CL_Postings_DA.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Craigslist Used Items Analysis\n",
    "\n",
    "This is my first project related to web scraping and data analytics/visualization. I've always been interested in the second-hand market.  There are many second-hand stores here in the Asheville area. I was curious to explore what was being sold on the most prominent site to post used goods, Craigslist. I used the Scrapy package to scrape the first page of items available close to where I live. This is simple introduction project and I may build this out in more depth in the future as a hobby. For now, this notebook provides a quick snapshot of what is available in the area for the day I scraped CL.\n",
    "\n",
    "Questions I want to answer:\n",
    "\n",
    "What the most common item types being sold around that area?  \n",
    "\n",
    "What items sell for the most money?\n",
    "\n",
    "What can I get for free?\n",
    "\n",
    "How much money should I expect to bring with me to do some treasure-hunting around Asheville and neighboring towns?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
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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>date</th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Apr 25</td>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>2015 Harley Davidson Softail Slim with Warrent...</td>\n",
       "      <td>$12899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Apr 25</td>\n",
       "      <td>(~ Hummer_ H3_ Luxury)</td>\n",
       "      <td>2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...</td>\n",
       "      <td>$14950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Apr 25</td>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE</td>\n",
       "      <td>$9499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Apr 25</td>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>2015 Hyosung Aquila GV250 PERFECT Starter Bike!</td>\n",
       "      <td>$3499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Apr 25</td>\n",
       "      <td>(Greenville)</td>\n",
       "      <td>02 Ford Excursion v10</td>\n",
       "      <td>$2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     date                 location  \\\n",
       "0  Apr 25              (GarageTRS)   \n",
       "1  Apr 25   (~ Hummer_ H3_ Luxury)   \n",
       "2  Apr 25              (GarageTRS)   \n",
       "3  Apr 25              (GarageTRS)   \n",
       "4  Apr 25             (Greenville)   \n",
       "\n",
       "                                                name   price  \n",
       "0  2015 Harley Davidson Softail Slim with Warrent...  $12899  \n",
       "1  2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...  $14950  \n",
       "2   MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE   $9499  \n",
       "3    2015 Hyosung Aquila GV250 PERFECT Starter Bike!   $3499  \n",
       "4                              02 Ford Excursion v10   $2500  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud \n",
    "%matplotlib inline\n",
    "\n",
    "#display all lines as ouput\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\"\n",
    "\n",
    "raw = pd.read_csv(\"avl_used_goods.csv\")\n",
    "\n",
    "raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'd prefer to simplify the dataframe so the index is the date column; this is just personal preference.  Also, I want to take a look at each remaining column and the data-types they are."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Apr 25', 'Apr 25', 'Apr 25', 'Apr 25', 'Apr 25'], dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adjust index of dataframe\n",
    "postings = raw.rename(index=raw['date'])\n",
    "postings.index[:5]\n",
    "\n",
    "#explain why I changed first column to index."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "del postings['date']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location    object\n",
       "name        object\n",
       "price       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>2015 Harley Davidson Softail Slim with Warrent...</td>\n",
       "      <td>$12899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(~ Hummer_ H3_ Luxury)</td>\n",
       "      <td>2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...</td>\n",
       "      <td>$14950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE</td>\n",
       "      <td>$9499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>2015 Hyosung Aquila GV250 PERFECT Starter Bike!</td>\n",
       "      <td>$3499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(Greenville)</td>\n",
       "      <td>02 Ford Excursion v10</td>\n",
       "      <td>$2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location  \\\n",
       "Apr 25              (GarageTRS)   \n",
       "Apr 25   (~ Hummer_ H3_ Luxury)   \n",
       "Apr 25              (GarageTRS)   \n",
       "Apr 25              (GarageTRS)   \n",
       "Apr 25             (Greenville)   \n",
       "\n",
       "                                                     name   price  \n",
       "Apr 25  2015 Harley Davidson Softail Slim with Warrent...  $12899  \n",
       "Apr 25  2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...  $14950  \n",
       "Apr 25   MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE   $9499  \n",
       "Apr 25    2015 Hyosung Aquila GV250 PERFECT Starter Bike!   $3499  \n",
       "Apr 25                              02 Ford Excursion v10   $2500  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings.dtypes #object data type is essentially string type.\n",
    "postings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before I begin I want to get an idea of how many items I'm starting off with to parse through."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "98"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>2015 Harley Davidson Softail Slim with Warrent...</td>\n",
       "      <td>$12899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(~ Hummer_ H3_ Luxury)</td>\n",
       "      <td>2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...</td>\n",
       "      <td>$14950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE</td>\n",
       "      <td>$9499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(GarageTRS)</td>\n",
       "      <td>2015 Hyosung Aquila GV250 PERFECT Starter Bike!</td>\n",
       "      <td>$3499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(Greenville)</td>\n",
       "      <td>02 Ford Excursion v10</td>\n",
       "      <td>$2500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       location  \\\n",
       "Apr 25              (GarageTRS)   \n",
       "Apr 25   (~ Hummer_ H3_ Luxury)   \n",
       "Apr 25              (GarageTRS)   \n",
       "Apr 25              (GarageTRS)   \n",
       "Apr 25             (Greenville)   \n",
       "\n",
       "                                                     name   price  \n",
       "Apr 25  2015 Harley Davidson Softail Slim with Warrent...  $12899  \n",
       "Apr 25  2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...  $14950  \n",
       "Apr 25   MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE   $9499  \n",
       "Apr 25    2015 Hyosung Aquila GV250 PERFECT Starter Bike!   $3499  \n",
       "Apr 25                              02 Ford Excursion v10   $2500  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(postings)\n",
    "postings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The price column will need to be converted to float-type so that numerical analysis can be performed on the items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "location     object\n",
       "name         object\n",
       "price       float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(downtown Asheville)</td>\n",
       "      <td>2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900</td>\n",
       "      <td>139000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(2015 Mercedes-Benz Sprinter Vans)</td>\n",
       "      <td>2015 Mercedes-Benz Sprinter Vans Van Sprinter ...</td>\n",
       "      <td>38189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(marion)</td>\n",
       "      <td>1968 Camaro</td>\n",
       "      <td>26500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(Knoxville)</td>\n",
       "      <td>07 cummins</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>(~ Hummer_ H3_ Luxury)</td>\n",
       "      <td>2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...</td>\n",
       "      <td>14950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   location  \\\n",
       "Apr 25                 (downtown Asheville)   \n",
       "Apr 25   (2015 Mercedes-Benz Sprinter Vans)   \n",
       "Apr 25                             (marion)   \n",
       "Apr 25                          (Knoxville)   \n",
       "Apr 25               (~ Hummer_ H3_ Luxury)   \n",
       "\n",
       "                                                     name     price  \n",
       "Apr 25  2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900  139000.0  \n",
       "Apr 25  2015 Mercedes-Benz Sprinter Vans Van Sprinter ...   38189.0  \n",
       "Apr 25                                        1968 Camaro   26500.0  \n",
       "Apr 25                                         07 cummins   20000.0  \n",
       "Apr 25  2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...   14950.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#adjust price column to float type and sort values descending\n",
    "postings['price'] = postings['price'].replace('[\\$]','', regex=True ).astype(float)\n",
    "postings = postings.sort_values('price', ascending=False)\n",
    "postings.dtypes\n",
    "postings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "The location column will need to be normalized so make analysis more efficient.  I will need to remove all erroenous characters and capitalize all remaining characters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>downtown Asheville</td>\n",
       "      <td>2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900</td>\n",
       "      <td>139000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>2015 Mercedes-Benz Sprinter Vans</td>\n",
       "      <td>2015 Mercedes-Benz Sprinter Vans Van Sprinter ...</td>\n",
       "      <td>38189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>marion</td>\n",
       "      <td>1968 Camaro</td>\n",
       "      <td>26500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>Knoxville</td>\n",
       "      <td>07 cummins</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>Hummer H3 Luxury</td>\n",
       "      <td>2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...</td>\n",
       "      <td>14950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 location  \\\n",
       "Apr 25                 downtown Asheville   \n",
       "Apr 25   2015 Mercedes-Benz Sprinter Vans   \n",
       "Apr 25                             marion   \n",
       "Apr 25                          Knoxville   \n",
       "Apr 25                   Hummer H3 Luxury   \n",
       "\n",
       "                                                     name     price  \n",
       "Apr 25  2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900  139000.0  \n",
       "Apr 25  2015 Mercedes-Benz Sprinter Vans Van Sprinter ...   38189.0  \n",
       "Apr 25                                        1968 Camaro   26500.0  \n",
       "Apr 25                                         07 cummins   20000.0  \n",
       "Apr 25  2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...   14950.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove all non-letter characters from location column using regex\n",
    "postings['location'].replace(['\\(', ',', '~', '_' ,'\\)'], '', regex=True, inplace=True)\n",
    "postings.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>DOWNTOWN ASHEVILLE</td>\n",
       "      <td>2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900</td>\n",
       "      <td>139000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>2015 MERCEDES-BENZ SPRINTER VANS</td>\n",
       "      <td>2015 Mercedes-Benz Sprinter Vans Van Sprinter ...</td>\n",
       "      <td>38189.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>MARION</td>\n",
       "      <td>1968 Camaro</td>\n",
       "      <td>26500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>KNOXVILLE</td>\n",
       "      <td>07 cummins</td>\n",
       "      <td>20000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>HUMMER H3 LUXURY</td>\n",
       "      <td>2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...</td>\n",
       "      <td>14950.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 location  \\\n",
       "Apr 25                 DOWNTOWN ASHEVILLE   \n",
       "Apr 25   2015 MERCEDES-BENZ SPRINTER VANS   \n",
       "Apr 25                             MARION   \n",
       "Apr 25                          KNOXVILLE   \n",
       "Apr 25                   HUMMER H3 LUXURY   \n",
       "\n",
       "                                                     name     price  \n",
       "Apr 25  2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900  139000.0  \n",
       "Apr 25  2015 Mercedes-Benz Sprinter Vans Van Sprinter ...   38189.0  \n",
       "Apr 25                                        1968 Camaro   26500.0  \n",
       "Apr 25                                         07 cummins   20000.0  \n",
       "Apr 25  2008 *Hummer* *H3* Luxury IN HOUSE FINANCE FRE...   14950.0  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings['location'] = postings['location'].apply(lambda x: x.upper())#upper-case locations\n",
    "postings = postings.sort_values(['price'], ascending=False)\n",
