iris _flower.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d7c64ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0ddfcd81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a dictionary called gdp\n",
    "gdp = {\"country\":[\"United States\", \"China\", \"Japan\", \"Germany\", \"United Kingdom\"],\n",
    "       \"capital\":[\"Washington, D.C.\", \"Beijing\", \"Tokyo\", \"Berlin\", \"London\"],\n",
    "       \"population\":[323, 1389, 127, 83, 66],\n",
    "       \"gdp\":[19.42, 11.8, 4.84, 3.42, 2.5],\n",
    "       \"continent\":[\"North America\", \"Asia\", \"Asia\", \"Europe\", \"Europe\"]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3644ddd2",
   "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>country</th>\n",
       "      <th>capital</th>\n",
       "      <th>population</th>\n",
       "      <th>gdp</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "      <td>323</td>\n",
       "      <td>19.42</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>1389</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>127</td>\n",
       "      <td>4.84</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>83</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>66</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          country           capital  population    gdp      continent\n",
       "0   United States  Washington, D.C.         323  19.42  North America\n",
       "1           China           Beijing        1389  11.80           Asia\n",
       "2           Japan             Tokyo         127   4.84           Asia\n",
       "3         Germany            Berlin          83   3.42         Europe\n",
       "4  United Kingdom            London          66   2.50         Europe"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_df = pd.DataFrame(gdp)\n",
    "gdp_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d709e62f",
   "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>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>United States</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "      <td>323</td>\n",
       "      <td>19.42</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>1389</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>127</td>\n",
       "      <td>4.84</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>83</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>66</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country           Capital  Population    GDP      Continent\n",
       "US   United States  Washington, D.C.         323  19.42  North America\n",
       "CN           China           Beijing        1389  11.80           Asia\n",
       "JP           Japan             Tokyo         127   4.84           Asia\n",
       "DE         Germany            Berlin          83   3.42         Europe\n",
       "UK  United Kingdom            London          66   2.50         Europe"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change index from 0-4 to their country names\n",
    "gdp_df.index = [\"US\", \"CN\", \"JP\", \"DE\", \"UK\"]\n",
    "gdp_df.columns = [\"Country\", \"Capital\", \"Population\", \"GDP\", \"Continent\"]\n",
    "gdp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5a8360f5",
   "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>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>United States</td>\n",
       "      <td>Washington, D.C.</td>\n",
       "      <td>323</td>\n",
       "      <td>19.42</td>\n",
       "      <td>North America</td>\n",
       "      <td>9.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>China</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>1389</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Asia</td>\n",
       "      <td>9.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>127</td>\n",
       "      <td>4.84</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>83</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>66</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country           Capital  Population    GDP      Continent  Area\n",
       "US   United States  Washington, D.C.         323  19.42  North America  9.15\n",
       "CN           China           Beijing        1389  11.80           Asia  9.38\n",
       "JP           Japan             Tokyo         127   4.84           Asia  0.37\n",
       "DE         Germany            Berlin          83   3.42         Europe  0.35\n",
       "UK  United Kingdom            London          66   2.50         Europe  0.24"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add one more column\n",
    "gdp_df[\"Area\"] = [9.15, 9.38, 0.37, 0.35, 0.24]\n",
    "gdp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e56a1c8d",
   "metadata": {},
   "source": [
    "## Choose relevant columns and rows "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cff9d806",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>GDP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>United States</td>\n",
       "      <td>19.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>China</td>\n",
       "      <td>11.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>Japan</td>\n",
       "      <td>4.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>3.42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country    GDP\n",
       "US   United States  19.42\n",
       "CN           China  11.80\n",
       "JP           Japan   4.84\n",
       "DE         Germany   3.42\n",
       "UK  United Kingdom   2.50"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using [[]] to choose tbe columns of country and gdp \n",
    "gdp_df[[\"Country\",\"GDP\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4b3d3c72",
   "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>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>Japan</td>\n",
       "      <td>Tokyo</td>\n",
       "      <td>127</td>\n",
       "      <td>4.84</td>\n",
       "      <td>Asia</td>\n",
       "      <td>0.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>83</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>66</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country Capital  Population   GDP Continent  Area\n",
       "JP           Japan   Tokyo         127  4.84      Asia  0.37\n",
       "DE         Germany  Berlin          83  3.42    Europe  0.35\n",
       "UK  United Kingdom  London          66  2.50    Europe  0.24"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using slicing to choose the rows between japan and uk\n",
    "gdp_df[2:5] # note the 5th rows is not included"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "257e4452",
   "metadata": {},
   "source": [
    "## using loc and iloc to choose columns and rows\n",
    "### The difference is: \n",
    "- loc is based on label name \n",
    "- iloc is based on index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "918e5884",
   "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>Country</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>Japan</td>\n",
       "      <td>4.84</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country   GDP Continent\n",
       "JP           Japan  4.84      Asia\n",
       "DE         Germany  3.42    Europe\n",
       "UK  United Kingdom  2.50    Europe"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# loc \n",
    "gdp_df.loc[[\"JP\",\"DE\",\"UK\"],[\"Country\",\"GDP\",\"Continent\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3cef77bc",
   "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>Country</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>United States</td>\n",
       "      <td>19.42</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>China</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>Japan</td>\n",
       "      <td>4.84</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country    GDP      Continent\n",
       "US   United States  19.42  North America\n",
       "CN           China  11.80           Asia\n",
       "JP           Japan   4.84           Asia\n",
       "DE         Germany   3.42         Europe\n",
       "UK  United Kingdom   2.50         Europe"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# choose all the rows and country, gdp, continent as columns \n",
    "gdp_df.loc[:,[\"Country\",\"GDP\",\"Continent\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "694219e9",
   "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>Country</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>Japan</td>\n",
       "      <td>4.84</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country   GDP Continent\n",
