250323TaniyaDubeyMRA ProjectM1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ea3c9bca",
   "metadata": {},
   "source": [
    "# Marketing & Retail Analytics Project"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fa383ba",
   "metadata": {},
   "source": [
    "## Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "446d5e29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Libraries to help with reading and manipulating data\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# Libraries to help with data visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from pylab import rcParams\n",
    "\n",
    "# Command to tell Python to actually display the graphs\n",
    "%matplotlib inline\n",
    "\n",
    "# this library contains a large number of probability distributions as well as a growing library of statistical functions\n",
    "import scipy.stats as stats  \n",
    "\n",
    "# set working directory\n",
    "import os\n",
    "os.getcwd()\n",
    "os.chdir('C:\\\\Users\\\\Dubey\\\\Downloads')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fd694c39",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Dubey\\AppData\\Local\\Temp\\ipykernel_13184\\1765676117.py:2: DeprecationWarning: Importing display from IPython.core.display is deprecated since IPython 7.14, please import from IPython display\n",
      "  from IPython.core.display import display, HTML\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>.container { width:99.9% !important; }<\\style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# this two lines of code is just to increase the size of the display\n",
    "from IPython.core.display import display, HTML \n",
    "display(HTML('<style>.container { width:99.9% !important; }<\\style>'))\n",
    "\n",
    "# set column number to see all columns\n",
    "pd.set_option('display.max_columns', 35)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ba2849",
   "metadata": {},
   "source": [
    "# Problem Statement:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9f61e46",
   "metadata": {},
   "source": [
    "An automobile parts manufacturing company has collected data of transactions for 3 years. They do not have any in-house data science team, thus they have hired you as their consultant. Your job is to use your magical data science skills to provide them with suitable insights about their data and their customers."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b90423a5",
   "metadata": {},
   "source": [
    "## Data Dictionary:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93409f2a",
   "metadata": {},
   "source": [
    "* ORDERNUMBER : Order Number\n",
    "\n",
    "* CUSTOMERNAME : customer\n",
    "\n",
    "* QUANTITYORDERED : Quantity ordered\n",
    "\n",
    "* PHONE : Phone of the customer\n",
    "\n",
    "* PRICEEACH : Price of Each item\n",
    "\n",
    "* ADDRESSLINE1 : Address of customer\n",
    "\n",
    "* ORDERLINENUMBER : order line\n",
    "\n",
    "* CITY : City of customer\n",
    "\n",
    "* SALES : Sales amount\n",
    "\n",
    "* POSTALCODE : Postal Code of customer\n",
    "\n",
    "* ORDERDATE : Order Date\n",
    "\n",
    "* COUNTRY : Country customer\n",
    "\n",
    "* DAYS_SINCE_LASTORDER : Days_ Since_Lastorder\n",
    "\n",
    "* CONTACTLASTNAME : Contact person customer\n",
    "\n",
    "* STATUS : Status of order like Shipped or not\n",
    "\n",
    "* CONTACTFIRSTNAME : Contact person customer\n",
    "\n",
    "* PRODUCTLINE : Product line – CATEGORY\n",
    "\n",
    "* DEALSIZE : Size of the deal based on Quantity and Item Price\n",
    "\n",
    "* MSRP : Manufacturer's Suggested Retail Price\n",
    "\n",
    "* PRODUCTCODE : Code of Product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d51966eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# READ DATASET\n",
    "df = pd.read_excel('Sales_Data.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b189b17d",
   "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>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>DAYS_SINCE_LASTORDER</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>PRODUCTLINE</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>PRODUCTCODE</th>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>CITY</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10107</td>\n",
       "      <td>30</td>\n",
       "      <td>95.70</td>\n",
       "      <td>2</td>\n",
       "      <td>2871.00</td>\n",
       "      <td>2018-02-24</td>\n",
       "      <td>828</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>S10_1678</td>\n",
       "      <td>Land of Toys Inc.</td>\n",
       "      <td>2125557818</td>\n",
       "      <td>897 Long Airport Avenue</td>\n",
       "      <td>NYC</td>\n",
       "      <td>10022</td>\n",
       "      <td>USA</td>\n",
       "      <td>Yu</td>\n",
       "      <td>Kwai</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10121</td>\n",
       "      <td>34</td>\n",
       "      <td>81.35</td>\n",
       "      <td>5</td>\n",
       "      <td>2765.90</td>\n",
       "      <td>2018-05-07</td>\n",
       "      <td>757</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>S10_1678</td>\n",
       "      <td>Reims Collectables</td>\n",
       "      <td>26.47.1555</td>\n",
       "      <td>59 rue de l'Abbaye</td>\n",
       "      <td>Reims</td>\n",
       "      <td>51100</td>\n",
       "      <td>France</td>\n",
       "      <td>Henriot</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Small</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10134</td>\n",
       "      <td>41</td>\n",
       "      <td>94.74</td>\n",
       "      <td>2</td>\n",
       "      <td>3884.34</td>\n",
       "      <td>2018-07-01</td>\n",
       "      <td>703</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>S10_1678</td>\n",
       "      <td>Lyon Souveniers</td>\n",
       "      <td>+33 1 46 62 7555</td>\n",
       "      <td>27 rue du Colonel Pierre Avia</td>\n",
       "      <td>Paris</td>\n",
       "      <td>75508</td>\n",
       "      <td>France</td>\n",
       "      <td>Da Cunha</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10145</td>\n",
       "      <td>45</td>\n",
       "      <td>83.26</td>\n",
       "      <td>6</td>\n",
       "      <td>3746.70</td>\n",
       "      <td>2018-08-25</td>\n",
       "      <td>649</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>S10_1678</td>\n",
       "      <td>Toys4GrownUps.com</td>\n",
       "      <td>6265557265</td>\n",
       "      <td>78934 Hillside Dr.</td>\n",
       "      <td>Pasadena</td>\n",
       "      <td>90003</td>\n",
       "      <td>USA</td>\n",
       "      <td>Young</td>\n",
       "      <td>Julie</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10168</td>\n",
       "      <td>36</td>\n",
       "      <td>96.66</td>\n",
       "      <td>1</td>\n",
       "      <td>3479.76</td>\n",
       "      <td>2018-10-28</td>\n",
       "      <td>586</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Motorcycles</td>\n",
       "      <td>95</td>\n",
       "      <td>S10_1678</td>\n",
       "      <td>Technics Stores Inc.</td>\n",
       "      <td>6505556809</td>\n",
       "      <td>9408 Furth Circle</td>\n",
       "      <td>Burlingame</td>\n",
       "      <td>94217</td>\n",
       "      <td>USA</td>\n",
       "      <td>Hirano</td>\n",
       "      <td>Juri</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ORDERNUMBER  QUANTITYORDERED  PRICEEACH  ORDERLINENUMBER    SALES  \\\n",
       "0        10107               30      95.70                2  2871.00   \n",
       "1        10121               34      81.35                5  2765.90   \n",
       "2        10134               41      94.74                2  3884.34   \n",
       "3        10145               45      83.26                6  3746.70   \n",
       "4        10168               36      96.66                1  3479.76   \n",
       "\n",
       "   ORDERDATE  DAYS_SINCE_LASTORDER   STATUS  PRODUCTLINE  MSRP PRODUCTCODE  \\\n",
       "0 2018-02-24                   828  Shipped  Motorcycles    95    S10_1678   \n",
       "1 2018-05-07                   757  Shipped  Motorcycles    95    S10_1678   \n",
       "2 2018-07-01                   703  Shipped  Motorcycles    95    S10_1678   \n",
       "3 2018-08-25                   649  Shipped  Motorcycles    95    S10_1678   \n",
       "4 2018-10-28                   586  Shipped  Motorcycles    95    S10_1678   \n",
       "\n",
       "           CUSTOMERNAME             PHONE                   ADDRESSLINE1  \\\n",
       "0     Land of Toys Inc.        2125557818        897 Long Airport Avenue   \n",
       "1    Reims Collectables        26.47.1555             59 rue de l'Abbaye   \n",
       "2       Lyon Souveniers  +33 1 46 62 7555  27 rue du Colonel Pierre Avia   \n",
       "3     Toys4GrownUps.com        6265557265             78934 Hillside Dr.   \n",
       "4  Technics Stores Inc.        6505556809              9408 Furth Circle   \n",
       "\n",
       "         CITY POSTALCODE COUNTRY CONTACTLASTNAME CONTACTFIRSTNAME DEALSIZE  \n",
