIAgAAkJQgCgAAQFKCKAAAAEkJogAAACQliAIAAJCUIAoAAEBSgigAAABJCaIAAAAkJYgCAACQlCAKAABAUoIoAAAASQmiAAAAJCWIAgAAkJQgCgAAQFKCKAAAAEkJogAAACQliAIAAJCUIAoAAEBSgigAAABJCaIAAAAkJYgCAACQlCAKAABAUoIoAAAASQmiAAAAJCWIAgAAkJQgCgAAQFKCKAAAAEkJogAAACQliAIAAJBUjYLo9OnTo3nz5tGsWbMYOXLk576/atWq+M53vhPNmjWLzp07x/z584tdJwAAAFuJaoNoRUVFDBkyJKZNmxZz5syJCRMmxJw5c6osM27cuGjQoEG89tprceGFF8Yll1yy0QoGAABgy1ZtEH322WejWbNm0bRp09huu+3ixBNPjMmTJ1dZZvLkyXHaaadFRMS3v/3teOSRRyLLso1TMQAAAFu0aoPo4sWLY6+99so9bty4cSxevPgLl6lTp07Uq1cvli1bVuRSAQAA2BqUZNVcupw0aVJMnz49brvttoiIuOOOO+KZZ56JMWPG5JZp06ZNTJ8+PRo3bhwREfvtt18888wzseuuu1Zpa+zYsTF27NiIiHjllVeiRYsW/7G4JUuWxG677Zb/T/Vf0M7mVIt20rSzOdWinTTtbE61aCdNO5tTLdpJ087mVIt20rSzOdWinTTtbE61pG5n/vz5sXTp0g1/M6vGk08+mfXq1Sv3+JprrsmuueaaKsv06tUre/LJJ7Msy7I1a9Zku+yyS7Z27drqmq5Whw4dat3G1trO5lSLdtK0sznVop007WxOtWgnTTubUy3aSdPO5lSLdtK0sznVop007WxOtWxO7VTbNbdTp05RXl4e8+bNi9WrV8fEiROjf//+VZbp379//Pa3v42IdVdQv/71r0dJSUkNszQAAAD/TepUu0CdOjFmzJjo3bt3VFRUxBlnnBGtW7eOsrKy6NixY/Tv3z/OPPPMOOWUU6JZs2ax8847x8SJE1PUDgAAwBZo2xEjRoyobqH9998/hg4dGhdccEF07949IiJ69uwZzZs3j4h1YXXAgAFx/vnnx1lnnRUNGjQoWoEdOnTQzkZsQztbVjubUy3aSdPO5lSLdtK0sznVop007WxOtWgnTTubUy3aSdPO5lTL5tJOtZMVAQAAQDFVO0YUAAAAikkQBQAAIClBFDZjTzzxRIwfPz4i1t2rad68eZu4IoAtz6pVqz733LvvvrsJKgHIz+rVq+Pll1+Ol19+OdasWVNwOwsWLIiHH344IiI++eST+OCDDwpu65133ol//etfua9CGSNajddffz0aN24c22+/fcyYMSNefPHFOPXUU6N+/fqburSCdOjQIc4444w4+eSTizqp1Jaubdu2//GWQy+++GLCata58sorY9asWTF37tx49dVX480334wTTjghZs6cmbyW9b3zzjuxcuXK3OO99957k9Xy8ssvx5w5c6rUc+qppxbU1urVq+PVV1+NiIjmzZvHl770pU1aT7G89957sXDhwmjXrt0mrWNr9d5770V5eXmVv3nlpH4pDRs2LC677LL48pe/HEcffXS8+OKLccMNN8T3vve95LVsjvr27Rt//OMfc+v1W2+9Ff369YvnnntuE1dWO5vL5y8iYtSoUXHBBRdU+1xNzJ49O/76179GRES3bt3iwAMPLKimre0Yjv+svLw8Lr300s/th994442821q2bFnssssuxSyvIDNmzIjTTjstmjRpElmWxcKFC+O3v/1t3uv5rbfeGmPHjo133303Xn/99SgvL49zzjknHnnkkbzamTJlSlx00UXx5ptvRsOGDWPBggXRsmXL+Mc//pFXO5U2myD6gx/8IH75y1/Gscceu8FAMGXKlLzbLMYGurS0NGbNmhXz58+PPn36xHHHHRf/+Mc/4sEHH0xezxFHHPG5D8yGnvtPXnvttRg/fnzcfffd0bFjxxg4cGD06tWr4Pu+Tp06Nf7xj39U+ZnKysryamPx4sWxYMGC+PTTT3PP5ft3WrlyZYwbN+5ztfzmN7+p0esXLFgQERG/+tWvIiLilFNOiYiIO++8MyIiRo4cmVc9EeuuYP785z//3Abx0UcfrdHrS0tL44UXXoj27dvHCy+8EBER7dq1yysUF3O9KvbGp7brw5VXXhkzZsyIOXPmRJ8+fWLatGnRtWvXmDRpUt61FGNDX8x6avu76dGjR0yZMiU+/fTT6NChQzRs2DAOO+ywuP766/OupVgBp7braETE/fffH5dcckm88847kWVZZFkWJSUlsWLFirxqiYjIsizuvPPOeOONN6KsrCz+9a9/xb///e84+OCDa9zGbbfdFqNGjYpFixZFaWlpPP3003HIIYfUeB2vNHPmzBgxYkRuO1j5c+Vz8FRaWhp///vf4w9/+EM88MADcf3110f37t1j9uzZedVSqbYnVZYvXx4jRozIhYnDDz88ysrKol69ejVu4/e//31873vf+8LP7Q9/+MMat3XrrbfGgw8+GJMmTYqFCxdG//7949prr41evXrVuI2Iddv1W2+9NebPn19ln5XP57hSbfd9xfr8RRRnfWjfvn08//zzVZ476KCDcvuvmho1alTceuut8c1vfjMiIv7whz/E4MGDY+jQoXm1E1GcY7ja7ssr1a1b93P74Xr16kXHjh3juuuui6ZNm9aonWKdfHj66adj6NCh8c9//jNWr14dFRUV8ZWvfCWv7Wkxgt/QoUP/4zHojTfeWOO2unbtGldeeWVceOGF8ac//SnGjx8fa9eujauuuqrGbVTaf//9o7S0NAYOHBjHHHNMwcfJtf17dejQIe66667cnUpeffXVOOmkk/I+iVZaWhrPPvtsdO7cObdOtm3bNl566aW82jnwwAPj0UcfjSOPPDJeeOGFeOyxx+L3v/99jBs3Lq92crLNxKxZs7Isy7IZM2Zs8Ctft956a9amTZusfv36WY8ePbIddtgh69mzZ97tHHTQQVmWZdn//M//ZDfeeGOWZVlWWlqatJ5PPvkkW7ZsWdauXbvs3XffzZYtW5YtW7YsmzdvXta8efO8a8myLKuoqMgmT56c7bnnntlee+2VlZWVZcuWLcurjbPPPjs75ZRTssaNG2cjRozI2rRpk51xxhl5tTFs2LBsn332yY455pisX79+Wb9+/bJjjz02rzayLMu+/e1vZ5dddlnWtGnT7Pbbb8+OOuqo7Pzzz8+7nQ39bSs/A/k66qijsttuuy1r0aJFNmPGjGzgwIHZsGHDavz6Tp06VXn/Dz/8MGvbtm1eNRRzvWrXrl22dOnS3O/o0UcfzfvvXakY62ebNm2yioqKrF27dlmWZdm///3v7Mgjjyyonvbt22evvPJK7vHcuXOz9u3bb5J6ivG7qfwb3XrrrVlZWVmWZVnen51KBx54YJZlWXb//fdnZ5xxRvb+++/nfsZ8FGMd3W+//bI5c+bk/d4bcs4552TnnXde1qJFiyzLsuzdd9/NOnbsmFcbbdq0yT755JPc7+if//xn9o1vfCPvWpo3b549+OCD2dtvv50tXbo095WP1q1bZ1mWZWeeeWY2bdq0LMuygv5OWZZlI0aMyHr06JE1bNgwO/3007Pdd989+9a3vpVXG9/85jezsrKy7PXXX89ef/31bMSIEXn/bm6++eZcPRv6yteYMWOyfv36ZW3atMlmzpyZ9+uzLMsOOeSQbNiwYdndd/gIUV0AACAASURBVN+dTZo0KfeVr2Ls+4r1+cuy2q0Pd911V9avX7+sfv362bHHHpv76tGjR/b1r38971ratm2bffjhh7nHhez7KhXjGK62+/JKl112WXbzzTdnK1asyJYvX57dcsst2bBhw7KJEydmhx9+eI3aKNaxbZZlWYcOHbLy8vKstLQ0+/TTT7Pf/OY32fDhw/Nq47DDDssefvjhrG3bttn8+fOzK664Irv88svzauP222/Pbr/99uyss87KDjvssOzGG2/Mbrzxxqxbt27Z2WefnVdblfvtNm3afO65fK1duzb785//nJ144onZfvvtl1166aXZ3Llz82qjGH+vDX32C1kfDj744CzL/u/zv2bNmoLa6dChQ5Zl6/YvFRUVuf8XarMJosVWrA30wQcfnN11111Z69atszfeeCPLsv/b4aeq55e//GXWpEmTbLvttsv23XffrEmTJlmTJk2ydu3aZaNHj867ltmzZ2c/+MEPsgMOOCAbOnRo9vTTT2fXXnttrraaqvwAV/77wQcfZF27ds2rjQMOOCBbuXJlXq/ZkMoVq7KW1atXZ507d867nQMPPDB74oknco9nzpyZ9++lUuXGb/0VPZ8D3V/84hfZ4MGDs3333TcbO3Zs1qVLl9yOtBCrVq3KZs+enb344ovZqlWr8n59MTc+xVg/K4N6+/bts+XLl2dr164t+MRMMTb0xaqnGL+bNm3aZG+++WZ21FFHZc8++2yWZYUH0WIFnGKso4ceemje7/tFKg9Q1z8ozffnqlyfDzzwwNx2rFWrVnnXUnmAUBuXXHJJ1rx586y0tDRbvXp19s477xTcbjFOqmxou1nItvTTTz/Nrr/++rxfV+m6667LfV177bVZu3btsu9973u55/JV6P7gs4qx7yvW5y/Larc+zJ8/P3vssceyLl26VDnR+dxzz2Vr1qzJu5bKbWClTz75pEqwyEcxjuFquy+vtKHfZ+Xnqaa/62KefKjcp6//c+Ub0osZ/Dp37lzl81LIPuKQQw7JKioqsm984xvZ6NGjs/vvvz874IADCqpnfY8++mi25557ZvXq1cu6d++ePfnkkzV6XTH+XgMHDszOPPPM7LHHHssee+yxbNCgQdnAgQPz/hkuvvji7Kc//WnWvHnz7M9//nN2/PHHZz/+8Y/zbueII47IPvjgg+z73/9+duKJJ2bnn39+dsghh+TdTqU6hV1HLb4BAwbEPffc84Vj9fIdo7fDDjvEDjvsEBHrJilo0aJFzJ07N++6xo8fHzfffHP8v//3/2LfffeNefPm5bptpqrnggsuiAsuuCBGjx5dUNeU9XXo0CHq168fZ555ZowcOTK23377iIjo3Llz3mMPv/zlL0dExI477hhvvvlm7LLLLvHWW2/l1UbTpk1jzZo1uToKVTnup379+vHyyy/H1772tXjnnXfybmfcuHFxxhlnxPLlyyPLsmjQoEFBXa7Wr2mPPfaIqVOnxp577pnX5Bg/+tGP4qGHHoqvfvWrMXfu3LjqqqviqKOOKqiWqVOnxjnnnBP77bdfZFkW8+bNi1tuuSWOOeaYGrdRv379+PDDD6N79+7x3e9+Nxo2bBhf+cpXCqqnGOtnx44d4/3334+zzjorOnToEDvttFMccsghBdXTsWPHGDRoUK676Z133hkdO3bcJPUU43dTVlYWvXv3jq5du0anTp3ijTfeiP333z/vWiIi+vXrFy1atIgvf/nLcdNNN8WSJUty9eWjGOtox44d4zvf+U4cf/zxVbYZlV348q2noqIit79ZsmRJbLNNfvP3NW7cON5///04/vjj46ijjooGDRrEPvvsU+PXV3Zj7NmzZ1x88cXxzW9+s8rP1b59+xq3NXLkyBg2bFjUq1cvtt122/jKV74SkydPrvkPs54vf/nLsc0220SdOnVixYoV0bBhw1i4cGHebTzxxBPRtWvXiFjX/bhyn5GPbbfdNiZMmBAXXnhh3q+NiM9NxlH5WSl0ko5+/frFgw8+GH369Cno9ZWKse+r7edvfbVZH/bZZ5/YZ5994qmnnirovT9r4MCB0blz5/jGN74RERF//OMf48wzzyyorWIcw9V2X15pxx13jHvuuSe+/e1vR0TEpEmTctvSmnb7LNaxbWU9q1evjtLS0hg2bFjssccesXbt2rza2H777WPt2rWx//77x5gxY6JRo0bx4YcfFlTPe++9FytWrIidd945IiI+/PDDeO+99/JqY9SoUfHxxx/HjTfeGJdffnk8+uij8dvf/ragepYtWxa///3v44477ojdd989Ro8eHf3794+///3vccIJJ9Ro8shi/L1uuumm+NWvfpXrotytW7c477zz8v55fv7zn8dtt90Wbdu2jVtuuSX69OkTgwYNyrudyZMnxw477BA33HBD3HnnnbF8+fK8h+Stb7MZI/rWW2/FHnvskRur91n5bly/8Y1vxPjx4+OXv/xlPProo9GgQYNYs2ZN3mM7izX4vlj1PPnkk58bm5LPuJ033nijxuMQqvOTn/wkhg4dGo888kgMGTIkSkpKYtCgQfGTn/ykxm1861vfitmzZ8cRRxxRZYecz5iAiHVjZb71rW/Fiy++GAMHDowPP/wwrrrqqjjnnHPyaqfS8uXLIyLyGs/0WQ888EB069YtFi5cGEOHDo0VK1bEiBEj4thjj61xGwsWLIjy8vI48sgj4+OPP46KioqoW7du3rW0aNEiHnjggWjWrFlErJvAoW/fvvHKK6/UuI2PPvoodthhh9xYouXLl8d3v/vdggbzF2t9qDR//vxYsWJFwRPyrFq1Kn71q1/FE088ERH/t6Ev9CCxNvUU+3dTDO+++24u4Hz88cexYsWK+NrXvpZXG8VYRwcOHPi550pKSgo6WXTnnXfG3XffHc8//3ycdtppMWnSpLj66qvjhBNOyLutiIjHH388li9fHkcffXRst912NXpNz549v/B7JSUleY9BK9ZkWeedd15cc801MXHixLjuuutip512itLS0twM3jXx97//PU477bTcSb2dd945br/99oImnbnwwgtjzZo18Z3vfKfKya+aBvWKioq45JJL4tprr837vT+rbt268dFHH8V2222XCyiFjFMu1r6vUiGfv/UVY31Yfwzk6tWrY82aNXmPOaz0/PPPV9keH3TQQXm3USwb2pdfccUV0b9//7zaeeONN+KCCy6Ip556KkpKSqJLly5xww03RKNGjeK5557LnbT5T4q5f1iwYEE0bNgw1qxZEzfccEMsX748zjvvvNxxQk387W9/i5YtW8b7778fl19+eSxfvjyGDRsWXbp0ybue8ePHx4gRI6Jnz56RZVn85S9/iREjRsRpp52Wd1vFcMABB8Qpp5wSAwcOjMaNG1f53s9//vO45JJLqm2jGH+vymOvbbfdNiLWbc9WrVoVO+64Y43bqKioiNatW+d1zJfKZhNEN6babKCLNfi+GPWccsop8frrr0dpaWnuA1lSUpLXjuvtt9+OH//4x/Hmm2/GtGnTYs6cOfHUU08VfLax0qpVq2LlypV5B7cvOlO1qTY8q1ativvuu+9zYb+Qsz0zZ86Mww47rNrnvkixZjiLiOjUqVP87W9/yz3OsiwOPvjgKs/V1IoVK6r8birPXhaq0PWhGJN3FdPGqKfyd3PMMcfkNYtvMSYGWt/mOBtwbaxduzaefvrp2HnnneORRx6JLMviiCOOiJYtW+bdVkVFRbz99ttV1olNMZN0MSfLWl9tT/JUhpCvfvWrBdewocCeb1A/5JBDinbFrhiKte8r5ufvlVdeqfX6UCnLspg8eXI8/fTTNZ7sb8WKFfHVr371C6825rOv+aJedtn/PxHYppgJv9hqe/Jhc/Tvf/87nnnmmYhY11Ovpic8N8aEp2PHjo3BgwdXeW748OEFTV4ZUfjfq0uXLvHwww/HTjvtFBHrrhT36tUrnnzyybze/7jjjovRo0cXvH3o2rVrPPHEE7kTTpXrUlaLSQMjNsMgWoxZvCIiLr/88ujevXsceuihBXUfnDBhQtx1113xxBNPRLdu3XLPf/DBB7HNNtvU+OCyuu4b+WxYW7ZsGXPmzCl45q6IiGOOOSYGDhwYP/3pT2P27Nnx6aefxkEHHZT3rFmVanuFtliKFSCPPvroqFevXnTo0CEX9iMiLrroorxr2tBJjA0990WKNcNZRMS5554bCxYsiAEDBkRJSUnce++9sffee8eRRx4ZETXr2njLLbfEFVdcETvssENss802Bc3sub5CD6BWrlwZH3/8cfTs2TNmzJgRlZuwFStWxNFHH13QGb/PzlpaqSY/W7HrKSsrqzLDX0VFRZx66qm5GZxr4oQTTogWLVrEXXfdFWVlZXHnnXdGy5YtY9SoUXnVElG8gFOMdXTRokUxdOjQ3DCCbt26xahRoz53tromantCMSJi9OjRceWVV8buu++e68ZY6EFubWcgb9u2bcyePTsOOuigmD17drz99tvxve99Lx566KEat/HKK69EixYtvnAbVZMrkNXNzJzPTLfFdO6558bixYvjhBNOqHJMUEi37ilTpsRf/vKXiFg3Q3W/fv2KVmc+ivn5i9g4J1XyWc/69esXDzzwQOy7774bDI/57GuK2ctu3rx5MXr06M9tu/INN7WZcTlFSK9Uk8/Pxgh+EYXPJP3cc89Fhw4d4vHHH9/g9w8//PC8a+nTp09897vfje9+97sRETFkyJDcSd7qFPPYv3JG9Oqeq0737t3jhRdeiIMPPrjKNrDQv1WxbDZjRCt9//vfj4kTJ8YJJ5wQs2bNit/97ne5e/vlo2nTpjFhwoQ4//zzo27dutGtW7fo3r17HHfccTV6/aGHHhp77LFHLF26tEoIqVu3bl5nhjt06JA7Y/Cvf/0rGjRoEFmWxfvvvx977713jfqYV2rTpk38+9//jj322KPGr/mspUuXxoABA+JnP/tZRETUqVOnSuDKxxddoa1JEC322crjjjsuFyBrM+Zm0aJFMX369IJfHxHx1FNPxZNPPhlLliypclC2YsWKqKioqHE722+/fZWzZp9++mnBJyFWrlwZu+++e24jvdtuu8Unn3wSf/rTn6KkpKRGB2PXXnttvPzyy7HrrrsWVMP6anMAdcstt8Qvf/nLePPNN6scGH/1q1+N73//+wXVc+aZZ8YNN9zwuRMQNVHsehYuXBg/+9nP4tJLL41Vq1bFgAED8u6W9tprr8W9994bkydPjtNOOy1OPvnkKifU8jFp0qRcwBk/fnwu4OSrGOvowIED4+STT4577703Itbd3mPgwIF5ha1KRxxxRNx3333xzW9+s+D1atSoUTF37txa32vunHPOiY8//jgee+yxGDRoUEyaNCmv22ZEFGdc5/XXXx9jx47d4Im3ml6BrBx7OXfu3Pjb3/6W6774pz/9Ke+fqdIX3Xohn6C+cuXK2GWXXar8DDXd9q1v+PDh8be//S13cDpq1KiYOXNmbp9aU8W47UWxPn8RVbfJ2267bUH74vvvvz/3/7Vr18asWbPyGk/+wAMPRETkdVz0RSqPk/bZZ594++23c71/Dj744GjYsGFebR1//PFx5plnxrHHHpv3OPL1HXfccdGtW7c48sgj897PnHzyyfHAAw9UOaaslG9IrzwhWfn7LkTlONsf/ehHBbfxWZdcckncfffd0bp16yrHBTUJoh06dIiIwgLnF7nvvvuif//+sc0228T06dOjfv36Nb5FSTGP/b/yla/E888/nzu+eO655woab5/PsLnqVHadLykpia5du9au63zB0xxtJMWYxWt9b731VjZq1Khsr732ynbaaae8X7+hKboLmbZ70KBB2dSpU3OPH3zwwWzw4MF5tdGjR4+sfv36Wa9evapMkZ6Pww8/PFu6dGluhrynnnoq6969e15tVGrRokW2du3agl775ptvZlm2bra9DX3lq5CZjDfkrLPOyl588cVatTFjxoxsxIgR2de+9rUqtxq47rrrsldffbXG7RRrhrNi6d27d/bRRx8Vpa399tsv79tTfFZtZhD+rGLMWlqsetauXZuddNJJ2TXXXJMdddRRBc0YWjmDb7du3bKXXnopW7JkSbbvvvsWVE+xZgMuxjparJlYsyzLdtppp6ykpCT70pe+lNWtWzfbaaedsrp16+bVRo8ePQqaFfSzijED+bnnnpu999572U033ZQ1a9YsKy0tzU4//fRa11aobt26ZStWrMg9XrFiRdatW7eC2rr22mtzX1dffXXWpUuXgmaNLIa2bdvmZg3PsnWz+hYyI3UxbntRrM9flhVnm3z66afnvgYNGpRdffXV2dtvv11QW4sWLcpmzpyZPf7447mvQtx9993Z3nvvnZ166qnZKaeckjVp0iS7995782qjGPuHLCvejMtbq2LMJP3EE09kRx55ZLb//vvn7jKR776v8haJy5Yty+bPn5+VlpZmQ4YMyT2Xj2Ic+z/77LNZ06ZNs65du2aHHXZYtt9+++VuzbcpXHnllVmbNm2ysrKyrKysLGvXrl32k5/8pOD2Nruuud27d4+HH344Bg0aFF/72tdijz32iNtvvz3vm3IPGjQo5syZE7vvvnt069YtunbtGu3bt486dfK7CLyhrpTt2rXL+4rdhrpU5tvNshhdDp5//vkYOnRovPzyy9GmTZtYsmRJTJo0qaDxPyeccELceOONtbpC+9FHH+XO5L/66qvxyiuv5D0eLiJyN7tu27ZtwbVERLRq1Spee+212HfffWP77bev1XiSBQsWxD777JObQa6yf39NrV27NsaNGxd//vOfI8uy6N27dwwaNKigqzeVkyQ8/fTTUVJSEoccckjccMMNeU1c9cILL+RmM6zt5Bo9e/aMhx56KO/1cX2rV6+Om2++uUoXubPPPjuvz07lun3PPfdERUVFrWYtrW09629n1qxZE2effXYcdthhufHb+dRSOTHQSy+9FKeffnqtJu8qxsQ1EcVZR4844ogYOHBgnHTSSRGxbgjF+PHjN9m44DPPPDPmzp0bffv2rfK5ybf7aefOneOZZ56JLl26xP333x+77LJLtG7dOl577bWC6qrtuM71r2xVqlevXrRt27bGV5OaN28eL774Yu73smrVqmjXrl3BM3yub9WqVdG7d++YMWNGjV9TrG7d7dq1ixkzZuS61r377rvRo0ePvPcRHTp0iOeee67KcUDlczVVrM9fRHG2ycVSeWWsVatWVXpbFdKF8MADD4yHHnoo97ldsmRJHHnkkXkdU951111RXl4evXr1Knj/EBFx2WWXxaGHHlrrGZcL7b76Wffff39ccskl8c4770S27naOeY/1++ywlqwWQ3aOOeaYuPfee/M+VlpfixYtNti7KZ9eA5Vdw7P1xkBWyvdnK8axf8S6Y4LKbWfz5s3zOs757NjOSoX8vSvff/bs2bneDp988kmUlpYWvG3f9Fucz7jjjjti7dq1MWbMmLjhhhti4cKFcd999+XdzrJly6KioiLq168fO++8c+y66655bWBvuumm+PWvfx2vv/56lZ35Bx98UOPJZta35557xtVXX13l9hB77rlnXm0cfvjhte5i0r59+3j88cdj7ty5kWVZ3h/o9S1dujRatWoVBx98cJWNcz47i+7du8df//rXeO+996JXr17RqVOnuPvuu/MaDxcR8cQTT8Ttt99e6wA5bdq0vJb/Tz744IM46KCDcmMFdt111/jtb38bbdq0qdHrt9lmmzjrrLPirLPOinfffTcWLVpUcBfCk08+OYYMGRJ/+MMfIiJi4sSJcdJJJ+UmBaiJs88+O77+9a9H27Zta9U9KWJd1/kePXrU6gDqvPPOizVr1uSmMb/jjjvi3HPPjdtuu63GbXy2C+KsWbNy/893MpTa1vPZWho0aBBz5syJiy66KO9aKqdk7969e8FjeCv9+te/joh13UePPvroggNOMdbR3/zmNzF06NC48MILo6SkJA499NC4/fbb866l0nvvvRfl5eVVukbmc0C39957x9577x2rV6+O1atXF1xHv3794v3334+LL7442rdvn5uBPB/jxo3LnbRo0qRJVFRUxJVXXhlXXHFF3vWMGzcunnrqqdwkQTNmzIgOHTrEvHnzoqysrEa3vzj11FPj4IMPrnILjmJNQvfxxx/HokWL8npNsbp1X3rppXHQQQdVmdmzkMlLinHbi2J9/iKKs00+7bTTYtSoUVG/fv2IWLd+XXTRRXlPkvbHP/4x5s6dW+vbukWsO6G7/nHSLrvskvctSl566aW444474tFHH63SZTTfWa1HjRoV11xzTWy//fbxpS99qaAg8EUhvZAgOmzYsPjTn/5Uq0mpajOs5bN23HHHKC0trdVM0vXq1cvrtnQbUoyu4ZVqc+z/6KOPxte//vXPnRisHK5Y02EFlbNPF3rLqs/ac889Y+XKlVVuS9OoUaOC29vsrog+8sgjceihhxbU/3lD/vnPf8b//u//xg033BAVFRU13nktX7483nvvvbj00kur7GTq1q1b0Cyh7777blx55ZW5qyXdu3ePK664Iq+27rnnnrj44oujR48ekWVZ/PWvf41f/OIXuXtS1cS9994bRx99dNStWzeuvvrqeP755+Oyyy7L+8xeRHGu0FZecR49enR88sknMWzYsIIGYRfrtj+V3nnnnSoHp4VM2HDo/8femcf1lP1//NU2BhPGkH3LUlRKKUqbvclky1IhSpYoZYkZQ9YYzFDGvs4gKTtlV5HKUFHIklBo0KKalNSn8/uj373fPi26534Ofcz0fDx6DLe5x+nTXc55L6+XiQl8fHykFnMLFy4UrHRmaWmJU6dOobi4GAYGBlBTU4OJiQk2bNhAPZfKsvi6urpUUWEW4i4cy5Ytq/Q4zaK5svnT/kwskaf5+Pn5wcnJCaqqqpgyZQri4uLwyy+/YNCgQdRjRUZGQk9PD/Xr18eBAwcQFxcHDw8P6nuLxT0qqxJ1WXbt2gU/Pz+8ePECenp6uH79OoyNjakXlywoLCyUyhxyL3mahbiDgwOys7Oxe/duZGVlYdKkSbCwsBBlWTJ48GDs27cPzZo1A1Cqtu7o6IiAgACYm5vj7t27gsaJjY3lF0Hm5uai+4jKaglIJBKkp6fD29ubqgebleAHUCqEUzYgTGtlBLC1vWABi2dyZe8IMe8NFpkxDi8vLyQkJPBVFIGBgejevTvWrFkjeIxOnTohMTFRLpRpy1cayEKfPn2o/ePLw1VzsICFkvSPP/4oc3UTx+bNmzFu3DipwEpAQACVh6csa/8lS5Zg2bJlTG3LZBEkc3d3h4KCAlJTU3Hz5k0MHDgQCgoKuHjxIoyMjCqtpBGC3G1EJ06ciOjoaDRu3JgXGDI1NcW3335LNU5wcDAiIiJw9epVZGdno3fv3jAzM4OzszP1nK5du4akpCQ4OTkhIyMD//zzDzp06EA9jqywKDHhNiTXrl3D4sWLMW/ePCxfvpzZg4SWHj16YMuWLZg9ezZ2794NLS0t0cqw8fHxiIiIAFBadiXGr+7UqVOYO3cu0tLSoKamhpSUFHTt2hX37t2jHkvWjQn3At+1axeeP3+OZcuWiSoLB0qjqN9++y3s7OygoKCAwMBAvH37Fl5eXgCEKbgtXLgQ7du3h42NjdQDXlb7FrHo6+vj8OHD6NixI4DS8uNRo0YJViUuj6yqpazmw2ITyV1n58+fx7Zt27By5UpMmDBB1GfTvXt3xMfHIyEhAZMmTYKLiwuCgoKqDERVhyxBHlmVqMuio6ODmzdvonfv3rh9+zYePHiAhQsXCnqZslaMZPVzBQYGYubMmahfvz4OHjwoaoMOlLYoJCYm8n8nhEBLSwuJiYnUGwsWQb2yQQxlZWU0a9aMuoSUVVl3Zb+Thg0bol27dp+trPVTKZbKiq6uLsLDw/n1WlZWFiwsLAS/z7mF7suXL5l6rB49elSqJJvL0gtl+PDh2LFjB3UFGgcLNWoOlpt0Dw8PvHr1CsOHD5f6nGkEvFhu/FjAwuqJo7JAFcuA/OdGVpXtqgIFHGIrXuSuNJf7QdPS0nDkyBHMnDkTaWlpUrt3IZw7dw5mZmbw8PCgLoEty7JlyxATE4OHDx/CyckJHz58wPjx4wVHkVi+MFiUmHClEyEhIZgyZQqGDBmCRYsWUY3BwcJqx8/PD6tXr8aIESOgpaWFJ0+efNTk/WPj7Ny5k3+Ajh8/nu9Jo2Hx4sW4fv06BgwYgFu3biEsLAwHDhygng9QWuq0YsUKvoztwIEDVD2ZxcXF+PvvvxEUFAQfHx9Rc+AICgoCUKrwWpZDhw4J7nkICAgAACl1SLG9IOnp6Vi7dm2FjR/Ny2LdunXo27cv1NXVQQhBSkoKdd8iBwvVUlbz2bNnDzw8PHD+/HlkZmZi//79mDBhAtVGlIsvnjlzBo6OjtDS0oLYmKOysjIUFBRw8uRJuLm5YfLkyYKVA8siS5CHlRJ1Wb7++mup0iJNTU3BPS6sFCNfvXqFly9foqCgQGqRmpubi/z8fKqxkpKS4OfnB1tbW9y/fx/79+9Hjx49qEzPOThLktGjRwMoXchbWlri3bt3fHagOsr/vlNTU6GpqSkqqNeuXTupQKO5ubng8nBXV1esWbOm0rJuMffnjBkzEBcXh+7du4MQgrt370JLSws5OTnYunWr4Pu0sjVBw4YN0bNnT0ybNu2jarMsFUtZrlHmzp0LY2NjjB49GoQQHDlyBD///LPg83v27AmgNBBiaWkJBQUFKCsry1whZ2trC1tbW9HnZ2dnQ1NTE4aGhqLakFioUXOwKF/lyM3NRb169XDhwgWp+dBsRLkkhixtLRwslKTDwsKo/92qkEgkfPk093faEngWa50JEyZg06ZNaNiwIYDSwJyzszN1EE1WlW1WrRXlkbuN6IEDBxAREYE7d+6gSZMmcHNzE2U7d3YJ3QAAIABJREFUsGnTJr6fMi4uTlQ/JQAcP34ct27d4qM7LVu2pKqzZvnCsLKywuDBg6VKTGhr4Vu1aoVp06bh4sWLWLBgAQoLC6k3sxwsrHbMzc2lehvU1dVFPVB3796Nv/76i/dGWrBgAYyNjak3oioqKvwGv6SkBH379oWnpyf1fIDSDcWSJUv4h7q5uTlVKYW3tzcGDx4MU1NTGBoa4smTJ+jcubOoubDoeWDZNzFu3DiMHTsWwcHB2LZtG/788080bdqUagx1dXUkJSVJNfCL9c+LiopCQkICunfvjiVLlmDu3LnU9xar+bDYRBoYGGDQoEF4+vQpVq9ezfsfi0FVVRWrV6/GgQMHcPXqVZSUlKCoqIh6HFmCPB8+fEBeXh6Ki4ulnr8NGjSg9jPlaN26NbKzszF8+HAMHDgQ3377reAyYVZWAefPn8cff/yBFy9eSL0jVFVVsWrVKqqxbGxssGnTJgwYMACEEKxfvx6GhoaiNn5btmzB0aNH+bJaR0dH2NraQkFBQfBCj2VQr3ygcdy4cYIDjerq6jAwMMCyZcuYZApbtmzJV+8AQGJiIry9vbF27VqMHDlS8EZUXV0d6enpUu9zVVVVPHr0CFOmTMH+/furPJelVQXLNYqjoyN69uzJL7KPHTuGbt26CT7fwcEBP//8M/bs2YN27drx1hdOTk7U9wMHC0GeqsqWhbJjxw4AbDZJQ4cO5S2RZEVs4JZDIpFg6NChmD17NpP5ODk5YdmyZZg9ezbCwsKwd+9ewevTAwcOYPz48VX6GIsR8LKyssLYsWMxbdo0AKWBfCsrK6oxWKx1TE1N0atXL6xfvx4vX77EunXr8Ntvv1GNAQBt2rThN7OyEBwcjMWLF1cQqKIVPeKQu9LcJk2aoGPHjpg+fTr69u2L9u3bixrn8OHDmDdvnkz9lEBp/8eNGzf4Mql3797B2NiYaoEpxpC+KmQtMcnPz8e5c+ego6ODzp074++//8adO3dE9Y717NkTMTExUuWitGULjx49wq+//lrB4Jk2msaV2XFR5Pfv38PQ0JC6xHfAgAE4ceIEfvrpJ2RkZEBNTQ03b94U3NdZFRKJBO/evUODBg1kGoeWqprdOWg99O7evVshWinEN7Y8nDpk2WvH0NCQ77sSOsapU6f4JvmrV69i5syZosq6WaiWspqPk5MTXr58iadPnyI+Ph4SiQSWlpZUapolJSW4ffs21NXV0ahRI2RmZuLly5eiRIZevXqFgwcPwtDQEGZmZkhNTUV4eDj17517Xujq6uLWrVtQVFSk7qHllKhZc+XKFeTk5MDKykpQH1hVRvC0AkzlFxMKCgpo2rQpTE1Nqds/OMP7sjx69AhdunShGkcikUBLSwsPHjygOq88LH7fHN27d0d0dDQfaKR9D798+RJz5sxBZmYmpk+fLhWUoX0GamtrV+iR5Y7R9JxW9rzjjmlpaX00gMDq+mMFd+1xwnzlEdq+MXv2bOTl5WH9+vVQVVXlx543bx7q1asHX19f6rl16tRJZkEeADILRQKl91ZISEiF9Y6YTRIL3r9/j927d1fI1tEEzLk1MgtkUZLevn07pk2bxqTXmaOkpATbt2/nM48DBw6Ei4sLlSgTi7UOUNoi2LdvXzRp0gS3bt2i6kvnNuf37t1jorLdqVMnHDt2rMrnEC1ylxHNyMjAvXv3cPXqVfz8889ISkqChobGR6ODlbFy5UrcvHmzQj8l7UZ0zJgxmDZtGrKzs7Fz507s2bMHU6ZMoRpDSUkJKSkp+PDhg8zN7ra2thg4cCD/EMvKyhL8kJdIJNDX15daYLRo0UK0/Uq9evXw4cMH6OnpYf78+WjRogV1dnX06NGYPn069c1dHs5WpKxCo5h+4JMnT+Lrr7/Ghg0b4O/vj5ycHKo+wbI4ODhg27ZtUFJSgqGhIXJzc+Hh4cH3ZVbH/PnzsWjRItStWxdWVlZISEjAhg0bePU1IVy5cgX9+vXD6dOnK3yPtgRn2bJlCA8PR2JiIqytrXH27FmYmpqK2ohySs0tWrRASEgIWrZsWeUipiq2bduG4cOHIzg4GLGxsfjpp59w5swZ6rkAbFRLWc1n9+7d/CayXr16yMzMpI5cKyoqolmzZkhMTKRuayhP8+bNpV5Ubdu2FfU7b9SoEfLy8mBubo5x48ZBTU2N31gIxc3NTXQ5Y3kWL14Mc3NzmJiYUGeWZDGCL0tlKqkpKSnw8fHB0qVLYWdnJ3isgoICzJ49Gy9fvsS5c+eQmJiI6Oho6o2okpISNDQ0kJqaKqqfk4PF75uDECL1flBSUqKqEmjVqhWGDBmCn3/+GadPn5bqj6LdiGppacHV1ZX/3XAKpoWFhVQK9Hl5eVKfcWpqKn89VLdOYHX9lUWWskgHBwcEBwfDwMCgUnsIoaWVwcHBePTokdQYDRo0wNatW6GpqSlqI9qsWTOZN6HlhSLd3d1FJTZsbGzw9ddfi1KeHzNmDIKCgqpc/IsJPkyYMAGampo4f/48vL294e/vT/1Z9enTB25ubhg7dqzU/S2mR1QWJWkuaylmw1kVioqKcHV1haurq+gxWKx19u/fjxUrVmDfvn1ISEiAtbU19u7dK1gHhasiYqWy3aZNG2hrazPZhAJymBHNzc1FZGQkrly5goiICGRkZKB3797VNsmWp7zgTUlJCXR1dUVlSy5evCjl5Thw4EDqMRwdHXH//n0MHTpU6maliURs374dS5Yswddffw1FRUVRfk3Dhg3D77//LtMCgyMlJQVqamooKirChg0bkJOTgxkzZqBTp06Cx6D1TfsYcXFxfCmZmZmZaIVGVnDRcX9/f15wxsDAQPALgzv/+PHjCA4Oxvr162Fubl5jqrA6OjqIj49Hjx49EB8fj9evX2P8+PHU9gdA6YLDzMwMz58/h7u7O3Jzc7FkyRLqkqPo6Gh+AxISEkJd8lIZnGqpmBIWVvOR1VaEpQ8fi9I24H+ewSUlJXyQZ/z48VRiVx4eHhXKGRs0aMDPhyZguXfvXkRERCA6Ohqqqqq8ON6wYcOofi4WmZLyZGVlYcCAAVRiRd9//z2cnJzg4+OD+Ph4FBcXo0ePHqLeeebm5rh16xaMjIyk3lc0109lv+9x48aJ6k9av349/vzzT4wYMQKEEJw8eRKTJk0S1DZx7949uLq6omXLltiwYYNMvtdA6YZ/y5Yt/LumT58+mDFjBr7++mvk5+cLFpE5c+YMpk+fjo4dO4IQgqdPn2LLli2wtLTEzp07BbeEsLr+TE1N+bLI06dP82WRy5cvFzWeGLp06VJle8/HvvcxWAjysBCKBMR50HP8/fffaNGiBVOHAK6CjZtXUVERzMzMcP36dcFjsBQHYqEkPWvWrArHuGAl7bOd8xMtD82am8Vap7xY1o0bNzB16lRRit8cJSUlyMvLE1Wld/PmTSxevBgWFhYy+xcDcrgR7d69O0xNTWFqagpzc3Nqs2kOFpLdQOnL9Ouvv4aSkhIePnyIhw8f4vvvv6f23mRRLtC5c2dER0ejSZMmVP92WVgsMFiydOlSqKmpYcSIETIpsU6ePBnu7u7Q09OTGnvp0qWCzueMfss2pgPiDX+B0sj57du34eDgADc3N1hYWFCVpnHlXi4uLhg1ahSsrKxEl7axUGLlSnAMDAwQFhYGVVVVdO3aVeYSPlrKi2okJiaiRYsWvFIjzbVcnUKqkMUKy/kAbGxFWEr8syptW7BgQYXnb2XHPoYs5YxV8erVKwQFBeHXX3/F27dvqTQAWFhqVYXQNofi4mIoKyvzn0PZ88Tak8hqzSWRSDBgwACmwiFiA41du3aFn5+fqPaTqigoKEBqaio0NDRkGqewsJB/fmpoaFBl9AG2158sZZEc/fv3ryCgUtmxqhg+fDhGjhxZoeLiwIEDCAoKErVOYWF9wSqxsWDBAvTv35/ptSgL3Dvd3NwcW7ZsQfPmzWFkZCSz93RNMnXqVDx48EBKaK1Dhw7IzMyEuro6VVY9MzOT//P79+9x+PBhZGVlUQVn0tPTmQTIgdLWOk58TkyFpaxVehyDBg3CN998UyGzLzYbLXeluax6G9atWyfVTzl16lTqfkqgdOMWERGBt2/fwsrKCj179kRgYCB1vyf3C+LKDMRIb3fs2FGUAmJZVqxYIdP5ANsSES7TvW7dOv6YGCXW8+fPIyYmBnPnzuVfYqdOnRK8EWVl9FuWadOmoX379tDV1YW5uTlSUlKook8//PADNDU1UbduXWzduhXp6enUCxUOFkqsPXv2RHZ2NqZMmQIDAwN88803MDY2pprH2rVrMX/+fF6mvzxChKpYiGpwcCXLb968QVRUFPr16wegVFTCxMRE0EaU5XyA0qABZysSFhbG24rQoK6ujqKiIiYbURalbUBpZUn5TefZs2epNqKylDOWx8XFBYmJiWjWrBnMzMxw5MgR6nIyHx8fJi0g5QkLCxNsWWZkZIS4uDjUr18fmZmZ/H11/fp10cIUFhYWMmXalJSUoKioiJycHCbiGMnJydDS0oK+vj7CwsIQERGBDh06CFLwvX37NpP7gOPUqVPw8vLChw8f8PTpU9y+fRve3t6CN0lV9e0nJycDoMvUsbz+ZCmLfP/+PfLz85GRkYG3b9/yZdO5ubl4+fKl4Dls3rwZI0eOxJ49e3hBppiYGBQUFOD48ePUPxMguyAPULlQpLW1NfU4vXv3xogRI1BSUgIVFRVRgW4uaF4WLuP322+/USnzT506FW/fvsWKFSswdOhQ5OXlUa8Rc3JypHwyLSws4O3tTXXfV6XYzEETgEhISEBkZCRfCeTq6gozMzNcu3YNOjo6gscBUKF6w9PTEwYGBlQb0T59+qB9+/YYO3YsRo4cSW1FCZRWWk2ePJl//8XHx2P79u3YsmUL1TiJiYlo0KAB/P398f333/NVerQb0bS0NMFe0kKQu40oC6ljDlklu4HSjFi9evWwe/duuLq6Yv78+VJZN6HcvXsXEyZM4GvDmzRpgn379vHKe0JYvXo1TExM0KtXL9Gy3RYWFkhJSUFSUhIGDBiA/Px8avsDPz8/AGz6VFgpsaqpqSEsLAzjx4/HX3/9BT8/P1F2FcnJyWjdujXq1KmD8PBwJCQkwNHRUbBlQVlmzZolVSbSrl07qgzBL7/8gvnz56Nhw4ZQUlJCvXr1cPLkSep5AGyUWLmH3vTp02FlZYXc3Fxq8RtuQ8PJ9IuBhVIkB7dIGTRoEJ/JBErLoCZNmvTZ5wPIZivCwVLiv2fPnhg7dqzo0ratW7diy5YtePLkidT18s8//1D7XP72228wNTWtUM747t07amn5zMxMSCQSNGrUCI0bN0aTJk2ofSBltdSqLJiXlZWFli1bYt++fYLG4O7j9evXY+jQoUhOTkafPn2Qnp4uWlGYRU8cFzEfOHCgVPWNmGvQ1tYWMTExePz4MaZPn46hQ4fCwcFBUA82y00oUFrddOPGDVhaWgIozTrTvMdY9u2zsHTj8PPzQ35+PjZu3IjFixcjNDRUcEvU9u3b4evri7S0NBgYGPDXZIMGDeDm5iZ4Dq1atcJff/2F0NBQvrrB2toa/fv3p/55WAQ9OVglNubMmYPo6GiZRF48PT3RunVrODg4gBCCQ4cOITk5Gfr6+nB2dkZ4eLjgsZycnKCkpAQLCwvRWVBnZ2doa2vzFnH79++Hk5OTID9mDpbB3Ldv3yIvL4/fCL979w5ZWVlQUlKifhaUbY0oKSlBTEwMtebCo0ePcOPGDRw6dAg+Pj7o1q0b7OzsqLQ+PD09cf78eb6cV1dXl9/401BUVISioiKcOHECbm5uUFFREXUdWltb48KFC8wy+3JXmjto0CCMHTsWv/76q5TUsdCoeWXRIkB8iWWPHj2wZcsWzJ49m5dsL1+mIQQTExP4+Pjw9fTh4eFYuHAhlRqrkZERTE1NK6TDaRZgO3fuxI4dO5CVlYXk5GQkJSVh+vTp1H5EwP96gBQVFfHo0SM8ePCAumy5qKgIW7du5W8qS0tLTJs2jbr0uWw52tKlS3Hp0iWkpaVRP1z19PQQExODZ8+ewdraGsOGDcO9e/dEic68fv0aCxcuRFpaGs6ePcuLh0yePFnQ+fn5+Vi/fj1SU1OxY8cO3hrkhx9+oJ4LCyVWoLSU9dq1a1BQUICpqamolzErWPjYcnTt2hX379/n/15SUgItLS2pY59rPiNGjMDevXvh6+uL0NBQfPvttygqKqK6BqtaQIrxAZO1tC0nJwdv377FTz/9hF9++YU/rqqqSl2CD8hezlie+/fv4/z589iwYQMkEglevHgh+FxZW0DK93spKCjgu+++oxL1ad26Nd+bU1JSgsLCQhBCUKdOHSgpKYnq22HRE8fyGuRU69euXYu6devC3d29xozle/fujevXr0v9+7L0/skCqxYkVvz+++/UlmmfitOnT8PGxobpdSgr5ubmCA8PF22lBaDS9hyuBJ+2dadt27a8RUm/fv1EbUoqK/8X2xJw+vRpDBkyRKbPZ/fu3Vi5ciUfRLt69SoWLlwIe3t7LF26VKr6rjrK9r8qKyujQ4cOmDt3ruiS/IyMDMyZMwf+/v5UCSBO1b/sM0dMm9bGjRuxZs0a6OrqIiQkBKmpqRg/fjzvzywUVVVVvHv3DnXq1BGd2S+L3GVEMzMzMXnyZPj5+cHCwgIWFhYwNDQUfD7rEktfX1+sXr0aI0aMgJaWFp48eVJpc3Z1vHv3Tuo8zhychqKioio9koSyefNm3LhxA7169QJQ2nf65s0bUWOVLVseNGgQDA0NqcuWXV1dUVRUhBkzZgAojaa5urpi165dVHMp2/i9dOlSGBgYYMOGDVRjAKUqacrKyjh+/Djc3d35BY8YJk2axIuHAKViC2PHjhW8EXVycoKBgQEfrGjVqhVGjx4taiNaXok1KyuLumRpxowZePz4Mb/o2b59Oy5duoTNmzcLHoNlCQ4LH1uO/v37Vyi9GjBgANUYrObDlaAtXboUffv25W1FaGC5yJK1tK1hw4Zo2LAhAgICIJFI8Pr1axQXFyMvLw95eXnUwmmxsbG8/QH3Ihaj4hscHIyIiAhcvXoV2dnZ6NevH5VnNSEEs2bNws2bN/neRdpMCQsrGolEgry8vAoVDvn5+aLHZJFpmzhxIj58+MDfAxoaGtQBRg4VFRUEBARg3759fCaR1sv28uXLMDExQd26dUXNgUNLSwsHDx6ERCJBUlISNm7cCBMTE+pxCgsLcfTo0QpWHkJV2llcfwCqFU2heSYrKioiOzubryB6+/YtAgIC+Pf758TGxgYAm2chK8E2dXV1WFpa4vvvvxct8lKvXj0EBQXx1QlHjhzhg3G0G8kHDx4gODgYmzdvhrOzM2xsbGBnZwdTU1PBY9StWxfXrl3jz4mMjBR9jwUGBsLT0xO2trZwdnaGpqYm1fklJSXo2rUroqKieEuZVatWoWXLlgBAtQkFStdN5Uudaav4cnNzcfz4cT5zPWLECGq7mzZt2iAqKgoKCgooKiqCn5+fqHYZWav0OFjvs+QuI8pFGwcPHoxZs2ahZcuWGDVqFN8/8aUyYsQI6Ovr8+bRBw4cQGxsLFXfw8KFC9G+fXvY2NiIFvYpH1kpLi6Gvr6+qGguF6X+/fffUVBQwJct00TCKovqiBXkYUGvXr3g6ekJHx8fnD59Gh06dKjUN04IsoqHcD58skbBgNKXg56eHurXr48DBw4gLi4OHh4eVAthTU1N3L9/n3/ZickaciIox44dw6tXr/jylICAADRr1owqeMDCx7Ysx44d4yOD5ubm1As6FvNh5eEoixVDeVh4zQHApk2bsHTpUjRr1kzKPoPm2TNhwgQkJydDT09PSg1YTLmnm5sbzMzMYGZmxi9UaBFTHcMa7jnMksoybTo6Oli7dq3gMcLDwzFx4kS0b98ehBA8f/4cf/75J5X6M0diYiK2bdsGY2Nj2Nvb4+nTpwgKCsKCBQsEjzFx4kRER0ejcePGvEKyqakpdc9Wfn4+fHx8cOHCBQDA4MGDsWjRIurMvJWVFRo2bAgDAwMpa5q5c+cKHoPF9de0aVO0adMG9vb26NWrV4WABk3rQWXvt5rKXLMMerISbGMhWvnkyRN4eHggOjoaCgoK6N27NzZs2IBWrVohNjaWahNZlrdv38LDw4M6WxcfHw9HR0fk5OQAAL799lv8+eefojyrgdKNW0BAAPbu3QsFBQU4OTnB3t6e95WtDpbXW2XPVloBrw4dOmD48OEYM2YMtaYGR0ZGBjw8PHDp0iUQQjBo0CD4+flRK5BnZmZi2bJlUlVt3t7e1ONUVRYs5tkOyGFGdNGiRcjJycFvv/3GSx2LyWyxom/fvpU+zGh7Vvfs2YMlS5bw/R9mZmbUC7mAgAAApb2iHLTCPhYWFli1ahUKCgpw8eJFbNmyhY8c0kIIQXR0NPz9/bF7924AoO43VVJSQnJyMjp27Aig9CFL4ydqamqKa9euVSjJFhux3Lt3L7Zt24aff/4ZHTp0wNOnT/ngAS2yiod89dVXKCgo4M9PTk4W3e/k6uqK+Ph4xMfH47fffoOLiwscHR2rVMesjE6dOiE1NZXfvD5//pzKqgf436Jm7ty5iImJ4Y/b2NhQ942y8LEty8iRI6k9BVnPh5WHo5OTE2/FEBYWxlsxiIGF1xxQWl3y8OFDUfYdHDExMUhMTGTiX7Zp0yaZx9DX18fNmzepqnZY8yliyevWrePL8AFxmba5c+fiwoULfBnbo0ePYG9vL8quq1u3bnywgVM2ptmEAv8rFU5LS8ORI0cwc+ZMpKWlUfV8SSQSDBkyBGFhYXyli1hevHiBc+fOyTQGi+vv1atXuHjxIgICAnDw4EEMGTIE9vb2VPoVHBKJREp5XiKRyORXKAss+w5ZCbbJKlopkUiwZcuWSvuLAYjahF65cgWBgYE4d+4cevbsyfd6Voefnx88PDyQl5eH+Ph4fq0lxg6kLA0aNMCoUaNQUFAAX19fHD9+HOvWrcOsWbMElX33798fR48exciRI0W/Jx48eIB79+4hJydHqtc1NzdXKhgrhCdPnkBBQUGmCpUmTZpQC6RWhp2dHczNzXH06FEAgL+/P8aOHYtLly5RjVM2s/z+/XveTUGMlg8AgNTyUWJiYviva9eukdmzZxMvL6/PPg+JREIOHTrEZJwdO3aQUaNGEVtbW7Jjxw5SUlIiaqzw8HBiY2NDfvnlF0IIIcnJycTd3Z1qjEuXLpE2bdoQCwsLYm5uTtq1a0dCQ0NFzUfeiI2NJSYmJqRBgwbExMSEdO7cmcTHxws+/8KFC8Tc3Jw0adKEODg4kHbt2pGwsDBRc+nRowchhJBly5aRXbt2SR2rjh9++IHY2NgQc3NzUrduXWJhYUEsLS35P4tBU1OTJCcn839/8uQJ0dTUpBrj2bNnpKCggOTk5JClS5eS2bNnk6SkJKoxvvnmG6Kqqlrhizv+uedDCCFmZmbkm2++If369SM2Njb8Fw36+vqEEEK0tbUrHKNFT0+PEEKIjo4OIYSQDx8+kF69elGPY2lpSYqKikTNgWPUqFEkLS1NpjE4oqOjSc+ePUn9+vWJiooKUVRUpP6da2hoECUlJaKurk50dHSItrY2/zl9LjIzMz/JuM+ePSMXL14khBDy7t07kpubS3V+ZZ+D2M/GwsKC5OTkkMzMTNK+fXtiZGREZs+eTTXG/v37ydSpU4mxsTGxsbEha9asIVFRUdRz6devH8nOzqY+rzxTpkwhCQkJMo3B+vp7//492bt3L2nSpAn5/fffqc+fN28eGT16NLl06RK5dOkSGT16NJkzZ47o+dQ0R48eJUePHiWzZs0iY8aMIQcPHuSPHT16lHq8O3fuED09PdK2bVvStm1boq+vT+7evUs1hphnb1W0a9eODB8+nBw8eJDk5eVRnaurq0sIEb6OEMKJEyfI8OHDiba2Nlm7di15/fo1IaT0+dOuXTtBY3zzzTdEQUGBqKioiH6XnzhxgkyaNIk0btyYTJo0if9yd3cnkZGRVGNFRUWRrl27kjZt2hBCCLl9+zZxdXUVdK67u/tHv2jR0tKqcKzsGkEsqampZOTIkaLPl5vS3KqUzTjElF59KjjvJSGwLA/hSv/+bRQWFvKqoBoaGtRZPxbljNWp2IkVoiguLsbDhw9BCBHVI5WZmYnr16+DEILevXuL9pC1sLCAlZUV9u7di6tXr0JNTU2wD1p1WVMxqrHnzp3D1KlToa6uDkIIUlJSsH37dgwePJhqnPT0dABg5tMlKyzmI6uHI1Aqjnbt2jWMGjUK/fr1Q6tWrfDjjz9Sq+8C7LzmJk+ejIcPH2LIkCGi+6P69u2L27dvw8jISGoMMf6CPXv2rLSnt2zFSXWwNJeXJ1iI2jk7O0NRUZEvv+dK/mgrgYD/ldvt2rULz58/x7Jly6gFgpo0aYKOHTti+vTp6Nu3L9q3b089DwAYNmwYbt26JVoNmHvXFBcXIykpCerq6qhTpw6fSaT5mVhdf4WFhQgJCUFAQACePXuGoUOHwtnZGa1ataIap6SkBNu3b+evk4EDB8LFxYWqyokVLN7plQm1cdB6kQJsRCtdXV3x8uVLjB49Wur6E1PNk5ubKzqDaW9vj5iYGKSlpfEVbQBEXccckyZNgrOzc6UlnpcvXxalniwL0dHRostpOXr16oUjR45g6NChfMmw0Havr776Ctra2hgzZgxatmxZofqFtv95zpw5MDIywpgxYwCU9hffuHEDv/76K9U45SGEQEtLC4mJiaLOl5uNaFllsyVLllSopa8JhTMAvN0KUPqQjY2NxaxZswQv6Fgu4n/88Uc0adIEY8eOlXoACekR/dhDuU6dOujYsSN++ukn6OrqVjuWp6cnfH19q9xk0ywKN2/ejHHjxsksbjBs2DD8/vvvossZq3qhc4hdWEZFRVUQo6ARVnn58iUL0Z34AAAgAElEQVRSUlKkzhdTh//q1SscPHgQhoaGMDMzQ2pqKsLDw0WJvLCirPqppqam4AAEIQTLli3Dpk2bUFJSAkIIlJWV4e7uLljogyWfYj6vXr3CjRs3oKCgAENDQzRv3pzq/Js3b6Jr167Izs7G4sWLkZOTgwULFvAiZTTs2rULtra2uHPnDiZNmsR7zU2bNo1qHBb9USw26Rwse4zfvHkjVbIlS1m1PKCnp8eL2nGfB20/YmFhITZv3syX95qZmWHGjBmi2gt0dHRw4cIFTJw4ET4+PjA0NBSlVHvv3j1cvXoV165dQ1JSEjQ0NLB//36qMbZu3Yri4mIoKChAWVmZF2YRukb5FO8aWa4/R0dH3L17F9bW1rCzs4O2tjb1v1+WgoICpKamilYWZcWneqfLAgtNDFlVzAF2iZ9Xr15h8ODBla75aD9fiUSCAQMGiBLPAUrLaTU1Navsl6f1iAZKA8s7d+6ssIaj+axlUbzNzMzE4cOHERgYCGVlZYwdOxajRo0SZScI/E/tltNoKCkp4fcSNK1sZa+fkpIS3L59G+3bt8eBAwdEzUtuNqJlqanm9sro0KEDFBQU+MVlhw4d4O3tLbgWX9Zer/JzKY/QHtGPPZSLi4tx9+5dLF26VNDnHhsbCwMDAyaLQlbiBubm5rh16xaMjIykbiyxvpsskFVYZcGCBQgMDISWlpaUuIuY7A8LWNqlAOI36evXr8fZs2exY8cO/p548uQJXF1dYWVlhdmzZ4uaj1hYz2fXrl1Yvnw5+vXrB0IIrly5Am9vbzg7Owseg7tHyxIcHEyluPz8+XO0adOm0u/RjlUWsf1RrDE3N8elS5fg4uKC5s2bo0WLFvjjjz+oFoWnTp3C3LlzkZaWBjU1NaSkpKBr1668B+KXCktROxYcPnwYK1asgKmpKe9J6+Xlxfc6CSE3NxeRkZG4cuUKIiIikJGRgd69ewv2yiwuLsbChQuxZ88etGvXDoQQpKamwsnJCatWraKudrl+/Tq0tLR4EZbc3Fzcv3+fKljE4vpTVFSUemdyEBE6C6dOnYKXlxc+fPiAp0+f4vbt2/D29q6xdxYrWAm2sRCtZEF113xNJX769++PY8eOUWlpcEydOhU7duyo1NVCQUFBVP+iiYkJzMzMKgiK2draCh5j1KhRmDNnDtzc3HiP+5iYGBw6dIhqLi9evMChQ4ewfv16rFmzRrR2CQvKXj/Kyspo3749tSe4FKKLej8hLGvOa5qyP4ssNdSfA29v78/+b2pra0v1qBYXF5Nu3bpRjxMeHs5/hYWFkeXLl4sahyWampqi+28JIaRLly7k/fv3Ms2hT58+hJCKvZBi+iYMDAxIUlIS0dPTI8XFxWTPnj3kxx9/FDWv8ePHE2NjY+Lq6krc3NyIm5ub4J4HPT09kp6eXuH4mzdv+H7Gzwnr+XTp0oVkZGTwf8/IyCBdunShGqNHjx7kzp07/N8DAgKIkZER1RgaGhrk6dOnFY7v2bOHqKurU41FiGz9USyvY45nz56R/Px8mXp6u3fvTjIyMvjfc2hoKHF2dhY1H3nCy8uL+Pj4EA0NDXLhwgUyfPhwsnDhQkHncn2KVX3VFDo6OsTV1ZX4+/uT58+fU5/v6elJJk+eLNUrm5OTQ6ZMmUI8PDyox9PT05N6P0gkEuq1j7xdf/r6+iQ7O1vquceiB00MLPv/R40aRRYtWkTU1dXJH3/8QQYOHEhmzZpFPaesrCzi7u5OevToQfT19YmHhwfJysoSdO6aNWsIIYR/V8raL0gIkalHefTo0YSQive7LH3KQ4cOJW3atCHOzs7UP9uNGzdE/Zsfg+uDlYX09HTi4OBA1NTUSNOmTcm4ceOk3u9CiI2NJfPmzSO6urrE2dmZ3Lt3T/R84uPjycmTJ2XqdWaN3KnmyiOylFeSMglnMdYJZcnPz8f69euRmpqKHTt2ICkpCQ8fPhSdmShPVaVzVcFli8tD83NyZspcmd/27dupPROB0izsrVu3cPDgQRw+fBgdOnTA9OnTqcdhiba2Nl69eoUWLVqIOl9dXR1FRUWilXIB8GVxrHyfOnXqBIlEAiUlJTg5OaFHjx5UPXUcsqifFhUVVdor27RpU2pvQRawns93330nJVWvqqpKrTR75MgRjBo1CgcPHkRERAT27dvH200IZf369Rg0aBBCQkLQuXNnAKWK3QcPHqRSW+aYOnUq1q9fL9UfNWXKFEH9UayvY+B/pWN169alKg8ui4qKCu+xWVJSgr59+8LT05PZHGuKX375Bbt374aOjg527NiBIUOGwMXFRdC5wcHBzOaxdu1azJ8/v8pSQhrtCC6bm5+fj3r16lHPJTg4GI8ePZKaR4MGDbB161ZoamrC19eXajxSRl0WKM1M0ij4AvJ3/amoqFTIZrFQuBYDy2fF48ePcfjwYZw8eRITJ06Eg4MDlecwx4sXL0TrnWzbtg0mJiYwMDBg9pnOmDEDhYWFmDRpEsaNG0eVifTz8wPA9n6XRbl+6tSpyMvLg52dHRwcHJioHP/www84c+YMrK2tRY8hi+Ktt7c3QkJC0LVrV9jZ2WH16tVQVha/bXN2dkZCQkKFKjuhn3lVLX5Ehr5gQI7sW8rab+Tn5/MN1ESkDQcrqiqvFLoRLftLk/Xh4eTkBAMDA37h1qpVK4wePZrZRpSWssJJ79+/x+HDh6V6aoWwZs0a7NixA1u3bgXwP3EDoTx69AgBAQEICAjg+2cJIaL7DAB2PS4ZGRno1q2baGGVevXqQU9PD/3795c6X8yLLDk5Ga1bt0adOnUQHh6OhIQEODo6UvUasLRLkWWT/tVXX4n63qeC1XzWr18PoHSz36tXLwwbNowvL6f1ZFNXV8ehQ4cwfPhwtG3bFhcuXKA2Gbe2tkadOnXw/fff48SJE9i1axdu3LiBq1evUnsvAsC7d++kyqYsLS3x7t076nEkEglev34ttWinaX9gKUzWqFEj5OXlwdzcHOPGjYOamppU//6XxsmTJ/HixQvMnDkTU6ZMwc6dO5Geno7Y2Fg0atQIo0aNqnaMsr1hr1+/xs2bNwGUil6pqalRzYdbTNJaO1VGdHQ0Jk+ejLy8PKSmpiI+Ph7bt2/Hli1bBJ2voKBQ6XWjpKQk6t2urq6OjRs3wtXVFQCwZcsWqKurU40hb9eflpYWDh48CIlEgqSkJGzcuBEmJiY1Nh9WcGXXjRo1wt27d9G8eXO8efOGehxu4+fk5AQHBweqjd+sWbPg5eWFv//+G2PGjIG9vT169OhBPYeyREREICkpCXv27IGBgQGMjIwwadIkDBo0qNpzuXc3y15bWUqCb926hYcPH+LQoUOwtbWFiooK7O3tYWdnJ1qYzM/PD6tWrcJXX32Fr776imo/snz58iq/p6CggMWLF1c7xsqVK9GhQwfeem/hwoUAxG/8rl+/LlpQCGAbdCiLXPaIyhNdu3aVybdOSUkJ9evXByEEBQUFfCRWzAabE9cQ0/TMcfnyZZiYmFAvSoVCa/ZblqysLLx48YJq0a2oqAgzMzPs3r2b97RUV1cXnX0+ffo05s2bx6THRdYe2qr6OMQ8rPX09BATE4Nnz57B2toaw4YNw71793DmzBnBY6SkpEBNTQ1FRUXYsGEDcnJyMGPGDGovUUA29VPunioPIQTv37//7FlRVvOpriJBSNau/CbrzZs3aNiwIf8Zi4lYRkREYMSIETAxMUFQUBC+/vpr6jEANv1Rv//+O5YtW4ZmzZpJRXRZKI1y0Cys3r17h7p166KkpAT+/v7IycnBuHHjZPJKrUn69OmDQ4cO8b3Benp6CA0NRV5eHpycnKhUc4OCguDl5QVLS0sQQhAREYF169YJ2sx+CmRRrwSA4cOHY+TIkRWC0AcOHEBQUBD1O+LNmzeYNWsWQkNDoaCggP79+8PX15dqsy5v119+fj58fHxw4cIFEEIwePBgLF68WPQzQ17gBNsSEhLg5OSEvLw8LF++XFTV1aNHj7B3714cPnwYRkZGcHJywsCBAwWfn5KSgkOHDuHQoUMoKCiAg4MD7O3t+aoVMUgkEpw4cQKzZs1CgwYNQAjBqlWrPpopK+/dziFL8igpKQk//fQTEhMTpXpxxazn4uPjcejQIQQFBaF58+aIjIykHkMWfvvttwrH3r17h927dyMzM5PXSvgYrAW3Jk+ejLlz56Jbt25U51WGrEFGKT57MfAXBkvfOlkxNjYm+fn5fB/J48ePiaGhIdUYjo6OpHPnzqRXr15k3rx55NSpU4J7FMoTGxvLf928eZNs3bqVdO/enWqMyvzhPD09BZ9//PhxMnbsWNK6dWvi4uJCLl26RNq3b0/7o/DIU48LS7hrZu3atWTjxo2EEFIj/ZQcZXt6y37V8j/evXtHfc6zZ88++kUD10v1zTffEBUVFVKvXj2Z+jLL9kf16NGDzJo1i/rZ07FjR+r+mo9R1iszPz9fsFdmUlISuXbtWoXjERER5PHjx8zm97np2bOn1N9nzpzJ/5nWv7B79+68DyAhpT3TtO+Hsj66lX3RwPVIl33u0cznxYsXxMjIiFhYWJA5c+aQOXPmEHNzc2JoaEhevHhBNRdZ+bdef/8ViouLyZEjR0jLli2JpqYm0dDQENWrFxcXR/T09IiioqKoecTHxxNPT0/SuXNnMmPGDBIbG0sIIeTly5ekbdu2osaUhT59+pBLly4RHR0d8uzZM7JkyRKyePFi6nEkEgm5cOECcXJyIs2aNSPDhw8XNZ+SkhKyf/9+snz5ckJIqV/mX3/9RT1Obm4uWbFiBWnfvj2ZP3++1HPxcxIeHk4aNGhAunTpIlM/b2BgIGnbti1xdHQkEyZMIO3btyeHDx8WPS+5Kc2VNzhrkn/++Uem8koWzJw5E/b29li6dCmsrKzw/PlzjBs3DpGRkfjjjz+oxuKybGlpaThy5AhmzpyJtLQ06t4UAJg7dy7/Z045KygoiGqMnJwcNGjQALt27YKjoyPvDyeU4cOHY/jw4Xj37h1OnjwJX19fvHnzBq6urhgxYoSgEpOysOxxOXbsGBYsWIA3b96AEEIdKYyMjMTSpUt5+xbufDHRQRUVFQQEBODPP//E6dOnAUBwpu5jpYwKCgpUGXkOMXYb/xVkKSFs164dE19dgG2PFQB8++23MvtBt2nTRpSiYmWU98p88eKFYK9MT0/PSnujGzZsCE9PT/4e+9J4+/at1N83bdrE/5nzyBVKSUmJVJSc62WkITo6Gm3atIG9vT169epVwUePhjZt2iAqKgoKCgooKiqCn58fVR9Zq1at8NdffyE0NJRXpbW2tqb2NmTR9yqv19+jR4/w66+/VtDUEKNYKg9w7RJVQeOBDJRWpOzduxchISEYOHAgTp8+DX19faSlpcHY2FhQr15xcTHOnj2LQ4cO4fLly7C0tMTSpUup5sHh7u4OFxcXrFq1SqpKrmXLlli5ciXVWCwsrAoKCtC/f38QQtCuXTssXboUBgYGHy1zLUtERAQCAgJw4sQJ6OjowM7ODhs2bBD9zpgxYwYUFRURGhqKxYsX45tvvsHMmTP5TGB1ZGVlYf369fD398fEiRMRFxcnqq2FFZMnT8b+/fuho6PDVxSJwcfHBzdv3uSf7+np6RgwYIDoapfajWgVzJs3r6anwNOlSxe+N2DgwIEYMGAA9PX14efnV6lIysc4cOAAIiIicOfOHTRp0gRubm6imu4ByNSHyVFcXIy///4bQUFB8PHxET1O/fr14eDgAAcHB7x9+xaHDx/GmjVrqDeiLHtc5s+fj9OnT4tump88eTI2bNhQQTpcDHv37sW2bdvw888/o0OHDnj69Klg+e/K+gIIIXj+/Dm1UNGnKOf5t+Hp6Ynz589j6NChAErL769evSr4fCUlJWhoaDC1jpIF7ueoCiFBPW5BqK6uDktLSwwZMkQqMEi7IARKPYw5r0wA6Ny5s+C+r9evX0NHR6fCcR0dHTx79ox6LvJCr169sHPnTkyZMkXq+Pbt22FkZEQ1lpWVFQYPHgx7e3sAQGBgILXox6tXr3Dx4kUEBATg4MGDGDJkCOzt7aGlpUU1DlAq9uLh4YGXL1+iVatWGDRoEDZv3kw9Tr9+/dCvXz/q8zhY9L3K6/U3evRoTJ8+HS4uLjK/s+QBVsE4X19fmJiYYM6cOZg2bZqojR93H5w5cwZGRkaws7PDjh07ZOoJLts+9PbtWzx//pxPBAhdH7C0sKpTpw5KSkrQuXNnbNq0Ca1atRJUwgqUBpratWsHOzs7LF26VLZS0f/nr7/+QlxcHN+L++233+LDhw+CzvXy8sKxY8cwdepU3Llzp8btyoBSAcXq3sdCYBFkLEttj2g1PH36FC1atOB7HAoKCvD69WvRzc+yUFlvgJ2dHbp06SJ4jCZNmqBjx46YPn06+vbtK9PPkZOTg2XLlvGLZAsLC3h7e1NFn1j4w7GEZY9Lnz59ZOpL4Lz8WFP+hUNDeWViW1tbuLm5MZ/jfxlZDLA55MlXt2nTph/NagnJjrPony2PLF6ZnTt3RlJSUqXf69SpEx4/fkw9H3ngzZs3GD58OOrUqcMbwMfGxqKwsBAnTpxAs2bNqh3j8ePHeP36Nfr06YNjx47xiseNGjXCuHHj0LFjR1FzKywsREBAALy8vLBkyZL/9HNHXq8/WTQi5JmsrCw0btxY6tjTp08r9XavjHnz5iEqKgr3799H9+7d0adPH5iYmMDExKTCuFXRr18/ODg4wNbWlllWzdLSEqdOnUJxcTEMDAygpqaGPn36VJsJLouuri5CQ0MxYMAA3Lp1C2FhYThw4AB2795NPZ+bN2+ia9euyM7OxuLFi5GTk4P58+ejd+/e1Z6bkpLCVDgJKH1HREVFwdDQEHFxcUhPT8egQYME+dwrKiqiTp06UFZWltmfl5W2y4wZM5CdnQ0bGxupQC6tUrGXlxcSEhKkgow6OjpYu3atqHnVbkSroWfPnoiKiuLVLz98+IA+ffoITs1/Km7dusVLMUskEqpz7927h6tXr+LatWtISkqChoYG9u/fTz0HW1tbaGtr8+I5+/fvR3x8PI4dO0Y91r8RDw8PvHr1il/YcQi96X/88UdIJBKMHDlS6nxugUiDLC+cypSJf/3112ob6WsRBwsD7LKRbvL/QjGHDh0SFaWWFYlEwkfzExISZMpqlaWkpAR5eXm8wjot8+fPR6NGjbBv3z78/vvv2LJlC7p16yaoMsPe3h79+vWrkDnctWsXLl68iMDAQFFzkhfKlp9qaWlRZQB/+OEHrF69ukLG7s6dO1i4cCF12WhhYSFCQkIQEBCAZ8+eYejQoXB2dkarVq0Enc9CvZIlLCoE5PX64zJRI0aMkHpnCd1sySt9+vTB2bNn+WfN/fv3MXr0aMFCVxwfPnxATEwMoqKiEB0djejoaDRq1EgmJVNZ4IJwu3btwvPnz/nWKBrxN05EU1dXF7du3YKioiJ14FRe8ff3R2BgIOLi4jBx4kQcOXIEK1euxOjRoz/rPCZOnIjo6Gg0btwYZmZmMDc3h6mpKXVAwsnJqcIxBQUF7Nmzh3pOZYOMZmZmGDFiBPUY/BxqN6IfR09PD7dv35Y6VlM3WWW9Afb29hg2bJjgMXJzcxEZGYkrV64gIiICGRkZ6N27d5UKrR+jss+msmOVwdIfjgVcT3BViOkJlvWmL2t1UfZ8Mf02srxwWCsTs0LWHlx5nU9GRgY8PDxw6dIlEEIwaNAg+Pn5USthls9ejxw5Eu7u7lRjsEbWrJaDgwO2bdsGJSUlGBoaIjc3Fx4eHvDy8qKeS0lJCXbv3i1V/eDi4iKoJ/z169cYMWIEvvrqKxgYGAAotbP68OEDjh8/jubNm1PP59+CoaFhlYFaHR0d3LlzR/BYjo6OuHv3LqytrWFnZwdtbW3q+bBQr2QJiwoBeb3+KssQitU1kCdCQkKwdu1ahISE4OHDh3B0dIS/vz/09PSoxsnJyUF0dDQiIyMRHR2N7Oxs6OjoYO/evZ9o5h9HR0cHFy5cwMSJE+Hj4wNDQ0PqjeiAAQNw4sQJ/PTTT8jIyICamhpu3rwpyB+ag0VwhiXPnz/nlcMfPHiAy5cvgxCC/v37Izk5ucYsEzltl19//VW0tosslK12Kcu1a9fQokUL0dUutRvRahg4cCDc3d35G+XkyZPYuHEjlYy9rFTWGzBs2DBRvQHdu3eHqakpTE1NYW5ujtatW4uel7GxMdatWwdTU1MApeI68+bNQ3R0dLXnrlixAoMHD0ZCQgLvz1QWWfykxMBlkI4dO4ZXr15h/PjxAICAgAA0a9YMGzZs+KzzYY0sL5wTJ07g0KFDiIyMhJWVFezs7ODi4oKnT59+hplXTadOnWTqwWWNPMxHXrPXsma1OLhAl7+/P+Li4vDLL7/AwMBAtJE2J8DTtGlTUeeHhYXxWRHazOG/FZZlo4qKilKl5Rxigzz//PMP/Pz8sHv3bowZMwZz585l0ktGA8sKgdrr7/Nx4sQJrF27Fv/88w+OHj1K1RI1depU3Lt3D6qqqujVqxd69+6N3r1716hwDSBbaxS3KdHT05OyEEpJScGQIUP4AIkQWARnOCIjIytslCo79jE0NTVx7ty5Cq1re/bsgY+PD5KTkwWPxYLy2i6mpqYwMzODsbEx1Tjv37/H7t27ce/ePSlhKaHJEdbVLhy1G9FqSE5Oxrhx45CWlgZCCNq0aYN9+/aJ8k4Uy6foDcjPz+c9TcUSHx8PR0dH5OTkACht5P7zzz8F9R6y6Jn4FHBlJtUd+xissr2vX7/GwoULkZaWhrNnzyIxMZFXVKWFRS8up0wcEBCA0NBQODo6ilImZoWsPbiskXU+LEoI5TF7zSKrxaGlpYXbt2/DwcEBbm5usLCwoK5QIYRg2bJl2LRpEy+woKSkBHd3d3h7e4ueWy2lyGPZaHn1Sg8PjxrfBAD/zr7XqKioCqq55b1XvxTKvsMJIQgNDUXHjh35DYrQd7mVlRUyMjKgra0NExMTGBsbQ1tbW7QivzzAclPCMjijr6+PuLi4ao99jDNnzsDT0xMhISG8P+svv/wCf39/nD17VqYEjhhYabuMHj0ampqaOHjwILy9veHv74+uXbvCz89P0Pksq13KUrsRFQhXviMPyleyIIs1RFVwUekGDRrA19cXnp6egs+Vt56Jrl27IiQkBOrq6gBKBQmsra1x//59wWOcPn0aNjY2VZY7C832fv/993BycoKPjw/i4+NRXFyMHj16iL7ZWcIpEwcGBn7W6gAAfA/ylStXZOrBlbf5sCghlMfsNcus1saNG7FmzRro6uoiJCQEqampGD9+PCIiIgSPsX79epw9exY7duzgSwmfPHkCV1dXWFlZYfbs2YLHqqUi8lY2Wla9cubMmXLxDmdVISBvTJgwAcnJydDT0+NVcxUUFD57qw0ruHd4QUEBv7FWVlbmRWNoKrcIIbh37x6ioqIQFRWFu3fvonHjxjA2Nq5WjO1TkZ6ejp07d1YIHAjJkH2qTYnY4Ex0dDSioqLg6+sr9QzPzc3F8ePHqdvpLl++jGnTpuHEiRPYtWsXbty4gZCQkBoLYLHQduFatLhquKKiIpiZmeH69euCzv9UImm1G1EBhISEVEhlf6mR8169euHIkSMYOnQor/ylra1N3XRfFW3btkVqaqrg/1/eeibOnTuHqVOnQl1dHYQQpKSkYMeOHZ8161dcXAxlZWX+QV9WPVVoDy6HvPXisqCy3lsOsY33svAp5iNrCaG8Za8/Jdz9IpQePXrg4sWLFayvaBQRa6keeSkbZaleyQKWFQLyRteuXZGYmPhFZ/rKUlRUhJ9//hl79uzhrbBSU1Ph5OSEVatWQUVFhXrMFy9eIDIyElFRUQgODkZmZiays7NZT10QJiYmMDMzq2ARZ2trW+25rDclsgZnrly5gvDwcGzbtg3Tp0/nj6uqqsLGxobPbNIQERGBESNGwMTEBEFBQaLcE1jAStvFyMgIN27cgLm5ObZs2YLmzZvDyMhIcMXUp6p2qfURrYbp06cjPz8fYWFhcHFxwZEjR6j91OQNrgmbg6Xfl9C4RvmeCc5jq6bLpaysrJCUlIQHDx4AKO0VKJvdEoKsjfdGRkaIi4tD/fr1kZmZyb/Ur1+/Tm3MzMKzTt7gghRV9YJ8yfNhZYDNyldXHpE1MFhUVFSp/3LTpk1RVFTEZI61lIqtVSa49rmRxd/uU3DgwAHUr18ffn5+UoHAmhZbY4G2tjZevXqFFi1a1PRUmDB//nzk5eXh6dOnUFVVBVC6KZg3bx68vLzg6+sraJyNGzfymVAVFRW+DcnZ2blSP9jPRX5+PtasWSPq3J49e1bqObxr1y6q/lBAOjizZMkSUcEZCwsLWFhYYNKkSbyNi1hldc7vnBCCwsJCXL58GWpqajV2j3K6LqampnBzcxNdGjx16lS8ffsWK1aswNChQ5GXl/fRlqDy+Pr6YsSIEfD396+02kUstRnRauBS2Nx/8/Ly8P3331OVgskTLKwhPobQjKi89kwUFRVh69atvDeqpaUlpk2bRhX5lLXxnsuAxsXFwd3dHXfv3oW2tjbS09Nx5MgRUf6f/0ZY9ILI03zksYRQ3qgqMEjjWfex3wnt9SNvys21/LeQt+uvb9++uH37NoyMjKQCuJ9b9ZQVnTt3xqNHjyqsSyQSCTQ1NavMCJZnzpw5vA6GPG3SFy1aBBMTE1hbW1Ofy7IEn2X7BktldXmDhbYLC1hXu9RuRKuBMz3v3bs3jh07hu+++w5aWlpfrGE5C2sILlpUHkKIVC9Fdchjz4SLiwuKioqkvFGVlJSwa9cuwWPI2njfunVrzJkzB0BpRK+wsBCEENSpUwdKSkr894Qgb7LoLGDdCyIv85G3EkJ5hEVgUElJqVLFcUII3r9/T5UVlQel5Fr+u8jb9VfWv7gsNKqn8kSXLlCk6sIAABXKSURBVF3w6NEj6u99KaiqquLdu3eoU6cOVFRURL1r5KUEn4O1sro8IKu2S3V+8TRryk9BbWluNfzwww/Izs6Gl5cX9PX1oaCgABcXl5qelmiaNGkCf39/mcb4559/mMxFQUEB2traaNSoERo2bIiGDRsiODgYN27cqLGN6M2bN6U2Dv369YOuri7VGEpKSrCysoKVlRXfeG9paSm48V4ikSAvL69CJjU/P59qHkDpA+xj2dkvkQ8fPiAvLw/FxcVS12KDBg1w5MiRL3Y+8lZCKI9wPTr16tVDWloavvvuO/z9999UY0gkEmbzadasmdxsAmr57yFv15+FhQVev37Ni9gYGRl9dnsclnTr1g379u2roPp74MABaGpq1tCs2MFiLScvJfgcRUVFKCoqwokTJ+Dm5gYVFZUar7STFU9PT5w/f55PLOjq6vJVe0Io+3vevn07pk2bxnyOslCbEaWgsLAQ79+/p+7TkwdYWEOwpKqeCRMTE+jo6EBRUfGzzodDX18fhw8f5o15nzx5glGjRlGXe8rSeM+yvJSlLLq8kZKSwveC1DQSiQRjxoyhssOpRTi+vr4wMTHBhQsX4ObmhtDQUMyYMYMPDK5YseKzzkfelJtr+W8hr9dfUFAQvLy8YGlpCUIIIiIisG7dOowaNapG5iMrL1++xMiRI1G3bl2p8tOCggIcP378i1c5Bkp/xpSUFKlKNnNz8xqckWywUFaXN7jKzLLClbS2ZRxlx6ClsLCQWjNFCLUb0Wo4fPgwrKysoKqqipUrVyIuLg6LFy9Gjx49anpqVLCwhmCJvPZMXL58GU5OTlKquXv37qWK+MmqiijLg+Jj/Ns86/r27VtppDM0NLQGZgMYGxsjOjq6Rv7tfzuV+Q4bGxvDxMSEqq2AFfKm3FzLfwt5vf50dXVx8eJFPguanp6OAQMGfPZ2CdaEhobi3r17AEqzpP3796/hGbFhwYIFCAwMRLdu3aTsdr7Elp2PQausLm+w1HaRJdHBnTthwgRq65iPUbsRrQauF+natWtYtGgRvLy8sHz5cvz11181PTXRyGoN8W+nsLAQDx8+BABoaGhQR4BkbbzPyspC48aNKWddNf9Wz7rY2Fj+z+/fv8fRo0ehrKyMtWvX1sh8XF1d8fLlS4wePVqqB7E2O8YOefMdrkopufyxWmr5FMjb9VfeP7KkpAS6urpy4X1dS0U0NDSQkJDwSbJcNcXr16+xcOFCpKWl4ezZs0hMTOR7LL9UWGi7cMiyEdXW1sbChQuxePFirFu3rsL3xa51vtwQwWeCixKFhIRg6tSpGDJkCBYtWlTDsxIHK2uIfzuxsbG8wTPn2Vm+R+RjyNrrx3ITykIWXV4pLxHfp0+fGrVWev/+Pb777jupjKyCgkLtRpQhBQUFyM3NRU5ODnJyctCyZcsatT9wd3ev8FKv7FgttXwK5O36s7KywuDBg2Fvbw8ACAwMFKXIWsvnQV1dHUVFRf+qjeikSZPg5OQEHx8fAKWiUmPHjv2iN6Kyarvo6OjwSZHHjx/zzgtcckSokNO2bdvg7++P7OxsnD59Wup7sqx1ajei1dCqVStMmzYNFy9exIIFC1BYWPhFioqUtYa4c+dOrTVEFUyYMAHJycnQ09OTKlWh2YjKE/9mz7qsrCz+zyUlJYiNjUVOTk6NzYfzE62FPfLmO8wpJaenp0spEubm5jIVQ6qllsqQt+vv8ePHeP36NdatW4djx47h2rVrAErbFcaNG/fZ51OLMOrVqwc9PT30799fajNadq3wpZGRkYExY8Zg9erVAABlZWV+LfelwUrbJTg4mMl8OC/Tnj17Mt3Y125EqyEoKAjnzp3DvHnz0KhRI/z999+VpqTlnd9++w116tTBypUr+UgR8O/YkLAkJiYGiYmJX7zKGseXGDQRioGBAW86raysjA4dOlD5SbLmxYsXcHd3R2RkJADAzMwMfn5+os2na/kfqampKCwsROfOndGqVSu0bt0ajRo1qrH5yJtycy3/LeTt+vP09OQX/iNHjuQzI3fu3IGnp2eF7Ekt8sHQoUOrtXj70qhfvz4yMzP5Ndz169e/SIFRAJXajJXVdhG6EWUt6jhhwgRs3LiRV+61sLDA9OnToaKiImq82h7RakhOTkbr1q1Rp04dhIeHIyEhAY6OjjW6CKrl0zF69Ghs3LhRrgSUavkyGDhwIBwcHDBhwgQApdlof39/XLx4sYZn9u9AHn2H5Um5uZb/FvKk1G1oaMhbtpSnfN9oLfJFQUEBUlNToaGhUdNTYUJsbCxmzZqFu3fvQltbG+np6Thy5AhfjvqlIk/aLi4uLigqKsLEiRMBAPv374eSkhJ27dolarzajWg16OnpISYmBs+ePYO1tTWGDRuGe/fu4cyZMzU9tVoYYmNjAwUFBfzzzz+4ffs2jIyMpEpV/m0qcv8GioqKsHXrVj4qZ2lpiWnTpomOyskKZ6Rd3bFaZOPFixeIjIxEVFQUgoODkZmZiezs7BqZi7wpN9fy30JelLo7d+6MpKSkSr/XqVMnPH78+DPPqBYhnD59GvPmzcOHDx/w9OlT3L59G97e3l/keoez+NLX1wcAPHz4EIQQaGho1NiagAXltV08PDxqXNulMusYsXYyQG1pbrUoKipCWVkZx44dg7u7O9zd3b8465ZaqmfevHk1PYVaKHF1dUVRURFmzPi/9u49pso6juP4B1BMHJiGK2SkZnmZBxHSuHpBhqEunFqZq9Q0NUinruyyzLCy5kYGJKXZUgpleaktpzYdc9MUFMU7M8ELLDGTgRAiqIfTH84ziYtyOJ3nAO/X5obPOXz9HufU7/P7PZ9fvKQ7d+Xi4uJsvivXUo888ojS09OtQR0ZGRmGHC3SFjV27vDMmTMNDStKTEy0fn1vcjPgCEOGDFFsbKzhSd1Dhw7V2rVrNXv27DrXv/vuu3qhcnAeCQkJOnTokEaNGiXpzp+n8+fPG9uUjf78808tXLhQZ86ckb+/v/V4wJ49e9o1ANKR7J3tkpmZqbCwMHXu3LlFddzc3HTu3Dn17dtXknT+/PkWPYfLiuh9BAcHa+HChVq+fLm2bdumPn36yGQy6dSpU0a3Bgeora1VRkYGgQtOyN535VqqsLBQ8+fPV1ZWllxcXBQWFqaUlBQ9/vjjhvTTljjrucMNeeaZZ3To0CGj20A70NB5okacI3rlyhVNnDhR7u7u1sHz8OHDunnzpn755Rc99thjDu0HDyYkJETZ2dl1zi6/e2Rha+VsR3y1hKurqzp16qQOHTrYdBTgf02fPl1ZWVnq3r27hg8frhEjRigiIqLZK6yZmZl67bXX9MQTT8hisaiwsFDr1q1TZGRks+rcxa3b+1i3bp1Wr16tDz74QH369NGFCxesz4Ch7aioqFBqaqouXbqk2NhYRUdHKzU1VYmJiQoICGAQdUL2vivXUr169WqVW5pag3uTQZ2JsyU3o31xlqTuRx99VAcOHNCePXusN+nHjx+v0aNHG9wZmjJo0CBt3LhRZrNZ+fn5SklJUVhYmNFttYizHfHVEvYOm0xLS5MkFRcXa8uWLXrzzTdVXFys27dvN6tOVFSU8vPz9ccff0i6cx5tS44AYkUUkDRhwgR169ZNoaGhyszM1N9//y2LxaLk5GQNGTLE6PbQAHvflbOVvSLW0fr06dOnXnLz0qVLFRERYXRraAdI6kZLVFVVafny5dq1a5ck6dlnn9WSJUv00EMPGdxZ8/33iK+QkBCFhIQY/jylM0lPT9e+fft08uRJeXt7KyIiQsOHD1doaKihfTGI3kd+fr7ef/995eXlqbq62nq9te6jR8PuTfYzm83y8fFRUVFRq/wLuT2pqamx2105W33xxRf1rt0bsV5ZWenwngC0fSR1wxbV1dVavXq1CgoK5O/vr1mzZrX6Z9tjYmJUUlIik8mksLAwhYaGymQytZmj+OzB29tbffv21RtvvKHIyEj17t3b6JYkMYjeV0REhJYtW6ZFixZp27ZtWrdunWpra5tcBUHrExQUpNzc3EZ/DueRk5MjPz8/63NHP/zwg7Zu3apevXopISHB0GACZ4pYx//P2ZKb0b6Q1A1bTJkyRR07dtTw4cO1c+dO9e7dW0lJSUa31WLOeMSXszl9+rT27t2r33//Xfn5+erfv79+/PFHQ3tyNfRXbwVu3LihqKgoWSwW6390t2/fbnRbsLPjx4/Ly8tLXl5e8vT01IkTJ6xfe3l5Gd0e7jF37ly5u7tLkvbu3av33ntP06ZNU9euXTVnzhxDeiotLdWSJUs0ePBg3b59W7m5uVqxYgVDaBsXFxenI0eOKD4+XvHx8Tpy5Iji4uKMbgvtxN2kbrPZLLPZrPT0dJK6cV95eXlKT0/X3LlztWXLFuuNtNbOxcVFJpNJ48aN09ixYxUeHq5z584pOTnZ6NacQkVFhYqKilRYWKiLFy+qvLxcrq7NHwOjoqIe6NqDat1r8Q7QqVMn1dbW6qmnntKqVavk6+vLVrs2yGw2G90CHpDZbLauev7000+aM2eOJk+erMmTJxvyPK+9I9bReuTk5NRJaR49erQCAgIM7Ajtyffff6/58+dr0aJF1qRuZwkwgvO6d8dGa9+Se5ezHvHlTCIiIqw/5s2b1+xnyaurq1VVVaWSkhKVlZXp7obaiooKXbp0yea+2Jp7Hzk5ORo4cKCuXbumDz/8UOXl5XrnnXcUEhJidGtAu2QymXTs2DF16NBBAwYM0LfffqsRI0ZYX3P00Ur2jlhH6xEUFKTNmzfXSW5+/vnn2dYPwGm5ublZz521WCy6ceOGPDw8WvW/Wa3piC+jVVVVycPDo9nfl5ycrKSkJBUXF8vX19c6iHp5eWn27NmaN2+eTf0wiAJoVZYvX64dO3bI29tbRUVFys3NlYuLiwoKCjR9+nRrgiTwf3OW5Ga0LyR1A2iurKwszZo1S5WVlSoqKtLx48e1Zs0aff31182q89VXX2n+/Pl264tBtBGxsbFNvs55gYBxsrOzdfnyZY0ZM8Z6Z/fs2bOqrKxUUFCQwd2hPXGG5Ga0LyR1A2iu4OBgbdmyRbGxsTp69Kgk23aRpaam6uWXX9bDDz8sSSorK1NGRobi4+Nt6otBtBE9evSQn5+fpk6dquDgYP33t2nkyJEGdQYAMJIzJzejfSGpG8CDCA4O1sGDBxUYGGgdRAMCAurkHDyIhpK5763ZXKTmNuKvv/7SZ599plOnTmnBggXavXu3vL29NXLkSIZQAGjHnDG5Ge0LSd0AmsPPz08HDhyQi4uLbt26pcTERA0cOLDZdcxmc53FObPZrJs3b9rcF4NoI9zc3BQTE6O0tDRlZ2frySef1KhRo7Rq1SqjWwMAGKix5OZPPvlEBQUFBneHtm7x4sUaNmyYPD09dfLkSSUkJKhbt25GtwXAia1evVqpqam6dOmSfH19dezYMaWmpja7TkxMjKZMmaLMzExlZmZq6tSpiomJsbkvtuY2oaamRtu3b1dGRoYuXryo2NhYzZw5U76+vka3BgAwiLMlN6N9IakbgFFqa2u1Zs0aZWZmSpKio6P1+uuvy83NzaZ6DKKNmDZtmk6dOqVx48bppZdekslkMrolAIATILkZANAaOHvKNoNoI1xdXa1pnNxxBADci+RmAICzs1fK9osvvqhNmzbJ39+/zlx014kTJ2zqj0EUAAAAANqwlqRsX758WT4+PiosLGzw9V69etnUUwebvgsAAAAA4NRKS0u1cuVKbdiwQdOnT1dubm6zA858fHxkNps1Y8YM7dmzx269kZoLAAAAAG2MPVO23dzc5OrqqvLycrv1x9ZcAAAAAGhj7J2yPWHCBB09elTR0dHWfARJSklJsak/tuYCAAAAQBtTW1tr13qTJk3SpEmT6lxrKLzoQTGIAgAAAACadO3aNS1YsKDOteTkZJvr8YwoAAAAAKBJaWlp9a6tX7/e5nqsiAIAAAAAGpSRkaGNGzfqwoULio2NtV6vqKhQ9+7dba7LIAoAAAAAaFBYWJh8fHxUUlKit956y3rd09NTgwcPtrkuqbkAAAAAgCZdv35dnTt3lqurq86ePaszZ85o7Nix6tixo031GEQBAAAAAE16+umntW/fPpWVlSk8PFzDhg2Tu7u7NmzYYFM9wooAAAAAAE2yWCzy8PDQzz//rPj4eG3evFmnT5+2uR6DKAAAAACgSRaLRVlZWdqwYYPGjx8vSTKbzTbXYxAFAAAAADQpKSlJn3/+uSZOnKhBgwbp/PnzioyMtLkez4gCAAAAAByK41sAAAAAAA1auHChkpKS9Nxzz8nFxaXe67/++qtNdRlEAQAAAAANevXVVyVJb7/9tl3rsjUXAAAAAHBfV69elST16NGjxbUIKwIAAAAANCohIUHe3t7q37+/+vXrpx49eujjjz9uUU0GUQAAAABAg1auXKn9+/crJydHpaWlKisr08GDB7V//359+eWXNtdlay4AAAAAoEGBgYHavXu3vL2961y/evWqxowZo6NHj9pUlxVRAAAAAECDbt26VW8Ile48J3rr1i2b6zKIAgAAAAAa5O7ubtNr98PWXAAAAABAg9zc3NSlS5d61y0Wi6qrq21eFWUQBQAAAAA4FFtzAQAAAAAOxSAKAAAAAHAoBlEAAAAAgEMxiAIAYCdJSUmqqqqy2/sAAGirCCsCAMBOevfurcOHDzd43pot7wMAoK1iRRQAABtcv35d48ePV0BAgEwmk5YtW6bi4mJFRkYqMjJSkhQXF6ehQ4dq0KBB+uijjyRJKSkp9d63a9cuhYaGKigoSC+88IIqKysN+1wAADgCK6IAANhg69at+u2337R27VpJUnl5uQICAuqsdJaWlqp79+4ym82KiopSSkqKBg8eXGdFtKSkRJMmTdLOnTvVpUsXrVixQjU1NVq6dKmRHw8AgP8VK6IAANjA399fu3fv1rvvvqt9+/apa9eu9d6zadMmBQUFKTAwUKdPn1ZeXl6992RnZysvL0/h4eEaMmSI0tLSVFhY6IiPAACAYToY3QAAAK1Rv379lJubqx07dmjJkiWKioqq8/qFCxeUmJionJwcdevWTTNmzFB1dXW9OhaLRdHR0crIyHBU6wAAGI4VUQAAbFBcXCwPDw+98sorWrx4sXJzc+Xp6al//vlHklRRUaEuXbqoa9euunLlinbu3Gn93nvfFxISov3796ugoEDSnWdPz5496/gPBACAA7EiCgCADU6ePKnFixfL1dVVHTt21DfffKOsrCzFxMSoZ8+e2rNnjwIDAzVgwAD5+fkpPDzc+r1z5syp877169dr6tSpqqmpkSR9+umn6tevn1EfDQCA/x1hRQAAAAAAh2JrLgAAAADAoRhEAQAAAAAOxSAKAAAAAHAoBlEAAAAAgEMxiAIAAAAAHIpBFAAAAADgUAyiAAAAAACHYhAFAAAAADjUvysySf+WXmw2AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "grouped = Y_pred[Y_pred['predicted'] > Y_pred['max_infection']].groupby('st').count().div(Y_pred.groupby('st').count()).fillna(0).iloc[:,0]\n",
