{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop = pd.read_csv(\"2016 MA Population.csv\") \n",
    "income = pd.read_csv(\"MA Median Income.csv\")\n",
    "covid = pd.read_csv(\"MA COVID Data 1-9-21.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Municipality</th>\n",
       "      <th>Population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abington</td>\n",
       "      <td>16335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acton</td>\n",
       "      <td>23607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acushnet</td>\n",
       "      <td>10483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams</td>\n",
       "      <td>8174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agawam</td>\n",
       "      <td>28696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>40341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Worcester</td>\n",
       "      <td>184859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Worthington</td>\n",
       "      <td>1189</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Wrentham</td>\n",
       "      <td>11728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Yarmouth</td>\n",
       "      <td>23400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Municipality  Population\n",
       "0       Abington       16335\n",
       "1          Acton       23607\n",
       "2       Acushnet       10483\n",
       "3          Adams        8174\n",
       "4         Agawam       28696\n",
       "..           ...         ...\n",
       "346       Woburn       40341\n",
       "347    Worcester      184859\n",
       "348  Worthington        1189\n",
       "349     Wrentham       11728\n",
       "350     Yarmouth       23400\n",
       "\n",
       "[351 rows x 2 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = [\n",
    "    (income['Median_Income'] < 25000),\n",
    "    (income['Median_Income'] >= 25000) & (income['Median_Income'] < 50000),\n",
    "    (income['Median_Income'] >= 50000) & (income['Median_Income'] < 75000),\n",
    "    (income['Median_Income'] >= 75000) & (income['Median_Income'] < 100000),\n",
    "    (income['Median_Income'] >= 100000) & (income['Median_Income'] < 150000),\n",
    "    (income['Median_Income'] >= 150000)\n",
    "    ]\n",
    "\n",
    "# create a list of the values we want to assign for each condition\n",
    "values = [0.6, 0.8, 1.6, 3.4, 3.0, 5.6]\n",
    "\n",
    "# create a new column and use np.select to assign values to it using our lists as arguments\n",
    "income['flights'] = np.select(conditions, values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "351"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(income)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Series' object has no attribute 'isNA'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-83baa2855968>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mcovid\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTotal_Cases\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misNA\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, name)\u001b[0m\n\u001b[0;32m   5272\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5273\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5274\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\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   5275\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5276\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m->\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: 'Series' object has no attribute 'isNA'"
     ]
    }
   ],
   "source": [
    "covid.Total_Cases.isNA()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Municipality</th>\n",
       "      <th>Total_Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Abington</td>\n",
       "      <td>1073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Acton</td>\n",
       "      <td>596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Acushnet</td>\n",
       "      <td>808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adams</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Agawam</td>\n",
       "      <td>1699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Woburn</td>\n",
       "      <td>2941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Worcester</td>\n",
       "      <td>16924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Worthington</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Wrentham</td>\n",
       "      <td>671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>351</th>\n",
       "      <td>Yarmouth</td>\n",
       "      <td>772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>352 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Municipality Total_Cases\n",
       "0       Abington        1073\n",
       "1          Acton         596\n",
       "2       Acushnet         808\n",
       "3          Adams         171\n",
       "4         Agawam        1699\n",
       "..           ...         ...\n",
       "347       Woburn        2941\n",
       "348    Worcester       16924\n",
       "349  Worthington          20\n",
       "350     Wrentham         671\n",
       "351     Yarmouth         772\n",
       "\n",
       "[352 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "covid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = income.merge(pop, 'left', left_on='Municipality', right_on='Municipality')\n",
    "merged = merged.merge(covid, 'left', left_on='Municipality', right_on='Municipality')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged.Total_Cases = merged.Total_Cases.replace({\"<5\": 4})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Municipality</th>\n",
       "      <th>Median_Income</th>\n",
       "      <th>flights</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total_Cases</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Municipality, Median_Income, flights, Population, Total_Cases]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[merged.Population.isna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged = merged.astype({'Population': 'float64', 'Total_Cases': 'float64'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Municipality</th>\n",
       "      <th>Median_Income</th>\n",
       "      <th>flights</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total_Cases</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dover</td>\n",
       "      <td>204018</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>149.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Weston</td>\n",
       "      <td>196651</td>\n",
       "      <td>5.6</td>\n",
       "      <td>12094.0</td>\n",
       "      <td>342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wellesley</td>\n",