    "postings.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "It appears there are a fair amount of erroenous locations in the dataframe.  To perform accurate analysis and visualization on items, I will need to consolidate and remove some of these locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([' DOWNTOWN ASHEVILLE', ' 2015 MERCEDES-BENZ SPRINTER VANS',\n",
       "       ' MARION', ' KNOXVILLE', '  HUMMER H3 LUXURY', ' GARAGETRS',\n",
       "       ' AMERICAN AUTO SALES', ' ASHEVILLE', ' HENDERSONVILLE',\n",
       "       ' GREENVILLE', ' HAYWOOD', ' FOREST CITY', ' RUTHERFORDTON',\n",
       "       ' NORTH ASHEVILLE', ' SWANNANOA', ' WEAVERVILLE', ' ETOWAH',\n",
       "       ' WAYNESVILLE NC', ' 224 SOUTH GROVE STREET UNIT L', ' WNC',\n",
       "       ' EAST ASHEVILLE', ' SPARTANBURG SC', ' CANDLER', ' FRANKLIN NC',\n",
       "       ' FLAT ROCK', ' OLD FORT', ' BREVARD NC', ' ASHEVILLE NC',\n",
       "       ' WEAVERVILLE N C', ' BREVARDNC', ' ASHEVILLE N C',\n",
       "       ' ASHEVILLE NEAR DOWNTOWN', ' NEAR ASHEVILLE AIRPORT',\n",
       "       ' WAYNESVILLE', ' DELIVERED', ' LEICESTER NC'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(postings.location.unique())\n",
    "postings.location.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "postings.loc[postings.location.str.contains('AMERICAN AUTO SALES'),'location'] = 'ARDEN'\n",
    "postings.loc[postings.location.str.contains('ASHEVILLE'),'location'] = 'ASHEVILLE'\n",
    "postings.loc[postings.location.str.contains('GARAGETRS'),'location'] = 'ASHEVILLE' #dealer in Asheville\n",
    "postings.loc[postings.location.str.contains('BREVARD'),'location'] = 'BREVARD'\n",
    "postings.loc[postings.location.str.contains('WAYNESVILLE'),'location'] = 'WAYNESVILLE'\n",
    "postings.loc[postings.location.str.contains('WEAVERVILLE'),'location'] = 'WEAVERVILLE'\n",
    "postings = postings[~postings.location.str.contains('KNOXVILLE')]#remove knoxville; distance too far and only one item.\n",
    "postings = postings[~postings.location.str.contains('SPARTANBURG')] #remove spartanburg; distance too far and only one item.\n",
    "postings = postings[~postings.location.str.contains('HUMMER H3 LUXURY')] #removed because location isn't provided; only one item.\n",
    "postings = postings[~postings.location.str.contains('2015 MERCEDES-BENZ SPRINTER VANS')] #removed because location isn't provided; only one item.\n",
    "postings = postings[~postings.location.str.contains('224 SOUTH GROVE STREET UNIT L')] #removed because irrelevant; room for rent, not item for sale."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array(['ASHEVILLE', ' MARION', 'ARDEN', ' HENDERSONVILLE', ' GREENVILLE',\n",
       "       ' HAYWOOD', ' FOREST CITY', ' RUTHERFORDTON', ' SWANNANOA',\n",
       "       'WEAVERVILLE', ' ETOWAH', 'WAYNESVILLE', ' WNC', ' CANDLER',\n",
       "       ' FRANKLIN NC', ' FLAT ROCK', ' OLD FORT', 'BREVARD', ' DELIVERED',\n",
       "       ' LEICESTER NC'], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(postings.location.unique())\n",
    "postings.location.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the dataframe has been cleaned up a bit, I want to start to get a feel for the dispersion of prices of the items in the area."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAMMTNADA8AQNADA8QQMADE/QAADDEzQAwPAEDQAwPEEDAAxP0AAAwxM0AMDwBA0AMDxB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17c91780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,9))\n",
    "plt.plot(postings.price.values, '.')\n",
    "# plt.figure(figsize=(9,9))\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "75"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(postings.loc[postings.price < 1000, :])\n",
    "len(postings.loc[postings.price >= 1000, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900</td>\n",
       "      <td>139000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>MARION</td>\n",
       "      <td>1968 Camaro</td>\n",
       "      <td>26500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2015 Harley Davidson Softail Slim with Warrent...</td>\n",
       "      <td>12899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ARDEN</td>\n",
       "      <td>2006 SUBARU BAJA TURBO</td>\n",
       "      <td>9900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE</td>\n",
       "      <td>9499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2007 Ford Mustang - Excellent Condition</td>\n",
       "      <td>8500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>HENDERSONVILLE</td>\n",
       "      <td>Ford explorer</td>\n",
       "      <td>7500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>GREENVILLE</td>\n",
       "      <td>2017 ktm 250sx BRAND NEW NEVER CRANKED</td>\n",
       "      <td>7500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>HAYWOOD</td>\n",
       "      <td>98 Jeep Wrangler</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ARDEN</td>\n",
       "      <td>2003 HONDA ELEMENT AWD</td>\n",
       "      <td>5900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location                                               name  \\\n",
       "Apr 25        ASHEVILLE  2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900   \n",
       "Apr 25           MARION                                        1968 Camaro   \n",
       "Apr 25        ASHEVILLE  2015 Harley Davidson Softail Slim with Warrent...   \n",
       "Apr 25            ARDEN                             2006 SUBARU BAJA TURBO   \n",
       "Apr 25        ASHEVILLE   MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE   \n",
       "Apr 25        ASHEVILLE            2007 Ford Mustang - Excellent Condition   \n",
       "Apr 25   HENDERSONVILLE                                      Ford explorer   \n",
       "Apr 25       GREENVILLE             2017 ktm 250sx BRAND NEW NEVER CRANKED   \n",
       "Apr 25          HAYWOOD                                   98 Jeep Wrangler   \n",
       "Apr 25            ARDEN                             2003 HONDA ELEMENT AWD   \n",
       "\n",
       "           price  \n",
       "Apr 25  139000.0  \n",
       "Apr 25   26500.0  \n",
       "Apr 25   12899.0  \n",
       "Apr 25    9900.0  \n",
       "Apr 25    9499.0  \n",
       "Apr 25    8500.0  \n",
       "Apr 25    7500.0  \n",
       "Apr 25    7500.0  \n",
       "Apr 25    7000.0  \n",
       "Apr 25    5900.0  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>PT 609 Pro Mag.</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Brand New Verbatim pack of 30 DVD-r</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>BREVARD</td>\n",
       "      <td>2 RCA Speaker</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>Car Batteries $5 for bad $10 for good</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>DELIVERED</td>\n",
       "      <td>Premium Horse Hay!  2017 Production</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>LEICESTER NC</td>\n",
       "      <td>All Man Magazine - 1959</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>MARION</td>\n",
       "      <td>1994 Honda Civic 2 Dr Coupe</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>2008 Kia Sedona parts</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>2006 Chrysler 300 parts</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>WTB Vintage Mandolins, Violins, Guitars</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             location                                     name  price\n",
       "Apr 25      ASHEVILLE                          PT 609 Pro Mag.   12.0\n",
       "Apr 25      ASHEVILLE      Brand New Verbatim pack of 30 DVD-r   10.0\n",
       "Apr 25        BREVARD                            2 RCA Speaker   10.0\n",
       "Apr 25    WAYNESVILLE    Car Batteries $5 for bad $10 for good   10.0\n",
       "Apr 25      DELIVERED      Premium Horse Hay!  2017 Production    7.0\n",
       "Apr 25   LEICESTER NC                  All Man Magazine - 1959    3.0\n",
       "Apr 25         MARION              1994 Honda Civic 2 Dr Coupe    2.0\n",
       "Apr 25    WAYNESVILLE                    2008 Kia Sedona parts    1.0\n",
       "Apr 25    WAYNESVILLE                  2006 Chrysler 300 parts    1.0\n",
       "Apr 25      ASHEVILLE  WTB Vintage Mandolins, Violins, Guitars    1.0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings.head(10)\n",
    "postings.tail(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears the most expensive items in the area are motor vehicles.  A fair amount of the least expensive items are vehicle related as well.  It also appears that there is one item that is abnormally high.  It will be best to remove this anamolous item so that it doesn't skew the data.\n",
    "\n",
    "Let's also take a look at items grouped by their locations.  I'm interested in finding out how much potential value is available per each location around where I live. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900</td>\n",
       "      <td>139000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         location                                               name     price\n",
       "Apr 25  ASHEVILLE  2009 Toyota Tacoma, 4x4 102,000, ext. cab $13,900  139000.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CANDLER</th>\n",
       "      <td>305.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DELIVERED</th>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ETOWAH</th>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FLAT ROCK</th>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FOREST CITY</th>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRANKLIN NC</th>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GREENVILLE</th>\n",
       "      <td>10000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HAYWOOD</th>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HENDERSONVILLE</th>\n",
       "      <td>8639.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LEICESTER NC</th>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MARION</th>\n",
       "      <td>26502.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OLD FORT</th>\n",
       "      <td>300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RUTHERFORDTON</th>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWANNANOA</th>\n",
       "      <td>2250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WNC</th>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ARDEN</th>\n",
       "      <td>21700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASHEVILLE</th>\n",
       "      <td>48678.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BREVARD</th>\n",
       "      <td>719.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAYNESVILLE</th>\n",
       "      <td>987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEAVERVILLE</th>\n",
       "      <td>755.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sum\n",
       "location                \n",
       " CANDLER           305.0\n",
       " DELIVERED           7.0\n",
       " ETOWAH            500.0\n",
       " FLAT ROCK         175.0\n",
       " FOREST CITY      2000.0\n",
       " FRANKLIN NC       200.0\n",
       " GREENVILLE      10000.0\n",
       " HAYWOOD          7000.0\n",
       " HENDERSONVILLE   8639.0\n",
       " LEICESTER NC        3.0\n",
       " MARION          26502.0\n",
       " OLD FORT          300.0\n",
       " RUTHERFORDTON    1200.0\n",
       " SWANNANOA        2250.0\n",
       " WNC               250.0\n",
       "ARDEN            21700.0\n",
       "ASHEVILLE        48678.0\n",
       "BREVARD            719.0\n",
       "WAYNESVILLE        987.0\n",
       "WEAVERVILLE        755.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings[postings.price==postings.price.max()] #Thats an expensive Tacoma!! Probably just a typo, but removing to avoid assumptions.\n",
    "postings = postings.loc[~postings.name.str.contains('2009 Toyota Tacoma, 4x4 102,000'),['location', 'name', 'price']] \n",
    "\n",
    "#group by location; get the sum of each location\n",
    "postings.groupby(['location']).agg(['sum']).loc[:,'price']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that the anamolous item is removed, let's take a look at the most expensive item avaiable in the area is.  We'll also visualize each location via a boxplot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>MARION</td>\n",
       "      <td>1968 Camaro</td>\n",
       "      <td>26500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       location         name    price\n",
       "Apr 25   MARION  1968 Camaro  26500.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings.loc[postings.price == postings.price.max(),]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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YSpIkqTAYSpIkCTAYSpIkqTAYSpIkCYClne6AJOnxImLC7Zk5Tz2RtJg4YihJO6DMfOxx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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a22264588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#update df to remove name column; necessary for clean boxplot visualization.\n",
    "postings_bp = postings.loc[:, ['location', 'price']]\n",
    "\n",
    "#create a boxplot; adjust x-axis labels so they are visible. specify labels and title.  \n",
    "postings_bp.boxplot(column='price', by='location',figsize=(10,10))\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Location')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Items Nearby')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The boxplot still appears to be cluttered with anomalous items. I'll need to clean this up and replot the items.\n",
    "But first, let's take a closer look at the vehicles for sale in the area to see how they are affecting the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>MARION</td>\n",
       "      <td>1968 Camaro</td>\n",
       "      <td>26500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2015 Harley Davidson Softail Slim with Warrent...</td>\n",
       "      <td>12899.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ARDEN</td>\n",
       "      <td>2006 SUBARU BAJA TURBO</td>\n",
