week-5/Exercise Part 1 - EDA and Data Cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercise Part 1: EDA and Data Cleaning\n",
    "Author: J. Tyler Bittner\n",
    "\n",
    "[Part 2: Data Visualizations](...)\n",
    "\n",
    "## Data  Set\n",
    "For this exercise the data set I'm using is [HCCI’s 2016 Health Care Cost and Utilization Report](https://www.healthcostinstitute.org/data) saved as [Machine Readable Data 2016 HCCUR.csv](datasets/HCCI’s 2016 Health Care Cost and Utilization Report - Machine readable data/Machine Readable Data 2016 HCCUR.csv).\n",
    "\n",
    "The variables and dimentions they're measured on are described in the [HCCI data description doc](datasets/HCCI’s 2016 Health Care Cost and Utilization Report - Machine readable data/Machine Readable Data Documentation 2016 HCCUR.pdf).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis & Cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Set defaults\n",
    "#plt.rcParams['figure.figsize'] = (20, 10)\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 28140 entries, 0 to 28139\n",
      "Data columns (total 8 columns):\n",
      "High-Level Category    28140 non-null object\n",
      "Mid-Level Category     28140 non-null object\n",
      "Subcategory            26070 non-null object\n",
      "Age Group              28140 non-null object\n",
      "Gender                 28140 non-null object\n",
      "Metric                 28140 non-null object\n",
      "Year                   28140 non-null int64\n",
      "Value                  28140 non-null float64\n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv(os.path.join('datasets', \n",
    "                                    'HCCI’s 2016 Health Care Cost and Utilization Report - Machine readable data', \n",
    "                                    'Machine Readable Data 2016 HCCUR.csv'))\n",
    "df.rename(columns={'HCCI_HL_CATEGORY': 'High-Level Category',\n",
    "          'HCCI_ML_CATEGORY': 'Mid-Level Category',\n",
    "          'HCCI_SUBCATEGORY': 'Subcategory',\n",
    "          'AGE_GROUP': 'Age Group',\n",
    "          'GENDER': 'Gender',\n",
    "          'METRIC': 'Metric',\n",
    "          'YEAR': 'Year',\n",
    "          'VALUE': 'Value'},\n",
    "          inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High-Level Category</th>\n",
       "      <th>Mid-Level Category</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>All</td>\n",
       "      <td>All</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2012</td>\n",
       "      <td>10.0975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>All</td>\n",
       "      <td>All</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2013</td>\n",
       "      <td>12.3736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>All</td>\n",
       "      <td>All</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2014</td>\n",
       "      <td>13.0115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>All</td>\n",
       "      <td>All</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2015</td>\n",
       "      <td>13.4226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>All</td>\n",
       "      <td>All</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2016</td>\n",
       "      <td>13.9617</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  High-Level Category Mid-Level Category     Subcategory Age Group Gender  \\\n",
       "0           Inpatient    inpatient_acute  Labor_Delivery       All    All   \n",
       "1           Inpatient    inpatient_acute  Labor_Delivery       All    All   \n",
       "2           Inpatient    inpatient_acute  Labor_Delivery       All    All   \n",
       "3           Inpatient    inpatient_acute  Labor_Delivery       All    All   \n",
       "4           Inpatient    inpatient_acute  Labor_Delivery       All    All   \n",
       "\n",
       "                    Metric  Year    Value  \n",
       "0  OOP_spending_per_person  2012  10.0975  \n",
       "1  OOP_spending_per_person  2013  12.3736  \n",
       "2  OOP_spending_per_person  2014  13.0115  \n",
       "3  OOP_spending_per_person  2015  13.4226  \n",
       "4  OOP_spending_per_person  2016  13.9617  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "> Age Group:\n",
      "['All' 'Ages_0to18' 'Ages_19to25' 'Ages_26to44' 'Ages_45to54' 'Ages_55to64']\n",
      "> Gender:\n",
      "['All' 'Female' 'Male']\n",
      "> Metric:\n",
      "['OOP_spending_per_person' 'Payer_spending_per_person' 'Price'\n",
      " 'Spending_per_person' 'Utilization_per_1000_persons']\n",
      "> Year:\n",
      "[2012 2013 2014 2015 2016]\n"
     ]
    }
   ],
   "source": [
    "# Categorical type values (non-medical related):\n",
    "\n",
    "print('> Age Group:')\n",
    "print(df['Age Group'].unique())\n",
    "\n",
    "print('> Gender:')\n",
    "print(df['Gender'].unique())\n",
    "\n",
    "print('> Metric:')\n",
    "print(df['Metric'].unique())\n",
    "\n",
    "print('> Year:')\n",
    "print(df['Year'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(28140, 8)\n",
      "High-Level Category       0\n",
      "Mid-Level Category        0\n",
      "Subcategory            2070\n",
      "Age Group                 0\n",
      "Gender                    0\n",
      "Metric                    0\n",
      "Year                      0\n",
      "Value                     0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Check for nulls\n",
    "print(df.shape)\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 26070 entries, 0 to 27419\n",
      "Data columns (total 8 columns):\n",
      "High-Level Category    26070 non-null object\n",
      "Mid-Level Category     26070 non-null object\n",
      "Subcategory            26070 non-null object\n",
      "Age Group              26070 non-null object\n",
      "Gender                 26070 non-null object\n",
      "Metric                 26070 non-null object\n",
      "Year                   26070 non-null int64\n",
      "Value                  26070 non-null float64\n",
      "dtypes: float64(1), int64(1), object(6)\n",
      "memory usage: 1.8+ MB\n"
     ]
    }
   ],
   "source": [
    "# Drop nulls\n",
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High-Level Category</th>\n",
       "      <th>Mid-Level Category</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "      <th>Age Group Upper Limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2012</td>\n",
       "      <td>0.5951</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2013</td>\n",
       "      <td>0.6936</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2014</td>\n",
       "      <td>0.6324</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2015</td>\n",
       "      <td>0.5548</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.4927</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>Payer_spending_per_person</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.9628</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>Payer_spending_per_person</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.0448</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>Payer_spending_per_person</td>\n",
       "      <td>2014</td>\n",
       "      <td>3.5116</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>Payer_spending_per_person</td>\n",
       "      <td>2015</td>\n",
       "      <td>2.9669</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Inpatient</td>\n",
       "      <td>inpatient_acute</td>\n",
       "      <td>Labor_Delivery</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>Payer_spending_per_person</td>\n",
       "      <td>2016</td>\n",
       "      <td>2.5772</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   High-Level Category Mid-Level Category     Subcategory   Age Group  \\\n",
       "50           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "51           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "52           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "53           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "54           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "55           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "56           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "57           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "58           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "59           Inpatient    inpatient_acute  Labor_Delivery  Ages_0to18   \n",
       "\n",
       "                       Metric  Year   Value  Age Group Upper Limit  \n",
       "50    OOP_spending_per_person  2012  0.5951                     18  \n",
       "51    OOP_spending_per_person  2013  0.6936                     18  \n",
       "52    OOP_spending_per_person  2014  0.6324                     18  \n",
       "53    OOP_spending_per_person  2015  0.5548                     18  \n",
       "54    OOP_spending_per_person  2016  0.4927                     18  \n",
       "55  Payer_spending_per_person  2012  3.9628                     18  \n",
       "56  Payer_spending_per_person  2013  4.0448                     18  \n",
       "57  Payer_spending_per_person  2014  3.5116                     18  \n",
       "58  Payer_spending_per_person  2015  2.9669                     18  \n",
       "59  Payer_spending_per_person  2016  2.5772                     18  "
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remove rows summarized by Age Group\n",
    "df = df[df['Age Group'] != 'All']\n",
    "\n",
    "# Remove rows summarized by Subcategory\n",
    "df = df[df['Subcategory'] != 'Total']\n",
    "\n",
    "# Convert  Age Group to be numeric for finding correlations later\n",
    "df['Age Group Upper Limit'] = df['Age Group'].str[-2:]\n",
    "df['Age Group Upper Limit'] = pd.to_numeric(df['Age Group Upper Limit'], errors='coerce')\n",
    "\n",
    "# Ignore gender for this exercise\n",
    "df.drop('Gender', axis=1, inplace=True)\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categories - Static visualization\n",
    "\n",
    "This data set has three types of medical categories so let's take a look at the values and frequencies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12406e4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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3eQZC0g6SjmxSZ5yk7fpzTEREdL9WprJvB74q6UFJJ0ga3+6gminXvDtOUiuL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\nooskMUdERHSR/x/DV5ciriGndwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118ef7668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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k+fDSlAQAAAAAzNJCP4TygqraP8nD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ABYIzQEAAAAANYIDQEAAACANUJDAAAAAGCN0BAAAAAAWCM0BAAAAADWCA0BAAAAgDVCQwAAAABg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\ni/ZcvOwWfOVy/H39l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f1vS3J1kk2WoeaHhhaq6s62vxlJPpHkO+1Y72vteye5NsnfAXcm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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118fdf048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(y='High-Level Category', data=df)\n",
    "plt.title('Count of High-Level Categories')\n",
    "plt.show()\n",
    "\n",
    "sns.countplot(y='Mid-Level Category', data=df)\n",
    "plt.title('Count of Mid-Level Categories')\n",
    "plt.show()\n",