       "JP           Japan  4.84      Asia\n",
       "DE         Germany  3.42    Europe\n",
       "UK  United Kingdom  2.50    Europe"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc \n",
    "gdp_df.iloc[2:,[0,3,4]] # =gdp_df.loc[[\"JP\",\"DE\",\"UK\"],[\"Country\",\"GDP\",\"Continent\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7ce69636",
   "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>Country</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>US</th>\n",
       "      <td>United States</td>\n",
       "      <td>19.42</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CN</th>\n",
       "      <td>China</td>\n",
       "      <td>11.80</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>JP</th>\n",
       "      <td>Japan</td>\n",
       "      <td>4.84</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country    GDP      Continent\n",
       "US   United States  19.42  North America\n",
       "CN           China  11.80           Asia\n",
       "JP           Japan   4.84           Asia\n",
       "DE         Germany   3.42         Europe\n",
       "UK  United Kingdom   2.50         Europe"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gdp_df.iloc[:,[0,3,4]] # =gdp_df.loc[:,[\"Country\",\"GDP\",\"Continent\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b05085a",
   "metadata": {},
   "source": [
    "## Filter relevant data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a8adc8b",
   "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>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>83</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>66</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country Capital  Population   GDP Continent  Area\n",
       "DE         Germany  Berlin          83  3.42    Europe  0.35\n",
       "UK  United Kingdom  London          66  2.50    Europe  0.24"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## filter all the data where the continent is in Europe\n",
    "gdp_df[gdp_df.Continent == \"Europe\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "26a3b239",
   "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>Country</th>\n",
       "      <th>Capital</th>\n",
       "      <th>Population</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Area</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>DE</th>\n",
       "      <td>Germany</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>83</td>\n",
       "      <td>3.42</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UK</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>London</td>\n",
       "      <td>66</td>\n",
       "      <td>2.50</td>\n",
       "      <td>Europe</td>\n",
       "      <td>0.24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Country Capital  Population   GDP Continent  Area\n",
       "DE         Germany  Berlin          83  3.42    Europe  0.35\n",
       "UK  United Kingdom  London          66  2.50    Europe  0.24"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or using loc\n",
    "gdp_df.loc[gdp_df.Continent == \"Europe\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e5611fb",
   "metadata": {},
   "source": [
    "# Iris flower dataset \n",
    "* Source (http://archive.ics.uci.edu/ml/datasets/Iris)\n",
    "* 4 characters namely sepal, petal, length and width are used as quantitative analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "360bf4de",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   sepal_length  sepal_width  petal_length  petal_width      species\n",
       "0           5.1          3.5           1.4          0.2  Iris-setosa\n",
       "1           4.9          3.0           1.4          0.2  Iris-setosa\n",
       "2           4.7          3.2           1.3          0.2  Iris-setosa\n",
       "3           4.6          3.1           1.5          0.2  Iris-setosa\n",
       "4           5.0          3.6           1.4          0.2  Iris-setosa\n",
       "5           5.4          3.9           1.7          0.4  Iris-setosa\n",
       "6           4.6          3.4           1.4          0.3  Iris-setosa\n",
       "7           5.0          3.4           1.5          0.2  Iris-setosa\n",
       "8           4.4          2.9           1.4          0.2  Iris-setosa\n",
       "9           4.9          3.1           1.5          0.1  Iris-setosa"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data \n",
    "iris = pd.read_csv('iris.txt')\n",
    "# add column names \n",
    "col_names = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
    "iris = pd.read_csv('iris.txt', names= col_names)\n",
    "iris.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "48ca837c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(150, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Iris-setosa        50\n",
       "Iris-versicolor    50\n",
       "Iris-virginica     50\n",
       "Name: species, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print the total number of iris flowers and their species \n",
    "print(iris.shape)\n",
    "iris.species.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "31be8722",
   "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>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     petal_length  petal_width\n",
       "0             1.4          0.2\n",
       "1             1.4          0.2\n",
       "2             1.3          0.2\n",
       "3             1.5          0.2\n",
       "4             1.4          0.2\n",
       "..            ...          ...\n",
       "145           5.2          2.3\n",
       "146           5.0          1.9\n",
       "147           5.2          2.0\n",
       "148           5.4          2.3\n",
       "149           5.1          1.8\n",
       "\n",
       "[150 rows x 2 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter petal_length and petal_width data \n",
    "iris.loc[:,[\"petal_length\",\"petal_width\"]] # = (iris.iloc[:,[2,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "390fea0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline \n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64711d02",
   "metadata": {},
   "source": [
    "# Scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "7a8c7a8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='petal_length', ylabel='petal_width'>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 262,
       "width": 386
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.plot(kind='scatter', x=\"petal_length\", y=\"petal_width\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "55e29ad4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='petal_length', ylabel='petal_width'>"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 479,
       "width": 720
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# divide the scatter plot into 3 categories based on petal length \n",
    "setosa = iris[iris.species == 'Iris-setosa']\n",
    "versicolor = iris[iris.species == 'Iris-versicolor']\n",
    "virginica = iris[iris.species == 'Iris-virginica']\n",
    "\n",
    "ax=setosa.plot(kind='scatter', x=\"petal_length\", y=\"petal_width\", color='Red', label='Setosa', figsize=(12,8))\n",
    "versicolor.plot(kind='scatter', x=\"petal_length\", y=\"petal_width\", color='Green', ax=ax, label='Versicolor')\n",