       "      <td>9900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE</td>\n",
       "      <td>9499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2007 Ford Mustang - Excellent Condition</td>\n",
       "      <td>8500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>HENDERSONVILLE</td>\n",
       "      <td>Ford explorer</td>\n",
       "      <td>7500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>GREENVILLE</td>\n",
       "      <td>2017 ktm 250sx BRAND NEW NEVER CRANKED</td>\n",
       "      <td>7500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>HAYWOOD</td>\n",
       "      <td>98 Jeep Wrangler</td>\n",
       "      <td>7000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ARDEN</td>\n",
       "      <td>2003 HONDA ELEMENT AWD</td>\n",
       "      <td>5900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ARDEN</td>\n",
       "      <td>2006 HONDA ACCORD</td>\n",
       "      <td>5900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2000 Jeep Cherokee Sport 4.0</td>\n",
       "      <td>4900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2015 Hyosung Aquila GV250 PERFECT Starter Bike!</td>\n",
       "      <td>3499.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>GREENVILLE</td>\n",
       "      <td>02 Ford Excursion v10</td>\n",
       "      <td>2500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>2004 Dodge Ram 2500 ^_^Great Body No Rust^_^</td>\n",
       "      <td>2075.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>FOREST CITY</td>\n",
       "      <td>HARLEY DAVIDSON seats  Ford Super Duty</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Ford explorer</td>\n",
       "      <td>1800.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location                                               name  \\\n",
       "Apr 25           MARION                                        1968 Camaro   \n",
       "Apr 25        ASHEVILLE  2015 Harley Davidson Softail Slim with Warrent...   \n",
       "Apr 25            ARDEN                             2006 SUBARU BAJA TURBO   \n",
       "Apr 25        ASHEVILLE   MUST SELL!! NEED SPACE! 2013 Polaris RZR 800S LE   \n",
       "Apr 25        ASHEVILLE            2007 Ford Mustang - Excellent Condition   \n",
       "Apr 25   HENDERSONVILLE                                      Ford explorer   \n",
       "Apr 25       GREENVILLE             2017 ktm 250sx BRAND NEW NEVER CRANKED   \n",
       "Apr 25          HAYWOOD                                   98 Jeep Wrangler   \n",
       "Apr 25            ARDEN                             2003 HONDA ELEMENT AWD   \n",
       "Apr 25            ARDEN                                  2006 HONDA ACCORD   \n",
       "Apr 25        ASHEVILLE                       2000 Jeep Cherokee Sport 4.0   \n",
       "Apr 25        ASHEVILLE    2015 Hyosung Aquila GV250 PERFECT Starter Bike!   \n",
       "Apr 25       GREENVILLE                              02 Ford Excursion v10   \n",
       "Apr 25        ASHEVILLE       2004 Dodge Ram 2500 ^_^Great Body No Rust^_^   \n",
       "Apr 25      FOREST CITY             HARLEY DAVIDSON seats  Ford Super Duty   \n",
       "Apr 25        ASHEVILLE                                      Ford explorer   \n",
       "\n",
       "          price  \n",
       "Apr 25  26500.0  \n",
       "Apr 25  12899.0  \n",
       "Apr 25   9900.0  \n",
       "Apr 25   9499.0  \n",
       "Apr 25   8500.0  \n",
       "Apr 25   7500.0  \n",
       "Apr 25   7500.0  \n",
       "Apr 25   7000.0  \n",
       "Apr 25   5900.0  \n",
       "Apr 25   5900.0  \n",
       "Apr 25   4900.0  \n",
       "Apr 25   3499.0  \n",
       "Apr 25   2500.0  \n",
       "Apr 25   2075.0  \n",
       "Apr 25   2000.0  \n",
       "Apr 25   1800.0  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# vehicles are skewing boxplot too much; all rows at or above 1.8k appear to be motor vehicles. \n",
    "motor_vehicles = postings.loc[postings.price >= 1800.0, :]\n",
    "motor_vehicles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A5gIXAhcBc7sZWetSBjaflpkJfBfYt3v+AcDX+3gNkiRp9VOthyciTgSeA2wcEYuAw4Hn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AHwm9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\nPBEREdF6KXgiIiKi9VLwREREROv9P0dr8X40NwggAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a205860b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "motor_vehicles.plot.bar('name', 'price', figsize=(9,9))\n",
    "plt.ylabel(\"Price\")\n",
    "plt.xlabel(\"Vehicle\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've taken a closer look at the vehicles in the area, lets remove them and examine the remaining items."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>76.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>188.131579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>238.855401</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>89.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1200.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             price\n",
       "count    76.000000\n",
       "mean    188.131579\n",
       "std     238.855401\n",
       "min       1.000000\n",
       "25%      28.000000\n",
       "50%      89.500000\n",
       "75%     250.000000\n",
       "max    1200.000000"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#new dataframe created to remove motor vehicles\n",
    "non_mv = postings.loc[postings.price < 1800.0, :]\n",
    "non_mv.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>RUTHERFORDTON</td>\n",
       "      <td>Vintage Porcelain True Value Sign</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Canon 5D MK II Body - Low Shutter Count</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>HENDERSONVILLE</td>\n",
       "      <td>Wedding Dress</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Antique Oak Cabinet/Bookcase</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Mission Oak Antique Cabinet/Bookcase</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Mission Oak Cabinet/Bookcase</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>SWANNANOA</td>\n",
       "      <td>27\" iMac (Late 2009)</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WEAVERVILLE</td>\n",
       "      <td>Canon EF 17-35mm f/2.8 L USM Lens -Exc</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>Large 6' - 8' Solid Wood Dining Table with 8 c...</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Moe's Home Collection Colvin Sideboard in Natural</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location                                               name  \\\n",
       "Apr 25    RUTHERFORDTON                  Vintage Porcelain True Value Sign   \n",
       "Apr 25        ASHEVILLE            Canon 5D MK II Body - Low Shutter Count   \n",
       "Apr 25   HENDERSONVILLE                                      Wedding Dress   \n",
       "Apr 25        ASHEVILLE                       Antique Oak Cabinet/Bookcase   \n",
       "Apr 25        ASHEVILLE               Mission Oak Antique Cabinet/Bookcase   \n",
       "Apr 25        ASHEVILLE                       Mission Oak Cabinet/Bookcase   \n",
       "Apr 25        SWANNANOA                               27\" iMac (Late 2009)   \n",
       "Apr 25      WEAVERVILLE             Canon EF 17-35mm f/2.8 L USM Lens -Exc   \n",
       "Apr 25      WAYNESVILLE  Large 6' - 8' Solid Wood Dining Table with 8 c...   \n",
       "Apr 25        ASHEVILLE  Moe's Home Collection Colvin Sideboard in Natural   \n",
       "\n",
       "         price  \n",
       "Apr 25  1200.0  \n",
       "Apr 25   900.0  \n",
       "Apr 25   800.0  \n",
       "Apr 25   625.0  \n",
       "Apr 25   625.0  \n",
       "Apr 25   625.0  \n",
       "Apr 25   600.0  \n",
       "Apr 25   525.0  \n",
       "Apr 25   500.0  \n",
       "Apr 25   500.0  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>PT 609 Pro Mag.</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>Car Batteries $5 for bad $10 for good</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Brand New Verbatim pack of 30 DVD-r</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>BREVARD</td>\n",
       "      <td>2 RCA Speaker</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>DELIVERED</td>\n",
       "      <td>Premium Horse Hay!  2017 Production</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>LEICESTER NC</td>\n",
       "      <td>All Man Magazine - 1959</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>MARION</td>\n",
       "      <td>1994 Honda Civic 2 Dr Coupe</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>2008 Kia Sedona parts</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>2006 Chrysler 300 parts</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>WTB Vintage Mandolins, Violins, Guitars</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             location                                     name  price\n",
       "Apr 25      ASHEVILLE                          PT 609 Pro Mag.   12.0\n",
       "Apr 25    WAYNESVILLE    Car Batteries $5 for bad $10 for good   10.0\n",
       "Apr 25      ASHEVILLE      Brand New Verbatim pack of 30 DVD-r   10.0\n",
       "Apr 25        BREVARD                            2 RCA Speaker   10.0\n",
       "Apr 25      DELIVERED      Premium Horse Hay!  2017 Production    7.0\n",
       "Apr 25   LEICESTER NC                  All Man Magazine - 1959    3.0\n",
       "Apr 25         MARION              1994 Honda Civic 2 Dr Coupe    2.0\n",
       "Apr 25    WAYNESVILLE                    2008 Kia Sedona parts    1.0\n",
       "Apr 25    WAYNESVILLE                  2006 Chrysler 300 parts    1.0\n",
       "Apr 25      ASHEVILLE  WTB Vintage Mandolins, Violins, Guitars    1.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_mv.sort_values(by='price', ascending=False).head(10)\n",
    "non_mv.sort_values(by='price', ascending=False).tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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LzIyIc4FZ41U0M6+kmmvUbc5g9fEq25IkqQGjPV3/0oj4s1o7kSRJatlor0XU\nB7wtIm4CHqDMMcrMF9bVmCRJUtNGG4z2rrULSZKkHjBsMIqIDYG3Ac8FrgYWZubqJhqTJElq2khz\njBZRTZK+mmrU6JO1dyRJktSSkQ6lvSAzdwaIiIXAD+tvSZIkqR0jjRg9PHDDQ2iSJGldN9KI0S4R\n8dtyO4CNyv2Bs9I2rbU7SZKkBo105etJTTUiSZLUttFe4FGSJGmdZzCSJEkqDEaSJEmFwUiSJKkY\n7UeCSKrBLh++iPsefHjYbaYfe8GQ6zbbaD2uOv5V492WJE1YBiOpRfc9+DA3nbDPkOuXLl3K7Nmz\nh1w/XGiSJD1xHkqTJEkqDEaSJEmFwUiSJKkwGEmSJBUGI0mS1LMWL17MzJkzmTNnDjNnzmTx4sW1\n1vOsNEmS1JMWL17M/PnzWbhwIY888giTJk1i3rx5AMydO7eWmo4YSZKknrRgwQIWLlxIX18fkydP\npq+vj4ULF7JgwYLaahqMJElST1q+fDl77rnnGsv23HNPli9fXltNg5EkSepJM2bMYNmyZWssW7Zs\nGTNmzKitpsFIkiT1pPnz5zNv3jz6+/tZvXo1/f39zJs3j/nz59dW08nXkiSpJw1MsD7qqKNYvnw5\nM2bMYMGCBbVNvAaDkSRJ6mFz585l7ty5I3525HjxUJokSVJhMJIkSYNq+uKKvcBDaZIk6XHauLhi\nL3DESJIkPU4bF1fsBY4YTVA7L9p55I0WjbzJ1YdePfZmJEk9p42LK/YCg9EENVKgaWr2vySpNw1c\nXLGvr+/RZXVfXLEXeChNkiQ9ThsXV+wFjhhJkqTHaePiir3AYCRJkgbV9MUVe4GH0iRJkgqDkSRJ\nUmEwkiRJKgxGkiRJhcFIkiSpMBhJkiQVBiNJkqTCYCRJklQYjCRJkgqDkSRJUmEwkiRJKgxGkiRJ\nhcFIkiSpMBhJkiQVBiNJkqTCYCRJklQYjCRJkgqDkSRJUmEwkiRJKgxGkiRJhcFIkiSpMBhJkiQV\nBiNJkqTCYCRJklQYjCRJkgqDkSRJUmEwkiRJKgxGkiRJhcFIkiSpaC0YRcSkiPhxRJxf7j87In4Q\nETdExFkRsX5ZvkG5v6Ksn95Wz5Ikad3W5ojRO4DlHfdPBD6VmTsCK4F5Zfk8YGVmPhf4VNlOkiRp\n3LUSjCJiGrAP8KVyP4BXAueUTRYB+5fb+5X7lPVzyvaSJEnjKjKz+aIR5wD/AmwCvAc4DLi0jAoR\nEdsB38zMmRFxDbBXZt5W1v0c2D0z7+ra5+HA4QBTp06dtWTJkjH1uGrVKqZMmTKmfVh/7e6hifqH\nXfgAp+218ZPuYaTHj9VEeA1clBi8AAAgAElEQVR6vYc26vf19Y24TX9/fwOdVNp+Der+fzYabT8H\nbdcfjx76+vquyMzdRtwwMxv9AvYF/r3cng2cDzwNWNGxzXbA1eX2tcC0jnU/B7YarsasWbNyrPr7\n+8e8D+uv3T00UX/7Y84fUw8jPX6sJsJr0Os9tF0/s/6fs5G0/Ry0/f1ntv8ctF1/PHoALs9R5JTJ\nTzp6PXkvA/46Il4DbAhsCnwa2DwiJmfmamAacHvZ/jaqoHRbREwGNgPuab5tSZK0rmt8jlFmvi8z\np2XmdOAg4JLMPBjoB15XNjsU+Hq5fV65T1l/SUl+kiRJ46qXrmN0DPDuiFgBbAUsLMsXAluV5e8G\njm2pP0mStI5r41DaozJzKbC03L4RePEg2zwEHNhoY5IkaULqpREjSZKkVhmMJEmSCoORJElSYTCS\nJEkqDEaSJEmFwUiSJKkwGEmSJBUGI0mSpMJgJEmSVBiMJEmSCoORJElSYTCSJEkqWv0QWUmS2rbL\nhy/ivgcfHnab6cdeMOz6zTZaj6uOf9V4tqWWGIwkSRPafQ8+zE0n7DPk+qVLlzJ79uxh9zFScNLa\nw0NpkiRJhcFIkiSpMBhJkiQVBiNJkqTCYCRJklQYjCRJkgqDkSRJUmEwkiRJKrzAoyRNcGO98rNX\nfda6xGAkSRPcWK/87FWftS7xUJokSVJhMJIkSSoMRpIkSYXBSJIkqTAYSZIkFQYjSZKkwmAkSZJU\neB0jqUWbzDiWnRcdO/xGi4Z7PMDQ15+RJD0xBiOpRfcvP8EL60lSD/FQmiRJUmEwkiRJKgxGkiRJ\nhcFIkiSpMBhJkiQVBiNJkqTCYCRJklQYjCRJkgqDkSRJUmEwkiRJKgxGkiRJhcFIkiSpMBhJkiQV\nBiNJkqTCYCRJklQYjCRJkorJbTcgSWrXJjOOZedFxw6/0aLhHg+wz5Ouv/OinUfeaJj6AFcfevWT\nri91MhhJ0gR3//ITuOmEoYPN0qVLmT179pDrpx97wZjqjxRqRqovjScPpUmSJBUGI0mSpMJgJEmS\nVBiMJEmSCoORJElSYTCSJEkqDEaSJEmF1zEqImLEbTKzgU4kSVJbHDEqMnONr+2POf9xyyRJ0rrN\nYCRJklQYjCRJkgqDkSRJUmEwkiRJKgxGkiRJRePBKCK2i4j+iFgeEddGxDvK8i0j4tsRcUP5d4uy\nPCLiMxGxIiJ+EhG7Nt2zJEmaGNoYMVoNHJ2ZM4A9gCMi4gXAscDFmbkjcHG5D7A3sGP5Ohz4XPMt\nS5KkiaDxYJSZd2Tmj8rt+4HlwLbAfsCistkiYP9yez/g9KxcCmweEc9suG1JkjQBRJsXLoyI6cB3\ngJnALZm5ece6lZm5RUScD5yQmcvK8ouBYzLz8q59HU41osTUqVNnLVmyZEy9HXbhA5y218Zj2sdY\nrFq1iilTpkzY+k30cNTNR415H6dsf8qYHj/Sz9lIz0HdP6dt/xy0Xb8Xemii/kT/ORzr9z+afYzV\nRPg5rLuHvr6+KzJztxE37L66c1NfwBTgCuCAcv/ervUry78XAHt2LL8YmDXcvmfNmpVjtf0x5495\nH2PR398/oev3Qg9N1B/p52ykHur+OZ0Ir0Gv9+DPYf3PwVi//9HsY6wmws9h3T0Al+co8kkrZ6VF\nxHrAfwNfycyvlsW/HjhEVv69syy/Ddiu4+HTgNub6lWSJE0cbZyVFsBCYHlmntSx6jzg0HL7UODr\nHcvfVM5O2wO4LzPvaKxhSZI0YUxuoebLgEOAqyPiyrLsOOAE4OyImAfcAhxY1n0DeA2wAvgd8Obx\naGKXD1/EfQ8+POw204+9YMh1m220Hlcd/6rxaEWSWjfc+x0AFw7/fiitKxoPRllNoo4hVs8ZZPsE\njhjvPu578GFuOmGfIdcvXbqU2bNnD7l+xDcRSVpLDPdeCNX73UjbSOsKr3wtSZJUGIwkSZIKg5Ek\nSVLRxuRrCYCdF+088kaLhl999aFXj08zkiRhMFKLRgo1I02AlyRpvBmMpJZ5mrQk9Q6DkdQiT5OW\npN7i5GtJkqTCYCRJklQYjCRJkgqDkSRJUjFhJ19vMuNYdl507PAbDXMNnU1mADgpVpKkdcmEDUb3\nLz/BD5GV5IVG1ZMihvqs9cdUn7Gu8TZhg5EkgRcaVW/qDj1euqM5zjGSJEkqDEaSJEmFwUiSJKkw\nGEmSJBUGI0mSpMJgJEmSVBiMJEmSCoORJElSYTCSJEkqDEaSJEnFhP5IkBE/7+zCoddvttF649yN\nJElq24QNRiN95oyfSyNJ0sTjoTRJkqRiwo4YSZKkwUXEiNtkZgOdNM8RI0mStIbMXONr+2POf9yy\ndZUjRpIkTXC7fPgi7nvw4WG3Ge6Epc02Wo+rjn/VeLfVCoORJGkNgx1GiRPXvL8ujxhMRPc9+PCw\nJxwtXbqU2bNnD7l+xLO81yIeSpMkraH7kEl/f/+EOYwiGYwkSZIKD6VJktQy5/j0DoORJEktc45P\n7zAYSZKk1nWPmt184r4jPmb7Y85/9PZ4jZoZjCRJUuseN2p2wpqT/JsaNXPytSRJUuGIUeF1OyRJ\nkiNGhdftkCRJjhhJkia0TWYcy86Ljh1+o0Uj7QNg6LPK6u5hrPX1GIORJGlCu3/5CWM6VR7GPvF3\nrD14uv748VCaJElS4YiRJEkTXC8cyuuFHsBgJEnShNcLh/J6oQfwUJokSdKjDEaSJEmFwUiSJKkw\nGEmSJBUGI0mSpMKz0iRJUk8Y8cyyC4dev9lG641LDwYjSZLUuuFO1YcqNI20zXjwUJokSVLhiJHU\nQyLi8ctOXPN+ZjbUjSRNPI4YST0kM9f46u/vf9wySVJ9DEaSJEmFh9IkSVLPaWtqgSNGkiSp57Q1\ntcARI0mSekAvXMNHBiNJklrXK9fwkcFIkqQxjdaAIzbrEoORJGlCc7Sm4qG8isFIkqQJznD4GM9K\nkyRJKtaaYBQRe0XETyNiRUQc23Y/kiTVJSLW+Lr5xH0ft0z1WCsOpUXEJODfgL8EbgMui4jzMvO6\ndjt78nZetPPIGy0afvXVh149Ps1IknpK9zV6li5dyuzZs9tpZoJZK4IR8GJgRWbeCBARS4D9gLU2\nGI0UavxPoCYY0CVpTbE2fChlRLwO2Csz31LuHwLsnplHdmxzOHA4wNSpU2ctWbJkTDVXrVrFlClT\nxrQP66/dPbRdvxd6qLv+UTcfNS77OWX7U8ZlP4NZ11+DtaGHpuv39fWNarv+/v6aO3lMLz4HTX7/\nMPbnoK+v74rM3G3EDbsvr92LX8CBwJc67h8CnDLU9rNmzcqx6u/vH/M+rL9299B2/V7oYaLX74Ue\n2q7fCz1M9Pq90EPb9cejB+DyHEXmWFsmX98GbNdxfxpwe0u9SJKkddTaEowuA3aMiGdHxPrAQcB5\nLfckSZLWMWvF5OvMXB0RRwLfAiYBp2bmtS23JUmS1jFrRTACyMxvAN9ouw9JkrTuWlsOpUmSJNXO\nYCRJklQYjCRJkgqDkSRJUmEwkiRJKgxGkiRJhcFIkiSpMBhJkiQVBiNJkqTCYCRJklQYjCRJkgqD\nkSRJUmEwkiRJKgxGkiRJhcFIkiSpMBhJkiQVkZlt9zDuIuI3wM1j3M3WwF3j0I71194e2q7fCz1M\n9Pq90EPb9Xuhh4levxd6aLv+ePSwfWY+baSN1slgNB4i4vLM3M367Wm7h7br90IPE71+L/TQdv1e\n6GGi1++FHtqu32QPHkqTJEkqDEaSJEmFwWho/2H91rXdQ9v1of0eJnp9aL+HtutD+z1M9PrQfg9t\n14eGenCOkSRJUuGIkSRJUmEwkiRJKgxGal1EbDbMuj9rshdJ0sTmHKNhRMTLMvO7bfdRp4h493Dr\nM/OkBnq4HPjLzFzZtfxVwMLM3K7m+n8GbJ2Z3+xa/tfALzPzijrrl1qbZuZvh1j3rMy8peb6TwOe\nlpnXdS3fCbgzM39TZ/1eEBEfy8zjWqz/puHWZ+bpTfXSKSKeA8wFDsrMmTXXejdwX2Yu7Fp+FDAp\nMz9dZ30JHDEiIiZFxNyIeE9EzCzL9o2I7wGfbaiHvoj4akRcW77OiYjZTdQGNun4ek/X/U0a6uEL\nQH/55QxARLyxLN+ngfofB5YPsvy6sq4JSwduRMTFXeu+1kD9U4DBrgg7DTi57uIR8Z8RceoQXwtH\n3sO42KuhOkP5s0G+Xgx8FDi1yUYi4pkR8c6I+CFwLTCJKhzV7e+AMwZZ/h9lXe0i4k86bm/QtW6P\nJnootZ4fEZ+MiAvK1yci4vkN1d50mHXPaqD+KztuP7tr3QG115/oI0YRcRqwHfBDYHeqjxJ5CXBs\nZtb+Cyki9qEKYB8BfgQEsCvwfuDIzPxG3T109PLjzPzTpup11T4EeC/wKuANwNuAvTLzpgZqX52Z\nOw+x7qrM3KWBHh597rtfhyZel4i4NjN3GmLdNQ2MFLx2kMXPAt5JNVIwrc76pYergNlU/wcfJzPv\nqbuHjl4COBg4hiqgL8jMnzRQ961UAWgacHb5+npmPnvYB45f/eH+Lw65bpx7+FFm7tp9e7D7Nfbw\nEuCrVH8c/pjqZ/JPgbcCB2TmpTXX73wOLs7MOYOta6h+46/B5Dp3vpbYDXhhZv4xIjak+hyW52bm\nrxqq/0/A/pl5VceyK8vhpVOAxoIR0FpKzswzIuIhqjeBW4CXZebdDZXfaJh1GzfUQw5xe7D7dVjv\nSa4bF5n53wO3I2IH4DjgFcAJQFMjRn8CXMHgwSiBHepuICImA4cBRwM/AF6XmT+tu26HfwO+D7wx\nMy8vPTX6vhARUzPz193LmmxhiNuD3a/LB4G5mbm0Y9nXIuIS4Hhg75rrd36fWw6zron6jb8GBiP4\nQ2b+ESAzH4qInzUYigCe0RWKKL38pOE3g9ZExNVUv3gCeCqwFdWhtQAyM19Ycwv/GxELgPdnxxBq\nRHwYuKTm2gOeXuZXRMdtyv0RP/RwHNwQEa/pHqGMiL2BGxuoT0TMAOZT/WX8ceBtmbm6idrFdW2N\nmAJExBHAO4CLqUZLx/pB2E/GNsCBwEnl/edsGgjGHT4OXBARR1ONoAPMAv4V+ERDPbT9RwrAc7pC\nUVU88/8ioomLHLb9HLRa32AEfxIRA0PUATyn3G/ql/IDT3LduOgIJQDP7Xoumvj+AfZtoMZwjga+\nBKyIiCvLsl2Ay4G3NNTDF3lsTlfnbUpvdXsXcH5EvJ5q1ASq0dSX0MDrExH/Vep9ovTyCLBplY2b\nPYzVolOAO4E9gf8Z+N5p8P9iZt4FfA74XERsR3VY+86IWA6cW/fk9Mw8PSJ+QzW1YODw7TXA8d0n\nR9RoWkR8hup5H7hNub9tQz3cP8y62n8v0P4fajtExHml3sDtgfq1H9Z1jlHE9sOtr/uvtoi4F/jO\nYKuAPTNzi5rrt/r9lx6eC0ztPgMwIl4O3J6ZP6+7h1JvB2Bgns21mdnISMlIIuKdTZyNUyaavpHH\nfiFdC5yZmQ81UPsmHgvoA6OHAzIzmziMdVhmnjbEuu0beC9o/f/iUMqk34My88Nt9dCUiDh0uPWZ\nuaiBHu4Elgy2Cnh9ZtZ6NCEijh9ufd0/BxHx5yPU/79a6xuM4k8y8/pye4PM/H3Huj0amOTW6g/A\nUCLiZVTzDI5ooNb5wHHdk0sjYjeqvxT/qub6w55lUfep8iOJiFsys/YzQfTopNdtge9k5p0R8ULg\nWODldV82oqOHZ1MF9ASWNx3QI+LpwBEdPVwH/Hv3vJ+aan9wmNWZmR+tu4fhRMQnMvM9DdRpPZwN\npak/1Iapf1ZmvqHWGgajdme/R8vXr+mq9yKqEYPXA78AvpqZpzRQd8iznpo4E6VrjtOApBoyfnpm\nTqqz/kgi4tYGruX0C4Y+dp+Z+Zw665ce1qc6E6vzF/KZnX+s1Fz/X4G/Aq4EngucD/wj8DHgC3WP\nnJVTpL9EdUjxSqqfx12oDm3OG+p9Ypx7eBlwJnAaj01E3xU4FDi4e1S3hvpHD7J4Y2AesFVmTqmz\n/kh64Y+UJkYvR6jf6nPQRH3nGLV/BsJSqjeex50WSXX9mrqD2fOAg6hO0b0bOIsqMPfVWbfLhsOs\nG+6MsXHRHbwiYjrVadJ/QfVLsW1N/PWyW9f9p1AF5PdQnSlYq4h4AXAe8F0e+4U8G5gfEftl5rV1\n90A1l+pPy0kYWwC3U52xekMDtQE+QxUGDxo4IaScgPABqkt6DHsByHHySaqzZDtf869HxLlUp47v\nXmfxzPzkwO2I2IRqMvqbqQ4rfXKoxzWoqbPShh29pLrETFsaew7aYjBqf/Z926dFXg/8P+CvMnMF\nQES8q4G6nS6LiLdm5hc7F0bEPB6bCFy7iNiR6qyo3anehN+emQ83VPt+Bv95GzhTr1YDl0aIiKcA\nh1BdRuJKYJ/suhp2TU4B/iEzv925MCL+gioUNBHUHxwYFcrMlRHx0wZDEVSXqDisc0E5S/IjEdFU\nH5t2haKBPq4sQaV2EbEl8G6q0cNFwK7ZdVX8BuoPuoqGQkFEfJwqqF8JHFOmGwyMXjZyocth1P57\nMSKGGhAIGjhL0mDU/hkIbQez11KNGPVHxIVUf5k1/RfBO4FzI+Jg1jwjan3gb+ouHtUVz+dTHcL5\nV6rDFo/UXbdTZjZ1lfFBRcR6VG+47wKWAfs1Nem92LY7FAFk5v9GRO2Hc4vndJ39Mr3jPpn51zXX\n74W/xCMitugOIiUs1P5JCSUQHEB1peudM3NV3TUHcQWPP7Q+oJE/lKiu+N/a6GXbf6gx/Ojg9XUX\nd45Ry5PcIuI24CSqH7h3lduU++9scMLnxsD+VIfUXkn1l9q5mXlRE/VLD310nBGVmY1cQygiHgFu\nBS6gOk18DZn59ib6GEoTx9TLz+Fq4NNUF9hcQ2Z+teb6P6P6Rfj7ruUbAldn5o511i+12j0TJmIR\n8HPgo5lrXE/rA8DzMvOQOuuXWodTXV35Pax5HaETgVMz8ws11/8j8Huqn8XOX04DlywY8qMqmhAR\n22bmLxuoc0Vmzuq4f2VmvqjuumuDiNg9M39Qa42JHoyG08QZCG2fFjmY8tfhgcAbMvOVI20/jnX7\n6Jh4m5n9DdXt2TNAoLHJ16cx/OTrWofvI+L9wB5UH4NzU1k2nWrezeWZ+ZE663f1siHV5OsEft7E\n5QpK3U2prvK9K9UhlKS62OWPqUYx72uoj32pPp7n0UtXAB/PzP9pon4va2ri8SCXcXlF5/0GRi+H\nNBEmXxuMhtEDPwAbZ2atF/OKiE9TTXj9XhN/CQ3Rw7ZUnwv0EGueCbMR8Ddt9dUr2v45bEpEHEn1\nC/mpVD8Dq4BPNHFmZKk/mcfmcNxMdehoGvCfwPy655sNnIUa1afZv4DqObi24UOarep4P/puZt7e\ndj/dmvgjpdTpycu4QHPPQZv1DUbDaPA/wbbAM4GfZOYfynVE3gkclpnb1Fz7SOCl5Qvge5SgBFw1\ncHZMzT2cS/VBlad1LX8T8NrM3K/uHtoWj11Z9nGrqH4pDzUhdJ0zMMk3M4e7+m8ddT9FdcXxdw3U\nLqM4n6CamP2Omus38gGlI/TQ6nWEeuH9aDht/5ES1dXID8rMj7fYQ9vPgSNGdRvhDISrsuZP9Y6I\nd1JN/F0BbACcTDXP6HTgXzPzjjrrd/XyTOBlVG9Kf011DZ/aj+mXs3+e/0TXrUt68ZBqk4YJhgBk\n5knDrR+nHm6gmsuTXcsnAdfXPc8pIn6cLX5WW+mhZ64j1OL70SkMPfH40KbnOUXE1lRTG+ZSnRB0\nbgNTPFr9Qy0i/oehX4NXZmatH+7tWWnDn4HwhwbqHw48PzPvieoKzCuAV2TNV9zuVK6VsjPVG9DL\nqIbxVwBnNNTCoBdQLKeOt3pxxaas68FnFFo9K6/I7lBUFj4SzXzC/LYdZ8U+ThMnAfTCdYR64P3o\n8ie5btyU5/5vqC64+zzgXGCHuv9Q7zDc/8eTG6g/3AcG1/5hwhN+xKht3cPnMcxVoGuq/21gU6rJ\nnpcCl2bm8qbqlx4+BUyhOgvvgbJsY+BTwEN1/0Jo+/BB6WGov1IHmmjlzLiI+EvgvZn5l23ULz3U\nPteu1Pka1dXeT+9a/rdUn09V64TXiLgZGPJnsamTAAa5jtDJTV1HqBfej3pBRDwI/BB4P7AsMzMi\nbswGPjNQjhgBj0663Bv4k7LoOuBbmbm6gfLTuv5KfHrn/QZ+Id5I9bEDO1Jd+fquiPhNVp+y3ZT3\nAv8C3Fx+OSSwPdWbcq2f5l0M9kv3qcBbgK2AJj6fqZG/RIcSEa8EPg9sQ3XF9Y9RHc4NYEFDPQw5\n1670VbcjgK9GxN/x2Ejyn1FOAmig/t09cAZk29cRav39aJjDOEBjZ4QdR3V9uc8BZ0bEWQ3UfFTb\nf6jFYx/TNFT9F9Zaf6KPGEXENkA/cAfVabFBdYrsM4C+us+M6JVTxcsk0z2ohq/3oPqcsGsyc9j+\nxrmHjahOkw5gRWb+rqnaHT0MHD6YB5wNfDIz72yg7uuA85s6NXyQ+j+muo7W96n+SDgd+EBmNjFs\n3mtz7V5Jdar6wFlhFzdU99LM3KOJWsP00BPXEWrz/aiXzgiLiB2o5hYdRBUWj6eaY/Szmuu2fX2/\n7UeoX+tnxRmMquu3XJldnxYcEW8HZjUZDLpFxOSGRq2IiA2o/joemOy4B3Bn1vwBriP01NhhnDYP\nH5T651I99xcCi4GLssGrbw9ySPfn2cAHx3bUuw7Ys+W5dgdkuZBlDHL154Z66B69Xg5c2NT7QK9o\n8/0oIt4DnJWZt9Zd64mIiJ2pQtIb6v6/2QN/qH2W6gOkv9dG/dov8b4W2KM7FAFk5meo/jPWKiKW\nddzunlz4wwbqfyoifgD8CvgI1aS7L1BNCG8kFEXEKyPiZxGxKiK+HBEviIjLgROohpLrrv9x4DLg\nfqrDBx9q+pdiZv4N1WjZxcDbgVsj4nMR8YqGWtg8Ig4Y+KKaA9t5v24PZeY9AJl5C/CzJkNR8f6O\n242MEnUqo9fXAkdTHTrcluoz664t61oRERtHxMERcUEDtQbej+6gpfcjquf9exHxnYj4h3JWWKMi\n4rMR8bLOZZl5dWYe19AfLAcDt0TE6RGxdzkzs0k3AJ+MiP/f3plHW1YWZ//3MCgzSIyKzShRMRAJ\nKgLiUhAREsWIgHRDNBjUqLAEDIifiiYSVCIIQZQsjFFMZFRAyOdAGB3AAYSmQRSVFgVEIiIyOAWe\n7496T9/dh3sPfNrvu7d96rfWWb3P3n1v1T3D3rXrrarnB5KOltR06ndmjCa0yLZon+3amOWuvYX9\nNxEzQq5umaEY86HvZZxBLB+M+fRHwJ6EcOS6Xv4nX99BdD6NmN993qL4fOy72Lx1fkjZa0mPAv6S\n6IraFfg0UZhedfr1EM5HxQ8R06bnA38FLCQyuee4wXwtSQcV2+sBZwCn2b6mtt0xH9YiauvmE3Vf\nnyl+fHHiDy5bHzYq9ucDqxDvwenVlxIzMNJNhC7QQw4RtQ21U5ZLgqFZAqMmA9/KSXBfOnIcRBrz\n1xN/cNnZ73UZZ2goRCP3JNLmTwY+bfvgfr2qS981DcWHbxOv+QrAfxJBwZIxHra/OcePLjP7tjeb\n41iTeV5l+XoBsAtRe3kG8EHbG9e2PVRKtuSFRAb7qbZbiKiObPcSGMziR9MbtTl82Ar4d0JMt2oG\nK7vS4DJgtzmOtYiM15G0O3EyXqezbCFg7drGJf0pcB4xXXYkx7ED8HZJL7X9rdo+sPTfXdyaee76\nAqYTh5WNlngq+7AmMyK+zyDek38CLnGDuxdJx4+CL0kHdbN1kj5ue7+a9ucKfBS6ZXN9P5c1P2ZG\nxPn2zjbEDUNt3cBfTjjWqhHhC8CXiHqvxQCSmmRuh0ip65kP7E10ybXokl1CKTI+Gji6Exi8i4bz\n3cqN2suJ12BdInvYyvbKRMZyPrATcb2uPvNt6jNGfSPpY5OO2351ZfsXAe+z/d9j+19ITDjdsab9\nYmvSa9BiGWcxcw/5dIvZIZJ+SlyUTieKbavqcs1iv/fMZcfeisCLmMlcfMn2nq3s90Xf2eviw1bE\nRWhPonX+dOCdtid2CS1PSHoy8RosAB4gXoPTbN/Ugy+zBQan2T63st3ZbtROp92N2ihz+WKi1vZ0\n4Fw3mGcGGRih0OOaC9uuOm1V0uNt/6SmjYexPyl9f4Ptp7X2aUhImucGIraSVnMP4wk69uesr2m4\npPs8YvlqdDLcnpj229vr0pK+b5LGKcW/C4A9iIGL59g+uaUPfVAC1NGS1aKefOg3MOj/Ru0S4j34\nVIuM/UPsZ2Ck2ZS7RaTv59muutwo6XZgEfEh+LTtu2vam8X+jUQn1q/H9q8CLHJlfaiho0aCieVE\nMKn4eafK9hcSS6grABeX7VEG7RLbW1a2fwvwQ6IL8Vzb90habHuTmnaTh0chzbMzIV7aNDjrA0lP\nc5m2LenR3XOjpG1bdEuW88GpxDWhfWAwdqNWMldbALe6wVy3CX6tAxxgu+rQ2akPjLqUToR9gcOJ\nAuSjbF9b2eaosG8+0QVyBREknWd7Us3BsrL/DmIswYG2f1D2bQycAFxp+921fRgykn7UotBQ0jNn\n2b0tMRX8DttbV7b/A+BBelpOLHUsLyNuEk4lOmAWtVjGHBLlfPAYl0nPpTFiP+CQFtlbxQypOXGM\nUliuGcKysqSdXAaLStpkVO9Vni+Zt1XR/r8SRffXS1qbuC49QNQYHWr7tMr2NyDGZ8wjJvGfSigQ\nvJJYSjyoqv0MjEAxVG0/Yn7I14D32v5OD348imhXnw/sCFxke98Gdg8kLsCjbov7gGNsz5ZNq2G/\nyV3Y70KrjNGYzecDRxAToN9j+3MNbG7kytNkH4EPIj73C4ibhLWICeSfdSNpCvU4YFHSfGJmz33E\nHJd/IIRTvwEcWbsrrvgwkmLoBsgmJk8/rnY30BB4mGXlJmMc+g7OJF1ve/OyfTCwg+2XSXoC8Lna\nr0HJmF1GBGS7EvVV142A06IAABcBSURBVBM3CLfXtA3ZlYakAwgJiIuAXfu8ODj0ob5FnIyfSahK\nV6XcfZwInFgK7nCDOR1jfJgo8OsFza0LJGCdhn7sQgREvyKylZe0sk2od/f2HkCkpYhlvItL6n50\nk/BhoPqQPc0uD/QSYtBcdXkg4g75mba/J+kZxEVhvu1zKttdgseGKJbs8eFEVvs9rfzoGc+xPdvz\nWmiO7dme1+A3ne2dgbMAbN8e9y/VWdf2P5TtL0j6CbD1eMlHLaY+MAI+CNwBPBc4v/Omj4b7VRWr\ngyXp672JO+XViYK3v3IbVel3AGdDLwHRUJgk4NpE3FXSN4i78vcTF0TKxRGoP0OHNifbR0wp9jwP\nOE+hodeC9wAnefYBi+8Fag9Y/I3t70G836XGqllQ1KV0Zr0d2AY4FnhT6wLcHhkJe4ulRb5FLO20\noO/g7OeSXgLcSjRB7A9LMqpNvo9lTMDovHQ7sJqk1aH+CJUMjKDX4k5JlxNftk8Br7Pdq8p6TzxJ\n0nlzHXR9NetrbC+c7YCkN1S2PeI+4F6iTXq8Nb3FDJ15nQvAQ3B9Ne2Hq+WrfoNCyAPtN77T9gmS\nWiytP07SmzvP1+g+t/2BWX5mmSJpCyIg2hz4Z2B/9ziBuicO62yPn49bnZ9H50Sx9PlRtLlm/R1R\nZ/oE4ODO8tVOQHVpGGKG32iu3ojRzaGBujWPWWM0O6VVdR/bB1S283zgiy1mQ8xh/35CsPMhh2iX\nMfsu8Jq5jruymnVpz93L9lVj+/8R2K1FsWXfSLoZeOdcx11fTfsa4oR3KnA+Y8MOWyxxT6ofaVFb\nIuldk47brj/YTnoA+BFx8XtIQFQ7QE6Ccl2Yk9rnxGknM0YdFEJ1+wCvABZTlpgqsyewx1zrtg1O\nRItpN1l4Lu7p+Yu+F3CWpH1tX1GKgE8CnkK0rTdB0uOAA1hamuVDjdpj76wd/EzC9p9L2oxYTj6V\nIksDXNCi8LmwtmYXzBVRCF6VFoHPI2B/2tXRDBJJ5zPhNWiQwZ4Y+GhMXLYGks60/YqyfbTtwzvH\nLrD9osr2/9r2f5bt7W1/pXPswFIXW8/+tGeMJD2FmSmndxLaQIe60aRXTdaIsu1PVLbfXCxzFh/O\ntj2rgruklVvUNkh6OlGAfADw2rJ7ge3fzP1Ty9T+9kQg8HFmUsjPIOpa9u2eGCrZ/6rtbWva+P9B\n0t7Ah4Cjbb+/kc3eByxK+gvg/xCNF6Pg+Gjbn61tOwk62RoBH2Esm93iJq6MbXgFUWbxedvXlZqf\ntwGrNshe9i1u3mtXXmaM4NuENtBuo8JHSYe0Mj7pLl3SMQ1cqHrBfSSMB0Wdtu19iGzW42vaV2il\n3UIEIecCFwIHEjUeTbTSiALXl9m+urPvM5LOIVq4t6lsf76ktV0GjErakZgrdDNwYosAUdI84iZl\nd+Au4BAiWG3CpMBH0h617Ut6LVHb8RZmalmeBbxP0vpuMHW6BIeTBo3uX9uHvukGPpLu7Smb/VFg\nA2Lq9QllqXs74K2uLAdSmJQxaZFN6bUrLwOjGHc/H7hE0ueJjrChdOi8gtm1k5YlS5Yp1IN4aBdJ\n2xDB0O7EILEDWLoQshZXMTO75R4iCPl6eV690K+w1lhQBIDta0ZjFCpzBvG6312WlM8iOrG2JNrl\n56wBWxZIugxYEziTmCk2CkYfJWndRsHpJI6jvnjmIYR4a/dvvbhkkb4MtJDj+K9Z9m0IHExD4dIB\n0deSyrMIFfkHFSoEPwX+pMUMn8JqCt28FYBVy7bKo0VXWq9deVO/lDaitAGORPNeAJxCaANd0KNP\n1acu952yLHaOIoLAHxJTv88hpm5PjRyEpBuA59i+a2z/usDlnkPPbhnav3ZUaF8ylQ/afotCDuKa\n2kX4isnbo5NR96Q0agLodQJ2o+/inNqEk45V9OdJxNLN84jA8KOtlpb7pHznRlzC0vI4TTLIfZ2L\nO/YuZXKdVVVx8U5TkIBNmWkQEqGfuHpN+5kxKjjE+T4JfLJ8MfYE3gpUDYzGvoRLHaJN5mpSyrIV\nrwO+QxQ8/5ftX0lqFrFrGDIIxwEXSDqUmbbUZwJHl2O16b73LyDqXCh3rNWN2964upHfjxafx19I\n2nJ8dISkLYlMZhMkPY1o2d+KmKv1+oYF8EOgm0GGme8jtMsgb9YZYSFg0/K8Sbew7R1q/v5HQK/i\n5VMfGEnaGnisO7ILtn+mEHdtvYwzTouBaiuUQVordLZHvrRKnT8BeBGRrTteMQ5+VUkrNToh/18m\nyCDQ4HWwfbKk2wg9oM3L7uuBf7J9fm37xJLNmcTU58cQE6iRtB5LT8FdbtGMHMZDDlG5zq3w98RA\ny48xc17Ymqh9++sG9pF0FrGMcwyxtPcAsNYoOB7AkmZ1BpKp7jUwkPS8Scdtf7GyC4cBp9q+vLKd\nWZn6pbSSMtzPRUC1s/9PgJNt1x6s1yvqWTx0Fn9WIWQYFhDTyC+yvU9jHzZmRgbhBDfSjOuTUvC+\nN7AecKbtW8v+rQiNrC/06V8LJE3sRG00S+nxzIxsEBEcf6hVbckcS5qjc0PvS5otKJ+Dn8/SiPAD\n4r3o5UZB0mOJsRr1a2xiZME4JmoO13dlzTxJBxG1v+sR9Y+n2b6mps2l7GdgpEUe0wfqHFtoe8vK\n9nud1zBkJK0FvNb2sY3sjcsgnNJiVECx/VrgUtvfLc//nWgMuJkI3KtKgkjazPa3y/aj3dEk0oBF\nfmsgaRNmZkndYPumRnY3bLRsm0xA0teA3W3fVhoRLiQaEZ4O/NZ21UaE4sO2wPuIJoQjCTHhxxKZ\n/VfZ/nxtH8b8eS5xbnwMoePYIos9ClLnl8cqRA3q6bZvrGrY9lQ/gO/9LseWof1vzrY92/NpfAA/\nbGBji/KFu5ZYslixh7/zOmDlsr0PsZTyR0TW6ksN7E/955AY4ngmcBMx3PWcsn0W0TXY8j34dN+v\nR8eXTYmL4nV9+9Lo7722s30M8M9le4Xusco+XEmUF+xFjK7YtuzfDLi64WuxE3ApUYS+c8/vy1aE\nuPMDtW2t8DtFU8sXF0o6SmMVpgo5iIsb2O9bRXnotHgNFhIzQr4EPBs4TtIJo0cD+wD/65ns1EuA\nT9i+0/aFhLBwbfJzGNpQ3yLaol9ue3ciKFgEtMjcdl/nvrvw1pN0sKSvE8t5KxHL29PAeCPCRRCN\nCLT7Lqxk+wLbZwG3u2RsXbK6tZH0YoWO56HA223vaPu/W9ge82NlSbtJ+iTwOeBGIpNelakvviYK\nHv8N+J5CrwliHfVKKs9uKfStojx0WrwGf9vAxsPxYCl0vou4Szuqc2y5nxsyELb32Nwux63quxV6\nfrWZ9B40oSzpLgDWJ7JnrwE+42HIlbRiUiPCrxr58GBn+5djx1p8Ns4nht7eCRw+3pnqyrIoknYm\nPocvJmbKnU6IrN9X0+6IqQ+Mygu9oMzsWNIN5EZ1Bcy0ZXZbMinPl/tCR+i/G8hzTB8vheCtdOTe\nSQTjKwLn2b6++PB8YjmnNuuX7Jg625Tn8xrYHwJ9Z8a2lPSL4seqZRtmWrSr67URMixXEALaVwK0\nHJ0xEA5mphHhuZ1M7pOJwbMtmPRZWKWB/apzih4BbyMkkg51D52QU1983TdD6ITpmyG9BgqNotHo\ngF2I+p49G9leCVjTnSGPZfAote+UNFmzb87gcXlC0inA94Ej3TkxSjoCeIrtV/bmXCNK59NexOf/\n8ZRJ5K483HKoaBZhcU9Hl+pbgGNtP9C3LyPKuXB3QsPyxVVtZWA0DCStQ9yRANzo0io6DQyh66nM\n7diHmdTt9sSE1ft79mtn4C22d65sZxUiKPufsf2PA35hu9USQm+ULsiPEuK91xBZzFHB5/59fSfL\nueEA20c97H9etnbXZ0ZgezVCCeBtLX3oA/UsLD4EJH2IOAce4MoC1g/jx6OAvyTOzbsSsjxnu3JX\nXAZGPVPe+JOJORmLiVTpRkRHzOs9HSP4u7IkV9jerrH9Wwg5kpOAc23fI2mxGw56k/QC4F+BJxJC\ntu8BPkF8Ho6yfXZl+ycTKt5nj+3fl1hOeENN+0NC0qaEur2IZfXvN7K7AXAEM5+BU4lW7VcRw+4O\nauHHHL49FZg/DbVGkh4kGjH294yw+E2eghlOXSQ9A/ggIbR+Ep26J9cfHzKqMdqF6Ig7A/igG03I\nz8CoZyS9m+h8eb3te8q+NYm1/pttH9Gnfy2QdLXtrca3G9r/FyIwXURcjD4DLGp5IpR0NTFp+Arg\nL4ig6Ah3RH0r2/+W7T+d49j1tjef7Viy7CgT3y8jPgO7EkX41wOHuN2Ax4WEYO3lwFc8Nvh2GpC0\nO5Exeg4wEhb/t5Y3SkNB0g5ElqZbB2pXHnzcCU73s7247GsWnGZg1DOSrgOePb5kI2kN4Ku2t+jH\ns3aUk/EOxJyQi+lHtFFEweECInW7FrA/8Fnb9zawPy4a+X3bm9a227E3KAHTaWR8oKyknwAbujNs\ns4EPWxABweixOhEkjQKlr7fypW80QGHxVpQl9GOJBqA3eky/r4H9rYjgdE+i+eR04J2tljMzMOoZ\ndVTNZzk251Tu5QkNT5ZkZeKOfQHwItuPbWDzJmJmyIhjus8bLKVdBhw2fuFTaAkea3uidlLy+9O5\nQRh9D5ZSdu+lOyeKsecTnVqbuLIUxFBRiH3vBexdO1syBMr56H3AR9xzkCBpe+JcvAdR+3eO7ZOr\n2szAqF9mORl2ucSVJUmSyUh6nusLJiLp48w9n8S2q85akvRsogPp48TUbQgx0VcRtSVfq2k/GcYN\nQunK3IrIFm1PLPPfSizvXWH7sto+JP0j6Y/HGzHK/g2I88H7e/BpBWBnIjitez7MwKhfhnAynHbK\nxeAVxLyez9u+TtJLiFkaq7aueeoLhYDpGwmJFIj6lhNt39GfV0lLJN0H3EDUOF46qu9IppexEQ7z\niIzNoZN/6ve22auGaAZGydRTsjUbEG362xDCrdsBb7V9biMfjrd9cNk+qFt0LenjHpvInCx/9H0x\nKHYWEJ/9ZwIPAN9gJlt0a237yTAoDUC7E23yTyG6pPe2vX4j+91O5fH6y6WeV7GfgVEy7ZQC+Kfb\nfrDM8/kpoZfVpBOo+NDviWDy9HHPVQeXLDv6/gzM4s9qhHbg9sB+wKOmaZbPNCPpl8SN4juAL9t2\n066wCZ3KLTqXp14SJEmA3zgEIrH9K0k3tgyKCpNEXFvwkh5sJkszCCHf0o21DTN1RlsDPwJ6G/SX\nNOdtRNH9ScCpks5obL9X7cYMjJJkRq8OWEqzrmW2ZAVJjyFGFoy2RxfDFp1Aq7ood0t6dLdFXNK2\nxPJiUpfehXzLPK0NmVlCO5YYG1J9ZEUyHGwfBxyn0BBdQAwcfaKkw4kaoxsru9CrhmgupSVTjwag\n1dZ3Ef7QlnGmEUn3A9+jXAzKNuX5k2yv3sCHpxPDTfPCMMVIOpgY9HmN7f8t+/6MCJL2rj1jTdIh\nxf5dwG/Hj9c+J2fGKElgPfes1dZq1P0EBrGMM+UMYYjmRsDdlAyhpHcS82NuBg7KLrWpYX3gBGYy\nN5cTS6nHuI1e3jzgX4DNgK79K5oM/M0bg2Ta6VurrePHSoQcyGZl17eAL4zu2CrbzoxRQrkIbmv7\n/jKy4gNElmArYC/bu/TqYNIUhZbns4h6s+3K4+eeQz5oebGfGaMkWTojskovDkhPJCYd/5hQcxdR\nEP0BSTvavq2yC+tLOqHYHW1Tns+rbDsBJN3D5M7AtRq44Y480cuBj9q+CrhK0hsb2E+GxaqEPNLa\n5XEboZu2XNvPwChJJhc+t5JieA9wku3juzslvQl4L/A3le0f1tm+cuzY+POkArbXHG33IaY8Y1pr\nAPcTIrYf7hzr5aYhaY+kk4HNgXuArxFLWR+wfdc02M/AKEniTuQqZoKhb3aOmQZdEMTyxX7jO22f\nIOk7tY3bPmWuY5KOqW0/eQh91TgcT+hR/QK4wfaVsETU88c9+ZS0Z0Pg0cB3CUmYW4CfT4v9rDFK\nkgEwKUPQY/ZgZP+Htjfsy/400mddl6R5wOOAhaP5XpLWA1a2/cM+fEraI0lE1uY55bEF8DOiAPpd\ny7P9zBglyTBYW9LLZ9kvYo29T7IrrQFj7/86458H22e38KNIf9w6ti+zRVNGGdlwnaSfE52KdxN1\nj88GqgdGfdrPjFGSDABJH5t03ParK9tfd65DROagiUbSNPMwnwHXVhRPkhGltnE0+fy3lFb58u+i\nUSZxubWfgVGSJJIWE3UtvQyYTCJj1CorlCSTkPQByuygPrKFvdvPwChJ+kfSqyYctu3/aOZM0gs5\nLypJhkHWGCXJMNh6ln0CdiPmCGVglCRJ0oDMGCXJwCjdGPsChxPTr4+yfe3kn0r+0OlopT3kEO3E\njJNk6smMUZIMhCIJsh/w98RQsz1tV59hlAyGxUSGMEmSHsnAKEkGgKQDgIOAi4Bda6tHJ4PkN/m+\nJ0n/5FJakgwASQ8CdwD/w9JTj3MZZUqQdKLtA/v2I0mmncwYJckw2KRvB5LeWSzpzZ3nBn4KfNn2\n4p58SpKpY4W+HUiSBIBVbd9cllJuH22X5+v17VzShDWANTuPtYBnAZ+TNL9Px5JkmsiltCQZAN0Z\nNuPzbHK+zXRTppJfmJ+BJGlDZoySZBhoju3ZnidThO2fkZ+BJGlGBkZJMgw8x/Zsz5MpQtILgLv6\n9iNJpoUsvk6SYbC+pBOIzMBom/J8Xn9uJa2QtIiHBsHrArcBf9PeoySZTrLGKEkGgKSJFz7bp7Ty\nJekHSRuN7TJwp+37+vAnSaaVDIySJEkGjKR1gANsH9W3L0kyDeRSWpIMAEnnM6GWyPZLG7qT9ICk\nDYAjgCcC5wKnAkcCryrbSZI0IAOjJBkGx5R/BXwEeE2PviT98AngMuDTwK7AV4HrgT+zfXufjiXJ\nNJFLaUkyMCRdbXurvv1I2iJpoe0tO89/Amxo+9c9upUkU0dmjJJkeOTdypQi6THMzCy6HVhN0uqw\nZJ5RkiSVycAoSQZAmW48YsWxC2ReFKeDtYGrWHqY4zfLvwae1NyjJJlCciktSQaApMXExW+2Cce2\nnRfFJEmSBmRglCRJkiRJUkhJkCRJkiRJkkIGRkmSJEmSJIUMjJIkSZIkSQoZGCVJkiRJkhQyMEqS\nJEmSJClkYJQkSZIkSVLIwChJkkEh6d6Kv/ttY88vr2UrSZI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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a17c83518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "non_mv_bp = non_mv.loc[:, ['location', 'price']]#prepping df for boxplot, removed name column\n",