    "\n",
    "# Need a longer size for this one\n",
    "plt.figure(figsize=(20, 30))\n",
    "\n",
    "sns.countplot(y='Subcategory', data=df)\n",
    "plt.title('Count of Subcategories')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## [In-progress] Categories Exploration (using Bokeh)\n",
    "df.to_csv(os.path.join('exercise_files', 'df_categories.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Focus on prescription drug spending and costs\n",
    "\n",
    "Prescription drug costs are a topic of much debate and interest in the U.S. so for this exercise I'll choose to examine drug spending and prices by subcategory (ignoring brand vs. generics)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Age Group</th>\n",
       "      <th>Metric</th>\n",
       "      <th>Year</th>\n",
       "      <th>Value</th>\n",
       "      <th>Age Group Upper Limit</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8625</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2012</td>\n",
       "      <td>3.8639</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8626</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.3137</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8627</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.4565</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8628</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.0359</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8629</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>Ages_0to18</td>\n",
       "      <td>OOP_spending_per_person</td>\n",
       "      <td>2016</td>\n",
       "      <td>3.3667</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Subcategory   Age Group                   Metric  Year   Value  \\\n",
       "8625  Anti_Infectives  Ages_0to18  OOP_spending_per_person  2012  3.8639   \n",
       "8626  Anti_Infectives  Ages_0to18  OOP_spending_per_person  2013  4.3137   \n",
       "8627  Anti_Infectives  Ages_0to18  OOP_spending_per_person  2014  4.4565   \n",
       "8628  Anti_Infectives  Ages_0to18  OOP_spending_per_person  2015  4.0359   \n",
       "8629  Anti_Infectives  Ages_0to18  OOP_spending_per_person  2016  3.3667   \n",
       "\n",
       "      Age Group Upper Limit  \n",
       "8625                     18  \n",
       "8626                     18  \n",
       "8627                     18  \n",
       "8628                     18  \n",
       "8629                     18  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter out irrelevant rows\n",
    "rx_df = df[df['High-Level Category'] == 'Prescription_Drugs']\n",
    "rx_df = rx_df.drop(['High-Level Category', 'Mid-Level Category'], axis=1)\n",
    "rx_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tidy the data\n",
    "\n",
    "The data is not in tidy form, so let's reorganize the variables and observations properly. The `Metric` column contains variables so I'll convert them into their own columns.  The `Value` column contains observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(225, 5)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Metric</th>\n",
       "      <th>OOP_spending_per_person</th>\n",
       "      <th>Payer_spending_per_person</th>\n",
       "      <th>Price</th>\n",
       "      <th>Spending_per_person</th>\n",
       "      <th>Utilization_per_1000_persons</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Age Group Upper Limit</th>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">Anti_Infectives</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">18</th>\n",
       "      <th>2012</th>\n",
       "      <td>4.841867</td>\n",
       "      <td>9.923122</td>\n",
       "      <td>12.610456</td>\n",
       "      <td>14.764956</td>\n",
       "      <td>3459.720944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>4.750900</td>\n",
       "      <td>10.753267</td>\n",
       "      <td>14.669633</td>\n",
       "      <td>15.504144</td>\n",
       "      <td>3254.608911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>4.448389</td>\n",
       "      <td>10.485811</td>\n",
       "      <td>17.521444</td>\n",
       "      <td>14.934200</td>\n",
       "      <td>3100.666556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>4.527733</td>\n",
       "      <td>9.593022</td>\n",
       "      <td>16.142189</td>\n",
       "      <td>14.120733</td>\n",
       "      <td>3105.522800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>4.390100</td>\n",
       "      <td>8.995678</td>\n",
       "      <td>15.473244</td>\n",
       "      <td>13.385800</td>\n",
       "      <td>2996.774789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">25</th>\n",
       "      <th>2012</th>\n",
       "      <td>3.760978</td>\n",
       "      <td>10.126200</td>\n",
       "      <td>15.922078</td>\n",
       "      <td>13.887222</td>\n",
       "      <td>3162.176233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>3.697722</td>\n",
       "      <td>11.264767</td>\n",
       "      <td>19.408356</td>\n",
       "      <td>14.962522</td>\n",
       "      <td>3080.970233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>3.468044</td>\n",
       "      <td>12.237178</td>\n",
       "      <td>26.833578</td>\n",
       "      <td>15.705233</td>\n",
       "      <td>2983.599222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>3.665744</td>\n",
       "      <td>13.623644</td>\n",
       "      <td>25.636567</td>\n",
       "      <td>17.289356</td>\n",
       "      <td>2957.567344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>3.629444</td>\n",
       "      <td>15.232278</td>\n",
       "      <td>23.723222</td>\n",
       "      <td>18.861722</td>\n",
       "      <td>2824.072611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Metric                                      OOP_spending_per_person  \\\n",
       "Subcategory     Age Group Upper Limit Year                            \n",
       "Anti_Infectives 18                    2012                 4.841867   \n",
       "                                      2013                 4.750900   \n",
       "                                      2014                 4.448389   \n",
       "                                      2015                 4.527733   \n",
       "                                      2016                 4.390100   \n",
       "                25                    2012                 3.760978   \n",
       "                                      2013                 3.697722   \n",
       "                                      2014                 3.468044   \n",
       "                                      2015                 3.665744   \n",
       "                                      2016                 3.629444   \n",
       "\n",
       "Metric                                      Payer_spending_per_person  \\\n",
       "Subcategory     Age Group Upper Limit Year                              \n",
       "Anti_Infectives 18                    2012                   9.923122   \n",
       "                                      2013                  10.753267   \n",
       "                                      2014                  10.485811   \n",
       "                                      2015                   9.593022   \n",
       "                                      2016                   8.995678   \n",
       "                25                    2012                  10.126200   \n",
       "                                      2013                  11.264767   \n",
       "                                      2014                  12.237178   \n",
       "                                      2015                  13.623644   \n",
       "                                      2016                  15.232278   \n",
       "\n",
       "Metric                                          Price  Spending_per_person  \\\n",
       "Subcategory     Age Group Upper Limit Year                                   \n",
       "Anti_Infectives 18                    2012  12.610456            14.764956   \n",
       "                                      2013  14.669633            15.504144   \n",
       "                                      2014  17.521444            14.934200   \n",
       "                                      2015  16.142189            14.120733   \n",
       "                                      2016  15.473244            13.385800   \n",
       "                25                    2012  15.922078            13.887222   \n",
       "                                      2013  19.408356            14.962522   \n",
       "                                      2014  26.833578            15.705233   \n",
       "                                      2015  25.636567            17.289356   \n",
       "                                      2016  23.723222            18.861722   \n",
       "\n",
       "Metric                                      Utilization_per_1000_persons  \n",
       "Subcategory     Age Group Upper Limit Year                                \n",
       "Anti_Infectives 18                    2012                   3459.720944  \n",
       "                                      2013                   3254.608911  \n",
       "                                      2014                   3100.666556  \n",
       "                                      2015                   3105.522800  \n",
       "                                      2016                   2996.774789  \n",
       "                25                    2012                   3162.176233  \n",
       "                                      2013                   3080.970233  \n",
       "                                      2014                   2983.599222  \n",
       "                                      2015                   2957.567344  \n",
       "                                      2016                   2824.072611  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rx_df_tidy = rx_df.pivot_table(index=['Subcategory', 'Age Group Upper Limit', 'Year'],\n",
    "                         columns='Metric',\n",
    "                         values='Value', aggfunc=np.mean)\n",
    "print(rx_df_tidy.shape)\n",
    "rx_df_tidy.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(225, 8)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metric</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Age Group Upper Limit</th>\n",
       "      <th>Year</th>\n",
       "      <th>OOP_spending_per_person</th>\n",
       "      <th>Payer_spending_per_person</th>\n",
       "      <th>Price</th>\n",
       "      <th>Spending_per_person</th>\n",
       "      <th>Utilization_per_1000_persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2012</td>\n",
       "      <td>4.841867</td>\n",
       "      <td>9.923122</td>\n",
       "      <td>12.610456</td>\n",
       "      <td>14.764956</td>\n",
       "      <td>3459.720944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.750900</td>\n",
       "      <td>10.753267</td>\n",
       "      <td>14.669633</td>\n",
       "      <td>15.504144</td>\n",
       "      <td>3254.608911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.448389</td>\n",
       "      <td>10.485811</td>\n",
       "      <td>17.521444</td>\n",
       "      <td>14.934200</td>\n",
       "      <td>3100.666556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.527733</td>\n",