    "virginica.plot(kind='scatter', x=\"petal_length\", y=\"petal_width\", color='Orange', ax=ax, label='Virginica')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "7f162509",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='sepal_length', ylabel='sepal_width'>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABaEAAAPACAYAAAAsRrWWAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAABYlAAAWJQFJUiTwAAB6+ElEQVR4nOz9f5hd910f+r6/1iCDtFORZCiO04wcEoWAfCCZcSLktA4Iwo+T0jnhnt4zOqc9bSinyZ05F+zazzM99BQCPXBSV8YCOtMEWn6FXok+t5g5h19pQBCXSqnjGYdcix9RQuydhBgQCWpGDnYsfe8fe1TLkma8Je01e83M6/U88yzPWp+99kff9Z01S28vrV1qrQEAAAAAgCbcMOwGAAAAAADYvITQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY0aG3cBmVkr5eJK/kuSxIbcCAAAAAHA9bknyX2qtL7/aF26KELqU8neT/NzKt/9LrfVf9/m6x5LsXmXzn9Rab7rO1v7Kl3zJl7zoq77qq150nfsBAAAAABia3//938/nP//5a3rthg+hSykvS/LjSZaTdK5hF2eSHL7C+uXraOuCx77qq77qRYuLiwPYFQAAAADAcExMTGRpaemxa3nthg6hSyklyU8n+fMkv5jknmvYzV/UWt8xyL4AAAAAAOjZ6B9M+N1JDiR5a5KzQ+4FAAAAAIBLbNg7oUspX5XknUl+tNb6YCnlwDXu6sZSyt9JMpZekP3hJA/WWs8NqFUAAAAAgC1rQ4bQpZSRJO9J0k3yvde5u5tW9nWxj5dS3lprfX+f/az20OdXX1dnAAAAAAAb3EZ9HMf3JXltkr9fa722j2Ts+ekk35heEL0zyX+T5N1Jbknya6WUr73OPgEAAAAAtrQNdyd0KeX16d39fF+t9cT17KvW+gOXrHo0ydtLKctJ7k7yjiRv6WM/E6v0uphk/Hp6BAAAAADYyDbUndAXPYbjI0n+aYNv9a6V5R0NvgcAAAAAwKa3oULoJJ0kr0ryVUn+spRSL3wl+f6Vmp9cWXf4Ot7nT1eWO69jHwAAAAAAW95GexzHU0n+zSrbxtN7TvTvJPnDJNfzqI79K8s/uo59AAAAAABseRsqhF75EMLvutK2Uso70guhf7bW+q8vWv9FSV6R5Au11o9dtH5vkk/XWj9zyX52J/mXK9/+/ED/AAAAAAAAW8yGCqGv0UuT/H6Sx5PcctH6v53kH5dSfivJx5N8Lr2w+s1JvjjJryY5tK6dAgAAAABsMlshhF7NbyX5yvTunt6f3vOf/yK9x3m8J8l7aq11aN0BAAAAAGwCmyaErrW+I8k7rrD+sSTlCuvfn+T9TfcFAAAAALCV3TDsBgAAAAAA2LyE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAbdbtJrOzye7dyc6dveXsbG89AADABiCEBgBoq2PHkr17k3vv7YXOTz7ZW957b2/9sWPD7hAAAOB5CaEBANqo200mJ5Pl5StvX17ubXdHNAAA0HJCaACANpqbWz2AvmB5OZmfX59+AAAArpEQGgCgjY4e7a/uyJFm+wAAALhOQmgAgDY6fXqwdQAAAEMihAYAaKPR0cHWAQAADIkQGgCgjaam+qs7eLDZPgAAAK6TEBoAoI1mZpJOZ+2aTieZnl6ffgAAAK6REBoAoI3GxpKFhdWD6E6nt31sbH37AgAAuEpCaACAtjpwIDl5Mpmd7YXNO3b0lrOzvfUHDgy7QwAAgOc1MuwGAABYw9hY8s539r4AAAA2IHdCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwDt0+0ms7PJ7t3Jzp295exsbz0AAAAbihAaAGiXY8eSvXuTe+/thc5PPtlb3ntvb/2xY8PuEAAAgKsghAYA2qPbTSYnk+XlK29fXu5td0c0AADAhiGEBgDaY25u9QD6guXlZH5+ffoBAADgugmhAYD2OHq0v7ojR5rtAwAAgIERQgMA7XH69GDrAAAAGDohNADQHqOjg60DAABg6ITQAEB7TE31V3fwYLN9AAAAMDBCaACgPWZmkk5n7ZpOJ5meXp9+AAAAuG5CaACgPcbGkoWF1YPoTqe3fWxsffsCAADgmgmhAYB2OXAgOXkymZ3thc07dvSWs7O99QcODLtDAAAArsLIsBsAALjM2Fjyznf2vgAAANjQ3AkNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0ABsbd1uMjub7N6d7NzZW87O9tYDAAAA100IDcDWdexYsndvcu+9vdD5ySd7y3vv7a0/dmzYHQIAAMCGJ4QGYGvqdpPJyWR5+crbl5d7290RDQAAANdFCA3A1jQ3t3oAfcHycjI/vz79AAAAwCYlhAZgazp6tL+6I0ea7QMAAAA2OSE0AFvT6dODrQMAAACuSAgNwNY0OjrYOgAAAOCKhNAAbE1TU/3VHTzYbB8AAACwyQmhAdiaZmaSTmftmk4nmZ5en34AAABgkxJCA7A1jY0lCwurB9GdTm/72Nj69gUAAACbjBAagK3rwIHk5MlkdrYXNu/Y0VvOzvbWHzgw7A4BAABgwxsZdgMAMFRjY8k739n7AgAAAAbOndAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGM2RQhdSvm7pZS68vVdV/nav1ZK+alSyh+XUp4qpTxWSjlcSnlhU/0CAAAAAGwVGz6ELqW8LMmPJ1m+hte+IslikrcmeSjJ/Un+KMn3JDlRSnnxAFsFANi4ut1kdjbZvTvZubO3nJ3trQcAAFjDhg6hSyklyU8n+fMk77qGXcwn+atJvrvW+t/VWv9xrfVAemH0Vyb5oYE1CwCwUR07luzdm9x7by90fvLJ3vLee3vrjx0bdocAAECLbegQOsl3JzmQ3p3MZ6/mhaWUr0jyzUkeSzJ3yebvX9nf3y2l7Lz+NgEANqhuN5mcTJZX+Udny8u97e6IBgAAVrFhQ+hSylcleWeSH621PngNuziwsvwPtdbzF2+otX4uyX9KsiPJ111XowAAG9nc3OoB9AXLy8n8/Pr0AwAAbDgbMoQupYwkeU+SbpLvvcbdfOXK8iOrbD+1snxVH/0sXukryauvsTcAgHY4erS/uiNHmu0DAADYsEaG3cA1+r4kr03y12utn7/GfexaWZ5ZZfuF9V96jfsHANj4Tp8ebB0AALDlbLgQupTy+vTufr6v1nqiybdaWdbnK6y1TlxxB727occH2RQAwLoaHe3vec+jo833AgAAbEgb6nEcFz2G4yNJ/ul17u7Cnc67Vtn+Vy6pAwDYeqam+qs7eLDZPgAAgA1rQ4XQSTrpPaP5q5L8ZSmlXvhK8v0rNT+5su7w8+zrD1eWqz3zec/KcrVnRgMAbH4zM0mns3ZNp5NMT69PPwAAwIaz0R7H8VSSf7PKtvH0nhP9O+kFzM/3qI7fWll+cynlhlrr+QsbSikvSPKGJJ9P8oHr6hgAYCMbG0sWFpLJyWR5+fLtnU5v+9jY+vcGAABsCBsqhF75EMLvutK2Uso70guhf7bW+q8vWv9FSV6R5Au11o9dtK+PlVL+Q5JvTjKT5Mcv2t0PJNmZ5N211rOD/nMAAGwoBw4kJ08m8/PJkSO9DyEcHe09gmN6WgANAACsaUOF0NfopUl+P8njSW65ZNt0kuNJfqyU8o0rdfuSfEN6j+H4J+vXJgBAi42NJe98Z+8LAADgKmy0Z0IP1Mqd0bcl+Zn0wue707tr+seS7K+1/vnwugMAAAAA2Pg2zZ3QtdZ3JHnHFdY/lqSs8bpPJHlrU30BAAAAAGxlW/pOaAAAAAAAmiWEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgCedeJEsm9fMjKSlNJb7tvXWw8AAADXQAgNAPTcd19y++3JQw8l58711p071/v+9tt72wEAAOAqCaEBgN6dzvfcs3bNPfe4IxoAAICrJoQGAJI77+yv7q67Gm0DAACAzUcIDQAki4v91T38cLN9AAAAsOkIoQGAZ58BPag6AAAAWCGEBgCSbdsGWwcAAAArhNAAQDIx0V/dbbc12wcAAACbjhAaAEgOH+6v7v77G20DAACAzUcIDQAk+/cnhw6tXXPoUK8OAAAAroIQGgDoufvu5PjxZN++Z5/9vG1b7/vjx3vbAQAA4CqNDLsBAKBF9u9PPvCBYXcBAADAJuJOaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGYHgeeCC5+eaklGe/br65tx7o6XaT2dlk9+5k587ecna2tx4AANbD2W7yyGzyS7uTX9jZWz4y21u/1RiLa1JqrcPuYdMqpSyOj4+PLy4uDrsVgPZ5+9uTd7979e1ve1vyrnetXz/QRseOJZOTyfLy5ds6nWRhITlwYP37AgBg63jiWPLgZPLMFa5JRzrJHQvJTVvkmnSLj8XExESWlpaWaq0TV/tad0IDsP4eeGDtADrpbXdHNFtZt7t6AJ301k9OuiMaAIDmnO2uHromvfUPTm6Nu4CNxXURQgOw/mZmBlsHm9Hc3OoB9AXLy8n8/Pr0AwDA1vORudVD1wueWU5ObYFrUmNxXYTQAKy/T396sHWwGR092l/dkSPN9gEAwNb1eJ/XpI9tgWtSY3FdhNAAAG10+vRg6wAA4Go91ee1Zr91G5mxuC5CaACANhodHWwdAABcrRv7vNbst24jMxbXRQgNwPp7yUsGWweb0dRUf3UHDzbbBwAAW9fuPq9Jb9kC16TG4roIoQFYf3Nzg62DzWhmJul01q7pdJLp6fXpBwCAredVM8nI81yTjnSSPVvgmtRYXBchNADr7y1vSd72trVr3va2Xh1sVWNjycLC6kF0p9PbPja2vn0BALB17BxL7lhYPXwd6fS279wC16TG4roIoQEYjne9K/nFX7z8kRsveUlv/bveNZy+oE0OHEhOnkxmZ3th844dveXsbG/9gQPD7hAAgM3upgPJm08mXz2b7BhLtu3oLb96trf+pi10TWosrlmptQ67h02rlLI4Pj4+vri4OOxWAAAAAACu2cTERJaWlpZqrRNX+1p3QgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AMPT7Sazs8nu3cnOnb3l7Gxv/VbTlrFoSx8AAABsGqXWOuweNq1SyuL4+Pj44uLisFsBaJ9jx5LJyWR5+fJtnU6ysJAcOLD+fQ1DW8aiLX0AAADQOhMTE1laWlqqtU5c7WvdCQ3A+ut2Vw87k976ycmtcfdtW8aiLX0AAACw6QihAVh/c3Orh50XLC8n8/Pr088wtWUs2tIHAAAAm44QGoD1d/Rof3VHjjTbRxu0ZSza0gcAAACbjhAagPV3+vRg6zaytoxFW/oAAABg0xFCA7D+RkcHW7eRtWUs2tIHAAAAm44QGoD1NzXVX93Bg8320QZtGYu29AEAAMCmI4QGYP3NzCSdzto1nU4yPb0+/QxTW8aiLX0AAACw6QihAVh/Y2PJwsLqoWen09s+Nra+fQ1DW8aiLX0AAACw6QihARiOAweSkyeT2dlesLljR285O9tbf+DAsDtcP20Zi7b0AQAAwKZSaq3D7mHTKqUsjo+Pjy8uLg67FQAAAACAazYxMZGlpaWlWuvE1b7WndAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDZtVt5vMzia7dyc7d/aWs7O99WB+AABAe5ztJo/MJr+0O/mFnb3lI7O99QCbQKm1DruHTauUsjg+Pj6+uLg47FbYao4dSyYnk+Xly7d1OsnCQnLgwPr3RTuYHwAA0B5PHEsenEyeucL1+UgnuWMhucn1OTB8ExMTWVpaWqq1Tlzta90JDZtNt7t6wJj01k9OuuN1qzI/AACgPc52Vw+gk976ByfdEQ1seEJo2Gzm5lYPGC9YXk7m59enH9rF/AAAgPb4yNzqAfQFzywnp1yfAxubEBo2m6NH+6s7cqTZPmgn8wMAANrj8T6vzx9zfQ5sbEJo2GxOnx5sHZuL+QEAAO3xVJ/X3f3WAbSUEBo2m9HRwdaxuZgfAADQHjf2ed3dbx1ASwmhYbOZmuqv7uDBZvugncwPAABoj919Xp/f4voc2NiE0LDZzMwknc7aNZ1OMj29Pv3QLuYHAAC0x6tmkpHnuT4f6SR7XJ8DG5sQGjabsbFkYWH1oLHT6W0fG1vfvmgH8wMAANpj51hyx8LqQfRIp7d9p+tzYGMTQsNmdOBAcvJkMjvbCxN37OgtZ2d76w8cGHaHDJP5AQAA7XHTgeTNJ5Ovnk12jCXbdvSWXz3bW3+T63Ng4yu11mH3sGmVUhbHx8fHFxcXh90KAAAAAMA1m5iYyNLS0lKtdeJqX+tOaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMRsyhC6l/PNSym+WUj5RSvl8KeUzpZRHSinfX0p58VXs57FSSl3l64km/wwAAAAAAFvBhgyhk9yVZGeS9yX50ST/NskzSd6R5MOllJddxb7OJPmBK3wdGmC/AO1y4kSyb18yMpKU0lvu29dbv9X66HaT2dlk9+5k587ecna2t57hcVwAAAA2jVJrHXYPV62U8sW11r+8wvofSvK9Sf5VrXW6j/08liS11lsG3ePK/hfHx8fHFxcXm9g9wLW5777knntW337oUHL33Vujj2PHksnJZHn58m2dTrKwkBw40GwPXM5xAQAAaJ2JiYksLS0t1Vonrva1G/JO6CsF0Cv+3cpyz3r1ArChnDixdvCb9LY3fSdyG/rodlcPOpPe+slJd96uN8cFAABg09mQIfQavn1l+eGreM2NpZS/U0r53lLK95RSvqGUsq2J5gCG7s47+6u7665G22hFH3NzqwedFywvJ/PzzfXA5RwXAACATWdDPo7jglLKPUk6SXYluS3JX08vgP6mWuuf9fH6x5LsvsKmjyd5a631/X32sdrzNl49Pj6+w+M4gNYYGUnOnXv+um3bkmee2dx97N7d3920Y2PJ44830wOXc1wAAABa6XoexzHSREPr6J4kX37R97+e5O/3E0Cv+Okk/zHJySSfS/IVSf7XJP8wya+VUvbXWn93gP0CDFc/we/V1G3kPk6fHmwdg+G4AAAAbDob+nEctdabaq0lyU1JviO9EPmRUsp4n6//gVrrsVrrn9Ran6y1PlprfXuSH0nyJUne0ed+Jq70leQPrukPBtCUbX0+bajfuo3cx+joYOsYDMcFAABg09nQIfQFKyHyA0m+OcmLk/zcde7yXSvLO65zPwDtMtHnv5i57bbN38fUVH91Bw821wOXc1wAAAA2nU0RQl9Qa308ye8l2VtKuZ5bpP50Zbnz+rsCaJHDh/uru//+RttoRR8zM0mns3ZNp5NMTzfXA5dzXAAAADadTRVCr7h5ZXk9DxLdv7L8o+vsBaBd9u9PDh1au+bQoV7dZu9jbCxZWFg98Ox0etvHxprrgcs5LgAAAJvOhguhSymvLqXcdIX1N5RSfijJX01yvNb62ZX1X7TymldcUr+3lPKiK+xnd5J/ufLtzw/+TwAwZHffnRw/nuzb9+wzl7dt631//Hhv+1bp48CB5OTJZHa2F2ru2NFbzs721h840HwPXM5xAQAA2FRKrXXYPVyVUsqdSf5FkgeTfCzJnyf58iRvTO+DCZ9I8o211t9bqb8lyceTPF5rveWi/bwjyT9O8lsr2z+X5BVJ3pzki5P8apK31Fqfvo5eF8fHx8cXFxevdRcAAAAAAEM3MTGRpaWlpVprnx/09KyRJhpq2G8k+Ykkb0jytUm+NMnZJB9J8p4kP1Zr/Uwf+/mtJF+Z5LXpPX5jZ5K/SPI7K/t5T91oCT0AAAAAQMtsuBC61vpokpmrqH8sSbnC+vcnef/gOgMAAAAA4FIb7pnQAAAAAABsHEJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJo2Ky63WR2Ntm9O9m5s7ecne2t34pOnEj27UtGRpJSest9+3rrt6K2jEcb5mkbemiTtswNaLnumW5m3zeb3Yd3Z+cP78zuw7sz+77ZdM9s0XPH2W7yyGzyS7uTX9jZWz4y21u/lXoAAOCKSq112D1sWqWUxfHx8fHFxcVht8JWc+xYMjmZLC9fvq3TSRYWkgMH1r+vYbnvvuSee1bffuhQcvfd69fPsLVlPNowT9vQQ5u0ZW5Ayx37+LFMHp3M8tOXnzs62ztZmFrIgZdvoXPHE8eSByeTZ65wLh3pJHcsJDc1PB5t6AEAYJObmJjI0tLSUq114mpfK4RukBCaoeh2k717rxyqXdDpJCdPJmNj69fXsJw4kdx++/PXHT+e7N/ffD/D1pbxaMM8bUMPbdKWuQEt1z3Tzd75vVcMoC/obO/k5PTJjO3aAueOs93kV/ZeOfy9YKSTvPlksrOh8WhDDwAAW8D1hNAexwGbzdzc2qFa0ts+P78+/QzbnXf2V3fXXY220RptGY82zNM29NAmbZkb0HJzD82tGUAnyfLTy5n/4BY5d3xkbu3wN+ltP9XgeLShBwAA1iSEhs3m6NH+6o4cabaPtuj3XyI8/HCzfbRFW8ajDfO0DT20SVvmBrTc0ZP9nTuOPLpFzh2P93kufazB8WhDDwAArEkIDZvN6dODrdvozp0bbN1G15bxaMM8bUMPbdKWuQEtd/rJ/s4J/dZteE/1+efst26j9gAAwJqE0LDZjI4Otm6j27ZtsHUbXVvGow3ztA09tElb5ga03OiO/s4J/dZteDf2+efst26j9gAAwJqE0LDZTE31V3fwYLN9tMVEn8/Kv+22Zvtoi7aMRxvmaRt6aJO2zA1ouam9/Z07Dt66Rc4du/s8l97S4Hi0oQcAANYkhIbNZmYm6XTWrul0kunp9eln2A4f7q/u/vsbbaM12jIebZinbeihTdoyN6DlZl4/k872tc8dne2dTL9ui5w7XjWTjDzPuXSkk+xpcDza0AMAAGsSQsNmMzaWLCysHq51Or3tY2Pr29ew7N+fHDq0ds2hQ726raAt49GGedqGHtqkLXMDWm5s11gWphZWDaI72ztZmFrI2K4tcu7YOZbcsbB6CDzS6W3f2eB4tKEHAADWVGqtw+5h0yqlLI6Pj48vLi4OuxW2om43mZ9PjhzpfbDa6GjvsQLT01snVLvYiRPJXXclDz/c+2C1bdt6jxW4//6tGaq1ZTzaME/b0EObtGVuQMt1z3Qz/8H5HHn0SE4/eTqjO0Zz8NaDmX7d9NYJoC92tpucmk8eO9L7AMAbR3uPv9gzvX7hbxt6AADYxCYmJrK0tLRUa+3zeY7PEkI3SAgNAAAAAGwG1xNCexwHAAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRloCF1KeWMp5ZdLKX9aSvlCKeXcFb6eGeR7AgAAAADQXiOD2lEp5c1JfinJtiTdJH+YROAMAAAAALCFDSyETvKOJF9I8uZa638Y4H4BAAAAANigBvk4jluT/IIAGgAAAACACwYZQi8n+cwA9wcAAAAAwAY3yBD6N5PsH+D+AAAAAADY4AYZQs8meUUp5X8vpZQB7hcAAAAAgA3qmj+YsJTyU1dYfTLJDyT5zlLKh5L8xRVqaq31H1zr+wIAAAAAsHFccwid5O+vse2Wla8rqUmE0AAAAAAAW8D1hNAvH1gXAAAAAABsStccQtdaHx9kIwAAAAAAbD4D+2DCUsr3lVLueJ6av1FK+b5BvScAAAAAAO02sBA6yTuSfP3z1NyR5PsH+J4AAAAAALTYIEPofowkOb/O7wkAAAAAwJCsdwg9keT0Or8nAAAAAABDcs0fTJgkpZRjl6z6+6WUr79C6bYkL0uyO8mR63lPAAAAAAA2jusKofPcZ0DXJLesfF3qfJI/T/ILSe66zvcE4Hp1u8ncXHL0aHL6dDI6mkxNJTMzydjY1usDgA3vU0+cyKkTd+aVy4t5YTmXz9Zt+WhnInv2H85Lb9o/7PYYtj87kSzdmXxmMannkrItedFEMn44+TLzAwCaVmqtg9lRKeeTvKPW+oMD2eEmUEpZHB8fH19cXBx2KwDPOnYsmZxMlpcv39bpJAsLyYEDW6cPADa8Rz50X/Y8ek86V3jY4PL55NSth/La19y9/o3RDr93X/Khe1bf/ppDyVebHwDwfCYmJrK0tLRUa5242tcO8pnQb03ySwPcHwCD1u2uHvwmvfWTk726rdAHABvep544sWoAnSSdG5I9j96TTz1xYn0box3+7MTaAXTS2/5n5gcANGlgIXSt9WdrrR8e1P4AaMDc3OrB7wXLy8n8/NboA4AN79SJO1cNoC/o3JCc+oCnAm5JS3f2WWd+AECTrvmZ0KWUO671tbXWB6/1tQBch6NH+6s7ciR55zs3fx8AbHivXF7sfQz689V97uHmm6F9PtPnoxE/Y34AQJOu54MJfzu9DyO8Fn1cJgIwcKdPD7Zuo/cBwIb3wnJuoHVsMrXP495vHQBwTa4nhP7BXB5C70vyrUk+luR3kjyR5KYkfz3JK5L8WpKHruM9Abgeo6P9PWd5dHRr9AHAhvfZui078/wBYq+OLads6y9gLu6TAoAmXfMzoWut76i1/sCFryTvTXIgyfck+cpa61trrf9brfWtSb4yyV0r2399EI0DcA2mpvqrO3hwa/QBwIb30U5/H87+0Rfc1nAntNKL+psfeZH5AQBNGtgHEyb5Z0l+o9b647XW8xdvqLWer7X+aJLfTO8OagCGYWYm6XTWrul0kunprdEHABvenv2Hs3x+7Zrl88mer7t/fRqiXcYP91lnfgBAkwYZQr8+yYeep+Z3k3zdAN8TgKsxNpYsLKweAHc6ve1jY1ujDwA2vJfetD+nbj20ahC9fD45deuhvPSm/evbGO3wZfuT1xxau+Y1h3p1AEBjBhlCl/Se+7yWVw7w/QC4FgcOJCdPJrOzvZB3x47ecna2t/7Aga3VBwAb3mtfc3fOfNPx/PaOffnkuW05ez755Llt+e0d+3Lmm47nta+5e9gtMkxffXfypuPJi/c9++znsq33/ZuO97YDAI0qtV762YLXuKNSfj29Zz5/R631l6+w/W8l+fdJ3ldr/W8H8qYtV0pZHB8fH19cXBx2KwAAAAAA12xiYiJLS0tLtdY+P3ThWSMD7OOfJHkwyUIp5f0r//0nSb48yRuT3JHk8yt1AAAAAABsAQMLoWuti6WUNyX5qSRfv/JV03tMR5L8YZJ/UGt9ZFDvCQAAAABAuw3yTujUWo8neXUp5fYk40l2JTmTZGllGwAAAAAAW8hAQ+gLVgJnoTMAAAAAwBZ3w7AbAAAAAABg87rmO6FLKd+X3jOf52qtn1n5vh+11vrPrvV9AQAAAADYOK7ncRzvSC+E/oUkn1n5vh81iRAaAAAAAGALuJ4Q+htWlt1LvgcAAAAAgCTXEULXWt+/1vcAAAAAAOCDCQEAAAAAaMzAQuhSymOllH9dSvmfSikvGdR+AQAAAADYuK7nmdCX+uIk35nkrUlSSvnDJL+Z5FiS36q1/sUA3wsAAAAAgA1gYCF0rfWmUsreJN+Y5JuS/I0kM0mmk5wvpfxuVkLpWut7B/W+AAAAAAC01yDvhE6t9WSSk0l+rJRyQ5Lb0gulDyR5Q5LXJrl70O8LAAAAAEA7NfbBhLXW80mWk5xN8vkkX0hSmnxPAAAAAADaZaB3JJdSxtK78/nC3c9fnl7w3E3y7/PsM6IBAAAAANgCBhZCl1JOJfmKlW9PJ/mtPPsM6I8N6n0AAAAAANg4Bnkn9CuS1CTvS3I4yYO11icHuH8AAAAAADaYQYbQ/0eSb1j5elOSL5RS/nN6d0P/ZpL/XGt9ZoDvBwAAAABAyw3sQwJrrd9Xa/0bSV6U5NuT/MskL0jy/UkeTPKZUsqvlFL+0aDeEwAAAACAdhtYCH1BrfVsrfVXa6331FrHk3xZkn+U5Mkk35bkXwz6PQEAAAAAaKeBh9BJUkrZVUqZLKX8WJLfSfIjSf7qyuY/b+I9IUnS7Sazs8nu3cnOnb3l7Gxv/VbTlrE4cSLZty8ZGUlK6S337eutZ3jaMj8ArlL3TDez75vN7sO7s/OHd2b34d2Zfd9sumecv2iHTz1xIr/9wL588j0jOfvzJZ98z0h++4F9+dQT63ztc7abPDKb/NLu5Bd29paPzPbWMxxtOSYt6cP5/BItOS4ATSm11sHsqJRvTHLhazy9gLskWU7vcRzHkvxmrfV3B/KGG0ApZXF8fHx8cXFx2K1sDceOJZOTyfLy5ds6nWRhITlwYP37Goa2jMV99yX33LP69kOHkrvvbr4Pnqst8wPgKh37+LFMHp3M8tOXn7862ztZmFrIgZc7fzE8j3zovux59J50rnCrz/L55NSth/La16zDtc8Tx5IHJ5NnrvC7fqST3LGQ3ORnZV215Zi0pA/n80u05LgAPJ+JiYksLS0t1Vonrva1gwyhz6/851NJTmQldE7yUK313EDeZIMRQq+jbjfZu/fKodoFnU5y8mQyNrZ+fQ1DW8bixInk9tufv+748WT//ub64LnaMj8ArlL3TDd75/deMbC4oLO9k5PTJzO2y/mL9fepJ05k12/cfsUA+oLl88mZbzqel97U4LXP2W7yK3uvHGZdMNJJ3nwy2elnZV205Zi0pA/n80u05LgA9ON6QuhBPo7jnUnelOSFtdYDtdb/o9Z6YqsG0Kyzubm1Q7Wkt31+fn36Gaa2jMWdd/ZXd9ddjbbBJdoyPwCu0txDc2sGFkmy/PRy5j/o/MVwnDpx55oBdJJ0bkhOfaDha5+PzK0dZiW97af8rKybthyTlvThfH6JlhwXgKYNLISutX5vrfU3a61/eTWvW3l29E8Nqg+2qKNH+6s7cqTZPtqgLWPR778AePjhZvvgudoyPwCu0tGT/Z2/jjzq/MVwvHK5v2ufV36u4Wufx/v8Xf+Yn5V105Zj0pI+nM8v0ZLjAtC0Rj6Y8Cq9JsnfG3YTbHCnTw+2biNry1ic6/MfQfRbx2C0ZX4AXKXTT/Z3Xuq3DgbthaW/a5p+667ZU33+DPRbx/VryzFpSR/O55doyXEBaFobQmi4fqOjg63byNoyFtu2DbaOwWjL/AC4SqM7+jsv9VsHg/bZ2t81Tb911+zGPn8G+q3j+rXlmLSkD+fzS7TkuAA0TQjN5jA11V/dwYPN9tEGbRmLiT6fUX/bbc32wXO1ZX4AXKWpvf2dvw7e6vzFcHy009+1z0df0PC1z+4+f9ff4mdl3bTlmLSkD+fzS7TkuAA0TQjN5jAzk3Q6a9d0Osn09Pr0M0xtGYvDh/uru//+RtvgEm2ZHwBXaeb1M+lsX/v81dneyfTrnL8Yjj37D2f5/No1y+eTPV/X8LXPq2aSkef5XT/SSfb4WVk3bTkmLenD+fwSLTkuAE0TQrM5jI0lCwurh2udTm/72Nj69jUMbRmL/fuTQ4fWrjl0qFfH+mnL/AC4SmO7xrIwtbBqcNHZ3snC1ELGdjl/MRwvvWl/Tt16aNUgevl8curWQ3npTQ1f++wcS+5YWD3UGun0tu/0s7Ju2nJMWtKH8/klWnJcAJpWaq3DbaCU70/yfbU2/XC09VdKWRwfHx9fXOzvk7IZgG43mZ9PjhzpfbDa6GjvsQLT01svVGvLWJw4kdx1V/Lww70PIdy2rfcIjvvvF0APU1vmB8BV6p7pZv6D8zny6JGcfvJ0RneM5uCtBzP9uumtE1jQap964kROfeCuvPJzD+eF5Vw+W7floy+4LXu+7v7mA+iLne0mp+aTx470PtDsxtHeP+ffMy3MGpa2HJOW9OF8fomWHBeAtUxMTGRpaWmp1trnM1ifJYRukBAaAAAAANgMrieE9jgOAAAAAAAaI4QGAAAAAKAxbQihH0vy4LCbAAAAAABg8IYeQtdaf7bW+g3D7gMAAAAAgMEbudYXllK+7xpfWmut/+xa3xcAAAAAgI3jmkPoJO+4xtfVJEJoAAAAAIAt4HpCaI/QAAAAAABgTdccQtda3z/IRgAAAAAA2HyG/sGEAAAAAABsXkJoAAAAAAAaM9AQupTyklLKXCnlo6WUz5dSzl3h65lBvicAAAAAAO11PR9M+ByllJcmeSjJlyc5meTGJI8neSrJV6y814eSnBnUewIAAAAA0G6DvBP6+5LclORba61fu7Lup2utr04vhH5vki9J8h0DfE8AAAAAAFpskCH0tyT59Vrrb1y6odb6ySR/O70Q+gcG+J4AAAAAALTYIEPom9J7DMcF59ILnZMktdblJO9LMjnA9wQAAAAAoMUGGUL/lyTbL/r+s0leeknNmSRfNsD3BAAAAACgxQYZQj+e5GUXff+7SQ6UUnYkSSnlhiTfnOSTA3xPAAAAAABabJAh9G8m+YZSyhetfP+zSW5OcryU8i+S/Kcke5P8wgDfEwAAAACAFhsZ4L7+TXqP4BhN8ula68+XUiaS/L+TfM1KzdEkPzTA9wQAAAAAoMUGFkLXWk8l+eeXrLurlPLDSb4iyWO11j8Z1PsBAAAAANB+g7wT+opqrX+W5M+afh8AAAAAANpnkM+E/q9KKS8rpfytUsrfXVm+7PlfBQxUt5vMzia7dyc7d/aWs7O99VtRW8bjgQeSm29OSnn26+abe+sBgI3vbDd5ZDb5pd3JL+zsLR+Z7a1naBZPPZCf+5mb8/jPlpz9+ZLHf7bk537m5iye2nrXYE889kC6/+7mPPNvS+q/LXnm35Z0/93NeeKxrTcWAKyfUmsd3M5K2ZNkPsmBK2w+lmSm1vqRgb1hy5VSFsfHx8cXFxeH3QpbzbFjyeRksrx8+bZOJ1lYSA5c6cd0k2rLeLz97cm737369re9LXnXu5rvAwBoxhPHkgcnk2eucM0x0knuWEhu2kLXYC3x737z7fm2T787L7jCLVifO5/82kvelv/nN26Na7BT/+nteeVj704pl2+rNfnoLW/LnjdsjbEA4OpNTExkaWlpqdY6cbWvHdid0KWUVyY5nuQbk/xRkp9Lcu/K8o9W1v/OSh3QlG539cA16a2fnNw6d0S3ZTweeGDtADrpbXdHNABsTGe7qwfQSW/9g5PuiF5ni6ceWDWATpIX3JB826ffvSXuiH7isQdWDaCT3j/Qe+Vj73ZHNACNGOTjOP7PJC9O8j1JvrLW+tZa6/9Wa31rkq9McleS0SQ/PMD3BC41N7d64HrB8nIyP78+/QxbW8ZjZmawdQBAu3xkbvUA+oJnlpNTW+QarCVO/qeZVQPoC15wQ3Ly+Oa/Bnv6oZlVA+gLSunVAcCgDTKE/sYkv1pr/fFa6/mLN9Raz9dafzTJryX5pgG+J3Cpo0f7qztypNk+2qIt4/HpTw+2DgBol8f7vOZ4bItcg7XEG0t/11ZvzOa/Brv5C/39GfutA4CrMcgQenuSDz1PzYeSfNEA3xO41OnTg63b6IwHALAenurzWqLfOgZidNtg6zayfv+IW2AoABiCQYbQv5vk+Z73/MokHx7gewKXGh0dbN1GZzwAgPVwY5/XEv3WMRCnzw22biPr94+4BYYCgCEYZAj9w0m+o5TybVfaWEp5c5K3JPmhAb4ncKmpqf7qDh5sto+2aMt4vOQlg60DANpld5/XHLdskWuwlnh/7e/a6v3Z/Ndgf/xF/f0Z+60DgKsxyBD6xek98/mXSyn/oZTyv5dS/peV5fuS/F9JfjXJaCnlf774a4A9ADMzSaezdk2nk0xPr08/w9aW8ZibG2wdANAur5pJRp7nmmOkk+zZItdgLbH3DXP53Pm1az53Ptl7++a/Btv++rnUunZNrb06ABi0QYbQP5PkbyYp6X344A8meffK8htX1v+tJD990dfPrCyBQRkbSxYWVg9eO53e9rGx9e1rWNoyHm95S/K2t61d87a39eoAgI1n51hyx8LqQfRIp7d95xa5BmuJiT1vya+95G2rBtGfO5/82kvelok9m/8a7KZb3pKP3vK2VYPoWpOP3vK23HTL5h8LANZfqc/3v0L73VEpf+9aX1tr/dmBNNEypZTF8fHx8cXFxWG3wlbU7Sbz88mRI70P3Rsd7T1yYnp66wTQF2vLeDzwQO/u7E9f9KnjL3lJ7w5oATQAbHxnu8mp+eSxI70PIbxxtPcIjj3TAughWjz1QE4en8kb8+mMbus9A/r9eUn23j63JQLoiz3x2AN5+qGZ3PyFT2dbes+A/uMvekm2v35OAA3AmiYmJrK0tLRUa5242tcOLITmckJoAAAAAGAzuJ4QepCP4wAAAAAAgOcYGfQOSylfluT/keSrkuystX7XRetfnuT/V2v9/KDfFwAAAACA9hloCF1K+QdJfizJF6f3QYQ1yXetbP7yJCeS/MMk/2aQ7wsAAAAAQDsN7HEcpZQ3JfmJJB9J8pYk/+ri7bXWR5OcTPLfDeo9AQAAAABot0HeCT2b5NNJ3lhr/S+llNdeoebDSfYP8D0BAAAAAGixQX4w4W1JfrnW+l/WqPlkkpsG+J4AAAAAALTYIEPo7UnOPk/NlyY5N8D3BAAAAACgxQYZQj+WZOJ5avYl+cMBvicAAAAAAC02yBB6IcnfKKX87SttLKW8NcnXJPn3A3xPAAAAAABabJAfTHhvkqkkR0op/32SXUlSSvlfk/yNJN+R5FSSHx/gewIAAAAA0GIDC6FrrZ8tpbwxyc8lufhu6B9bWf7HJP9jrfX5nhsNAAAAAMAmMcg7oVNr7Sb5+lLK1yTZn+TFSc4k+UCtdXGQ7wUAAAAAQPsNNIS+oNb64SQfTpJSymh6z4oeTfIbtdZzTbwnAAAAAADtM7APJiyl/L9KKf+5lPKii9ZNJPn9JP/fJL+a5HgpZeeg3hMAAAAAgHYbWAid5H9IUmutn7lo3b9I8sIkP51eCP26JG8f4HsCAAAAANBigwyh92TlERzJf30MxxuT/Jta63fVWr89yQeT/I8DfE8AAAAAAFpskCH0i5P86UXfv2Fl+cBF6/5jkt0DfE8AAAAAAFpskCH0Z5KMXvT9G5OcT3L8onU1yRcP8D0BAAAAAGixQYbQv5/k20spLy6lfGl6z4j+YK31v1xUc0uSJwb4ngAAAAAAtNggQ+gfTfKSJJ9M8okkNyWZv7CxlLItyV9P8rsDfE8AAAAAAFpsYCF0rfX/SvL2JCeT/GGSe2qtP39RyTel9yiO917ve5VS/nkp5TdLKZ8opXy+lPKZUsojpZTvL6W8+Cr39ddKKT9VSvnjUspTpZTHSimHSykvvN4+AQAAAAC2ukHeCZ1a60/UWm9b+br/km3vrbW+sNb6EwN4q7uS7EzyvvTuwP63SZ5J8o4kHy6lvKyfnZRSXpFkMclbkzyU5P4kf5Tke5KcuNpAe0vrdpPZ2WT37mTnzt5ydra3fiv2QfucOJHs25eMjCSl9Jb79vXWr6e2zNG29EHrdM90M/u+2ew+vDs7f3hndh/endn3zaZ7ZmvOjROfOJF9P7kvIz84kvIDJSM/OJJ9P7kvJz6xfucOx+S5PvXEifz2A/vyyfeM5OzPl3zyPSP57Qf25VNPrPP5/Gw3eWQ2+aXdyS/s7C0fme2tX0etGQ/ap/tA8os3J/+f8uzXL97cW7+ebbTkHNaKn5WWnDe4RFuOS1v6oH3aMjfa0gcbVqm1DruHq1ZK+eJa619eYf0PJfneJP+q1jrdx37em+Sbk3x3rfXHL1r/I+kF3e+utb79OvpcHB8fH19cXLzWXWwMx44lk5PJ8vLl2zqdZGEhOXBg6/RB+9x3X3LPPatvP3Qoufvu5vtoyxxtSx+0zrGPH8vk0cksP3353Ohs72RhaiEHXr515sZ9x+/LPe9b/dxx6E2HcvftzZ47HJPneuRD92XPo/ekc4XbKJbPJ6duPZTXvmYdzudPHEsenEyeucJ5dKST3LGQ3NT8cWnNeNA+//ntycfevfr2V7wt2feuxttoyzmsFT8rLTlvcIm2HJe29EH7tGVutKUPhm5iYiJLS0tLtdaJq33thgyhV1NK+dokH0ryG7XWNz1P7Vck+ViSx5K8otZ6/qJtL0jy6SQlyV+ttZ69xn42