Investigate-FBI-Gun-Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Project: Investigate a Dataset - FBI Gun Data\n",
    "\n",
    "## Table of Contents\n",
    "<ul>\n",
    "<li><a href=\"#intro\">Introduction</a></li>\n",
    "<li><a href=\"#wrangling\">Data Wrangling</a></li>\n",
    "<li><a href=\"#eda\">Exploratory Data Analysis</a></li>\n",
    "<li><a href=\"#conclusions\">Conclusions</a></li>\n",
    "</ul>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='intro'></a>\n",
    "## Introduction\n",
    "\n",
    "### Dataset Description \n",
    "\n",
    "The data comes from the FBI's National Instant Criminal Background Check System. \n",
    "The NICS is used by to determine whether a prospective buyer is eligible to buy firearms or explosives. \n",
    "Gun shops call into this system to ensure that each customer does not have a criminal record or isn’t otherwise ineligible to make a purchase. \n",
    "\n",
    "The data has been supplemented with state level data from census.gov.\n",
    "\n",
    "### The NICS data is found in one sheet of an .xlsx file. \n",
    "* It contains the number of firearm checks by\n",
    "    1. month \n",
    "    2. state\n",
    "    3. permit\n",
    "    4. types of guns on many columns\n",
    "### The U.S. census data is found in a .csv file. \n",
    "* It contains several variables at the state level. \n",
    "* Most variables just have one data point per state (2016), but a few have data for more than one year.\n",
    "\n",
    "\n",
    "### Questions for Analysis\n",
    "    1. What is the overall trend of gun purchases?\n",
    "    2. Which states have had the highest growth in gun registrations? \n",
    "    3. What census data is most associated with high gun per capita? \n",
    " \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='wrangling'></a>\n",
    "## Data Wrangling\n",
    "\n",
    "### General Properties\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the packages we will use \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load our data which in two sources as follows \n",
    "df_gun = pd.read_excel('gun_data.xlsx')\n",
    "df_us = pd.read_csv('US_Census_Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12485 entries, 0 to 12484\n",
      "Data columns (total 27 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   month                      12485 non-null  object \n",
      " 1   state                      12485 non-null  object \n",
      " 2   permit                     12461 non-null  float64\n",
      " 3   permit_recheck             1100 non-null   float64\n",
      " 4   handgun                    12465 non-null  float64\n",
      " 5   long_gun                   12466 non-null  float64\n",
      " 6   other                      5500 non-null   float64\n",
      " 7   multiple                   12485 non-null  int64  \n",
      " 8   admin                      12462 non-null  float64\n",
      " 9   prepawn_handgun            10542 non-null  float64\n",
      " 10  prepawn_long_gun           10540 non-null  float64\n",
      " 11  prepawn_other              5115 non-null   float64\n",
      " 12  redemption_handgun         10545 non-null  float64\n",
      " 13  redemption_long_gun        10544 non-null  float64\n",
      " 14  redemption_other           5115 non-null   float64\n",
      " 15  returned_handgun           2200 non-null   float64\n",
      " 16  returned_long_gun          2145 non-null   float64\n",
      " 17  returned_other             1815 non-null   float64\n",
      " 18  rentals_handgun            990 non-null    float64\n",
      " 19  rentals_long_gun           825 non-null    float64\n",
      " 20  private_sale_handgun       2750 non-null   float64\n",
      " 21  private_sale_long_gun      2750 non-null   float64\n",
      " 22  private_sale_other         2750 non-null   float64\n",
      " 23  return_to_seller_handgun   2475 non-null   float64\n",
      " 24  return_to_seller_long_gun  2750 non-null   float64\n",
      " 25  return_to_seller_other     2255 non-null   float64\n",
      " 26  totals                     12485 non-null  int64  \n",
      "dtypes: float64(23), int64(2), object(2)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "# see is ther any null or any not correct dtypes\n",
    "df_gun.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 85 entries, 0 to 84\n",
      "Data columns (total 52 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   Fact            80 non-null     object\n",
      " 1   Fact Note       28 non-null     object\n",
      " 2   Alabama         65 non-null     object\n",
      " 3   Alaska          65 non-null     object\n",
      " 4   Arizona         65 non-null     object\n",
      " 5   Arkansas        65 non-null     object\n",
      " 6   California      65 non-null     object\n",
      " 7   Colorado        65 non-null     object\n",
      " 8   Connecticut     65 non-null     object\n",
      " 9   Delaware        65 non-null     object\n",
      " 10  Florida         65 non-null     object\n",
      " 11  Georgia         65 non-null     object\n",
      " 12  Hawaii          65 non-null     object\n",
      " 13  Idaho           65 non-null     object\n",
      " 14  Illinois        65 non-null     object\n",
      " 15  Indiana         65 non-null     object\n",
      " 16  Iowa            65 non-null     object\n",
      " 17  Kansas          65 non-null     object\n",
      " 18  Kentucky        65 non-null     object\n",
      " 19  Louisiana       65 non-null     object\n",
      " 20  Maine           65 non-null     object\n",
      " 21  Maryland        65 non-null     object\n",
      " 22  Massachusetts   65 non-null     object\n",
      " 23  Michigan        65 non-null     object\n",
      " 24  Minnesota       65 non-null     object\n",
      " 25  Mississippi     65 non-null     object\n",
      " 26  Missouri        65 non-null     object\n",
      " 27  Montana         65 non-null     object\n",
      " 28  Nebraska        65 non-null     object\n",
      " 29  Nevada          65 non-null     object\n",
      " 30  New Hampshire   65 non-null     object\n",
      " 31  New Jersey      65 non-null     object\n",
      " 32  New Mexico      65 non-null     object\n",
      " 33  New York        65 non-null     object\n",
      " 34  North Carolina  65 non-null     object\n",
      " 35  North Dakota    65 non-null     object\n",
      " 36  Ohio            65 non-null     object\n",
      " 37  Oklahoma        65 non-null     object\n",
      " 38  Oregon          65 non-null     object\n",
      " 39  Pennsylvania    65 non-null     object\n",
      " 40  Rhode Island    65 non-null     object\n",
      " 41  South Carolina  65 non-null     object\n",
      " 42  South Dakota    65 non-null     object\n",
      " 43  Tennessee       65 non-null     object\n",
      " 44  Texas           65 non-null     object\n",
      " 45  Utah            65 non-null     object\n",
      " 46  Vermont         65 non-null     object\n",
      " 47  Virginia        65 non-null     object\n",
      " 48  Washington      65 non-null     object\n",
      " 49  West Virginia   65 non-null     object\n",
      " 50  Wisconsin       65 non-null     object\n",
      " 51  Wyoming         65 non-null     object\n",
      "dtypes: object(52)\n",
      "memory usage: 34.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_us.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Data Cleaning\n",
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# we can see some of missing values in both our data \n",
    "# but most importantly alots of columns that we didn't need \n",
    "\n",
    "# cleaninng process \n",
    "# will i will start with US_data \n",
    "# make it in amanageable forme then exrtract the columns that i need \n",
    "#  make the same steps for df_gun too \n",
    "# lets start \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th>Fact</th>\n",
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       "      <td>Persons 65 years and over, percent,  July 1, 2...</td>\n",
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       "      <th>Fact Note</th>\n",
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       "      <th>Alabama</th>\n",
       "      <td>4,863,300</td>\n",
       "      <td>4,780,131</td>\n",
       "      <td>1.70%</td>\n",
       "      <td>4,779,736</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>22.60%</td>\n",
       "      <td>23.70%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>741,894</td>\n",
       "      <td>710,249</td>\n",
       "      <td>4.50%</td>\n",
       "      <td>710,231</td>\n",
       "      <td>7.30%</td>\n",
       "      <td>7.60%</td>\n",
       "      <td>25.20%</td>\n",
       "      <td>26.40%</td>\n",
       "      <td>10.40%</td>\n",
       "      <td>7.70%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>6,931,071</td>\n",
       "      <td>6,392,301</td>\n",
       "      <td>8.40%</td>\n",
       "      <td>6,392,017</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>23.50%</td>\n",
       "      <td>25.50%</td>\n",
       "      <td>16.90%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>2,988,248</td>\n",
       "      <td>2,916,025</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2,915,918</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>23.60%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>16.30%</td>\n",
       "      <td>14.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>California</th>\n",
       "      <td>39,250,017</td>\n",
       "      <td>37,254,522</td>\n",
       "      <td>5.40%</td>\n",
       "      <td>37,253,956</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>23.20%</td>\n",
       "      <td>25.00%</td>\n",
       "      <td>13.60%</td>\n",
       "      <td>11.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>Colorado</th>\n",
       "      <td>5,540,545</td>\n",
       "      <td>5,029,324</td>\n",
       "      <td>10.20%</td>\n",
       "      <td>5,029,196</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>22.80%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>13.40%</td>\n",
       "      <td>10.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>3,576,452</td>\n",
       "      <td>3,574,114</td>\n",
       "      <td>0.10%</td>\n",
       "      <td>3,574,097</td>\n",
       "      <td>5.20%</td>\n",
       "      <td>5.70%</td>\n",
       "      <td>21.10%</td>\n",
       "      <td>22.90%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>14.20%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>952,065</td>\n",
       "      <td>897,936</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>897,934</td>\n",
       "      <td>5.80%</td>\n",
       "      <td>6.20%</td>\n",
       "      <td>21.50%</td>\n",
       "      <td>22.90%</td>\n",
       "      <td>17.50%</td>\n",
       "      <td>14.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>20,612,439</td>\n",
       "      <td>18,804,592</td>\n",
       "      <td>9.60%</td>\n",
       "      <td>18,801,310</td>\n",
       "      <td>5.50%</td>\n",
       "      <td>5.70%</td>\n",
       "      <td>20.10%</td>\n",
       "      <td>21.30%</td>\n",
       "      <td>19.90%</td>\n",
       "      <td>17.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>10,310,371</td>\n",
       "      <td>9,688,680</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>9,687,653</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>25.70%</td>\n",
       "      <td>13.10%</td>\n",
       "      <td>10.70%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>1,428,557</td>\n",
       "      <td>1,360,301</td>\n",
       "      <td>5.00%</td>\n",
       "      <td>1,360,301</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>21.60%</td>\n",
       "      <td>22.30%</td>\n",
       "      <td>17.10%</td>\n",
       "      <td>14.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>1,683,140</td>\n",
       "      <td>1,567,650</td>\n",
       "      <td>7.40%</td>\n",
       "      <td>1,567,582</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>7.80%</td>\n",
       "      <td>26.00%</td>\n",
       "      <td>27.40%</td>\n",
       "      <td>15.10%</td>\n",
       "      <td>12.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12,801,539</td>\n",
       "      <td>12,831,574</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>12,830,632</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>22.90%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>14.60%</td>\n",
       "      <td>12.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>6,633,053</td>\n",
       "      <td>6,484,136</td>\n",
       "      <td>2.30%</td>\n",
       "      <td>6,483,802</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.70%</td>\n",
       "      <td>23.80%</td>\n",
       "      <td>24.80%</td>\n",
       "      <td>14.90%</td>\n",
       "      <td>13.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>3,134,693</td>\n",
       "      <td>3,046,869</td>\n",
       "      <td>2.90%</td>\n",
       "      <td>3,046,355</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.60%</td>\n",
       "      <td>23.30%</td>\n",
       "      <td>23.90%</td>\n",
       "      <td>16.40%</td>\n",
       "      <td>14.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>2,907,289</td>\n",
       "      <td>2,853,129</td>\n",
       "      <td>1.90%</td>\n",
       "      <td>2,853,118</td>\n",
       "      <td>6.70%</td>\n",
       "      <td>7.20%</td>\n",
       "      <td>24.60%</td>\n",
       "      <td>25.50%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>13.20%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>4,436,974</td>\n",
       "      <td>4,339,344</td>\n",
       "      <td>2.20%</td>\n",
       "      <td>4,339,367</td>\n",
       "      <td>6.20%</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>22.80%</td>\n",
       "      <td>23.60%</td>\n",
       "      <td>15.60%</td>\n",
       "      <td>13.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>4,681,666</td>\n",
       "      <td>4,533,479</td>\n",
       "      <td>3.30%</td>\n",
       "      <td>4,533,372</td>\n",
       "      <td>6.60%</td>\n",
       "      <td>6.90%</td>\n",
       "      <td>23.80%</td>\n",
       "      <td>24.70%</td>\n",
       "      <td>14.40%</td>\n",
       "      <td>12.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>1,331,479</td>\n",
       "      <td>1,328,364</td>\n",
       "      <td>0.20%</td>\n",
       "      <td>1,328,361</td>\n",
       "      <td>4.90%</td>\n",
       "      <td>5.20%</td>\n",
       "      <td>19.10%</td>\n",
       "      <td>20.70%</td>\n",
       "      <td>19.40%</td>\n",
       "      <td>15.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>6,016,447</td>\n",
       "      <td>5,773,786</td>\n",
       "      <td>4.20%</td>\n",
       "      <td>5,773,552</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>22.40%</td>\n",
       "      <td>23.40%</td>\n",
       "      <td>14.60%</td>\n",
       "      <td>12.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>6,811,779</td>\n",
       "      <td>6,547,813</td>\n",
       "      <td>4.00%</td>\n",
       "      <td>6,547,629</td>\n",
       "      <td>5.30%</td>\n",
       "      <td>5.60%</td>\n",
       "      <td>20.20%</td>\n",
       "      <td>21.70%</td>\n",
       "      <td>15.80%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>9,928,300</td>\n",
       "      <td>9,884,129</td>\n",
       "      <td>0.40%</td>\n",
       "      <td>9,883,640</td>\n",
       "      <td>5.80%</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>22.10%</td>\n",
       "      <td>23.70%</td>\n",
       "      <td>16.20%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>5,519,952</td>\n",
       "      <td>5,303,924</td>\n",
       "      <td>4.10%</td>\n",
       "      <td>5,303,925</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.70%</td>\n",
       "      <td>23.30%</td>\n",
       "      <td>24.20%</td>\n",
       "      <td>15.10%</td>\n",
       "      <td>12.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>2,988,726</td>\n",
       "      <td>2,968,103</td>\n",
       "      <td>0.70%</td>\n",
       "      <td>2,967,297</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>24.10%</td>\n",
       "      <td>25.50%</td>\n",
       "      <td>15.10%</td>\n",
       "      <td>12.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>6,093,000</td>\n",
       "      <td>5,988,928</td>\n",
       "      <td>1.70%</td>\n",
       "      <td>5,988,927</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>22.80%</td>\n",
       "      <td>23.80%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>14.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>1,042,520</td>\n",
       "      <td>989,414</td>\n",
       "      <td>5.40%</td>\n",
       "      <td>989,415</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>21.80%</td>\n",
       "      <td>22.60%</td>\n",
       "      <td>17.70%</td>\n",
       "      <td>14.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>1,907,116</td>\n",
       "      <td>1,826,334</td>\n",
       "      <td>4.40%</td>\n",
       "      <td>1,826,341</td>\n",
       "      <td>7.00%</td>\n",
       "      <td>7.20%</td>\n",
       "      <td>24.80%</td>\n",
       "      <td>25.10%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>13.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>2,940,058</td>\n",
       "      <td>2,700,691</td>\n",
       "      <td>8.90%</td>\n",
       "      <td>2,700,551</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>6.90%</td>\n",
       "      <td>23.00%</td>\n",
       "      <td>24.60%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>12.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>1,334,795</td>\n",
       "      <td>1,316,461</td>\n",
       "      <td>1.40%</td>\n",
       "      <td>1,316,470</td>\n",
       "      <td>4.80%</td>\n",
       "      <td>5.30%</td>\n",
       "      <td>19.50%</td>\n",
       "      <td>21.80%</td>\n",
       "      <td>17.00%</td>\n",
       "      <td>13.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>8,944,469</td>\n",
       "      <td>8,791,953</td>\n",
       "      <td>1.70%</td>\n",
       "      <td>8,791,894</td>\n",
       "      <td>5.80%</td>\n",
       "      <td>6.20%</td>\n",
       "      <td>22.20%</td>\n",
       "      <td>23.50%</td>\n",
       "      <td>15.30%</td>\n",
       "      <td>13.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>2081015</td>\n",
       "      <td>2059198</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2059179</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.132</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19745289</td>\n",
       "      <td>19378110</td>\n",
       "      <td>0.019</td>\n",
       "      <td>19378102</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.135</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>10146788</td>\n",
       "      <td>9535688</td>\n",
       "      <td>0.064</td>\n",
       "      <td>9535483</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.129</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>757952</td>\n",
       "      <td>672591</td>\n",
       "      <td>0.127</td>\n",
       "      <td>672591</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.145</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>11614373</td>\n",
       "      <td>11536727</td>\n",
       "      <td>0.007</td>\n",
       "      <td>11536504</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.141</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>3923561</td>\n",
       "      <td>3751615</td>\n",
       "      <td>0.046</td>\n",
       "      <td>3751351</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.135</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>4093465</td>\n",
       "      <td>3831072</td>\n",
       "      <td>0.068</td>\n",
       "      <td>3831074</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.139</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>12784227</td>\n",
       "      <td>12702857</td>\n",
       "      <td>0.006</td>\n",
       "      <td>12702379</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.154</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>1056426</td>\n",
       "      <td>1052940</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1052567</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.144</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>4961119</td>\n",
       "      <td>4625410</td>\n",
       "      <td>0.073</td>\n",
       "      <td>4625364</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.137</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>865454</td>\n",
       "      <td>814195</td>\n",
       "      <td>0.063</td>\n",
       "      <td>814180</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>6651194</td>\n",
       "      <td>6346298</td>\n",
       "      <td>0.048</td>\n",
       "      <td>6346105</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.134</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>27,862,596</td>\n",
       "      <td>25,146,100</td>\n",
       "      <td>10.80%</td>\n",
       "      <td>25,145,561</td>\n",
       "      <td>7.20%</td>\n",
       "      <td>7.70%</td>\n",
       "      <td>26.20%</td>\n",
       "      <td>27.30%</td>\n",
       "      <td>12.00%</td>\n",
       "      <td>10.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>3,051,217</td>\n",
       "      <td>2,763,888</td>\n",
       "      <td>10.40%</td>\n",
       "      <td>2,763,885</td>\n",
       "      <td>8.30%</td>\n",
       "      <td>9.50%</td>\n",
       "      <td>30.20%</td>\n",
       "      <td>31.50%</td>\n",
       "      <td>10.50%</td>\n",
       "      <td>9.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>624,594</td>\n",
       "      <td>625,741</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>625,741</td>\n",
       "      <td>4.90%</td>\n",
       "      <td>5.10%</td>\n",
       "      <td>19.00%</td>\n",
       "      <td>20.70%</td>\n",
       "      <td>18.10%</td>\n",
       "      <td>14.60%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>8,411,808</td>\n",
       "      <td>8,001,041</td>\n",
       "      <td>5.10%</td>\n",
       "      <td>8,001,024</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>22.20%</td>\n",
       "      <td>23.20%</td>\n",
       "      <td>14.60%</td>\n",
       "      <td>12.20%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>7,288,000</td>\n",
       "      <td>6,724,545</td>\n",
       "      <td>8.40%</td>\n",
       "      <td>6,724,540</td>\n",
       "      <td>6.20%</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>22.40%</td>\n",
       "      <td>23.50%</td>\n",
       "      <td>14.80%</td>\n",
       "      <td>12.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>1,831,102</td>\n",
       "      <td>1,853,011</td>\n",
       "      <td>-1.20%</td>\n",
       "      <td>1,852,994</td>\n",
       "      <td>5.50%</td>\n",
       "      <td>5.60%</td>\n",
       "      <td>20.50%</td>\n",
       "      <td>20.90%</td>\n",
       "      <td>18.80%</td>\n",
       "      <td>16.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>5,778,708</td>\n",
       "      <td>5,687,289</td>\n",
       "      <td>1.60%</td>\n",
       "      <td>5,686,986</td>\n",
       "      <td>5.80%</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>22.30%</td>\n",
       "      <td>23.60%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>13.70%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>585,501</td>\n",
       "      <td>563,767</td>\n",
       "      <td>3.90%</td>\n",
       "      <td>563,626</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>23.70%</td>\n",
       "      <td>24.00%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>12.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                          0   \\\n",
       "Fact            Population estimates, July 1, 2016,  (V2016)   \n",
       "Fact Note                                                NaN   \n",
       "Alabama                                            4,863,300   \n",
       "Alaska                                               741,894   \n",
       "Arizona                                            6,931,071   \n",
       "Arkansas                                           2,988,248   \n",
       "California                                        39,250,017   \n",
       "Colorado                                           5,540,545   \n",
       "Connecticut                                        3,576,452   \n",
       "Delaware                                             952,065   \n",
       "Florida                                           20,612,439   \n",
       "Georgia                                           10,310,371   \n",
       "Hawaii                                             1,428,557   \n",
       "Idaho                                              1,683,140   \n",
       "Illinois                                          12,801,539   \n",
       "Indiana                                            6,633,053   \n",
       "Iowa                                               3,134,693   \n",
       "Kansas                                             2,907,289   \n",
       "Kentucky                                           4,436,974   \n",
       "Louisiana                                          4,681,666   \n",
       "Maine                                              1,331,479   \n",
       "Maryland                                           6,016,447   \n",
       "Massachusetts                                      6,811,779   \n",
       "Michigan                                           9,928,300   \n",
       "Minnesota                                          5,519,952   \n",
       "Mississippi                                        2,988,726   \n",
       "Missouri                                           6,093,000   \n",
       "Montana                                            1,042,520   \n",
       "Nebraska                                           1,907,116   \n",
       "Nevada                                             2,940,058   \n",
       "New Hampshire                                      1,334,795   \n",
       "New Jersey                                         8,944,469   \n",
       "New Mexico                                           2081015   \n",
       "New York                                            19745289   \n",
       "North Carolina                                      10146788   \n",
       "North Dakota                                          757952   \n",
       "Ohio                                                11614373   \n",
       "Oklahoma                                             3923561   \n",
       "Oregon                                               4093465   \n",
       "Pennsylvania                                        12784227   \n",
       "Rhode Island                                         1056426   \n",
       "South Carolina                                       4961119   \n",
       "South Dakota                                          865454   \n",
       "Tennessee                                            6651194   \n",
       "Texas                                             27,862,596   \n",
       "Utah                                               3,051,217   \n",
       "Vermont                                              624,594   \n",
       "Virginia                                           8,411,808   \n",
       "Washington                                         7,288,000   \n",
       "West Virginia                                      1,831,102   \n",
       "Wisconsin                                          5,778,708   \n",
       "Wyoming                                              585,501   \n",
       "\n",
       "                                                               1   \\\n",
       "Fact            Population estimates base, April 1, 2010,  (V2...   \n",
       "Fact Note                                                     NaN   \n",
       "Alabama                                                 4,780,131   \n",
       "Alaska                                                    710,249   \n",
       "Arizona                                                 6,392,301   \n",
       "Arkansas                                                2,916,025   \n",
       "California                                             37,254,522   \n",
       "Colorado                                                5,029,324   \n",
       "Connecticut                                             3,574,114   \n",
       "Delaware                                                  897,936   \n",
       "Florida                                                18,804,592   \n",
       "Georgia                                                 9,688,680   \n",
       "Hawaii                                                  1,360,301   \n",
       "Idaho                                                   1,567,650   \n",
       "Illinois                                               12,831,574   \n",
       "Indiana                                                 6,484,136   \n",
       "Iowa                                                    3,046,869   \n",
       "Kansas                                                  2,853,129   \n",
       "Kentucky                                                4,339,344   \n",
       "Louisiana                                               4,533,479   \n",
       "Maine                                                   1,328,364   \n",
       "Maryland                                                5,773,786   \n",
       "Massachusetts                                           6,547,813   \n",
       "Michigan                                                9,884,129   \n",
       "Minnesota                                               5,303,924   \n",
       "Mississippi                                             2,968,103   \n",
       "Missouri                                                5,988,928   \n",
       "Montana                                                   989,414   \n",
       "Nebraska                                                1,826,334   \n",
       "Nevada                                                  2,700,691   \n",
       "New Hampshire                                           1,316,461   \n",
       "New Jersey                                              8,791,953   \n",
       "New Mexico                                                2059198   \n",
       "New York                                                 19378110   \n",
       "North Carolina                                            9535688   \n",
       "North Dakota                                               672591   \n",
       "Ohio                                                     11536727   \n",
       "Oklahoma                                                  3751615   \n",
       "Oregon                                                    3831072   \n",