    "\n",
    "non_mv_bp.boxplot(column='price', by='location', figsize=(9,9))\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Location')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Items Nearby')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It appears that the anomalous locations are still cluttering the boxplot.  Let's take a look at what those items are and clean up the dataset to refine the visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([' RUTHERFORDTON', 'ASHEVILLE', ' HENDERSONVILLE', ' SWANNANOA',\n",
       "       'WEAVERVILLE', ' ETOWAH', 'WAYNESVILLE', ' WNC', ' CANDLER',\n",
       "       ' FRANKLIN NC', ' FLAT ROCK', ' OLD FORT', 'BREVARD', ' DELIVERED',\n",
       "       ' LEICESTER NC', ' MARION'], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "non_mv.location.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>DELIVERED</td>\n",
       "      <td>Premium Horse Hay!  2017 Production</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          location                                 name  price\n",
       "Apr 25   DELIVERED  Premium Horse Hay!  2017 Production    7.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ETOWAH</td>\n",
       "      <td>Propane fireplace/stove and mantle</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       location                                name  price\n",
       "Apr 25   ETOWAH  Propane fireplace/stove and mantle  500.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>FLAT ROCK</td>\n",
       "      <td>WHEEL DOLLIES (SET OF 4)</td>\n",
       "      <td>175.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          location                      name  price\n",
       "Apr 25   FLAT ROCK  WHEEL DOLLIES (SET OF 4)  175.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>FOREST CITY</td>\n",
       "      <td>HARLEY DAVIDSON seats  Ford Super Duty</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location                                    name   price\n",
       "Apr 25   FOREST CITY  HARLEY DAVIDSON seats  Ford Super Duty  2000.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>FRANKLIN NC</td>\n",
       "      <td>Complete 305 Chevy Engine</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            location                       name  price\n",
       "Apr 25   FRANKLIN NC  Complete 305 Chevy Engine  200.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>LEICESTER NC</td>\n",
       "      <td>All Man Magazine - 1959</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             location                     name  price\n",
       "Apr 25   LEICESTER NC  All Man Magazine - 1959    3.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>MARION</td>\n",
       "      <td>1968 Camaro</td>\n",
       "      <td>26500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>MARION</td>\n",
       "      <td>1994 Honda Civic 2 Dr Coupe</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       location                         name    price\n",
       "Apr 25   MARION                  1968 Camaro  26500.0\n",
       "Apr 25   MARION  1994 Honda Civic 2 Dr Coupe      2.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>OLD FORT</td>\n",
       "      <td>Marvin Bailey Face Jug</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>OLD FORT</td>\n",
       "      <td>Maple Book Cases</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         location                    name  price\n",
       "Apr 25   OLD FORT  Marvin Bailey Face Jug  150.0\n",
       "Apr 25   OLD FORT        Maple Book Cases  150.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>RUTHERFORDTON</td>\n",
       "      <td>Vintage Porcelain True Value Sign</td>\n",
       "      <td>1200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              location                               name   price\n",
       "Apr 25   RUTHERFORDTON  Vintage Porcelain True Value Sign  1200.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WNC</td>\n",
       "      <td>AKAI  Tuner/Amp ,25 WATT ,and all accessories</td>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       location                                           name  price\n",
       "Apr 25      WNC  AKAI  Tuner/Amp ,25 WATT ,and all accessories  250.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "postings[postings.location.str.contains('DELIVERED')]\n",
    "postings[postings.location.str.contains('ETOWAH')]\n",
    "postings[postings.location.str.contains('FLAT ROCK')]\n",
    "postings[postings.location.str.contains('FOREST CITY')]\n",
    "postings[postings.location.str.contains('FRANKLIN')]\n",
    "postings[postings.location.str.contains('LEICESTER')]\n",
    "postings[postings.location.str.contains('MARION')]\n",
    "postings[postings.location.str.contains('OLD FORT')]\n",
    "postings[postings.location.str.contains('RUTHERFORDTON')]\n",
    "postings[postings.location.str.contains('WNC')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ASHEVILLE          27\n",
       "BREVARD            11\n",
       "WAYNESVILLE        10\n",
       " SWANNANOA          8\n",
       "WEAVERVILLE         5\n",
       " HENDERSONVILLE     3\n",
       "Name: location, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Canon 5D MK II Body - Low Shutter Count</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         location                                     name  price\n",
       "Apr 25  ASHEVILLE  Canon 5D MK II Body - Low Shutter Count  900.0"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Removing all locations with 2 or less items.\n",
    "counts = non_mv.location.value_counts()\n",
    "loc_gt2 = counts[counts > 2]\n",
    "loc_gt2 \n",
    "\n",
    "popular_locations = non_mv[non_mv.location.isin(loc_gt2.keys())]\n",
    "\n",
    "popular_locations[popular_locations.price == popular_locations.price.max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can take a deeper look at the items only located in the primary locations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>64.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>177.437500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>219.255803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>28.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>82.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>250.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>900.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            price\n",
       "count   64.000000\n",
       "mean   177.437500\n",
       "std    219.255803\n",
       "min      1.000000\n",
       "25%     28.000000\n",
       "50%     82.000000\n",
       "75%    250.000000\n",
       "max    900.000000"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_locations.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I'm concerned that there may be duplicate items.  Let's take a look; if there are some we'll remove them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Moe's Home Collection Colvin Sideboard in Natural</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Cardinal Health NPWT Black Foam Dressing Kit X...</td>\n",
       "      <td>79.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>PT 609 Pro Mag.</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         location                                               name  price\n",
       "Apr 25  ASHEVILLE  Moe's Home Collection Colvin Sideboard in Natural  500.0\n",
       "Apr 25  ASHEVILLE  Cardinal Health NPWT Black Foam Dressing Kit X...   79.0\n",
       "Apr 25  ASHEVILLE                                    PT 609 Pro Mag.   12.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "popular_locations.loc[popular_locations.duplicated(),:]#extracting out all the duplicates\n",
    "popular_locations.drop_duplicates(inplace=True)\n",
    "popular_locations = popular_locations[~popular_locations.name.str.contains('Mission Oak')]#removing this item that shows up 3 times with different names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Canon 5D MK II Body - Low Shutter Count</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>HENDERSONVILLE</td>\n",
       "      <td>Wedding Dress</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>Antique Oak Cabinet/Bookcase</td>\n",
       "      <td>625.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>SWANNANOA</td>\n",
       "      <td>27\" iMac (Late 2009)</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WEAVERVILLE</td>\n",
       "      <td>Canon EF 17-35mm f/2.8 L USM Lens -Exc</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               location                                     name  price\n",
       "Apr 25        ASHEVILLE  Canon 5D MK II Body - Low Shutter Count  900.0\n",
       "Apr 25   HENDERSONVILLE                            Wedding Dress  800.0\n",
       "Apr 25        ASHEVILLE             Antique Oak Cabinet/Bookcase  625.0\n",
       "Apr 25        SWANNANOA                     27\" iMac (Late 2009)  600.0\n",
       "Apr 25      WEAVERVILLE   Canon EF 17-35mm f/2.8 L USM Lens -Exc  525.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>location</th>\n",
       "      <th>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>BREVARD</td>\n",
       "      <td>2 RCA Speaker</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>Car Batteries $5 for bad $10 for good</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>2008 Kia Sedona parts</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>WAYNESVILLE</td>\n",
       "      <td>2006 Chrysler 300 parts</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apr 25</th>\n",
       "      <td>ASHEVILLE</td>\n",
       "      <td>WTB Vintage Mandolins, Violins, Guitars</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           location                                     name  price\n",
       "Apr 25      BREVARD                            2 RCA Speaker   10.0\n",
       "Apr 25  WAYNESVILLE    Car Batteries $5 for bad $10 for good   10.0\n",
       "Apr 25  WAYNESVILLE                    2008 Kia Sedona parts    1.0\n",
       "Apr 25  WAYNESVILLE                  2006 Chrysler 300 parts    1.0\n",
       "Apr 25    ASHEVILLE  WTB Vintage Mandolins, Violins, Guitars    1.0"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_locations.head()\n",
    "popular_locations.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we've extracted and removed the anamolous locations and erroneous items from the dataset, let's take a deeper dive into each location and the items available there."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/numpy/core/fromnumeric.py:57: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
      "  return getattr(obj, method)(*args, **kwds)\n"
     ]
    },
    {
     "data": {
      "image/png": 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5gI4BXpjkHLoxPuv69euA2/TrXwgcO4HaJElSA4YeBA1AVc0AM/3yucCD5tnn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\n6l7QePg7O5ypOrcGoDEbmZH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RdL7Wn5rGyaQWS5L7AN/0slepbUleQDcp6llVtalfd2+6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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a2053f0b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "popular_postings_bp = popular_locations.loc[:, ['location', 'price']]\n",
    "\n",
    "#create a boxplot; specify labels and title.  \n",
    "popular_postings_bp.boxplot(column='price', by='location', figsize=(9,9))\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.xlabel('Location')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Items Nearby')\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HENDERSONVILLE</th>\n",
       "      <td>3.0</td>\n",
       "      <td>379.666667</td>\n",
       "      <td>386.368132</td>\n",
       "      <td>40.0</td>\n",
       "      <td>169.50</td>\n",
       "      <td>299.0</td>\n",