    "\n",
    "\n",
    "#Y_pred.groupby('st').count()\n",
    "\n",
    "#Y_pred.groupby('state')['predicted_above_reported'].median().sort_values()\n",
    "(\n",
    "    Y_pred[['st', 'state']]\n",
    "    .set_index('st')\n",
    "    .drop_duplicates()\n",
    "    .assign(\n",
    "        predicted_above_reported=lambda x:grouped, \n",
    "        count=lambda x:Y_pred.groupby('st').count().iloc[:,0]\n",
    "    )\n",
    "    .sort_values(by='predicted_above_reported')\n",
    "    .set_index('state')\n",
    "    ['predicted_above_reported']\n",
    "    .plot.bar(x='index', figsize=(16,9))\n",
    ")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 370,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interested_county = county_percentages_raw.loc[48039]\n",
    "peaks, heights = find_peaks(interested_county, height=10, prominence=10)\n",
    "len(tuple(peaks))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fips\n",
       "48041    48041 ha\n",
       "48039    48039 ha\n",
       "Name: fips, dtype: object"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H49GGDRtiHQEAAABAktrb3KN0m1Wzi3PMjnJWLBaL5pcl5+Cl1+o7lWazaPG0vLP+rj0KeU5z6aWX6tJLL5UkzZo1Szt27IjFawEAAACkmH3NXlWV5CjdHvO1uUmrLndq0+4mGYYhi8VidpyI2X2sSwumuCY0sTnx/hQBAAAAYBR7j/dowZTEGbZ0qupyp7z+QTV29psdJWKCobDebOrSedPPfnVVorACAAAASBJt3gG5fX5VlydqYc2VNLStOVnsb/ZqIBie8JliCisAAACApLCv2SvpneKXaOaV5cpiSa5JwbvqOyVJS1lhBQAAAJDKhoveggRdYc1Kt2tmYXZSFdbd9Z0qyc3Q1LzMCX2fwgoAAAAgKexr7tEUl0N5WelmR5mw6nLnyZXiZLCrvkvnTc+f8BApCisAAACApJDIA5eGVZfnqr6jT96BoNlRJs3t86u+o2/C24ElCisAAACAJDAQDKnW3ZuwA5eGDec/0JL4q6y7jg2dXz1vxsQGLkkUVgAAAABJ4GCrV6GwkbDnV4cNF9ZkOMe6u6FLdqtFi6a6JvwMCisAAACAhDdc8BJ9hbXc5ZArM037kmSFdcEUpxxptgk/g8IKAAAAIOHtPd6j7HSbphdkmR1lUiwWi6rLcxN+hXUwFNabjd0Tvn91GIUVAAAAQMLb1+zV/HKnrNaJTaONJ9XlTh1o8SocNsyOMmH7W7zqD4YmNXBJorACAAAASHCGYWhfc4+qy3PNjhIR1WVO9QVCOtbRZ3aUCdtdf2LgEiusAAAAAFJZY2e/vP5BLSif+HCfeJIMg5d21XepKCdDFfmZk3oOhRUAAABAQtt7cuBScqywzinNkc1qSejCuru+U+dNz5PFMrkt2hRWAAAAAAlt7/EeWSzSvLLkKKyONJtmFWXHXWH9n+31+vkLR8b8nMfn11FP36TuXx1GYQUAAACQ0PY192hmUbay0u1mR4mY6nKn9jXH19U2j7zWoP945oAaxjhbu7u+S5K0dNrkBi5JFFYAAAAACW5vc0/C37/6XtXlTjV19au7L2h2lJPcPr9CYUP3vFR7xs/tbuiU3WrRuRUUVgAAAAAprLs/qMbOfi1IusI6tL15X0v8bAv2+AKyWqSHaxrU7vWP+rldx7pUXe5UZrpt0u+ksAIAAABIWPtPnPNMtsK6IM4mBfcFBtUXCOnDy6YpGArr1y/Xjfi5wVBYbzR2Tfr+1WEUVgAAAAAJa9/JCcHJVViLczNUmJ0eN4XV4wtIkpZV5uvqReV64NVj6u4/fbvywVaf+gKhSd+/OozCCgAAACBh7Wv2qiA7XaXODLOjRJTFYtGCKU691RQfhbXdN7QFuCgnXZ+7ZLa8/kE9sO3YaZ/bVd8pSRRWAAAAABgauJQ76fs+49HyGQXa39ITF4OXhldYi3IydM5Uly6ZW6xfb61TfyD0rs/tqu9UYXa6phVkRuS9FFYAAAAACWkwFNaBVm/SnV8dtmpWgQxD2nm0w+wocp9YYS3MGVrJ/j9rq+TpDejhmoZ3fe71+i4tnZ4fsb9AoLACAAAASEi17l4FBsNJd3512JJpeUq3W7W9zmN2FHmGC2t2uiRp5cwCLZ+Rr3teqlUwFJYkdfYGVOvu1XkzIjNwSaKwAgAAAEhQwwOJFkxJzsLqSLNp6bQ8bauNhxXWgHIz7HKkvXNVzefXzlZTV7+eeP24pKH7V6XInV+VKKwAAAAAEtTe4z1Kt1k1uzjH7ChRs2pWod4+3q2eAXPPsbp9fhXlvnuw1dp5JZpflqufv3hE4bCh3fVdslktOrfCFbH3UlgBAAAAJKS9zT2qKslRmi15a83qmQUKG9JrRztNzeH2+U9uBx5msVj0uUtn63CbT8/sbdWu+k7NL8tVVro9Yu9N3j9ZAAAAAEltX3NP0m4HHrZ0er7SbVZtM/kcq8cXUFHO6VcHXbuoXNMLsvSzFw7rjYbuiG4HliisAAAAABJQm3dAbl8gaQcuDctMt2nxNJfp51jdPr8Kc9JP+327zaq/u2S23mzsls8/qKXTIzdwSaKwAgAAAEhA+5q9kpS0V9qcatXMQu1pGiqEZhgMhdXZFxxxhVWSblo2VSUnzreywgoAAAAg5e09fmJCcCoU1lkFCoUNvXbMnHOsHb0BSVLRCCuskpRht+kf3z9fa6qKNKMwK6LvprACAAAASDj7mns0xeWQKyvN7ChRt2xGvuxWi7bXmnOO1e0bLqwjr7BK0s3LKvTAp1fJYrFE9N0UVgAAAAAJZ28KDFwalpVu17kVLm0zrbD6JUmFZyis0UJhBQAAAJBQBoIh1bb7kn7g0qlWzSrUm43d6gvE/hyrp3eosI62JTiaKKwAAAAAEsqBFq/CRmqcXx22amaBBsOGdh3rivm73d6hLcGssAIAAADAGF482C5JWjwtsleoxLPllQWyWS3absJ9rO5ev9JtVjkd9pi/m8IKAAAAIGGEwoY27mzQmqoiTcnLNDtOzORk2HXOVHPOsbq9ARXmpEd8oNJ4UFgBAAAAJIwth9rV1NWvj6ycbnaUmFs9s0BvNHRrIBiK6Xs9vf4zTgiOJgorAAAAgITx4I56FWan630LSs2OEnOrZhUoEAprV31s72N1+/wqNGHgkkRhBQAAAJAg2noG9Ny+Nt28rELp9tSrMssrC2S1SNtrO2L6Xo8vwAorAAAAAJzJI681ajBsaN2KaWZHMYXTkaYFU5wxPcdqGIY8vgArrAAAAAAwmvCJYUurZxVoVnGO2XFMs3pmoXY3dMXsHGvPwKACobCKWWEFAAAAgJG9csSj+o6+lBy2dKpVswoVGAzrjYbY3Mfq8fkliRVWAAAAABjNgzvrlZeVpqsWlpkdxVQrKwtksUjb62JzjtXtC0gSZ1gBAAAAYCQen1/PvN2iDy2tkCPNZnYcU7my0jS/LHbnWE+usGZTWAEAAADgNH/c1ahgyNBHVqbmsKX3Wj2rQLvqOxUYDEf9Xe4ThbUoly3BAAAAAPAuhmHooR0NWj4jX3NKc82OExdWzSzUQDCsNxujf47V7QvIYpEKsiisAAAAAPAu2+s6VOvu1foUH7Z0qpUzCyTF5hyr2+dXfla67DZzqiOFFQAAAEDcemhHvXIddl27qNzsKHGjIDtd80pzY3KO1eMLqDDbnNVVicIKAAAAIE519QX01J4WfXDpVGWmp/awpfdaObNArx3rlGEYUX2P2+c3bUKwRGEFAAAAEKce29WkwGBY61ewHfi9Kouy1RcIqasvGNX3eHoDpt3BKlFYAQAAAMQhwzD04I56LZ6WpwVTnGbHiTulzqFVz1bvQFTf4/aywgoAAAAA77KrvlOH2nz6KFfZjKjM6ZAktXRHr7AOBEPy+gdVxAorAAAAALzj2b1tSrNZdN25U8yOEpdKTxTW1p7oFVZPb0CSWGEFAAAAgFO19QyoJNeh7Ay72VHiUsnwluAef9Te4fENPbuQwgoAAAAA72j3+VWUa15RincZdpsKstPVEsUVVveJwsqWYAAAAAA4hdsXULGJRSkRlORmqC2qhZUtwQAAAABwmnaTp9MmgjKXIyYrrFxrAwAAAAAnhMKGOnoprGMpczrU0h3NM6wBZaXblJVu3jliCisAAACAuNLZF1DYkIo5w3pGJU6HPL1+BUPhqDzf7TP/Lw0orAAAAADiyjvDfiisZ1LmdMgwhrZPR4PHFzB1O7BEYQUAAAAQZ9ze4WE/DF06k9KTV9tE5xwrK6wAAAAA8B7tvqECxrU2Z1bqdEiKZmENmP6XBhRWAAAAAHHlnRVWCuuZlLmGCmtLd+QLa7wMvqKwAgAAAIgrbp9f6XarnA7zptMmgoKsdKXZLGqNwhnWrhODrwqzWWEFAAAAgJPafX4V52TIYrGYHSWuWa0WleQ61BqFFVa378Qqt8nbsimsAAAAAOJKu9dv+tnJRFHizFCrN/KF1RMnk5oprAAAAADiytCwH86vjkeZ0xGVM6ztJwsrW4IBAAAA4KR4uE4lUZQ6HWrtifwZVo8vPgZfUVgBAAAAxI1w2FBHb0DFXGkzLqVOh3z+Qfn8gxF9rtvnl91qkdORFtHnni0KKwAAAIC40dkXUChsmL4VNVGUuYaKfaTvYvX4AirMSZfVau7gKworAAAAgLhx8uwkK6zjUuocuos10oXV7fOrMNv8PwMKKwAAAIC44fbGx9nJRBG1wtobiIu/NKCwAgAAAIgb7ji5TiVRDBfWlu7IDl5ye/0qyjZ/WzaFFQAAAEDcGC6sDF0an5wMu3Iy7BFdYTUMQ55ePyusAAAAAHCqdp9f6TarnA672VESRqkzI6KFtTcQ0kAwrEJWWAEAAADgHe1ev4py0mWxmDudNpGUuRxqiWBh9cTRtmwKKwAAAIC44fbFx7CfRFKa61BbT+TOsA5vyy6Mg6uFKKwAAAAA4obb64+Llb1EUupyqLVnQOGwEZHnuX3xM6mZwgoAAAAgbrh9fhXHQVFKJKW5GRoMG+roC0TkefE0qZnCCgAAACAuhMOGPL0BFeWavxU1kZS5hq+2icw5Vs+JFdYChi4BAAAAwJDOvoBCYSMuVvYSyfBdrJGaFOz2+eXKTFO63fy6aH4CAAAAAFB8nZ1MJO8U1sgMXvL4AiqKg4FLEoUVAAAAQJyIp7OTiaQ4N0MWiyJ2tU27z6/COPkzoLACAAAAiAvDhbWYa23OSprNqsLsDLVFqLB64mjwFYUVAAAAQFxo954orHFSlhJJmSsjYiusbl8gLu5glSisAAAAAOJEu8+vdJtVzky72VESTpnTEZEpwYHBsLr7g3GzLZvCCgAAACAuuL1DK3sWi8XsKAmnxOlQm3fyQ5c6eocGX7HCCgAAAACncPv8cbOyl2jKnA519AbkHwxN6jnxNviKwgoAAAAgLrh9fgYuTVDZiatt2iZ5tY2nd/hqIVZYAQAAAOCkdq8/bopSoilxDhX91kkOXnJ7WWEFAAAAgHcJhw15egNxU5QSTZlraIV1spOCPb1DhZV7WAEAAADghK7+oEJhg8I6QaW5Q4W1dZJbgt2+gBxpVmWn2yIRa9IorAAAAABMd3LYD2dYJyQvK03pduvktwT7/CrMzoibSc0UVgAAAACmGz47WcwK64RYLBaVOR0RKKyBuPpLAworAAAAANO1n1hhLc5l6NJElToz1NI9yTOsPr+KsuPnz4DCCgAAAMB07XE2nTYRlU5yhdUwDLX2+FUYR5OaKawAAAAATOf2BZRms8iVmWZ2lIQ1VFj9MgxjQt9/+bBHbp9fKyoLIpxs4iisAAAAAEwXb8N+ElGZ06H+YEg9A4MT+v69W2pVnJuhG5ZMiXCyiaOwAgAAADCd2+dXcRwN+0lEpSfuYm2bwLbgg61evXiwXbefP0MZ9vi40kaisAIAAACIA+1ev4ri6OxkIio9UfhbJlBYf7WlVo40q25dNSPSsSaFwgoAAADAdG6fn4FLk1R2YoX1bCcFt3kHtGn3cd28rEL5cTQhWIpiYR0YGNDKlSu1ePFiLVy4UN/61rckSXV1dVq1apWqqqq0bt06BQKBaEUAAAAAkADCYUOeOLv/MxGVOk9sCT4xcXm8fv/qMQXDYW1YMysasSYlaoU1IyNDmzdv1htvvKHXX39dTz/9tLZt26avfvWruuuuu3T48GHl5+frvvvui1YEAAAAAAmguz+owbDBCuskOdJscmWmndUKa38gpAe2HdMV1aWaWZQdxXQTE7XCarFYlJOTI0kKBoMKBoOyWCzavHmzbr75ZknS7bffrk2bNkUrAgAAAIAE4PYNrQgydGnyys7yLtY/7mpUZ19Qn7ko/lZXpSifYQ2FQlqyZIlKSkr0vve9T7Nnz1ZeXp7sdrskqaKiQk1NTSN+95577tHy5cu1fPlytbe3RzMmAAAAABO1n9jCytClyStxZoy7sIbDhn69tU6LK1xaUZkf5WQTE9XCarPZ9Prrr6uxsVE7duzQ/v37x/3dO+64QzU1NaqpqVFxcXEUUwIAAAAwU/vwCitbgietzOkY95Tg5/a3qdbdqw0XzYrb+29jMiU4Ly9Pa9eu1auvvqquri4NDg5dZNvY2KipU6fGIgIAAACAOOX2DQ1i5Qzr5JW5HGr3+hUKG2N+9t4ttZqal6lrzimLQQN7n/4AACAASURBVLKJiVphbW9vV1dXlySpv79fzz77rKqrq7V27Vo9+uijkqT7779fN954Y7QiAAAAAEgAbp9faTaLXJlpZkdJeCVOh8LGO+eCR/NmY5d21HXokxdWym6L39tO7dF6cHNzs26//XaFQiGFw2Hdcsstuu6667RgwQKtX79e3/jGN7R06VJt2LAhWhEAAAAAJIB2r1+F2RmyWuNzW2oiKTtxtU1rz8DJa25Gcu+WOuVm2LVuxbRYRZuQqBXWc889V7t37z7t92fNmqUdO3ZE67UAAAAAEozb51dRLgOXIqHUObStuqV7QOdWjPyZpq5+PfVWsz51YaVyHfG9qh2/a78AAAAAUoLb5+f8aoScusI6mt++XCdJ+sSFM2OSaTIorAAAAABM5fYGKKwRUpiTIZvVotaekc+wHm7z6aEdDbp2Ubmm5mXGON3Zo7ACAAAAME04bMjT61dxLoU1EmxWi4pzMka82ub5A2364E9fVrrdqi9eXmVCurMXtTOsAAAAADCW7v6ggiGDFdYIKnU53rUl2DAM/WpLnb7z132aX+bUvbcvT4jVVYnCCgAAAMBEw9evFOUwdClSSnMzdNTTK0kaCIb0z4+/pcd2NemaRWX6jw8vVlZ64tTAxEkKAAAAIOm0nyisxaywRkyZy6FttR619Qzosw+8pt31Xbrrirn64uVVslgS6+ogzrACAAAAOKPGzj59+eHX1dkbiPiz3b6hZxZxhjViSp0O9QwM6vqfbNX+Zq9+8bHzdOcVcxKurEqssAIAAAAYw+9ePabHdjWp1z+oX3xsWUSLj9vLCmuklZ642sZuteqPn7tAC6Y4TU40cRRWAAAAAKMKhQ098fpxuTLT9Le3W7VxZ4PWr5wesee3+/yyWy1yZaZF7Jmp7qqFpWrqnKuPrZ6uwgT/iwC2BAMAAAAY1fY6j1p6BvSvNy7UhVWF+v//vFe17b6IPd/t9aswJ11Wa+JtV41XuY403XnFnIQvqxKFFQAAAMAZ/Gn3cWWn23TlgjL954eXKN1u1Zc2vq5gKByR57t9fq60wagorAAAAABGNBAM6ak9zbpqYZky020qczn03Q8t0puN3br7fw+O6xnHu/o1EAyN+nO3L0BhxagorAAAAABG9MKBNnkHBnXj0qknf+/qReVat3yafvbCEW2v9Yz63YFgSN9/er8u+v7z+sRvdmhwlBVZt8+vYiYEYxQUVgAAAAAj2rT7uIpy0nXh7MJ3/f6/XL9AMwqydNfG19XdHzzte9trPbrmR1v0sxeOaPWsAm2r7dAPnjlw2ucMw2BLMM6IwgoAAADgNN39QW3e36brzp0iu+3dtSE7w6671y9Vq9evb2zaI8MwJEnegaC+/vhbWnfPNgXDYT2wYZX+8OnVunXVdP3yxVo9vafltHcEQ4aKctJj9t8LiYVrbQAAAACc5uk9zQqEwvrAKduBT7VkWp7uumKO/uOZg7psfrGcjjR9Y9MetfYM6NNrZurLV85VVvpQ3fiX6xdoT1O3/uGRNzSvLFczi7IlDW0HlsSWYIyKFVYAAAAAp/nT68dVWZilxRWuUT/zuUurtKIyX//wyJvacH+NnI40Pfb5C/WN6xacLKuSlGG36WcfWya7zaLPPfCa+gKDkqR2b0CS2BKMUVFYAQAAALxLS/eAXq316MYlU2WxjH4/qs1q0X+tW6L55bm664q5+vMX1mjJtLwRPzs1L1N3r1+qA61eff3xPSfPr0qssGJ0bAkGAAAA8C5/fuO4DEO6ccmUMT9bkZ+lv3zhonE995K5xfrS5XP1X/97UMtm5CswODQ5mBVWjIbCCgAAAOBdNr3epHMrXJpVnBPxZ3/hsirtbujUv/55r1bNKpDNalFeZlrE34PkwJZgAAAAACcdbvPq7eM9unHJyMOWJstqtejudUtU4szQlkNuFWany2odfdsxUhuFFQAAAMBJm3Yfl9UiXb+4PGrvyMtK189vXaZ0u5XzqzgjtgQDAAAAkCQZhqE/vdGkC6uKVJLriOq7FlW49Kvblkf1HUh8FFYAAAAAkqRd9Z1q6OjXnZfPjcn7Lp5bHJP3IHGxJRgAAACApKHtwBl2q65aWGp2FEAShRUAAACApGAorCffatYVC0qV62BqL+IDhRUAAACA/rqnRR29AX0gStOBgYngDCsAAACQogzD0Ku1Hv38hSPacsitivxMXcK5UsQRCisAAACQYsJhQ8/ua9XPXjiiNxq6VJSToa++f75uXT1d6XY2YSJ+UFgBAACAFBEYDOtPrzfpFy8e0ZH2Xk0vyNK3P3iObjqvQo40m9nxgNNQWAEAAIAU8aWNu/XUWy2qLnfqvz+yVNecUya7jRVVxC8KKwAAAJACjnf16697WrRhzUx949pqWSwWsyMBY+KvUwAAAIAU8EhNowxDuv38SsoqEgaFFQAAAEhy4bChR15r0AWzCzW9MMvsOMC4UVgBAACAJPfKEY8aO/u1bsU0s6MAZ4XCCgAAACS5jTUNcmWm6aqFZWZHAc4KhRUAAABIYl19Af3t7RZ9YMkUrq5BwqGwAgAAAEls0+4mBQbDuoXtwEhAFFYAAAAgSRmGoYd2NuicqU4tnOIyOw5w1iisAAAAQJJ6q6lb+1u8WrdiutlRgAmhsAIAAABJauPOBmXYrbph8RSzowATQmEFAAAAklB/IKQnXj+uaxaVy5WZZnYcYEIorAAAAEAS+uueZnn9g7plOcOWkLgorAAAAEAS2rizQZWFWVo9q8DsKMCEUVgBAACAJHPU3avtdR368PJpslgsZscBJuysCms4HFZPT0+0sgAAAACIgIdrGmS1SDcvqzA7CjApYxbWj370o+rp6VFvb6/OOeccLViwQD/4wQ9ikQ0AAADAWRoMhfXoa41aO69EpU6H2XGASRmzsO7du1dOp1ObNm3S1Vdfrbq6Ov3+97+PRTYAAAAAZ+mFA+1q8/p1ywqGLSHxjVlYg8GggsGgNm3apBtuuEFpaWnsgwcAAADi1MaaBhXlZOiy+SVmRwEmbczC+tnPflaVlZXq7e3VxRdfrGPHjsnpdMYiGwAAAICz0OYd0Ob9bbpp2VSl2ZivisQ35v+Kv/jFL6qpqUlPPfWULBaLZsyYoeeffz4W2QAAAACchcd2NSkUNrh7FUljzMLa2tqqDRs26Oqrr5Y0dKb1/vvvj3owAAAAAONnGIYe3tmgFZX5ml2cY3YcICLGLKyf+MQndNVVV+n48eOSpLlz5+ruu++OejAAAAAA47fzaKdq3b2sriKpjFlY3W63brnlFlmtQx+12+2y2WxRDwYAAABg/DbubFBOhl3XnltudhQgYsYsrNnZ2fJ4PCcnA2/btk0ulyvqwQAAAACMj3cgqKfeatb1i6coK91udhwgYsb8X/MPf/hD3XDDDTpy5IguvPBCtbe369FHH41FNgAAAADj8Oc3mtUfDGkdd68iyYxZWM877zy9+OKLOnDggAzD0Lx585SWlhaLbAAAAADGYWNNg+aV5mpxBTshkVzGLKy/+93v3vXrXbt2SZJuu+226CQCAAAAMG77W3r0RkOXvnndgpPH+IBkMWZh3blz58n/f2BgQM8995zOO+88CisAAAAQBzbubFCazaIPLp1qdhQg4sYsrD/+8Y/f9euuri6tX78+aoEAAAAAjI9/MKTHdzfpygVlKshONzsOEHFjTgl+r+zsbNXV1UUjCwAAAICz8OzeVnX1BRm2hKQ15grr9ddff3IvfDgc1t69e3XLLbdEPRgAAACAM9u4s0FT8zK1pqrI7ChAVIxZWL/yla+882G7XTNmzFBFRUVUQwEAAAA4s8bOPm097NYXL5sjq5VhS0hOYxbWSy65JBY5AAAAAJyFR2oaJUkfXs5iEpLXqIU1Nzd3xLHYhmHIYrGop6cnqsEAAAAAjCwUNvToa41aU1Wkivwss+MAUTNqYfV6vbHMAQAAAGCcXj7sVlNXv7529XyzowBRNeaW4GFtbW0aGBg4+evp06dHJRAAAACAM9tY06C8rDRdubDU7ChAVI15rc0TTzyhOXPmaObMmbrkkktUWVmpq6++OhbZAAAAALxHV19Az77dqg8unaoMu83sOEBUjVlYv/nNb2rbtm2aO3eu6urq9Nxzz2n16tWxyAYAAADgPV482K5AKKwbFk8xOwoQdWMW1rS0NBUWFiocDiscDmvt2rWqqamJRTYAAAAA77F5f5sKs9O1uCLP7ChA1I15hjUvL08+n08XXXSRbr31VpWUlCg7OzsW2QAAAACcYjAU1gsH2nV5dQl3ryIljLnCunbtWnV3d+tHP/qR3v/+92v27Nn685//HItsAAAAAE6xu6FL3f1BXT6fYUtIDWMW1sHBQV155ZW69NJL5fV6tW7dOhUWFsYiGwAAAIBTbN7fJrvVoovmFpkdBYiJMQvrt771Lb399tv66U9/qubmZl1yySW64oorYpENAAAAwCme39+m5ZX5cjrSzI4CxMSYhXVYSUmJysrKVFhYqLa2tmhmAgAAAPAeTV392t/i1WXzS8yOAsTMmIX1Zz/7mS699FJdfvnl8ng8uvfee/Xmm2/GIhsAAACAEzbvH1o0uozzq0ghY04Jbmho0N13360lS5bEIg8AAACAETy/v03TC7I0u5gbO5A6xiys3/nOd2KRAwAAAMAoBoIhvXLErfUrpsti4TobpI5xn2EFAAAAYI5Xj3g0EAxrLedXkWIorAAAAECce25/q7LSbVo1s8DsKEBMUVgBAACAOGYYhp7f364Lq4rkSLOZHQeIKQorAAAAEMcOtvrU1NXPdTZISRRWAAAAII4NX2ezdh6FFamHwgoAAADEsef3t2nhFKfKXA6zowAxR2EFAAAA4lRXX0A1xzrYDoyURWEFAAAA4tSLB9sVNsR1NkhZFFYAAAAgTj2/v00F2elaXJFndhTAFBRWAAAAIA6FwoZeONiuS+cVy2a1mB0HMAWFFQAAADDB5v2t+twDr+nlw24ZhnHaz3fXd6qrL8j5VaQ0u9kBAAAAgFQzEAzpG4/v0fHuAf11T4vOrXDpc5fM1pULy06upm7e3yab1aKL5hSbnBYwD4UVAAAAiLEHth3T8e4B/eaTK9TSPaBfvnhEn/vDLs0qytZnL5mlDyydqs3727R8Rr5cmWlmxwVMQ2EFAAAAYqhnIKifPn9YF80p0tp5Q9t9b1k+TU/vadHPXzysr/7xLf3nMwfV5vXrn66eb3JawFwUVgAAACCG7n2pVp19Qf3jVe+UUZvVomvPLdc1i8q09bBbP3/hiLr6g7pqYZmJSQHzUVgBAACAGGn3+vWrLXW69txyLapwnfZzi2XozOpFc4oVDIWVZmNGKlIb/wQAAAAAMfKTzYcUCIX1lSvnjflZyipAYQUAAABiot7Tp//ZUa91K6ZpZlG22XGAhEBhBQAAAGLgh88ekM1q0Z2XzzE7CpAwKKwAAABAlO093qM/vXFcn7xwpkqdDrPjAAmDwgoAAABE2Q/+tl+5GXb93cWzzY4CJBQKKwAAABBF22s9ev5Auz6/tkqurDSz4wAJhcIKAAAARIlhGPr+3w6o1Jmh28+vNDsOkHAorAAAAECUPPlWs1471qk7L5+rzHSb2XGAhGOP1oMbGhp02223qbW1VRaLRXfccYfuvPNOdXR0aN26dTp69KgqKyv18MMPKz8/P1oxAAAAgJjxD4a0o65Dm/e36fn9bTrq6dPs4mzdsrzC7GhAQrIYhmFE48HNzc1qbm7WeeedJ6/Xq2XLlmnTpk367W9/q4KCAn3ta1/Td7/7XXV2dup73/veGZ+1fPly1dTURCMmAAAAMCken1/P7m3V5v1tevmwW72BkNLtVl0wu1CXzS/RtYvKVZiTYXZMIK6N1vmitsJaXl6u8vJySVJubq6qq6vV1NSkP/3pT3rhhRckSbfffrsuvfTSMQsrAAAAEI88Pr+uuvsluX0BTXE59IGlU3XZ/BJdMLuILcBABEStsJ7q6NGj2r17t1atWqXW1taTRbasrEytra0jfueee+7RPffcI0lqb2+PRUwAAADgrPzbk/vU3R/Uw589Xysq82WxWMyOBCSVqA9d8vl8uummm3T33XfL6XS+62cWi2XUf6jvuOMO1dTUqKamRsXFxdGOCQAAAJyVLYfa9fjuJn3uktlaObOAsgpEQVQLazAY1E033aRbb71VH/rQhyRJpaWlam5uljR0zrWkpCSaEQAAAICI6w+E9PXH92hWUbY+v7bK7DhA0opaYTUMQxs2bFB1dbW+/OUvn/z9G264Qffff78k6f7779eNN94YrQgAAABAVPz35kOq7+jTtz+4SI40zqoC0RK1M6wvv/yyfv/732vRokVasmSJJOnf//3f9bWvfU233HKL7rvvPs2YMUMPP/xwtCIAAAAAEbevuUf3vFSrW5ZX6PzZhWbHAZJa1ArrmjVrNNqNOc8991y0XgsAAABETShs6GuPvaW8zDT98zXVZscBkl7Uhy4BAAAAyeKBbcf0RkOX/uX6BcrLSjc7DpD0KKwAAADAOBzv6tf3n96vi+cW64bFU8yOA6QECisAAAAwDt964m2FDEPf/sA5XGEDxAiFFQAAABjD03ta9OzeVt11xVxNK8gyOw6QMiisAAAAwBn0BQb1rSf2aEG5UxvWzDQ7DpBSKKwAAADAGfzljWa19vj1L9cvkN3Gvz4DscQ/cQAAAMAZPLizXlUlOVo1s8DsKEDKobACAAAAo9jf0qPd9V36yMrpDFoCTEBhBQAAAEbx0I4Gpdus+tDSqWZHAVIShRUAAAAYwUAwpMd2NerqRWXKz043Ow6QkiisAAAAwAieeqtZPQODWr9iutlRgJRFYQUAAABG8OCOes0sytbqWQxbAsxCYQUAAADe43CbVzuPdmr9imkMWwJMRGEFAAAA3uPBHQ1Ks1l007IKs6MAKY3CCgAAAJxiIBjSH3c16soFZSrKyTA7DpDSKKwAAADAKf72dou6+oL6yEqGLQFmo7ACAAAAp3hwR72mFWTqgtmFZkcBUh6FFQAAADihtt2nbbUdWr9iuqxWhi0BZqOwAgAAACds3Nkgm9WiDzNsCYgLFFYAAABAUmAwrEdfa9QV1SUqcTrMjgNAFFYAAABAkvTs3lZ5egNaz7AlIG5QWAEAAABJD+2s19S8TF08p9jsKABOsJsdAAAAAIiFfc09enBHvUJh47SfhQ1DWw65ddcVc2Vj2BIQNyisAAAASHpt3gHd/usd6hkIKidj5H8FnlmUrfUrp8U4GYAzobACAAAgqQ2GwvrC/+xWz0BQj3/+QlWXO82OBGCcKKwAAABIaj/42wFtr+vQf61bTFkFEgxDlwAAAJC0nt7TrF++VKuPrZ6uDy7lblUg0bDCCgAAECUDwdCIA34kyWa1yJFmi3Gi1FLb7tNXHnlTi6fl6ZvXLTA7DoAJoLACAABEwatHPLr1V9s0Sl+VJP34I0t1/eIpsQuVQvoCg/rcA7uUZrPoZ7eepww7fzkAJCIKKwAAQBQ8vadZ6Xarvvy+uSP+/JGaRv3HMwf0/nPKlGbjlFYkGYahrz++RwfbvPrdp1Zqal6m2ZEATBCFFQAAIAq2HHZr5cxC3XHx7BF/Prs4Rxvur9EfX2vU+pXTY5wuuT2w7Zge392kv3/fXF00p9jsOAAmgb/OAwAAiLDjXf2qbe/VxXOKRv3MZfNLtGRanv77uUPyD4ZimC657a7v1L/+Za8um1+i/7O2yuw4ACaJwgoAABBhWw+5JUlrzlBYLRaLvnLlPB3vHtBDOxpiFS2pGYahf3rsLZXkOvRftyyR1WoxOxKASaKwAgAARNiWw24V52ZoXmnuGT93YVWhVs4s0E+eP6z+AKusk7XlkFv7W7z60hVz5MpKMzsOgAigsAIAAERQOGzo5cNurakqksVy5hU+i8Wiv3/fXLV7/Xpg27EYJUxev9pap+LcDN2whMnLQLKgsAIAAETQ3uYedfQGtKZq9O3Ap1o1q1AXzSnSz188Ip9/MMrpkteBFq9eOtiuT1xQyRU2QBKhsAIAAETQ1sNjn199r7+/cp46egO6/5WjUUqV/H61pVaONKs+ysRlIKlQWAEAACJo6yG35pbmqNTpGPd3lkzL0xXVJfrli0fU3R+MYrrk1OYd0J9eP64PL5um/Ox0s+MAiCAKKwAAQIQMBEPacbRDa6rO/u7Pu943Vz0Dg7pvS20UkiW3371yTMFwWBvWzDQ7CoAIo7ACAABEyM6jHQoMhnXRWWwHHrZwikvXLirXfVvr1NEbiEK65NQfCOmB7cf0vupSVRZlmx0HQIRRWAEAACJkyyG30mwWrZpVMKHvf+mKOeoLhvTLl45EOFnyenRXo7r6gvrMxbPMjgIgCiisAAAAEbLlkFvLZuQrK90+oe/PKc3VB5ZM1f2vHFWbdyDC6ZJPOGzo11vrtHhanpbPyDc7DoAooLACAABEQLvXr33NPbpoztmfXz3VnZfPUTBk6CebD0coWfL6332tqnP36jMXzRzzzlsAiYnCCgAAEAGvHDlxnc04718dTWVRtj6ycpr+sL1eh9u8kYiWtH61pU5T8zL1/oVlZkcBECUUVgAAgAjYcsgtV2aazpnqmvSz7rpirrLSbfr2k/sikCw5vdHQpR1HO/TJCytlt/GvtECy4p9uAACASTIMQ1sPuXVhVaFs1slvTS3MydAXLqvS8wfa9dLB9ggkTD6/2lqn3Ay71q2YZnYUAFFEYQUAAJikI+0+tfQMTOj+1dHcfkGlZhRm6d+e3KvBUDhiz00GTV39euqtZn1k1XTlOtLMjgMgiiisAAAAk7Tl0ND51YncvzqaDLtN/3T1fB1s9WljTUPEnpsMfrO1ThZJn7ig0uwoAKKMwgoAADBJWw+5VVmYpWkFWRF97lULy7RyZoF++MxB9QwEI/rsRLWt1qPfbzuma88t15S8TLPjAIgyCisAAMAkBAbD2lbr0ZoIrq4Os1gs+ua1C9TRF9BPn+eam9cburThtzs1rSBL/3LdArPjAIgBCisAAMAk7K7vVG8gFNHzq6daVOHSh5ZW6Ddbj6re0xeVdySC/S09uv3XO1SQk64HNqxSYU6G2ZEAxACFFQAAYBK2HnbLapHOn10YtXf84/vnyWa16LtPp+Y1N3XuXn3sVzvkSLPqfz69WmUuh9mRAMQIhRUAAGASthxya/G0PLkyozetttTp0N9dMltPvdWinUc7ovaeeNTU1a9b792msGHoD59eFfFzwgDiG4UVAABggrr7gnqzsUsXVUX+/Op73XHxLJU5Hfq/f9mrcNiI+vviQZt3QLfeu01e/6B+96mVqirJNTsSgBijsAIAAEzQq7VuhQ1pzZzonF89VWa6Tf/4/nl6s7Fbj+9uivr7zNbVF9Bt9+1Qm9ev335yhc6Z6jI7EgATUFgBAAAmaMsht7LTbVo6PS8m7/vAkqlaMi1PX3vsTd37Um3SrrT2B0K6/Tc7Vdveq3tvW65lMwrMjgTAJBRWAACACdp62K3VswqVZovNv1JZrRb95hMrdNn8En37qX36+K+3q6V7ICbvjhXDMPT1x9/Sm41d+slHl+rCGGy3BhC/KKwAAAAT0NDRp2OePl0UhftXzyQ/O12/+NgyffdDi7TrWJfe/6OX9PSe5phmiKY/bK/XY7ub9KXL5+rKhWVmxwFgMgorAADABGw55JYUm/Or72WxWLR+5XQ9+cU1mpafpb97YJe++uib+n/t3Xd4lfX9//HXySIJZJOQkIQwAgkzkCEbAUVREAeiIA5UtFpHtbZ+tbU/bdVKqx22rqpYUTG4QRFxMJQIBMKGkBAgm0yy91m/P4KpKCOBJOfk5Pm4Li4uzrlz3+8P4YTzOp/3/fnUNpo6vZb2tDu3Qn/6LFVTowJ13/RIW5cDwA4QWAEAAM7BpowShfi4a1BgT5vVMDCwlz66e4J+OXWQ3t+Rq1n/2qStR4/LaLbYrKZzVVbbpF++s0OBXj30z+tHy8nJYOuSANgBF1sXAAAA0NWYLVZtPnJclwzrI4PBtsHKzcVJD8+M1pQhgfr1e7s1/9WtcnN2UmRQLw0N8dbQEC8NC/HW0BBv+fV0s2mtp2O2WPWrFbtUWtOkj+6eIF9P+6wTQOcjsAIAALTRvvxKVdYbNamT7189k3EDA7T2wSnakFas1IIqHSyo1ncZJfpoZ17LMX193BXX318XDPDX2AH+igzsZRczmc9/c0ibMkr1zDUjNTKM7WsA/A+BFQAAoI2SMkokye5WsPV2d9WVo0N15ejQlsdKaxp1sKBKBwuqtC+/Stsyj+uzPcckSX6erko4EWDHDQywyV6n69OK9K/1h3VtXJjmJ4R3+vUB2DcCKwAAQBttyijVsBBv9e7Vw9alnFXvXj00eXCgJp9YHMpqtSq3rF7Jmce1LbNM27LK9FVqkSTpySuH66bx/TutttyyOj343h4NDfHWU1eNsHl7NQD7Q2AFAABog9pGk3bmlOu2SQNsXco5MRgM6hfgqX4BnpoX3zyjWVjZoIc+2K2/fpmuy0eGKKATgniD0ay7l++QxWrVKzfGyt3VucOvCaDrYZVgAACANkjOPC6j2arJkZ2/nU1HCfZx1x/njFB9k1nPfZXeKddcsS1H+/Or9Ld5MYoIsN1KywDsG4EVAACgDTZllKqHi5Pi+/vZupR2FRnUS4sm9NeK7bnal1fZodeyWq1anpyjmHBfXTI8uEOvBaBrI7ACAAC0QVJGqS4Y4O+QLaz3XzxYAT3d9Pin+2W1WjvsOtuzypVRXKOFF/TrsGsAcAwEVgAAgFYqrGxQRnGNJtnZ6sDtxdvdVQ/PjNbOnAqt3J3fYddZnpwtL3cXzY4J6bBrAHAMBFYAAIBWSjpcKkl2tf9qe7s2NkwxYT56Zk2aahpN7X7+4zWN+mJfoebGhsnTjfU/AZwZgRUAAKCVkjJKFNDTTUODvW1d06taKAAAIABJREFUSodxcjLo8TnDVVzdqBc3HG7383+4I09NZotuGEs7MICzI7ACAAC0gsViVdLhUk2M7C0nJ8feLzS2n5/mxoZp6aZMZZbWttt5LRar3t2Wowv6+2tIH692Oy8Ax0VgBQAAaIW0wmqV1jRpsgO3A//Y/82MkpuLk55andpu59x85Liyj9dp4ThmVwG0DoEVAACgFZIOl0iSJg92nP1XzyTI2133TY/UurRibUgrbpdzLk/Oln9PN80cwVY2AFqHwAoAANAKmzJKFRnUS8E+7rYupdPcOnGABvbuqSdXp6rJZDmvcxVVNeir1CLNiwtTDxfH2xIIQMcgsAIAAJxFg9GsbZllDrudzem4uTjpD1cM09HSWv2/VftV32Q+53O9vz1XZotVC9h7FUAbEFgBAADOYkd2uRpNlm5z/+qPTYsK0uJJA7Rie65mPv+dthw53uZzmC1WJW7L0aTI3urfu2cHVAnAURFYAQAAzuK7jBK5OBk0dmCArUuxicdmD1PiHeMkSQte26rffbJP1Q3GVn/9xvRiHats0EK2sgHQRgRWAACAs0jKKFVsPz/16uFi61JsZvygAK391ZTm2dZtObrkH9+1ejGm5ck5CvTqoYuH9engKgE4GgIrAADAGRyvadSBY1Xdsh34pzzcnPXY7GH66O4J8nJ30a1vbteD7+1WWW3Tab8mr7xOG9KLNT8hXK7OvPUE0Dbd92NCAACAVvj+xD2bkwisLcb089Nn903SixuO6KUNh/X5vgJdFB2kK0f31dSoILm7/m8V4Pe258ogaT6LLQE4BwRWAACAM9h0qETe7i4aFeZr61LsSg8XZ/16xhBdMSpEy5NztHrvMX2xv1BePVw0c0Swrhwdqvj+flqxPVfTooIU6uth65IBdEEEVgAAgNOoaTTpi/2FmjGsj5ydDLYuxy4N7uOlJ+YM12OzhmrL0eNatbs5uH6wI09ePVxU3WjSwnHMrgI4NwRWAACA0/h4Z55qGk26eXyErUuxey7OTpo8OFCTBwfqqatGaH1asVbuyleT2aILhwTZujwAXRSBFQAA4BSsVquWbc5STJiPxvTzs3U5XYq7q7MuHxmiy0eG2LoUAF0cS7UBAACcQtLhUh0pqdUtE/rbuhQA6LYIrAAAAKewbHOWAnq6adYoZgkBwFYIrAAAAD+RW1andWnFWnBBP/VwcT77FwAAOgSBFQAA4Cfe3potJ4OB1W0BwMYIrAAAAD9S32TWe9tzNXN4sEJ82DsUAGyJwAoAAPAjK3fnq7LeyGJLAGAHCKwAAAAn/LCVTXSwlxL6s5UNANgagRUAAOCEbZllSius1qIJ/WUwGGxdDgB0ewRWAACAE5ZtyZKPh6uuHB1q61IAACKwAgAASJKOVdTrywNFmp8QLg83trIBAHtAYAUAAJC0PDlbFqtVN46LsHUpAIATCKwAAKDbazCalbgtVxdF91G4v6etywEAnEBgBQAA3d7newtUVtukRWxlAwB2hcAKAAC6vbe2ZCkyqJcmRgbYuhQAwI8QWAEAQLeWfbxWe/IqNT8hnK1sAMDOEFgBAEC39uWBQknSpcODbVwJAOCnOiyw3nbbbQoKCtKIESNaHisrK9OMGTM0ePBgzZgxQ+Xl5R11eQAAgFZZu79QI0K9WWwJAOxQhwXWRYsWae3atSc9tmTJEl100UXKyMjQRRddpCVLlnTU5QEAAM6qsLJBO3MqNJPZVQCwSx0WWKdMmSJ/f/+THlu1apVuueUWSdItt9yilStXdtTlAQAAzuqr1OZ24JkjCKwAYI869R7WoqIihYSESJKCg4NVVFTUmZcHAAA4ydr9hRoU2FORQV62LgUAcAo2W3TJYDCccSW+V199VfHx8YqPj1dJSUknVgYAALqDstomJWeW6bIRIbYuBQBwGp0aWPv06aOCggJJUkFBgYKCgk577J133qmUlBSlpKQoMDCws0oEAADdxDcHi2S2WGkHBgA71qmBdc6cOVq2bJkkadmyZbryyis78/IAAAAtvtxfqFBfDw3v623rUgAAp9FhgXXBggUaP3680tPTFRYWpqVLl+qRRx7R119/rcGDB+ubb77RI4880lGXBwAAOK2aRpM2ZZRq5ojgM96iBACwLZeOOnFiYuIpH1+3bl1HXRIAAKBV1qcVq8lsoR0YAOyczRZdAgAAsJUv9xeqd68eiu3nZ+tSAABnQGAFAADdSoPRrA3pxbpkeB85O9EODAD2jMAKAAC6lU0ZpaprMmvmcNqBAcDeEVgBAEC3snZ/obzdXTRuYICtSwEAnAWBFQAAdBtGs0XfHCzSxcP6yM2Ft0EAYO/4SQ0AALqN5KNlqqw30g4MAF0EgRUAAHQbX+wvkIers6YMCbR1KQCAViCwAgCAbsFsserLA0WaFh0od1dnW5cDAGgFAisAAOgWduWUq7SmUZfSDgwAXQaBFQAAdAtr9xfKzdlJ06ODbF0KAKCVCKwAAMDhWa1WrT1QqImRAfJyd7V1OQCAViKwAgAAh7c3r1J55fWaOYJ2YADoSgisAADA4X2yK19uLk6aOSLE1qUAANqAwAoAABya0WzRZ3uO6eKhQfLxoB0YALoSAisAAHBomzJKdLy2SVeNDrV1KQCANiKwAgAAh/bJrmPy83TV1ChWBwaArobACgAAHFZ1g1FfHSjU7FF95ebC2x4A6Gr4yQ0AABzWF/sL1Wiy6KoxtAMDQFdEYAUAAA5r5a58RQR4Krafr61LAQCcAwIrAABwSAWV9dpy9LiuGh0qg8Fg63IAAOeAwAoAABzSqt3HZLVKV9MODABdFoEVAAA4HKvVqk925iu2n6/69+5p63IAAOeIwAoAABxOakGV0ouqmV0FgC6OwAoAABzOyl35cnU2aPaovrYuBQBwHgisAADAoZgtVq3afUxTo4Lk19PN1uUAAM4DgRUAADiU7w+Xqri6kXZgAHAABFYAAOBQVu7Kl5e7i6ZHB9m6FADAeSKwAgAAh1HXZNLaA4WaNTJE7q7Oti4HAHCeCKwAAMBhfHWgSHVNZtqBAcBBEFgBAIDD+HhXvkJ9PZTQ39/WpQAA2gGBFQAAOITiqgYlZZToqjF95eRksHU5AIB2QGAFAAAO4fN9BbJYRTswADgQAisAAHAIa/YVKDrYS5FBXrYuBQDQTgisAACgyyusbND2rHJdPjLE1qUAANoRgRUAAHR5X+wvkCQCKwA4GAIrAADo8v7XDtzL1qUAANoRgRUAAHRptAMDgOMisAIAgC6NdmAAcFwEVgAA0KXRDgwAjovACgAAuizagQHAsRFYAQBAl0U7MAA4NgIrAADosmgHBgDHRmAFAABd0g/twLOYXQUAh0VgBQAAXVJLO/AoAisAOCoCKwAA6JJ+aAceFEg7MAA4KgIr0MV9d6hE459Zp8dW7lNaYZWtywGATkE7MAB0Dy62LgDAuatqMOr/Ptoro9mi91Py9M7WHCX099ON4yI0c0Swerg427pEAOgQtAMDQPdAYAW6sGfWpKmoqkEf/3KiIvw99eGOPC1PztavVuxWQE83zYsP18Kx/RTu72nrUgGgXdEODADdA4EVsBMNRrMsVqs83Vr3svz+cKkSt+XoF1MGanS4ryTpjikDdfukAfr+SKne3pKtV787ole+PaJwfw9F9fFWdLCXooK9FB3spQG9e8rFmbsCAHQ9P7QDPzRjiK1LAQB0MAIrYAeSMkr10Ae75erspMQ7xp11RrS20aRHPt6rAb176sGfvGFzcjJo8uBATR4cqILKeq3cdUz7j1UqvbBaG9KLZbZYJUluzk4aFNRLfp6up73O9OggLZ488PwHCADtiHZgAOg+CKyADTUYzXr2y3QtTcrUwMCeOl7TpPmvbtWKO88cWp/9Ml155fV6/xfj5e56+vtUQ3w8dPfUQSdd70hJjdILq5t/FVWrttF0yq8trm7UX9amaU5MXwV5u5/7IAGgndEODADdB4EVsJG0wio9sGK30gqrdfP4CD162VAdKanRwteTzxhaU7LKtGxLlm4eF6GE/v5tuqa7q7OG9/XR8L4+Zz02q7RW0/62UW9tydZvLo1q03UAoKPQDgwA3Qs3sAGdzGKx6vVNRzXn39+rtKZJ/12UoD9dOUIebs4aEeqj5YvHqqbRpPmvblVuWd1JX9tgNOvhD/eqr4+HHp4Z3aF19u/dUzOG9tE7ydmqazr1LCwAdDbagQGgeyGwAp2ooLJeN72RrKc+P6gLowL15QOTNS066KRjfhpac47/L7T+85sMHS2t1V/mjlLPHh3fIHHHlIGqqDPqox157XZOo9nSbudCM6vVqt99sk8PrNgly4l7lAFH9fle2oEBoDshsAKdpMFo1tyXNmtndoWWXDNSr94Up4BePU557A+htbbJpAWvNYfWPbkVevW7I5qfEK5Jg3t3Ss3xEX6KCfPR0qTMlsWazsfBgirFPvm1lm3OOv/i0OKDlDy9m5yjlbuP6ZXvjti6HKDDFFY2KCW7XLNGMrsKAN0FgRXoJMuTc3SsskFLF8Vr/gX9ZDAYznj8j0Pr/Fe36KEP9ijIy12/mzW0kyqWDAaDFk8eqKzjdVp3sOi8zmWxWPXYyv2qbjDpqc9TtSe3op2q7N4yS2v1xGcHNH5ggGaNCtHfvjqkHdllti4L6BC0AwNA90NgBTpBg9GsV749ogmDAjRhUOtnR4f3bQ6tdUazDhfX6OmrR8jb/fTb0HSEy0YEK9TXQ69vyjyv83y4M087ssv1+8uHKsjLXfcm7lRVg7GdqrQfRrNFX+wr0PPfZGhjenGHjtFotuiBFbvk6uykv18fo2euGalQXw/d9+4uVdQ1ddh1W6PBaFbNaVagBs4V7cAA0P2wSjDQCd7Zmq2S6ka9eENsm792eF8ffXjXBKUWVOmioX06oLozc3F20q0T++upzw9qT26FYsJ923yOiromLfkiTXERfrp90gDFRvjpuv9s0SMf7dWLN8Sedbb5fKxPK9JXB4p01ZhQjRsY0GHXKaisV2JyjlZsz1VxdWPL4waDFB3srYT+forv76+E/n4K8fFol2s+/02G9uRV6uWFsS3nfOGGMZr78mb95oO9eu3muA79uz2VjKJqvbM1Wx/vzFdNk6nDxo7u54d2YFYHBoDuhcAKdLD6JrNe+faoJkYG6IIBbduG5geRQb0UGWS7GYXrE8L1/DcZem3TUb1wDqH7r1+mq7LeqKeuGiEnJ4PiIvz020ujtOSLNC1PztGN4yLaveajJTV6cnWqNqSXyMXJoBXbczVlSKAevjRKI0LPvq1Pa1gsViUdLtU7W7O1Lq1YFqtVFw4J1J/HRmjsQH/ty6vUtqwypWSV68MdeXprS7Ykqa+Pu4b19VZUsJeigr0VHeylAb17ytW59U0vyUeP68WNh3VdfJgu+9H9fKPCfPXIZUP15OpU/ff7LN02aUC7jPVMmkwWrT1QqHe2ZmtbZpncnJ10+chgRQT01I7sk8ce5uehhP7+mhYdpCtGhXRaoLZarZ0e3tG+aAcGgO6JwAp0sOXJ2SqtadRLF7U96NkLL3dXLRjbT0uTMpVXXqcwv5/vD3s6u3LKlbgtR7dNHKChId4tj985eaA2HzmuP61OVWw/Pw3r632Gs7ReTaNJL6w/rKVJR9XDxVmPzRqq6xPClbgtRy9tPKLZ/07SrFEhemjGEA1sY1thk8mio6U1Si+s1sGCaq3dX6Cs43Xy7+mmOyYP1A0X9FO/gP/93UyI7K0Jkc0t4CazRQcLqrU9q0w7c8qVXlitDeklLYtZuTk7aWBgTw0L8db1CeEae4bZ4Mp6o379/h718/fU41cM/9nzt03sry1HjuuZLw4qvr+fRoW1fVa8NY5V1Oudrdl6PyVXpTVNCvf30COXRWteXNhJC4r9eOwp2WVKOlyqT3bl67tDJXr66hHq4eLc7rU1mSzaf6xSKVll2p5Vrh3Z5Yrt56vXb0lo92t1Z1arVZX1Rvl6unX4tWgHBoDuyWC1Wu1+D4T4+HilpKTYugygzeqbzJr81/WKCvbS8sXjbF3OecmvqNeUv27QrRP667HZw1r1NWaLVXNeSFJpTaO++fWF8vrJ/belNY26/PlN6uXuos/unXReW/VYrVat3J2vZ9akqbi6UdfGhenhmVEK8nJvOaaqwajXvzuq15My1WiyaF5cmH518WCF+Hio0WRWdYPpxC+jqhtMqqo3KvN4rdILq5VeWK0jJTUympt/ZLo4GRTbz08Lx/XTzBHB5xS6Gk1mHSmuVXpRldJOXGNPboXK64y6cEigfnua2eD7E3fp830F+vCu8RrTz++U566oa9Llz2+Si7OTVt8/qd3vfa5pNGnqsxtUVtuk6dF9dOO4fpoyOFBOTmefxbRYrHp+XYaeX5eh2H6+euWmuJO+T+dqR3a5NqQVa3tWmXbnVqjR1LyFUv8AT/Xu1UMp2eVaec9EjT6Htnb83IFjlXri0wPanlWuy0YE63eXD1W4f+s/zGqLwsoGjXtmnR6aMUT3XTS4Q64BALCt02U+AivQgV7fdFRPfX5QH9w1Xgn9z60d2J7cn7hL69OKtfnR6a0KQMs2Z+nxTw/ohRvGaPaovqc8ZsuR41r4+lZdNSZUf79u9CmPKalu1LeHSnSsov601/r2UIl2ZJcrJsxHT8wZftogJzUH5RfWH9a7yTmyWK1ycjKoyXT6/WFDfT1OtO96KfrE7wN795KbS/uvW1ffZNayLVl6eeMRVdYbNXtUiB66JEoDeveUJK3cla8H3tvdqjfuKVlluv7VrZo5IlgvLBjTri2xL288or+sTTuvf9tr9hXooff3yNfTVa/dHH/OrdoVdU16cvVBfbQzT85OBg3v6634CH9dMMBPcRH+CvTqoZpGk8Y/s06TB/fWSwvjzuk6aFZe26S/fZ2ud5Nz5OvpppkjgvXxzjxZrdLdUwfprgsHyd21fWfN//t9pv74WarWPXQhM6wA4KAIrHbMarXqWGWDQn1ZjMSR1DWZNOWvGxQd7K13Fo+1dTntYl9epa54IUm/v3yo7pgy8IzHFlc36KLnvtXofr5667YLzhiW/vH1IT2/LkPPzYvRtXFhslisOnCsSuvTirU+vVh78yp0tp9UgV499NtLo3RtbFirZvkkKa+8Tu8m58hsscrL3UVe7q4/+d1F4f6enb4ys9Tc9vvad0e1NClTTWaLrosP09zYMN363+2KDvHSijvHy7kV43xxw2E9+2W6/nz1SN0wtl+71FbfZNakv6zX8FAfvXXbBed1rgPHKnXnWzt0vLZRz14boytiTv3BxqlYrVZ9vq9AT3x6QBV1Rt114SDdNXWQep1mpv6va9P08rdHtOGhqep/4gMAtJ7JbFHithw999Uh1TSadNO4CD148RD5eLoqv6Jez6w5qNV7CxTq66HHZg3VzBHB7fYhybUvb1ZNo0lrH5jSLucDANgfAqudMpktevjDvfp4V37Lm3U4hte+O6qn1zjO7OoPrv/PFuWW1enbh6edcZGgB1bs0pp9hVr7wOSz3itqtli18PWt2pNbqVmjQvTtoRKVVDfKYJBiwnw1PTpI06ODFB3sddo3wE4GOeSiOiXVjXpxw2EtT86W0WyVVw8XrfnV5Fa3XlosVt3y321KzizTx3dPaJcFp5YmZerJ1ant9m+7tKZRd729QynZ5bp3WqR+PWPIWT90KKxs0GMr9+ubg0UaFeajJdeMOut90MXVDZq0ZIPmxYfp6atHnnfd3cnWo8f1xKcHlFZYrfEDA/T4nGGKDv753/ePj5sYGaDHrxiuIX28zuvatAMDQPdAYLVDDUaz7k/cpa9SixTm56Hi6ka9d+e4M7YyomtwxNnVH3yTWqTFb6XoXwvGaM5pZsO2HDmuBa9t1X3TI/XQJVGtOm9RVYNm/StJjSazpgwJ1PSoIF0YFajeP1q8pzvLLavT0qRMXRgVqGlRQW362tKaRs3+V5JcXQxafe9k+Xie+4xxg9GsKX/doIGBPbXizvHnfJ6fajJZ9IeV+/VeSq4uHBKoi4cGqa+vR8svb3cXGQwGWSxWrdieq2fWHJTRYtFDM6J068T+cmnlCsuPfrxXH+3M1/f/N12BXvzbOpuUrDK9vPGI1qUVK9TXQ7+fNVSXnWXm1GS26N1tOfrbiZnYBy8erF9OjWx158NPvZGUqT+tph0YABwdgdXO1DaadOfbKfr+8HH9cc5wzYnpqzkvJqnJZNFn905SkPf5L0AC23n1uyP685o0fXjXeMU70Oyq1Dxjd/Hfv1WjyaIJgwLk5e6qXu4u8j7RQuvl7qq/f31IjSazvn7wwjbdy1bXZJKrs1ObtndB6+zMKdf1/9miKYMD9drN8eccHt7ekqU/rDqgdxePbVkBub1YrVa9uTlLf12brnqj+aTnevVwUV9fdxlkUHpR8yzfkrkjFRHQttbeIyU1uvjv3+reaa3/MKW7sVqt2pheopc2Htb2rHL5ebrqtokDtHjyQHm4tf71XFbbpCc+PaBP9xzT1KhA/eO60fLr2fbVhGkHBoDugcBqRyrrjFr05jbtya3Qs9fGaO6JNuC0wipd89JmRQV7acWd4zpkqwd0vLomkyb/ZYOG9fXW27c71uzqD748UKi/f3VIVSdW061pNP3smDcWxWt6dB8bVIfT+WERrN9eGqV7pkW2+eubTBZNe26j+nj30Ed3T+iwFmyLxarS2kYdq2jQsYp6HauoV/6J38tqmzQ3NkzXJ4Sf8/XvfCtFyZll2vzI9PNamdrRmMwWfb6vQC9vPKK0wmr19XHXHVMG6vqEcHm6ndvfk9Vq1fLkHP3ps1T17uWmFxbGKrYNXUS0AwNA93G6zMf/1J2suLpBNy/dpqMltXppYZxmjghueS462Ft/mxeju5fv1GOf7Ndfrx3lkPfknY3RbFFRVUPLm9X8inqV1zYp0KtHS3tgqK+HAr16tGrRmc72ztZsHa9t0gMXO+6bq0uHB+vS4f/7t2u2WFXT2LwdTFW9Sc5OBkUFn999a2h/N4+P0I7scv3tq3TFhPlq0uC2zZB+sitP+RX1eurqER36s8nJyaAgL3cFebl3yBY0v7hwkL5KLdJ723N126QB7X7+rqC6wXjSz9i88nqt3ntMeeX1GhzUS3+bF6M5o/ued7eDwWDQjeMiFBPmq1++u0PX/2eLHr1sqG6d2L9V/4bW7CuQJF0+KuS86gAAdF0E1k6UV16nG19PVlFVo95YlHDKN4uXjQzR/dMj9a/1hzW8r7cWTXT8N1MNRrPe3pKttQcKdayiXkVVDbL8ZN7f3dVJDcaTtx1xcTIo2MddIT7uZ5yNHh3uq/kXhCvMr2P2B2wwmpVRVKO0wiqlF1brgx15mjy4t+IiHKsV+EycnQzy8XCVj4erxC3YdstgMGjJ3JFKK6zS/St2afV9k9S3lauTm8wWvbjhiEaG+mjqkMAOrrRjxUX4KaG/n5YmZeqm8REO34JutVq15ehxvb89V2mF1cqvqFd1w8ldES5OBsWE++r/zR6mi4f2OeeW8dMZGeaj1fdO1kMf7NGfVqcqJbtMf5k76md7M//Umn0Fig724t5VAOjGCKxtcKyiXr/9cI+OVTS0bHnh1ePkbTB6uDrJoJ//R2+VVW9vyVZto0nvLB6ruIjTv6t/4OIhSi2o1pOfH9SQYC9NGNS+94nZC5PZog925On5bzJUWNWg0eG+mhjZ+8QMqvv/Flzx8ZCHm7OqGowq+NGMwLGKehVUNv/5p/e7/cBotujFjYf10sbDmh4dpIXjInTh4MBzfjNmtli1O7dc3x8+rrTCKqUVViurtLYlYPdwcVJ0iLcemzXsXP9agA7l6eail2+M05x/J+mXy3fq/V+Mb9V+sp/uOaacsjr956Y4h+j8+MWUQVr8Voo+31ugq8aE2rqcDlFZb9RHO/K0PDlbR0pq5ePhqoT+fho7wP+kBa06q2PFx9NVr90cp1e/O6q/fpmu1GNJen7+GMWcZha9sLJBKdnlemjGkA6tCwBg37iHtZV2ZJfpF2/vUKPRogujAk+0Pza3QDb/fur7+H4s2NtdbyxKOOvWC1Jzu9Y1L21WaU2jPr13Uqu3sOhomaW1+mRXvqZHB51zq57F0rx34t+/PqTM0lqN6eerhy+N1vhBAe1cbbO88jolbsvRe9tzVVrTpHB/D91wQYSuiw9TQCtWoK2oa9K3h0q0Ia1Y3x4qUXmdUQaDFOHvqahgL0UFeys62EtRwV7qH9DTLtuUgZ9as69Av1y+UzePj9CfrhxxxmPNFqsu+ce3cnV20pr7J7f77JstWCxWXfLP7+TiZNAXv5rsECH8B/vyKvXO1myt2pOvBqNFo8N9deO4CM0eFdKmRdA60rbMMt2XuFPF1Y2anxCu31wS9bOfx6wODADdC4sunYf3t+fq9yv3KdTXQ6/fEq/IoFPfm2e2WGU0W075nCS5Oju1KcxkldZqzgtJ6uvrobunDjrtcX283RUd7CVfz7avvthahZUNen5dht5PyZXZYpWTQbp14gA9dMmQVi/GYbVa9e2hEj37ZboOHKtSVB8v/ebSKF08NKhT3iw2mSz68kCh3tmareTMMrk5O2l0uK+8PVzl7e6iXj9a5dbL3UUVdUZtTC/WjuxyWaySf083TY0K1PToIE0eHNjc/gp0YU9/nqrXNmXqn9ePPuMs4+q9x3Tvu7v0wg1jNHvUqbcy6oreT8nVwx/u1bLbLtCFXbzNub7JrM/2HtPyrdnak1cpD1dnXTWmrxaOjWiXvXc7QlWDUf/6JkNvbs6Sh5uzfj1jiG4aF9GyRRGrAwNA90JgPQcms0V/XpOmN77P1OTBvfXCgtjz2r/wXHx3qES3L9suo/ns36Y+3j0UFeytqD69Wmb9/Hq6tSyE87/ZYKOqGkxydTYotp+fRob5nPYe0PLaJr387REt25wli9WqhWMjdPP4CC1NytTy5ByF+3t1QC+jAAASmElEQVTomatHnXHxlgajWZ/vLdDbW7O1O7dC4f4e+vWMIZoTE2qz2ciMomotT85RakHVT2bKjSfdPzu8r7emRwdpWnSQYsJ8mT2FQzGaLVr4WrL25FXomthQzYkJ1dgB/ifNoFosVl3+r01qMlv09YMXOtRroNHUvKdsZFAvLV88ztblnJMjJTVavjVHH+7IVVWDSZFBvXTj2H66Ji5M3me5P9ReHC6u1h8/S9WmjFIN6dNLT1wxXAMDe7E6MAB0MwTWNqqsM+rexJ3alFGqWyf21+8vH9rqjenb2/GaRlXWG0/5nMUq5VfUK/3E/ZTphdXKKK5Rk+n0M70/5ebipJgwH8X391dCfz/F9fOXi7NBS5My9dp3R1XTZNI1Y8L0wMWDT2pN3nr0uB79eJ8yS2s1Ly5Mj80adlKgzyqt1fLkbH2wI08VdUYNCuypRRMH6Pr48FbdM2cLVqtVdU1mVZ8I9K1pGQa6spLqRv15zUF9eaBQdU1mBXu7a87ovpoT01fD+3rr69Qi3fn2Dv39uhhdExtm63Lb3X++PaJnvkjTZ/dO0sgw+5yJ/Cmj2aJvUov0TnK2vj98XK7OBl06PFg3jovQ2AH+XbK92Wq16qvUIj25OlV55fUaGNhTR0tqaQcGgG6EwNoGh4urtXhZSvP2DVeN0PUJ/Trt2u3BZLYo63id0gurVdNobGlx/d/vLvJ2d1Vto0k7ssuVkl2u7Vll2pdXKdOJ6UVPN2fVNZl1ybA++s2lURrS59Rt0A1Gs55fl6FXvzsqP083/XHOcDk7GbQ8OVubMkrl4tT8RmrhuH4aPzCgS76RArqDuiaTvjlYrE9352tjeolMFqsig3q1fPi1/qELbfahXUeqajBq4jPrNWVIoF5cGGvrcn7GbLEqp6zupA8lU7LLVVLdqFBfD90wtp+uiw9XoJdjfLjWYDTrte+O6sWNhxUZ1Eur75ts65IAAJ2EwHoaJdWNSi+sbtmSJL2oWmmF1fJ2d9ErN8Ypvn/32ZqkvsmsPXkVSskqU25ZveZfEK4xrdzgfX9+pR75eK/251dJkkJ83HXDBf10fUK4grzdO7JsAO2svLZJa/YXaNXuY9qWWeaws6s/ePbLNL244YhuGR+hx2YPs/k2N7tzK/RucrbSC6t1qKimZRX0HxZ7G97XR1ePCdW06CCHatH+seM1jZJElwsAdCPdLrB+daBQ723PleU0w6s3mnW4uEalNU0tj/Xu5aaoYC8NDfbWrZMGKLSV+xOimcls0ardx+Tt4appUYEOORsDdDf1TWZ5uNnHyrIdxWS2aMkXaXo9KVMTBgXopYWxHbqI3elYrVb99/ss/XnNQXm6OWtEqE/L/0lRwV4a3KdXqxe5AwCgqzld5nPI//le33RUT685qL4+Hgrodeo3Ha7OTpoeHXTSliS9+ST3vLg4O2lunOPOwgDdkaOHVan5Z9djs4cpKthLv/9kv6588Xu9fnO8Bp/mVoiOUN1g1MMf7tUX+wt18dA++tu8mE5f5A8AAHvkUIHVYrHqqc8P6o3vM3XZiGD94/rRdrPnHADAvs2LD9fAwJ76xds7dfVLm/X8/NG6aGifDr/uwYIq/XL5TuWU1enRy6J155SB3O8PAMAJDtOz2WA06553d+qN7zN1+6QBevGGWMIqAKBN4iL89em9E9W/t6cWv5WilzceUUfeOfP+9lxd9eL3qm00KfGOcfrFhYMIqwAA/IhDzLCW1zZp8Vsp2plTrj/MHqbbJw2wdUkAgC6qr6+HPvjFBP32wz36y9o07cguV/8Az+a9mhub92yuOrFvc12jWf493dTX10Ohvu7q6+vR8ivU10OePU79wanR1Hzf7Ac78jQxMkDPzx/DbSkAAJxClw+sOcfrtOi/25RXUa+XbojVZSNDbF0SAKCL83Bz1r8XjFF0sJf+te6wvncytGwL5uXuKm93F4X5esjTzVlltU3KK6/TtszjqmowtfoaBoN0//RI/eriIQ672i8AAOerywZWo9mipMOl+u0He2SyWPXu4rHdagsaAEDHMhgMunf6YN0zLbLVbbrVDUYVVDYov6JexyrqVd9kPu2xo8N9+X8LAICz6FKBtaS6URvTi7UhvVibDpWqutGkcH8PvXnrBRoU2MvW5QEAHFBb7in1cneVl7urhnTiCsMAADiyLhFYi6sadeULSdqbXymrVQry6qFZo0I0LTpIkwf3Zl86AAAAAHBAXSLpFVU3yGAw6MGLh2h6dJCG9/VmFUUAAAAAcHBdIrAODfHWynsm2roMAAAAAEAn6hL7sLqweiIAAAAAdDtdIrACAAAAALofAisAAAAAwC4RWAEAAAAAdonACgAAAACwSwRWAAAAAIBdIrACAAAAAOwSgRUAAAAAYJcIrAAAAAAAu0RgBQAAAADYJQIrAAAAAMAuEVgBAAAAAHaJwAoAAAAAsEsEVgAAAACAXSKwAgAAAADskk0C69q1axUVFaXIyEgtWbLEFiUAAAAAAOxcpwdWs9mse+65R1988YVSU1OVmJio1NTUzi4DAAAAAGDnOj2wbtu2TZGRkRo4cKDc3Nw0f/58rVq1qrPLAAAAAADYuU4PrPn5+QoPD2/5c1hYmPLz83923Kuvvqr4+HjFx8erpKSkM0sEAAAAANgBu1106c4771RKSopSUlIUGBho63IAAAAAAJ2s0wNraGiocnNzW/6cl5en0NDQzi4DAAAAAGDnOj2wJiQkKCMjQ5mZmWpqatKKFSs0Z86czi4DAAAAAGDnXDr9gi4ueuGFF3TppZfKbDbrtttu0/Dhwzu7DAAAAACAnTNYrVarrYs4m169eik6OvqUz1VWVsrHx6fN5ywpKbGre2PPdRz2yFHG4ijjkDp/LB31+nKU74mjjENynLE4yjgk+/v/7Vw5yvfEUcYhOc5Yzmcc9vb64ntiXxxlHJJtxpKWlqaampqfP2HtAuLi4k773B133NHu57SFcx2HPXKUsTjKOKzWzh9LR72+HOV74ijjsFodZyyOMg6r1f7+fztXjvI9cZRxWK2OM5bzGYe9vb74ntgXRxmH1WqbsZzu9WW3qwS31hVXXGHrEtqFo4xDcpyxOMo4JMcZC+OwP44yFkcZhyNxlO+Jo4xDcpyxOMo4JMcZC+OwP/Y0li7REhwfH6+UlBS7PyeAZry+ANvh9Qd0HF5fQMc53evL+Yknnnii88tpu7i4uC5xTgDNeH0BtsPrD+g4vL6AjnOq11eXmGEFAAAAAHQ/Xf4eVgAAAACAYyKwAgAAAADsksMH1pUrV8pgMCgtLc3WpQAOwWAw6MYbb2z5s8lkUmBgoGbPnm3DqoDupVevXrYuAXB4Z3udTZ06lQWYgE7g8IE1MTFRkyZNUmJiYpu+zmw2d1BFQNfWs2dP7d+/X/X19ZKkr7/+WqGhoTauCgAAAI7IoQNrTU2NkpKStHTpUq1YsUKStHHjRk2ZMkWzZs1SVFSU7rrrLlksFknNn6Q99NBDiomJ0ZYtW2xZOmDXLr/8cn3++eeSmj8UWrBgQctz27Zt0/jx4zVmzBhNmDBB6enpkqQpU6Zo9+7dLcdNmjRJe/bs6dzCAQeycePGkzob7r33Xr355puSpP79++vxxx9XbGysRo4cSZcRcI7O9DoD0DkcOrCuWrVKM2fO1JAhQxQQEKAdO3ZIan5D/e9//1upqak6cuSIPv74Y0lSbW2txo4dqz179mjSpEm2LB2wa/Pnz9eKFSvU0NCgvXv3auzYsS3PRUdHa9OmTdq1a5f+9Kc/6Xe/+50k6fbbb2/5T/7QoUNqaGhQTEyMLcoHuoXevXtr586duvvuu/Xcc8/ZuhwAAM6JQwfWxMREzZ8/X1LzG+wf2oIvuOACDRw4UM7OzlqwYIGSkpIkSc7Ozpo7d67N6gW6ilGjRikrK0uJiYm6/PLLT3qusrJS8+bN04gRI/Tggw/qwIEDkqR58+Zp9erVMhqNeuONN7Ro0SIbVA50H9dcc42k5j3tsrKybFsMAADnyMXWBXSUsrIyrV+/Xvv27ZPBYJDZbJbBYNCsWbNkMBhOOvaHP7u7u8vZ2dkW5QJdzpw5c/Sb3/xGGzdu1PHjx1se/8Mf/qBp06bpk08+UVZWlqZOnSpJ8vT01IwZM7Rq1Sq9//77LR0PAM6Ni4tLyy0tktTQ0HDS8z169JDU/GGsyWTq1NoAR3G21xmAjuewM6wffvihbrrpJmVnZysrK0u5ubkaMGCANm3apG3btikzM1MWi0Xvvfce7b/AObjtttv0+OOPa+TIkSc9XllZ2bII00/v81m8eLHuv/9+JSQkyM/Pr7NKBRxSRESEUlNT1djYqIqKCq1bt87WJQEOh9cZYHsOG1gTExN19dVXn/TY3LlzlZiYqISEBN17770aOnSoBgwY8LPjAJxdWFiY7r///p89/vDDD+vRRx/VmDFjfjarExcXJ29vb916662dVSbgcEwmk3r06KHw8HBdd911GjFihK677jqNGTPG1qUBDoPXGWA/DFar1WrrIjrTxo0b9dxzz2n16tW2LgXodo4dO6apU6cqLS1NTk4O+3kZ0KH27NmjO+64Q9u2bbN1KYDD4nUG2A/eMQLoFG+99ZbGjh2rp59+mrAKnKNXXnlFCxYs0FNPPWXrUgCHxesMsC/dboYVAAAAANA1ONQ0R25urqZNm6Zhw4Zp+PDhev755yU1rxg8Y8YMDR48WDNmzFB5ebkkafny5Ro1apRGjhypCRMmaM+ePS3nWrt2raKiohQZGaklS5bYZDwAAAAA0J051AxrQUGBCgoKFBsbq+rqasXFxWnlypV688035e/vr0ceeURLlixReXm5/vKXv2jz5s0aOnSo/Pz89MUXX+iJJ55QcnKyzGazhgwZoq+//lphYWFKSEhQYmKihg0bZushAgAAAEC34VAzrCEhIYqNjZUkeXl5aejQocrPz9eqVat0yy23SJJuueUWrVy5UpI0YcKElq01xo0bp7y8PEnStm3bFBkZqYEDB8rNzU3z58/XqlWrbDAiAAAAAOi+HCqw/lhWVpZ27dqlsWPHqqioSCEhIZKk4OBgFRUV/ez4pUuX6rLLLpMk5efnKzw8vOW5sLAw5efnd07hAAAAAABJkoutC+gINTU1mjt3rv75z3/K29v7pOcMBoMMBsNJj23YsEFLly5VUlJSZ5YJAAAAADgDh5thNRqNmjt3rhYuXKhrrrlGktSnTx8VFBRIar7PNSgoqOX4vXv3avHixVq1apUCAgIkSaGhocrNzW05Ji8vT6GhoZ04CgAAAACAQwVWq9Wq22+/XUOHDtWvf/3rlsfnzJmjZcuWSZKWLVumK6+8UpKUk5Oja665Rm+//baGDBnScnxCQoIyMjKUmZmppqYmrVixQnPmzOncwQAAAABAN+dQqwQnJSVp8uTJGjlypJycmrP4n//8Z40dO1bXXXedcnJyFBERoffff1/+/v5avHixPvroI0VEREiSXFxclJKSIklas2aNHnjgAZnNZt122236/e9/b7NxAQAAAEB35FCBFQAAAADgOByqJRgAAAAA4DgIrAAAAAAAu0RgBQAAAADYJQIrAAAAAMAuEVgBAAAAAHaJwAoAgA088cQTeu655077/MqVK5WamtqJFQEAYH8IrAAA2CECKwAA7MMKAECnefrpp7Vs2TIFBQUpPDxccXFx8vHx0auvvqqmpiZFRkbq7bff1u7duzV79mz5+PjIx8dHH330kSTpnnvuUUlJiTw9PfXaa68pOjraxiMCAKBjEVgBAOgEO3bs0KJFi5ScnCyTyaTY2FjddddduvXWWxUQECBJeuyxx9SnTx/dd999WrRokWbPnq1rr71WknTRRRfplVde0eDBg5WcnKxHH31U69evt+WQAADocC62LgAAgO5g06ZNuvrqq+Xp6SlJmjNnjiRp//79euyxx1RRUaGamhpdeumlP/vampoabd68WfPmzWt5rLGxsXMKBwDAhgisAADY0KJFi7Ry5UrFxMTozTff1MaNG392jMVika+vr3bv3t35BQIAYEMsugQAQCeYMmWKVq5cqfr6elVXV+uzzz6TJFVXVyskJERGo1HLly9vOd7Ly0vV1dWSJG9vbw0YMEAffPCBJMlqtWrPnj2dPwgAADoZgRUAgE4QGxur66+/XjExMbrsssuUkJAgSXryySc1duxYTZw48aRFlObPn69nn31WY8aM0ZEjR7R8+XItXbpUMTExGj58uFatWmWroQAA0GlYdAkAAAAAYJeYYQUAAAAA2CUCKwAAAADALhFYAQAAAAB2icAKAAAAALBLBFYAAAAAgF0isAIAAAAA7BKBFQAAAABglwisAAAAAAC79P8B/+gSX2aLuX8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1152x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = interested_county.plot.line(figsize=(16,10))\n",
    "\n",
    "interested_county.iloc[peaks].rename('values').reset_index().plot.scatter(x='date', y='values', ax = ax, color='red', s=50)\n",
    "\n",
    "county_details.loc[[48041, 48039]].index.to_series().apply(lambda x:str(x) + ' ha')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {},
   "outputs": [],
   "source": [
    "means = (\n",
    "    df\n",
    "    .dropna()\n",
    "    .query('YEAR==12 & AGEGRP > 0')\n",
    "    .assign(fips=lambda x:x['STATE'].astype(int)*1000+x['COUNTY'].astype(int))\n",
    "    [['fips', 'AGEGRP', 'TOT_POP']]\n",
    "    .assign(\n",
    "        AGEGRP2=lambda x:x['AGEGRP'] * 5 - 2.5, \n",
    "        SUMAGE=lambda x:x['AGEGRP2'] * x['TOT_POP']\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 277,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fips\n",
       "16065    23.309476\n",
       "46121    23.644958\n",
       "2158     23.708882\n",
       "51678    23.734971\n",
       "51750    24.683106\n",
       "           ...    \n",
       "35021    59.550000\n",
       "12015    60.118506\n",
       "35003    61.824866\n",
       "15005    63.000000\n",
       "12119    67.858652\n",
       "Length: 3142, dtype: float64"
      ]
     },
     "execution_count": 277,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def median_from_frequency(df):\n",
    "    total_pop = df['TOT_POP'].sum()\n",
    "    median = total_pop / 2\n",
    "    cumsum = df['TOT_POP'].cumsum()\n",
    "    above = cumsum[cumsum > median][:1]\n",
    "    below = cumsum[cumsum <= median][-1:]\n",
    "    startage = (df[cumsum <= median][-1:]['AGEGRP'] * 5).iloc[0]\n",
    "    return startage + (median - below.iloc[0]) / (above.iloc[0] - below.iloc[0]) * 5\n",
    "\n",
    "means.groupby('fips').apply(median_from_frequency).sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1682,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "st                                                              Texas\n",
       "county                                                Brazoria County\n",
       "area                                                           1357.7\n",
       "pop                                                            374264\n",
       "density                                                        275.66\n",
       "longitude                                                    -95.4458\n",
       "latitude                                                      29.1884\n",
       "conservative                                                 0.627557\n",
       "median_age                                                       36.1\n",
       "mean_age                                                      36.8205\n",
       "hispanic                                                     0.315689\n",
       "black                                                        0.151262\n",
       "asian                                                        0.069467\n",
       "max_height                                                    46.4531\n",
       "peak_info_raw       ([78, 112], {'peak_heights': [16.8664365260885...\n",
       "peak_info                                                          []\n",
       "peak_count                                                          0\n",
       "peak_date                                                         NaT\n",
       "median_infection                                              4.10329\n",
       "mean_infection                                                10.0893\n",
       "max_infection                                                 46.4531\n",
       "adjusted_density                                               659.58\n",
       "Name: 48039, dtype: object"
      ]
     },
     "execution_count": 1682,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_details.loc[48039]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1684,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13944b950>"
      ]
     },
     "execution_count": 1684,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "county_details.query('(adjusted_density > 609) & (adjusted_density < 709) & peak_count == 1').plot.scatter(x='density', y='max_infection')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1688,
   "metadata": {},
   "outputs": [],
   "source": [
    "counties_like_brazoria = county_details.query('(adjusted_density > 609) & (adjusted_density < 709)')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1689,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16.058779031239588\n"
     ]
    },
    {
     "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>st</th>\n",
       "      <th>county</th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>conservative</th>\n",
       "      <th>median_age</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>...</th>\n",
       "      <th>asian</th>\n",
       "      <th>max_height</th>\n",
       "      <th>peak_info_raw</th>\n",
       "      <th>peak_info</th>\n",
       "      <th>peak_count</th>\n",
       "      <th>peak_date</th>\n",
       "      <th>median_infection</th>\n",
       "      <th>mean_infection</th>\n",
       "      <th>max_infection</th>\n",
       "      <th>adjusted_density</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22051</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Jefferson Parish</td>\n",
       "      <td>295.632</td>\n",
       "      <td>432493.0</td>\n",
       "      <td>1462.943795</td>\n",
       "      <td>-90.135227</td>\n",
       "      <td>29.827561</td>\n",
       "      <td>0.576775</td>\n",
       "      <td>39.6</td>\n",
       "      <td>40.442394</td>\n",
       "      <td>...</td>\n",
       "      <td>0.042697</td>\n",
       "      <td>90.141839</td>\n",
       "      <td>([9, 39, 78, 109], {'peak_heights': [90.141838...</td>\n",
       "      <td>[(9, 90.14183879441813)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-07</td>\n",
       "      <td>16.796308</td>\n",
       "      <td>23.434688</td>\n",
       "      <td>90.141839</td>\n",
       "      <td>663.519334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51175</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Southampton County</td>\n",
       "      <td>599.145</td>\n",
       "      <td>17631.0</td>\n",
       "      <td>29.426933</td>\n",
       "      <td>-77.102646</td>\n",
       "      <td>36.723332</td>\n",
       "      <td>0.583430</td>\n",
       "      <td>47.4</td>\n",
       "      <td>44.167801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005105</td>\n",
       "      <td>80.215854</td>\n",
       "      <td>([28, 111], {'peak_heights': [80.2158535696054...</td>\n",
       "      <td>[(28, 80.21585356960547)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>4.051306</td>\n",
       "      <td>9.687596</td>\n",
       "      <td>80.215854</td>\n",
       "      <td>650.292127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6099</th>\n",
       "      <td>California</td>\n",
       "      <td>Stanislaus County</td>\n",
       "      <td>1494.827</td>\n",
       "      <td>550660.0</td>\n",
       "      <td>368.377076</td>\n",
       "      <td>-120.997492</td>\n",
       "      <td>37.558041</td>\n",
       "      <td>0.490156</td>\n",
       "      <td>34.4</td>\n",
       "      <td>36.551284</td>\n",
       "      <td>...</td>\n",
       "      <td>0.061424</td>\n",
       "      <td>65.194494</td>\n",
       "      <td>([108], {'peak_heights': [65.19449388007119]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.633204</td>\n",
       "      <td>9.459371</td>\n",
       "      <td>65.194494</td>\n",
       "      <td>627.304860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12099</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Palm Beach County</td>\n",
       "      <td>1969.763</td>\n",
       "      <td>1496770.0</td>\n",
       "      <td>759.873142</td>\n",
       "      <td>-80.465018</td>\n",
       "      <td>26.647331</td>\n",
       "      <td>0.420974</td>\n",
       "      <td>45.2</td>\n",
       "      <td>44.349362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.029446</td>\n",
       "      <td>49.611592</td>\n",
       "      <td>([81, 111], {'peak_heights': [18.7737594954468...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6.781269</td>\n",