       "Pennsylvania                                             12702857   \n",
       "Rhode Island                                              1052940   \n",
       "South Carolina                                            4625410   \n",
       "South Dakota                                               814195   \n",
       "Tennessee                                                 6346298   \n",
       "Texas                                                  25,146,100   \n",
       "Utah                                                    2,763,888   \n",
       "Vermont                                                   625,741   \n",
       "Virginia                                                8,001,041   \n",
       "Washington                                              6,724,545   \n",
       "West Virginia                                           1,853,011   \n",
       "Wisconsin                                               5,687,289   \n",
       "Wyoming                                                   563,767   \n",
       "\n",
       "                                                               2   \\\n",
       "Fact            Population, percent change - April 1, 2010 (es...   \n",
       "Fact Note                                                     NaN   \n",
       "Alabama                                                     1.70%   \n",
       "Alaska                                                      4.50%   \n",
       "Arizona                                                     8.40%   \n",
       "Arkansas                                                    2.50%   \n",
       "California                                                  5.40%   \n",
       "Colorado                                                   10.20%   \n",
       "Connecticut                                                 0.10%   \n",
       "Delaware                                                    6.00%   \n",
       "Florida                                                     9.60%   \n",
       "Georgia                                                     6.40%   \n",
       "Hawaii                                                      5.00%   \n",
       "Idaho                                                       7.40%   \n",
       "Illinois                                                   -0.20%   \n",
       "Indiana                                                     2.30%   \n",
       "Iowa                                                        2.90%   \n",
       "Kansas                                                      1.90%   \n",
       "Kentucky                                                    2.20%   \n",
       "Louisiana                                                   3.30%   \n",
       "Maine                                                       0.20%   \n",
       "Maryland                                                    4.20%   \n",
       "Massachusetts                                               4.00%   \n",
       "Michigan                                                    0.40%   \n",
       "Minnesota                                                   4.10%   \n",
       "Mississippi                                                 0.70%   \n",
       "Missouri                                                    1.70%   \n",
       "Montana                                                     5.40%   \n",
       "Nebraska                                                    4.40%   \n",
       "Nevada                                                      8.90%   \n",
       "New Hampshire                                               1.40%   \n",
       "New Jersey                                                  1.70%   \n",
       "New Mexico                                                  0.011   \n",
       "New York                                                    0.019   \n",
       "North Carolina                                              0.064   \n",
       "North Dakota                                                0.127   \n",
       "Ohio                                                        0.007   \n",
       "Oklahoma                                                    0.046   \n",
       "Oregon                                                      0.068   \n",
       "Pennsylvania                                                0.006   \n",
       "Rhode Island                                                0.003   \n",
       "South Carolina                                              0.073   \n",
       "South Dakota                                                0.063   \n",
       "Tennessee                                                   0.048   \n",
       "Texas                                                      10.80%   \n",
       "Utah                                                       10.40%   \n",
       "Vermont                                                    -0.20%   \n",
       "Virginia                                                    5.10%   \n",
       "Washington                                                  8.40%   \n",
       "West Virginia                                              -1.20%   \n",
       "Wisconsin                                                   1.60%   \n",
       "Wyoming                                                     3.90%   \n",
       "\n",
       "                                               3   \\\n",
       "Fact            Population, Census, April 1, 2010   \n",
       "Fact Note                                     NaN   \n",
       "Alabama                                 4,779,736   \n",
       "Alaska                                    710,231   \n",
       "Arizona                                 6,392,017   \n",
       "Arkansas                                2,915,918   \n",
       "California                             37,253,956   \n",
       "Colorado                                5,029,196   \n",
       "Connecticut                             3,574,097   \n",
       "Delaware                                  897,934   \n",
       "Florida                                18,801,310   \n",
       "Georgia                                 9,687,653   \n",
       "Hawaii                                  1,360,301   \n",
       "Idaho                                   1,567,582   \n",
       "Illinois                               12,830,632   \n",
       "Indiana                                 6,483,802   \n",
       "Iowa                                    3,046,355   \n",
       "Kansas                                  2,853,118   \n",
       "Kentucky                                4,339,367   \n",
       "Louisiana                               4,533,372   \n",
       "Maine                                   1,328,361   \n",
       "Maryland                                5,773,552   \n",
       "Massachusetts                           6,547,629   \n",
       "Michigan                                9,883,640   \n",
       "Minnesota                               5,303,925   \n",
       "Mississippi                             2,967,297   \n",
       "Missouri                                5,988,927   \n",
       "Montana                                   989,415   \n",
       "Nebraska                                1,826,341   \n",
       "Nevada                                  2,700,551   \n",
       "New Hampshire                           1,316,470   \n",
       "New Jersey                              8,791,894   \n",
       "New Mexico                                2059179   \n",
       "New York                                 19378102   \n",
       "North Carolina                            9535483   \n",
       "North Dakota                               672591   \n",
       "Ohio                                     11536504   \n",
       "Oklahoma                                  3751351   \n",
       "Oregon                                    3831074   \n",
       "Pennsylvania                             12702379   \n",
       "Rhode Island                              1052567   \n",
       "South Carolina                            4625364   \n",
       "South Dakota                               814180   \n",
       "Tennessee                                 6346105   \n",
       "Texas                                  25,145,561   \n",
       "Utah                                    2,763,885   \n",
       "Vermont                                   625,741   \n",
       "Virginia                                8,001,024   \n",
       "Washington                              6,724,540   \n",
       "West Virginia                           1,852,994   \n",
       "Wisconsin                               5,686,986   \n",
       "Wyoming                                   563,626   \n",
       "\n",
       "                                                               4   \\\n",
       "Fact            Persons under 5 years, percent, July 1, 2016, ...   \n",
       "Fact Note                                                     NaN   \n",
       "Alabama                                                     6.00%   \n",
       "Alaska                                                      7.30%   \n",
       "Arizona                                                     6.30%   \n",
       "Arkansas                                                    6.40%   \n",
       "California                                                  6.30%   \n",
       "Colorado                                                    6.10%   \n",
       "Connecticut                                                 5.20%   \n",
       "Delaware                                                    5.80%   \n",
       "Florida                                                     5.50%   \n",
       "Georgia                                                     6.40%   \n",
       "Hawaii                                                      6.40%   \n",
       "Idaho                                                       6.80%   \n",
       "Illinois                                                    6.00%   \n",
       "Indiana                                                     6.40%   \n",
       "Iowa                                                        6.40%   \n",
       "Kansas                                                      6.70%   \n",
       "Kentucky                                                    6.20%   \n",
       "Louisiana                                                   6.60%   \n",
       "Maine                                                       4.90%   \n",
       "Maryland                                                    6.10%   \n",
       "Massachusetts                                               5.30%   \n",
       "Michigan                                                    5.80%   \n",
       "Minnesota                                                   6.40%   \n",
       "Mississippi                                                 6.30%   \n",
       "Missouri                                                    6.10%   \n",
       "Montana                                                     6.00%   \n",
       "Nebraska                                                    7.00%   \n",
       "Nevada                                                      6.30%   \n",
       "New Hampshire                                               4.80%   \n",
       "New Jersey                                                  5.80%   \n",
       "New Mexico                                                  0.062   \n",
       "New York                                                    0.059   \n",
       "North Carolina                                               0.06   \n",
       "North Dakota                                                0.073   \n",
       "Ohio                                                         0.06   \n",
       "Oklahoma                                                    0.068   \n",
       "Oregon                                                      0.058   \n",
       "Pennsylvania                                                0.056   \n",
       "Rhode Island                                                0.052   \n",
       "South Carolina                                              0.059   \n",
       "South Dakota                                                0.071   \n",
       "Tennessee                                                   0.061   \n",
       "Texas                                                       7.20%   \n",
       "Utah                                                        8.30%   \n",
       "Vermont                                                     4.90%   \n",
       "Virginia                                                    6.10%   \n",
       "Washington                                                  6.20%   \n",
       "West Virginia                                               5.50%   \n",
       "Wisconsin                                                   5.80%   \n",
       "Wyoming                                                     6.50%   \n",
       "\n",
       "                                                           5   \\\n",
       "Fact            Persons under 5 years, percent, April 1, 2010   \n",
       "Fact Note                                                 NaN   \n",
       "Alabama                                                 6.40%   \n",
       "Alaska                                                  7.60%   \n",
       "Arizona                                                 7.10%   \n",
       "Arkansas                                                6.80%   \n",
       "California                                              6.80%   \n",
       "Colorado                                                6.80%   \n",
       "Connecticut                                             5.70%   \n",
       "Delaware                                                6.20%   \n",
       "Florida                                                 5.70%   \n",
       "Georgia                                                 7.10%   \n",
       "Hawaii                                                  6.40%   \n",
       "Idaho                                                   7.80%   \n",
       "Illinois                                                6.50%   \n",
       "Indiana                                                 6.70%   \n",
       "Iowa                                                    6.60%   \n",
       "Kansas                                                  7.20%   \n",
       "Kentucky                                                6.50%   \n",
       "Louisiana                                               6.90%   \n",
       "Maine                                                   5.20%   \n",
       "Maryland                                                6.30%   \n",
       "Massachusetts                                           5.60%   \n",
       "Michigan                                                6.00%   \n",
       "Minnesota                                               6.70%   \n",
       "Mississippi                                             7.10%   \n",
       "Missouri                                                6.50%   \n",
       "Montana                                                 6.30%   \n",
       "Nebraska                                                7.20%   \n",
       "Nevada                                                  6.90%   \n",
       "New Hampshire                                           5.30%   \n",
       "New Jersey                                              6.20%   \n",
       "New Mexico                                               0.07   \n",
       "New York                                                 0.06   \n",
       "North Carolina                                          0.066   \n",
       "North Dakota                                            0.066   \n",
       "Ohio                                                    0.062   \n",
       "Oklahoma                                                 0.07   \n",
       "Oregon                                                  0.062   \n",
       "Pennsylvania                                            0.057   \n",
       "Rhode Island                                            0.055   \n",
       "South Carolina                                          0.065   \n",
       "South Dakota                                            0.073   \n",
       "Tennessee                                               0.064   \n",
       "Texas                                                   7.70%   \n",
       "Utah                                                    9.50%   \n",
       "Vermont                                                 5.10%   \n",
       "Virginia                                                6.40%   \n",
       "Washington                                              6.50%   \n",
       "West Virginia                                           5.60%   \n",
       "Wisconsin                                               6.30%   \n",
       "Wyoming                                                 7.10%   \n",
       "\n",
       "                                                               6   \\\n",
       "Fact            Persons under 18 years, percent, July 1, 2016,...   \n",
       "Fact Note                                                     NaN   \n",
       "Alabama                                                    22.60%   \n",
       "Alaska                                                     25.20%   \n",
       "Arizona                                                    23.50%   \n",
       "Arkansas                                                   23.60%   \n",
       "California                                                 23.20%   \n",
       "Colorado                                                   22.80%   \n",
       "Connecticut                                                21.10%   \n",
       "Delaware                                                   21.50%   \n",
       "Florida                                                    20.10%   \n",
       "Georgia                                                    24.40%   \n",
       "Hawaii                                                     21.60%   \n",
       "Idaho                                                      26.00%   \n",
       "Illinois                                                   22.90%   \n",
       "Indiana                                                    23.80%   \n",
       "Iowa                                                       23.30%   \n",
       "Kansas                                                     24.60%   \n",
       "Kentucky                                                   22.80%   \n",
       "Louisiana                                                  23.80%   \n",
       "Maine                                                      19.10%   \n",
       "Maryland                                                   22.40%   \n",
       "Massachusetts                                              20.20%   \n",
       "Michigan                                                   22.10%   \n",
       "Minnesota                                                  23.30%   \n",
       "Mississippi                                                24.10%   \n",
       "Missouri                                                   22.80%   \n",
       "Montana                                                    21.80%   \n",
       "Nebraska                                                   24.80%   \n",
       "Nevada                                                     23.00%   \n",
       "New Hampshire                                              19.50%   \n",
       "New Jersey                                                 22.20%   \n",
       "New Mexico                                                  0.236   \n",
       "New York                                                    0.212   \n",
       "North Carolina                                              0.227   \n",
       "North Dakota                                                0.233   \n",
       "Ohio                                                        0.225   \n",
       "Oklahoma                                                    0.245   \n",
       "Oregon                                                      0.212   \n",
       "Pennsylvania                                                0.209   \n",
       "Rhode Island                                                0.197   \n",
       "South Carolina                                              0.221   \n",
       "South Dakota                                                0.246   \n",
       "Tennessee                                                   0.226   \n",
       "Texas                                                      26.20%   \n",
       "Utah                                                       30.20%   \n",
       "Vermont                                                    19.00%   \n",
       "Virginia                                                   22.20%   \n",
       "Washington                                                 22.40%   \n",
       "West Virginia                                              20.50%   \n",
       "Wisconsin                                                  22.30%   \n",
       "Wyoming                                                    23.70%   \n",
       "\n",
       "                                                            7   \\\n",
       "Fact            Persons under 18 years, percent, April 1, 2010   \n",
       "Fact Note                                                  NaN   \n",
       "Alabama                                                 23.70%   \n",
       "Alaska                                                  26.40%   \n",
       "Arizona                                                 25.50%   \n",
       "Arkansas                                                24.40%   \n",
       "California                                              25.00%   \n",
       "Colorado                                                24.40%   \n",
       "Connecticut                                             22.90%   \n",
       "Delaware                                                22.90%   \n",
       "Florida                                                 21.30%   \n",
       "Georgia                                                 25.70%   \n",
       "Hawaii                                                  22.30%   \n",
       "Idaho                                                   27.40%   \n",
       "Illinois                                                24.40%   \n",
       "Indiana                                                 24.80%   \n",
       "Iowa                                                    23.90%   \n",
       "Kansas                                                  25.50%   \n",
       "Kentucky                                                23.60%   \n",
       "Louisiana                                               24.70%   \n",
       "Maine                                                   20.70%   \n",
       "Maryland                                                23.40%   \n",
       "Massachusetts                                           21.70%   \n",
       "Michigan                                                23.70%   \n",
       "Minnesota                                               24.20%   \n",
       "Mississippi                                             25.50%   \n",
       "Missouri                                                23.80%   \n",
       "Montana                                                 22.60%   \n",
       "Nebraska                                                25.10%   \n",
       "Nevada                                                  24.60%   \n",
       "New Hampshire                                           21.80%   \n",
       "New Jersey                                              23.50%   \n",
       "New Mexico                                               0.252   \n",
       "New York                                                 0.223   \n",
       "North Carolina                                           0.239   \n",
       "North Dakota                                             0.223   \n",
       "Ohio                                                     0.237   \n",
       "Oklahoma                                                 0.248   \n",
       "Oregon                                                   0.226   \n",
       "Pennsylvania                                              0.22   \n",
       "Rhode Island                                             0.213   \n",
       "South Carolina                                           0.234   \n",
       "South Dakota                                             0.249   \n",
       "Tennessee                                                0.236   \n",
       "Texas                                                   27.30%   \n",
       "Utah                                                    31.50%   \n",
       "Vermont                                                 20.70%   \n",
       "Virginia                                                23.20%   \n",
       "Washington                                              23.50%   \n",
       "West Virginia                                           20.90%   \n",
       "Wisconsin                                               23.60%   \n",
       "Wyoming                                                 24.00%   \n",
       "\n",
       "                                                               8   \\\n",
       "Fact            Persons 65 years and over, percent,  July 1, 2...   \n",
       "Fact Note                                                     NaN   \n",
       "Alabama                                                    16.10%   \n",
       "Alaska                                                     10.40%   \n",
       "Arizona                                                    16.90%   \n",
       "Arkansas                                                   16.30%   \n",
       "California                                                 13.60%   \n",
       "Colorado                                                   13.40%   \n",
       "Connecticut                                                16.10%   \n",
       "Delaware                                                   17.50%   \n",
       "Florida                                                    19.90%   \n",
       "Georgia                                                    13.10%   \n",
       "Hawaii                                                     17.10%   \n",
       "Idaho                                                      15.10%   \n",
       "Illinois                                                   14.60%   \n",
       "Indiana                                                    14.90%   \n",
       "Iowa                                                       16.40%   \n",
       "Kansas                                                     15.00%   \n",
       "Kentucky                                                   15.60%   \n",
       "Louisiana                                                  14.40%   \n",
       "Maine                                                      19.40%   \n",
       "Maryland                                                   14.60%   \n",
       "Massachusetts                                              15.80%   \n",
       "Michigan                                                   16.20%   \n",
       "Minnesota                                                  15.10%   \n",
       "Mississippi                                                15.10%   \n",
       "Missouri                                                   16.10%   \n",
       "Montana                                                    17.70%   \n",
       "Nebraska                                                   15.00%   \n",
       "Nevada                                                     15.00%   \n",
       "New Hampshire                                              17.00%   \n",
       "New Jersey                                                 15.30%   \n",
       "New Mexico                                                  0.165   \n",
       "New York                                                    0.154   \n",
       "North Carolina                                              0.155   \n",
       "North Dakota                                                0.145   \n",
       "Ohio                                                        0.162   \n",
       "Oklahoma                                                     0.15   \n",
       "Oregon                                                      0.168   \n",
       "Pennsylvania                                                0.174   \n",
       "Rhode Island                                                0.165   \n",
       "South Carolina                                              0.167   \n",
       "South Dakota                                                 0.16   \n",
       "Tennessee                                                   0.157   \n",
       "Texas                                                      12.00%   \n",
       "Utah                                                       10.50%   \n",
       "Vermont                                                    18.10%   \n",
       "Virginia                                                   14.60%   \n",
       "Washington                                                 14.80%   \n",
       "West Virginia                                              18.80%   \n",
       "Wisconsin                                                  16.10%   \n",
       "Wyoming                                                    15.00%   \n",
       "\n",
       "                                                               9   ...   75  \\\n",
       "Fact            Persons 65 years and over, percent, April 1, 2010  ...  NaN   \n",
       "Fact Note                                                     NaN  ...  NaN   \n",
       "Alabama                                                    13.80%  ...  NaN   \n",
       "Alaska                                                      7.70%  ...  NaN   \n",
       "Arizona                                                    13.80%  ...  NaN   \n",
       "Arkansas                                                   14.40%  ...  NaN   \n",
       "California                                                 11.40%  ...  NaN   \n",
       "Colorado                                                   10.90%  ...  NaN   \n",
       "Connecticut                                                14.20%  ...  NaN   \n",
       "Delaware                                                   14.40%  ...  NaN   \n",
       "Florida                                                    17.30%  ...  NaN   \n",
       "Georgia                                                    10.70%  ...  NaN   \n",
       "Hawaii                                                     14.30%  ...  NaN   \n",
       "Idaho                                                      12.40%  ...  NaN   \n",
       "Illinois                                                   12.50%  ...  NaN   \n",
       "Indiana                                                    13.00%  ...  NaN   \n",