       "0         NYC      10022     USA              Yu             Kwai    Small  \n",
       "1       Reims      51100  France         Henriot             Paul    Small  \n",
       "2       Paris      75508  France        Da Cunha           Daniel   Medium  \n",
       "3    Pasadena      90003     USA           Young            Julie   Medium  \n",
       "4  Burlingame      94217     USA          Hirano             Juri   Medium  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TOP ROWs OF DATASET\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "75735501",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2747 entries, 0 to 2746\n",
      "Data columns (total 20 columns):\n",
      " #   Column                Non-Null Count  Dtype         \n",
      "---  ------                --------------  -----         \n",
      " 0   ORDERNUMBER           2747 non-null   int64         \n",
      " 1   QUANTITYORDERED       2747 non-null   int64         \n",
      " 2   PRICEEACH             2747 non-null   float64       \n",
      " 3   ORDERLINENUMBER       2747 non-null   int64         \n",
      " 4   SALES                 2747 non-null   float64       \n",
      " 5   ORDERDATE             2747 non-null   datetime64[ns]\n",
      " 6   DAYS_SINCE_LASTORDER  2747 non-null   int64         \n",
      " 7   STATUS                2747 non-null   object        \n",
      " 8   PRODUCTLINE           2747 non-null   object        \n",
      " 9   MSRP                  2747 non-null   int64         \n",
      " 10  PRODUCTCODE           2747 non-null   object        \n",
      " 11  CUSTOMERNAME          2747 non-null   object        \n",
      " 12  PHONE                 2747 non-null   object        \n",
      " 13  ADDRESSLINE1          2747 non-null   object        \n",
      " 14  CITY                  2747 non-null   object        \n",
      " 15  POSTALCODE            2747 non-null   object        \n",
      " 16  COUNTRY               2747 non-null   object        \n",
      " 17  CONTACTLASTNAME       2747 non-null   object        \n",
      " 18  CONTACTFIRSTNAME      2747 non-null   object        \n",
      " 19  DEALSIZE              2747 non-null   object        \n",
      "dtypes: datetime64[ns](1), float64(2), int64(5), object(12)\n",
      "memory usage: 429.3+ KB\n"
     ]
    }
   ],
   "source": [
    "# Data Type of Columns\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a08dba9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2747, 20)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Rows and Columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "196fbb74",
   "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>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>DAYS_SINCE_LASTORDER</th>\n",
       "      <th>MSRP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2747.000000</td>\n",
       "      <td>2747.000000</td>\n",
       "      <td>2747.000000</td>\n",
       "      <td>2747.000000</td>\n",
       "      <td>2747.000000</td>\n",
       "      <td>2747.000000</td>\n",
       "      <td>2747.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10259.761558</td>\n",
       "      <td>35.103021</td>\n",
       "      <td>101.098951</td>\n",
       "      <td>6.491081</td>\n",
       "      <td>3553.047583</td>\n",
       "      <td>1757.085912</td>\n",
       "      <td>100.691664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>91.877521</td>\n",
       "      <td>9.762135</td>\n",
       "      <td>42.042548</td>\n",
       "      <td>4.230544</td>\n",
       "      <td>1838.953901</td>\n",
       "      <td>819.280576</td>\n",
       "      <td>40.114802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>10100.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>26.880000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>482.130000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>33.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>10181.000000</td>\n",
       "      <td>27.000000</td>\n",
       "      <td>68.745000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2204.350000</td>\n",
       "      <td>1077.000000</td>\n",
       "      <td>68.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>10264.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>95.550000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3184.800000</td>\n",
       "      <td>1761.000000</td>\n",
       "      <td>99.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10334.500000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>127.100000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>4503.095000</td>\n",
       "      <td>2436.500000</td>\n",
       "      <td>124.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10425.000000</td>\n",
       "      <td>97.000000</td>\n",
       "      <td>252.870000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>14082.800000</td>\n",
       "      <td>3562.000000</td>\n",
       "      <td>214.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ORDERNUMBER  QUANTITYORDERED    PRICEEACH  ORDERLINENUMBER  \\\n",
       "count   2747.000000      2747.000000  2747.000000      2747.000000   \n",
       "mean   10259.761558        35.103021   101.098951         6.491081   \n",
       "std       91.877521         9.762135    42.042548         4.230544   \n",
       "min    10100.000000         6.000000    26.880000         1.000000   \n",
       "25%    10181.000000        27.000000    68.745000         3.000000   \n",
       "50%    10264.000000        35.000000    95.550000         6.000000   \n",
       "75%    10334.500000        43.000000   127.100000         9.000000   \n",
       "max    10425.000000        97.000000   252.870000        18.000000   \n",
       "\n",
       "              SALES  DAYS_SINCE_LASTORDER         MSRP  \n",
       "count   2747.000000           2747.000000  2747.000000  \n",
       "mean    3553.047583           1757.085912   100.691664  \n",
       "std     1838.953901            819.280576    40.114802  \n",
       "min      482.130000             42.000000    33.000000  \n",
       "25%     2204.350000           1077.000000    68.000000  \n",
       "50%     3184.800000           1761.000000    99.000000  \n",
       "75%     4503.095000           2436.500000   124.000000  \n",
       "max    14082.800000           3562.000000   214.000000  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Statistical Summary\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "647b10b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORDERNUMBER             0\n",
       "QUANTITYORDERED         0\n",
       "PRICEEACH               0\n",
       "ORDERLINENUMBER         0\n",
       "SALES                   0\n",
       "ORDERDATE               0\n",
       "DAYS_SINCE_LASTORDER    0\n",
       "STATUS                  0\n",
       "PRODUCTLINE             0\n",
       "MSRP                    0\n",
       "PRODUCTCODE             0\n",
       "CUSTOMERNAME            0\n",
       "PHONE                   0\n",
       "ADDRESSLINE1            0\n",
       "CITY                    0\n",
       "POSTALCODE              0\n",
       "COUNTRY                 0\n",
       "CONTACTLASTNAME         0\n",
       "CONTACTFIRSTNAME        0\n",
       "DEALSIZE                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nulls in data set\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "934dae4a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# duplicate in data set\n",
    "df.duplicated().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3a58dd93",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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VWuvuWuvuJG/L0i4bsZT2zy27YAAAGBFnCAMAsBl8f5L/VEp5cZLpJGeTfDzJi7ofBNfx8SQPllJe2Ln/e6WU73a+fjzJr6VzDeGe7/mVJF8Z0P5b12YoAACwdkqtddQ1AAAAAACwDlwyAgAAAACgEQJhAAAAAIBGCIQBAAAAABohEAYAAAAAaIRAGAAAAACgEQJhAAAAAIBGCIQBAAAAABohEAYAAAAAaMT/Bxu9rBQnjK5tAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Boxplot\n",
    "for column in df.columns:\n",
    "    if df[column].dtype == 'int64' or df[column].dtype == 'float64':\n",
    "        plt.figure(figsize=(25,6))\n",
    "        sns.boxplot(df[column], color='Salmon')\n",
