Pokemon Database Web Scraping and Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1 - Scraping the pokedex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1.1 Use BeautifulSoup to extract all the table rows as a list. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use BeautifulSoup to extract all the table rows as a list. \n",
    "#Create an HTTP request for the page \n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "url='https://pokemondb.net/pokedex/all'\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Response [200]>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "page = requests.get(url)\n",
    "# Response code is returned\n",
    "page"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "page_soup = BeautifulSoup(page.text, 'html.parser')\n",
    "#page_soup.prettify()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "56"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import sys\n",
    "sys.getsizeof(page)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "header_raw=page_soup.find_all(name=\"div\", class_=\"sortwrap\")\n",
    "header=[]\n",
    "for i in header_raw:\n",
    "    header.append(i.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['#',\n",
       " 'Name',\n",
       " 'Type',\n",
       " 'Total',\n",
       " 'HP',\n",
       " 'Attack',\n",
       " 'Defense',\n",
       " 'Sp. Atk',\n",
       " 'Sp. Def',\n",
       " 'Speed']"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of results: 1\n"
     ]
    }
   ],
   "source": [
    "body = page_soup.find_all('tbody')\n",
    "print(\"Number of results: \" + str(len(body)))\n",
    "#page_list[0].find_all(\"tr\")\n",
    "content=[]\n",
    "for each in page_soup.findAll(name = 'td'):\n",
    "    content.append(each.contents)\n",
    "rows=[content[i:i+10] for i in range(0, len(content), 10)]   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "rows.append(header)\n",
    "rowsfinal=[]\n",
    "for row in rows:\n",
    "    row=list(row)\n",
    "    rowsfinal.append(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "927"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(rowsfinal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 927 rows in the extracted list, including the header"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1.2 Extract name,url, types,total points and statistics of the first pokemon as a test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[<span class=\"infocard-cell-img\"><span class=\"img-fixed icon-pkmn\" data-alt=\"Bulbasaur icon\" data-src=\"https://img.pokemondb.net/sprites/sun-moon/icon/bulbasaur.png\"></span></span>, <span class=\"infocard-cell-data\">001</span>][<a class=\"ent-name\" href=\"/pokedex/bulbasaur\" title=\"View pokedex for #001 Bulbasaur\">Bulbasaur</a>][<a class=\"type-icon type-grass\" href=\"/type/grass\">Grass</a>, <br/>, \\' \\', <a class=\"type-icon type-poison\" href=\"/type/poison\">Poison</a>][\\'318\\'][\\'45\\'][\\'49\\'][\\'49\\'][\\'65\\'][\\'65\\'][\\'45\\']'"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bulbasaur=rowsfinal[0]\n",
    "string=''.join(str(e) for e in bulbasaur)\n",
    "string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['bulbasaur']\n"
     ]
    }
   ],
   "source": [
    "#The name of the pokemon\n",
    "regexName = r'/pokedex/(.*)\"\\stitle'\n",
    "Name = re.findall(regexName, string)\n",
    "print(Name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['/pokedex/bulbasaur']\n"
     ]
    }
   ],
   "source": [
    "#The url of the pokemon\n",
    "regexLink = r'href=\"(.*)\"\\stitle'\n",
    "Link = re.findall(regexLink, string)\n",
    "print(Link)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Grass Poison']\n"
     ]
    }
   ],
   "source": [
    "#The types of the pokemon\n",
    "regexType1 = r'(?<=/type/).*>(\\w+)(?=</a>\\,)'\n",
    "regexType2 =  r'(?<=/type/).*>(\\w+)(?=</a>\\])'\n",
    "Type1 = re.findall(regexType1, string)\n",
    "Type2 = re.findall(regexType2, string)\n",
    "Type_List=Type1+Type2\n",
    "Type=[' '.join(str(t) for t in Type_List)]\n",
    "print(Type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['318']\n"
     ]
    }
   ],
   "source": [
    "#The total points of the pokemon\n",
    "regexTotal = r'(?<=\\[\\')(\\d{3})'\n",
    "Total = re.findall(regexTotal, string)\n",
    "print(Total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['001', '45', '49', '49', '65', '65', '45']\n"
     ]
    }
   ],
   "source": [
    "#The list of ID Number, HP, Attack, Defense, Sp. Atk, Sp. Def, Speed of the pokemon\n",
    "regexID = r'(\\d+)</span>'\n",
    "ID = re.findall(regexID, string)\n",
    "\n",
    "regexNumeric = r'(?<=[\\d+]\\'\\]\\[\\')(\\d+)'\n",
    "Numeric = re.findall(regexNumeric, string)\n",
    "\n",
    "statistics=ID+Numeric\n",
    "print(statistics)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1.3 Define a function that takes in a row of the pokedex table and returns it as a DataFrame with a single row. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#Create a single DataFrame by appending these rows. \n",
    "def pokedex(rowsfinal):\n",
    "    import pandas as pd\n",
    "    statistics=[]\n",
    "    Name=[]\n",
    "    Link=[]\n",
    "    Type=[]\n",
    "    Total=[]\n",
    "    regexName = r'/pokedex/(.*)\"\\stitle'\n",
    "    regexLink = r'href=\"(.*)\"\\stitle'\n",
    "    regexType1 = r'(?<=/type/).*>(\\w+)(?=</a>\\,)'\n",
    "    regexType2 =  r'(?<=/type/).*>(\\w+)(?=</a>\\])'\n",
    "    regexTotal = r'(?<=\\[\\')(\\d{3}?)'\n",
    "    regexID = r'(\\d+)</span>'\n",
    "    regexNumeric = r'(?<=[\\d+]\\'\\]\\[\\')(\\d+)'\n",
    "    for i in range(len(rowsfinal)-1):\n",
    "        string=''.join(str(e) for e in rowsfinal[i])\n",
    "        Name.append(re.findall(regexName, string))\n",
    "        ID = re.findall(regexID, string)\n",
    "        Numeric = re.findall(regexNumeric, string)\n",
    "        statistics.append(ID+Numeric)\n",
    "        Link.append(re.findall(regexLink, string))\n",
    "        Type1 = re.findall(regexType1, string)\n",
    "        Type2 = re.findall(regexType2, string)\n",
    "        Type_List=Type1+Type2\n",
    "        Type.append([' '.join(str(t) for t in Type_List)])\n",
    "        Total.append(re.findall(regexTotal, string)[0])\n",
    "    df=pd.DataFrame((list(zip(statistics,Name,Link,Type,Total))))\n",
    "    return df\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>[001, 45, 49, 49, 65, 65, 45]</td>\n",
       "      <td>[bulbasaur]</td>\n",
       "      <td>[/pokedex/bulbasaur]</td>\n",
       "      <td>[Grass Poison]</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>[002, 60, 62, 63, 80, 80, 60]</td>\n",
       "      <td>[ivysaur]</td>\n",
       "      <td>[/pokedex/ivysaur]</td>\n",
       "      <td>[Grass Poison]</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>[003, 80, 82, 83, 100, 100, 80]</td>\n",
       "      <td>[venusaur]</td>\n",
       "      <td>[/pokedex/venusaur]</td>\n",
       "      <td>[Grass Poison]</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>[003, 80, 100, 123, 122, 120, 80]</td>\n",
       "      <td>[venusaur]</td>\n",
       "      <td>[/pokedex/venusaur]</td>\n",
       "      <td>[Grass Poison]</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>[004, 39, 52, 43, 60, 50, 65]</td>\n",
       "      <td>[charmander]</td>\n",
       "      <td>[/pokedex/charmander]</td>\n",
       "      <td>[Fire]</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   0             1                      2  \\\n",
       "0      [001, 45, 49, 49, 65, 65, 45]   [bulbasaur]   [/pokedex/bulbasaur]   \n",
       "1      [002, 60, 62, 63, 80, 80, 60]     [ivysaur]     [/pokedex/ivysaur]   \n",
       "2    [003, 80, 82, 83, 100, 100, 80]    [venusaur]    [/pokedex/venusaur]   \n",
       "3  [003, 80, 100, 123, 122, 120, 80]    [venusaur]    [/pokedex/venusaur]   \n",
       "4      [004, 39, 52, 43, 60, 50, 65]  [charmander]  [/pokedex/charmander]   \n",
       "\n",
       "                3    4  \n",
       "0  [Grass Poison]  318  \n",
       "1  [Grass Poison]  405  \n",
       "2  [Grass Poison]  525  \n",
       "3  [Grass Poison]  625  \n",
       "4          [Fire]  309  "
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pokedex(rowsfinal)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2 - Cleaning the Pokedex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.1 Add column names to the DataFrame. Convert strings to numeric where appropriate. Make the ID number the first column in the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df.columns = ['statistics','Name','URL','Type','Total']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['statistics']=df['statistics'].apply(', '.join)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df['Name'] = df['Name'].str.get(0)\n",
    "df['URL'] = df['URL'].str.get(0)\n",
    "df['Type'] = df['Type'].str.get(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>statistics</th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Type</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>001, 45, 49, 49, 65, 65, 45</td>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>/pokedex/bulbasaur</td>\n",
       "      <td>Grass Poison</td>\n",
       "      <td>318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>002, 60, 62, 63, 80, 80, 60</td>\n",
       "      <td>ivysaur</td>\n",
       "      <td>/pokedex/ivysaur</td>\n",
       "      <td>Grass Poison</td>\n",
       "      <td>405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>003, 80, 82, 83, 100, 100, 80</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>/pokedex/venusaur</td>\n",
       "      <td>Grass Poison</td>\n",
       "      <td>525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>003, 80, 100, 123, 122, 120, 80</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>/pokedex/venusaur</td>\n",
       "      <td>Grass Poison</td>\n",
       "      <td>625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>004, 39, 52, 43, 60, 50, 65</td>\n",