       "Iowa                                                       14.90%  ...  NaN   \n",
       "Kansas                                                     13.20%  ...  NaN   \n",
       "Kentucky                                                   13.30%  ...  NaN   \n",
       "Louisiana                                                  12.30%  ...  NaN   \n",
       "Maine                                                      15.90%  ...  NaN   \n",
       "Maryland                                                   12.30%  ...  NaN   \n",
       "Massachusetts                                              13.80%  ...  NaN   \n",
       "Michigan                                                   13.80%  ...  NaN   \n",
       "Minnesota                                                  12.90%  ...  NaN   \n",
       "Mississippi                                                12.80%  ...  NaN   \n",
       "Missouri                                                   14.00%  ...  NaN   \n",
       "Montana                                                    14.80%  ...  NaN   \n",
       "Nebraska                                                   13.50%  ...  NaN   \n",
       "Nevada                                                     12.00%  ...  NaN   \n",
       "New Hampshire                                              13.50%  ...  NaN   \n",
       "New Jersey                                                 13.50%  ...  NaN   \n",
       "New Mexico                                                  0.132  ...  NaN   \n",
       "New York                                                    0.135  ...  NaN   \n",
       "North Carolina                                              0.129  ...  NaN   \n",
       "North Dakota                                                0.145  ...  NaN   \n",
       "Ohio                                                        0.141  ...  NaN   \n",
       "Oklahoma                                                    0.135  ...  NaN   \n",
       "Oregon                                                      0.139  ...  NaN   \n",
       "Pennsylvania                                                0.154  ...  NaN   \n",
       "Rhode Island                                                0.144  ...  NaN   \n",
       "South Carolina                                              0.137  ...  NaN   \n",
       "South Dakota                                                0.143  ...  NaN   \n",
       "Tennessee                                                   0.134  ...  NaN   \n",
       "Texas                                                      10.30%  ...  NaN   \n",
       "Utah                                                        9.00%  ...  NaN   \n",
       "Vermont                                                    14.60%  ...  NaN   \n",
       "Virginia                                                   12.20%  ...  NaN   \n",
       "Washington                                                 12.30%  ...  NaN   \n",
       "West Virginia                                              16.00%  ...  NaN   \n",
       "Wisconsin                                                  13.70%  ...  NaN   \n",
       "Wyoming                                                    12.40%  ...  NaN   \n",
       "\n",
       "                         76  \\\n",
       "Fact            Value Flags   \n",
       "Fact Note               NaN   \n",
       "Alabama                 NaN   \n",
       "Alaska                  NaN   \n",
       "Arizona                 NaN   \n",
       "Arkansas                NaN   \n",
       "California              NaN   \n",
       "Colorado                NaN   \n",
       "Connecticut             NaN   \n",
       "Delaware                NaN   \n",
       "Florida                 NaN   \n",
       "Georgia                 NaN   \n",
       "Hawaii                  NaN   \n",
       "Idaho                   NaN   \n",
       "Illinois                NaN   \n",
       "Indiana                 NaN   \n",
       "Iowa                    NaN   \n",
       "Kansas                  NaN   \n",
       "Kentucky                NaN   \n",
       "Louisiana               NaN   \n",
       "Maine                   NaN   \n",
       "Maryland                NaN   \n",
       "Massachusetts           NaN   \n",
       "Michigan                NaN   \n",
       "Minnesota               NaN   \n",
       "Mississippi             NaN   \n",
       "Missouri                NaN   \n",
       "Montana                 NaN   \n",
       "Nebraska                NaN   \n",
       "Nevada                  NaN   \n",
       "New Hampshire           NaN   \n",
       "New Jersey              NaN   \n",
       "New Mexico              NaN   \n",
       "New York                NaN   \n",
       "North Carolina          NaN   \n",
       "North Dakota            NaN   \n",
       "Ohio                    NaN   \n",
       "Oklahoma                NaN   \n",
       "Oregon                  NaN   \n",
       "Pennsylvania            NaN   \n",
       "Rhode Island            NaN   \n",
       "South Carolina          NaN   \n",
       "South Dakota            NaN   \n",
       "Tennessee               NaN   \n",
       "Texas                   NaN   \n",
       "Utah                    NaN   \n",
       "Vermont                 NaN   \n",
       "Virginia                NaN   \n",
       "Washington              NaN   \n",
       "West Virginia           NaN   \n",
       "Wisconsin               NaN   \n",
       "Wyoming                 NaN   \n",
       "\n",
       "                                                               77  \\\n",
       "Fact                                                            -   \n",
       "Fact Note       Either no or too few sample observations were ...   \n",
       "Alabama                                                       NaN   \n",
       "Alaska                                                        NaN   \n",
       "Arizona                                                       NaN   \n",
       "Arkansas                                                      NaN   \n",
       "California                                                    NaN   \n",
       "Colorado                                                      NaN   \n",
       "Connecticut                                                   NaN   \n",
       "Delaware                                                      NaN   \n",
       "Florida                                                       NaN   \n",
       "Georgia                                                       NaN   \n",
       "Hawaii                                                        NaN   \n",
       "Idaho                                                         NaN   \n",
       "Illinois                                                      NaN   \n",
       "Indiana                                                       NaN   \n",
       "Iowa                                                          NaN   \n",
       "Kansas                                                        NaN   \n",
       "Kentucky                                                      NaN   \n",
       "Louisiana                                                     NaN   \n",
       "Maine                                                         NaN   \n",
       "Maryland                                                      NaN   \n",
       "Massachusetts                                                 NaN   \n",
       "Michigan                                                      NaN   \n",
       "Minnesota                                                     NaN   \n",
       "Mississippi                                                   NaN   \n",
       "Missouri                                                      NaN   \n",
       "Montana                                                       NaN   \n",
       "Nebraska                                                      NaN   \n",
       "Nevada                                                        NaN   \n",
       "New Hampshire                                                 NaN   \n",
       "New Jersey                                                    NaN   \n",
       "New Mexico                                                    NaN   \n",
       "New York                                                      NaN   \n",
       "North Carolina                                                NaN   \n",
       "North Dakota                                                  NaN   \n",
       "Ohio                                                          NaN   \n",
       "Oklahoma                                                      NaN   \n",
       "Oregon                                                        NaN   \n",
       "Pennsylvania                                                  NaN   \n",
       "Rhode Island                                                  NaN   \n",
       "South Carolina                                                NaN   \n",
       "South Dakota                                                  NaN   \n",
       "Tennessee                                                     NaN   \n",
       "Texas                                                         NaN   \n",
       "Utah                                                          NaN   \n",
       "Vermont                                                       NaN   \n",
       "Virginia                                                      NaN   \n",
       "Washington                                                    NaN   \n",
       "West Virginia                                                 NaN   \n",
       "Wisconsin                                                     NaN   \n",
       "Wyoming                                                       NaN   \n",
       "\n",
       "                                                               78  \\\n",
       "Fact                                                            D   \n",
       "Fact Note       Suppressed to avoid disclosure of confidential...   \n",
       "Alabama                                                       NaN   \n",
       "Alaska                                                        NaN   \n",
       "Arizona                                                       NaN   \n",
       "Arkansas                                                      NaN   \n",
       "California                                                    NaN   \n",
       "Colorado                                                      NaN   \n",
       "Connecticut                                                   NaN   \n",
       "Delaware                                                      NaN   \n",
       "Florida                                                       NaN   \n",
       "Georgia                                                       NaN   \n",
       "Hawaii                                                        NaN   \n",
       "Idaho                                                         NaN   \n",
       "Illinois                                                      NaN   \n",
       "Indiana                                                       NaN   \n",
       "Iowa                                                          NaN   \n",
       "Kansas                                                        NaN   \n",
       "Kentucky                                                      NaN   \n",
       "Louisiana                                                     NaN   \n",
       "Maine                                                         NaN   \n",
       "Maryland                                                      NaN   \n",
       "Massachusetts                                                 NaN   \n",
       "Michigan                                                      NaN   \n",
       "Minnesota                                                     NaN   \n",
       "Mississippi                                                   NaN   \n",
       "Missouri                                                      NaN   \n",
       "Montana                                                       NaN   \n",
       "Nebraska                                                      NaN   \n",
       "Nevada                                                        NaN   \n",
       "New Hampshire                                                 NaN   \n",
       "New Jersey                                                    NaN   \n",
       "New Mexico                                                    NaN   \n",
       "New York                                                      NaN   \n",
       "North Carolina                                                NaN   \n",
       "North Dakota                                                  NaN   \n",
       "Ohio                                                          NaN   \n",
       "Oklahoma                                                      NaN   \n",
       "Oregon                                                        NaN   \n",
       "Pennsylvania                                                  NaN   \n",
       "Rhode Island                                                  NaN   \n",
       "South Carolina                                                NaN   \n",
       "South Dakota                                                  NaN   \n",
       "Tennessee                                                     NaN   \n",
       "Texas                                                         NaN   \n",
       "Utah                                                          NaN   \n",
       "Vermont                                                       NaN   \n",
       "Virginia                                                      NaN   \n",
       "Washington                                                    NaN   \n",
       "West Virginia                                                 NaN   \n",
       "Wisconsin                                                     NaN   \n",
       "Wyoming                                                       NaN   \n",
       "\n",
       "                                 79                                      80  \\\n",
       "Fact                              F                                      FN   \n",
       "Fact Note       Fewer than 25 firms  Footnote on this item in place of data   \n",
       "Alabama                         NaN                                     NaN   \n",
       "Alaska                          NaN                                     NaN   \n",
       "Arizona                         NaN                                     NaN   \n",
       "Arkansas                        NaN                                     NaN   \n",
       "California                      NaN                                     NaN   \n",
       "Colorado                        NaN                                     NaN   \n",
       "Connecticut                     NaN                                     NaN   \n",
       "Delaware                        NaN                                     NaN   \n",
       "Florida                         NaN                                     NaN   \n",
       "Georgia                         NaN                                     NaN   \n",
       "Hawaii                          NaN                                     NaN   \n",
       "Idaho                           NaN                                     NaN   \n",
       "Illinois                        NaN                                     NaN   \n",
       "Indiana                         NaN                                     NaN   \n",
       "Iowa                            NaN                                     NaN   \n",
       "Kansas                          NaN                                     NaN   \n",
       "Kentucky                        NaN                                     NaN   \n",
       "Louisiana                       NaN                                     NaN   \n",
       "Maine                           NaN                                     NaN   \n",
       "Maryland                        NaN                                     NaN   \n",
       "Massachusetts                   NaN                                     NaN   \n",
       "Michigan                        NaN                                     NaN   \n",
       "Minnesota                       NaN                                     NaN   \n",
       "Mississippi                     NaN                                     NaN   \n",
       "Missouri                        NaN                                     NaN   \n",
       "Montana                         NaN                                     NaN   \n",
       "Nebraska                        NaN                                     NaN   \n",
       "Nevada                          NaN                                     NaN   \n",
       "New Hampshire                   NaN                                     NaN   \n",
       "New Jersey                      NaN                                     NaN   \n",
       "New Mexico                      NaN                                     NaN   \n",
       "New York                        NaN                                     NaN   \n",
       "North Carolina                  NaN                                     NaN   \n",
       "North Dakota                    NaN                                     NaN   \n",
       "Ohio                            NaN                                     NaN   \n",
       "Oklahoma                        NaN                                     NaN   \n",
       "Oregon                          NaN                                     NaN   \n",
       "Pennsylvania                    NaN                                     NaN   \n",
       "Rhode Island                    NaN                                     NaN   \n",
       "South Carolina                  NaN                                     NaN   \n",
       "South Dakota                    NaN                                     NaN   \n",
       "Tennessee                       NaN                                     NaN   \n",
       "Texas                           NaN                                     NaN   \n",
       "Utah                            NaN                                     NaN   \n",
       "Vermont                         NaN                                     NaN   \n",
       "Virginia                        NaN                                     NaN   \n",
       "Washington                      NaN                                     NaN   \n",
       "West Virginia                   NaN                                     NaN   \n",
       "Wisconsin                       NaN                                     NaN   \n",
       "Wyoming                         NaN                                     NaN   \n",
       "\n",
       "                           81  \\\n",
       "Fact                      NaN   \n",
       "Fact Note       Not available   \n",
       "Alabama                   NaN   \n",
       "Alaska                    NaN   \n",
       "Arizona                   NaN   \n",
       "Arkansas                  NaN   \n",
       "California                NaN   \n",
       "Colorado                  NaN   \n",
       "Connecticut               NaN   \n",
       "Delaware                  NaN   \n",
       "Florida                   NaN   \n",
       "Georgia                   NaN   \n",
       "Hawaii                    NaN   \n",
       "Idaho                     NaN   \n",
       "Illinois                  NaN   \n",
       "Indiana                   NaN   \n",
       "Iowa                      NaN   \n",
       "Kansas                    NaN   \n",
       "Kentucky                  NaN   \n",
       "Louisiana                 NaN   \n",
       "Maine                     NaN   \n",
       "Maryland                  NaN   \n",
       "Massachusetts             NaN   \n",
       "Michigan                  NaN   \n",
       "Minnesota                 NaN   \n",
       "Mississippi               NaN   \n",
       "Missouri                  NaN   \n",
       "Montana                   NaN   \n",
       "Nebraska                  NaN   \n",
       "Nevada                    NaN   \n",
       "New Hampshire             NaN   \n",
       "New Jersey                NaN   \n",
       "New Mexico                NaN   \n",
       "New York                  NaN   \n",
       "North Carolina            NaN   \n",
       "North Dakota              NaN   \n",
       "Ohio                      NaN   \n",
       "Oklahoma                  NaN   \n",
       "Oregon                    NaN   \n",
       "Pennsylvania              NaN   \n",
       "Rhode Island              NaN   \n",
       "South Carolina            NaN   \n",
       "South Dakota              NaN   \n",
       "Tennessee                 NaN   \n",
       "Texas                     NaN   \n",
       "Utah                      NaN   \n",
       "Vermont                   NaN   \n",
       "Virginia                  NaN   \n",
       "Washington                NaN   \n",
       "West Virginia             NaN   \n",
       "Wisconsin                 NaN   \n",
       "Wyoming                   NaN   \n",
       "\n",
       "                                                             82  \\\n",
       "Fact                                                          S   \n",
       "Fact Note       Suppressed; does not meet publication standards   \n",
       "Alabama                                                     NaN   \n",
       "Alaska                                                      NaN   \n",
       "Arizona                                                     NaN   \n",
       "Arkansas                                                    NaN   \n",
       "California                                                  NaN   \n",
       "Colorado                                                    NaN   \n",
       "Connecticut                                                 NaN   \n",
       "Delaware                                                    NaN   \n",
       "Florida                                                     NaN   \n",
       "Georgia                                                     NaN   \n",
       "Hawaii                                                      NaN   \n",
       "Idaho                                                       NaN   \n",
       "Illinois                                                    NaN   \n",
       "Indiana                                                     NaN   \n",
       "Iowa                                                        NaN   \n",
       "Kansas                                                      NaN   \n",
       "Kentucky                                                    NaN   \n",
       "Louisiana                                                   NaN   \n",
       "Maine                                                       NaN   \n",
       "Maryland                                                    NaN   \n",
       "Massachusetts                                               NaN   \n",
       "Michigan                                                    NaN   \n",
       "Minnesota                                                   NaN   \n",
       "Mississippi                                                 NaN   \n",
       "Missouri                                                    NaN   \n",
       "Montana                                                     NaN   \n",
       "Nebraska                                                    NaN   \n",
       "Nevada                                                      NaN   \n",
       "New Hampshire                                               NaN   \n",
       "New Jersey                                                  NaN   \n",
       "New Mexico                                                  NaN   \n",
       "New York                                                    NaN   \n",
       "North Carolina                                              NaN   \n",
       "North Dakota                                                NaN   \n",
       "Ohio                                                        NaN   \n",
       "Oklahoma                                                    NaN   \n",
       "Oregon                                                      NaN   \n",
       "Pennsylvania                                                NaN   \n",
       "Rhode Island                                                NaN   \n",
       "South Carolina                                              NaN   \n",
       "South Dakota                                                NaN   \n",
       "Tennessee                                                   NaN   \n",
       "Texas                                                       NaN   \n",
       "Utah                                                        NaN   \n",
       "Vermont                                                     NaN   \n",
       "Virginia                                                    NaN   \n",
       "Washington                                                  NaN   \n",
       "West Virginia                                               NaN   \n",
       "Wisconsin                                                   NaN   \n",
       "Wyoming                                                     NaN   \n",
       "\n",
       "                            83  \\\n",
       "Fact                         X   \n",
       "Fact Note       Not applicable   \n",
       "Alabama                    NaN   \n",
       "Alaska                     NaN   \n",
       "Arizona                    NaN   \n",
       "Arkansas                   NaN   \n",
       "California                 NaN   \n",
       "Colorado                   NaN   \n",
       "Connecticut                NaN   \n",
       "Delaware                   NaN   \n",
       "Florida                    NaN   \n",
       "Georgia                    NaN   \n",
       "Hawaii                     NaN   \n",
       "Idaho                      NaN   \n",
       "Illinois                   NaN   \n",
       "Indiana                    NaN   \n",
       "Iowa                       NaN   \n",
       "Kansas                     NaN   \n",
       "Kentucky                   NaN   \n",
       "Louisiana                  NaN   \n",
       "Maine                      NaN   \n",
       "Maryland                   NaN   \n",
       "Massachusetts              NaN   \n",
       "Michigan                   NaN   \n",
       "Minnesota                  NaN   \n",
       "Mississippi                NaN   \n",
       "Missouri                   NaN   \n",
       "Montana                    NaN   \n",
       "Nebraska                   NaN   \n",
       "Nevada                     NaN   \n",
       "New Hampshire              NaN   \n",
       "New Jersey                 NaN   \n",
       "New Mexico                 NaN   \n",
       "New York                   NaN   \n",
       "North Carolina             NaN   \n",
       "North Dakota               NaN   \n",
       "Ohio                       NaN   \n",
       "Oklahoma                   NaN   \n",
       "Oregon                     NaN   \n",
       "Pennsylvania               NaN   \n",
       "Rhode Island               NaN   \n",
       "South Carolina             NaN   \n",
       "South Dakota               NaN   \n",
       "Tennessee                  NaN   \n",
       "Texas                      NaN   \n",
       "Utah                       NaN   \n",
       "Vermont                    NaN   \n",
       "Virginia                   NaN   \n",
       "Washington                 NaN   \n",
       "West Virginia              NaN   \n",
       "Wisconsin                  NaN   \n",
       "Wyoming                    NaN   \n",
       "\n",
       "                                                               84  \n",
       "Fact                                                            Z  \n",
       "Fact Note       Value greater than zero but less than half uni...  \n",
       "Alabama                                                       NaN  \n",
       "Alaska                                                        NaN  \n",
       "Arizona                                                       NaN  \n",
       "Arkansas                                                      NaN  \n",
       "California                                                    NaN  \n",
       "Colorado                                                      NaN  \n",
       "Connecticut                                                   NaN  \n",
       "Delaware                                                      NaN  \n",
       "Florida                                                       NaN  \n",
       "Georgia                                                       NaN  \n",
       "Hawaii                                                        NaN  \n",
       "Idaho                                                         NaN  \n",
       "Illinois                                                      NaN  \n",
       "Indiana                                                       NaN  \n",
       "Iowa                                                          NaN  \n",
       "Kansas                                                        NaN  \n",
       "Kentucky                                                      NaN  \n",
       "Louisiana                                                     NaN  \n",
       "Maine                                                         NaN  \n",
       "Maryland                                                      NaN  \n",
       "Massachusetts                                                 NaN  \n",
       "Michigan                                                      NaN  \n",
       "Minnesota                                                     NaN  \n",
       "Mississippi                                                   NaN  \n",
       "Missouri                                                      NaN  \n",
       "Montana                                                       NaN  \n",
       "Nebraska                                                      NaN  \n",
       "Nevada                                                        NaN  \n",
       "New Hampshire                                                 NaN  \n",
       "New Jersey                                                    NaN  \n",
       "New Mexico                                                    NaN  \n",
       "New York                                                      NaN  \n",
       "North Carolina                                                NaN  \n",
       "North Dakota                                                  NaN  \n",
       "Ohio                                                          NaN  \n",
       "Oklahoma                                                      NaN  \n",
       "Oregon                                                        NaN  \n",
       "Pennsylvania                                                  NaN  \n",
       "Rhode Island                                                  NaN  \n",