    "        plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "beee8a4a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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+9j6taCjlwySAM6xuLFPvcEwnuof8LgUAACDrETYByBtHugY1PJbQSnqyAJhivFfT/o4BnysBAADIfoRNAPLGntY+BUxaVlfidykAMkxJNKTa0qgOdvb7XQoAAEDWI2wCkDf2tPVqUXWxCsJBv0sBkIEW1xTr8KlBJZzzuxQAAICsRtgEIC90D46qrXdEKxuYQgdgeourizUSS+hk97DfpQAAAGQ1wiYAeWG8D8ty+jUBOIvFNcWSxFQ6AACAi0TYBCAvHOkaVEE4oLrSqN+lAMhQZYVhVRdHdPDUoN+lAAAAZDXCJgB54WjXoOZXFilg5ncpADLYoppiHeocoG8TAADARSBsApDzhsfiausd1oKqIr9LAZDhFtcUa2gsrvbeEb9LAQAAyFqETQBy3rHTQ3ISYROA86JvEwAAwMUjbAKQ8450DcgkzSdsAnAelUURVRSGdbBzwO9SAAAAshZhE4Ccd6RrULWlURWEg36XAiALLK4p1sFTg3L0bQIAALgghE0AclrCOR3tGmIKHYAZW1xTrIGRmDr66dsEAABwIQibAOS0zv4RDY3FCZsAzNiiib5NTKUDAAC4EIRNAHLa0a5BSfRrAjBz1cURlRaEdIiwCQAA4IKkNWwys1vMbI+ZtZjZXdM8HjWzB7zHN5vZIm//zWb2rJlt925fPemcJ7xrPu991aXzOQDIbke6BlUQDqi2NOp3KQCyhJlpUXWxDnYO0LcJAADgAqQtbDKzoKR7Jb1O0hpJt5vZmimHfVDSaefcMkn3SPq0t79T0pucc5dKulPS16ec9x7n3OXeV3u6ngOA7He0a0jzK4sUMPO7FABZZHFNsXqHYzo9OOZ3KQAAAFknnSObNkhqcc4dcM6NSrpf0sYpx2yU9FXv/oOSbjIzc84955w74e3fKanQzBiWAGBWhsfiausdpl8TgFlbPNG3qd/nSgAAALJPOsOmJklHJ20f8/ZNe4xzLiapR1L1lGPeLulXzrnJS8L8uzeF7hNmDFcAML1jp4fkJMImALNWVxpVUSRIk3AAAIALkNENws3sEiWn1v32pN3v8abXvdz7eu9Zzv2wmW01s60dHR3pLxZAxjnSNSATzcEBzJ6ZaXFNMWETAADABUhn2HRc0vxJ283evmmPMbOQpHJJp7ztZknflfQ+59z+8ROcc8e92z5J31Ryut6vcc7d55xb75xbX1tbm5InBCC7HOkaVG1pVAXhoN+lAMhCi2uKdXpwTN2Do36XAgAAkFXSGTZtkbTczBabWUTSbZI2TTlmk5INwCXpVkmPO+ecmVVI+oGku5xzPx8/2MxCZlbj3Q9LeqOkHWl8DgCyVMI5He0aYgodgAu2qDrZt+nQKUY3AQAAzEbawiavB9NHJT0i6UVJ33bO7TSzT5nZm73DviSp2sxaJH1c0l3e/o9KWibpbq830/NmVicpKukRM3tB0vNKjoz6YrqeA4Ds1dk/oqGxOGETgAvWUF6gSDCgo6eH/C4FAAAgq4TSeXHn3EOSHpqy7+5J94clvWOa8/5a0l+f5bJXpbJGALnpaNegJJqDA7hwATPNqyjQccImAACAWcnoBuEAcKGOdA2qIBxQTWnU71IAZLGmikKd7BlSPOH8LgUAACBrEDYByElHuga1oKpIATO/SwGQxZoqizQWd2rvG/a7FAAAgKxB2AQg5wyPxdXeO6L5TKEDcJGaKwslial0AAAAs0DYBCDnHD09KCf6NQG4eFXFERWEAzrWTdgEAAAwU4RNAHLO+AiE5grCJgAXJ9kkvJCRTQAAALNA2AQg55zsGVZlUViFkaDfpQDIAc0VRWrtGVYsnvC7FAAAgKxA2AQg57T2DquhvNDvMgDkiKbKQsWdU2svTcIBAABmgrAJQE4ZiyfU2TeihrICv0sBkCOaK5Lh9TGm0gEAAMwIYROAnNLWOywnqbGcsAlAalQUhVUUCeo4TcIBAABmZEZhk5l9x8zeYGaEUwAyWmtPcpoLYROAVDEzNVfSJBwAAGCmZhoe/Yukd0vaZ2Z/a2Yr01gTAFywkz3DioQCqiyO+F0KgBzSVFGo9r5hjcZoEg4AAHA+MwqbnHOPOufeI+lKSYckPWpmvzCzD5hZOJ0FAsBsnOwZVkNZgQJmfpcCIIc0VxYp4aSTPYxuAgAAOJ8ZT4szs2pJ75f0IUnPSfqMkuHTj9NSGQDMknNOrb1DamAKHYAUa/KahNO3CQAA4PxCMznIzL4raaWkr0t6k3PupPfQA2a2NV3FAcBs9AyNaXgswUp0AFKurDCs0oIQK9IBAADMwIzCJklfdM49NHmHmUWdcyPOufVpqAsAZu0kzcEBpFFTBU3CAQAAZmKm0+j+epp9T6eyEAC4WONhEyObAKRDc2WhOvtHNDwW97sUAACAjHbOkU1m1iCpSVKhmV0habzjbpmkojTXBgCz0tozpKriiKLhoN+lAMhBTRVFcpJO0LcJAADgnM43je43lGwK3izpnybt75P052mqCQAuyMmeYabQAUibpkqahAMAAMzEOcMm59xXJX3VzN7unPvPOaoJAGZtNJZQ18CoLl9Q4XcpAHJUSTSkiqIwTcIBAADO43zT6O5wzv0/SYvM7ONTH3fO/dM0pwHAnGvrHZaT1FhW6HcpAHJYU0UhI5sAAADO43wNwou92xJJpdN8AUBGmGgOzjQ6AGnUXFGoroFRdQ+O+l0KAABAxjrfNLoveLd/NTflAMCFOdkzpGgooMqisN+lAMhhTZXJ9VFeONajV6yo9bkaAACAzHS+kU2SJDP7OzMrM7OwmT1mZh1mdke6iwOAmWrtGVZDeYHM7PwHA8AFaqpITtXdfrzH50oAAAAy14zCJkmvdc71SnqjpEOSlkn6k3QVBQCzkXBOrb2sRAcg/QojQVUWhbXrZK/fpQAAAGSsmYZN49Pt3iDpP5xz/DkPQMboHhzTSCxBc3AAc6KhvFC7CZsAAADOaqZh03+b2W5JV0l6zMxqJQ2nrywAmLmTPcmVoWgODmAuNJQV6GDngIbH4n6XAgAAkJFmFDY55+6SdL2k9c65MUkDkjamszAAmKnWnmGZpPoywiYA6ddQXqCEk/a19ftdCgAAQEY652p0U6yStMjMJp/ztRTXAwCzdrJnWNUlEUVCMx2sCQAXrtELtl9s7dWlzeU+VwMAAJB5ZhQ2mdnXJS2V9Lyk8THjToRNADJAa++w5lXQrwnA3KgqiaggHNCL9G0CAACY1kxHNq2XtMY559JZDADM1vBYXF0Do7pqYaXfpQDIEwEzrWwo0+6TfX6XAgAAkJFmOudkh6SGdBYCABeirTe5VkEj/ZoAzKHVDaXa3dor/g4HAADw62YaNtVI2mVmj5jZpvGvdBYGADNxsicZNrESHYC5tKqhVKcHx9TeN+J3KQAAABlnptPoPpnOIgDgQrX1DqsgHFB5YdjvUgDkkVWNZZKkF0/2shImAADAFDMa2eSc+6mkQ5LC3v0tkn6VxroAYEba+0ZUWxKVmfldCoA8srohGTbtbqVvEwAAwFQzCpvM7LckPSjpC96uJknfS1NNADBjHX0jqmNUAYA5Vl4U1rzyAlakAwAAmMZMezZ9RNLLJPVKknNun6S6dBUFADMxOBpT/0hMdaVRv0sBkIdWNbIiHQAAwHRmGjaNOOdGxzfMLCSJ5VcA+KrDa8xL2ATAD6saSrW/o18jsbjfpQAAAGSUmYZNPzWzP5dUaGY3S/oPSd9PX1kAcH7tveNhE9PoAMy9VY1liiWc9rcP+F0KAABARplp2HSXpA5J2yX9tqSHJP1FuooCgJlo7xtWOGgqL2IlOgBzb01jqSRpdyt9mwAAACYLzeQg51zCzL4n6XvOuY70lgQAM9PeN6La0qgCrEQHwAeLqosVCQVYkQ4AAGCKc45ssqRPmlmnpD2S9phZh5ndPTflAcDZtfeNMIUOgG9CwYBW1JewIh0AAMAU55tG9zElV6G72jlX5ZyrknSNpJeZ2cfOd3Ezu8XM9phZi5ndNc3jUTN7wHt8s5kt8vbfbGbPmtl27/bVk865ytvfYmafNWNIA5CPRsbi6hkaozk4AF+taijTi6xIBwAAcIbzhU3vlXS7c+7g+A7n3AFJd0h637lONLOgpHslvU7SGkm3m9maKYd9UNJp59wySfdI+rS3v1PSm5xzl0q6U9LXJ53zeUm/JWm593XLeZ4DgBzU0c9KdAD8t6qhVJ39IxOrYwIAAOD8YVPYOdc5dafXt+l8HXk3SGpxzh1wzo1Kul/SxinHbJT0Ve/+g5JuMjNzzj3nnDvh7d+p5Cp4UTNrlFTmnPulc85J+pqkt5ynDgA5qN37YFfLNDoAPlrTWCZJ2kPfJgAAgAnnC5tGL/AxSWqSdHTS9jFv37THOOdiknokVU855u2SfuWcG/GOP3aea0qSzOzDZrbVzLZ2dNDTHMg17b0jCpqpqjjidykA8tjKBlakAwAAmOp8q9GtM7Pp3j2ZpLQPJzCzS5ScWvfa2Z7rnLtP0n2StH79epfi0gD4rL1vWNUlEQUDtG0D4J/qkqjqSqP0bQIAAJjknGGTcy54Edc+Lmn+pO1mb990xxwzs5CkckmnJMnMmiV9V9L7nHP7Jx3ffJ5rAsgDHX0jaqwo9LsMANCqxjJWpAMAAJjkfNPoLsYWScvNbLGZRSTdJmnTlGM2KdkAXJJulfS4c86ZWYWkH0i6yzn38/GDnXMnJfWa2bXeKnTvk/RfaXwOADLQWDyhroFRmoMDyAirG0rV0t6vsXjC71IAAAAyQtrCJq8H00clPSLpRUnfds7tNLNPmdmbvcO+JKnazFokfVzSXd7+j0paJuluM3ve+6rzHvs9Sf8mqUXSfkk/TNdzAJCZOvtH5MRKdAAyw+rGMo3GEzrYOeB3KQAAABnhfD2bLopz7iFJD03Zd/ek+8OS3jHNeX8t6a/Pcs2tktamtlIA2WR8Jbo6VqIDkAFWNSabhL94slcr6kt9rgYAAMB/6ZxGBwBp0dE3IpNUU8JKdAD8t6SmROGg0SQcAADAQ9gEIOu09w6rqjiiUJB/wgD4LxIKaGltifa2ETYBAABIhE0AslB73wj9mgBklOX1pYRNAAAAHsImAFklnnA61T+qujL6NQHIHCvqSnTs9JAGRmJ+lwIAAOA7wiYAWeXUwIjizjGyCUBGWe41Bm9p7/e5EgAAAP8RNgHIKh3eSnS1hE0AMsjKhmTYtIepdAAAAIRNALJLO2ETgAy0oKpI0VBA+wibAAAACJsAZJeOvhFVFIYVDQX9LgUAJgQD5q1IxzQ6AAAAwiYAWaW9d1h1ZYxqApB5VtSXMLIJAABAhE0Askgi4dTRP6LaEsImAJlneX2pTvQMq294zO9SAAAAfEXYBCBrHO8e0ljcqa6swO9SAODXrPBWpGMqHQAAyHeETQCyxr725PSUOpqDA8hAK72wial0AAAg3xE2AcgaLe3J0QJ1pYxsApB5misLVRgOMrIJAADkPcImAFmjpb1fJdGQCiOsRAcg8wQCpmV1JROjMAEAAPIVYROArLG/Y0C1TKEDkMGW15doL9PoAABAniNsApAVnHNqae9nJToAGW1FfanaekfUM8iKdAAAIH8RNgHICl0Do+oZGmNkE4CMNt4kfC9T6QAAQB4jbAKQFQ50DkgSYROAjLa8vkSSmEoHAADyGmETgKyw31uJjml0ADJZU0WhiiNB7WNFOgAAkMcImwBkhf0d/YqGAiovCvtdCgCclZlpWX0pI5sAAEBeI2wCkBX2dwxocU2xAmZ+lwIA57SijhXpAABAfiNsApAVDnT0a2ldid9lAMB5ragvVWf/qLoGRv0uBQAAwBeETQAy3kgsriNdg1paS9gEIPOtaPBWpGN0EwAAyFOETQAy3uFTg0o4aWltsd+lAMB5rfBWpNtH2AQAAPIUYROAjDe+Eh0jmwBkg4ayApVGQ9rLinQAACBPETYByHgHOgckSYtrGNkEIPOZmZbXl2gPI5sAAECeImwCkPH2t/ersbxAxdGQ36UAwIysqC/VvrY+Oef8LgUAAGDOETYByHj7O/qZQgcgq6yoL9XpwTF19rMiHQAAyD+ETQAymnNO+zsGaA4OIKusqE+uSEeTcAAAkI8ImwBktI6+EfWPxLS0jpFNALLH+Ip0ewmbAABAHiJsApDRWjqSqzktqSFsApA9akujKi8Maw8r0gEAgDxE2AQgo+3vSK5Et7SOaXQAsoeZaUV9CdPoAABAXiJsApDR9rf3qygSVENZgd+lAMCsrKgv1V5WpAMAAHmIsAlARjvQOaCltSUyM79LAYBZWVFfqt7hmNr7RvwuBQAAYE4RNgHIaPvb+7WElegAZKHlNAkHAAB5irAJQMYaGo3rePeQltbSHBxA9llRXypJ2tNK2AQAAPILYROAjHWw02sOTtgEIAvVlERVVRzRPlakAwAAeYawCUDG2t+R/IDGSnQAstXyuhLtbWdkEwAAyC+ETQAy1v6OfplJi6oJmwBkp5UNpWpp62dFOgAAkFcImwBkrP0dA2quLFRBOOh3KQBwQZbXl6pvJKaTPcN+lwIAADBn0ho2mdktZrbHzFrM7K5pHo+a2QPe45vNbJG3v9rMfmJm/Wb2uSnnPOFd83nvqy6dzwGAfw509NOvCUBWW1GX/DdsDyvSAQCAPJK2sMnMgpLulfQ6SWsk3W5ma6Yc9kFJp51zyyTdI+nT3v5hSZ+Q9Mdnufx7nHOXe1/tqa8egN8SCacDHQOETQCy2viKdPsImwAAQB5J58imDZJanHMHnHOjku6XtHHKMRslfdW7/6Ckm8zMnHMDzrmnlAydAOShk73DGhqLa0kt/ZoAZK/K4ohqSqLay4p0AAAgj6QzbGqSdHTS9jFv37THOOdiknokVc/g2v/uTaH7hJlZKooFkFn2t3sr0TGyCUCWW9lQwsgmAACQV7KxQfh7nHOXSnq59/Xe6Q4ysw+b2VYz29rR0TGnBQK4eAc6CJsA5IbldaXa196vRIIV6QAAQH5IZ9h0XNL8SdvN3r5pjzGzkKRySafOdVHn3HHvtk/SN5Wcrjfdcfc559Y759bX1tZe0BMA4J/9HQMqKwippiTidykAcFFW1JdqcDSu491DfpcCAAAwJ9IZNm2RtNzMFptZRNJtkjZNOWaTpDu9+7dKetw5d9Y/+5lZyMxqvPthSW+UtCPllQPwXUt7v5bUloiZsgCy3Yr65AjNvUylAwAAeSJtYZPXg+mjkh6R9KKkbzvndprZp8zszd5hX5JUbWYtkj4u6a7x883skKR/kvR+MzvmrWQXlfSImb0g6XklR0Z9MV3PAYB/Wjr6tayOKXQAst9yb0U6moQDAIB8EUrnxZ1zD0l6aMq+uyfdH5b0jrOcu+gsl70qVfUByEw9Q2Pq6BshbAKQE8oLw6ovi9IkHAAA5I1sbBAOIMft95qDL6M5OIAcsaK+VHvbCZsAAEB+IGwCkHFa2r2wiZFNAHLEivpS7WvrV5wV6QAAQB4gbAKQcfa39ysSCmh+VZHfpQBASqyoL9FILKGjXYN+lwIAAJB2hE0AMk5Le7+W1BQrGGAlOgC54aUm4UylAwAAuY+wCUDGaeno11Km0AHIIcu9f9P2tbMiHQAAyH2ETQAyyvBYXEe7BrWU5uAAckhpQVhNFYWMbAIAAHmBsAlARjnYOaCEozk4gNyzvL5Ee1oJmwAAQO4jbAKQUSZWomNkE4Acs6K+VAc6BhSLJ/wuBQAAIK0ImwBklJb2fplJS2qL/S4FAFJqeV2JRuMJHWZFOgAAkOMImwBklJaOfs2vLFJBOOh3KQCQUiu8Fen20bcJAADkOMImABllf3s//ZoA5KTl9cl/2/a2sSIdAADIbYRNADJGPOF0oHOAsAlATiqKhDS/qlB7GNkEAAByHGETgIxxtGtQo7EEzcEB5KwVdaXay4p0AAAgxxE2AcgY+zuSU0uWMrIJQI5a1ViqA50DGonF/S4FAAAgbQibAGSMlvZk2MTIJgC5alVDmeIJN/HvHQAAQC4ibAKQMVra+1VTElV5UdjvUgAgLVY3Jlek232SqXQAACB3ETYByBgtHf1aVlfsdxkAkDaLqosVCQW0u7XX71IAAADShrAJQEZwLjmthJXoAOSyUDCgFfUl2k2TcAAAkMMImwBkhI6+EfUNx+jXBCDnrWoo04tMowMAADmMsAlARphoDl5X6nMlAJBeqxpK1dk/oo6+Eb9LAQAASAvCJgAZoaVjPGxiZBOA3La6sUyStIepdAAAIEcRNgHICC3t/SqJhlRfFvW7FABIq1UN3op0NAkHAAA5irAJQEZoae/X0roSmZnfpQBAWlWXRFVbGqVvEwAAyFmETQAyQkt7P83BAeSN1Y1ljGwCAAA5i7AJgO96h8fU3jdCvyYAeWN1Q6n2tfUrFk/4XQoAAEDKETYB8N1LK9ERNgHID6saSzUaT+hg54DfpQAAAKQcYRMA342HTUtri32uBADmxqqG5Ip0L7IiHQAAyEGETQB8t7+jX5FgQAuqivwuBQDmxNLaEoUCpt0n6dsEAAByD2ETAN/tb+/XopoihYL8kwQgP0RCAS2rK9FuRjYBAIAcxCc7AL7b29av5XWlfpcBAHNqVUMpI5sAAEBOImwC4Kv+kZiOdA1qVQNhE4D8sqqxTCd6htUzOOZ3KQAAAClF2ATAV3vbklNIVhI2Acgz4yH77lZGNwEAgNxC2ATAV3u8fiXjKzMBQL5Y3Zj8d4++TQAAINcQNgHw1Z7WPhVFgmquLPS7FACYU3WlUVUWhRnZBAAAcg5hEwBfvXiyVysbShUImN+lAMCcMjOtaijTiycZ2QQAAHILYRMA3zjntKetj+bgAPLWqsZS7WntUyLh/C4FAAAgZQibAPimvW9E3YNjWllP2AQgP61uKNPQWFxHugb9LgUAACBlQn4XACB/vXgy2adkVSPNwQFkl29uPpKS6xw7nQyZ7nvygNY2lUuS3n3NgpRcGwAAwC+MbALgm5dWomNkE4D8VFdaIJPU2jvsdykAAAApQ9gEwDd7WvtUXxZVRVHE71IAwBeRUEDVJVG19hA2AQCA3EHYBMA3u1v7tLKBKXQA8ltDeQEjmwAAQE5Ja9hkZreY2R4zazGzu6Z5PGpmD3iPbzazRd7+ajP7iZn1m9nnppxzlZlt9875rJmxXjqQhcbiCbW092s1U+gA5Lmm8gJ1DYxqaDTudykAAAApkbawycyCku6V9DpJayTdbmZrphz2QUmnnXPLJN0j6dPe/mFJn5D0x9Nc+vOSfkvScu/rltRXDyDdDnUOaDSe0ErCJgB5rqmySJJ0rJsV6QAAQG5I58imDZJanHMHnHOjku6XtHHKMRslfdW7/6Ckm8zMnHMDzrmnlAydJphZo6Qy59wvnXNO0tckvSWNzwFAmuz2moMTNgHId00VhZKk46eHfK4EAAAgNdIZNjVJOjpp+5i3b9pjnHMxST2Sqs9zzWPnuSaALLC7tVfBgGlZXYnfpQCArwojQVUXR3SMsAkAAOSInG0QbmYfNrOtZra1o6PD73IATLGntU9LaooVDQX9LgUAfNdUWajj3YRNAAAgN6QzbDouaf6k7WZv37THmFlIUrmkU+e5ZvN5rilJcs7d55xb75xbX1tbO8vSAaRbciU6ptABgCQ1VxapZ2hMfcNjfpcCAABw0dIZNm2RtNzMFptZRNJtkjZNOWaTpDu9+7dKetzrxTQt59xJSb1mdq23Ct37JP1X6ksHkE59w2M6dnpIqxvL/C4FADJCM32bAABADgml68LOuZiZfVTSI5KCkr7snNtpZp+StNU5t0nSlyR93cxaJHUpGUhJkszskKQySREze4uk1zrndkn6PUlfkVQo6YfeF4AssrfNaw5ez8gmAJCkeRWFMknHmEoHAAByQNrCJklyzj0k6aEp++6edH9Y0jvOcu6is+zfKmlt6qoEMNdYiQ4AzhQJBVRXFtWx04N+lwIAAHDRcrZBOIDMtae1TyXRkJorC/0uBQAyRnNFkY6fHtI5OgoAAABkBcImAHNuvDl4svUaAEBKrkg3MBpnVToAAJD1CJsAzCnnnHaf7GUKHQBMMT7a84VjPT5XAgAAcHEImwDMqdbeYfUOx7SKsAkAztBQVqCgGWETAADIeoRNAObUeHPwVQ1lPlcCAJklFAyoobxALxzr9rsUAACAi0LYBGBO7T7prURXz8gmAJiqqbJQ24/1KJGgSTgAAMhehE0A5tSe1l41lheovCjsdykAkHGaKwrVNxLToVMDfpcCAABwwQibAMyp3a199GsCgLNoriySRJNwAACQ3QibAMyZ4bG49rX3a808+jUBwHRqS6MqCAe0jb5NAAAgixE2AZgzO0/0KJ5wWtdc4XcpAJCRggHT2nnl2s7IJgAAkMUImwDMmeePJj88XT6/wt9CACCDXdZcoR0nehSLJ/wuBQAA4IIQNgGYM9uOdquxvEB1ZQV+lwIAGeuy5nINjyW0r73f71IAAAAuCGETgDnzwrFuptABwHlc1lwuKflvJgAAQDYibAIwJ7oHR3Xo1KDWMYUOAM5pUXWxSgtCrEgHAACyFmETgDmxzfvQtG5+uc+VAEBmCwRMlzWXEzYBAICsRdgEYE5sO9otM+nSJsImADify5ortLu1V0Ojcb9LAQAAmDXCJgBzYtvRbi2tLVFpQdjvUgAg421YVKWxuNOvjpz2uxQAAIBZI2wCkHbOOW2jOTgAzNjVi6sUDJie3n/K71IAAABmjbAJQNod7x5SZ/+oLqdfEwDMSEk0pEubyvX0AcImAACQfQibAKTdtqPjzcEr/C0EALLIdUurte1otwZGYn6XAgAAMCuETQDS7oVj3YoEA1rVUOZ3KQCQNa5bUq1YwmnrYfo2AQCA7ELYBCDtnj/arTXzyhQJ8U8OAMzU+kWVCgdNv2QqHQAAyDJ88gOQVvGE0/bjPVrXTL8mAJiNokhI65oraBIOAACyDmETgLRqae/X4Gicfk0AcAGuW1qt7cd71E/fJgAAkEUImwCk1baj3ZJoDg4AF+K6JdWKJ5y2HOzyuxQAAIAZI2wCkFbPH+tWaUFIi6uL/S4FALLOlQsrFQkG9DR9mwAAQBYhbAKQVtuOdmtdc4UCAfO7FADIOgXhoC5fQN8mAACQXQibAKTN8Fhce1r7tG4+zcEB4EJdt6RaO0/0qGdozO9SAAAAZoSwCUDa7DzRq1jCaV1zhd+lAEDWum5ptRJOeoa+TQAAIEsQNgFIG5qDA8DFu2JBhaKhAFPpAABA1iBsApA22451q6GsQPVlBX6XAgBZKxoK6qqFlTQJBwAAWYOwCUDabDvaTb8mAEiB65ZUa3drr7oHR/0uBQAA4LwImwCkRXvvsA6dGtQVCyr9LgUAst51S6vlnPTLA/RtAgAAmY+wCUBaPNXSKUm6YVmNz5UAQPa7rLlCheGgfslUOgAAkAUImwCkxVMtnaosCmtNY5nfpQBA1ouEAlq/qJIm4QAAICsQNgFIOeecntrXqeuX1SgQML/LAYCccN3Sau1p61N777DfpQAAAJwTYROAlGtp71d734hezhQ6AEiZm1fXS5Ie3tnqcyUAAADnRtgEIOV+ts/r17ScsAkAUmV5falW1pfqv7ed9LsUAACAcyJsApByT7V0alF1kZori/wuBQByyhsua9SWw11q7WEqHQAAyFyETQBSaiye0C8PnGJUEwCkwRsua5Rz0kPbGd0EAAAyV1rDJjO7xcz2mFmLmd01zeNRM3vAe3yzmS2a9Nifefv3mNlvTNp/yMy2m9nzZrY1nfUDmL3njnRrcDSuG+jXBAApt7S2RKsby/QDwiYAAJDB0hY2mVlQ0r2SXidpjaTbzWzNlMM+KOm0c26ZpHskfdo7d42k2yRdIukWSf/iXW/cq5xzlzvn1qerfgAX5ql9HQqYdN1SwiYASIc3XtaoZw+f1onuIb9LAQAAmFY6RzZtkNTinDvgnBuVdL+kjVOO2Sjpq979ByXdZGbm7b/fOTfinDsoqcW7HoAM91RLpy5rrlB5YdjvUgAgJ73xskZJTKUDAACZK51hU5Oko5O2j3n7pj3GOReT1COp+jznOkk/MrNnzezDaagbwAXqHR7TtmM9TKEDgDRaWF2sS5vK9f0XCJsAAEBmysYG4Tc4565UcnreR8zsFdMdZGYfNrOtZra1o6NjbisE8tTT+08pnnA0BweANHvDZY3adrRbR7sG/S4FAADg16QzbDouaf6k7WZv37THmFlIUrmkU+c61zk3ftsu6bs6y/Q659x9zrn1zrn1tbW1F/1kAJzfz1s6VRgO6soFlX6XAgA57Q2XJqfS0SgcAABkonSGTVskLTezxWYWUbLh96Ypx2ySdKd3/1ZJjzvnnLf/Nm+1usWSlkt6xsyKzaxUksysWNJrJe1I43MAMAtP7evUNUuqFAll46BJAMge86uKtG5+hX7AVDoAAJCB0vaJ0OvB9FFJj0h6UdK3nXM7zexTZvZm77AvSao2sxZJH5d0l3fuTknflrRL0sOSPuKci0uql/SUmW2T9IykHzjnHk7XcwAwc8e7h3Sgc4B+TQAwR954aaO2H+/Roc4Bv0sBAAA4QyidF3fOPSTpoSn77p50f1jSO85y7t9I+psp+w5IWpf6SgFcrJ/v65Qk+jUBwBx5/WWN+puHXtQPtp/UR161zO9yAAAAJjDXBUBK/KylU7WlUa2sL/W7FADIC00VhbpyQYX+m6l0AAAgwxA2AbhoiYTTL1o6dcOyGpmZ3+UAQN5442Xz9OLJXu1u7fW7FAAAgAmETQAu2tbDp3VqYFSvXMHKjwAwl956RZOKIkF9/on9fpcCAAAwgbAJwEX77nPHVBQJ6rWX1PtdCgDklcriiN577UJ9f9sJHaRROAAAyBCETQAuyvBYXP/9wkndsrZBRZG0rjkAAJjGh16+RJFQQPf+pMXvUgAAACQRNgG4SI++2Ka+4ZjefmWz36UAQF6qLY3q9g0L9N3njuto16Df5QAAABA2Abg43/nVcTWWF+jaJdV+lwIAeeu3X7FUQTN9/qf0bgIAAP4jbAJwwTr6RvTTvR16yxVNCgZYhQ4A/NJQXqB3Xt2sB7ce08meIb/LAQAAeY6wCcAF+/62E4onnN52RZPfpQBA3vudVy5Vwjl94acH/C4FAADkOcImABfsO88d06VN5VpeX+p3KQCQ95ori/T2K5v1rWeOqL1v2O9yAABAHiNsAnBB9rT2acfxXr3tSkY1AUCm+L1XLVUs4fTFJxndBAAA/MM65QAuyHeeO6ZQwPSmdfP8LgUA4FlYXayN6+bp//3yiH77lUtVUxKdeOybm4+k7fu++5oFabs2AADIPoxsAjBr8YTT9547rhtX1p7xQQYA4L+PvHqZ4gmnP/6PbUoknN/lAACAPETYBGDWfrG/U229I3rrFc1+lwIAmGJpbYk+8aY1emJPhz7/0/1+lwMAAPIQYROAWfvur46rtCCkm1bX+V0KAGAad1yzQG9eN0//+KM9+sX+Tr/LAQAAeYawCcCs9I/E9MMdrXrjZfNUEA76XQ4AYBpmpv/ztku1uKZYf/Ct59Xey+p0AABg7hA2AZiVr/z8oIbG4nrX1fP9LgUAcA7F0ZA+f8dVGhiJ6aPfek5x+jcBAIA5QtgEYMZOD4zqCz89oJvX1Ovy+RV+lwMAOI8V9aX6m7eu1TMHu/Toi21+lwMAAPIEYROAGbv3Jy0aGI3pT39jpd+lAABm6G1XNuv2DfP1070deuZgl9/lAACAPEDYBGBGjncP6WtPH9bbr2zW8vpSv8sBAMzCX77pEi2vK9H3nj+u7z53XLF4wu+SAABADiNsAjAj9/x4r2TSH928wu9SAACzVBAO6s7rF+kVy2u15VCXvvizA+odGvO7LAAAkKMImwCc1962Pn3nV8f0vmsXqqmi0O9yAAAXIGCmW9Y26PYNC9TWO6J7f9Kiw6cG/C4LAADkIMImAOf1dw/vUXEkpI+8apnfpQAALtKlTeX6nRuXKhIK6Is/O6BHdrZqYCTmd1kAACCHEDYBOKeth5IrGP32K5eosjjidzkAgBRoKCvQ7924TJc2levJvR36+0f26OEdJ9VP6AQAAFIg5HcBADKXc06ffni3akuj+s0bFvtdDgAghQojQb3r6gW6ceWwntjTrp/t69TTB05pw6IqXb+sRpVF/IEBAABcGMImAGf1H1uPacuh0/pfb1mrogj/XABALqovK9C7rl6gm1aN6Im97Xr6wCn9fP8pLawu0uXzK7R2XrmKo7wGAACAmeOdA4BpbTnUpf/5ve26YVmNbr96vt/lAADSrKY0qluvmq+bVtdr29FuPX+0W//1/Al9f9sJLa8r1WXN5VrVUKbCSNDvUgEAQIYjbALwa452Deq3v/6s5lcW6d53X6lQkPZuAJAvKosiunFlnV65olYne4a17Vi3XjjWoz3P9ilg0pKaEq2ZV6bVjWUqLwz7XS4AAMhAhE0AztA/EtOHvrpVsXhC/3bnepUX8UECAPKRmWleRaHmVRTqNy5p0LHTQ9p1okc7T/Rq07YT2rTthOZXFmpNY5muWVKlpbUlfpcMAAAyBGETgAnxhNMffus5tXT066sf2KAlfHAAAEgKmGlBVZEWVBXpNy5pUHvfiHad7NWuE716ZFebHtnVpmV1JfqNS+r1G5c06NKmcpmZ32UDAACfEDYBmPB3D+/WY7vb9b82XqIbltf4XQ4AIAOZmerLClRfVqBXraxT9+CoiiJBPbKzTf/60wO69yf71VheoNeuSQZPGxZXMR0bAIA8Q9gEQLF4Qvc8uldfePKA3nvtQr33ukV+lwQAyBIVRRG9+5oFev/LFuv0wKge292uR3a26v4tR/XVpw+roiisV6+q040r6/TyZTWqLI74XTIAAEgzwiYgzx3tGtQf3v+cfnWkW+9c36y737TG75IAAFmqsjiiW69q1q1XNWtwNKYn93boRzvb9Pjudn3nV8cVMGnd/ArduKJOL19Ro0ubyhVm1BMAADmHsAnIYz944aTu+s4LkpM+c9vl2nh5k98lAQByRFEkpFvWNuqWtY2KJ5y2HevWT/d06Im9Hfrnx/bqnkf3qiAc0LrmCq1fVKn1C6t05YJKFqYAACAHEDYBeahveEx/84MXdf+Wo7p8foU+e9sVWlBd5HdZAIAcFQyYrlxQqSsXVOpjN69Q18Cont5/SlsPd+lXh0/rCz89oHsT+yVJjeUFWl5fquV1JVpRX6JldaVqrixUTUlUwQBNxwEAyAaETUAeaWnv09eePqz/fPaYBsfi+t0bl+rjN69IyxSGb24+kvJrAgByQ1VxRG+4rFFvuKxRkjQ4GtO2oz16/mi39rX1aW97n76x+ZSGxxIT5wRMKisIq6wwrPLCsIoiQRVGgioMB5P3w0EVeLfJfSGFgzbtqnjvvmbBnD1XAADyEWETkOPG4gk9vrtdX3v6kH7eckqRYEBvXNeoD1y/WJc2l/tdHgAAKoqEdN3Sal23tHpiXzzh9Pkn9qu9b1jdg2PqHRpTz9CYuofGdKJ7SIOjcQ2PxeXOcd2gmaLhgArCQRWEAop6t1sOdam0IKSSaEglBSGVFoRVGg2dsa+sIDxxn75SAADMDmETkAVmM0poZCyuI6cHdahzUIdPDejo6UGNxZ3KC8N67Zp6rV9UpZJoSNuP9xA2AQAyVjBgqiqOqOocq9clnNNoLKGh0bgGx+IaGo1raCyuYe92aCwZSA2PxTUSS2h4LKHuoTFtPdyl/uGY+oZjiiXOFVclFYQDKomGVVrwUiCVvE3uK4oEFTBTwCQzm3T/17edkxJOcnJyTnLOu/Wez/j9UMAUCprCgYDCQdNzR7sVCSaDs0gooIJQUNFwYGJU13QjuGaKkV4AgFQjbAKySDzhNOy9mR4ci6tnaEynB0Z1enBUXd7tqf5ROUkmqbGiQOsXVWlZbYlW1JfS6wIAkFMCZslRS+GgKi/gfOecYt5r68hYQsOxuIbHEhqZejsW13BsfDuunsExDcdeOmc0lpgIifwQMKkkGlJxNBmElRWEVVEcVmVhRBVFYVUURVReGOZ9AABgzhA2ASninNNILKGBkZgGR+MaGI1pYCTubSfv/9qb2DP+2nrmY5PfxHb1j06cO53CcFBVxRE1lBXo0qYKLaou0vyqIhWEg3P8XwEAgOxhZgoHTeFgQKUFqbmmc05O8sKnZAI1se3cxAgks+QfhmSSyTQ+MMm8uhLOKZ4482s0ntBILPn+Yfy9w+BoXP0jseTXcPK2tXdYfcOxM5+rpLLCsCqKwqosSoZQlYURlReF1dLer6aKQhVGeN8AAEgNwiZgiuHxEUODo+oeHFO3d3t6cEzdQ6PqHkjenh4cU89gsn/EgPcmb7Z/0QwFkm9ww0FTKBiY2A55b3xDAVNxJKTymrAKwslGqEWR8UaoySH8VcURQiUAADKEmU2ESN7/XJCAmQJB04W+xMfiCe/9TPK9zOTbQ6cG1HtsTOMzCL/yi0OSpOriiOZVFKqpolBNlcnb8e2a0uSUxmgot99zpGuBE6YqAsg3aQ2bzOwWSZ+RFJT0b865v53yeFTS1yRdJemUpHc55w55j/2ZpA9Kikv6A+fcIzO5JvKPc05jcaeRWPKvfJP/4jc4GlOv15Ohb3hMfcMxr0dD8n6vd793ODYRKg2Nxc/6vSLBwMRfBMuLwlpUU6TywrCKoyEd6hxQJJTsoxANBhQJJb+i3m0kGFA4FFA4kAyTQoHpV8gBAAC4WKFgQNUlUVWXRKd9PJ5w6htOhlFr5pXqRPewjp0e0vHuIe1r79MTe9unHVFdGg2puiSi6pKoqoojqimJqLo4quqSZBhVVphstj7eeL3Em9qXbVP4xlsXDE/0/kqcdXtsYvRZYmIUWrKlgXl9u6SHtp9UJBRIrpY4aRXFkmhoYqrjxIiz4ogqvBUXea8IIFulLWwys6CkeyXdLOmYpC1mtsk5t2vSYR+UdNo5t8zMbpP0aUnvMrM1km6TdImkeZIeNbMV3jnnuyYyQCLhNOQN7U72F4q9dH80rsHR2MT95HHJx5NBUUKj8YT2t/crlkhoLO4UiycUSzjF4k6xRMK7fen+bEYUmeT1d0g22YyGkreN5YVaWlsysZRyUSQ00XSzKHLuJZQlaXldaUr+2wEAAKRbMGCqKIqooiiit17R/GuPO+d0enBMx08P6UTPkLoGRnWqf0Sd/aM6NTCqroERHe0a1HNHunV6cFTx8zRaTzY1DygaSr4HC3t/iJv4g5x3PxwMKBK0icdDwYCCJgUCyUbrwYDptWvqJ0aEJ/+AF1DApLhLvlccn4I4FncaGI1pcCTu3cY04L0PHRjxbkeTLQ/6hmPqHRpTW++whsbiGouf+/lMfj8ZDgYUDCRrC5p5tY5PnUw2fh8cjal7KNnMfnJD+5HY9C0SpDP/yFlRFJ5omF9VnAymqoojqiyOqNq7rSqKMBUSQMZI58imDZJanHMHJMnM7pe0UdLkYGijpE969x+U9DlLfpLfKOl+59yIpINm1uJdTzO4Zl5xziVXNPFuX1rFZNJ2Irk95gU1YzGnsURCY/FkUDMWfynQGe8FMDwpKBqatLrLxP2z7feCo7P1FjqbYMBU6L1gR0PJAGhwND7xBiIaDqo4kJxqFp70xmL8NhxMvsBPfjwYSI4qmljq2Lt+JBjgr0SedA0VBwBcuGz8tzkba8a5mb20GuD5Vq9NJJx6hsZ0amBEvcMv9Y56dFebhr33lZP7TA3HEt4qgmMajSXfk47Gk+9R4+78f0L80lMHL+q5BQOm4khQxdHkHxaLvabq9WVRlReGJ5rOF3p/mHxp+6U/VkZCAQVS8H4yFk9M/IE2+d47NnF//A+0g6NxtfWO6GDnQDIsO0frhmgo4I0oC6qkIKTiyEsrKBZ7I85KIsn7yXDvpffU4SktHcbfY5+xiuL4Z4+J1RS9zxtK3sa9PwjHvT8KJ2/dS7fxhJ45dFqJhJNzTnHv/IRzSiQm3f+1be9+wkneVNXxvmc2sW1aWlucnIIaSP6cQ4GAd/vS54OQFwyGZ7EdGv+s4V0vPMvt5IyG6X9mqf5ckkhM+W/mha8Jd/bHnNPEzyjhhbbjP8Mzt50ee7Ft0vnTXzORcGce82v7vO2JepP7F1YXKeF9HzOb+Fm89DMJnLGdvA1M+vl4+8bD6MmfGwN2Rkg9+XrhKT+3ie8z6fdj/P9jgSl99gJmZ/x/cXxVUqQ3bGqSdHTS9jFJ15ztGOdczMx6JFV7+3855dwm7/75rpmTHtnZqt//1nO/Fi7NlYBJRZGQ1zcooKJwSAWR5ItwbWn014YEj/cWKoyEztwXTo4QOqP3UCQ4bQDEG1cAAIDMFwiYKr3RNZN1D47N+lrxhDsjgIol3MSH2fEPzDeurPVGvL808j3hnEKB5KiioJmCweQHyKJoUMWRl0Kl4uj07zvHzfX7z1AwoNJgMiCaqYRLTvEbH7F11cJKb2XiZM/RvuHYRD/R/pGYTnQPa2D0pRDwXKOp/BIwr0+ZFxRN3J80qm3yh/jxhvvjH4ec9wf3kz1DyeDECzMm//9kPCzJBWfLMmaQ1fou6P2Mx4OZgL00IrC1ZzgZBHlPcGpwORb/9e1MNh5CvRRIJXcETLphWY3+7c6r/S4xrXK2QbiZfVjSh73NfjPb42c9KVQjqdPvIoAMwe8DcCZ+J4Az8TsxA+/xuwDMJX4ngDP58juxR9KX3j/X3zVtFk63M51h03FJ8ydtN3v7pjvmmJmFJJUr2Sj8XOee75qSJOfcfZLuu9DiM5WZbXXOrfe7DiAT8PsAnInfCeBM/E4AZ+J3AjgTvxPpE0jjtbdIWm5mi80somTD701Tjtkk6U7v/q2SHnfOOW//bWYWNbPFkpZLemaG1wQAAAAAAIBP0jayyevB9FFJj0gKSvqyc26nmX1K0lbn3CZJX5L0da8BeJeS4ZG8476tZOPvmKSPOOfikjTdNdP1HAAAAAAAADA75rKhixgmmNmHvSmCQN7j9wE4E78TwJn4nQDOxO8EcCZ+J9KHsAkAAAAAAAApk86eTQAAAAAAAMgzhE1ZwsxuMbM9ZtZiZnf5XQ8w18xsvpn9xMx2mdlOM/tDb3+Vmf3YzPZ5t5V+1wrMJTMLmtlzZvbf3vZiM9vsvV