       "      <td>9.593022</td>\n",
       "      <td>16.142189</td>\n",
       "      <td>14.120733</td>\n",
       "      <td>3105.522800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.390100</td>\n",
       "      <td>8.995678</td>\n",
       "      <td>15.473244</td>\n",
       "      <td>13.385800</td>\n",
       "      <td>2996.774789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Metric      Subcategory  Age Group Upper Limit  Year  OOP_spending_per_person  \\\n",
       "0       Anti_Infectives                     18  2012                 4.841867   \n",
       "1       Anti_Infectives                     18  2013                 4.750900   \n",
       "2       Anti_Infectives                     18  2014                 4.448389   \n",
       "3       Anti_Infectives                     18  2015                 4.527733   \n",
       "4       Anti_Infectives                     18  2016                 4.390100   \n",
       "\n",
       "Metric  Payer_spending_per_person      Price  Spending_per_person  \\\n",
       "0                        9.923122  12.610456            14.764956   \n",
       "1                       10.753267  14.669633            15.504144   \n",
       "2                       10.485811  17.521444            14.934200   \n",
       "3                        9.593022  16.142189            14.120733   \n",
       "4                        8.995678  15.473244            13.385800   \n",
       "\n",
       "Metric  Utilization_per_1000_persons  \n",
       "0                        3459.720944  \n",
       "1                        3254.608911  \n",
       "2                        3100.666556  \n",
       "3                        3105.522800  \n",
       "4                        2996.774789  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Flatten out the the MultiIndex to work nicely with plotting libs\n",
    "rx_df_tidy.reset_index(inplace=True)\n",
    "print(rx_df_tidy.shape)\n",
    "rx_df_tidy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot key CDFs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JzpRMJUy7WN6sGfKOmCK722txpQAAoK8jyAGKTDfgawzrzmumWF0KoohphCOD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\n1U9XBAAAAICBxhgjy7KUmnplDyzsM8iNHDlSVVV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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1235f8240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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C4wgdhbGEjtBVO8PaLH7x8fHavn27Z7mkpESxsbGe5ZycHNntdo0ePVqSdN11\n1+l///d/OyEqgK50rKham7Yf06btx1Re1aCwYH/NSkvRjAuSNbBPBCdVBwAA6EbaLH5TpkzRb37z\nG5WXlysoKEgbN25scZhnSkqKCgsLdejQIQ0YMEAffvihRo0a1amhAXSOugaHPt1xXO9n5mr/0QpZ\nrRZNGBarWy4YpQtHxMnfz2Z2RAAAALRDm8UvLi5Oy5Yt0+LFi+VwOLRw4UKNHj1aS5Ys0dKlSzVq\n1Cg99thjuvPOO2UYhqKjo/Xoo492RXYAHcAwDOXkVui9LUf16Y7jamhyKTk+TN9PH6lLxiepV3jb\nh3ICAADAu53VefzS09OVnp7eYt1zzz3nuT5t2jRNmzatY5MB6FTVdU36KOuYNm45qqOF1QoMsOni\nsX00Ky1FQ5J7cSgnAACADzmr4gfANxiGoeyDZdq49ag+25kvh9OtwX0j9eOFYzR1XB8FB/qbHREA\nAACdgOIH9AAV1Q3atO2YNm49qvzSWoUE+mnmpBTNSktR/0TOuwkAAODrKH6Ajzp1Gob3thxV5u5C\nudyGRg6I1nWXD9V3xiTK7s9ELQAAAD0FxQ/wMSUV9fog86je35arkop6hYcEKP3iAZo5KUV948LM\njgcAAAATUPwAH+ByubVtb5He23JUn+8rktuQxg6J0ffTR2rSyAT5+1nNjggAAAATUfyAbqyiqkEb\ntx7Vhi1HVXqiXlHhgVo4Y4guvzBZ8dEhZscDAACAl6D4Ad2MYRjafahM72QcUcbOfLnchsYOidEt\nV6XqwhHxstnYuwcAAICWKH5AN1HX4NBH24/pnc1HlFtYrZAgf82/aIDmTOmnPjGhZscDAACAF6P4\nAV7ucH6l3s04oo+yjqmhyaVBfSN1x3VjddHYPgoM4H9hAAAAtI1PjYAXcrnc+mxnvt7612HtPVKu\nAD+rpo5L0pwp/TQkuZfZ8QAAANDNUPwAL1LX4NAHmbl645ODKq6oV0LvEP3gipGacUGywoIDzI4H\nAACAboriB3iBssp6vfWvw3p38xHV1js0ckC0brlqlC4YES+r1WJ2PAAAAHRzFD/AREXldXrlgxxt\n2p4rt9vQ5NGJunraQA1NiTI7GgAAAHwIxQ8wQXF5nV75MEcfZObKarVoVlo/XTl1oBJ6c+49AAAA\ndDyKH9CFSirq9fcPc/R+5lFJFs2e3E/Xzhis6Iggs6MBAADAh1H8gC5QU9ekv27cr3cyjkgydPmF\nKbp2xhDv1AzPAAAgAElEQVTF9KLwAQAAoPNR/IBO5HIb2rj1qP78zl7V1jdpxgXJuv7yoYqNCjY7\nGgAAAHoQih/QSXYfKtPa13bpUH6lZ5bOAX0izI4FAACAHojiB3Sw0hP1emH9bn2y47h6Rwbp7v+Y\nqIvGJspi4bQMAAAAMAfFD+gghmHoo6w8rXltp5xOt66/fKiumT5IgQH8bwYAAABz8YkU6ACVNY16\n5tUvtXlXgUb0j9KyG8YrPppTMwAAAMA7UPyA87RtT6FWv7JDNXVN+s95I3TVJYNks3JYJwAAALwH\nxQ9op/pGp37/Zrbe23JU/RLC9fAtk9U/kclbAAAA4H0ofkA75JfW6Be/36rjJTW65tJBWjR7mPz9\nbGbHAgAAAFpF8QPO0c4DJXr8/7ZJkn7x/6ZozOAYkxMBAAAAZ0bxA87Be1uO6Lf/2KmE3iH6+Q8m\nKbF3qNmRAAAAgDZR/ICz4HK59Ye3duvNTw5p/NBY3X3TRIUE+ZsdCwAAADgrFD+gDbX1Dj3xl+36\nfF+xrrh4gL6fPlI2m9XsWAAAAMBZo/gBZ1Bd16QVv8vQ0YIq3bZwjOZM7md2JAAAAOCcUfyAb1FT\n79D9azKUW1itFd+fpInD48yOBAAAALQLx6sBrahrcOjBtZt1pKBK9/7nBZQ+AAAAdGsUP+Ab6hud\nevC5LTqQd0J333SBLhwRb3YkAAAA4LxQ/ICvaWh06qHnt2h/boXuummiJo9KMDsSAAAAcN4ofsBJ\nDU1O/eIPW7X3cJl+euMEfWd0otmRAAAAgA5B8QMkud2GfvXnLO06WKo7bxivi8f1MTsSAAAA0GEo\nfoCklzbuU+aeQt1y1ShdOqGv2XEAAACADkXxQ4+3eVe+Xn4/R5dfmKx53+lvdhwAAACgw1H80KPl\nFlZp1V8/15DkSN26YLQsFovZkQAAAIAOR/FDj1VT79AjL2TKHuCn+/7zQgX428yOBAAAAHQKih96\nJJfb0K9fzFJReZ3uWXyBoiOCzI4EAAAAdBqKH3qkl97bp+17i3TL1aM0ckC02XEAAACATkXxQ4+z\neVe+XvmgeTKXOZP7mR0HAAAA6HQUP/QoFdUN+s0rOzS4b6R+dA2TuQAAAKBnOKvit379es2dO1cz\nZ87Uiy++eNr2Q4cO6aabbtIVV1yhH/zgB6qsrOzwoEBH+N26nWpocmnZDePl78dkLgAAAOgZ2ix+\nRUVFWrVqlV566SW9/vrrevnll3XgwAHPdsMw9KMf/UhLlizRm2++qeHDh2vt2rWdGhpoj399eVwZ\nOwt046xh6hsXZnYcAAAAoMu0WfwyMjKUlpamyMhIBQcHa9asWdqwYYNn++7duxUcHKypU6dKkm69\n9VYtWrSo8xID7VBZ06jfrdupQX0jdfW0gWbHAQAAALqUX1s3KC4uVkxMjGc5NjZWO3fu9Czn5uaq\nd+/euu+++7R3714NGDBAP//5z88pRHZ29jndHl0nKyvL7Agd4tXPylRd16Qbp/bSjh1fmB2nR/KV\nsQTzMZbQERhH6CiMJXQXbRY/t9vdYgIMwzBaLDudTmVmZuovf/mLRo0apaeeekqPP/64Hn/88bMO\nkZqaKrvdfo7R0dmysrI0YcIEs2Octy3ZBco+mqdFs4dp7oyhZsfpkXxlLMF8jCV0BMYROgpjCR2h\nsbGxS3aEtXmoZ3x8vEpKSjzLJSUlio2N9SzHxMQoJSVFo0aNkiTNnz+/xR5BwEzVdU169tUvNSAx\nQgunDzY7DgAAAGCKNovflClTtHnzZpWXl6u+vl4bN270fJ9PksaNG6fy8nLt27dPkrRp0yaNHDmy\n8xID5+D5N7JVVdukO64fJz8bZy8BAABAz9TmoZ5xcXFatmyZFi9eLIfDoYULF2r06NFasmSJli5d\nqlGjRumZZ57RihUrVF9fr/j4eD3xxBNdkR04o+17i7Rp+zFdd9kQDegTYXYcAAAAwDRtFj9JSk9P\nV3p6eot1zz33nOf6mDFj9Oqrr3ZsMuA8OJxurX1tl/rGhem6y4eYHQcAAAAwFce+wSe9m3FYBWW1\n+uEVqZyoHQAAAD0exQ8+p6beob+9v19jh8Ro/LDYtu8AAAAA+DiKH3zO3z/IUU29Q99PZ5IhAAAA\nQKL4wccUldfpzU8PafrEvuqfyIQuAAAAgETxg4/58zt7ZbVa9B+zh5sdBQAAAPAaFD/4jK+OVeif\nX+TpqmkD1TsyyOw4AAAAgNeg+MEnGIahP6zfrYjQAF1z6SCz4wAAAABeheIHn7BtT5GyD5bphpnD\nFBzob3YcAAAAwKtQ/NDtuVxuvfDWbvWJCdWstBSz4wAAAABeh+KHbm/j1qPKK67Rf84fIT8bQxoA\nAAD4Jj4lo1tzON165YMcDe8XpUkj482OAwAAAHglih+6tX9+fkyllQ267vIhslgsZscBAAAAvBLF\nD92Wy23o1U1faUCfCI0fGmt2HAAAAMBrUfzQbW3ZVaDjJbVaOH0we/sAAACAM6D4oVsyDEN/35Sj\nxN4hmjI60ew4AAAAgFej+KFb+mJ/iQ7mVeqa6YNls7K3DwAAADgTih+6pb9vylF0RKAundDX7CgA\nAACA16P4odvZe7hc2QfLdPUlg+TvxxAGAAAA2sKnZnQ7f9+Uo7DgAM2alGJ2FAAAAKBboPihWzmc\nX6lte4p0xdQBCrT7mR0HAAAA6BYofuhWXt30lYLsNs3/Tn+zowAAAADdBsUP3UZBaa3+teO4Zk/u\nr9DgALPjAAAAAN0GxQ/dxmsfH5DVatVV0waaHQUAAADoVih+6BZq6h3alHVMl4xPUlR4oNlxAAAA\ngG6F4odu4cNtuWpscmneRXy3DwAAADhXFD94Pbfb0NufHdbwflEalBRpdhwAAACg26H4wet9kVOs\ngtJazWUmTwAAAKBdKH7wem/967Aiw+z6zuhEs6MAAAAA3RLFD16tsKxWWfuKNCstRf5+DFcAAACg\nPfgkDa/2TsYRWSwWzZncz+woAAAAQLdF8YPXamhy6v2tRzV5VIKiI4LMjgMAAAB0WxQ/eK1Pvjiu\nmnqH5jOpCwAAAHBeKH7wSoZh6O1/HVZKfJhGDog2Ow4AAADQrVH84JX2HinXofxKzbtogCwWi9lx\nAAAAgG6N4gev9Pa/Disk0E+XjE8yOwoAAADQ7VH84HXKqxr02c58zbgwWUF2P7PjAAAAAN0exQ9e\n570tR+VyG5o3hUldAAAAgI5A8YNXcbsNvZ95VGOHxCgxJtTsOAAAAIBPoPjBq+w8UKKSinrNvDDF\n7CgAAACAz6D4wau8n5mrkCB/TUqNNzsKAAAA4DMofvAaNXVN2ryrQJeMT1KAv83sOAAAAIDPoPjB\na/zzi+NyON267MJks6MAAAAAPuWsit/69es1d+5czZw5Uy+++OK33u7jjz/W9OnTOywcepYPMo+q\nf2K4BvaJMDsKAAAA4FPaPElaUVGRVq1apXXr1ikgIEDXX3+9Jk2apEGDBrW4XWlpqX75y192WlD4\ntsP5lTqQV6klV6XKYrGYHQcAAADwKW3u8cvIyFBaWpoiIyMVHBysWbNmacOGDafdbsWKFbr99ts7\nJSR83weZufKzWXXJ+L5mRwEAAAB8TpvFr7i4WDExMZ7l2NhYFRUVtbjNn/70J40YMUJjxozp+ITw\neQ6nWx9l5WlSarzCQwLMjgMAAAD4nDYP9XS73S0OvTMMo8VyTk6ONm7cqD/+8Y8qLCxsV4js7Ox2\n3Q+dLysrq9OfY09unarrmtSvV1OXPB/Mwd8tOgpjCR2BcYSOwlhCd9Fm8YuPj9f27ds9yyUlJYqN\njfUsb9iwQSUlJbrmmmvkcDhUXFysG2+8US+99NJZh0hNTZXdbj/H6OhsWVlZmjBhQqc/z1tfbFF0\nRKCunTdFNivf7/NFXTWW4PsYS+gIjCN0FMYSOkJjY2OX7Ahr81DPKVOmaPPmzSovL1d9fb02btyo\nqVOnerYvXbpU7733nt544w2tXbtWsbGx51T60LOVVdbr831Fmj6xL6UPAAAA6CRtFr+4uDgtW7ZM\nixcv1lVXXaX58+dr9OjRWrJkiXbt2tUVGeHDNm0/Jrchzt0HAAAAdKI2D/WUpPT0dKWnp7dY99xz\nz512u6SkJG3atKljksHnGYah9zNzNXJAtBJ7h5odBwAAAPBZZ3UCd6Az7DlcroLSWl3O3j4AAACg\nU1H8YJoPMnMVZLfpO6MTzY4CAAAA+DSKH0xR1+DQv748rovHJinQflZHHAMAAABoJ4ofTPHZl/lq\naHJxmCcAAADQBSh+MMX7mbnqExOqoSm9zI4CAAAA+DyKH7pcXnG19h4p1+UXJsti4dx9AAAAQGej\n+KHLfZCZK6vVoksn9jU7CgAAANAjUPzQpVwutzZtP6aJw+IUFR5odhwAAACgR6D4oUt9vr9YFdWN\nuoxJXQAAAIAuQ/FDl3o/M1eRoXZdMCLO7CgAAABAj0HxQ5eprGlU5u5CXTIhSX42hh4AAADQVfj0\njS7zUVaeXG6DwzwBAACALkbxQ5cwDEPvZx7V0OReSokPNzsOAAAA0KNQ/NAlvjp2QrmF1ZrB3j4A\nAACgy1H80CU+yMxVgJ9VU8f2MTsKAAAA0ONQ/NDpGh0uffJFnqaMSVRIkL/ZcQAAAIAeh+KHTrd5\nV4FqG5y6nMM8AQAAAFNQ/NDpPsg8qrioYKUO6G12FAAAAKBHovihUxWV1+nLr0p12YXJslotZscB\nAAAAeiSKHzrVh9tyZbFI0yf2NTsKAAAA0GNR/NBp3G5DH2zL1djBMYrtFWx2HAAAAKDHovih0+w8\nUKKSinpdxqQuAAAAgKkofug0H2QeU0iQv9JSE8yOAgAAAPRoFD90ipq6JmXsytcl45MU4G8zOw4A\nAADQo1H80Ck+2XFcDqebwzwBAAAAL0DxQ6d4PzNX/RPDNbBPhNlRAAAAgB6P4ocOdzi/UgeOndBl\nFybLYuHcfQAAAIDZKH7ocB9sy5WfzapLxnPuPgAAAMAbUPzQoRxOtz7anqdJqfEKDwkwOw4AAAAA\nUfzQwTL3FKq6rkmXXcCkLgAAAIC3oPihQ32QmavoiECNGxprdhQAAAAAJ1H80GHKKuv1+b4iTZ/Y\nVzYrk7oAAAAA3oLihw6zafsxuQ1x7j4AAADAy1D80CHcbkPvbTmq1IHRSuwdanYcAAAAAF9D8UOH\n+CKnWEXldZo7ub/ZUQAAAAB8A8UPHeLdjCOKCA1Q2qgEs6MAAAAA+AaKH85bSUW9tu0p1OUXpsjf\njyEFAAAAeBs+peO8bdx6VIakWWkpZkcBAAAA0AqKH86L0+XWxq1HNH5orOKjQ8yOAwAAAKAVFD+c\nl8zdhSqvatTcKUzqAgAAAHgrih/Oy7ubj6h3ZJAmDI8zOwoAAACAb0HxQ7vll9RoR06JZqWlyGa1\nmB0HAAAAwLeg+KHdNmw5KqvVopmTmNQFAAAA8GZnVfzWr1+vuXPnaubMmXrxxRdP2/7BBx/oyiuv\n1BVXXKHbbrtNlZWVHR4U3qXJ4dIHmblKS41XVHig2XEAAAAAnEGbxa+oqEirVq3SSy+9pNdff10v\nv/yyDhw44NleU1OjBx98UGvXrtWbb76poUOH6je/+U2nhob5PtuZr+q6Js2dzKQuAAAAgLdrs/hl\nZGQoLS1NkZGRCg4O1qxZs7RhwwbPdofDoQceeEBxcc2TewwdOlQFBQWdlxhe4d2MI0rsHaJRg3qb\nHQUAAABAG9osfsXFxYqJifEsx8bGqqioyLPcq1cvXX755ZKkhoYGrV27VpdddlknRIW3OJxfqb1H\nyjV7cj9ZmdQFAAAA8Hp+bd3A7XbLYvn3h3vDMFosn1JdXa0f//jHGjZsmK6++upzCpGdnX1Ot0fX\nycrKOm3d+swK2axS74DyVrcDrWGsoKMwltARGEfoKIwldBdtFr/4+Hht377ds1xSUqLY2NgWtyku\nLtYPfvADpaWl6b777jvnEKmpqbLb7ed8P3SurKwsTZgwocW6E9WN2vnKRk2fmKyLp4wzKRm6m9bG\nEtAejCV0BMYROgpjCR2hsbGxS3aEtXmo55QpU7R582aVl5ervr5eGzdu1NSpUz3bXS6Xbr31Vs2Z\nM0fLly9vdW8gfMdbnx2S0+XWgksHmR0FAAAAwFlqc49fXFycli1bpsWLF8vhcGjhwoUaPXq0lixZ\noqVLl6qwsFB79uyRy+XSe++9J6l5D94jjzzS6eHRteobnXr7X4c1aWS8kmLDzI4DAAAA4Cy1Wfwk\nKT09Xenp6S3WPffcc5KkUaNGad++fR2fDF7n/cyjqql36JpLB5sdBQAAAMA5OKsTuANOl1uv//Og\nRvSP0rB+UWbHAQAAAHAOKH44K//6Ml8lFfXs7QMAAAC6IYof2mQYhtZ99JX6xoVq4vA4s+MAAAAA\nOEcUP7Tpi5wSHc6v0oJLBnHCdgAAAKAbovihTa99dEBR4XZNG59kdhQAAAAA7UDxwxkdyDuhHV+V\n6IqLB8rfz2Z2HAAAAADtQPHDGb320QEF2f00e3I/s6MAAAAAaCeKH75VRY1T//ryuOZM7qeQIH+z\n4wAAAABoJ4ofvtVne6pltVp0xdQBZkcBAAAAcB4ofmhVXnG1sg7WauakFEVHBJkdBwAAAMB5oPih\nVf/39h752yy6YeYws6MAAAAAOE8UP5xm96Eybcku1EUjwhQZZjc7DgAAAIDzRPFDC4Zh6IX1uxUV\nHqi0YaFmxwEAAADQASh+aOGznfnan1uh/5g9TAF+DA8AAADAF/DJHh4Op1t/enuvUuLDNP2CZLPj\nAAAAAOggFD94vJtxWAVltfrP+SNls1rMjgMAAACgg1D8IEmqqXfob+/naMzg3powLNbsOAAAAAA6\nEMUPkqRXP8xRdV2Tvjd/pCwW9vYBAAAAvoTiBxVX1OnNTw/pkglJGpgUaXYcAAAAAB2M4tfDGYah\n37+ZLUm6afZwk9MAAAAA6AwUvx7uo6w8Zews0A0zhyo2KtjsOAAAAAA6AcWvByuuqNOa13ZqeL8o\nLbh0sNlxAAAAAHQSil8P5XYbeuqvX8gwDP3XjeM5fQMAAADgwyh+PdSbnx7UroOlWnLlKMVHh5gd\nBwAAAEAnovj1QEcLqvR/b+/VpJHxuuzCZLPjAAAAAOhkFL8exuF06dcvZSk0yF+3XzuWc/YBAAAA\nPQDFr4d5ccM+Hc6v0k++O1aRYXaz4wAAAADoAhS/HiT7YKnWfXxAs9JSdOHIeLPjAAAAAOgiFL8e\n4mhhlR79Y6bio0P0gytSzY4DAAAAoAtR/HqAwrJa3b9ms/z9rHr4lskKsvuZHQkAAABAF6L4+bjy\nqgb9fE2GmhwuPXzLFE7dAAAAAPRAFD8fVl3XpPvXZOhEdaMeXJKmlIRwsyMBAAAAMAHFz0fVNzr1\n0PNbdLykViu+N0lDU6LMjgQAAADAJBQ/H+RwuvToHzP1VW6F7r5pgsYMiTE7EgAAAAATMcuHj6mo\natCv/pKlXQdLdcd14zR5