       "      <td>charmander</td>\n",
       "      <td>/pokedex/charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        statistics        Name                  URL  \\\n",
       "0      001, 45, 49, 49, 65, 65, 45   bulbasaur   /pokedex/bulbasaur   \n",
       "1      002, 60, 62, 63, 80, 80, 60     ivysaur     /pokedex/ivysaur   \n",
       "2    003, 80, 82, 83, 100, 100, 80    venusaur    /pokedex/venusaur   \n",
       "3  003, 80, 100, 123, 122, 120, 80    venusaur    /pokedex/venusaur   \n",
       "4      004, 39, 52, 43, 60, 50, 65  charmander  /pokedex/charmander   \n",
       "\n",
       "           Type Total  \n",
       "0  Grass Poison   318  \n",
       "1  Grass Poison   405  \n",
       "2  Grass Poison   525  \n",
       "3  Grass Poison   625  \n",
       "4          Fire   309  "
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "new = df['statistics'].str.split(\",\", n = 6, expand = True) \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ID Number']=new[0]\n",
    "df['HP']=new[1]\n",
    "df['Attack']=new[2]\n",
    "df['Defense']=new[3]\n",
    "df['Sp.Atk']=new[4]\n",
    "df['Sp.Def']=new[5]\n",
    "df['Speed']=new[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop('statistics', axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Total']=df['Total'].astype(float)\n",
    "df['HP']=df['HP'].astype(float)\n",
    "df['Attack']=df['Attack'].astype(float)\n",
    "df['Defense']=df['Defense'].astype(float)\n",
    "df['Sp.Atk']=df['Sp.Atk'].astype(float)\n",
    "df['Sp.Def']=df['Sp.Def'].astype(float)\n",
    "df['Speed']=df['Speed'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.set_index('ID Number')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>URL</th>\n",
       "      <th>Type</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp.Atk</th>\n",
       "      <th>Sp.Def</th>\n",
       "      <th>Speed</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID Number</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>001</td>\n",
       "      <td>bulbasaur</td>\n",
       "      <td>/pokedex/bulbasaur</td>\n",
       "      <td>Grass Poison</td>\n",
       "      <td>318.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>002</td>\n",
       "      <td>ivysaur</td>\n",
       "      <td>/pokedex/ivysaur</td>\n",
       "      <td>Grass Poison</td>\n",
       "      <td>405.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>60.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>/pokedex/venusaur</td>\n",
       "      <td>Grass Poison</td>\n",
       "      <td>525.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>003</td>\n",
       "      <td>venusaur</td>\n",
       "      <td>/pokedex/venusaur</td>\n",
       "      <td>Grass Poison</td>\n",
       "      <td>625.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>80.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>004</td>\n",
       "      <td>charmander</td>\n",
       "      <td>/pokedex/charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>309.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Name                  URL          Type  Total    HP  Attack  \\\n",
       "ID Number                                                                       \n",
       "001         bulbasaur   /pokedex/bulbasaur  Grass Poison  318.0  45.0    49.0   \n",
       "002           ivysaur     /pokedex/ivysaur  Grass Poison  405.0  60.0    62.0   \n",
       "003          venusaur    /pokedex/venusaur  Grass Poison  525.0  80.0    82.0   \n",
       "003          venusaur    /pokedex/venusaur  Grass Poison  625.0  80.0   100.0   \n",
       "004        charmander  /pokedex/charmander          Fire  309.0  39.0    52.0   \n",
       "\n",
       "           Defense  Sp.Atk  Sp.Def  Speed  \n",
       "ID Number                                  \n",
       "001           49.0    65.0    65.0   45.0  \n",
       "002           63.0    80.0    80.0   60.0  \n",
       "003           83.0   100.0   100.0   80.0  \n",
       "003          123.0   122.0   120.0   80.0  \n",
       "004           43.0    60.0    50.0   65.0  "
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.2 Create 18 dummy variables for each type of pokemon. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import pandas as pd\n",
    "dummy=df['Type'].str.get_dummies(sep=' ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "28"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df1=pd.concat([df,dummy],axis=1)\n",
    "len(df1.columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.3 Remove duplicate values of pokemon based on the URL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "809"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df1.drop_duplicates(subset =\"URL\", \n",
    "                     keep = 'first', inplace = True) \n",
    "len(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2.4 create a sample of the pokemon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Add a dummy variable to the DataFrame called \"sample\" that tags every 4th pokemon to be included in the sample\n",
    "df1['sample']=(df1.index.astype('int64') % 4==0).astype('int64')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "samples=df1['Name'][df1['sample']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 3 - Scraping Individual Pages"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3.1 Scrape the main image for Bulbasaur in a general way that could be applied to other pokemon pages by searching for the relevant tag and extracting the image URL. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from IPython.display import Image\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "urlimage='https://pokemondb.net/pokedex/bulbasaur'\n",
    "image = requests.get(urlimage)\n",
    "# Response code is returned\n",
    "image_soup = BeautifulSoup(image.text, 'html.parser')\n",
    "#image_soup.prettify()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<img alt=\"Bulbasaur artwork by Ken Sugimori\" height=\"336\" src=\"https://img.pokemondb.net/artwork/bulbasaur.jpg\" width=\"360\"/><img alt=\"Bulbasaur  sprite from Red &amp; Blue\" class=\"img-fixed img-sprite-v1\" src=\"https://img.pokemondb.net/sprites/red-blue/normal/bulbasaur.png\"/><img alt=\"Bulbasaur  sprite from Silver\" class=\"img-fixed img-sprite-v3\" src=\"https://img.pokemondb.net/sprites/silver/normal/bulbasaur.png\"/><img alt=\"Bulbasaur  sprite from Ruby &amp; Sapphire\" class=\"img-fixed img-sprite-v5\" src=\"https://img.pokemondb.net/sprites/ruby-sapphire/normal/bulbasaur.png\"/><img alt=\"Bulbasaur  sprite from Diamond &amp; Pearl\" class=\"img-fixed img-sprite-v8\" src=\"https://img.pokemondb.net/sprites/diamond-pearl/normal/bulbasaur.png\"/><img alt=\"Bulbasaur  sprite from Black &amp; White\" class=\"img-fixed img-sprite-v11\" src=\"https://img.pokemondb.net/sprites/black-white/normal/bulbasaur.png\"/><img alt=\"Bulbasaur  sprite from X &amp; Y\" class=\"img-fixed img-sprite-v13\" src=\"https://img.pokemondb.net/sprites/x-y/normal/bulbasaur.png\" style=\"width:120px; height:120px\"/><img alt=\"Bulbasaur Shiny sprite from Silver\" class=\"img-fixed img-sprite-v3\" src=\"https://img.pokemondb.net/sprites/silver/shiny/bulbasaur.png\"/><img alt=\"Bulbasaur Shiny sprite from Ruby &amp; Sapphire\" class=\"img-fixed img-sprite-v5\" src=\"https://img.pokemondb.net/sprites/ruby-sapphire/shiny/bulbasaur.png\"/><img alt=\"Bulbasaur Shiny sprite from Diamond &amp; Pearl\" class=\"img-fixed img-sprite-v8\" src=\"https://img.pokemondb.net/sprites/diamond-pearl/shiny/bulbasaur.png\"/><img alt=\"Bulbasaur Shiny sprite from Black &amp; White\" class=\"img-fixed img-sprite-v11\" src=\"https://img.pokemondb.net/sprites/black-white/shiny/bulbasaur.png\"/><img alt=\"Bulbasaur Shiny sprite from X &amp; Y\" class=\"img-fixed img-sprite-v13\" src=\"https://img.pokemondb.net/sprites/x-y/shiny/bulbasaur.png\" style=\"width:120px; height:120px\"/>\n"
     ]
    }
   ],
   "source": [
    "image_string=''.join(str(e) for e in image_soup.find_all('img'))\n",
    "print(image_string)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'https://img.pokemondb.net/artwork/bulbasaur.jpg'"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Display image\n",
    "image= re.findall('https://.*/.*/.*.jpg', image_string)\n",
    "image[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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n9rP9awlyUw4uB1Pt1F/GyHOShyjpXbV5pskJ3HMQW6DcBaeQ/KZipK/gKV6hamU7mFFuGqqI2vQxIP7ag+pCvGFTW22yab/i5dl8m4nVFbHqovMOcxzjqDuYJIeebjtN3km3NFBJllGxusiBLaUEZERDwC1MW73Qg1zJx69ThM3f8XiJkzlPAq65urisn9g5RyjbSZObhsw4bmTSmUpSR9jBq2Cj3+HMObTJiNdOR7pv+dNbAmIsIP4b65boq5VOueF4nX7st3VYpjPVQir7KLax/jKeVcZbsIh7p07hPcWdrFrG5lym4sPXWxFT3DVuwozHOep9vhyhXYrJHXj1bnXrFtpeb4YWPPS/hkPnkF7uDMOLSy2rLmX3YNFeNWiq+4kkeLdRScLgJUrD4gTji2DTiK2UqorZoJZu4g98fYDeQVjoPIEyVlj9lZNwcUq4I5ibStXMLFBVGRL0Ib7fAiBh5fZ9X3VyegrYRZx43lP4M1t3KihgRE10HcIb5g7jTVipiKzEb0V50UW4220MEJGLNWs9lhuO32SFvA1b6ZJGVLqq+X6+Br40SnRxzYOLHk3VdK2E2EmdFwLJDuqz53r/ALrmcmS1DZYb50IQD+HJuJ17X1yncwSFZPYGF5DbqFPYe61phJ7DqGY5zBqBnorw+Mb/AKULTxokgayILeC3NgtfMZmLZtXpG3ScQ2e5LcPH7hh9uSz8mbxhl2qgPS30l3BjYOOJaRIypbxOlKmklLcVJnuEp3HpF277baI7QItDMb6r/a+KxzpZGOw5yIG6DfMG8QW8DUN9a/ZlxsH3Lh5ZHBl/GfKagw8OxVUdpsEOYGoXl97UHm3rFZrJIU7uDMJTuGSetHoUJmsigi0M9tdtHP2yPM39NedQE6FOEDeBr3HMPPw2Ez9NPb0ukOMorpqLGB8LWeeUW4b86GYvKhu5ixpjvMZ6PPIjoiUsiyJpCI7QLQz0Ivg6Hu6slSaavqcxEZ6bDlGw21LsNxOi+th1cj1sLYKSS08MrbpN65ZcqrIFbXIroSk8oSrlBOEJljGr2oeRt2M81GYtKZm1RLTJqBDjTLRMGijQndS0MF5+DxhXc7BgpFfjVmmfXlpzDcbmN9y+BGKdXRlgx1fack1UtV1lK5MqwkpiSMduhFkomRrRRqyqkT/ei121MF3ESW3NQZ7DyLK4iU7ULN6ia5q8fbSupjdgQZ6JrPnTvp/v4EQSZtZSk9yV5vInrqrH7D3WjGW3B0tGtC4TDvpsao5DkmRaOH028bP/AB++jvpuqCE+hfnQiBqSlSgQ/cLFa5DqGEMtK/dJflS5OR18erMoLLbEW2kYqI77cthZ7E+oGoeRUq6FxZ1apC4U4piRv8dgRaOl/lST2Cu4T54Vf0qUZNLTZZfhkX3IZzJN+fdMkxAkHuxQlyUgIhsCIW+QuuPWkBqHBMEncKUlpGM7y2HD7WCnpciqp49FVweexflNkTa0ktOGvnBdeUHXNzCS2Ik9HJ99hb165Cq2zRP+BJHKNtSYSufrg0joZMYmSlyoNJX+01Bz/dLr0BLw1f3x4bXQjba3