484C2oAeQFM6swswfNbLeZvWhm1/E6gXxmZh/z3jftMLNvmVkBrxPIJ2b2ZTNrN7Mdk/ZN+7pgSZ/1fjdeMLMr/as8+xE2ZQEzC0q6V9LrJK2RdLuZrfG3KmDOxST9f865NZKulfQR7/fgLkmPOeeWS3rM2wbyyR9KenHS9qcl3eOcWybptKQP+lIV4I/PSHrYObdK0jolfzd4nUBeMrMmSX8gab1zbq2SCyzdJl4nkF++IumWKfvO9rrwOknLva8PS/r8HNWYkwibssMGSS3OuQPOuVFJ90va6HNNwJxyzp10zv3Ku9+n5AeIJiV/F77qHfZVSW/xpUDAB2bWLOkNkv7N2zZJr5b0oHcIvxPIG2ZWLukVSq52LOfcqHOuW7xOIL+FJBWaWUhSkaST4nUCecQ596SSK99PdrbXhY2SvuaSfimpwswa56TQHETYlB2aJB2dtH3M2wfkJTNbJOkKSZsl1TvnTnoPtUqq96suwAf/LOlPJSW87WpJ3c65mLfN6wXyyWJJHZL+3Zta+m9mVixeJ5CnnHPHJf2DpCNKhkw9kp4VrxPA2V4X+NydQoRNALKKmZVI+k9Jf+Sc6538mEsur8kSm8gLZvZGSe3OuWf9rgXIECFJV0r6vHPuCkkDmjJljtcJ5BOvD81GJYPYeZKK9evTiYC8xutC+hA2ZYfjkuZP2m729gF5xczCSgZN33DOfcfb3TY+vNW7bferPmCOvUzSm83skJLTq1+tZL+aCm+6hMTrBfLLMUnHnHObve0HlQyfeJ1AvnqNpIPOuQ7n3Jik7yj52sHrBPLd2V4X+NydQoRN2WGLpOXeyhERJRv7bfK5JmBOeb1oviTpRefcP016aJOkO737d0r6r7m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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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0RAAAAGBaYE0lIIistXrmSIdWFiUpIYapb0AoMcbopuJk3VScrH0t/frPF07qq8/X65tbT+o9K/L0wdUFSvVEOR0TAAAAcAwjlYAgqu86q+a+87pzHqOUgFC2JNerb7+/Us98bI02VKTpW9satfpfX9BnfnVYrf7zTscDAAAAHEGpBATRM0c6ZYx0W0Wa01EATILydI/+/YHF2vxX6/SOJdn6aU2b1v3bFv3lowd0ouuM0/EAAACAKUWpBATRM0c7VZnnVWo8U2SAmSQ/OVb/8vb52vrXt+gDq/L19JFO3fbVrfqTH9Soptkva63TEQEAAICgo1QCguRU3znVdgyxQDcwg6UnROkzb67Qjk/dqo9tKNHeU/2677936u3/9ZKeOdKhkVHKJQAAAMxclEpAkDx7tFOSKJWAWcAXG6GPbSjVS59ar/9zz1z1nb2s//3wPm34yot6pLpFF6+MOB0RAAAAmHSUSkCQPHOkU3MzPcrxxTgdBcAUiY5w670r8/XCJ9bpGw8uUXxUmD792GGt/uJmfX3zCQ2cv+x0RAAAAGDSUCoBQdA1dFH7WgZ0J6OUgFnJ7TK6e0GGHv/wTfrJn67QvKwE/dtz9Vr1hc367BNH1dLHjnEAAAAIfWFOBwBmoufGp77dOY9SCZjNjDFaWZSklUVJOt45pG9ubdTDu07phzubdVtFmj64ulDL8r0yxjgdFQAAAAgYpRIQBM8c7VRhSqyKU+OcjgJgmihP9+gr71ykv76jXD/c2axHdrfo2aNdmp+VoP+1Ol93z89URBgDiAEAABA6+OkVmGQD5y9rV6Nfd85NZ/QBgD+QnhClv76zXDs/tV7//LZ5On95WB9/9OAr6y75z7HuEgAAAELDDZVKxphfGmPuNsZQQgGv4fnabo2MWqa+AXhV0RFuvbsqT7/9+M36/geWqSw9Xv/2XL1W/ssm/e0vD+tE1xmnIwIAAACv6kanv/2npA9I+g9jzM8kfc9aWxe8WEDoeuZIpzITojQ/K8HpKABCgMtltK4sVevKUlXfdUbf29GkX+5r0092t2h1cbLeuzJP68tTnY4JAAAA/IEbGnlkrX3eWvtuSUskNUt63hjzkjHmA8aY8GAGBELJuUvD2nqiR3fMY+obgMCVpsXrX96+QDv/dr0+eUeZTvac1Z/9aK/W/usL2lLXrbOXhp2OCAAAALzihhfqNsYkSXqPpPdK2i/px5JWS3q/pHXBCAeEmi11Pbo8PKo75zL1DcDr54uN0IdvKdafrS3U87Xd+tGuZj13rEubjndrflaCVhYmKdsbTXkNAAAAR91QqWSMeUxSmaQfSXqLtbZj/NSjxpg9wQoHhJpNtV3yxoSrMt/ndBQAM0CY26U756Xrznnp+tpv67Wrya/9Lf060DqgrMRorShM0oLsBIW7WfIQAAAAU+9GRyp9y1r71MQDxphIa+0la21lEHIBIWdk1OqFum7dUpYqt4vRAwAmV6onSm9dmKk7KtK0v3VAuxr79It9bXrqcIeW5nm1LN+nlPhIp2MCAABgFrnRUunzkp666thOja2xBEDS/pZ+9Z+/olvnsKAugOCJDHdrRWGSqgp8auo9p12NfXrpZK+2N/SqIDlWy/N9mpvpURijlwAAABBkr1oqGWPSJWVJijbGLJb08vALj6SYIGcDQsqm490KcxmtLU1xOgqAWcAYo8KUOBWmxOnMxSvad6pfNaf69eieVsVEuLUk16vKfK9S46OcjgoAAIAZ6rVGKt0h6Y8lZUv6yoTjZyR9OkiZgJC0qbZLywt88kSxISKAqRUfFa6by1K1pjRFjT3ntLvZ/3ujl5aNj15i7SUAAABMplctlay1P5D0A2PMO6y1v5iiTEDIafWfV33XWb2zMsfpKABmMZcxKk6NU3Hq+OillgHVNPv10z2tig53a0luopbl+5TqmT2jlx6pbnE6wjU9WJXrdAQAAIA37LWmv73HWvuwpHxjzF9efd5a+5VrPAyYdTYf75YkrZ+T5nASABgTHxWum0tTtKYkWY0951TT7NeuRr92nOxTflKMlhf4NDeTneMAAADw+r3W9LfY8Y9xwQ4ChLLna7tUmBKrguTY174YAKbQxNFLZy8Na9+pfu1u9uune9oUHd6hxeOjl9Jm0eglAAAATI7Xmv72P+Mf/2lq4gCh5+ylYVU3+vX+VXlORwGAVxUXGaa1pSlaXZKspt5z2t3kV3WjXy+d7FOuL0bL8r2an5WoiDBGLwEAAOC1vdZIJUmSMeZfJX1e0gVJz0haIOnj41PjgFlt+4leXR4Z1a3lTH0DEBpcxqgoJU5FKWOjl/a39KumuV+/2Neu3xzq0MLssdFLWd5op6MCAABgGruhUknS7dbavzbGvE1Ss6S3S9oqiVIJs96m2i55osJUme91OgoABCwuMkxrSlK0ujhZp/rOq6bZr30tY1PkMhOiVJnv06KcREWFu52OCgAAgGnmRkull6+7W9LPrLWDxpggRQJCx+io1Qt13bq5LJXFbgGENGOM8pNjlZ8cqzcvyNSBtgHtafbriYOn9fSRDs3PStCyfJ9yfTHiZwAAAABIN14q/cYYc1xj09/+3BiTIuli8GIBoeFQ+6B6z17W+vJUp6MAwKSJjnBrZWGSVhT41D5wQTXN/TrYNqB9LQNKjY9UZb5Pi3MSFRt5oz9GAAAAYCa6oZ8GrbWfGl9XadBaO2KMOSfpnuBGA6a/TbVdchlpXVmK01EAYNIZY5TtjVG2N0Z3zU/X4bZB1TT79dThDj17tFMVGR4ty/epMCVWLkYvAQAAzDqB/IqxXFK+MWbiY344yXmAkLKptluVeT4lxkQ4HQUAgioyzK3KfJ8q833qHLyomlN+HWgZ0OH2QfliI1SZ59WSPK88UeFORwUAAMAUudHd334kqUjSAUkj44etKJUwi3UMXtCxjiF96k3lTkcBgCmVnhCltyzI1J1z03X09JBqmv167liXnq/tUnm6R8sLfCpOjWP0EgAAwAx3oyOVKiVVWGttMMMAoWTz8W5J0oY5rKcEYHYKd7u0KCdRi3IS1Xv2kvY0+7W3ZUDHOobki41QVYFPS3O9imHtJQAAgBnpRn/KOyIpXVJHELMAIWVLXY+yvdEqSolzOgoAOC45LlJ3zsvQhoo0HT09pOrGPj19pFO/Pdal+VkJqipMUo43mp3jAAAAZpAbLZWSJR0zxuyWdOnlg9batwYlFTDNXRkZ1c6TfXrrokzeIAHABGEulxZmJ2phdqI6hy6qurFPB1oHtL91QJkJUaoqSNLCnERFhLmcjgoAAIA36EZLpc8GMwQQavad6tfZS8NaW8KubwBwPemeKN2zKEt3zk3XgbYBVTf69diBdj11pENLcr1aUZiklPhIp2MCAADgdbqhUsla+6IxJk9SibX2eWNMjCR3cKMB09eL9T1yu4xWFSc5HQUApr3IcLeqCpK0PN+nFv957Wrs0+5mv3Y29qk0LU6ripJVkhrHyE8AAIAQc6O7v/2ppIck+TS2C1yWpP+WtD540YDpa+uJHi3JTWTrbAAIgDFGeUmxykuK1V0Xr2h3s1/VjX59/6VmpcRFamVRkpbkepkaBwAAECJu9Ke2D0u6SdKQJFlrT0hiyyvMSr1nL+lI+xBT3wDgDYiPCtf68jT99R1lum9ptiLCXHri4Gl94ZlaPX2kQ/3nLzsdEQAAAK/hRtdUumStvfzysHRjTJgkG7RUwDS2/USvJGltKaUSALxRYW6XFud6tSgnUS3+89pxsk87Gnq1/USvKjI9uqkoWXlJMUyNAwAAmIZutFR60RjzaUnRxpjbJH1I0q+DFwuYvrbW98gbE655WQlORwGAGWPi1LiB85e1q7FPNc39Onp6SFmJ0VpTkqy5mQlyuyiXAAAAposbLZU+JemDkg5L+jNJT0n6drBCAdPV6KjV1hO9Wl2SwhsbAAiSxJgI3TkvQ7eWp2l/a7+2n+jVxppW+WK7dFNxspay7hIAAMC0cKO7v40aY34l6VfW2p7gRgKmr9rOIfWevaS1JclORwGAGS8izKWqgiQty/fp2OkhbTvRo18fPK1NtV1aUZiklYVJio280d+PAQAAYLK96k9iZmwBg3+U9BGNL+ptjBmR9P+stZ8Lfjxgetlaz3pKADDVXMZoXlaC5mZ61Nx3XttO9Gjz8W5tO9GjJblerS5OVlJcpNMxAQAAZp3X+vXexzW269sya22TJBljCiX9lzHm49barwY7IDCdbK3vUXl6vNI8UU5HAYBZxxijguRYFSTHqmvoorY39GpPc792N/k1LytBa0tSlOWNdjomAADArPFapdJ7Jd1mre19+YC1ttEY8x5Jz0miVMKsce7SsPac8usDNxU4HQUAZr00T5TesSRbt81J00sn+1Td1KfD7YMqSY3TurJUFSTHOh0RAABgxnutUil8YqH0MmttjzEmPEiZgGlpV2OfroxYrS1h6hsATBee6HDdOS9d68pSVN3Yp+0NvfrWtkblJ8VoXVmqSlLjNDabHwAAAJPttbZOufw6z0mSjDF3GmPqjDENxphPXeN8pDHm0fHz1caY/AnnFhhjdhpjjhpjDhtjmG8ER22t71FUuEuV+V6nowAArhIV7tbNZan65B3levOCDPWfv6Lvv9Ss/9xyUsdOD2rUWqcjAgAAzDivNVJpoTFm6BrHjaRXLXmMMW5J35B0m6Q2STXGmCestccmXPZBSf3W2mJjzAOSvijpfmNMmKSHJb3XWnvQGJMk6cqNfUlAcGw90asVhUmKCnc7HQUAcB0RYS6tKkrW8nyf9rcM6MUTPXq4ukVpnkitK03V/OwEuRi5BAAAMCledaSStdZtrfVc40+8tfa1pr8tl9RgrW201l6WtFHSPVddc4+kH4zf/rmk9eM7zt0u6ZC19uB4jj5r7UigXxwwWVr959XUe46pbwAQIsLcLi0r8OnjG0r1zspsWSs9uqdVX/1tvfY0+zUyysglAACAN+q1pr+9EVmSWifcbxs/ds1rrLXDkgYlJUkqlWSNMc8aY/YZY/46iDmB1/RifY8kaW0ppRIAhBK3y2hRjld/sb5EDy7PVWS4S7/c366v/LaOcgkAAOANeq3pb04Jk7Ra0jJJ5yVtMsbstdZumniRMeYhSQ9JUm5u7pSHxOyxtb5HWYnRKkphNyEACEUuYzQvK0FzMz2q7zqj52u79cv97Xqhrlu3lKVqca5XbhfT4gAAAAIRzJFK7ZJyJtzPHj92zWvG11FKkNSnsVFNW621vdba85KekrTk6iew1n7TWltpra1MSWEECYLjysioXjrZp7WlyewgBAAhzhijsnSPPrSuSO9bmaeYiDBGLgEAALxOwRypVCOpxBhToLHy6AFJD151zROS3i9pp6R7JW221lpjzLOS/toYE6OxXeZulvTVIGYFrmt/y4DOXhpmPSUAmEGMMSpP96gsLV51XWe0aXzk0pb6Ht1SlqJFOcEdufRIdUvQPvcb9WAVo78BAMCNCVqpZK0dNsZ8RNKzktySvmutPWqM+ZykPdbaJyR9R9KPjDENkvwaK55kre03xnxFY8WUlfSUtfbJYGUFXs3W+h65XUaripOdjgIAmGS/Vy51ntGm4936xb52vVA3NeUSAABAKAvqmkrW2qc0NnVt4rF/mHD7oqT7rvPYhyU9HMx8wI3YeqJHi3ISlRD9WhseAgBClTFG5RkelaVTLgEAANyoYK6pBIQ8/7nLOtw+qJvZ9Q0AZoWXy6UPrSvS+1bkKTrcrV/sa9dXn6/X3lP9rLkEAAAwwXTd/Q2YFrad6JG10lpKJQCYVSaOXDreeUabjnfpF/vaXtktblFOIiOXAADArEepBLyKrfW9SowJ1/ysBKejAAAcYIzRnAyPyl8ul2rHyqUtdd26tTxVC7IplwAAwOxFqQRch7VW2070aHVxMm8YAGCWm1gu1XaMjVz62d6xkUsvl0suw/cKAAAwu1AqAddxvPOMus9cYuobAOAVxhhVZHpUnhGv2o4hbart1k/3tOmF4z26tTxV87MTKJcAAMCsQakEXMfW+h5J0toSSiUAwO9zGaO5mQmak+HR0dND2lTbpUf3tL4ycmleFuUSAACY+SiVgOvYeqJHZWnxSk+IcjoKAGCachmj+VkJmpvp0ZH2QW0+3q2NNa1Kq+vWreVpmpvpoVwCAAAzFqUScA3nLw+rpqlf71+V53QUAEAIcBmjBdmJmpeVoMPtg9pc262f7G5RuidKt5anqoJyCQAAzECUSsA1VDf6dXlklPWUAAABcRmjhdmJmp+VoENtA9p8vFuP7G5RRkKU1penak6GR4ZyCQAAzBCUSsA1vFjfo6hwl5bl+5yOAgAIQS5jtCjHq/lZia+USw9XtygzIUrr56SpPD2ecgkAAIQ8SiXgGrae6FFVQZKiwt1ORwEAhDC3y2hxrlcLshN1sHVAm+u69aNdp5SVGK31c1JVlka5BAAAQhelEnCVtv7zauw5p3dXsZ4SAGByuF1GS/K8WpiTqAOt/dp8vFs/3HlK2d5orS9PVSnlEgAACEGUSsBVttb3SpJuLk12OAkAYKZxu4yW5vm0KMerfS39eqGuWz/YOTZy6ZayFJVnsKA3AAAIHZRKwFW21vcoMyFKRSlxTkcBAMxQbpfRsnyfFucm6kDLgLbU9+jh6rHd4taVpWheVoJj5dIj1S2OPO+NeLAq1+kIAABgAkolYILhkVHtONmru+dnMA0BABB0YS6XKvN9Wpzr1aG2AW2p69HGmlal1HZrXVmKFmQnyu3i+xEAAJieKJWACQ60DujMxWGtLU1xOgoAYBZ5eUHvhTmJOtI+qC11PfrZ3jZtOt6tm0tTtDg3UWEul9MxAQAAfg+lEjDB1voeuYx0UxHrKQEApp7LGC3ITtS8rAQd7zijF+q69dj+dr1wvFtrSlNUmedVuJtyCQAATA+USsAEL57o1aKcRCXEhDsdBQAwi7mMUUWmR3My4lXfdVYv1HXr1wdPa/Pxbt1UlKSqgiRFR7idjgkAAGY5SiVgnP/cZR1qG9BH15c4HQUAAEmSMUZl6fEqTYtTU985ba3v0XPHuvRifY+WF/h0U1GyPNH8IgQAADiDUgkYt72hV9aK9ZQAANOOMUaFyXEqTI7T6YEL2nqiR9tP9Oqlk31akpuoNSUpSo6LdDomAACYZSiVgHFb63uUEB2uhdmJTkcBAOC6MhOj9cCyXN0255K2NfRq36l+7Wnu19ysBN1ckqIsb7TTEQEAwCxBqQRIstZq24kerS5OZutmAEBISIqL1B8tytL68lS9dLJPuxr7dKR9UMWpcVpdnKyS1DgZw/c0AAAQPJRKgKS6rjPqGrqktaXs+gYACC3xUeG6Y266bi5N0e4mv3Y09Or7LzUrzROp1cXJWpidqDB2jAMAAEFAqQRobOqbxHpKAIDQFRXu1trSFK0qStKhtkFtb+jVL/a169mjXVpRmKSqAp9iI/nRDwAATB5+sgAkba3vVUlqnDISWIcCABDawtwuLcnzanFuok72nNP2hh49X9ulLXXdWpLr1U3FyUqJZ1FvAADwxlEqYda7cHlEu5v9eu+KPKejAAAwaYwxKk6NU3FqnLqGLmpHQ6/2tfRrd7Nf5enxWl2crILkWNZdAgAArxulEma9XU19ujw8ytQ3AMCMleaJ0tuXZOu2ijRVN/m1q7FP397epHRPlFYVJWlhTqLCWXcJAAAEiFIJs97W+h5FhrlUVeBzOgoAAEEVHxWuDXPSdHNpig60DmjnyT79cn+7nj7SqWX5Pq0o9CkxJsLpmAAAIERQKmHW21rfo+UFPkWFu52OAgDAlAh3u7Qs36fKPK+a+s5p58k+bTvRo20nelSR6dGqomTlJ8UwNQ4AALwqSiXMau0DF3Sy55zetTzX6SgAAEw5Y4wKk+NUmByn/vOXVd3oV02zX0dPDzE1DgAAvCZKJcxqW+t7JIn1lAAAs543JkJ3zkvXreWpOtQ2oJcmTI2rzPeqqiBJvlimxgEAgN+hVMKstrW+R+meKJWkxjkdBQCAaSEizKXKfJ+WTpgat6OhV9tP9KokLU5VBUkqS4+Xi6lxAADMepRKmLWGR0a1vaFXb5qXzpoRAABcZeLUuMELV1TT7NeeZr9+tOuUEqPDtbxgrHiKjwp3OioAAHAIpRJmrYNtAzpzcZipbwAAvIaE6LFd424pS1Vtx5Cqm/r03LEubart1twsj6oKkljYGwCAWYhSCbPWi/W9chlpdXGy01EAAAgJbpfRvKwEzctKUM+ZS9rd1Ke9Lf061Dao1PhIVRUmaXFOIjuqAgAwS1AqYdbaWt+jBdmJSoxh0VEAAAKVEh+puxdk6raKdB1qG1B1k1+/Pnhazx7p1KKcRFUV+pSREO10TAAAEESUSpiVBs5f1qG2AX3k1hKnowAAENJeXti7Mt+ntv7z2tXo176Wfu1u9ivXF6OqAp/mZSUo3O1yOioAAJhklEqYlbY39GrUSjeXMvUNAIDJku2N0b1LY3TX/HTtO9Wv6ia/fra3Tb851KEluYlaVuBTanyU0zEBAMAkoVTCrLS1vkfxUWFamJ3odBQAAGacmIgwrS5J0ariZDX2nNPupj7tbOzTjpN9KkiO1fJ8n+ZmehQW4OilR6pbgpT4jXuwKtfpCAAATDlKJcw61lptre/V6uLkgH+YBQAAN85ljIpT41ScGqczF69o76l+1TT79eieVsVEuLU016tlBT4lx0U6HRUAALwOlEqYdU50n1Xn0EWtLU1xOgoAALNGfFS41pWlam1pik52n9XuZr92nOzVtoZeFaaMjV6qyPQozMUvfAAACBWUSph1ttb3SBKlEgAADnAZo5K0eJWkxWvowhXtOdWvPc1+baxpVWxkmJbmerW8wCdfLLuzAgAw3VEqYdZ5sb5HRSmxykpkm2MAAJzkiQ7XreWpWleWohNdY6OXtp3o0dYTPSpOjdPyfJ/mZHjkdhmnowIAgGugVMKscvHKiHY3+VlMEwCAacRljMrS41WWHq/BC1e055Rfe5r79cjuFsVHhmlpnlfL8n3yMnoJAIBphVIJs0p1k1+XhkeZ+gYAwDSVEB2u9eVpuqUsVfWdZ7S72a8X63v0Yn2PStLGRi+VpTN6CQCA6YBSCbPK1voeRYS5tKIgyekoAADgVbiMUXmGR+UZHg2cv/zK2ksPV7fIExWmpXk+Lcv3KjGG0UsAADiFUgmzytb6Hi3P9yk6wu10FAAAcIMSYyK0Yc7Y6KW6zjPa3dynLXXd2lLXrdK0eC0v8KksPV4uw+glAACmEqUSZo3TAxd0ovus7qvMdjoKAAB4Hdwuo4pMjyoyPeo/d1k1p/za29yvH+06pYTocFXmeVWZ71NCdLjTUQEAmBUolTBrbDvRI0mspwQAwAzgjY3Q7RXpWl+eptqOIdU0+7XpeLc2H+9WefrY6KWSNEYvAQAQTJRKmDW21vcqzROpsrR4p6MAAIBJ4nYZzctK0LysBPnPXVZNs197TvWrtvOUEqPDVZnvU2WeVx5GLwEAMOkolTArDI+MantDr26vSJPhN5YAAMxIvtgI3TE3XevnpKq244x2N/Xp+doubT7epfJ0j5YX+FScGsfoJQAAJgmlEmaFA60DGrxwRTeXMfUNAICZLszl0vysBM3PSlDv2UuqafZr76l+HesYkjcmXMvyfVqa51V8FKOXAAB4IyiVMCtsqeuRy0hriimVAACYTZLjIvWmeRm6bU6ajnYMaXeTX88d69LztV2qyPBoeUGSClNiGb0EAMDrQKmEWWFLfbeW5HqVEMNvJAEAmI3C3C4tzE7UwuxE9Zz53eilI6eH5IuN0PJ8n5bkeRUXyY/HAADcKL5rYsbrPnNRR9qH9Mk7ypyOAgAApoGU+EjdNT9Dt1Wk6ejpQe1u8uuZo5367bEuVWSOrb1UmBzLOowAALwGSiXMeFvreyVJN5cy9Q0AAPxOuNulRTleLcrxqmvoomqa/drX0q/D7YNKio3QMkYvAQDwqoL6HdIYc6ekf5fklvRta+0XrjofKemHkpZK6pN0v7W2ecL5XEnHJH3WWvtvwcyKmWtLXbdS4iM1N9PjdBQAADBNpXmi9OYFmbpjbroOtw+qpvl3o5fmZMRrWb5PRSG6c9wj1S1OR7iuB6tynY4AAHgDglYqGWPckr4h6TZJbZJqjDFPWGuPTbjsg5L6rbXFxpgHJH1R0v0Tzn9F0tPByoiZb3hkVNtO9Oq2ijSGsAMAgNcU7nZpSa5XS3K96h66qD2n+rWvZWztJW9MuJbmje0clxDNOo0AAARzpNJySQ3W2kZJMsZslHSPxkYeveweSZ8dv/1zSV83xhhrrTXG/JGkJknngpgRM9zBtgENXriidWVMfQMAAIFJ9UTprvkZur1ibOe4mma/nq/t0qbaLpWlj41eKk2Ll9vFL64AALNTMEulLEmtE+63Saq63jXW2mFjzKCkJGPMRUl/o7FRTp8IYkbMcC8c75HLSGuKKZUAAMDrM3HnuL6zl7TnVL/2nurX8c5T8kSFaWmeV2tKkpXji3E6KgAAU2q6rjr4WUlftdaefbUpS8aYhyQ9JEm5uczHxh/aUt+tJbleJcQwRB0AALxxSXGRumNuujbMSdPxzrHRS1vqerT2Sy9oTUmK3rUsRxsq0hTudjkdFQCAoAtmqdQuKWfC/ezxY9e6ps0YEyYpQWMLdldJutcY86+SEiWNGmMuWmu/PvHB1tpvSvqmJFVWVtpgfBEIXd1nLupI+5A+cXup01EAAMAM43YZzc1M0NzMBA2cv6xLw6P66Z5W/fmP9yk5LkLvWJqtB5blqiA51umoAAAETTBLpRpJJcaYAo2VRw9IevCqa56Q9H5JOyXdK2mztdZKWvPyBcaYz0o6e3WhBLyWrfW9kqR1ZakOJwEAADNZYkyEHqzK1V+sL9GL9d36ye5WfXtbk/7nxUatKPTpXctzdcfcdEWFu52OCgDApApaqTS+RtJHJD0ryS3pu9bao8aYz0naY619QtJ3JP3IGNMgya+x4gmYFFvqupUcF6mKDI/TUQAAwCzgdhndWp6mW8vT1DV0UT/f26aNNS366MYDSowJ19sWZ+ldy3NVmhbvdFQAACZFUNdUstY+Jempq479w4TbFyXd9xqf47NBCYcZbXhkVNtO9GrDnDS52JEFAABMsTRPlD58S7H+/OYivXSyTz+padHDu07pezuatTTPqweW5ejNCzIVHcHoJQBA6JquC3UDb8jBtgENXriidWXs+gYAAJzjchmtLknW6pJk9Z29pF/ua9dPalr0yZ8f0ud+fUz3LM7UA8tyNS8rwemoAAAEjFIJM9KWuh65jLSmJNnpKAAAAJLGdo7707WF+pM1Bapp7tfG3S362Z42PbyrRfOyPHpgWa7uWZSp+Ch2rQUAhAZKJcxIW+p6tCTXq8SYCKejAAAA/B5jjJYX+LS8wKd/fMtc/epAu36yu0Wf+dUR/fOTtXrzggw9sDxXS3ITZQzT+AEA0xelEmac7jMXdbh9UJ+4vdTpKAAAAK8qISZc71+Vr/etzNPBtkFt3N2iJw6e1s/2tqk0LU73L8vV2xdnyRvLL8oAANMPpRJmnM213ZKkW8vTHE4CAABwY4wxWpSTqEU5ifrMmyv0m4On9ZOaVv2f3xzTF585rjvnpuuB5TlaWZjE6CUAwLRBqYQZZ9PxbmUmRGlOBtv1AgCA0BMXGaYHlufqgeW5qu0Y0sbdLXpsf7ueOHha+Ukxun9Zru5dmq2U+EinowIAZjmX0wGAyXTxyoi2n+jV+jlp/BYPAACEvDkZHv3TPfO0++826Kv3L1SqJ0pffOa4Vv7LJv3vH+3VC3XdGhm1TscEAMxSjFTCjLLzZJ8uXBnR+jmpTkcBAACYNFHhbr1tcbbetjhbJ3vO6tGaVv1ib5ueOdqprMRo3VeZrXdW5igzMdrpqACAWYRSCTPK87Vdiolwa0VhktNRAAAAgqIoJU6fvmuOPnF7mX57rEsba1r0tedP6D82ndDNpSl6YHmubi1PVbibSQkAgOCiVMKMYa3V5uPdWlOSrKhwt9NxAAAAgioizKW7F2To7gUZavWf10/3tOqne1r1Zz/aq5T4SN27NFsPLMtxOiYAYAajVMKMcfT0kDoGL+rjG0qdjgIAADClcnwx+qvby/TR9SXaUtejjTUt+p8XT+q/tpxUUUqsqgqSNCfDI7eLNScBAJOHUgkzxubj3TJGuqWc9ZQAAMDsFOZ2aUNFmjZUpKlz8KJ+tqdV397epEd2t8gTFaZl+T4ty/fJEx3udFQAwAxAqYQZY1NtlxZmJ7K9LgAAgKT0hCj9f+tL5I2NUF3nGVU39WnT8W69UNetORkerShMUmFyLDvmAgBeN0olzAjdQxd1sG1Qn7idqW8AAAATuYzRnAyP5mR41Hf2knY3+bXnVL+Onh5Sclykqgp8WpLrVXQEa1ICAAJDqYQZYfPxbknS+jlpDicBAACYvpLiIvWm+RnaUJGmw+2Dqm7s05OHO/TcsU4tzE5UVWGSshKjpyzPI9UtU/ZcgXiwKtfpCAAQEiiVMCM8X9utrMRolafHOx0FAABg2gt3u7Qk16sluV6dHrig6qY+HWgd0J5T/cr2RmtFQZLmZyco3O1yOioAYBqjVELIu3hlRNsbenTf0hzWBAAAAAhQZmK03rY4W3fOzdD+1n5VN/r1831tevJwhyrzvFpRmCRvbITTMQEA0xClEkLezpN9unhlVOvnsOsbAADA6xUd4daqomStLExSY+85VTf2acfJXm1v6FVFpkeripKVnxTDL/EAAK+gVELIe762SzERbq0oTHI6CgAAQMgzxqgoJU5FKXEaOH9Z1U1+7W7y6+jpIWUmRGlVUbIWZCcojKlxADDrUSohpFlrtfl4t9aUJCsqnB1LAAAAJlNiTITumJuuW8pSdaB1QC+d7NXP97Xp6aOdqirwqarAp/iocKdjAgAcQqmEkHakfUgdgxf18dtKnY4CAAAwY0WEubS8wKdl+V6d7DmnHQ292ny8Wy/W92hBVoJWFSdP6a5xAIDpgVIJIe2Zox1yu4xum5PmdBQAAIAZzxij4tQ4FafGqffsJe082ae9Lf3a3zqgwuRYrSlJUWlaHOsuAcAsQamEkGWt1dNHOrWi0MeOJAAAAFMsOS5Sb1mYqdsq0lTT7NdLJ/v0g53NSvNEak1xihbkJCjMxbpLADCTUSohZDV0n1Vjzzl9YFW+01EAAABmrahwt9aUpGhlUZIOtw1q24mxdZeeO9apVUXJWl7gY+1LAJihKJUQsp4+0ilJun1uusNJAAAAEOZyaXGuV4tyEnWi+6y2nejRM0c79UJdt5bn+7SqOFkJ0SzqDQAzCaUSQtYzRzq1NM+rNE+U01EAAAAwzhij0rR4labFq33ggrad6NGOk73acbJXC7MTtbY0hZ/fAGCGoFRCSGrpO69jHUP6u7vmOB0FAAAA15GVGK0HluWqv+Kydpzs1Z7msUW9KzI8WleWomxvjNMRAQBvAKUSQtIzRzskSXfOY+obAADAdOeNjdCbF2Tq1rJUvdTYp5dO9upYx5BKUuN0c1mKCpJi2TEOAEIQpRJC0jNHOjU306McH7/dAgAACBUxkWHaMCdNq4uTtbvJr20Nvfr2tibl+WK0rixFpWnxlEsAEEIolRByOgcval/LgD5xe6nTUQAAAPA6RIW7tbZ0bMe4Paf6ta2+Rz/YeUoZCVFaV5aquZkeuSiXAGDao1RCyHnu2Niub0x9AwAACG3hbpdWFiZpWb5XB1sH9WJ9t36yu0XJcZFaV5aihdmJcrsolwBguqJUQsh5+nCnilJiVZwa73QUAAAATIIwl0tL87xanJuoo6eHtKWuWz/f26YXjnfr1vJULaBcAoBpiVIJIcV/7rKqm/r0oXXFTkcBAADAJHMZo/lZCZqb6dHxjiFtOt6tn+1t0wt13bqljHIJAKYbSiWElOePdWnUMvUNAABgJnMZo4rMBJVneFTbMaRNtb9fLi3MSWTNJQCYBiiVEFKePtKhbG+05mZ6nI4CAACAIHMZo7mZCZqT4dGx00Pa/MrIpR7dWp6iBdmUSwDgJEolhIyhi1e0o6FP71uZx1azAAAAs4jLGM3LSlBF5u/KpZ/uadPm4z3jay4lUC4BgAMolRAyfnu0S5dHRvWm+RlORwEAAIADri6XNh3v0k/3tGrzKwt6Uy4BwFSiVELIePzgaWV7o7UkN9HpKAAAAHDQxHLp6OkhbR4vl17eLW4+5RIATAlKJYSEnjOXtKOhV//75kKmvgEAAEDS7+8Wd/T0kDbVdunRPa3aXNet9eWpmpdFuQQAwUSphJDw1OEOjYxa3bMoy+koAAAAmGYmlktH2ge16Xi3Nta0Kr2uR+vnpKoiw8MvJgEgCCiVEBIeP9Cu8vR4labFOx0FAAAA05TLGC3ITtS8rAQdahvU5uNd+nF1izITorRhTprK0uMplwBgElEqYdpr6TuvfS0D+ps7y52OAgAAgBDgMkaLchI1PytBB1sHtLmuWz/cdUrZ3mhtmJOmktQ4yiUAmASUSpj2fn3otCTpLQvZ9Q0AAAA3zu0yWpLn1cKcRO1v6dfmum59/6Vm5flitKEiTYXJsZRLAPAGUCphWrPW6lf727Us36tsb4zTcQAAABCC3C6jynyfFuUmau+pfr1wvFvf2d6kguRYbZiTpoLkWKcjAkBIcjkdAHg1xzvP6ET3Wb2VBboBAADwBoW5XKoqSNJf3V6mNy/IUO+ZS/rWtkZ9d3uTWvrOOR0PAEIOI5UwrT1+4LTCXEZ3z2fqGwAAACZHuNulVUXJqszzaXdTn16s79F/b21UaVqcNsxJczoeAIQMSiVMW6OjVr8+eFprSpLli41wOg4AAABmmIgwl1aXpGhZgU+7Gv3aWt+j/9xyUvVdZ/Xx20o0NzPB6YgAMK0x/Q3T1t6WfrUPXNA9TH0DAABAEEWGuXVzaYo+eUeZNsxJ0+6mPt39H9v15w/vVV3nGafjAcC0xUglTFuPH2hXVLhLt1UwBBkAAADBFxXu1q3lqVpZmKQdJ3u1+Xi3njnSqXlZCVo/J1Wp8VFOR9SDVblORwCAV1AqYVq6MjKqJw916LaKdMVG8tcUAAAAUyc6wq0Nc9K0qihJ2070aufJPh1pH9SinETdWp6qpLhIpyMCwLTAu3VMSy/W9aj//BW9dWGm01EAAAAwS8VEhOmOuem6qThZ2+p7tKupTwfbBrQ416tbylJZ9xPArEephGnp0T2tSo6L0LqyFKejAAAAYJaLiwzTm+ZnaHVJsl6s79HuJr/2t/RraZ5Pt5SlKDGGcgnA7ESphGmne+iiNh/v1p+sKVC4m7XkAQAAMD3ER4XrzQsytaYkRVvqurWnuV/7Wvq1LN+rdaWp8kSHOx0RAKYUpRKmnZ/va9PIqNUDy1iEEAAAANNPQnS47lmUpZtLU/RC3djIpT3N/aoq8GltaYrioyiXAMwOlEqYVkZHrR6taVVVgU8FybFOxwEAAACuKzEmQm9bPF4uHe/WzsY+VTf5tSzfpzUlyUyLAzDjUSphWtnV1KdTfef1sQ0lTkcBAAAAbogvNkLvWJqtdWUp2lLfo+qmPu1u8mtJXqJuLmVBbwAzV1AXrDHG3GmMqTPGNBhjPnWN85HGmEfHz1cbY/LHj99mjNlrjDk8/vHWYObE9PFoTas8UWF607wMp6MAAAAAAUmKi9Q7lmTrr24rU2W+V/taBvSV39bp53tb1XPmktPxAGDSBW2kkjHGLekbkm6T1CapxhjzhLX22ITLPiip31pbbIx5QNIXJd0vqVfSW6y1p40x8yQ9KykrWFkxPQycv6ynj3TqXctyFBXudjoOAAAA8Lp4YyN0z6IsrStL1fYTPdrd7Nf+lgHNz07QurJUpXuinI4IAJMimNPflktqsNY2SpIxZqOkeyRNLJXukfTZ8ds/l/R1Y4yx1u6fcM1RSdHGmEhrLfX+DPbY/nZdHh7V/SzQDQAAgBkgITpcdy/I1NrSFO1o6NWuJr8OtQ2qIsOjW8pSleWNdjoiALwhwSyVsiS1TrjfJqnqetdYa4eNMYOSkjQ2Uull75C0j0JpZrPWauPuVi3MTlBFpsfpOAAAAMCkiY8K153zMrS2JEU7TvZpZ2OvjnUMqTglTmtLU1SUEitjjNMxASBg03qhbmPMXI1Nibv9OucfkvSQJOXmMrollB1sG1Rd1xn937fNdzoKAAAAEBQxkWG6rSJNa0qStbvJrx0NvfrujiZlJUbr5tIUVWR65KJcAhBCglkqtUvKmXA/e/zYta5pM8aESUqQ1CdJxphsSY9Jep+19uS1nsBa+01J35SkyspKO6npMaU27m5RdLhbb1nIAt0AAACY2aLC3VpbmqKVRUna3zKgbSd69MjuFiXFRmhtaYoW5yQqzB3UPZUAYFIEs1SqkVRijCnQWHn0gKQHr7rmCUnvl7RT0r2SNltrrTEmUdKTkj5lrd0RxIyYBs5eGtYTB0/rLQszFB8V7nQcAAAAYEqEu11aXuBTZb5XR08P6cX6bj22v13P13ZpdXGyluX72MAGwLQWtFJpfI2kj2hs5za3pO9aa48aYz4naY+19glJ35H0I2NMgyS/xoonSfqIpGJJ/2CM+YfxY7dba7uDlRfOeeLAaZ2/PKL7l+W89sUAAADADOMyRvOzEjQv06OGnrPaWt+jp4906oW6bq0oSNKKoiR5+OUrgGkoqGsqWWufkvTUVcf+YcLti5Luu8bjPi/p88HMhunBWqvv7mjSvCyPluR6nY4DAAAAOMYYo5LUeJWkxqut/7xerO/Ri/U92tbQq4XZiVpdkux0RAD4PdN6oW7MfC/W96ih+6y+dv8idrwAAAAAxmV7Y/Tuqjz1nb2kHSd7tfdUv/a19Gt/S7/+dE2h1pQk8/MzAMdRKsFR39nepDRPpO6azwLdAAAAwNWS4iL11oVZ2jAnTbub/NrfOqD3fXe3ytPj9SdrCvXWhZmKCGNRbwDO4F8fOOZ455C2nejV+1fl840QAAAAeBUxEWFaV5aq7X9zi7507wJZK33iZwe1+oub9Y0XGjRw/rLTEQHMQoxUgmO+s61J0eFuPbg81+koAAAAQEj4xd52SdL7VubpRPdZ7Wjo1ZeerdPXnq/X4lyvVhUmKdUT5Ui2B6v4uR6YbSiV4IjuMxf1+IHTemB5jhJjIpyOAwAAAIQUY4xK0+JVmhavjsELeulkn/ad6tfuJr+KU+O0qihJpWnxcrHuEoAgolSCIx7eeUpXRkf1gZsKnI4CAAAAhLSMhGi9Y0m27pibrj3Nfu1q7NMPd55SUmyEVhYlaUmuV1HhbqdjApiBKJUw5S5eGdHD1S3aMCdNBcmxTscBAAAAZoS4yLF1l9aUpOjo6UG9dLJPvznUod8e69KSvLGpcUlxkU7HBDCDUCphyj22v13+c5f1J6sZpQQAAABMNrfLaEF2ohZkJ6qt/7xeOtmn3Y1+7TrZp7L0eK0sSlJxSpwMU+MAvEGUSphSo6NW39nepHlZHi0v8DkdBwAAAJjRsr0xemdljO6cl67dTX5VN/l1fEezUuIjtaooSYtzvOzEDOB1o1TClHrxRI8aus/qa/cv4jcjAAAAwBTxRIVrw5w0rStN0eH2salxjx84rWePdmpZnk9VhUnyxbKBDoDAUCphylhr9R+bTigzIUp3zc9wOg4AAAAw64S5XVqc69WinES1+Memxu042avtDb0qTYvXisIklaTFsWscgBtCqYQp80Jdt/a3DOgLb5/PEFsAAADAQcYY5SXFKi8pVoMXrmh3k181zX79YGezfLERqirwaWmeVzERvGUEcH38C4EpYa3Vl5+rV64vRu9Ymu10HAAAAADjEqLDdVtFmm4pT9HR00Pa1dinp4906rfHurQwJ1ErCpOUlRjtdEwA0xClEqbEs0c7dfT0kL7yzoUKdzNKCQAAAJhuwlwuLcxO1MLsRHUMXlB1o1/7W/u191S/crzRWlGYpHlZCfw8D+AVlEoIupFRq6/8tl5FKbG6Z1GW03EAAAAAvIaMhGj90eIs3TkvXfta+rWrsU8/29umJw93aFm+T8sLfPLGsLA3MNtRKiHofnPotOq7zurrDy6W28WCfwAAAECoiAp3a1VRslYUJqmx55x2NfZpa32Pttb3qDx9bGHvolQW9gZmK0olBNXwyKi+9vwJlafH66557PgGAAAAhCKXMSpOjVNxapwGzl9+ZWHv2s4zSoqN0IrCJN09P0MJMeFORwUwhSiVEFS/3N+upt5z+tb7KuVilBIAAAAQ8hJjInT73HTdWp6qI6cHtavRrycPd2jT8S790aIsvXdlnuZmJjgdE8AUoFRC0FweHtW/P39CC7MTtGFOqtNxAAAAAEyiMLdLi3K8WpTj1emBC+o5c0m/OtCujTWtWpKbqPesyNNd8zMUFe52OiqAIGHZfgTNozUtah+4oL+8vUyGOdYAAADAjJWZGK0v3rtA1X+7QZ+5e44Gzl/RX/70oFb8yyb985PH1NR7zumIAIKAkUoICv+5y/ryb+u1otCntSXJTscBAAAAMAUSYsL1J2sK9cHVBdp5sk8/rm7R93Y061vbmnRTcZLeXZWn2yrSFO5mfAMwE1AqISj+9ZnjOntxWJ+7Zx6jlAAAAIBZxhijVcXJWlWcrO4zF/XTmlb9ZHerPvTjfUqJj9QDy3L0wPJcZSVGOx0VwBtAqYRJt/dUvzbWtOqhtYUqTYt3Og4AAAAAB6XGR+kjt5boz9cV68X6bv14V4u+/kKDvvFCg24pS9V7VuRpbWmK3GzsA4QcSiVMquGRUf39r44o3ROlj64vcToOAAAAgGnC7TK6tTxNt5anqa3/vDbubtXGmlZt+n6NshKj9WBVru6rzFZqfJTTUQHcICayYlI9vOuUjnUM6e/fXKHYSDpLAAAAAH8o2xujT9xRpp1/e6v+891LlJ8coy89W6dV/7JZH35kn1462StrrdMxAbwG3vVj0nSfuagvP1evNSXJumt+utNxAAAAAExz4W6X7pqfobvmZ+hkz1n9pLpFP9vbpicPdagwJVYPLs/VvUuzlRgT4XRUANdAqYRJ8y9PHdel4VH901vnsjg3AAAAMMs8Ut3yhj9HYUqc/vK2Uh1pH1R1k1+ff7JWX3j6uBZkJ2hZvk+5vpjX9V7jwarcN5wNwB+iVMKk2NXYp8f2t+sjtxSrMCXO6TgAAAAAQlS426XFuV4tzvWqY/CCdjf5daB1QPtaBpQSH6lleV4tyvUqjuU2AMfxKsQbdvbSsD71i0PK9kbrw7cUOx0HAAAAwAyRkRCtexZl6c556TrSPqia5n49daRTzx7tUkWmR5X5XhWlxMnFTAnAEZRKeMP+4fEjavGf18aHVio6wu10HAAAAAAzTGSYW0vzfFqa51PX0EXtafZrX8uADrcPyhsTPn7Oq4TocKejArMKpRLekMf2t+mX+9r1sQ0lWl7gczoOAAAAgBkuzROluxdk6o656TraMaQ9zX49X9ulTbVdKkuP17J8n0rT4uV2MXoJCDZKJbxuzb3n9JnHjmh5vk8fYdobAAAAgCkU5nZpYXaiFmYnqu/sJe091a+9Lf06vuuU4qPCtDTXq6V5XiXFRTodFZixKJXwulweHtVfbNyvMLdLX31gkcLcLqcjAQAAAJilkuIidfvcdK2fk6b6rjOqafbrxfoebanvUVFKrGIj3bq9Ip3lOoBJRqmE1+XLz9XpUNug/vs9S5WVGO10HAAAAACQ22U0J8OjORkeDV64on0t/drT7NdHNx5QfGSY3rwwQ/cuzdaSXK8Mi3sDbxilEgK2tb5H/7O1Ue9Zkas756U7HQcAAAAA/kBCdLhuKUvVzaUpKk6N08/3tunxA6f1k92tKkiO1b1Ls/W2xVnK5JfkwOtGqYSANHSf1V9s3K/StDh95u4Kp+MAAAAAwKtyGaMVhUlaUZikf3rrXD19pFM/39uqLz1bp397rk6ri5N179JspscBrwOlEm5Y99BFvf+7uxXmMvr2+5YpKpx/cAEAAACEjtjIMN27NFv3Ls1Wq/+8frGvTT/f2/bK9Li75mfonsWZWlGQJBe7xwGviVIJN+TMxSv64+/VqP/8ZT360ErlJsU4HQkAAAAAXrccX4w+tqFUf3FriXY3+/WzPW36zaHTenRPq9I9UXrrokzdsyhTFRke1l8CroNSCa/p8vCo/vzhfarrOqPvvL9S87MTnI4EAAAAAJPC5frd9LjP/9E8PV/bpccPtOu725v0za2NKkmN0x8tztJbF2Yqx8cv14GJKJXwqqy1+ptfHNL2hl596d4FWleW6nQkAAAAAAiK6Ai33rIwU29ZmKn+c5f15OEOPX6gXV96tk5ferZOlXle3bM4S3fPz5AvNsLpuIDjKJVwXdZafeHp43psf7s+cXup7qvMcToSAAAAAEwJb2yE3rMiT+9ZkadW/3k9cfC0Hj/Qrr//1RF99omjWlWUpDfNy9Adc9OUFBfpdFzAEZRKuKaRUau/f/yIHqlu0XtX5OnDtxQ7HQkAAAAAHJHji9GHbynWh9YVqbbjjH5z6LSeOtyhTz92WH//+BGtKPTprvkZumNuupIpmDCLUCrhD1y8MqKPbtyvZ4926UPrivTJO8pYmA4AAADArGeMUUWmRxWZHn3yjjId6xjS04c79dThDv3dY0f09786oqqCJN21IEN3zk1XSjwFE2Y2SiX8nsELV/SnP9ijmlN+/eNbKvSBmwqcjgQAAAAA044xRnMzEzQ3M0F/dXupjnee0VOHO/Tk4Q79/a+O6B8fP6Jl+T7dVpGmDXPSlJ8c63RkYNJRKuEVnYMX9cff262TPWf1Hw8s1lsWZjodCQAAAACmPWOM5mR4NCfDo7+8rVR1XWf01OFOPXukU59/slaff7JWxalxWj8nVbfNSdPiXK/cLmaDIPRRKkGSVN3Yp489ekBnLg7r+x9YrpuKk52OBAAAAAAhxxij8nSPytPHCqZW/3k9X9ulTbXd+s62Jv3Pi43yxUbolrJUbZiTqjWlKYqL5K05QhN/c2e5KyOj+trz9frPLSeV54vRt/9sheZmJjgdCwAAAABmhBxfjD5wU4E+cFOBhi5e0Yt1PdpU26Xna7v0i31tinC7tKIoSWtLkrWmJEWlaXGsaYuQQak0izX1ntPHNu7XwbZBvbMyW//4lrmKpSEHAAAAMMM8Ut3idITfs7wgSUvzfDrlPydZafPxbn3+yVpJtUqNj9TqkmStKUnWTcXJSo2PcjoucF00CLPQ6KjVz/a26p9+fUzhbpf+891LdNf8DKdjAQAAAMCs4XYZFSbH6cGqXH3mzRVqH7ig7Sd6tO1Er1443q1f7muXJJWnx2tNSbJWl6Roeb5P0RFuh5MDv0OpNMvsauzTvzxVq4Ntg1pZmKSv3L9QGQnRTscCAAAAgFktKzFa9y/L1f3LcjU6anX09JC2NfRo+4le/eClU/rWtiZFhLm0KDtRlfleLcv3aUmeVwnR4U5HxyxGqTRLNHSf0ReePq7na7uVkRClf7tvod6+OEsudhwAAAAAgGnF5TKan52g+dkJ+tC6Yl24PKLdzX5tP9GjmuZ+fXNro/5zy0kZI5WlxWtZvu+VoikzkUEDmDqUSjNcU+85fWtbox6taVV0uFufvKNMH1xdoKhwhkwCAAAAQCiIjnDr5tIU3VyaIkk6f3lYB1oHtKe5XzXNfv1yX5t+tOuUpLERT5X5Xi3OSdT87ATNyfAoJoK3/ggO/mbNQCOjVi8c79YPd53S1voehbmM3lOVq79YX6KkuEin4wEAAAAA3oCYiDCtKkrWqqJkSdLwyKiOd55RTbNfe5r79dLJPj1+4LQkyWWkopQ4zc9K0LzxP3MzPWzShEnB36IZpK3/vH59sEM/rj6ltv4LSvNE6uMbSvWu5TlK9bBjAAAAAADMRGFu1yuF0QduKpC1Vp1DF3WkfUiH2wd1tH1Q2xt69cv9Y4t/GyMVJsdqXlaCKjI8Kk6NU0lqvLK80XKzRAoCQKkUwqy1Ot55Rs8d7dJzxzp19PSQJGlFoU+fvmuObqtIU7jb5XBKAAAAAMBUMsYoIyFaGQnRuq0i7ZXj3UMXdeT0oA63jZVN1Y3+V0Y0SVJkmEuFKXEqTo1TcUqcStLGbucnxSoijPeW+EOUSiHEWqvG3nPa29yvvaf69VJjr1r9F2SMtDTXq0/fVa7bK9KVnxzrdFQAAAAAwDST6onSrZ4o3Vr+u6Jp8PwVNfScUUP3WTV0n9WJ7rPa39KvXx/8XdnkdhnleKOV44tRtjda2d6XP47dTomLZBOoWSqopZIx5k5J/y7JLenb1tovXHU+UtIPJS2V1Cfpfmtt8/i5v5X0QUkjkv7CWvtsMLNON5eHR9XUe071XWd0ovusjp0e1N5T/eo/f0WSlBAdrmX5Xn1oXbHWz0lVajzT2wAAAAAAgUmICdfSPJ+W5vl+7/iFyyM62XNWJ3vGyqbGnnNqG7ig3x7rUu/Zy793bYTbpazxkikrMVop8ZFjf+IilTz+MSU+knWcZqCg/R81xrglfUPSbZLaJNUYY56w1h6bcNkHJfVba4uNMQ9I+qKk+40xFZIekDRXUqak540xpdbakWDlnS6+tbVRG2ta1Nx3XiOjVtLYwmr5ybFaPydNlXleVeZ7VZgcRxMMAAAAAAiK6Aj3K+s0Xe3C5RG1D5xXa/8FtfVfUFv/+fGPF1Rb2y3/uUsafzv7e2Ii3EoeL5iS4yKUGB2hhJhwJUSHyxMdLk9UmBKif3f/5dss6zJ9BbMmXC6pwVrbKEnGmI2S7pE0sVS6R9Jnx2//XNLXjTFm/PhGa+0lSU3GmIbxz7cziHmnhahwl4pT4/SmeRkqSRtbLK0wJVZR4W6nowEAAAAAoOgIt4pT41WcGn/N8yOjVv5zl9Vz5pJ6zl5S7/jHnjOX1Dv+sbHnnAYvDGjwwhVdGh591eeLCHMpJsKtmHC3oiPciokIG/849ic6PEwxEW5FhbsU7h77ExHmUrjbKMLtUnjY+DH3y+eNwsOuuu92yWWMXC7JbYyMMXKZsal/LmNkrr5txm67jJFx/e7+xOtcZmx9q5ksmKVSlqTWCffbJFVd7xpr7bAxZlBS0vjxXVc9Nit4UaeP967M13tX5jsdAwAAAACA18XtMq9MgbsRF6+MaOjiFQ1duKLBC8PjH69o6OIVDZ6/orOXh3Xh8ojOXx4Z/zis85dH5D93WW39vzt28cqohkdHdWXkGsOkHPDJO8r04VuKnY4RVCE9odEY85Ckh8bvnjXG1DmZB1MqWVKv0yEAvGJGvCbf7XQAYHLMiNdjqOHfD7wKXpN4Vfz7MaWm9PX4kS9KH5mqJwuuvOudCGap1C4pZ8L97PFj17qmzRgTJilBYwt238hjZa39pqRvTmJmhAhjzB5rbaXTOQCM4TUJTB+8HoHphdckMH3wepx8wVztqkZSiTGmwBgTobGFt5+46ponJL1//Pa9kjZba+348QeMMZHGmAJJJZJ2BzErAAAAAAAAAhC0kUrjayR9RNKzktySvmutPWqM+ZykPdbaJyR9R9KPxhfi9museNL4dT/V2KLew5I+PBt2fgMAAAAAAAgVZmxgEBBajDEPjU9/BDAN8JoEpg9ej8D0wmsSmD54PU4+SiUAAAAAAAAELJhrKgEAAAAAAGCGolRCyDHG3GmMqTPGNBhjPuV0HmC2M8Y0G2MOG2MOGGP2OJ0HmE2MMd81xnQbY45MOOYzxvzWGHNi/KPXyYzAbHGd1+NnjTHt498jDxhj7nIyIzCbGGNyjDEvGGOOGWOOGmM+On6c75OTiFIJIcUY45b0DUlvklQh6V3GmApnUwGQdIu1dhFbtAJT7vuS7rzq2KckbbLWlkjaNH4fQPB9X3/4epSkr45/j1xkrX1qijMBs9mwpL+y1lZIWiHpw+PvHfk+OYkolRBqlktqsNY2WmsvS9oo6R6HMwEA4Ahr7VaN7aA70T2SfjB++weS/mgqMwGz1XVejwAcYq3tsNbuG799RlKtpCzxfXJSUSoh1GRJap1wv238GADnWEnPGWP2GmMecjoMAKVZazvGb3dKSnMyDAB9xBhzaHx6HNNsAAcYY/IlLZZULb5PTipKJQDAG7XaWrtEY9NSP2yMWet0IABj7Ng2v2z1CzjnvyQVSVokqUPSlx1NA8xCxpg4Sb+Q9DFr7dDEc3yffOMolRBq2iXlTLifPX4MgEOste3jH7slPaaxaaoAnNNljMmQpPGP3Q7nAWYta22XtXbEWjsq6VvieyQwpYwx4RorlH5srf3l+GG+T04iSiWEmhpJJcaYAmNMhKQHJD3hcCZg1jLGxBpj4l++Lel2SUde/VEAguwJSe8fv/1+SY87mAWY1V5+4zrubeJ7JDBljDFG0nck1VprvzLhFN8nJ5EZG+0FhI7xrVi/Jskt6bvW2n92NhEwexljCjU2OkmSwiQ9wmsSmDrGmJ9IWicpWVKXpH+U9CtJP5WUK+mUpHdaa1k8GAiy67we12ls6puV1Czpzyas5QIgiIwxqyVtk3RY0uj44U9rbF0lvk9OEkolAAAAAAAABIzpbwAAAAAAAAgYpRIAAAAAAAACRqkEAAAAAACAgFEqAQAAAAAAIGCUSgAAAAAAAAgYpRIAAAgpxphsY8zjxpgTxpiTxph/N8ZEGGPWGWMGjTEHjDHHjTH/NuExf2yM6THG7B9/3LPGmFUTzn/fGNM0/tgDxpiXrnrcy5/z4xMe81ljzCeuke/s+Md8Y4w1xvx/E8593RjzxzfwnKPGmAUTHnfEGJM/8fNf9bV9fUIma4wpnnD+Y+PHKsfvNxtjDo8/52FjzD0Trh2ZkOeAMeZT48e3GGPqjDEHjTE1xphFAf1PAwAAMxKlEgAACBnGGCPpl5J+Za0tkVQqKU7SP49fss1au0jSYklvNsbcNOHhj1prF48/7guSfmmMmTPh/CettYvG/6y66nGLJN0k6e+MMTkBRO6W9FFjTMR1zl/vOdsk/V0AzzPRYUkPTLh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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in df.columns:\n",
    "    if df[column].dtype == 'int64' or df[column].dtype == 'float64':\n",
    "        plt.figure(figsize=(20,8))\n",
    "        sns.distplot(df[column])\n",
    "        plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "adaeb544",
   "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>ORDERNUMBER</th>\n",
       "      <th>QUANTITYORDERED</th>\n",
       "      <th>PRICEEACH</th>\n",
       "      <th>ORDERLINENUMBER</th>\n",
       "      <th>SALES</th>\n",
       "      <th>ORDERDATE</th>\n",
       "      <th>DAYS_SINCE_LASTORDER</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>PRODUCTLINE</th>\n",
       "      <th>MSRP</th>\n",
       "      <th>PRODUCTCODE</th>\n",
       "      <th>CUSTOMERNAME</th>\n",
       "      <th>PHONE</th>\n",
       "      <th>ADDRESSLINE1</th>\n",
       "      <th>CITY</th>\n",
       "      <th>POSTALCODE</th>\n",
       "      <th>COUNTRY</th>\n",
       "      <th>CONTACTLASTNAME</th>\n",
       "      <th>CONTACTFIRSTNAME</th>\n",
       "      <th>DEALSIZE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>399</th>\n",
       "      <td>10405</td>\n",
       "      <td>97</td>\n",
       "      <td>93.280000</td>\n",
       "      <td>5</td>\n",
       "      <td>9048.16</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>447</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Classic Cars</td>\n",