VKLZkQAAAACYjOLnQ778qkRPvpiluganlt0wTtMnJpsdCQAAAIAXoPj5\nAJfb0Csf5OhvG/cpMSZUK//fFCZyAQAAAOBB8evmTlQ36tcvZmnHVyW6ZHySbls4hvP0AQAAAGiB\nhtBNGYahzN2FevYfX6qmzqHbrx2rmZOSZbFYzI4GAAAAwMtQ/LoZwzC0bU+RXtq4TwfzKpUUG6oH\nl0xW/8QIs6MBAAAA8FIUv27i1B6+v76/XwfzKpUQHaI7rhunSyYkyc/GWTkAAAAAfDuKn5c7Ud2o\nzD2Fevuzwzp0vLnw3Xn9OF0yPkk2Ch8AAACAs0Dx80LHS2q0NbtAW7ILte9ouQxD6hMTomU3jNO0\ncRQ+AAAAAOfmrIrf+vXr9dvf/lZOp1M333yzFi1a1GL73r17tXz5ctXW1mrixIl66KGH5OdHpzwb\ndQ0OHc6v0pGC5kv2wVLlFddIkgb0idANlw/VpNQE9U8MZ+IWAAAAAO3SZjsrKirSqlWrtG7dOgUE\nBOj666/XpEmTNGjQIM9t7rrrLq1cuVJjx47Vfffdp1deeUU33nhjpwbvDgzDUF2DU+VVDaqoblB5\nZYPKqxpUXtWowrJaHS6oUnF5nef2IUH+Gtw3UnOn9NekkfGKjQo2MT0AAAAAX9Fm8cvIyFBaWpoi\nIyMlSbNmzdKGDRt0++23S5KOHz+uhoYGjR07VpK0YMECrV69+qyKn2EYkqSmpqZ2v4DOcKKmUZ/u\nOC6HwyWX22i+uAy5DUMul1tOlyG3Z71bLsOQ0+lWQ5NTDU0uNTSe/LPJqZMvsYUAf5uiwgM1ekCk\n+k7uq6TYUPWNDVOvcHuLvXqNjY1d+Kpb5w0Z4BsYS+gojCV0BMYROgpjCefrVBcyWisOHajN4ldc\nXKyYmBjPcmxsrHbu3Pmt22NiYlRUVHRWT+5wOCRJOTk5Zx24q/SPbOsWlpOXr3/fzt6OZ6qVGmuV\nf6xI+e24d2fLzs42OwJ8BGMJHYWxhI7AOEJHYSyhozgcDgUGBnba47dZ/Nxud4u9UIZhtFhua/uZ\nhISEaMiQIfL39+f7awAAAAB6HMMw5HA4FBIS0qnP02bxi4+P1/bt2z3LJSUlio2NbbG9pKTEs1xa\nWtpi+5lYrVaFhYWdS14AAAAA8CmduafvlDbPCzBlyhRt3rxZ5eXlqq+v18aNGzV16lTP9j59+shu\ntysrK0uS9MYbb7TYDgAAAAAwl8U4i28Rrl+/XmvWrJHD4dDChQu1ZMkSLVmyREuXLtWoUaO0b98+\nrVixQjU1NRo5cqQee+wxBQQEdEV+AAAAAEAbzqr4AQAAAAC6rzYP9QQAAAAAdG8UPwAAAADwcRQ/\nAAAAAPBxFD8AAAAA8HEUP5xm/fr1mjt3rmbOnKkXX3zR7DjwIk8//bTmzZunefPm6YknnpAkZWRk\nKD09XTNnztSqVas8t927d68WLFigWbNmafny5XI6nZKk/Px8LVq0SLNnz9aPfvQj1dbWSpKqqqp0\nyy23aM6cOVq0aFGL84PCN/3yl7/UPffcI6njxktTU5PuuusuzZkzR1dffbUOHjxozotDl9i0aZMW\nLFigOXPmaOXKlZJ4T0L7vPHGG55/3375y19K4n0J56ampkbz589XXl6epM5/L2rXuDKAryksLDQu\nvfRSo6KiwqitrTXS09ONr776yuxY8AKfffaZcd111xmNjY1GU1OTsXjxYmP9+vXGtGnTjNzcXMPh\ncBjf//73jY8//tgwDMOYN2+e8cUXXxiGYRj33nuv8eKLLxqGYRi33HKL8dZbbxmGYRhPP/208cQT\nTxiGYRgPPfSQsWbNGsMwDOO1114z7rjjjq5+iehCGRkZxqRJk4yf/exnhmF03Hh5/vnnjZ///OeG\nYRhGZmamce2113bdi0KXys3NNS666CKjoKDAaGpqMm644Qbj448/5j0J56yurs644IILjLKyMsPh\ncBgLFy40PvvsM96XcNZ27NhhzJ8/3xg5cqRx7Ngxo76+vtPfi9ozrtjjhxYyMjKUlpamyMhIBQcH\na9asWdqwYYPZseAFYmJidM899yggIED+/v4aOHCgjhw5opSUFPXt21d+fn5KT0/Xhg0bdPz4cTU0\nNGjs2LGSpAULFmjDhg1yOBzatm2bZs2a1WK9JH388cdKT0+XJM2fP1+ffPKJHA6HOS8WnerEiRNa\ntWqVbr31Vknq0PHy8ccf64orrpAkXXDBBSovL1d+fn5Xv0R0gffff19z585VfHy8/P39tWrVKgUF\nBfGehHPmcrnkdrtVX18vp9Mpp9MpPz8/3pdw1l555RU98MADio2NlSTt3Lmz09+L2jOuKH5oobi4\nWDExMZ7l2NhYFRUVmZgI3mLw4MGeN6ojR47o3XfflcViaXW8fHMcxcTEqKioSBUVFQr9/+3cPUhy\nfRgG8MtQa7APKirTojGQaJHAAiVpKBqiaGiMIAiCIoIoEFssG6ShrZaiIYhUlMShoQa/MHKJBqFB\nQqIok+gTMTvPEAg+T728+aY+r1y/7f9HD+eGi/ucW4/KZBCLxRn7QGb2xGIxZDIZ4vF4vsqjPDIa\njZiZmUFFRQWAP/vOf8nLZ8e6vr7OV2mURxcXF0ilUpiYmMDAwAB2dna+vIaxJ9E/kclkmJ6eRl9f\nH3Q6HRQKBSQSCfsS/WtLS0tQq9XpdT56UTa54uBHGd7f3yESidJrQRAy1kTn5+cYGxvD3Nwcmpqa\nPs3LVzn6LE9f5UsQBJSUsEUVm729Pcjlcmg0mvTeT+bl9/cwR8UrlUohEAhgeXkZu7u7OD09RTQa\nZU+ibwuHw7DZbDg6OoLH40FJSQl8Ph/7EmXtq55T6OsdU0cZGhoaMn7Afnt7m/7amigUCmF0dBSz\ns7MYHBz8Mi+/78diMdTV1aG6uhqPj49IpVIZrwc+Pg2LxWIAgLe3Nzw/P6OqqiqP1VE+uN1u+Hw+\nDAwMYG1tDYeHh7BarT+Wl/r6etzc3PxxLCo+tbW10Gg0qK6uRllZGXp6euD3+9mT6Nu8Xi80Gg1q\namoglUoxNDSEYDDIvkRZy8f9UTa54uBHGTo7OxEIBBCPx/H6+oqDgwNotdpCnxb9Ba6urjA5OQmL\nxYL+/n4AQHt7OyKRSPqRK5fLBa1WC4VCgdLSUoRCIQAf/5am1WohkUigVqvhdrsBAA6HI50vnU4H\nh8MB4GM4UKvVkEgkBaiUcmlzcxMulwtOpxNTU1PQ6/Uwm80/lhedTgen0wkAODk5QWlpKRobGwtQ\nKeVad3c3vF4vHh4ekEql4PF40Nvby55E39ba2gq/34+XlxcIgoDDw0N0dHSwL1HW8nF/lE2uRIIg\nCLkqmv6f9vf3sb6+jmQyieHhYYyPjxf6lOgvYDKZYLPZ0NzcnN4bGRlBS0sLzGYzEokEdDodFhYW\nIBKJEA6HYTAY8PT0BJVKBbPZDKlUisvLS8zPz+Pu7g5yuRyrq6uorKzE/f095ufnEY1GUV5eDovF\nArqEE88AAADHSURBVKVSWcCKKdfsdjuOj4+xsrLyY3lJJBIwGo04OzuDVCqFyWSCSqUqdKmUI1ar\nFVtbW0gmk+jq6oLBYEAwGGRPom/b2NiA3W6HRCJBW1sbFhcXEYlE2JfoW/R6Pba3t6FUKhEIBHLa\ni7LJFQc/IiIiIiKiIsdHPYmIiIiIiIocBz8iIiIiIqIix8GPiIiIiIioyHHwIyIiIiIiKnIc/IiI\niIiIiIocBz8iIiIiIqIix8GPiIiIiIioyHHwIyIiIiIiKnK/AMqI/YUpuOcTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122f3ee80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (15, 5))\n",
    "sns.kdeplot(rx_df_tidy.loc[:,'Price'], cumulative=True)\n",
    "sns.kdeplot(rx_df_tidy.loc[:,'Spending_per_person'], cumulative=True)\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize = (15, 5))\n",
    "sns.kdeplot(rx_df_tidy.loc[:,'Utilization_per_1000_persons'], cumulative=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "An interesting observation from the CDFs is the very long tails in spending and utilization, which indicate a small number people are willing to spend and use a very high amount of medical services."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Look for correlations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metric</th>\n",
       "      <th>Age Group Upper Limit</th>\n",
       "      <th>Year</th>\n",
       "      <th>OOP_spending_per_person</th>\n",
       "      <th>Payer_spending_per_person</th>\n",
       "      <th>Price</th>\n",
       "      <th>Spending_per_person</th>\n",
       "      <th>Utilization_per_1000_persons</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Metric</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Age Group Upper Limit</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.522301</td>\n",
       "      <td>0.503733</td>\n",
       "      <td>0.113305</td>\n",
       "      <td>0.529634</td>\n",
       "      <td>0.483740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.057604</td>\n",
       "      <td>0.107881</td>\n",
       "      <td>0.170600</td>\n",
       "      <td>0.082755</td>\n",
       "      <td>0.004156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOP_spending_per_person</th>\n",
       "      <td>0.522301</td>\n",
       "      <td>-0.057604</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.707380</td>\n",
       "      <td>0.012466</td>\n",
       "      <td>0.791997</td>\n",
       "      <td>0.963403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Payer_spending_per_person</th>\n",
       "      <td>0.503733</td>\n",
       "      <td>0.107881</td>\n",
       "      <td>0.707380</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.193111</td>\n",
       "      <td>0.991782</td>\n",
       "      <td>0.624053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price</th>\n",
       "      <td>0.113305</td>\n",
       "      <td>0.170600</td>\n",
       "      <td>0.012466</td>\n",
       "      <td>0.193111</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.169055</td>\n",
       "      <td>-0.059679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spending_per_person</th>\n",
       "      <td>0.529634</td>\n",
       "      <td>0.082755</td>\n",
       "      <td>0.791997</td>\n",
       "      <td>0.991782</td>\n",
       "      <td>0.169055</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.713400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilization_per_1000_persons</th>\n",
       "      <td>0.483740</td>\n",
       "      <td>0.004156</td>\n",
       "      <td>0.963403</td>\n",
       "      <td>0.624053</td>\n",
       "      <td>-0.059679</td>\n",
       "      <td>0.713400</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Metric                        Age Group Upper Limit      Year  \\\n",
       "Metric                                                          \n",
       "Age Group Upper Limit                      1.000000  0.000000   \n",
       "Year                                       0.000000  1.000000   \n",
       "OOP_spending_per_person                    0.522301 -0.057604   \n",
       "Payer_spending_per_person                  0.503733  0.107881   \n",
       "Price                                      0.113305  0.170600   \n",
       "Spending_per_person                        0.529634  0.082755   \n",
       "Utilization_per_1000_persons               0.483740  0.004156   \n",
       "\n",
       "Metric                        OOP_spending_per_person  \\\n",
       "Metric                                                  \n",
       "Age Group Upper Limit                        0.522301   \n",
       "Year                                        -0.057604   \n",
       "OOP_spending_per_person                      1.000000   \n",
       "Payer_spending_per_person                    0.707380   \n",
       "Price                                        0.012466   \n",
       "Spending_per_person                          0.791997   \n",
       "Utilization_per_1000_persons                 0.963403   \n",
       "\n",
       "Metric                        Payer_spending_per_person     Price  \\\n",
       "Metric                                                              \n",
       "Age Group Upper Limit                          0.503733  0.113305   \n",
       "Year                                           0.107881  0.170600   \n",
       "OOP_spending_per_person                        0.707380  0.012466   \n",
       "Payer_spending_per_person                      1.000000  0.193111   \n",
       "Price                                          0.193111  1.000000   \n",
       "Spending_per_person                            0.991782  0.169055   \n",
       "Utilization_per_1000_persons                   0.624053 -0.059679   \n",
       "\n",
       "Metric                        Spending_per_person  \\\n",
       "Metric                                              \n",
       "Age Group Upper Limit                    0.529634   \n",
       "Year                                     0.082755   \n",
       "OOP_spending_per_person                  0.791997   \n",
       "Payer_spending_per_person                0.991782   \n",
       "Price                                    0.169055   \n",
       "Spending_per_person                      1.000000   \n",
       "Utilization_per_1000_persons             0.713400   \n",
       "\n",
       "Metric                        Utilization_per_1000_persons  \n",
       "Metric                                                      \n",
       "Age Group Upper Limit                             0.483740  \n",
       "Year                                              0.004156  \n",
       "OOP_spending_per_person                           0.963403  \n",
       "Payer_spending_per_person                         0.624053  \n",
       "Price                                            -0.059679  \n",
       "Spending_per_person                               0.713400  \n",