       "South Carolina                                                NaN  \n",
       "South Dakota                                                  NaN  \n",
       "Tennessee                                                     NaN  \n",
       "Texas                                                         NaN  \n",
       "Utah                                                          NaN  \n",
       "Vermont                                                       NaN  \n",
       "Virginia                                                      NaN  \n",
       "Washington                                                    NaN  \n",
       "West Virginia                                                 NaN  \n",
       "Wisconsin                                                     NaN  \n",
       "Wyoming                                                       NaN  \n",
       "\n",
       "[52 rows x 85 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# first lets transpose the data so we can make calculation easy \n",
    "\n",
    "#transpose the df_us dataframe \n",
    "df_ust = df_us.transpose()\n",
    "df_ust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "        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>Population estimates, July 1, 2016,  (V2016)</th>\n",
       "      <th>Population estimates base, April 1, 2010,  (V2016)</th>\n",
       "      <th>Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)</th>\n",
       "      <th>Population, Census, April 1, 2010</th>\n",
       "      <th>Persons under 5 years, percent, July 1, 2016,  (V2016)</th>\n",
       "      <th>Persons under 5 years, percent, April 1, 2010</th>\n",
       "      <th>Persons under 18 years, percent, July 1, 2016,  (V2016)</th>\n",
       "      <th>Persons under 18 years, percent, April 1, 2010</th>\n",
       "      <th>Persons 65 years and over, percent,  July 1, 2016,  (V2016)</th>\n",
       "      <th>Persons 65 years and over, percent, April 1, 2010</th>\n",
       "      <th>...</th>\n",
       "      <th>NaN</th>\n",
       "      <th>Value Flags</th>\n",
       "      <th>-</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>FN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>S</th>\n",
       "      <th>X</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Fact</th>\n",
       "      <td>Population estimates, July 1, 2016,  (V2016)</td>\n",
       "      <td>Population estimates base, April 1, 2010,  (V2...</td>\n",
       "      <td>Population, percent change - April 1, 2010 (es...</td>\n",
       "      <td>Population, Census, April 1, 2010</td>\n",
       "      <td>Persons under 5 years, percent, July 1, 2016, ...</td>\n",
       "      <td>Persons under 5 years, percent, April 1, 2010</td>\n",
       "      <td>Persons under 18 years, percent, July 1, 2016,...</td>\n",
       "      <td>Persons under 18 years, percent, April 1, 2010</td>\n",
       "      <td>Persons 65 years and over, percent,  July 1, 2...</td>\n",
       "      <td>Persons 65 years and over, percent, April 1, 2010</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Value Flags</td>\n",
       "      <td>-</td>\n",
       "      <td>D</td>\n",
       "      <td>F</td>\n",
       "      <td>FN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "      <td>X</td>\n",
       "      <td>Z</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fact Note</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Either no or too few sample observations were ...</td>\n",
       "      <td>Suppressed to avoid disclosure of confidential...</td>\n",
       "      <td>Fewer than 25 firms</td>\n",
       "      <td>Footnote on this item in place of data</td>\n",
       "      <td>Not available</td>\n",
       "      <td>Suppressed; does not meet publication standards</td>\n",
       "      <td>Not applicable</td>\n",
       "      <td>Value greater than zero but less than half uni...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>4,863,300</td>\n",
       "      <td>4,780,131</td>\n",
       "      <td>1.70%</td>\n",
       "      <td>4,779,736</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>22.60%</td>\n",
       "      <td>23.70%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>741,894</td>\n",
       "      <td>710,249</td>\n",
       "      <td>4.50%</td>\n",
       "      <td>710,231</td>\n",
       "      <td>7.30%</td>\n",
       "      <td>7.60%</td>\n",
       "      <td>25.20%</td>\n",
       "      <td>26.40%</td>\n",
       "      <td>10.40%</td>\n",
       "      <td>7.70%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>6,931,071</td>\n",
       "      <td>6,392,301</td>\n",
       "      <td>8.40%</td>\n",
       "      <td>6,392,017</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>23.50%</td>\n",
       "      <td>25.50%</td>\n",
       "      <td>16.90%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>2,988,248</td>\n",
       "      <td>2,916,025</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2,915,918</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>23.60%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>16.30%</td>\n",
       "      <td>14.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>39,250,017</td>\n",
       "      <td>37,254,522</td>\n",
       "      <td>5.40%</td>\n",
       "      <td>37,253,956</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>23.20%</td>\n",
       "      <td>25.00%</td>\n",
       "      <td>13.60%</td>\n",
       "      <td>11.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>5,540,545</td>\n",
       "      <td>5,029,324</td>\n",
       "      <td>10.20%</td>\n",
       "      <td>5,029,196</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>22.80%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>13.40%</td>\n",
       "      <td>10.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>3,576,452</td>\n",
       "      <td>3,574,114</td>\n",
       "      <td>0.10%</td>\n",
       "      <td>3,574,097</td>\n",
       "      <td>5.20%</td>\n",
       "      <td>5.70%</td>\n",
       "      <td>21.10%</td>\n",
       "      <td>22.90%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>14.20%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>952,065</td>\n",
       "      <td>897,936</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>897,934</td>\n",
       "      <td>5.80%</td>\n",
       "      <td>6.20%</td>\n",
       "      <td>21.50%</td>\n",
       "      <td>22.90%</td>\n",
       "      <td>17.50%</td>\n",
       "      <td>14.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>20,612,439</td>\n",
       "      <td>18,804,592</td>\n",
       "      <td>9.60%</td>\n",
       "      <td>18,801,310</td>\n",
       "      <td>5.50%</td>\n",
       "      <td>5.70%</td>\n",
       "      <td>20.10%</td>\n",
       "      <td>21.30%</td>\n",
       "      <td>19.90%</td>\n",
       "      <td>17.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>10,310,371</td>\n",
       "      <td>9,688,680</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>9,687,653</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>25.70%</td>\n",
       "      <td>13.10%</td>\n",
       "      <td>10.70%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>1,428,557</td>\n",
       "      <td>1,360,301</td>\n",
       "      <td>5.00%</td>\n",
       "      <td>1,360,301</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>21.60%</td>\n",
       "      <td>22.30%</td>\n",
       "      <td>17.10%</td>\n",
       "      <td>14.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>1,683,140</td>\n",
       "      <td>1,567,650</td>\n",
       "      <td>7.40%</td>\n",
       "      <td>1,567,582</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>7.80%</td>\n",
       "      <td>26.00%</td>\n",
       "      <td>27.40%</td>\n",
       "      <td>15.10%</td>\n",
       "      <td>12.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>12,801,539</td>\n",
       "      <td>12,831,574</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>12,830,632</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>22.90%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>14.60%</td>\n",
       "      <td>12.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>6,633,053</td>\n",
       "      <td>6,484,136</td>\n",
       "      <td>2.30%</td>\n",
       "      <td>6,483,802</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.70%</td>\n",
       "      <td>23.80%</td>\n",
       "      <td>24.80%</td>\n",
       "      <td>14.90%</td>\n",
       "      <td>13.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>3,134,693</td>\n",
       "      <td>3,046,869</td>\n",
       "      <td>2.90%</td>\n",
       "      <td>3,046,355</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.60%</td>\n",
       "      <td>23.30%</td>\n",
       "      <td>23.90%</td>\n",
       "      <td>16.40%</td>\n",
       "      <td>14.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>2,907,289</td>\n",
       "      <td>2,853,129</td>\n",
       "      <td>1.90%</td>\n",
       "      <td>2,853,118</td>\n",
       "      <td>6.70%</td>\n",
       "      <td>7.20%</td>\n",
       "      <td>24.60%</td>\n",
       "      <td>25.50%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>13.20%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>4,436,974</td>\n",
       "      <td>4,339,344</td>\n",
       "      <td>2.20%</td>\n",
       "      <td>4,339,367</td>\n",
       "      <td>6.20%</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>22.80%</td>\n",
       "      <td>23.60%</td>\n",
       "      <td>15.60%</td>\n",
       "      <td>13.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>4,681,666</td>\n",
       "      <td>4,533,479</td>\n",
       "      <td>3.30%</td>\n",
       "      <td>4,533,372</td>\n",
       "      <td>6.60%</td>\n",
       "      <td>6.90%</td>\n",
       "      <td>23.80%</td>\n",
       "      <td>24.70%</td>\n",
       "      <td>14.40%</td>\n",
       "      <td>12.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>1,331,479</td>\n",
       "      <td>1,328,364</td>\n",
       "      <td>0.20%</td>\n",
       "      <td>1,328,361</td>\n",
       "      <td>4.90%</td>\n",
       "      <td>5.20%</td>\n",
       "      <td>19.10%</td>\n",
       "      <td>20.70%</td>\n",
       "      <td>19.40%</td>\n",
       "      <td>15.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>6,016,447</td>\n",
       "      <td>5,773,786</td>\n",
       "      <td>4.20%</td>\n",
       "      <td>5,773,552</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>22.40%</td>\n",
       "      <td>23.40%</td>\n",
       "      <td>14.60%</td>\n",
       "      <td>12.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>6,811,779</td>\n",
       "      <td>6,547,813</td>\n",
       "      <td>4.00%</td>\n",
       "      <td>6,547,629</td>\n",
       "      <td>5.30%</td>\n",
       "      <td>5.60%</td>\n",
       "      <td>20.20%</td>\n",
       "      <td>21.70%</td>\n",
       "      <td>15.80%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>9,928,300</td>\n",
       "      <td>9,884,129</td>\n",
       "      <td>0.40%</td>\n",
       "      <td>9,883,640</td>\n",
       "      <td>5.80%</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>22.10%</td>\n",
       "      <td>23.70%</td>\n",
       "      <td>16.20%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>5,519,952</td>\n",
       "      <td>5,303,924</td>\n",
       "      <td>4.10%</td>\n",
       "      <td>5,303,925</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.70%</td>\n",
       "      <td>23.30%</td>\n",
       "      <td>24.20%</td>\n",
       "      <td>15.10%</td>\n",
       "      <td>12.90%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>2,988,726</td>\n",
       "      <td>2,968,103</td>\n",
       "      <td>0.70%</td>\n",
       "      <td>2,967,297</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>24.10%</td>\n",
       "      <td>25.50%</td>\n",
       "      <td>15.10%</td>\n",
       "      <td>12.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>6,093,000</td>\n",
       "      <td>5,988,928</td>\n",
       "      <td>1.70%</td>\n",
       "      <td>5,988,927</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>22.80%</td>\n",
       "      <td>23.80%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>14.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>1,042,520</td>\n",
       "      <td>989,414</td>\n",
       "      <td>5.40%</td>\n",
       "      <td>989,415</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>21.80%</td>\n",
       "      <td>22.60%</td>\n",
       "      <td>17.70%</td>\n",
       "      <td>14.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>1,907,116</td>\n",
       "      <td>1,826,334</td>\n",
       "      <td>4.40%</td>\n",
       "      <td>1,826,341</td>\n",
       "      <td>7.00%</td>\n",
       "      <td>7.20%</td>\n",
       "      <td>24.80%</td>\n",
       "      <td>25.10%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>13.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>2,940,058</td>\n",
       "      <td>2,700,691</td>\n",
       "      <td>8.90%</td>\n",
       "      <td>2,700,551</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>6.90%</td>\n",
       "      <td>23.00%</td>\n",
       "      <td>24.60%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>12.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>1,334,795</td>\n",
       "      <td>1,316,461</td>\n",
       "      <td>1.40%</td>\n",
       "      <td>1,316,470</td>\n",
       "      <td>4.80%</td>\n",
       "      <td>5.30%</td>\n",
       "      <td>19.50%</td>\n",
       "      <td>21.80%</td>\n",
       "      <td>17.00%</td>\n",
       "      <td>13.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>8,944,469</td>\n",
       "      <td>8,791,953</td>\n",
       "      <td>1.70%</td>\n",
       "      <td>8,791,894</td>\n",
       "      <td>5.80%</td>\n",
       "      <td>6.20%</td>\n",
       "      <td>22.20%</td>\n",
       "      <td>23.50%</td>\n",
       "      <td>15.30%</td>\n",
       "      <td>13.50%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>2081015</td>\n",
       "      <td>2059198</td>\n",
       "      <td>0.011</td>\n",
       "      <td>2059179</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.252</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.132</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>19745289</td>\n",
       "      <td>19378110</td>\n",
       "      <td>0.019</td>\n",
       "      <td>19378102</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.135</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>10146788</td>\n",
       "      <td>9535688</td>\n",
       "      <td>0.064</td>\n",
       "      <td>9535483</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.227</td>\n",
       "      <td>0.239</td>\n",
       "      <td>0.155</td>\n",
       "      <td>0.129</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>757952</td>\n",
       "      <td>672591</td>\n",
       "      <td>0.127</td>\n",
       "      <td>672591</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.066</td>\n",
       "      <td>0.233</td>\n",
       "      <td>0.223</td>\n",
       "      <td>0.145</td>\n",
       "      <td>0.145</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>11614373</td>\n",
       "      <td>11536727</td>\n",
       "      <td>0.007</td>\n",
       "      <td>11536504</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.237</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.141</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>3923561</td>\n",
       "      <td>3751615</td>\n",
       "      <td>0.046</td>\n",
       "      <td>3751351</td>\n",
       "      <td>0.068</td>\n",
       "      <td>0.07</td>\n",
       "      <td>0.245</td>\n",
       "      <td>0.248</td>\n",
       "      <td>0.15</td>\n",
       "      <td>0.135</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>4093465</td>\n",
       "      <td>3831072</td>\n",
       "      <td>0.068</td>\n",
       "      <td>3831074</td>\n",
       "      <td>0.058</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.212</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.139</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>12784227</td>\n",
       "      <td>12702857</td>\n",
       "      <td>0.006</td>\n",
       "      <td>12702379</td>\n",
       "      <td>0.056</td>\n",
       "      <td>0.057</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.154</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>1056426</td>\n",
       "      <td>1052940</td>\n",
       "      <td>0.003</td>\n",
       "      <td>1052567</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.213</td>\n",
       "      <td>0.165</td>\n",
       "      <td>0.144</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>4961119</td>\n",
       "      <td>4625410</td>\n",
       "      <td>0.073</td>\n",
       "      <td>4625364</td>\n",
       "      <td>0.059</td>\n",
       "      <td>0.065</td>\n",
       "      <td>0.221</td>\n",
       "      <td>0.234</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.137</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>865454</td>\n",
       "      <td>814195</td>\n",
       "      <td>0.063</td>\n",
       "      <td>814180</td>\n",
       "      <td>0.071</td>\n",
       "      <td>0.073</td>\n",
       "      <td>0.246</td>\n",
       "      <td>0.249</td>\n",
       "      <td>0.16</td>\n",
       "      <td>0.143</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>6651194</td>\n",
       "      <td>6346298</td>\n",
       "      <td>0.048</td>\n",
       "      <td>6346105</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.064</td>\n",
       "      <td>0.226</td>\n",
       "      <td>0.236</td>\n",
       "      <td>0.157</td>\n",
       "      <td>0.134</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>27,862,596</td>\n",
       "      <td>25,146,100</td>\n",
       "      <td>10.80%</td>\n",
       "      <td>25,145,561</td>\n",
       "      <td>7.20%</td>\n",
       "      <td>7.70%</td>\n",
       "      <td>26.20%</td>\n",
       "      <td>27.30%</td>\n",
       "      <td>12.00%</td>\n",
       "      <td>10.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>3,051,217</td>\n",
       "      <td>2,763,888</td>\n",
       "      <td>10.40%</td>\n",
       "      <td>2,763,885</td>\n",
       "      <td>8.30%</td>\n",
       "      <td>9.50%</td>\n",
       "      <td>30.20%</td>\n",
       "      <td>31.50%</td>\n",
       "      <td>10.50%</td>\n",
       "      <td>9.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>624,594</td>\n",
       "      <td>625,741</td>\n",
       "      <td>-0.20%</td>\n",
       "      <td>625,741</td>\n",
       "      <td>4.90%</td>\n",
       "      <td>5.10%</td>\n",
       "      <td>19.00%</td>\n",
       "      <td>20.70%</td>\n",
       "      <td>18.10%</td>\n",
       "      <td>14.60%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>8,411,808</td>\n",
       "      <td>8,001,041</td>\n",
       "      <td>5.10%</td>\n",
       "      <td>8,001,024</td>\n",
       "      <td>6.10%</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>22.20%</td>\n",
       "      <td>23.20%</td>\n",
       "      <td>14.60%</td>\n",
       "      <td>12.20%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>7,288,000</td>\n",
       "      <td>6,724,545</td>\n",
       "      <td>8.40%</td>\n",
       "      <td>6,724,540</td>\n",
       "      <td>6.20%</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>22.40%</td>\n",
       "      <td>23.50%</td>\n",
       "      <td>14.80%</td>\n",
       "      <td>12.30%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>1,831,102</td>\n",
       "      <td>1,853,011</td>\n",
       "      <td>-1.20%</td>\n",
       "      <td>1,852,994</td>\n",
       "      <td>5.50%</td>\n",
       "      <td>5.60%</td>\n",
       "      <td>20.50%</td>\n",
       "      <td>20.90%</td>\n",
       "      <td>18.80%</td>\n",
       "      <td>16.00%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>5,778,708</td>\n",
       "      <td>5,687,289</td>\n",
       "      <td>1.60%</td>\n",
       "      <td>5,686,986</td>\n",
       "      <td>5.80%</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>22.30%</td>\n",
       "      <td>23.60%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>13.70%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>585,501</td>\n",
       "      <td>563,767</td>\n",
       "      <td>3.90%</td>\n",
       "      <td>563,626</td>\n",
       "      <td>6.50%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>23.70%</td>\n",
       "      <td>24.00%</td>\n",
       "      <td>15.00%</td>\n",
       "      <td>12.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>52 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Population estimates, July 1, 2016,  (V2016)  \\\n",
       "Fact            Population estimates, July 1, 2016,  (V2016)   \n",
       "Fact Note                                                NaN   \n",
       "Alabama                                            4,863,300   \n",
       "Alaska                                               741,894   \n",
       "Arizona                                            6,931,071   \n",
       "Arkansas                                           2,988,248   \n",
       "California                                        39,250,017   \n",
       "Colorado                                           5,540,545   \n",
       "Connecticut                                        3,576,452   \n",
       "Delaware                                             952,065   \n",
       "Florida                                           20,612,439   \n",
       "Georgia                                           10,310,371   \n",
       "Hawaii                                             1,428,557   \n",
       "Idaho                                              1,683,140   \n",
       "Illinois                                          12,801,539   \n",
       "Indiana                                            6,633,053   \n",
       "Iowa                                               3,134,693   \n",
       "Kansas                                             2,907,289   \n",
       "Kentucky                                           4,436,974   \n",
       "Louisiana                                          4,681,666   \n",
       "Maine                                              1,331,479   \n",
       "Maryland                                           6,016,447   \n",
       "Massachusetts                                      6,811,779   \n",
       "Michigan                                           9,928,300   \n",
       "Minnesota                                          5,519,952   \n",
       "Mississippi                                        2,988,726   \n",
       "Missouri                                           6,093,000   \n",
       "Montana                                            1,042,520   \n",
       "Nebraska                                           1,907,116   \n",
       "Nevada                                             2,940,058   \n",
       "New Hampshire                                      1,334,795   \n",
       "New Jersey                                         8,944,469   \n",
       "New Mexico                                           2081015   \n",
       "New York                                            19745289   \n",
       "North Carolina                                      10146788   \n",
       "North Dakota                                          757952   \n",
       "Ohio                                                11614373   \n",
       "Oklahoma                                             3923561   \n",
       "Oregon                                               4093465   \n",
       "Pennsylvania                                        12784227   \n",
       "Rhode Island                                         1056426   \n",
       "South Carolina                                       4961119   \n",
       "South Dakota                                          865454   \n",
       "Tennessee                                            6651194   \n",
       "Texas                                             27,862,596   \n",
       "Utah                                               3,051,217   \n",
       "Vermont                                              624,594   \n",
       "Virginia                                           8,411,808   \n",
       "Washington                                         7,288,000   \n",
       "West Virginia                                      1,831,102   \n",
       "Wisconsin                                          5,778,708   \n",
       "Wyoming                                              585,501   \n",
       "\n",
       "               Population estimates base, April 1, 2010,  (V2016)  \\\n",
       "Fact            Population estimates base, April 1, 2010,  (V2...   \n",
       "Fact Note                                                     NaN   \n",
       "Alabama                                                 4,780,131   \n",
       "Alaska                                                    710,249   \n",
       "Arizona                                                 6,392,301   \n",
       "Arkansas                                                2,916,025   \n",
       "California                                             37,254,522   \n",
       "Colorado                                                5,029,324   \n",
       "Connecticut                                             3,574,114   \n",
       "Delaware                                                  897,936   \n",
       "Florida                                                18,804,592   \n",
       "Georgia                                                 9,688,680   \n",
       "Hawaii                                                  1,360,301   \n",
       "Idaho                                                   1,567,650   \n",
       "Illinois                                               12,831,574   \n",
       "Indiana                                                 6,484,136   \n",
       "Iowa                                                    3,046,869   \n",
       "Kansas                                                  2,853,129   \n",
       "Kentucky                                                4,339,344   \n",
       "Louisiana                                               4,533,479   \n",
       "Maine                                                   1,328,364   \n",
       "Maryland                                                5,773,786   \n",
       "Massachusetts                                           6,547,813   \n",
       "Michigan                                                9,884,129   \n",
       "Minnesota                                               5,303,924   \n",
       "Mississippi                                             2,968,103   \n",
       "Missouri                                                5,988,928   \n",
       "Montana                                                   989,414   \n",
       "Nebraska                                                1,826,334   \n",
       "Nevada                                                  2,700,691   \n",
       "New Hampshire                                           1,316,461   \n",
       "New Jersey                                              8,791,953   \n",
       "New Mexico                                                2059198   \n",
       "New York                                                 19378110   \n",
       "North Carolina                                            9535688   \n",
       "North Dakota                                               672591   \n",
       "Ohio                                                     11536727   \n",
       "Oklahoma                                                  3751615   \n",
       "Oregon                                                    3831072   \n",
       "Pennsylvania                                             12702857   \n",
       "Rhode Island                                              1052940   \n",
       "South Carolina                                            4625410   \n",
       "South Dakota                                               814195   \n",
       "Tennessee                                                 6346298   \n",
       "Texas                                                  25,146,100   \n",
       "Utah                                                    2,763,888   \n",
       "Vermont                                                   625,741   \n",
       "Virginia                                                8,001,041   \n",
       "Washington                                              6,724,545   \n",
       "West Virginia                                           1,853,011   \n",
       "Wisconsin                                               5,687,289   \n",
       "Wyoming                                                   563,767   \n",
       "\n",
       "               Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)  \\\n",
       "Fact            Population, percent change - April 1, 2010 (es...                                      \n",
       "Fact Note                                                     NaN                                      \n",
       "Alabama                                                     1.70%                                      \n",
       "Alaska                                                      4.50%                                      \n",
       "Arizona                                                     8.40%                                      \n",
       "Arkansas                                                    2.50%                                      \n",
       "California                                                  5.40%                                      \n",
       "Colorado                                                   10.20%                                      \n",
       "Connecticut                                                 0.10%                                      \n",
       "Delaware                                                    6.00%                                      \n",
       "Florida                                                     9.60%                                      \n",
       "Georgia                                                     6.40%                                      \n",
       "Hawaii                                                      5.00%                                      \n",
       "Idaho                                                       7.40%                                      \n",
       "Illinois                                                   -0.20%                                      \n",
       "Indiana                                                     2.30%                                      \n",
       "Iowa                                                        2.90%                                      \n",