       "      <td>115</td>\n",
       "      <td>S12_4675</td>\n",
       "      <td>Mini Caravy</td>\n",
       "      <td>88.60.1555</td>\n",
       "      <td>24, place Kluber</td>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>67000</td>\n",
       "      <td>France</td>\n",
       "      <td>Citeaux</td>\n",
       "      <td>Frederique</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2511</th>\n",
       "      <td>10401</td>\n",
       "      <td>85</td>\n",
       "      <td>88.750000</td>\n",
       "      <td>10</td>\n",
       "      <td>7543.75</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>2570</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>Planes</td>\n",
       "      <td>99</td>\n",
       "      <td>S700_2466</td>\n",
       "      <td>Tekni Collectables Inc.</td>\n",
       "      <td>2015559350</td>\n",
       "      <td>7476 Moss Rd.</td>\n",
       "      <td>Newark</td>\n",
       "      <td>94019</td>\n",
       "      <td>USA</td>\n",
       "      <td>Brown</td>\n",
       "      <td>William</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>10401</td>\n",
       "      <td>77</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>9</td>\n",
       "      <td>7084.00</td>\n",
       "      <td>2020-04-03</td>\n",
       "      <td>2673</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>Planes</td>\n",
       "      <td>80</td>\n",
       "      <td>S700_3167</td>\n",
       "      <td>Tekni Collectables Inc.</td>\n",
       "      <td>2015559350</td>\n",
       "      <td>7476 Moss Rd.</td>\n",
       "      <td>Newark</td>\n",
       "      <td>94019</td>\n",
       "      <td>USA</td>\n",
       "      <td>Brown</td>\n",
       "      <td>William</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>571</th>\n",
       "      <td>10407</td>\n",
       "      <td>76</td>\n",
       "      <td>185.300000</td>\n",
       "      <td>2</td>\n",
       "      <td>14082.80</td>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>611</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>Vintage Cars</td>\n",
       "      <td>170</td>\n",
       "      <td>S18_1749</td>\n",
       "      <td>The Sharp Gifts Warehouse</td>\n",
       "      <td>4085553659</td>\n",
       "      <td>3086 Ingle Ln.</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>94217</td>\n",
       "      <td>USA</td>\n",
       "      <td>Frick</td>\n",
       "      <td>Sue</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>10407</td>\n",
       "      <td>76</td>\n",
       "      <td>94.500000</td>\n",
       "      <td>6</td>\n",
       "      <td>7182.00</td>\n",
       "      <td>2020-04-22</td>\n",
       "      <td>1696</td>\n",
       "      <td>On Hold</td>\n",
       "      <td>Classic Cars</td>\n",
       "      <td>90</td>\n",
       "      <td>S24_2766</td>\n",
       "      <td>The Sharp Gifts Warehouse</td>\n",
       "      <td>4085553659</td>\n",
       "      <td>3086 Ingle Ln.</td>\n",
       "      <td>San Jose</td>\n",
       "      <td>94217</td>\n",
       "      <td>USA</td>\n",
       "      <td>Frick</td>\n",
       "      <td>Sue</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1930</th>\n",
       "      <td>10405</td>\n",
       "      <td>76</td>\n",
       "      <td>154.469737</td>\n",
       "      <td>3</td>\n",
       "      <td>11739.70</td>\n",
       "      <td>2020-04-14</td>\n",
       "      <td>1978</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Classic Cars</td>\n",
       "      <td>140</td>\n",
       "      <td>S24_3856</td>\n",
       "      <td>Mini Caravy</td>\n",
       "      <td>88.60.1555</td>\n",
       "      <td>24, place Kluber</td>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>67000</td>\n",
       "      <td>France</td>\n",
       "      <td>Citeaux</td>\n",
       "      <td>Frederique</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1609</th>\n",
       "      <td>10412</td>\n",
       "      <td>70</td>\n",
       "      <td>121.400000</td>\n",
       "      <td>10</td>\n",
       "      <td>8498.00</td>\n",
       "      <td>2020-05-03</td>\n",
       "      <td>1638</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Trucks and Buses</td>\n",
       "      <td>127</td>\n",
       "      <td>S24_2300</td>\n",
       "      <td>Euro Shopping Channel</td>\n",
       "      <td>(91) 555 94 44</td>\n",
       "      <td>C/ Moralzarzal, 86</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>28034</td>\n",
       "      <td>Spain</td>\n",
       "      <td>Freyre</td>\n",
       "      <td>Diego</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1931</th>\n",
       "      <td>10419</td>\n",
       "      <td>70</td>\n",
       "      <td>132.000000</td>\n",
       "      <td>8</td>\n",
       "      <td>9240.00</td>\n",
       "      <td>2020-05-17</td>\n",
       "      <td>1946</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Classic Cars</td>\n",
       "      <td>140</td>\n",
       "      <td>S24_3856</td>\n",
       "      <td>Salzburg Collectables</td>\n",
       "      <td>6562-9555</td>\n",
       "      <td>Geislweg 14</td>\n",
       "      <td>Salzburg</td>\n",
       "      <td>5020</td>\n",
       "      <td>Austria</td>\n",
       "      <td>Pipps</td>\n",
       "      <td>Georg</td>\n",
       "      <td>Large</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      ORDERNUMBER  QUANTITYORDERED   PRICEEACH  ORDERLINENUMBER     SALES  \\\n",
       "399         10405               97   93.280000                5   9048.16   \n",
       "2511        10401               85   88.750000               10   7543.75   \n",
       "2614        10401               77   92.000000                9   7084.00   \n",
       "571         10407               76  185.300000                2  14082.80   \n",
       "1656        10407               76   94.500000                6   7182.00   \n",
       "1930        10405               76  154.469737                3  11739.70   \n",
       "1609        10412               70  121.400000               10   8498.00   \n",
       "1931        10419               70  132.000000                8   9240.00   \n",
       "\n",
       "      ORDERDATE  DAYS_SINCE_LASTORDER   STATUS       PRODUCTLINE  MSRP  \\\n",
       "399  2020-04-14                   447  Shipped      Classic Cars   115   \n",
       "2511 2020-04-03                  2570  On Hold            Planes    99   \n",
       "2614 2020-04-03                  2673  On Hold            Planes    80   \n",
       "571  2020-04-22                   611  On Hold      Vintage Cars   170   \n",
       "1656 2020-04-22                  1696  On Hold      Classic Cars    90   \n",
       "1930 2020-04-14                  1978  Shipped      Classic Cars   140   \n",
       "1609 2020-05-03                  1638  Shipped  Trucks and Buses   127   \n",
       "1931 2020-05-17                  1946  Shipped      Classic Cars   140   \n",
       "\n",
       "     PRODUCTCODE               CUSTOMERNAME           PHONE  \\\n",
       "399     S12_4675                Mini Caravy      88.60.1555   \n",
       "2511   S700_2466    Tekni Collectables Inc.      2015559350   \n",
       "2614   S700_3167    Tekni Collectables Inc.      2015559350   \n",
       "571     S18_1749  The Sharp Gifts Warehouse      4085553659   \n",
       "1656    S24_2766  The Sharp Gifts Warehouse      4085553659   \n",
       "1930    S24_3856                Mini Caravy      88.60.1555   \n",
       "1609    S24_2300      Euro Shopping Channel  (91) 555 94 44   \n",
       "1931    S24_3856      Salzburg Collectables       6562-9555   \n",
       "\n",
       "            ADDRESSLINE1        CITY POSTALCODE  COUNTRY CONTACTLASTNAME  \\\n",
       "399     24, place Kluber  Strasbourg      67000   France         Citeaux   \n",
       "2511       7476 Moss Rd.      Newark      94019      USA           Brown   \n",
       "2614       7476 Moss Rd.      Newark      94019      USA           Brown   \n",
       "571       3086 Ingle Ln.    San Jose      94217      USA           Frick   \n",
       "1656      3086 Ingle Ln.    San Jose      94217      USA           Frick   \n",
       "1930    24, place Kluber  Strasbourg      67000   France         Citeaux   \n",
       "1609  C/ Moralzarzal, 86      Madrid      28034    Spain          Freyre   \n",
       "1931         Geislweg 14    Salzburg       5020  Austria           Pipps   \n",
       "\n",
       "     CONTACTFIRSTNAME DEALSIZE  \n",
       "399        Frederique    Large  \n",
       "2511          William    Large  \n",
       "2614          William    Large  \n",
       "571               Sue    Large  \n",
       "1656              Sue    Large  \n",
       "1930       Frederique    Large  \n",
       "1609            Diego    Large  \n",
       "1931            Georg    Large  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.QUANTITYORDERED >=70].sort_values(by='QUANTITYORDERED',ascending=False).head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "29c244d4",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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