opioid-us.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Opioid Prescriptions in the United States\n",
    "An Exploration of the [Washington Post Opioid Dataset](https://www.washingtonpost.com/graphics/2019/investigations/dea-pain-pill-database/)\n",
    "\n",
    "### Table of Contents\n",
    "\n",
    "* [Opioid prescriptions for the US (2006-2017)](#opioid-us)\n",
    "* [Opioid prescriptions by State (2006-2017)](#opioid-by-state)\n",
    "* [Opioid prescriptions by County (2006-2017)](#opioid-by-county)\n",
    "* [Opioid prescriptions in Tennessee (2006-2017)](#opioid-tn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import calendar\n",
    "from ipyleaflet import *\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# magic function making plot outputs appear within the notebook\n",
    "%matplotlib inline\n",
    "\n",
    "# change the default plot output size\n",
    "plt.rcParams['figure.figsize'] = [18, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get current working directory\n",
    "cwd = os.getcwd()\n",
    "data_dir = os.path.join(cwd, 'data')\n",
    "\n",
    "# set input and output Excel files\n",
    "in_nation = os.path.join(data_dir, 'OpioidsPerYearTotalAndRate.xlsx')\n",
    "in_state = os.path.join(data_dir, 'OpioidRxRateByState.xlsx')\n",
    "in_county = os.path.join(data_dir, 'OpioidRxRate_County.xlsx')\n",
    "in_rate = os.path.join(data_dir, 'US_Opioid_OverdoseDeathsByState.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opioid prescriptions for the US (2006-2017) <a class='anchor' id='opioid-us'></a>\n",
    "\n",
    "### Total prescriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Year</th>\n",
       "      <th>TotalPrescriptions</th>\n",
       "      <th>PrescriptionRatePer100People</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>215917663</td>\n",
       "      <td>72.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>228543773</td>\n",
       "      <td>75.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>237860213</td>\n",
       "      <td>78.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>243738090</td>\n",
       "      <td>79.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>251088904</td>\n",
       "      <td>81.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  TotalPrescriptions  PrescriptionRatePer100People\n",
       "0  2006           215917663                          72.4\n",
       "1  2007           228543773                          75.9\n",
       "2  2008           237860213                          78.2\n",
       "3  2009           243738090                          79.5\n",
       "4  2010           251088904                          81.2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nation_total_rx = pd.read_excel(in_nation, sheet_name='TotalRx')\n",
    "df_nation_total_rx.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '0'),\n",
       " Text(0, 0, '50,000,000'),\n",
       " Text(0, 0, '100,000,000'),\n",
       " Text(0, 0, '150,000,000'),\n",
       " Text(0, 0, '200,000,000'),\n",
       " Text(0, 0, '250,000,000'),\n",
       " Text(0, 0, '300,000,000')]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the trend by year\n",
    "ax = df_nation_total_rx.plot.bar(x=0, y=1, title=\"Total Prescriptions by Year\", rot=0)\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Total Prescriptions\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the trend by year\n",
    "ax = df_nation_total_rx.plot.bar(x=0, y=2, title=\"Prescription Rate Per 100 People\", rot=0)\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Prescription Rate\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "# ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### County participation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>TotalCounties</th>\n",
       "      <th>CountiesWithData</th>\n",
       "      <th>PercCountiesWithData</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2006</td>\n",
       "      <td>3143</td>\n",
       "      <td>2754</td>\n",
       "      <td>87.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>3143</td>\n",
       "      <td>2746</td>\n",
       "      <td>87.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008</td>\n",
       "      <td>3143</td>\n",
       "      <td>2758</td>\n",
       "      <td>87.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2009</td>\n",
       "      <td>3143</td>\n",
       "      <td>2750</td>\n",
       "      <td>87.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>3143</td>\n",
       "      <td>2741</td>\n",
       "      <td>87.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  TotalCounties  CountiesWithData  PercCountiesWithData\n",
       "0  2006           3143              2754                  87.6\n",
       "1  2007           3143              2746                  87.4\n",
       "2  2008           3143              2758                  87.8\n",
       "3  2009           3143              2750                  87.5\n",
       "4  2010           3143              2741                  87.2"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nation_county_ptcp = pd.read_excel(in_nation, sheet_name='CountyParticipation')\n",
    "df_nation_county_ptcp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the trend by year\n",
    "ax = df_nation_county_ptcp.plot.bar(x=0, y=2, title=\"Counties with Data\", rot=0)\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Total Prescriptions\")\n",
    "vals = ax.get_yticks().astype(int)\n",
    "# ax.set_yticklabels(['{:,}'.format(x) for x in vals])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the trend by year\n",
    "ax = df_nation_county_ptcp.plot.bar(x=0, y=3, title=\"Percent Counties with Data\", rot=0)\n",
    "ax.set_xlabel(\"Year\")\n",
    "ax.set_ylabel(\"Total Prescriptions\")\n",
    "vals = ax.get_yticks().astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opioid Precriptions by State (2006-2017)   <a class='anchor' id='opioid-by-state'></a>\n",
    "\n",
    "### Merging each yearly spreadsheet into a single spreadsheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "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>State</th>\n",
       "      <th>State ABBR</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>115.6</td>\n",
       "      <td>120.3</td>\n",
       "      <td>126.1</td>\n",
       "      <td>131.6</td>\n",
       "      <td>134.3</td>\n",
       "      <td>136.6</td>\n",
       "      <td>143.8</td>\n",
       "      <td>142.4</td>\n",
       "      <td>135.2</td>\n",
       "      <td>125.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>107.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>63.4</td>\n",
       "      <td>66.6</td>\n",
       "      <td>68.5</td>\n",
       "      <td>67.3</td>\n",
       "      <td>68.4</td>\n",
       "      <td>68.0</td>\n",
       "      <td>66.8</td>\n",
       "      <td>63.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>60.8</td>\n",
       "      <td>58.9</td>\n",
       "      <td>52.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>74.3</td>\n",
       "      <td>77.8</td>\n",
       "      <td>80.9</td>\n",
       "      <td>84.2</td>\n",
       "      <td>88.5</td>\n",
       "      <td>88.6</td>\n",
       "      <td>85.3</td>\n",
       "      <td>80.4</td>\n",
       "      <td>79.7</td>\n",
       "      <td>75.5</td>\n",
       "      <td>70.2</td>\n",
       "      <td>61.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>98.3</td>\n",
       "      <td>108.2</td>\n",
       "      <td>112.1</td>\n",
       "      <td>116.0</td>\n",
       "      <td>120.8</td>\n",
       "      <td>115.2</td>\n",
       "      <td>121.8</td>\n",
       "      <td>120.9</td>\n",
       "      <td>123.2</td>\n",
       "      <td>117.2</td>\n",
       "      <td>114.6</td>\n",
       "      <td>105.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>51.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>55.1</td>\n",
       "      <td>55.6</td>\n",
       "      <td>55.8</td>\n",
       "      <td>55.9</td>\n",
       "      <td>56.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>52.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>44.8</td>\n",
       "      <td>39.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State State ABBR   2006   2007   2008   2009   2010   2011   2012  \\\n",
       "0     Alabama         AL  115.6  120.3  126.1  131.6  134.3  136.6  143.8   \n",
       "1      Alaska         AK   63.4   66.6   68.5   67.3   68.4   68.0   66.8   \n",
       "2     Arizona         AZ   74.3   77.8   80.9   84.2   88.5   88.6   85.3   \n",
       "3    Arkansas         AR   98.3  108.2  112.1  116.0  120.8  115.2  121.8   \n",
       "4  California         CA   51.0   53.6   55.1   55.6   55.8   55.9   56.4   \n",
       "\n",
       "    2013   2014   2015   2016   2017  \n",
       "0  142.4  135.2  125.0  121.0  107.2  \n",
       "1   63.7   62.7   60.8   58.9   52.0  \n",
       "2   80.4   79.7   75.5   70.2   61.2  \n",
       "3  120.9  123.2  117.2  114.6  105.4  \n",
       "4   54.4   52.7   47.7   44.8   39.5  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_state = pd.read_excel(in_state, sheet_name=None, ignore_index=True)\n",
    "\n",
    "df_template = pd.read_excel(in_state, sheet_name=0)\n",
    "\n",
    "for index, key in enumerate(df_state.keys()):\n",
    "    df_year = pd.read_excel(in_state, sheet_name=key)\n",
    "    df_template[key] = df_year.iloc[:, 2]\n",
    "        \n",
    "df_state=df_template.drop(columns=['2006 Prescribing Rate'])\n",
    "df_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>State ABBR</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>115.6</td>\n",
       "      <td>120.3</td>\n",
       "      <td>126.1</td>\n",
       "      <td>131.6</td>\n",
       "      <td>134.3</td>\n",
       "      <td>136.6</td>\n",
       "      <td>143.8</td>\n",
       "      <td>142.4</td>\n",
       "      <td>135.2</td>\n",
       "      <td>125.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>107.2</td>\n",
       "      <td>128.258333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>AK</td>\n",
       "      <td>63.4</td>\n",
       "      <td>66.6</td>\n",
       "      <td>68.5</td>\n",
       "      <td>67.3</td>\n",
       "      <td>68.4</td>\n",
       "      <td>68.0</td>\n",
       "      <td>66.8</td>\n",
       "      <td>63.7</td>\n",
       "      <td>62.7</td>\n",