       "Kansas                                                      1.90%                                      \n",
       "Kentucky                                                    2.20%                                      \n",
       "Louisiana                                                   3.30%                                      \n",
       "Maine                                                       0.20%                                      \n",
       "Maryland                                                    4.20%                                      \n",
       "Massachusetts                                               4.00%                                      \n",
       "Michigan                                                    0.40%                                      \n",
       "Minnesota                                                   4.10%                                      \n",
       "Mississippi                                                 0.70%                                      \n",
       "Missouri                                                    1.70%                                      \n",
       "Montana                                                     5.40%                                      \n",
       "Nebraska                                                    4.40%                                      \n",
       "Nevada                                                      8.90%                                      \n",
       "New Hampshire                                               1.40%                                      \n",
       "New Jersey                                                  1.70%                                      \n",
       "New Mexico                                                  0.011                                      \n",
       "New York                                                    0.019                                      \n",
       "North Carolina                                              0.064                                      \n",
       "North Dakota                                                0.127                                      \n",
       "Ohio                                                        0.007                                      \n",
       "Oklahoma                                                    0.046                                      \n",
       "Oregon                                                      0.068                                      \n",
       "Pennsylvania                                                0.006                                      \n",
       "Rhode Island                                                0.003                                      \n",
       "South Carolina                                              0.073                                      \n",
       "South Dakota                                                0.063                                      \n",
       "Tennessee                                                   0.048                                      \n",
       "Texas                                                      10.80%                                      \n",
       "Utah                                                       10.40%                                      \n",
       "Vermont                                                    -0.20%                                      \n",
       "Virginia                                                    5.10%                                      \n",
       "Washington                                                  8.40%                                      \n",
       "West Virginia                                              -1.20%                                      \n",
       "Wisconsin                                                   1.60%                                      \n",
       "Wyoming                                                     3.90%                                      \n",
       "\n",
       "                Population, Census, April 1, 2010  \\\n",
       "Fact            Population, Census, April 1, 2010   \n",
       "Fact Note                                     NaN   \n",
       "Alabama                                 4,779,736   \n",
       "Alaska                                    710,231   \n",
       "Arizona                                 6,392,017   \n",
       "Arkansas                                2,915,918   \n",
       "California                             37,253,956   \n",
       "Colorado                                5,029,196   \n",
       "Connecticut                             3,574,097   \n",
       "Delaware                                  897,934   \n",
       "Florida                                18,801,310   \n",
       "Georgia                                 9,687,653   \n",
       "Hawaii                                  1,360,301   \n",
       "Idaho                                   1,567,582   \n",
       "Illinois                               12,830,632   \n",
       "Indiana                                 6,483,802   \n",
       "Iowa                                    3,046,355   \n",
       "Kansas                                  2,853,118   \n",
       "Kentucky                                4,339,367   \n",
       "Louisiana                               4,533,372   \n",
       "Maine                                   1,328,361   \n",
       "Maryland                                5,773,552   \n",
       "Massachusetts                           6,547,629   \n",
       "Michigan                                9,883,640   \n",
       "Minnesota                               5,303,925   \n",
       "Mississippi                             2,967,297   \n",
       "Missouri                                5,988,927   \n",
       "Montana                                   989,415   \n",
       "Nebraska                                1,826,341   \n",
       "Nevada                                  2,700,551   \n",
       "New Hampshire                           1,316,470   \n",
       "New Jersey                              8,791,894   \n",
       "New Mexico                                2059179   \n",
       "New York                                 19378102   \n",
       "North Carolina                            9535483   \n",
       "North Dakota                               672591   \n",
       "Ohio                                     11536504   \n",
       "Oklahoma                                  3751351   \n",
       "Oregon                                    3831074   \n",
       "Pennsylvania                             12702379   \n",
       "Rhode Island                              1052567   \n",
       "South Carolina                            4625364   \n",
       "South Dakota                               814180   \n",
       "Tennessee                                 6346105   \n",
       "Texas                                  25,145,561   \n",
       "Utah                                    2,763,885   \n",
       "Vermont                                   625,741   \n",
       "Virginia                                8,001,024   \n",
       "Washington                              6,724,540   \n",
       "West Virginia                           1,852,994   \n",
       "Wisconsin                               5,686,986   \n",
       "Wyoming                                   563,626   \n",
       "\n",
       "               Persons under 5 years, percent, July 1, 2016,  (V2016)  \\\n",
       "Fact            Persons under 5 years, percent, July 1, 2016, ...       \n",
       "Fact Note                                                     NaN       \n",
       "Alabama                                                     6.00%       \n",
       "Alaska                                                      7.30%       \n",
       "Arizona                                                     6.30%       \n",
       "Arkansas                                                    6.40%       \n",
       "California                                                  6.30%       \n",
       "Colorado                                                    6.10%       \n",
       "Connecticut                                                 5.20%       \n",
       "Delaware                                                    5.80%       \n",
       "Florida                                                     5.50%       \n",
       "Georgia                                                     6.40%       \n",
       "Hawaii                                                      6.40%       \n",
       "Idaho                                                       6.80%       \n",
       "Illinois                                                    6.00%       \n",
       "Indiana                                                     6.40%       \n",
       "Iowa                                                        6.40%       \n",
       "Kansas                                                      6.70%       \n",
       "Kentucky                                                    6.20%       \n",
       "Louisiana                                                   6.60%       \n",
       "Maine                                                       4.90%       \n",
       "Maryland                                                    6.10%       \n",
       "Massachusetts                                               5.30%       \n",
       "Michigan                                                    5.80%       \n",
       "Minnesota                                                   6.40%       \n",
       "Mississippi                                                 6.30%       \n",
       "Missouri                                                    6.10%       \n",
       "Montana                                                     6.00%       \n",
       "Nebraska                                                    7.00%       \n",
       "Nevada                                                      6.30%       \n",
       "New Hampshire                                               4.80%       \n",
       "New Jersey                                                  5.80%       \n",
       "New Mexico                                                  0.062       \n",
       "New York                                                    0.059       \n",
       "North Carolina                                               0.06       \n",
       "North Dakota                                                0.073       \n",
       "Ohio                                                         0.06       \n",
       "Oklahoma                                                    0.068       \n",
       "Oregon                                                      0.058       \n",
       "Pennsylvania                                                0.056       \n",
       "Rhode Island                                                0.052       \n",
       "South Carolina                                              0.059       \n",
       "South Dakota                                                0.071       \n",
       "Tennessee                                                   0.061       \n",
       "Texas                                                       7.20%       \n",
       "Utah                                                        8.30%       \n",
       "Vermont                                                     4.90%       \n",
       "Virginia                                                    6.10%       \n",
       "Washington                                                  6.20%       \n",
       "West Virginia                                               5.50%       \n",
       "Wisconsin                                                   5.80%       \n",
       "Wyoming                                                     6.50%       \n",
       "\n",
       "                Persons under 5 years, percent, April 1, 2010  \\\n",
       "Fact            Persons under 5 years, percent, April 1, 2010   \n",
       "Fact Note                                                 NaN   \n",
       "Alabama                                                 6.40%   \n",
       "Alaska                                                  7.60%   \n",
       "Arizona                                                 7.10%   \n",
       "Arkansas                                                6.80%   \n",
       "California                                              6.80%   \n",
       "Colorado                                                6.80%   \n",
       "Connecticut                                             5.70%   \n",
       "Delaware                                                6.20%   \n",
       "Florida                                                 5.70%   \n",
       "Georgia                                                 7.10%   \n",
       "Hawaii                                                  6.40%   \n",
       "Idaho                                                   7.80%   \n",
       "Illinois                                                6.50%   \n",
       "Indiana                                                 6.70%   \n",
       "Iowa                                                    6.60%   \n",
       "Kansas                                                  7.20%   \n",
       "Kentucky                                                6.50%   \n",
       "Louisiana                                               6.90%   \n",
       "Maine                                                   5.20%   \n",
       "Maryland                                                6.30%   \n",
       "Massachusetts                                           5.60%   \n",
       "Michigan                                                6.00%   \n",
       "Minnesota                                               6.70%   \n",
       "Mississippi                                             7.10%   \n",
       "Missouri                                                6.50%   \n",
       "Montana                                                 6.30%   \n",
       "Nebraska                                                7.20%   \n",
       "Nevada                                                  6.90%   \n",
       "New Hampshire                                           5.30%   \n",
       "New Jersey                                              6.20%   \n",
       "New Mexico                                               0.07   \n",
       "New York                                                 0.06   \n",
       "North Carolina                                          0.066   \n",
       "North Dakota                                            0.066   \n",
       "Ohio                                                    0.062   \n",
       "Oklahoma                                                 0.07   \n",
       "Oregon                                                  0.062   \n",
       "Pennsylvania                                            0.057   \n",
       "Rhode Island                                            0.055   \n",
       "South Carolina                                          0.065   \n",
       "South Dakota                                            0.073   \n",
       "Tennessee                                               0.064   \n",
       "Texas                                                   7.70%   \n",
       "Utah                                                    9.50%   \n",
       "Vermont                                                 5.10%   \n",
       "Virginia                                                6.40%   \n",
       "Washington                                              6.50%   \n",
       "West Virginia                                           5.60%   \n",
       "Wisconsin                                               6.30%   \n",
       "Wyoming                                                 7.10%   \n",
       "\n",
       "               Persons under 18 years, percent, July 1, 2016,  (V2016)  \\\n",
       "Fact            Persons under 18 years, percent, July 1, 2016,...        \n",
       "Fact Note                                                     NaN        \n",
       "Alabama                                                    22.60%        \n",
       "Alaska                                                     25.20%        \n",
       "Arizona                                                    23.50%        \n",
       "Arkansas                                                   23.60%        \n",
       "California                                                 23.20%        \n",
       "Colorado                                                   22.80%        \n",
       "Connecticut                                                21.10%        \n",
       "Delaware                                                   21.50%        \n",
       "Florida                                                    20.10%        \n",
       "Georgia                                                    24.40%        \n",
       "Hawaii                                                     21.60%        \n",
       "Idaho                                                      26.00%        \n",
       "Illinois                                                   22.90%        \n",
       "Indiana                                                    23.80%        \n",
       "Iowa                                                       23.30%        \n",
       "Kansas                                                     24.60%        \n",
       "Kentucky                                                   22.80%        \n",
       "Louisiana                                                  23.80%        \n",
       "Maine                                                      19.10%        \n",
       "Maryland                                                   22.40%        \n",
       "Massachusetts                                              20.20%        \n",
       "Michigan                                                   22.10%        \n",
       "Minnesota                                                  23.30%        \n",
       "Mississippi                                                24.10%        \n",
       "Missouri                                                   22.80%        \n",
       "Montana                                                    21.80%        \n",
       "Nebraska                                                   24.80%        \n",
       "Nevada                                                     23.00%        \n",
       "New Hampshire                                              19.50%        \n",
       "New Jersey                                                 22.20%        \n",
       "New Mexico                                                  0.236        \n",
       "New York                                                    0.212        \n",
       "North Carolina                                              0.227        \n",
       "North Dakota                                                0.233        \n",
       "Ohio                                                        0.225        \n",
       "Oklahoma                                                    0.245        \n",
       "Oregon                                                      0.212        \n",
       "Pennsylvania                                                0.209        \n",
       "Rhode Island                                                0.197        \n",
       "South Carolina                                              0.221        \n",
       "South Dakota                                                0.246        \n",
       "Tennessee                                                   0.226        \n",
       "Texas                                                      26.20%        \n",
       "Utah                                                       30.20%        \n",
       "Vermont                                                    19.00%        \n",
       "Virginia                                                   22.20%        \n",
       "Washington                                                 22.40%        \n",
       "West Virginia                                              20.50%        \n",
       "Wisconsin                                                  22.30%        \n",
       "Wyoming                                                    23.70%        \n",
       "\n",
       "                Persons under 18 years, percent, April 1, 2010  \\\n",
       "Fact            Persons under 18 years, percent, April 1, 2010   \n",
       "Fact Note                                                  NaN   \n",
       "Alabama                                                 23.70%   \n",
       "Alaska                                                  26.40%   \n",
       "Arizona                                                 25.50%   \n",
       "Arkansas                                                24.40%   \n",
       "California                                              25.00%   \n",
       "Colorado                                                24.40%   \n",
       "Connecticut                                             22.90%   \n",
       "Delaware                                                22.90%   \n",
       "Florida                                                 21.30%   \n",
       "Georgia                                                 25.70%   \n",
       "Hawaii                                                  22.30%   \n",
       "Idaho                                                   27.40%   \n",
       "Illinois                                                24.40%   \n",
       "Indiana                                                 24.80%   \n",
       "Iowa                                                    23.90%   \n",
       "Kansas                                                  25.50%   \n",
       "Kentucky                                                23.60%   \n",
       "Louisiana                                               24.70%   \n",
       "Maine                                                   20.70%   \n",
       "Maryland                                                23.40%   \n",
       "Massachusetts                                           21.70%   \n",
       "Michigan                                                23.70%   \n",
       "Minnesota                                               24.20%   \n",
       "Mississippi                                             25.50%   \n",
       "Missouri                                                23.80%   \n",
       "Montana                                                 22.60%   \n",
       "Nebraska                                                25.10%   \n",
       "Nevada                                                  24.60%   \n",
       "New Hampshire                                           21.80%   \n",
       "New Jersey                                              23.50%   \n",
       "New Mexico                                               0.252   \n",
       "New York                                                 0.223   \n",
       "North Carolina                                           0.239   \n",
       "North Dakota                                             0.223   \n",
       "Ohio                                                     0.237   \n",
       "Oklahoma                                                 0.248   \n",
       "Oregon                                                   0.226   \n",
       "Pennsylvania                                              0.22   \n",
       "Rhode Island                                             0.213   \n",
       "South Carolina                                           0.234   \n",
       "South Dakota                                             0.249   \n",
       "Tennessee                                                0.236   \n",
       "Texas                                                   27.30%   \n",
       "Utah                                                    31.50%   \n",
       "Vermont                                                 20.70%   \n",
       "Virginia                                                23.20%   \n",
       "Washington                                              23.50%   \n",
       "West Virginia                                           20.90%   \n",
       "Wisconsin                                               23.60%   \n",
       "Wyoming                                                 24.00%   \n",
       "\n",
       "               Persons 65 years and over, percent,  July 1, 2016,  (V2016)  \\\n",
       "Fact            Persons 65 years and over, percent,  July 1, 2...            \n",
       "Fact Note                                                     NaN            \n",
       "Alabama                                                    16.10%            \n",
       "Alaska                                                     10.40%            \n",
       "Arizona                                                    16.90%            \n",
       "Arkansas                                                   16.30%            \n",
       "California                                                 13.60%            \n",
       "Colorado                                                   13.40%            \n",
       "Connecticut                                                16.10%            \n",
       "Delaware                                                   17.50%            \n",
       "Florida                                                    19.90%            \n",
       "Georgia                                                    13.10%            \n",
       "Hawaii                                                     17.10%            \n",
       "Idaho                                                      15.10%            \n",
       "Illinois                                                   14.60%            \n",
       "Indiana                                                    14.90%            \n",
       "Iowa                                                       16.40%            \n",
       "Kansas                                                     15.00%            \n",
       "Kentucky                                                   15.60%            \n",
       "Louisiana                                                  14.40%            \n",
       "Maine                                                      19.40%            \n",
       "Maryland                                                   14.60%            \n",
       "Massachusetts                                              15.80%            \n",
       "Michigan                                                   16.20%            \n",
       "Minnesota                                                  15.10%            \n",
       "Mississippi                                                15.10%            \n",
       "Missouri                                                   16.10%            \n",
       "Montana                                                    17.70%            \n",
       "Nebraska                                                   15.00%            \n",
       "Nevada                                                     15.00%            \n",
       "New Hampshire                                              17.00%            \n",
       "New Jersey                                                 15.30%            \n",
       "New Mexico                                                  0.165            \n",
       "New York                                                    0.154            \n",
       "North Carolina                                              0.155            \n",
       "North Dakota                                                0.145            \n",
       "Ohio                                                        0.162            \n",
       "Oklahoma                                                     0.15            \n",
       "Oregon                                                      0.168            \n",
       "Pennsylvania                                                0.174            \n",
       "Rhode Island                                                0.165            \n",
       "South Carolina                                              0.167            \n",
       "South Dakota                                                 0.16            \n",
       "Tennessee                                                   0.157            \n",
       "Texas                                                      12.00%            \n",
       "Utah                                                       10.50%            \n",
       "Vermont                                                    18.10%            \n",
       "Virginia                                                   14.60%            \n",
       "Washington                                                 14.80%            \n",
       "West Virginia                                              18.80%            \n",
       "Wisconsin                                                  16.10%            \n",
       "Wyoming                                                    15.00%            \n",
       "\n",
       "                Persons 65 years and over, percent, April 1, 2010  ...  NaN  \\\n",
       "Fact            Persons 65 years and over, percent, April 1, 2010  ...  NaN   \n",
       "Fact Note                                                     NaN  ...  NaN   \n",
       "Alabama                                                    13.80%  ...  NaN   \n",
       "Alaska                                                      7.70%  ...  NaN   \n",
       "Arizona                                                    13.80%  ...  NaN   \n",
       "Arkansas                                                   14.40%  ...  NaN   \n",
       "California                                                 11.40%  ...  NaN   \n",
       "Colorado                                                   10.90%  ...  NaN   \n",
       "Connecticut                                                14.20%  ...  NaN   \n",
       "Delaware                                                   14.40%  ...  NaN   \n",
       "Florida                                                    17.30%  ...  NaN   \n",
       "Georgia                                                    10.70%  ...  NaN   \n",
       "Hawaii                                                     14.30%  ...  NaN   \n",
       "Idaho                                                      12.40%  ...  NaN   \n",
       "Illinois                                                   12.50%  ...  NaN   \n",
       "Indiana                                                    13.00%  ...  NaN   \n",
       "Iowa                                                       14.90%  ...  NaN   \n",
       "Kansas                                                     13.20%  ...  NaN   \n",