       "      <td>14.179238</td>\n",
       "      <td>49.611592</td>\n",
       "      <td>645.956575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12115</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Sarasota County</td>\n",
       "      <td>555.868</td>\n",
       "      <td>433742.0</td>\n",
       "      <td>780.296761</td>\n",
       "      <td>-82.331502</td>\n",
       "      <td>27.184592</td>\n",
       "      <td>0.559755</td>\n",
       "      <td>57.1</td>\n",
       "      <td>51.479128</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018387</td>\n",
       "      <td>46.900824</td>\n",
       "      <td>([111], {'peak_heights': [46.9008238603989]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.486666</td>\n",
       "      <td>7.749199</td>\n",
       "      <td>46.900824</td>\n",
       "      <td>642.538548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51139</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Page County</td>\n",
       "      <td>310.856</td>\n",
       "      <td>23902.0</td>\n",
       "      <td>76.890908</td>\n",
       "      <td>-78.481092</td>\n",
       "      <td>38.621787</td>\n",
       "      <td>0.756984</td>\n",
       "      <td>45.8</td>\n",
       "      <td>43.619572</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005062</td>\n",
       "      <td>46.618932</td>\n",
       "      <td>([32, 65, 100], {'peak_heights': [46.618932067...</td>\n",
       "      <td>[(32, 46.618932067848476), (65, 18.52803710388...</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>9.562858</td>\n",
       "      <td>11.445021</td>\n",
       "      <td>46.618932</td>\n",
       "      <td>662.694779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48039</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Brazoria County</td>\n",
       "      <td>1357.701</td>\n",
       "      <td>374264.0</td>\n",
       "      <td>275.660105</td>\n",
       "      <td>-95.445825</td>\n",
       "      <td>29.188391</td>\n",
       "      <td>0.627557</td>\n",
       "      <td>36.1</td>\n",
       "      <td>36.820493</td>\n",
       "      <td>...</td>\n",
       "      <td>0.069467</td>\n",
       "      <td>46.453077</td>\n",
       "      <td>([78, 112], {'peak_heights': [16.8664365260885...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.103291</td>\n",
       "      <td>10.089309</td>\n",
       "      <td>46.453077</td>\n",
       "      <td>659.579960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48141</th>\n",
       "      <td>Texas</td>\n",
       "      <td>El Paso County</td>\n",
       "      <td>1012.693</td>\n",
       "      <td>839238.0</td>\n",
       "      <td>828.719069</td>\n",
       "      <td>-106.235906</td>\n",
       "      <td>31.772824</td>\n",
       "      <td>0.272981</td>\n",
       "      <td>32.7</td>\n",
       "      <td>35.656512</td>\n",
       "      <td>...</td>\n",
       "      <td>0.013597</td>\n",
       "      <td>43.525879</td>\n",
       "      <td>([73, 105], {'peak_heights': [12.0857934731949...</td>\n",
       "      <td>[(105, 43.52587874783009)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-12</td>\n",
       "      <td>7.447232</td>\n",
       "      <td>11.358615</td>\n",
       "      <td>43.525879</td>\n",
       "      <td>659.629818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48139</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Ellis County</td>\n",
       "      <td>935.487</td>\n",
       "      <td>184826.0</td>\n",
       "      <td>197.571960</td>\n",
       "      <td>-96.796901</td>\n",
       "      <td>32.347956</td>\n",
       "      <td>0.734402</td>\n",
       "      <td>36.4</td>\n",
       "      <td>37.178508</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008489</td>\n",
       "      <td>42.279147</td>\n",
       "      <td>([107], {'peak_heights': [42.2791474916176]})</td>\n",
       "      <td>[(107, 42.2791474916176)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>3.400882</td>\n",
       "      <td>8.950368</td>\n",
       "      <td>42.279147</td>\n",
       "      <td>686.228931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36027</th>\n",
       "      <td>New York</td>\n",
       "      <td>Dutchess County</td>\n",
       "      <td>795.630</td>\n",
       "      <td>294218.0</td>\n",
       "      <td>369.792491</td>\n",
       "      <td>-73.743311</td>\n",
       "      <td>41.763490</td>\n",
       "      <td>0.498130</td>\n",
       "      <td>42.5</td>\n",
       "      <td>41.914856</td>\n",
       "      <td>...</td>\n",
       "      <td>0.037126</td>\n",
       "      <td>41.174523</td>\n",
       "      <td>([10, 51], {'peak_heights': [41.17452268143252...</td>\n",
       "      <td>[(10, 41.17452268143252), (51, 15.100561973968...</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>8.691320</td>\n",
       "      <td>12.506710</td>\n",
       "      <td>41.174523</td>\n",
       "      <td>626.711338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51620</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Franklin city</td>\n",
       "      <td>8.206</td>\n",
       "      <td>7967.0</td>\n",
       "      <td>970.874970</td>\n",
       "      <td>-76.939048</td>\n",
       "      <td>36.682607</td>\n",
       "      <td>0.360660</td>\n",
       "      <td>39.5</td>\n",
       "      <td>40.048011</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009665</td>\n",
       "      <td>39.448439</td>\n",
       "      <td>([21, 55, 106], {'peak_heights': [16.137997812...</td>\n",
       "      <td>[(106, 39.44843909698937)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-13</td>\n",
       "      <td>7.172443</td>\n",
       "      <td>9.327322</td>\n",
       "      <td>39.448439</td>\n",
       "      <td>699.406388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13217</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Newton County</td>\n",
       "      <td>272.161</td>\n",
       "      <td>111744.0</td>\n",
       "      <td>410.580502</td>\n",
       "      <td>-83.851276</td>\n",
       "      <td>33.557022</td>\n",
       "      <td>0.487983</td>\n",
       "      <td>36.5</td>\n",
       "      <td>37.295515</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012117</td>\n",
       "      <td>34.645516</td>\n",
       "      <td>([112], {'peak_heights': [34.64551628211422]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5.369416</td>\n",
       "      <td>8.510546</td>\n",
       "      <td>34.645516</td>\n",
       "      <td>631.998610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12069</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Lake County</td>\n",
       "      <td>938.381</td>\n",
       "      <td>367118.0</td>\n",
       "      <td>391.224886</td>\n",
       "      <td>-81.711578</td>\n",
       "      <td>28.760584</td>\n",
       "      <td>0.619224</td>\n",
       "      <td>47.4</td>\n",
       "      <td>45.363779</td>\n",
       "      <td>...</td>\n",
       "      <td>0.022595</td>\n",
       "      <td>32.725951</td>\n",
       "      <td>([110], {'peak_heights': [32.72595109552164]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.906744</td>\n",
       "      <td>7.378819</td>\n",
       "      <td>32.725951</td>\n",
       "      <td>631.040532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42103</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Pike County</td>\n",
       "      <td>544.961</td>\n",
       "      <td>55809.0</td>\n",
       "      <td>102.409163</td>\n",
       "      <td>-75.031473</td>\n",
       "      <td>41.330298</td>\n",
       "      <td>0.634324</td>\n",
       "      <td>49.1</td>\n",
       "      <td>45.441551</td>\n",
       "      <td>...</td>\n",
       "      <td>0.016001</td>\n",
       "      <td>30.717012</td>\n",
       "      <td>([16], {'peak_heights': [30.71701184908732]})</td>\n",
       "      <td>[(16, 30.71701184908732)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>4.095602</td>\n",
       "      <td>7.831940</td>\n",
       "      <td>30.717012</td>\n",
       "      <td>611.413116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51023</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Botetourt County</td>\n",
       "      <td>541.202</td>\n",
       "      <td>33419.0</td>\n",
       "      <td>61.749587</td>\n",
       "      <td>-79.812802</td>\n",
       "      <td>37.556307</td>\n",
       "      <td>0.748503</td>\n",
       "      <td>48.2</td>\n",
       "      <td>44.839986</td>\n",
       "      <td>...</td>\n",
       "      <td>0.007840</td>\n",
       "      <td>27.358261</td>\n",
       "      <td>([96], {'peak_heights': [27.35826069857609]})</td>\n",
       "      <td>[(96, 27.35826069857609)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>2.564837</td>\n",
       "      <td>4.270978</td>\n",
       "      <td>27.358261</td>\n",
       "      <td>650.320949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9003</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Hartford County</td>\n",
       "      <td>735.097</td>\n",
       "      <td>891720.0</td>\n",
       "      <td>1213.064398</td>\n",
       "      <td>-72.732326</td>\n",
       "      <td>41.803893</td>\n",
       "      <td>0.381323</td>\n",
       "      <td>40.2</td>\n",
       "      <td>40.815901</td>\n",
       "      <td>...</td>\n",
       "      <td>0.060615</td>\n",
       "      <td>26.241421</td>\n",
       "      <td>([28, 60], {'peak_heights': [26.24142107387969...</td>\n",
       "      <td>[(28, 26.241421073879696), (60, 12.47989439349...</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-05-28</td>\n",
       "      <td>9.476069</td>\n",
       "      <td>11.705013</td>\n",
       "      <td>26.241421</td>\n",
       "      <td>660.197441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51065</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Fluvanna County</td>\n",
       "      <td>286.005</td>\n",
       "      <td>27270.0</td>\n",
       "      <td>95.347983</td>\n",
       "      <td>-78.275447</td>\n",
       "      <td>37.841775</td>\n",
       "      <td>0.549472</td>\n",
       "      <td>43.6</td>\n",
       "      <td>42.742391</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009424</td>\n",
       "      <td>26.193096</td>\n",
       "      <td>([17], {'peak_heights': [26.193095500026192]})</td>\n",
       "      <td>[(17, 26.193095500026192)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-15</td>\n",
       "      <td>2.619310</td>\n",
       "      <td>4.512565</td>\n",
       "      <td>26.193096</td>\n",
       "      <td>618.464238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12053</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Hernando County</td>\n",
       "      <td>472.539</td>\n",
       "      <td>193920.0</td>\n",
       "      <td>410.378826</td>\n",
       "      <td>-82.423249</td>\n",
       "      <td>28.553330</td>\n",
       "      <td>0.649700</td>\n",
       "      <td>48.7</td>\n",
       "      <td>46.008354</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014331</td>\n",
       "      <td>26.078501</td>\n",
       "      <td>([107], {'peak_heights': [26.07850070721358]})</td>\n",
       "      <td>[(107, 26.07850070721358)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-14</td>\n",
       "      <td>1.031353</td>\n",
       "      <td>4.825259</td>\n",
       "      <td>26.078501</td>\n",
       "      <td>616.856528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17163</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>St. Clair County</td>\n",
       "      <td>657.759</td>\n",
       "      <td>259686.0</td>\n",
       "      <td>394.804176</td>\n",
       "      <td>-89.930509</td>\n",
       "      <td>38.470092</td>\n",
       "      <td>0.469903</td>\n",
       "      <td>39.2</td>\n",
       "      <td>39.722434</td>\n",
       "      <td>...</td>\n",
       "      <td>0.015619</td>\n",
       "      <td>25.690367</td>\n",
       "      <td>([58, 113], {'peak_heights': [10.1771260016217...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>8.141701</td>\n",
       "      <td>8.662862</td>\n",
       "      <td>23.104827</td>\n",
       "      <td>703.119642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48157</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Fort Bend County</td>\n",
       "      <td>861.481</td>\n",
       "      <td>811688.0</td>\n",
       "      <td>942.200699</td>\n",
       "      <td>-95.773000</td>\n",
       "      <td>29.529543</td>\n",
       "      <td>0.465483</td>\n",
       "      <td>36.8</td>\n",
       "      <td>36.522438</td>\n",
       "      <td>...</td>\n",
       "      <td>0.209220</td>\n",
       "      <td>21.489608</td>\n",
       "      <td>([112], {'peak_heights': [21.489608252009567]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.197602</td>\n",
       "      <td>5.883034</td>\n",
       "      <td>21.489608</td>\n",
       "      <td>667.879866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9013</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Tolland County</td>\n",
       "      <td>410.214</td>\n",
       "      <td>150721.0</td>\n",
       "      <td>367.420420</td>\n",
       "      <td>-72.336335</td>\n",
       "      <td>41.861181</td>\n",
       "      <td>0.470344</td>\n",
       "      <td>37.6</td>\n",
       "      <td>39.812053</td>\n",
       "      <td>...</td>\n",
       "      <td>0.050484</td>\n",
       "      <td>21.420847</td>\n",
       "      <td>([19, 52], {'peak_heights': [10.04694577587538...</td>\n",
       "      <td>[(19, 10.046945775875386), (52, 21.42084665422...</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>3.341083</td>\n",
       "      <td>5.316966</td>\n",
       "      <td>21.420847</td>\n",
       "      <td>655.566729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37029</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Camden County</td>\n",
       "      <td>240.556</td>\n",
       "      <td>10867.0</td>\n",
       "      <td>45.174512</td>\n",
       "      <td>-76.179064</td>\n",
       "      <td>36.365041</td>\n",
       "      <td>0.735685</td>\n",
       "      <td>41.8</td>\n",
       "      <td>41.005107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.021441</td>\n",
       "      <td>21.033535</td>\n",
       "      <td>([87], {'peak_heights': [21.033535342912355]})</td>\n",
       "      <td>[(87, 21.033535342912355)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>0.657298</td>\n",
       "      <td>3.494058</td>\n",
       "      <td>21.033535</td>\n",
       "      <td>668.763702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55127</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Walworth County</td>\n",
       "      <td>555.127</td>\n",
       "      <td>103868.0</td>\n",
       "      <td>187.106734</td>\n",
       "      <td>-88.541542</td>\n",
       "      <td>42.668420</td>\n",
       "      <td>0.606661</td>\n",
       "      <td>40.8</td>\n",
       "      <td>40.914093</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010783</td>\n",
       "      <td>23.106250</td>\n",
       "      <td>([36, 115], {'peak_heights': [10.0402158783951...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6.533017</td>\n",
       "      <td>7.249658</td>\n",
       "      <td>20.768118</td>\n",
       "      <td>658.704622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13057</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Cherokee County</td>\n",
       "      <td>421.674</td>\n",
       "      <td>258773.0</td>\n",
       "      <td>613.680236</td>\n",
       "      <td>-84.474934</td>\n",
       "      <td>34.241765</td>\n",
       "      <td>0.761702</td>\n",
       "      <td>39.5</td>\n",
       "      <td>38.978187</td>\n",
       "      <td>...</td>\n",
       "      <td>0.020779</td>\n",
       "      <td>22.027028</td>\n",
       "      <td>([115], {'peak_heights': [22.027027549242]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>5.548161</td>\n",
       "      <td>6.646462</td>\n",
       "      <td>19.708393</td>\n",
       "      <td>705.426904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51053</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Dinwiddie County</td>\n",
       "      <td>503.716</td>\n",
       "      <td>28544.0</td>\n",
       "      <td>56.666852</td>\n",
       "      <td>-77.634920</td>\n",
       "      <td>37.079832</td>\n",
       "      <td>0.563654</td>\n",
       "      <td>43.3</td>\n",
       "      <td>41.947344</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008408</td>\n",
       "      <td>17.516816</td>\n",
       "      <td>([64], {'peak_heights': [17.516816143497756]})</td>\n",
       "      <td>[(64, 17.516816143497756)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>4.879684</td>\n",
       "      <td>5.048706</td>\n",
       "      <td>17.516816</td>\n",
       "      <td>627.920792</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13113</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Fayette County</td>\n",
       "      <td>194.342</td>\n",
       "      <td>114421.0</td>\n",
       "      <td>588.761050</td>\n",
       "      <td>-84.491165</td>\n",
       "      <td>33.410331</td>\n",
       "      <td>0.600837</td>\n",
       "      <td>43.3</td>\n",
       "      <td>41.503854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051153</td>\n",
       "      <td>19.726649</td>\n",
       "      <td>([116], {'peak_heights': [19.72664857974373]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.183731</td>\n",
       "      <td>4.557106</td>\n",
       "      <td>17.479309</td>\n",
       "      <td>697.138528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51075</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Goochland County</td>\n",
       "      <td>281.415</td>\n",
       "      <td>23753.0</td>\n",
       "      <td>84.405593</td>\n",
       "      <td>-77.913181</td>\n",
       "      <td>37.719753</td>\n",
       "      <td>0.631658</td>\n",
       "      <td>48.9</td>\n",
       "      <td>45.386162</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017219</td>\n",
       "      <td>17.441406</td>\n",
       "      <td>([20], {'peak_heights': [17.441405897600905]})</td>\n",
       "      <td>[(20, 17.441405897600905)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-18</td>\n",
       "      <td>4.209995</td>\n",
       "      <td>4.927487</td>\n",
       "      <td>17.441406</td>\n",
       "      <td>661.659661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51165</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Rockingham County</td>\n",
       "      <td>849.086</td>\n",
       "      <td>81948.0</td>\n",
       "      <td>96.513192</td>\n",
       "      <td>-78.873964</td>\n",
       "      <td>38.509544</td>\n",
       "      <td>0.735094</td>\n",
       "      <td>41.2</td>\n",
       "      <td>41.344328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009762</td>\n",
       "      <td>16.386698</td>\n",
       "      <td>([30, 64], {'peak_heights': [16.38669818491168...</td>\n",
       "      <td>[(30, 16.386698184911687), (64, 12.90016665620...</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-06-01</td>\n",
       "      <td>8.803492</td>\n",
       "      <td>8.675806</td>\n",
       "      <td>16.386698</td>\n",
       "      <td>679.164231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51770</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Roanoke city</td>\n",
       "      <td>42.562</td>\n",
       "      <td>99143.0</td>\n",
       "      <td>2329.378319</td>\n",
       "      <td>-79.959473</td>\n",
       "      <td>37.278788</td>\n",
       "      <td>0.398894</td>\n",
       "      <td>38.7</td>\n",
       "      <td>39.774442</td>\n",
       "      <td>...</td>\n",
       "      <td>0.035595</td>\n",
       "      <td>16.282397</td>\n",
       "      <td>([97], {'peak_heights': [16.282397287611975]})</td>\n",
       "      <td>[(97, 16.282397287611975)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-04</td>\n",
       "      <td>4.178668</td>\n",
       "      <td>5.542487</td>\n",
       "      <td>16.282397</td>\n",
       "      <td>670.663574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51069</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Frederick County</td>\n",
       "      <td>413.496</td>\n",
       "      <td>89313.0</td>\n",
       "      <td>215.994834</td>\n",
       "      <td>-78.263122</td>\n",
       "      <td>39.203874</td>\n",
       "      <td>0.686124</td>\n",
       "      <td>40.9</td>\n",
       "      <td>40.721927</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017612</td>\n",
       "      <td>15.835161</td>\n",
       "      <td>([65], {'peak_heights': [15.835160774867198]})</td>\n",
       "      <td>[(65, 15.835160774867198)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-06-02</td>\n",
       "      <td>5.118436</td>\n",
       "      <td>5.644591</td>\n",
       "      <td>15.835161</td>\n",
       "      <td>615.314320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51775</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Salem city</td>\n",
       "      <td>14.437</td>\n",
       "      <td>25301.0</td>\n",
       "      <td>1752.510909</td>\n",
       "      <td>-80.054263</td>\n",
       "      <td>37.284994</td>\n",
       "      <td>0.632307</td>\n",
       "      <td>40.4</td>\n",
       "      <td>41.136022</td>\n",
       "      <td>...</td>\n",
       "      <td>0.018774</td>\n",
       "      <td>15.809652</td>\n",
       "      <td>([37, 96], {'peak_heights': [10.72797800199879...</td>\n",
       "      <td>[(96, 15.809651792419272)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-03</td>\n",
       "      <td>2.258522</td>\n",
       "      <td>3.739438</td>\n",
       "      <td>15.809652</td>\n",
       "      <td>640.444454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17119</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>Madison County</td>\n",
       "      <td>715.582</td>\n",
       "      <td>262966.0</td>\n",
       "      <td>367.485487</td>\n",
       "      <td>-89.904854</td>\n",
       "      <td>38.829857</td>\n",
       "      <td>0.582191</td>\n",
       "      <td>40.4</td>\n",
       "      <td>40.780082</td>\n",
       "      <td>...</td>\n",
       "      <td>0.010306</td>\n",
       "      <td>15.971647</td>\n",
       "      <td>([115], {'peak_heights': [15.971646524645772]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.694122</td>\n",
       "      <td>4.677220</td>\n",
       "      <td>15.754345</td>\n",
       "      <td>685.901117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13297</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Walton County</td>\n",
       "      <td>325.680</td>\n",
       "      <td>94593.0</td>\n",
       "      <td>290.447679</td>\n",
       "      <td>-83.732818</td>\n",
       "      <td>33.779720</td>\n",
       "      <td>0.789634</td>\n",
       "      <td>39.0</td>\n",
       "      <td>39.094727</td>\n",
       "      <td>...</td>\n",
       "      <td>0.017010</td>\n",
       "      <td>17.518663</td>\n",
       "      <td>([62, 112], {'peak_heights': [11.9308133643232...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.228643</td>\n",
       "      <td>5.693830</td>\n",
       "      <td>15.404341</td>\n",
       "      <td>627.807510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17133</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>Monroe County</td>\n",
       "      <td>385.011</td>\n",
       "      <td>34637.0</td>\n",
       "      <td>89.963663</td>\n",
       "      <td>-90.178221</td>\n",
       "      <td>38.275371</td>\n",
       "      <td>0.695276</td>\n",
       "      <td>42.7</td>\n",
       "      <td>41.819514</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006034</td>\n",
       "      <td>14.847871</td>\n",
       "      <td>([16, 110], {'peak_heights': [14.8478711864686...</td>\n",
       "      <td>[(16, 14.847871186468641)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>3.299527</td>\n",
       "      <td>4.620061</td>\n",
       "      <td>14.847871</td>\n",
       "      <td>651.676381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6067</th>\n",
       "      <td>California</td>\n",
       "      <td>Sacramento County</td>\n",
       "      <td>964.644</td>\n",
       "      <td>1552058.0</td>\n",
       "      <td>1608.943818</td>\n",
       "      <td>-121.341842</td>\n",
       "      <td>38.452044</td>\n",
       "      <td>0.367942</td>\n",
       "      <td>36.6</td>\n",
       "      <td>38.276234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.170105</td>\n",
       "      <td>18.804526</td>\n",
       "      <td>([103], {'peak_heights': [14.662559554567457]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.698206</td>\n",
       "      <td>3.648563</td>\n",
       "      <td>14.837443</td>\n",
       "      <td>669.454023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17037</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>DeKalb County</td>\n",
       "      <td>631.307</td>\n",
       "      <td>104897.0</td>\n",
       "      <td>166.158462</td>\n",
       "      <td>-88.769975</td>\n",
       "      <td>41.893577</td>\n",
       "      <td>0.482620</td>\n",
       "      <td>31.7</td>\n",
       "      <td>36.185472</td>\n",
       "      <td>...</td>\n",
       "      <td>0.027074</td>\n",
       "      <td>13.754990</td>\n",
       "      <td>([66, 109], {'peak_heights': [13.7549895884261...</td>\n",
       "      <td>[(66, 13.754989588426199)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-06-03</td>\n",
       "      <td>5.175145</td>\n",
       "      <td>5.713924</td>\n",
       "      <td>13.754990</td>\n",
       "      <td>667.560794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13117</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Forsyth County</td>\n",
       "      <td>224.021</td>\n",
       "      <td>244252.0</td>\n",
       "      <td>1090.308498</td>\n",
       "      <td>-84.122798</td>\n",
       "      <td>34.227224</td>\n",
       "      <td>0.748567</td>\n",
       "      <td>39.0</td>\n",
       "      <td>37.588495</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155483</td>\n",
       "      <td>14.270975</td>\n",
       "      <td>([106], {'peak_heights': [13.686099368099926]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.392281</td>\n",
       "      <td>4.668234</td>\n",
       "      <td>13.686099</td>\n",
       "      <td>699.343689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26161</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Washtenaw County</td>\n",
       "      <td>705.965</td>\n",
       "      <td>367601.0</td>\n",
       "      <td>520.707117</td>\n",
       "      <td>-83.838673</td>\n",
       "      <td>42.253101</td>\n",
       "      <td>0.282675</td>\n",
       "      <td>34.1</td>\n",
       "      <td>37.818838</td>\n",
       "      <td>...</td>\n",
       "      <td>0.093767</td>\n",
       "      <td>13.446256</td>\n",
       "      <td>([4, 68], {'peak_heights': [13.446255975520042...</td>\n",
       "      <td>[(4, 13.446255975520042), (68, 12.202671607841...</td>\n",
       "      <td>2</td>\n",
       "      <td>2020-06-05</td>\n",
       "      <td>3.789046</td>\n",
       "      <td>4.726507</td>\n",
       "      <td>13.446256</td>\n",
       "      <td>697.891433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51127</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>New Kent County</td>\n",
       "      <td>209.729</td>\n",
       "      <td>23091.0</td>\n",
       "      <td>110.099223</td>\n",
       "      <td>-76.993126</td>\n",
       "      <td>37.507830</td>\n",
       "      <td>0.695988</td>\n",
       "      <td>43.1</td>\n",
       "      <td>41.780023</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012213</td>\n",
       "      <td>14.229415</td>\n",
       "      <td>([116], {'peak_heights': [14.22941529476543]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.371569</td>\n",
       "      <td>3.109632</td>\n",
       "      <td>12.992075</td>\n",
       "      <td>707.194397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27037</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Dakota County</td>\n",
       "      <td>562.168</td>\n",
       "      <td>429021.0</td>\n",
       "      <td>763.154431</td>\n",
       "      <td>-93.070602</td>\n",
       "      <td>44.671186</td>\n",
       "      <td>0.474056</td>\n",
       "      <td>38.4</td>\n",
       "      <td>38.847801</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052196</td>\n",
       "      <td>12.919780</td>\n",
       "      <td>([53, 109], {'peak_heights': [12.2205140141325...</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>6.826173</td>\n",
       "      <td>5.965672</td>\n",
       "      <td>12.919780</td>\n",
       "      <td>697.874073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51161</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Roanoke County</td>\n",
       "      <td>250.518</td>\n",
       "      <td>94186.0</td>\n",
       "      <td>375.965001</td>\n",
       "      <td>-80.052881</td>\n",
       "      <td>37.269754</td>\n",
       "      <td>0.646149</td>\n",
       "      <td>44.4</td>\n",
       "      <td>43.261844</td>\n",
       "      <td>...</td>\n",
       "      <td>0.034846</td>\n",
       "      <td>11.830694</td>\n",
       "      <td>([97], {'peak_heights': [11.830693673005692]})</td>\n",
       "      <td>[(97, 11.830693673005692)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-07-04</td>\n",
       "      <td>2.426809</td>\n",
       "      <td>3.253042</td>\n",
       "      <td>11.830694</td>\n",
       "      <td>637.556824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51079</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Greene County</td>\n",
       "      <td>156.246</td>\n",
       "      <td>19819.0</td>\n",
       "      <td>126.844847</td>\n",
       "      <td>-78.458827</td>\n",
       "      <td>38.296094</td>\n",
       "      <td>0.670312</td>\n",
       "      <td>41.0</td>\n",
       "      <td>40.551869</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019880</td>\n",
       "      <td>15.857799</td>\n",
       "      <td>([91], {'peak_heights': [11.532944576993216]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.883236</td>\n",
       "      <td>4.008710</td>\n",
       "      <td>11.532945</td>\n",
       "      <td>688.416499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26093</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Livingston County</td>\n",
       "      <td>565.254</td>\n",
       "      <td>191995.0</td>\n",
       "      <td>339.661462</td>\n",
       "      <td>-83.912221</td>\n",
       "      <td>42.602918</td>\n",
       "      <td>0.656380</td>\n",
       "      <td>43.7</td>\n",
       "      <td>41.863707</td>\n",
       "      <td>...</td>\n",
       "      <td>0.009828</td>\n",
       "      <td>10.947706</td>\n",
       "      <td>([72], {'peak_heights': [10.947705731424058]})</td>\n",
       "      <td>[(72, 10.947705731424058)]</td>\n",
       "      <td>1</td>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>2.008981</td>\n",
       "      <td>2.878364</td>\n",
       "      <td>10.947706</td>\n",
       "      <td>627.526789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41051</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Multnomah County</td>\n",
       "      <td>431.297</td>\n",
       "      <td>812855.0</td>\n",
       "      <td>1884.675757</td>\n",
       "      <td>-122.420855</td>\n",
       "      <td>45.546839</td>\n",
       "      <td>0.188491</td>\n",
       "      <td>37.7</td>\n",
       "      <td>39.209321</td>\n",
       "      <td>...</td>\n",
       "      <td>0.080792</td>\n",
       "      <td>10.544843</td>\n",
       "      <td>([113], {'peak_heights': [10.544843264085934]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.434101</td>\n",
       "      <td>3.506777</td>\n",
       "      <td>10.544843</td>\n",
       "      <td>688.926897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51101</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>King William County</td>\n",
       "      <td>273.941</td>\n",
       "      <td>17148.0</td>\n",
       "      <td>62.597421</td>\n",
       "      <td>-77.098117</td>\n",
       "      <td>37.710650</td>\n",
       "      <td>0.684030</td>\n",
       "      <td>40.1</td>\n",
       "      <td>40.136459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.011255</td>\n",
       "      <td>9.997001</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.499250</td>\n",
       "      <td>3.113101</td>\n",
       "      <td>9.997001</td>\n",
       "      <td>618.644930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51680</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Lynchburg city</td>\n",
       "      <td>49.128</td>\n",
       "      <td>82168.0</td>\n",
       "      <td>1672.528904</td>\n",
       "      <td>-79.192012</td>\n",
       "      <td>37.399706</td>\n",
       "      <td>0.548667</td>\n",
       "      <td>28.4</td>\n",
       "      <td>35.725282</td>\n",
       "      <td>...</td>\n",
       "      <td>0.026945</td>\n",
       "      <td>16.342824</td>\n",
       "      <td>([115], {'peak_heights': [16.34282376177031]})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.738598</td>\n",
       "      <td>2.330332</td>\n",
       "      <td>9.910010</td>\n",
       "      <td>610.272330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51085</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Hanover County</td>\n",
       "      <td>468.536</td>\n",
       "      <td>107766.0</td>\n",
       "      <td>230.005805</td>\n",
       "      <td>-77.499373</td>\n",
       "      <td>37.765620</td>\n",
       "      <td>0.671558</td>\n",
       "      <td>42.8</td>\n",
       "      <td>41.530167</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019431</td>\n",
       "      <td>9.809614</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.844308</td>\n",
       "      <td>4.090828</td>\n",
       "      <td>9.809614</td>\n",
       "      <td>684.978074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51145</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Powhatan County</td>\n",
       "      <td>260.221</td>\n",
       "      <td>29652.0</td>\n",
       "      <td>113.949297</td>\n",
       "      <td>-77.915137</td>\n",
       "      <td>37.551349</td>\n",
       "      <td>0.745375</td>\n",
       "      <td>46.0</td>\n",
       "      <td>43.380716</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006003</td>\n",
       "      <td>9.153803</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.408896</td>\n",
       "      <td>2.983650</td>\n",
       "      <td>9.153803</td>\n",
       "      <td>643.073923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51009</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Amherst County</td>\n",
       "      <td>473.934</td>\n",
       "      <td>31605.0</td>\n",
       "      <td>66.686501</td>\n",
       "      <td>-79.144294</td>\n",
       "      <td>37.603520</td>\n",
       "      <td>0.657756</td>\n",
       "      <td>45.5</td>\n",
       "      <td>43.338317</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005695</td>\n",
       "      <td>11.752209</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>0.904016</td>\n",
       "      <td>1.280689</td>\n",
       "      <td>9.040161</td>\n",
       "      <td>693.748847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26115</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Monroe County</td>\n",
       "      <td>549.394</td>\n",
       "      <td>150500.0</td>\n",
       "      <td>273.938194</td>\n",
       "      <td>-83.537421</td>\n",
       "      <td>41.928414</td>\n",
       "      <td>0.616921</td>\n",
       "      <td>43.0</td>\n",
       "      <td>41.851429</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006884</td>\n",
       "      <td>8.922639</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.512103</td>\n",
       "      <td>4.059984</td>\n",
       "      <td>8.922639</td>\n",
       "      <td>638.839633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53033</th>\n",
       "      <td>Washington</td>\n",
       "      <td>King County</td>\n",
       "      <td>2115.566</td>\n",
       "      <td>2252782.0</td>\n",
       "      <td>1064.860184</td>\n",
       "      <td>-122.258812</td>\n",
       "      <td>47.484886</td>\n",
       "      <td>0.231463</td>\n",
       "      <td>37.0</td>\n",
       "      <td>38.693362</td>\n",
       "      <td>...</td>\n",
       "      <td>0.197391</td>\n",
       "      <td>8.782791</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.048962</td>\n",
       "      <td>4.412089</td>\n",
       "      <td>8.782791</td>\n",
       "      <td>649.949210</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29099</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>656.629</td>\n",
       "      <td>225081.0</td>\n",
       "      <td>342.782606</td>\n",
       "      <td>-90.539312</td>\n",
       "      <td>38.265035</td>\n",
       "      <td>0.686215</td>\n",
       "      <td>40.0</td>\n",
       "      <td>39.896804</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008170</td>\n",
       "      <td>13.074658</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.904076</td>\n",
       "      <td>2.582752</td>\n",
       "      <td>8.504875</td>\n",
       "      <td>650.694528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55131</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Washington County</td>\n",
       "      <td>430.703</td>\n",
       "      <td>136034.0</td>\n",
       "      <td>315.841775</td>\n",
       "      <td>-88.230419</td>\n",
       "      <td>43.368646</td>\n",
       "      <td>0.712678</td>\n",
       "      <td>43.5</td>\n",
       "      <td>42.006594</td>\n",
       "      <td>...</td>\n",
       "      <td>0.014680</td>\n",
       "      <td>13.337002</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.415363</td>\n",
       "      <td>3.321814</td>\n",
       "      <td>8.401261</td>\n",
       "      <td>616.620353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39153</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Summit County</td>\n",
       "      <td>412.748</td>\n",
       "      <td>541013.0</td>\n",
       "      <td>1310.758623</td>\n",
       "      <td>-81.533176</td>\n",
       "      <td>41.124021</td>\n",
       "      <td>0.454869</td>\n",
       "      <td>41.1</td>\n",
       "      <td>41.291194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.038966</td>\n",
       "      <td>7.921648</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.657161</td>\n",
       "      <td>4.029849</td>\n",
       "      <td>7.921648</td>\n",
       "      <td>683.173957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39103</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Medina County</td>\n",
       "      <td>421.358</td>\n",
       "      <td>179746.0</td>\n",
       "      <td>426.587368</td>\n",
       "      <td>-81.899719</td>\n",
       "      <td>41.117862</td>\n",
       "      <td>0.630058</td>\n",
       "      <td>42.7</td>\n",
       "      <td>41.554277</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012963</td>\n",
       "      <td>7.788769</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>2.583010</td>\n",
       "      <td>3.094035</td>\n",
       "      <td>7.788769</td>\n",
       "      <td>610.721668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39055</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Geauga County</td>\n",
       "      <td>400.164</td>\n",
       "      <td>93649.0</td>\n",
       "      <td>234.026549</td>\n",
       "      <td>-81.179194</td>\n",
       "      <td>41.499614</td>\n",
       "      <td>0.632417</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.621304</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006727</td>\n",
       "      <td>7.169629</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>4.042450</td>\n",
       "      <td>4.184291</td>\n",
       "      <td>7.169629</td>\n",
       "      <td>706.783925</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51109</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Louisa County</td>\n",
       "      <td>496.300</td>\n",
       "      <td>37591.0</td>\n",
       "      <td>75.742494</td>\n",
       "      <td>-77.962143</td>\n",
       "      <td>37.977320</td>\n",
       "      <td>0.628913</td>\n",
       "      <td>45.2</td>\n",
       "      <td>42.999721</td>\n",
       "      <td>...</td>\n",
       "      <td>0.006464</td>\n",
       "      <td>6.460513</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>3.040241</td>\n",
       "      <td>3.343599</td>\n",
       "      <td>6.460513</td>\n",
       "      <td>660.756407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51019</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Bedford County</td>\n",
       "      <td>753.021</td>\n",
       "      <td>78997.0</td>\n",
       "      <td>104.906769</td>\n",
       "      <td>-79.526608</td>\n",
       "      <td>37.311302</td>\n",
       "      <td>0.758379</td>\n",
       "      <td>47.0</td>\n",
       "      <td>43.902965</td>\n",
       "      <td>...</td>\n",
       "      <td>0.012342</td>\n",
       "      <td>7.233548</td>\n",
       "      <td>([], {'peak_heights': []})</td>\n",
       "      <td>[]</td>\n",
       "      <td>0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>1.627548</td>\n",
       "      <td>1.954327</td>\n",
       "      <td>6.148516</td>\n",
       "      <td>642.108672</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>58 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   st               county      area        pop      density  \\\n",
       "fips                                                                           \n",
       "22051       Louisiana     Jefferson Parish   295.632   432493.0  1462.943795   \n",
       "51175        Virginia   Southampton County   599.145    17631.0    29.426933   \n",
       "6099       California    Stanislaus County  1494.827   550660.0   368.377076   \n",
       "12099         Florida    Palm Beach County  1969.763  1496770.0   759.873142   \n",
       "12115         Florida      Sarasota County   555.868   433742.0   780.296761   \n",
       "51139        Virginia          Page County   310.856    23902.0    76.890908   \n",
       "48039           Texas      Brazoria County  1357.701   374264.0   275.660105   \n",
       "48141           Texas       El Paso County  1012.693   839238.0   828.719069   \n",
       "48139           Texas         Ellis County   935.487   184826.0   197.571960   \n",
       "36027        New York      Dutchess County   795.630   294218.0   369.792491   \n",
       "51620        Virginia        Franklin city     8.206     7967.0   970.874970   \n",
       "13217         Georgia        Newton County   272.161   111744.0   410.580502   \n",
       "12069         Florida          Lake County   938.381   367118.0   391.224886   \n",
       "42103    Pennsylvania          Pike County   544.961    55809.0   102.409163   \n",
       "51023        Virginia     Botetourt County   541.202    33419.0    61.749587   \n",
       "9003      Connecticut      Hartford County   735.097   891720.0  1213.064398   \n",