       "Utilization_per_1000_persons                      1.000000  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rx_df_tidy.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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brLW7AYwxS3E7zCqRJyIikqWeWILmUP8TmqaOCDfdt4U9h7sBeNfJ8/jE+qX9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\nsDTopX62m7BL17erKsnN34gltWU52Q8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DfOjKU3jdWScoISIlJ+N6tPam+MGT+/jMA7kZ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5paZ+ZbqxvebLFtWPq40jBfK+9Pa1/d/nEz7nl6T5uaL83HZe7ndr/u7nw+7rV1e/mmTWJZlx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PdfVzz+a9HBqIA7CoPkCNV2fn8QgArSEfn766nXOXNow7f8Cr01TrxWeUJ2IukrRorDHo\njZpZz19dg2NDCTo6u6v6OlLpiLzJmO4VKt/7804ZhBA34abfsmzZsml+rKIYskp85huRY45XGQnT\nRgjKNnCUg2rot3c92kk4ZqJrwo3UGFN5rhQiCZOx8ZRO+nipLG2sYV9vdFxE3srm4oSVXHJTfiFd\nAdeRU0r5VVRHvy2FYrw+pjOeWbbDsUE3xeD+5w8xFE8xnHJ/CQKo9eg8+tpxZNrrUQJdvTFufWA7\n/zpGMB+76+o3dFK2Q280mRXyCqXMqoq0kzPX+m6pbNrchUfTSExS3Tvg0YibTjoN0kGg4SAJ+T0q\nHbsKqaZ+mztG+T06MdMm3zBp2RKPDscHE7TW+cdFB48VBPf0DLuVvg2N44MJth0M8+ENp3HzlXOz\nhl6+604sZbGg3s/xwQQAC+r9hd5+UlBN/VahKJZq7LelVqGdzpy2UPTfN3+3h3+57hye6+rnrsc6\nGYiZ2dMf6B8RFN/U3srH3rR6VDE3AI+u0VTrLWglM1WWNtbw4qEwXl3LrvMcKRHpv6Wa58WV9sib\njGJXySeEEAsB0veZhO/DwNKc1y0Bjub9ICnvkVJeKKW8sKWlZartVZRANGXTWGNgOg4Jy8F0HBpr\njKxqXw0kTJu+aJKDfTGODsRJTrK4mW2qod929Q4jpcS0HZKmg2k7SCmnVD24J5pCF65YkfFc0oV7\nvFQubWuiJ5oiZTtowt3d74mmpuSR5zXcwgOOlEgkjnRDBaeS8gtw9+O7OOcLj3HaPzzMOV94jLsf\n3zWl88xVqqHflsJkXh/gjmeLG/wYusCWEkMXLG7wM5yy6ejs5sZ7nuHyu57gxnueGeVJkhHxTNvB\nkZKdx4eyIh64F8FIysZySO+Mahi6ji4E0aSV9SvJfMZz+/s5PphgKO6K3y0hH0hIWo6qJFoG5lrf\nLZVdJ4byetaMZWVzkNagF4HbRw1dMK/Wi9fQVd+qQqqp3+7ujnCgb5hXjgwynBov4gnc6z6MeObd\n8fa14xZTuYvF3mgKpMQBUpaDoQkcKflWx9456ymXue4MJ00s22EoniJuOvzF+Uvca9GUyinNLaqp\n3yoUxVKN/baYeWwuE81pgQnntYfCMSzboasnSufxIfZ2R0iYdjb67/7nDxGJm3lHsNagl89cc8Yo\nEU8IQWONlyUT+EFPh43r20iaDinbIW7aJCwby3aYX+er+gyDao/IK5YHgb8G7kzf/zLn+EeEEPfj\nFtYYVP545WO6vmghn8HxwQQeXUMI0pFcFqtbK7vDmDBthpMWw0m7qAXNqY7tSGw5Ev4qpRtFpzlT\nm2RmxIqR80/tO9jS1U9ryMtQfCQiry4wNY+81a0h9vdFR5+r1sOKeaVH9939+C6+/vhuIC3SJKzs\n47kaOXCys6G9lesOD3DvU/sYTtnUenU+ePnKUeNdZrezrWWkT8RSFl5dG5XqmjEYvgP4ozUtWREv\nZTl8+ZGdE24WaJpAMBIVajtuqnhuZIpPF25K2qCbrlAX8NAcshlO2gzGTZUyqyhIR2c30aQrGk+E\nwO3b8+sD6LpgOGlTH/CovqWYlI7ObsIxE7tAHxOQnRMammBBvZ/WkD+vhUBuVeWxhXqEcKP0TNup\n+oiKfEgpubitiU9cuYbvbtnP8cE4C+oC3HDRUi6ewmakQqE4tcm1T9l9YoiULfEaWnYzeOwYWWhO\n2xryj7NwyZ3XbmhvHZWWm/HOPT6YYPm8WgCODcWxCiwRw/HRGVhBn0FTrRcjj0deOTF0kc2+QoLQ\nBEnLmVIW12xS7ULeuNRYIcR9uIUtmoUQh4HP4wp4PxFCfAA4CPxl+uUPA28F9gAx4P2z0OZTgsl+\nxMVQTam1SrybOh7d9U4Y+60ZeumZ8Svn1bCnZxjhyKy460hY1Vx6qtahcIx5tT6ag9P3yNu4vo1b\nH9iOne6btpRTjmr69pN7R/1byZzjSsirTjo6u3lg2xFaQj6WpQtBPLDtCOcsaciOd4WKRXg0mTfF\n4DtP7mXV/CCm7TAYN/ncL17l1aMTey5K6Qp4mf/P+EblRqa01vk5OpBAIumNJjF0gUfXcJjDVwAA\nIABJREFUufuGc+bcYlYxu2za3EWNV5s08lwCR8JxmkO26luKkti0uSvrvZgPQ3dLq0gH6mo9ea+z\n+aoqp4PkAfc+ablVvn363PFslFISN22iSYtY0saRknOW1vO1pedWumkKheIkIHOdvv3BHdTrgoBH\nL7h+n6gA2mSFMzLreJmzlnMPuAW0tCKc03wenXm13ilXoi2FTZu7aA566YuaCDGyUR6OmdxZ5RkG\nFRXyhBD5tpUiUsqMHPumsU9KKW8scLp8r5XAh6feQkUhylFNK5qy8eqCWE6V2RqPNmuptUq8Kw+1\nPoOEmRolTon08VK57eozuPm+bURTNk7aDyzo1bnt6jNKPlepfhCTkTJtUpaDI90BPqVNrZ+qastz\nj2LGu0LFIj77y1fHVf6aV+vhQN8wKcvh2GCc2/73FQ6F45O2w3YkUri/NEdCg8+T/YyMiXHI72FR\nA3QPJUhYDq0hv4qSUhTF8/v7iorGA1csGU7aSsRTlMShSUQ1vyEQQsNraKyYF8yKeDfe80w2+2Mg\nlsKju9F6RwcSOHmSTJ30JqDfoOo9G+OptHiXsiYUORUKhWK6FLt+n6gA2q0PbCeWtDAdNyK6JeQj\n6DOymybRlM2ieh9HBxNYOUulY0MJ/v6n2zk2lCjYvnk1HppDPur8noKvKTeZwA+fodMTSZKyHTya\noMZnVP38ptIRedtwPezCuPPDBuCYEKIb+Fsp5dZKNk5RmHKUpU6Y9igRDyBmOtQUWXU2482T73gh\nUpbDcNIimrQwC+V2FHjfq0cH2XkswtvPXZS3is6pSkvQR3g45RapSJf70ZhapVkAr0fH67i7Nrom\n8E5xN2bj+jY++cB2joTjWI6DobnFLj53zZkln+vOR3YSTdrkbCoRTdrc+cjOkgf5qfRbRWXZ3R0h\nEjezxU18hpbXOyNfsYjQo6Mrf5m2w5EBN8Xg9eMR/uHnrxCOTV7MpTnopSXoy3pPrmqu4barz2BD\neytLN48WrUN+D7omaA35ue+mS8rxT6A4yblx0x8mFfHA9QU1NMHK5loG42bVT3IV1cXSxhqO5Gxa\n5F4LNQGNtf5RfnhjC1q8eChMwnTw6YKFDQEaazx0R5IFPy+aclhQ5y373zFda5lETuSd2khWKBSz\nQcaSILP+1YTAZxRev+eb03Z0dhNJWDhSZqvZHh1IMC84Yje0uD5A5/GhcXOKuOnw8uFBAHy6IJmn\nYKCuiVkV8WAk8CPk9xBKf3YmjbjaqbSQ9yjwcynlYwBCiDcDVwE/Af4D19dOUYVMpyx1ht4CBQwK\nHZ8qlu0wnLSJJE1SRRarcKSkq2eYrQfCbD0Q5uUjg9n3eg1NCXk5SCkRQuAVIhtCbUs5pRTpTZu7\nqA94WFgfyB4rNdJzVNsAhOuXgyi+es5Y9vRER1XTzRhw7+mJlnwuQxOYeXbdDU1JedVIR2c3g3ET\n05ZZsTVhORwOx1kzPzTp+3MtBGRO/lcsafHxH780aXXQDM21Xh65ZX3e5yZKgVAoJqOjs5st+8JF\nvda0HRbW10wrullx6rJxfRuvHRtkMG6Ne6651otHF6Ou95noEcuWHB1MoKVdQpO2u3jUhDsnmygd\n/OFXT/COzu6yic5TtZZJWjbRRPmyQCzHoTuSmBOLTYVCUVky45aUEifta25LSSq9uVzs+n3T5i4a\nazz0DaeQjpuG6iDpHzb552vbiKUsrr1gMbf9rL/gOb587Vl8/sEd6MKNnE7HgKBr0Ds8+cZ2uZnL\nc+hKC3kXSik/lHkgpfy1EOLLUspPCCGmFs6jmBWqyd8uH5btMJxyU2cTRUb4HR9KsC0t3G07OMBg\nfPxg0r4gxMJ6NWnKJVPZqDddIdaraywI+qaUIn0oHMumCGYIePQpedxkFgC6ENhIdCHGLRKKJc+m\n0YTHJ6JQpblToQLdXGTT5i6ctPCa+w3ZsrjxLvf3kbQcPLpGwNA5PkEUyVg0wYS/p4lSIBSKyfjs\nz18u+rUZb8a5MslVVBcb2lv5xvXn87lfvjrKTqA16GV+fQApJbu7I9lU2p5IkgV1PnqjKTQEmiYw\nJJiOe81MWBKvLgpGuoObUVHOghe5qWmRhElPJEnCsrn5/he5+4bzR32OabtZIJFEaVkg+RiMm+w8\nNsSOo0PsODpI5/EI5y5p4McbL53un6RQKE5yNm3uwrTtbGZJZry0bDeyrtj1+6FwjOagm4baG01m\n130Bj8a6FY2cGEpy8cqJC/Jc0jYPcOe2bnEjd5t8qsUNp8tcnkNXWsjrF0J8Crg//fh6ICyE0AEV\na17FlFO8mSqayC+kCOBg/+TCTzRhse1QmG0HBth6IMyRgfEeVfPrfKxb1si65Y2cv6yBVfNDsx7y\nW+1MVNloqucqh6/d7u4IgzETLSdqtDeSwrQjJZ9LEyJb6GLs8dLJv+QQKrm2KtndHSk4zhQz3mX6\n9NKmGmzboW84RX8RqbQZDAGGoU36G8iXAqFQTMbdj+/i8GBxonIm4lr5Liqmw4b2Vn7ffgU33vPM\nuOt933CSSMKiO5KgIeChN5rkyEACCXjSUeuapuETEkPXMG0bTRM0Bzz0RlMFxbzdJyYuJFQKmQ3H\nSMLk6EAiW2E3lrK5/cEd3O44XLRiHtGURbLIjeSxOFJyoC/GjqNDvJYW7vL5qL58eBDTdjeIFAqF\nohCZNVE+Fjf4i16/Z+a0dQEPdYGRNNTGGi896Q3qyTYtfrBlPyvm1dLVE8UduWS2uOHqltoi/6Ly\nMlfn0JUW8t6NW3X2F7jroqfSx3TgXRVsl2ISyineTBWvoeUtEOAx8gsiKcvhtWND6Yi7MK8fjzA2\nwzHoMzhvaQPrljdy4fJGFjX4szsFivyUMyS5nOdKWQ62lNjpinaZr7HY9OpcfLpGzBl/kfNNYfKc\nqfI7lqlU+VXMPCnLyUqv2aEg/fUVIzBf2tbEN3+3x03NTacRgLsRIRhJK8iHwP3QoM9Q0U+KGeHe\np/YV9TqBK1YEPLryXVSUhcz1vjeaYDBmkrQdHAfqA0ZW3Jsf8nNkID4qHUxKWNQQcD10dbdAmkd3\nR+me6OiFqgA0jWwUSjnIzH97IkmESG/02ZnIQMk3n9jL164v3mYGYDhpsfPYEK+lI+5eOzbEcHL8\nnEPXBKtagqxdVMf5yxp40xnzlYinUCgmJWU5rod5eh4q0hNbIcDQtaLX75e2NfGtjr1YjoNP16iv\n8aAJjesuWALAgb5hvvRw54TneGDrYf79xgv45APbiSQsLNv1MW+s8fCpq9qn+6eeUlRUyJNS9gIf\nLfD0ntlsi6I0yllIoFRsRzKcsvAZGgnTGVcttSZdHMGRkn29rs/dtgNhXj48OM6PyqML1i6q44J0\n1N2a+SF05VVWEuUMSS7nuRzpjBJqs9ngsnQhL+DVSFj2qPNpAmq8pU+ga30GSTOFTEe3CAFCTq3K\nr2Lm8egj3o9jgzInE9c6Orv58QuHCfp0BuJWtv+saQ2yuzuKoYMmXWF37DgmAI+hsXLeSFELhaLc\nRBLjvcryIXEn/x+8fOXMNkhxyrChvZXrDg/wrY692I7Eb+jEUjbDKZuhuJmN9tA1gWVJUrbEq8PC\nen82vftz17iLvk2bu0hZDrowsxHUmnDfi3Q3fkthomIWGQEybtoYGtiOwEHSUOPDa2gcH5q4ArmU\nksPh+Ihod3SIfb3DeTd0GgIezlxUx9pFdZy5qI7T54fwp+e4Aa8+yk9YoVAoCuHRBcMpmZ2Hypz7\nobhZ1Pq9o7ObB7YdobHGQyThWlf1RVP81cXLuGhlI7948QjfSY/FhewOPJpb2HJDeytfue7cOZnO\nWk1UdOUohFgD3AqsyG2LlPKKSrVJUTzTLSSg5USnjD0+lox453reOUgpWTEvyOHwMMMpO5ta4Dc0\ngn4PX3poJ9sOhvNWg2xrqc2my569pJ7AFKuiKkYoZ0hyuc5VKLJ7CgF5tIb8hIfNrKCT2clqmUIE\n6urWEK87QwzEzazBa0ONh9WtkxdOUMw+a+bX8fLhMMOp8R3n5cMDE/bVb/5uD46URJMjonKd3yDg\n1Qn6dBKWg6FrCCGxHPc1uib4r/deqCYzillB0wR2vgtxmsxkXNcEH7tiFTdfuWbW2qY4+dnS1c+S\nxkA2Aq+rJ0rKduiNuilaRwddUazGo1EX8BCOmQynbFa3jl70Ze5vvOcZ9vdFGYpbWduXuoCRraYI\nk1ecnaiYxRtXNXPu0gZuvmIVn39wBwnTxmsImmt8BH0GcdNmQd1ocS1h2rx+PMKOo0O8enSQncci\neT2YNQErm2td4W5hHWsX1ausEIVCURZaQ3768xSSmMhfdCwZf9D6gJ/moMS0JbGUxXP7w+w4NsRz\n+92iWQvq/Hz66nY+9bOXSaQz5wSuH55EUpted8/VdNZqotIhID8FvgPcC8yeuZpi2pS7umg+JitY\nccNFS/n6b3dR6xWYjluZNm5ahOPWKC+RlqCPdctHfO6aar1Ffb4mBD6Phs/Q8RkaPkPDUCkMeZls\nYlyJc2XSV0XmP+kUxnxprZMhpXS99spQmffStiae29+Prgk8aTF7MGFxadvE5rCKyrBxfRvv/+7z\neZ/7zpNdBYWNwZhJV2+UgZiZFfE8uiDk0+mLJvnbP2rjG0/swXKcbOSIkPCxK1YBZM3ep/t7Uigy\nZMbWXSeGMG2J19AmHMME4PO4KTd3vH2t6oOKstDR2c2dj+xkX1+MpOXg0wULGwKE/B5aQj6OhOMk\nrRExTyBorfMT8nuo9Rm0hvwF07sz0XIL6o289hzFVJzNLWYB4Pfo2I7F3b/dzbJ5rp3CBcsb+dw1\nZ/KNJ3ZjaAK/R0t/lsNb1s7ntzu70xF3g+ztGc4rlgd9BmcuDLF2UT1nLqrjjIWhUX6BCoVCUS4i\n8fweooYuqA94ilq/7+6OEEu6myQeXaOp1osjJa8dH8pG+L35zPl85IpVBH0Gf33Jcv7zqX3omkAT\nbqVt2wGf4XDjPc+ouW0ZqPQVw5JSfrvCbVBMgUPhGLoY2T316hrNQW9J1UUL6SmOhKMD8bzinWm7\nPneZ6rLdQ8lxA1OtV+e8ZQ1cuLyR85c1srQxMOmOZq5o502Ldsp3pDiKmRiXcq5bH9hONGlhO5Le\naJJbH9jOv153bsnn0oTA0Nww8kwUnS6mVqCinMVdtnT10xL0EkmMRAyE/AZbuvq5ueSzKWaaDe2t\nBXcrYwWMzIcSJr/ZeZxwzMxObgzNFUaODyVZMz+UFQDvfWofwymbWq/OBy9fyTlLGsr2e1IoMmTG\n6ZRlM5ROp42n7ILXYXA3PqREiXiKspG5xg/EzKxPaNJ2U02XNELI76E5ZDOQjrwD8Of4Hk9WxX4y\ne46xIl2N1xi3CX0oHKPOb2DZTnr+IPHoIhsdmOHitiY+bJ/G97Yc4PhgHE3TcKTkrsdez9u25U01\no9JklzXVTLFglkKhUJRGz7CJnqdIpGlLLNuZdP3e0dlNJGFhOw6aJjBth6PpQkQAIb/Bx69czYbT\nW/F5dObVevn0NWdS6zO496l9RJMWEmgIGCxJ+4yque30qbSQ9yshxN8BPweyZdOklP2Va5KiGEI+\nwy0WkX5s2jbx/jinLyhPemBGxJNSsr8vli1Q8dKhgWyYbgZDE5y5qI51yxq5YHkD7QvqJvS5E0Jk\nxTr3ppfsn6IYYdPmLobiKYYSrgeYJtz0walEZ975yE76h1PZxaVpS1JWijsf2Vnyudqaa9l1IuJ6\nm0E2Iq+tufSKSOUs7pIp3Z6blut65hQvgiuqg3yjTDRpcf9zB/mXR18f5annpnS7ByIJN73h5ivX\ncPOVa7KRUj/Zeph7n9pHjVenPuD2j3wLTYWiVO58ZGdR1dxz8Rsa5y9rVP1OUTZyr/G5KV2WIzk2\nEEdvcr3vgj4DiYWT9g89OpBgUYMbuZyvyFBuJH/QqyOEyLsBk6k4C+443BNJkrRsDvXHeOzVY6xb\n0URL0EdvNDnKdiVhOiyoC9AXTbLj6FD2trs7gpldGY9s7AQ8Ou0LQ65ot9AV70J+T1n+DRUKhWIq\niAJ5tEcGEqxunbhAz6bNXUjpuHPZMTuAp7XU8uVrz2ZBvZ/GWi91OWNdZp574z3PsK83SiRh8fqJ\nSDaIQc1tp0elhby/Tt9/MueYBFR5viqnJ5JgrGOUkz5eDBN58gD8esdxXjgQZtvBAfqHU+OeX9lc\ny7rlDVywrJFzlzQQ8Ob3ucsV7bw5wp2ifLxyJEw0OdIbHAkDcYtXj4RLPtfunuFxESKOdI+XytVn\nLaDzeCR7zcqYtV991oKSz1XOarpLG2uyF7PciLyVzaVVuVPMDh2d3QWf84ypNDycNPm3x3dz7+8n\nrgR6ZCBBR2c3G9pbx0W0HhuMEzdtfIaeNXufLApFoZiIjs5uOk9ES35fyK+qJSvKR0dnN7t7oiNm\n62OeT9qSpGkTSVgkc8xsDc3dIDw+mKC1zj+uT+aOobqAPen5QmONwYuHwnzg+y+wpjXIp65qz27K\n2Y7kSDhO2uIZBHzhV6/xsStWc/2FS/nGE7uJpSyEgEjCJmHZRJIWf7npmbx/28J6P2sXub52axfV\nsbK5VhVOO8VYcdtDEz6//85rZqklCsV4WkO+UbZTucgirIJeOhQmXxKSJuCe915IQ8BDY40XrcC4\nt+vEEEMJCw3XpsiyJX3DKSx7qOS/RTFCpavWqvJnc5TePIaZEx2HkYIVsaRNvEBKWoY7Hx2dmtAc\n9GYry65b3ljQ586bFurcNFkNr64po+AZZjiZv3pEtMDxiSgk8E4m/Obj4VeOFTxeqmF7prre2DTI\nqewiZTzytHQZ+JTt0BNN8e6LJ/fIk1JiOxIrc7Od9L3ElhKP5noJKcrHps1dWV/EsfhzIjYicZN/\n+MUr/Gp7/n6XiwQ2/nArFyxrJDycHO3HZOhZs/eMkBc37bxRKApFMdz5yM6S37O0McA/veMstVOu\nKBubNnfh0TQc6eQdTwEOD4xsBmeCR2xHIjXXnzZfmnduumxXTxRdCGzp0Bs18eoauoB9vcPc/uAO\nzltSx9YD/aTSUXQ6IDRoqfVlz3XpaU34PTr7+4ZHtTOTDeI1NE6fH8yKdmcsrCvae1mhUCgqQa1X\nz5taC+78tjdP0EyGvmiSuJl/TedIN0Bhssy2TOSyk17HZMbWqVgUKUaoiJAnhLhCSvmEEOLP8z0v\npfzZbLdJMTOMFe+kdMWHzuORCd9X49U5d0kD65Y3sG55I8uaasYJcoamZQW7TEGKQjsBipmjkMRW\nuvRWXvb0DGerwmaQwN4pRPd1dHbzg2cOpEuqS1KWww+eOcA5SxpKXuhu6eqnzq8zGLcw06nI9QHX\nI+/DjsRMi3O2LTEdB8uWaYNYOamgqRWITFVMnUPhWMGqXpnCKf3DST78Py+ypasPKFyRe+x7uyMJ\n9vcNs6RhpGhQrtm7lHJa0Z8Kxd2P7yo6Gi/Tz89YEOKRW9bPaLsUpx6HwjHm1/k4HI4XVd0uO4QK\n8GiiYJp3brpsynbQhcBx3Pdnpo2W4xBLWTyy4wQNAYOeqLvpbANeIeiJJrMLzX19o+cILUGfG223\n2E2TXdUarKiHsiYEuibwKh9nhUJRJNGUzZLGAAf6R6LyMusjXRekrNFCXUdnN995ci/7+4bx6hOv\nLYqxp/IaGtGENS6bL2462QwVRelUKiLvj4EngD/L85wElJBXBOWsFFpOMuKdW23WwXEcDvbH2Hpg\ngBcO9LP90OCkEXm/+Ls3jqoQqyrIVi+FRI6pSKoBj5Z31yfgKf27zopeY5S8qVStvevRTvqiKWS2\nYq1DKprirkc7i/7NuZF0Dq8fH2QgZmX/zRwJAzGLzmODHOgrXWRUzCxLG2s4WiAdIZay+eKvXuVH\nzx8mlt5V/JM1LfxuV8+k53XSkZQeTeNEJEldwI3oyJi9DydtBuPmOLN2haJY7n58F1//7e6iX2/o\nAk0IPnVV+wy2SnGqkklrbQ76OBFJTv6GNFK6Al2hzYzMeWu8Bl5dw7Sc7EZKIr049WgwFDcxbclw\navQcI5UToiKA0xeEOHNRHWel/e1mI8pdCDfdTNfT99rIzRjz/yrLRKFQlMrSxhr2943e1MsNdvDq\nYlRl+8G46a6ZyhSVsbo1xHPDfXkXjFPxQVe4VETIk1J+Pn3//kp8/slAR2c3G3+4NesjcjgcZ9vB\nMJves67iP4YDfcP0RZNsOzjA1gNhth4M0xctHLKbjxqfMSpFVvnaVS9LGgN5fReWNAbyvHpirlo7\nn5+/ND418aq180s+l6G7ptljU3gMvfRJ8O7udGGXjLdPunDG7u5IVqBzHLClG0mXjaCTMh1RN+I/\nEUna465jEne3TFF9bFzfxnP7+wuG2N379IFRj7ce7C8obucigN5o0o1QGUiM8l/06Dp333BOxcdy\nxdylo7Obb3XsLZjCmEumv2pC8OENp6l+p5gRMl6z4Vhp80Fwo5zzcffju3jxYD+J9GrTbwhSecZq\nM+cCHhtzrRVAXcDA0DRuedNqLlvdXHL78pER5zTNzSDJ3GdFupznlJ+eQqGYSS5ta+LZfX3jjhu6\nYF6tl1jK5n3ffX7GPn/j+rZs1spY9vSU7uGrcKlUau0nJnpeSvm12WrLXOXm+18cZQYMkLQcbr7/\nRV7+wlsq1CqXD3zvBfb1jo8saqr1csGyBi5c3sj5yxq5/p78psFA0X5Qdz++a5xvWan+Z4rpceHy\nhrxC3oXLG0o+185j+VOuCx2fiPlBL4cHx+/6zw8W52WTK9BZBez+LIeSo+jMfAYV6eM9kSRx0yZh\n2tn7hOm4j1Mjj3NfE88cS9mkbIcljQG++/6LS2qTYoSxkc6XtjWV5NE4lLDRKCK1PO2PaOgaa1qD\nNNR4ORyOqQg8RVnYtLlr3ByhEJoGq1uC3Hb1GarfKWaMDe2t3AG8/3vuYrGYDY8MdX4PN9//Ip50\nCljGD/noYILcvblECeEjAtAFCE2wcl6QGy5aysVtE3vV5oucMzSBlhMxp+VE1CkUCkWl6ejs5jub\nu/LuRwtgKGGN2+AoliUNxUUsb2hvHTfmZ31QK+3FNIepVGptKH1/OnAR8GD68Z8BmyvSoirh4/dv\n48GXj2M7El0TvP2cBXz9hgvGvW4oYeV9f6Hj5SKTNjvRBCwj4vk9Wtrnzi1QsWJeDXrW1276EXZ3\nP76LbzyxB024Vc3ips03ntgDoMS8WeSxHfmrehY6PhF7CvjXTcXXjgLpJxKIJi0cKZGO653jSLeQ\nRKZwhOUUX6hj24HwGGHNSQtuGfHNyRHlJr5QTiRuF8tkn6EozN2P7+JbHXuxHAefrmE7Ds/tz7+D\nOBEOUOfTiabsURMnPe2dl6mg7NXcqNHPXdOuBBRFWTlUZJXjOr/B3Tecr/qfYlbY0N6KV9eQUqJr\n2qQ2K+BG4/XHxhRSS9pA2uduiotAiSvi/dPbz+LitiYMTcPQhXvLiZwzRkXQKXFOoVDMHTo6u/nk\nA9sLCnUpW5Kyi183ZDygNQFBr84X33l20e