fwjd7SZ79145zLqg00lOnkzGxjZ/H7TPiRPJ7bc/f93x48n+/c310ZY52pY+aJ3umW72zu+9YlBwQWd7JyenT2Zs1+afGyc+cSK3/9TznzuOf+fx7H9ZM+cOx+S5PvXEiez6jduvGCJdsHw+OfNNx/PSmxo8n5/tJr+y98p/CbtgpJO8+WSys7nj0prxoH26DyS/8x3PX/fXfzEZe0tzbbTkHNaKn5WWnDe4RFuOS1v6oH3aMjfa0getcD0h9EAfx9EC376y/HAftRf+F81/uDiATpJa6+eS/KckO5J83eDa24Tm5tYOs5Le9vn5tWs2Sx+0z5139ld3112NttGaOdqWPmiduYfm1gwKkmT56eXMf3BrzI07f/3Ovuruem9z5w7H5LlOnbhzzRApSTo3JKc+0PD5/CNza/8lLOltP9XscWnNeNA+D88Mtu4ateUc1oqflZacN7hEW45LW/qgfdoyN9rSBxvehg6hSyn3lFLeUUq5v5TyH5P8s/QC6Hf28fKvXFl+ZJXtp1aWr+qjj8UrfSV5dR99bGxHj/ZXd+TI1uiD9un3XyI8/HCzfbRljralD1rn6Mn+5saRR7fG3Fj8dH/njof/uLlzh2PyXK9c7u+YvPJzDZ/PH+/zPPpYs8elNeNB+/zlpwdbd43acg5rxc9KS84bXKItx6UtfdA+bZkbbemDDW9k2A1cp3uSfPlF3/96kr9fa/2zPl67a2V5ZpXtF9Z/6bW1tkWcPj3Yuo3eB+1z7txg665VW+ZoW/qgdU4/2d8x77duoztX+zsn9Ft3LRyT53ph6W+s+627Zk/1Od791l2j1owHrKIt57BW/Ky05LzBJdpyXNrSB+3TlrnRlj7Y8Db0ndC11ptqrSXJTUm+I8lXJHmklDI+gN2XC2/TRx8TV/pK8gcD6KPdRkcHW7fR+6B9tm0bbN21asscbUsftM7ojv6Oeb91G9220t85od+6a+GYPNdna39j3W/dNbuxz/Hut+4atWY8YBVtOYe14melJecNLtGW49KWPmiftsyNtvTBhrehQ+gLaq1/Umt9IMk3J3lxkp/r42UX7nTetcr2v3JJHVcyNdVf3cGDW6MP2meiz2fl33Zbs320ZY62pQ9aZ2pvf3Pj4K1bY25MvKS/c8dtNzd37nBMnuujnf6OyUdf0PD5fHef59Fbmj0urRkP2ueLXzLYumvUlnNYK35WWnLe4BJtOS5t6YP2acvcaEsfbHibIoS+oNb6eJLfS7K3lPJ8/wvmD1eWqz3zec/KcrVnRpMkMzNJp7N2TaeTTE9vjT5on8OH+6u7//5G22jNHG1LH7TOzOtn0tm+9tzobO9k+nVbY24c/tbDfdXd/y3NnTsck+fas/9wls+vXbN8PtnzdQ2fz1810/sE+LWMdJI9zR6X1owH7XPb3GDrrlFbzmGt+FlpyXmDS7TluLSlD9qnLXOjLX2w4W2qEHrFzSvL53uo12+tLL+5lPKccSilvCDJG5J8PskHBtveJjM2liwsrB5qdTq97WNjW6MP2mf//uTQobVrDh3q1TWpLXO0LX3QOmO7xrIwtbBqYNDZ3snC1ELGdm2NubH/Zftz6E1rnzsOvelQ9r+suXOHY/JcL71pf07demjVMGn5fHLq1kN56U0Nn893jiV3LKz+l7GRTm/7zmaPS2vGg/YZe0vyiretXfOKt/XqmmyjJeewVvystOS8wSXaclza0gft05a50ZY+2PBKrc/7yONWKaW8Oslf1FqfuGT9DUn+WZLvTXK81vqGlfVflOQVSb5Qa/3YJa95b3qP8PjuWuuPX7T+R5LcleTdtda3X0evi+Pj4+OLi/19IvOG1u0m8/PJkSO9DzQbHe39c/7p6fUNs9rSB+1z4kRy113Jww/3PoRw27beIzjuv7/5APpibZmjbemD1ume6Wb+g/M58uiRnH7ydEZ3jObgrQcz/brpLRN2XuzEJ07krvfelYf/+OGcq+eyrWzLbTfflvu/5f5GA+iLOSbP9aknTuTUB+7KKz/3cF5YzuWzdVs++oLbsufr7l/fwPVsNzk13/sk+KdO956DeMvB3l1A6/iXsNaMB+3TfSB5eCb5y08/u+6LX9K7A7rhAPo5bbTkHNaKn5WWnDe4RFuOS1v6oH3aMjfa0gdDNTExkaWlpaWVz8K7KhsxhL4zyb9I8mCSjyX58yRfnuSN6X0w4RNJvrHW+nsr9bck+XiSx2utt1yyr1ckOZ7kryZZSPL7SfYl+Yb0HsNxe631z6+j160TQgMAAAAAm9b1hNAjTTTUsN9I8hPpPS7ja5N8aZKz6YXG70nyY7XWz/Szo1rrx0optyX5wSTfmuS/TfLpJD+W5Af63Q8AAAAAAFe24ULoWuujSWauov6xJGWN7Z9I8tbr7wwAAAAAgEttxg8mBAAAAACgJYTQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY0aG3QAMVLebzM0lR48mp08no6PJ1FQyM5OMjQ27OwA2mO6ZbuYemsvRk0dz+snTGd0xmqm9U5l5/UzGdq3P75U29MDl2nJc9HGRs93kI3PJ40eTp04nN44mu6eSV80kO7fYWLSI8YANpiXnUmDzKbXWYfewaZVSFsfHx8cXFxeH3crWcOxYMjmZLC9fvq3TSRYWkgMH1r8vADakYx8/lsmjk1l++vLfK53tnSxMLeTAy5v9vdKGHrhcW46LPi7yxLHkwcnkmStcB450kjsWkpu2yFi0iPGADaYl51KgvSYmJrK0tLRUa5242tcKoRskhF5H3W6yd++VA+gLOp3k5El3RAPwvLpnutk7v/eKwckFne2dnJw+2didfG3ogcu15bjo4yJnu8mv7L1yaHLBSCd588lG7+JrxVi0iPGADaYl51Kg3a4nhPZMaDaHubm1A+ikt31+fn36AWBDm3tobs3gJEmWn17O/Aeb+73Shh64XFuOiz4u8pG5tUOTpLf91BYYixYxHrDBtORcCmxeQmg2h6NH+6s7cqTZPgDYFI6e7O/3ypFHm/u90oYeuFxbjos+LvJ4n9eBj22BsWgR4wEbTEvOpcDmJYRmczh9erB1AGxpp5/s7/dFv3UbtQcu15bjoo+LPNXnvvutu0atGIsWMR6wwbTkXApsXkJoNofR0cHWAbClje7o7/dFv3UbtQcu15bjoo+L3Njnvvutu0atGIsWMR6wwbTkXApsXkJoNoepqf7qDh5stg8ANoWpvf39Xjl4a3O/V9rQA5dry3HRx0V293kdeMsWGIsWMR6wwbTkXApsXkJoNoeZmaTTWbum00mmp9enHwA2tJnXz6Szfe3fK53tnUy/rrnfK23ogcu15bjo4yKvmklGnuc6cKST7NkCY9EixgM2mJacS4HNSwjN5jA2liwsrB5Edzq97WNj69sXABvS2K6xLEwtrBqgdLZ3sjC1kLFdzf1eaUMPXK4tx0UfF9k5ltyxsHp4MtLpbd+5BcaiRYwHbDAtOZcCm1eptQ67h02rlLI4Pj4+vri4OOxWto5uN5mfT44c6X0I4eho7xEc09MCaACuWvdMN/MfnM+RR4/k9JOnM7pjNAdvPZjp102vW3DShh64XFuOiz4ucrabnJpPHjvS++CsG0d7/2x8z/S6hiatGIsWMR6wwbTkXAq008TERJaWlpZqrRNX+1ohdIOE0AAAAADAZnA9IbTHcQAAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQmJFhNwAA0Fpnu8lH5pLHjyZPnU5uHE12TyWvmkl2jq1LCyc+cSJ3/vqdWfz0Ys7Vc9lWtmXiJRM5/K2Hs/9l+9elhyStGAsuZ360rIck+bMTydKdyWcWk3ouKduSF00k44eTL1u/Y9I9083cQ3M5evJoTj95OqM7RjO1dyozr5/J2C4/s8PgmECf2nI+57kcF65TqbUOu4dNq5SyOD4+Pr64uDjsVgCAq/XEseTByeSZ5cu3jXSSOxaSmw402sJ9x+/LPe+7Z9Xth950KHfffnejPSRpxVhwOfOjZT0kye/dl3xo9WOS1xxKvrr5Y3Ls48cyeXQyy09fPh6d7Z0sTC3kwMv9zK4nxwT61JbzOc/luLBiYmIiS0tLS7XWiat9rRC6QUJoANigznaTX9l75QvtC0Y6yZtPNnbnx4lPnMjtP3X789Yd/87jzd7x2oKx4HLmR8t6SHp3QL/v+Y9J3nS80Tuiu2e62Tu/94ph5wWd7Z2cnD7p7tt14phAn9pyPue5HBcucj0htGdCAwBc6iNza19oJ73tp+Yba+HOX7+zr7q73ntXYz0kacVYcDnzo2U9JL1HcPRV1+wxmXtobs2wM0mWn17O/Af9zK4XxwT61JbzOc/luDAgQmgAgEs9frS/useONNbC4qf7+5dUD//xw431kKQVY8HlzI+W9ZD0ngHdV12zx+Toyf7G48ijfmbXi2MCfWrL+ZznclwYECE0AMClnjo92LprcK6eG2jdNWvBWHA586NlPSS9DyEcZN01Ov1kf3/Ofuu4fo4J9Kkt53Oey3FhQITQAACXunF0sHXXYFvZNtC6a9aCseBy5kfLekiSfse64WMyuqO/P2e/dVw/xwT61JbzOc/luDAgQmgAgEvtnuqv7paDjbUw8ZL+Puvjtptva6yHJK0YCy5nfrSshyR5UZ+fz/OiZo/J1N7+xuPgrX5m14tjAn1qy/mc53JcGBAhNADApV410/uU77WMdJI90421cPhbD/dVd/+33N9YD0laMRZczvxoWQ9JMn64z7pmj8nM62fS2b72eHS2dzL9Oj+z68UxgT615XzOczkuDIgQGgDgUjvHkjsWVr/gHun0tu8ca6yF/S/bn0NvOrRmzaE3Hcr+l+1vrIckrRgLLmd+tKyHJPmy/clr1j4mec2hXl2DxnaNZWFqYdXQs7O9k4WphYzt8jO7XhwT6FNbzuc8l+PCgJRa67B72LRKKYvj4+Pji4t9flI2ANAuZ7vJqfnep30/dbr3rLtbDvbu9FinC+0TnziRu957Vx7+44dzrp7LtrItt918W+7/lvubDxgv1oKx4HLmR8t6SJI/O5Es3ZV85uHehxCWbb1HcIzf33gAfbHumW7mPzifI48eyeknT2d0x2gO3now06+bFnYOiWMCfWrL+ZznclxIMjExkaWlpaVaa5/PIXuWELpBQmgAAAAAYDO4nhDa4zgAAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMaMDLsBABim7plu5h6ay9GTR3P6ydMZ3TGaqb1TmXn9TMZ2jQ27PYbM/HjWhx/9idzwyD/Kq284m21JziX5g/M7c/61P5KvufUfDru9dWdutNDZbvKRueTxo8lTp5MbR5PdU8mrZpKd63hM2tIHAECLlFrrsHvYtEopi+Pj4+OLi4vDbgWAKzj28WOZPDqZ5aeXL9vW2d7JwtRCDrz8wBA6ow3Mj2f99q99e974mV9OKZdvqzV5/4v+Zr7+2/7v9W9sSMyNFnriWPLgZPLM5cckI53kjoXkpnU4Jm3pAwCgARMTE1laWlqqtU5c7Ws9jgOALal7prtqiJQky08vZ/LoZLpnuuvcGW1gfjzrw4/+xKoBdJKUkrzxM7+cDz/6E+vb2JCYGy10trt68Jv01j842avbCn0AALSQEBqALWnuoblVQ6QLlp9ezvwH59epI9rE/HjWDY/8o1UD6AtK6dVtBeZGC31kbvXg94JnlpNTDR+TtvQBANBCQmgAtqSjJ4/2VXfk0SMNd0IbmR/PevUNZwdat9GZGy30eH/HJI81fEza0gcAQAsJoQHYkk4/eXqgdWwu5seztg24bqMzN1roqT7Hut+6jd4HAEALCaEB2JJGd4wOtI7Nxfx41rkB12105kYL3djnWPdbt9H7AABoISE0AFvS1N6pvuoO3nqw4U5oI/PjWX9wfudA6zY6c6OFdvd3THJLw8ekLX0AALSQEBqALWnm9TPpbO+sWdPZ3sn066bXqSPaxPx41vnX/khqXbum1l7dVmButNCrZpKRtY9JRjrJnoaPSVv6AABoISE0AFvS2K6xLEwtrBomdbZ3sjC1kLFdY+vcGW1gfjzra279h3n/i/7mqkF0rcn7X/Q38zW3/sP1bWxIzI0W2jmW3LGwegA80ult39nwMWlLHwAALVTq893awjUrpSyOj4+PLy4uDrsVAFbRPdPN/Afnc+TRIzn95OmM7hjNwVsPZvp100IkzI+LfPjRn8gNj/yjvPqGs9mW3jOg/+D8zpx/7Y9smQD6YuZGC53tJqfmk8eO9D7878bR3qMv9kyvb/Dblj4AAAZsYmIiS0tLS7XWiat9rRC6QUJoAAAAAGAzuJ4Q2uM4AAAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGjMyLAbAAC4VPdMN3MPzeXoyaM5/eTpjO4YzdTeqcy8fiZju8aG3d66astYtKUPLnG2m3xkLnn8aPLU6eTG0WT3VPKqmWSn4wIADIhrDq5TqbUOu4dNq5SyOD4+Pr64uDjsVgBgwzj28WOZPDqZ5aeXL9vW2d7JwtRCDrz8wBA6W39tGYu29MElnjiWPDiZPHP5cclIJ7ljIbnJcQEArpNrDlZMTExkaWlpqdY6cbWv9TgOAKA1ume6q4adSbL89HImj06me6a7zp2tv7aMRVv64BJnu6v/ZTDprX9wslcHAHCtXHMwIEJoAKA15h6aWzXsvGD56eXMf3B+nToanraMRVv64BIfmVv9L4MXPLOcnHJcAIDr4JqDARFCAwCtcfTk0b7qjjx6pOFOhq8tY9GWPrjE4/0dlzzmuAAA18E1BwMihAYAWuP0k6cHWreRtWUs2tIHl3iqz/Hutw4A4EpcczAgQmgAoDVGd4wOtG4ja8tYtKUPLnFjn+Pdbx0AwJW45mBAhNAAQGtM7Z3qq+7grQcb7mT42jIWbemDS+zu77jkFscFALgOrjkYECE0ANAaM6+fSWd7Z82azvZOpl83vU4dDU9bxqItfXCJV80kI2sfl4x0kj2OCwBwHVxzMCBCaACgNcZ2jWVhamHV0LOzvZOFqYWM7Rpb587WX1vGoi19cImdY8kdC6v/pXCk09u+03EBAK6Daw4GpNRah93DplVKWRwfHx9fXFwcdisAsKF0z3Qz/8H5HHn0SE4/eTqjO0Zz8NaDmX7d9JYLO9syFm3pg0uc7San5nufSP/U6d7zGG852LsbyV8GAYBBcc1BkomJiSwtLS3VWieu9rVC6AYJoQEAAACAzeB6QmiP4wAAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGiOEBgAAAACgMUJoAAAAAAAaI4QGAAAAAKAxQmgAAAAAABojhAYAAAAAoDFCaAAAAAAAGrPhQuhSyotLKd9VSnmglPLRUsrnSylnSim/U0r5B6WUvv9MpZTHSil1la8nmvxzAAAAAABsBSPDbuAa/O0k/yrJp5P8VpJuki9P8h1J/nWSbyul/O1aa+1zf2eSHL7C+uXrbxXgEme7yUfmksePJk+dTm4cTXZPJa+aSXaODbu7ddc9083cQ3M5evJoTj95OqM7RjO1dyozr5/J2K6tNx48qy1zoxV9OG9AXz71xImcOnFnXrm8mBeWc/ls3ZaPdiayZ//hvPSm/cNuj2FzLgWAoSr9Z7XtUEo5kGRnkl+ptZ6/aP1NSR5K8rIk/32t9d/3sa/HkqTWektDvS6Oj4+PLy4uNrF7YKN54ljy4GTyzBX+H9dIJ7ljIbnpwPr3NSTHPn4sk0cns/z05ePR2d7JwtRCDrx864wHz2rL3GhFH84b0JdHPnRf9jx6TzpX+DeRy+eTU7ceymtfc/f6N0Y7OJcCwEBMTExkaWlpqdY6cbWv3XCP46i1Hqu1/t8XB9Ar659I8q6Vb79+3RsDWMvZ7up/+Ul66x+c7NVtAd0z3VXDvSRZfno5k0cn0z2zNcaDZ7VlbrSiD+cN6MunnjixagCdJJ0bkj2P3pNPPXFifRujHZxLAaAVNlwI/Ty+sLJ85ipec2Mp5e+UUr63lPI9pZRvKKVsa6I5YAv7yNzqf/m54Jnl5NT8+vQzZHMPza0a7l2w/PRy5j+4NcaDZ7VlbrSiD+cN6MupE3euGkBf0LkhOfWBu9anIdrFuRQAWmHThNCllJEk//PKt79+FS+9Kcl7kvxQes+GPpbkVCnljVfx3otX+kry6qvoA9jMHj/aX91jR5rtoyWOnuxvPI48ujXGg2e1ZW60og/nDejLK5f7e/TdKz/3cMOd0ErOpQDQCpsmhE7yziS3JvnVWut7+3zNTyf5xvSC6J1J/psk705yS5JfK6V8bQN9AlvRU6cHW7fBnX6yvz9nv3VsHm2ZG63ow3kD+vLCcm6gdWwyzqUA0AqbIoQupXx3kruT/EGSv9vv62qtP7DyjOk/qbU+WWt9tNb69iQ/kuRLkryjz/1MXOlrpR+A3iewD7Jugxvd0d+fs986No+2zI1W9OG8AX35bO3vSXr91rHJOJcCQCts+BC6lDKT5EeT/F6Sb6i1fmYAu73wAYd3DGBfAMnuqf7qbjnYbB8tMbW3v/E4eOvWGA+e1Za50Yo+nDegLx/t9Pfh7B99wW0Nd0IrOZcCQCts6BC6lHJnkn+Z5NH0AugnBrTrP11Z7hzQ/oCt7lUzyUhn7ZqRTrJnen36GbKZ18+ks33t8ehs72T6dVtjPHhWW+ZGK/pw3oC+7Nl/OMvn165ZPp/s+br716ch2sW5FABaYcOG0KWU2ST3J/lQegH0n679iquyf2X5RwPcJ7CV7RxL7lhY/S9BI53e9p1j69vXkIztGsvC1MKqIV9neycLUwsZ27U1xoNntWVutKIP5w3oy0tv2p9Ttx5aNYhePp+cuvVQXnrT/isXsLk5lwJAK5Ra67B7uGqllH+a5AeTLCb55rUewVFK+aIkr0jyhVrrxy5avzfJpy99bSlld5LfSPLKJP+k1vrD19Hn4vj4+PjiYn+f2A1sAWe7yan53iewP3W69/zBWw727r7Zgn/56Z7pZv6D8zny6JGcfvJ0RneM5uCtBzP9umkB9BbXlrnRij6cN6Avn3riRE594K688nMP54XlXD5bt+WjL7gte77ufgE0zqUAMAATExNZWlpaWvksvKuy4ULoUsrfS/IzSc4l+fEkZ65Q9lit9WdW6m9J8vEkj9dab7loP+9I8o+T/NbK9s+lF1a/OckXJ/nVJG+ptT59Hb0KoQEAAACADe96QuiRJhpq2MtXltuS3LlKzfvTC6rX8ltJvjLJa9N7/MbOJH+R5HeSvCfJe+pGS+gBAAAAAFpmw4XQtdZ3JHnHVdQ/lqRcYf370wurAQAAAABoyIb9YEIAAAAAANpPCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA2wjrpnupl932x2H96dnT+8M7sP787s+2bTPdMddmvQKic+cSL7fnJfRn5wJOUHSkZ+cCT7fnJfTnzixLBbAwAA4CoJoQHWybGPH8ve+b259/i96Z7p5skvPJnumW7uPX5v9s7vzbGPHxt2i9AK9x2/L7f/1O156I8fyrl6Lklyrp7LQ3/8UG7/qdtz3/H7htwhAAAAV0MIDbAOume6mTw6meWnl6+4ffnp5UwenXRHNFveiU+cyD3vu2fNmnved487ogEAADYQITTAOph7aG7VAPqC5aeXM//B+XXqCNrpzl+/s6+6u957V7ONAAAAMDBCaIB1cPTk0b7qjjx6pOFOoN0WP73YV93Df/xww50AAAAwKEJogHVw+snTA62DzerCM6AHVQcAAMDwCaEB1sHojtGB1sFmta1sG2gdAAAAwyeEBlgHU3un+qo7eOvBhjuBdpt4yURfdbfdfFvDnQAAADAoQmiAdTDz+pl0tnfWrOls72T6ddPr1BG00+FvPdxX3f3fcn+zjQAAADAwQmiAdTC2aywLUwurBtGd7Z0sTC1kbNfYOncG7bL/Zftz6E2H1qw59KZD2f+y/evUEQAAANdLCA2wTg68/EBOTp/M7BtmM7ZrLDu+aEfGdo1l9g2zOTl9MgdefmDYLUIr3H373Tn+ncez76X7/uuzn7eVbdn30n05/p3Hc/ftdw+5QwAAAK5GqbUOu4dNq5SyOD4+Pr64uDjsVgAAAAAArtnExESWlpaWaq39fZjPRdwJDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNAAAAAAADRGCA0AAAAAQGOE0AAAAAAANEYIDQAAAABAY4TQAAAAAAA0RggNAAAAAEBjhNDQhG43mZ1Ndu9Odu7sLWdne+uhBbpnupl932x2H96dnT+8M7sP787s+2bTPWOO0g7mKHC1nDcAANqr1FqH3cOmVUpZHB8fH19cXBx2K6ynY8eSyclkefnybZ1OsrCQHDiw/n3BimMfP5bJo5NZfvryOdrZ3snC1EIOvNwcZXjMUeBqOW8AADRvYmIiS0tLS7XWiat9rTuhYZC63dUD6KS3fnLSHdEMTfdMd9W/pCfJ8tPLmTw66a4xhsYcBa6W8wYAQPsJoWGQ5uZWD6AvWF5O5ufXpx+4xNxDc6v+Jf2C5aeXM/9Bc5ThMEeBq+W8AQDQfkJoGKSjR/urO3Kk2T5gFUdP9jdHjzxqjjIc5ihwtZw3AADaTwgNg3T69GDrYMBOP9nf3Ou3DgbNHAWulvMGAED7CaFhkEZHB1sHAza6o7+5128dDJo5Clwt5w0AgPYTQsMgTU31V3fwYLN9wCqm9vY3Rw/eao4yHOYocLWcNwAA2k8IDYM0M5N0OmvXdDrJ9PT69AOXmHn9TDrb156jne2dTL/OHGU4zFHgajlvAAC0nxAaBmlsLFlYWD2I7nR628fG1rcvWDG2aywLUwur/mW9s72ThamFjO0yRxkOcxS4Ws4bAADtV2qtw+5h0yqlLI6Pj48vLi4OuxXWW7ebzM8nR470PoRwdLT3CI7paQE0rdA90838B+dz5NEjOf3k6YzuGM3BWw9m+nXT/pJOK5ijwNVy3gAAaNbExESWlpaWaq0TV/taIXSDhNAAAAAAwGZwPSG0x3EAAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0BghNAAAAAAAjRFCAwAAAADQGCE0AAAAAACNEUIDAAAAANAYITQAAAAAAI0RQgMAAAAA0JiRYTcAAAAbzaeeOJFTJ+7MK5cX88JyLp+t2/LRzkT27D+cl960f9jtrbvumW7mHprL0ZNHc/rJ0xndMZqpvVOZef1MxnaNDbs9AACGzJ3QAABwFR750H3Z9Ru35+s//1D+2rZz2XlD8te2ncvXf/6h7PqN2/PIh+4bdovr6tjHj2Xv/N7ce/zedM908+QXnkz3TDf3Hr83e+f35tjHjw27RQAAhkwIDQAAffrUEyey59F70lnlKrpzQ7Ln0XvyqSdOrG9jQ9I9083k0cksP718xe3LTy9n8uhkume669wZAABtIoQGAIA+nTpx56oB9AWdG5JTH7hrfRoasrmH5lYNoC9Yfno58x+cX6eOAABoIyE0AAD06ZXLi/3Vfe7hhjtph6Mnj/ZVd+TRIw13AgBAmwmhAQCgTy8s5wZat9GdfvL0QOsAANichNAAANCnz9ZtA63b6EZ3jA60DgCAzUkIDQAAffpoZ6K/uhfc1nAn7TC1d6qvuoO3Hmy4EwAA2kwIDQAAfdqz/3CWz69ds3w+2fN1969PQ0M28/qZdLZ31qzpbO9k+nXT69QRAABtJIQGAIA+vfSm/Tl166FVg+jl88mpWw/lpTftX9/GhmRs11gWphZWDaI72ztZmFrI2K6xde4MAIA2EUIDAMBVeO1r7s6Zbzqe396xL588ty1nzyefPLctv71jX8580/G89jV3D7vFdXXg5QdycvpkZt8wm7FdY9nxRTsytmsss2+Yzcnpkznw8gPDbhEAgCErtdZh97BplVIWx8fHxxcXF4fdCgAAAADANZuYmMjS0tJSrbW/D0q5iDuhAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIoQEAAAAAaIwQGgAAAACAxgihAQAAAABojBAaAAAAAIDGCKEBAAAAAGiMEBoAAAAAgMYIof//7d17lCdleSfw76NA5OZ4wdGTNeMgEUWJRlBUEIExIuKFVdfsqNGA8RbYiImrGGMU3JN4OUdEEeI9KJrgekEwXiAK4oUYVljciCgKDIMKIqAj4ABe3v2jqk3bds909+9X3T09n885fcqueuutt9qXp6e/Xf0WAAAAAACDEUIDAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwAAAAAwGCE0AAAAAACDEUIDAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwAAAAAwGCE0AAAAAACDEUIDAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwAAAAAwGCE0AAAAAACDEUIDAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwAAAAAwGCE0AAAAAACDEUIDAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwAAAAAwGCE0AAAAAACD2eJC6Kq6e1U9v6pOr6rvVtXGqtpQVV+uqj+rqjndU1Xdu6reV1U/qKrbqmpdVZ1QVXcd6h4AAAAAALYW2yz2AObhGUn+Ick1Sc5Nsj7JPZM8Lcl7kjyhqp7RWmub66iqdktyfpKVSc5I8q0k+yQ5OskhVbVfa+2GQe4CYBGt37A+J11wUk675LRc/7Prs8sOu2Ttg9bmqH2OyqoVqxZ7eAAAAMAysiWG0JcleUqST7XWfjWxs6peleSCJE9PF0h/bBZ9nZwugH5Ja+3ESX0dn+Qvk/xdkhePb+gAi++cK8/JYacdlptvv/nX+9ZvWJ83nf+mnPy1k3PG2jOyZtc1izhCAAAAYDnZ4pbjaK2d01r75OQAut9/bZJ39J8euLl+quq+SQ5Osi7JSVMOvzbJLUmeU1U7jjpmgKVi/Yb1vxVAT3bz7TfnsNMOy/oN6xd4ZAAAAMBytcWF0Jvx8377i1m0nXjM7+xpAu2bknwlyQ5JHjm+4QEsrpMuOGnGAHrCzbffnJP/z8kLNCIAAABguVs2IXRVbZPkuf2nn53FKffvt5fNcPw7/Xb3WVz7wuk+kjxgFuMAWDCnXXLarNr98zf+eeCRAAAAAFuLZRNCJ3lDkj2TfLq1dtYs2q/otxtmOD6x/y4jjgtgybj+Z9ePtR0AAADA5myJLyb8LVX1kiQvS/KtJM8ZV7f9tm2uYWtt7xnGdWGSvcY0HoCR7bLDLrNa73mXHXZZgNEAAAAAW4Mt/knoqjoqyVuTfDPJQa21G2d56sSTzitmOH7nKe0AtnhrH7R2Vu2eueczBx4JAAAAsLXYokPoqnppkrcn+Ua6APraOZz+7X4705rP9+u3M60ZDbDFOWqfo7LTdjttss1O2+2UIx9+5AKNCAAAAFjuttgQuqqOSfKWJBenC6Cvm2MX5/bbg6vqN74OVbVzkv2SbEzy1RGHCrBkrFqxKmesPWPGIHqn7XbKGWvPyKoVqxZ4ZAAAAMBytUWG0FX1t+leRHhhkse21mZ8g1ZVbVtVD6iq3Sbvb61dnuTsJKuTHDXltOOS7JjkA621W8Y5doDFtmbXNbnkyEtyzH7HZNWKVdlh2x2yasWqHLPfMbnkyEuyZtc1iz1EAAAAYBmp1jb73r0lpar+NMkpSX6Z5MRMv2bzutbaKX371UmuTHJVa231lL52S3J+kpVJzkhyaZJHJDko3TIc+7bWbhhhrBfutddee1144YXz7QIAAAAAYNHtvffeueiiiy5qre0913O3GWJAA9u1394xyUtnaHNeuqB6k1prl1fVw5K8LskhSQ5Nck2StyU5bg4vOQQAAAAAYBpbXAjdWjs2ybFzaL8uSW3i+NVJjhh1XAAAAAAA/LYtck1oAAAAAAC2DEJoAAAAAAAGI4QGAAAAAGAwQmgAAAAAAAYjhAYAAAAAYDBCaAAAAAAABiOEBgAAAABgMEJoAAAAAAAGI4QGAAAAAGAwQmgAAAAAAAYjhAYAAAAAYDBCaAAAAAAABiOEBgAAAABgMEJoAAAAAAAGI4QGAAAAAGAwQmgAAAAAAAYjhAYAAAAAYDBCaAAAAAAABiOEBgAAAABgMEJoAAAAAAAGI4QGAAAAAGAwQmgAAAAAAAYjhAYAAAAAYDBCaAAAAAAABiOEBgAAAABgMEJoAAAAAAAGI4QGAAAAAGAwQmgAAAAAAAYjhAYAAAAAYDBCaAAAAAAABlOttcUew7JVVTdsv/32d9tjjz0WeygAAAAAAPN26aWXZuPGjTe21u4+13OF0AOqqiuT3DnJukUeCovnAf32W4s6CpiZOcpSZ46y1JmjLHXmKEudOcpSZ46y1C3kHF2d5KettV3neqIQGgZUVRcmSWtt78UeC0zHHGWpM0dZ6sxRljpzlKXOHGWpM0dZ6raUOWpNaAAAAAAABiOEBgAAAABgMEJoAAAAAAAGI4QGAAAAAGAwQmgAAAAAAAZTrbXFHgMAAAAAAMuUJ6EBAAAAABiMEBoAAAAAgMEIoQEAAAAAGIwQGgAAAACAwQihAQAAAAAYjBAaAAAAAIDBCKEBAAAAABiMEBpGUFXPqarWfzx/Duetm3Te1I9rhxwzy9c451VV3buq3ldVP6iq2/q+T6iquw41fpa/cc1RNZShVdX+VfWxqrqmr4HXVNXZVXXoHPpQRxnUqPNULWUIVXX4JubVxMcv59CfWspYjXOOqqMMqaqe2H9f/15VbayqK6rqI1X1qDn2s2Tq6DYLfUFYLqrq95KcmOTmJDvNo4sNSU6YZv/NIwwLRp5XVbVbkvOTrExyRpJvJdknydFJDqmq/VprN4w+VLZS46p9aiiDqKpXJ/lfSa5P8i9JrkmyS5KHJjkwyadn0Yc6yqDGMU97ainjdnGS42Y4tn+SNUk+M5uO1FIGcnHGNEd76ihjV1VvTPKKJDck+US67/e/n+SwJE+vque21j44i36WVB2t1tpCXQuWjaqqJP+aZNckH0/yP5O8oLX2nlmevy5JWmurBxoiW6FxzauqOivJwUle0lo7cdL+45P8ZZJ3ttZePMo12DqNcY6OpR+YqqqekeR/J/lckqe11m6acnzb1trPZ9GPOspgxjhP1yVqKQunqv4tySOTHNZaO3MW7dVSFtQ85ui6RB1lvKrqXkm+n+RHSR7cWrtu0rGDkpyT5MrW2n1n0deSqqNCaJiHqjo6yVvSPWmyJslrI4RmkY1jXlXVfZNcnmRdkt1aa7+adGzndE9aVZKVrbVbRhguWyEhNEtZVd0hyXeT3DPJ6tbaj+bZjzrKYMY1T/u+1iVqKQujqvZM8h/pgpX7tNY2udyBWspCm+sc7c9Zl6ijjFdVPSLJV5Oc2Vo7bJrjP02X5+68mX6WXB21HAfMUVXtkeQNSd7aWvtiVa2ZZ1e/U1V/kmRVkluS/L8kX5zNNzvYhFHn1cR8PnvyN6kkaa3dVFVfSfeb1Ecm+fyYxszWZVy1Tw1l3PZN9xdOH03y46p6YpI9k9ya5ILW2r/Nsh91lCGNa55OUEtZKC/qt++d5fxSS1loc52jE9RRxu07SW5Psk9V7dJau37iQFU9JsnO6Zbo2JwlV0eF0DAHVbVNklOTrE/yqhG7u1ff12RXVtURrbXzRuybrdeo8+r+/fayGY5/J903qt3jH/zMz7hqnxrKuD283/4wyUVJ/mDywar6YpL/NosnT9VRhjSueTpBLWVwVbV9kj9J8qsks/rL0ailLKB5ztEJ6ihj1Vq7saqOSXJ8km9W1SfSrQ29W5KnpFsa9kUz9/BrS66O3mEhLgLLyGvSvfDl8NbaxhH6+cckj033DWvHdD9AvDPJ6iSfqaqHjDhOtk7jmFcr+u2GGY5P7L/LvEfJ1mxctU8NZQgr++2Lk2yf5I/SPWmyZ5KzkjwmyUdm0Y86ypDGNU8TtZSF88fpat5nWmtXz/IctZSFNJ85mqijDKS1dkKSp6V7ePgFSV6Z5BlJrk5yyuR1ojdhydVRITTMUlXtk+7p5zfP408df0Nr7bjW2jmttR+21n7WWvtGvxj88el+oDh29BGztVmgeVUTlxtDX2xlxjVH1VAGcsd+W+meJP18a+3m1tolSZ6a5HtJDqiqR414HXWUUYxtnqqlLKAX9tt3jrFPtZRxmtccVUcZSlW9It3SW6ekewJ6xyR7J7kiyYeq6k3juEy/XbA6KoSGWZi0DMdlSf52wEu9o98+ZsBrsPWZy7ya+G3oihmO33lKOxiHcdU+NZRR/LjfXtFa+/rkA/1fP53Vf7rPZvpRRxnSuObppqiljE1VPTDdWubfS/LpOZyqlrIgRpijm6KOMm9VdWCSN6Z7MeFftdau6H/JcVG6Xzh/P8nL+hcPbsqSq6NCaJidndKtk7NHklurqk18JHlt3+bd/b4TRrjOxJ9U7DhCHzDVXObVt/vt7jMcv1+/nWldKZiPcdU+NZRRTNS/n8xwfCL8236W/aijDGFc83RT1FLGab4ve1NLWSjznaOboo4yiif123OnHmit/SzJBeny3Idupp8lV0e9mBBm57Yk753h2F7p/uP/crr/yEdZqmPiTyevGKEPmGou82riG93BVXWHyW/Rraqdk+yXZGOSr453iGzlxlX71FBG8cUkv0hyv6rarrV2+5Tje/bbdZvpRx1lSOOap5uiljIWVXWnJM9J97K3mX6WmolayuBGnKOboo4yit/pt/eY4fjE/qn/BphqydVRT0LDLLTWNrbWnj/dR5Iz+2bv7/d9OEmqatuqekBV7Ta5r6p6UFXdbeo1quo+Sd7ef/rBIe+H5Weu82qm+dlauzzJ2elepnHUlO6OS/fb/A+01m4Z4/DZCoxrjqqhDKW1dn2SD6f7k8XXTD5WVY9L8vh0f6742X6fOsqCG9c8VUtZIM9Ictckn57pZW9qKYts3nNUHWVAX+q3L6yq/zL5QFU9IV14fGuS8/t9W0wdrdas4w+jqKpj0y3J8YLW2nsm7V+d5