       "51065        Virginia      Fluvanna County   286.005    27270.0    95.347983   \n",
       "12053         Florida      Hernando County   472.539   193920.0   410.378826   \n",
       "17163        Illinois     St. Clair County   657.759   259686.0   394.804176   \n",
       "48157           Texas     Fort Bend County   861.481   811688.0   942.200699   \n",
       "9013      Connecticut       Tolland County   410.214   150721.0   367.420420   \n",
       "37029  North Carolina        Camden County   240.556    10867.0    45.174512   \n",
       "55127       Wisconsin      Walworth County   555.127   103868.0   187.106734   \n",
       "13057         Georgia      Cherokee County   421.674   258773.0   613.680236   \n",
       "51053        Virginia     Dinwiddie County   503.716    28544.0    56.666852   \n",
       "13113         Georgia       Fayette County   194.342   114421.0   588.761050   \n",
       "51075        Virginia     Goochland County   281.415    23753.0    84.405593   \n",
       "51165        Virginia    Rockingham County   849.086    81948.0    96.513192   \n",
       "51770        Virginia         Roanoke city    42.562    99143.0  2329.378319   \n",
       "51069        Virginia     Frederick County   413.496    89313.0   215.994834   \n",
       "51775        Virginia           Salem city    14.437    25301.0  1752.510909   \n",
       "17119        Illinois       Madison County   715.582   262966.0   367.485487   \n",
       "13297         Georgia        Walton County   325.680    94593.0   290.447679   \n",
       "17133        Illinois        Monroe County   385.011    34637.0    89.963663   \n",
       "6067       California    Sacramento County   964.644  1552058.0  1608.943818   \n",
       "17037        Illinois        DeKalb County   631.307   104897.0   166.158462   \n",
       "13117         Georgia       Forsyth County   224.021   244252.0  1090.308498   \n",
       "26161        Michigan     Washtenaw County   705.965   367601.0   520.707117   \n",
       "51127        Virginia      New Kent County   209.729    23091.0   110.099223   \n",
       "27037       Minnesota        Dakota County   562.168   429021.0   763.154431   \n",
       "51161        Virginia       Roanoke County   250.518    94186.0   375.965001   \n",
       "51079        Virginia        Greene County   156.246    19819.0   126.844847   \n",
       "26093        Michigan    Livingston County   565.254   191995.0   339.661462   \n",
       "41051          Oregon     Multnomah County   431.297   812855.0  1884.675757   \n",
       "51101        Virginia  King William County   273.941    17148.0    62.597421   \n",
       "51680        Virginia       Lynchburg city    49.128    82168.0  1672.528904   \n",
       "51085        Virginia       Hanover County   468.536   107766.0   230.005805   \n",
       "51145        Virginia      Powhatan County   260.221    29652.0   113.949297   \n",
       "51009        Virginia       Amherst County   473.934    31605.0    66.686501   \n",
       "26115        Michigan        Monroe County   549.394   150500.0   273.938194   \n",
       "53033      Washington          King County  2115.566  2252782.0  1064.860184   \n",
       "29099        Missouri     Jefferson County   656.629   225081.0   342.782606   \n",
       "55131       Wisconsin    Washington County   430.703   136034.0   315.841775   \n",
       "39153            Ohio        Summit County   412.748   541013.0  1310.758623   \n",
       "39103            Ohio        Medina County   421.358   179746.0   426.587368   \n",
       "39055            Ohio        Geauga County   400.164    93649.0   234.026549   \n",
       "51109        Virginia        Louisa County   496.300    37591.0    75.742494   \n",
       "51019        Virginia       Bedford County   753.021    78997.0   104.906769   \n",
       "\n",
       "        longitude   latitude  conservative  median_age   mean_age  ...  \\\n",
       "fips                                                               ...   \n",
       "22051  -90.135227  29.827561      0.576775        39.6  40.442394  ...   \n",
       "51175  -77.102646  36.723332      0.583430        47.4  44.167801  ...   \n",
       "6099  -120.997492  37.558041      0.490156        34.4  36.551284  ...   \n",
       "12099  -80.465018  26.647331      0.420974        45.2  44.349362  ...   \n",
       "12115  -82.331502  27.184592      0.559755        57.1  51.479128  ...   \n",
       "51139  -78.481092  38.621787      0.756984        45.8  43.619572  ...   \n",
       "48039  -95.445825  29.188391      0.627557        36.1  36.820493  ...   \n",
       "48141 -106.235906  31.772824      0.272981        32.7  35.656512  ...   \n",
       "48139  -96.796901  32.347956      0.734402        36.4  37.178508  ...   \n",
       "36027  -73.743311  41.763490      0.498130        42.5  41.914856  ...   \n",
       "51620  -76.939048  36.682607      0.360660        39.5  40.048011  ...   \n",
       "13217  -83.851276  33.557022      0.487983        36.5  37.295515  ...   \n",
       "12069  -81.711578  28.760584      0.619224        47.4  45.363779  ...   \n",
       "42103  -75.031473  41.330298      0.634324        49.1  45.441551  ...   \n",
       "51023  -79.812802  37.556307      0.748503        48.2  44.839986  ...   \n",
       "9003   -72.732326  41.803893      0.381323        40.2  40.815901  ...   \n",
       "51065  -78.275447  37.841775      0.549472        43.6  42.742391  ...   \n",
       "12053  -82.423249  28.553330      0.649700        48.7  46.008354  ...   \n",
       "17163  -89.930509  38.470092      0.469903        39.2  39.722434  ...   \n",
       "48157  -95.773000  29.529543      0.465483        36.8  36.522438  ...   \n",
       "9013   -72.336335  41.861181      0.470344        37.6  39.812053  ...   \n",
       "37029  -76.179064  36.365041      0.735685        41.8  41.005107  ...   \n",
       "55127  -88.541542  42.668420      0.606661        40.8  40.914093  ...   \n",
       "13057  -84.474934  34.241765      0.761702        39.5  38.978187  ...   \n",
       "51053  -77.634920  37.079832      0.563654        43.3  41.947344  ...   \n",
       "13113  -84.491165  33.410331      0.600837        43.3  41.503854  ...   \n",
       "51075  -77.913181  37.719753      0.631658        48.9  45.386162  ...   \n",
       "51165  -78.873964  38.509544      0.735094        41.2  41.344328  ...   \n",
       "51770  -79.959473  37.278788      0.398894        38.7  39.774442  ...   \n",
       "51069  -78.263122  39.203874      0.686124        40.9  40.721927  ...   \n",
       "51775  -80.054263  37.284994      0.632307        40.4  41.136022  ...   \n",
       "17119  -89.904854  38.829857      0.582191        40.4  40.780082  ...   \n",
       "13297  -83.732818  33.779720      0.789634        39.0  39.094727  ...   \n",
       "17133  -90.178221  38.275371      0.695276        42.7  41.819514  ...   \n",
       "6067  -121.341842  38.452044      0.367942        36.6  38.276234  ...   \n",
       "17037  -88.769975  41.893577      0.482620        31.7  36.185472  ...   \n",
       "13117  -84.122798  34.227224      0.748567        39.0  37.588495  ...   \n",
       "26161  -83.838673  42.253101      0.282675        34.1  37.818838  ...   \n",
       "51127  -76.993126  37.507830      0.695988        43.1  41.780023  ...   \n",
       "27037  -93.070602  44.671186      0.474056        38.4  38.847801  ...   \n",
       "51161  -80.052881  37.269754      0.646149        44.4  43.261844  ...   \n",
       "51079  -78.458827  38.296094      0.670312        41.0  40.551869  ...   \n",
       "26093  -83.912221  42.602918      0.656380        43.7  41.863707  ...   \n",
       "41051 -122.420855  45.546839      0.188491        37.7  39.209321  ...   \n",
       "51101  -77.098117  37.710650      0.684030        40.1  40.136459  ...   \n",
       "51680  -79.192012  37.399706      0.548667        28.4  35.725282  ...   \n",
       "51085  -77.499373  37.765620      0.671558        42.8  41.530167  ...   \n",
       "51145  -77.915137  37.551349      0.745375        46.0  43.380716  ...   \n",
       "51009  -79.144294  37.603520      0.657756        45.5  43.338317  ...   \n",
       "26115  -83.537421  41.928414      0.616921        43.0  41.851429  ...   \n",
       "53033 -122.258812  47.484886      0.231463        37.0  38.693362  ...   \n",
       "29099  -90.539312  38.265035      0.686215        40.0  39.896804  ...   \n",
       "55131  -88.230419  43.368646      0.712678        43.5  42.006594  ...   \n",
       "39153  -81.533176  41.124021      0.454869        41.1  41.291194  ...   \n",
       "39103  -81.899719  41.117862      0.630058        42.7  41.554277  ...   \n",
       "39055  -81.179194  41.499614      0.632417        45.0  42.621304  ...   \n",
       "51109  -77.962143  37.977320      0.628913        45.2  42.999721  ...   \n",
       "51019  -79.526608  37.311302      0.758379        47.0  43.902965  ...   \n",
       "\n",
       "          asian  max_height  \\\n",
       "fips                          \n",
       "22051  0.042697   90.141839   \n",
       "51175  0.005105   80.215854   \n",
       "6099   0.061424   65.194494   \n",
       "12099  0.029446   49.611592   \n",
       "12115  0.018387   46.900824   \n",
       "51139  0.005062   46.618932   \n",
       "48039  0.069467   46.453077   \n",
       "48141  0.013597   43.525879   \n",
       "48139  0.008489   42.279147   \n",
       "36027  0.037126   41.174523   \n",
       "51620  0.009665   39.448439   \n",
       "13217  0.012117   34.645516   \n",
       "12069  0.022595   32.725951   \n",
       "42103  0.016001   30.717012   \n",
       "51023  0.007840   27.358261   \n",
       "9003   0.060615   26.241421   \n",
       "51065  0.009424   26.193096   \n",
       "12053  0.014331   26.078501   \n",
       "17163  0.015619   25.690367   \n",
       "48157  0.209220   21.489608   \n",
       "9013   0.050484   21.420847   \n",
       "37029  0.021441   21.033535   \n",
       "55127  0.010783   23.106250   \n",
       "13057  0.020779   22.027028   \n",
       "51053  0.008408   17.516816   \n",
       "13113  0.051153   19.726649   \n",
       "51075  0.017219   17.441406   \n",
       "51165  0.009762   16.386698   \n",
       "51770  0.035595   16.282397   \n",
       "51069  0.017612   15.835161   \n",
       "51775  0.018774   15.809652   \n",
       "17119  0.010306   15.971647   \n",
       "13297  0.017010   17.518663   \n",
       "17133  0.006034   14.847871   \n",
       "6067   0.170105   18.804526   \n",
       "17037  0.027074   13.754990   \n",
       "13117  0.155483   14.270975   \n",
       "26161  0.093767   13.446256   \n",
       "51127  0.012213   14.229415   \n",
       "27037  0.052196   12.919780   \n",
       "51161  0.034846   11.830694   \n",
       "51079  0.019880   15.857799   \n",
       "26093  0.009828   10.947706   \n",
       "41051  0.080792   10.544843   \n",
       "51101  0.011255    9.997001   \n",
       "51680  0.026945   16.342824   \n",
       "51085  0.019431    9.809614   \n",
       "51145  0.006003    9.153803   \n",
       "51009  0.005695   11.752209   \n",
       "26115  0.006884    8.922639   \n",
       "53033  0.197391    8.782791   \n",
       "29099  0.008170   13.074658   \n",
       "55131  0.014680   13.337002   \n",
       "39153  0.038966    7.921648   \n",
       "39103  0.012963    7.788769   \n",
       "39055  0.006727    7.169629   \n",
       "51109  0.006464    6.460513   \n",
       "51019  0.012342    7.233548   \n",
       "\n",
       "                                           peak_info_raw  \\\n",
       "fips                                                       \n",
       "22051  ([9, 39, 78, 109], {'peak_heights': [90.141838...   \n",
       "51175  ([28, 111], {'peak_heights': [80.2158535696054...   \n",
       "6099      ([108], {'peak_heights': [65.19449388007119]})   \n",
       "12099  ([81, 111], {'peak_heights': [18.7737594954468...   \n",
       "12115      ([111], {'peak_heights': [46.9008238603989]})   \n",
       "51139  ([32, 65, 100], {'peak_heights': [46.618932067...   \n",
       "48039  ([78, 112], {'peak_heights': [16.8664365260885...   \n",
       "48141  ([73, 105], {'peak_heights': [12.0857934731949...   \n",
       "48139      ([107], {'peak_heights': [42.2791474916176]})   \n",
       "36027  ([10, 51], {'peak_heights': [41.17452268143252...   \n",
       "51620  ([21, 55, 106], {'peak_heights': [16.137997812...   \n",
       "13217     ([112], {'peak_heights': [34.64551628211422]})   \n",
       "12069     ([110], {'peak_heights': [32.72595109552164]})   \n",
       "42103      ([16], {'peak_heights': [30.71701184908732]})   \n",
       "51023      ([96], {'peak_heights': [27.35826069857609]})   \n",
       "9003   ([28, 60], {'peak_heights': [26.24142107387969...   \n",
       "51065     ([17], {'peak_heights': [26.193095500026192]})   \n",
       "12053     ([107], {'peak_heights': [26.07850070721358]})   \n",
       "17163  ([58, 113], {'peak_heights': [10.1771260016217...   \n",
       "48157    ([112], {'peak_heights': [21.489608252009567]})   \n",
       "9013   ([19, 52], {'peak_heights': [10.04694577587538...   \n",
       "37029     ([87], {'peak_heights': [21.033535342912355]})   \n",
       "55127  ([36, 115], {'peak_heights': [10.0402158783951...   \n",
       "13057       ([115], {'peak_heights': [22.027027549242]})   \n",
       "51053     ([64], {'peak_heights': [17.516816143497756]})   \n",
       "13113     ([116], {'peak_heights': [19.72664857974373]})   \n",
       "51075     ([20], {'peak_heights': [17.441405897600905]})   \n",
       "51165  ([30, 64], {'peak_heights': [16.38669818491168...   \n",
       "51770     ([97], {'peak_heights': [16.282397287611975]})   \n",
       "51069     ([65], {'peak_heights': [15.835160774867198]})   \n",
       "51775  ([37, 96], {'peak_heights': [10.72797800199879...   \n",
       "17119    ([115], {'peak_heights': [15.971646524645772]})   \n",
       "13297  ([62, 112], {'peak_heights': [11.9308133643232...   \n",
       "17133  ([16, 110], {'peak_heights': [14.8478711864686...   \n",
       "6067     ([103], {'peak_heights': [14.662559554567457]})   \n",
       "17037  ([66, 109], {'peak_heights': [13.7549895884261...   \n",
       "13117    ([106], {'peak_heights': [13.686099368099926]})   \n",
       "26161  ([4, 68], {'peak_heights': [13.446255975520042...   \n",
       "51127     ([116], {'peak_heights': [14.22941529476543]})   \n",
       "27037  ([53, 109], {'peak_heights': [12.2205140141325...   \n",
       "51161     ([97], {'peak_heights': [11.830693673005692]})   \n",
       "51079     ([91], {'peak_heights': [11.532944576993216]})   \n",
       "26093     ([72], {'peak_heights': [10.947705731424058]})   \n",
       "41051    ([113], {'peak_heights': [10.544843264085934]})   \n",
       "51101                         ([], {'peak_heights': []})   \n",
       "51680     ([115], {'peak_heights': [16.34282376177031]})   \n",
       "51085                         ([], {'peak_heights': []})   \n",
       "51145                         ([], {'peak_heights': []})   \n",
       "51009                         ([], {'peak_heights': []})   \n",
       "26115                         ([], {'peak_heights': []})   \n",
       "53033                         ([], {'peak_heights': []})   \n",
       "29099                         ([], {'peak_heights': []})   \n",
       "55131                         ([], {'peak_heights': []})   \n",
       "39153                         ([], {'peak_heights': []})   \n",
       "39103                         ([], {'peak_heights': []})   \n",
       "39055                         ([], {'peak_heights': []})   \n",
       "51109                         ([], {'peak_heights': []})   \n",
       "51019                         ([], {'peak_heights': []})   \n",
       "\n",
       "                                               peak_info peak_count  \\\n",
       "fips                                                                  \n",
       "22051                           [(9, 90.14183879441813)]          1   \n",
       "51175                          [(28, 80.21585356960547)]          1   \n",
       "6099                                                  []          0   \n",
       "12099                                                 []          0   \n",
       "12115                                                 []          0   \n",
       "51139  [(32, 46.618932067848476), (65, 18.52803710388...          3   \n",
       "48039                                                 []          0   \n",
       "48141                         [(105, 43.52587874783009)]          1   \n",
       "48139                          [(107, 42.2791474916176)]          1   \n",
       "36027  [(10, 41.17452268143252), (51, 15.100561973968...          2   \n",
       "51620                         [(106, 39.44843909698937)]          1   \n",
       "13217                                                 []          0   \n",
       "12069                                                 []          0   \n",
       "42103                          [(16, 30.71701184908732)]          1   \n",
       "51023                          [(96, 27.35826069857609)]          1   \n",
       "9003   [(28, 26.241421073879696), (60, 12.47989439349...          2   \n",
       "51065                         [(17, 26.193095500026192)]          1   \n",
       "12053                         [(107, 26.07850070721358)]          1   \n",
       "17163                                                 []          0   \n",
       "48157                                                 []          0   \n",
       "9013   [(19, 10.046945775875386), (52, 21.42084665422...          2   \n",
       "37029                         [(87, 21.033535342912355)]          1   \n",
       "55127                                                 []          0   \n",
       "13057                                                 []          0   \n",
       "51053                         [(64, 17.516816143497756)]          1   \n",
       "13113                                                 []          0   \n",
       "51075                         [(20, 17.441405897600905)]          1   \n",
       "51165  [(30, 16.386698184911687), (64, 12.90016665620...          2   \n",
       "51770                         [(97, 16.282397287611975)]          1   \n",
       "51069                         [(65, 15.835160774867198)]          1   \n",
       "51775                         [(96, 15.809651792419272)]          1   \n",
       "17119                                                 []          0   \n",
       "13297                                                 []          0   \n",
       "17133                         [(16, 14.847871186468641)]          1   \n",
       "6067                                                  []          0   \n",
       "17037                         [(66, 13.754989588426199)]          1   \n",
       "13117                                                 []          0   \n",
       "26161  [(4, 13.446255975520042), (68, 12.202671607841...          2   \n",
       "51127                                                 []          0   \n",
       "27037                                                 []          0   \n",
       "51161                         [(97, 11.830693673005692)]          1   \n",
       "51079                                                 []          0   \n",
       "26093                         [(72, 10.947705731424058)]          1   \n",
       "41051                                                 []          0   \n",
       "51101                                                 []          0   \n",
       "51680                                                 []          0   \n",
       "51085                                                 []          0   \n",
       "51145                                                 []          0   \n",
       "51009                                                 []          0   \n",
       "26115                                                 []          0   \n",
       "53033                                                 []          0   \n",
       "29099                                                 []          0   \n",
       "55131                                                 []          0   \n",
       "39153                                                 []          0   \n",
       "39103                                                 []          0   \n",
       "39055                                                 []          0   \n",
       "51109                                                 []          0   \n",
       "51019                                                 []          0   \n",
       "\n",
       "       peak_date  median_infection mean_infection  max_infection  \\\n",
       "fips                                                               \n",
       "22051 2020-04-07         16.796308      23.434688      90.141839   \n",
       "51175 2020-04-26          4.051306       9.687596      80.215854   \n",
       "6099         NaT          2.633204       9.459371      65.194494   \n",
       "12099        NaT          6.781269      14.179238      49.611592   \n",
       "12115        NaT          2.486666       7.749199      46.900824   \n",
       "51139 2020-07-07          9.562858      11.445021      46.618932   \n",
       "48039        NaT          4.103291      10.089309      46.453077   \n",
       "48141 2020-07-12          7.447232      11.358615      43.525879   \n",
       "48139 2020-07-14          3.400882       8.950368      42.279147   \n",
       "36027 2020-05-19          8.691320      12.506710      41.174523   \n",
       "51620 2020-07-13          7.172443       9.327322      39.448439   \n",
       "13217        NaT          5.369416       8.510546      34.645516   \n",
       "12069        NaT          1.906744       7.378819      32.725951   \n",
       "42103 2020-04-14          4.095602       7.831940      30.717012   \n",
       "51023 2020-07-03          2.564837       4.270978      27.358261   \n",
       "9003  2020-05-28          9.476069      11.705013      26.241421   \n",
       "51065 2020-04-15          2.619310       4.512565      26.193096   \n",
       "12053 2020-07-14          1.031353       4.825259      26.078501   \n",
       "17163        NaT          8.141701       8.662862      23.104827   \n",
       "48157        NaT          4.197602       5.883034      21.489608   \n",
       "9013  2020-05-20          3.341083       5.316966      21.420847   \n",
       "37029 2020-06-24          0.657298       3.494058      21.033535   \n",
       "55127        NaT          6.533017       7.249658      20.768118   \n",
       "13057        NaT          5.548161       6.646462      19.708393   \n",
       "51053 2020-06-01          4.879684       5.048706      17.516816   \n",
       "13113        NaT          3.183731       4.557106      17.479309   \n",
       "51075 2020-04-18          4.209995       4.927487      17.441406   \n",
       "51165 2020-06-01          8.803492       8.675806      16.386698   \n",
       "51770 2020-07-04          4.178668       5.542487      16.282397   \n",
       "51069 2020-06-02          5.118436       5.644591      15.835161   \n",
       "51775 2020-07-03          2.258522       3.739438      15.809652   \n",
       "17119        NaT          3.694122       4.677220      15.754345   \n",
       "13297        NaT          4.228643       5.693830      15.404341   \n",
       "17133 2020-04-14          3.299527       4.620061      14.847871   \n",
       "6067         NaT          1.698206       3.648563      14.837443   \n",
       "17037 2020-06-03          5.175145       5.713924      13.754990   \n",
       "13117        NaT          3.392281       4.668234      13.686099   \n",
       "26161 2020-06-05          3.789046       4.726507      13.446256   \n",
       "51127        NaT          2.371569       3.109632      12.992075   \n",
       "27037        NaT          6.826173       5.965672      12.919780   \n",
       "51161 2020-07-04          2.426809       3.253042      11.830694   \n",
       "51079        NaT          2.883236       4.008710      11.532945   \n",
       "26093 2020-06-09          2.008981       2.878364      10.947706   \n",
       "41051        NaT          2.434101       3.506777      10.544843   \n",
       "51101        NaT          2.499250       3.113101       9.997001   \n",
       "51680        NaT          1.738598       2.330332       9.910010   \n",
       "51085        NaT          3.844308       4.090828       9.809614   \n",
       "51145        NaT          2.408896       2.983650       9.153803   \n",
       "51009        NaT          0.904016       1.280689       9.040161   \n",
       "26115        NaT          3.512103       4.059984       8.922639   \n",
       "53033        NaT          4.048962       4.412089       8.782791   \n",
       "29099        NaT          1.904076       2.582752       8.504875   \n",
       "55131        NaT          2.415363       3.321814       8.401261   \n",
       "39153        NaT          3.657161       4.029849       7.921648   \n",
       "39103        NaT          2.583010       3.094035       7.788769   \n",
       "39055        NaT          4.042450       4.184291       7.169629   \n",
       "51109        NaT          3.040241       3.343599       6.460513   \n",
       "51019        NaT          1.627548       1.954327       6.148516   \n",
       "\n",
       "       adjusted_density  \n",
       "fips                     \n",
       "22051        663.519334  \n",
       "51175        650.292127  \n",
       "6099         627.304860  \n",
       "12099        645.956575  \n",
       "12115        642.538548  \n",
       "51139        662.694779  \n",
       "48039        659.579960  \n",
       "48141        659.629818  \n",
       "48139        686.228931  \n",
       "36027        626.711338  \n",
       "51620        699.406388  \n",
       "13217        631.998610  \n",
       "12069        631.040532  \n",
       "42103        611.413116  \n",
       "51023        650.320949  \n",
       "9003         660.197441  \n",
       "51065        618.464238  \n",
       "12053        616.856528  \n",
       "17163        703.119642  \n",
       "48157        667.879866  \n",
       "9013         655.566729  \n",
       "37029        668.763702  \n",
       "55127        658.704622  \n",
       "13057        705.426904  \n",
       "51053        627.920792  \n",
       "13113        697.138528  \n",
       "51075        661.659661  \n",
       "51165        679.164231  \n",
       "51770        670.663574  \n",
       "51069        615.314320  \n",
       "51775        640.444454  \n",
       "17119        685.901117  \n",
       "13297        627.807510  \n",
       "17133        651.676381  \n",
       "6067         669.454023  \n",
       "17037        667.560794  \n",
       "13117        699.343689  \n",
       "26161        697.891433  \n",
       "51127        707.194397  \n",
       "27037        697.874073  \n",
       "51161        637.556824  \n",
       "51079        688.416499  \n",
       "26093        627.526789  \n",
       "41051        688.926897  \n",
       "51101        618.644930  \n",
       "51680        610.272330  \n",
       "51085        684.978074  \n",
       "51145        643.073923  \n",
       "51009        693.748847  \n",
       "26115        638.839633  \n",
       "53033        649.949210  \n",
       "29099        650.694528  \n",
       "55131        616.620353  \n",
       "39153        683.173957  \n",
       "39103        610.721668  \n",
       "39055        706.783925  \n",
       "51109        660.756407  \n",
       "51019        642.108672  \n",
       "\n",
       "[58 rows x 22 columns]"
      ]
     },
     "execution_count": 1689,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(counties_like_brazoria['max_infection'].median())\n",
    "counties_like_brazoria.sort_values(by='max_infection', ascending=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1677,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "origin = (county_details.loc[48039]['latitude'], county_details.loc[48039]['longitude'])\n",
    "\n",
    "\n",
    "#distance(county_details, 48039)\n",
    "#county_details[['latitude', 'longitude']].dropna().apply(distance, axis=1).to_frame(name='miles').query('miles > 0 & miles < 100')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.27550772043691224"
      ]
     },
     "execution_count": 349,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(1-((county_percentages_raw.loc[48039][-21:].sum() * 7) / 100000)) ** 25\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.38814430081286716"
      ]
     },
     "execution_count": 345,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1619,
   "metadata": {
    "scrolled": false
   },
   "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>st</th>\n",
       "      <th>county</th>\n",
       "      <th>density</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13095</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Dougherty County</td>\n",
       "      <td>267.596421</td>\n",
       "      <td>118.0</td>\n",
       "      <td>21.458561</td>\n",
       "      <td>22.349015</td>\n",
       "      <td>0.974513</td>\n",
       "      <td>5.562846</td>\n",
       "      <td>10.719646</td>\n",
       "      <td>27.733022</td>\n",
       "      <td>83.320881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19013</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>Black Hawk County</td>\n",
       "      <td>231.946254</td>\n",
       "      <td>118.0</td>\n",
       "      <td>17.588559</td>\n",
       "      <td>17.612727</td>\n",
       "      <td>0.108862</td>\n",
       "      <td>5.633597</td>\n",
       "      <td>11.430487</td>\n",
       "      <td>24.058454</td>\n",
       "      <td>78.380485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12097</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Osceola County</td>\n",
       "      <td>283.061623</td>\n",
       "      <td>118.0</td>\n",
       "      <td>14.464012</td>\n",
       "      <td>21.307437</td>\n",
       "      <td>0.950477</td>\n",
       "      <td>1.976993</td>\n",
       "      <td>3.231623</td>\n",
       "      <td>14.399734</td>\n",
       "      <td>73.604975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48245</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>287.077982</td>\n",
       "      <td>118.0</td>\n",
       "      <td>15.068384</td>\n",
       "      <td>19.205219</td>\n",
       "      <td>0.681448</td>\n",
       "      <td>3.137502</td>\n",
       "      <td>4.287446</td>\n",
       "      <td>23.708726</td>\n",
       "      <td>73.142130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48309</th>\n",
       "      <td>Texas</td>\n",
       "      <td>McLennan County</td>\n",
       "      <td>247.442631</td>\n",
       "      <td>118.0</td>\n",
       "      <td>11.443096</td>\n",
       "      <td>18.940213</td>\n",
       "      <td>0.111336</td>\n",
       "      <td>0.445345</td>\n",
       "      <td>0.890690</td>\n",
       "      <td>13.610850</td>\n",
       "      <td>67.636739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12005</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Bay County</td>\n",
       "      <td>230.342049</td>\n",
       "      <td>118.0</td>\n",
       "      <td>10.560174</td>\n",
       "      <td>18.708134</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.756377</td>\n",
       "      <td>1.430984</td>\n",
       "      <td>6.582525</td>\n",
       "      <td>65.988789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45051</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Horry County</td>\n",
       "      <td>312.269379</td>\n",
       "      <td>118.0</td>\n",
       "      <td>16.572589</td>\n",
       "      <td>20.282960</td>\n",
       "      <td>0.645534</td>\n",
       "      <td>1.422193</td>\n",
       "      <td>3.086461</td>\n",
       "      <td>34.525998</td>\n",
       "      <td>63.141323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46099</th>\n",
       "      <td>South Dakota</td>\n",
       "      <td>Minnehaha County</td>\n",
       "      <td>239.279834</td>\n",
       "      <td>118.0</td>\n",
       "      <td>17.014495</td>\n",
       "      <td>17.659046</td>\n",
       "      <td>1.479358</td>\n",
       "      <td>5.122276</td>\n",
       "      <td>7.655677</td>\n",
       "      <td>25.574398</td>\n",
       "      <td>61.985091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13185</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Lowndes County</td>\n",
       "      <td>236.674152</td>\n",
       "      <td>118.0</td>\n",
       "      <td>16.747208</td>\n",
       "      <td>17.312602</td>\n",
       "      <td>1.338457</td>\n",
       "      <td>3.163625</td>\n",
       "      <td>6.023056</td>\n",
       "      <td>29.081016</td>\n",
       "      <td>61.447334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10005</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Sussex County</td>\n",
       "      <td>250.219266</td>\n",
       "      <td>118.0</td>\n",
       "      <td>19.307401</td>\n",
       "      <td>15.222766</td>\n",
       "      <td>3.171554</td>\n",
       "      <td>6.526081</td>\n",
       "      <td>13.448607</td>\n",
       "      <td>30.632938</td>\n",
       "      <td>59.466630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5143</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Washington County</td>\n",
       "      <td>253.923178</td>\n",
       "      <td>118.0</td>\n",
       "      <td>18.740845</td>\n",
       "      <td>19.136201</td>\n",
       "      <td>0.477809</td>\n",
       "      <td>0.955618</td>\n",
       "      <td>9.675633</td>\n",
       "      <td>35.970063</td>\n",
       "      <td>54.828589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48361</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Orange County</td>\n",
       "      <td>249.936314</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.756395</td>\n",
       "      <td>12.055124</td>\n",
       "      <td>0.171300</td>\n",
       "      <td>1.027799</td>\n",
       "      <td>2.226897</td>\n",
       "      <td>8.136738</td>\n",
       "      <td>53.959423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28049</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>Hinds County</td>\n",
       "      <td>266.562114</td>\n",
       "      <td>118.0</td>\n",
       "      <td>13.913328</td>\n",
       "      <td>12.414804</td>\n",
       "      <td>2.587992</td>\n",
       "      <td>5.792172</td>\n",
       "      <td>7.671547</td>\n",
       "      <td>21.597407</td>\n",
       "      <td>51.944691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22073</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Ouachita Parish</td>\n",
       "      <td>251.109917</td>\n",
       "      <td>118.0</td>\n",
       "      <td>20.367123</td>\n",
       "      <td>12.011870</td>\n",
       "      <td>3.262025</td>\n",
       "      <td>11.859793</td>\n",
       "      <td>16.030525</td>\n",
       "      <td>28.379621</td>\n",
       "      <td>50.235192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45035</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Dorchester County</td>\n",
       "      <td>284.018889</td>\n",
       "      <td>118.0</td>\n",
       "      <td>11.306494</td>\n",
       "      <td>16.003886</td>\n",
       "      <td>0.175490</td>\n",
       "      <td>0.943261</td>\n",
       "      <td>2.544612</td>\n",
       "      <td>15.223799</td>\n",
       "      <td>48.610861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32003</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>Clark County</td>\n",
       "      <td>287.237511</td>\n",
       "      <td>118.0</td>\n",
       "      <td>11.931272</td>\n",
       "      <td>12.864259</td>\n",
       "      <td>2.212138</td>\n",
       "      <td>4.057161</td>\n",
       "      <td>4.884350</td>\n",
       "      <td>14.161463</td>\n",
       "      <td>47.324621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1101</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Montgomery County</td>\n",
       "      <td>288.794219</td>\n",
       "      <td>118.0</td>\n",
       "      <td>19.597232</td>\n",
       "      <td>12.870088</td>\n",
       "      <td>1.135359</td>\n",
       "      <td>6.244473</td>\n",
       "      <td>22.234108</td>\n",
       "      <td>28.888572</td>\n",
       "      <td>46.865085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48039</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Brazoria County</td>\n",
       "      <td>275.660105</td>\n",
       "      <td>118.0</td>\n",
       "      <td>11.056405</td>\n",
       "      <td>11.945619</td>\n",
       "      <td>1.793997</td>\n",
       "      <td>3.177665</td>\n",
       "      <td>4.179632</td>\n",
       "      <td>15.470840</td>\n",
       "      <td>46.453077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12085</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Martin County</td>\n",
       "      <td>296.247774</td>\n",
       "      <td>118.0</td>\n",
       "      <td>16.820568</td>\n",
       "      <td>13.774057</td>\n",
       "      <td>1.863354</td>\n",
       "      <td>3.305235</td>\n",
       "      <td>12.732919</td>\n",
       "      <td>29.525288</td>\n",
       "      <td>43.921917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12091</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Okaloosa County</td>\n",
       "      <td>226.539833</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.992206</td>\n",
       "      <td>11.616329</td>\n",
       "      <td>0.542312</td>\n",
       "      <td>1.169360</td>\n",
       "      <td>2.474298</td>\n",
       "      <td>8.609201</td>\n",
       "      <td>42.232535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34011</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Cumberland County</td>\n",
       "      <td>309.129776</td>\n",
       "      <td>118.0</td>\n",
       "      <td>17.480465</td>\n",
       "      <td>12.394236</td>\n",
       "      <td>0.955394</td>\n",
       "      <td>6.592216</td>\n",
       "      <td>14.139826</td>\n",
       "      <td>29.641087</td>\n",
       "      <td>42.228398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47189</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Wilson County</td>\n",
       "      <td>253.416978</td>\n",
       "      <td>118.0</td>\n",
       "      <td>9.690620</td>\n",
       "      <td>9.787649</td>\n",
       "      <td>1.777604</td>\n",
       "      <td>3.555208</td>\n",
       "      <td>5.563242</td>\n",
       "      <td>11.208781</td>\n",
       "      <td>40.884891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22017</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Caddo Parish</td>\n",
       "      <td>273.413019</td>\n",
       "      <td>118.0</td>\n",
       "      <td>17.817281</td>\n",
       "      <td>10.775317</td>\n",
       "      <td>3.687342</td>\n",
       "      <td>8.876384</td>\n",
       "      <td>13.113853</td>\n",
       "      <td>25.989813</td>\n",
       "      <td>40.858128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54061</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Monongalia County</td>\n",
       "      <td>293.316151</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.987814</td>\n",
       "      <td>10.278700</td>\n",
       "      <td>0.405798</td>\n",
       "      <td>0.811596</td>\n",
       "      <td>1.623192</td>\n",
       "      <td>3.922714</td>\n",
       "      <td>40.715070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5131</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Sebastian County</td>\n",
       "      <td>240.316519</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.963245</td>\n",
       "      <td>10.915859</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.111758</td>\n",
       "      <td>0.670549</td>\n",
       "      <td>14.081532</td>\n",
       "      <td>37.103719</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12083</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Marion County</td>\n",
       "      <td>230.715296</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.214229</td>\n",
       "      <td>10.298276</td>\n",
       "      <td>0.312616</td>\n",
       "      <td>0.908539</td>\n",
       "      <td>1.250463</td>\n",
       "      <td>5.666159</td>\n",
       "      <td>37.084031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48423</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Smith County</td>\n",
       "      <td>252.591014</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.809801</td>\n",
       "      <td>10.155878</td>\n",
       "      <td>0.306888</td>\n",
       "      <td>0.982043</td>\n",
       "      <td>1.595819</td>\n",
       "      <td>6.613444</td>\n",
       "      <td>36.826603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13045</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Carroll County</td>\n",
       "      <td>240.428312</td>\n",
       "      <td>118.0</td>\n",
       "      <td>9.619285</td>\n",
       "      <td>8.976031</td>\n",
       "      <td>0.952444</td>\n",
       "      <td>3.690722</td>\n",
       "      <td>6.548056</td>\n",
       "      <td>10.060194</td>\n",
       "      <td>36.788167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12061</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Indian River County</td>\n",
       "      <td>318.019297</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.835971</td>\n",
       "      <td>11.512982</td>\n",
       "      <td>0.178657</td>\n",
       "      <td>0.803958</td>\n",
       "      <td>2.054560</td>\n",
       "      <td>17.821076</td>\n",
       "      <td>35.731481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1081</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Lee County</td>\n",
       "      <td>270.834979</td>\n",
       "      <td>118.0</td>\n",
       "      <td>10.532574</td>\n",
       "      <td>9.375079</td>\n",
       "      <td>1.649601</td>\n",
       "      <td>3.668191</td>\n",
       "      <td>5.556549</td>\n",
       "      <td>18.275838</td>\n",
       "      <td>34.467969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1117</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Shelby County</td>\n",
       "      <td>277.352121</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.938847</td>\n",
       "      <td>9.839803</td>\n",
       "      <td>0.656205</td>\n",
       "      <td>2.706846</td>\n",
       "      <td>4.199712</td>\n",
       "      <td>10.728952</td>\n",
       "      <td>34.319522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24045</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>Wicomico County</td>\n",
       "      <td>276.700922</td>\n",
       "      <td>118.0</td>\n",
       "      <td>9.741644</td>\n",
       "      <td>8.626930</td>\n",
       "      <td>0.275762</td>\n",
       "      <td>3.860668</td>\n",
       "      <td>6.342527</td>\n",
       "      <td>14.443037</td>\n",
       "      <td>34.056611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48091</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Comal County</td>\n",
       "      <td>279.205900</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.376657</td>\n",
       "      <td>10.060608</td>\n",
       "      <td>0.182905</td>\n",