       "      <td>60.8</td>\n",
       "      <td>58.9</td>\n",
       "      <td>52.0</td>\n",
       "      <td>63.925000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>AZ</td>\n",
       "      <td>74.3</td>\n",
       "      <td>77.8</td>\n",
       "      <td>80.9</td>\n",
       "      <td>84.2</td>\n",
       "      <td>88.5</td>\n",
       "      <td>88.6</td>\n",
       "      <td>85.3</td>\n",
       "      <td>80.4</td>\n",
       "      <td>79.7</td>\n",
       "      <td>75.5</td>\n",
       "      <td>70.2</td>\n",
       "      <td>61.2</td>\n",
       "      <td>78.883333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>98.3</td>\n",
       "      <td>108.2</td>\n",
       "      <td>112.1</td>\n",
       "      <td>116.0</td>\n",
       "      <td>120.8</td>\n",
       "      <td>115.2</td>\n",
       "      <td>121.8</td>\n",
       "      <td>120.9</td>\n",
       "      <td>123.2</td>\n",
       "      <td>117.2</td>\n",
       "      <td>114.6</td>\n",
       "      <td>105.4</td>\n",
       "      <td>114.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>CA</td>\n",
       "      <td>51.0</td>\n",
       "      <td>53.6</td>\n",
       "      <td>55.1</td>\n",
       "      <td>55.6</td>\n",
       "      <td>55.8</td>\n",
       "      <td>55.9</td>\n",
       "      <td>56.4</td>\n",
       "      <td>54.4</td>\n",
       "      <td>52.7</td>\n",
       "      <td>47.7</td>\n",
       "      <td>44.8</td>\n",
       "      <td>39.5</td>\n",
       "      <td>51.875000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State State ABBR   2006   2007   2008   2009   2010   2011   2012  \\\n",
       "0     Alabama         AL  115.6  120.3  126.1  131.6  134.3  136.6  143.8   \n",
       "1      Alaska         AK   63.4   66.6   68.5   67.3   68.4   68.0   66.8   \n",
       "2     Arizona         AZ   74.3   77.8   80.9   84.2   88.5   88.6   85.3   \n",
       "3    Arkansas         AR   98.3  108.2  112.1  116.0  120.8  115.2  121.8   \n",
       "4  California         CA   51.0   53.6   55.1   55.6   55.8   55.9   56.4   \n",
       "\n",
       "    2013   2014   2015   2016   2017        mean  \n",
       "0  142.4  135.2  125.0  121.0  107.2  128.258333  \n",
       "1   63.7   62.7   60.8   58.9   52.0   63.925000  \n",
       "2   80.4   79.7   75.5   70.2   61.2   78.883333  \n",
       "3  120.9  123.2  117.2  114.6  105.4  114.475000  \n",
       "4   54.4   52.7   47.7   44.8   39.5   51.875000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_state['mean'] = df_state.mean(axis=1)\n",
    "df_state.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_state_file = in_state.replace(\".xlsx\", \"_Merge.xlsx\")\n",
    "df_state.to_excel(out_state_file, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_state.plot.bar(x=1, y=range(2, 14), rot=0, title=\"Prescription Rate by State\")\n",
    "ax.set_xlabel(\"State\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>State ABBR</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>115.6</td>\n",
       "      <td>120.3</td>\n",
       "      <td>126.1</td>\n",
       "      <td>131.6</td>\n",
       "      <td>134.3</td>\n",
       "      <td>136.6</td>\n",
       "      <td>143.8</td>\n",
       "      <td>142.4</td>\n",
       "      <td>135.2</td>\n",
       "      <td>125.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>107.2</td>\n",
       "      <td>128.258333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>WV</td>\n",
       "      <td>129.9</td>\n",
       "      <td>135.1</td>\n",
       "      <td>145.5</td>\n",
       "      <td>146.9</td>\n",
       "      <td>143.1</td>\n",
       "      <td>139.6</td>\n",
       "      <td>136.9</td>\n",
       "      <td>129.0</td>\n",
       "      <td>126.4</td>\n",
       "      <td>111.3</td>\n",
       "      <td>96.0</td>\n",
       "      <td>81.3</td>\n",
       "      <td>126.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>TN</td>\n",
       "      <td>124.8</td>\n",
       "      <td>128.8</td>\n",
       "      <td>132.9</td>\n",
       "      <td>138.4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>138.5</td>\n",
       "      <td>136.1</td>\n",
       "      <td>127.1</td>\n",
       "      <td>121.3</td>\n",
       "      <td>114.9</td>\n",
       "      <td>107.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>125.391667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>KY</td>\n",
       "      <td>122.6</td>\n",
       "      <td>130.8</td>\n",
       "      <td>136.6</td>\n",
       "      <td>135.2</td>\n",
       "      <td>136.5</td>\n",
       "      <td>137.0</td>\n",
       "      <td>127.9</td>\n",
       "      <td>111.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>97.2</td>\n",
       "      <td>86.8</td>\n",
       "      <td>119.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>98.3</td>\n",
       "      <td>108.2</td>\n",
       "      <td>112.1</td>\n",
       "      <td>116.0</td>\n",
       "      <td>120.8</td>\n",
       "      <td>115.2</td>\n",
       "      <td>121.8</td>\n",
       "      <td>120.9</td>\n",
       "      <td>123.2</td>\n",
       "      <td>117.2</td>\n",
       "      <td>114.6</td>\n",
       "      <td>105.4</td>\n",
       "      <td>114.475000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            State State ABBR   2006   2007   2008   2009   2010   2011   2012  \\\n",
       "0         Alabama         AL  115.6  120.3  126.1  131.6  134.3  136.6  143.8   \n",
       "48  West Virginia         WV  129.9  135.1  145.5  146.9  143.1  139.6  136.9   \n",
       "42      Tennessee         TN  124.8  128.8  132.9  138.4  140.0  138.5  136.1   \n",
       "17       Kentucky         KY  122.6  130.8  136.6  135.2  136.5  137.0  127.9   \n",
       "3        Arkansas         AR   98.3  108.2  112.1  116.0  120.8  115.2  121.8   \n",
       "\n",
       "     2013   2014   2015   2016   2017        mean  \n",
       "0   142.4  135.2  125.0  121.0  107.2  128.258333  \n",
       "48  129.0  126.4  111.3   96.0   81.3  126.750000  \n",
       "42  127.1  121.3  114.9  107.5   94.4  125.391667  \n",
       "17  111.7  110.0  102.6   97.2   86.8  119.575000  \n",
       "3   120.9  123.2  117.2  114.6  105.4  114.475000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_state_sort = df_state.sort_values(by='mean', ascending=False)\n",
    "df_state_sort.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_state_sort.plot.bar(x=1, y=range(2, 14), rot=0, title=\"Prescription Rate by State\")\n",
    "ax.set_xlabel(\"State\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>State ABBR</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>AL</td>\n",
       "      <td>115.6</td>\n",
       "      <td>120.3</td>\n",
       "      <td>126.1</td>\n",
       "      <td>131.6</td>\n",
       "      <td>134.3</td>\n",
       "      <td>136.6</td>\n",
       "      <td>143.8</td>\n",
       "      <td>142.4</td>\n",
       "      <td>135.2</td>\n",
       "      <td>125.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>107.2</td>\n",
       "      <td>128.258333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>WV</td>\n",
       "      <td>129.9</td>\n",
       "      <td>135.1</td>\n",
       "      <td>145.5</td>\n",
       "      <td>146.9</td>\n",
       "      <td>143.1</td>\n",
       "      <td>139.6</td>\n",
       "      <td>136.9</td>\n",
       "      <td>129.0</td>\n",
       "      <td>126.4</td>\n",
       "      <td>111.3</td>\n",
       "      <td>96.0</td>\n",
       "      <td>81.3</td>\n",
       "      <td>126.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Tennessee</td>\n",
       "      <td>TN</td>\n",
       "      <td>124.8</td>\n",
       "      <td>128.8</td>\n",
       "      <td>132.9</td>\n",
       "      <td>138.4</td>\n",
       "      <td>140.0</td>\n",
       "      <td>138.5</td>\n",
       "      <td>136.1</td>\n",
       "      <td>127.1</td>\n",
       "      <td>121.3</td>\n",
       "      <td>114.9</td>\n",
       "      <td>107.5</td>\n",
       "      <td>94.4</td>\n",
       "      <td>125.391667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>KY</td>\n",
       "      <td>122.6</td>\n",
       "      <td>130.8</td>\n",
       "      <td>136.6</td>\n",
       "      <td>135.2</td>\n",
       "      <td>136.5</td>\n",
       "      <td>137.0</td>\n",
       "      <td>127.9</td>\n",
       "      <td>111.7</td>\n",
       "      <td>110.0</td>\n",
       "      <td>102.6</td>\n",
       "      <td>97.2</td>\n",
       "      <td>86.8</td>\n",
       "      <td>119.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>AR</td>\n",
       "      <td>98.3</td>\n",
       "      <td>108.2</td>\n",
       "      <td>112.1</td>\n",
       "      <td>116.0</td>\n",
       "      <td>120.8</td>\n",
       "      <td>115.2</td>\n",
       "      <td>121.8</td>\n",
       "      <td>120.9</td>\n",
       "      <td>123.2</td>\n",
       "      <td>117.2</td>\n",
       "      <td>114.6</td>\n",
       "      <td>105.4</td>\n",
       "      <td>114.475000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>MS</td>\n",
       "      <td>102.7</td>\n",
       "      <td>109.9</td>\n",
       "      <td>113.2</td>\n",
       "      <td>117.3</td>\n",
       "      <td>118.1</td>\n",
       "      <td>117.2</td>\n",
       "      <td>121.8</td>\n",
       "      <td>119.6</td>\n",
       "      <td>116.3</td>\n",
       "      <td>110.9</td>\n",
       "      <td>105.6</td>\n",
       "      <td>92.9</td>\n",
       "      <td>112.125000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>OK</td>\n",
       "      <td>108.7</td>\n",
       "      <td>114.4</td>\n",
       "      <td>111.3</td>\n",
       "      <td>115.0</td>\n",
       "      <td>119.6</td>\n",
       "      <td>122.3</td>\n",
       "      <td>127.4</td>\n",
       "      <td>123.3</td>\n",
       "      <td>110.9</td>\n",
       "      <td>104.4</td>\n",
       "      <td>97.9</td>\n",
       "      <td>88.1</td>\n",
       "      <td>111.941667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>LA</td>\n",
       "      <td>109.2</td>\n",
       "      <td>110.1</td>\n",
       "      <td>113.7</td>\n",
       "      <td>113.0</td>\n",
       "      <td>112.6</td>\n",
       "      <td>111.7</td>\n",
       "      <td>113.0</td>\n",
       "      <td>112.4</td>\n",
       "      <td>108.9</td>\n",
       "      <td>100.4</td>\n",
       "      <td>98.1</td>\n",