       "Kentucky                                                   13.30%  ...  NaN   \n",
       "Louisiana                                                  12.30%  ...  NaN   \n",
       "Maine                                                      15.90%  ...  NaN   \n",
       "Maryland                                                   12.30%  ...  NaN   \n",
       "Massachusetts                                              13.80%  ...  NaN   \n",
       "Michigan                                                   13.80%  ...  NaN   \n",
       "Minnesota                                                  12.90%  ...  NaN   \n",
       "Mississippi                                                12.80%  ...  NaN   \n",
       "Missouri                                                   14.00%  ...  NaN   \n",
       "Montana                                                    14.80%  ...  NaN   \n",
       "Nebraska                                                   13.50%  ...  NaN   \n",
       "Nevada                                                     12.00%  ...  NaN   \n",
       "New Hampshire                                              13.50%  ...  NaN   \n",
       "New Jersey                                                 13.50%  ...  NaN   \n",
       "New Mexico                                                  0.132  ...  NaN   \n",
       "New York                                                    0.135  ...  NaN   \n",
       "North Carolina                                              0.129  ...  NaN   \n",
       "North Dakota                                                0.145  ...  NaN   \n",
       "Ohio                                                        0.141  ...  NaN   \n",
       "Oklahoma                                                    0.135  ...  NaN   \n",
       "Oregon                                                      0.139  ...  NaN   \n",
       "Pennsylvania                                                0.154  ...  NaN   \n",
       "Rhode Island                                                0.144  ...  NaN   \n",
       "South Carolina                                              0.137  ...  NaN   \n",
       "South Dakota                                                0.143  ...  NaN   \n",
       "Tennessee                                                   0.134  ...  NaN   \n",
       "Texas                                                      10.30%  ...  NaN   \n",
       "Utah                                                        9.00%  ...  NaN   \n",
       "Vermont                                                    14.60%  ...  NaN   \n",
       "Virginia                                                   12.20%  ...  NaN   \n",
       "Washington                                                 12.30%  ...  NaN   \n",
       "West Virginia                                              16.00%  ...  NaN   \n",
       "Wisconsin                                                  13.70%  ...  NaN   \n",
       "Wyoming                                                    12.40%  ...  NaN   \n",
       "\n",
       "                Value Flags  \\\n",
       "Fact            Value Flags   \n",
       "Fact Note               NaN   \n",
       "Alabama                 NaN   \n",
       "Alaska                  NaN   \n",
       "Arizona                 NaN   \n",
       "Arkansas                NaN   \n",
       "California              NaN   \n",
       "Colorado                NaN   \n",
       "Connecticut             NaN   \n",
       "Delaware                NaN   \n",
       "Florida                 NaN   \n",
       "Georgia                 NaN   \n",
       "Hawaii                  NaN   \n",
       "Idaho                   NaN   \n",
       "Illinois                NaN   \n",
       "Indiana                 NaN   \n",
       "Iowa                    NaN   \n",
       "Kansas                  NaN   \n",
       "Kentucky                NaN   \n",
       "Louisiana               NaN   \n",
       "Maine                   NaN   \n",
       "Maryland                NaN   \n",
       "Massachusetts           NaN   \n",
       "Michigan                NaN   \n",
       "Minnesota               NaN   \n",
       "Mississippi             NaN   \n",
       "Missouri                NaN   \n",
       "Montana                 NaN   \n",
       "Nebraska                NaN   \n",
       "Nevada                  NaN   \n",
       "New Hampshire           NaN   \n",
       "New Jersey              NaN   \n",
       "New Mexico              NaN   \n",
       "New York                NaN   \n",
       "North Carolina          NaN   \n",
       "North Dakota            NaN   \n",
       "Ohio                    NaN   \n",
       "Oklahoma                NaN   \n",
       "Oregon                  NaN   \n",
       "Pennsylvania            NaN   \n",
       "Rhode Island            NaN   \n",
       "South Carolina          NaN   \n",
       "South Dakota            NaN   \n",
       "Tennessee               NaN   \n",
       "Texas                   NaN   \n",
       "Utah                    NaN   \n",
       "Vermont                 NaN   \n",
       "Virginia                NaN   \n",
       "Washington              NaN   \n",
       "West Virginia           NaN   \n",
       "Wisconsin               NaN   \n",
       "Wyoming                 NaN   \n",
       "\n",
       "                                                                -  \\\n",
       "Fact                                                            -   \n",
       "Fact Note       Either no or too few sample observations were ...   \n",
       "Alabama                                                       NaN   \n",
       "Alaska                                                        NaN   \n",
       "Arizona                                                       NaN   \n",
       "Arkansas                                                      NaN   \n",
       "California                                                    NaN   \n",
       "Colorado                                                      NaN   \n",
       "Connecticut                                                   NaN   \n",
       "Delaware                                                      NaN   \n",
       "Florida                                                       NaN   \n",
       "Georgia                                                       NaN   \n",
       "Hawaii                                                        NaN   \n",
       "Idaho                                                         NaN   \n",
       "Illinois                                                      NaN   \n",
       "Indiana                                                       NaN   \n",
       "Iowa                                                          NaN   \n",
       "Kansas                                                        NaN   \n",
       "Kentucky                                                      NaN   \n",
       "Louisiana                                                     NaN   \n",
       "Maine                                                         NaN   \n",
       "Maryland                                                      NaN   \n",
       "Massachusetts                                                 NaN   \n",
       "Michigan                                                      NaN   \n",
       "Minnesota                                                     NaN   \n",
       "Mississippi                                                   NaN   \n",
       "Missouri                                                      NaN   \n",
       "Montana                                                       NaN   \n",
       "Nebraska                                                      NaN   \n",
       "Nevada                                                        NaN   \n",
       "New Hampshire                                                 NaN   \n",
       "New Jersey                                                    NaN   \n",
       "New Mexico                                                    NaN   \n",
       "New York                                                      NaN   \n",
       "North Carolina                                                NaN   \n",
       "North Dakota                                                  NaN   \n",
       "Ohio                                                          NaN   \n",
       "Oklahoma                                                      NaN   \n",
       "Oregon                                                        NaN   \n",
       "Pennsylvania                                                  NaN   \n",
       "Rhode Island                                                  NaN   \n",
       "South Carolina                                                NaN   \n",
       "South Dakota                                                  NaN   \n",
       "Tennessee                                                     NaN   \n",
       "Texas                                                         NaN   \n",
       "Utah                                                          NaN   \n",
       "Vermont                                                       NaN   \n",
       "Virginia                                                      NaN   \n",
       "Washington                                                    NaN   \n",
       "West Virginia                                                 NaN   \n",
       "Wisconsin                                                     NaN   \n",
       "Wyoming                                                       NaN   \n",
       "\n",
       "                                                                D  \\\n",
       "Fact                                                            D   \n",
       "Fact Note       Suppressed to avoid disclosure of confidential...   \n",
       "Alabama                                                       NaN   \n",
       "Alaska                                                        NaN   \n",
       "Arizona                                                       NaN   \n",
       "Arkansas                                                      NaN   \n",
       "California                                                    NaN   \n",
       "Colorado                                                      NaN   \n",
       "Connecticut                                                   NaN   \n",
       "Delaware                                                      NaN   \n",
       "Florida                                                       NaN   \n",
       "Georgia                                                       NaN   \n",
       "Hawaii                                                        NaN   \n",
       "Idaho                                                         NaN   \n",
       "Illinois                                                      NaN   \n",
       "Indiana                                                       NaN   \n",
       "Iowa                                                          NaN   \n",
       "Kansas                                                        NaN   \n",
       "Kentucky                                                      NaN   \n",
       "Louisiana                                                     NaN   \n",
       "Maine                                                         NaN   \n",
       "Maryland                                                      NaN   \n",
       "Massachusetts                                                 NaN   \n",
       "Michigan                                                      NaN   \n",
       "Minnesota                                                     NaN   \n",
       "Mississippi                                                   NaN   \n",
       "Missouri                                                      NaN   \n",
       "Montana                                                       NaN   \n",
       "Nebraska                                                      NaN   \n",
       "Nevada                                                        NaN   \n",
       "New Hampshire                                                 NaN   \n",
       "New Jersey                                                    NaN   \n",
       "New Mexico                                                    NaN   \n",
       "New York                                                      NaN   \n",
       "North Carolina                                                NaN   \n",
       "North Dakota                                                  NaN   \n",
       "Ohio                                                          NaN   \n",
       "Oklahoma                                                      NaN   \n",
       "Oregon                                                        NaN   \n",
       "Pennsylvania                                                  NaN   \n",
       "Rhode Island                                                  NaN   \n",
       "South Carolina                                                NaN   \n",
       "South Dakota                                                  NaN   \n",
       "Tennessee                                                     NaN   \n",
       "Texas                                                         NaN   \n",
       "Utah                                                          NaN   \n",
       "Vermont                                                       NaN   \n",
       "Virginia                                                      NaN   \n",
       "Washington                                                    NaN   \n",
       "West Virginia                                                 NaN   \n",
       "Wisconsin                                                     NaN   \n",
       "Wyoming                                                       NaN   \n",
       "\n",
       "                                  F                                      FN  \\\n",
       "Fact                              F                                      FN   \n",
       "Fact Note       Fewer than 25 firms  Footnote on this item in place of data   \n",
       "Alabama                         NaN                                     NaN   \n",
       "Alaska                          NaN                                     NaN   \n",
       "Arizona                         NaN                                     NaN   \n",
       "Arkansas                        NaN                                     NaN   \n",
       "California                      NaN                                     NaN   \n",
       "Colorado                        NaN                                     NaN   \n",
       "Connecticut                     NaN                                     NaN   \n",
       "Delaware                        NaN                                     NaN   \n",
       "Florida                         NaN                                     NaN   \n",
       "Georgia                         NaN                                     NaN   \n",
       "Hawaii                          NaN                                     NaN   \n",
       "Idaho                           NaN                                     NaN   \n",
       "Illinois                        NaN                                     NaN   \n",
       "Indiana                         NaN                                     NaN   \n",
       "Iowa                            NaN                                     NaN   \n",
       "Kansas                          NaN                                     NaN   \n",
       "Kentucky                        NaN                                     NaN   \n",
       "Louisiana                       NaN                                     NaN   \n",
       "Maine                           NaN                                     NaN   \n",
       "Maryland                        NaN                                     NaN   \n",
       "Massachusetts                   NaN                                     NaN   \n",
       "Michigan                        NaN                                     NaN   \n",
       "Minnesota                       NaN                                     NaN   \n",
       "Mississippi                     NaN                                     NaN   \n",
       "Missouri                        NaN                                     NaN   \n",
       "Montana                         NaN                                     NaN   \n",
       "Nebraska                        NaN                                     NaN   \n",
       "Nevada                          NaN                                     NaN   \n",
       "New Hampshire                   NaN                                     NaN   \n",
       "New Jersey                      NaN                                     NaN   \n",
       "New Mexico                      NaN                                     NaN   \n",
       "New York                        NaN                                     NaN   \n",
       "North Carolina                  NaN                                     NaN   \n",
       "North Dakota                    NaN                                     NaN   \n",
       "Ohio                            NaN                                     NaN   \n",
       "Oklahoma                        NaN                                     NaN   \n",
       "Oregon                          NaN                                     NaN   \n",
       "Pennsylvania                    NaN                                     NaN   \n",
       "Rhode Island                    NaN                                     NaN   \n",
       "South Carolina                  NaN                                     NaN   \n",
       "South Dakota                    NaN                                     NaN   \n",
       "Tennessee                       NaN                                     NaN   \n",
       "Texas                           NaN                                     NaN   \n",
       "Utah                            NaN                                     NaN   \n",
       "Vermont                         NaN                                     NaN   \n",
       "Virginia                        NaN                                     NaN   \n",
       "Washington                      NaN                                     NaN   \n",
       "West Virginia                   NaN                                     NaN   \n",
       "Wisconsin                       NaN                                     NaN   \n",
       "Wyoming                         NaN                                     NaN   \n",
       "\n",
       "                          NaN  \\\n",
       "Fact                      NaN   \n",
       "Fact Note       Not available   \n",
       "Alabama                   NaN   \n",
       "Alaska                    NaN   \n",
       "Arizona                   NaN   \n",
       "Arkansas                  NaN   \n",
       "California                NaN   \n",
       "Colorado                  NaN   \n",
       "Connecticut               NaN   \n",
       "Delaware                  NaN   \n",
       "Florida                   NaN   \n",
       "Georgia                   NaN   \n",
       "Hawaii                    NaN   \n",
       "Idaho                     NaN   \n",
       "Illinois                  NaN   \n",
       "Indiana                   NaN   \n",
       "Iowa                      NaN   \n",
       "Kansas                    NaN   \n",
       "Kentucky                  NaN   \n",
       "Louisiana                 NaN   \n",
       "Maine                     NaN   \n",
       "Maryland                  NaN   \n",
       "Massachusetts             NaN   \n",
       "Michigan                  NaN   \n",
       "Minnesota                 NaN   \n",
       "Mississippi               NaN   \n",
       "Missouri                  NaN   \n",
       "Montana                   NaN   \n",
       "Nebraska                  NaN   \n",
       "Nevada                    NaN   \n",
       "New Hampshire             NaN   \n",
       "New Jersey                NaN   \n",
       "New Mexico                NaN   \n",
       "New York                  NaN   \n",
       "North Carolina            NaN   \n",
       "North Dakota              NaN   \n",
       "Ohio                      NaN   \n",
       "Oklahoma                  NaN   \n",
       "Oregon                    NaN   \n",
       "Pennsylvania              NaN   \n",
       "Rhode Island              NaN   \n",
       "South Carolina            NaN   \n",
       "South Dakota              NaN   \n",
       "Tennessee                 NaN   \n",
       "Texas                     NaN   \n",
       "Utah                      NaN   \n",
       "Vermont                   NaN   \n",
       "Virginia                  NaN   \n",
       "Washington                NaN   \n",
       "West Virginia             NaN   \n",
       "Wisconsin                 NaN   \n",
       "Wyoming                   NaN   \n",
       "\n",
       "                                                              S  \\\n",
       "Fact                                                          S   \n",
       "Fact Note       Suppressed; does not meet publication standards   \n",
       "Alabama                                                     NaN   \n",
       "Alaska                                                      NaN   \n",
       "Arizona                                                     NaN   \n",
       "Arkansas                                                    NaN   \n",
       "California                                                  NaN   \n",
       "Colorado                                                    NaN   \n",
       "Connecticut                                                 NaN   \n",
       "Delaware                                                    NaN   \n",
       "Florida                                                     NaN   \n",
       "Georgia                                                     NaN   \n",
       "Hawaii                                                      NaN   \n",
       "Idaho                                                       NaN   \n",
       "Illinois                                                    NaN   \n",
       "Indiana                                                     NaN   \n",
       "Iowa                                                        NaN   \n",
       "Kansas                                                      NaN   \n",
       "Kentucky                                                    NaN   \n",
       "Louisiana                                                   NaN   \n",
       "Maine                                                       NaN   \n",
       "Maryland                                                    NaN   \n",
       "Massachusetts                                               NaN   \n",
       "Michigan                                                    NaN   \n",
       "Minnesota                                                   NaN   \n",
       "Mississippi                                                 NaN   \n",
       "Missouri                                                    NaN   \n",
       "Montana                                                     NaN   \n",
       "Nebraska                                                    NaN   \n",
       "Nevada                                                      NaN   \n",
       "New Hampshire                                               NaN   \n",
       "New Jersey                                                  NaN   \n",
       "New Mexico                                                  NaN   \n",
       "New York                                                    NaN   \n",
       "North Carolina                                              NaN   \n",
       "North Dakota                                                NaN   \n",
       "Ohio                                                        NaN   \n",
       "Oklahoma                                                    NaN   \n",
       "Oregon                                                      NaN   \n",
       "Pennsylvania                                                NaN   \n",
       "Rhode Island                                                NaN   \n",
       "South Carolina                                              NaN   \n",
       "South Dakota                                                NaN   \n",
       "Tennessee                                                   NaN   \n",
       "Texas                                                       NaN   \n",
       "Utah                                                        NaN   \n",
       "Vermont                                                     NaN   \n",
       "Virginia                                                    NaN   \n",
       "Washington                                                  NaN   \n",
       "West Virginia                                               NaN   \n",
       "Wisconsin                                                   NaN   \n",
       "Wyoming                                                     NaN   \n",
       "\n",
       "                             X  \\\n",
       "Fact                         X   \n",
       "Fact Note       Not applicable   \n",
       "Alabama                    NaN   \n",
       "Alaska                     NaN   \n",
       "Arizona                    NaN   \n",
       "Arkansas                   NaN   \n",
       "California                 NaN   \n",
       "Colorado                   NaN   \n",
       "Connecticut                NaN   \n",
       "Delaware                   NaN   \n",
       "Florida                    NaN   \n",
       "Georgia                    NaN   \n",
       "Hawaii                     NaN   \n",
       "Idaho                      NaN   \n",
       "Illinois                   NaN   \n",
       "Indiana                    NaN   \n",
       "Iowa                       NaN   \n",
       "Kansas                     NaN   \n",
       "Kentucky                   NaN   \n",
       "Louisiana                  NaN   \n",
       "Maine                      NaN   \n",
       "Maryland                   NaN   \n",
       "Massachusetts              NaN   \n",
       "Michigan                   NaN   \n",
       "Minnesota                  NaN   \n",
       "Mississippi                NaN   \n",
       "Missouri                   NaN   \n",
       "Montana                    NaN   \n",
       "Nebraska                   NaN   \n",
       "Nevada                     NaN   \n",
       "New Hampshire              NaN   \n",
       "New Jersey                 NaN   \n",
       "New Mexico                 NaN   \n",
       "New York                   NaN   \n",
       "North Carolina             NaN   \n",
       "North Dakota               NaN   \n",
       "Ohio                       NaN   \n",
       "Oklahoma                   NaN   \n",
       "Oregon                     NaN   \n",
       "Pennsylvania               NaN   \n",
       "Rhode Island               NaN   \n",
       "South Carolina             NaN   \n",
       "South Dakota               NaN   \n",
       "Tennessee                  NaN   \n",
       "Texas                      NaN   \n",
       "Utah                       NaN   \n",
       "Vermont                    NaN   \n",
       "Virginia                   NaN   \n",
       "Washington                 NaN   \n",
       "West Virginia              NaN   \n",
       "Wisconsin                  NaN   \n",
       "Wyoming                    NaN   \n",
       "\n",
       "                                                                Z  \n",
       "Fact                                                            Z  \n",
       "Fact Note       Value greater than zero but less than half uni...  \n",
       "Alabama                                                       NaN  \n",
       "Alaska                                                        NaN  \n",
       "Arizona                                                       NaN  \n",
       "Arkansas                                                      NaN  \n",
       "California                                                    NaN  \n",
       "Colorado                                                      NaN  \n",
       "Connecticut                                                   NaN  \n",
       "Delaware                                                      NaN  \n",
       "Florida                                                       NaN  \n",
       "Georgia                                                       NaN  \n",
       "Hawaii                                                        NaN  \n",
       "Idaho                                                         NaN  \n",
       "Illinois                                                      NaN  \n",
       "Indiana                                                       NaN  \n",
       "Iowa                                                          NaN  \n",
       "Kansas                                                        NaN  \n",
       "Kentucky                                                      NaN  \n",
       "Louisiana                                                     NaN  \n",
       "Maine                                                         NaN  \n",
       "Maryland                                                      NaN  \n",
       "Massachusetts                                                 NaN  \n",
       "Michigan                                                      NaN  \n",
       "Minnesota                                                     NaN  \n",
       "Mississippi                                                   NaN  \n",
       "Missouri                                                      NaN  \n",
       "Montana                                                       NaN  \n",
       "Nebraska                                                      NaN  \n",
       "Nevada                                                        NaN  \n",
       "New Hampshire                                                 NaN  \n",
       "New Jersey                                                    NaN  \n",
       "New Mexico                                                    NaN  \n",
       "New York                                                      NaN  \n",
       "North Carolina                                                NaN  \n",
       "North Dakota                                                  NaN  \n",
       "Ohio                                                          NaN  \n",
       "Oklahoma                                                      NaN  \n",
       "Oregon                                                        NaN  \n",
       "Pennsylvania                                                  NaN  \n",
       "Rhode Island                                                  NaN  \n",
       "South Carolina                                                NaN  \n",
       "South Dakota                                                  NaN  \n",
       "Tennessee                                                     NaN  \n",