       "      <td>0.640168</td>\n",
       "      <td>1.463241</td>\n",
       "      <td>14.540958</td>\n",
       "      <td>33.197282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42089</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Monroe County</td>\n",
       "      <td>279.919314</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.248095</td>\n",
       "      <td>8.348373</td>\n",
       "      <td>0.755099</td>\n",
       "      <td>2.202372</td>\n",
       "      <td>3.481844</td>\n",
       "      <td>9.040211</td>\n",
       "      <td>33.140448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22103</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>St. Tammany Parish</td>\n",
       "      <td>307.987336</td>\n",
       "      <td>118.0</td>\n",
       "      <td>12.322756</td>\n",
       "      <td>8.809545</td>\n",
       "      <td>3.949679</td>\n",
       "      <td>5.705092</td>\n",
       "      <td>8.283354</td>\n",
       "      <td>16.923277</td>\n",
       "      <td>33.133417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45077</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Pickens County</td>\n",
       "      <td>255.604776</td>\n",
       "      <td>118.0</td>\n",
       "      <td>9.673093</td>\n",
       "      <td>10.724964</td>\n",
       "      <td>0.675533</td>\n",
       "      <td>1.238477</td>\n",
       "      <td>3.096191</td>\n",
       "      <td>21.391867</td>\n",
       "      <td>33.101100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19103</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>Johnson County</td>\n",
       "      <td>246.140317</td>\n",
       "      <td>118.0</td>\n",
       "      <td>9.056273</td>\n",
       "      <td>7.980712</td>\n",
       "      <td>0.850678</td>\n",
       "      <td>2.457513</td>\n",
       "      <td>6.663642</td>\n",
       "      <td>14.461521</td>\n",
       "      <td>32.892871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48187</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Guadalupe County</td>\n",
       "      <td>234.566287</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.931725</td>\n",
       "      <td>9.131192</td>\n",
       "      <td>0.428108</td>\n",
       "      <td>0.941838</td>\n",
       "      <td>1.384217</td>\n",
       "      <td>14.705517</td>\n",
       "      <td>32.279359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34041</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Warren County</td>\n",
       "      <td>294.933290</td>\n",
       "      <td>118.0</td>\n",
       "      <td>10.159799</td>\n",
       "      <td>9.869083</td>\n",
       "      <td>0.678547</td>\n",
       "      <td>2.171349</td>\n",
       "      <td>5.089100</td>\n",
       "      <td>18.693961</td>\n",
       "      <td>31.891693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47009</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Blount County</td>\n",
       "      <td>238.207572</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.846969</td>\n",
       "      <td>6.341485</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.456197</td>\n",
       "      <td>1.234414</td>\n",
       "      <td>3.112871</td>\n",
       "      <td>31.450726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47105</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Loudon County</td>\n",
       "      <td>235.882312</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.789047</td>\n",
       "      <td>6.613434</td>\n",
       "      <td>0.528435</td>\n",
       "      <td>1.585305</td>\n",
       "      <td>4.755916</td>\n",
       "      <td>9.974212</td>\n",
       "      <td>30.649235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13015</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Bartow County</td>\n",
       "      <td>234.445450</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.723296</td>\n",
       "      <td>6.779775</td>\n",
       "      <td>2.386742</td>\n",
       "      <td>4.906082</td>\n",
       "      <td>5.966856</td>\n",
       "      <td>8.353599</td>\n",
       "      <td>30.629861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12001</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Alachua County</td>\n",
       "      <td>307.470686</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.516870</td>\n",
       "      <td>10.295865</td>\n",
       "      <td>0.139383</td>\n",
       "      <td>1.448587</td>\n",
       "      <td>2.625045</td>\n",
       "      <td>13.486957</td>\n",
       "      <td>29.575729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37105</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Lee County</td>\n",
       "      <td>242.309548</td>\n",
       "      <td>118.0</td>\n",
       "      <td>13.890017</td>\n",
       "      <td>7.505781</td>\n",
       "      <td>0.231239</td>\n",
       "      <td>10.983853</td>\n",
       "      <td>14.683677</td>\n",
       "      <td>18.152262</td>\n",
       "      <td>29.367353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51800</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Suffolk city</td>\n",
       "      <td>230.173327</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.151569</td>\n",
       "      <td>6.692986</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.140723</td>\n",
       "      <td>5.273313</td>\n",
       "      <td>8.220164</td>\n",
       "      <td>28.848122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21227</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Warren County</td>\n",
       "      <td>245.376662</td>\n",
       "      <td>118.0</td>\n",
       "      <td>12.916762</td>\n",
       "      <td>7.033739</td>\n",
       "      <td>0.644973</td>\n",
       "      <td>9.352103</td>\n",
       "      <td>12.953201</td>\n",
       "      <td>16.876784</td>\n",
       "      <td>28.701283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37089</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Henderson County</td>\n",
       "      <td>314.733507</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.272290</td>\n",
       "      <td>6.916178</td>\n",
       "      <td>1.338331</td>\n",
       "      <td>3.771661</td>\n",
       "      <td>5.596659</td>\n",
       "      <td>8.759987</td>\n",
       "      <td>28.591625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48251</th>\n",
       "      <td>Texas</td>\n",
       "      <td>Johnson County</td>\n",
       "      <td>242.609274</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.059052</td>\n",
       "      <td>6.387067</td>\n",
       "      <td>0.365640</td>\n",
       "      <td>1.300053</td>\n",
       "      <td>2.031333</td>\n",
       "      <td>5.179899</td>\n",
       "      <td>26.976101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37101</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Johnston County</td>\n",
       "      <td>264.551074</td>\n",
       "      <td>118.0</td>\n",
       "      <td>9.891622</td>\n",
       "      <td>8.455868</td>\n",
       "      <td>0.477694</td>\n",
       "      <td>2.269047</td>\n",
       "      <td>6.619475</td>\n",
       "      <td>18.203556</td>\n",
       "      <td>26.819110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37109</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Lincoln County</td>\n",
       "      <td>289.023220</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.670085</td>\n",
       "      <td>6.638642</td>\n",
       "      <td>0.331798</td>\n",
       "      <td>1.078342</td>\n",
       "      <td>1.907837</td>\n",
       "      <td>8.709689</td>\n",
       "      <td>26.709712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12017</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Citrus County</td>\n",
       "      <td>257.276997</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.929755</td>\n",
       "      <td>7.048880</td>\n",
       "      <td>0.190913</td>\n",
       "      <td>0.572738</td>\n",
       "      <td>1.431846</td>\n",
       "      <td>6.061480</td>\n",
       "      <td>26.536871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12119</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Sumter County</td>\n",
       "      <td>242.113751</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.410549</td>\n",
       "      <td>6.606543</td>\n",
       "      <td>0.030823</td>\n",
       "      <td>0.863055</td>\n",
       "      <td>3.452219</td>\n",
       "      <td>5.852590</td>\n",
       "      <td>26.215289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45007</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>Anderson County</td>\n",
       "      <td>283.129213</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.118476</td>\n",
       "      <td>7.289681</td>\n",
       "      <td>0.634739</td>\n",
       "      <td>1.419347</td>\n",
       "      <td>2.538955</td>\n",
       "      <td>8.762922</td>\n",
       "      <td>25.530606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12015</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Charlotte County</td>\n",
       "      <td>277.694887</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.699579</td>\n",
       "      <td>6.154039</td>\n",
       "      <td>1.058705</td>\n",
       "      <td>2.873628</td>\n",
       "      <td>3.932334</td>\n",
       "      <td>6.049744</td>\n",
       "      <td>23.745245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37147</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Pitt County</td>\n",
       "      <td>277.222286</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.838188</td>\n",
       "      <td>5.097771</td>\n",
       "      <td>0.055327</td>\n",
       "      <td>2.134060</td>\n",
       "      <td>3.912443</td>\n",
       "      <td>8.318882</td>\n",
       "      <td>23.474661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20045</th>\n",
       "      <td>Kansas</td>\n",
       "      <td>Douglas County</td>\n",
       "      <td>268.187711</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.624267</td>\n",
       "      <td>5.773238</td>\n",
       "      <td>0.116848</td>\n",
       "      <td>0.467392</td>\n",
       "      <td>0.817936</td>\n",
       "      <td>2.424595</td>\n",
       "      <td>23.019047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47001</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Anderson County</td>\n",
       "      <td>228.311613</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.574808</td>\n",
       "      <td>5.283650</td>\n",
       "      <td>0.185582</td>\n",
       "      <td>0.556745</td>\n",
       "      <td>1.113491</td>\n",
       "      <td>2.783727</td>\n",
       "      <td>23.012141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39109</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Miami County</td>\n",
       "      <td>263.138866</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.518437</td>\n",
       "      <td>4.719330</td>\n",
       "      <td>0.400583</td>\n",
       "      <td>1.735858</td>\n",
       "      <td>2.670551</td>\n",
       "      <td>5.875213</td>\n",
       "      <td>22.966742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49049</th>\n",
       "      <td>Utah</td>\n",
       "      <td>Utah County</td>\n",
       "      <td>317.569058</td>\n",
       "      <td>118.0</td>\n",
       "      <td>8.668010</td>\n",
       "      <td>5.939112</td>\n",
       "      <td>0.987955</td>\n",
       "      <td>3.996726</td>\n",
       "      <td>6.926910</td>\n",
       "      <td>15.032631</td>\n",
       "      <td>22.947496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34037</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Sussex County</td>\n",
       "      <td>270.682486</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.328312</td>\n",
       "      <td>7.272163</td>\n",
       "      <td>0.041871</td>\n",
       "      <td>1.957463</td>\n",
       "      <td>3.253964</td>\n",
       "      <td>13.778503</td>\n",
       "      <td>22.371001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26021</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Berrien County</td>\n",
       "      <td>270.192533</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.273418</td>\n",
       "      <td>5.095535</td>\n",
       "      <td>0.097783</td>\n",
       "      <td>3.166305</td>\n",
       "      <td>5.261653</td>\n",
       "      <td>8.451239</td>\n",
       "      <td>22.164132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34019</th>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Hunterdon County</td>\n",
       "      <td>290.709389</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.286058</td>\n",
       "      <td>5.852205</td>\n",
       "      <td>0.344591</td>\n",
       "      <td>1.837819</td>\n",
       "      <td>5.743185</td>\n",
       "      <td>11.945826</td>\n",
       "      <td>21.594377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10001</th>\n",
       "      <td>Delaware</td>\n",
       "      <td>Kent County</td>\n",
       "      <td>308.414324</td>\n",
       "      <td>118.0</td>\n",
       "      <td>9.450259</td>\n",
       "      <td>4.667098</td>\n",
       "      <td>1.580401</td>\n",
       "      <td>5.630177</td>\n",
       "      <td>9.166323</td>\n",
       "      <td>13.295119</td>\n",
       "      <td>21.256387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37159</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Rowan County</td>\n",
       "      <td>277.855347</td>\n",
       "      <td>118.0</td>\n",
       "      <td>10.368536</td>\n",
       "      <td>5.365208</td>\n",
       "      <td>1.407578</td>\n",
       "      <td>5.831396</td>\n",
       "      <td>9.752508</td>\n",
       "      <td>14.980656</td>\n",
       "      <td>20.812052</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55063</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>La Crosse County</td>\n",
       "      <td>261.278853</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.838881</td>\n",
       "      <td>6.142854</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.484196</td>\n",
       "      <td>1.210490</td>\n",
       "      <td>8.715525</td>\n",
       "      <td>20.336226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37097</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Iredell County</td>\n",
       "      <td>316.827370</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.418650</td>\n",
       "      <td>5.856571</td>\n",
       "      <td>0.785767</td>\n",
       "      <td>1.885841</td>\n",
       "      <td>2.985914</td>\n",
       "      <td>10.961449</td>\n",
       "      <td>19.565597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12035</th>\n",
       "      <td>Florida</td>\n",
       "      <td>Flagler County</td>\n",
       "      <td>237.055088</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.982279</td>\n",
       "      <td>5.373941</td>\n",
       "      <td>0.372409</td>\n",
       "      <td>1.489634</td>\n",
       "      <td>2.172383</td>\n",
       "      <td>6.827489</td>\n",
       "      <td>19.489378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39045</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Fairfield County</td>\n",
       "      <td>312.392077</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.041638</td>\n",
       "      <td>4.526508</td>\n",
       "      <td>0.997264</td>\n",
       "      <td>2.447830</td>\n",
       "      <td>3.263773</td>\n",
       "      <td>5.416956</td>\n",
       "      <td>18.766693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18095</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Madison County</td>\n",
       "      <td>286.710997</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.691471</td>\n",
       "      <td>3.931760</td>\n",
       "      <td>0.661534</td>\n",
       "      <td>1.764090</td>\n",
       "      <td>3.638436</td>\n",
       "      <td>6.477519</td>\n",
       "      <td>18.522949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53063</th>\n",
       "      <td>Washington</td>\n",
       "      <td>Spokane County</td>\n",
       "      <td>296.405867</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.475592</td>\n",
       "      <td>4.761432</td>\n",
       "      <td>0.218604</td>\n",
       "      <td>1.065694</td>\n",
       "      <td>2.773538</td>\n",
       "      <td>5.827162</td>\n",
       "      <td>18.308082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18067</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Howard County</td>\n",
       "      <td>281.665342</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.950582</td>\n",
       "      <td>3.683028</td>\n",
       "      <td>1.038407</td>\n",
       "      <td>3.807493</td>\n",
       "      <td>6.403511</td>\n",
       "      <td>9.345665</td>\n",
       "      <td>17.652923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13297</th>\n",
       "      <td>Georgia</td>\n",
       "      <td>Walton County</td>\n",
       "      <td>290.447679</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.010202</td>\n",
       "      <td>4.178115</td>\n",
       "      <td>0.755115</td>\n",
       "      <td>3.020459</td>\n",
       "      <td>4.379666</td>\n",
       "      <td>8.306262</td>\n",
       "      <td>17.518663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24037</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>St. Mary's County</td>\n",
       "      <td>317.794949</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.826624</td>\n",
       "      <td>3.429242</td>\n",
       "      <td>0.629271</td>\n",
       "      <td>3.177819</td>\n",
       "      <td>4.719534</td>\n",
       "      <td>8.495161</td>\n",
       "      <td>17.116176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25015</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Hampshire County</td>\n",
       "      <td>305.032669</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.484563</td>\n",
       "      <td>3.710437</td>\n",
       "      <td>0.977074</td>\n",
       "      <td>2.065180</td>\n",
       "      <td>4.441247</td>\n",
       "      <td>8.083069</td>\n",
       "      <td>16.521438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37057</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Davidson County</td>\n",
       "      <td>303.268648</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.270033</td>\n",
       "      <td>4.764437</td>\n",
       "      <td>0.767091</td>\n",
       "      <td>3.153598</td>\n",
       "      <td>6.051499</td>\n",
       "      <td>11.868609</td>\n",
       "      <td>16.364617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25019</th>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Nantucket County</td>\n",
       "      <td>253.480098</td>\n",
       "      <td>118.0</td>\n",
       "      <td>0.966484</td>\n",
       "      <td>3.865050</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>16.292156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27019</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Carver County</td>\n",
       "      <td>296.589289</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.694510</td>\n",
       "      <td>4.091310</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.577742</td>\n",
       "      <td>4.485994</td>\n",
       "      <td>6.627036</td>\n",
       "      <td>16.176764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39043</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Erie County</td>\n",
       "      <td>295.224163</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.324811</td>\n",
       "      <td>3.802937</td>\n",
       "      <td>0.192359</td>\n",
       "      <td>1.538870</td>\n",
       "      <td>3.366278</td>\n",
       "      <td>5.241776</td>\n",
       "      <td>15.773417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27109</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Olmsted County</td>\n",
       "      <td>242.279394</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.540343</td>\n",
       "      <td>3.101074</td>\n",
       "      <td>2.978202</td>\n",
       "      <td>4.692925</td>\n",
       "      <td>7.174760</td>\n",
       "      <td>9.724282</td>\n",
       "      <td>15.613000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37133</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Onslow County</td>\n",
       "      <td>259.507777</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.645312</td>\n",
       "      <td>3.024376</td>\n",
       "      <td>0.072173</td>\n",
       "      <td>0.544904</td>\n",
       "      <td>1.299108</td>\n",
       "      <td>3.716893</td>\n",
       "      <td>15.372779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9015</th>\n",
       "      <td>Connecticut</td>\n",
       "      <td>Windham County</td>\n",
       "      <td>227.685169</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.726217</td>\n",
       "      <td>3.023318</td>\n",
       "      <td>0.733968</td>\n",
       "      <td>2.691217</td>\n",
       "      <td>4.220318</td>\n",
       "      <td>5.718836</td>\n",
       "      <td>15.046350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50007</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>Chittenden County</td>\n",
       "      <td>305.219372</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.282516</td>\n",
       "      <td>3.411090</td>\n",
       "      <td>0.087228</td>\n",
       "      <td>1.046739</td>\n",
       "      <td>2.006249</td>\n",
       "      <td>3.663585</td>\n",
       "      <td>14.654341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18019</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Clark County</td>\n",
       "      <td>317.286881</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.721598</td>\n",
       "      <td>2.638701</td>\n",
       "      <td>1.932101</td>\n",
       "      <td>3.773635</td>\n",
       "      <td>5.554791</td>\n",
       "      <td>6.732165</td>\n",
       "      <td>14.370002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19113</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>Linn County</td>\n",
       "      <td>316.239817</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.095296</td>\n",
       "      <td>3.503883</td>\n",
       "      <td>1.260286</td>\n",
       "      <td>2.725367</td>\n",
       "      <td>5.797313</td>\n",
       "      <td>7.782263</td>\n",
       "      <td>14.241226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39089</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Licking County</td>\n",
       "      <td>259.138462</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.669706</td>\n",
       "      <td>3.182713</td>\n",
       "      <td>0.484639</td>\n",
       "      <td>1.615465</td>\n",
       "      <td>2.423197</td>\n",
       "      <td>3.614602</td>\n",
       "      <td>14.135315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23001</th>\n",
       "      <td>Maine</td>\n",
       "      <td>Androscoggin County</td>\n",
       "      <td>231.397205</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.990527</td>\n",
       "      <td>3.571626</td>\n",
       "      <td>0.395810</td>\n",
       "      <td>1.055494</td>\n",
       "      <td>2.638735</td>\n",
       "      <td>6.201027</td>\n",
       "      <td>14.117231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29019</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>Boone County</td>\n",
       "      <td>263.290508</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.545494</td>\n",
       "      <td>4.212294</td>\n",
       "      <td>0.056544</td>\n",
       "      <td>0.474969</td>\n",
       "      <td>1.979036</td>\n",
       "      <td>4.017444</td>\n",
       "      <td>13.774094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37085</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Harnett County</td>\n",
       "      <td>228.536086</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.638547</td>\n",
       "      <td>3.771691</td>\n",
       "      <td>0.630363</td>\n",
       "      <td>2.022416</td>\n",
       "      <td>4.517604</td>\n",
       "      <td>8.798822</td>\n",
       "      <td>13.657872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17143</th>\n",
       "      <td>Illinois</td>\n",
       "      <td>Peoria County</td>\n",
       "      <td>289.367564</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.079003</td>\n",
       "      <td>3.399114</td>\n",
       "      <td>0.318915</td>\n",
       "      <td>1.514846</td>\n",
       "      <td>3.109421</td>\n",
       "      <td>5.979655</td>\n",
       "      <td>13.474156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6097</th>\n",
       "      <td>California</td>\n",
       "      <td>Sonoma County</td>\n",
       "      <td>313.695030</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.739700</td>\n",
       "      <td>3.485043</td>\n",
       "      <td>0.780267</td>\n",
       "      <td>1.271547</td>\n",
       "      <td>2.283005</td>\n",
       "      <td>4.941694</td>\n",
       "      <td>13.466838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55131</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Washington County</td>\n",
       "      <td>315.841775</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.590115</td>\n",
       "      <td>2.631399</td>\n",
       "      <td>0.840126</td>\n",
       "      <td>1.575236</td>\n",
       "      <td>2.467870</td>\n",
       "      <td>5.355804</td>\n",
       "      <td>13.337002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54037</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Jefferson County</td>\n",
       "      <td>272.597610</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.008254</td>\n",
       "      <td>2.993228</td>\n",
       "      <td>0.249986</td>\n",
       "      <td>1.999890</td>\n",
       "      <td>2.999835</td>\n",
       "      <td>4.937228</td>\n",
       "      <td>13.249271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26145</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Saginaw County</td>\n",
       "      <td>238.139815</td>\n",
       "      <td>118.0</td>\n",
       "      <td>7.111850</td>\n",
       "      <td>3.295306</td>\n",
       "      <td>1.649456</td>\n",
       "      <td>4.442285</td>\n",
       "      <td>6.522849</td>\n",
       "      <td>9.803017</td>\n",
       "      <td>13.195649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55117</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Sheboygan County</td>\n",
       "      <td>225.597293</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.958933</td>\n",
       "      <td>3.093937</td>\n",
       "      <td>0.247715</td>\n",
       "      <td>0.867002</td>\n",
       "      <td>1.672075</td>\n",
       "      <td>3.777651</td>\n",
       "      <td>13.005029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21113</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Jessamine County</td>\n",
       "      <td>314.410049</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.959798</td>\n",
       "      <td>2.696568</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.319940</td>\n",
       "      <td>2.128404</td>\n",
       "      <td>3.255853</td>\n",
       "      <td>12.935415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55105</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Rock County</td>\n",
       "      <td>227.467548</td>\n",
       "      <td>118.0</td>\n",
       "      <td>6.200975</td>\n",
       "      <td>3.412400</td>\n",
       "      <td>0.699620</td>\n",
       "      <td>3.279468</td>\n",
       "      <td>5.946769</td>\n",
       "      <td>8.898291</td>\n",
       "      <td>12.855516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39003</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Allen County</td>\n",
       "      <td>254.290726</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.948337</td>\n",
       "      <td>2.903603</td>\n",
       "      <td>0.558303</td>\n",
       "      <td>1.971507</td>\n",
       "      <td>2.931090</td>\n",
       "      <td>5.268983</td>\n",
       "      <td>12.701390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21145</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>McCracken County</td>\n",
       "      <td>262.993278</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.320054</td>\n",
       "      <td>2.370504</td>\n",
       "      <td>0.436752</td>\n",
       "      <td>1.747007</td>\n",
       "      <td>2.675105</td>\n",
       "      <td>3.930766</td>\n",
       "      <td>12.447426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18059</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Hancock County</td>\n",
       "      <td>255.437624</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.544648</td>\n",
       "      <td>2.469301</td>\n",
       "      <td>1.279296</td>\n",
       "      <td>3.883577</td>\n",
       "      <td>4.934427</td>\n",
       "      <td>6.761992</td>\n",
       "      <td>12.427446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24015</th>\n",
       "      <td>Maryland</td>\n",
       "      <td>Cecil County</td>\n",
       "      <td>297.034421</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.617563</td>\n",
       "      <td>2.257815</td>\n",
       "      <td>1.250026</td>\n",
       "      <td>3.333403</td>\n",
       "      <td>4.375091</td>\n",
       "      <td>5.520948</td>\n",
       "      <td>12.361369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42073</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Lawrence County</td>\n",
       "      <td>238.743631</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.413889</td>\n",
       "      <td>2.977026</td>\n",
       "      <td>0.167061</td>\n",
       "      <td>0.501183</td>\n",
       "      <td>1.002366</td>\n",
       "      <td>2.631210</td>\n",
       "      <td>12.195448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21073</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Franklin County</td>\n",
       "      <td>245.447588</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.155378</td>\n",
       "      <td>3.009072</td>\n",
       "      <td>0.061285</td>\n",
       "      <td>0.280161</td>\n",
       "      <td>3.081776</td>\n",
       "      <td>5.042907</td>\n",
       "      <td>12.046944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41047</th>\n",
       "      <td>Oregon</td>\n",
       "      <td>Marion County</td>\n",
       "      <td>294.181131</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.255873</td>\n",
       "      <td>2.529239</td>\n",
       "      <td>1.683968</td>\n",
       "      <td>3.367935</td>\n",
       "      <td>4.435817</td>\n",
       "      <td>6.602386</td>\n",
       "      <td>12.034208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54107</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Wood County</td>\n",
       "      <td>228.029269</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.129422</td>\n",
       "      <td>2.856946</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.342099</td>\n",
       "      <td>0.855248</td>\n",
       "      <td>2.223644</td>\n",
       "      <td>11.973467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55087</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Outagamie County</td>\n",
       "      <td>294.710936</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.768211</td>\n",
       "      <td>3.024913</td>\n",
       "      <td>0.152069</td>\n",
       "      <td>1.140515</td>\n",
       "      <td>3.269477</td>\n",
       "      <td>4.828181</td>\n",
       "      <td>11.785325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6061</th>\n",
       "      <td>California</td>\n",
       "      <td>Placer County</td>\n",
       "      <td>283.103377</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.003467</td>\n",
       "      <td>3.239226</td>\n",
       "      <td>0.143456</td>\n",
       "      <td>0.394505</td>\n",
       "      <td>1.560089</td>\n",
       "      <td>4.590606</td>\n",
       "      <td>11.763427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54009</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Brooke County</td>\n",
       "      <td>245.939129</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1.556153</td>\n",
       "      <td>2.778689</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.790679</td>\n",
       "      <td>11.720810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39155</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Trumbull County</td>\n",
       "      <td>320.192399</td>\n",
       "      <td>118.0</td>\n",
       "      <td>5.067679</td>\n",
       "      <td>2.080874</td>\n",
       "      <td>2.236946</td>\n",
       "      <td>3.481698</td>\n",
       "      <td>4.582131</td>\n",
       "      <td>5.826883</td>\n",
       "      <td>10.896092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18035</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Delaware County</td>\n",
       "      <td>291.068642</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.805866</td>\n",
       "      <td>2.304294</td>\n",
       "      <td>0.125165</td>\n",
       "      <td>1.877476</td>\n",
       "      <td>3.629787</td>\n",
       "      <td>5.006602</td>\n",
       "      <td>10.513865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42125</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Washington County</td>\n",
       "      <td>241.385829</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.365534</td>\n",
       "      <td>2.984713</td>\n",
       "      <td>0.069058</td>\n",
       "      <td>0.483407</td>\n",
       "      <td>1.104930</td>\n",
       "      <td>1.795512</td>\n",
       "      <td>10.496839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21019</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Boyd County</td>\n",
       "      <td>292.235901</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.264890</td>\n",
       "      <td>2.609768</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.611572</td>\n",
       "      <td>1.223144</td>\n",
       "      <td>3.440093</td>\n",
       "      <td>10.396727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26017</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Bay County</td>\n",
       "      <td>233.157435</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.967971</td>\n",
       "      <td>2.462258</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.354956</td>\n",
       "      <td>4.017277</td>\n",
       "      <td>5.679599</td>\n",
       "      <td>10.250983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51085</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Hanover County</td>\n",
       "      <td>230.005805</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.135833</td>\n",
       "      <td>2.004443</td>\n",
       "      <td>0.397687</td>\n",
       "      <td>2.932942</td>\n",
       "      <td>3.910589</td>\n",
       "      <td>5.037369</td>\n",
       "      <td>9.809614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21029</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Bullitt County</td>\n",
       "      <td>274.981146</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.217467</td>\n",
       "      <td>1.457628</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.224350</td>\n",
       "      <td>1.923978</td>\n",
       "      <td>2.798514</td>\n",
       "      <td>9.619892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26115</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Monroe County</td>\n",
       "      <td>273.938194</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.121854</td>\n",
       "      <td>2.300931</td>\n",
       "      <td>0.569530</td>\n",
       "      <td>2.586616</td>\n",
       "      <td>3.607024</td>\n",
       "      <td>6.229236</td>\n",
       "      <td>8.922639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26075</th>\n",
       "      <td>Michigan</td>\n",
       "      <td>Jackson County</td>\n",
       "      <td>225.905203</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.108931</td>\n",
       "      <td>2.697062</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.685091</td>\n",
       "      <td>2.433375</td>\n",
       "      <td>5.936280</td>\n",
       "      <td>8.742125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36083</th>\n",
       "      <td>New York</td>\n",
       "      <td>Rensselaer County</td>\n",
       "      <td>243.265571</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.341018</td>\n",
       "      <td>2.123093</td>\n",
       "      <td>0.630064</td>\n",
       "      <td>1.710174</td>\n",
       "      <td>2.700275</td>\n",
       "      <td>4.747983</td>\n",
       "      <td>8.640880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18167</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Vigo County</td>\n",
       "      <td>265.396851</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.437414</td>\n",
       "      <td>1.570726</td>\n",
       "      <td>0.266928</td>\n",
       "      <td>1.334640</td>\n",
       "      <td>2.001959</td>\n",
       "      <td>3.470063</td>\n",
       "      <td>8.007837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29165</th>\n",
       "      <td>Missouri</td>\n",
       "      <td>Platte County</td>\n",
       "      <td>248.501868</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1.722913</td>\n",
       "      <td>1.591304</td>\n",
       "      <td>0.136813</td>\n",
       "      <td>0.820877</td>\n",
       "      <td>1.162908</td>\n",
       "      <td>1.915379</td>\n",
       "      <td>7.251076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36007</th>\n",
       "      <td>New York</td>\n",
       "      <td>Broome County</td>\n",
       "      <td>269.902489</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.012887</td>\n",
       "      <td>1.462052</td>\n",
       "      <td>1.049935</td>\n",
       "      <td>3.074809</td>\n",
       "      <td>4.049749</td>\n",
       "      <td>5.005940</td>\n",
       "      <td>7.199554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39055</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Geauga County</td>\n",
       "      <td>234.026549</td>\n",
       "      <td>118.0</td>\n",
       "      <td>4.214387</td>\n",
       "      <td>1.066008</td>\n",
       "      <td>1.220362</td>\n",
       "      <td>3.546678</td>\n",
       "      <td>4.118723</td>\n",
       "      <td>4.881449</td>\n",
       "      <td>7.169629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39139</th>\n",
       "      <td>Ohio</td>\n",
       "      <td>Richland County</td>\n",
       "      <td>244.622619</td>\n",
       "      <td>118.0</td>\n",
       "      <td>3.117718</td>\n",
       "      <td>1.532668</td>\n",
       "      <td>0.235827</td>\n",
       "      <td>2.240360</td>\n",
       "      <td>2.829928</td>\n",
       "      <td>3.979587</td>\n",
       "      <td>6.956907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42019</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Butler County</td>\n",
       "      <td>238.209545</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.190549</td>\n",
       "      <td>1.625812</td>\n",
       "      <td>0.456284</td>\n",
       "      <td>0.912568</td>\n",
       "      <td>1.292804</td>\n",
       "      <td>3.498176</td>\n",
       "      <td>6.007737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42013</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Blair County</td>\n",
       "      <td>231.702168</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1.030500</td>\n",
       "      <td>1.104143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.351781</td>\n",
       "      <td>0.586302</td>\n",
       "      <td>1.260549</td>\n",
       "      <td>5.393977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36091</th>\n",
       "      <td>New York</td>\n",
       "      <td>Saratoga County</td>\n",
       "      <td>283.787087</td>\n",
       "      <td>118.0</td>\n",
       "      <td>2.157828</td>\n",
       "      <td>1.095287</td>\n",
       "      <td>0.683637</td>\n",
       "      <td>1.367274</td>\n",
       "      <td>1.864465</td>\n",
       "      <td>2.765623</td>\n",
       "      <td>5.096203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51121</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Montgomery County</td>\n",
       "      <td>254.603193</td>\n",
       "      <td>118.0</td>\n",
       "      <td>1.910556</td>\n",
       "      <td>1.462627</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.724906</td>\n",
       "      <td>1.594792</td>\n",
       "      <td>3.153339</td>\n",
       "      <td>4.929358</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   st               county     density  count       mean  \\\n",
       "fips                                                                       \n",
       "13095         Georgia     Dougherty County  267.596421  118.0  21.458561   \n",
       "19013            Iowa    Black Hawk County  231.946254  118.0  17.588559   \n",
       "12097         Florida       Osceola County  283.061623  118.0  14.464012   \n",
       "48245           Texas     Jefferson County  287.077982  118.0  15.068384   \n",
       "48309           Texas      McLennan County  247.442631  118.0  11.443096   \n",
       "12005         Florida           Bay County  230.342049  118.0  10.560174   \n",
       "45051  South Carolina         Horry County  312.269379  118.0  16.572589   \n",
       "46099    South Dakota     Minnehaha County  239.279834  118.0  17.014495   \n",
       "13185         Georgia       Lowndes County  236.674152  118.0  16.747208   \n",
       "10005        Delaware        Sussex County  250.219266  118.0  19.307401   \n",
       "5143         Arkansas    Washington County  253.923178  118.0  18.740845   \n",
       "48361           Texas        Orange County  249.936314  118.0   7.756395   \n",
       "28049     Mississippi         Hinds County  266.562114  118.0  13.913328   \n",
       "22073       Louisiana      Ouachita Parish  251.109917  118.0  20.367123   \n",
       "45035  South Carolina    Dorchester County  284.018889  118.0  11.306494   \n",
       "32003          Nevada         Clark County  287.237511  118.0  11.931272   \n",
       "1101          Alabama    Montgomery County  288.794219  118.0  19.597232   \n",
       "48039           Texas      Brazoria County  275.660105  118.0  11.056405   \n",
       "12085         Florida        Martin County  296.247774  118.0  16.820568   \n",
       "12091         Florida      Okaloosa County  226.539833  118.0   7.992206   \n",
       "34011      New Jersey    Cumberland County  309.129776  118.0  17.480465   \n",
       "47189       Tennessee        Wilson County  253.416978  118.0   9.690620   \n",
       "22017       Louisiana         Caddo Parish  273.413019  118.0  17.817281   \n",
       "54061   West Virginia    Monongalia County  293.316151  118.0   5.987814   \n",
       "5131         Arkansas     Sebastian County  240.316519  118.0   7.963245   \n",
       "12083         Florida        Marion County  230.715296  118.0   6.214229   \n",
       "48423           Texas         Smith County  252.591014  118.0   6.809801   \n",
       "13045         Georgia       Carroll County  240.428312  118.0   9.619285   \n",