VyqQ40yiKzZp6qfJkMkSpdDCjIgQrayRVwMNp3WoufPnHxRhJJaxSvRKlZ5VIJqQ8cJXqSktGYSnSw+y7UY8i9XEkuVdymUoEC15hzfHF3OXiHl88qzGa6oJt/wVbJ52umaOHdfucDUhIr3cck9lEbXVt9VKDjfu+WEWw4mObUzD/wBmE7KrMtSSseMiUMbeNdMSdbz7Un5tFqlWqUkhM2A1ORHt3a9STJREPANQPsGXuuovGuGNJkZ7xDeUpiKv/LjLcqlB+hmL/wDXSOyARd9DSSim407EOoYmu3w3HMNxgMc5FsOKG+w4fyCVAzN7pUAx7dDutw11a5X7r6N+nJRKSFIS4lMOVVnW20eySo9xKsGIZpbesgRDxqoxwxST15mkzrZtHkNRJ/kRGUtPy7QvpHb7kpB9viagQMFpw5QZYmOKTBLphj9k5TTspuE3J+mFS305wM9HNnE6Ez6CToSthY1bdgVZPeffjQY8LRBamHXSYb4UQTj4vetIVxBdZQ+117aqjyLXpZE46zLd5e4UYMxzDcGe48gkgy0Wf2YAe+HDPoC7HE4HLKjkW2kmUzDbopZ2ClK2Hn4bC4iHIitOk+0NwSgom/cTI8eWjZZDcbg1DJJJogUsE6yoyOQTfEBFr/ckGFoS4hipgxXjPcIIH8OXcEW2pkHG+o3iWTz8Zr4+X18lUzKocdLUSdDmejuFn7HYLEeghR1Ge3xItXWfb5+s/s24lLiW+bH5mhiVKbhs8PqWTkt+OJFI+7HU2zkkKHfSa5bE5LiScJRCFLblmvztoRkfwM9vmbhEHHNwtSW0Rpfvdx8N9bGD69g6+waBsT0gmpxk1Tzpb/8AuZEanRquS4w8JE1DSqykl5naV9fHqoemQYe5XSmJUeyYRQnCV9RHClV1yxNJhTLTnOStLGGqfGgQUV7Ghn8edJBT5Bb2xS8prIw9wsp4+n0PmhCW06NuIeb0IhsNtNtFK1vq5yTGZs4T0KkxebkZ1tZFqInwyjClTpUe5Sc+fX+sOyQ4qzm2UylaYyGahDWZQA1k1e6PqWADyWADymvIOZlWEZ5lXj6tZUf1ItY99nLBzrd0+WxdOtfp4M+NVIsL/TyCQLufu6wwmMxyBLQNHbYbabgz3BeUsmYWnlG4q62JU5/87nHa+/asqGwxomZDFhHk410X27Bs3SSpQOv6wKobBUzI9paHtxEDhKIKjLIG2ZaLWltPqJNi5Armq9JdxyDlHgOzCJ2DWEwpMXcIjJIGgjDjXOpadjefJp8wYhLTYNtsEkLSRJd0uq33Svwi8O/x78Gf49HrmU25wnn4zUxlMGZVBnKUIcadQ+j4y5caGly8TNNjHes8RElJdwRbaWFpGrEJOyuxX1jcRpLaUFv8H/NsrpzjEdPvykpJBS5zEBiJk0SxlODYeBh0v2nMfwcRD2qZUZmY0rG5FYPfUxjQtuU05j8U1Jj3MYLmXzSysLlQckXjgTBsXkxqSFHX/oEQUpLSVZFGUbx2k91mHDrURYxkjwR9/gYdPdWSHsidzz5MdhEVmfe8qvSm4/YqNsOdlbgz3EtaIs78HEz7MU8ggZcyZWOQzcVW2UceusWC9/YbL6irFA8grUhWV1SQjK4Cw5eSd2plvIBMzFEmmhk4nsLEzNqnaK6tAo+z+QuTFVMyUi0BBw9kqPcZrN9FBx2KpqHYWztipplEdpQulf21R8x6XbXVpaWT6yn+ecwisMRrXvVV4mZIy25MnW6QlSXE8vZ0IW3JQqIwsNR22hvsDVp50IWijSxUV6KquFjHObBr1fpnl9GcCD6goZHH94vZCFLjVJG1DMOK2E37okIzVCBEHi52OG75yML+cpgpUbDXV+zTJbuQusR24rTxly0SzOq3CkiTX7vR5iepyhaNwaT15dK5v1+SS8jjMOrctJpUDziLOyplvvRcfmyJY8E8ruYpovr8t2F/HVXSluEROKDn9QqVXPUaSV8kbhj/APEfgzaC9W5Gwy3FYeV9zv7aNz9RopAejtymnothVlEyKFLMKQNgZ7A1EkqX1VsiPGaiMvK7YognZerqtiPud5aJpq2gjlXVfqQt1LiZUZdKDVziTNTHOqiqhVuly70anBGSj4f+DiXWHJpI0hMlh399tOKDHo6w6yBtqaA+8mO3iOGxLSumcNOgU529qbIskW2G8jrnRc2cd2tiISynm2Kxl+kh47A9tpdXVb6ZU97y82vnZ3HfR7G4rj8SBHgJ1ytzp0ddG9HA/A8yiQzDqLKqsVRrZAg0sgpuwM9hvvpuK+CWVZARbEMzonZzaX0SWzbSsWFez9Qpc2HW5hYJ9W6ffV1ewM9xklx7PXx2IqKSkk+sqPhsPGpwffMn/FnLXtk3mIOq5lKVt8Lu0Kpg4Ljyseodc8qkUi4VjHnp6povgpxLTeONKsrTRxwKVzBSibThlKnJLOVitPNbVWJxq81Ih4HkbacO69Uiw/FmDZP41U2HsyDtFsSvIU+0h4LWltvDaFV1Y/DJqFd/ETw3pfTYhHbTULxRxBxcQgNLItiMOOhSuYbhuK9mVuww3GZGZ0J28CJKRMZ21VpuHkO2c2DCZrof4s3RZrarqqRaZtk1rRzmKjKmfY04ixIxyFlMdEaDiM6+kJSSE/G6s2KerxWQcSKCnxfUKWRBx8c24NQS9MyqdQ0kbHqzW4x2JLzx1f07ZpZXdXi3ZFLZzZhRlSnp1Mc6a3AawrG10sP8j/DyBNvYFRBq0XeIVl9HZwdCxAo6+q/BxIjPyYWSFLyCIqymWj1viFW5WwbVcQQqZ3IK6tt1JEbF7LI1VdVFpofwt8RrrqaWE0SWDwqRRWdlg93fOK4fya+zPH7m+YpcLqaI/wCXlGOJyKGzXZDYiNh1QwSsOua9qix+Jj0X+F//xAA6EQABAwIEBAQDBwMDBQAAAAABAAIDERIEECExEyBBUQUiMDIUI2EzQEJScYGRBkNQcqGxFSQ0wdH/2gAIAQMBAT8B/wAHxWh9n+Me8MFSpXX6lYaTiRglSStjUk8khoCgKCn+He9rBVyll4hqnLCPLA49EXdVhW3zV7ffqgbrisHVcePuuPH3XHj7rix91xo+6OIjHVHFjoE7EvOyJJ1KKsc/RqcQBYzbLDuEcbnDdGSQ7lYRp1d95dI1m5TsT+UJ0j3bnMn0NToFww33lOf0boM9mAJrS80Ca0NFo+7ueGCpUmJLtGocleQAnZcN/UKnfMvdtXkKk0db2WFFZPu8soiGqc90hqc6quYFdAuGI/tCrgPa1F7j1y0zpmE0VcAn6uJWD95+7OcGipUjzI64ocwqTQIngi1u6H1zJVeSmcR+YMsOaSD0yaCqfiXE+XRfFSDqm478wUc8cmx58W/ZmQzoqIMc7RoUcJjaS40KLYx+NPYWFBV9EGhqpBR5VaJjr2hw9LFy08gVUUGkoYZ27zRRziNto1XxLuyOJeviZF8RL3XxcgRnvNXtV0XZVi7lBrXe1wXCf2XCp7zRXsb7R/KMj3dUTRE1UBvjMZ6bekcn60dlhJPweiSGipUjy9xdk1tU02e1HM5nM5RvMbaNW60Cc9VrlhnWvBTm2kj0SUENWHJjixwcE03Co9DGvowN7oAk0CbH3QCPJRBjjsEWDq4L5Xf/AGXyvqpG2OtRQNFcVVE5lRqTUh3fnqiVSuUe9D1RFEFhpKGw+himulktavK0WtQzplRrfeUZPyiiJJ3R1yZ5POU411yAyJ5CmI/ZA5Vyqqqqqg3vnVP1o5DKJ97a8+ImqbWJuVVVAXanZX00ZplVb5Bob5np7y41OQGVaZ0yKYm6xuH7qqLlcqlW1Vipyt1aW/uhlh32Op35sVNaLBk3kbJafopG01btmAXGgVWx7alF1chkTlTkcm7KHV1vdWq1U9FptdVOba4tzjde0O5HuDG3FOJkNxRFEDRXBDXZPFG3VVUx5Ygxsns/hENZ7kXmlo25BkUOXcoJho4FYiOx1e6GVPRfqGuzwr9SzkxclTwwooq6uWIjFlwG2RFDQo/YindH7P8AfkpyBFpboUTTKON0ho0J2GlaK05G75vf5qO2KcwsNPTpWH984nWyB2ZNoqoWmV5e5NbVY3wxsGBc4+7KX3uTC1zLSaaqQt9rcwFRFBbpg6ohHdQQcTzO2WCwnxMgZsE2GKzhgaLxXwcN+dFsiCDQopmbxVjSmup5XbJ7bPSH2J/XIoJhq0HLGOpHQdVBFYKFeHt+defw/wDKxrjLhZGnstlKavdnRAJsYAueo3lzqHbIBAJwt8nZOUEV7qnZBqwcXw8X1coXG6iIDhQrxnA/CS1GxR1KGe8X6HJjgPK7ZPjLP09GvlpyYY1jyk+ZiA3tlgW0iu7leIPNRGiUTU15GtsFzkXVNSojqT9CgMotHglVrqU1pkdQKOMNFAmMveGd0/3LGyljQ1vVeEzm4wnZeLYX4nCuHUaqlDryN9js47m6O9qkip5m+31MJ7MsMb5nOywDA7CMqsea4h6dvyn5wqPdlH7X/pyE9FhGVq7LBi7Exj6qSPzLxIWyhv0Xh5pimKldCsZHwsQ9vJH1H0yHlZcETXVMkLDojGJNY/45iKcuF+zUxtjJWC3OXh5/7VixwtxEn65gcl7X/afynBrGGjq1zqqrDikYy8P/APKZl4uykrX9wvDG3Ylv0y8cjsxhPfkj0dlGa+Q9VTKpGoV7ZNJN+6fGWb5NYXbKoZ7dTzQCkYWLNIisNJbIMvC33YYDsvFsKaOxDdqa5AcxPKzQZYY2zsP1GWMwwxUdvVeH4M4arn7nL+oqfEN/Tkrm7zC/LbKOSzQ7KRgaKxAJznO3PIMgKmiAoKLHGjAEDTUKLFnZ68JxkTbmOduvGZm/BG07qmRVVVVRNVuqIjMZA0NQo5OKwPHXN8jIhc80Xi+IbicRcznYdaFEEGh5Gk2kdtURxdR7ubDsuf8ApljG1YEWUbcmnK80oTkEeSnIUNE3ERH8SM8Y1qviYu6wnjUeGbw36hSf1KPwMUn9RYl3t0UmLmnNXu9E+Zt3JFv/ACqpw4rbxv15GtLjQKKMRtoMnsDxaVUwuLXBOga7zRfwtW7qtcgNKlFU5iac1cg0oBObw2UO59BjrSvllUZ+ZWt/MrmMHl1OTXFpqFI0H5jdsg0nXooIqa8k0QlH1TmujOq4l2kgqvh7xVidG5m6vPVVyabTVOcXGpzrzWkqxNw7zsFbG33Gv6Li09goia6nMimh9SJwHldsUY3h1rWqPD080mp5nsEgoVJA6PVRyGMqGSOQUT8LG7YJ/hz+idg5B0RgeOi4D+y4MnZcCTsvhZT0QwUh6IYCU9F/06VDwx/dN8NHUpuAiG6kj4Zo1TTeWxp5Y2/3DsETU1OVVXpz3gIGuWGkvbTqPRlwwOrEQWnVQ4stPnUczJRVpVCrFYFa3srQrQrQrVbnPjGxaN3UkjpTV3KG9TsnydOiLiUFRbJpQFQTm/5Zod0STuimZRP4brlv6MoaWm4Ky4XR6/8AKDi3ZQ+IOZo9R4uOTqt+YkN3UuMjjU2MfJtytY5+yPDj+p/2TpC8rZV5Buo/a7Jx4X+r/jJrS80apIHMbcU3bPCvujp29GX7NyDi01C4zX/aj91wrtYzVGrd1HiZY9im+JuG4Q8Tadwj4mzsj4mOgT/EZD7U+aR+55eER7tFWNv1KL3yeVHVbczVD+IfRA2DiH9lUnUpsfV6u0o3QJuoLe4Tc8KaSU9F2rTyMmfsSr4z7m/wrWHZy4R6EFcGTsuDJ2Qw8vZfDvG64bOr0eEOpKvb0ajK86VyZ3T/AJbbepyKEQb9onhpbVozKCwrbnEfRSuudQbBMYGeZ/8AH/1E1NSgo/eEM4tHg+k8UcRk2E0q7QINi9tP35KFuiud3VSeePfXZOeXuLjk11rgU7eqHtcPpkF1yhdw43yJtAdVJq67vlsmuoQnbnMHX0sU22X9UyPhC526JJNSnKT3VyCDtLXIs0qNR6B8sX+r/wBJsLjqdArY29KqUVaHUTH0FHbIyMaCGZtykNsLWd9covOLP4yKrqpPecxqfSmaNJD0RNdSijsne1p5AS3UIFj/AH6J8L2/XKnI8NjI6kIkk1KKm0DW8hQCij4jrVPV76rVAFV4n6opkRfr0Tzc4kZxCrx6Tm3Ci20KKaziGikfe7Tbl30CjZY0NTomP3CkgjZ1ojB+VwKOHkHRRRkPqRstzXJrbnUUrrnk8jddcoPl07uRyplxTTUVTnudvyYcVkHp4oMa6p6ocM/i/wBk94pYxDlwkX9w5yM4jbU4FpoVWijeeE6ubfKC7tyb5Qx8R30TnOMgcQpRa8j0cG3d3p4iPiMyHLFHxXUW3JNCJBVPjcw+ZAfKa3rvkBXZS+VtnXMlAUyhj4baZYtmof6MDbYwPUngrJc3qpMO6MVWyoSK5bqCLhN+vLuqA6LFGktB0TZ6+8Izu2Gme6Aplhof7js3tvaWp7Sw2u58PFe6p29Qqlq1JUuHPFo0aKgYLQnYZ9xsGihw4j1O/oYqFweX9DlaaVoiVuhplBhvxP5XAbqaGovaFh4wRc4KWEUuYmRmQ6J+H08iYwyGgTWhgoPuFB6Vld0zCUf5tk49FNh7XVaNCmYcMFXalSYd13kGihwwZ5nb+gRVBoCDGt0agyn363rkRX7p/8QAKBEAAQMDBAEFAAMBAAAAAAAAAQACERASIAMhMDFAEyIyQVFCUGFx/9oACAECAQE/Af6O4Tb/AFhNolEzutN1zU5wai9zj/UEgdp7rkVpOtBRK0hLvOlXt/Ve39XqNV7f1Xt/Ve39XqNR1R9I6pXaKAJ6TiPiKMNrSVcStMffkl4b2jrfiLiaRw/8VsfJE/QoF9Qhvsht45Ib2napPVIULpSjWJVpwLji5afy8dzrUTd3WVNJpaG/JSB0FceFvaJkrT78Y7IuJNZU1AR9ggYEqcIUUZ8qM78bUP1kUGl3SbpkBel/qc2xBTnNBsnfKgM+BKlSpoQCrGr0QfteiVY4fSGmT2gxow1Bc1DiCd+qVpn68ucSI24/40aYMrvwoUqRx6oh2Jyaoppu+uEnOakoVng1x0aTWKxgf2rTIzJynAlR+1J4AtUe2hQNIUZDcRVhg5E5DKMpxCO4hQjWVOQMGUdjFQZHNHgPEGsKOB30a6Z+sCmady1GW4DiFYzInYotIPH/AAq074NFxTnW7BeoXOoe8xTpf7VrblqEaYgKT2muu2OEUKInZERSOD+GA6qPa1P6QEHGadIEk5Mbci4N2CcbinCgNwmgFDUtlObbhKnCdow0z7afdHdpqGPS7QqKdqbRAoE1ag+0w7oChNRQwU5sVNYUY6XWDz7kzrI70GLf2jviUCmdJ/xoOpROJqWfmEVOGn8aCj/kUzoZ9rqsYP8AjTT6T/jRh9uRwLQ5OaWoIAlbDrEJvVBTU+S03fXBGbuqNdaU913VNP44AVIxc2PiiSaGoy1Gz0tMe7GFFZzO1YlNFoQGMo74Qmp7J3GICYJNAo4p4XsB3Xphem1dZGrhgKajPsYASgLeA+EOG1WqENq6jLd6AJreA4k8MeCBU77K0zACaz7dwTCL7zui0KwqFChQorBVhVhVhViFrT7k4yZGE4E+MDCDgabLZbYxUu/KyprKlT5IcQg7OVeu+858+4q4q5XFScJpCtXSJ4BhKHgypqMIKgq1WirjxHgPLNW4xgUUcBgKnwiYoBh0iaDCaPKjhNZ8KKDqs1FJwJ44UK37pHhzlKmlwTjtXtHIBRGEeEOJtH0KGcCMp8KOFpPVH0K64B4pEFRxAxuKGk8wqeQbFEnUKNgbaaabLp4jhFIyYJKe21NElPbagi0t8K49cTnNtgKFpsuG6iExtxR24WutTnlyBhHUJ2871DECgcW9eJ//xABPEAABAgMDBQsHCAcIAgMAAAABAgMABBEFEiEQEzFBURQgIiMwMkJSYXGBM0NykaGxwSQ0U2JzgpLRBhUlQGPh8DVEUGR0k6KyVNImo8L/2gAIAQEABj8C/wADem3pVp2xM+tCXGa51KQaXu0Q2+w4l1lwXkrToIyVcWlA+saReQoKG0H/AAhFjWar9ozQ4S/oG9ajDMq0OLaTdFdcT9iqIEvd3XL16IPOEOS9gJbzTZuuWg8Kor9Qa4cmbQP6xfSklb0zwvUNCfCN1GqVTyy9m+ihOqg/wYqWoJSNJMKk7CAtKfOF9PkWu0qh1193dE9MKvvvnWdndks9lh1Tcw7eaVcNOK6VYS20kIQkUCUiNzo8rMuJaT64YbdebaaZbSi+shIwEBpm0pR106EIeSSf3u9OzjMt2LXifCLsrKT872tM4H2xh+jk/TVWgjD9GXvGYH5Qf/jLl3/Up/KOM/RuZSjah5KjFP1Fav8AtD84qz+j1oK+0uogZv8AR5DQ156Zi8udk7OGpDDOcPti/aU9N2keq85RH4RAbZbS02OigUGS84rsCRzlHYO2HJ+cAE48LoR9Ejq9+3JJsLSdypBWSFaf6w9cBW5myRovC974k5QNJzEsRMTKwNA6KfH94U9MOoYaTpW4qgEKRY0i9aStGfVxbP4jpi7aNpCXYVpl7PTdr984xxUm1e+kcF9XrMYYDsyaIpv77ziGkdZaqRds6WMwP/Id4DQ8dKvCN1TTu6pzUsiiW+xA1ZZp3UjiE+9X9dkOvuc1ArDb7g+Vzvyh5XadA8B+7rs6ymf1haY5w8216RhMzbcybQfBvJZ0MN9yYAAAA0Aat7XLeeebZG1agICW5lL6j0WAXD7IC25dZJ6DnA9cY5M5mErd67nDV6zvQ9reWp31mJORT/e5ltmEoSKJSKAfuz1i2O5mmkYTM8k6PqpjNy6LtcVK1qO079Tjq0ttp0qUaAQr9VSgUyMN1TJuoPcNJis9acwuvm5fikfnFUSTRV1nBfPrMUSkJGxIpGjfzC+q2o+yJZs6UtpHsiwnViqBOpB8cB+7NyMkf2lO8BunQTrVCGUGutSusdu/W6tQShAqpR1Ru6eQtEgj5vKr6f1lQEgAJGgDk5wD6MwKaKQp5vnsKS8PAwxMt4oeQFjx5JUytJcXUIaaGlxZ0JjPWpNvzMyrE5t5TbaOxITT1xeBmW3dTiZld4e2AqStpyYSPM2gnOA+IxiltSDkpT+8S/GtH1YiA/KTDcw0ek2qu+tW0HDeQysyjHYE6f67d/WYfQ13nH1RLNy8nMWhJtm+pLaDxitXgIvGw1hP+pRe9UFxsFtaTdW0vnIPbybrfXSU+yJZestiFNr5qhQw5ZjivlNnuFsjWUE1SeScnFKvSMissyqdSl9Nz4DKpxxSW0JxKlGgEFmzZZ+1HBpzKaIHjBn5R6XsXODhsM8YFekNEDdH6QzV7/LNobEVmLXtWYOu9NECL2dnSf8AUqgXZieQaUwmlQM3bNrIA1CaMK3Paky2VKKznEpXUnTHAn5Z77Vgp9xiu55OY+zeKPeI+V2ROMjWtujoHqgDdjbaj0HeAfbCmrMl3LTdHSbwbHesx+0LR3Mg+YkRT/kYFyTbWsY5x4X1V7zFBgNgyStoJ4LMydzzHf0TykxK6m130+irH31ySVog8RMncswnsOg/1s5F0y6qTr5DEv6av6MMSqNDSbtdp1nLftBapuiryW63Wk9l3X4wG2WkNIHRQmg5MTVor3SGzxUuMEJHbtgNtIS02NCUCg3sy2jn3bye8YxLzH0jYVyNYrEsfpW1Nnw4Q+ORxhWAWNOztgy0yr9pSZzT4JxVsX48hmieJsxoEDVnV6/BMKcdVcQNcJUQU1FaKFDv7r802lf0Y4SvUI4mzpx3YpSQ2PaY4NnMD05n8hFUy0ikbFOK98MTRRmy4Ob48k7KnTKvKb+7pT7Dv8N5nUCrjCg8PDT7KwlaeaoVHdkYttlRQEkNzSRjfa1nwhDrSw42sXkqToI39q2s8q63MTargGJUE8EAQi0LQFHdLErqYHxVvVLWoIQnEqUaAR+zZTPjVMPm434azH7QnFvg+Zb4tr1DT4xRltLQ+oKZTJNEpb/vDo6Cer6RhDbYuoQLqQNQ5KfZw41lt0eHB5SaltAbXVCdiCNXZWuQg4g6omrBdJuo4+UKj5s6U+B3zz