       "      <td>89.5</td>\n",
       "      <td>107.716667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>IN</td>\n",
       "      <td>94.3</td>\n",
       "      <td>99.7</td>\n",
       "      <td>103.3</td>\n",
       "      <td>105.6</td>\n",
       "      <td>107.1</td>\n",
       "      <td>106.7</td>\n",
       "      <td>110.5</td>\n",
       "      <td>106.3</td>\n",
       "      <td>96.7</td>\n",
       "      <td>89.1</td>\n",
       "      <td>83.9</td>\n",
       "      <td>74.2</td>\n",
       "      <td>98.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>South Carolina</td>\n",
       "      <td>SC</td>\n",
       "      <td>90.4</td>\n",
       "      <td>91.7</td>\n",
       "      <td>94.1</td>\n",
       "      <td>95.8</td>\n",
       "      <td>98.6</td>\n",
       "      <td>96.8</td>\n",
       "      <td>104.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>101.3</td>\n",
       "      <td>95.1</td>\n",
       "      <td>89.4</td>\n",
       "      <td>79.3</td>\n",
       "      <td>94.958333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             State State ABBR   2006   2007   2008   2009   2010   2011  \\\n",
       "0          Alabama         AL  115.6  120.3  126.1  131.6  134.3  136.6   \n",
       "48   West Virginia         WV  129.9  135.1  145.5  146.9  143.1  139.6   \n",
       "42       Tennessee         TN  124.8  128.8  132.9  138.4  140.0  138.5   \n",
       "17        Kentucky         KY  122.6  130.8  136.6  135.2  136.5  137.0   \n",
       "3         Arkansas         AR   98.3  108.2  112.1  116.0  120.8  115.2   \n",
       "24     Mississippi         MS  102.7  109.9  113.2  117.3  118.1  117.2   \n",
       "36        Oklahoma         OK  108.7  114.4  111.3  115.0  119.6  122.3   \n",
       "18       Louisiana         LA  109.2  110.1  113.7  113.0  112.6  111.7   \n",
       "14         Indiana         IN   94.3   99.7  103.3  105.6  107.1  106.7   \n",
       "40  South Carolina         SC   90.4   91.7   94.1   95.8   98.6   96.8   \n",
       "\n",
       "     2012   2013   2014   2015   2016   2017        mean  \n",
       "0   143.8  142.4  135.2  125.0  121.0  107.2  128.258333  \n",
       "48  136.9  129.0  126.4  111.3   96.0   81.3  126.750000  \n",
       "42  136.1  127.1  121.3  114.9  107.5   94.4  125.391667  \n",
       "17  127.9  111.7  110.0  102.6   97.2   86.8  119.575000  \n",
       "3   121.8  120.9  123.2  117.2  114.6  105.4  114.475000  \n",
       "24  121.8  119.6  116.3  110.9  105.6   92.9  112.125000  \n",
       "36  127.4  123.3  110.9  104.4   97.9   88.1  111.941667  \n",
       "18  113.0  112.4  108.9  100.4   98.1   89.5  107.716667  \n",
       "14  110.5  106.3   96.7   89.1   83.9   74.2   98.116667  \n",
       "40  104.0  103.0  101.3   95.1   89.4   79.3   94.958333  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_state_top10 = df_state_sort.head(10)\n",
    "df_state_top10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top 10 States with the highest prescription rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_state_top10.plot.bar(x=1, y=range(2, 14), rot=0, title=\"Prescription Rate by State\")\n",
    "ax.set_xlabel(\"State\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_state_sort.plot.bar(x=1, y=14, rot=0, title=\"Mean Prescription Rate by State\")\n",
    "ax.set_xlabel(\"State\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opioid Precriptions by County (2006-2017)   <a class='anchor' id='opioid-by-county'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017', 'AllCounties2006-2017']\n",
      "Total number of sheets: 13\n"
     ]
    }
   ],
   "source": [
    "df_county = pd.ExcelFile(in_county)\n",
    "sheet_names =df_county.sheet_names\n",
    "print(sheet_names)\n",
    "\n",
    "num_sheets = len(df_county.sheet_names)\n",
    "print(f\"Total number of sheets: {num_sheets}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>FIPS County Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1009</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        County State  FIPS County Code\n",
       "0  Autauga, AL    AL              1001\n",
       "1  Baldwin, AL    AL              1003\n",
       "2  Barbour, AL    AL              1005\n",
       "3     Bibb, AL    AL              1007\n",
       "4   Blount, AL    AL              1009"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sheet_template = df_county.parse(sheet_name=sheet_names[0])[[\"County\", \"State\", \"FIPS County Code\"]]\n",
    "sheet_template.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging each yearly spreadsheet into a single spreadsheet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2006\n",
      "2007\n",
      "2008\n",
      "2009\n",
      "2010\n",
      "2011\n",
      "2012\n",
      "2013\n",
      "2014\n",
      "2015\n",
      "2016\n",
      "2017\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>County_x</th>\n",
       "      <th>State_x</th>\n",
       "      <th>FIPS County Code</th>\n",
       "      <th>County_y</th>\n",
       "      <th>State_y</th>\n",
       "      <th>2006 Prescribing Rate</th>\n",
       "      <th>County</th>\n",
       "      <th>State</th>\n",
       "      <th>2007 Prescribing Rate</th>\n",
       "      <th>County _x</th>\n",
       "      <th>...</th>\n",
       "      <th>2014 Prescribing Rate</th>\n",
       "      <th>County _y</th>\n",
       "      <th>State _y</th>\n",
       "      <th>2015 Prescribing Rate</th>\n",
       "      <th>County _x</th>\n",
       "      <th>State _x</th>\n",
       "      <th>2016 Prescribing Rate</th>\n",
       "      <th>County _y</th>\n",
       "      <th>State _y</th>\n",
       "      <th>2017 Prescribing Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1001</td>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>134.8</td>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>135.8</td>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>...</td>\n",
       "      <td>145.3</td>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>129.9</td>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>129.6</td>\n",
       "      <td>AUTAUGA</td>\n",
       "      <td>AL</td>\n",
       "      <td>106.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1003</td>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>127.8</td>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>133.2</td>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>...</td>\n",
       "      <td>143.5</td>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>132.1</td>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>123.8</td>\n",
       "      <td>BALDWIN</td>\n",
       "      <td>AL</td>\n",
       "      <td>106.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1005</td>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>78.1</td>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>72.7</td>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>...</td>\n",
       "      <td>102.0</td>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>93.3</td>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>92.7</td>\n",
       "      <td>BARBOUR</td>\n",
       "      <td>AL</td>\n",
       "      <td>90.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1007</td>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>114.3</td>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>116.1</td>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>...</td>\n",
       "      <td>75.8</td>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>69.4</td>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>97.2</td>\n",
       "      <td>BIBB</td>\n",
       "      <td>AL</td>\n",
       "      <td>80.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1009</td>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>40.3</td>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>44.6</td>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>...</td>\n",
       "      <td>63.2</td>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>57.9</td>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>56.9</td>\n",
       "      <td>BLOUNT</td>\n",
       "      <td>AL</td>\n",
       "      <td>48.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 39 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      County_x State_x  FIPS County Code     County_y State_y  \\\n",
       "0  Autauga, AL      AL              1001  Autauga, AL      AL   \n",
       "1  Baldwin, AL      AL              1003  Baldwin, AL      AL   \n",
       "2  Barbour, AL      AL              1005  Barbour, AL      AL   \n",
       "3     Bibb, AL      AL              1007     Bibb, AL      AL   \n",
       "4   Blount, AL      AL              1009   Blount, AL      AL   \n",
       "\n",
       "   2006 Prescribing Rate       County State  2007 Prescribing Rate  \\\n",
       "0                  134.8  Autauga, AL    AL                  135.8   \n",
       "1                  127.8  Baldwin, AL    AL                  133.2   \n",
       "2                   78.1  Barbour, AL    AL                   72.7   \n",
       "3                  114.3     Bibb, AL    AL                  116.1   \n",
       "4                   40.3   Blount, AL    AL                   44.6   \n",
       "\n",
       "      County _x  ... 2014 Prescribing Rate     County _y State _y  \\\n",
       "0  Autauga, AL   ...                 145.3  Autauga, AL       AL    \n",
       "1  Baldwin, AL   ...                 143.5  Baldwin, AL       AL    \n",
       "2  Barbour, AL   ...                 102.0  Barbour, AL       AL    \n",
       "3     Bibb, AL   ...                  75.8     Bibb, AL       AL    \n",
       "4   Blount, AL   ...                  63.2   Blount, AL       AL    \n",
       "\n",
       "  2015 Prescribing Rate     County _x State _x 2016 Prescribing Rate  \\\n",
       "0                 129.9  Autauga, AL       AL                  129.6   \n",
       "1                 132.1  Baldwin, AL       AL                  123.8   \n",
       "2                  93.3  Barbour, AL       AL                   92.7   \n",
       "3                  69.4     Bibb, AL       AL                   97.2   \n",
       "4                  57.9   Blount, AL       AL                   56.9   \n",
       "\n",
       "   County _y State _y 2017 Prescribing Rate  \n",
       "0   AUTAUGA       AL                  106.6  \n",
       "1   BALDWIN       AL                  106.7  \n",