       "Texas                                                         NaN  \n",
       "Utah                                                          NaN  \n",
       "Vermont                                                       NaN  \n",
       "Virginia                                                      NaN  \n",
       "Washington                                                    NaN  \n",
       "West Virginia                                                 NaN  \n",
       "Wisconsin                                                     NaN  \n",
       "Wyoming                                                       NaN  \n",
       "\n",
       "[52 rows x 85 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# make colums name right by makeing first row as columns header \n",
    "df_ust.rename(columns=df_ust.iloc[0], inplace = True)\n",
    "df_ust"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# then drop first two rows to make our data clean \n",
    "df_ust.drop(df_ust.index[0:2], inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>Population estimates, July 1, 2016,  (V2016)</th>\n",
       "      <th>Population estimates base, April 1, 2010,  (V2016)</th>\n",
       "      <th>Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)</th>\n",
       "      <th>Population, Census, April 1, 2010</th>\n",
       "      <th>Persons under 5 years, percent, July 1, 2016,  (V2016)</th>\n",
       "      <th>Persons under 5 years, percent, April 1, 2010</th>\n",
       "      <th>Persons under 18 years, percent, July 1, 2016,  (V2016)</th>\n",
       "      <th>Persons under 18 years, percent, April 1, 2010</th>\n",
       "      <th>Persons 65 years and over, percent,  July 1, 2016,  (V2016)</th>\n",
       "      <th>Persons 65 years and over, percent, April 1, 2010</th>\n",
       "      <th>...</th>\n",
       "      <th>NaN</th>\n",
       "      <th>Value Flags</th>\n",
       "      <th>-</th>\n",
       "      <th>D</th>\n",
       "      <th>F</th>\n",
       "      <th>FN</th>\n",
       "      <th>NaN</th>\n",
       "      <th>S</th>\n",
       "      <th>X</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>4,863,300</td>\n",
       "      <td>4,780,131</td>\n",
       "      <td>1.70%</td>\n",
       "      <td>4,779,736</td>\n",
       "      <td>6.00%</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>22.60%</td>\n",
       "      <td>23.70%</td>\n",
       "      <td>16.10%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>741,894</td>\n",
       "      <td>710,249</td>\n",
       "      <td>4.50%</td>\n",
       "      <td>710,231</td>\n",
       "      <td>7.30%</td>\n",
       "      <td>7.60%</td>\n",
       "      <td>25.20%</td>\n",
       "      <td>26.40%</td>\n",
       "      <td>10.40%</td>\n",
       "      <td>7.70%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>6,931,071</td>\n",
       "      <td>6,392,301</td>\n",
       "      <td>8.40%</td>\n",
       "      <td>6,392,017</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>7.10%</td>\n",
       "      <td>23.50%</td>\n",
       "      <td>25.50%</td>\n",
       "      <td>16.90%</td>\n",
       "      <td>13.80%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>2,988,248</td>\n",
       "      <td>2,916,025</td>\n",
       "      <td>2.50%</td>\n",
       "      <td>2,915,918</td>\n",
       "      <td>6.40%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>23.60%</td>\n",
       "      <td>24.40%</td>\n",
       "      <td>16.30%</td>\n",
       "      <td>14.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>39,250,017</td>\n",
       "      <td>37,254,522</td>\n",
       "      <td>5.40%</td>\n",
       "      <td>37,253,956</td>\n",
       "      <td>6.30%</td>\n",
       "      <td>6.80%</td>\n",
       "      <td>23.20%</td>\n",
       "      <td>25.00%</td>\n",
       "      <td>13.60%</td>\n",
       "      <td>11.40%</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 85 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population estimates, July 1, 2016,  (V2016)  \\\n",
       "Alabama                                       4,863,300   \n",
       "Alaska                                          741,894   \n",
       "Arizona                                       6,931,071   \n",
       "Arkansas                                      2,988,248   \n",
       "California                                   39,250,017   \n",
       "\n",
       "           Population estimates base, April 1, 2010,  (V2016)  \\\n",
       "Alabama                                             4,780,131   \n",
       "Alaska                                                710,249   \n",
       "Arizona                                             6,392,301   \n",
       "Arkansas                                            2,916,025   \n",
       "California                                         37,254,522   \n",
       "\n",
       "           Population, percent change - April 1, 2010 (estimates base) to July 1, 2016,  (V2016)  \\\n",
       "Alabama                                                 1.70%                                      \n",
       "Alaska                                                  4.50%                                      \n",
       "Arizona                                                 8.40%                                      \n",
       "Arkansas                                                2.50%                                      \n",
       "California                                              5.40%                                      \n",
       "\n",
       "           Population, Census, April 1, 2010  \\\n",
       "Alabama                            4,779,736   \n",
       "Alaska                               710,231   \n",
       "Arizona                            6,392,017   \n",
       "Arkansas                           2,915,918   \n",
       "California                        37,253,956   \n",
       "\n",
       "           Persons under 5 years, percent, July 1, 2016,  (V2016)  \\\n",
       "Alabama                                                 6.00%       \n",
       "Alaska                                                  7.30%       \n",
       "Arizona                                                 6.30%       \n",
       "Arkansas                                                6.40%       \n",
       "California                                              6.30%       \n",
       "\n",
       "           Persons under 5 years, percent, April 1, 2010  \\\n",
       "Alabama                                            6.40%   \n",
       "Alaska                                             7.60%   \n",
       "Arizona                                            7.10%   \n",
       "Arkansas                                           6.80%   \n",
       "California                                         6.80%   \n",
       "\n",
       "           Persons under 18 years, percent, July 1, 2016,  (V2016)  \\\n",
       "Alabama                                                22.60%        \n",
       "Alaska                                                 25.20%        \n",
       "Arizona                                                23.50%        \n",
       "Arkansas                                               23.60%        \n",
       "California                                             23.20%        \n",
       "\n",
       "           Persons under 18 years, percent, April 1, 2010  \\\n",
       "Alabama                                            23.70%   \n",
       "Alaska                                             26.40%   \n",
       "Arizona                                            25.50%   \n",
       "Arkansas                                           24.40%   \n",
       "California                                         25.00%   \n",
       "\n",
       "           Persons 65 years and over, percent,  July 1, 2016,  (V2016)  \\\n",
       "Alabama                                                16.10%            \n",
       "Alaska                                                 10.40%            \n",
       "Arizona                                                16.90%            \n",
       "Arkansas                                               16.30%            \n",
       "California                                             13.60%            \n",
       "\n",
       "           Persons 65 years and over, percent, April 1, 2010  ...  NaN  \\\n",
       "Alabama                                               13.80%  ...  NaN   \n",
       "Alaska                                                 7.70%  ...  NaN   \n",
       "Arizona                                               13.80%  ...  NaN   \n",
       "Arkansas                                              14.40%  ...  NaN   \n",
       "California                                            11.40%  ...  NaN   \n",
       "\n",
       "           Value Flags    -    D    F   FN  NaN    S    X    Z  \n",
       "Alabama            NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "Alaska             NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "Arizona            NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "Arkansas           NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "California         NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN  \n",
       "\n",
       "[5 rows x 85 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_ust.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we just need firts two columns in our analysis \n",
    "# getting usa population in 2016 and 2010\n",
    "# we will put it in new datafram called df_pop\n",
    "df_pop = df_ust.iloc[:,:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Population estimates, July 1, 2016,  (V2016)</th>\n",
       "      <th>Population estimates base, April 1, 2010,  (V2016)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>4,863,300</td>\n",
       "      <td>4,780,131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>741,894</td>\n",
       "      <td>710,249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>6,931,071</td>\n",
       "      <td>6,392,301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>2,988,248</td>\n",
       "      <td>2,916,025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>39,250,017</td>\n",
       "      <td>37,254,522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Population estimates, July 1, 2016,  (V2016)  \\\n",
       "Alabama                                       4,863,300   \n",
       "Alaska                                          741,894   \n",
       "Arizona                                       6,931,071   \n",
       "Arkansas                                      2,988,248   \n",
       "California                                   39,250,017   \n",
       "\n",
       "           Population estimates base, April 1, 2010,  (V2016)  \n",
       "Alabama                                             4,780,131  \n",
       "Alaska                                                710,249  \n",
       "Arizona                                             6,392,301  \n",
       "Arkansas                                            2,916,025  \n",
       "California                                         37,254,522  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\engsa\\AppData\\Local\\Temp\\ipykernel_22676\\2690296075.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pop.rename(columns = {'Population estimates, July 1, 2016,  (V2016)':'pop_2016', 'Population estimates base, April 1, 2010,  (V2016)' : 'pop_2010'}, inplace=True)\n"
     ]
    }
   ],
   "source": [
    "# rename the columns\n",
    "df_pop.rename(columns = {'Population estimates, July 1, 2016,  (V2016)':'pop_2016', 'Population estimates base, April 1, 2010,  (V2016)' : 'pop_2010'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>pop_2016</th>\n",
       "      <th>pop_2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>4,863,300</td>\n",
       "      <td>4,780,131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>741,894</td>\n",
       "      <td>710,249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>6,931,071</td>\n",
       "      <td>6,392,301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>2,988,248</td>\n",
       "      <td>2,916,025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>39,250,017</td>\n",
       "      <td>37,254,522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              pop_2016    pop_2010\n",
       "Alabama      4,863,300   4,780,131\n",
       "Alaska         741,894     710,249\n",
       "Arizona      6,931,071   6,392,301\n",
       "Arkansas     2,988,248   2,916,025\n",
       "California  39,250,017  37,254,522"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the new naming \n",
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop_2016    object\n",
       "pop_2010    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for types of data \n",
    "df_pop.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\engsa\\AppData\\Local\\Temp\\ipykernel_22676\\2973951752.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pop['pop_2010']= df_pop['pop_2010'].str.replace(',', '').astype(int)\n"
     ]
    }
   ],
   "source": [
    "# convert string type to int \n",
    "df_pop['pop_2010']= df_pop['pop_2010'].str.replace(',', '').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\engsa\\AppData\\Local\\Temp\\ipykernel_22676\\2310752916.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_pop['pop_2016']= df_pop['pop_2016'].str.replace(',', '').astype(int)\n"
     ]
    }
   ],
   "source": [
    "df_pop['pop_2016']= df_pop['pop_2016'].str.replace(',', '').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pop_2016    int32\n",
       "pop_2010    int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for types again\n",
    "df_pop.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pop_2016</th>\n",
       "      <th>pop_2010</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>4863300</td>\n",
       "      <td>4780131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>741894</td>\n",
       "      <td>710249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>6931071</td>\n",
       "      <td>6392301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>2988248</td>\n",
       "      <td>2916025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>39250017</td>\n",
       "      <td>37254522</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            pop_2016  pop_2010\n",
       "Alabama      4863300   4780131\n",
       "Alaska        741894    710249\n",
       "Arizona      6931071   6392301\n",
       "Arkansas     2988248   2916025\n",
       "California  39250017  37254522"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pop.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>permit_recheck</th>\n",
       "      <th>handgun</th>\n",
       "      <th>long_gun</th>\n",
       "      <th>other</th>\n",
       "      <th>multiple</th>\n",
       "      <th>admin</th>\n",
       "      <th>prepawn_handgun</th>\n",
       "      <th>...</th>\n",
       "      <th>returned_other</th>\n",
       "      <th>rentals_handgun</th>\n",
       "      <th>rentals_long_gun</th>\n",
       "      <th>private_sale_handgun</th>\n",
       "      <th>private_sale_long_gun</th>\n",
       "      <th>private_sale_other</th>\n",
       "      <th>return_to_seller_handgun</th>\n",
       "      <th>return_to_seller_long_gun</th>\n",
       "      <th>return_to_seller_other</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5734.0</td>\n",
       "      <td>6320.0</td>\n",
       "      <td>221.0</td>\n",
       "      <td>317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2320.0</td>\n",
       "      <td>2930.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>382.0</td>\n",
       "      <td>11063.0</td>\n",
       "      <td>7946.0</td>\n",
       "      <td>920.0</td>\n",
       "      <td>631</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>28394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>632.0</td>\n",
       "      <td>4347.0</td>\n",
       "      <td>6063.0</td>\n",
       "      <td>165.0</td>\n",
       "      <td>366</td>\n",
       "      <td>51.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37165.0</td>\n",
       "      <td>24581.0</td>\n",
       "      <td>2984.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>123506</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     month       state   permit  permit_recheck  handgun  long_gun   other  \\\n",
       "0  2017-09     Alabama  16717.0             0.0   5734.0    6320.0   221.0   \n",
       "1  2017-09      Alaska    209.0             2.0   2320.0    2930.0   219.0   \n",
       "2  2017-09     Arizona   5069.0           382.0  11063.0    7946.0   920.0   \n",
       "3  2017-09    Arkansas   2935.0           632.0   4347.0    6063.0   165.0   \n",
       "4  2017-09  California  57839.0             0.0  37165.0   24581.0  2984.0   \n",
       "\n",
       "   multiple  admin  prepawn_handgun  ...  returned_other  rentals_handgun  \\\n",
       "0       317    0.0             15.0  ...             0.0              0.0   \n",
       "1       160    0.0              5.0  ...             0.0              0.0   \n",
       "2       631    0.0             13.0  ...             0.0              0.0   \n",
       "3       366   51.0             12.0  ...             0.0              0.0   \n",
       "4         0    0.0              0.0  ...             0.0              0.0   \n",
       "\n",
       "   rentals_long_gun  private_sale_handgun  private_sale_long_gun  \\\n",
       "0               0.0                   9.0                   16.0   \n",
       "1               0.0                  17.0                   24.0   \n",
       "2               0.0                  38.0                   12.0   \n",
       "3               0.0                  13.0                   23.0   \n",
       "4               0.0                   0.0                    0.0   \n",
       "\n",
       "   private_sale_other  return_to_seller_handgun  return_to_seller_long_gun  \\\n",
       "0                 3.0                       0.0                        0.0   \n",
       "1                 1.0                       0.0                        0.0   \n",
       "2                 2.0                       0.0                        0.0   \n",
       "3                 0.0                       0.0                        2.0   \n",
       "4                 0.0                       0.0                        0.0   \n",
       "\n",
       "   return_to_seller_other  totals  \n",
       "0                     3.0   32019  \n",
       "1                     0.0    6303  \n",
       "2                     0.0   28394  \n",
       "3                     1.0   17747  \n",
       "4                     0.0  123506  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets clean the other data set df_gun\n",
    "df_gun.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12485 entries, 0 to 12484\n",
      "Data columns (total 27 columns):\n",
      " #   Column                     Non-Null Count  Dtype  \n",
      "---  ------                     --------------  -----  \n",
      " 0   month                      12485 non-null  object \n",
      " 1   state                      12485 non-null  object \n",
      " 2   permit                     12461 non-null  float64\n",
      " 3   permit_recheck             1100 non-null   float64\n",
      " 4   handgun                    12465 non-null  float64\n",
      " 5   long_gun                   12466 non-null  float64\n",
      " 6   other                      5500 non-null   float64\n",
      " 7   multiple                   12485 non-null  int64  \n",
      " 8   admin                      12462 non-null  float64\n",
      " 9   prepawn_handgun            10542 non-null  float64\n",
      " 10  prepawn_long_gun           10540 non-null  float64\n",
      " 11  prepawn_other              5115 non-null   float64\n",
      " 12  redemption_handgun         10545 non-null  float64\n",
      " 13  redemption_long_gun        10544 non-null  float64\n",
      " 14  redemption_other           5115 non-null   float64\n",
      " 15  returned_handgun           2200 non-null   float64\n",
      " 16  returned_long_gun          2145 non-null   float64\n",
      " 17  returned_other             1815 non-null   float64\n",
      " 18  rentals_handgun            990 non-null    float64\n",
      " 19  rentals_long_gun           825 non-null    float64\n",
      " 20  private_sale_handgun       2750 non-null   float64\n",
      " 21  private_sale_long_gun      2750 non-null   float64\n",
      " 22  private_sale_other         2750 non-null   float64\n",
      " 23  return_to_seller_handgun   2475 non-null   float64\n",
      " 24  return_to_seller_long_gun  2750 non-null   float64\n",
      " 25  return_to_seller_other     2255 non-null   float64\n",
      " 26  totals                     12485 non-null  int64  \n",
      "dtypes: float64(23), int64(2), object(2)\n",
      "memory usage: 2.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df_gun.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>32019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>6303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>28394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>17747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "      <td>123506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12480</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1.0</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12482</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12483</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12484</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>8.0</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12485 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         month          state   permit  totals\n",
       "0      2017-09        Alabama  16717.0   32019\n",
       "1      2017-09         Alaska    209.0    6303\n",
       "2      2017-09        Arizona   5069.0   28394\n",
       "3      2017-09       Arkansas   2935.0   17747\n",
       "4      2017-09     California  57839.0  123506\n",
       "...        ...            ...      ...     ...\n",
       "12480  1998-11       Virginia      0.0      24\n",
       "12481  1998-11     Washington      1.0     361\n",
       "12482  1998-11  West Virginia      3.0     408\n",
       "12483  1998-11      Wisconsin      0.0     241\n",
       "12484  1998-11        Wyoming      8.0     107\n",
       "\n",
       "[12485 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# it looks that we has our columns in correct types \n",
    "# we will get 4 columns month state permit totals and will drop the rest\n",
    "# put it in new datafram called df_gun_clean\n",
    "\n",
    "df_gun_clean = df_gun.drop(columns=['permit_recheck', 'handgun', 'long_gun',\n",
    "       'other', 'multiple', 'admin', 'prepawn_handgun', 'prepawn_long_gun',\n",
    "       'prepawn_other', 'redemption_handgun', 'redemption_long_gun',\n",
    "       'redemption_other', 'returned_handgun', 'returned_long_gun',\n",
    "       'returned_other', 'rentals_handgun', 'rentals_long_gun',\n",
    "       'private_sale_handgun', 'private_sale_long_gun', 'private_sale_other',\n",
    "       'return_to_seller_handgun', 'return_to_seller_long_gun',\n",
    "       'return_to_seller_other'])\n",
    "df_gun_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>date</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>32019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>6303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>28394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>17747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "      <td>123506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12480</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12481</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Washington</td>\n",
       "      <td>1.0</td>\n",
       "      <td>361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12482</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>3.0</td>\n",
       "      <td>408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12483</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>0.0</td>\n",
       "      <td>241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12484</th>\n",
       "      <td>1998-11</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>8.0</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12485 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          date          state   permit  totals\n",
       "0      2017-09        Alabama  16717.0   32019\n",
       "1      2017-09         Alaska    209.0    6303\n",
       "2      2017-09        Arizona   5069.0   28394\n",
       "3      2017-09       Arkansas   2935.0   17747\n",
       "4      2017-09     California  57839.0  123506\n",
       "...        ...            ...      ...     ...\n",
       "12480  1998-11       Virginia      0.0      24\n",
       "12481  1998-11     Washington      1.0     361\n",
       "12482  1998-11  West Virginia      3.0     408\n",
       "12483  1998-11      Wisconsin      0.0     241\n",
       "12484  1998-11        Wyoming      8.0     107\n",
       "\n",
       "[12485 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# rename the month colum to date \n",
    "df_gun_clean.rename(columns = {'month':'date'}, inplace= True)\n",
    "df_gun_clean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      datetime64[ns]\n",
       "state             object\n",
       "permit           float64\n",
       "totals             int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making data datetime type so we can make our analysis on yearly bases \n",
    "# convert the date \n",
    "df_gun_clean.date = pd.to_datetime(df_gun_clean.date)\n",
    "# checking for it \n",
    "df_gun_clean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>32019</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>6303</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>28394</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>17747</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "      <td>123506</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        date       state   permit  totals  year\n",
       "0 2017-09-01     Alabama  16717.0   32019  2017\n",
       "1 2017-09-01      Alaska    209.0    6303  2017\n",
       "2 2017-09-01     Arizona   5069.0   28394  2017\n",
       "3 2017-09-01    Arkansas   2935.0   17747  2017\n",
       "4 2017-09-01  California  57839.0  123506  2017"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  adding new column named year \n",
    "df_gun_clean['year'] = df_gun_clean['date'].dt.year\n",
    "df_gun_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date       0\n",
       "state      0\n",
       "permit    24\n",
       "totals     0\n",
       "year       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for missing values \n",
    "df_gun_clean.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# droping missing values becuses its small numbers \n",
    "df_gun_clean.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "date      0\n",
       "state     0\n",
       "permit    0\n",
       "totals    0\n",
       "year      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for any other nulls \n",
    "df_gun_clean.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>totals</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>32019</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>6303</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>28394</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>17747</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "      <td>123506</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "        date       state   permit  totals  year\n",
       "0 2017-09-01     Alabama  16717.0   32019  2017\n",
       "1 2017-09-01      Alaska    209.0    6303  2017\n",
       "2 2017-09-01     Arizona   5069.0   28394  2017\n",
       "3 2017-09-01    Arkansas   2935.0   17747  2017\n",
       "4 2017-09-01  California  57839.0  123506  2017"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this our cleand data set number 2\n",
    "df_gun_clean.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# checking for states number \n",
    "df_gun_clean.state.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_list = df_pop.index.values\n",
    "len(state_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we found 5 states not in US data so we will drop them \n",
    "# to get 50 States in two data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['District of Columbia',\n",
       " 'Guam',\n",
       " 'Mariana Islands',\n",
       " 'Puerto Rico',\n",
       " 'Virgin Islands']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we will limit the state to our 50 state in the US_Census_data\n",
    "#  getting the states to remove \n",
    "all_states = df_gun_clean.state.unique()\n",
    "states_rm=[]\n",
    "for state in all_states:\n",
    "    if state not in state_list:\n",
    "        states_rm.append(state)\n",
    "\n",
    "states_rm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "# we will limit the state to our 50 state in the US_Census_data\n",
    "for state in states_rm:\n",
    "    ind_state = df_gun_clean[(df_gun_clean['state'] == state)].index\n",
    "    df_gun_clean.drop(ind_state , inplace=True)\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gun_clean.state.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "      <th>totals</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "      <td>32019</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "      <td>6303</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5069.0</td>\n",
       "      <td>28394</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2935.0</td>\n",
       "      <td>17747</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-09-01</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "      <td>123506</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "        date       state   permit  totals  year\n",
       "0 2017-09-01     Alabama  16717.0   32019  2017\n",
       "1 2017-09-01      Alaska    209.0    6303  2017\n",
       "2 2017-09-01     Arizona   5069.0   28394  2017\n",
       "3 2017-09-01    Arkansas   2935.0   17747  2017\n",
       "4 2017-09-01  California  57839.0  123506  2017"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gun_clean.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='eda'></a>\n",
    "## Exploratory Data Analysis\n",
    "\n",
    "### Research Question 1 \n",
    "### What is the overall trend of Gun Purchases?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "vscode": {
     "languageId": "ruby"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\engsa\\AppData\\Local\\Temp\\ipykernel_22676\\1574616230.py:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_line = df_gun_clean.groupby('year').sum()\n"
     ]
    },
    {