7zgaabQVKWrVDU7P3lu4qZYXobr0qbd6022yZiaerm2waDDSSdkBdoPbopoZSKMp8Nfed7mJLBIwdmui33bVQlhhNEDHHEqO09vJ2ZMjzl6XV4iojTGnLp38tM9BXEL7Kngn1+/KzaMthNSKs8jtHSES803zHm0uDxG9Uykn9UyK8dj7w//KffvrhObfRwmXtbaoXLTSA1OtYLTt+sOzLecVdTo74vTdZSUPmPOOD6x6I7NMJaaQG20iiUpFAOULwTUsOIdw7DFRoOPJus6L6cDsOqGXjgpacR268hSdBwMTVgvuVfk1FTN7pNH8vjvEsy5+XzhzMuNhpiruGmGpZnybYpU69p3mcmHUtI2q1wqWbZeTRvOZxxN2uNNGRF8qbeb8m83zkR8sRnGdUywmqfvDSmAphrc7B89MDT3J/OM/wn5n6Z3SO4aBy0y0em0oeyEN6HmUhKk9mo8paEt1Hr6R2KFctkWmODxwlnT9RW8m59WMtK1lJbHX5xXrwhUrZ11KEcF6cWKpbPVSOkr2CGGXp5+fl58KCVP9BxONPEZG32/JuJvCJm/jcl0Zquoa6Q6f8ALD/vyKlsm+2DdC9Su7LnJp4NbE6VHuEJbEwWlqwGdTdHr34clVBufk3nWgdSxe5pgkAtuoN1xpWlCtm+071SdT0rU94OWZaAqq7eT3jGJGbwq6ylRptpjkedb+cuUZYH8RWA/PwiTseQVSZdTcDnUSOe5/Wswp24RLSqOCgaVHZ3kxJ2jNPremEzKODXgoSTS6BCzsBMWf8AYphuZalXZlK5fNnNDQQquPrh6bmG8wt1IQlompSkbe3HeBJUAo6ATid41Z7Bop7hOqHQa1+vRCUISEISKBI0AZFSVmpSqYHlH1+TY79quyE2TKqL89NJvzc6/wANwI2DZWAzmU5rqkQaJMzZqlYt14TPdDbzSr7SxeSoaxvbXY2rQ8PFMbqlCGp1IpjzXR1VfnChdLTzeDjKtKDyTBp/dVe/eTssb1WJxxOPhklZOvF2eyZlWzOKwFe4e+H7YVWkzwGQdTSTh6zUxJ2cnySEmYc79CPjCnACc2Urw7FCHDtSYkUbGU+7euSdm0vp4Ls0rmtHYNphTyLxmgtBD61ErrXblKlG6lIqTsh601ii5xVUjqtjBA+PjkRZsm4Gpp5N9Tv0LetXfqEXEVTLtArWtWKlayo9sTNpv+Um1Xwnqo6I9UVgpUKg4EGHrLWqqPLS5PV6Q3qv8xK18UqyJmpQhE62KAnQsdUwpBTmZlvBxlWlPIl+nCQ3mwe/H8t5akpU3X2W5kdhHBPwyW7akv5a0prc8vTWmtwfGJSTrXMtJbr3CLXmtW6c0n0UYRbcxS8pTC0juAjDWmEN6kigrvNxSDv28wg+T+qD1vdCW2xdQnQIlm/pJhtPtyz6UVKlNFIpDLCMENICBD025ilsc3adQhc/PJpPThzi0/Rp6KPCJ0IN1Tt1kfeUBCQNAhx5xIUlqlAdsNzyEhKq3HKa9kNTqE3lyqs5TanpD1QhxJ4C0hQ3lkr2l1s+Kf5Zb8s4v9ZNc1csm94K1RueYGYnk6WzoV2jlGx1pBQ/5RaMxrDJSn0jgPaY/RmzVaGnM6vtKEE/9jkW6nDOOuL9azE1qUqSdV6wYlb3OzSK+rev3gVWY6u+h0Y5onUrs7YqMRFkN/5i/wDhSTvKRZdnXL7DFZx/wwR7ckm3WinJxsDwqfhkdXrLp9wicrqAPtihxENtnEsqUz6jvJJ36OabPrw+ORcq6SJdDaVpbBoF98BKQEpGoQA4KKTzVp5yYSxaOKTgiaGg+lsgEGoOsbzGLyPI6ldb+W9mXNOYkgB2EmLKkgaImp1AX3J4X5RZQ2tPnxonI2BqKh/yMPn/ACKk+ykJ1Yb0gioOBBhTllKSE6TJu8z7p6MMuTEg5KtsNL4SyDVRoMN7b1pKxvP7mR3IGSxTUBO6j67pyTDOtDl71j+UPjrlKfbktBvVnQ4PvD+W8cGwpV6lCDAnGxx0twsOkjpJgEYg4jIUrSFJOlJjOWerOM6VSbhw+7HFm66Oe0vBScgDi+MOhtAvKPhB3QgsS30B5y/S7OyMN7+kUxsU2174sKTHmW3Hz4inwiXmXbqUoqm+rog5J9gYZibdbp96vxics81DiiA2e9Yw5WVcI4TynHT4rOSSe1szjZ9eGQPUvNqF1adohlti9mUcLhClTkmu1CD795d1ZXJToeUZ9DZ4bzOoOYnE8x5OHrhxq05mZbuquVSq4muw0jiGUNk6wMfXvluHQgFUbpUOMnHlvH3fCC5rRZ6R61mFtuJC21iiknWIZFnPiYYZAG45jSoDUF/nE+7Nyjllom7jtHubfpjwokWkuBeemEUuGtaY/DkMN4ruiyvsck+20KupSHU/dNfhDbydC01y333EtJ2qMTUyGyhglKGyoYqpr9u/vt+XZ4xv4jxhDieaoVG8l1LQlSH/AJO6DoUDor4iLiytdnHmOHhZk9U9kBQNQcQRvdzNArmJo5ptA0mJKUNCphlLZI7BDqNBVIo8aKMNyikc9ouBddh0ZChYCkHSlWIjPNSjDTvXQgA8opHWBEN2balmTTjTSrrcxLovgI7aRdG6UfaSy0j3QeKmZg9Vpgmvwh1qSs2YXZpUVNZ8pbW3Xo6dEHiJVgaitwrPsjjrRuDYwyE+01i/ms+79I+b59vIqZGDL3GNdh6SfjvGTrD7ZH4oUhYCknAg64TKuEmQePEOKPkz1DvFOuquoEC3JoXZKVUQyk61aqd2Rq2ZBJVOyXOSPONax/XbEvNS7ypd5GKHEaUHWDGYtWjfUmk+TX37DANQUnQRoMacj4TzmF5tVdtK/HeYGvKla1BCBpUo0AgvN4yMpUIX13Dr9XIJQF5paVhaV3a0MYCXmO0KLZ+MYyQV6D4j+z1/7qIaVM5qVYbWF3G1XlqI7ci2Hk3m16YVZ00avtCqF/So25M2njn9TKNPjs8YzKF/J2jx8wnybX1EbVdsNSsq2GmGhRKRlftOyGr6HOFMSKel9ZHb2a4VcIdQeCtCho7CILlnPqkydLR4bR8ITKzks628U3r0uM4gjbtirD6HuwHEeEOuJRdW7S+dsacmZDy2UqPCLeBI2VhLTQohIoByGnJU4DbBvTiFEdFrhe6AZOVTKNKxz02cfBAgLtCZetBem6s3Wx90QEoSEIGhKRQZUrbUFoVoUNfKJmJbgzstw2zt2phD0zaDilEYs3ruOy6mE0YNk2Mecbt117uGodsIlpNlLDCNCU70WnZa0StogcNJHAmOxX5wqQm2VyE+jSy7r9E64ZcQ6qXmmFX2nkaUn8ocetdtvh3UtTLAuIHjqPfDSmJpyZqq6lh4X73jpi9M2W4kU0sOBZ/DFHHHZZfUeaUCI4M+x95d33x/aEv/ALgj+0Jb/cEf2hL/AI4pu5J9FKj8I4LzjnoNKji5add7UsRwbNnT6QSn4xwbLUPtJhIjBMnLj7zhjjbSKB1ZdoJ9sTUvbWcmnAc4087fcFD0Snb2w7aRlNyyqKJl2Ci7Wg51NW9bsxi9umZwKkeaRrVDbKOY2kJGSvIY5ZGYmUDckws5vqpd1V8eQCJ6WS9Tmr0KT3GLyc7atnDp6XmR2jpDti82tD7KhQ0xHjDExIrIzJKhLOK4GOmh1RmXQZV/6J7Anu2xrjhtJV6SRGLTX4BHkmvwCOYj8IjCg8I0xrylS1BKRpKjQQhMgLrHSm3E8H7o1wq5VTizVbq+cvelhmjsz1NSfS2Qt1Zz0y5z3iMT2DYMmOOSg0ZGGz50kD1Vyhxk3mzoVTTvXGRg5zmzsUNES7zhrMt8U+NYWP6ryLdpWaNwzzsyhta2uau9tTogS9qo3E9oS75pztB1eMXXUIfaOpQqI/Z7qXWf/FmdXoq1QG7QlnbOX1nMW/xRfbWlxHWQajfXpl1tkfxDSLtmSS53+MeA2PGM/aT2611qlkeST4a4AAoBqG8rMOhB1I0qV3CMAqy5P/7l/wDrAbZRcRp7+07+yTqMwU/8DkUB8wQqhV9Mf/X3xQYQXph1LLY6SjAl20vJvirbjiLqV9g3k3JGuZtFvPo+0Gn48jJY0SZ9iv4oU0+2l1s6UrFYvWRM3G9O5JjhNnuOkQG7SlnZBzrKF5vwUIq0tD7f1TeEFcvfknevLKu+zRBuWi1M9kyz8RA+TST6fqLKffFf1fKo7FP4xwESDHeVLj5Taq6dWWbCPbF8MBxw+ce4avbvKqISNpNIuyyXZ1exhGH4jhGLosyWHRbN5w+OqN0BBW4fOL4TiidEJvih2chZ6+rONwmzmVFN8Xn1p6DezvMIabSEISKACFsSSBMzAwUoni2z2n4CBMTTqpuYGhSxwUeiNUSr45zMwhQ8TQ+/eWTOq4Jl5xuq9iVYK5F1zW28ysfjGUg6IK0tbndPnJc5tXsj5PaOdHVm26+0RR6zc7TSuWdBr4GOPYm5X7Rg/CPnrQ9KojGeZ8DHzsHuSr8o4sTDvoMmOKsiZUNqyEwfk8rKfaKKz7I460F9zCEo/OL5ZzrnWeUVn2xQYDZDTQ886lB7tJ90KmCPkskq619dzWfDJWCmzEIW2MDNu8z7o6UbnmZkzKXWitJUgJooHHR2HeyqwkKImEroezGM+6PlMzxrnedXgIVLSCi0yCUuzdP+KPzhLbabqE6BkfpqFfbFcs6P4RI7xjEi/evZxhC6+HIWo0dTBcHenhfCJV7rtJV7MimJNsz8yMCls0Sj0laI3W46wltrhGVaRW8nXwjAUk1SoVByUGmL6SlxJwrGLDR+4I4LaE+ikDfpufOCq6z6ZwhmWbxCBp2nWckxLpcLRdQUBY1QGijNLZ4pbfVIizHdkyE/iBG9sqzb1Emrjnd/Qh1DRDbhQQk7DSNzqRm3Jcllaa1xH56cr4OgoV7olycCW0+7K4nakj2RZhIOCCjHsURyDrKua4goPjG5nsHpNxTCxsoYWzLOKbs1s3VvNnF89UdnbCWmkBttOhIgjSIZQdLVWj900yVgvy68y8ecCKoc7x8YzDyDLzGpCtCvROvkP4ck3XvWr+UKYYC56aTpalxWnedAir0ymRR9HK4q8VH4ROSmedeaS2hwZ5d4gmtcY3VKuBqZoApK+Y4O3Ye2GH595pLbK84mXYqaq1VUd7Pz3m5YbnQdqqY5P1kkVl10TMgDm7F/AxhjkI2ikSZ/hDK8o6kKPsize5f/AHPIzEpLEoZtu6tSupTyns98IaaTcbQKJEHJPsg1CVpX3FQxHs3hbdQFoPRVF+SXu1kf3Z/nD0VRdU5uZ8c5p/gkb0k4AYmJg1XKycw4XFqSaLc1BI2CkBplsNNjopikWtNA1CnUtA+iP5752ZOKuagbVaoYa0rIvrVtUcTkKVAKSdIOgwacZIdFXSZ7D2dsV1GA2kF2YVzWk6TEswvnoQArvyzi9jSoslIFOICvXjyKbQa+c2cvPp7U9If1shC0GqVCoyXrt9xRuoQOkrZAS5wphw5x5X1jvVOOKuNpFSTqh20rWk0zD88rOJS8PJt9EeqFKse0XpI6mHuMa7toh6QdlpecebQldWVXbyTrFYpNWdNS6tZCbwj5yEH64IhbbEw2448Q0LiscTAbSKJSLoyPzHUQSO+JVlXlLt9fpHE76bCFHc1no0jQpyGz9UbzONrelqmpQyuiT4aoowyluukjSe87x5PSdKW0+JiWYoBmm0ooNGA5FbTgvIWClQ2iJ+y2nGFiUXwEPVBLasQQoR8wac+zmR8RG7Z+5nUi6yyg3g321277cJF6zpOjk1sWrotxQYDIzaEki/PSteB9K2ecj4iEvNKqhYqIxSD3iLOzSEoIBcWEimA0ZZKS1zD6QfRHCPu31UU3Q6bjVdR2+EPSjUyy6otqK1hYNVU0xKO1qS2Ae8YcjZFn1GbbVut7uToHJyFtJFGknc00f4auafBXv36nBwn1cBlA0qVDbb3zx4558/WOrw3jVoyLRUuaezbsogeUVQm8nYcPGOJcBVrbPOT3iJyZKPkqAlhbvUOn1ZCtaghHWUaCFWhcIlWmy0wpQ55OlQ7N6VKISkYknVD1uTjF6TQMzJtupqFDWukXHbMlVJ+yA90TVlIvCVUN0St7qnnCvYeRtO2Vo4Dp3PLK2oTzj6xyc+ydLreaT6SsE+2Jyy5x689Z6iAqmLjfRp/WyG5adk37PfdFWw9Si+4jX2ZEtFxAdVzUFWJ8MhUohKUipJ1QLenW/kyRSQZWNX0lPdvWG2ptUk606HUvITU6CPjAQtpx2YreM2p054nbeh83i+l990ku4lQrdx9UXZKeVLy/0bqM5d9EwHJjOT7o6Uyq8Pw6IoMAN6LOl/7MZUDOvp1/UEIaaQG20C6lKdAGRL8ui9aEmc6x9bajxjON1ArdIUKFJ1g9u/YsqUVSYmeeseaa6SvyhmVYTcZaSEJT2cnZxs2T3bm5oOuN3wnQDT2+6Ev2pLMNfqxkOlDRvcJXMSo66acIelp+abWo8IIZN5wK1Xaa4Ls0+hUy1XgE8NezCG2ZtsLtKYTn3HxgtLnRodVNENNzazu8VbW02m8oqBpDS7Ub3HZiTe3Jeq46dV+mgdkBKRQDAAb6Ym5hzNNNp53uiVsybYXKTWaDqQ4fKpONR+WTMbpZz+jNZwXvVvV2bYxogfOJ481sdkNSUqmjaNJ1qOsneOhwFyWck90zEve4Kl1up0f1hCJJ+ZDsq8OIW4oX2z1FfAwLPRMrl2GWd0PrYPD00SmuqEyc6svS73zabIpU9RXbDTSUl6ZfVcZZTpWfy7YbNptMhhxV3Py6qhB2Kr74vLxUrBDacVLVqAELmZyi7UmuE+rq7EDu5WatGafmJgPkEyxXRvRTGmmLkpKMyyf4SAIU0/LpQVKvZxkXV174pP2nP2k0BTNOu3U+N3THySSYlztbbAPIWddknZ2Tbm0uzLbAqu6OzXHySxZ/dLRzrD60pZzZHea+EWfZjkvNWK/Om6p59F2gAxCNpMCzmZdDFwVQ8gcYlfWrpJh6QtR5tqelDcWVrHGDSFDwjdbky/JSzvzYMm6pQ+kV+UPSc7eXaEs4ppaWmysrp0qCCmbQuyrM1jDPvdn1RCJWTZSwynop+O9RNzCHA+lGbvsulskdtIzP6rliitcUVJ8dMTMzYSJXc8ygJXKvKUm6RrBx9UNNztoSsvJVCltyqDeqNhMMWnIWkuammKpDc/ilSTqvJ0adMOMzwlrNlHQUuIQc86R2HQIQuXlQt9Pn3jfX6zo8P3xtKXTLTbCw7Lvp6CxHyp2VsymClMDOrX2prgn2wSqSbmnlVvvzIzji66akxuKyrVbbs4k03QirsuNiDr8YLMsk3lm846s1W4raT+5//8QAKhABAAIBAgYCAgIDAQEAAAAAAQARITFBEFFhcYGRobHB8CDRMOHxQFD/2gAIAQEAAT8h/wDhLRcKO7KVQy6sNswYAVbVucASU0I29woj80f/ACGwHFsnyhLD/k7haXmXv+YmCTFVyj6W37medwg5B19XH3HCCuMAtNM6CJXic4sIeoZ8nL/4xZGtdB5ljO0z+lPY/wBQnHfGeW5DYh8sb3oUSsqm2ve5Rx8gAlyMD3qv69xaVkJYb6aRYyV2Egc/+toGqwR2tT4JqBlAPBdviU6+1PB1vSKHwnA6E2LdULRVdwO1H3KZbY5sOKjqeBu3EtRsg18BGn+wgl1DXCzdLtSTTTYUJvTEtbqmyPdQZ4h98+yLWYRi8K3/AAuN8QmB8osMM0q3evbl/wCgYJWZ5zFBg0Ct9ngRP9btnQdoUva2yvW5hx9lgS1AMUWLYESDXBfBLVEZ1qI/MDL3M64R6HmUrK0pA67F11Y8Alnb8OT2hGvl315HlxDHECtmm+yge/8Az03/AIWuv+M+JRDyF6Te7usHQ1GoHQlecqlH9RtsQ0W1l5nRlj8R1+2zQxt8AWW6cggzrbQ+ekAjQdwd+A/QszVZq8FibzTea+Wry18BBXy5msLbKsQE2DB/5jlvHFrer1e+dDMFFOQzzxuwZLYUMsYySjWHhS/PBlv05ZXThlL1rhdMZR4t+9Y88s6T3A+JoJFvTEtVjcQwjHMi03L0UASlnelztufyLPf/AJnzXu50OlyPLtLqkbeu7Fwqa7ynOB7ysqpti3QIPUGwOi+v61KWtDoDoT2JvAlJrhvidJ5imW84goN3gynq5eRVCHiD8oX0bcV43gX/AIlkJrpOi7votlth4da+wObLrMPh9OFUAAFUBbaR7zn+4nrvySaN5iC+TyejL4XL4DhPGpiI759oNi0XEYd5Y6xYTOi6GZ21Mr+4cKcWzqZ1i/TNhoP1pGxfw5NhBU3GA3wUavALgQlGgVQ456onLEW+YU/JMwj+0lMMHsbqDscp4/xVuQnQ4TnTfaWJTWXQIJU4HVmKzC6QtxmqGYs6I3tFovJ8KFmvAWEHg0iG0Yzzr2ltCMn5sXqkRoHyQFrCf1RsNWAXVGC07srhb2kpJh1GErm3uPhvpT7hJeXgRv8AgepRF1OH6OZgbctU+b+iOGuwEy4BmhWltXya9Is1HQ4GjrNe8/WayuKiU9UuAakA7YPjeqfEZWxUFv8ARtP+3+HBGtGbqE7FvEzwl/QMtvmFoi0hfV2xNqHybeJpNiF6iDBjuSMSFaiLNUCOIhtwJ9pfPITSHypeCWWdZixiJqi0TpIXz6fUMve+6Z+bjx6HPCjLwOaRUc6lS6FOtYMMZ3AV37eDTOgrUjHY+1/4KtjdHLN5Ue4BXUrd2A3XYJhGrCHc2YAciOuJLLn2io73ftmJ0p1KfMepUZ3dGojFA5bfgES3YW1liLHcas4Dzq2HYcCrWWPDBzbeYUFQM6njPy4brwlqqDWW2NRNgicUF3b2DvRE8sj9RZAmUANKKhRzwfEB5da1ZE/kyq8/RoE3Wo9Qr3E3eaau2hLuARpA7xhA5q6RqM+8/UafWiKlEvTOmp5I05SDb1HeZuNIAWtNqGj9AzygFTdCAoIx3tDEpjaVwdKPSKk4T31KeYULuLMsE0nNUTnrjOmJaz5TTvwrWxn7Enoek31MdgqVom5M4b2S0wfpl/lonsCIaxQwO7FfQevTvpTzFTAB14O0961ai0Fy09d3l6b/AHfEMiLEsc5ZdiprOBZbm3paG8edcSrTVt1z4X5eAQtsDgqGoxJBoCzun2IxxkDWdtmNppwS50iuqfU0gWxkOWSe0n0KjdLjc58GqdXokBajF2Aa+f4q2EK4W06+h5o5mXFQTMhKvYvbmSy4aeh256jrLxRKbLqOquQar0IKDajXzA5O5JhtWIOhwFsNJRFtuWyq4EfDLeOdxTb6WFd7wnoyhLY2jNyWdZr2mvAow7OyAa6i9hHFULyjgexjAG2aHRhON6Gcwrufh/DM0Jt/J6OXjnCkepT1TqtvngLFxPSZzdQlW5BqvQgZ+rSHYWfdTcI+XMupHXuO44jjfup9ieyap7jBPPXe9CA1SKEvs3wkuEJo4yfxasyZpCfJouswm4KHo8Cx8pblwMiakrhfAZzByoaZ5uVCBM8hu2jaZ7Z/gugIGQhw99TpLk96sDkH8nKB7tPOkG6pHStODSqBXWoGe6ld+5ng9Aoi3CWjDJBULD9FQmY1eYvF8NblKY5/oJliMwoez4fM1ztwcHEY303jMgFpU9unHKedTjYv7isafwKIejc2zwSCxxC7M+OALRP6tP76rzCCXs/GKeG+DTZN2foYZQPPLrnrnHODw6Xd6jqRr1WNCK2J8iC9Z/zHCWPzaPvaRrSLLQJ8JkbxIhb6zo2KmYqUzROWh2DeVuKiArpGuGdWPD7vHuu0JEI2gaBNTlcFoF6ddOo283EVFKs2Vudsbd5pFFUae8vppWfU7en/AGGDL0BOE2NQDrBZxljkrSfkhP8AbBB22RKItN5V5nJ3hiOPDrLmWE08FznMvqlqK1ytb0jRwGgoBEV9+eCVScnIe0sXKHbG8MefsSbLbOmVD8/ECeOmqmSsuS8kXHY+NIc8A55RDqVhV7gf9ZLglDOsuLOusudIY5RegMrPdM2foWusU15x7ZNVQ1TzODnnaV6Y5t6ruo7IrM5VeOkQDaDNNQanKKMMdUy+Fp8s1HlMSQncJdjQV4X0xn/y+dHvJ2gjztzunM6yq1m8ZZCCnFLfLRb69uJM9gE/9H9IqM4ICg2rFrU9UjCRMDdAfm4y1WXbA/l8xsLT/XIfn6mGDAY8QAhDbygqFu0CtIEdVEa05858JWFFfpvH3inmrOVzCB0EBtyh+YAdAegVDlzIGnSDqrDglKNjD0Hysr6tV1rPwsBIAoCDiWlsvv4D5gTiaml1dxx5nQ+IYq8ifUytMfMSyWSrLwwsO8qtixlOiEFuXnr7O7iO9KfcHftNpqmmMMxBMoZP4LpYTnXV+IqrxJr/AE5KpWGZFyO0YUioSrsRzC6Vxlf5jeCKolN1lQI4mCMuLYVlM75ChIIWJkSUpnJ3y4ASrl1Q+NYd41A/OpQqDiZTyYGpylV5GfEKUaJ3DLQLMI8owyrdnr4SVZdeFwI+Fx6JfAZN9UiotcoOK0mnDQ6DxG6ZTqTozOIKP/edf+w0RrEseFaZlukVFVBuxmq2c7zP1fbXjLUZOXTR/hZpDMd3pPZ6SpehX6Nrl0OjiMz3wAQ12dB1v9oaFYArliBKY1XgTY1BYnJjRK2VW3/G07RValYwRGHR4UCOcvcvFou6GZXlPXCrEEb10X5hML/jv7FMiZN9X8cLr0E9MvzFR4cwi+6PxPmRsh0mr+oydSOmCENybwuj0Wxm4lIXybP7mO7edS3s/MsS/Jq52RzMk3ontmnR8zSBQYA2mjgtSyAy8JywX+pquKDzQX+9ZZnKVaEc7bS6WQpkHaMXHxBlyHFiXVy39xozrMkGX8FolriLhp5mDLtmVHlrdui+K4Zy6N5WX2Ss1DI6h69DqQEDnNh+nQ+4vrEHs93s/XCggy4Ra14J57y7MZZNz9+OycDhASQ2m17HRKLf0A8yLL2dGXSDZznfUzQmS+BNM1j6dguYjrAKauvfU+ZgjdO/4ZGrBJsTUlUVQDyXMdWmsxKEVIw1GM6wjsOlUr+I3d4UUS6Uw5o1JYziVoGJSSuaM0hBt9Sz1WJ8vB7Z5NbLHwo26zX76kHQcOwLVfbn4jAlgAhuvKNhrLVNJc0xvMSUG10rVD0jHqNHZ7uTxKMDaBLsC/F8kxRXq3sel20lCBrCxOcGv4dRIfeRL/rzEBARoURlIcIarJfn4gNcOf4Bp5GHVQVK0Cw6kMnIlVDrpLkwW8ZliQ3oA4JcoYDShanUqV0RwQcU1U8toZtHR9saQXKzn/tf5MrSuTWTS9h0vMShsozwCCb5v8uFLY6v/jHgmmutgm87cNO8qL3i8DDZXJ/QByXhUqP8ypWCJNCsHJi1OkbqnLl/3+D5IRXYObLsImcPsNS864CYND3ZrG9a9ov+w/p+6IYHbE/qt4ZytrsOjMWiw7xL3B+g+g4CWBLuimGn+FKPDrM+IS+E24Lib8gFmVJQ8BqRzC3vrNoTTvDHDcZpAsyp03wpMUF/oRB8xfBdT9hNPDySPuMBYOMLB0h2jnCUKpQ3OSdSZPyN7Pcb8Mp31N/1esNy4B1Dfmxr9TXlFW+fl68QxbXr78v91xntrNFu/R6MyGxD7PTuMvIMWFaisDGSsTD96+1LJ6hMnIN4UX4ly6S4wy0Tm/KvmZgzDj6BNOFMW8y44lhvNxA9JapTqsHuH9/xemz5lcmgZU5/kSndNLg6QHrNUAcgneAvaGFC22DpwqLGSKmqGCW4JSE2ihtoWzc52fuYO7VVwuQFl2KZwfluHN8zQK4/l5vV/izMxVF0d/1zKlpXyn0cdHOfvNtla3IH4hdo3ijbLr9JX0I/4jXzYYpkoh5U/MXVsZ7gKEgq4WoC+kpLx6yGhml+CrTNh1/qIu0tyV+IUPJ0U+2AvsE8ygyvMj4GecOfxCNpuwH7WxIw2DcYVlOis7QC2YEwBe4OfaW3e8Z0SjXMdnS5oani6hA0B9AqFtYtWMSm8axhmE0EGwuIXj0gWmPeMPNJC2YW2tZ1TlD+f+vLxuSbgaSdkNLkyZxGmVoDDsPwzLmZVqUNb5naVhVrD1D0HUilBHnnmfcUfoPEDsdv6oAUD+3KH0dSaF8pqQpqBKqbbTkPMChRcxTk2vrpOZJ3N1eXTQgViAqVgEVyItD2VbOmrtH6iMVyA2diMtK6xm4G1WJW4AS7JShdu1seHqaJgK0HNjVVJaCjVl6nWDzQM1vCzW3df1DzKTqucjb6pXl/hy6WxQ0d9nOkOKq6qv8A40UyzYBup/qJOZ3YoeWv2OImWYqu8IDUtA+4/l2ua27GrENGw5d3ZYnPEA7P5PmEAFQFAQKxKHCzQ6f5QxsVequPpyfuYNU3qVumq9WdVcBw0hJfREXkYsQdvDOC1Dof0QWYDAE1tdUr6HN6EtV4jSZbc3WY8sLcGJTO9C2p3LeD/C4sAPp/tU2Vvojzl1r4BcWPuzddl3jxp1T8lS7rt3c+up6hNP2oPtlYb6JfKaWN6t+BCzrNt9VKcVFP0vcRsK1H3XlUAwGgbcLYgGy0QDnmJrSqw/Ep5gZsqxj66fCL6Z59qBc28iMWBLvdMxiitngQiolpKIxfXvZ+Yl4bjdRto9rYMMR+ANCHeW1T50+btK40oo5lOPtEfb0KL1GYcmBCI401QA1Xqyf4XsPdg/ui2XnwGCtajpNTLLv4HsjFnTpvrsBF1YIXMqZ2LNL7sSjjoUvpIXehvV+oR6LoQRE5++kxtOxl/bMBSG8YJ2wiPV7N9op1/Zi9mYTEbNCcrV/X9FPMubtwYprsnB1ZoQBJoMqxRgZHLo57sEF1LmVUKaNldJcUtJcldLZMNWkG5RdWxj0Ca1mwLGnmut6TaAwJQfuvWOMaKnHoGd8Z4XLbt07WinyE1pWm3al/wZxMEa2HzH/RvM3iMH/DkuxbDWyrYBoc11nBrKTkc4JZEqhUsTbLwAcF5MI/1Kcpcl/1Q85bAw60S7SDU6uGuK+ZT6aXwK+JoTGvXWTu3FmrgbK5VzAjDmmPR00TokHmH9vypwGJiqKDkgrbf8P3Fhi/RqD4gIx44Ny98vaPEr9ZVDQT7Q17Wl3t4cyGkWV3lB7HrQI+D/B8dwgr8wWuf+2vw/EYmin71djuNZgaDoyOYWYTpHxt/OB8VLy6EJv3QJ6c948rWpvq6P36RGlZljGII0uaQthzTSKWH05x6+8xfw/Uc3YQqa6/U/pGdceHwos0gYiNELrFoVyGw83AN8HANWsroSouSWxZliZPYx+Bz7jChRc+Ux477FTMBbNkyqs5XEw7Qxm8Por8cLgaAY9o1sK/wiPAgaLNKdWjBjBS7E0LSOQP1CsnN54VMtk1b3hZ+9YuTvqy9r2fmbLgytyzhlHuX5gQhQ5dJTkbzTgO46tqIy72zS3WFH++7KqNWYWntis9ef4huzMXD3R+nxHw5T654+/iaIBpaHYckhZua05BdHflGm8UF7zCaMx0CPbbdiUAYYb5n5eNHZflFfmVxi8u85vv/Do3rxB6sxSkdzcckyUJlE682kNFa8xrHjSE6TDCcmIHpLThM1KTXTLkuXk5Qt55q+gfySzHpGwhqyI41mBY2F+o93g/Gwx4ibgXp0v3AUFibBiIsYaWviTHzUa3aq6v9pXjitFy9XBDQ69ZpZ4PrrHY3TStiX5sy3lBKkLuPmj4R7tsL75l8yuNR3URuh+BlG8iqoMev8JCXR0QpIIsrnjWh2RNZUXd/VRMB6Kc3XnOaJQioupap7zi36MW9kgEAGAOGOE1WvuHyHWKIvDTuJsmiT4W5mJtIRZlR1xFzNYolymKRhd36flFZeG0ERkzPWpSjcfT+ku5DDLJZ5y0QoTuPqby6JrwLxAS4MapmE5tjB1cef8AHpsRzctekiANiOlbzJ7E0hrHrw2h0OCVp0o6awnxRu3ePRR3v+A0q4MlCOavbuzBLQNhzjUmdpW6OUvkkrvUunmQce1pHlmKLwF71AFyppKDGkZQ742VA5wuzg7cNdbrzymvfHDy9aQJK5UFZrBzVHfMqprK4C0iqmiBbUpPGS6igOSHp/x2yqxNVgvYgrTS6lkd3IB1ioYpV0lJsyzNUNetiB9NTwdeTTgasZIe0I9i/wBtj+IpdgFkFLuOaUcE8yCetOk5clwCdVmXeDaar5GIh0bgyJWVjt/RAIAFAaBFUDxF7I0loVoWKZH3Ws1+MgsuFQtAOGU6qYfwjjvUwaip36N0EwhxDUcI+TMfkvl26mEjPLYH+O27KLBkFdLD0pF4axKxUcOjbCdwr3eZf3nEGPQ1AT5nA+4AQg1uWNUXAaURC102xGmM1zg9zrwduYb1F6w8BqCgOX8iFoquegOq1BesmN1tm+c6iVFUpbBk8srgusvdfEbbWUkFJg5TdTnX/nOUvLlPtpf4ZDlyMzJ2X3i40mJDVbutLO0ZboOWp1JdxJz0Gug47t/OP0lsOg3WkJzS/X0DKunEA3OrLLuysUChLoPUB8+P8msvDWZko0EFYLm6XEtXm1rLvTJKogoM1e/IlQeBvtdh8mZ6vUuXd1/wF9yoLaNptnM2TzbFwappn1FIPpq4a9EaVcIS4JbD3i3MOpGMFNk63q/3EtxejImtLpjjLrC0ckDWC6+I36GVocxnzZg47wGrzW71f46iBiTuky/9zSKDbkbry15wq8fSyw5hreGsD62pGQp69cdocAKOCRqCstDOdF08ltX86U7wMBgedn4D/wBgFdqt0jW5zIjaiy+74ou1igqpoqNTmv8AkIZp1ZbQdWMKjqtn+9O1f/H/AP/aAAwDAQACAAMAAAAQkkkkkkkkkkkkkkkkklkgkkkkkkkkkkkkkkkkkkklvHckkkkkkkkkkkkkkkkkkjLa4EkkkkkkkkkkkkAJYN476spuEkkkkkkkkkkgH9b1aezUipskkkkkkkkkkAIeAbqDoUZ04kkkkkkkkkkHEKaNjWNDyPbEkkkkgAkkkgvnx1RIlW2PHwkkkklA8AAgnBRbTviYW265Ekkkg3BWObrz2PPxggGHFEgEkkkKJISvFrMfDpGHqGhyFCkkkguIA41N8trar/SbSiHoQkkgN8vM5rRplx2/J6qNqMgckkm2hCmfq1iycBri0HiHLMkkCqk8KOh0Crk1E5s3Mth0qkgNGDsp1h6V4kYN9Q1QlcRQgzqG+S5qJO/MedRz8IeDe1EM7mN6g1jkkLaF8V8sJ7yNIi6klTGSIaSxzE1OVAit/wB8TAP3TP6J4V8brm/G4CHf4P8AMkXlyDKxlcBmdWQCKHeYxfmNBD2ZElrIQOdrzkRs0MBVgtSBFyCcRfOKWG/h1nF43YRrA5Yu6zJ1kS2Gxk8dLBRoGuABiRVV9ow/DzOEpV443ARQQcIb7h47Bahfp3ohNb0MzTeQC410OU/CkEiQ+mtSQX/B4SQYYHlHGlYiSmQ294uDeOQSSSTxC7UoSonrK2QLpiuOzmSSSC26HJAamD+BlkRIanwtSSSSSimjb0tRVLYQm85RhYQSSSQ7uxv7fdv2F0vvBZyBYySSSTrDw95cDcqkGKL4IglASSSQP+/JjXdxkjISUenUMUCSSSBJErG6eB71Xx02/XX0MSSSSMkh4GqP6skmnX6B/wDF4kkkkBGqqxEUBVCfImLfEIvEkkkkQrSrMgfJtxczA2qBuUkkkkgn5ockl8ITloEUJowIkkkkkFajwkkgU8i3EghShbkkkkkgYkAkkkbd7yEknk1BkkkkkkkkkkkkgE4kEkkkkkkkk//EACoRAQACAQIEBgMBAQEBAAAAAAEAESExQRBRYXGBkaGxwdEg4fAwQPFQ/9oACAEDAQE/EP8A4be8Pp27/wDzGcktJtcGHwmNcvKAaStARgTdf/HtRUa9gNI7I7jR67HjGcm2MGyz8f8Ac6xUc5PB/wB7nTzpfOJbZzZCtM+k2cRWy2LYgmr6Or0h/Sb83n9R0ipZoH95+UFpa7zOXGh/1fInucAO/CiXefx0g3AWm2GRV0Mv68YCVe493f2jExK61n4PT3gHWYT0D/nujUssB6wbvFQi3SLcrGINkVo3Ks0d8e8oNL5i1Bitg5GPaHDVgxDgch5EznIfr/nsmS6EuHcILChLxecW4jAtjzRyMvi6ENMHVy/Uwyvb2jTrEpHLKjBGCdQE94+vr7xADy+f+ZHpkXf+BNEuKy2UzSARWsK/t6vLoR23A8AIp14EZ1SiYgFvPhbnfH+ZMtCWxUl630ETTyZhc3Jw/wB2/PTO78cLGOGspGjF7DAgLjXQ38WaqPJlKcjomjFUVxC5ideGvCt5ElF6wSE2gbg/yqed9RwiuI0GYIoHrr5TIjrcRfIZsgIponlKtnlHCo8v3AqC90+YNqzs37kVao7g+zBsrvj3iHN2z7QQuh6+Ut67z+k1+h5EPg+7zL5EG2bcAuaTXixYhMPNPUx7VLSXC9sn+LjSIqN3gsMMa67+f1Gra2wYMWeDAsRNYs1QvSUnpdXf9RV2s5k5EXql1AQ0vPjidDWbQ14a8bxFqUFzTmYPkj54fiazYQgjpP8Ag5P/AIJWvMozFZFRxGZ5ZmcEBoPN9pQyt7ftBeF8h9y1tdcBZQaXlGsW+GiaI+kB+H1IyprxWPJwRK2BEcph56etRUjqTRmPtTp3/wADZZCc183n+uR4sNvBI0gK0QZucjL9Ep/cf7tM6ridECAAfY5vPser4xUrV4I5hiURb46YZlySnzLjFkYZTFuk34qppCMLbme581XCayvWu/5i2hu8/wBcA1CkZzEoav11jUhTnv57eEsIzblwN8g3e/I99pvA/vThdl4IhbgXwBcNEGLjeQ18GPXAvWUNJZpLaoQE468OrjDw19M+EcuKK0w+vyyTl16H74auK1Gu1WpzIdXt6Pw9SLUpcsq3b/ek96NjtzeunIjtVteAuGkoi2wtrKqVw5IKgZTgJ5kqYZS5QlEtly5UW+Ndy/zOhH8SoKQTN/wUaBEuoxI1EEiXRtilQ5qjPXtFuJcmo6jo/wBzmqc82vg6PoxynbyPl0PC47Qch87vjL4H4AL4LxdCCiURskR9mR9cF1OqUSpVcKZTLZcXUinuY+uBKvGfhQJ1fiU9rY5xGC6OX64I2oTHmlfhpctU/mIFEW4EtGFJErMFxTc4DK3MkXtFSsHLPAjpDduGkzisx6mNTr7y9Mjo8yBNJbDWMvpCEZUaLy9xDJFrDP6zDxNHoZmgxq9+UVAGX1YhSkFdu3hz37QLamPeYjDS2e1QQNYW31eXlFWBKJouVuKiA9sMF7ad/wBazcdeEq/9H66+UqzDlrQD52OsCSp2ofPm9Yw49xy78zk7aO0VBSTEqDFy6ms7Rp5MMaq8zqfW8Vcx0eZwuacFl8A4LB0e0uplwdWDg/OleEU9Tfv/AGILQsFnoPl8ItmuHmS7EXvn34BcIohbf0N36IP2jg005RVlk0AhIH/rf68IqKg1eHq8vuWZcEQegvQ2PJuOCiALH1JQ9PfvqaeU0EFFcVo/iyMM136PM+eccG7WjszSGYwJUrjc5K38cKmjKQ5NcKp6ZPv9QKJf8x6B9ykNAt7v695kllzMC4FcCo+xz6vQ9Y7NmdnJ6SyGIjRgxdtZzAGswnoH9fjGK3B5s1iJ1Tq7H3L52izomp4mfCERyXhr/dINDUYgVxzPs+sdZ3lSktqLXib3ENlrfl0ZdQzCKmkFdZcv8Ganf6i0Wzqz9/RwrBzfVgUtBryAgL1zZUDiwtEUnOuXXpG7pmCPR5pwInCDZ7fLwcDk9Mx20ZLYeqxqHNPRlBsMHkK/g7W3Xpnhha6pfLofcRWl2Wupsw51993hzIFOYcFlXNC6w4sNW5rHC5QFvb54Wdn5ZUHmfmECUPEsyQwBnk18Tf3gUCRpyL14XUXeJa54f988EFrv8PBtgo8n9yxdT6PDDWm/P+fwVHj7MISdD0Oz8S10xogiymA9DkfMT5NnZjmH2Mc9DzjqnU2O3Xr5TeLU14OZV+nvPIfvF5Zxw6iE9b+Y+gp3MYvreIFNSyJX4LRbLdILFuFSrxBSf2McOmfuTSZDoZHr9MDq6WM0fuXMma1vzeDLhTSY0j0mF4Hv+/e5WY7CZY41lqQY1ZfN7lzVZ/uXA5xFYEMxiEN8QAG065PsRGYEmJ5n6ioAUR6m3SAE2wYbs1dPCYsKMRRpCcJYzM4EKyiaQQZo4IWoQNHAfv141FDr8c/Cbdgry36Xy4a8dOAmwcP34RlsSoEwxAHODpz9JoQ5jS+p15kbunhcuLHN2y+uF0dn4irfAh5kDSCjZFbBItw8+ILiMLwA04JcFRWHlBcDxx7wfD4QXbLaV6VqdOSXB2ef/ExVOw/veXTPjLg7y7/B4KrcYfh+HhfDJnT2QWEcyy0Ojn1PmXxqRmAtTfgj0WFsg6juc5mXvuPsjdUBgraVjcmuIZVSvxC3nhtKnWWg3pENsRoMs8TM5H7ZocMEu+DwBFLEp2nUTwH6l2nof3DlvJ+pYZIVbgL6SoIXJDDVanJ5fXAS2OZ/s9iALFHXV6vwbaufw5IGjKSaT+wxr8Q0fOCZ7OTh+ovhXeAFbJVeFJQe8RazwuNIt54JUuoZhoiGecza3viXXO5fZ+JfEj0y+cRWTvLiKimVXCpXE43scEXkT8MWj71fqy6J7B+Vd4FuOf3FCZHUjDfyZnqF5QBbvv8AqJ16Gau51flN6/lOt8obj6T9lm0PX6g2x6zeA8P3BzB3QcKHlC1BdX4uaQ44X7R2I770UlLqBX8WsXEcAj5y4Nv9G3+Gs2GeW36lOFMHag8+AANKOsOTwnTnR4CecXtETWKBbL5Li/8AO0qVwStL3ducsAYGh/as0CW3Yhhk0SwnQCOnBNF2cBACaOCns37QQWf4qVgF/wAzXNNzZ3N/COWqYcHZ/f2PKaXAgsfyEtVMetv94xlMT+8o5yw4u0frzn8an3LotsoyYqDx1oLHp8kY1eb+ePtwphbLIGNQbTvDARMTMNcPDb/G+Dk+0ILTDFseTD4mjBCgfJ8mWKNPlFTIdfvWb1f3hNoPB+IFx7pv3+8YHQr+6RHPM8AgXgmqR3PxrA+DvYPLWMWaORgP/Ir40mBFvXgQ0jggxcVvMUJx0wOb9EUqWusvpKPV7fb6zCv2N+7v7QiWiehZ7TTBmsflUfT/ABNDow4awZnHJyesNid3wzl7oj7mJd6QnzUQ3w2HNx6z7jsB3SFWLwtg0+wA94j8yv0QalDpj2mbzKirYX6gR2OehseO80mUyOrka+Lt7zCZTWro6ay4OI8QUVLed3riH0VPDfxYMBbt8ujpvEZIIcEFFcUjcz/Lowvvwdb7mr2NYjBW4s6eEyYYMUii2GVaLzY6hXx4NzMMFs6zWEUbFvYz+pqJP8RYKJYNw52scjzv+z1hv+CkeGia0jicyMB3/mUDkXNAbMjx+tIEuEV1PeVCOb7y5coD/leJ1QqC3oO3V68jaMltioxFb5qfMlxQcCz1O3To4iFvqcu5t7deDrFxwvlK4PHuvT7MBO8Pwas2FnN08j5YFOFUwVpUKivZzP10hFNuLeXQlRaIN5rA3FeTaJUZZ1M/I+TrNMRVF0eUNd+VOkxQ5n+V/mf69YiUyx5xwjfTp5P7rw4kYspi9C3M08SZQKczMuHAwy0QI0AHI3XqrLkWzkmTbF+b9cSK8SgqIY8e0sDpodjThs2Mw2fV7dem+0DvOXty6fuHppY8KP1P8id7xtOoTVFz6NV5EK/Swdj7gRhBgKAtYBm0z2bnvKZXbxgOfHKZqF+1MOPDLJyhaLVlSq52cmduxpHgtQWtwFb6nkTUkxDkhUuoXUmfOI53/cuDAnb2f8wRpzHM6fucwO/0YPPpyrq8jtBwq5pLlJuunbn48TVfzHB5JZYY+/ZgPHWGJrH0M+bg41HKiBRUwDoy/XjErlJWNuU7r8BouLNYEY8Mnh/P+duBkyRa1g3hLqLNo4nTftABR+F2NJv9ynDx284NBysHPbzhFVC4zOybenI4aRHBwgLgletXLwpB2fiVF/DWUJGKxrr5/wCjAGPfv9+cWYoctpZAgTBNYDQCBS6tf7p+KApiSxiHpVAPeBna8zF9/wBRJoun3rNYsVVEPgroOx8/XEUN4v1OJwGLiiYqw9X/AEFlQAzAuBrMlrVnY5/3hK8cRxdA/wBymZ79B2/wUOU8u8rab6rnBIDrgIuI0fY+/rz/ABuDMm8DWRqHufMW3ti4BFpqfJK5o3ZSlWmzv2+pVf8AUOng/wBa45Lr/FirmljXMRp17ypNka9PodPqFxE8ib7LPbpcdMvoPv8AJB14V64IahVwFawA0P8AtqtoYgq3/k//xAAoEQADAAMAAgICAgICAwAAAAAAAREQITFBUSAwYXFAgVDwscGRofH/2gAIAQIBAT8Q/wAHK3f8YugvbEBs6nSCTglFP8OtrDXvgajGXz/sY9s2Hr+c0XWQw35cb8Aa/Aa/IlxRnmhttWMNYIlxX/t+xCkHR6VHbb+T2h6p12dFA3BexbxCYTuyN6EdJ+OsqoRf73CHF/sJSQRJF/HWVjW6oS8vCiLZjZ8GujxDgm4R71CKyj0JjOrrKEaOetC2v46ErHvWIirBsOdY/BCTbiWzyO/SPAP72NvWPfRpDO6Y1MIQlVCmMlMv4zJKyyH+ThRRTo1uIZW9+WJNusW0UgbfFBo5UnseL9tZfo5BtDaooMiYkNUgyPjeJzRjXRUHhbzwY2PSZqyNp1ECa+psQ3S4qGKKKEx9Whu9j4A18aZwMb7Q2krLDuLk6sUMlOY4NiEoew1nv/0Ji6f2Gy+hLy/lsdELF9FGETCVUKWFnuG8IfobiglW/H/w4MUgmkq+jwSjfjCWXiNiTyQMGoxYbg3RKiUwuiLH5x+jp+RsJUQl5YldsZWPzocaI0b7+iaPAspluG0ulPgl7xA9mJXDjRaJUSylsWAqH4D5hiCF7DVO4twX2JLqNt8/AhfBex1wlOHSAvIJV3HgWEqJQbhSieFm/BciXhXkhkS5jo3CjU/tiGsfn5Q18UqJBqHT8s2JR+2ezw3BiVFpDFwxDQl2FEQojDKlwkOD2RolBKCi0yQ/g9D3ilEqJTeLSejTvwY8Ibc+DELRRrn4YxIQgiJGeSrDQ0bL8H/BRPZsfwegzbwI4w1HDwjgbpMXDExuDEppEo2+CypAmmJJHemsPhX6P2frDY916eE4SV/DXCUCkvAjoJahNQkw3cnvBe2FzQ/RHnYOlPYv2B6whMxpI2EJQ99j2IP1iESGxvCe/wCxDYmNUeGMtXWM3HsWloXRqPFmDdNbM3AWuDcOj1rDmglPdvwIhxn5QInt4as8CVELGMbfDvMIeC5pZkW/THghusex6F8iRj6NwuL0zqsWMbgzot2JVD8q8jcTZyJXWa5JQ/IhCR4llNHBURj9lzDwWFgeso7/ALwut4cnhxH0ozmE6XRaG7h4YsweBpHtHTDpoKaWULg7XMKnn/8AEYxKigX4Goo+/AkLA+HnmbE8QeXOhKHvCTF7DVJFjph9kPGxRPguiFqIXRsU777HmxIbFcG/af8AvB6R3CVEFij4L1YWMK04/ktigYh0Tg8JXWNx4FwuMf8AJhIhDbKIk7lF8PhBUR0XrD+MPBKxCUUHzRsT9jIaDFTXBsgxKjwJThzbGE7hLPdCU1lM0SHwZTpIfrHd4JEZzXwM7HjLc0NUhMG99Y2HAsNt6Ep0Y8MTHHBvKZTpCQYw9i8zEjwRaISLo/eOjXQliG8XHRPRXMhjRC0iwnDo17GsRjTQxNr5JUmPJcQjFobomHmEw1VBihuhKp4rxZHMM69L2SV/3/fx8E4KMbXgVfBnw3lOFMpUkwniYRi10exJs4focR0/QmNiY2Uo9shtlTcPJK1waf8AS/tkXSv5wTNUbArbQ/WN14+IJELcR+EXpECTyx50op6IhiOdH6iEJnhRSlwlRKbOos0sQa+mEHsn6OCRk8j9D9Cr0Veio08GPXTwl7dHF3M3cPC38YT95QipDYm2xYe/uBL6W8+TRbY55sbb0eRtLo3cLZpDG2RfBCEWI7hCGxbGp9dExM5wSD8BfooW8jWXA6xMxexElKMSotYeUIcG6znR0dCVRBKD+t4jCm2fkNoTQ23wpgVCQNKDPFhGl06qQYhC1sYbhbhNJiy3j6nrDc6JbMNnsVPhEujfrCglOcKcGips8JQajOnhiEuU3nVKUbJ9TP7A63s8hFKJIrWhN8DGUeCe8OD9MeA/IUXDqo2uCaXMpC0NOCY9qDC5mEJ9TcVNtijuNsKcDZRisUDdEqQs8NjcSXwXTh1jVYiw7GmJm9CWsv6mqoJQYlWcGOjRWJDEywT9rCnyFsbgtobP5amxJhUVsnycLlu/W0iL2QuDYlSJEIN+MoaobSI8C08O78FhKsSGSYncSnMOBOkw9fW1VhcFR+hoXR/FRwqWmzOo6OJlLC3wSmG1C05hsW2JQuxj79j4CdbY2kWnRdH8WPTY226xG3RtIp8ykQSb4SaynGUo3k0QafYyQ2qJKSGoODWxCVw5ov0UlcRiQhBDnPjomXa4NkNf8GwsrLBu/bWuZ0ba+lDMQlohrDpBbkxGzTL8k2uYssEkfCioWwp/OgqodFjXkd/h/wD/xAAqEAEAAgICAgICAQQDAQEAAAABABEhMUFRYXGBkRChsSAwwfBA0eFQ8f/aAAgBAQABPxD/AOETJANrxFtEbkfdfFwo3xkSyBSFiHH4o8SMLoUWxyLIGasNI1/8hXxGuljjFQucqZzVjTpCAPRVKvtHjmjADZcCAYKXLFGm8+a5Zb2OqROLyCNxPFoJV5VtmaJqOKPAOZsDQ/8AjULJHP2rAe5XmikE03hzhkXbiyvGjIrQ108BzRgDa3Nr+oWf4YYpwgEcWFQzRTjigoA4z2y/9G9EpT1gGMYOShc2gG1S7kq2dWq9f8pagpBQ2e9fGkjRy0c8lL7CEL5W/wAFjRS8X8sHN5trDce8IrgILAXdUveL6mugviTYa85eIjQOzNZowyqN3KF3ckutCwXtDBmVAoKXtkwD7/cR0kw8UYWtvSxiEQYY24ALWDGJ9z0aSjb5bZzj1BtJX93oc4gB3bRAnJMITTeVhO8dzduIKtWvhMQbKovggRL5ZeZajje41/LZauIIg7XS+If8eqfIGeWAi84UdmlaQG7M1YczMPxqq8X7phADKsEoJ7b0TQh0EIjA4avQYhIGYjVSG1QG6IRRRiLo46jd1fuPjUC83V4I6Wx3AGPf2XRcafqP/wCHgKmMUSE86FO53zPEX0SlnWWUk3krN1+ZOJhjm8swPlD5TC4dpCFlo5Lrbl/4y1O3DoOsJCy8td4VYlsryxrVorpDTI7T4qDcYBQHgiG4SbtgCmjw3H4XxipYqWLcrcrUPQbYS87DD7ETIyLyuyl7aIgDdChxdVN4GBA0zMBTINFg80X4lRp4prDM142k3jZSFXFJLQtWiszUoTAFatl45lxUvPjGHiW9gklhdL40fUCZryCA+g/4q1M94SKU2lNlg2gFCkOCaftvORxotoI/Zlhzq8xd6/cZ0qvMvCPmChV+f+pQV8MXaYP8w4ZNXlpo4fZ5C4uaejZAoeVFg4Ar9+xFcTpG0j4ARtcDTZ+MPYBM6YeSbsfEZaMzEIgSi1GfdSsoNu6sfssXjmgU8i8YV/HDUNH9u6l+/qWPP9h5dTfnEygbOGHs0JTq73VBwB5mwagrRr+oNxfmeD8rlRgJQ6PRqLCLox4NugDKocw/RKSXebSN4clGiwLKpE7QFAHRKbcRyrUKzl6Ija0dRcSvDC6op5izRja5ZyYeiZrt8S6slfNHygPmExMg4EIfBiMbWWUyayeFfiGNP2K5LMYuvj+0SMWVsK6ci00UNRHtEIlFWastdnNRKvZSb2+kPYzAKd7DDMNGV93Aeuss2UivN02nMwInGdBxZAS6E5Ig2hLP9JTz9Snn6i4/8m9BGRWfFrX3OoQgpZVUMZyTDkSpd0+41VZWqI4dlBXuotvRFzEIrXbLLkdUTpzYwLzTDwDPEYRawuTo3VjWcmxAhMjNSvQ0eImTmBcAF7eJkKyvcvFGuXuI6CZhuWjTUZoOAGov5SXErbq2qb5sgcUHq879DEWHhAAR2BevZ/ZWiAK0M4O8WjIBt21nTqAfE8/IoCI0HuFxBXJTAtU05qqHLBoN5FCNg7RUVlMrZt5sHOgU9qyuekue+Dw8DCMSsjXZmjnnPPeZYMvPkW82ddFHZFhIFk5pBvpKjNtJJrEq8jmKduFX5GvYfEIRz9PaQfuG5ggHVkofV8zKJk9gkH+1MGvmPrFTmNWvhjXLkwjy3HdFOYbeLrJztpvOAeITEBQR9BiM7a6m5kKTFrGLoFfyYmTDeTqoIKmu4dBn+EtebVHBQ55QLFa4JVt3MtLIvKOr5PmMJVtcWEePoEO+mH+c2QCjxM705QbP7CLgPcugAna4E3GMc/LSyna7MZFwquHPiD6cZTXmGqtpdUxA5YUkrGgL8uZQz4ZedcQS9MYoD7iTYS6G1fMCl74SXD6AzVcmpduBSEwkQ9AZARsEP7RLGKJ2vuWzCUUq1kJW93X5irj6oWvxV8R1RLqXft0TOFj29Qzg1/MoQ1CkG3RLx54JyRLpBp5aM8uTXTMMYhT9gtl5APUxhUGdcGFAy1p2L/rIOWqQWwMIKDaKcSlr4JVGhLXUAVWgmIUDMZdNnsNmoIUA0SpjFaPxzFqWa2uhxNtk1/Wj8hHQO3Y1y0HNvwi1dFqVdJt9TLp4S9rSAZ+JRRMmaFQKrFZEiL/zPiguPYAjDuvUqKrYtz9dQyRWPEQLFLtHk6jIGaK2Y8IV6i2rHG/glPAWfUTj7YBDaMcsFxA+Ahq+ZX/NEFDS7BO18l1GDVe0okr0kD3VnMUfK2m2ipZjq8ZIMTUmJGxEf6tIcfFexsgTa0DmguXFiJsqg+Uy82jiiGYqW42R75xFIPHD7QAeWYVIbcpjuHgA4pSbNDD17RX2/qLUe/mJFvzctatY+eH84W9Avc5e4paDvXbWPj5QPQBB1M64/lDxIZjO3LD6QLnxuaEAuNOp1POxXwREwk9QopXzEmyO0MylSPJKKqXUHYtcN1m4IGtts8f/ABHSbHYljHbPU2FPwADpXBBi7yQ3ig20FIdIp8x9M8dsvOcyhlrSH9Ww67FFXfoytBaw5CAtYWJrBpRrcAMuVFxYgLNwIdscaXUBFRiDIK6Ic3wlAVtDhF0RsBiOAqsRFdjwSlaxeu4dv2o0vJo2uXxHSDY4rbyzKt6KAIt2rnuXqMcEqC2/UW55jui4Ucwuw1O54BCZXDmrR7q+54iIdPcG2V5ncqb4B+4XIvTHXPaZRYi4KE8uZr5f1PXQ+o2ChOthPAz9IfRhjTmL1Fuxgo5C2cgnLEeNutGV5MPj+hajXrkNvreaDRoXQKSlW1bV5gfTDEthSWnTHahhTK0DSnkJnyYdfXjzZgcYxE1tv8ShyXbMG5oFHRrXoBXqVfgRW8AHiXejGQQ9uiQ+gNRbbW2Z50TAEM4yxFRlirJRACjH4BMYfP4nGgbzWvVzxACBAmxyH6SGpeWKFLcDkyzWPUdOCbxDcW18NS7eWABcT5xF29Iw4fkZ8TymYaOd1IhPOK0ARPpZS2GASO27O6CMGPyyRdgLR6dtC8ggKik2dezej3CovTqJzvE2rbzEP+8phLAviNrGDrYEVtXI01Has+Iwn+FEC8VsJXhzK+c0rCnXeBlDLhNQE4BeF0nXaI2NwGVAIjsID6l5WLdTbMAs6JxEDmMTvEEIv4JQvgKjuQMbYZrdWfdStVrGSOwAp6TyQ7ZWY3MmL8oPqKOII7slUVnOpc4YrzcEcxDJhMxzMb1Rxy4q64VlEtJaOYlulD1gvyseKOCGvwtEGwhtIwYEK7J1IHipQWnDAlRyVmwFDxieAHAQZDEu8wiqi1bqzhmdqmhbuQwOFTLQXiZrYmhht/8AX7gXO+Y6oca1BWrzjHM2NK9svWs8wg1iK74iJWO2VVy2K011hpwo0oXCNbYIaXfcAYXtf+lDVHKQ9UUMTQORXixuCAGUWJpOyVHa9ErWcrcW25wCLaDycke6xtVs93C3brTJR4AWhzprkebIhRgl8M+YiLRruW9waHFmfEyukwckLS5QFcGY7NP1EPFCsWkv0T5m92bm0aNwMto0tNT5bQLaqsdJesKuKr8VPzBt7w5uqOlE0MYI9926y0umA8wStAEuiWsqyEUJ2D0u0cnJRXcNLWaaux/xCLbHV7RXfV18TEWzO6gQKVu0AxWI/llYYFmmi2Vl4OIWKPiYyx8SpWAW2N4ZwQAvjMFreZjWDzGNGNkKLwz41A5qnhKEaAACMUf/AAhrzjKReMaGTzqjYNx3WPIFU2qKBa5TD1m+Cc4Zbyu7mI8mRWk1RdmWLAwttFeZgsfHScInExHeJYB8R1DIlyOX9S9WIg2jU8P+GWh7m4XQm/sXNaLrkzCZB2bHAjMktjNIFZfiBdrU9T6l6L+I7v8AUMZK8fitgM9SNFVcVZnm43jZSxRt2L9cxCqwxTX4p/8A0/GVp6lyi7oeeEG8TV9ODA28DxDBRpQj56wZ4PECKDuw3xrbjM18s24t/wC4qg3efBq88w9LlrQW/wARb09ErEcPiZlDCUnTal23SR74iDLZYuCj6ifO3utXGzaRPPk0Eo8AL8Q0ttrb8tUdxZijmEdqsuN2CDAtOkasPOG7TUVfAFATQQYcSCnH2mFcHlxFFCvwhSiN3EFUqW9tR0nMEWcCIy0cvcRRWWbbbLDSNZWBU05x/TH0csQj43z+/lyOQyKy4/Qdts9I1ZfFv+jcRQc9dRZo0Srr7lHUDyY/jARng0/lPgt3+d41EWjCmh/T5QHUBtWqII97FIymktPZxUMrR9VAeEL5jKInLsts4tMHaLEGZEY/fYz6xL1hxuk/4YK6aV0gvzRGfXaDgKJYxpz9ipcgxvFL2bcBDpirVtqrlSqrlVZdNQx5sqfq/iN/qzPeZTAKl5eYy1IWXt8XvUtWtHS+zV/MZ/kpEiVOADDvxKZZMUAMVd9POzK720rUGzOZwNNIoAKAOqISVQjLpRwtg6Q8RWRwBk21sGW0HUEJUr2RX/8A1BEpigbpkXnCS60Yee4yqzwfhLIGWqA56HTzPqSGgVhYMrXnFS3WRysSxrYzToHaAmcr6szNW5eYMJxRI+JbbU0GfMHgIsRYFXv8mIy3DClg5nO2PmKYLf0hqPMaB0cXCKeqH0QkSgWsz2GyAJr0kD6KSMEt+hEKIJBliORvhgZwEFqiQNmS5ciG5WwAsOnZgtLaoZE+IZETCPcEhZJFlwf2fKQwEOYjB1CBOjMybA6K4h2WLN2dQzH33BiNuChf8H7QCKsaWqjZBFHRD+WUoNqeESADWzrFYR+IwEwW2g+IX4hVydOoNRyjB+Dm82tND6m4eGoi4P6WMgVRvZG4fWqL9YYI2iOyWPuppxjhzHDrMVuAb0+W3mKSkED4RMJ5JzhWKPYFEMFIqlAdq4CWkCXMtmesUg8rTAYJ9vyBz9Q/xYDsb8/sS/SVLL2FxS5YpOjJwjD7gGXArNbIZCCfGCvxXOe4NtCkLsUPpU2remhj4lqFbl68GY5EI1aYRIpBwiYR3ExfcKnKy07vd/hHe5ishdgCYbptDm42TiKisEWeUb5irV0MOq+kTWL+sL0kcZLFesCubJYMYmFIlTaan6EJemRNqg+lHT5hRitBkC/sfI9wUFE5mIrySFD8utpTx3/mKXXczsFbOVq3lim3CsQsR9JKvD9zGV7F9I7iGkKEOVfO7C/4QzE8Ki0XYPDHdOItA74Ii1yMP1Z+TR5jy0YtV3bHUZLeaYSEw4BQAoANFcQoSDcq+YqTbzEjW6mBP2MExZZLr1xQ25wiKODJvopf0QAoI0iaYhEkFjVeMAeo+3VmNG4L4IuSnxWjHuEh4JUm6WWfg2VoJisDxuJoYlcsIzuMgGH+mYLKpivagf4Pw5ysrUbtiV1DLdYlo8nUlsbcmS95MXLfr0gVG2rC+VdZIXQlnPqwZ+lfgtktTJBgYfakf5CULblWFaUDSCxOnuAKoE2RXYvm2heTFo8Rt3HYjmXs6NrGQb8tndYjZ0VdRo6lFsKHPMBpaH1ucl3XeVuOjljPQRG4U3CsXxomcqX8QBaNKGNVMMR1LidcCwwzeG7XDKsMEGUh0jBaVYQksl7QKUp4SqIE4sHkx2FrIjTiWF7JrfKdAW8X6I3tMlWNQ2S055gKjL1MlCrgsrHzqQTxIChbe5d00/qVU8SjUjXQ2yxVmyklaO/mr+fwXhS7TcHdAgvBMZyB+Bs+IfiJdOYryXRruhteAsbHp59saaQDptmlko1G2Vya8EbYMH8xEA2i7HD1HxVQLLFndlHduInFp4YLMd8fEuvwEOvMW8txk1tKAHZqlEFhcEV2lDbYuZZRFiwBIsCbEzcPEKOpTqL6CI8wcEsbUlTzZ7mBXj2rIDhRjzbPdEm+cbeRxMJwQBbZbauGs3jI0N9MYigilsSxPcBIdGRQEFWYa3KChP5YNwymLjeSpy93A0BcemssdcCAAKIz1ISGM8RrCRKQax9y9XxWhCFuKNmwJZkpMC5nquPlIOnineAog5N0GG4eJJeBZQRbUo5ImtD4VLOX5D3LkHMoHoR7rqA62F3n2NvhpdGEGRWjQeDggXdzzFpoAsT5YHv/AAhXNv1KyZsTCN2R0Ffdqz6pT9ATZ+DQgbkM5aNfSwEKmlcIciR9zRhJtJs5bW/gKKOE2M3FiAVqF7Vp7RwB/AwwYxIBpCkOWXUXSBiKvoJdKILauPLlUYbYYvALCDVlllJhyg+xSmdgrVig9HI6EBe4CxPUuRPi4Ew30ijaRqoiTORQfMBciGSUIB6CO2J0E09Mvghj3NwvNwRKq7Y4IZEZbrHBKDdp5XbA5Xa3MbRCB7t6hVb8cSjIBAO0AfM2EIi2P5YBrB4S6/hArgnhnt1MtywVBO4pYGVeTzLSigWbhwD0XQefqEd0dbXPGbj9kshW4+1P+sAcNrIkBe3k/wAwiHbjUDre3YZVwmR4SVe3tmRD8QA3jwwVoC14JT3BlM9rXbUHlxM6QCh2nGCTKapqBPYukuVVbUVVKqq5fwlxOBUgxtTqlVRWVIgj4goPUC2GkPVxOPW5vl7fVo6inATakEKllG8r3EVt1WN1Sb3GPx17dPkYXuqOCCwS/Mo6SCMwD5CiinAdAoS4lRKVHfKqtqqtqswJZW5QZbs5lO8w80T3N/H5jnBfu5Ymflh0dLXX7WCGhQ2i7VENvYjcFU5fIKcJfMfm6kONVb262Ss0zQXAoD1HdqvBuA4KjpAdklrkxKstaOI6VzEF8eYg+K/CYsLQQ9BUqOvljcODqUmu450D4tWIjQmjlxymfWhWEa4WIKtlWl5zLYKO5MXUpyI2cQFuVXlu0cvtRXl/o3G5aHwKbBjRRcl6IALgKUdil3SqNl5hYYQ+LSI8AF2Hu6oQyKWyHDZnIOBCJDetxcYKM8jRzBXJtVVtoPoSag6ARQooKAGrMwefgA+Lomq0U6fVxeWLqn/Y19we1TeD+BFWL4WsGlpd3PswcKdUz8Je8yynBnBU1zHkmSQLAzwjJkwQbuFar/bzBTIUnv2/mKbJrDBYEcWoo2olI/VqZV6GGgZDWSiJKrKrlmTnMLQFuFRzdcRxTfpVWYZrVZy3oXkoBusL84v5myviZMnDG5TsoXEsXU6NdQUMQrjgFhFSKzg5grE4ggG+5YFNBwy00i6s6EFdIg2lKx/WnOLcLt28z00NNZGI2wDTsVmGpQCyQEszqCMyzVmwHxLKV7WQCNhoaPlF9PBMborvUr6l0B6zBo1zRpXvBlHEdKxf093ThQGQECsNRXoYKE6HwTJEvhEPUT3cbynsqKwSoNlbED5SBL4NG0sKA3X5StWxYKykZzZBQ4NcBzKAMsBk2yxLCGYKhrWMFDp/i8IcglDDAlUmKgD0Krmftgh4RHeoFYOgRwpkniY3qmojxELW8JaV32VDl7jGAC1KAMqvAE2DG4UUixMDCZLIasF5YwARRYLaFLVaKHi8p/wlh9L7AuXP9hVLiijWWiitbFgK3tZaOuiDbsKvElmeJh3BIHkz9r5iD7TOAMXBaO4kHUWwXoArFGEyZhzxtQHsSFOvxv8AH8xWtFisPI/QMC5oUWmtJTtAPFxLTFs9wJmmsAxmBvYMAaAMAdEpQo5YFR99/gWabzRYAPImcHmc1iw+ApDF7hVVkCo7G18orLkg9mLWwoisGDYQC0PhtYTkWKcmHzbmZPuDercpHS4iLeZWjZWTAKANFTVj0gujfiF8SkiMqtorDnCy+pd5Gc6IUVxAdC7d8G3aZ4Ov7LCDFtVc39ItJCEr5p09JSdwAApVR3zj855mAgCK1rInwFczHthr+LE+GUnOvlGSlovinuXUdMbBxkZ7bizVqbr3ZV8DLUatX1d1f7qBdhhGe0w+HiIc9CM5LIvkCoO7jUpzdT4qAPAqBQPBxEjsXBGCbJA2lAHnUHHnVCmlaEPIty3eCv45r+5im7ivhQ6nG1tAA3fcyvq4gyXzTZfNTSABoIli1ohlhoPUeCArLheo74FUZBPX+sRQkGayCnIleQ9UqKRGGoDoKIjIdggUWT8nZtFVxmNcg1K85RVsQ95gaVtZp/SR0rYh9MtP0QR11EAoPK+T5Fnohr+wNiIBqqH+FMqWJT5nKFkPQrXs0xjeCHHObYt8lxsLUG49hfaRA+IbzdCj0qwvHLLOL897gxaCiI14bB7VoCuvQW/G5hiaW2viuUGjxWRPC6z77lG6XT8LptQ1ze8Ylde2q+ReJ8qa1KHiEQq+ricNkXmQBaLu2pd5wTAhhBV6AwRHU2pa0OO39FAGqzD1jWFPI+ACQIhKFAGVWJGAZWWkqIdm3lmLv6gZyACy05RqywIWRLh7YyAk4yQrNLRiDaomogq/FGeHth00Mh5C8oKNmTmdM719q8pyrKqswNR7SrzYbK+Nb43KQohLjZkvfO4sKYOeZV+obuWeoncV0IbleVV/sFMoJoJl6pl6ALQFAtf2sNALXABuVePtXdKo0/AaI50suGUp5wgUICoQtGJ8iSrDMrAHBx6mollFRsCxGxWTklLS6G09kD1WUBA6wEutLmpqWUFHqLKqtYiU2/BBd33AJzD7SRyN+hvEVJpGpa+SRfMpFWjzNnPARx2rNNZpazBFf39gCuystcqjMqsipVdd5SvHiVWOZULlDD4lJjuPrX8U/HaEgbIjq8UGaSPxGc3PC83BQANFzYoaA30jT6YC0Hih5wTFFtybiQFHSX9ldrEP8wzzlV2EMuKK9aD+wzle0C/1AFLztKRFnhDr4SmW0jQxd2q2OBqB35ErPbvKq5W15YZAFrIrCPxcvApHOfhFZaN16hl5EH2OWp6qRMaKSbpiD7fBm6eDxg8mJbV5GoTiQcStA8iJolQYCLVqVhB9y4VZTI4wPm+m1Azun5aM5sTqEoVhV8KHwJfLCXvIJxsA2aHVWxuyEl7iKBS0g0BpSJ1wWqCbXQF9bh9ob9CHRcwcX0Euei8GiAen3MMLN3AZGWdP1lKNmziaKACWJwj1D3aNeWyti0X+4KTbjzd/cWQRFQpo8Df4mlgJgyb8f2HMa6aNPC5m0O25NeMyRMe3leVWMljaVI7hncTtgPobM2Z4cxyt8TALa4Ok5E4FJwwnJ3Z6fgOLWii0RXAE8Ghpk6b8EVB4FiaTs7gmhX+YSJyEFAmOgLXwCy/apYetBJApQGIaqK+fHleRXuKExH+JYilnNMfHM1+CYbxcSnnuGzT8FtPRSvC7jxTULyjbcgzwInRHPUTv0+wUowiYRhvq1lAeprWQKBVMIxJb2DpE37lJZXz+LBycvGoiywFL2I80hAhiKSwU8qIxgKha5rO1vi6/snKlE3tRo0k/9mAFc1YIT6ZSjqEQAS8WR7y+DtJmrkl4lD0aHpeZfuIHogpUI2I8KZqDtf8Ac4mOBbiCrd1DRGh3F2p3Xe7oUPNuWfsK2JSKFCFDzcxeS1cirulX3FQ7xWM1VtL9MGMy/VHSWsT6HMEkIQAwUGNEdtjg7jO/OwoftF3CkGk8/Nju/ImWgj5GXc9ZYnItFngcOY+cLNl+sis449Q0VfMUKzPLAGaeGzcRNheGmiQna18A5gjnlg7tP7MLa/UqoFst7Zgq1UvUQkkVboW2a4X/AGdiE6zK8Ip8zDifXExwyEMLNRxWhtBWOee2Tyr8uqQcSwKL8BhAZvqI3xBLpiyGq7jUpE0q7MnBR5u5VEPyIBQBoDj8UreF5WbNaiOqoonuNo42CBRkRGAFNx/lCBRvlm8tGWwa4OondhuWQoMVDvUKjeaMpQfCYAoVa6lSto5BwRSXcvQHEbBlOgRvVvC4tAFIles1QC9AcziIUGj+WdiUu5ir6gVHFuPEyDEZqYVtExMyM5vEvmoFf2qIKNjRUXVDJmq5JWyFp2DhHke5beAQuR0ittWwVeKiX5RQg4BaiGUSL4NpMbk1uZwLfQ0Rv+hOiM116jMUQpQFi+1LRxhHBHC68yxRPymUWmDdCSlzuCF2ckyJtIB5oJkPCPDTOaE0zb5D9YoLcQUFXJil44InNtKQtTwALBUpVMoRRuhVt6WIFJC4hSUD2IzPWvLhNXhqtws3uZLddyrXCzA6g2TIv/yIE8xgG1oijwZ268Qa8tRWX+1V8YYiQHazppixcGdVkqrit4N0CxDkJBG1AWsu6bqTzEnzJU2s0YchHDGXoHRSo9ALMtkVktksJLtnJyf6aADw3PgCktuumAP5pHlOpDVa15WTTMVA+irXC1KuIEtJeKoKCsd8RWikKndFEvQqof0B1AKADABxBKrRCCX6dxDrpA3yuAMrKw4zS53UAyVlcQIky8gAmAAAPwbW0kUUotYrRcB8ZD3NvT1QluEf8zJkpIathqK06muksoQ4mKS0yJpG6r9E6XaiUW8vKuVV5/tstx0q6EbLTZKgEU0c5YACFUruVVI2Pg9gHRZi9jCIv3+g0PPQ6fKWx+hAkizA7FmWKNwbiqDmwmBl4lpCThFCHVSDdUcgGfhgigBgAKD+q3NvFrKIypAGbYUBr0t+YLNDemg0Bg4CZDTsNJbylI1VxdeXRMMQfUICU4Mq6IKKKzSEjjWFNucASMuxOHhbNo+AAwB+UuY5mkuH4cnLeW81CTrHiXIFWiOzukZUmVQISwIpsqzU5JAwdcCnAANGAoYtAx7cXLxAO3Eu6hRfjRoTYrRFLIP8cAywGMA3ZLKz2sN0dLU1u2UP7hKUxkQSCIRRGQAtGyXpJMB6rAKrFqsJmBprLpiNWFsGoFXtk4oSX63jyMetMFK6Et8s1/XXAzRdAuUE9UaGy3oI2hEr9IqmS6h+oH7pyLFOiraupX4aWizGrGbGTfFGXzkqKBmDTN0pdAlog0XZjQWdNxQFwzxRyl6g5sFl90dT7cklgQ5dnSI21R5VVtQO49ZZXv8ApfD/AEERgI3VvLxVVulMkgopltbe5d9N4+HVNkBw0Uyq5bCtk3wXBbnVJqoyREUU7FSlFROS0mnSYQqiN4BHyJcFCsoNzYTnX/K3KOpz/QqILFuCw0OyBLavFYVgiwdbYUE4eRF+PVfCgMAMTI+Yr/Axy2OCOZfa1ppyRLcFBbRluq/4X//Z\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import requests\n",
    "import IPython.display as Disp\n",
    "Disp.Image(requests.get(image[0]).content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3.2 Extract the location table."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "url='https://pokemondb.net/pokedex/bulbasaur'\n",
    "tables = pd.read_html(requests.get(url, headers={'User-agent': 'Mozilla/5.0'}).text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>RedBlue</td>\n",
       "      <td>Pallet Town</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Yellow</td>\n",
       "      <td>Cerulean City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>GoldSilverCrystal</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>RubySapphire</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>FireRedLeafGreen</td>\n",
       "      <td>Pallet Town</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Emerald</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>DiamondPearlPlatinum</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>HeartGoldSoulSilver</td>\n",
       "      <td>Pallet Town</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>BlackWhiteBlack 2White 2</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>XY</td>\n",
       "      <td>Lumiose City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>Omega RubyAlpha Sapphire</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>11</td>\n",
       "      <td>SunMoon</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>12</td>\n",
       "      <td>Ultra SunUltra Moon</td>\n",
       "      <td>Route 2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>13</td>\n",
       "      <td>Let's Go PikachuLet's Go Eevee</td>\n",
       "      <td>Cerulean City, Viridian Forest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0                                1\n",
       "0                          RedBlue                      Pallet Town\n",
       "1                           Yellow                    Cerulean City\n",
       "2                GoldSilverCrystal  Trade/migrate from another game\n",
       "3                     RubySapphire  Trade/migrate from another game\n",
       "4                 FireRedLeafGreen                      Pallet Town\n",
       "5                          Emerald  Trade/migrate from another game\n",
       "6             DiamondPearlPlatinum  Trade/migrate from another game\n",
       "7              HeartGoldSoulSilver                      Pallet Town\n",
       "8         BlackWhiteBlack 2White 2  Trade/migrate from another game\n",
       "9                               XY                     Lumiose City\n",
       "10        Omega RubyAlpha Sapphire  Trade/migrate from another game\n",
       "11                         SunMoon  Trade/migrate from another game\n",
       "12             Ultra SunUltra Moon                          Route 2\n",
       "13  Let's Go PikachuLet's Go Eevee   Cerulean City, Viridian Forest"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#table that contains the locations for Bulbasaur\n",
    "tables[15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3.3 Transpose the DataFrame such that each column is a video game and each row/cell is the location "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "table=tables[15].T\n",
    "table.columns = table.iloc[0]\n",
    "table=table.drop(table.index[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RedBlue</th>\n",
       "      <th>Yellow</th>\n",
       "      <th>GoldSilverCrystal</th>\n",
       "      <th>RubySapphire</th>\n",
       "      <th>FireRedLeafGreen</th>\n",
       "      <th>Emerald</th>\n",
       "      <th>DiamondPearlPlatinum</th>\n",
       "      <th>HeartGoldSoulSilver</th>\n",
       "      <th>BlackWhiteBlack 2White 2</th>\n",
       "      <th>XY</th>\n",
       "      <th>Omega RubyAlpha Sapphire</th>\n",
       "      <th>SunMoon</th>\n",
       "      <th>Ultra SunUltra Moon</th>\n",
       "      <th>Let's Go PikachuLet's Go Eevee</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Pallet Town</td>\n",
       "      <td>Cerulean City</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "      <td>Pallet Town</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "      <td>Pallet Town</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "      <td>Lumiose City</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "      <td>Route 2</td>\n",
       "      <td>Cerulean City, Viridian Forest</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0      RedBlue         Yellow                GoldSilverCrystal  \\\n",
       "1  Pallet Town  Cerulean City  Trade/migrate from another game   \n",
       "\n",
       "0                     RubySapphire FireRedLeafGreen  \\\n",
       "1  Trade/migrate from another game      Pallet Town   \n",
       "\n",
       "0                          Emerald             DiamondPearlPlatinum  \\\n",
       "1  Trade/migrate from another game  Trade/migrate from another game   \n",
       "\n",
       "0 HeartGoldSoulSilver         BlackWhiteBlack 2White 2            XY  \\\n",
       "1         Pallet Town  Trade/migrate from another game  Lumiose City   \n",
       "\n",
       "0         Omega RubyAlpha Sapphire                          SunMoon  \\\n",
       "1  Trade/migrate from another game  Trade/migrate from another game   \n",
       "\n",
       "0 Ultra SunUltra Moon  Let's Go PikachuLet's Go Eevee  \n",
       "1             Route 2  Cerulean City, Viridian Forest  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3.4 Extract the location table and transpose it for all the pokemon in the sample "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set up the pokeman route names\n",
    "import time\n",
    "samples=df1['Name'][df1['sample']==1]\n",
    "route_names=list(map(lambda x:x.lower(),samples))\n",
    "del route_names[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Finished\n"
     ]
    }
   ],
   "source": [
    "#Extract the location table and transpose it for all the pokemon in the sample \n",
    "dfs = []\n",
    "for route_name in route_names:\n",
    "    url='https://pokemondb.net/pokedex/'+route_name\n",
    "    tables = pd.read_html(requests.get(url, headers={'User-agent': 'Mozilla/5.0'}).text)\n",
    "    tables=tables[-2].T\n",
    "    tables.columns = tables.iloc[0]\n",
    "    tables=tables.drop(tables.index[0])\n",
    "    tables['Name']=route_name\n",
    "    time.sleep(0.5)\n",
    "    dfs.append(tables)\n",