       "2   BARBOUR       AL                   90.7  \n",
       "3      BIBB       AL                   80.6  \n",
       "4    BLOUNT       AL                   48.9  \n",
       "\n",
       "[5 rows x 39 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for sheet_name in sheet_names[:-1]:\n",
    "    print(sheet_name)\n",
    "    sheet_year = df_county.parse(sheet_name)\n",
    "#     print(sheet_year.head()).\n",
    "    sheet_template= pd.merge(sheet_template, sheet_year, how='left', on=['FIPS County Code'])\n",
    "#     print(sheet_template.head())\n",
    "\n",
    "sheet_template.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter and select columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['County',\n",
       " 'State',\n",
       " 'FIPS County Code',\n",
       " '2006 Prescribing Rate',\n",
       " '2007 Prescribing Rate',\n",
       " '2008 Prescribing Rate',\n",
       " '2009 Prescribing Rate',\n",
       " '2010 Prescribing Rate',\n",
       " '2011 Prescribing Rate',\n",
       " '2012 Prescribing Rate',\n",
       " '2013 Prescribing Rate',\n",
       " '2014 Prescribing Rate',\n",
       " '2015 Prescribing Rate',\n",
       " '2016 Prescribing Rate',\n",
       " '2017 Prescribing Rate']"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter_col = [\"County\", \"State\", \"FIPS County Code\"] + [col for col in sheet_template if col.startswith('20')]\n",
    "filter_col"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Display selected columns:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>FIPS County Code</th>\n",
       "      <th>2006 Prescribing Rate</th>\n",
       "      <th>2007 Prescribing Rate</th>\n",
       "      <th>2008 Prescribing Rate</th>\n",
       "      <th>2009 Prescribing Rate</th>\n",
       "      <th>2010 Prescribing Rate</th>\n",
       "      <th>2011 Prescribing Rate</th>\n",
       "      <th>2012 Prescribing Rate</th>\n",
       "      <th>2013 Prescribing Rate</th>\n",
       "      <th>2014 Prescribing Rate</th>\n",
       "      <th>2015 Prescribing Rate</th>\n",
       "      <th>2016 Prescribing Rate</th>\n",
       "      <th>2017 Prescribing Rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1001</td>\n",
       "      <td>134.8</td>\n",
       "      <td>135.8</td>\n",
       "      <td>144.9</td>\n",
       "      <td>147.5</td>\n",
       "      <td>151.7</td>\n",
       "      <td>144.1</td>\n",
       "      <td>157.8</td>\n",
       "      <td>166.7</td>\n",
       "      <td>145.3</td>\n",
       "      <td>129.9</td>\n",
       "      <td>129.6</td>\n",
       "      <td>106.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1003</td>\n",
       "      <td>127.8</td>\n",
       "      <td>133.2</td>\n",
       "      <td>138.3</td>\n",
       "      <td>143.9</td>\n",
       "      <td>143.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>156.4</td>\n",
       "      <td>154.3</td>\n",
       "      <td>143.5</td>\n",
       "      <td>132.1</td>\n",
       "      <td>123.8</td>\n",
       "      <td>106.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1005</td>\n",
       "      <td>78.1</td>\n",
       "      <td>72.7</td>\n",
       "      <td>79.6</td>\n",
       "      <td>88.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>118.3</td>\n",
       "      <td>107.5</td>\n",
       "      <td>102.0</td>\n",
       "      <td>93.3</td>\n",
       "      <td>92.7</td>\n",
       "      <td>90.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1007</td>\n",
       "      <td>114.3</td>\n",
       "      <td>116.1</td>\n",
       "      <td>119.6</td>\n",
       "      <td>109.6</td>\n",
       "      <td>58.9</td>\n",
       "      <td>57.7</td>\n",
       "      <td>69.2</td>\n",
       "      <td>70.5</td>\n",
       "      <td>75.8</td>\n",
       "      <td>69.4</td>\n",
       "      <td>97.2</td>\n",
       "      <td>80.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1009</td>\n",
       "      <td>40.3</td>\n",
       "      <td>44.6</td>\n",
       "      <td>58.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>60.1</td>\n",
       "      <td>64.8</td>\n",
       "      <td>66.6</td>\n",
       "      <td>65.9</td>\n",
       "      <td>63.2</td>\n",
       "      <td>57.9</td>\n",
       "      <td>56.9</td>\n",
       "      <td>48.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        County State  FIPS County Code  2006 Prescribing Rate  \\\n",
       "0  Autauga, AL    AL              1001                  134.8   \n",
       "1  Baldwin, AL    AL              1003                  127.8   \n",
       "2  Barbour, AL    AL              1005                   78.1   \n",
       "3     Bibb, AL    AL              1007                  114.3   \n",
       "4   Blount, AL    AL              1009                   40.3   \n",
       "\n",
       "   2007 Prescribing Rate  2008 Prescribing Rate  2009 Prescribing Rate  \\\n",
       "0                  135.8                  144.9                  147.5   \n",
       "1                  133.2                  138.3                  143.9   \n",
       "2                   72.7                   79.6                   88.5   \n",
       "3                  116.1                  119.6                  109.6   \n",
       "4                   44.6                   58.2                   56.2   \n",
       "\n",
       "   2010 Prescribing Rate  2011 Prescribing Rate  2012 Prescribing Rate  \\\n",
       "0                  151.7                  144.1                  157.8   \n",
       "1                  143.8                  150.0                  156.4   \n",
       "2                   97.0                   99.4                  118.3   \n",
       "3                   58.9                   57.7                   69.2   \n",
       "4                   60.1                   64.8                   66.6   \n",
       "\n",
       "   2013 Prescribing Rate  2014 Prescribing Rate  2015 Prescribing Rate  \\\n",
       "0                  166.7                  145.3                  129.9   \n",
       "1                  154.3                  143.5                  132.1   \n",
       "2                  107.5                  102.0                   93.3   \n",
       "3                   70.5                   75.8                   69.4   \n",
       "4                   65.9                   63.2                   57.9   \n",
       "\n",
       "   2016 Prescribing Rate  2017 Prescribing Rate  \n",
       "0                  129.6                  106.6  \n",
       "1                  123.8                  106.7  \n",
       "2                   92.7                   90.7  \n",
       "3                   97.2                   80.6  \n",
       "4                   56.9                   48.9  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_county_merge = sheet_template[filter_col]\n",
    "df_county_merge.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['County', 'State', 'FIPS County Code', '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013', '2014', '2015', '2016', '2017']\n"
     ]
    }
   ],
   "source": [
    "col_names = [\"County\", \"State\", \"FIPS County Code\"] + sheet_names[:-1]\n",
    "print(col_names)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>FIPS County Code</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01001</td>\n",
       "      <td>134.8</td>\n",
       "      <td>135.8</td>\n",
       "      <td>144.9</td>\n",
       "      <td>147.5</td>\n",
       "      <td>151.7</td>\n",
       "      <td>144.1</td>\n",
       "      <td>157.8</td>\n",
       "      <td>166.7</td>\n",
       "      <td>145.3</td>\n",
       "      <td>129.9</td>\n",
       "      <td>129.6</td>\n",
       "      <td>106.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01003</td>\n",
       "      <td>127.8</td>\n",
       "      <td>133.2</td>\n",
       "      <td>138.3</td>\n",
       "      <td>143.9</td>\n",
       "      <td>143.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>156.4</td>\n",
       "      <td>154.3</td>\n",
       "      <td>143.5</td>\n",
       "      <td>132.1</td>\n",
       "      <td>123.8</td>\n",
       "      <td>106.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01005</td>\n",
       "      <td>78.1</td>\n",
       "      <td>72.7</td>\n",
       "      <td>79.6</td>\n",
       "      <td>88.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>118.3</td>\n",
       "      <td>107.5</td>\n",
       "      <td>102.0</td>\n",
       "      <td>93.3</td>\n",
       "      <td>92.7</td>\n",
       "      <td>90.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01007</td>\n",
       "      <td>114.3</td>\n",
       "      <td>116.1</td>\n",
       "      <td>119.6</td>\n",
       "      <td>109.6</td>\n",
       "      <td>58.9</td>\n",
       "      <td>57.7</td>\n",
       "      <td>69.2</td>\n",
       "      <td>70.5</td>\n",
       "      <td>75.8</td>\n",
       "      <td>69.4</td>\n",
       "      <td>97.2</td>\n",
       "      <td>80.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01009</td>\n",
       "      <td>40.3</td>\n",
       "      <td>44.6</td>\n",
       "      <td>58.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>60.1</td>\n",
       "      <td>64.8</td>\n",
       "      <td>66.6</td>\n",
       "      <td>65.9</td>\n",
       "      <td>63.2</td>\n",
       "      <td>57.9</td>\n",
       "      <td>56.9</td>\n",
       "      <td>48.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        County State FIPS County Code   2006   2007   2008   2009   2010  \\\n",
       "0  Autauga, AL    AL            01001  134.8  135.8  144.9  147.5  151.7   \n",
       "1  Baldwin, AL    AL            01003  127.8  133.2  138.3  143.9  143.8   \n",
       "2  Barbour, AL    AL            01005   78.1   72.7   79.6   88.5   97.0   \n",
       "3     Bibb, AL    AL            01007  114.3  116.1  119.6  109.6   58.9   \n",
       "4   Blount, AL    AL            01009   40.3   44.6   58.2   56.2   60.1   \n",
       "\n",
       "    2011   2012   2013   2014   2015   2016   2017  \n",
       "0  144.1  157.8  166.7  145.3  129.9  129.6  106.6  \n",
       "1  150.0  156.4  154.3  143.5  132.1  123.8  106.7  \n",
       "2   99.4  118.3  107.5  102.0   93.3   92.7   90.7  \n",
       "3   57.7   69.2   70.5   75.8   69.4   97.2   80.6  \n",