81NIHpyGyl8IyQYCi7gClTqdTafwQQQvwauEBKGUk/\n/gLw00q0qRr4+P3bRqUV2o5MP96WV8ybLWxHEk2a7Dw2xNN7+th2IDzhLur/c8kyLljeyNpF9dT6\njKynnd+jjzIInq632r1P7XPTF/McV0Le5AzEUgzFXeE3M4bmjqW5Piwi9zXpb0gI93iswEQ8Ztr0\np6sg5X6nuZ8hZeY87sFC/nWmIxmMZSbt0hXhJMicHiSlezNtm+GUzbHBRN5zHRtKsunJvSNiW05E\n27hjRYhvtz7w8oTPTwePLgh4dPzZm5Z9HMg55vfoBLw6dX4PK5trZ6w9JzOZVERHSnQhiJsOsTwR\nncXywT9qY0tXP9sOhrPVho30AjbjybSiqUZFQSnKTkdnN4cL+DqORYl4itmmpdbjRssXuXicgq1t\nQYx01cbMKX264BcvHeHff7ebZU21KhpaoVCcVGQ8vsuBVxesW9405eyRVa1Bdp2IpIMnRqKiV7UG\ny9K+U5FKReRlWAbk9q4UsKIyTak8+bzBMse/fsPstsWyHQ6GY/x+Vw9b9vaz9UCYnmhxi9pb39Ke\nFe8mMuVdXO/Lm/q4uN5X1OdUKirxZMGRlBR5NhUGCnjhOFKStEbEs2IEs9sffHVS4S1h2pMWs3Ak\n/Hs6crMcaIK8QtuI2DZafPv+MwcKnut7779o1PtKLegS8OosrC/dm1DhpiLajsRx5LiqWlPhgW1H\nuOPtawG4/cEdmLbNYMwk6Uh8hsaHN5ymNhwUZaejs5u//cELRb1WEyjRQjGr/Pa1E/SNja6bRTyG\njgd37mPZkmjK4aVDA8yv89EdSXD7gzu4A/W7UCgUJwd7uqNlmdOCG8F/302XTPn9n7qqnU8+sJ1I\nwsJyHAxNI+Q3VIGtaVBpIe8HwHNCiJ/jbpBdC3y/sk06dRmIpXh6Ty9P7enl2X39dOVJZ2ys8XD+\nskae6CycNlkf8BT1eV+89hw+9MMXRnma+A3BF689p/TGK2YE0x4rtjmjhLd8FWZzufWn27OvyxXe\nEkV6N+Xyy5eOTvXPGMfyphr8Xp1AOprNb+jZqLZ8UW9fenhnwXP95uPrS6oiN5GQt7SpOG9IRflx\nUxHLI+JpArqHEtz1aCeP3LKeO3CFwsOa8sBTzByZIliFfDjHcvr80OQvUiimSNJyK2+btlsEypaS\nLz28c9J5w0xiOw6OlFnfW124i49jg0kWNfjx6IJNm7vU+KxQKE4KzDKGNAe805ONNrS38pXrznXn\nw8oTuixUVMiTUn5JCPEocHn60PullC9Wsk2nErYj2Xqgnyd39fKHPb28enRw3ALAb2ics6SBi1c2\nctmqZtYuqsPvMVjz2UfK0oaQ3wNjlHnFzPOjZw+yZW8vg3EzK7SNLp7ginalmPznY9vBgTK1GC5f\n1ZwV2vxGRnRzxbZATnppwKNT4zX44PcLR6X8eOOl2RTfTHpvhqx3A27KryaYUMhZAN/kAAAgAElE\nQVSrr/GOTvPNucutGCmLsPXWNZFNI5LFlJtUlA1B4aImpaBrAo8ucBzJru4oHZ3dbGhvVRMVxYzS\n0dnNrQ9sL7rAha6hdsEVM4ppS6I5GRIDsRT78xRCmy10XMsQ25Z4dYHlSDy6mzni4BabWtlcy+Ei\n/SUVCoWimumYIOhmKpwYym9ZVApqPlxeqkE1eQk4RrotQohlUsqDlW3SyYmUkn29wzzR2c3Te3p5\nfn+YaHJ0Gqom4PQFIS5eOY/LV83jDSvnEfIbJUUcFcumzV2uaJReeFiOg2Haajd0FnhuXx+/ejl/\nKncxeA0tm/55YqhwyvVfvWHZiIdbPp+3jBhnuMevvvupguf65rvPRwhXWNPTleGEwL0nXU2uSCPq\nBfXFVVkqhuZgcangxbB83mh/O5n1Akz/f/b4eH9AcP8tFKXT0dnNkYHiPMUmwmdo2e9AAB4dNZ4p\nZoVNm7tK8sH52BWrVb9UzBi2I+k8PsRTu3vZcXSI144OlWWMnYjJquCuWRDikVvWc/ldT9AQ8LCv\ndxjLlq5Pb7qKeNy0WdKoIuMVCsXcZ9PmrrKeL6XKy1YdFRXyhBAfBT4PnABsRq7DKreyTPRFkzy5\nq4ff7+5lS1cfx/MUAFjSGOANK5u4fHUzl61qpjVUPpFjIrYfHhhXRSeatNl+uHxRXIr8XLSyCcuR\naEKM93XLSS8NeMYXVfB79FGC2RVffbLg53zg8pWAK7JlK7/lVIIzdA09LchNJsI11HjL88fPIUT6\n3yb9qJJNOam569HOaRuqC0bEVinBQbIo5FfRHYpZ4dmuviJifl0+ceVq5c+omFFu/+Wr/M+zs7sn\nP1H/F5DduF7aWEN3JEFz0MfRwTg47saYLtwq4hvXt81KexUKhWIm2d0dKev5VKxA9VHpiLyPAadL\nKfsq3I6Tio7Xu3lyVw9P7+ll14nouOcbAh4uXtnEZaua2bCmheUVqnJZqBR2oeOK8vFXb1jO1Wct\nLFiMolgmi9Rc3BjA0LSiI+UUikqwq3v8OFksAvj4lat5+JVj7O+PYTsSr67RHPRj6GLWNkYUpy6n\nf+ahor0dlYinmA3OXdLA/zx7kKZaL2sX1XHmwjrWLqrjEz95qSwWBqUigcPhOKd/9hFaaj0MJSxS\njsSRYEsHKaF9QZBPXdWuIlUVCsVJQaTMxR8X15UvA0lRHiot5B0CBivchpOO9/1/z4967DU0LljW\nwOWrmtmwppUzF9WhTVNY0QR5I1iUXnNyIIRAFwJdF3gyEXS6lo2q08RIZN1E+Ax9llqsUEydqXhB\nBn06TbU+7nj7Wja0t3LOkgZuf3AHHl0Q8OjETVtFdyhmnNM/8xDJIve+Ll3ZqEQ8xazwlrULOHdp\nA4Y2suF337MHKiLiZchYVBwdTCIBTQM73R6PLrj6rAVKxFMoFCcNxXrmFoNXQxWjrEIqLeR1AR1C\niIeArNGWlPJrlWtS9TOZWbAA2hfWcfmqefzxmhYuXNGE31NeQaXQureMxXEUM4xHFwT9Bh5Nw9BF\nNnKuGIFOoTjV0YTIinjgGvhmq9OqalyKWeDGTX8oWsRrn1/LfRvfOLMNUijS1Nd40HXBUNyk1mvw\nwv5+vv9M5e2vLSdtfwA4jutrKoS7mfOtjr2cs6RBjdkKhWLO0xsp7F8+EZeubOTS05r59pN7iZsO\nAteC65/ecZYaG6uQSgt5B9M3b/qmKMADWw/x9J4+nunq41gen7tcXrz9T09JPzFFaYT8HrdqsEJx\nCnP347um9L76gGfcpEZV41LMFh2d3WzZFy769Y9+fMPMNUahyEPQZxD0ucuMe5/aN6XI53LjjKok\nD6bt5BSLkqo4kUKhmPM8tuM4t/50+5Te+8KBMPdtfKOK3p8jVFTIk1L+I4AQolZKWbma9HOAW3/6\nctGvVSKeQqFQTE5HZzdfe3z3lN6rKhsqKsn7vvv85C9K8933XTSDLVEoJudQOFaVRulSAiJdoEiW\n3xxeoVAoZovhpMUdv9rBj184POVzmBW0P1CUjlbJDxdCXCqEeA3YmX58rhDiP2bgc64SQrwuhNgj\nhLit3OefDinLYTCW4qndPRO+zqML1i1v5CN/sooHPnTpLLVOoVAoTl4+8L3ixZCxKO87RaVY9emH\nin7td993kYowUlScpY01mPbMRuS1BD34PRq6cOfMxZCpMg5uRcZUJU38FAqFYoo8t6+Pa+7+/bRE\nPMXco9Kptf8GvAV4EEBKuV0Isb6cHyCE0IFvAX8KHAaeF0I8KKV8rZyfUwy2I0laNknTYV9vlC1d\nfTy/L8yLhwYYjJsTvveFz15JfUBF2uVy6crGvKlFl65srEBrFArFXOL7T+9jquvKS1c2KnFEURHu\nfnwXVpH9Vol4imph4/o2tnT1zdj5DQ38HoN5tT76hlPUBTwc6BvGdlyxLh8C9zlNuFENQhN4ixQA\nFQqFohqIpyy++bu9bHpyL5Yj0QT8zWUrufepfZVummIWqLSQh5TykBgdb1+kdXPRXAzskVJ2AQgh\n7gfeAcyokCelJGk56ZtNbyTJc/v62XowzNYDYY4OTOxzNxYl4o3nvo1v5MZNfxgl5l26slEZeisU\nignpeL2b2381tUvAtect5Os3XFDmFikUxVFsKvgnrlytRDxF1TCTfVETcO97R0Trjs7udNGhOJqQ\n2I7E0EeKWmhC0NZcSziWIpKwSNkOXl0j5DdY2RycsXYqFApFuXAcyc5jQ3zmF6/y0qEBwC1K8YU/\nW8uftLdOWchb2hgoZzMVM0ylhbxDQog3AlII4QVuJp1mW0YWA4dyHh8G3lDmz8CyHRKWQ9K0SVgO\n0YTFq0cG2HZwgBcOhNl9IjKuomvIb3D+0gbWrWji0rYm3rXpmXI3q6ppn19L54nx1ojt82uLPocS\n7RQKRSn86NmD3P7LV0t+n9okUMwVPnHlamVUrag6NKDciasCuOVNo0XrTNGhjs5ubn9wB6ZtMxgz\nSdoOhqbx4Q2ncc6SBm5/cAcL6g0CHp24aWPaUlkmKBSKqieSMHlg62G++utdRJMWAG87ZyG3vvl0\nls+rQRRpSKrjjslpq1DqAgb/9I6zZqrZihmg0kLeh4Bv4IptR4DHgA+X+TPy9eZxkfZCiJuAmwCW\nLVs24QltR5JKR9olLYek6ZCybfb1DGcj7l4+PEhyjNeGRxesXVTPhcsbuWB5A+csaaAu4KHWa6Br\ncyucvxwi3KMf38BVX+8YdZ72+bWqul4JlNJvFYpqoRL91rYdvvabXXyrY29J7xPAO1UUniJNtY+5\nPh0l4inGUQ39ds38IJ0nomU7n89wRblC/X1Deyt3gBudp8VY0ljDxvVtWdEv+1x4/HOK6qAa+q1C\nUSoz1W+Tls2B3hhf+fXr/Oa1EwA0BDzc+pY1vGXtQlpCvqLP1T6/ltuuPlONgXOcSlet7QX+aoY/\n5jCwNOfxEuBonrbcA9wDcOGFF+a11BhKmAzGTEzbFehODCXYdiDM1oMDvHgwTDg23uduVUuQ85c1\ncOGKRs5eXE9DjZdan0GtV8fQK1prZFqUS4RTot30KKbfzjTzagz6Ylbe45U8l0fLX33JM4WfXaFI\ngqn8gst5rrnKbPfbWNLi0z9/hV++5A795y1tyKYiTMT+O6+Z6aYp5hiVHHPvf+7gpK95/UuqzyrG\nUw1zhduuPoOP3reNSHJ6DjqGJqjx6tx9w/mTLjoz0XmlPqeoDqqh3yoUpVLufus4knAsxdN7evnS\nwzs5MZQE4A0rm/jkW07ntJYgjbXFW3AFvVp2/a3GwLlNRYU8IUQbbkTeJbhRcluAj2f87MrE88Bq\nIcRK3Ki/G4B3T+VEfdEkv9/Vy9YDYbYeDHM4HB/3mtaQj3XLG7lgmRt111jjxe/RixLvSolyu/a8\nhfz8pWN5j88WSoRTAHz1XefzoR++QCLHgd1vCL76rvMreq6PXrE6r5/UR69YXfK5brky/7luubKy\n51JMzomhOB//8Xb+sNc1Wr+ivZV/u/483vjPjxNN5U/0UpHBimrjD3t6+ewvJk4JV8KzoprZ0N7K\nv994AZ/9xSscLtEnOsPCeh8eXeeOt69VC1CFQnHSE0mYdA8l+e+n93HfcwdxJHgNjQ+tb+Od5y+m\nOeSjzu8Z975CgREA33z3uplutmKWqHRq7Y9wK8pem358A3AfZfSwk1JaQoiP4Kbt6sB/Syl3lHqe\nD37vBZ7oPDHO567Wp3Pe0gYuXN7IuuWNLG4IIITA59Gp9boCnqfIyLtSotzcNK9tPPjycWxHomuC\nt5+zQKV/KWadDe2tfOc9F5YlPLuc58qk29z71D6GUza1Xp0PXr5ySmln1XouRWGklOw6EeXm+1/k\n9eMRAG68eCmffeuZ1PoNXr3jas66/ZFRYl7Qq/HqHVdXqskKRV729kT50A+3YjmS9gUhDvVFGTZH\nJiOq3yrmChvaW3nqtjflFKQYuc6/fHiAbz+5l3g6lD5TVVbgVqWtr/GyYl5QpX8pFIqTnoRp0zec\nYveJCF9+eCe70rYEq1qDfOat7axsDtJa56PGm1/K2Xr7W1h3x2OjxDxDE9z73gvV+HkSIaSsXKSy\nEOJZKeUbxhx7Rkp5SaXaBG4Y7AsvvDDq2C33v8gvXjqKoQnOXFTHuuWNrFvWyOkLQll/u2Ij7xQn\nLRU1OszXbxWKIjjp+m3KcnjhQD9//5PtHBtMoAk3EvOm9W3U+iq9f6UoExU3lp2NMTc8nOLa/3ia\n/X0xmoNefvmRy1ncoKrKzWFOiX6rOCk56eYKpbLitoem/F4VMV0x5lS/tR1J/3CKoXiKX718jG93\n7CVpOQjg+ouW8v7LVuD36Myv8+P36DPXcEWlKarfVnpF8zshxG3A/bgbb9cDDwkhmgCklP2VbFwu\n779sJVec0cppLUECOT8cn0cn6DWo9SnxTqFQKCpNNGnx5OvdfPpnrzCUsPAZGp+95gyuvWCJEvEU\nc4qU5fChH25lf18Mn6Hxn++9UIl4CoVCoVCchAzGTQZiKXqjSb7y2Os80+XKIK0hH5++up1zlzbg\n0TUW1PuLzvZTnNxUelVzffr+pvR9Rn38G1xhr2rqwJ+7tIHFjQGG4iZeQyPoM0pKm1UoFArFzCGl\npG84xUMvH+NLD+8kZTk0BDx86dqzWL+mhaAS8RRzCCkln/n5Kzy7z53If/Vd53L+ssYKt0qhUCgU\nCkU5SZg2vdEkKcvhma4+vvLY69kCmle0t3LLm1YT9BvZSLxMJqBCUZGVjRDiIuCQlHJl+vFfA38B\n7Ae+UE2ReLnUeg1CfgOfoUJZFQqFopo4OpjgvmcP8K3f7UUCixsC3PkXZ3POkgZCeYyAFYpq5jtP\ndvHTrYcB+Ps/XcPbzllU4RYpFAqFQqEoF5bt0D+cIpq0SJg233myiwe3HwVcD/5b3rSaN50xP/3Y\noDXkQwgl4ilGqFSIwibgSgAhxHrgn4GPAufhlmu+rkLtmpCAVwl4CoVCUW04juTffrMrK3ycuTDE\nF995Fm0tQeoDSsRTzC0effU4//JYJwDXnr+Yj1yxqsItUigUCoVCUQ6klOk0WhNHSnadiPClh3Zy\nKBwH4Jwl9dx2dTsL6vwAhPweWkK+SjZZUaVUSsjTc6LurgfukVL+L/C/QoiXKtQmhUKhUMwxEqbN\n3/9kOw+9cgyAy1bN4zNvPYMF9QEaarwVbp1CURqvHB7klh+/iJSwblkjd/7/7N15mFxVnf/x97e2\nrt7TnaSTkAXSkJgQNgERlIlRUDZFnHGBWUBHB1xhHGVARWVww3FB8OcojCvjKCqOQ5RNiEbUASUg\naxKS0ATS2TpLp7fq2u49vz+qqtPd6aWqU91d1f15PU89nTp176lTqVOnTn3vWf7meF2BFxERmQJi\nyTT7upOkPB/Pd/zk0W187/+24vmOUMB416uP4u2nLuybPttYHVFfVoY1aYE8Mws559LAWRxcI28y\nyyQiImXmd5v29AXx3vLy+bx/1dE0VkdorFbHR8rLro4477n9UeIpnwUNldx26SlaykNERKTMpbLT\naHsSaQB2dcb5wj0beXp7BwCLGqv4+PnLWDqnFgAzY3ZthdZ3lhFNVu34MfA7M9sL9AK/BzCzY4CO\nSSqTiIiUmXNWzOXKs5aQTHu87ZQFzKiKMLNGUxCk/OzrSeA7qKkI8b13vkL1WEREpMy19yQ50JvC\nOYdzjjUb27j5wc30JD0A3nziEVzxmmai4cyFu2DAmFMX7bsvMpxJCeQ55z5nZmuAecCvnXMu+1CA\nzFp5IiIiefmX1y+lZU83dZVhBT+kbK04op7//cCrad0fY0n2qryIiJS/o669e8THt954wQSVRCZS\nyvNpjyUB6Iqn+NqDm/ntc3sAaKgKc/U5L+P05pl9x4eDAebWRwkHA5NSXikvkzZe0zn3yBBpmyaj\nLCIiUt5qo2FmKYgnZW7+jErmz6ic7GKIiMgEGinQpyBf+coNVXpi2wG+cM9G9nQnADijeSYfPWcp\nDf3Wv6sIB5lbF+1bH09kNJp4LSIiZU87eomIiIhIqXDArb97np+ua8UB0VCA97/2aC44ft6Ajayq\nIiGaaisIKIgnBVAgT0RERERERESkSF7c18NP1rUC8LI5tXz8/GUsbKwacExNNMTsmgrtUC8FUyBP\nRERERERERKRIEmmfgMElpy3isjOOJDRo7buGqggN1ZFhzhYZmQJ5IiIiIiIiIiJFEg4GuOntJ3H8\ngvoB6WbGzJoIddHwJJVMpgIF8kREREREREREiuSomVWHBPECZjTVVVAVURhGDo9qkIiIiIiIiEiJ\nGWlHW9CutqUsMGjdu1AgwJz6CipCwUkqkUwlCuSJiIiIiIiIiIyDcDDA3Poo4UHr5ImMlQJ5IiIi\nIiIiIiJFVhEOMrcuSjCgnWmleBTIExEREREREREpospIkDm1UQIK4kmRKZAnIiIiIiIiIlIkwYAx\nty6KmYJ4UnyapC0iIiIiIiIiUiTBgCmIJ+NGI/JEREREREREppiRdr3Vjrci5Usj8kRERERERERE\nRMqARuSJiIiIiIiIlJmRRtyJyNSlEXkiIiIiIiIiIiJlQCPyRERERERERKaR8RzNp/X3RMaXRuSJ\niIiIiIiIiIiUAXPOTXYZSo6Z7QFezN6dBeydxOLkoxzKCOVRzsMp417n3LnFLEwhBtXbyVKq77HK\nNTzV29J4HwYrtTKVWnkmtd5CSdTdUntPVJ6RzQI2TuN6W2rvx0SZKq97uvcVpsr7WAzl9H+hels+\n71WOypxnvVUgbxRmts45d+pkl2Mk5VBGKI9ylkMZS1mp/v+pXDKSUnwfSq1MpVYeKb33ROUZWamV\nZ6JN19c/XV/3VKP38SD9X5SPcnyvVOb8aWqtiIiIiIiIiIhIGVAgT0REREREREREpAwokDe62ya7\nAHkohzJCeZSzHMpYykr1/0/lkpGU4vtQamUqtfJI6b0nKs/ISq08E226vv7p+rqnGr2PB+n/onyU\n43ulMudJa+SJiIiIiIiIiIiUAY3IExERERERERERKQMK5ImIiIiIiIiIiJQBBfJERERERERERETK\ngAJ5IiIiIiIiIiIiZUCBPBERERERERERkTKgQJ6IiIiIiIiIiEgZUCBPRERERERERESkDCiQJyIi\nIiIiIiIiUgYUyBMRERERERERESkDCuSJiIiIiIiIiIiUAQXyREREREREREREyoACeSIiIiIiIiIi\nImVAgTwREREREREREZEyoECeiIiIiIiIiIhIGVAgT0REREREREREpAwokCciIiIiIiIiIlIGFMgT\nEREREREREREpAwrkiYiIiIiIiIiIlAEF8kRERERERERERMqAAnkiIiIiIiIiIiJlQIE8ERERERER\nERGRMqBAnoiIiIiIiIiISBlQIE9ERERERERERKQMKJAnIiIiIiIiIiJSBhTIExERERERERERKQMK\n5A3h3HPPdYBuuhV6m1Sqt7qN8TapVG91G+Nt0qnu6jaG26RTvdVtjLdJpXqr2xhvk0r1Vrcx3vIy\nIYE8M/uumbWZ2TP90r5kZhvN7Ckz+4WZzcimH2VmvWb2RPb2rX7nnGJmT5vZFjO7xcwsm95oZg+Y\n2ebs34ZsumWP25J9npPzKe/evXuL+x8gMgFUb6Ucqd5KuVLdlXKkeivlSPVWypHqrYynvAN5ZvY2\nM6vN/vs6M/uffANjwPeBcwelPQAc55w7AdgEfKzfY887507K3t7bL/2bwOXAkuwtl+e1wBrn3BJg\nTfY+wHn9jr08e76IiIiIiIiIiEjZKWRE3iedc11mdiZwDvAD8gyMOeceAvYPSvu1cy6dvfsIsGCk\nPMxsHlDnnHvYOeeA24GLsg+/OVsesn/7p9/uMh4BZmTzERERERERERERKSuFBPK87N8LgG865+4C\nIkUqxz8C9/a7v9jM/mJmvzOzv8qmzQda+x3Tmk0DmOOc2wmQ/dvU75xtw5wjIiIiIiIiIiJSNgoJ\n5G03s1uBtwP3mFlFgecPycw+AaSB/84m7QQWOedeDvwL8CMzqwNsiNNHWwww73PM7HIzW2dm6/bs\n2ZNf4UUmmeqtlCPVWylXqrtSjlRvpRyp3ko5Ur2ViVJIIO7twP3Auc65A0AjcPXhPLmZXQa8Efi7\n7HRZnHMJ59y+7L8fA54HlpIZTdd/+u0CYEf237tzU2azf9uy6a3AwmHOGcA5d5tz7lTn3KmzZ88+\nnJclMmFUb6Ucqd5KuVLdlXKkeivlSPVWypHqrUyUUL4HOudiZnYXMMfMFmWTN471ic3sXOAa4DXO\nuVi/9NnAfuecZ2bNZDaqaHHO7TezLjM7HfgTcCnw9expq4HLgBuzf+/ql/5BM7sDeCXQkZuCKyIi\nIpOvozdFfWV4soshIiIiMqUcde3dwz629cYLJrAkUmx5B/LM7EPAp4HdgJ9NdsAJeZz7Y2AVMMvM\nWrP5fAyoAB4wM4BHsjvUrgRuMLM0mXX53uucy22U8T4yO+BWkllTL7eu3o3AT83s3cBLwNuy6fcA\n5wNbgBjwrnxfr4iIiIyvzniKA7GkAnkiIiIiInnKO5AHXAW8LDfttRDOuUuGSP7OMMf+HPj5MI+t\nA44bIn0fcNYQ6Q74QEGFFRERkXEXS6bZ150kMNRqtiIiIiIiMqRC1sjbBnSMV0FERERkeoinPHZ3\nJsgujysiIiIiInkqZEReC7DWzO4GErlE59xXi14qERERmZJSns/uzriCeCIiIiIiY1BIIO+l7C2S\nvYmIiIjkzfMduzrieP7BIF53Ik3K8wkHC5kkICIiIiIyPRWya+2/AZhZbeau6x63UomIiMiU4vuO\nXZ1xUp7fl9adSHP1nU9x1Mwqbrnk5VSEgpNYQhERERGR0pf35W8zO87M/gI8AzxrZo+Z2YrxK5qI\niIhMBc452roSJFJeX1pvyuPj//M0z+3q4oH1u3n8xQOTWEIRERERkfJQyDyW24B/cc4d6Zw7EvgI\n8J/jUywRERGZKvZ0J4gl0333EymP6/73GZ7Z0YkBX37biZxx9MzJK6CIiIiISJkoJJBX7Zz7be6O\nc24tUF30EomIiMiUsb8nSXf8YBAvmfb59C/X85eXMiPwPvKGpfz1yQsmq3giIiIiImWloF1rzeyT\nwH9l7/898ELxiyQiIiJTQUdvigOxZN/9tOfz2bs38OcX9gPwwdcezZtOPGKyiiciIiIiUnYKGZH3\nj8Bs4H+AXwCzgHeNR6FERESkvPUk0uzrTvTd