    "print(\"Finished\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1672"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Get the size of the table list we just created\n",
    "import sys\n",
    "sys.getsizeof(dfs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Because the location table is not in the same format for every page, so we will be extracting only the information for the X and Y games"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Check if the the column 'XY' is in the DataFrame.create a new DataFrame with only the name for the pokemon and the 'XY' column. \n",
    "#Append that DataFrame to a list to concatenate with the other pokemon that have the XY location column. \n",
    "dfs1=[]\n",
    "for df in dfs:\n",
    "    if 'XY' in df.columns:\n",
    "        dfs1.append(df[['Name','XY']])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>XY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>charmander</td>\n",
       "      <td>Lumiose City</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>wartortle</td>\n",
       "      <td>Evolve Squirtle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>butterfree</td>\n",
       "      <td>Evolve Caterpie/Metapod</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>pidgey</td>\n",
       "      <td>Route 2, 3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>raticate</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "0        Name                               XY\n",
       "0  charmander                     Lumiose City\n",
       "1   wartortle                  Evolve Squirtle\n",
       "2  butterfree          Evolve Caterpie/Metapod\n",
       "3      pidgey                       Route 2, 3\n",
       "4    raticate  Trade/migrate from another game"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Create a single DataFrame that contains the name or URL of the pokemon and the XY location.\n",
    "#Append all the sample pokemon and their XY locations to a single DataFrame.\n",
    "XY=pd.DataFrame()\n",
    "XY=XY.append(dfs1,ignore_index=True)\n",
    "XY.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "141"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#test length of the new dataframe\n",
    "len(XY)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 4 - Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4.1 Which type has the highest and lowest average attack? Average defense? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create a table that shows the average  attack and defense for each type. \n",
    "sampleDF=df1[df1['sample']==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bug',\n",
       " 'Dark',\n",
       " 'Dragon',\n",
       " 'Electric',\n",
       " 'Fairy',\n",
       " 'Fighting',\n",
       " 'Fire',\n",
       " 'Flying',\n",
       " 'Ghost',\n",
       " 'Grass',\n",
       " 'Ground',\n",
       " 'Ice',\n",
       " 'Normal',\n",
       " 'Poison',\n",
       " 'Psychic',\n",
       " 'Rock',\n",
       " 'Steel',\n",
       " 'Water']"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummies=list(sampleDF.columns[10:28])\n",
    "dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot=[]\n",
    "for dummy in dummies:\n",
    "    pivot.append(sampleDF.pivot_table(values=['Attack','Defense'],columns=dummy,aggfunc='mean'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table=pd.concat(pivot, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table.drop(0, axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [],
   "source": [
    "pivot_table.columns=dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Each type should be a row. Average attack and defense should be columns.\n",
    "pivot_table=pivot_table.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "MeanAttackDefense=pivot_table.plot(kind='bar', stacked=True, width=0.5,figsize=(20,10)).get_figure()\n",
    "MeanAttackDefense.savefig('MeanAttackDefense.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Max values of columns are at row index position :\n",
      "Attack     Dragon\n",
      "Defense     Steel\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# get the index position of max values in every column\n",
    "maxValueType = pivot_table.idxmax()\n",
    " \n",
    "print(\"Max values of columns are at row index position :\")\n",
    "print(maxValueType)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Min values of columns are at row index position :\n",
      "Attack      Fairy\n",
      "Defense    Normal\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# get the index position of min values in every column\n",
    "minValueType = pivot_table.idxmin()\n",
    " \n",
    "print(\"Min values of columns are at row index position :\")\n",
    "print(minValueType)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Pokemon type that has the maximum average attack is Dragon, the type that has the maximum avaerage defense is steel"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The Pokemon type that has the minimum average attack is Fairy, the type that has the minimum avaerage defense is Normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4.2 For the locations in pokemon X/Y, which location has the highest average total point score?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>XY</th>\n",
       "      <th>URL</th>\n",
       "      <th>Type</th>\n",
       "      <th>Total</th>\n",
       "      <th>HP</th>\n",
       "      <th>Attack</th>\n",
       "      <th>Defense</th>\n",
       "      <th>Sp.Atk</th>\n",
       "      <th>Sp.Def</th>\n",
       "      <th>...</th>\n",
       "      <th>Grass</th>\n",
       "      <th>Ground</th>\n",
       "      <th>Ice</th>\n",
       "      <th>Normal</th>\n",
       "      <th>Poison</th>\n",
       "      <th>Psychic</th>\n",
       "      <th>Rock</th>\n",
       "      <th>Steel</th>\n",
       "      <th>Water</th>\n",
       "      <th>sample</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>charmander</td>\n",
       "      <td>Lumiose City</td>\n",
       "      <td>/pokedex/charmander</td>\n",
       "      <td>Fire</td>\n",
       "      <td>309.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>wartortle</td>\n",
       "      <td>Evolve Squirtle</td>\n",
       "      <td>/pokedex/wartortle</td>\n",
       "      <td>Water</td>\n",
       "      <td>405.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>butterfree</td>\n",
       "      <td>Evolve Caterpie/Metapod</td>\n",
       "      <td>/pokedex/butterfree</td>\n",
       "      <td>Bug Flying</td>\n",
       "      <td>395.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>pidgey</td>\n",
       "      <td>Route 2, 3</td>\n",
       "      <td>/pokedex/pidgey</td>\n",
       "      <td>Normal Flying</td>\n",
       "      <td>251.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>raticate</td>\n",
       "      <td>Trade/migrate from another game</td>\n",
       "      <td>/pokedex/raticate</td>\n",
       "      <td>Normal</td>\n",
       "      <td>413.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name                               XY                  URL  \\\n",
       "0  charmander                     Lumiose City  /pokedex/charmander   \n",
       "1   wartortle                  Evolve Squirtle   /pokedex/wartortle   \n",
       "2  butterfree          Evolve Caterpie/Metapod  /pokedex/butterfree   \n",
       "3      pidgey                       Route 2, 3      /pokedex/pidgey   \n",
       "4    raticate  Trade/migrate from another game    /pokedex/raticate   \n",
       "\n",
       "            Type  Total    HP  Attack  Defense  Sp.Atk  Sp.Def  ...  Grass  \\\n",
       "0           Fire  309.0  39.0    52.0     43.0    60.0    50.0  ...      0   \n",
       "1          Water  405.0  59.0    63.0     80.0    65.0    80.0  ...      0   \n",
       "2     Bug Flying  395.0  60.0    45.0     50.0    90.0    80.0  ...      0   \n",
       "3  Normal Flying  251.0  40.0    45.0     40.0    35.0    35.0  ...      0   \n",
       "4         Normal  413.0  55.0    81.0     60.0    50.0    70.0  ...      0   \n",
       "\n",
       "   Ground  Ice  Normal  Poison  Psychic  Rock  Steel  Water  sample  \n",
       "0       0    0       0       0        0     0      0      0       1  \n",
       "1       0    0       0       0        0     0      0      1       1  \n",
       "2       0    0       0       0        0     0      0      0       1  \n",
       "3       0    0       1       0        0     0      0      0       1  \n",
       "4       0    0       1       0        0     0      0      0       1  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Join the pokedex data to the  location DataFrame created in Step 3.4. \n",
    "merged=pd.merge(XY, df1, on='Name', how='inner')\n",
    "merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {},
   "outputs": [],
   "source": [
    "XY_table=merged.set_index('XY').groupby(by='XY').mean()['Total']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x5760 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#XY_table.plot.bar(rot=270)\n",
    "MaxAverageTotal_in_XY=XY_table.plot(kind='bar', stacked=True, width=0.5,figsize=(20,80)).get_figure()\n",
    "MaxAverageTotal_in_XY.savefig('MaxAverageTotal_in_XY.pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Location has the highest average total point score :\n",
      "Sea Spirit's Den, Roaming Kalos\n"
     ]
    }
   ],
   "source": [
    "maxValuelocation = XY_table.idxmax()\n",
    " \n",
    "print(\"Location has the highest average total point score :\")\n",
    "print(maxValuelocation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "580.0"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XY_table[maxValuelocation]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Location has the highest average total point score is Sea Spirit's Den, Roaming Kalos; the highest average total points is 580"
   ]
  }
 ],
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