     "data": {
      "text/html": [
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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>permit</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>78169.0</td>\n",
       "      <td>891376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>1037700.0</td>\n",
       "      <td>9038315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>1227814.0</td>\n",
       "      <td>8421486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>1408338.0</td>\n",
       "      <td>8812457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>1363211.0</td>\n",
       "      <td>8358574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>1403496.0</td>\n",
       "      <td>8393942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>1345672.0</td>\n",
       "      <td>8571726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>1350193.0</td>\n",
       "      <td>8857599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>2036569.0</td>\n",
       "      <td>9945475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>3077655.0</td>\n",
       "      <td>11151998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>3697854.0</td>\n",
       "      <td>12684240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>4449333.0</td>\n",
       "      <td>13974323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>4882948.0</td>\n",
       "      <td>14309926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>5544418.0</td>\n",
       "      <td>16323039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>5683545.0</td>\n",
       "      <td>19446504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>6168970.0</td>\n",
       "      <td>20946761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>7768805.0</td>\n",
       "      <td>20840268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>8780938.0</td>\n",
       "      <td>23006228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>11133748.0</td>\n",
       "      <td>27405549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>7469045.0</td>\n",
       "      <td>17974940</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          permit    totals\n",
       "year                      \n",
       "1998     78169.0    891376\n",
       "1999   1037700.0   9038315\n",
       "2000   1227814.0   8421486\n",
       "2001   1408338.0   8812457\n",
       "2002   1363211.0   8358574\n",
       "2003   1403496.0   8393942\n",
       "2004   1345672.0   8571726\n",
       "2005   1350193.0   8857599\n",
       "2006   2036569.0   9945475\n",
       "2007   3077655.0  11151998\n",
       "2008   3697854.0  12684240\n",
       "2009   4449333.0  13974323\n",
       "2010   4882948.0  14309926\n",
       "2011   5544418.0  16323039\n",
       "2012   5683545.0  19446504\n",
       "2013   6168970.0  20946761\n",
       "2014   7768805.0  20840268\n",
       "2015   8780938.0  23006228\n",
       "2016  11133748.0  27405549\n",
       "2017   7469045.0  17974940"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can answer this Q by grouping the df_gun_clean data by year \n",
    "# putting it in new data fram \n",
    "df_line = df_gun_clean.groupby('year').sum()\n",
    "df_line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def line_plot(df,x,y,title):\n",
    "    sns.lineplot(data=df, x= x, y= y).set_title(title)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now we plot the totals number of guns through years so \n",
    "# we get a good feel about this trend \n",
    "line_plot(df_line,'year','totals','Overall Trend of Gun Purchases')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> as we can see its growing trend with two peaks one around 1998 and 2016\n",
    "although it slows down with sharp decrease in 2017 as we can see."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "line_plot(df_line,'year','permit','Overall Trend of Gun Permits')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h3> as we can see its growing trend too as in previous with one peak  in 2016\n",
    "although it slows down also  with sharp decrease in 2017 as we can see."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 2  \n",
    "### Which states have had the highest growth in gun registrations?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\engsa\\AppData\\Local\\Temp\\ipykernel_22676\\1688906.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_gun_clean.groupby(['year','state']).sum()\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>permit</th>\n",
       "      <th>totals</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">1998</th>\n",
       "      <th>Alabama</th>\n",
       "      <td>0.0</td>\n",
       "      <td>36568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>1733.0</td>\n",
       "      <td>17453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>0.0</td>\n",
       "      <td>21752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>5366.0</td>\n",
       "      <td>67445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2017</th>\n",
       "      <th>Virginia</th>\n",
       "      <td>9107.0</td>\n",
       "      <td>366994</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>119467.0</td>\n",
       "      <td>419522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>19079.0</td>\n",
       "      <td>152784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>137868.0</td>\n",
       "      <td>387537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>3447.0</td>\n",
       "      <td>37736</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      permit  totals\n",
       "year state                          \n",
       "1998 Alabama             0.0   36568\n",
       "     Alaska              4.0    3985\n",
       "     Arizona          1733.0   17453\n",
       "     Arkansas            0.0   21752\n",
       "     California       5366.0   67445\n",
       "...                      ...     ...\n",
       "2017 Virginia         9107.0  366994\n",
       "     Washington     119467.0  419522\n",
       "     West Virginia   19079.0  152784\n",
       "     Wisconsin      137868.0  387537\n",
       "     Wyoming          3447.0   37736\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# let group our data and see\n",
    "df_gun_clean.groupby(['year','state']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>16717.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>209.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>5069.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2935.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year       state   permit\n",
       "0  2017     Alabama  16717.0\n",
       "1  2017      Alaska    209.0\n",
       "2  2017     Arizona   5069.0\n",
       "3  2017    Arkansas   2935.0\n",
       "4  2017  California  57839.0"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# as we can see its larg data sets \n",
    "# so i will choose top 5 states in permit numbers \n",
    "# and trak their record through 10 yeas to make scope managable \n",
    "\n",
    "# make new data fram for this Q name it df_gun_growth\n",
    "# we will choose 3 columns that we need to make our top5 later \n",
    "df_gun_growth =df_gun_clean[['year','state','permit']].copy()\n",
    "df_gun_growth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>permit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>455769</td>\n",
       "      <td>24926779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>455769</td>\n",
       "      <td>9498639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>455769</td>\n",
       "      <td>6530543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>455769</td>\n",
       "      <td>3872974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>455769</td>\n",
       "      <td>3313076.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  year      permit\n",
       "state                             \n",
       "Kentucky        455769  24926779.0\n",
       "Illinois        455769   9498639.0\n",
       "California      455769   6530543.0\n",
       "North Carolina  455769   3872974.0\n",
       "Michigan        455769   3313076.0"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group our data by state and sum all permit \n",
    "# then sort them ascending and take top 5 \n",
    "df_top5 = df_gun_growth.groupby(['state']).sum().sort_values(by=['permit'], axis = 0, ascending = False).head(5).copy()\n",
    "df_top5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>permit</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>24926779.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>9498639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>6530543.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>3872974.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>3313076.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    permit\n",
       "state                     \n",
       "Kentucky        24926779.0\n",
       "Illinois         9498639.0\n",
       "California       6530543.0\n",
       "North Carolina   3872974.0\n",
       "Michigan         3313076.0"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets drop year columns becuse it has no meaning here\n",
    "df_top5.drop('year',axis=1,inplace=True)\n",
    "df_top5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lets see bar chart to get a sense of differnc betweeb them \n",
    "df_top5.plot(kind='bar').set_title('Top 5 States with Gun Permits')\n",
    "plt.ylabel('permits')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "# as we can see Kentucky has the lead \n",
    "# we will see the trend of permit in theis 5 states to know the answer to our Q \n",
    "# Which states have had the highest growth in gun registrations?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Kentucky', 'Illinois', 'California', 'North Carolina', 'Michigan'], dtype='object', name='state')"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the list of states \n",
    "df_top5.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>15712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>378384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>16571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2017</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>19292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12434</th>\n",
       "      <td>1998</td>\n",
       "      <td>California</td>\n",
       "      <td>434.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12444</th>\n",
       "      <td>1998</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12448</th>\n",
       "      <td>1998</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12454</th>\n",
       "      <td>1998</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>579.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12465</th>\n",
       "      <td>1998</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>524.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1135 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       year           state    permit\n",
       "4      2017      California   57839.0\n",
       "14     2017        Illinois   15712.0\n",
       "18     2017        Kentucky  378384.0\n",
       "24     2017        Michigan   16571.0\n",
       "35     2017  North Carolina   19292.0\n",
       "...     ...             ...       ...\n",
       "12434  1998      California     434.0\n",
       "12444  1998        Illinois       4.0\n",
       "12448  1998        Kentucky       0.0\n",
       "12454  1998        Michigan     579.0\n",
       "12465  1998  North Carolina     524.0\n",
       "\n",
       "[1135 rows x 3 columns]"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the data for these 5 only \n",
    "df_top5_states = df_gun_growth.query(\"state == 'Kentucky' | state == 'Illinois' | state == 'California'| state == 'North Carolina'| state == 'North Carolina'| state == 'Michigan'\")\n",
    "df_top5_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>permit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>California</td>\n",
       "      <td>57839.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>15712.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>378384.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>16571.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>2017</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>19292.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7044</th>\n",
       "      <td>2007</td>\n",
       "      <td>California</td>\n",
       "      <td>16445.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7054</th>\n",
       "      <td>2007</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>35972.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7058</th>\n",
       "      <td>2007</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>105742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7064</th>\n",
       "      <td>2007</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>13629.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7075</th>\n",
       "      <td>2007</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>11547.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>645 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      year           state    permit\n",
       "4     2017      California   57839.0\n",
       "14    2017        Illinois   15712.0\n",
       "18    2017        Kentucky  378384.0\n",
       "24    2017        Michigan   16571.0\n",
       "35    2017  North Carolina   19292.0\n",
       "...    ...             ...       ...\n",
       "7044  2007      California   16445.0\n",
       "7054  2007        Illinois   35972.0\n",
       "7058  2007        Kentucky  105742.0\n",
       "7064  2007        Michigan   13629.0\n",
       "7075  2007  North Carolina   11547.0\n",
       "\n",
       "[645 rows x 3 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# limitting the years to 10 years also \n",
    "df_top5_states= df_top5_states[(df_top5_states['year'] > 2006) & (df_top5_states['year'] <= 2017)]\n",
    "df_top5_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we can see the which state has the highest growth in these 5 \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#define index column\n",
    "df_top5_year = df_top5_states.set_index('year')\n",
    "\n",
    "#group data by product and display sales as line chart\n",
    "plt.figure(figsize=(20,8))\n",
    "df_top5_year.groupby('state')['permit'].plot(kind='line', legend=True)\n",
    "plt.title('Top 5 States in Gun Permits in (2007-2017)')\n",
    "plt.ylabel('permits')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Kentucky is the Highest state in all of USA with North Carolina taking the top rank in 2014 only \n",
    "#### so the answer to our Q is \"Kentucky\" "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<hr>\n",
    "<hr>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Research Question 3  \n",
    "### What census data is most associated with high gun per capita? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# to answer this Q we need 2 info \n",
    "# 1- population\n",
    "# 2- total num of guns which we can only get  in 2016 and 2010\n",
    "# so we will calculate the gun per capita for these 2 years only "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get 2016 and 2010 data from df_gun_clean \n",
    "# so we can extrcat the total num of guns in these two years \n",
    "df_gun_10 = df_gun_clean.query(\" year == 2010\")\n",
    "df_gun_16 = df_gun_clean.query(\" year == 2016\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 600 entries, 495 to 1154\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   date    600 non-null    datetime64[ns]\n",
      " 1   state   600 non-null    object        \n",
      " 2   permit  600 non-null    float64       \n",
      " 3   totals  600 non-null    int64         \n",
      " 4   year    600 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# checkin \n",
    "df_gun_16.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 600 entries, 4455 to 5114\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype         \n",
      "---  ------  --------------  -----         \n",
      " 0   date    600 non-null    datetime64[ns]\n",
      " 1   state   600 non-null    object        \n",
      " 2   permit  600 non-null    float64       \n",
      " 3   totals  600 non-null    int64         \n",
      " 4   year    600 non-null    int64         \n",
      "dtypes: datetime64[ns](1), float64(1), int64(2), object(1)\n",
      "memory usage: 28.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# checkin \n",
    "df_gun_10.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\engsa\\AppData\\Local\\Temp\\ipykernel_22676\\1360375285.py:2: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_gun_16 = df_gun_16.groupby('state').sum()\n",
      "C:\\Users\\engsa\\AppData\\Local\\Temp\\ipykernel_22676\\1360375285.py:3: FutureWarning: The default value of numeric_only in DataFrameGroupBy.sum is deprecated. In a future version, numeric_only will default to False. Either specify numeric_only or select only columns which should be valid for the function.\n",
      "  df_gun_10 = df_gun_10.groupby('state').sum()\n"
     ]
    }
   ],
   "source": [
    "# group our data at state level \n",
    "df_gun_16 = df_gun_16.groupby('state').sum()\n",
    "df_gun_10 = df_gun_10.groupby('state').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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>permit</th>\n",
       "      <th>totals</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alabama</th>\n",
       "      <td>4621.0</td>\n",
       "      <td>308607</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alaska</th>\n",
       "      <td>0.0</td>\n",
       "      <td>65909</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arizona</th>\n",
       "      <td>24751.0</td>\n",
       "      <td>206050</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Arkansas</th>\n",
       "      <td>31305.0</td>\n",
       "      <td>191448</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>California</th>\n",
       "      <td>295252.0</td>\n",
       "      <td>816399</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Colorado</th>\n",
       "      <td>12928.0</td>\n",
       "      <td>300820</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Connecticut</th>\n",
       "      <td>90942.0</td>\n",
       "      <td>179595</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Delaware</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19240</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida</th>\n",
       "      <td>35.0</td>\n",
       "      <td>559347</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Georgia</th>\n",
       "      <td>95959.0</td>\n",
       "      <td>333247</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hawaii</th>\n",
       "      <td>10527.0</td>\n",
       "      <td>10533</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Idaho</th>\n",
       "      <td>13758.0</td>\n",
       "      <td>92031</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Illinois</th>\n",
       "      <td>463699.0</td>\n",
       "      <td>695300</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Indiana</th>\n",
       "      <td>105419.0</td>\n",
       "      <td>345650</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iowa</th>\n",
       "      <td>82079.0</td>\n",
       "      <td>124738</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kansas</th>\n",
       "      <td>4008.0</td>\n",
       "      <td>144156</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kentucky</th>\n",
       "      <td>2149338.0</td>\n",
       "      <td>2385579</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Louisiana</th>\n",
       "      <td>0.0</td>\n",
       "      <td>239204</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <td>0.0</td>\n",
       "      <td>65988</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maryland</th>\n",
       "      <td>4.0</td>\n",
       "      <td>88162</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massachusetts</th>\n",
       "      <td>58474.0</td>\n",
       "      <td>115904</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Michigan</th>\n",
       "      <td>194283.0</td>\n",
       "      <td>348200</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Minnesota</th>\n",
       "      <td>109840.0</td>\n",
       "      <td>297390</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mississippi</th>\n",
       "      <td>3826.0</td>\n",
       "      <td>169873</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Missouri</th>\n",
       "      <td>23095.0</td>\n",
       "      <td>349672</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montana</th>\n",
       "      <td>7857.0</td>\n",
       "      <td>101095</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nebraska</th>\n",
       "      <td>27728.0</td>\n",
       "      <td>58283</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nevada</th>\n",
       "      <td>13332.0</td>\n",
       "      <td>104789</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Hampshire</th>\n",
       "      <td>22799.0</td>\n",
       "      <td>79940</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Jersey</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50522</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Mexico</th>\n",
       "      <td>8100.0</td>\n",
       "      <td>109069</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York</th>\n",
       "      <td>30047.0</td>\n",
       "      <td>241495</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Carolina</th>\n",
       "      <td>150722.0</td>\n",
       "      <td>331637</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North Dakota</th>\n",
       "      <td>4051.0</td>\n",
       "      <td>47083</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ohio</th>\n",
       "      <td>1.0</td>\n",
       "      <td>393250</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oklahoma</th>\n",
       "      <td>0.0</td>\n",
       "      <td>233909</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Oregon</th>\n",
       "      <td>486.0</td>\n",
       "      <td>184835</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pennsylvania</th>\n",
       "      <td>2569.0</td>\n",
       "      <td>626677</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rhode Island</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14835</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Carolina</th>\n",
       "      <td>48351.0</td>\n",
       "      <td>201628</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South Dakota</th>\n",
       "      <td>0.0</td>\n",
       "      <td>63151</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tennessee</th>\n",
       "      <td>102940.0</td>\n",
       "      <td>411024</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Texas</th>\n",
       "      <td>122062.0</td>\n",
       "      <td>968071</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utah</th>\n",
       "      <td>481563.0</td>\n",
       "      <td>553134</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vermont</th>\n",
       "      <td>0.0</td>\n",
       "      <td>24276</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Virginia</th>\n",
       "      <td>0.0</td>\n",
       "      <td>320783</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Washington</th>\n",
       "      <td>80101.0</td>\n",
       "      <td>335342</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Virginia</th>\n",
       "      <td>179.0</td>\n",
       "      <td>159550</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wisconsin</th>\n",
       "      <td>1.0</td>\n",
       "      <td>194797</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wyoming</th>\n",
       "      <td>5916.0</td>\n",
       "      <td>47709</td>\n",
       "      <td>24120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   permit   totals   year\n",
       "state                                    \n",
       "Alabama            4621.0   308607  24120\n",
       "Alaska                0.0    65909  24120\n",
       "Arizona           24751.0   206050  24120\n",
       "Arkansas          31305.0   191448  24120\n",
       "California       295252.0   816399  24120\n",
       "Colorado          12928.0   300820  24120\n",
       "Connecticut       90942.0   179595  24120\n",
       "Delaware              0.0    19240  24120\n",
       "Florida              35.0   559347  24120\n",
       "Georgia           95959.0   333247  24120\n",
       "Hawaii            10527.0    10533  24120\n",
       "Idaho             13758.0    92031  24120\n",
       "Illinois         463699.0   695300  24120\n",
       "Indiana          105419.0   345650  24120\n",
       "Iowa              82079.0   124738  24120\n",
       "Kansas             4008.0   144156  24120\n",
       "Kentucky        2149338.0  2385579  24120\n",
       "Louisiana             0.0   239204  24120\n",
       "Maine                 0.0    65988  24120\n",
       "Maryland              4.0    88162  24120\n",
       "Massachusetts     58474.0   115904  24120\n",
       "Michigan         194283.0   348200  24120\n",
       "Minnesota        109840.0   297390  24120\n",
       "Mississippi        3826.0   169873  24120\n",
       "Missouri          23095.0   349672  24120\n",
       "Montana            7857.0   101095  24120\n",
       "Nebraska          27728.0    58283  24120\n",
       "Nevada            13332.0   104789  24120\n",
       "New Hampshire     22799.0    79940  24120\n",
       "New Jersey            0.0    50522  24120\n",
       "New Mexico         8100.0   109069  24120\n",
       "New York          30047.0   241495  24120\n",
       "North Carolina   150722.0   331637  24120\n",
       "North Dakota       4051.0    47083  24120\n",
       "Ohio                  1.0   393250  24120\n",
       "Oklahoma              0.0   233909  24120\n",
       "Oregon              486.0   184835  24120\n",
       "Pennsylvania       2569.0   626677  24120\n",
       "Rhode Island          0.0    14835  24120\n",
       "South Carolina    48351.0   201628  24120\n",
       "South Dakota          0.0    63151  24120\n",
       "Tennessee        102940.0   411024  24120\n",
       "Texas            122062.0   968071  24120\n",
       "Utah             481563.0   553134  24120\n",
       "Vermont               0.0    24276  24120\n",
       "Virginia              0.0   320783  24120\n",
       "Washington        80101.0   335342  24120\n",
       "West Virginia       179.0   159550  24120\n",
       "Wisconsin             1.0   194797  24120\n",
       "Wyoming            5916.0    47709  24120"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_gun_10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping year and permit columns \n",
    "df_gun_10.drop(['year','permit'],axis=1,inplace=True)\n",
    "df_gun_16.drop(['year','permit'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 50 entries, Alabama to Wyoming\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype\n",
      "---  ------  --------------  -----\n",
      " 0   totals  50 non-null     int64\n",
      "dtypes: int64(1)\n",
      "memory usage: 800.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df_gun_10.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Alabama           0.064560\n",
       "Alaska            0.092797\n",
       "Arizona           0.032234\n",
       "Arkansas          0.065654\n",
       "California        0.021914\n",
       "Colorado          0.059813\n",
       "Connecticut       0.050249\n",
       "Delaware          0.021427\n",
       "Florida           0.029745\n",
       "Georgia           0.034396\n",
       "Hawaii            0.007743\n",
       "Idaho             0.058706\n",
       "Illinois          0.054187\n",
       "Indiana           0.053307\n",
       "Iowa              0.040940\n",
       "Kansas            0.050526\n",
       "Kentucky          0.549756\n",
       "Louisiana         0.052764\n",
       "Maine             0.049676\n",
       "Maryland          0.015269\n",
       "Massachusetts     0.017701\n",
       "Michigan          0.035228\n",
       "Minnesota         0.056070\n",
       "Mississippi       0.057233\n",
       "Missouri          0.058386\n",
       "Montana           0.102177\n",
       "Nebraska          0.031913\n",
       "Nevada            0.038801\n",
       "New Hampshire     0.060723\n",
       "New Jersey        0.005746\n",
       "New Mexico        0.052967\n",
       "New York          0.012462\n",
       "North Carolina    0.034779\n",
       "North Dakota      0.070002\n",
       "Ohio              0.034087\n",
       "Oklahoma          0.062349\n",
       "Oregon            0.048246\n",
       "Pennsylvania      0.049334\n",
       "Rhode Island      0.014089\n",
       "South Carolina    0.043591\n",
       "South Dakota      0.077563\n",
       "Tennessee         0.064766\n",
       "Texas             0.038498\n",
       "Utah              0.200129\n",
       "Vermont           0.038796\n",
       "Virginia          0.040093\n",
       "Washington        0.049868\n",
       "West Virginia     0.086103\n",
       "Wisconsin         0.034251\n",
       "Wyoming           0.084625\n",
       "dtype: float64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# now lets calculate the gunper capita in 2010\n",
    "df_gun_cap_10 = df_gun_10['totals'] / df_pop['pop_2010']\n",
    "df_gun_cap_10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_gun_cap_16 = df_gun_16['totals'] / df_pop['pop_2016']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Kentucky         0.828683\n",
       "Indiana          0.216601\n",
       "Illinois         0.150300\n",
       "West Virginia    0.132352\n",
       "Montana          0.130776\n",
       "dtype: float64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get the states that has the high gun percapita in 2016\n",
    "df_gun_cap_16.nlargest(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state\n",
       "Kentucky         0.549756\n",
       "Utah             0.200129\n",
       "Montana          0.102177\n",
       "Alaska           0.092797\n",
       "West Virginia    0.086103\n",
       "dtype: float64"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to get the states that has the high gun percapita in 2010\n",
    "df_gun_cap_10.nlargest(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id='conclusions'></a>\n",
    "## Conclusions\n",
    "\n",
    "> Finally, we answer the 3 Questions that we have \n",
    "    1. What is the overall trend of gun purchases?\n",
    "        it's growing trend but slows down in 2017\n",
    "    2. Which states have had the highest growth in gun registrations? \n",
    "        Kentucky is the biggest state on guns and also gun per capita \n",
    "\n",
    "> limitations, no advanced model has implemented and more population data where need to get trend for gun per captia . \n"
   ]
  }
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