       "      <td>549.50</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWANNANOA</th>\n",
       "      <td>8.0</td>\n",
       "      <td>281.250000</td>\n",
       "      <td>157.972647</td>\n",
       "      <td>100.0</td>\n",
       "      <td>187.50</td>\n",
       "      <td>250.0</td>\n",
       "      <td>325.00</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASHEVILLE</th>\n",
       "      <td>22.0</td>\n",
       "      <td>166.590909</td>\n",
       "      <td>239.687229</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.00</td>\n",
       "      <td>49.5</td>\n",
       "      <td>221.25</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BREVARD</th>\n",
       "      <td>11.0</td>\n",
       "      <td>65.363636</td>\n",
       "      <td>45.149247</td>\n",
       "      <td>10.0</td>\n",
       "      <td>25.00</td>\n",
       "      <td>75.0</td>\n",
       "      <td>92.50</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAYNESVILLE</th>\n",
       "      <td>10.0</td>\n",
       "      <td>98.700000</td>\n",
       "      <td>147.143203</td>\n",
       "      <td>1.0</td>\n",
       "      <td>16.25</td>\n",
       "      <td>65.0</td>\n",
       "      <td>105.00</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEAVERVILLE</th>\n",
       "      <td>5.0</td>\n",
       "      <td>151.000000</td>\n",
       "      <td>210.227258</td>\n",
       "      <td>30.0</td>\n",
       "      <td>46.00</td>\n",
       "      <td>65.0</td>\n",
       "      <td>89.00</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                price                                                        \\\n",
       "                count        mean         std    min     25%    50%     75%   \n",
       "location                                                                      \n",
       " HENDERSONVILLE   3.0  379.666667  386.368132   40.0  169.50  299.0  549.50   \n",
       " SWANNANOA        8.0  281.250000  157.972647  100.0  187.50  250.0  325.00   \n",
       "ASHEVILLE        22.0  166.590909  239.687229    1.0   20.00   49.5  221.25   \n",
       "BREVARD          11.0   65.363636   45.149247   10.0   25.00   75.0   92.50   \n",
       "WAYNESVILLE      10.0   98.700000  147.143203    1.0   16.25   65.0  105.00   \n",
       "WEAVERVILLE       5.0  151.000000  210.227258   30.0   46.00   65.0   89.00   \n",
       "\n",
       "                        \n",
       "                   max  \n",
       "location                \n",
       " HENDERSONVILLE  800.0  \n",
       " SWANNANOA       600.0  \n",
       "ASHEVILLE        900.0  \n",
       "BREVARD          150.0  \n",
       "WAYNESVILLE      500.0  \n",
       "WEAVERVILLE      525.0  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>sum</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HENDERSONVILLE</th>\n",
       "      <td>1139.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWANNANOA</th>\n",
       "      <td>2250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASHEVILLE</th>\n",
       "      <td>3665.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BREVARD</th>\n",
       "      <td>719.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAYNESVILLE</th>\n",
       "      <td>987.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEAVERVILLE</th>\n",
       "      <td>755.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    sum\n",
       "location               \n",
       " HENDERSONVILLE  1139.0\n",
       " SWANNANOA       2250.0\n",
       "ASHEVILLE        3665.0\n",
       "BREVARD           719.0\n",
       "WAYNESVILLE       987.0\n",
       "WEAVERVILLE       755.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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>name</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>location</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>HENDERSONVILLE</th>\n",
       "      <td>Wedding Dress</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SWANNANOA</th>\n",
       "      <td>27\" iMac (Late 2009)</td>\n",
       "      <td>600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASHEVILLE</th>\n",
       "      <td>Canon 5D MK II Body - Low Shutter Count</td>\n",
       "      <td>900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BREVARD</th>\n",
       "      <td>Wood Pantry Cabinet</td>\n",
       "      <td>150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAYNESVILLE</th>\n",
       "      <td>Large 6' - 8' Solid Wood Dining Table with 8 c...</td>\n",
       "      <td>500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WEAVERVILLE</th>\n",
       "      <td>Canon EF 17-35mm f/2.8 L USM Lens -Exc</td>\n",
       "      <td>525.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                              name  price\n",
       "location                                                                 \n",
       " HENDERSONVILLE                                      Wedding Dress  800.0\n",
       " SWANNANOA                                    27\" iMac (Late 2009)  600.0\n",
       "ASHEVILLE                  Canon 5D MK II Body - Low Shutter Count  900.0\n",
       "BREVARD                                        Wood Pantry Cabinet  150.0\n",
       "WAYNESVILLE      Large 6' - 8' Solid Wood Dining Table with 8 c...  500.0\n",
       "WEAVERVILLE                 Canon EF 17-35mm f/2.8 L USM Lens -Exc  525.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "popular_locations.groupby(['location']).describe()\n",
    "\n",
    "popular_locations.groupby(['location']).agg(['sum']).loc[:,'price']\n",
    "\n",
    "popular_locations.sort_values(by='price', ascending=False).groupby('location').first()#most expensive item in each location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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t7z4NCNQh3ahCCCFCKSEcF9Faf2uAu77Qz2M1cNMA53keeD6IpUWU9vZ2AHRiku+APQmU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\n4mRWmN2ROnuXm6Pp8Pcbbwx1VlQpUalSJZ59/nnSnU4+EMFbhJq4L4A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xw1dI47KVWUGP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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20614780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,5))\n",
    "sns.violinplot(x=\"location\", y=\"price\", data=popular_locations, scale=\"width\", inner=\"stick\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VdRpw2pS2t/ZMHwkcOei4JA2HQwQlLThJtgQ+C7yhqm6eutwLzSVJ0lxZYEla\nUJJsTlNcHV9Vnxt2PJIkabxYYElaMJIE+BiwsqrePex4JEnS+LHAkrSQ7AG8DHhmkgvbxz7DDkqS\nJI0Pb3IhacGoqrOZ/kdBJUmSOuEZLEmSJEnqiAWWJEmSJHXEAkuSJEmSOmKBJUmSJEkdscCSJEmS\npI5YYEmSJElSRyywJEmSJKkjFliSJEmS1BELLEmSJEnqiAWWJEmSJHVk0bADkCQNztIjTh12CFqP\n1UftO+wQZqUf/47my2uXpA3xDJYkSZIkdcQCS5IkSZI6YoElSZIkSR2xwJIkSZKkjlhgSZIkSVJH\nLLAkSZIkqSMWWJIkSZLUEQssSZIkSeqIBZYkSZIkdcQCS5IkSZI6YoElSZIkSR2xwJK0oCTZK8nl\nSVYlOWLY8Uia/zaUV5JskeTT7fJzkywdfJSSBsUCS9KCkWQz4APA3sDOwEFJdh5uVJLms1nmlcOA\nG6rqkcB7gHcNNkpJg2SBJWkh2Q1YVVVXVNWvgJOAA4Yck6T5bTZ55QDguHb6ZOBZSTLAGCUN0KJh\nByBJA7QYuKpnfg3w5KmdkiwHlreztya5fBbr3ha4bpMjHLz5GjfM39hnjDujf16jb/t8I177w/qx\n/U0wm7zymz5VtS7JTcA2TNmXc8w9/dTZ+93hv+1R/dyPYlxDj2ma933oMU0n75p1XLPKPxZYkhaS\n6Y4Y190aqo4Gjt6oFScrqmrZXAMblvkaN8zf2Odr3DC/Y++j2eSVvuWefhrF93sUY4LRjMuYZq/r\nuBwiKGkhWQPs0DO/PXD1kGKRNB5mk1d+0yfJIuD+wPUDiU7SwFlgSVpIzgN2SrJjknsCBwKnDDkm\nSfPbbPLKKcDB7fQLgW9U1d3OYEkaDw4RlLRgtNc+vBb4KrAZcExVXdrR6kdmWM9Gmq9xw/yNfb7G\nDfM79r6YKa8keQewoqpOAT4GfDLJKpozVwcOL+KNMorv9yjGBKMZlzHNXqdxZT4cQFm2bFmtWLFi\ng/2WHnFq59tefdS+na9TGgdJzh/FcdSSJEnD5BBBSZIkSeqIBZYkSZIkdcQCS5I2QZK9klyeZFWS\nI4Ydz1RJdkhyRpKVSS5N8vq2/YFJTk/y/fbvA9r2JHlf+3ouTrLrkOPfLMl3knypnd8xyblt3J9u\nbypAki3a+VXt8qVDjnvrJCcn+V67758yH/Z5kje2/04uSXJiknvNl32uuVtPnnh7kh8nubB97DOE\n2FYn+W67/RVt27SfpQHF8+ie/XFhkpuTvGEY+yrJMUmuTXJJT9tQ88wMMf1DmwsvTvL5JFu37UuT\n3N6zzz48wJhmfL+SHNnup8vreqPFAAAgAElEQVSTPG8u2+xbgTXKH1ZJ6kKSzYAPAHsDOwMHJdl5\nuFHdzTrg8Kp6DLA78Jo2xiOAr1fVTsDX23loXstO7WM58KHBh3wXrwdW9sy/C3hPG/cNwGFt+2HA\nDVX1SOA9bb9h+mfgK1X1W8ATaF7DSO/zJIuB1wHLqupxNDdsOJD5s881dzPlCWje+13ax2lDiu8Z\n7fYnr/ud6bPUd1V1+eT+AH4buA34fLt40PvqWGCvKW3DzjPTxXQ68LiqejzwP8CRPct+0LPPXjXA\nmGCa96v9d38g8Nj2OR9s/6/fKP08gzXqH1ZJ2lS7Aauq6oqq+hVwEnDAkGO6i6q6pqouaKdvofmi\nv5gmzuPabscBL2inDwA+UY1zgK2TbDfgsAFIsj2wL/DRdj7AM4GT2y5T4558PScDz2r7D1yS+wFP\no7lzHFX1q6q6kXmwz2nuLnzvNL/VdB/gGubBPtemWU+eGFUzfZYG7Vk0BcIPh7HxqjqLu/+e2lDz\nzHQxVdXXqmpdO3sOzW/FDcwM+2kmBwAnVdUvq+pKYBXN//UbpW8F1jz8sErSxloMXNUzv4YRznPt\nEK4nAucCD66qa6DJ18CD2m6j9JreC7wJ+HU7vw1wY89/1L2x/SbudvlNbf9heDiwFvh4muGNH01y\nX0Z8n1fVj4F/BH5EU1jdBJzP/Njn6siUPAHw2nZo1zGDHIrXo4CvJTk/yfK2babP0qAdCJzYMz/s\nfQUjnmeAVwBf7pnfsc2TZyb53QHHMt371cl+Gsg1WHP5sCZZnmRFkhVr164dRJiStLGmO1o/kr99\nkWRL4LPAG6rq5vV1naZt4K8pyX7AtVV1fm/zNF1rFssGbRGwK/Chqnoi8HPWP4RpJGJv/z8+ANgR\neChwX5phRVON4j5XB6bJEx8CHgHsQlN0/9MQwtqjqnal+bf4miRPG0IMd5PmWsT9gX9rm0ZhX63P\n0D+vSf6SZoTb8W3TNcCSNk/+OXBCOwJgEGZ6vzrZT30vsOb6Ya2qo6tqWVUtm5iY6HeYkjQXa4Ad\neua3B64eUiwzSrI5TR4+vqo+1zb/dHJ4SPv32rZ9VF7THsD+SVbTDL18Js0Zra3b4WtTY/tN3O3y\n+zP7ISFdWwOsqarJg4on0xRco77Pnw1cWVVrq+oO4HPA7zA/9rk20XR5oqp+WlV3VtWvgY8wh6FS\nm6qqrm7/XktzrdNuzPxZGqS9gQuq6qdtfEPfV62RzDNJDgb2A15S7Y/wtsPwftZOnw/8AHjUIOJZ\nz/vVyX7qa4E1qh9WSerIecBOae6ydk+a4SKnDDmmu2ivifkYsLKq3t2z6BTg4Hb6YOCLPe0vb+84\ntTtw0+Rwk0GqqiOravuqWkqzX79RVS8BzgBeOEPck6/nhW3/oZxNqaqfAFcleXTb9CzgMkZ8n9MM\nDdw9yX3afzeTcY/8PtemmSlPTLlG5/eAS6Y+t89x3TfJVpPTwHPbGGb6LA3SQfQMDxz2vuoxcnkm\nyV7Am4H9q+q2nvaJyRtIJHk4zQ04rhhQTDO9X6cAB6a5S+qObUz/vbHrX7ThLnOzvg9rzxs6zH+A\nkrRJqmpdktcCX6W549oxVXXpkMOaag/gZcB3k1zYtr0FOAr4TJLDaL5Y/2G77DRgH5oLe28DDh1s\nuBv0ZuCkJH8DfIf2RhLt308mWUVzFuXAIcU36c+A49vC+wqa/XgPRnifV9W5SU4GLqAZxvMd4Gjg\nVObHPtfczZQnDkqyC80QqdXAKwcc14OBz7f3TlkEnFBVX0lyHtN/lgYiyX2A53DX/fH3g95XSU4E\n9gS2TbIGeBtDzu0zxHQksAVwevtentPeMfBpwDuSrAPuBF5VVZ2fBZ8hpj2ne7+q6tIkn6E5uLQO\neE1V3bnR2+zXwaYkTwW+BXyX/71A+S00Ff9dXtCGKuhly5bVihUrNrjNpUecugkRT2/1Uft2vk5p\nHCQ5v/73lrmSJEmij2ewqupspr9QzNuyS5IkSRpLA7mLoCRJkiQtBBZYkiRJktQRCyxJkiRJ6ogF\nliRJkiR1xAJLkjQvJbkzyYVJLknyb+2tk6frd1qSrQcdn6TxZf7R+lhgSZLmq9urapeqehzwK+BV\nvQvbH9S8R1XtU1U3DidESWPK/KMZWWBJksbBt4BHJlmaZGWSD9L8YO4OSVYn2RYgycuTXJzkoiSf\nbNsmknw2yXntY48hvg5J84/5R3fRt9/BkiRpEJIsAvYGvtI2PRo4tKr+tF0+2e+xwF8Ce1TVdUke\n2Pb/Z+A9VXV2kiXAV4HHDPAlSJqnzD+ajgWWJGm+uneSC9vpbwEfAx4K/LCqzpmm/zOBk6vqOoCq\nur5tfzaw8+QXIeB+Sbaqqlv6F7qkec78oxlZYEmS5qvbq2qX3ob2S8rPZ+gfoKZpvwfwlKq6vdvw\nJI0x849m5DVYkqSF4uvAi5JsA9AzROdrwGsnOyXZZZrnStKmMP8sIBZYkqQFoaouBd4JnJnkIuDd\n7aLXAcvai88vY8rdwCRpU5l/FhaHCEqS5qWq2nKattXA46a0Le2ZPg44bsry64A/6kuQksaS+Ufr\n4xksSZIkSeqIBZYkSZIkdcQCS5IkSZI6YoElSZIkSR2xwJIkSZKkjlhgSZIkSVJHLLAkSZIkqSMW\nWJIkSZLUEQssSZIkSeqIBZYkSZIkdcQCS5IkSZI6YoElSZIkSR2xwJIkjbwkxyb5m2HHMVtJ3pLk\no+300iSVZFE7/80kfzzcCCVJ/WKBJUmakySrkzx7StshSc4eVkyjoqr+tqosoqQhS/LUJP+V5KYk\n1yf5zyRPGnZcGm+Lhh2AJEn9kmRRVa0bdhySBi/J/YAvAa8GPgPcE/hd4JfDjEvjzzNYkqS+SPLQ\nJJ9NsjbJlUle17Ps7Uk+k+QTSW5JcmmSZT3Ln5jkgnbZp4F7TVn3fkkuTHJje3T68T3LVid5c5KL\ngZ8nWdTO/7hd3+VJntX23SLJe5Nc3T7em2SLdtmeSdYkOTzJtUmuSXJou2z3JD9JslnPdn+v3ebk\n6/vULPfTK5KsTHJDkq8medhc9reku3kUQFWdWFV3VtXtVfW1qro4yQ+T/DZAkpe2w3h3buf/OMkX\n2undkny7zTXXJHl/kntObqB93quSfL/9DH8gSdplhyQ5O8k/tsuuTLJ3z3MPbT/7tyS5Iskre5bN\nmH/a5fdv8+fa9rX8VZJ7tMsekeQbSX6W5LokxyfZur+7Wr0ssCRJnWv/o/934CJgMfAs4A1JntfT\nbX/gJGBr4BTg/e1z7wl8Afgk8EDg34A/6Fn3rsAxwCuBbYB/BU6ZLIxaBwH7tut+BPBa4ElVtRXw\nPGB12+8vgd2BXYAnALsBf9WznocA929fw2HAB5I8oKrOAX4OPLOn74uBE2a/lyDJC4C3AL8PTADf\nAk7cmHVImtH/AHcmOS7J3kke0LPsTGDPdvppwBXA03vmz2yn7wTeCGwLPIUml/3plO3sBzyJJoe8\niCbHTHoycHn7/L8HPjZZgAHXts+9H3Ao8J42v02aNv+0y/6lXfbwNu6Xt+sACPB3wEOBxwA7AG+f\nfhepHyywJEmb4gvtkd0bk9wIfLBtfxIwUVXvqKpfVdUVwEeAA3uee3ZVnVZVd9IUU09o23cHNgfe\nW1V3VNXJwHk9z/sT4F+r6tz2qPRxNEN+du/p876quqqqbqf5grQFsHOSzatqdVX9oO33EuAdVXVt\nVa0F/hp4Wc967miX31FVpwG3Ao9ul51IU8iRZCtgHza+OHol8HdVtbIdyvi3wC6exZI2XVXdDDwV\nKJr8szbJKUkeTFNATRZUv0tTkEzOP71dTlWdX1XnVNW6qlpNc0Dn6dzVUVV1Y1X9CDiD5oDNpB9W\n1UfaPHccsB3w4Hbdp1bVD6pxJvC1NpZJ0+af9sz5HwFHVtUtbVz/RJu7qmpVVZ1eVb9s89q7p4lZ\nfWSBJUnaFC+oqq0nH/zvkd2HAQ+dUny9hfaLResnPdO3AfdKc6e9hwI/rqrqWf7DnumHAYdPWfcO\n7fMmXTU5UVWrgDfQHMG9NslJSSb7PnTKun84ZT0/m3IN123Alu30CcDvt2fOfh+4oKp61zUbDwP+\nued1XE9z9HnxRq5H0jTagxeHVNX2wONoPt/vpSmgfjfJQ4DNgE8DeyRZSnNm6EKAJI9K8qV2SPDN\nNAdBtp2ymam5bMvpllXVbe3klu26905yTpqbb9xIc5Cmd90z5Z9taa4nm5q7FrfrfVCb537cxvyp\naWJWH1lgSZL64Srgyt7iq6q2qqp9ZvHca4DFPcNoAJZMWfc7p6z7PlXVe/aotzijqk6oqqfSFDQF\nvKtddHXb1rudq2fzAqvqMpovNXszh+GBPa/llVNey72r6r/msC5J61FV3wOOBR7XHni5DXgdcFZV\n3UJTDC2nObv+6/ZpHwK+B+xUVfejOVCUqeveWO2Bmc8C/wg8uD1Addos130dzdmtqbnrx+3039Hk\nuce3Mb+0i5g1exZYkqR++G/g5jQ3l7h3ks2SPC6zuz3yt4F1wOvS3KDi92mujZr0EeBVSZ6cxn2T\n7NsO07ubJI9O8sz2C80vgMlhg9AM6furJBNJtgXeSnO0d7ZOoPmC9jSaa8U21oeBI5M8to31/kn+\ncA7rkTRFkt9qbxKxfTu/A82w3nPaLmfSXJ85eb3VN6fMA2wF3AzcmuS3aO5I2IV70gxdXgusa29+\n8dzZPLEdbvgZ4J1JtmqHFP85/5u7tqIZTnhjksXA/+koZs2SBZYkqXPtF4Dn01yLcCXNEdeP0gy9\n2dBzf0Uz5O4Q4Aaaaw0+17N8Bc11WO9vl69q+85kC+CoNoafAA+iOQoN8DfACuBi4LvABW3bbJ1I\nc6H8N6rquo14HgBV9Xmas2kntUN5LqE5IyZp091Cc5OJc5P8nKawugQ4vF1+Jk0xctYM8wB/QXOG\n+haagzuf7iKw9ozZ62gKpRvabZyyEav4M5ob7VwBnE1zsOeYdtlfA7sCNwGn0pM/NRi56xD30bRs\n2bJasWLFBvstPeLUzre9+qh9O1+nNA6SnF9VyzbcU5IkaeHwDJYkSZIkdcQCS5IkSZI6YoElSZIk\nSR2xwJIkSZKkjiwadgCSNMq23XbbWrp06bDDkNQ6//zzr6uqiWHH0W/mHmn0zDb/WGBJ0nosXbqU\n2dzFVNJgJPnhsGMYBHOPNHpmm38cIihJkiRJHbHAkiRJkqSOWGBJkiRJUkcssCRJkiSpIxZYkiRJ\nktQRCyxJYyHJMUmuTXLJDMuT5H1JViW5OMmug45R0vyVZIckZyRZmeTSJK+fps+MeSbJwUm+3z4O\nHmz0kgbJAkvSuDgW2Gs9y/cGdmofy4EPDSAmSeNjHXB4VT0G2B14TZKdp/SZNs8keSDwNuDJwG7A\n25I8YFCBSxosCyxJY6GqzgKuX0+XA4BPVOMcYOsk2w0mOknzXVVdU1UXtNO3ACuBxVO6zZRnngec\nXlXXV9UNwOms/4CQpHnMAkvSQrEYuKpnfg13/3IkSRuUZCnwRODcKYtmyjPmH2kBWTTsACRpQDJN\nW03bMVlOM7yHJUuWzGrlS484dc6BzWT1Uft2vs5+WMivfaFayO95ki2BzwJvqKqbpy6e5im1nvap\n697o3LOQLeR/hxptnsGStFCsAXbomd8euHq6jlV1dFUtq6plExMTAwlO0uhLsjlNcXV8VX1umi4z\n5ZlZ5R9zjzQeLLAkLRSnAC9v7/K1O3BTVV0z7KAkzQ9JAnwMWFlV756h20x55qvAc5M8oL25xXPb\nNkljyCGCksZCkhOBPYFtk6yhuWPX5gBV9WHgNGAfYBVwG3DocCKVNE/tAbwM+G6SC9u2twBLYP15\npqquT/J/gfPa572jqtZ3Ux5J85gFlqSxUFUHbWB5Aa8ZUDiSxkxVnc3011L19pkxz1TVMcAxfQhN\n0ohxiKAkSZIkdcQCS5IkSZI6YoElSZIkSR2xwJIkSZKkjlhgSZIkSVJHLLAkSZIkqSMWWJIkSZLU\nEQssSZIkSeqIBZYkSZIkdcQCS5IkSZI6YoElSZIkSR2xwJIkSZKkjlhgSZIkSVJHLLAkSZIkqSMW\nWJIkSZLUEQssSZIkSeqIBZYkSZIkdcQCS5IkSZI6