       "12061         Florida  Indian River County  318.019297  118.0   8.835971   \n",
       "1081          Alabama           Lee County  270.834979  118.0  10.532574   \n",
       "1117          Alabama        Shelby County  277.352121  118.0   8.938847   \n",
       "24045        Maryland      Wicomico County  276.700922  118.0   9.741644   \n",
       "48091           Texas         Comal County  279.205900  118.0   7.376657   \n",
       "42089    Pennsylvania        Monroe County  279.919314  118.0   7.248095   \n",
       "22103       Louisiana   St. Tammany Parish  307.987336  118.0  12.322756   \n",
       "45077  South Carolina       Pickens County  255.604776  118.0   9.673093   \n",
       "19103            Iowa       Johnson County  246.140317  118.0   9.056273   \n",
       "48187           Texas     Guadalupe County  234.566287  118.0   6.931725   \n",
       "34041      New Jersey        Warren County  294.933290  118.0  10.159799   \n",
       "47009       Tennessee        Blount County  238.207572  118.0   3.846969   \n",
       "47105       Tennessee        Loudon County  235.882312  118.0   6.789047   \n",
       "13015         Georgia        Bartow County  234.445450  118.0   8.723296   \n",
       "12001         Florida       Alachua County  307.470686  118.0   8.516870   \n",
       "37105  North Carolina           Lee County  242.309548  118.0  13.890017   \n",
       "51800        Virginia         Suffolk city  230.173327  118.0   7.151569   \n",
       "21227        Kentucky        Warren County  245.376662  118.0  12.916762   \n",
       "37089  North Carolina     Henderson County  314.733507  118.0   8.272290   \n",
       "48251           Texas       Johnson County  242.609274  118.0   5.059052   \n",
       "37101  North Carolina      Johnston County  264.551074  118.0   9.891622   \n",
       "37109  North Carolina       Lincoln County  289.023220  118.0   5.670085   \n",
       "12017         Florida        Citrus County  257.276997  118.0   4.929755   \n",
       "12119         Florida        Sumter County  242.113751  118.0   5.410549   \n",
       "45007  South Carolina      Anderson County  283.129213  118.0   6.118476   \n",
       "12015         Florida     Charlotte County  277.694887  118.0   6.699579   \n",
       "37147  North Carolina          Pitt County  277.222286  118.0   5.838188   \n",
       "20045          Kansas       Douglas County  268.187711  118.0   3.624267   \n",
       "47001       Tennessee      Anderson County  228.311613  118.0   3.574808   \n",
       "39109            Ohio         Miami County  263.138866  118.0   4.518437   \n",
       "49049            Utah          Utah County  317.569058  118.0   8.668010   \n",
       "34037      New Jersey        Sussex County  270.682486  118.0   7.328312   \n",
       "26021        Michigan       Berrien County  270.192533  118.0   6.273418   \n",
       "34019      New Jersey     Hunterdon County  290.709389  118.0   7.286058   \n",
       "10001        Delaware          Kent County  308.414324  118.0   9.450259   \n",
       "37159  North Carolina         Rowan County  277.855347  118.0  10.368536   \n",
       "55063       Wisconsin     La Crosse County  261.278853  118.0   4.838881   \n",
       "37097  North Carolina       Iredell County  316.827370  118.0   6.418650   \n",
       "12035         Florida       Flagler County  237.055088  118.0   4.982279   \n",
       "39045            Ohio     Fairfield County  312.392077  118.0   5.041638   \n",
       "18095         Indiana       Madison County  286.710997  118.0   4.691471   \n",
       "53063      Washington       Spokane County  296.405867  118.0   4.475592   \n",
       "18067         Indiana        Howard County  281.665342  118.0   6.950582   \n",
       "13297         Georgia        Walton County  290.447679  118.0   6.010202   \n",
       "24037        Maryland    St. Mary's County  317.794949  118.0   5.826624   \n",
       "25015   Massachusetts     Hampshire County  305.032669  118.0   5.484563   \n",
       "37057  North Carolina      Davidson County  303.268648  118.0   7.270033   \n",
       "25019   Massachusetts     Nantucket County  253.480098  118.0   0.966484   \n",
       "27019       Minnesota        Carver County  296.589289  118.0   4.694510   \n",
       "39043            Ohio          Erie County  295.224163  118.0   4.324811   \n",
       "27109       Minnesota       Olmsted County  242.279394  118.0   7.540343   \n",
       "37133  North Carolina        Onslow County  259.507777  118.0   2.645312   \n",
       "9015      Connecticut       Windham County  227.685169  118.0   4.726217   \n",
       "50007         Vermont    Chittenden County  305.219372  118.0   3.282516   \n",
       "18019         Indiana         Clark County  317.286881  118.0   5.721598   \n",
       "19113            Iowa          Linn County  316.239817  118.0   6.095296   \n",
       "39089            Ohio       Licking County  259.138462  118.0   3.669706   \n",
       "23001           Maine  Androscoggin County  231.397205  118.0   3.990527   \n",
       "29019        Missouri         Boone County  263.290508  118.0   3.545494   \n",
       "37085  North Carolina       Harnett County  228.536086  118.0   5.638547   \n",
       "17143        Illinois        Peoria County  289.367564  118.0   4.079003   \n",
       "6097       California        Sonoma County  313.695030  118.0   3.739700   \n",
       "55131       Wisconsin    Washington County  315.841775  118.0   3.590115   \n",
       "54037   West Virginia     Jefferson County  272.597610  118.0   4.008254   \n",
       "26145        Michigan       Saginaw County  238.139815  118.0   7.111850   \n",
       "55117       Wisconsin     Sheboygan County  225.597293  118.0   2.958933   \n",
       "21113        Kentucky     Jessamine County  314.410049  118.0   2.959798   \n",
       "55105       Wisconsin          Rock County  227.467548  118.0   6.200975   \n",
       "39003            Ohio         Allen County  254.290726  118.0   3.948337   \n",
       "21145        Kentucky     McCracken County  262.993278  118.0   3.320054   \n",
       "18059         Indiana       Hancock County  255.437624  118.0   5.544648   \n",
       "24015        Maryland         Cecil County  297.034421  118.0   4.617563   \n",
       "42073    Pennsylvania      Lawrence County  238.743631  118.0   2.413889   \n",
       "21073        Kentucky      Franklin County  245.447588  118.0   3.155378   \n",
       "41047          Oregon        Marion County  294.181131  118.0   5.255873   \n",
       "54107   West Virginia          Wood County  228.029269  118.0   2.129422   \n",
       "55087       Wisconsin     Outagamie County  294.710936  118.0   3.768211   \n",
       "6061       California        Placer County  283.103377  118.0   3.003467   \n",
       "54009   West Virginia        Brooke County  245.939129  118.0   1.556153   \n",
       "39155            Ohio      Trumbull County  320.192399  118.0   5.067679   \n",
       "18035         Indiana      Delaware County  291.068642  118.0   3.805866   \n",
       "42125    Pennsylvania    Washington County  241.385829  118.0   2.365534   \n",
       "21019        Kentucky          Boyd County  292.235901  118.0   2.264890   \n",
       "26017        Michigan           Bay County  233.157435  118.0   3.967971   \n",
       "51085        Virginia       Hanover County  230.005805  118.0   4.135833   \n",
       "21029        Kentucky       Bullitt County  274.981146  118.0   2.217467   \n",
       "26115        Michigan        Monroe County  273.938194  118.0   4.121854   \n",
       "26075        Michigan       Jackson County  225.905203  118.0   3.108931   \n",
       "36083        New York    Rensselaer County  243.265571  118.0   3.341018   \n",
       "18167         Indiana          Vigo County  265.396851  118.0   2.437414   \n",
       "29165        Missouri        Platte County  248.501868  118.0   1.722913   \n",
       "36007        New York        Broome County  269.902489  118.0   4.012887   \n",
       "39055            Ohio        Geauga County  234.026549  118.0   4.214387   \n",
       "39139            Ohio      Richland County  244.622619  118.0   3.117718   \n",
       "42019    Pennsylvania        Butler County  238.209545  118.0   2.190549   \n",
       "42013    Pennsylvania         Blair County  231.702168  118.0   1.030500   \n",
       "36091        New York      Saratoga County  283.787087  118.0   2.157828   \n",
       "51121        Virginia    Montgomery County  254.603193  118.0   1.910556   \n",
       "\n",
       "             std       min        25%        50%        75%        max  \n",
       "fips                                                                    \n",
       "13095  22.349015  0.974513   5.562846  10.719646  27.733022  83.320881  \n",
       "19013  17.612727  0.108862   5.633597  11.430487  24.058454  78.380485  \n",
       "12097  21.307437  0.950477   1.976993   3.231623  14.399734  73.604975  \n",
       "48245  19.205219  0.681448   3.137502   4.287446  23.708726  73.142130  \n",
       "48309  18.940213  0.111336   0.445345   0.890690  13.610850  67.636739  \n",
       "12005  18.708134  0.000000   0.756377   1.430984   6.582525  65.988789  \n",
       "45051  20.282960  0.645534   1.422193   3.086461  34.525998  63.141323  \n",
       "46099  17.659046  1.479358   5.122276   7.655677  25.574398  61.985091  \n",
       "13185  17.312602  1.338457   3.163625   6.023056  29.081016  61.447334  \n",
       "10005  15.222766  3.171554   6.526081  13.448607  30.632938  59.466630  \n",
       "5143   19.136201  0.477809   0.955618   9.675633  35.970063  54.828589  \n",
       "48361  12.055124  0.171300   1.027799   2.226897   8.136738  53.959423  \n",
       "28049  12.414804  2.587992   5.792172   7.671547  21.597407  51.944691  \n",
       "22073  12.011870  3.262025  11.859793  16.030525  28.379621  50.235192  \n",
       "45035  16.003886  0.175490   0.943261   2.544612  15.223799  48.610861  \n",
       "32003  12.864259  2.212138   4.057161   4.884350  14.161463  47.324621  \n",
       "1101   12.870088  1.135359   6.244473  22.234108  28.888572  46.865085  \n",
       "48039  11.945619  1.793997   3.177665   4.179632  15.470840  46.453077  \n",
       "12085  13.774057  1.863354   3.305235  12.732919  29.525288  43.921917  \n",
       "12091  11.616329  0.542312   1.169360   2.474298   8.609201  42.232535  \n",
       "34011  12.394236  0.955394   6.592216  14.139826  29.641087  42.228398  \n",
       "47189   9.787649  1.777604   3.555208   5.563242  11.208781  40.884891  \n",
       "22017  10.775317  3.687342   8.876384  13.113853  25.989813  40.858128  \n",
       "54061  10.278700  0.405798   0.811596   1.623192   3.922714  40.715070  \n",
       "5131   10.915859  0.000000   0.111758   0.670549  14.081532  37.103719  \n",
       "12083  10.298276  0.312616   0.908539   1.250463   5.666159  37.084031  \n",
       "48423  10.155878  0.306888   0.982043   1.595819   6.613444  36.826603  \n",
       "13045   8.976031  0.952444   3.690722   6.548056  10.060194  36.788167  \n",
       "12061  11.512982  0.178657   0.803958   2.054560  17.821076  35.731481  \n",
       "1081    9.375079  1.649601   3.668191   5.556549  18.275838  34.467969  \n",
       "1117    9.839803  0.656205   2.706846   4.199712  10.728952  34.319522  \n",
       "24045   8.626930  0.275762   3.860668   6.342527  14.443037  34.056611  \n",
       "48091  10.060608  0.182905   0.640168   1.463241  14.540958  33.197282  \n",
       "42089   8.348373  0.755099   2.202372   3.481844   9.040211  33.140448  \n",
       "22103   8.809545  3.949679   5.705092   8.283354  16.923277  33.133417  \n",
       "45077  10.724964  0.675533   1.238477   3.096191  21.391867  33.101100  \n",
       "19103   7.980712  0.850678   2.457513   6.663642  14.461521  32.892871  \n",
       "48187   9.131192  0.428108   0.941838   1.384217  14.705517  32.279359  \n",
       "34041   9.869083  0.678547   2.171349   5.089100  18.693961  31.891693  \n",
       "47009   6.341485  0.000000   0.456197   1.234414   3.112871  31.450726  \n",
       "47105   6.613434  0.528435   1.585305   4.755916   9.974212  30.649235  \n",
       "13015   6.779775  2.386742   4.906082   5.966856   8.353599  30.629861  \n",
       "12001  10.295865  0.139383   1.448587   2.625045  13.486957  29.575729  \n",
       "37105   7.505781  0.231239  10.983853  14.683677  18.152262  29.367353  \n",
       "51800   6.692986  0.000000   3.140723   5.273313   8.220164  28.848122  \n",
       "21227   7.033739  0.644973   9.352103  12.953201  16.876784  28.701283  \n",
       "37089   6.916178  1.338331   3.771661   5.596659   8.759987  28.591625  \n",
       "48251   6.387067  0.365640   1.300053   2.031333   5.179899  26.976101  \n",
       "37101   8.455868  0.477694   2.269047   6.619475  18.203556  26.819110  \n",
       "37109   6.638642  0.331798   1.078342   1.907837   8.709689  26.709712  \n",
       "12017   7.048880  0.190913   0.572738   1.431846   6.061480  26.536871  \n",
       "12119   6.606543  0.030823   0.863055   3.452219   5.852590  26.215289  \n",
       "45007   7.289681  0.634739   1.419347   2.538955   8.762922  25.530606  \n",
       "12015   6.154039  1.058705   2.873628   3.932334   6.049744  23.745245  \n",
       "37147   5.097771  0.055327   2.134060   3.912443   8.318882  23.474661  \n",
       "20045   5.773238  0.116848   0.467392   0.817936   2.424595  23.019047  \n",
       "47001   5.283650  0.185582   0.556745   1.113491   2.783727  23.012141  \n",
       "39109   4.719330  0.400583   1.735858   2.670551   5.875213  22.966742  \n",
       "49049   5.939112  0.987955   3.996726   6.926910  15.032631  22.947496  \n",
       "34037   7.272163  0.041871   1.957463   3.253964  13.778503  22.371001  \n",
       "26021   5.095535  0.097783   3.166305   5.261653   8.451239  22.164132  \n",
       "34019   5.852205  0.344591   1.837819   5.743185  11.945826  21.594377  \n",
       "10001   4.667098  1.580401   5.630177   9.166323  13.295119  21.256387  \n",
       "37159   5.365208  1.407578   5.831396   9.752508  14.980656  20.812052  \n",
       "55063   6.142854  0.000000   0.484196   1.210490   8.715525  20.336226  \n",
       "37097   5.856571  0.785767   1.885841   2.985914  10.961449  19.565597  \n",
       "12035   5.373941  0.372409   1.489634   2.172383   6.827489  19.489378  \n",
       "39045   4.526508  0.997264   2.447830   3.263773   5.416956  18.766693  \n",
       "18095   3.931760  0.661534   1.764090   3.638436   6.477519  18.522949  \n",
       "53063   4.761432  0.218604   1.065694   2.773538   5.827162  18.308082  \n",
       "18067   3.683028  1.038407   3.807493   6.403511   9.345665  17.652923  \n",
       "13297   4.178115  0.755115   3.020459   4.379666   8.306262  17.518663  \n",
       "24037   3.429242  0.629271   3.177819   4.719534   8.495161  17.116176  \n",
       "25015   3.710437  0.977074   2.065180   4.441247   8.083069  16.521438  \n",
       "37057   4.764437  0.767091   3.153598   6.051499  11.868609  16.364617  \n",
       "25019   3.865050  0.000000   0.000000   0.000000   0.000000  16.292156  \n",
       "27019   4.091310  0.000000   0.577742   4.485994   6.627036  16.176764  \n",
       "39043   3.802937  0.192359   1.538870   3.366278   5.241776  15.773417  \n",
       "27109   3.101074  2.978202   4.692925   7.174760   9.724282  15.613000  \n",
       "37133   3.024376  0.072173   0.544904   1.299108   3.716893  15.372779  \n",
       "9015    3.023318  0.733968   2.691217   4.220318   5.718836  15.046350  \n",
       "50007   3.411090  0.087228   1.046739   2.006249   3.663585  14.654341  \n",
       "18019   2.638701  1.932101   3.773635   5.554791   6.732165  14.370002  \n",
       "19113   3.503883  1.260286   2.725367   5.797313   7.782263  14.241226  \n",
       "39089   3.182713  0.484639   1.615465   2.423197   3.614602  14.135315  \n",
       "23001   3.571626  0.395810   1.055494   2.638735   6.201027  14.117231  \n",
       "29019   4.212294  0.056544   0.474969   1.979036   4.017444  13.774094  \n",
       "37085   3.771691  0.630363   2.022416   4.517604   8.798822  13.657872  \n",
       "17143   3.399114  0.318915   1.514846   3.109421   5.979655  13.474156  \n",
       "6097    3.485043  0.780267   1.271547   2.283005   4.941694  13.466838  \n",
       "55131   2.631399  0.840126   1.575236   2.467870   5.355804  13.337002  \n",
       "54037   2.993228  0.249986   1.999890   2.999835   4.937228  13.249271  \n",
       "26145   3.295306  1.649456   4.442285   6.522849   9.803017  13.195649  \n",
       "55117   3.093937  0.247715   0.867002   1.672075   3.777651  13.005029  \n",
       "21113   2.696568  0.000000   1.319940   2.128404   3.255853  12.935415  \n",
       "55105   3.412400  0.699620   3.279468   5.946769   8.898291  12.855516  \n",
       "39003   2.903603  0.558303   1.971507   2.931090   5.268983  12.701390  \n",
       "21145   2.370504  0.436752   1.747007   2.675105   3.930766  12.447426  \n",
       "18059   2.469301  1.279296   3.883577   4.934427   6.761992  12.427446  \n",
       "24015   2.257815  1.250026   3.333403   4.375091   5.520948  12.361369  \n",
       "42073   2.977026  0.167061   0.501183   1.002366   2.631210  12.195448  \n",
       "21073   3.009072  0.061285   0.280161   3.081776   5.042907  12.046944  \n",
       "41047   2.529239  1.683968   3.367935   4.435817   6.602386  12.034208  \n",
       "54107   2.856946  0.000000   0.342099   0.855248   2.223644  11.973467  \n",
       "55087   3.024913  0.152069   1.140515   3.269477   4.828181  11.785325  \n",
       "6061    3.239226  0.143456   0.394505   1.560089   4.590606  11.763427  \n",
       "54009   2.778689  0.000000   0.000000   0.000000   1.790679  11.720810  \n",
       "39155   2.080874  2.236946   3.481698   4.582131   5.826883  10.896092  \n",
       "18035   2.304294  0.125165   1.877476   3.629787   5.006602  10.513865  \n",
       "42125   2.984713  0.069058   0.483407   1.104930   1.795512  10.496839  \n",
       "21019   2.609768  0.000000   0.611572   1.223144   3.440093  10.396727  \n",
       "26017   2.462258  0.000000   2.354956   4.017277   5.679599  10.250983  \n",
       "51085   2.004443  0.397687   2.932942   3.910589   5.037369   9.809614  \n",
       "21029   1.457628  0.000000   1.224350   1.923978   2.798514   9.619892  \n",
       "26115   2.300931  0.569530   2.586616   3.607024   6.229236   8.922639  \n",
       "26075   2.697062  0.000000   0.685091   2.433375   5.936280   8.742125  \n",
       "36083   2.123093  0.630064   1.710174   2.700275   4.747983   8.640880  \n",
       "18167   1.570726  0.266928   1.334640   2.001959   3.470063   8.007837  \n",
       "29165   1.591304  0.136813   0.820877   1.162908   1.915379   7.251076  \n",
       "36007   1.462052  1.049935   3.074809   4.049749   5.005940   7.199554  \n",
       "39055   1.066008  1.220362   3.546678   4.118723   4.881449   7.169629  \n",
       "39139   1.532668  0.235827   2.240360   2.829928   3.979587   6.956907  \n",
       "42019   1.625812  0.456284   0.912568   1.292804   3.498176   6.007737  \n",
       "42013   1.104143  0.000000   0.351781   0.586302   1.260549   5.393977  \n",
       "36091   1.095287  0.683637   1.367274   1.864465   2.765623   5.096203  \n",
       "51121   1.462627  0.000000   0.724906   1.594792   3.153339   4.929358  "
      ]
     },
     "execution_count": 1619,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    pd.concat([county_details[['st', 'county', 'density']], county_percentages.loc[counties_like_brazoria.index].T.describe().T], axis=1)\n",
    "    .dropna()\n",
    "    .sort_values(by='max', ascending=False)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1707,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1717,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUMLEV</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2012</th>\n",
       "      <th>POPESTIMATE2013</th>\n",
       "      <th>POPESTIMATE2014</th>\n",
       "      <th>POPESTIMATE2015</th>\n",
       "      <th>POPESTIMATE2016</th>\n",
       "      <th>POPESTIMATE2017</th>\n",
       "      <th>POPESTIMATE2018</th>\n",
       "      <th>POPESTIMATE2019</th>\n",
       "      <th>NPOPCHG_2010</th>\n",
       "      <th>NPOPCHG_2011</th>\n",
       "      <th>NPOPCHG_2012</th>\n",
       "      <th>NPOPCHG_2013</th>\n",
       "      <th>NPOPCHG_2014</th>\n",
       "      <th>NPOPCHG_2015</th>\n",
       "      <th>NPOPCHG_2016</th>\n",
       "      <th>NPOPCHG_2017</th>\n",
       "      <th>NPOPCHG_2018</th>\n",
       "      <th>NPOPCHG_2019</th>\n",
       "      <th>BIRTHS2010</th>\n",
       "      <th>BIRTHS2011</th>\n",
       "      <th>BIRTHS2012</th>\n",
       "      <th>BIRTHS2013</th>\n",
       "      <th>BIRTHS2014</th>\n",
       "      <th>BIRTHS2015</th>\n",
       "      <th>BIRTHS2016</th>\n",
       "      <th>BIRTHS2017</th>\n",
       "      <th>BIRTHS2018</th>\n",
       "      <th>BIRTHS2019</th>\n",
       "      <th>DEATHS2010</th>\n",
       "      <th>DEATHS2011</th>\n",
       "      <th>DEATHS2012</th>\n",
       "      <th>DEATHS2013</th>\n",
       "      <th>DEATHS2014</th>\n",
       "      <th>DEATHS2015</th>\n",
       "      <th>DEATHS2016</th>\n",
       "      <th>DEATHS2017</th>\n",
       "      <th>DEATHS2018</th>\n",
       "      <th>DEATHS2019</th>\n",
       "      <th>NATURALINC2010</th>\n",
       "      <th>NATURALINC2011</th>\n",
       "      <th>NATURALINC2012</th>\n",
       "      <th>NATURALINC2013</th>\n",
       "      <th>NATURALINC2014</th>\n",
       "      <th>NATURALINC2015</th>\n",
       "      <th>NATURALINC2016</th>\n",
       "      <th>NATURALINC2017</th>\n",
       "      <th>NATURALINC2018</th>\n",
       "      <th>NATURALINC2019</th>\n",
       "      <th>INTERNATIONALMIG2010</th>\n",
       "      <th>INTERNATIONALMIG2011</th>\n",
       "      <th>INTERNATIONALMIG2012</th>\n",
       "      <th>INTERNATIONALMIG2013</th>\n",
       "      <th>INTERNATIONALMIG2014</th>\n",
       "      <th>INTERNATIONALMIG2015</th>\n",
       "      <th>INTERNATIONALMIG2016</th>\n",
       "      <th>INTERNATIONALMIG2017</th>\n",
       "      <th>INTERNATIONALMIG2018</th>\n",
       "      <th>INTERNATIONALMIG2019</th>\n",
       "      <th>DOMESTICMIG2010</th>\n",
       "      <th>DOMESTICMIG2011</th>\n",
       "      <th>DOMESTICMIG2012</th>\n",
       "      <th>DOMESTICMIG2013</th>\n",
       "      <th>DOMESTICMIG2014</th>\n",
       "      <th>DOMESTICMIG2015</th>\n",
       "      <th>DOMESTICMIG2016</th>\n",
       "      <th>DOMESTICMIG2017</th>\n",
       "      <th>DOMESTICMIG2018</th>\n",
       "      <th>DOMESTICMIG2019</th>\n",
       "      <th>NETMIG2010</th>\n",
       "      <th>NETMIG2011</th>\n",
       "      <th>NETMIG2012</th>\n",
       "      <th>NETMIG2013</th>\n",
       "      <th>NETMIG2014</th>\n",
       "      <th>NETMIG2015</th>\n",
       "      <th>NETMIG2016</th>\n",
       "      <th>NETMIG2017</th>\n",
       "      <th>NETMIG2018</th>\n",
       "      <th>NETMIG2019</th>\n",
       "      <th>RESIDUAL2010</th>\n",
       "      <th>RESIDUAL2011</th>\n",
       "      <th>RESIDUAL2012</th>\n",
       "      <th>RESIDUAL2013</th>\n",
       "      <th>RESIDUAL2014</th>\n",
       "      <th>RESIDUAL2015</th>\n",
       "      <th>RESIDUAL2016</th>\n",
       "      <th>RESIDUAL2017</th>\n",
       "      <th>RESIDUAL2018</th>\n",
       "      <th>RESIDUAL2019</th>\n",
       "      <th>GQESTIMATESBASE2010</th>\n",
       "      <th>GQESTIMATES2010</th>\n",
       "      <th>GQESTIMATES2011</th>\n",
       "      <th>GQESTIMATES2012</th>\n",
       "      <th>GQESTIMATES2013</th>\n",
       "      <th>GQESTIMATES2014</th>\n",
       "      <th>GQESTIMATES2015</th>\n",
       "      <th>GQESTIMATES2016</th>\n",
       "      <th>GQESTIMATES2017</th>\n",
       "      <th>GQESTIMATES2018</th>\n",
       "      <th>GQESTIMATES2019</th>\n",
       "      <th>RBIRTH2011</th>\n",
       "      <th>RBIRTH2012</th>\n",
       "      <th>RBIRTH2013</th>\n",
       "      <th>RBIRTH2014</th>\n",
       "      <th>RBIRTH2015</th>\n",
       "      <th>RBIRTH2016</th>\n",
       "      <th>RBIRTH2017</th>\n",
       "      <th>RBIRTH2018</th>\n",
       "      <th>RBIRTH2019</th>\n",
       "      <th>RDEATH2011</th>\n",
       "      <th>RDEATH2012</th>\n",
       "      <th>RDEATH2013</th>\n",
       "      <th>RDEATH2014</th>\n",
       "      <th>RDEATH2015</th>\n",
       "      <th>RDEATH2016</th>\n",
       "      <th>RDEATH2017</th>\n",
       "      <th>RDEATH2018</th>\n",
       "      <th>RDEATH2019</th>\n",
       "      <th>RNATURALINC2011</th>\n",
       "      <th>RNATURALINC2012</th>\n",
       "      <th>RNATURALINC2013</th>\n",
       "      <th>RNATURALINC2014</th>\n",
       "      <th>RNATURALINC2015</th>\n",
       "      <th>RNATURALINC2016</th>\n",
       "      <th>RNATURALINC2017</th>\n",
       "      <th>RNATURALINC2018</th>\n",
       "      <th>RNATURALINC2019</th>\n",
       "      <th>RINTERNATIONALMIG2011</th>\n",
       "      <th>RINTERNATIONALMIG2012</th>\n",
       "      <th>RINTERNATIONALMIG2013</th>\n",
       "      <th>RINTERNATIONALMIG2014</th>\n",
       "      <th>RINTERNATIONALMIG2015</th>\n",
       "      <th>RINTERNATIONALMIG2016</th>\n",
       "      <th>RINTERNATIONALMIG2017</th>\n",
       "      <th>RINTERNATIONALMIG2018</th>\n",
       "      <th>RINTERNATIONALMIG2019</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RDOMESTICMIG2016</th>\n",
       "      <th>RDOMESTICMIG2017</th>\n",
       "      <th>RDOMESTICMIG2018</th>\n",
       "      <th>RDOMESTICMIG2019</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "      <th>RNETMIG2016</th>\n",
       "      <th>RNETMIG2017</th>\n",
       "      <th>RNETMIG2018</th>\n",
       "      <th>RNETMIG2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2971</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>51</td>\n",
       "      <td>600</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>Fairfax city</td>\n",
       "      <td>22565</td>\n",
       "      <td>22554</td>\n",
       "      <td>22614</td>\n",
       "      <td>22460</td>\n",
       "      <td>22894</td>\n",
       "      <td>23230</td>\n",
       "      <td>23310</td>\n",
       "      <td>23230</td>\n",
       "      <td>23246</td>\n",
       "      <td>23435</td>\n",
       "      <td>23725</td>\n",
       "      <td>24019</td>\n",
       "      <td>60</td>\n",
       "      <td>-154</td>\n",
       "      <td>434</td>\n",
       "      <td>336</td>\n",
       "      <td>80</td>\n",
       "      <td>-80</td>\n",
       "      <td>16</td>\n",
       "      <td>189</td>\n",
       "      <td>290</td>\n",
       "      <td>294</td>\n",
       "      <td>77</td>\n",
       "      <td>216</td>\n",
       "      <td>337</td>\n",
       "      <td>270</td>\n",
       "      <td>272</td>\n",
       "      <td>238</td>\n",
       "      <td>305</td>\n",
       "      <td>556</td>\n",
       "      <td>631</td>\n",
       "      <td>673</td>\n",
       "      <td>34</td>\n",
       "      <td>186</td>\n",
       "      <td>188</td>\n",
       "      <td>171</td>\n",
       "      <td>159</td>\n",
       "      <td>226</td>\n",
       "      <td>246</td>\n",
       "      <td>229</td>\n",
       "      <td>214</td>\n",
       "      <td>203</td>\n",
       "      <td>43</td>\n",
       "      <td>30</td>\n",
       "      <td>149</td>\n",
       "      <td>99</td>\n",
       "      <td>113</td>\n",
       "      <td>12</td>\n",
       "      <td>59</td>\n",
       "      <td>327</td>\n",
       "      <td>417</td>\n",
       "      <td>470</td>\n",
       "      <td>25</td>\n",
       "      <td>141</td>\n",
       "      <td>228</td>\n",
       "      <td>206</td>\n",
       "      <td>291</td>\n",
       "      <td>274</td>\n",
       "      <td>263</td>\n",
       "      <td>251</td>\n",
       "      <td>137</td>\n",
       "      <td>140</td>\n",
       "      <td>-7</td>\n",
       "      <td>-327</td>\n",
       "      <td>54</td>\n",
       "      <td>34</td>\n",
       "      <td>-334</td>\n",
       "      <td>-371</td>\n",
       "      <td>-307</td>\n",
       "      <td>-390</td>\n",
       "      <td>-264</td>\n",
       "      <td>-315</td>\n",
       "      <td>18</td>\n",
       "      <td>-186</td>\n",
       "      <td>282</td>\n",
       "      <td>240</td>\n",
       "      <td>-43</td>\n",
       "      <td>-97</td>\n",
       "      <td>-44</td>\n",
       "      <td>-139</td>\n",
       "      <td>-127</td>\n",
       "      <td>-175</td>\n",
       "      <td>-1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>-3</td>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>521</td>\n",
       "      <td>9.584239</td>\n",
       "      <td>14.860872</td>\n",
       "      <td>11.707571</td>\n",
       "      <td>11.68887</td>\n",
       "      <td>10.227761</td>\n",
       "      <td>13.125054</td>\n",
       "      <td>23.821255</td>\n",
       "      <td>26.759966</td>\n",
       "      <td>28.192024</td>\n",
       "      <td>8.253095</td>\n",
       "      <td>8.290338</td>\n",
       "      <td>7.414795</td>\n",
       "      <td>6.832832</td>\n",
       "      <td>9.712076</td>\n",
       "      <td>10.586109</td>\n",
       "      <td>9.811272</td>\n",
       "      <td>9.075488</td>\n",
       "      <td>8.503686</td>\n",
       "      <td>1.331144</td>\n",
       "      <td>6.570534</td>\n",
       "      <td>4.292776</td>\n",
       "      <td>4.856038</td>\n",
       "      <td>0.515685</td>\n",
       "      <td>2.538945</td>\n",
       "      <td>14.009983</td>\n",
       "      <td>17.684478</td>\n",
       "      <td>19.688338</td>\n",
       "      <td>6.256378</td>\n",
       "      <td>10.05424</td>\n",
       "      <td>8.932443</td>\n",
       "      <td>12.505372</td>\n",
       "      <td>11.774817</td>\n",
       "      <td>11.317669</td>\n",
       "      <td>10.75384</td>\n",
       "      <td>5.810008</td>\n",
       "      <td>5.864611</td>\n",
       "      <td>-14.509473</td>\n",
       "      <td>2.381267</td>\n",
       "      <td>1.474287</td>\n",
       "      <td>-14.353245</td>\n",
       "      <td>-15.943275</td>\n",
       "      <td>-13.21112</td>\n",
       "      <td>-16.709154</td>\n",
       "      <td>-11.195929</td>\n",
       "      <td>-13.195375</td>\n",
       "      <td>-8.253095</td>\n",
       "      <td>12.435507</td>\n",
       "      <td>10.40673</td>\n",
       "      <td>-1.847873</td>\n",
       "      <td>-4.168457</td>\n",
       "      <td>-1.89345</td>\n",
       "      <td>-5.955314</td>\n",
       "      <td>-5.38592</td>\n",
       "      <td>-7.330764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SUMLEV  REGION  DIVISION  STATE  COUNTY    STNAME       CTYNAME  \\\n",
       "2971      50       3         5     51     600  Virginia  Fairfax city   \n",
       "\n",
       "      CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010  POPESTIMATE2011  \\\n",
       "2971          22565              22554            22614            22460   \n",
       "\n",
       "      POPESTIMATE2012  POPESTIMATE2013  POPESTIMATE2014  POPESTIMATE2015  \\\n",
       "2971            22894            23230            23310            23230   \n",
       "\n",
       "      POPESTIMATE2016  POPESTIMATE2017  POPESTIMATE2018  POPESTIMATE2019  \\\n",
       "2971            23246            23435            23725            24019   \n",
       "\n",
       "      NPOPCHG_2010  NPOPCHG_2011  NPOPCHG_2012  NPOPCHG_2013  NPOPCHG_2014  \\\n",
       "2971            60          -154           434           336            80   \n",
       "\n",
       "      NPOPCHG_2015  NPOPCHG_2016  NPOPCHG_2017  NPOPCHG_2018  NPOPCHG_2019  \\\n",
       "2971           -80            16           189           290           294   \n",
       "\n",
       "      BIRTHS2010  BIRTHS2011  BIRTHS2012  BIRTHS2013  BIRTHS2014  BIRTHS2015  \\\n",
       "2971          77         216         337         270         272         238   \n",
       "\n",
       "      BIRTHS2016  BIRTHS2017  BIRTHS2018  BIRTHS2019  DEATHS2010  DEATHS2011  \\\n",
       "2971         305         556         631         673          34         186   \n",
       "\n",
       "      DEATHS2012  DEATHS2013  DEATHS2014  DEATHS2015  DEATHS2016  DEATHS2017  \\\n",
       "2971         188         171         159         226         246         229   \n",
       "\n",
       "      DEATHS2018  DEATHS2019  NATURALINC2010  NATURALINC2011  NATURALINC2012  \\\n",
       "2971         214         203              43              30             149   \n",
       "\n",
       "      NATURALINC2013  NATURALINC2014  NATURALINC2015  NATURALINC2016  \\\n",
       "2971              99             113              12              59   \n",
       "\n",
       "      NATURALINC2017  NATURALINC2018  NATURALINC2019  INTERNATIONALMIG2010  \\\n",
       "2971             327             417             470                    25   \n",
       "\n",
       "      INTERNATIONALMIG2011  INTERNATIONALMIG2012  INTERNATIONALMIG2013  \\\n",
       "2971                   141                   228                   206   \n",
       "\n",
       "      INTERNATIONALMIG2014  INTERNATIONALMIG2015  INTERNATIONALMIG2016  \\\n",
       "2971                   291                   274                   263   \n",
       "\n",
       "      INTERNATIONALMIG2017  INTERNATIONALMIG2018  INTERNATIONALMIG2019  \\\n",
       "2971                   251                   137                   140   \n",
       "\n",
       "      DOMESTICMIG2010  DOMESTICMIG2011  DOMESTICMIG2012  DOMESTICMIG2013  \\\n",
       "2971               -7             -327               54               34   \n",
       "\n",
       "      DOMESTICMIG2014  DOMESTICMIG2015  DOMESTICMIG2016  DOMESTICMIG2017  \\\n",
       "2971             -334             -371             -307             -390   \n",
       "\n",
       "      DOMESTICMIG2018  DOMESTICMIG2019  NETMIG2010  NETMIG2011  NETMIG2012  \\\n",
       "2971             -264             -315          18        -186         282   \n",
       "\n",
       "      NETMIG2013  NETMIG2014  NETMIG2015  NETMIG2016  NETMIG2017  NETMIG2018  \\\n",
       "2971         240         -43         -97         -44        -139        -127   \n",
       "\n",
       "      NETMIG2019  RESIDUAL2010  RESIDUAL2011  RESIDUAL2012  RESIDUAL2013  \\\n",
       "2971        -175            -1             2             3            -3   \n",
       "\n",
       "      RESIDUAL2014  RESIDUAL2015  RESIDUAL2016  RESIDUAL2017  RESIDUAL2018  \\\n",
       "2971            10             5             1             1             0   \n",
       "\n",
       "      RESIDUAL2019  GQESTIMATESBASE2010  GQESTIMATES2010  GQESTIMATES2011  \\\n",
       "2971            -1                  521              521              521   \n",
       "\n",
       "      GQESTIMATES2012  GQESTIMATES2013  GQESTIMATES2014  GQESTIMATES2015  \\\n",
       "2971              521              521              521              521   \n",
       "\n",
       "      GQESTIMATES2016  GQESTIMATES2017  GQESTIMATES2018  GQESTIMATES2019  \\\n",
       "2971              521              521              521              521   \n",
       "\n",
       "      RBIRTH2011  RBIRTH2012  RBIRTH2013  RBIRTH2014  RBIRTH2015  RBIRTH2016  \\\n",
       "2971    9.584239   14.860872   11.707571    11.68887   10.227761   13.125054   \n",
       "\n",
       "      RBIRTH2017  RBIRTH2018  RBIRTH2019  RDEATH2011  RDEATH2012  RDEATH2013  \\\n",
       "2971   23.821255   26.759966   28.192024    8.253095    8.290338    7.414795   \n",
       "\n",
       "      RDEATH2014  RDEATH2015  RDEATH2016  RDEATH2017  RDEATH2018  RDEATH2019  \\\n",
       "2971    6.832832    9.712076   10.586109    9.811272    9.075488    8.503686   \n",
       "\n",
       "      RNATURALINC2011  RNATURALINC2012  RNATURALINC2013  RNATURALINC2014  \\\n",
       "2971         1.331144         6.570534         4.292776         4.856038   \n",
       "\n",
       "      RNATURALINC2015  RNATURALINC2016  RNATURALINC2017  RNATURALINC2018  \\\n",
       "2971         0.515685         2.538945        14.009983        17.684478   \n",
       "\n",
       "      RNATURALINC2019  RINTERNATIONALMIG2011  RINTERNATIONALMIG2012  \\\n",
       "2971        19.688338               6.256378               10.05424   \n",
       "\n",
       "      RINTERNATIONALMIG2013  RINTERNATIONALMIG2014  RINTERNATIONALMIG2015  \\\n",
       "2971               8.932443              12.505372              11.774817   \n",
       "\n",
       "      RINTERNATIONALMIG2016  RINTERNATIONALMIG2017  RINTERNATIONALMIG2018  \\\n",
       "2971              11.317669               10.75384               5.810008   \n",
       "\n",
       "      RINTERNATIONALMIG2019  RDOMESTICMIG2011  RDOMESTICMIG2012  \\\n",
       "2971               5.864611        -14.509473          2.381267   \n",
       "\n",
       "      RDOMESTICMIG2013  RDOMESTICMIG2014  RDOMESTICMIG2015  RDOMESTICMIG2016  \\\n",
       "2971          1.474287        -14.353245        -15.943275         -13.21112   \n",
       "\n",
       "      RDOMESTICMIG2017  RDOMESTICMIG2018  RDOMESTICMIG2019  RNETMIG2011  \\\n",
       "2971        -16.709154        -11.195929        -13.195375    -8.253095   \n",
       "\n",
       "      RNETMIG2012  RNETMIG2013  RNETMIG2014  RNETMIG2015  RNETMIG2016  \\\n",
       "2971    12.435507     10.40673    -1.847873    -4.168457     -1.89345   \n",
       "\n",
       "      RNETMIG2017  RNETMIG2018  RNETMIG2019  \n",
       "2971    -5.955314     -5.38592    -7.330764  "
      ]
     },
     "execution_count": 1717,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"co-est2019-alldata.csv\", encoding = \"ISO-8859-1\").query('SUMLEV == 50').query('CTYNAME==\"Fairfax city\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1721,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SUMLEV</th>\n",
       "      <th>REGION</th>\n",
       "      <th>DIVISION</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>CENSUS2010POP</th>\n",
       "      <th>ESTIMATESBASE2010</th>\n",
       "      <th>POPESTIMATE2010</th>\n",
       "      <th>POPESTIMATE2011</th>\n",
       "      <th>POPESTIMATE2012</th>\n",
       "      <th>POPESTIMATE2013</th>\n",
       "      <th>POPESTIMATE2014</th>\n",
       "      <th>POPESTIMATE2015</th>\n",
       "      <th>POPESTIMATE2016</th>\n",
       "      <th>POPESTIMATE2017</th>\n",
       "      <th>POPESTIMATE2018</th>\n",
       "      <th>POPESTIMATE2019</th>\n",
       "      <th>NPOPCHG_2010</th>\n",
       "      <th>NPOPCHG_2011</th>\n",
       "      <th>NPOPCHG_2012</th>\n",
       "      <th>NPOPCHG_2013</th>\n",
       "      <th>NPOPCHG_2014</th>\n",
       "      <th>NPOPCHG_2015</th>\n",
       "      <th>NPOPCHG_2016</th>\n",
       "      <th>NPOPCHG_2017</th>\n",
       "      <th>NPOPCHG_2018</th>\n",
       "      <th>NPOPCHG_2019</th>\n",
       "      <th>BIRTHS2010</th>\n",
       "      <th>BIRTHS2011</th>\n",
       "      <th>BIRTHS2012</th>\n",
       "      <th>BIRTHS2013</th>\n",
       "      <th>BIRTHS2014</th>\n",
       "      <th>BIRTHS2015</th>\n",
       "      <th>BIRTHS2016</th>\n",
       "      <th>BIRTHS2017</th>\n",
       "      <th>BIRTHS2018</th>\n",
       "      <th>BIRTHS2019</th>\n",
       "      <th>DEATHS2010</th>\n",
       "      <th>DEATHS2011</th>\n",
       "      <th>DEATHS2012</th>\n",
       "      <th>DEATHS2013</th>\n",
       "      <th>DEATHS2014</th>\n",
       "      <th>DEATHS2015</th>\n",
       "      <th>DEATHS2016</th>\n",
       "      <th>DEATHS2017</th>\n",
       "      <th>DEATHS2018</th>\n",
       "      <th>DEATHS2019</th>\n",
       "      <th>NATURALINC2010</th>\n",
       "      <th>NATURALINC2011</th>\n",
       "      <th>NATURALINC2012</th>\n",
       "      <th>NATURALINC2013</th>\n",
       "      <th>NATURALINC2014</th>\n",
       "      <th>NATURALINC2015</th>\n",
       "      <th>NATURALINC2016</th>\n",
       "      <th>NATURALINC2017</th>\n",
       "      <th>NATURALINC2018</th>\n",
       "      <th>NATURALINC2019</th>\n",
       "      <th>INTERNATIONALMIG2010</th>\n",
       "      <th>INTERNATIONALMIG2011</th>\n",
       "      <th>INTERNATIONALMIG2012</th>\n",
       "      <th>INTERNATIONALMIG2013</th>\n",
       "      <th>INTERNATIONALMIG2014</th>\n",
       "      <th>INTERNATIONALMIG2015</th>\n",
       "      <th>INTERNATIONALMIG2016</th>\n",
       "      <th>INTERNATIONALMIG2017</th>\n",
       "      <th>INTERNATIONALMIG2018</th>\n",
       "      <th>INTERNATIONALMIG2019</th>\n",
       "      <th>DOMESTICMIG2010</th>\n",
       "      <th>DOMESTICMIG2011</th>\n",
       "      <th>DOMESTICMIG2012</th>\n",
       "      <th>DOMESTICMIG2013</th>\n",
       "      <th>DOMESTICMIG2014</th>\n",
       "      <th>DOMESTICMIG2015</th>\n",
       "      <th>DOMESTICMIG2016</th>\n",
       "      <th>DOMESTICMIG2017</th>\n",
       "      <th>DOMESTICMIG2018</th>\n",
       "      <th>DOMESTICMIG2019</th>\n",
       "      <th>NETMIG2010</th>\n",
       "      <th>NETMIG2011</th>\n",
       "      <th>NETMIG2012</th>\n",
       "      <th>NETMIG2013</th>\n",
       "      <th>NETMIG2014</th>\n",
       "      <th>NETMIG2015</th>\n",
       "      <th>NETMIG2016</th>\n",
       "      <th>NETMIG2017</th>\n",
       "      <th>NETMIG2018</th>\n",
       "      <th>NETMIG2019</th>\n",
       "      <th>RESIDUAL2010</th>\n",
       "      <th>RESIDUAL2011</th>\n",
       "      <th>RESIDUAL2012</th>\n",
       "      <th>RESIDUAL2013</th>\n",
       "      <th>RESIDUAL2014</th>\n",
       "      <th>RESIDUAL2015</th>\n",
       "      <th>RESIDUAL2016</th>\n",
       "      <th>RESIDUAL2017</th>\n",
       "      <th>RESIDUAL2018</th>\n",
       "      <th>RESIDUAL2019</th>\n",
       "      <th>GQESTIMATESBASE2010</th>\n",
       "      <th>GQESTIMATES2010</th>\n",
       "      <th>GQESTIMATES2011</th>\n",
       "      <th>GQESTIMATES2012</th>\n",
       "      <th>GQESTIMATES2013</th>\n",
       "      <th>GQESTIMATES2014</th>\n",
       "      <th>GQESTIMATES2015</th>\n",
       "      <th>GQESTIMATES2016</th>\n",
       "      <th>GQESTIMATES2017</th>\n",
       "      <th>GQESTIMATES2018</th>\n",
       "      <th>GQESTIMATES2019</th>\n",
       "      <th>RBIRTH2011</th>\n",
       "      <th>RBIRTH2012</th>\n",
       "      <th>RBIRTH2013</th>\n",
       "      <th>RBIRTH2014</th>\n",
       "      <th>RBIRTH2015</th>\n",
       "      <th>RBIRTH2016</th>\n",
       "      <th>RBIRTH2017</th>\n",
       "      <th>RBIRTH2018</th>\n",
       "      <th>RBIRTH2019</th>\n",
       "      <th>RDEATH2011</th>\n",
       "      <th>RDEATH2012</th>\n",
       "      <th>RDEATH2013</th>\n",
       "      <th>RDEATH2014</th>\n",
       "      <th>RDEATH2015</th>\n",
       "      <th>RDEATH2016</th>\n",
       "      <th>RDEATH2017</th>\n",
       "      <th>RDEATH2018</th>\n",
       "      <th>RDEATH2019</th>\n",
       "      <th>RNATURALINC2011</th>\n",
       "      <th>RNATURALINC2012</th>\n",
       "      <th>RNATURALINC2013</th>\n",
       "      <th>RNATURALINC2014</th>\n",
       "      <th>RNATURALINC2015</th>\n",
       "      <th>RNATURALINC2016</th>\n",
       "      <th>RNATURALINC2017</th>\n",
       "      <th>RNATURALINC2018</th>\n",
       "      <th>RNATURALINC2019</th>\n",
       "      <th>RINTERNATIONALMIG2011</th>\n",
       "      <th>RINTERNATIONALMIG2012</th>\n",
       "      <th>RINTERNATIONALMIG2013</th>\n",
       "      <th>RINTERNATIONALMIG2014</th>\n",
       "      <th>RINTERNATIONALMIG2015</th>\n",
       "      <th>RINTERNATIONALMIG2016</th>\n",
       "      <th>RINTERNATIONALMIG2017</th>\n",