       "      <td>176852</td>\n",
       "      <td>5.6</td>\n",
       "      <td>28876.0</td>\n",
       "      <td>737.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sudbury</td>\n",
       "      <td>170945</td>\n",
       "      <td>5.6</td>\n",
       "      <td>18816.0</td>\n",
       "      <td>510.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sherborn</td>\n",
       "      <td>170872</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4294.0</td>\n",
       "      <td>112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>North Adams</td>\n",
       "      <td>38774</td>\n",
       "      <td>0.8</td>\n",
       "      <td>13082.0</td>\n",
       "      <td>263.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>37954</td>\n",
       "      <td>0.8</td>\n",
       "      <td>40262.0</td>\n",
       "      <td>3514.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Springfield</td>\n",
       "      <td>37118</td>\n",
       "      <td>0.8</td>\n",
       "      <td>154028.0</td>\n",
       "      <td>13098.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Monroe</td>\n",
       "      <td>31458</td>\n",
       "      <td>0.8</td>\n",
       "      <td>116.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Gosnold</td>\n",
       "      <td>22344</td>\n",
       "      <td>0.6</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Municipality  Median_Income  flights  Population  Total_Cases\n",
       "0          Dover         204018      5.6      5980.0        149.0\n",
       "1         Weston         196651      5.6     12094.0        342.0\n",
       "2      Wellesley         176852      5.6     28876.0        737.0\n",
       "3        Sudbury         170945      5.6     18816.0        510.0\n",
       "4       Sherborn         170872      5.6      4294.0        112.0\n",
       "..           ...            ...      ...         ...          ...\n",
       "346  North Adams          38774      0.8     13082.0        263.0\n",
       "347      Holyoke          37954      0.8     40262.0       3514.0\n",
       "348  Springfield          37118      0.8    154028.0      13098.0\n",
       "349       Monroe          31458      0.8       116.0          4.0\n",
       "350      Gosnold          22344      0.6        76.0          5.0\n",
       "\n",
       "[351 rows x 5 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_pop = sum(merged.Population * merged.flights)\n",
    "merged.Calculated = merged.flights * merged.Population / total_pop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Municipality</th>\n",
       "      <th>Median_Income</th>\n",
       "      <th>flights</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total_Cases</th>\n",
       "      <th>Calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Dover</td>\n",
       "      <td>204018</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5980.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>0.002088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Weston</td>\n",
       "      <td>196651</td>\n",
       "      <td>5.6</td>\n",
       "      <td>12094.0</td>\n",
       "      <td>342.0</td>\n",
       "      <td>0.004223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wellesley</td>\n",
       "      <td>176852</td>\n",
       "      <td>5.6</td>\n",
       "      <td>28876.0</td>\n",
       "      <td>737.0</td>\n",
       "      <td>0.010082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Sudbury</td>\n",
       "      <td>170945</td>\n",
       "      <td>5.6</td>\n",
       "      <td>18816.0</td>\n",
       "      <td>510.0</td>\n",
       "      <td>0.006569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sherborn</td>\n",
       "      <td>170872</td>\n",
       "      <td>5.6</td>\n",
       "      <td>4294.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.001499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>North Adams</td>\n",
       "      <td>38774</td>\n",
       "      <td>0.8</td>\n",
       "      <td>13082.0</td>\n",
       "      <td>263.0</td>\n",
       "      <td>0.000652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>Holyoke</td>\n",
       "      <td>37954</td>\n",
       "      <td>0.8</td>\n",
       "      <td>40262.0</td>\n",
       "      <td>3514.0</td>\n",
       "      <td>0.002008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>348</th>\n",
       "      <td>Springfield</td>\n",
       "      <td>37118</td>\n",
       "      <td>0.8</td>\n",
       "      <td>154028.0</td>\n",
       "      <td>13098.0</td>\n",
       "      <td>0.007682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349</th>\n",
       "      <td>Monroe</td>\n",
       "      <td>31458</td>\n",
       "      <td>0.8</td>\n",
       "      <td>116.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.000006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>350</th>\n",
       "      <td>Gosnold</td>\n",
       "      <td>22344</td>\n",
       "      <td>0.6</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.000003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>351 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Municipality  Median_Income  flights  Population  Total_Cases  Calculated\n",
       "0          Dover         204018      5.6      5980.0        149.0    0.002088\n",
       "1         Weston         196651      5.6     12094.0        342.0    0.004223\n",
       "2      Wellesley         176852      5.6     28876.0        737.0    0.010082\n",
       "3        Sudbury         170945      5.6     18816.0        510.0    0.006569\n",
       "4       Sherborn         170872      5.6      4294.0        112.0    0.001499\n",
       "..           ...            ...      ...         ...          ...         ...\n",
       "346  North Adams          38774      0.8     13082.0        263.0    0.000652\n",
       "347      Holyoke          37954      0.8     40262.0       3514.0    0.002008\n",
       "348  Springfield          37118      0.8    154028.0      13098.0    0.007682\n",
       "349       Monroe          31458      0.8       116.0          4.0    0.000006\n",
       "350      Gosnold          22344      0.6        76.0          5.0    0.000003\n",
       "\n",
       "[351 rows x 6 columns]"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18464161460>"
      ]
     },
     "execution_count": 89,
     "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": [
    "plt.scatter(merged.Median_Income, merged.Total_Cases/merged.Population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.16033062582245966"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged.Median_Income.corr(merged.Total_Cases/merged.Population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1846421c190>"