smjYAUiSJI26JMcA+wHXVtXjpln+f4CXtLOL\ngMcAE1V1fZLVwC3AncC6qlo2mKglDYNnsCRJkjbsWGCvmRZW1T9U1S5VtQtwJHBmVV3f0+UZ7XKL\nK2nMWWBJkiRtQFWdBVy/wY6Ng4AT+xiOpBFmgSVJktSRJPehOdP12Z7mAr6W5Pwky4cTmaRB8Ros\nSZKk7jwf+M8pwwP3qKqrkzwIOD3J99ozYnfRFl/LAZYsWTKYaCV1zjNYkiRJ3TmQKcMDq+rq9u+1\nwOeB3aZ7YlUdXVXLqmrZxMRE3wOV1B8WWJIkSR1Icn/g6cAXe9rum2SryWngucAlw4lQ0iA4RFCS\nJGkDkpwI7Alsm2QN8DZgc4Cq+nDb7feAr1XVz3ue+mDg80mg+d51QlV9ZVBxSxo8CyxJkqQNqKqD\nZtHnWJrbufe2XQE8oT9RSRpFDhGUJEmSpI5YYEmSJElSRyywJEmSJKkjFliSJEmS1BELLEmSJEnq\niAWWJEmSJHXEAkuSJEmSOmKBJUmSJEkdscCSNBaS7JXk8iSrkhwxzfIlSc5I8p0kFyfZZxhxSpKk\n8WaBJWneS7IZ8AFgb2Bn4KAkO0/p9lfAZ6rqicCBwAcHG6UkSVoILLAkjYPdgFVVdUVV/Qo4CThg\nSp8C7tdO3x+4eoDxSZKkBcICS9I4WAxc1TO/pm3r9XbgpUnWAKcBfzbTypIsT7IiyYq1a9d2Hask\nSRpjFliSxkGmaasp8wcBx1bV9sA+wCeTTJsDq+roqlpWVcsmJiY6DlWSJI0zCyxJ42ANsEPP/Pbc\nfQjgYcBnAKrq28C9gG0HEp0kSVowLLAkjYPzgJ2S7JjknjQ3sThlSp8fAc8CSPIYmgLL8X+SJKlT\nFliS5r2qWge8FvgqsJLmboGXJnlHkv3bbocDf5LkIuBE4JCqmjqMUJIkaZMsGnYAktSFqjqN5uYV\nvW1v7Zm+DNhj0HFJkqSFxTNYkiRJktQRCyxJkqQNSHJMkmuTXDLD8j2T3JTkwvbx1p5leyW5PMmq\nJEcMLmpJw9C3AivJDknOSLIyyaVJXt+2PzDJ6Um+3/59QL9ikCRJ6sixwF4b6POtqtqlfbwDIMlm\nwAeAvYGdgYOS7NzXSCUNVT/PYK0DDq+qxwC7A69pE8oRwNeraifg6+28JEnSyKqqs4Dr5/DU3YBV\nVXVFVf0KOAk4oNPgJI2UvhVYVXVNVV3QTt9Cc2evxTRJ5bi223HAC/oVgyRJ0gA9JclFSb6c5LFt\n22Lgqp4+a9o2SWNqINdgJVkKPBE4F3hwVV0DTREGPGiG5yxPsiLJirVr/akaSZI00i4AHlb/j707\nj5usLO/8//kKLRpFUWmV0N00UWJURoE8QQmZCSoaEAZMBk2TqGjI9G8cGTRxfg6YDCYYI2ZxQdyI\nILgAKm4daUVcEIiCNMjeknSAhE6j3SyyuKCN1/xRp7V8eLZ++tTyVH3er1e9nnPuc59TV1HVF3XV\nuc99qp4JvAv4TNOeKfpOeYsIv/tIo6HnBVaSRwKfBF5bVffMdb+qOrWqJqpqYvHixb0LUJIkaRtV\n1T1VdV+zvBpYlGRnOmeslnZ1XQJsmOYYfveRRkBPC6wki+gUVx+tqk81zd9NskuzfRdgYy9jkCRJ\n6rUkT0ySZnlfOt+x7gAuB/ZIsnuShwIrgFWDi1RSr/XsRsNNkjkNWFtVb+vatAo4Cjip+fvZXsUg\nSZLUhiRnAwcAOydZD7wRWARQVe8DjgBelWQz8ENgRVUVsDnJMcD5wHbA6VV1/QBegqQ+6VmBBewP\nvAy4NslVTdsb6BRWH09yNPDvwIt7GIMkSdI2q6ojZ9l+CnDKNNtWA6t7EZek4dOzAquqLmHqCzsB\nnter55UkSZKkQenLLIKSJEmSNA4ssCRJkiSpJRZYkiRJktQSCyxJkiRJaokFliRJkiS1xAJLkiRJ\nklpigSVJkiRJLbHAkiRJkqSWWGBJkiRJUksssCRJkiSpJRZYkiRJktQSCyxJkiRJaokFliRJkiS1\nxAJLkiRJklpigSVJkiRJLbHAkiRJkqSWWGBJkiRJUksssCRJkmaR5PQkG5NcN832P0xyTfP4epJn\ndm27Jcm1Sa5KsqZ/UUsahFkLrCRPSrJDs3xAkmOT7NT70CSNI3OOpF7ahhxzBnDQDNtvBn67qp4B\nvAk4ddL251TVXlU1MZ+4JS0cczmD9UnggSRPBk4DdgfO6mlUksaZOUdSL80rx1TVRcCdM2z/elXd\n1axeCixpIVZJC9BcCqyfVtVm4HeBd1TVnwC79DYsSWPMnCOpl/qRY44GPt+1XsAXk1yRZGXLzyVp\nyGw/hz4/SXIkcBTwX5u2Rb0LSdKYM+dI6qWe5pgkz6FTYP1WV/P+VbUhyeOBC5J8uzkjNnnflcBK\ngGXLlrUVkqQ+m8sZrFcC+wFvrqqbk+wOfKS3YUkaY+YcSb3UsxyT5BnAB4DDq+qOLe1VtaH5uxH4\nNLDvVPtX1alVNVFVE4sXL24jJEkDMGuBVVU3VNWxVXV2s35zVZ3U+9AkjaP55pwkByW5Mcm6JMdN\n0+clSW5Icn0Sr+uSxlCvvtckWQZ8CnhZVf1zV/sjkuy4ZRl4ATDlTISSRsOsQwSTHEpnNpzdmv4B\nqqoe1ePYJI2h+eScJNsB7waeD6wHLk+yqqpu6OqzB3A8naE6dzVDdSSNmfl+r0lyNnAAsHOS9cAb\naYYWVtX7gBOAxwHvSQKwuZkx8AnAp5u27YGzquoL7b8yScNiLtdgvQP4PeDaqqoexyNJ88k5+wLr\nquomgCTnAIcDN3T1+e/Au7fM8tUM1ZE0fub1vaaqjpxl+x8DfzxF+03AMx+8h6RRNZdrsG4FrrO4\nktQn88k5uzb7bbG+aev2q8CvJvmnJJcmmfZ+NklWJlmTZM2mTZu2IgxJC4DfayT11FzOYL0eWJ3k\na8D9Wxqr6m09i0rSOJtPzskUbZO/PG0P7EFniM8S4OIke1bV9x60Y9WpNDcJnZiY8EuYNFr8XiOp\np+ZSYL0ZuA94GPDQ3oYjSfPKOeuBpV3rS4ANU/S5tKp+Atyc5EY6Bdfl2xaupAXG7zWSemouBdZj\nq+oFPY9Ekjrmk3MuB/Zoplv+D2AF8AeT+nwGOBI4I8nOdIYM3rStwUpacPxeI6mn5nIN1peSmIgk\n9ctW55yq2gwcA5wPrAU+XlXXJzkxyWFNt/OBO5LcAHwV+P+771MjaWz4vUZST83lDNargdcn+THw\nY5ymXVJvzSvnVNVqYPWkthO6lgv40+YhaXz5vUZST81aYFXVjv0IRJLAnCOpt8wxknpt1iGC6Xhp\nkv/brC9Nsm/vQ5M0jsw5knrJHCOp1+ZyDdZ7gP34+QXj9wHv7llEksadOUdSL5ljJPXUXK7BelZV\n7ZPkWwBVdVcSpzWV1CvmHEm9ZI6R1FNzOYP1kyTb0dy0M8li4Kc9jUrSODPnSOolc4yknppLgXUy\n8Gng8UneDFwCvKWnUUkaZ+YcSb1kjpHUU3OZRfCjSa4AnkdnKtMXVdXankcmaSyZcyT1kjlGUq/N\nWmAl+XBVvQz49hRtktQqc46kXjLHSOq1uQwRfHr3SjNu+dd7E44kmXMk9ZQ5RlJPTVtgJTk+yb3A\nM5Lck+TeZn0j8Nm+RShpLJhzJPWSOUZSv0xbYFXVW5q7nf9tVT2qqnZsHo+rquP7GKOkMWDOkdRL\n5hhJ/TLtNVhJ9mkWP9G1/DNVdWXPopI0dsw5knrJHCOpX2aa5OLvZ9hWwHNbjkXSeDPnSOqlbcox\nSU4HDgU2VtWeU2wP8E7ghcAPgFdsKdqSHAX8edP1r6rqzK0PX9JCMW2BVVXP6WcgksabOUdSL7WQ\nY84ATgE+NM32g4E9msezgPcCz0ryWOCNwASdQu6KJKuq6q5tjEfSkJrLLIKSJEljraouAu6cocvh\nwIeq41JgpyS7AL8DXFBVdzZF1QXAQb2PWNKgzHofLEmSJM1qV+DWrvX1Tdt07Q+SZCWwEmDZsmVz\nfuLlx523laFqOr34b3nLSYe0fsyFEmcvLITXPtM07fs3f3do9RklaQrmHEm91Icckynaaob2BzdW\nnVpVE1U1sXjx4laDk9Q/Mw0RPLn5+41+BCJp7JlzJPVSr3PMemBp1/oSYMMM7ZJG1ExDBH+S5IPA\nrklOnryxqo7tXViSxpA5R1Iv9TrHrAKOSXIOnUku7q6q25KcD/x1ksc0/V4AeN8taYTNVGAdChxI\nZ9rSK/oTjqQxZs6R1EvblGOSnA0cAOycZD2dmQEXAVTV+4DVdKZoX0dnmvZXNtvuTPIm4PLmUCdW\n1UyTZUha4Gaapv124Jwka6vq6j7GJGkMmXMk9dK25piqOnKW7QW8epptpwOnb+1zSlqY5jJN+x1J\nPp1kY5LvJvlkkiU9j0zSuDLnSOolc4yknppLgfVBOuOKf5nOtKL/2LRJUi+YcyT1kjlGUk/NpcB6\nfFV9sKo2N48zAOcOldQr5hxJvWSOkdRTcymwNiV5aZLtmsdLgTt6HZiksWXOkdRL5hhJPTWXAuuP\ngJcA3wFuA45o2iSpF8w5knrJHCOpp2aaph2Aqvp34LA+xCJJ5hxJPWWOkdRrczmDJUmSJEmaAwss\nSZIkSWqJBZYkSZIktWTWAivJn3ct79DbcCSNO3OOpF4yx0jqtWkLrCSvT7Ifndl1tvhG70OSNI7M\nOZJ6yRwjqV9mmkXwRuDFwK8kuRhYCzwuyVOq6sa+RCdpnJhzJPWSOUZSX8w0RPAu4A3AOuAA4OSm\n/bgkX+9xXJLGjzlHUi+ZYyT1xUwF1kHAecCTgLcB+wLfr6pXVtVv9iM4SWNlm3JOkoOS3JhkXZLj\nZuh3RJJKMtFa5JIWAtmQOt8AACAASURBVL/XSOqLaQusqnpDVT0PuAX4CJ3hhIuTXJLkH/sUn6Qx\nsS05J8l2wLuBg4GnAUcmedoU/XYEjgUuazl8SUPO7zWS+mUu07SfX1WXV9WpwPqq+i3glT2OS9L4\nmk/O2RdYV1U3VdWPgXOAw6fo9ybgb4AftRqxpIXE7zWSemrWAquqXt+1+oqm7fZeBSRpvM0z5+wK\n3Nq1vr5p+5kkewNLq+pzs8WQZGWSNUnWbNq0aU5xS1oY/F4jqde26kbDVXV1rwKRpMm2Iudkqt1/\ntjF5CPB24HVzfN5Tq2qiqiYWL148xxAkLTR+r5HUC1tVYEnSkFoPLO1aXwJs6FrfEdgTuDDJLcCz\ngVVOdCFpa8w2mU6Stye5qnn8c5LvdW17oGvbqv5GLqmfZroPliQtFJcDeyTZHfgPYAXwB1s2VtXd\nwM5b1pNcCPzvqlrT5zglLVBdk+k8n86POpcnWVVVN2zpU1V/0tX/fwF7dx3ih1W1V7/ilTQ4nsGS\ntOBV1WbgGOB8OjcP/XhVXZ/kxCSHDTY6SSNirpPpbHEkcHZfIpM0VDyDJWkkVNVqYPWkthOm6XtA\nP2KSNFKmmkznWVN1TLIbsDvwla7mhyVZA2wGTqqqz0yx30pgJcCyZctaCltSv3kGS5IkaXYzTqYz\nyQrg3Kp6oKttWVVN0Bm+/I4kT3rQwZxgRxoJFliSJEmzm20ynW4rmDQ8sKo2NH9vAi7kF6/PkjRC\nLLAkSZJm97PJdJI8lE4R9aDZAJM8BXgM8I2utsck2aFZ3hnYH7hh8r6SRoPXYEmSJM2iqjYn2TKZ\nznbA6Vsm0wHWVNWWYutI4Jyq6h4++FTg/Ul+SufH7ZO6Zx+UNFossCRJkuZgLpPpVNVfTLHf14H/\n1NPgJA0NhwhKkiRJUksssCRJkiSpJRZYkiRJktQSCyxJkiRJaokFliRJkiS1xAJLkiRJklpigSVJ\nkiRJLbHAkiRJkqSWWGBJkiRJUksssCRJkiSpJRZYkiRJktQSCyxJkiRJaokFliRJkiS1xAJLkiRJ\nklpigSVJkiRJLbHAkiRJkqSWWGBJkiRJUksssCRJkiSpJRZYkiRJc5DkoCQ3JlmX5Lgptr8iyaYk\nVzWPP+7adlSSf2keR/U3ckn9tP2gA5AkSRp2SbYD3g08H1gPXJ5kVVXdMKnrx6rqmEn7PhZ4IzAB\nFHBFs+9dfQhdUp95BkuSJGl2+wLrquqmqvoxcA5w+Bz3/R3ggqq6symqLgAO6lGckgbMAkuSJGl2\nuwK3dq2vb9om+29JrklybpKlW7NvkpVJ1iRZs2nTprbiltRnPSuwkpyeZGOS67ra/iLJf3SNTX5h\nr55fkiSpRZmirSat/yOwvKqeAXwJOHMr9qWqTq2qiaqaWLx48TYFK2lwenkG6wymPv399qraq3ms\n7uHzS5IktWU9sLRrfQmwobtDVd1RVfc3q/8A/Ppc95U0OnpWYFXVRcCdvTq+JElSH10O7JFk9yQP\nBVYAq7o7JNmla/UwYG2zfD7wgiSPSfIY4AVNm6QRNIhZBI9J8nJgDfA6Z9CRJEnDrqo2JzmGTmG0\nHXB6VV2f5ERgTVWtAo5Nchiwmc6PzK9o9r0zyZvoFGkAJ1aVP0JLI6rfBdZ7gTfRGXf8JuDvgT+a\nqmOSlcBKgGXLlvUrPkmSpCk1lzasntR2Qtfy8cDx0+x7OnB6TwOUNBT6OotgVX23qh6oqp/SGZu8\n7wx9vdBTkiRJ0oLS1wJr0tjk3wWum66vJEmSJC00vZym/WzgG8BTkqxPcjTwN0muTXIN8BzgT3r1\n/JLGS5KDktyYZF2S46bY/qdJbmjuT/PlJLsNIk5JkjTaenYNVlUdOUXzab16PknjK8l2wLuB59OZ\nDvnyJKuq6oaubt8CJqrqB0leBfwN8Pv9j1aSJI2yvg4RlKQe2RdYV1U3VdWPgXOAw7s7VNVXq+oH\nzeqldO5DI0mS1CoLLEmjYFfg1q719U3bdI4GPj/dxiQrk6xJsmbTpk0thShJksaBBZakUZAp2mrK\njslLgQngb6c7mLOYSpKk+RrEjYYlqW3rgaVd60uADZM7JTkQ+DPgt6vq/j7FJkmSxohnsCSNgsuB\nPZLsnuShwApgVXeHJHsD7wcOq6qNA4hRkiSNAQssSQteVW0GjgHOB9YCH6+q65OcmOSwptvfAo8E\nPpHkqiSrpjmcJEnSvDlEUNJIqKrVwOpJbSd0LR/Y96AkSdLY8QyWJEmSJLXEAkuSJEmSWmKBJUmS\nJEktscCSJEmSpJZYYEmSJElSSyywJEmSJKklFliSJEmS1BILLEmSpDlIclCSG5OsS3LcFNv/NMkN\nSa5J8uUku3Vte6C5ybk3OpdGnDcaliRJmkWS7YB3A88H1gOXJ1lVVTd0dfsWMFFVP0jyKuBvgN9v\ntv2wqvbqa9CSBsIzWJIkSbPbF1hXVTdV1Y+Bc4DDuztU1Ver6gfN6qXAkj7HKGkIWGBJkiTNblfg\n1q719U3bdI4GPt+1/rAka5JcmuRFU+2QZGXTZ82mTZu2PWJJA+EQQUmSpNlliraasmPyUmAC+O2u\n5mVVtSHJrwBfSXJtVf3rLxys6lTgVICJiYkpjy1p+HkGS5IkaXbrgaVd60uADZM7JTkQ+DPgsKq6\nf0t7VW1o/t4EXAjs3ctgJQ2OBZYkSdLsLgf2SLJ7kocCK4BfmA0wyd7A++kUVxu72h+TZIdmeWdg\nf6B7cgxJI8QhgpIkSbOoqs1JjgHOB7YDTq+q65OcCKypqlXA3wKPBD6RBODfq+ow4KnA+5P8lM6P\n2ydNmn1Q0gixwJIkSZqDqloNrJ7UdkLX8oHT7Pd14D/1NjpJw8IhgpIkSZLUEgssSZIkSWqJBZYk\nSZIktcQCS5IkSZJaYoElSZIkSS2xwJIkSZKkllhgSZIkSVJLLLAkSZIkqSUWWJIkSZLUEgssSZIk\nSWqJBZYkSZIktcQCS5IkSZJaYoElSZIkSS2xwJIkSZKkllhgSZIkSVJLLLAkSZIkqSUWWJIkSZLU\nEgssSZIkSWqJBZakkZDkoCQ3JlmX5Lgptu+Q5GPN9suSLO9/lJIWsm3JM0mOb9pvTPI7/YxbUn9Z\nYEla8JJsB7wbOBh4GnBkkqdN6nY0cFdVPRl4O/DW/kYpaSHbljzT9FsBPB04CHhPczxJI8gCS9Io\n2BdYV1U3VdWPgXOAwyf1ORw4s1k+F3hekvQxRkkL27bkmcOBc6rq/qq6GVjXHE/SCNp+0AFIUgt2\nBW7tWl8PPGu6PlW1OcndwOOA2ycfLMlKYGWzel+SG1uPeA7S23NsOzPFax8W2/jah/q1baORfW15\n65xf2269jmUa25JndgUunbTvrpOfYBtyz0L4XCyEGKEHcfYol49tnL3Qdv6xwJI0CqY6E1Xz6NNp\nrDoVOHVbgxpmSdZU1cSg4+gFX9vCtABe27bkmTnln/nmngXw325BxAjG2bZxjdMhgpJGwXpgadf6\nEmDDdH2SbA88GrizL9FJGgXbkmfmsq+kEWGBJWkUXA7skWT3JA+lczH5qkl9VgFHNctHAF+pqinP\nYEnSFLYlz6wCVjSzDO4O7AF8s09xS+ozhwhKWvCaax2OAc4HtgNOr6rrk5wIrKmqVcBpwIeTrKPz\ni/KKwUU8FEZ5CKSvbWEa6te2LXmm6fdx4AZgM/DqqnqgxfCG+r9dYyHECMbZtrGMMwvhB9yJiYla\ns2bNrP2WH3de6899y0mHtH5MaRQkuWIhjKuWJEnqJ4cISpIkSVJLLLAkSZIkqSUWWJI0YpIsTfLV\nJGuTXJ/kNU37Y5NckORfmr+PadqT5OQk65Jck2Sfwb6C2SXZLsm3knyuWd89yWXNa/tYMwkBzaQC\nH2te22VJlg8y7tkk2SnJuUm+3bx/+43K+5bkT5rP43VJzk7ysFF533opyelJNia5rqtt6D4TCyXv\nNJ+7bya5uonzL5v2ofssLoQ8l+SWJNcmuSrJmqZtqN7z5rn7mlstsCRp9GwGXldVTwWeDbw6ydOA\n44AvV9UewJebdYCD6cxqtgedm5y+t/8hb7XXAGu71t8KvL15bXcBRzftRwN3VdWTgbc3/YbZO4Ev\nVNWvAc+k8xoX/PuWZFfgWGCiqvakM0nECkbnfeulM4CDJrUN42dioeSd+4HnVtUzgb2Ag5I8m+H8\nLC6UPPecqtqr67rsYXvPoc+51QJLkkZMVd1WVVc2y/fS+R/JrsDhwJlNtzOBFzXLhwMfqo5LgZ2S\n7NLnsOcsyRLgEOADzXqA5wLnNl0mv7Ytr/lc4HlN/6GT5FHAf6EzEx1V9eOq+h4j8r7Rmbn44enc\nH+qXgNsYgfet16rqIh58z76h+0wslLzTPN99zeqi5lEM2Wdxgee5oXrPB5FbLbAkaYQ1Q0X2Bi4D\nnlBVt0HnyxDw+KbbrsCtXbutb9qG1TuA1wM/bdYfB3yvqjY3693x/+y1NdvvbvoPo18BNgEfbIYF\nfSDJIxiB962q/gP4O+Df6RRWdwNXMBrv2yAM9Wdi2PNOM/TuKmAjcAHwrwzfZ3Gh5LkCvpjkiiQr\nm7Zhe8/7nlstsCRpRCV5JPBJ4LVVdc9MXadoG8p7eCQ5FNhYVVd0N0/RteawbdhsD+wDvLeq9ga+\nz8+HrExlwby25tqGw4HdgV8GHkFnGM5kC/F9GyYD/++2EPJOVT1QVXsBS4B9gafOEEvf41xgeW7/\nqtqHzr/nVyf5LzP0HVScfc+tFliSNIKSLKLzJeejVfWppvm7W4Y5NH83Nu3rgaVduy8BNvQr1q20\nP3BYkluAc+gMmXkHnSEc2zd9uuP/2Wtrtj+aBw+3GhbrgfVVdVmzfi6dLwWj8L4dCNxcVZuq6ifA\np4DfZDTet0EYys/EQss7zTCxC+lcMzZMn8UFk+eqakPzdyPwaToF67C9533PrRZYkjRimrH3pwFr\nq+ptXZtWAUc1y0cBn+1qf3kzc9Kzgbu3DJsYNlV1fFUtqarldCZJ+EpV/SHwVeCIptvk17blNR/R\n9B/KMyFV9R3g1iRPaZqeB9zACLxvdIYGPjvJLzWfzy2vbcG/bwMydJ+JhZJ3kixOslOz/HA6xf9a\nhuizuFDyXJJHJNlxyzLwAuA6huw9H0Ru3X72LpKkBWZ/4GXAtc11BgBvAE4CPp7kaDpfeF/cbFsN\nvBBYB/wAeGV/w23F/wHOSfJXwLdoLmZu/n44yTo6v+iuGFB8c/W/gI+mM/3yTXTei4ewwN+3qros\nybnAlXRmm/sWcCpwHqPxvvVMkrOBA4Cdk6wH3shw/lteKHlnF+DMJNvR/Nuqqs8luYHh/ywOW557\nAvDpZj6N7YGzquoLSS5nuN5z6HNuzUL4QWhiYqLWrFkza7/lx53X+nPfctIhrR9TGgVJruiaklWS\nJEk4RFCSJEmSWmOBJUmSJEktscCSJEmSpJZYYEmSJElSSyywJEmSJKklFliSpAUpyQNJrkpyXZJP\nJPmlafqt3nLfG0lqg/lHM7HAkiQtVD+sqr2qak/gx8D/6N7Y3CTyIVX1wqr63mBClDSizD+algWW\nJGkUXAw8OcnyJGuTvIfOTW2XJrklyc4ASV6e5JokVyf5cNO2OMknk1zePPYf4OuQtPCYf/QLth90\nAJIkbYsk2wMHA19omp4CvLKq/mezfUu/pwN/BuxfVbcneWzT/53A26vqkiTLgPOBp/bxJUhaoMw/\nmooFliRpoXp4kqua5YuB04BfBv6tqi6dov9zgXOr6naAqrqzaT8QeNqWL0LAo5LsWFX39i50SQuc\n+UfTssCSJC1UP6yqvbobmi8p35+mf4Caov0hwH5V9cN2w5M0wsw/mpbXYEmSxsWXgZckeRxA1xCd\nLwLHbOmUZK8p9pWkbWH+GSMWWJKksVBV1wNvBr6W5Grgbc2mY4GJ5uLzG5g0G5gkbSvzz3hxiKAk\naUGqqkdO0XYLsOektuVdy2cCZ07afjvw+z0JUtJIMv9oJp7BkiRJkqSWWGBJkiRJUksssCRJkiSp\nJRZYkiRJktQSCyxJkiRJaokFliRJkiS1xAJLkiRJklpigSVJkiRJLbHAkiRJkqSWWGBJkiRJUkss\nsCRJkiSpJRZYkiRJktQSCyxJkrZSkuuTHNAs/0WSjzTLy5NUku0HGqCkkZPkDUk+0Cz/Qq5JcmGS\nPx5shNrCAkuS1BNJjk+yelLbv0zTtqK/0W2bqnp6VV046DgkTW0U809V/XVVWUQtABZYkqReuQjY\nP8l2AEmeCCwC9pnU9uSmryS1ZcHlny1xaeGzwJIk9crldL7Q7NWs/xfgq8CNk9r+tao2JHlnkluT\n3JPkiiT/GTpfgpL8IMnjthw4ya8n2ZRkUZJXJLkkyd8luSvJzUkO7ur76CSnJbktyX8k+auuL1hP\nTvK1JHcnuT3Jx5r29yX5u+4Xk+SzSf60Wb4lyYGz/QeY6bkl9dTW5p9fS3JBkjuT3JjkJVsOlOSQ\nJN9qctOtSf6ia9sXkhzT/cRJrk7ye83yTMc9I8l7k6xO8n3g+CTf6c4RSX43yTXN8s+GI88myR8l\nWdvkxPOT7DbX/3DadhZYkqSeqKofA5fR+RJD8/di4JJJbVt+Pb6czhefxwJnAZ9I8rCq+g5wIfCz\nLybAS4Fzquonzfqz6Hxx2hn4G+C0JGm2nQlspvNL9d7AC4Atw2zeBHwReAywBHhX034W8PtbjpHk\nMc1+52zlf4aZnltSj2xN/knyCOACOv/uHw8cCbwnydObft8HXg7sBBwCvCrJi5ptZzX9AUjyNGA3\n4Lw5HBfgD4A3AzsCf9c813MnbT9ra157E9sbgN8DFjev++ytOYa2jQWWJKmXvsbPv8z8Zzr/o794\nUtvXAKrqI1V1R1Vtrqq/B3YAntL0O5NOUbVlGM2RwIe7nuffquofquqBpu8uwBOSPAE4GHhtVX2/\nqjYCbwe2XHPxEzpfhn65qn5UVZc07RcD1cQHcATwjaraMNcXPofnltRbc80/hwK3VNUHm/xzJfBJ\nOv/uqaoLq+raqvppVV1Dp1j57eYYnwb26jpD9IfAp6rq/tmO2/hsVf1Tc+wfNcc+EiDJjsAL2fri\n6P8D3lJVa6tqM/DXk2JUj1lgSZJ66SLgt5ozQIur6l+ArwO/2bTt2fQhyeuaIS13J/ke8Gg6Z6QA\nPgs8LcmvAM8H7q6qb3Y9z3e2LFTVD5rFR9IpnhYBtyX5XnPc99P5NRng9UCAb6YzM+AfNccoOmer\ntvwy/QfAR7fytc/23JJ6a675ZzfgWVv+nTb/Vv8QeCJAkmcl+WozLPlu4H/Q5Kaquhc4j5//cLKC\nn+eKGY/buHVSzGcBv5dkBzpnoK6sqn/byte9G/DOrue8k06e23Urj6N5chpZSVIvfYNOobQS+CeA\nqronyYambUNV3dxcb/V/gOcB11fVT5PcRedLAVX1oyQfp/Pl5Nf4xbNXM7kVuB/Yufkl9xc0ww//\nO0CS3wK+lOSiqlpH51fjLyY5ic4QxN/dytc+43NL6rm55p9bga9V1fOnOc5ZwCnAwU0uegc///EH\nOrnijUkuAh5O51ov6OSAmY4LnTPlP1+puiHJv9E5+73VwwO7nvfNVbW1PwqpJZ7BkiT1TFX9EFgD\n/CmdoTlbXNK0bbn+akc61yptArZPcgLwqEmH+xDwCuAwYE4XelfVbXSusfr7JI9K8pAkT0ry2wBJ\nXpxkSdP9Ljpfdh5o9v1WE88HgPOr6ntzfd1zeW5JvbUV+edzwK8meVk6E+csSvIbSZ7abN8RuLMp\nrvalU/h0W03nrNGJwMeq6qdzPO50zgKOpTOU8RNb/cLhfXQmzHg6/GyynRfP4ziaJwssSVKvfY3O\nsLhLutoubtq2fME5H/g88M/AvwE/YtLQmar6J+CndIbM3LIVz/9y4KHADXSKqHPpXKMF8BvAZUnu\nA1YBr6mqm7v2PRs4kPn9ijzbc0vqvVnzTzPM7wV0hvdtoDPk+K10rgMF+J/AiUnuBU4APt79BM31\nVp9iUq6Yw3GnczZwAPCVqrp9a15s87yfbp7nnCT3ANfROSOmPklnmPlwm5iYqDVr1szab/lx57X+\n3LecdEjrx5RGQZIrqmpi0HFovCT5CnBWVX1g0LFIkjQVr8GSJC0ISX4D2Ac4fNCxSJI0HYcISpKG\nXpIzgS/RmfL83kHHI0nSdDyDJUkaelV11KBjkCRpLjyDJUmSJEkt8QyWJM1g5513ruXLlw86DEmN\nK6644vaqWjzoOHrN3CMNn7nmn54VWElOBw4FNlbVnk3bY4GPAcuBW4CXVNVdvYpBkrbV8uXLmcss\nppL6o7kJ68gz90jDZ675p5dDBM8ADprUdhzw5araA/hysy5JkiRJI6FnBVZVXQTcOan5cODMZvlM\n4EW9en5JkiRJ6rd+T3LxhKq6DaD5+/jpOiZZmWRNkjWbNm3qW4CSJEmSNF9DO4tgVZ1aVRNVNbF4\n8chfyypJkiRpBPS7wPpukl0Amr8b+/z8kiRJktQz/S6wVgFbbhZ5FPDZPj+/JEmSJPVMzwqsJGcD\n3wCekmR9kqOBk4DnJ/kX4PnNuiS1Isl2Sb6V5HNTbNshyceSrEtyWZLl/Y9Q0qhJ8rAk30xydZLr\nk/zlFH3MP9IY6dl9sKrqyGk2Pa9Xzylp7L0GWAs8aoptRwN3VdWTk6wA3gr8fj+DkzSS7geeW1X3\nJVkEXJLk81V1aVcf8480RoZ2kgtJ2hpJlgCHAB+Ypkv3bSLOBZ6XJP2ITdLoqo77mtVFzaMmdTP/\nSGOkZ2ewJKnP3gG8Hthxmu27ArcCVNXmJHcDjwNun9wxyUpgJcCyZcvm9OTLjztv6yOexS0nHdL6\nMSW1L8l2wBXAk4F3V9Vlk7rMKf/MJ/eA+UcaNp7BkrTgJTkU2FhVV8zUbYq2yb8ydxq9TYSkrVBV\nD1TVXsASYN8ke07qMqf8Y+6RRoMFlqRRsD9wWJJbgHOA5yb5yKQ+64GlAEm2Bx4N3NnPICWNtqr6\nHnAhcNCkTeYfaYxYYEla8Krq+KpaUlXLgRXAV6rqpZO6dd8m4oimz5RnsCRprpIsTrJTs/xw4EDg\n25O6mX+kMeI1WJJGVpITgTVVtQo4DfhwknV0fjleMdDgJI2KXYAzm+uwHgJ8vKo+Z/6RxpcFlqSR\nUlUX0hmiQ1Wd0NX+I+DFg4lK0qiqqmuAvadoN/9IY8ohgpIkSZLUEgssSZIkSWqJBZYkSZIktcQC\nS5IkSZJaYoElSZIkSS2xwJIkSZKkllhgSZIkSVJLLLAkSZIkqSUWWJIkSZLUku0HHcCwW37cea0f\n85aTDmn9mJIkSZIGzzNYkiRJktQSCyxJkiRJaokFliRJkiS1xAJLkiRJklpigSVJkiRJLbHAkiRJ\nkqSWWGBJkiRJUksssCRJkiSpJRZYkha8JA9L8s0kVye5PslfTtHnFUk2JbmqefzxIGKVJEmjbftB\nByBJLbgfeG5V3ZdkEXBJks9X1aWT+n2sqo4ZQHySJGlMWGBJWvCqqoD7mtVFzaMGF5EkSRpXDhGU\nNBKSbJfkKmAjcEFVXTZFt/+W5Jok5yZZOsOxViZZk2TNpk2behazJEkaPRZYkkZCVT1QVXsBS4B9\nk+w5qcs/Asur6hnAl4AzZzjWqVU1UVUTixcv7l3QkiRp5FhgSRopVfU94ELgoEntd1TV/c3qPwC/\n3ufQJEnSGLDAkrTgJVmcZKdm+eHAgcC3J/XZpWv1MGBt/yKUJEnjwkkuJI2CXYAzk2xH54ejj1fV\n55KcCKypqlXAsUkOAzYDdwKvGFi0kiRpZFlgSVrwquoaYO8p2k/oWj4eOL6fcUmSpPHjEEFJkqR5\nSrI0yVeTrG1udP6aKfockOTurhudnzDVsSSNBs9gSZIkzd9m4HVVdWWSHYErklxQVTdM6ndxVR06\ngPgk9ZlnsCRJkuapqm6rqiub5XvpTKCz62CjkjRIFliSJEktSLKczvWgU93ofL8kVyf5fJKnT7O/\nNzmXRoAFliRJ0jZK8kjgk8Brq+qeSZuvBHarqmcC7wI+M9UxvMm5NBossCRJkrZBkkV0iquPVtWn\nJm+vqnuq6r5meTWwKMnOfQ5TUp9YYEmSJM1TkgCnAWur6m3T9Hli048k+9L5/nVH/6KU1E/OIihJ\nkjR/+wMvA65NclXT9gZgGUBVvQ84AnhVks3AD4EVVVWDCFZS71lgSZIkzVNVXQJklj6nAKf0JyJJ\ng+YQQUmSJElqiQWWJEmSJLXEAkuSJEmSWmKBJUmSJEktscCSJEmSpJZYYEmSJElSSyywJEmSJKkl\nFliSJEmS1BILLEmSJElqiQWWpJGQ5GFJvpnk6iTXJ/nLKfrskORjSdYluSzJ8v5HKkmSRpkFlqRR\ncT/w3Kp6JrAXcFCSZ0/qczRwV1U9GXg78NY+xyhJkkacBZakkVAd9zWri5pHTep2OHBms3wu8Lwk\n6VOIkiRpDGw/6AAkqS1JtgOuAJ4MvLuqLpvUZVfgVoCq2pzkbuBxwO2TjrMSWAmwbNmyXoc9reXH\nndf6MW856ZDWjylJkn7OM1iSRkZVPVBVewFLgH2T7Dmpy1Rnqyaf5aKqTq2qiaqaWLx4cS9ClSRJ\nI8oCS9LIqarvARcCB03atB5YCpBke+DRwJ19DU6SJI00CyxJIyHJ4iQ7NcsPBw4Evj2p2yrgqGb5\nCOArVfWgM1iSJEnz5TVYkkbFLsCZzXVYDwE+XlWfS3IisKaqVgGnAR9Oso7OmasVgwtXkiSNIgss\nSSOhqq4B9p6i/YSu5R8BL+5nXJIkabw4RFCSJEmSWmKBJUmSJEktscCSJEmSpJZYYEmSJElSSyyw\nJEmSJKklFliSJEmS1BILLEmSJElqiQWWJEmSJLXEAkuSJEmSWmKBJUmSNE9Jlib5apK1Sa5P8pop\n+iTJyUnWJbkmyT6DiFVSf2w/6AAkSZIWsM3A66rqyiQ7AlckuaCqbujqczCwR/N4FvDe5q+kEeQZ\nLEmSpHmqqtuqVhLGywAAF8NJREFU6spm+V5gLbDrpG6HAx+qjkuBnZLs0udQJfWJBZYkSVILkiwH\n9gYum7RpV+DWrvX1PLgII8nKJGuSrNm0aVOvwpTUYxZYkiRJ2yjJI4FPAq+tqnsmb55il3pQQ9Wp\nVTVRVROLFy/uRZiS+sACS5IkaRskWUSnuPpoVX1qii7rgaVd60uADf2ITVL/WWBJkiTNU5IApwFr\nq+pt03RbBby8mU3w2cDdVXVb34KU1FfOIihJkjR/+wMvA65NclXT9gZgGUBVvQ9YDbwQWAf8AHjl\nAOKU1CcWWJIkSfNUVZcw9TVW3X0KeHV/IpI0aA4RlDRUkjwpyQ7N8gFJjk2y0yz7zOVGnwckuTvJ\nVc3jhF69BkkLz3xyjyRNxQJL0rD5JPBAkifTua5hd+CsWfbZcqPPpwLPBl6d5GlT9Lu4qvZqHie2\nGrWkhW4+uUeSHsQCS9Kw+WlVbQZ+F3hHVf0JMOMNOed4o09JmslW5x5JmooFlqRh85MkRwJHAZ9r\n2hbNdecZbvQJsF+Sq5N8PsnTZziGN/uUxs825R5J2sICS9KweSWwH/Dmqro5ye7AR+ay4yw3+rwS\n2K2qngm8C/jMdMfxZp/SWJp37pGkbs4iKGmoVNUNwLFd6zcDJ82232w3+uwuuKpqdZL3JNm5qm5v\nJ3JJC9l8c48kTeYZLElDJcmhSb6V5M4k9yS5N8nks1GT95n1Rp9Jntj0I8m+dPLfHW3HL2lhmk/u\nkaSpeAZL0rB5B/B7wLXNvWPmYi43+jwCeFWSzcAPgRVbcXxJo28+uUeSHsQCS9KwuRW4bmu+4Mzx\nRp+nAKdsY2ySRtdW5x5JmooFlqRh83pgdZKvAfdvaZxu6J8ktcTcI6kVFliShs2bgfuAhwEPHXAs\nksaHuUdSKyywJA2bx1bVCwYdhKSxY+6R1ApnEZQ0bL6UxC85kvrN3COpFRZYkobNq4EvJPmRUyVL\n6iNzj6RWOERQ0lCpqh0HHYOk8WPukdQWz2BJGirpeGmS/9usL21uDCxJPWPukdQWCyxJw+Y9wH7A\nHzTr9wHvHlw4ksaEuUdSKxwiKGnYPKuq9knyLYCquiuJUyZL6jVzj6RWeAZL0rD5SZLtgAJIshj4\n6WBDkjQGzD2SWmGBJWnYnAx8Gnh8kjcDlwBvGWxIksaAuUdSKxwiKGmoVNVHk1wBPA8I8KKqWjvg\nsCSNOHOPpLZYYEkaKkk+XFUvA749RZsk9YS5R1JbHCIoadg8vXuluSbi1wcUi6TxYe6R1IqBFFhJ\nbklybZKrkqwZRAyShkuS45PcCzwjyT1J7m3WNwKfHXB4kkaUuUdS2wY5RPA5VXX7AJ9f0hCpqrcA\nb0nylqo6ftDxSBoP5h5JbfMaLElDIck+zeInupZ/pqqu7HNIksaAuUdS2wZVYBXwxSQFvL+qTp3c\nIclKYCXAsmXL+hyepAH4+xm2FfDcfgUiaayYeyS1alAF1v5VtSHJ44ELkny7qi7q7tAUXacCTExM\n1CCClNQ/VfWc+e6bZCnwIeCJdG4MempVvXNSnwDvBF4I/AB4hb9MS9qW3AOQ5HTgUGBjVe05xfYD\n6FzLdXPT9KmqOnFbnlPScBvIJBdVtaH5u5HOTf32HUQckkbGZuB1VfVU4NnAq5M8bVKfg4E9msdK\n4L39DVHSiDoDOGiWPhdX1V7Nw+JKGnF9L7CSPCLJjluWgRcA1/U7Dkmjo6pu23I2qqruBdYCu07q\ndjjwoeq4FNgpyS59DlXSiGlG4Nw56DgkDY9BDBF8AvDpzmgdtgfOqqovDCAOSUMkyf5V9U9Jdqiq\n+7fhOMuBvYHLJm3aFbi1a31903bbFMcY2WtAlx93XuvHvOWkQ1o/ptQvbeWeWeyX5GpgA/C/q+r6\naWIZ2dwjjZO+n8Gqqpuq6pnN4+lV9eZ+xyBpKJ3c/P3GfA+Q5JHAJ4HXVtU9kzdPscuU13dW1alV\nNVFVE4sXL55vOJIWhm3OPbO4Etitqp4JvAv4zHQdzT3SaHCadknD4idJPgjsmuTkyRur6tiZdk6y\niE5x9dGq+tQUXdYDS7vWl9D5NVnSeNum3DOb7h97qmp1kvck2dl7gUqjywJL0rA4FDiQzpTIV2zN\njs0MgacBa6vqbdN0WwUck+Qc4FnA3VX1oOGBksbOvHPPXCR5IvDdqqok+9IZPXRH288jaXhYYEka\nCs2vueckWVtVV2/l7vsDLwOuTXJV0/YGYFlz7PcBq+lM0b6OzjTtr2wlcEkL2jbmHpKcDRwA7Jxk\nPfBGYFFz7PcBRwCvSrIZ+CGwoqq8/Yw0wiywJA2bO5J8mk7RVMAlwGuqav10O1TVJUx9jVV3nwJe\n3WagkkbKVucegKo6cpbtpwCntBalpKE3kPtgSdIMPkhnON8v05nl7x+bNknqJXOPpFZYYEkaNo+v\nqg9W1ebmcQbgdFqSes3cI6kVFliShs2mJC9Nsl3zeCleEC6p98w9klphgSVp2PwR8BLgO3RuAnxE\n0yZJvWTukdQKJ7mQNFSq6t+BwwYdh6TxYu6R1BbPYEmSJElSSyywJEmSJKklFliSJEmS1BILLElD\nJcmfdy3vMMhYJI0Pc4+ktlhgSRoKSV6fZD86M3dt8Y1BxSNpPJh7JLXNWQQlDYsbgRcDv5LkYmAt\n8LgkT6mqGwcbmqQRZu6R1CrPYEkaFncBbwDWAQcAJzftxyX5+qCCkjTyzD2SWuUZLEnD4iDgjcCT\ngLcBVwPfr6pXDjQqSaPO3COpVRZYA7D8uPNaP+YtJx3S+jGlfqqqNwAkuRr4CLA3sDjJJcBdVfVf\nBxmfpNFk7pHUNgssScPm/Kq6HLg8yauq6reS7DzooCSNPHOPpFZ4DZakoVJVr+9afUXTdvtgopE0\nLsw9ktpigSVpaFXV1YOOQdL4MfdI2hYWWJIkSZLUEgssSSMhyelJNia5bprtByS5O8lVzeOEfsco\nSZJGn5NcSBoVZwCnAB+aoc/FVXVof8KRJEnjyDNYkkZCVV0E3DnoOCRJ0nizwJI0TvZLcnWSzyd5\n+nSdkqxMsibJmk2bNvUzPkmStMBZYEkaF1cCu1XVM4F3AZ+ZrmNVnVpVE1U1sXjx4r4FKEmSFj4L\nLEljoaruqar7muXVwCJvIipJktpmgSVpLCR5YpI0y/vSyX93DDYqSZI0apxFUNJISHI2cACwc5L1\nwBuBRQBV9T7gCOBVSTYDPwRWVFUNKFxJkjSiLLAkjYSqOnKW7afQmcZdkiSpZxwiKEmSNE9zuMl5\nkpycZF2Sa5Ls0+8YJfWXBZYkSdL8nQEcNMP2g4E9msdK4L19iEnSAFlgSZIkzdMcbnJ+OPCh6rgU\n2CnJLv2JTtIgeA2WJElS7+wK3Nq1vr5pu21yxyQr6ZzlYtmyZX0JbjrLjzuv1ePdctIhrR5P46vt\nzya0//n0DJYkSVLvZIq2KWcw9Sbn0miwwJIkSeqd9cDSrvUlwIYBxSKpDyywJEmSemcV8PJmNsFn\nA3dX1YOGB0oaHV6DJUmSNE9zuMn5auCFwDrgB8ArBxOppH6xwBoRC+GCP1g4cUqSNBdzuMl5Aa/u\nUziShoBDBCVJkiSpJRZYkiRJktQSCyxJkiRJaokFliRJkiS1xAJLkiRJklpigSVJkiRJLbHAkiRJ\nkqSWWGBJkiRJUksssCRJkiSpJRZYkkZCktOTbExy3TTbk+TkJOuSXJNkn37HKEmSRp8FlqRRcQZw\n0AzbDwb2aB4rgff2ISZJkjRmLLAkjYSqugi4c4YuhwMfqo5LgZ2S7NKf6CRJ0rjY/v+1d/+xktXl\nHcffn+4KCv5AYNsgu3QhEAttdKUbhNI0FrUFbOCPkgbSBDQ0m6YQbNOkWWJio0kTSZqiTagtrVRD\njBDRtlskokXb2D+KLAoIrFtX3ZYttCwFabVGRZ/+Md+7zF7v/rp77pxz5r5fyeTO+c5h5jPnnHnY\nZ86Zc/oOIEkzcirwxNT0njb21OIZk2xhspeL0047bSbhpDHZuPVTnT/n7ve9rfPnlKQ+uAdL0mqR\nJcZqqRmr6taq2lxVm9etW7fCsSRJ0jyxwZK0WuwBNkxNrwee7CmLJEmaUzZYklaLbcDV7WyC5wPP\nV9WPHR4oSZJ0NPwNlqS5kORjwJuAk5PsAf4QeAlAVf05cA9wKbAL+D/gHf0klSRJ88wGS9JcqKqr\nDvF4AdfNKI4kSVqlPERQkiRJkjpigyVJkiRJHbHBkiRJkqSO+BssHdBKXEhyJXjBS0mSJA2Fe7Ak\nSZIkqSM2WJIkSZLUERssSZIkSeqIDZYkSZIkdcQGS5IkSZI6YoMlSZJ0FJJcnGRnkl1Jti7x+NuT\n7E3yULv9Vh85Jc2Gp2mXJElapiRrgFuAtwJ7gAeSbKuqxxfNemdVXT/zgJJmzj1YkiRJy3cesKuq\nvlFV3wfuAC7vOZOkHtlgSZIkLd+pwBNT03va2GK/nuSRJHcl2bDUEyXZkmR7ku179+5diaySZsAG\nS5IkafmyxFgtmv57YGNVvQ74B+AjSz1RVd1aVZuravO6des6jilpVmywJEmSlm8PML1Haj3w5PQM\nVfXfVfW9NvmXwM/PKJukHthgSZIkLd8DwFlJTk9yDHAlsG16hiSnTE1eBuyYYT5JM+ZZBCVJkpap\nql5Icj1wL7AGuK2qHkvyXmB7VW0DbkhyGfAC8Czw9t4CS1pxNliSJElHoaruAe5ZNPbuqfs3AjfO\nOpekfniIoKS54IU+JUnSELgHS9LoeaFPSZI0FO7BkjQPvNCnJEkaBBssSfOgswt9ghf7lCRJy2eD\nJWkedHahT/Bin5IkaflssCTNAy/0KUmSBsEGS9I88EKfkiRpEDyLoKTR80KfkiRpKGywpBnZuPVT\nfUc4pN3ve1vfEZbNC31KkqQh8BBBSZIkSeqIDZYkSZIkdcQGS5IkSZI6YoMlSZIkSR2xwZIkSZKk\njthgSZIkSVJHbLAkSZIkqSM2WJIkSZLUERssSZIkSeqIDZYkSZIkdcQGS5IkSZI6YoMlSZIkSR2x\nwZIkSZKkjthgSZIkSVJHbLAkSZIkqSM2WJIkSZLUERssSZIkSeqIDZYkSZIkdaSXBivJxUl2JtmV\nZGsfGSTNl0PVlSTHJrmzPX5/ko2zTylpHll/JE2beYOVZA1wC3AJcA5wVZJzZp1D0vw4zLpyLfBc\nVZ0J3AzcNNuUkuaR9UfSYn3swToP2FVV36iq7wN3AJf3kEPS/DicunI58JF2/y7gzUkyw4yS5pP1\nR9J+1vbwmqcCT0xN7wHeuHimJFuALW3y20l2HsZznww8c9QJ+zHW7GPNDQfJnmF/t7hiy/wI3/dP\nr0SGZTqcurJvnqp6IcnzwEkssSznuP6sSMYV+LyMYVmCOTuVmw4755BqD3RYf5ZZe/pyRNtVj/9f\nHcX234wl69zlPILt87DqTx8N1lLf2NSPDVTdCtx6RE+cbK+qzcsN1qexZh9rbhhv9rHmXmGHU1cO\nq/bA/NafMWQEc3bNnCuus/qznNrTl7Gsr7HkhPFkNeeh9XGI4B5gw9T0euDJHnJImh+HU1f2zZNk\nLfAq4NmZpJM0z6w/kvbTR4P1AHBWktOTHANcCWzrIYek+XE4dWUbcE27fwXwuapacg+WJB0B64+k\n/cz8EMF27PH1wL3AGuC2qnqso6cfxW71Axhr9rHmhvFmH2vuFXOgupLkvcD2qtoGfAi4PckuJt8c\nX9lxjDGslzFkBHN2zZwraCD1pw9jWV9jyQnjyWrOQ4hfoEiSJElSN3q50LAkSZIkzSMbLEmSJEnq\nyNw0WEkuTrIzya4kW/vOMy3JhiSfT7IjyWNJ3tnGT0zy2SRfa39f3caT5E/be3kkybk951+T5MtJ\n7m7Tpye5v+W+s/2olyTHtuld7fGNPec+IcldSb7alv0FY1jmSX6vbSePJvlYkpeOZZmvRkOqPUlu\nS/J0kkenxga3zY+lJrbP3heTPNxyvqeND+7zOIY6nWR3kq8keSjJ9jY2qHWuF1lPOs85mnrSXt+a\nchTmosFKsga4BbgEOAe4Ksk5/abazwvA71fV2cD5wHUt31bgvqo6C7ivTcPkfZzVbluAD84+8n7e\nCeyYmr4JuLnlfg64to1fCzxXVWcCN7f5+vQB4NNV9TPA65m8h0Ev8ySnAjcAm6vq55j8YPpKxrPM\nV5UB1p4PAxcvGhviNj+Wmvg94KKqej2wCbg4yfkM8/M4ljr9y1W1aeraNENb53rRh7GedGlM9QSs\nKUenqkZ/Ay4A7p2avhG4se9cB8n7d8BbgZ3AKW3sFGBnu/8XwFVT8++br4es69sGehFwN5OLJT4D\nrF287JmcQemCdn9tmy895X4l8M3Frz/0ZQ6cCjwBnNiW4d3Ar45hma/G2xBrD7AReHRqetDbfHvt\nwddE4DjgS8Abh/Z5HEudBnYDJy8aG+w692Y9WcGMg60n7fWsKUd5m4s9WLz4j9IFe9rY4LRdp28A\n7gd+qqqeAmh/f7LNNqT3837gD4AftemTgG9V1Qttejrbvtzt8efb/H04A9gL/HXbxf1XSY5n4Mu8\nqv4D+GPg34GnmCzDBxnHMl+NBrHdHMKgt/mh18R2mMxDwNPAZ4GvM7zP41jqdAGfSfJgki1tbHDr\nXAc16PVlPemMNeUozUuDlSXGBnf++SQvBz4B/G5V/c/BZl1ibObvJ8mvAU9X1YPTw0vMWofx2Kyt\nBc4FPlhVbwC+w4u7iZcyiOztWOHLgdOB1wDHM9mtvdgQl/lqNObl33v2MdTEqvphVW1i8o3uecDZ\nB8ky85wjq9MXVtW5TGradUl+6SDz9r596oj0vr6sJ92wpnRjXhqsPcCGqen1wJM9ZVlSkpcw+eB/\ntKo+2Yb/K8kp7fFTmHyjAcN5PxcClyXZDdzBZFfx+4ETkixcpHo6277c7fFXMbmgYh/2AHuq6v42\nfReThmvoy/wtwDeram9V/QD4JPALjGOZr0ZD2W4OZpDb/NhqYlV9C/hHJr/xGNLncTR1uqqebH+f\nBv6GyT8wB7vOtaRBri/rSaesKR2YlwbrAeCsdoaTY5icFGBbz5n2SRImV3HfUVV/MvXQNuCadv8a\nJscNL4xf3c54cj7w/MLuzlmqqhuran1VbWSyTD9XVb8JfB644gC5F97PFW3+Xr5xrKr/BJ5I8to2\n9GbgcQa+zJkcGnh+kuPadrOQe/DLfJUadO1pBrfNj6UmJlmX5IR2/2VMvgDZwYA+j2Op00mOT/KK\nhfvArwCPMrB1rkMa3PqynnTLmtKRlfpx16xvwKXAvzI5nvVdfedZlO0XmeyGfAR4qN0uZXKM6n3A\n19rfE9v8YXJmsq8DX2FyRrm+38ObgLvb/TOALwK7gI8Dx7bxl7bpXe3xM3rOvAnY3pb73wKvHsMy\nB94DfJVJobgdOHYsy3w13oZUe4CPMfnt3g+YfFt37RC3+bHUROB1wJdbzkeBd7fxQX4eh1ynW56H\n2+2xhc/K0Na5t/3WmfWk25yjqictgzVlmbe0F5UkSZIkHaV5OURQkiRJknpngyVJkiRJHbHBkiRJ\nkqSO2GBJkiRJUkdssCRJkiSpIzZYOmpJfpjkoSSPJvl4kuMOMN89C9eAkKSjZe2R1Bfrjw7G07Tr\nqCX5dlW9vN3/KPBgTV3sr10EMFX1o74ySpo/1h5JfbH+6GDcg6WufQE4M8nGJDuS/BnwJWBDkt1J\nTgZIcnWSR5I8nOT2NrYuySeSPNBuF/b4PiSNi7VHUl+sP9rP2r4DaH4kWQtcAny6Db0WeEdV/U57\nfGG+nwXeBVxYVc8kObHN/wHg5qr65ySnAfcCZ8/wLUgaIWuPpL5Yf7QUGyx14WVJHmr3vwB8CHgN\n8G9V9S9LzH8RcFdVPQNQVc+28bcA5ywUI+CVSV5RVf+7ctEljZi1R1JfrD86IBssdeG7VbVpeqAV\niu8cYP4AS/347yeAC6rqu93GkzSnrD2S+mL90QH5Gyz14T7gN5KcBDC1m/wzwPULMyXZtMR/K0nL\nZe2R1Bfrzypig6WZq6rHgD8C/inJw8DCWXduADa3H4A+Dvx2XxklzR9rj6S+WH9WF0/TLkmSJEkd\ncQ+WJEmSJHXEBkuSJEmSOmKDJUmSJEkdscGSJEmSpI7YYEmSJElSR2ywJEmSJKkjNliSJEmS1JH/\nBwQDcml8R4kcAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20461dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(12,12)) #opening a figure\n",
    "\n",
    "#first subplot\n",
    "ax1 = fig.add_subplot(1,3,1) #first subplot\n",
    "ax1.hist(popular_locations.price.values)\n",
    "plt.title(\"All Locations\")\n",
    "plt.ylabel(\"# of Items\")\n",
    "plt.xlabel(\"Price\")\n",
    "\n",
    "ax2 = fig.add_subplot(3,3,2)\n",
    "ax2.hist(popular_locations[popular_locations.location.str.contains('ASHEVILLE')]['price'])\n",
    "plt.title(\"Asheville\")\n",
    "plt.ylabel(\"# of Items\")\n",
    "plt.xlabel(\"Price\")\n",
    "ax3 = fig.add_subplot(3,3,3)\n",
    "ax3.hist(popular_locations[popular_locations.location.str.contains('BREVARD')]['price'])\n",
    "plt.title(\"Brevard\")\n",
    "plt.ylabel(\"# of Items\")\n",
    "plt.xlabel(\"Price\")\n",
    "ax4 = fig.add_subplot(3,3,5)\n",
    "ax4.hist(popular_locations[popular_locations.location.str.contains('HENDERSONVILLE')]['price'])\n",
    "plt.title(\"Hendersonville\")\n",
    "plt.ylabel(\"# of Items\")\n",
    "plt.xlabel(\"Price\")\n",
    "ax5 = fig.add_subplot(3,3,6)\n",
    "ax5.hist(popular_locations[popular_locations.location.str.contains('SWANNANOA')]['price'])\n",
    "plt.title(\"Swannanoa\")\n",
    "plt.ylabel(\"# of Items\")\n",
    "plt.xlabel(\"Price\")\n",
    "ax6 = fig.add_subplot(3,3,8)\n",
    "ax6.hist(popular_locations[popular_locations.location.str.contains('WAYNESVILLE')]['price'])\n",
    "plt.title(\"Waynesville\")\n",
    "plt.ylabel(\"# of Items\")\n",
    "plt.xlabel(\"Price\")\n",
    "ax7 = fig.add_subplot(3,3,9)\n",
    "ax7.hist(popular_locations[popular_locations.location.str.contains('WEAVERVILLE')]['price'])\n",
    "plt.title(\"Weaverville\")\n",
    "plt.ylabel(\"# of Items\")\n",
    "plt.xlabel(\"Price\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the numerical analysis and visualizations that we can be confident $500.00 will allow us to purchase the majority of items in the primary locations around the Asheville area.\n",
    "\n",
    "Now that we've taken a deep dive into the data, let's take a step back and take create a wordcloud to get one last quick glimpse of the raw used items scraped from Craigslist on April 25th."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1cHn5KJwP/BW/dsjfIAddSBHHIfCVv+yRlxR6kXbjVghatFmi4jO2a+8d/Lfr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\nhSaWmEhr7eKjUaBt+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j8KYQYAcemZaLtxj8ZzVVu8AXuH9IazlSVyCplXEORCbV/H3mhT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\nz/6h72haDotq87MYTqii2QHZwtAGy7xP8opiFUGkVoA5m1YokTnlipMv8T9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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a20269d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,10))\n",
    "wordcloud = WordCloud(background_color='white', mode = \"RGB\", width = 2000, height=1000).generate(str(postings['name']))\n",
    "plt.title(\"Craigslist Used Items Word Cloud\")\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis(\"off\")\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In conclusion, this sample size snapshot of used items in the area illuminates that there are plenty of pickings for any vehicle enthusiast.  If someone is hunting for a deal that outside of purchasing a used vehicle, they can expect to pay around $500.00 or less. Free items are seperated in another area of Craigslist so it will require its own separate search another time. One man's junk is another man's treasure and happy hunting!"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}