       "4   64.8   66.6   65.9   63.2   57.9   56.9   48.9  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_county_merge.columns = col_names\n",
    "df_county_merge['FIPS County Code']= df_county_merge['FIPS County Code'].astype(str)\n",
    "df_county_merge['FIPS County Code'] = df_county_merge['FIPS County Code'].apply(lambda x: x.zfill(5))\n",
    "df_county_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a mean column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>FIPS County Code</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Autauga, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01001</td>\n",
       "      <td>134.8</td>\n",
       "      <td>135.8</td>\n",
       "      <td>144.9</td>\n",
       "      <td>147.5</td>\n",
       "      <td>151.7</td>\n",
       "      <td>144.1</td>\n",
       "      <td>157.8</td>\n",
       "      <td>166.7</td>\n",
       "      <td>145.3</td>\n",
       "      <td>129.9</td>\n",
       "      <td>129.6</td>\n",
       "      <td>106.6</td>\n",
       "      <td>141.225000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Baldwin, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01003</td>\n",
       "      <td>127.8</td>\n",
       "      <td>133.2</td>\n",
       "      <td>138.3</td>\n",
       "      <td>143.9</td>\n",
       "      <td>143.8</td>\n",
       "      <td>150.0</td>\n",
       "      <td>156.4</td>\n",
       "      <td>154.3</td>\n",
       "      <td>143.5</td>\n",
       "      <td>132.1</td>\n",
       "      <td>123.8</td>\n",
       "      <td>106.7</td>\n",
       "      <td>137.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Barbour, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01005</td>\n",
       "      <td>78.1</td>\n",
       "      <td>72.7</td>\n",
       "      <td>79.6</td>\n",
       "      <td>88.5</td>\n",
       "      <td>97.0</td>\n",
       "      <td>99.4</td>\n",
       "      <td>118.3</td>\n",
       "      <td>107.5</td>\n",
       "      <td>102.0</td>\n",
       "      <td>93.3</td>\n",
       "      <td>92.7</td>\n",
       "      <td>90.7</td>\n",
       "      <td>93.316667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Bibb, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01007</td>\n",
       "      <td>114.3</td>\n",
       "      <td>116.1</td>\n",
       "      <td>119.6</td>\n",
       "      <td>109.6</td>\n",
       "      <td>58.9</td>\n",
       "      <td>57.7</td>\n",
       "      <td>69.2</td>\n",
       "      <td>70.5</td>\n",
       "      <td>75.8</td>\n",
       "      <td>69.4</td>\n",
       "      <td>97.2</td>\n",
       "      <td>80.6</td>\n",
       "      <td>86.575000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Blount, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>01009</td>\n",
       "      <td>40.3</td>\n",
       "      <td>44.6</td>\n",
       "      <td>58.2</td>\n",
       "      <td>56.2</td>\n",
       "      <td>60.1</td>\n",
       "      <td>64.8</td>\n",
       "      <td>66.6</td>\n",
       "      <td>65.9</td>\n",
       "      <td>63.2</td>\n",
       "      <td>57.9</td>\n",
       "      <td>56.9</td>\n",
       "      <td>48.9</td>\n",
       "      <td>56.966667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        County State FIPS County Code   2006   2007   2008   2009   2010  \\\n",
       "0  Autauga, AL    AL            01001  134.8  135.8  144.9  147.5  151.7   \n",
       "1  Baldwin, AL    AL            01003  127.8  133.2  138.3  143.9  143.8   \n",
       "2  Barbour, AL    AL            01005   78.1   72.7   79.6   88.5   97.0   \n",
       "3     Bibb, AL    AL            01007  114.3  116.1  119.6  109.6   58.9   \n",
       "4   Blount, AL    AL            01009   40.3   44.6   58.2   56.2   60.1   \n",
       "\n",
       "    2011   2012   2013   2014   2015   2016   2017        mean  \n",
       "0  144.1  157.8  166.7  145.3  129.9  129.6  106.6  141.225000  \n",
       "1  150.0  156.4  154.3  143.5  132.1  123.8  106.7  137.816667  \n",
       "2   99.4  118.3  107.5  102.0   93.3   92.7   90.7   93.316667  \n",
       "3   57.7   69.2   70.5   75.8   69.4   97.2   80.6   86.575000  \n",
       "4   64.8   66.6   65.9   63.2   57.9   56.9   48.9   56.966667  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_county_merge = df_county_merge.replace(-9999, np.nan)\n",
    "df_county_merge['mean'] = df_county_merge.mean(axis=1)\n",
    "df_county_merge.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the merged opioid data as a new Excel file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_county_file = in_county.replace(\".xlsx\", \"_Merge.xlsx\")\n",
    "df_county_merge.to_excel(out_county_file, index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sort mean prescription rate by county:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>FIPS County Code</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2937</th>\n",
       "      <td>Martinsville, VA</td>\n",
       "      <td>VA</td>\n",
       "      <td>51690</td>\n",
       "      <td>403.3</td>\n",
       "      <td>438.4</td>\n",
       "      <td>474.7</td>\n",
       "      <td>520.1</td>\n",
       "      <td>566.4</td>\n",
       "      <td>583.8</td>\n",
       "      <td>548.1</td>\n",
       "      <td>521.8</td>\n",
       "      <td>532.5</td>\n",
       "      <td>444.4</td>\n",
       "      <td>399.9</td>\n",
       "      <td>336.4</td>\n",
       "      <td>480.816667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2940</th>\n",
       "      <td>Norton, VA</td>\n",
       "      <td>VA</td>\n",
       "      <td>51720</td>\n",
       "      <td>251.6</td>\n",
       "      <td>257.6</td>\n",
       "      <td>296.0</td>\n",
       "      <td>300.9</td>\n",
       "      <td>361.6</td>\n",
       "      <td>412.0</td>\n",
       "      <td>570.1</td>\n",
       "      <td>578.1</td>\n",
       "      <td>563.3</td>\n",
       "      <td>504.9</td>\n",
       "      <td>470.3</td>\n",
       "      <td>396.1</td>\n",
       "      <td>413.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2929</th>\n",
       "      <td>Galax, VA</td>\n",
       "      <td>VA</td>\n",
       "      <td>51640</td>\n",
       "      <td>383.9</td>\n",
       "      <td>400.8</td>\n",
       "      <td>428.0</td>\n",
       "      <td>398.5</td>\n",
       "      <td>360.0</td>\n",
       "      <td>409.4</td>\n",
       "      <td>439.2</td>\n",
       "      <td>457.1</td>\n",
       "      <td>460.3</td>\n",
       "      <td>431.1</td>\n",
       "      <td>394.4</td>\n",
       "      <td>345.1</td>\n",
       "      <td>408.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>Bell, KY</td>\n",
       "      <td>KY</td>\n",
       "      <td>21013</td>\n",
       "      <td>346.3</td>\n",
       "      <td>377.3</td>\n",
       "      <td>382.8</td>\n",
       "      <td>365.1</td>\n",
       "      <td>366.3</td>\n",
       "      <td>347.1</td>\n",
       "      <td>281.9</td>\n",
       "      <td>266.0</td>\n",
       "      <td>271.8</td>\n",
       "      <td>252.7</td>\n",
       "      <td>249.7</td>\n",
       "      <td>228.4</td>\n",
       "      <td>311.283333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1028</th>\n",
       "      <td>Floyd, KY</td>\n",
       "      <td>KY</td>\n",
       "      <td>21071</td>\n",
       "      <td>263.3</td>\n",
       "      <td>292.4</td>\n",
       "      <td>342.9</td>\n",
       "      <td>353.3</td>\n",
       "      <td>386.0</td>\n",
       "      <td>426.4</td>\n",
       "      <td>387.2</td>\n",
       "      <td>290.1</td>\n",
       "      <td>263.1</td>\n",
       "      <td>245.0</td>\n",
       "      <td>226.2</td>\n",
       "      <td>215.9</td>\n",
       "      <td>307.650000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1110</th>\n",
       "      <td>Whitley, KY</td>\n",
       "      <td>KY</td>\n",
       "      <td>21235</td>\n",
       "      <td>301.6</td>\n",
       "      <td>312.2</td>\n",
       "      <td>316.1</td>\n",
       "      <td>308.6</td>\n",
       "      <td>325.4</td>\n",
       "      <td>345.6</td>\n",
       "      <td>364.2</td>\n",
       "      <td>312.0</td>\n",
       "      <td>300.1</td>\n",
       "      <td>265.2</td>\n",
       "      <td>239.6</td>\n",
       "      <td>220.9</td>\n",
       "      <td>300.958333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Walker, AL</td>\n",
       "      <td>AL</td>\n",
       "      <td>1127</td>\n",
       "      <td>304.1</td>\n",
       "      <td>306.5</td>\n",
       "      <td>312.4</td>\n",
       "      <td>329.8</td>\n",
       "      <td>338.1</td>\n",
       "      <td>340.2</td>\n",
       "      <td>335.1</td>\n",
       "      <td>317.1</td>\n",
       "      <td>291.1</td>\n",
       "      <td>249.9</td>\n",
       "      <td>235.4</td>\n",
       "      <td>216.1</td>\n",
       "      <td>297.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1090</th>\n",
       "      <td>Pike, KY</td>\n",
       "      <td>KY</td>\n",
       "      <td>21195</td>\n",
       "      <td>387.5</td>\n",
       "      <td>408.7</td>\n",
       "      <td>332.1</td>\n",
       "      <td>324.1</td>\n",
       "      <td>315.4</td>\n",
       "      <td>315.5</td>\n",
       "      <td>306.2</td>\n",
       "      <td>267.7</td>\n",
       "      <td>263.1</td>\n",
       "      <td>242.4</td>\n",
       "      <td>220.0</td>\n",
       "      <td>191.4</td>\n",
       "      <td>297.841667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3015</th>\n",
       "      <td>Logan, WV</td>\n",
       "      <td>WV</td>\n",
       "      <td>54045</td>\n",
       "      <td>350.3</td>\n",
       "      <td>354.7</td>\n",
       "      <td>342.4</td>\n",
       "      <td>320.3</td>\n",
       "      <td>287.4</td>\n",
       "      <td>291.0</td>\n",
       "      <td>300.1</td>\n",
       "      <td>272.4</td>\n",
       "      <td>263.0</td>\n",
       "      <td>206.6</td>\n",
       "      <td>194.3</td>\n",
       "      <td>170.7</td>\n",
       "      <td>279.433333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2933</th>\n",
       "      <td>Lexington, VA</td>\n",
       "      <td>VA</td>\n",
       "      <td>51678</td>\n",
       "      <td>205.0</td>\n",
       "      <td>238.3</td>\n",
       "      <td>274.3</td>\n",
       "      <td>279.9</td>\n",
       "      <td>276.5</td>\n",
       "      <td>288.2</td>\n",
       "      <td>334.2</td>\n",
       "      <td>335.3</td>\n",