MokV7XWVk9p/8p0v5W6MslN6Raaf2KSO6Vbh+qp0zzZAjOa67yaaX72x3ZL9w1tZZIzklya5BFJDkr3pzr7ttZuGPqeWF7GNUfVUIZUVSuTfCXJ76f7AeCCJPdJt/5eS/Ks1tpH+raro46yCMYxT9VSFkJVfSnJo5M8pbX2yRnarI5ayiIZZY6qowylqu6Q7h0Pf5RuXp2e5Np0y8M+Kd0LBV/aWntr3351tpA6ajkOWHjnJrl/uiU8HpXut08/Sbecx6lJTm1+O8TcjW1etdYur6qHJXldkkOSHJrkmiRvS3Jca+3GsY+ercG45qgaymBaa9dV1SOSvDpdoPfIdP/4/1SS17fWZvXniuooQxrTPFVLGVRV7ZEu3Jv3y97UUoY0hjmqjjKI1tqvqurQdE8vr033vX6HJDemm6tva62dPcu+llQd9SQ0AAAAAACDsSY0AAAAAACDEUIDAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwAAAAAwGCE0AAAAAACDEUIDAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwDAElBVB1ZVq6pj53n+Kf35q8c7soVXVYf393L4Yo8FAIDRCaEBAIAFNWrgDgDAlkUIDQAAAADAYITQAAAAAAAMRggNAMCyUlVPqarPV9U1VXVbVf2gqs6rqiOntLtbVb2+qi6tqo1VtaE/7+Bp+vz1GsVV9cSqOr+qbqmqH1fVR6vqftOcs3tVvaGqvlZVP+rHclVVvauq7j3k12CasTyiH+e1VXV7VV1dVe+sqt+dpu0X+nvdpqpeVVXf6cd+dVW9saq2m+Eaz66qi/qv5XVVdWpV/e5Ef5PanZLk3P7T1/bXmvg4cJp+D+r7uKmqflpVn6qqPcbyhQEAYEFss9gDAACAcamqFyZ5Z5Jrk3wyyfVJViZ5cJIjkpzct7tPki8kWZ3kS0k+m2THJE9K8tmqelFr7d3TXOJpSZ6Q5PT+/D9M8vQkB1XVvq21b09p++J0gev5SW5P8qAkz0/y5Kp6WGvt++O585lV1RFJ3p3ktiRnJrk6yf0mjeORrbX105z6T0n2T/KZJD9NcmiSV6T7eh4x5RovT/KmJD9O8v4kG5I8LslX+v892Sf67Z8mOS/d13HCuiltn5TksH4M70jywH4cD6+qB7bWrt/M7QMAsAQIoQEAWE5elC7sfUhr7brJB6pql0mfvj/JfZI8s7V22qQ2d0kXir6tqs5srf1wSv9PTvLk1tq/TDrn6CQnpAu4Hzup7alJ3tJau23KOA5OF6q+Osmfz/0WZ6+qdk8Xyq9LcsDk0Luq1iT51yRvTfLUaU7fLcmDWms39u3/JsnXkzy3qv66tXZtv/++Sf4+XeC/V2vt6n7/K9MF2Wsnd9pa+0RV/SRdCP2F1tqxm7iF/5rk8a21z08a9+uTvDLJ89IF3wAALHGW4wAAYLn5RZKfT9058dRsVT0kyQFJPjY5gO7b/CTJa5PcKd0TzlOdMzmA7r09yeVJ1vRPWE/09f2pAXS//+wklyR5/Bzuab7+PMm2SY6e+tR1a+2cdE9GP7mqdp7m3GMmAui+/S1JPpTuZ4iHTWr3rHQPt5w4EUD37Vu6sPiXI4z/tMkBdO9d/XafEfoFAGABeRIaAIDl5ENJ3pzkkqr6cLrlHr7SWvvRpDaP6rcrqurYafq4R7+dbt3h86buaK39sqq+nO7J4YcmuSpJqqqSPDvJ4UkekuSuSe446dTbZ3dLI5m41wOq6uHTHF+Zbky7J7lwyrGvTdN+ImS+66R9D+23X57auLV2VVVdnW7Zk/mY7RgAAFjChNAAACwbrbXjq+r6JEcmeUmSlyZpVXVekpe31r6W5O5988f1HzPZaZp9U5fnmHBtv10xad/x/fWvSXJWku8n2dgfOzzdciBDm7jXl2+m3W/da/9U+FS/6LeTw/SJe57pa/PDzD+E/q0xtNZ+0eX7vzEGAACWMCE0AADLSmvtA0k+0K/vvG+69Y6fl+Ssqtoj//mivKNba2+bY/f3nGH/vfrthiSpqpXpQvBvJNm3tXbT5MZV9cw5Xne+Ju51RWvtpwNdY6Lfe6ZbZmSqmb5mAABsJawJDQDAstRa+0lr7dOttRckOSXJ3ZLsn+SrfZP959HtAVN3VNUdkzy6//T/9tv7pvu39tnTBND37o8vhFHudbYm7vnRUw/0a2T/3jTnTKwT7WlmAICtgBAaAIBlo6oOqarp/tpvZb/9Wb8kx5eSPK2qnjdDP3/QP8081ZqqetKUff8j3XrQ57bWrur3reu3j+5D6ol+d0ry7izcXyS+Pd1LGt9SVbtPPVhV21XVqAH1P6VbpuMvqurXgXO/JvbrM33QfEO/XTXitQEA2AJYjgMAgOXktCS39i8KXJek0j0F/PB0L977XN/uWUnOSfLeqnpJkn9Pt/7wvZM8OMme6V7qd92U/j+Z5PSqOj3Jd9O9cPDQJDemW4c6SdJau7aqTkuyNsnFVXV2urWTH5fk1iQXJ/nDsd31DFpr3+qD9vele1njZ5NclmTbdAHw/kl+lOQBI1zj8qp6TZK/T/L1/oWQG9Ld692SfD3d13Syb6dbI3ttVd2eZH2SluTUSUE+AADLhBAaAIDl5JVJHp9kr3Th8K1JrkpyTJJ/aK39PElaa9+rqr2T/EWSpyd5drondq9N8s0kJyb5j2n6/3iSdyX5myRPTPeU8ceT/HVr7bIpbf8syRVJ/nuSo9KFvWcmeU2Sj43ndjevtfbBqvp6kpclOSjJwUluSfKDJB9N8uExXOP1VfW9JH+V5IgkN6V7GeMrkpyd/1w3eqL9L6vqqUnekOSPk+yc7hcGX073/xcAAMtItdYWewwAALCkVdXhSf4xyRGttVMWdzRbjqq6c5IfJrm4tfaoxR4PAACLw5rQAADASKrqHlW17ZR92yR5c5I7JTl9UQYGAMCSYDkOAABgVE9P8rqq+lySq9OtBf2YJLunW//6xMUbGgAAi00IDQAAS1C/BMjqWTS9uLX2iUEHs3n/nm4958ckuXu/78okf5fkja21jYs1MAAAFp81oQEAYAmqqi8kOWAWTd/fWjt82NEAAMD8CaEBAAAAABiMFxMCAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwAAAAAwGCE0AAAAAACDEUIDAAAAADAYITQAAAAAAIMRQgMAAAAAMBghNAAAAAAAgxFCAwAAAAAwGCE0AAAAAACDEUIDAAAAADCY/w8Jal7/Pwx5fgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 480,
       "width": 720
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# divide the scatter plot into 3 categories based on sepal length \n",
    "setosa = iris[iris['species'] == 'Iris-setosa']\n",
    "versicolor = iris[iris['species'] == 'Iris-versicolor']\n",
    "virginica = iris[iris['species'] == 'Iris-virginica']\n",
    "\n",
    "ax = setosa.plot(kind='scatter', x='sepal_length', y='sepal_width', color='red', figsize=(12, 8))\n",
    "versicolor.plot(kind='scatter', x='sepal_length', y='sepal_width', color='green', ax=ax)\n",
    "virginica.plot(kind='scatter', x='sepal_length', y='sepal_width', color='orange', ax=ax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be762fa9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "235a5c50",
   "metadata": {},
   "source": [
    "# box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "bf057d64",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'petal_width'}, xlabel='[species]'>"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 522,
       "width": 728
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# draw petal_width as y axis and species as x axis \n",
    "iris[['species','petal_width',]].boxplot(grid=False, by='species', figsize=(12, 8))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "9db548fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:title={'center':'petal_length'}, xlabel='[species]'>,\n",
       "        <AxesSubplot:title={'center':'petal_width'}, xlabel='[species]'>],\n",
       "       [<AxesSubplot:title={'center':'sepal_length'}, xlabel='[species]'>,\n",
       "        <AxesSubplot:title={'center':'sepal_width'}, xlabel='[species]'>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 402,
       "width": 603
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.boxplot(by='species', grid=False, figsize=(10, 6))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db393069",
   "metadata": {},
   "source": [
    "# pandas groupby, aggregate, and function \n",
    "* groupby means : split data -> function -> aggregate "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "a04c9fa1",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                              \n",
       "Iris-setosa               5.8          4.4           1.9          0.6\n",
       "Iris-versicolor           7.0          3.4           5.1          1.8\n",
       "Iris-virginica            7.9          3.8           6.9          2.5"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# based on three species, what are the max value of each species' sepal and petal length, width\n",
    "\n",
    "iris.groupby('species').max()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "b2f9bfa7",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>1.5</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>2.1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 sepal_length  sepal_width  petal_length  petal_width\n",
       "species                                                              \n",
       "Iris-setosa               1.5          2.1           0.9          0.5\n",
       "Iris-versicolor           2.1          1.4           2.1          0.8\n",
       "Iris-virginica            3.0          1.6           2.4          1.1"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# we can also define agg func if we want to calculate the range of max legnth and min length of sepal etc  \n",
    "\n",
    "def range_iris(arr):\n",
    "    return arr.max() - arr.min()\n",
    "\n",
    "iris.groupby('species').agg(range_iris)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "fe84b04e",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"4\" halign=\"left\">sepal_length</th>\n",
       "      <th colspan=\"4\" halign=\"left\">sepal_width</th>\n",
       "      <th colspan=\"4\" halign=\"left\">petal_length</th>\n",
       "      <th colspan=\"4\" halign=\"left\">petal_width</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>range_iris</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>range_iris</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>range_iris</th>\n",
       "      <th>min</th>\n",
       "      <th>max</th>\n",
       "      <th>mean</th>\n",
       "      <th>range_iris</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</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",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>4.3</td>\n",
       "      <td>5.8</td>\n",
       "      <td>5.006</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.3</td>\n",
       "      <td>4.4</td>\n",
       "      <td>3.418</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>1.464</td>\n",
       "      <td>0.9</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.244</td>\n",
       "      <td>0.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>4.9</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.936</td>\n",
       "      <td>2.1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2.770</td>\n",
       "      <td>1.4</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>4.260</td>\n",
       "      <td>2.1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.326</td>\n",
       "      <td>0.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>4.9</td>\n",