      ]
     },
     "execution_count": 91,
     "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": [
    "plt.scatter(merged.flights * merged.Population, merged.Total_Cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7931628893288134"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(merged.flights * merged.Population).corr(merged.Total_Cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "merged_wo_boston = merged[merged['Municipality'] != \"Boston\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x184646d5880>"
      ]
     },
     "execution_count": 95,
     "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": [
    "plt.scatter(merged_wo_boston.flights * merged_wo_boston.Population, merged_wo_boston.Total_Cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5200241203809173"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(merged_wo_boston.flights * merged_wo_boston.Population).corr(merged_wo_boston.Total_Cases)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x184647a8250>"
      ]
     },
     "execution_count": 104,
     "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": [
    "plt.scatter(merged.Calculated, merged.Total_Cases/merged.Population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>Municipality</th>\n",
       "      <th>Median_Income</th>\n",
       "      <th>flights</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total_Cases</th>\n",
       "      <th>Calculated</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>Boston</td>\n",
       "      <td>62021</td>\n",
       "      <td>1.6</td>\n",
       "      <td>679848.0</td>\n",
       "      <td>46455.0</td>\n",
       "      <td>0.067818</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Municipality  Median_Income  flights  Population  Total_Cases  Calculated\n",
       "289       Boston          62021      1.6    679848.0      46455.0    0.067818"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged[merged['Municipality'] == 'Boston']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06249875432469157"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Average risk of COVID in MA\n",
    "sum(merged.Total_Cases)/sum(merged.Population)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Average Risk weighted by number of flights\n",
    "\n",
    "#Pivot table to get groups based on income/number of flights\n",
    "t = pd.pivot_table(merged, index = 'flights', aggfunc = sum)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Median_Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total_Cases</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flights</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>22344</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>783895</td>\n",
       "      <td>927920.0</td>\n",
       "      <td>88342.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.6</th>\n",
       "      <td>7763929</td>\n",
       "      <td>2582571.0</td>\n",
       "      <td>181457.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>8771618</td>\n",
       "      <td>1285052.0</td>\n",
       "      <td>53756.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.4</th>\n",
       "      <td>10578401</td>\n",
       "      <td>1839554.0</td>\n",
       "      <td>97634.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.6</th>\n",
       "      <td>2183069</td>\n",
       "      <td>188435.0</td>\n",
       "      <td>5273.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Median_Income  Population  Total_Cases\n",
       "flights                                        \n",
       "0.6              22344        76.0          5.0\n",
       "0.8             783895    927920.0      88342.0\n",
       "1.6            7763929   2582571.0     181457.0\n",
       "3.0            8771618   1285052.0      53756.0\n",
       "3.4           10578401   1839554.0      97634.0\n",
       "5.6            2183069    188435.0       5273.0"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.05509943070834175"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate average risk for each group\n",
    "t['Risk'] = t.Total_Cases / t.Population\n",
    "\n",
    "#Calculate overall number of total flights\n",
    "total_flights = sum(t.index * t.Population)\n",
    "\n",
    "#Weighted risk calculation - this is just sum(flights*cases)/total_flights\n",
    "sum(t.index * t.Population * t.Calculated) / total_flights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Median_Income</th>\n",
       "      <th>Population</th>\n",
       "      <th>Total_Cases</th>\n",
       "      <th>Risk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>flights</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.6</th>\n",
       "      <td>22344</td>\n",
       "      <td>76.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.065789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.8</th>\n",
       "      <td>783895</td>\n",
       "      <td>927920.0</td>\n",
       "      <td>88342.0</td>\n",
       "      <td>0.095204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1.6</th>\n",
       "      <td>7763929</td>\n",
       "      <td>2582571.0</td>\n",
       "      <td>181457.0</td>\n",
       "      <td>0.070262</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.0</th>\n",
       "      <td>8771618</td>\n",
       "      <td>1285052.0</td>\n",
       "      <td>53756.0</td>\n",
       "      <td>0.041832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.4</th>\n",
       "      <td>10578401</td>\n",
       "      <td>1839554.0</td>\n",
       "      <td>97634.0</td>\n",
       "      <td>0.053075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5.6</th>\n",
       "      <td>2183069</td>\n",
       "      <td>188435.0</td>\n",
       "      <td>5273.0</td>\n",
       "      <td>0.027983</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Median_Income  Population  Total_Cases      Risk\n",
       "flights                                                  \n",
       "0.6              22344        76.0          5.0  0.065789\n",
       "0.8             783895    927920.0      88342.0  0.095204\n",
       "1.6            7763929   2582571.0     181457.0  0.070262\n",
       "3.0            8771618   1285052.0      53756.0  0.041832\n",
       "3.4           10578401   1839554.0      97634.0  0.053075\n",
       "5.6            2183069    188435.0       5273.0  0.027983"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x18464839e20>"
      ]
     },
     "execution_count": 117,
     "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": [
    "plt.scatter(t.index, t.Calculated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}