       "      <td>325.7</td>\n",
       "      <td>269.0</td>\n",
       "      <td>237.0</td>\n",
       "      <td>213.5</td>\n",
       "      <td>273.075000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                County State  FIPS County Code   2006   2007   2008   2009  \\\n",
       "2937  Martinsville, VA    VA             51690  403.3  438.4  474.7  520.1   \n",
       "2940        Norton, VA    VA             51720  251.6  257.6  296.0  300.9   \n",
       "2929         Galax, VA    VA             51640  383.9  400.8  428.0  398.5   \n",
       "999           Bell, KY    KY             21013  346.3  377.3  382.8  365.1   \n",
       "1028         Floyd, KY    KY             21071  263.3  292.4  342.9  353.3   \n",
       "1110       Whitley, KY    KY             21235  301.6  312.2  316.1  308.6   \n",
       "63          Walker, AL    AL              1127  304.1  306.5  312.4  329.8   \n",
       "1090          Pike, KY    KY             21195  387.5  408.7  332.1  324.1   \n",
       "3015         Logan, WV    WV             54045  350.3  354.7  342.4  320.3   \n",
       "2933     Lexington, VA    VA             51678  205.0  238.3  274.3  279.9   \n",
       "\n",
       "       2010   2011   2012   2013   2014   2015   2016   2017        mean  \n",
       "2937  566.4  583.8  548.1  521.8  532.5  444.4  399.9  336.4  480.816667  \n",
       "2940  361.6  412.0  570.1  578.1  563.3  504.9  470.3  396.1  413.541667  \n",
       "2929  360.0  409.4  439.2  457.1  460.3  431.1  394.4  345.1  408.983333  \n",
       "999   366.3  347.1  281.9  266.0  271.8  252.7  249.7  228.4  311.283333  \n",
       "1028  386.0  426.4  387.2  290.1  263.1  245.0  226.2  215.9  307.650000  \n",
       "1110  325.4  345.6  364.2  312.0  300.1  265.2  239.6  220.9  300.958333  \n",
       "63    338.1  340.2  335.1  317.1  291.1  249.9  235.4  216.1  297.983333  \n",
       "1090  315.4  315.5  306.2  267.7  263.1  242.4  220.0  191.4  297.841667  \n",
       "3015  287.4  291.0  300.1  272.4  263.0  206.6  194.3  170.7  279.433333  \n",
       "2933  276.5  288.2  334.2  335.3  325.7  269.0  237.0  213.5  273.075000  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_county = pd.read_excel(out_county_file, sheet_name=0, ignore_index=True)\n",
    "df_county_sort = df_county.sort_values(by='mean', ascending=False)\n",
    "df_county_top10 = df_county_sort.head(10)\n",
    "df_county_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_county_top10.plot.bar(x=0, y=range(3, 15), rot=0, title=\"Top 10 County\")\n",
    "ax.set_xlabel(\"County\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_county_top10.plot.bar(x=0, y=15, rot=0, title=\"Mean Prescription Rate by County\")\n",
    "ax.set_xlabel(\"County\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opioid Prescriptions in Tennessee (2006-2017)   <a class='anchor' id='opioid-tn'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select Tennessee opioid data by county:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>FIPS County Code</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2428</th>\n",
       "      <td>Anderson, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47001</td>\n",
       "      <td>203.1</td>\n",
       "      <td>197.1</td>\n",
       "      <td>206.5</td>\n",
       "      <td>204.0</td>\n",
       "      <td>209.2</td>\n",
       "      <td>209.8</td>\n",
       "      <td>202.1</td>\n",
       "      <td>185.4</td>\n",
       "      <td>180.3</td>\n",
       "      <td>167.9</td>\n",
       "      <td>147.4</td>\n",
       "      <td>126.2</td>\n",
       "      <td>186.583333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2429</th>\n",
       "      <td>Bedford, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47003</td>\n",
       "      <td>97.8</td>\n",
       "      <td>99.2</td>\n",
       "      <td>102.1</td>\n",
       "      <td>114.2</td>\n",
       "      <td>118.8</td>\n",
       "      <td>120.4</td>\n",
       "      <td>124.6</td>\n",
       "      <td>126.7</td>\n",
       "      <td>124.2</td>\n",
       "      <td>111.1</td>\n",
       "      <td>101.5</td>\n",
       "      <td>94.2</td>\n",
       "      <td>111.233333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2430</th>\n",
       "      <td>Benton, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47005</td>\n",
       "      <td>17.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>21.6</td>\n",
       "      <td>21.5</td>\n",
       "      <td>22.2</td>\n",
       "      <td>62.6</td>\n",
       "      <td>167.6</td>\n",
       "      <td>143.8</td>\n",
       "      <td>151.8</td>\n",
       "      <td>143.5</td>\n",
       "      <td>136.7</td>\n",
       "      <td>119.0</td>\n",
       "      <td>85.658333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2431</th>\n",
       "      <td>Bledsoe, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47007</td>\n",
       "      <td>103.6</td>\n",
       "      <td>111.1</td>\n",
       "      <td>119.6</td>\n",
       "      <td>124.0</td>\n",
       "      <td>121.7</td>\n",
       "      <td>104.0</td>\n",
       "      <td>110.7</td>\n",
       "      <td>91.0</td>\n",
       "      <td>86.2</td>\n",
       "      <td>87.8</td>\n",
       "      <td>83.6</td>\n",
       "      <td>72.7</td>\n",
       "      <td>101.333333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2432</th>\n",
       "      <td>Blount, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47009</td>\n",
       "      <td>118.9</td>\n",
       "      <td>126.0</td>\n",
       "      <td>135.3</td>\n",
       "      <td>138.0</td>\n",
       "      <td>139.5</td>\n",
       "      <td>131.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>111.9</td>\n",
       "      <td>110.1</td>\n",
       "      <td>102.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>80.2</td>\n",
       "      <td>117.408333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2433</th>\n",
       "      <td>Bradley, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47011</td>\n",
       "      <td>136.0</td>\n",
       "      <td>142.7</td>\n",
       "      <td>156.4</td>\n",
       "      <td>169.4</td>\n",
       "      <td>182.6</td>\n",
       "      <td>183.7</td>\n",
       "      <td>185.1</td>\n",
       "      <td>159.2</td>\n",
       "      <td>148.9</td>\n",
       "      <td>141.3</td>\n",
       "      <td>129.9</td>\n",
       "      <td>113.7</td>\n",
       "      <td>154.075000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2434</th>\n",
       "      <td>Campbell, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47013</td>\n",
       "      <td>193.6</td>\n",
       "      <td>242.7</td>\n",
       "      <td>224.6</td>\n",
       "      <td>233.7</td>\n",
       "      <td>209.1</td>\n",
       "      <td>257.3</td>\n",
       "      <td>257.0</td>\n",
       "      <td>235.6</td>\n",
       "      <td>244.1</td>\n",
       "      <td>228.1</td>\n",
       "      <td>195.6</td>\n",
       "      <td>172.0</td>\n",
       "      <td>224.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2435</th>\n",
       "      <td>Cannon, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47015</td>\n",
       "      <td>127.9</td>\n",
       "      <td>125.8</td>\n",
       "      <td>123.5</td>\n",
       "      <td>127.6</td>\n",
       "      <td>129.6</td>\n",
       "      <td>136.3</td>\n",
       "      <td>120.3</td>\n",
       "      <td>125.6</td>\n",
       "      <td>121.1</td>\n",
       "      <td>108.2</td>\n",
       "      <td>101.9</td>\n",
       "      <td>95.1</td>\n",
       "      <td>120.241667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2436</th>\n",
       "      <td>Carroll, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47017</td>\n",
       "      <td>209.2</td>\n",
       "      <td>208.8</td>\n",
       "      <td>255.1</td>\n",
       "      <td>265.8</td>\n",
       "      <td>240.5</td>\n",
       "      <td>210.4</td>\n",
       "      <td>186.1</td>\n",
       "      <td>174.9</td>\n",
       "      <td>164.2</td>\n",
       "      <td>148.7</td>\n",
       "      <td>139.8</td>\n",
       "      <td>119.0</td>\n",
       "      <td>193.541667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2437</th>\n",
       "      <td>Carter, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47019</td>\n",
       "      <td>126.8</td>\n",
       "      <td>132.4</td>\n",
       "      <td>129.2</td>\n",
       "      <td>140.6</td>\n",
       "      <td>139.9</td>\n",
       "      <td>136.8</td>\n",
       "      <td>133.0</td>\n",
       "      <td>121.8</td>\n",
       "      <td>119.2</td>\n",
       "      <td>113.2</td>\n",
       "      <td>109.5</td>\n",
       "      <td>98.8</td>\n",
       "      <td>125.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            County State  FIPS County Code   2006   2007   2008   2009   2010  \\\n",
       "2428  Anderson, TN    TN             47001  203.1  197.1  206.5  204.0  209.2   \n",
       "2429   Bedford, TN    TN             47003   97.8   99.2  102.1  114.2  118.8   \n",
       "2430    Benton, TN    TN             47005   17.6   20.0   21.6   21.5   22.2   \n",
       "2431   Bledsoe, TN    TN             47007  103.6  111.1  119.6  124.0  121.7   \n",
       "2432    Blount, TN    TN             47009  118.9  126.0  135.3  138.0  139.5   \n",
       "2433   Bradley, TN    TN             47011  136.0  142.7  156.4  169.4  182.6   \n",
       "2434  Campbell, TN    TN             47013  193.6  242.7  224.6  233.7  209.1   \n",
       "2435    Cannon, TN    TN             47015  127.9  125.8  123.5  127.6  129.6   \n",
       "2436   Carroll, TN    TN             47017  209.2  208.8  255.1  265.8  240.5   \n",
       "2437    Carter, TN    TN             47019  126.8  132.4  129.2  140.6  139.9   \n",
       "\n",
       "       2011   2012   2013   2014   2015   2016   2017        mean  \n",
       "2428  209.8  202.1  185.4  180.3  167.9  147.4  126.2  186.583333  \n",
       "2429  120.4  124.6  126.7  124.2  111.1  101.5   94.2  111.233333  \n",
       "2430   62.6  167.6  143.8  151.8  143.5  136.7  119.0   85.658333  \n",
       "2431  104.0  110.7   91.0   86.2   87.8   83.6   72.7  101.333333  \n",
       "2432  131.0  125.0  111.9  110.1  102.0   91.0   80.2  117.408333  \n",
       "2433  183.7  185.1  159.2  148.9  141.3  129.9  113.7  154.075000  \n",