       "      <td>7.9</td>\n",
       "      <td>6.588</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.974</td>\n",
       "      <td>1.6</td>\n",
       "      <td>4.5</td>\n",
       "      <td>6.9</td>\n",
       "      <td>5.552</td>\n",
       "      <td>2.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.026</td>\n",
       "      <td>1.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                sepal_length                        sepal_width              \\\n",
       "                         min  max   mean range_iris         min  max   mean   \n",
       "species                                                                       \n",
       "Iris-setosa              4.3  5.8  5.006        1.5         2.3  4.4  3.418   \n",
       "Iris-versicolor          4.9  7.0  5.936        2.1         2.0  3.4  2.770   \n",
       "Iris-virginica           4.9  7.9  6.588        3.0         2.2  3.8  2.974   \n",
       "\n",
       "                           petal_length                        petal_width  \\\n",
       "                range_iris          min  max   mean range_iris         min   \n",
       "species                                                                      \n",
       "Iris-setosa            2.1          1.0  1.9  1.464        0.9         0.1   \n",
       "Iris-versicolor        1.4          3.0  5.1  4.260        2.1         1.0   \n",
       "Iris-virginica         1.6          4.5  6.9  5.552        2.4         1.4   \n",
       "\n",
       "                                        \n",
       "                 max   mean range_iris  \n",
       "species                                 \n",
       "Iris-setosa      0.6  0.244        0.5  \n",
       "Iris-versicolor  1.8  1.326        0.8  \n",
       "Iris-virginica   2.5  2.026        1.1  "
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add min and max names in the column heades \n",
    "iris.groupby('species').agg(['min','max', 'mean',range_iris])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "9160d175",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>petal_width</th>\n",
       "      <th>petal_length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>range_iris</th>\n",
       "      <th>range_iris</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Iris-setosa</th>\n",
       "      <td>0.5</td>\n",
       "      <td>0.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-versicolor</th>\n",
       "      <td>0.8</td>\n",
       "      <td>2.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Iris-virginica</th>\n",
       "      <td>1.1</td>\n",
       "      <td>2.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                petal_width petal_length\n",
       "                 range_iris   range_iris\n",
       "species                                 \n",
       "Iris-setosa             0.5          0.9\n",
       "Iris-versicolor         0.8          2.1\n",
       "Iris-virginica          1.1          2.4"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the range of petal width and petal length\n",
    "\n",
    "iris.groupby('species').agg({'petal_width':[range_iris],'petal_length':['min'], 'petal_length':[range_iris]})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "afcc5ff1",
   "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></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>species</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Iris-setosa</th>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Iris-versicolor</th>\n",
       "      <th>50</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.4</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>4.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.5</td>\n",
       "      <td>Iris-versicolor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Iris-virginica</th>\n",
       "      <th>100</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.3</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>7.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     sepal_length  sepal_width  petal_length  petal_width  \\\n",
       "species                                                                     \n",
       "Iris-setosa     0             5.1          3.5           1.4          0.2   \n",
       "                1             4.9          3.0           1.4          0.2   \n",
       "                2             4.7          3.2           1.3          0.2   \n",
       "Iris-versicolor 50            7.0          3.2           4.7          1.4   \n",
       "                51            6.4          3.2           4.5          1.5   \n",
       "                52            6.9          3.1           4.9          1.5   \n",
       "Iris-virginica  100           6.3          3.3           6.0          2.5   \n",
       "                101           5.8          2.7           5.1          1.9   \n",
       "                102           7.1          3.0           5.9          2.1   \n",
       "\n",
       "                             species  \n",
       "species                               \n",
       "Iris-setosa     0        Iris-setosa  \n",
       "                1        Iris-setosa  \n",
       "                2        Iris-setosa  \n",
       "Iris-versicolor 50   Iris-versicolor  \n",
       "                51   Iris-versicolor  \n",
       "                52   Iris-versicolor  \n",
       "Iris-virginica  100   Iris-virginica  \n",
       "                101   Iris-virginica  \n",
       "                102   Iris-virginica  "
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# filter the first 4 lines of data of each species \n",
    "\n",
    "def first_n(df, n = 3):\n",
    "    return(df[0:n])\n",
    "iris.groupby(\"species\").apply(first_n, n = 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "0d33d834",
   "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>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.6</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.5</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>7.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.8</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>7.6</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.2</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.6</td>\n",
       "      <td>6.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>Iris-virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     sepal_length  sepal_width  petal_length  petal_width         species\n",
       "125           7.2          3.2           6.0          1.8  Iris-virginica\n",
       "130           7.4          2.8           6.1          1.9  Iris-virginica\n",
       "135           7.7          3.0           6.1          2.3  Iris-virginica\n",
       "109           7.2          3.6           6.1          2.5  Iris-virginica\n",
       "107           7.3          2.9           6.3          1.8  Iris-virginica\n",
       "131           7.9          3.8           6.4          2.0  Iris-virginica\n",
       "105           7.6          3.0           6.6          2.1  Iris-virginica\n",
       "117           7.7          3.8           6.7          2.2  Iris-virginica\n",
       "122           7.7          2.8           6.7          2.0  Iris-virginica\n",
       "118           7.7          2.6           6.9          2.3  Iris-virginica"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the maximum 10 largest petal length\n",
    "# sort_values is from min to max, using -10 to count backwards \n",
    "iris.sort_values(by = \"petal_length\")[-10:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "f0071260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Iris-versicolor    20\n",
       "Iris-virginica     41\n",
       "dtype: int64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find septal length which is longer than 6 cm \n",
    "# solution 1 \n",
    "iris[iris['sepal_length'] > 6].groupby('species').size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "ed030559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "species\n",
       "Iris-setosa         0\n",
       "Iris-versicolor    20\n",
       "Iris-virginica     41\n",
       "dtype: int64"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# solution 2\n",
    "def more_len(group, length=6):\n",
    "    return len(group[group['sepal_length'] > length])\n",
    "\n",
    "iris.groupby(['species']).apply(more_len, 6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7796bc18",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}