       "      <th>RINTERNATIONALMIG2018</th>\n",
       "      <th>RINTERNATIONALMIG2019</th>\n",
       "      <th>RDOMESTICMIG2011</th>\n",
       "      <th>RDOMESTICMIG2012</th>\n",
       "      <th>RDOMESTICMIG2013</th>\n",
       "      <th>RDOMESTICMIG2014</th>\n",
       "      <th>RDOMESTICMIG2015</th>\n",
       "      <th>RDOMESTICMIG2016</th>\n",
       "      <th>RDOMESTICMIG2017</th>\n",
       "      <th>RDOMESTICMIG2018</th>\n",
       "      <th>RDOMESTICMIG2019</th>\n",
       "      <th>RNETMIG2011</th>\n",
       "      <th>RNETMIG2012</th>\n",
       "      <th>RNETMIG2013</th>\n",
       "      <th>RNETMIG2014</th>\n",
       "      <th>RNETMIG2015</th>\n",
       "      <th>RNETMIG2016</th>\n",
       "      <th>RNETMIG2017</th>\n",
       "      <th>RNETMIG2018</th>\n",
       "      <th>RNETMIG2019</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2586</th>\n",
       "      <td>50</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>48</td>\n",
       "      <td>39</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Brazoria County</td>\n",
       "      <td>313166</td>\n",
       "      <td>313117</td>\n",
       "      <td>314428</td>\n",
       "      <td>319147</td>\n",
       "      <td>324224</td>\n",
       "      <td>329890</td>\n",
       "      <td>337570</td>\n",
       "      <td>345227</td>\n",
       "      <td>353155</td>\n",
       "      <td>361853</td>\n",
       "      <td>368887</td>\n",
       "      <td>374264</td>\n",
       "      <td>1311</td>\n",
       "      <td>4719</td>\n",
       "      <td>5077</td>\n",
       "      <td>5666</td>\n",
       "      <td>7680</td>\n",
       "      <td>7657</td>\n",
       "      <td>7928</td>\n",
       "      <td>8698</td>\n",
       "      <td>7034</td>\n",
       "      <td>5377</td>\n",
       "      <td>1203</td>\n",
       "      <td>4699</td>\n",
       "      <td>4502</td>\n",
       "      <td>4551</td>\n",
       "      <td>4762</td>\n",
       "      <td>4882</td>\n",
       "      <td>4877</td>\n",
       "      <td>4683</td>\n",
       "      <td>4728</td>\n",
       "      <td>4681</td>\n",
       "      <td>507</td>\n",
       "      <td>1970</td>\n",
       "      <td>1956</td>\n",
       "      <td>2135</td>\n",
       "      <td>2247</td>\n",
       "      <td>2149</td>\n",
       "      <td>2307</td>\n",
       "      <td>2330</td>\n",
       "      <td>2507</td>\n",
       "      <td>2498</td>\n",
       "      <td>696</td>\n",
       "      <td>2729</td>\n",
       "      <td>2546</td>\n",
       "      <td>2416</td>\n",
       "      <td>2515</td>\n",
       "      <td>2733</td>\n",
       "      <td>2570</td>\n",
       "      <td>2353</td>\n",
       "      <td>2221</td>\n",
       "      <td>2183</td>\n",
       "      <td>82</td>\n",
       "      <td>306</td>\n",
       "      <td>434</td>\n",
       "      <td>424</td>\n",
       "      <td>648</td>\n",
       "      <td>495</td>\n",
       "      <td>476</td>\n",
       "      <td>415</td>\n",
       "      <td>316</td>\n",
       "      <td>171</td>\n",
       "      <td>517</td>\n",
       "      <td>1683</td>\n",
       "      <td>2101</td>\n",
       "      <td>2793</td>\n",
       "      <td>4433</td>\n",
       "      <td>4399</td>\n",
       "      <td>4870</td>\n",
       "      <td>5911</td>\n",
       "      <td>4496</td>\n",
       "      <td>3025</td>\n",
       "      <td>599</td>\n",
       "      <td>1989</td>\n",
       "      <td>2535</td>\n",
       "      <td>3217</td>\n",
       "      <td>5081</td>\n",
       "      <td>4894</td>\n",
       "      <td>5346</td>\n",
       "      <td>6326</td>\n",
       "      <td>4812</td>\n",
       "      <td>3196</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>-4</td>\n",
       "      <td>33</td>\n",
       "      <td>84</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>-2</td>\n",
       "      <td>10559</td>\n",
       "      <td>10587</td>\n",
       "      <td>10670</td>\n",
       "      <td>10558</td>\n",
       "      <td>10270</td>\n",
       "      <td>10577</td>\n",
       "      <td>10294</td>\n",
       "      <td>9957</td>\n",
       "      <td>10451</td>\n",
       "      <td>10475</td>\n",
       "      <td>10474</td>\n",
       "      <td>14.833287</td>\n",
       "      <td>13.995036</td>\n",
       "      <td>13.915006</td>\n",
       "      <td>14.26902</td>\n",
       "      <td>14.300004</td>\n",
       "      <td>13.966568</td>\n",
       "      <td>13.099154</td>\n",
       "      <td>12.940307</td>\n",
       "      <td>12.597709</td>\n",
       "      <td>6.21868</td>\n",
       "      <td>6.080473</td>\n",
       "      <td>6.527914</td>\n",
       "      <td>6.732988</td>\n",
       "      <td>6.294697</td>\n",
       "      <td>6.606699</td>\n",
       "      <td>6.51741</td>\n",
       "      <td>6.861538</td>\n",
       "      <td>6.722725</td>\n",
       "      <td>8.614608</td>\n",
       "      <td>7.914563</td>\n",
       "      <td>7.387092</td>\n",
       "      <td>7.536032</td>\n",
       "      <td>8.005308</td>\n",
       "      <td>7.359869</td>\n",
       "      <td>6.581745</td>\n",
       "      <td>6.078769</td>\n",
       "      <td>5.874984</td>\n",
       "      <td>0.965947</td>\n",
       "      <td>1.349144</td>\n",
       "      <td>1.29641</td>\n",
       "      <td>1.941689</td>\n",
       "      <td>1.449918</td>\n",
       "      <td>1.363151</td>\n",
       "      <td>1.160826</td>\n",
       "      <td>0.864877</td>\n",
       "      <td>0.460203</td>\n",
       "      <td>5.31271</td>\n",
       "      <td>6.531224</td>\n",
       "      <td>8.539796</td>\n",
       "      <td>13.283193</td>\n",
       "      <td>12.885235</td>\n",
       "      <td>13.946522</td>\n",
       "      <td>16.534081</td>\n",
       "      <td>12.305334</td>\n",
       "      <td>8.14101</td>\n",
       "      <td>6.278657</td>\n",
       "      <td>7.880368</td>\n",
       "      <td>9.836206</td>\n",
       "      <td>15.224882</td>\n",
       "      <td>14.335154</td>\n",
       "      <td>15.309673</td>\n",
       "      <td>17.694907</td>\n",
       "      <td>13.170211</td>\n",
       "      <td>8.601213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      SUMLEV  REGION  DIVISION  STATE  COUNTY STNAME          CTYNAME  \\\n",
       "2586      50       3         7     48      39  Texas  Brazoria County   \n",
       "\n",
       "      CENSUS2010POP  ESTIMATESBASE2010  POPESTIMATE2010  POPESTIMATE2011  \\\n",
       "2586         313166             313117           314428           319147   \n",
       "\n",
       "      POPESTIMATE2012  POPESTIMATE2013  POPESTIMATE2014  POPESTIMATE2015  \\\n",
       "2586           324224           329890           337570           345227   \n",
       "\n",
       "      POPESTIMATE2016  POPESTIMATE2017  POPESTIMATE2018  POPESTIMATE2019  \\\n",
       "2586           353155           361853           368887           374264   \n",
       "\n",
       "      NPOPCHG_2010  NPOPCHG_2011  NPOPCHG_2012  NPOPCHG_2013  NPOPCHG_2014  \\\n",
       "2586          1311          4719          5077          5666          7680   \n",
       "\n",
       "      NPOPCHG_2015  NPOPCHG_2016  NPOPCHG_2017  NPOPCHG_2018  NPOPCHG_2019  \\\n",
       "2586          7657          7928          8698          7034          5377   \n",
       "\n",
       "      BIRTHS2010  BIRTHS2011  BIRTHS2012  BIRTHS2013  BIRTHS2014  BIRTHS2015  \\\n",
       "2586        1203        4699        4502        4551        4762        4882   \n",
       "\n",
       "      BIRTHS2016  BIRTHS2017  BIRTHS2018  BIRTHS2019  DEATHS2010  DEATHS2011  \\\n",
       "2586        4877        4683        4728        4681         507        1970   \n",
       "\n",
       "      DEATHS2012  DEATHS2013  DEATHS2014  DEATHS2015  DEATHS2016  DEATHS2017  \\\n",
       "2586        1956        2135        2247        2149        2307        2330   \n",
       "\n",
       "      DEATHS2018  DEATHS2019  NATURALINC2010  NATURALINC2011  NATURALINC2012  \\\n",
       "2586        2507        2498             696            2729            2546   \n",
       "\n",
       "      NATURALINC2013  NATURALINC2014  NATURALINC2015  NATURALINC2016  \\\n",
       "2586            2416            2515            2733            2570   \n",
       "\n",
       "      NATURALINC2017  NATURALINC2018  NATURALINC2019  INTERNATIONALMIG2010  \\\n",
       "2586            2353            2221            2183                    82   \n",
       "\n",
       "      INTERNATIONALMIG2011  INTERNATIONALMIG2012  INTERNATIONALMIG2013  \\\n",
       "2586                   306                   434                   424   \n",
       "\n",
       "      INTERNATIONALMIG2014  INTERNATIONALMIG2015  INTERNATIONALMIG2016  \\\n",
       "2586                   648                   495                   476   \n",
       "\n",
       "      INTERNATIONALMIG2017  INTERNATIONALMIG2018  INTERNATIONALMIG2019  \\\n",
       "2586                   415                   316                   171   \n",
       "\n",
       "      DOMESTICMIG2010  DOMESTICMIG2011  DOMESTICMIG2012  DOMESTICMIG2013  \\\n",
       "2586              517             1683             2101             2793   \n",
       "\n",
       "      DOMESTICMIG2014  DOMESTICMIG2015  DOMESTICMIG2016  DOMESTICMIG2017  \\\n",
       "2586             4433             4399             4870             5911   \n",
       "\n",
       "      DOMESTICMIG2018  DOMESTICMIG2019  NETMIG2010  NETMIG2011  NETMIG2012  \\\n",
       "2586             4496             3025         599        1989        2535   \n",
       "\n",
       "      NETMIG2013  NETMIG2014  NETMIG2015  NETMIG2016  NETMIG2017  NETMIG2018  \\\n",
       "2586        3217        5081        4894        5346        6326        4812   \n",
       "\n",
       "      NETMIG2019  RESIDUAL2010  RESIDUAL2011  RESIDUAL2012  RESIDUAL2013  \\\n",
       "2586        3196            16             1            -4            33   \n",
       "\n",
       "      RESIDUAL2014  RESIDUAL2015  RESIDUAL2016  RESIDUAL2017  RESIDUAL2018  \\\n",
       "2586            84            30            12            19             1   \n",
       "\n",
       "      RESIDUAL2019  GQESTIMATESBASE2010  GQESTIMATES2010  GQESTIMATES2011  \\\n",
       "2586            -2                10559            10587            10670   \n",
       "\n",
       "      GQESTIMATES2012  GQESTIMATES2013  GQESTIMATES2014  GQESTIMATES2015  \\\n",
       "2586            10558            10270            10577            10294   \n",
       "\n",
       "      GQESTIMATES2016  GQESTIMATES2017  GQESTIMATES2018  GQESTIMATES2019  \\\n",
       "2586             9957            10451            10475            10474   \n",
       "\n",
       "      RBIRTH2011  RBIRTH2012  RBIRTH2013  RBIRTH2014  RBIRTH2015  RBIRTH2016  \\\n",
       "2586   14.833287   13.995036   13.915006    14.26902   14.300004   13.966568   \n",
       "\n",
       "      RBIRTH2017  RBIRTH2018  RBIRTH2019  RDEATH2011  RDEATH2012  RDEATH2013  \\\n",
       "2586   13.099154   12.940307   12.597709     6.21868    6.080473    6.527914   \n",
       "\n",
       "      RDEATH2014  RDEATH2015  RDEATH2016  RDEATH2017  RDEATH2018  RDEATH2019  \\\n",
       "2586    6.732988    6.294697    6.606699     6.51741    6.861538    6.722725   \n",
       "\n",
       "      RNATURALINC2011  RNATURALINC2012  RNATURALINC2013  RNATURALINC2014  \\\n",
       "2586         8.614608         7.914563         7.387092         7.536032   \n",
       "\n",
       "      RNATURALINC2015  RNATURALINC2016  RNATURALINC2017  RNATURALINC2018  \\\n",
       "2586         8.005308         7.359869         6.581745         6.078769   \n",
       "\n",
       "      RNATURALINC2019  RINTERNATIONALMIG2011  RINTERNATIONALMIG2012  \\\n",
       "2586         5.874984               0.965947               1.349144   \n",
       "\n",
       "      RINTERNATIONALMIG2013  RINTERNATIONALMIG2014  RINTERNATIONALMIG2015  \\\n",
       "2586                1.29641               1.941689               1.449918   \n",
       "\n",
       "      RINTERNATIONALMIG2016  RINTERNATIONALMIG2017  RINTERNATIONALMIG2018  \\\n",
       "2586               1.363151               1.160826               0.864877   \n",
       "\n",
       "      RINTERNATIONALMIG2019  RDOMESTICMIG2011  RDOMESTICMIG2012  \\\n",
       "2586               0.460203           5.31271          6.531224   \n",
       "\n",
       "      RDOMESTICMIG2013  RDOMESTICMIG2014  RDOMESTICMIG2015  RDOMESTICMIG2016  \\\n",
       "2586          8.539796         13.283193         12.885235         13.946522   \n",
       "\n",
       "      RDOMESTICMIG2017  RDOMESTICMIG2018  RDOMESTICMIG2019  RNETMIG2011  \\\n",
       "2586         16.534081         12.305334           8.14101     6.278657   \n",
       "\n",
       "      RNETMIG2012  RNETMIG2013  RNETMIG2014  RNETMIG2015  RNETMIG2016  \\\n",
       "2586     7.880368     9.836206    15.224882    14.335154    15.309673   \n",
       "\n",
       "      RNETMIG2017  RNETMIG2018  RNETMIG2019  \n",
       "2586    17.694907    13.170211     8.601213  "
      ]
     },
     "execution_count": 1721,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv(\"co-est2019-alldata.csv\", encoding = \"ISO-8859-1\").query('SUMLEV == 50').query('CTYNAME==\"Brazoria County\"')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1727,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3074"
      ]
     },
     "execution_count": 1727,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pd.read_csv(\"co-est2019-alldata.csv\", encoding = \"ISO-8859-1\").query('SUMLEV == 50 & COUNTY % 2 == 1'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1844,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_report_data_raw = get_daily_report_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1934,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>county</th>\n",
       "      <th>area</th>\n",
       "      <th>pop</th>\n",
       "      <th>density</th>\n",
       "      <th>longitude</th>\n",
       "      <th>latitude</th>\n",
       "      <th>conservative</th>\n",
       "      <th>median_age</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>hispanic</th>\n",
       "      <th>black</th>\n",
       "      <th>asian</th>\n",
       "      <th>median_income</th>\n",
       "      <th>adjusted_density</th>\n",
       "      <th>max_height</th>\n",
       "      <th>peak_info_raw</th>\n",
       "      <th>peak_info</th>\n",
       "      <th>peak_count</th>\n",
       "      <th>peak_date</th>\n",
       "      <th>median_infection</th>\n",
       "      <th>mean_infection</th>\n",
       "      <th>max_infection</th>\n",
       "      <th>tests_performed</th>\n",
       "      <th>median_infection_log</th>\n",
       "      <th>tests_performed_log</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>st</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               county  area  pop  density  longitude  latitude  conservative  \\\n",
       "st                                                                             \n",
       "Arkansas            6     6    6        6          6         6             6   \n",
       "California          2     2    2        2          2         2             2   \n",
       "Colorado            4     4    4        4          4         4             4   \n",
       "Georgia             1     1    1        1          1         1             1   \n",
       "Idaho               3     3    3        3          3         3             3   \n",
       "Illinois            4     4    4        4          4         4             4   \n",
       "Indiana             2     2    2        2          2         2             2   \n",
       "Iowa               12    12   12       12         12        12            12   \n",
       "Kansas             13    13   13       13         13        13            13   \n",
       "Kentucky            7     7    7        7          7         7             7   \n",
       "Louisiana           1     1    1        1          1         1             1   \n",
       "Maine               1     1    1        1          1         1             1   \n",
       "Michigan            7     7    7        7          7         7             7   \n",
       "Minnesota           4     4    4        4          4         4             4   \n",
       "Missouri            3     3    3        3          3         3             3   \n",
       "Nebraska           10    10   10       10         10        10            10   \n",
       "North Dakota        3     3    3        3          3         3             3   \n",
       "Oklahoma            6     6    6        6          6         6             6   \n",
       "Oregon              2     2    2        2          2         2             2   \n",
       "South Dakota        3     3    3        3          3         3             3   \n",
       "Tennessee           2     2    2        2          2         2             2   \n",
       "Texas              17    17   17       17         17        17            17   \n",
       "Virginia            2     2    2        2          2         2             2   \n",
       "West Virginia       2     2    2        2          2         2             2   \n",
       "Wisconsin           3     3    3        3          3         3             3   \n",
       "Wyoming             3     3    3        3          3         3             3   \n",
       "\n",
       "               median_age  mean_age  hispanic  black  asian  median_income  \\\n",
       "st                                                                           \n",
       "Arkansas                6         6         6      6      6              6   \n",
       "California              2         2         2      2      2              2   \n",
       "Colorado                4         4         4      4      4              4   \n",
       "Georgia                 1         1         1      1      1              1   \n",
       "Idaho                   3         3         3      3      3              3   \n",
       "Illinois                4         4         4      4      4              4   \n",
       "Indiana                 2         2         2      2      2              2   \n",
       "Iowa                   12        12        12     12     12             12   \n",
       "Kansas                 13        13        13     13     13             13   \n",
       "Kentucky                7         7         7      7      7              7   \n",
       "Louisiana               1         1         1      1      1              1   \n",
       "Maine                   1         1         1      1      1              1   \n",
       "Michigan                7         7         7      7      7              7   \n",
       "Minnesota               4         4         4      4      4              4   \n",
       "Missouri                3         3         3      3      3              3   \n",
       "Nebraska               10        10        10     10     10             10   \n",
       "North Dakota            3         3         3      3      3              3   \n",
       "Oklahoma                6         6         6      6      6              6   \n",
       "Oregon                  2         2         2      2      2              2   \n",
       "South Dakota            3         3         3      3      3              3   \n",
       "Tennessee               2         2         2      2      2              2   \n",
       "Texas                  17        17        17     17     17             17   \n",
       "Virginia                2         2         2      2      2              2   \n",
       "West Virginia           2         2         2      2      2              2   \n",
       "Wisconsin               3         3         3      3      3              3   \n",
       "Wyoming                 3         3         3      3      3              3   \n",
       "\n",
       "               adjusted_density  max_height  peak_info_raw  peak_info  \\\n",
       "st                                                                      \n",
       "Arkansas                      6           6              6          6   \n",
       "California                    2           2              2          2   \n",
       "Colorado                      4           4              4          4   \n",
       "Georgia                       1           1              1          1   \n",
       "Idaho                         3           3              3          3   \n",
       "Illinois                      4           4              4          4   \n",
       "Indiana                       2           2              2          2   \n",
       "Iowa                         12          12             12         12   \n",
       "Kansas                       13          13             13         13   \n",
       "Kentucky                      7           7              7          7   \n",
       "Louisiana                     1           1              1          1   \n",
       "Maine                         1           1              1          1   \n",
       "Michigan                      7           7              7          7   \n",
       "Minnesota                     4           4              4          4   \n",
       "Missouri                      3           3              3          3   \n",
       "Nebraska                     10          10             10         10   \n",
       "North Dakota                  3           3              3          3   \n",
       "Oklahoma                      6           6              6          6   \n",
       "Oregon                        2           2              2          2   \n",
       "South Dakota                  3           3              3          3   \n",
       "Tennessee                     2           2              2          2   \n",
       "Texas                        17          17             17         17   \n",
       "Virginia                      2           2              2          2   \n",
       "West Virginia                 2           2              2          2   \n",
       "Wisconsin                     3           3              3          3   \n",
       "Wyoming                       3           3              3          3   \n",
       "\n",
       "               peak_count  peak_date  median_infection  mean_infection  \\\n",
       "st                                                                       \n",
       "Arkansas                6          6                 6               6   \n",
       "California              2          2                 2               2   \n",
       "Colorado                4          4                 4               4   \n",
       "Georgia                 1          1                 1               1   \n",
       "Idaho                   3          3                 3               3   \n",
       "Illinois                4          4                 4               4   \n",
       "Indiana                 2          2                 2               2   \n",
       "Iowa                   12         12                12              12   \n",
       "Kansas                 13         13                13              13   \n",
       "Kentucky                7          7                 7               7   \n",
       "Louisiana               1          1                 1               1   \n",
       "Maine                   1          1                 1               1   \n",
       "Michigan                7          7                 7               7   \n",
       "Minnesota               4          4                 4               4   \n",
       "Missouri                3          3                 3               3   \n",
       "Nebraska               10         10                10              10   \n",
       "North Dakota            3          3                 3               3   \n",
       "Oklahoma                6          6                 6               6   \n",
       "Oregon                  2          2                 2               2   \n",
       "South Dakota            3          3                 3               3   \n",
       "Tennessee               2          2                 2               2   \n",
       "Texas                  17         17                17              17   \n",
       "Virginia                2          2                 2               2   \n",
       "West Virginia           2          2                 2               2   \n",
       "Wisconsin               3          3                 3               3   \n",
       "Wyoming                 3          3                 3               3   \n",
       "\n",
       "               max_infection  tests_performed  median_infection_log  \\\n",
       "st                                                                    \n",
       "Arkansas                   6                6                     6   \n",
       "California                 2                2                     2   \n",
       "Colorado                   4                4                     4   \n",
       "Georgia                    1                1                     1   \n",
       "Idaho                      3                3                     3   \n",
       "Illinois                   4                4                     4   \n",
       "Indiana                    2                2                     2   \n",
       "Iowa                      12               12                    12   \n",
       "Kansas                    13               13                    13   \n",
       "Kentucky                   7                7                     7   \n",
       "Louisiana                  1                1                     1   \n",
       "Maine                      1                1                     1   \n",
       "Michigan                   7                7                     7   \n",
       "Minnesota                  4                4                     4   \n",
       "Missouri                   3                3                     3   \n",
       "Nebraska                  10               10                    10   \n",
       "North Dakota               3                3                     3   \n",
       "Oklahoma                   6                6                     6   \n",
       "Oregon                     2                2                     2   \n",
       "South Dakota               3                3                     3   \n",
       "Tennessee                  2                2                     2   \n",
       "Texas                     17               17                    17   \n",
       "Virginia                   2                2                     2   \n",
       "West Virginia              2                2                     2   \n",
       "Wisconsin                  3                3                     3   \n",
       "Wyoming                    3                3                     3   \n",
       "\n",
       "               tests_performed_log  \n",
       "st                                  \n",
       "Arkansas                         6  \n",
       "California                       2  \n",
       "Colorado                         4  \n",
       "Georgia                          1  \n",
       "Idaho                            3  \n",
       "Illinois                         4  \n",
       "Indiana                          2  \n",
       "Iowa                            12  \n",
       "Kansas                          13  \n",
       "Kentucky                         7  \n",
       "Louisiana                        1  \n",
       "Maine                            1  \n",
       "Michigan                         7  \n",
       "Minnesota                        4  \n",
       "Missouri                         3  \n",
       "Nebraska                        10  \n",
       "North Dakota                     3  \n",
       "Oklahoma                         6  \n",
       "Oregon                           2  \n",
       "South Dakota                     3  \n",
       "Tennessee                        2  \n",
       "Texas                           17  \n",
       "Virginia                         2  \n",
       "West Virginia                    2  \n",
       "Wisconsin                        3  \n",
       "Wyoming                          3  "
      ]
     },
     "execution_count": 1934,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_report_data = daily_report_data_raw.copy()\n",
    "# 51 confirmed\n",
    "# 207 per 100000\n",
    "\n",
    "daily_report_data['Tests'] = daily_report_data['Confirmed'].div(daily_report_data['Incidence_Rate']) * 100000\n",
    "\n",
    "#daily_report_data.plot.scatter(x='Incidence_Rate', y='Tests', figsize=(16,10))\n",
    "\n",
    "#daily_report_data.query('Tests > 1000000')\n",
    "\n",
    "starter = (\n",
    "    daily_report_data\n",
    "    [['Tests', 'FIPS', 'date']]                           # Keep these three columns\n",
    "    .rename(columns={'Tests':'tests', 'FIPS':'fips'}) # Rename for simplicity\n",
    "    .drop_duplicates(['date','fips'])                         # There are a few duplicates. Drop them. \n",
    "    .pivot(values='tests', index='date', columns='fips')  # Create the pivot table.                                               # Fill NA values here.\n",
    ")\n",
    "\n",
    "\n",
    "(\n",
    "    county_details\n",
    "    .query('median_infection != 0')\n",
    "    .assign(\n",
    "        median_infection_log=lambda x:np.log(x['median_infection']),\n",
    "        tests_performed_log=lambda x:np.log(x['tests_performed'])\n",
    "    )\n",
    "    .query('abs(tests_performed_log - (median_infection_log * -1 + 9.56)) < 0.01')\n",
    "    \n",
    ")#.groupby('st').count()\n",
    "\n",
    "#.plot.scatter(x='median_infection_log', y='tests_performed_log', figsize=(16, 10))\n",
    "#county_details.tests_performed.apply(lambda x:np.log(x)).plot.hist(bins=100, figsize=(16,10))\n",
    "#county_details.query('median_infection != 0')['median_infection'].apply(lambda x:np.log(x)).plot.hist(bins=100, figsize=(16,10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "chsi = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "scrolled": false
   },
   "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>obesity</th>\n",
       "      <th>diabetes</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fips</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1001</th>\n",
       "      <td>24.500000</td>\n",
       "      <td>14.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1003</th>\n",
       "      <td>23.600000</td>\n",
       "      <td>7.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1005</th>\n",
       "      <td>25.600000</td>\n",
       "      <td>6.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>26.897561</td>\n",
       "      <td>13.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1009</th>\n",
       "      <td>24.200000</td>\n",
       "      <td>8.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56037</th>\n",
       "      <td>23.600000</td>\n",
       "      <td>5.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56039</th>\n",
       "      <td>9.000000</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56041</th>\n",
       "      <td>22.700000</td>\n",
       "      <td>4.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56043</th>\n",
       "      <td>21.100000</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56045</th>\n",
       "      <td>24.200000</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3074 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         obesity  diabetes\n",
       "fips                      \n",
       "1001   24.500000      14.2\n",
       "1003   23.600000       7.2\n",
       "1005   25.600000       6.6\n",
       "1007   26.897561      13.1\n",
       "1009   24.200000       8.4\n",
       "...          ...       ...\n",
       "56037  23.600000       5.3\n",
       "56039   9.000000       2.4\n",
       "56041  22.700000       4.8\n",
       "56043  21.100000       9.4\n",
       "56045  24.200000       5.7\n",
       "\n",
       "[3074 rows x 2 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "st                                                            Alabama\n",
       "county                                                    Bibb County\n",
       "area                                                          622.582\n",
       "pop                                                             22394\n",
       "density                                                       35.9696\n",
       "longitude                                                     -87.125\n",
       "latitude                                                      32.9965\n",
       "conservative                                                 0.782397\n",
       "median_age                                                         40\n",
       "mean_age                                                      40.5307\n",
       "hispanic                                                      0.02782\n",
       "black                                                        0.212691\n",
       "asian                                                      0.00214343\n",
       "white                                                        0.767661\n",
       "median_income                                                   46064\n",
       "poverty_percent                                                  21.8\n",
       "adjusted_density                                              110.711\n",
       "max_height                                                    33.1722\n",
       "peak_info_raw       ([24, 78, 113], {'peak_heights': [10.206815601...\n",
       "peak_info                                                          []\n",
       "peak_count                                                          0\n",
       "peak_date                                                         NaT\n",
       "median_infection                                              7.65511\n",
       "mean_infection                                                10.6786\n",
       "max_infection                                                 33.1722\n",
       "Name: 1007, dtype: object"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "county_details.loc[1007]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "bhrf = pd.read_sas('/Users/davidmorton/Downloads/LLCP2018.XPT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
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       "    .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>SUMLEV</th>\n",
       "      <th>STATE</th>\n",
       "      <th>COUNTY</th>\n",
       "      <th>STNAME</th>\n",
       "      <th>CTYNAME</th>\n",
       "      <th>YEAR</th>\n",
       "      <th>POPESTIMATE</th>\n",
       "      <th>POPEST_MALE</th>\n",
       "      <th>POPEST_FEM</th>\n",
       "      <th>UNDER5_TOT</th>\n",
       "      <th>UNDER5_MALE</th>\n",
       "      <th>UNDER5_FEM</th>\n",
       "      <th>AGE513_TOT</th>\n",
       "      <th>AGE513_MALE</th>\n",
       "      <th>AGE513_FEM</th>\n",
       "      <th>AGE1417_TOT</th>\n",
       "      <th>AGE1417_MALE</th>\n",
       "      <th>AGE1417_FEM</th>\n",
       "      <th>AGE1824_TOT</th>\n",
       "      <th>AGE1824_MALE</th>\n",
       "      <th>AGE1824_FEM</th>\n",
       "      <th>AGE16PLUS_TOT</th>\n",
       "      <th>AGE16PLUS_MALE</th>\n",
       "      <th>AGE16PLUS_FEM</th>\n",
       "      <th>AGE18PLUS_TOT</th>\n",
       "      <th>AGE18PLUS_MALE</th>\n",
       "      <th>AGE18PLUS_FEM</th>\n",
       "      <th>AGE1544_TOT</th>\n",
       "      <th>AGE1544_MALE</th>\n",
       "      <th>AGE1544_FEM</th>\n",
       "      <th>AGE2544_TOT</th>\n",
       "      <th>AGE2544_MALE</th>\n",
       "      <th>AGE2544_FEM</th>\n",
       "      <th>AGE4564_TOT</th>\n",
       "      <th>AGE4564_MALE</th>\n",
       "      <th>AGE4564_FEM</th>\n",
       "      <th>AGE65PLUS_TOT</th>\n",
       "      <th>AGE65PLUS_MALE</th>\n",
       "      <th>AGE65PLUS_FEM</th>\n",
       "      <th>AGE04_TOT</th>\n",
       "      <th>AGE04_MALE</th>\n",
       "      <th>AGE04_FEM</th>\n",
       "      <th>AGE59_TOT</th>\n",
       "      <th>AGE59_MALE</th>\n",
       "      <th>AGE59_FEM</th>\n",
       "      <th>AGE1014_TOT</th>\n",
       "      <th>AGE1014_MALE</th>\n",
       "      <th>AGE1014_FEM</th>\n",
       "      <th>AGE1519_TOT</th>\n",
       "      <th>AGE1519_MALE</th>\n",
       "      <th>AGE1519_FEM</th>\n",
       "      <th>AGE2024_TOT</th>\n",
       "      <th>AGE2024_MALE</th>\n",
       "      <th>AGE2024_FEM</th>\n",
       "      <th>AGE2529_TOT</th>\n",
       "      <th>AGE2529_MALE</th>\n",
       "      <th>AGE2529_FEM</th>\n",
       "      <th>AGE3034_TOT</th>\n",
       "      <th>AGE3034_MALE</th>\n",
       "      <th>AGE3034_FEM</th>\n",
       "      <th>AGE3539_TOT</th>\n",
       "      <th>AGE3539_MALE</th>\n",
       "      <th>AGE3539_FEM</th>\n",
       "      <th>AGE4044_TOT</th>\n",
       "      <th>AGE4044_MALE</th>\n",
       "      <th>AGE4044_FEM</th>\n",
       "      <th>AGE4549_TOT</th>\n",
       "      <th>AGE4549_MALE</th>\n",
       "      <th>AGE4549_FEM</th>\n",
       "      <th>AGE5054_TOT</th>\n",
       "      <th>AGE5054_MALE</th>\n",
       "      <th>AGE5054_FEM</th>\n",
       "      <th>AGE5559_TOT</th>\n",
       "      <th>AGE5559_MALE</th>\n",
       "      <th>AGE5559_FEM</th>\n",
       "      <th>AGE6064_TOT</th>\n",
       "      <th>AGE6064_MALE</th>\n",
       "      <th>AGE6064_FEM</th>\n",
       "      <th>AGE6569_TOT</th>\n",
       "      <th>AGE6569_MALE</th>\n",
       "      <th>AGE6569_FEM</th>\n",
       "      <th>AGE7074_TOT</th>\n",
       "      <th>AGE7074_MALE</th>\n",
       "      <th>AGE7074_FEM</th>\n",
       "      <th>AGE7579_TOT</th>\n",
       "      <th>AGE7579_MALE</th>\n",
       "      <th>AGE7579_FEM</th>\n",
       "      <th>AGE8084_TOT</th>\n",
       "      <th>AGE8084_MALE</th>\n",
       "      <th>AGE8084_FEM</th>\n",
       "      <th>AGE85PLUS_TOT</th>\n",
       "      <th>AGE85PLUS_MALE</th>\n",
       "      <th>AGE85PLUS_FEM</th>\n",
       "      <th>MEDIAN_AGE_TOT</th>\n",
       "      <th>MEDIAN_AGE_MALE</th>\n",
       "      <th>MEDIAN_AGE_FEM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>1</td>\n",
       "      <td>54571</td>\n",
       "      <td>26569</td>\n",
       "      <td>28002</td>\n",
       "      <td>3579</td>\n",
       "      <td>1866</td>\n",
       "      <td>1713</td>\n",
       "      <td>7418</td>\n",
       "      <td>3747</td>\n",
       "      <td>3671</td>\n",
       "      <td>3616</td>\n",
       "      <td>1842</td>\n",
       "      <td>1774</td>\n",
       "      <td>4617</td>\n",
       "      <td>2335</td>\n",
       "      <td>2282</td>\n",
       "      <td>41804</td>\n",
       "      <td>20046</td>\n",
       "      <td>21758</td>\n",
       "      <td>39958</td>\n",
       "      <td>19114</td>\n",
       "      <td>20844</td>\n",
       "      <td>22100</td>\n",
       "      <td>10867</td>\n",
       "      <td>11233</td>\n",
       "      <td>14730</td>\n",
       "      <td>7115</td>\n",
       "      <td>7615</td>\n",
       "      <td>14065</td>\n",
       "      <td>6843</td>\n",
       "      <td>7222</td>\n",
       "      <td>6546</td>\n",
       "      <td>2821</td>\n",
       "      <td>3725</td>\n",
       "      <td>3579</td>\n",
       "      <td>1866</td>\n",
       "      <td>1713</td>\n",
       "      <td>3991</td>\n",
       "      <td>2001</td>\n",
       "      <td>1990</td>\n",
       "      <td>4290</td>\n",
       "      <td>2171</td>\n",
       "      <td>2119</td>\n",
       "      <td>4290</td>\n",
       "      <td>2213</td>\n",
       "      <td>2077</td>\n",
       "      <td>3080</td>\n",
       "      <td>1539</td>\n",
       "      <td>1541</td>\n",
       "      <td>3157</td>\n",
       "      <td>1543</td>\n",
       "      <td>1614</td>\n",
       "      <td>3330</td>\n",
       "      <td>1594</td>\n",
       "      <td>1736</td>\n",
       "      <td>4157</td>\n",
       "      <td>2004</td>\n",
       "      <td>2153</td>\n",
       "      <td>4086</td>\n",
       "      <td>1974</td>\n",
       "      <td>2112</td>\n",
       "      <td>4332</td>\n",
       "      <td>2174</td>\n",
       "      <td>2158</td>\n",
       "      <td>3873</td>\n",
       "      <td>1866</td>\n",
       "      <td>2007</td>\n",
       "      <td>3083</td>\n",
       "      <td>1524</td>\n",
       "      <td>1559</td>\n",
       "      <td>2777</td>\n",
       "      <td>1279</td>\n",
       "      <td>1498</td>\n",
       "      <td>2277</td>\n",
       "      <td>1014</td>\n",
       "      <td>1263</td>\n",
       "      <td>1736</td>\n",
       "      <td>807</td>\n",
       "      <td>929</td>\n",
       "      <td>1251</td>\n",
       "      <td>546</td>\n",
       "      <td>705</td>\n",
       "      <td>731</td>\n",
       "      <td>295</td>\n",
       "      <td>436</td>\n",
       "      <td>551</td>\n",
       "      <td>159</td>\n",
       "      <td>392</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.9</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>2</td>\n",
       "      <td>54597</td>\n",
       "      <td>26584</td>\n",
       "      <td>28013</td>\n",
       "      <td>3584</td>\n",
       "      <td>1869</td>\n",
       "      <td>1715</td>\n",
       "      <td>7425</td>\n",
       "      <td>3752</td>\n",
       "      <td>3673</td>\n",
       "      <td>3618</td>\n",
       "      <td>1843</td>\n",
       "      <td>1775</td>\n",
       "      <td>4617</td>\n",
       "      <td>2335</td>\n",
       "      <td>2282</td>\n",
       "      <td>41817</td>\n",
       "      <td>20052</td>\n",
       "      <td>21765</td>\n",
       "      <td>39970</td>\n",
       "      <td>19120</td>\n",
       "      <td>20850</td>\n",
       "      <td>22116</td>\n",
       "      <td>10874</td>\n",
       "      <td>11242</td>\n",
       "      <td>14744</td>\n",
       "      <td>7121</td>\n",
       "      <td>7623</td>\n",
       "      <td>14067</td>\n",
       "      <td>6845</td>\n",
       "      <td>7222</td>\n",
       "      <td>6542</td>\n",
       "      <td>2819</td>\n",
       "      <td>3723</td>\n",
       "      <td>3584</td>\n",
       "      <td>1869</td>\n",
       "      <td>1715</td>\n",
       "      <td>3994</td>\n",
       "      <td>2004</td>\n",
       "      <td>1990</td>\n",
       "      <td>4294</td>\n",
       "      <td>2173</td>\n",
       "      <td>2121</td>\n",
       "      <td>4292</td>\n",
       "      <td>2214</td>\n",
       "      <td>2078</td>\n",
       "      <td>3080</td>\n",
       "      <td>1539</td>\n",
       "      <td>1541</td>\n",
       "      <td>3162</td>\n",
       "      <td>1546</td>\n",
       "      <td>1616</td>\n",
       "      <td>3333</td>\n",
       "      <td>1595</td>\n",
       "      <td>1738</td>\n",
       "      <td>4160</td>\n",
       "      <td>2006</td>\n",
       "      <td>2154</td>\n",
       "      <td>4089</td>\n",
       "      <td>1974</td>\n",
       "      <td>2115</td>\n",
       "      <td>4334</td>\n",
       "      <td>2176</td>\n",
       "      <td>2158</td>\n",
       "      <td>3873</td>\n",
       "      <td>1866</td>\n",
       "      <td>2007</td>\n",
       "      <td>3083</td>\n",
       "      <td>1524</td>\n",
       "      <td>1559</td>\n",
       "      <td>2777</td>\n",
       "      <td>1279</td>\n",