       "2434  257.3  257.0  235.6  244.1  228.1  195.6  172.0  224.450000  \n",
       "2435  136.3  120.3  125.6  121.1  108.2  101.9   95.1  120.241667  \n",
       "2436  210.4  186.1  174.9  164.2  148.7  139.8  119.0  193.541667  \n",
       "2437  136.8  133.0  121.8  119.2  113.2  109.5   98.8  125.100000  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tn = df_county[df_county['State']=='TN']\n",
    "df_tn.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_tn.plot.bar(x=0, y=15, rot=0, title=\"Mean Prescription Rate by County\")\n",
    "ax.set_xlabel(\"County\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_tn_sort = df_tn.sort_values(by='mean', ascending=False)\n",
    "ax = df_tn_sort.plot.bar(x=0, y=15, rot=0, title=\"Mean Prescription Rate by County\")\n",
    "ax.set_xlabel(\"County\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "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>County</th>\n",
       "      <th>State</th>\n",
       "      <th>FIPS County Code</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2459</th>\n",
       "      <td>Hamblen, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47063</td>\n",
       "      <td>281.3</td>\n",
       "      <td>289.3</td>\n",
       "      <td>286.1</td>\n",
       "      <td>283.1</td>\n",
       "      <td>281.9</td>\n",
       "      <td>261.7</td>\n",
       "      <td>251.6</td>\n",
       "      <td>226.1</td>\n",
       "      <td>199.9</td>\n",
       "      <td>180.4</td>\n",
       "      <td>163.2</td>\n",
       "      <td>143.6</td>\n",
       "      <td>237.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2434</th>\n",
       "      <td>Campbell, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47013</td>\n",
       "      <td>193.6</td>\n",
       "      <td>242.7</td>\n",
       "      <td>224.6</td>\n",
       "      <td>233.7</td>\n",
       "      <td>209.1</td>\n",
       "      <td>257.3</td>\n",
       "      <td>257.0</td>\n",
       "      <td>235.6</td>\n",
       "      <td>244.1</td>\n",
       "      <td>228.1</td>\n",
       "      <td>195.6</td>\n",
       "      <td>172.0</td>\n",
       "      <td>224.450000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2467</th>\n",
       "      <td>Henry, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47079</td>\n",
       "      <td>223.9</td>\n",
       "      <td>254.7</td>\n",
       "      <td>251.8</td>\n",
       "      <td>264.7</td>\n",
       "      <td>258.9</td>\n",
       "      <td>220.7</td>\n",
       "      <td>214.6</td>\n",
       "      <td>215.3</td>\n",
       "      <td>203.8</td>\n",
       "      <td>196.1</td>\n",
       "      <td>188.7</td>\n",
       "      <td>166.8</td>\n",
       "      <td>221.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2509</th>\n",
       "      <td>Sullivan, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47163</td>\n",
       "      <td>250.4</td>\n",
       "      <td>260.0</td>\n",
       "      <td>237.9</td>\n",
       "      <td>238.1</td>\n",
       "      <td>237.5</td>\n",
       "      <td>240.6</td>\n",
       "      <td>221.7</td>\n",
       "      <td>201.6</td>\n",
       "      <td>189.7</td>\n",
       "      <td>178.2</td>\n",
       "      <td>165.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>213.808333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2512</th>\n",
       "      <td>Trousdale, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47169</td>\n",
       "      <td>107.2</td>\n",
       "      <td>114.3</td>\n",
       "      <td>162.9</td>\n",
       "      <td>226.3</td>\n",
       "      <td>267.1</td>\n",
       "      <td>309.3</td>\n",
       "      <td>296.4</td>\n",
       "      <td>280.9</td>\n",
       "      <td>236.8</td>\n",
       "      <td>212.2</td>\n",
       "      <td>171.7</td>\n",
       "      <td>128.0</td>\n",
       "      <td>209.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2442</th>\n",
       "      <td>Cocke, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47029</td>\n",
       "      <td>153.9</td>\n",
       "      <td>219.5</td>\n",
       "      <td>235.6</td>\n",
       "      <td>240.7</td>\n",
       "      <td>216.3</td>\n",
       "      <td>224.4</td>\n",
       "      <td>203.4</td>\n",
       "      <td>205.0</td>\n",
       "      <td>213.9</td>\n",
       "      <td>201.5</td>\n",
       "      <td>188.3</td>\n",
       "      <td>162.6</td>\n",
       "      <td>205.425000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2440</th>\n",
       "      <td>Claiborne, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47025</td>\n",
       "      <td>190.7</td>\n",
       "      <td>183.9</td>\n",
       "      <td>187.3</td>\n",
       "      <td>210.0</td>\n",
       "      <td>202.7</td>\n",
       "      <td>203.3</td>\n",
       "      <td>240.8</td>\n",
       "      <td>242.2</td>\n",
       "      <td>232.2</td>\n",
       "      <td>219.4</td>\n",
       "      <td>189.4</td>\n",
       "      <td>159.2</td>\n",
       "      <td>205.091667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2443</th>\n",
       "      <td>Coffee, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47031</td>\n",
       "      <td>219.1</td>\n",
       "      <td>213.5</td>\n",
       "      <td>212.4</td>\n",
       "      <td>217.5</td>\n",
       "      <td>225.5</td>\n",
       "      <td>219.9</td>\n",
       "      <td>234.8</td>\n",
       "      <td>220.1</td>\n",
       "      <td>192.9</td>\n",
       "      <td>175.5</td>\n",
       "      <td>169.5</td>\n",
       "      <td>156.4</td>\n",
       "      <td>204.758333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2504</th>\n",
       "      <td>Sequatchie, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47153</td>\n",
       "      <td>177.7</td>\n",
       "      <td>183.4</td>\n",
       "      <td>221.0</td>\n",
       "      <td>240.0</td>\n",
       "      <td>259.1</td>\n",
       "      <td>253.9</td>\n",
       "      <td>213.6</td>\n",
       "      <td>194.1</td>\n",
       "      <td>186.2</td>\n",
       "      <td>191.3</td>\n",
       "      <td>175.2</td>\n",
       "      <td>159.7</td>\n",
       "      <td>204.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2441</th>\n",
       "      <td>Clay, TN</td>\n",
       "      <td>TN</td>\n",
       "      <td>47027</td>\n",
       "      <td>123.1</td>\n",
       "      <td>144.9</td>\n",
       "      <td>243.7</td>\n",
       "      <td>239.0</td>\n",
       "      <td>237.3</td>\n",
       "      <td>198.8</td>\n",
       "      <td>243.6</td>\n",
       "      <td>206.0</td>\n",
       "      <td>185.9</td>\n",
       "      <td>189.0</td>\n",
       "      <td>189.9</td>\n",
       "      <td>191.3</td>\n",
       "      <td>199.375000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              County State  FIPS County Code   2006   2007   2008   2009  \\\n",
       "2459     Hamblen, TN    TN             47063  281.3  289.3  286.1  283.1   \n",
       "2434    Campbell, TN    TN             47013  193.6  242.7  224.6  233.7   \n",
       "2467       Henry, TN    TN             47079  223.9  254.7  251.8  264.7   \n",
       "2509    Sullivan, TN    TN             47163  250.4  260.0  237.9  238.1   \n",
       "2512   Trousdale, TN    TN             47169  107.2  114.3  162.9  226.3   \n",
       "2442       Cocke, TN    TN             47029  153.9  219.5  235.6  240.7   \n",
       "2440   Claiborne, TN    TN             47025  190.7  183.9  187.3  210.0   \n",
       "2443      Coffee, TN    TN             47031  219.1  213.5  212.4  217.5   \n",
       "2504  Sequatchie, TN    TN             47153  177.7  183.4  221.0  240.0   \n",
       "2441        Clay, TN    TN             47027  123.1  144.9  243.7  239.0   \n",
       "\n",
       "       2010   2011   2012   2013   2014   2015   2016   2017        mean  \n",
       "2459  281.9  261.7  251.6  226.1  199.9  180.4  163.2  143.6  237.350000  \n",
       "2434  209.1  257.3  257.0  235.6  244.1  228.1  195.6  172.0  224.450000  \n",
       "2467  258.9  220.7  214.6  215.3  203.8  196.1  188.7  166.8  221.666667  \n",
       "2509  237.5  240.6  221.7  201.6  189.7  178.2  165.0  145.0  213.808333  \n",
       "2512  267.1  309.3  296.4  280.9  236.8  212.2  171.7  128.0  209.425000  \n",
       "2442  216.3  224.4  203.4  205.0  213.9  201.5  188.3  162.6  205.425000  \n",
       "2440  202.7  203.3  240.8  242.2  232.2  219.4  189.4  159.2  205.091667  \n",
       "2443  225.5  219.9  234.8  220.1  192.9  175.5  169.5  156.4  204.758333  \n",
       "2504  259.1  253.9  213.6  194.1  186.2  191.3  175.2  159.7  204.600000  \n",
       "2441  237.3  198.8  243.6  206.0  185.9  189.0  189.9  191.3  199.375000  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tn_top10 = df_tn_sort.head(10)\n",
    "df_tn_top10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wGEEEAAAAMBhBBAAAADAYQQQAAAAwGEEEAAAAMBhBBAAAADAYQQQAAAAwGEEEAAAAMBhBBAAAADAYQQQAAAAwGEEEAAAAMBhBBAAAADAYQQQAAAAwGEEEAAAAMBhBBAAAADAYQQQAAAAwGEEEAAAAMBhBBAAAADAYQQQAAAAwGEEEAAAAMBhBBAAAADAYQQQAAAAwGEEEAAAAMBhBBAAAADAYQQQAAAAwmLEFEVX17qq6uKrOHxl2VFX9pKrO7v8eNfLc/6yqC6vqu1X18HHVBQAAACydcbaIeG+SR0wz/I2ttT36v08lSVXtkuTQJPftp3lbVW06xtoAAACAJTC2IKK19qUkl89x9IOTfKi19pvW2g+TXJhkn3HVBgAAACyNpegj4rlVdW5/6sad+2F3S/LfI+Os6YcBAAAAE2ToIOLtSe6VZI8kFyU5ph9e04zbpptBVR1eVWdU1RmXXHLJeKoEAAAAxmLQIKK19vPW2g2ttRuT/GtuPv1iTZK7j4y6Q5KfzjCPY1trq1prq7beeuvxFgwAAAAsqkGDiKrabuTh45JMXVHjpCSHVtUWVbVjkp2SnD5kbQAAAMD4bbauEarq99OdUrFta23Xqto9yZ+21l61juk+mGT/JFtV1Zokr0iyf1Xtke60i9VJ/jpJWmsXVNVHknwryfVJntNau2He7woAAABYltYZRKQ7heLvkrwjSVpr51bVB5LMGkS01p40zeB3zTL+q5O8eg71AAAAABuouZyacZvW2tqnSVw/jmIAAACAyTaXIOLSqrpX+qtYVNUh6a54AQAAALBe5nJqxnOSHJtk56r6SZIfJjlsrFUBAAAAE2kuQURrrR1YVbdNsklr7er+yhYAAAAA62Uup2Z8PElaa9e01q7uh31sfCUBAAAAk2rGFhFVtXOS+ya5Y1U9fuSpOyRZMe7CAAAAgMkz26kZ90lyUJI7JXnMyPCrkzxznEUBAAAAk2nGIKK1dmKSE6vqga21rw1YEwAAADCh5tJZ5VlV9Zx0p2ncdEpGa+1pY6sKAAAAmEhz6azy+CR3TfLwJF9MskO60zMAAAAA1stcgoh7t9aOTHJNa+24JI9Ostt4ywIAAAAm0VyCiOv62yuqatckd0yycmwVAQAAABNrLn1EHFtVd07ysiQnJbldkpePtSoAAABgIq0ziGitvbO/+6Uk9xxvOQAAAMAkm/XUjKratKq2Gnl8q6p6ZlV9e/ylAQAAAJNmxiCiqg5NcnmSc6vqi1V1QJIfJHlUksMGqg8AAACYILOdmvGyJHu11i6sqj2TfC3Joa21E4YpDQAAAJg0s52a8dvW2oVJ0lr7ZpIfCiEAAACAhZitRcQ2VfW3I49vN/q4tfaG8ZUFAAAATKLZgoh/TXL7WR4DAAAArJcZg4jW2iuHLAQAAACYfLNevhMAAABgMQkiAAAAgMEIIgAAAIDBzNZZZZKkqrZI8mdJVo6O31r7h/GVBQAAAEyidQYRSU5McmWSM5P8ZrzlAAAAAJNsLkHEDq21R4y9EgAAAGDizaWPiK9W1W5jrwQAAACYeHNpEfHgJE+pqh+mOzWjkrTW2u5jrQwAAACYOHMJIh459ioAAACAjcI6T81orf0oyZ2SPKb/u1M/DAAAAGC9rDOIqKoXJHl/km36v3+rqueNuzAAAABg8szl1IynJ3lAa+2aJKmqf0rytSRvHmdhAAAAwOSZy1UzKskNI49v6IcBAAAArJe5tIh4T5KvV9UJ/ePHJnnX+EoCAAAAJtU6g4jW2huq6pR0l/GsJE9trZ017sIAAACAyTNjEFFVd2itXVVVd0myuv+beu4urbXLx18eAAAAMElmaxHxgSQHJTkzSRsZXv3je46xLgAAAGACzRhEtNYO6m93HK4cAAAAYJKt86oZVXXyXIYBAAAArMtsfUSsSHKbJFtV1Z1z8yU775Bk+wFqAwAAACbMbH1E/HWSF6YLHb45MvyqJG8dZ1EAAADAZJqtj4h/TvLPVfW81tqbB6wJAAAAmFCztYiY8s6q+tskD053tYwvJ/mX1tq1Y60MAAAAmDhzCSKOS3J1kqlWEU9KcnySJ4yrKAAAAGAyzSWIuE9r7X4jj79QVeeMqyAAAABgcq3z8p1JzqqqfaceVNUDkpw6vpIAAACASTWXFhEPSPJXVfXj/vE9kny7qs5L0lpru4+tOgAAAGCizCWIeMTYqwAAAAA2CjMGEVV1h9baVek6qvwdrbXLx1YVAAAAMJFmaxHxgSQHJTkz3WU7a+S5luSeY6wLAAAAmEAzBhGttYOqqpL8UWvtxzONBwAAADBXs141o7XWkpwwUC0AAADAhJvL5TtPq6q9x14JAAAAMPHmctWMA5L8dVX9KMk16fqKcNlOAAAAYL3NJYh45NirAAAAADYKczk1Y7skl7fWftRa+1GSy5PcdbxlAQAAAJNoLkHE25P8cuTxNf0wAAAAgPUylyCi+qtnJElaazdmbqd0AAAAANzCXIKIH1TV86tq8/7vBUl+MO7CAAAAgMkzlyDiWUkelOQnSdYkeUCSw8dZFAAAADCZ1nmKRWvt4iSHDlALAAAAMOHW2SKiql5bVXfoT8s4uaouraq/GKI4AAAAYLLM5dSMh7XWrkpyULpTM34/yd+NtSoAAABgIs0liNi8v31Ukg+21i4fYz0AAADABJvLZTj/X1V9J8mvkzy7qrZOcu14ywIAAAAm0TpbRLTWjkjywCSrWmvXJflVkoPHXRgAAAAweebSWeVtkjwnydv7QdsnWTXOogAAAIDJNJc+It6T5LdJHtQ/XpPkVWOrCAAAAJhYcwki7tVae22S65KktfbrJDXWqgAAAICJNJcg4rdVdeskLUmq6l5JfjPWqgAAAICJNJerZrwiyaeT3L2q3p9kvyRPGWdRAAAAwGSaNYioqkrynSSPT7JvulMyXtBau3SA2gAAAIAJM2sQ0VprVfV/W2t7JfnkQDUBAAAAE2oufUScVlV7j70SAAAAYOLNpY+IA5I8q6pWJ7km3ekZrbW2+zgLAwAAACbPXIKIR469CgAAAGCjMGMQUVUrkjwryb2TnJfkXa2164cqDAAAAJg8s/URcVySVelCiEcmOWaQigAAAICJNVsQsUtr7S9aa+9IckiSh6zPjKvq3VV1cVWdPzLsLlX12ar6fn975354VdWbqurCqjq3qvac17sBAAAAlrXZgojrpu7M85SM9yZ5xFrDjkhycmttpyQn94+TrsXFTv3f4UnePo/XAwAAAJa52YKI+1XVVf3f1Ul2n7pfVVeta8attS8luXytwQenO+Uj/e1jR4a/r3VOS3Knqtpu/d4KAAAAsNzN2Flla23TMbzetq21i/r5X1RV2/TD75bkv0fGW9MPu2jtGVTV4elaTeQe97jHGEoEAAAAxmW2FhFDqmmGtelGbK0d21pb1VpbtfXWW4+5LAAAAGAxDR1E/HzqlIv+9uJ++Jokdx8Zb4ckPx24NgAAAGDMhg4iTkry5P7+k5OcODL8r/qrZ+yb5MqpUzgAAACAyTFjHxELVVUfTLJ/kq2qak2SVyQ5OslHqurpSX6c5An96J9K8qgkFyb5VZKnjqsuAAAAYOmMLYhorT1phqceOs24LclzxlULAAAAsDwsl84qAQAAgI2AIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGIwgAgAAABiMIAIAAAAYjCACAAAAGMxmS/GiVbU6ydVJbkhyfWttVVXdJcmHk6xMsjrJ/2it/WIp6gMAAADGYylbRBzQWtujtbaqf3xEkpNbazslObl/DAAAAEyQ5XRqxsFJjuvvH5fksUtYCwAAADAGSxVEtCSfqaozq+rwfti2rbWLkqS/3WaJagMAAADGZEn6iEiyX2vtp1W1TZLPVtV35jphH1wcniT3uMc9xlUfAAAAMAZL0iKitfbT/vbiJCck2SfJz6tquyTpby+eYdpjW2urWmurtt5666FKBgAAABbB4EFEVd22qm4/dT/Jw5Kcn+SkJE/uR3tykhOHrg0AAAAYr6U4NWPbJCdU1dTrf6C19umq+kaSj1TV05P8OMkTlqA2AAAAYIwGDyJaaz9Icr9phl+W5KFD1wMAAAAMZzldvhMAAACYcIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwgggAAABgMIIIAAAAYDCCCAAAAGAwyy6IqKpHVNV3q+rCqjpiqesBAAAAFs+yCiKqatMkb03yyCS7JHlSVe2ytFUBAAAAi2VZBRFJ9klyYWvtB6213yb5UJKDl7gmAAAAYJEstyDibkn+e+Txmn4YAAAAMAGqtbbUNdykqp6Q5OGttWf0j/8yyT6tteeNjHN4ksP7h/dJ8t3BC118WyW5dKmL4Bask+XF+lh+rJPlxfpYfqyT5cX6WH6sk+XF+lheJml9/F5rbeu1B262FJXMYk2Su4883iHJT0dHaK0dm+TYIYsat6o6o7W2aqnr4GbWyfJifSw/1snyYn0sP9bJ8mJ9LD/WyfJifSwvG8P6WG6nZnwjyU5VtWNV3SrJoUlOWuKaAAAAgEWyrFpEtNaur6rnJvnPJJsmeXdr7YIlLgsAAABYJMsqiEiS1tqnknxqqesY2ESdajIhrJPlxfpYfqyT5cX6WH6sk+XF+lh+rJPlxfpYXiZ+fSyrzioBAACAybbc+ogAAAAAJpggoldVv1zr8VOq6i2LNO/VVbXVNMOPqqoXL8ZrjMzz61V1dlX9uKou6e+fXVUr+zo+PjLuIVX13sV8/cVQVXetqg9V1X9V1beq6lNV9ftjfL39q+oT6znNKVW1qr8/7fodGY8nW4UAABOKSURBVPeEfh1cWFVXjqyTB/XzOWNk3FVVdcq838wSGefnZx61TPzyXltVvbSqLqiqc/v3+oB1jH/T+hndDlXVP1TVgQPUu9vIerm8qn7Y3/9cv61qVTV62ea3VNVTxl3XfFTVliPv5WdV9ZORx7ca4PW/UlV7LHScGaZ7+ch7uWHk/nOq6lVV9cvRbd/a24HlbrH2NaP7gwXU8tIZlvXz+8/or6pqm5HxJ2JZ95/389cx7fZV9bH+/pLtW9aqaWK2YdOZz2ejql7X74deV1VbV3c8elZVPWSRanrqyDL/bVWd198/uv+/uLGqdh8Z//yqWrkYrz0O67vfXuTXfmFV3WYd48z4HaWqvjrP153447OFbOvW83VmPfbot0HHjIz/4qo6arFef7Esuz4iWJjW2gOSbmedZFVr7blTz1VVkqyqqvsu105AqyvyhCTHtdYO7YftkWTbJN9bytrmq7X2uKQLPJK8uLV20NRz/TrZpqoe2Vr7j6WpcPnq/x+qtXbjXKfZ2JZ3VT0wyUFJ9myt/ab/YjivL8CttZcvanEzv855SfZIkurC0E+01qa+aKxMcnGSF1TVO1prvx2ipvlqrV2Wm9/LUUl+2Vp7/eg48/k/Xg5aa/+Q5B+qarMkl7bWbgozqupVSS5P8jdJXrpEJc7bctvXtNZeneTVfR2/XGtZH5XuWvIvSvL3Q9e2UOtY1v+9rulbaz9Ncsgi1bEon8NJ2oatbQGfjb9OsnW/Hzo0yXdaa09erLpaa+9J8p6+ntVJDmitXdo/fkqSNem2RU9crNccl8Xcb8/TC5P8W5JfzWfi1tqD5jndRB+fLXRbtz7WdexRVb9J8viq+sepz8lypEXEHFTVY0aS3c9V1bb98KOq6riq+kx1v4o/vqpe26e0n66qzUdm83dVdXr/d+9pXuNe/TRnVtWXq2rnfvh7q+pNVfXVqvpBVS10Z/z6JP9rgfMYpwOSXNda+5epAa21s1trX66q21XVyVX1zX4ZH5x0O/2q+k5VvbO6BPz9VXVgVZ1aVd+vqn368Y6qquOr6vP98GeOvO4dqktqv1VV/1JVm/TTPKyqvta/5ker6nZjeM+vS/KyMcx3Wajul5GPV9U3+r/9+uFHVdW7q0vBf1BVz++Hr6yqb1fV25J8M8mRVfXGkfk9s6resICSJm15b5fuS+JvkqS1dml/4H6L1jo1h18X+u3NIVX1yKr6yMjw/avq//X3315VZ1T3S84rR8ZZXVWvHPl87ryA93RJkpOTLNpB7NCq6t799uhf0v0fb1dVf9Evm/Or6jX9eJtV1RUj0x1aVe8cuX9+VZ1TVV/oh92m3xadW1UfSrJiZNpjR9bNtKFSv26ntmkfrqrbLuBtvjPJYVV1pwXMY6nMtq+p6n7VPb9fXzd9samql/TDzqmqo0dnWFWb9McEr+ofL+b+491JnlhVd1nAPJbKjMt6dKR+2//lfnl9s6oeNDJ89JfEu1d3vPTdqnrFyPR/26+z86vqhSPTju5P7l5dS55X9+vwtLr5mG7afdU8bcjbsPX+bFTVSUlum+TrVfX3SV6b5FHV/Tp765k+C1W1V1V9sbpj3/+squ0WUPcnkty3qu6zgHkMZbb99rTLpB9+Tr8cXzf1mai1WglV1Seq+6I/7f66umOt7ZN8YWS/8oh+3ZxTVSeP1LlLrXWM1o//y5H7f9d/Xs6tkWOCedrQj88Wuq07vvrvNv3j91fVn86zluvTdXb5N/OcfhCCiJvdum5u0nJ2kn8Yee4rSfZtrd0/yYeSvGTkuXsleXSSg9Oli19ore2W5Nf98ClXtdb2SfKWJP9nmtc/NsnzWmt7JXlxkreNPLddkgenS0+Pnmba9fGRJHvWNGHIMrFrkjNneO7aJI9rre2Z7sN+TFUXoSa5d5J/TrJ7kp2T/Hm6Zfbi3DJ42T3denlgkpdX1fb98H3S/dq0W7p1+vjqvsC9LMmB/WuekeRvF+NNruVrSX5TVQeMYd5Dme3z889J3tha2zvJn6X78jJl5yQPT7f8X1E3h3f3SfK+/jP3+iR/OvLcU9P/KjJPk7C8R30m3cH196rqbVX1R4swz88m2bdu/pL6xCQf7u+/tLW2Kt1n6Y9qpClsugOrPZO8Pd1nbyGOTvKiqtp0gfNZSrskeVf/f1xJXpVu23X/JPtV1UGzTZzkFUke2lq7X5LH9cOem+QXrbXdk/xTP68pR/Tr5n5J/qSqdhmdWXVN+4/o57lnknOTvGAB7++qJO9L8rx1jbgMzbaveXy6X5rul+TAJK+rqu2q6pFJHpvkAf06ee3INJsleX+S77XWXjaG/ccv04URC1lfS2W2ZT3q4iR/0i+vJyZ50wzj7ZPksHTr6AnVhax7pds3PCDJvkmeWVVTn42b9iettR+l+8J8Wr8Ov5Rk6keJ2fZV87GhbsPW+7PRWvvTJL9ure3RWvunJC9P8uG+Zc9tM81nod+nvznJIf2x77vTtwqapxvTfSaX849tU6bdb69jmbwnyfNbaw9cj9f5nf11a+1NSX6arkXJAVW1dZJ/TfJn/WfiCSPTz3SMlr7ehyXZqX9+jyR7VdUfrkd9a9vQj88Wuq17Z7rtWKrqjkkelIVdSfKt6X4suOMC5jFWTs242a/Xagr5lCRT53zukOTDfSp5qyQ/HJnuP1pr11XVeUk2TfLpfvh5SVaOjPfBkds3jgxPnww/KMlHb/5enS1GRvm/fVPCb00l9wtwQ7rE8X8m2dCaPlWS1/QbuRuT3C1dc6ck+WHfVDJVdUGSk1trrV8vK0fmcWJr7ddJft0nwfskuSLJ6a21H/TTfzBdiHFtui8Sp/br5VbpNpLj8Kp0O+oNrtltb7bPz4HpUvWpp+9QVbfv73+y/0XgN1V1cW5enz9qrZ2WJK21a6rq80kOqqpvJ9l8al0vwIa+vG/SWvtlfxD+kHRfcj9cVUe01t67gHleX1WfTvKY6s7NfnRuDmD/R1Udnm7/sV26z8i5/XP/3t+eme6Add5aaz+sqtPThYobqv9qrX2jv/+AJJ8faUr8gSR/mJv3GdM5Ncn7quqjuXnZ/mH6L8CttbP67d2UJ1XV09Otm+3TrZtvjTz/oH7YV0e2aV+Z/9tL0gXr36yRVksT4MFJPthauyHJz6vqi0n2TvJHSd7TWvtVkrTWLh+Z5h1JPtKfYpF0X4YXe//xpiRn18h5vxNm8yRvqa4p8w1JZuqT4LN9s+RU1b+nW18tyQmttWtGhj8kyUkZ2Z/0fpvu1/Ok21b9SX9/2n1Va+3q+byZCdmGrW2mz8ZJs0wz02fhPum+uH22H75pkosWWN8Hkry0qnZc4HzGaqb9drqQ5neWSf9F8k6ttS/2szg+ySPn8FKz7a+n7JvkS621H/a1jW7XpjtGWzPy/MP6v7P6x7dLF0x8aQ61zWRijs9mMe22rrX2xap6a/+jweOTfLy1dv18X6S1dlVVvS/J89P9QL7sCCLm5s1J3tBaO6lv7nTUyHNTzapurKrrWrvpeqg35pbLt81wP+laplwx+kVuLb8ZuV8zjLM+jk8XRCzHfiIuyMzngh6WZOske/Xhz+rc3Cx5dBndOPJ4tvUw+ni64ZXugOdJc65+nlprn6+q/51uhzBpNknywD4Aukm/kx1dbzfk5nV1zVrzeGe6Xzm+k4W1hkgyecu7Pyg8Jckpffj25CTvTdc0b6rl24ppJ57Zh5M8J10/AN9orV3dH9y9OMnerbVfVHdu9Oh8p9bn6LpciNck+VgWdlCzlEb/j2fadt+41nOjy/OZ6QKMg5KcM9L65Heuu11VO6X7tXyf1toVVfVv+d11Xkk+3Vr7y7m/hdm11i6v7jSeZy3WPAcy275mpnVVmWbZ976a5ICqOqa1dm3GsP/o1+sHkjx7seY5kNmW9ai/SfLzdL+2b5Lux4DpzLS/nsna+5PRY7XRbdW0+6oF2hC3YfP5bMxm2s9CVe2W5IL1/IV/Vn2Ifkw2gC+xM+y3z8w0y6S6099m2vaM7ueTfrs/h/31TbOfZd4zHaONTvuPrbV3zDD9etvAj88WY1t3fLrvO4cmedoi1PR/0p2WtuBj53Fwasbc3DHJT/r78z3f74kjt7f4VaS1dlWSH1bVE5Kus5Oqut+6ZlhV35lPIa2169K1ynjhfKYfs88n2aJG+m+oqr37Zmt3THJxH0IckOT35jH/g6tqRVVtmWT/JFO/Vu5TVTtW1zfEE9P9SnhauubT9+7ruE2tu9fok6vqbvOoK+ma371knWNteD6Trjl5kps67lkvrbWvJ7l7ul+WploXWd5Jquo+/ZfQKXsk+VF/f3WSvfr7f7aesz4lyZ7pvgxPnZZxh3QH9Vf2rbPW+YtMVe3TJ/LrrbX2nXS/6K/rFIYNwWnpvqhuWV3nj4cm+WLf2u0XVbVTv/153Mg09+x/yT0yyS/StQL7UrqDlPT7ifv2494hydVJrupb7z18mhq+mq557j376W879b9TXf9Gj5nnezsm3ZfjDemYYrZ9zZfS9cewaXXNlv8wyenptmVPq763+bplfw3vSteE9qP9+p1x/1FV/1hVo+t5fbwhXaeAG9IPSbMt61F3THJR/5n4y3S/Bk/nT6rqLlV163Snypyabp09tl/Ot033OfryDNPPZNp91Ua4DZvPZ2M2M30Wvptk6+o6bkxVbV5V9+3vP7eqnjvjHGf33nStW7ae5/RjN8t+e9pl0lq7It1+98H9+IeNTLs6yR7V9VFz93QtfZPZ99dXJ5lqmfq1dPuFHfvXXJ9+aP4z3TZxqs+Pu/W/5m+sx2eLsa17b/rvZ62/sEC/XE/OPPQtXD6S5OnzmX7cNqSDhqV0VLqDiy+n67l6Praoqq+n+8Vquo5DDkvy9Ko6J12idvA049ykuvNPF9I64l1Zhgcy/a8Uj0t3oPFf1TU7Pird+WzvT3fVjzPSLa/5BDGnJ/lkuh3j/57qHCjdhvjoJOenO/XmhNbaJUmekuSDVXVuP82MHfD1XyLune4X5PXWWvtUug6uJs3z0623c6vqW5n/L6cfSXJqa+0XieU94nZJjquuo9Vz0zW9PKp/7pVJ/rnfdt2wPjPtf635RLqDl0/0w85J1wTzgnTnrp46h1ndIwtrEvjqdKfHbdBaa2vSnTd9SpKz052j/sn+6b9Pd4rGyblls9c39r+UnZfkc62189P1M7Rlv67/Jl1T3qT7xeNb6bZh/5pp1k1r7efpDkY+3O9rvpqbm7/vnuRn83xvP0/3PzJkr+8Lso59zQnpmi+fk+7A8iWttZ+11j6drgn6GdX1hfPiteb5hnTr4fgkl2Xm/cdumf+yvrSvb4t1jbtcrGNZj3pbkidX1Wnp/i/Xbskw5SvplvHZ6Zoun9Fa+2a6A/jTk3w9yTtba2fNMP1MZtpXbVTbsPl8NtYxv2mPpVp3NZFDkvxTvz06O93pY0n3WblsnvX/Nt1pTNusa9wlNO1+ex3L5KlJ3lpVX8st/x9PTXfcel66PrW+maxzf31skv+oqi/06+fwJP/ev+aHM0ettc+kOx3ma/2+6mNJbr+xHp8txrau359+O7dswbBdupYv83VMkq3WOdYSqJtbp7Ehqa6Ts3v2nc4wBzXDpfUWcf67Jnlaa20cHVpu9KrqE+k6Eju5f2x5bwCq6nVJjm+trX1eKstEVU2dsjFdKwoWWVX9p2W94bANG16/v39828AufTqU6i4R+4nW2q5LXMq0HJ/NX9/i7rx0l3a9sh/23CQ/bq3N1hfLBkkQwUZj3EEE41HduZGnJzmntfaEdY0PADCplnsQwfxU1YHpWq+8obU23RUWJ44gAgAAABiMPiIAAACAwQgiAAAAgMEIIgAAAIDBCCIAgEVRVXetqg/1ly77VlV9qqp+f91Tznn++1fVg9Y9JgCwnAkiAIAF6y8DekKSU1pr92qt7ZLkfyXZdhFfZv8kgggA2MAJIgCAxXBAkutaa/8yNaC1dnaSr1TV66rq/Ko6r6qemNzUuuETU+NW1Vuq6in9/dVV9cqq+mY/zc79Jeuelfz/9u7eNYooCsP48xJDbKII2qcWwaCk86MQC7WQiKCNRUAbO0Gs/QPsbCwEQYgBQRHsRQUbJfhBUoukSUCw0EY0HIudFIYsDG4yy8rza2Y4d+7Mme7ycpnhRpIPSY4n+ZxkvJmzp5k33tkbS5Kkf7Jr2A1IkqT/wiFgcYv6BWAaOAzsB94led3ifl+r6kiS68DNqrqa5B7wo6ruACR5CZwDngGXgSdV9WvwV5EkSTvJHRGSJGknHQMWqmq9qtaAV8BMi3lPm+MiMNXnmvvAXHM+BzwYoE9JktQRgwhJkrQdloGjW9TT5/rf/L0O2b1p/GdzXKfPDs6qegNMJTkJjFXVUvt2JUnSsBhESJKk7fACmEhybaOQZAb4BlxKMpbkAHACeAt8AQ4mmUiyFzjV4hnfgclNtYfAAu6GkCRpZBhESJKkgVVVAbPA6eb3ncvAbeAR8An4SC+suFVVq1W1AjxuxuaB9y0e8xyY3fhYZVObB/bRCyMkSdIISG/dIEmSNHqSXATOV9WVYfciSZLa8a8ZkiRpJCW5C5wBzg67F0mS1J47IiRJkiRJUmf8RoQkSZIkSeqMQYQkSZIkSeqMQYQkSZIkSeqMQYQkSZIkSeqMQYQkSZIkSeqMQYQkSZIkSerMHy0/Ug7dODgTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_tn_top10.plot.bar(x=0, y=15, rot=0, title=\"Top 10 TN County\")\n",
    "ax.set_xlabel(\"County\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Prescription Rate')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_tn_top10.plot.bar(x=0, y=range(3, 15), rot=0, title=\"Top 10 County\")\n",
    "ax.set_xlabel(\"County\")\n",
    "ax.set_ylabel(\"Prescription Rate\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py37",
   "language": "python",
   "name": "py37"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}