93zHF+7dyB+27AXgn/5qsUbiiYiIiIgUqJBda9uB\nKwHMLEhmqm3neBVMREREylM85dHWdTCI5zvHV369id8+tweAS08/kktOWzRZxRMRERERKVuF7Fr7\nIzOrM7Nq4FngOTO7evyKJiIiIuUmmfbZ3RnHOQdkdqy9Zc0W7nt2FwDvOHUBl73qyMksooiIiIhI\n2Spkau2x2RF4FwH3AIuAfxiXUomIiEjZSXs+uzrieP7BIN43f/c8q5/cAcBFJx3B5SubMbPJLKZI\n3pxztPckRz9QREREZIIUEsgLm1mYTCDvLudcCnDjUywREREpJ77v2NUZJ+37fWnf+7+t3PnYdgDO\nP34uH3zdMQOCeGbGrJqKCS+rSD6cc+zuTBBLeZNdFBEREZE+hQTyvgVsBaqBh8zsSCCvNfLM7Ltm\n1mZmz/RLazSzB8xsc/ZvQzbdzOwWM9tiZk+Z2cn9zrkse/xmM7usX/opZvZ09pxbLPsrYbjnEBER\nkeJxLhPES6YPBvH++08v8sNHXgLg7OVNfPjspQQGBfHm1FVQXZH3cr0iEyZXp2PJ9GQXRURERGSA\nvAJ5ZhYAdjvn5jvnzneZhW9eAl6b5/N8Hzh3UNq1wBrn3BJgTfY+wHnAkuztcuCb2TI0Ap8GXgmc\nBny6X2Dum9ljc+edO8pziIiISJHs6UoQ7zdq6WePtfKdP2wFYOWSWVxz7jKCgYNBvIAZc+uiVEUU\nxJPSkwvi9SY1Ek9ERERKT16BPOecD3xwUJpzzuV1mdI59xCwf1Dym4EfZP/9AzJTdnPpt2fzfwSY\nYWbzgHOAB5xz+7M76D4AnJt9rM4593A2wHj7oLyGeg4REREpgn3dCboTB7sDdz2xg2+ufR6A05sb\n+cQFyw8N4tVHqYwEJ7ysIqPxfcfODgXxREREpHQVMrX2ATP7qJktzE5ZbcyOkhurOc65nQDZv03Z\n9PnAtn7HtWbTRkpvHSJ9pOc4hJldbmbrzGzdnj17xvyiRCaS6q2UI9XbqaMjlqKjN9V3/75ndnHz\nms0AnLJoBte/aQXh4MGuRjBgzJsRJRouzyCe6u7UllvnMT7F1sRTvZVypHor5Uj1ViZKIYG8fwQ+\nADwEPJa9rRuHMg21lZ0bQ3pBnHO3OedOdc6dOnv27EJPF5kUqrdSjlRvp4aueIp9PYm++7/Z2MaX\nf/0cAMfPr+eGi44jEjrYzQgFAsyrr6QiVJ5BPFDdncp837FzCgbxQPVWylLIvCUAACAASURBVJPq\nrZQj1VuZKHkvTuOcW1zk595tZvOcczuz02PbsumtwMJ+xy0AdmTTVw1KX5tNXzDE8SM9h4iIiIxR\nb9Jjb3ey7/7vN+/l8/dswHewfF4tX/jr46jsN+ouHAwwtz46YHSeSKnIBfESUzCIJyIiIlNP3j1q\nM6sys+vM7Lbs/SVm9sbDeO7VQG7n2cuAu/qlX5rdvfZ0oCM7LfZ+4A1m1pDd5OINwP3Zx7rM7PTs\nbrWXDsprqOcQERGRMUikPXZ3xsksSwt/emEfn/nVenwHx8yu4ca/Pn7AJhbhYIB5CuJJifIUxBMR\nEZEyU0iv+ntAEnhV9n4r8Nl8TjSzHwMPAy8zs1YzezdwI/B6M9sMvD57H+AeoAXYAvwn8H4A59x+\n4DPAo9nbDdk0gPcB386e8zxwbzZ9uOcQERGRAqU8n90dCfxsEO/xl9r59Or1pH3HUTOr+Pe3Hk9t\nNNx3fCQU4IgZlYQUxJMS5PmOnR29CuKJiEwAbSIkUjx5T60FjnbOvcPMLgFwzvVmR8CNyjl3yTAP\nnTXEsY7MWnxD5fNd4LtDpK8Djhsifd9QzyEiIiKF8XzHro44ad8H4JntHVz3v8+QTPssaKjky287\nkRlVkb7jK8JB5tZFB+xYK1IqckG8ZNqf7KKIiEx5PYk0e7sTHDmzerKLIjIlFBLIS5pZJdmNJMzs\naCAx8ikiIiJS7pzL7OaZ8jJBj427Orn2f54mnvKZWxfly289gcbqg0G8aDaIF1AQT0qQgngiIhOn\nJ5GmrSsx5A6VIjI2hQTyPg3cByw0s/8GXg28czwKJSIiIqWjrSvRN/3w+bZurvn508SSHrNqInzl\n7SfQVBftO7Yykgni5TloX2RCeb5jx4HevqC0iIiMn1wQzzmnfoFIERWya+0DZvY4cDpgwFXOub3j\nVjIRERGZdHu6EvQk0gC8uK+Hq+98iq54moaqMF9524nMq6/sO7a6IkRTbYU661KS0p7Pzo64gngi\nIhOgfxBPRIqrkBF5AK8BziQzvTYM/KLoJRIREZGS0N6TpCueAmB7ey8f/dlTHOhNURcN8eW3ncjC\nxqq+Y2sqQsxWEE9KlIJ4IiITR0E8kfGVdyDPzP4DOAb4cTbpCjM72zk35MYUIhPhlgc38e0/vEBP\n0qM6EuQ9Zy7myrOXTnaxpp21G9u49aEWtrXHWNhQxRUrm1m1rGmyi1VU0+E1ytRQrLraGU/RHksC\nsKszzkd+9iT7epIEzQgFja+v2cLFr1jIac2N1EbDzK6tKPZLkUkwXP0p5zZQQbzSMrgundHcyMMt\n+8uybomMp3zb3VJrnxXEExl/hYzIew1wXHZXWczsB8DT41IqkTzc8uAmvvrg5r77nfF0330F8ybO\n2o1tfGr1s4SDxozKMG1dcT61+llugDF1IorZGSlWXqX8GkXgYJ3a3NbVN+11Vk0FbV1xrr7zSWZW\nR+hOenn/EDiivpK/Pnk+py1uZE9Xgo/89Mm+hapn1kSorwyxryfBzb/ZzMciy7jw5fMn/kVL0d3y\n4Ca+sfZ50p6PIzMK85GWfVSEDIcNqFeH0wZOJAXxSsvajW189M4n6U6kSXmO1vZeHm7ZB0BF0PB8\nv2zqlsh4WbuxjS/et5FNbd2Eg0ZtRZC/vNTOu29/lHl1UWoqQn3f6Wc0N3Ln49uL1kc9XAriiUyM\nQAHHPgcs6nd/IfBUcYsjkr9b1mwuKF3Gx60PtRAOGlWREGaZv+GgcetDLQXnlQuYtXXFB3RG1m5s\nm9S8SvU1isDAOhVLpPGdY19Pkq54Gs93tMdSbN0fG7G+9c+jLhpiV2cvN6/ZzG82tHH1nU+xsyOO\nGcyqiTCjMoxhVIaDVIQC/PjRbZP0yqWY1m5sywTxfB/Pge8y66g4IJ52pDy/r14dThs4kVIK4pWc\nG+/dwIFYirTn8PyBP/QTnmNPV5KU55V83RIZL7nv4xf29hA0SHuOPd0p0r6PAdsPxNmyJ/NYW1ec\nb6x9nmTaK0of9XApiCcycQoJ5M0ENpjZWjNbC6wHZpvZajNbPS6lExlBepjviOHSZXxsa49RGQ4O\nSKsMB2ltjxWcVzEDZsXMq1RfowgMrFMp3xEMGAGMvd0J9nQlCFhmp86R6lsuj8pwkLTviIaCGPDl\nB57jpf0xwkGjvjLMjKpw3znBoFFTERrT50BKz60PtZD2fZzL7Gg2lFy9grG3gRMl5fnsPKAgXql5\nYV+MgIE/xA99I9NWdcRSJV23RMZT7vvYc45AwPo+K74Dz89cXAmasbc7SVUkRNr36YqnB+QxGe2z\ngngiE6uQqbWfGrdSiEjZWthQRVtXnKrIweakN+WxoKFqhLOGtq09M2qov7F2RoqZV6m+RhEYWKci\nwQBp32EGyWwAw7LpOUPVt23tMeqjIVJeZhiW52dG9SXSPsGAcf2bVvCzda3s60lQGQ4SCgYIBoxY\nMj2mz4GUnm3tMSqCAWIpHzMyvxb78R0D6tVY28CJkEz77OqIk/YVxCtVQ/7Wt0x6wvNLtm6JjLfc\nd3ru+zz3WXHZUdIBG9gWVwQDJNID27qJbp8VxBOZeHmPyHPO/W6km5k9PJ4FFRlsuBED2i9xYl2x\nspmU54gl0ziX+ZvyHFesbC44r4UNVfSmvAFpY+2MFDOvUn2NIjCwTs2urcA58JwjEgwQNMN3MKvm\n4EYUQ9W3BTMq6Upk6rfvO7Z39Pb9MLjuguWccfRMLn7FQtK+I+n5BIzD+hxI6VnYUEV9VTjzHTro\nt1iA7GipbL0q5fdeQbzS1jyrGt8xZGctFwMIBQIlWbdEJkLuOz33fd6fkQnkOXfwAl19Vbjvwtrh\n9lHHQkE8kclRyNTa0USLmJfIqC46aV5B6TI+Vi1r4oYLV9BUG6WjN0VTbZQbLlwxpgV2ixkwK2Ze\npfoaRWBgnaqpCDGzJkzAjMpwgMWzqplRFSYUtGHrm3OOd5y6kJTn6Emm2d7RSzyVCYK849QFvGbp\nbABOa27kUxccy7z6ysP+HEjpuWJlM+FgkIaq8OA4HsGgMaMq1FevSvW9H68g3o4DvezujBc1z+nq\nmnOX0VAVJhwY+rJrOGh8YNXRJVe3RCZK7js9GDDm1VcQDmY+K6EAzKoJA5lpt7NqIsSSacLBIB9Y\ndXRR+qiFiiUVxBOZLIVMrR2NPsEyoW66+GTgcVY/tQsvuy7UhSfMzabLRFq1rKkoHYZVy5q4gcz6\nIK3tMRYcxo6uxcwrl1+pvUYROLROHTWzhi+85WCdyu1GO1x929ud5MRFM/jAa47mi/c/R282iPfX\nL5/PFa85GgAzo6m2gubZNZx3gi6WTEX969Hmti6SaR/nMmsrRoLGkjl1Jd1WJdIeuzrih2ygcLjW\nbGjja2s2ceKCGfzw3a8kMEwASvKzalkTX3rriX31LJbwSHoewUCA5lnVXHPuspKtYyITYfB3+ilH\nNnJGcyMPt+yntT3GkqYozjl6kh5NtdG+dvnKCS5nLJlmd6eCeCKTpZiBvIKZ2cuAn/RLaiazFt8M\n4J+APdn0jzvn7sme8zHg3YAHXOmcuz+bfi5wMxAEvu2cuzGbvhi4A2gEHgf+wTmXHOeXJhPkpotP\n5qaLJ7sUUkzFCpgVO69iKtVySfkaqU6N9Fh7T5KueIq053Pvs7voSmQWzP7Q647hLS+fD2SCeHPq\nKgasESlTU7m2TeMRxIsl03z9N1u4/9ndAGza3c229hhHzqwu2nNMV+Vaz0QmylCfkYkO1I2kN+kp\niCcyyYo5tbbgS5TOueeccyc5504CTgFiwC+yD9+Ue6xfEO9Y4GJgBXAu8B9mFjSzIPAN4DzgWOCS\n7LEAX8zmtQRoJxMEFBERmdY64ynaY0k83/GFezfyx+f3AXD5Xy3uC+IFzJhXH1UQT0rWeATxntvV\nxRX/9XhfEO/05pnc989/pSCeiEx7vUmPXZ1xBfFEJlnePXMzmwPMJzOFdodzbvegQ/7hMMtyFvC8\nc+5Fs2Fjgm8G7nDOJYAXzGwLcFr2sS3OuZZsWe8A3mxmG4DXAX+bPeYHwPXANw+zrCIiImUrlkyz\ntyuB7xxf/vVz/Pa5zAD4y844kotPWwRAMGDMqYsSDQcns6giw4qnMkE8v0g/KH3n+Omj2/jOH7fi\n+Y5w0Lh8ZTOXnLZowIYxIiLT0eEG8fZ1J/Cc4+jZNUUumcj0M2ogz8xOAr4F1APbs8kLzOwA8H7n\n3OMAzrlnDrMsFwM/7nf/g2Z2KbAO+Ihzrp1MIPGRfse0ZtMAtg1KfyUwEzjgnEsPcbyIiMi0E08d\nnBJz85rNfaOOLn7FQi4940ggE8SbWx+lIqQgnpSmYgfx9nUnuPHejTz20gEAFjVWcd0FyzmmqYYR\nLjCLiEwLmb7D2IN4T7Ye4DO/2sCMyjB3ffDV1EbDRS6hyPSSz9Ta7wNXOeeWO+fOzt6WAf8MfK8Y\nhTCzCHAh8LNs0jeBo4GTgJ3AV3KHDnH6MJvYj5g+VBkuN7N1ZrZuz549Qx0iUnJUb6Ucqd5OnmTa\nZ3dnHN/3+Y+1z/PLJ3cC8JaXz+ef/moxZkYoEGBefaWCeENQ3S0NxQ7iPfz8Pt5z+2N9Qbw3njCP\nb/39yRzTNDVGjajeSjlSvS0dh9PmOuf4yaPb+MhPn2R/T5I93Qk2t3WPQylLg+qtTJR8AnnVzrk/\nDU50zj0CFGuxkPOAx3PTdZ1zu51znnPOB/6Tg9NnW4GF/c5bAOwYIX0vMMPMQoPSD+Gcu805d6pz\n7tTZs2cX6WWJjC/VWylHqreTI+1lgnhpz+c7f3iBnz+eGWR/wfHz+OBrj8bMCAcDzJsRJRIq5hK6\nU4fq7uQrZhAvmfb5+m+28In/fYaO3hS10RDXX3gs//L6pVNqSrnqrZQj1dvScDhtbk8izfW/XM+t\nD7XgOzh6djW//OCZnLyoYRxKWhpUb2Wi5LNG3r1mdjdwOwenry4ELgXuK1I5LqHftFozm+ec25m9\n+xYgN213NfAjM/sqcASwBPgzmZF3S7I71G4nM033b51zzsx+C7yVzM61lwF3FanMIiIiZcH3Hbs6\n46Q8nx/+6SV+9OfM1/nZy5v48OuXHAzi1UcJBRXEk9JUzEXWX9jbw+fu3kDL3h4ATlhQz8fPW0ZT\nXfSw8xYRmQoOJ4jXsqeb63+5ntb2XgDOWTGHD5+9lKNmadMgkWIYNZDnnLvSzM4js9HEfDJBs1bg\nG7ndZA+HmVUBrweu6Jf879m1+RywNfeYc+5ZM/spsB5IAx9wznnZfD4I3A8Ege86557N5nUNcIeZ\nfRb4C/Cdwy2ziIhIuXDOsbsrTjLt89N12/jeH7cCsHLpLK45dxkBMyKhzHTaYEBrgUlpKlYQzznH\n6id38s3fPU8y7RMwuOxVR/G3py1S/RcRyTqcIN4D63fz1Qc2kUj7hIPGh153DBccP49gQBcKRYol\nr11rnXP3AveORwGcczEym1L0Txt2B1zn3OeAzw2Rfg9wSGAxu5PtaYPTRUREpoM9XQl6kx53PbGd\nb/2uBYAzmmfyifOXEwwYFeEg8+qiBBTEkBIVS6b7Nmg5HB29Kb786+f445Z9AMyti/KJC5ax4oj6\nYhRTRGRKGGsQL5nOrL+7+snMSlZz66Jcf+GxLJ1TOx7FFJnW8tm1th74GJkReU3Z5DYyU1RvdM4d\nGL/iiYiIyFjt607QnUhz79M7uXnNFgBOObKBT7/pWMLBANFwkLkK4kkJK1YQ7y8vtfOFezeytzsJ\nwFnLmrjq7CXUVOR1TVtEZFoYaxBvd2ecf/vlejbu6gLglYsb+dh5y6ir1O60IuMhn97LT4HfAK91\nzu0CMLO5wDvJ7DL7+nErnYiIiIzJgViSjt4Uaza08eVfbwIy64B95s0riIQCVEVCzKmrwExBPClN\nxQjipT2fHzz8Ij/600s4IBoOcNVZS3jDsXNU90VE+hlrEO/Rrfv53N0b6IynMeCdrzqKvzt9EQG1\nsSLjJp9A3lHOuS/2T8gG9G40s3eNT7FERERkrLriKfb3JHlo8x6+cO8GHHDsvFo+/5bjiIaDVFeE\naKpVEE9KVzGCeDsO9PK5ezawYWdmhMjL5tRy3QXLmd9QWaxiiohMCWMJ4vnO8V8Pv8jtD7+IA+qi\nIa67YDmnHtU4fgUVESC/QN6LZvavwA+cc7sBzGwOmRF520Y6UURERCZWb9Jjb3eSR1r28dlfbcB3\nsKSphhv/+gSqIiFqoiGaarUzp5SuYgTxHli/m5vXbCaW9DDgHa9YyLtefRRh7cosIjLAWIJ4Hb0p\nvnDPBv68tR2A5fNq+fQbj9XO3yITJJ9A3juAa4HfZQN4ALuA1cDbx6tgIiIiUphE2mN3Z5x1W/fz\n6dXPkvYdi2dV8+9vPYGaaIi6yjCzaiomu5giwzrcIF5PIs3Nazbz4IY2AGZWR7j2vGWccmRDMYsp\nIjIljCWIt3FXJ9evXk9bVwKAN590BO9fdbQulIhMoFEDec65duCa7E1ERERKUMrz2dUR58ltB/jk\n/z5DynMsaKjkS289gfrKMPWVYWYqiCcl7HCDeBt2dvLZuzewsyMOZHZnvvqcpcyoioy5TJFQgMbD\nOF9EpFQVGsRzzvGrp3by/367hZTniIYCfOQNSzlr+ZzRTxaRosprqy4zOwe4CJgPOGAHcJdz7r5x\nLJuIiIjkwfMduzriPLO9g4/94mniaZ959VG+8rYTaayO0FAVoaFawQgpXT2JNG1dYwvieb7jJ49u\n43v/txXPd4SDxntfczQXnXTEmNeBDAUCzKgOUxfVjosiMvUUGsSLpzy+9uBmfr1+NwALGyq5/sIV\nLJ5VPZ7FFJFhjBrIM7OvAUuB24HWbPIC4EozO885d9U4lk9ERERG4JxjV2ecDTs7uebnTxNLesyu\nqeArbzuR2bUVzKyuoL5KwQgpXYcTxNvTleAL927kiW0HADhqZhXXXbCc5tk1YyqLmTEjO4I1ENBm\nMCIy9RQaxGttj3H96vW07O0B4DVLZ3P1OUupiuQ1JgjItK0NGt0sUjT5fPrOd84tHZxoZj8BNgEK\n5ImIiEyS3Z0JntvVydV3PkV3Ik1jdYSvvP0E5tZHmVVboRFFUtIOJ4j3xy17+dL9z9EZTwNw4YlH\n8L7XNFMRDo6pLDXREI1VEUJa50lEpqjcWrr5BvF+v3kv/37fRnqSHgGDK1Y289ZTFhQ02rm6IkRj\ndURr6IkUUT6BvLiZneac+/Og9FcA8XEok4iIiORhT1eCTbs7+ejPnqKjN0VdNMSX3noCCxurmVUT\noVZBPClh3Yk0e8YQxEukPL75uxZWP7kDgLpoiKvPeRmvPmbWmMpRFQnRUB2mIjS2AKCISDlIpDMj\n8Tx/9DbX8x3f/n0LP1mXmZA3szrCp954LMcvqM/7+cLBADNrIgWN3BOR/OTzqXon8E0zq+Xg1NqF\nQGf2MREREZlg7T1JNu/u4qM/e4r9PUlqKjJBvObZNTTVVlBdoY6zlK7uRJq2zsKvB7fs6eYzd2/g\nxX0xAE5aOIOPnbeM2bWFb+QSCQWYWV1BZUQBPBGZ2goJ4u3vSfKZX63nydYOAE5cUM8n33gsjXmu\ntWtm1FeGaagKj3mdUhEZWT671j4OvNLM5pLZ7MKAVufcrvEunIiIiByqK55i0+4uPvKzJ2nrSlAV\nCfLFvzmepXPrmFNXoavfUtK64in2dCUKOsc5x/8+sYNv/e55Up4jGDDe9aqjeMcrFhIscC27YMBo\nqI5o2rmITAvJtJ93EO+p1gPc8KsN7O9JAnDxKxby7jMX593OVkaCzKyuIBLSNFqR8ZR3Tz8buBsQ\nvDOzZc65jUUvlYiIiAwplkyzaXc3H/3Zk+zsiBMNBfj8W45jxRH1zK2PEh3j+mAiE2EsQbwDsSRf\nun8TD7fsA2BefZTrLljO8nl1BeWTGyUyQxtZiMg0kUz77OzoHTWI55zjZ4+1cttDLfgOqiuCXHvu\nsryXLAgFAjRUh7Wkh8gEOdxL9r8GFh1OBma2FegCPCDtnDvVzBqBnwBHAVuBtzvn2i0zNvdm4Hwg\nBrwzO2IQM7sMuC6b7Wedcz/Ipp8CfB+oBO4BrnJjWVFZRERkksVTHpt2dfHRnz3JtvZewkHjMxcd\nx8sXNTCnTkE8KW2d8RR7CwziPfZiOzfeu5F92dEhrz92DleddUzBo061kYWITDf5jsTrSaT59/uf\n4/eb9wLQPLuaf3vTCuY3VOb1PHWVYRqrIrpAIjKBRu0Fmdktwz0EzChSOV7rnNvb7/61wBrn3I1m\ndm32/jXAecCS7O2VwDfJTPttBD4NnAo44DEzW+2ca88ecznwCJlA3rnAvUUqt4iIyIRIeT5b2rq4\n+s6neGFvD6GA8W8XruC0xY3MrY9qoX4paYUG8VKez/f+uJWfPLoNB1RFgvzz2Us4e/mcgp63MhKk\nsTqiz4eITCspLxPES/v+iMe17Onm+l+up7W9F4BzVszhqrOW5HVhsCIcZGZ1RBcRRSZBPpcz3wV8\nBBiq93VJcYvT583Aquy/fwCsJRPIezNwe3ZE3SNmNsPM5mWPfcA5tx/AzB4AzjWztUCdc+7hbPrt\nwEUokCciImXE8x0tbd38651Psbmtm4DBdRcs58xjZjO3Pqq1aKSkdfSm2NedfxCvtT3G5+7eyHO7\nuwBYNreWT1ywnPkz8hsdApndEhurI9r0RUSmnZTns/PA6EG8B9bv5qsPbCKR9gkHjQ+97hguOH7e\nqBtUBMxorNE6oyKTKZ/ezaPAM865/xv8gJldX4QyOODXZuaAW51ztwFznHM7AZxzO82sKXvsfGBb\nv3Nbs2kjpbcOkX4IM7uczMg9Fi06rNnCIhNG9VbKkeptYZxzbN3Xw7/+/CnW7+zCgGvPW8ZZy+cw\ntz5KWFMFJ4zqbuEKCeI55/j1+t3cvGYz8ZSPAZectpB3vuqovKfEBgPGjKoIddGQdkvMUr2VcqR6\nOza56bQjBfGSaZ//WPs8q5/cAcCcugquf9MKXja3dtT8a6IhZlZXFLzJ0HSheisTJZ9A3luB+FAP\nOOcWF6EMr3bO7cgG6x4ws5E2zxiqxXBjSD80MRNAvA3g1FNP1Rp6ZeKWBzfx7T+8QE/SozoS5D1n\nLubKs5dOdrEmjOrtxFm7sY1bH2phW3uMhQ1VXLGymVXLmkY/cZzzKkeqt4dau7GNL963kZa9PQAs\nnlnFtect5zUvm81L+2Ncc+dTPNnaAcBH37CU846bx7z6qNb7mmBTue4OVwcPp206EEv27Xw4mu5E\nmpse2MRvn9sDwKyaCB8/fzknLcxvFRdtZDG8cq63+X5fTvfv1amonOvtWBSjDc4niLe7M871v1zP\nc7syI55PW9zIx89bRl3lyKPrwsEAs2srNI12FNOt3srkGTWQl5uuOhoz+7lz7m8KLYBzbkf2b5uZ\n/QI4DdhtZvOyo/HmAW3Zw1uBhf1OXwDsyKavGpS+Npu+YIjjZQq45cFN3PTg5r7IbGc8zU0PbgaY\nVsG8UlCqQa5i5bV2YxufWv0s4aAxozJMW1ecT61+lhug4PyKmZdMDWs3tnH1nU/SHksRMPCdY+Pu\nbt71/UdpnllJJBxiQ7bDHTC4ec1mVj+xnY+df6zqjBTFcHXwnd9/FIDKcID3vebogr5b23uStMfy\nC+I9u6ODz929kV2dmevGrz5mJh99w8uoH+WHZU5NRYiG6ohGp04xo31f5r7jN+3upDOexncOz4fW\n9l7WvbifD732GPUHpSwMboMBNrdl+gG53zmjtcP5BPEe3bqfz929gc54GgPe+aqj+LvTFxEYYfRy\nwIyGqgh1lRrlLFJKitnjaS70BDOrNrPa3L+BNwDPAKuBy7KHXQbclf33auBSyzgd6MhOwb0feIOZ\nNZhZQzaf+7OPdZnZ6dkdby/tl5eUua//dsshwytdNl0mTq6j3dYVH9DRXruxbfSTyySvWx9qIRw0\nqiKZTkxVJEQ4aNz6UMuk5iVTw60PtdAVT2enqRhetg/ugBf29/YF8QwIWmb64fN7Y3z0zifHVJ9F\nBhtcB9ODfgf2pnxuenAztzy4Ka/88g3ieb7jvx55kavueIJdnXEioQD/fPYSbrhwRV5BvGg4yBEz\nKmmq0xTzqWik78v+3/HdCY+U5/raTgNSnuPrv92iNlLKQv82OBgIAIbnBk4jG6kdHi2I5zvHD/5v\nK9f+/Gk642nqoiFu/Jvj+YczjhwxiFddEWJBQyX1VWEF8URKTDFXAB7L0NE5wC+yDUMI+JFz7j4z\nexT4qZm9G3gJeFv2+HuA84EtQIzMRhw45/ab2WfIrOcHcEO/kYTvA74PVJLZ5EIbXZSQw5kam/KG\nrnLDpcv46N/RBqiKhIgl09z6UEvBo4VKNa9t7TGCltnZK+n5RIIBZtVEaG2PFZRPsfOS8jDadJlt\n7THSvk8oGCDlDeyE+/2as3DQCJhhZvi+ozsxtvos00Mh07RGqoP9ffsPL4z6HZ1vEK+tM87n793I\nU9kp482zqrnujcs5amb1qOdqI4vyNtxo+cHpm9u6mFsXHXBuZThIa3tswHd8cnDk2QCX6Q+qjZTJ\nUOg02f5tMHBIQM4MXLY/MLgdHi2I19Gb4gv3bODPW9sBWD6vlk+/8ViaBn22+gsHA8ysifT1oUWk\n9Ezqp9M51wKcOET6PuCsIdId8IFh8vou8N0h0tcBxx12YaXoNDV2aijVIFcx86qtCLG5rTt7pdRI\n+47tB+IsaaqZ1Lyk9I00bfZlc2q49rzl1FaE2O4f2nEfzIy+K+JmmdFMCgDLUPrXO3CkffqmygaA\npdm6l/tRubChir1dCZw7+GNxMAf0JL0RnzffIN5Dm/bwlQc20RVPA/CWl8/nipXNo+6+rI0syt8t\nD27iG2ufJ+X5OAc7D2SmwdZVhDgQTxEOBJhTV0FbV5yueJpwMMGsmoMBh96Ux4KGKra1x5gxyqhN\nA7WRMuEGt79ev/Y3GjIioSDhoLF0Tl1fELt/G9w/aDfY4HZ4tCDe6Q+ldQAAIABJREFUxl2dXL96\nPW1dmQ2HLjrpCN636uhhRzCbZaaxz9AIPJGSV8x5CPq0S0E0NXZqqK0Isf1AnLTvBgSmasYwUqJU\n83K5HpXrd+ufPkl5SekbbtoswNb9Ma6+80la22MMtza/DXPPuUxQY0FD1TiUWspdrt6Zccg0WR/Y\ntLt7wNTsK1Y2UxsN4flu2OkVBlRHhl/kPJ8gXjzl8dUHNnH9L9fTFU9TXxnmcxcdx4ded8yIQbzc\nRhYLG6qor9QPzHK1dmMb31j7POlsEA/Ay46c2xdL5QbSsbMjgec7GqrC7O9JEUumcc4RS6ZJeY4r\nVjazsKGK3lQmoBEJDqwPubxDAdRGyoTr3/4OHuAcT2dG03fG07ywt7tvyZf+bbA3wkW9/u1wyhs+\niOec45dP7uCqO56grStBNBTgE+cv48qzlgwbxKuKZKbRNlRH1MaKlIG8AnlmFjSzH45y2DVFKI9M\nI4c7NfYtJ80rKF3GR6kGuYqZV3fSY/6MKKGg4TlHKGjMnxEddXTKeOclpS83XSYTUDnY2XZkRtR1\nxdOkfMeChirCg76RDQhZZl28QHYEnuf7mZtz1FSEuGJlwcvTyjSQq3eeP3R756BvajZkNg740ltP\nZElTDeERdnx9z5mLh0zPJ4i3pa2b9/7wcX711E4ATlk0g29fegpnHD1zxPNqsms0zayp0G60Ze7W\nh1rwfHdwyYBBb6fnk10+APZ0JZhVU0FtRZCm2igdvSmaaqPccOEKVi1r4oqVzaS8THBvbv3AKYJG\n5gfOjKqI2kiZcIPb36Fa4QCWHXFqfdO/c22wmREK2oCPR/+u63vOXEzK89l5YOggXjzlceN9z3HT\ng5tJeY6FDZV84+9O5qzlc4YsbygQYE5dlLn1WmtUpJzkNTTFOeeZ2WwzizjnhuypOed+XdyiiYzs\npotPZlfH//HwC+19aWcsbuCmi0+exFJNP7nA1N7uZN8U1rk1FYcV5Cq1vBY2VNHWFad59sHpr7Fk\nmqba4dcXGSmvF/Z2D0hLpH0Wz9LU2qnokCmL2SEnBkSCARJpDzOjNhqirjLCvp6DX7EO8MiMRgoC\nc+oq2NOdefyYWSOvtyPTW67exQcPx8vKBZL7Tztctayprz6t3djGJ+96htb2Xhwj75a4vyfJgRGC\neM45bnpgM3c/vbPvB+15K+bykXOWjrjIejQcpLE6QjQ8/ChAKS/b2mNUhAIHv4cHRTj64nsGSc+n\nN+WxZE4dP7789EPyWrWsiRvIBAdb22Msm1NDdyLd10Y2z6rmmnOXqY2UCde//bXcMNN+ctNnk57f\nt+YjDGyDYfh2+H2vPWbYkXit7TGuX72+b22+lUtn8bqlTXx9zRZ2dvYyr66Si1+xkNOaGzEz6qIh\nGqoiukgiUoYKmWO2Ffijma0GenKJzrmvFrtQIvlYu7GNdS+2D0hb92I7aze2qeM2gRY2VLFhZwe9\nKQ/fged7dPSmWD6vftLzKlbA7IqVzbz3h+uIpw/2xqIh45MXHFtwXmc0N/KnF/b1jUhIeR69KY+/\nPW1RwXlJ6btiZXPfWjkO+jr0lh1pl4mzOJ7Z0Tnk+b4DnCMYNAKBALf+/Slq32RUuXoX7x46wGYc\nnJp9y4Ob+NZDLcT6XeQYKXDX31BBvD+37OeOR7exs7OXWdUVHOhNsv1AHLLPaea4f/0uHn9pP9UV\nYXqS6QE/LsPBAA3VkbyWQRhu0wQpTQsbqkh7/ogX1HLTZQFa9vawpyvBJbc9MuR7m7ufqwM1FSF6\nkx77e1Ns2NXF+3/0OO9d2ax1l2VC9W9/h5oEklsDLxIM0Noeoyvh0fyxu8l+3QMH2+DfX/O6Aeem\nPZ+dHfEhNyX6/ea9fP6eDSSyF3COqI/S3FjFrb9vIZX26El67O1K8PT2AzTVVRAwY1FjdUHtptpc\nkdJRSCBvR/YWAGrHpzgyndREAnQnD/0iqonkN6z7gz96jNSg01N+Jv2ZG84rRhHz5pybtutJzK2L\n8HBLuu++7+BAb5q5dZFJzeuM5kYebtnXdz/lZToxYwmY3fVE64AgHmTWObnridaCOzD3PrPrkI6d\nc5l0/diYenLTZb5430a2tHWT8h3hQKaT3pnIf3So5ztSnqcdGCUvuXr3ybueYVt77yGPG5kpq3Pr\nInxtzWYGz8DtTfnc9OBmXtjbza7O5JC7i/6/325hW3tsQBDuzy37ufk3mwkFjKDBhl2dfXlXhoMk\n0x5G5lfs7q4kwe4kc+oq2NeT4ObfbOYT0eVccMI8fvfcnlF/LK7d2ManVj9LOJhZnL2tK86nVj/L\nDTDksfrxOfmuWNnMp1Y/yxCDlIbkHPQk0mzc1cEVP3yM2miIJU21A+phrg4ELbOhQH+xpMfX1mgT\nNZlYw7W//eu9j8M5ONDrZTfCGpjHUG3wghmV/M3JCzjlqIYBF0zm1kaprwrz0Oa9QGYpjiPqo5gZ\n//3oNirDAWLJTNtrQNpl1qE8srGyr918a+sBHm7ZX5Q2V+2tyMTIO5DnnPs3ADOrds71jHa8yGiG\nCuKNlF7s8w+X52fWZoklPVKeP20XVF6zcc8hnZCAZdILdf+zbQWlj+Snj740bHqhHfrVT+0CMldR\nc5zLpN90cWHl2tzWNeQmL5vbugrLSMrG4CmLtz7UMiDInA/PQUcsRWtAOzBKflYta+L3y17H2o1t\nfYHktHMEDY6Zndm19so7/nLID8gcB/ziiZ1EQwd3Ec394PvJulYCBnXRUF8Q7iqWcMej2wgadMfT\ntPem+vLKrP/oCJgRyG4+BBAIGO2xFEfOrCaZ9vjvP71ETUUorx+Ltz7UQjhoVEUyXdmqSIhYMn1I\nsLuQgJ+Mr1XLmjjliVZe2p9/O+Y5aI9l1hKLJdID3r/+daBlz8AgXm5Ko+/g2394QYE8mVD9299b\nH2phc1sXybTfd+E/EjTae9MEAyPvUJtrg5tqI+zo6OWrD27i3GPncN/63YQCRlU4wMbdXX2j8CpC\ngcw6zIHMoAjfd3TFvczFlYCR6HdRem93kubZNezpivONtc+zoKHysNtctbciEyfvQJ6ZnQF8B6gB\nFpnZicAVzrn3j1fhREpNyvOJJTxiqTS9/aaGTOfFYbsTaXw3cM1q32XSCxXLTqkZHDCLpQpf1257\nZ2LIvHLphRhuwfjh0kfMq9/Uyv7lynOPFylzuaDeUdfeXfC5iWl8wUDGbvC6S/3ls2ZobhfRI2ZE\nCQeN237fQkNVhMrs2nWV4SC9KY87Ht3GtvYeuhNe34/KaCiAw5HyHM7z+9bEyzV3Act8r4aDAUIB\no7U9lneAblt7jBmV4QFl7b/eVE6++cn4W7uxjXue2T2mc4MBI+W7Ae9f/zqQHGKqYW4IlDaTksky\nUvt79MfvIRQwkumRO4C+c+zsSNBUV0EoYPz0sVYaqyM4By+1x/v6ogGDhQ1RAnbwN0kkFKA35RMw\nBswcCtjBz0xXPE3a94vS5qq9FZk4hUQfvgacA+wDcM49Cawcj0KJlJJ4yqO9J0lre4xt+2Ps60kM\nCOJNd30dA+t3gzFNNR7ulLHMWh72CucYAmbBYRYBHi59JKHcixm0m25omk7NlvyFAgHtwChFVR0Z\nfSOJ/ruIhgNGLOkRHbTFckXIaNnbTXss1RfEa6wKs7ChktqKEMFAZiSecw4/2wgHDcCoCGXK0Jvy\nWNBQxbb2WF+QMGeoAN3ChqoB66n1z6O/fPOT8ZfbtXYscmuKwcH3r38diAx1QTX7VPnUc5GJVh0J\nZi6Ej9L9MzOwzJqk0ew02d6UR+uBXjzfETCYV19xyIg7DGqjmYCaDzgOXnUPBqzvM5NI+1QM+vyM\ntc1VeysycQoaRuSc2zYoSdEMGbNlc6oLSp8ovu/oSaTZ05XgpX0xdhzopT2WJDnM7n/TXVX2B11u\nV85coKwqXPgoxfn10SHzyqUXVK5cxz3Xp3GD0gtw4QlzhyxXLr0QxzTVZH7A5jpu2U0PjmnSrrXT\nxa+e3DGm8z6w6mhd0Zaies+ZixnpekSw78IMJLKLpVeGg8T7LVDr+Y7tBxJ0xjOjswMGs2s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D0dEeLq3aspVcFxdb8mKn9/bhv//cdVOe1bFbKpiMbosHWQafsJBV5cFZ9IUunnE6+cdkfjVJYF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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118d9b128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(rx_df_tidy, kind=\"reg\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation findings\n",
    "\n",
    "As confirmed in the correlations, 'Spending_per_person' is the sum of 'OOP_spending_per_person' and 'Payer_spending_per_person', so let's remove them.\n",
    "\n",
    "The relevant metrics I'll pay attention to,  are:\n",
    "- 'Spending_per_person' (the total of 'OOP_spending_per_person' and 'Payer_spending_per_person')\n",
    "- 'Price'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metric</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Age Group Upper Limit</th>\n",
       "      <th>Year</th>\n",
       "      <th>Price</th>\n",
       "      <th>Spending_per_person</th>\n",
       "      <th>Utilization_per_1000_persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2012</td>\n",
       "      <td>12.610456</td>\n",
       "      <td>14.764956</td>\n",
       "      <td>3459.720944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2013</td>\n",
       "      <td>14.669633</td>\n",
       "      <td>15.504144</td>\n",
       "      <td>3254.608911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2014</td>\n",
       "      <td>17.521444</td>\n",
       "      <td>14.934200</td>\n",
       "      <td>3100.666556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2015</td>\n",
       "      <td>16.142189</td>\n",
       "      <td>14.120733</td>\n",
       "      <td>3105.522800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>15.473244</td>\n",
       "      <td>13.385800</td>\n",
       "      <td>2996.774789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Metric      Subcategory  Age Group Upper Limit  Year      Price  \\\n",
       "0       Anti_Infectives                     18  2012  12.610456   \n",
       "1       Anti_Infectives                     18  2013  14.669633   \n",
       "2       Anti_Infectives                     18  2014  17.521444   \n",
       "3       Anti_Infectives                     18  2015  16.142189   \n",
       "4       Anti_Infectives                     18  2016  15.473244   \n",
       "\n",
       "Metric  Spending_per_person  Utilization_per_1000_persons  \n",
       "0                 14.764956                   3459.720944  \n",
       "1                 15.504144                   3254.608911  \n",
       "2                 14.934200                   3100.666556  \n",
       "3                 14.120733                   3105.522800  \n",
       "4                 13.385800                   2996.774789  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop redundant columns\n",
    "rx_df_tidy.drop(labels=['OOP_spending_per_person', 'Payer_spending_per_person'], axis=1, inplace=True)\n",
    "rx_df_tidy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Finalize cleanup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Metric</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>Age Group Upper Limit</th>\n",
       "      <th>Year</th>\n",
       "      <th>Price per filled day</th>\n",
       "      <th>Spending per person</th>\n",
       "      <th>Utilization per 1000 persons</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2012</td>\n",
       "      <td>12.610456</td>\n",
       "      <td>14.764956</td>\n",
       "      <td>3459.720944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2013</td>\n",
       "      <td>14.669633</td>\n",
       "      <td>15.504144</td>\n",
       "      <td>3254.608911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2014</td>\n",
       "      <td>17.521444</td>\n",
       "      <td>14.934200</td>\n",
       "      <td>3100.666556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2015</td>\n",
       "      <td>16.142189</td>\n",
       "      <td>14.120733</td>\n",
       "      <td>3105.522800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Anti_Infectives</td>\n",
       "      <td>18</td>\n",
       "      <td>2016</td>\n",
       "      <td>15.473244</td>\n",
       "      <td>13.385800</td>\n",
       "      <td>2996.774789</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Metric      Subcategory  Age Group Upper Limit  Year  Price per filled day  \\\n",
       "0       Anti_Infectives                     18  2012             12.610456   \n",
       "1       Anti_Infectives                     18  2013             14.669633   \n",
       "2       Anti_Infectives                     18  2014             17.521444   \n",
       "3       Anti_Infectives                     18  2015             16.142189   \n",
       "4       Anti_Infectives                     18  2016             15.473244   \n",
       "\n",
       "Metric  Spending per person  Utilization per 1000 persons  \n",
       "0                 14.764956                   3459.720944  \n",
       "1                 15.504144                   3254.608911  \n",
       "2                 14.934200                   3100.666556  \n",
       "3                 14.120733                   3105.522800  \n",
       "4                 13.385800                   2996.774789  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rename cols to be more friendly\n",
    "rx_df_tidy.rename(columns={\n",
    "                  'Spending_per_person': 'Spending per person',\n",
    "                  'Utilization_per_1000_persons': 'Utilization per 1000 persons',\n",
    "                  'Price': 'Price per filled day'\n",
    "                  },\n",
    "                 inplace=True)\n",
    "rx_df_tidy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Save to file for use in Part 2\n",
    "rx_df_tidy.to_csv(os.path.join('exercise_files', 'rx_df_tidy.csv'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Summary\n",
    "Now that our data set is clean and tidy we can move on to creating visualizations in [Part 2](Exercise%20Part%202%20-%20Data%20Visualizations.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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