       "      <td>1498</td>\n",
       "      <td>2277</td>\n",
       "      <td>1014</td>\n",
       "      <td>1263</td>\n",
       "      <td>1734</td>\n",
       "      <td>806</td>\n",
       "      <td>928</td>\n",
       "      <td>1250</td>\n",
       "      <td>546</td>\n",
       "      <td>704</td>\n",
       "      <td>730</td>\n",
       "      <td>294</td>\n",
       "      <td>436</td>\n",
       "      <td>551</td>\n",
       "      <td>159</td>\n",
       "      <td>392</td>\n",
       "      <td>37.0</td>\n",
       "      <td>35.9</td>\n",
       "      <td>37.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>3</td>\n",
       "      <td>54773</td>\n",
       "      <td>26672</td>\n",
       "      <td>28101</td>\n",
       "      <td>3575</td>\n",
       "      <td>1863</td>\n",
       "      <td>1712</td>\n",
       "      <td>7400</td>\n",
       "      <td>3720</td>\n",
       "      <td>3680</td>\n",
       "      <td>3566</td>\n",
       "      <td>1820</td>\n",
       "      <td>1746</td>\n",
       "      <td>4671</td>\n",
       "      <td>2362</td>\n",
       "      <td>2309</td>\n",
       "      <td>42050</td>\n",
       "      <td>20194</td>\n",
       "      <td>21856</td>\n",
       "      <td>40232</td>\n",
       "      <td>19269</td>\n",
       "      <td>20963</td>\n",
       "      <td>22205</td>\n",
       "      <td>10944</td>\n",
       "      <td>11261</td>\n",
       "      <td>14824</td>\n",
       "      <td>7189</td>\n",
       "      <td>7635</td>\n",
       "      <td>14138</td>\n",
       "      <td>6871</td>\n",
       "      <td>7267</td>\n",
       "      <td>6599</td>\n",
       "      <td>2847</td>\n",
       "      <td>3752</td>\n",
       "      <td>3575</td>\n",
       "      <td>1863</td>\n",
       "      <td>1712</td>\n",
       "      <td>3964</td>\n",
       "      <td>1984</td>\n",
       "      <td>1980</td>\n",
       "      <td>4292</td>\n",
       "      <td>2163</td>\n",
       "      <td>2129</td>\n",
       "      <td>4229</td>\n",
       "      <td>2182</td>\n",
       "      <td>2047</td>\n",
       "      <td>3152</td>\n",
       "      <td>1573</td>\n",
       "      <td>1579</td>\n",
       "      <td>3191</td>\n",
       "      <td>1574</td>\n",
       "      <td>1617</td>\n",
       "      <td>3372</td>\n",
       "      <td>1615</td>\n",
       "      <td>1757</td>\n",
       "      <td>4143</td>\n",
       "      <td>2002</td>\n",
       "      <td>2141</td>\n",
       "      <td>4118</td>\n",
       "      <td>1998</td>\n",
       "      <td>2120</td>\n",
       "      <td>4321</td>\n",
       "      <td>2162</td>\n",
       "      <td>2159</td>\n",
       "      <td>3908</td>\n",
       "      <td>1884</td>\n",
       "      <td>2024</td>\n",
       "      <td>3119</td>\n",
       "      <td>1533</td>\n",
       "      <td>1586</td>\n",
       "      <td>2790</td>\n",
       "      <td>1292</td>\n",
       "      <td>1498</td>\n",
       "      <td>2289</td>\n",
       "      <td>1018</td>\n",
       "      <td>1271</td>\n",
       "      <td>1752</td>\n",
       "      <td>814</td>\n",
       "      <td>938</td>\n",
       "      <td>1259</td>\n",
       "      <td>552</td>\n",
       "      <td>707</td>\n",
       "      <td>743</td>\n",
       "      <td>299</td>\n",
       "      <td>444</td>\n",
       "      <td>556</td>\n",
       "      <td>164</td>\n",
       "      <td>392</td>\n",
       "      <td>37.1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>37.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>4</td>\n",
       "      <td>55227</td>\n",
       "      <td>26981</td>\n",
       "      <td>28246</td>\n",
       "      <td>3551</td>\n",
       "      <td>1852</td>\n",
       "      <td>1699</td>\n",
       "      <td>7355</td>\n",
       "      <td>3683</td>\n",
       "      <td>3672</td>\n",
       "      <td>3477</td>\n",
       "      <td>1782</td>\n",
       "      <td>1695</td>\n",
       "      <td>4816</td>\n",
       "      <td>2487</td>\n",
       "      <td>2329</td>\n",
       "      <td>42613</td>\n",
       "      <td>20573</td>\n",
       "      <td>22040</td>\n",
       "      <td>40844</td>\n",
       "      <td>19664</td>\n",
       "      <td>21180</td>\n",
       "      <td>22260</td>\n",
       "      <td>11055</td>\n",
       "      <td>11205</td>\n",
       "      <td>14832</td>\n",
       "      <td>7242</td>\n",
       "      <td>7590</td>\n",
       "      <td>14335</td>\n",
       "      <td>6979</td>\n",
       "      <td>7356</td>\n",
       "      <td>6861</td>\n",
       "      <td>2956</td>\n",
       "      <td>3905</td>\n",
       "      <td>3551</td>\n",
       "      <td>1852</td>\n",
       "      <td>1699</td>\n",
       "      <td>3902</td>\n",
       "      <td>1938</td>\n",
       "      <td>1964</td>\n",
       "      <td>4318</td>\n",
       "      <td>2201</td>\n",
       "      <td>2117</td>\n",
       "      <td>4030</td>\n",
       "      <td>2071</td>\n",
       "      <td>1959</td>\n",
       "      <td>3398</td>\n",
       "      <td>1742</td>\n",
       "      <td>1656</td>\n",
       "      <td>3216</td>\n",
       "      <td>1608</td>\n",
       "      <td>1608</td>\n",
       "      <td>3446</td>\n",
       "      <td>1665</td>\n",
       "      <td>1781</td>\n",
       "      <td>4003</td>\n",
       "      <td>1939</td>\n",
       "      <td>2064</td>\n",
       "      <td>4167</td>\n",
       "      <td>2030</td>\n",
       "      <td>2137</td>\n",
       "      <td>4196</td>\n",
       "      <td>2073</td>\n",
       "      <td>2123</td>\n",
       "      <td>4035</td>\n",
       "      <td>1985</td>\n",
       "      <td>2050</td>\n",
       "      <td>3247</td>\n",
       "      <td>1582</td>\n",
       "      <td>1665</td>\n",
       "      <td>2857</td>\n",
       "      <td>1339</td>\n",
       "      <td>1518</td>\n",
       "      <td>2323</td>\n",
       "      <td>1021</td>\n",
       "      <td>1302</td>\n",
       "      <td>1843</td>\n",
       "      <td>844</td>\n",
       "      <td>999</td>\n",
       "      <td>1312</td>\n",
       "      <td>585</td>\n",
       "      <td>727</td>\n",
       "      <td>782</td>\n",
       "      <td>315</td>\n",
       "      <td>467</td>\n",
       "      <td>601</td>\n",
       "      <td>191</td>\n",
       "      <td>410</td>\n",
       "      <td>37.3</td>\n",
       "      <td>36.2</td>\n",
       "      <td>38.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga County</td>\n",
       "      <td>5</td>\n",
       "      <td>54954</td>\n",
       "      <td>26826</td>\n",
       "      <td>28128</td>\n",
       "      <td>3402</td>\n",
       "      <td>1791</td>\n",
       "      <td>1611</td>\n",
       "      <td>7255</td>\n",
       "      <td>3658</td>\n",
       "      <td>3597</td>\n",
       "      <td>3511</td>\n",
       "      <td>1801</td>\n",
       "      <td>1710</td>\n",
       "      <td>4802</td>\n",
       "      <td>2488</td>\n",
       "      <td>2314</td>\n",
       "      <td>42544</td>\n",
       "      <td>20473</td>\n",
       "      <td>22071</td>\n",
       "      <td>40786</td>\n",
       "      <td>19576</td>\n",
       "      <td>21210</td>\n",
       "      <td>21955</td>\n",
       "      <td>10865</td>\n",
       "      <td>11090</td>\n",
       "      <td>14539</td>\n",
       "      <td>7029</td>\n",
       "      <td>7510</td>\n",
       "      <td>14254</td>\n",
       "      <td>6952</td>\n",
       "      <td>7302</td>\n",
       "      <td>7191</td>\n",
       "      <td>3107</td>\n",
       "      <td>4084</td>\n",
       "      <td>3402</td>\n",
       "      <td>1791</td>\n",
       "      <td>1611</td>\n",
       "      <td>3794</td>\n",
       "      <td>1913</td>\n",
       "      <td>1881</td>\n",
       "      <td>4358</td>\n",
       "      <td>2198</td>\n",
       "      <td>2160</td>\n",
       "      <td>4050</td>\n",
       "      <td>2119</td>\n",
       "      <td>1931</td>\n",
       "      <td>3366</td>\n",
       "      <td>1717</td>\n",
       "      <td>1649</td>\n",
       "      <td>3203</td>\n",
       "      <td>1576</td>\n",
       "      <td>1627</td>\n",
       "      <td>3328</td>\n",
       "      <td>1599</td>\n",
       "      <td>1729</td>\n",
       "      <td>3822</td>\n",
       "      <td>1826</td>\n",
       "      <td>1996</td>\n",
       "      <td>4186</td>\n",
       "      <td>2028</td>\n",
       "      <td>2158</td>\n",
       "      <td>4081</td>\n",
       "      <td>1999</td>\n",
       "      <td>2082</td>\n",
       "      <td>4121</td>\n",
       "      <td>2082</td>\n",
       "      <td>2039</td>\n",
       "      <td>3273</td>\n",
       "      <td>1545</td>\n",
       "      <td>1728</td>\n",
       "      <td>2779</td>\n",
       "      <td>1326</td>\n",
       "      <td>1453</td>\n",
       "      <td>2459</td>\n",
       "      <td>1090</td>\n",
       "      <td>1369</td>\n",
       "      <td>1907</td>\n",
       "      <td>863</td>\n",
       "      <td>1044</td>\n",
       "      <td>1359</td>\n",
       "      <td>608</td>\n",
       "      <td>751</td>\n",
       "      <td>834</td>\n",
       "      <td>340</td>\n",
       "      <td>494</td>\n",
       "      <td>632</td>\n",
       "      <td>206</td>\n",
       "      <td>426</td>\n",
       "      <td>37.7</td>\n",
       "      <td>36.4</td>\n",
       "      <td>38.8</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>8</td>\n",
       "      <td>7208</td>\n",
       "      <td>3799</td>\n",
       "      <td>3409</td>\n",
       "      <td>442</td>\n",
       "      <td>223</td>\n",
       "      <td>219</td>\n",
       "      <td>786</td>\n",
       "      <td>392</td>\n",
       "      <td>394</td>\n",
       "      <td>328</td>\n",
       "      <td>173</td>\n",
       "      <td>155</td>\n",
       "      <td>554</td>\n",
       "      <td>330</td>\n",
       "      <td>224</td>\n",
       "      <td>5826</td>\n",
       "      <td>3106</td>\n",
       "      <td>2720</td>\n",
       "      <td>5652</td>\n",
       "      <td>3011</td>\n",
       "      <td>2641</td>\n",
       "      <td>2504</td>\n",
       "      <td>1416</td>\n",
       "      <td>1088</td>\n",
       "      <td>1703</td>\n",
       "      <td>957</td>\n",
       "      <td>746</td>\n",
       "      <td>2123</td>\n",
       "      <td>1129</td>\n",
       "      <td>994</td>\n",
       "      <td>1272</td>\n",
       "      <td>595</td>\n",
       "      <td>677</td>\n",
       "      <td>442</td>\n",
       "      <td>223</td>\n",
       "      <td>219</td>\n",
       "      <td>413</td>\n",
       "      <td>203</td>\n",
       "      <td>210</td>\n",
       "      <td>454</td>\n",
       "      <td>233</td>\n",
       "      <td>221</td>\n",
       "      <td>386</td>\n",
       "      <td>208</td>\n",
       "      <td>178</td>\n",
       "      <td>415</td>\n",
       "      <td>251</td>\n",
       "      <td>164</td>\n",
       "      <td>403</td>\n",
       "      <td>234</td>\n",
       "      <td>169</td>\n",
       "      <td>479</td>\n",
       "      <td>250</td>\n",
       "      <td>229</td>\n",
       "      <td>436</td>\n",
       "      <td>242</td>\n",
       "      <td>194</td>\n",
       "      <td>385</td>\n",
       "      <td>231</td>\n",
       "      <td>154</td>\n",
       "      <td>401</td>\n",
       "      <td>218</td>\n",
       "      <td>183</td>\n",
       "      <td>529</td>\n",
       "      <td>287</td>\n",
       "      <td>242</td>\n",
       "      <td>574</td>\n",
       "      <td>291</td>\n",
       "      <td>283</td>\n",
       "      <td>619</td>\n",
       "      <td>333</td>\n",
       "      <td>286</td>\n",
       "      <td>407</td>\n",
       "      <td>219</td>\n",
       "      <td>188</td>\n",
       "      <td>272</td>\n",
       "      <td>130</td>\n",
       "      <td>142</td>\n",
       "      <td>237</td>\n",
       "      <td>99</td>\n",
       "      <td>138</td>\n",
       "      <td>164</td>\n",
       "      <td>74</td>\n",
       "      <td>90</td>\n",
       "      <td>192</td>\n",
       "      <td>73</td>\n",
       "      <td>119</td>\n",
       "      <td>42.3</td>\n",
       "      <td>41.3</td>\n",
       "      <td>43.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>272</th>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>9</td>\n",
       "      <td>7220</td>\n",
       "      <td>3788</td>\n",
       "      <td>3432</td>\n",
       "      <td>439</td>\n",
       "      <td>225</td>\n",
       "      <td>214</td>\n",
       "      <td>808</td>\n",
       "      <td>408</td>\n",
       "      <td>400</td>\n",
       "      <td>312</td>\n",
       "      <td>159</td>\n",
       "      <td>153</td>\n",
       "      <td>529</td>\n",
       "      <td>307</td>\n",
       "      <td>222</td>\n",
       "      <td>5806</td>\n",
       "      <td>3063</td>\n",
       "      <td>2743</td>\n",
       "      <td>5661</td>\n",
       "      <td>2996</td>\n",
       "      <td>2665</td>\n",
       "      <td>2482</td>\n",
       "      <td>1355</td>\n",
       "      <td>1127</td>\n",
       "      <td>1724</td>\n",
       "      <td>935</td>\n",
       "      <td>789</td>\n",
       "      <td>2108</td>\n",
       "      <td>1133</td>\n",
       "      <td>975</td>\n",
       "      <td>1300</td>\n",
       "      <td>621</td>\n",
       "      <td>679</td>\n",
       "      <td>439</td>\n",
       "      <td>225</td>\n",
       "      <td>214</td>\n",
       "      <td>413</td>\n",
       "      <td>207</td>\n",
       "      <td>206</td>\n",
       "      <td>478</td>\n",
       "      <td>247</td>\n",
       "      <td>231</td>\n",
       "      <td>378</td>\n",
       "      <td>197</td>\n",
       "      <td>181</td>\n",
       "      <td>380</td>\n",
       "      <td>223</td>\n",
       "      <td>157</td>\n",
       "      <td>393</td>\n",
       "      <td>217</td>\n",
       "      <td>176</td>\n",
       "      <td>492</td>\n",
       "      <td>253</td>\n",
       "      <td>239</td>\n",
       "      <td>469</td>\n",
       "      <td>260</td>\n",
       "      <td>209</td>\n",
       "      <td>370</td>\n",
       "      <td>205</td>\n",
       "      <td>165</td>\n",
       "      <td>403</td>\n",
       "      <td>228</td>\n",
       "      <td>175</td>\n",
       "      <td>496</td>\n",
       "      <td>271</td>\n",
       "      <td>225</td>\n",
       "      <td>574</td>\n",
       "      <td>299</td>\n",
       "      <td>275</td>\n",
       "      <td>635</td>\n",
       "      <td>335</td>\n",
       "      <td>300</td>\n",
       "      <td>430</td>\n",
       "      <td>231</td>\n",
       "      <td>199</td>\n",
       "      <td>283</td>\n",
       "      <td>145</td>\n",
       "      <td>138</td>\n",
       "      <td>217</td>\n",
       "      <td>86</td>\n",
       "      <td>131</td>\n",
       "      <td>172</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>198</td>\n",
       "      <td>73</td>\n",
       "      <td>125</td>\n",
       "      <td>42.4</td>\n",
       "      <td>41.8</td>\n",
       "      <td>43.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>273</th>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>10</td>\n",
       "      <td>6968</td>\n",
       "      <td>3660</td>\n",
       "      <td>3308</td>\n",
       "      <td>379</td>\n",
       "      <td>183</td>\n",
       "      <td>196</td>\n",
       "      <td>746</td>\n",
       "      <td>378</td>\n",
       "      <td>368</td>\n",
       "      <td>309</td>\n",
       "      <td>155</td>\n",
       "      <td>154</td>\n",
       "      <td>461</td>\n",
       "      <td>271</td>\n",
       "      <td>190</td>\n",
       "      <td>5676</td>\n",
       "      <td>3016</td>\n",
       "      <td>2660</td>\n",
       "      <td>5534</td>\n",
       "      <td>2944</td>\n",
       "      <td>2590</td>\n",
       "      <td>2367</td>\n",
       "      <td>1306</td>\n",
       "      <td>1061</td>\n",
       "      <td>1682</td>\n",
       "      <td>918</td>\n",
       "      <td>764</td>\n",
       "      <td>2029</td>\n",
       "      <td>1088</td>\n",
       "      <td>941</td>\n",
       "      <td>1362</td>\n",
       "      <td>667</td>\n",
       "      <td>695</td>\n",
       "      <td>379</td>\n",
       "      <td>183</td>\n",
       "      <td>196</td>\n",
       "      <td>398</td>\n",
       "      <td>196</td>\n",
       "      <td>202</td>\n",
       "      <td>433</td>\n",
       "      <td>220</td>\n",
       "      <td>213</td>\n",
       "      <td>356</td>\n",
       "      <td>193</td>\n",
       "      <td>163</td>\n",
       "      <td>329</td>\n",
       "      <td>195</td>\n",
       "      <td>134</td>\n",
       "      <td>399</td>\n",
       "      <td>225</td>\n",
       "      <td>174</td>\n",
       "      <td>444</td>\n",
       "      <td>236</td>\n",
       "      <td>208</td>\n",
       "      <td>488</td>\n",
       "      <td>270</td>\n",
       "      <td>218</td>\n",
       "      <td>351</td>\n",
       "      <td>187</td>\n",
       "      <td>164</td>\n",
       "      <td>390</td>\n",
       "      <td>227</td>\n",
       "      <td>163</td>\n",
       "      <td>443</td>\n",
       "      <td>231</td>\n",
       "      <td>212</td>\n",
       "      <td>566</td>\n",
       "      <td>304</td>\n",
       "      <td>262</td>\n",
       "      <td>630</td>\n",
       "      <td>326</td>\n",
       "      <td>304</td>\n",
       "      <td>438</td>\n",
       "      <td>242</td>\n",
       "      <td>196</td>\n",
       "      <td>330</td>\n",
       "      <td>178</td>\n",
       "      <td>152</td>\n",
       "      <td>211</td>\n",
       "      <td>88</td>\n",
       "      <td>123</td>\n",
       "      <td>182</td>\n",
       "      <td>91</td>\n",
       "      <td>91</td>\n",
       "      <td>201</td>\n",
       "      <td>68</td>\n",
       "      <td>133</td>\n",
       "      <td>43.6</td>\n",
       "      <td>42.9</td>\n",
       "      <td>44.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>274</th>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>11</td>\n",
       "      <td>6924</td>\n",
       "      <td>3627</td>\n",
       "      <td>3297</td>\n",
       "      <td>359</td>\n",
       "      <td>181</td>\n",
       "      <td>178</td>\n",
       "      <td>738</td>\n",
       "      <td>375</td>\n",
       "      <td>363</td>\n",
       "      <td>345</td>\n",
       "      <td>179</td>\n",
       "      <td>166</td>\n",
       "      <td>417</td>\n",
       "      <td>251</td>\n",
       "      <td>166</td>\n",
       "      <td>5634</td>\n",
       "      <td>2973</td>\n",
       "      <td>2661</td>\n",
       "      <td>5482</td>\n",
       "      <td>2892</td>\n",
       "      <td>2590</td>\n",
       "      <td>2336</td>\n",
       "      <td>1275</td>\n",
       "      <td>1061</td>\n",
       "      <td>1678</td>\n",
       "      <td>899</td>\n",
       "      <td>779</td>\n",
       "      <td>1961</td>\n",
       "      <td>1046</td>\n",
       "      <td>915</td>\n",
       "      <td>1426</td>\n",
       "      <td>696</td>\n",
       "      <td>730</td>\n",
       "      <td>359</td>\n",
       "      <td>181</td>\n",
       "      <td>178</td>\n",
       "      <td>420</td>\n",
       "      <td>204</td>\n",
       "      <td>216</td>\n",
       "      <td>422</td>\n",
       "      <td>225</td>\n",
       "      <td>197</td>\n",
       "      <td>353</td>\n",
       "      <td>192</td>\n",
       "      <td>161</td>\n",
       "      <td>305</td>\n",
       "      <td>184</td>\n",
       "      <td>121</td>\n",
       "      <td>365</td>\n",
       "      <td>207</td>\n",
       "      <td>158</td>\n",
       "      <td>437</td>\n",
       "      <td>232</td>\n",
       "      <td>205</td>\n",
       "      <td>514</td>\n",
       "      <td>257</td>\n",
       "      <td>257</td>\n",
       "      <td>362</td>\n",
       "      <td>203</td>\n",
       "      <td>159</td>\n",
       "      <td>367</td>\n",
       "      <td>212</td>\n",
       "      <td>155</td>\n",
       "      <td>438</td>\n",
       "      <td>231</td>\n",
       "      <td>207</td>\n",
       "      <td>544</td>\n",
       "      <td>292</td>\n",
       "      <td>252</td>\n",
       "      <td>612</td>\n",
       "      <td>311</td>\n",
       "      <td>301</td>\n",
       "      <td>476</td>\n",
       "      <td>268</td>\n",
       "      <td>208</td>\n",
       "      <td>337</td>\n",
       "      <td>171</td>\n",
       "      <td>166</td>\n",
       "      <td>221</td>\n",
       "      <td>103</td>\n",
       "      <td>118</td>\n",
       "      <td>198</td>\n",
       "      <td>88</td>\n",
       "      <td>110</td>\n",
       "      <td>194</td>\n",
       "      <td>66</td>\n",
       "      <td>128</td>\n",
       "      <td>43.9</td>\n",
       "      <td>43.1</td>\n",
       "      <td>44.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>275</th>\n",
       "      <td>50</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>12</td>\n",
       "      <td>6927</td>\n",
       "      <td>3624</td>\n",
       "      <td>3303</td>\n",
       "      <td>350</td>\n",
       "      <td>166</td>\n",
       "      <td>184</td>\n",
       "      <td>724</td>\n",
       "      <td>369</td>\n",
       "      <td>355</td>\n",
       "      <td>341</td>\n",
       "      <td>168</td>\n",
       "      <td>173</td>\n",
       "      <td>426</td>\n",
       "      <td>253</td>\n",
       "      <td>173</td>\n",
       "      <td>5675</td>\n",
       "      <td>3003</td>\n",
       "      <td>2672</td>\n",
       "      <td>5512</td>\n",
       "      <td>2921</td>\n",
       "      <td>2591</td>\n",
       "      <td>2363</td>\n",
       "      <td>1296</td>\n",
       "      <td>1067</td>\n",
       "      <td>1680</td>\n",
       "      <td>914</td>\n",
       "      <td>766</td>\n",
       "      <td>1928</td>\n",
       "      <td>1033</td>\n",
       "      <td>895</td>\n",
       "      <td>1478</td>\n",
       "      <td>721</td>\n",
       "      <td>757</td>\n",
       "      <td>350</td>\n",
       "      <td>166</td>\n",
       "      <td>184</td>\n",
       "      <td>429</td>\n",
       "      <td>213</td>\n",
       "      <td>216</td>\n",
       "      <td>379</td>\n",
       "      <td>195</td>\n",
       "      <td>184</td>\n",
       "      <td>371</td>\n",
       "      <td>196</td>\n",
       "      <td>175</td>\n",
       "      <td>312</td>\n",
       "      <td>186</td>\n",
       "      <td>126</td>\n",
       "      <td>356</td>\n",
       "      <td>210</td>\n",
       "      <td>146</td>\n",
       "      <td>421</td>\n",
       "      <td>231</td>\n",
       "      <td>190</td>\n",
       "      <td>518</td>\n",
       "      <td>250</td>\n",
       "      <td>268</td>\n",
       "      <td>385</td>\n",
       "      <td>223</td>\n",
       "      <td>162</td>\n",
       "      <td>370</td>\n",
       "      <td>212</td>\n",
       "      <td>158</td>\n",
       "      <td>405</td>\n",
       "      <td>214</td>\n",
       "      <td>191</td>\n",
       "      <td>575</td>\n",
       "      <td>317</td>\n",
       "      <td>258</td>\n",
       "      <td>578</td>\n",
       "      <td>290</td>\n",
       "      <td>288</td>\n",
       "      <td>499</td>\n",
       "      <td>280</td>\n",
       "      <td>219</td>\n",
       "      <td>352</td>\n",
       "      <td>180</td>\n",
       "      <td>172</td>\n",
       "      <td>229</td>\n",
       "      <td>107</td>\n",
       "      <td>122</td>\n",
       "      <td>198</td>\n",
       "      <td>82</td>\n",
       "      <td>116</td>\n",
       "      <td>200</td>\n",
       "      <td>72</td>\n",
       "      <td>128</td>\n",
       "      <td>44.2</td>\n",
       "      <td>43.5</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37704 rows × 96 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     SUMLEV  STATE  COUNTY   STNAME         CTYNAME  YEAR  POPESTIMATE  \\\n",
       "0        50      1       1  Alabama  Autauga County     1        54571   \n",
       "1        50      1       1  Alabama  Autauga County     2        54597   \n",
       "2        50      1       1  Alabama  Autauga County     3        54773   \n",
       "3        50      1       1  Alabama  Autauga County     4        55227   \n",
       "4        50      1       1  Alabama  Autauga County     5        54954   \n",
       "..      ...    ...     ...      ...             ...   ...          ...   \n",
       "271      50     56      45  Wyoming   Weston County     8         7208   \n",
       "272      50     56      45  Wyoming   Weston County     9         7220   \n",
       "273      50     56      45  Wyoming   Weston County    10         6968   \n",
       "274      50     56      45  Wyoming   Weston County    11         6924   \n",
       "275      50     56      45  Wyoming   Weston County    12         6927   \n",
       "\n",
       "     POPEST_MALE  POPEST_FEM  UNDER5_TOT  UNDER5_MALE  UNDER5_FEM  AGE513_TOT  \\\n",
       "0          26569       28002        3579         1866        1713        7418   \n",
       "1          26584       28013        3584         1869        1715        7425   \n",
       "2          26672       28101        3575         1863        1712        7400   \n",
       "3          26981       28246        3551         1852        1699        7355   \n",
       "4          26826       28128        3402         1791        1611        7255   \n",
       "..           ...         ...         ...          ...         ...         ...   \n",
       "271         3799        3409         442          223         219         786   \n",
       "272         3788        3432         439          225         214         808   \n",
       "273         3660        3308         379          183         196         746   \n",
       "274         3627        3297         359          181         178         738   \n",
       "275         3624        3303         350          166         184         724   \n",
       "\n",
       "     AGE513_MALE  AGE513_FEM  AGE1417_TOT  AGE1417_MALE  AGE1417_FEM  \\\n",
       "0           3747        3671         3616          1842         1774   \n",
       "1           3752        3673         3618          1843         1775   \n",
       "2           3720        3680         3566          1820         1746   \n",
       "3           3683        3672         3477          1782         1695   \n",
       "4           3658        3597         3511          1801         1710   \n",
       "..           ...         ...          ...           ...          ...   \n",
       "271          392         394          328           173          155   \n",
       "272          408         400          312           159          153   \n",
       "273          378         368          309           155          154   \n",
       "274          375         363          345           179          166   \n",
       "275          369         355          341           168          173   \n",
       "\n",
       "     AGE1824_TOT  AGE1824_MALE  AGE1824_FEM  AGE16PLUS_TOT  AGE16PLUS_MALE  \\\n",
       "0           4617          2335         2282          41804           20046   \n",
       "1           4617          2335         2282          41817           20052   \n",
       "2           4671          2362         2309          42050           20194   \n",
       "3           4816          2487         2329          42613           20573   \n",
       "4           4802          2488         2314          42544           20473   \n",
       "..           ...           ...          ...            ...             ...   \n",
       "271          554           330          224           5826            3106   \n",
       "272          529           307          222           5806            3063   \n",
       "273          461           271          190           5676            3016   \n",
       "274          417           251          166           5634            2973   \n",
       "275          426           253          173           5675            3003   \n",
       "\n",
       "     AGE16PLUS_FEM  AGE18PLUS_TOT  AGE18PLUS_MALE  AGE18PLUS_FEM  AGE1544_TOT  \\\n",
       "0            21758          39958           19114          20844        22100   \n",
       "1            21765          39970           19120          20850        22116   \n",
       "2            21856          40232           19269          20963        22205   \n",
       "3            22040          40844           19664          21180        22260   \n",
       "4            22071          40786           19576          21210        21955   \n",
       "..             ...            ...             ...            ...          ...   \n",
       "271           2720           5652            3011           2641         2504   \n",
       "272           2743           5661            2996           2665         2482   \n",
       "273           2660           5534            2944           2590         2367   \n",
       "274           2661           5482            2892           2590         2336   \n",
       "275           2672           5512            2921           2591         2363   \n",
       "\n",
       "     AGE1544_MALE  AGE1544_FEM  AGE2544_TOT  AGE2544_MALE  AGE2544_FEM  \\\n",
       "0           10867        11233        14730          7115         7615   \n",
       "1           10874        11242        14744          7121         7623   \n",
       "2           10944        11261        14824          7189         7635   \n",
       "3           11055        11205        14832          7242         7590   \n",
       "4           10865        11090        14539          7029         7510   \n",
       "..            ...          ...          ...           ...          ...   \n",
       "271          1416         1088         1703           957          746   \n",
       "272          1355         1127         1724           935          789   \n",
       "273          1306         1061         1682           918          764   \n",
       "274          1275         1061         1678           899          779   \n",
       "275          1296         1067         1680           914          766   \n",
       "\n",
       "     AGE4564_TOT  AGE4564_MALE  AGE4564_FEM  AGE65PLUS_TOT  AGE65PLUS_MALE  \\\n",
       "0          14065          6843         7222           6546            2821   \n",
       "1          14067          6845         7222           6542            2819   \n",
       "2          14138          6871         7267           6599            2847   \n",
       "3          14335          6979         7356           6861            2956   \n",
       "4          14254          6952         7302           7191            3107   \n",
       "..           ...           ...          ...            ...             ...   \n",
       "271         2123          1129          994           1272             595   \n",
       "272         2108          1133          975           1300             621   \n",
       "273         2029          1088          941           1362             667   \n",
       "274         1961          1046          915           1426             696   \n",
       "275         1928          1033          895           1478             721   \n",
       "\n",
       "     AGE65PLUS_FEM  AGE04_TOT  AGE04_MALE  AGE04_FEM  AGE59_TOT  AGE59_MALE  \\\n",
       "0             3725       3579        1866       1713       3991        2001   \n",
       "1             3723       3584        1869       1715       3994        2004   \n",
       "2             3752       3575        1863       1712       3964        1984   \n",
       "3             3905       3551        1852       1699       3902        1938   \n",
       "4             4084       3402        1791       1611       3794        1913   \n",
       "..             ...        ...         ...        ...        ...         ...   \n",
       "271            677        442         223        219        413         203   \n",
       "272            679        439         225        214        413         207   \n",
       "273            695        379         183        196        398         196   \n",
       "274            730        359         181        178        420         204   \n",
       "275            757        350         166        184        429         213   \n",
       "\n",
       "     AGE59_FEM  AGE1014_TOT  AGE1014_MALE  AGE1014_FEM  AGE1519_TOT  \\\n",
       "0         1990         4290          2171         2119         4290   \n",
       "1         1990         4294          2173         2121         4292   \n",
       "2         1980         4292          2163         2129         4229   \n",
       "3         1964         4318          2201         2117         4030   \n",
       "4         1881         4358          2198         2160         4050   \n",
       "..         ...          ...           ...          ...          ...   \n",
       "271        210          454           233          221          386   \n",
       "272        206          478           247          231          378   \n",
       "273        202          433           220          213          356   \n",
       "274        216          422           225          197          353   \n",
       "275        216          379           195          184          371   \n",
       "\n",
       "     AGE1519_MALE  AGE1519_FEM  AGE2024_TOT  AGE2024_MALE  AGE2024_FEM  \\\n",
       "0            2213         2077         3080          1539         1541   \n",
       "1            2214         2078         3080          1539         1541   \n",
       "2            2182         2047         3152          1573         1579   \n",
       "3            2071         1959         3398          1742         1656   \n",
       "4            2119         1931         3366          1717         1649   \n",
       "..            ...          ...          ...           ...          ...   \n",
       "271           208          178          415           251          164   \n",
       "272           197          181          380           223          157   \n",
       "273           193          163          329           195          134   \n",
       "274           192          161          305           184          121   \n",
       "275           196          175          312           186          126   \n",
       "\n",
       "     AGE2529_TOT  AGE2529_MALE  AGE2529_FEM  AGE3034_TOT  AGE3034_MALE  \\\n",
       "0           3157          1543         1614         3330          1594   \n",
       "1           3162          1546         1616         3333          1595   \n",
       "2           3191          1574         1617         3372          1615   \n",
       "3           3216          1608         1608         3446          1665   \n",
       "4           3203          1576         1627         3328          1599   \n",
       "..           ...           ...          ...          ...           ...   \n",
       "271          403           234          169          479           250   \n",
       "272          393           217          176          492           253   \n",
       "273          399           225          174          444           236   \n",
       "274          365           207          158          437           232   \n",
       "275          356           210          146          421           231   \n",
       "\n",
       "     AGE3034_FEM  AGE3539_TOT  AGE3539_MALE  AGE3539_FEM  AGE4044_TOT  \\\n",
       "0           1736         4157          2004         2153         4086   \n",
       "1           1738         4160          2006         2154         4089   \n",
       "2           1757         4143          2002         2141         4118   \n",
       "3           1781         4003          1939         2064         4167   \n",
       "4           1729         3822          1826         1996         4186   \n",
       "..           ...          ...           ...          ...          ...   \n",
       "271          229          436           242          194          385   \n",
       "272          239          469           260          209          370   \n",
       "273          208          488           270          218          351   \n",
       "274          205          514           257          257          362   \n",
       "275          190          518           250          268          385   \n",
       "\n",
       "     AGE4044_MALE  AGE4044_FEM  AGE4549_TOT  AGE4549_MALE  AGE4549_FEM  \\\n",
       "0            1974         2112         4332          2174         2158   \n",
       "1            1974         2115         4334          2176         2158   \n",
       "2            1998         2120         4321          2162         2159   \n",
       "3            2030         2137         4196          2073         2123   \n",
       "4            2028         2158         4081          1999         2082   \n",
       "..            ...          ...          ...           ...          ...   \n",
       "271           231          154          401           218          183   \n",
       "272           205          165          403           228          175   \n",
       "273           187          164          390           227          163   \n",
       "274           203          159          367           212          155   \n",
       "275           223          162          370           212          158   \n",
       "\n",
       "     AGE5054_TOT  AGE5054_MALE  AGE5054_FEM  AGE5559_TOT  AGE5559_MALE  \\\n",
       "0           3873          1866         2007         3083          1524   \n",
       "1           3873          1866         2007         3083          1524   \n",
       "2           3908          1884         2024         3119          1533   \n",
       "3           4035          1985         2050         3247          1582   \n",
       "4           4121          2082         2039         3273          1545   \n",
       "..           ...           ...          ...          ...           ...   \n",
       "271          529           287          242          574           291   \n",
       "272          496           271          225          574           299   \n",
       "273          443           231          212          566           304   \n",
       "274          438           231          207          544           292   \n",
       "275          405           214          191          575           317   \n",
       "\n",
       "     AGE5559_FEM  AGE6064_TOT  AGE6064_MALE  AGE6064_FEM  AGE6569_TOT  \\\n",
       "0           1559         2777          1279         1498         2277   \n",
       "1           1559         2777          1279         1498         2277   \n",
       "2           1586         2790          1292         1498         2289   \n",
       "3           1665         2857          1339         1518         2323   \n",
       "4           1728         2779          1326         1453         2459   \n",
       "..           ...          ...           ...          ...          ...   \n",
       "271          283          619           333          286          407   \n",
       "272          275          635           335          300          430   \n",
       "273          262          630           326          304          438   \n",
       "274          252          612           311          301          476   \n",
       "275          258          578           290          288          499   \n",
       "\n",
       "     AGE6569_MALE  AGE6569_FEM  AGE7074_TOT  AGE7074_MALE  AGE7074_FEM  \\\n",
       "0            1014         1263         1736           807          929   \n",
       "1            1014         1263         1734           806          928   \n",
       "2            1018         1271         1752           814          938   \n",
       "3            1021         1302         1843           844          999   \n",
       "4            1090         1369         1907           863         1044   \n",
       "..            ...          ...          ...           ...          ...   \n",
       "271           219          188          272           130          142   \n",
       "272           231          199          283           145          138   \n",
       "273           242          196          330           178          152   \n",
       "274           268          208          337           171          166   \n",
       "275           280          219          352           180          172   \n",
       "\n",
       "     AGE7579_TOT  AGE7579_MALE  AGE7579_FEM  AGE8084_TOT  AGE8084_MALE  \\\n",
       "0           1251           546          705          731           295   \n",
       "1           1250           546          704          730           294   \n",
       "2           1259           552          707          743           299   \n",
       "3           1312           585          727          782           315   \n",
       "4           1359           608          751          834           340   \n",
       "..           ...           ...          ...          ...           ...   \n",
       "271          237            99          138          164            74   \n",
       "272          217            86          131          172            86   \n",
       "273          211            88          123          182            91   \n",
       "274          221           103          118          198            88   \n",
       "275          229           107          122          198            82   \n",
       "\n",
       "     AGE8084_FEM  AGE85PLUS_TOT  AGE85PLUS_MALE  AGE85PLUS_FEM  \\\n",
       "0            436            551             159            392   \n",
       "1            436            551             159            392   \n",
       "2            444            556             164            392   \n",
       "3            467            601             191            410   \n",
       "4            494            632             206            426   \n",
       "..           ...            ...             ...            ...   \n",
       "271           90            192              73            119   \n",
       "272           86            198              73            125   \n",
       "273           91            201              68            133   \n",
       "274          110            194              66            128   \n",
       "275          116            200              72            128   \n",
       "\n",
       "     MEDIAN_AGE_TOT  MEDIAN_AGE_MALE  MEDIAN_AGE_FEM  \n",
       "0              37.0             35.9            37.9  \n",
       "1              37.0             35.9            37.8  \n",
       "2              37.1             36.0            37.9  \n",
       "3              37.3             36.2            38.4  \n",
       "4              37.7             36.4            38.8  \n",
       "..              ...              ...             ...  \n",
       "271            42.3             41.3            43.7  \n",
       "272            42.4             41.8            43.2  \n",
       "273            43.6             42.9            44.4  \n",
       "274            43.9             43.1            44.9  \n",
       "275            44.2             43.5            45.0  \n",
       "\n",
       "[37704 rows x 96 columns]"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_all_census_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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