EDA_DataSpark.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f39142dc-365b-41f6-a468-ca4390df6d81",
   "metadata": {},
   "source": [
    "# EDA Capstone Project - DataSpark\n",
    "## Detect file encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4181669b-4ff6-4bea-aa94-ecac688cfd2c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Detected encoding for <_io.BufferedReader name='C:\\\\Users\\\\16132\\\\Downloads\\\\Capstone2Data\\\\Customers.csv'> : Windows-1252\n",
      "Detected encoding for <_io.BufferedReader name='C:\\\\Users\\\\16132\\\\Downloads\\\\Capstone2Data\\\\Exchange_rates.csv'> : ascii\n",
      "Detected encoding for <_io.BufferedReader name='C:\\\\Users\\\\16132\\\\Downloads\\\\Capstone2Data\\\\Products.csv'> : ascii\n",
      "Detected encoding for <_io.BufferedReader name='C:\\\\Users\\\\16132\\\\Downloads\\\\Capstone2Data\\\\Sales.csv'> : ascii\n",
      "Detected encoding for <_io.BufferedReader name='C:\\\\Users\\\\16132\\\\Downloads\\\\Capstone2Data\\\\Stores.csv'> : utf-8\n"
     ]
    }
   ],
   "source": [
    "import chardet\n",
    "\n",
    "def detect_encoding(file):\n",
    "    with open(file, 'rb') as file:\n",
    "        result = chardet.detect(file.read())\n",
    "        encoding = result['encoding']\n",
    "        print(f\"Detected encoding for {file} : {encoding}\")\n",
    "\n",
    "list_of_csvs=[r'C:\\Users\\16132\\Downloads\\Capstone2Data\\Customers.csv',r'C:\\Users\\16132\\Downloads\\Capstone2Data\\Exchange_rates.csv',r'C:\\Users\\16132\\Downloads\\Capstone2Data\\Products.csv',r'C:\\Users\\16132\\Downloads\\Capstone2Data\\Sales.csv',r'C:\\Users\\16132\\Downloads\\Capstone2Data\\Stores.csv']\n",
    "\n",
    "for i in list_of_csvs:\n",
    "    detect_encoding(i)\n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "51c3df99-95d2-4099-9353-8b72ca9eaab5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "1bb0d051-e209-4f73-933e-5f7fa9976e7f",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "## EDA "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d2329e74-bc47-44fb-9bc7-71edb8dadca4",
   "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>CustomerKey</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301</td>\n",
       "      <td>Female</td>\n",
       "      <td>Lilly Harding</td>\n",
       "      <td>WANDEARAH EAST</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5523</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>7/3/1939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325</td>\n",
       "      <td>Female</td>\n",
       "      <td>Madison Hull</td>\n",
       "      <td>MOUNT BUDD</td>\n",
       "      <td>WA</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>6522</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9/27/1979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>554</td>\n",
       "      <td>Female</td>\n",
       "      <td>Claire Ferres</td>\n",
       "      <td>WINJALLOK</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3380</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>5/26/1947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>786</td>\n",
       "      <td>Male</td>\n",
       "      <td>Jai Poltpalingada</td>\n",
       "      <td>MIDDLE RIVER</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5223</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>9/17/1957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1042</td>\n",
       "      <td>Male</td>\n",
       "      <td>Aidan Pankhurst</td>\n",
       "      <td>TAWONGA SOUTH</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3698</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>11/19/1965</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerKey  Gender               Name            City State Code  \\\n",
       "0          301  Female      Lilly Harding  WANDEARAH EAST         SA   \n",
       "1          325  Female       Madison Hull      MOUNT BUDD         WA   \n",
       "2          554  Female      Claire Ferres       WINJALLOK        VIC   \n",
       "3          786    Male  Jai Poltpalingada    MIDDLE RIVER         SA   \n",
       "4         1042    Male    Aidan Pankhurst   TAWONGA SOUTH        VIC   \n",
       "\n",
       "               State Zip Code    Country  Continent    Birthday  \n",
       "0    South Australia     5523  Australia  Australia    7/3/1939  \n",
       "1  Western Australia     6522  Australia  Australia   9/27/1979  \n",
       "2           Victoria     3380  Australia  Australia   5/26/1947  \n",
       "3    South Australia     5223  Australia  Australia   9/17/1957  \n",
       "4           Victoria     3698  Australia  Australia  11/19/1965  "
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#File Customers\n",
    "import pandas as pd\n",
    "df_customers=pd.DataFrame()\n",
    "df_customers=pd.read_csv(list_of_csvs[0],encoding='windows-1252')\n",
    "\n",
    "df_customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "9aee60b8-37ef-4e2a-9f52-829fb9611dc4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 15266 entries, 0 to 15265\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype \n",
      "---  ------       --------------  ----- \n",
      " 0   CustomerKey  15266 non-null  int64 \n",
      " 1   Gender       15266 non-null  object\n",
      " 2   Name         15266 non-null  object\n",
      " 3   City         15266 non-null  object\n",
      " 4   State Code   15256 non-null  object\n",
      " 5   State        15266 non-null  object\n",
      " 6   Zip Code     15266 non-null  object\n",
      " 7   Country      15266 non-null  object\n",
      " 8   Continent    15266 non-null  object\n",
      " 9   Birthday     15266 non-null  object\n",
      "dtypes: int64(1), object(9)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df_customers.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "10e7a4d6-4cc7-46ef-948c-7757eb2c248b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert Birthday to Datetime format\n",
    "df_customers['Birthday']=pd.to_datetime(df_customers['Birthday'], format='%m/%d/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bab2c0d3-880f-4f27-be1b-e8d834445b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CustomerKey     0\n",
       "Gender          0\n",
       "Name            0\n",
       "City            0\n",
       "State Code     10\n",
       "State           0\n",
       "Zip Code        0\n",
       "Country         0\n",
       "Continent       0\n",
       "Birthday        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check for null values\n",
    "df_customers.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "ea26458f-6669-46e3-94dd-d0fd40f9fb48",
   "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>CustomerKey</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>City</th>\n",
       "      <th>State Code</th>\n",
       "      <th>State</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Country</th>\n",
       "      <th>Continent</th>\n",
       "      <th>Birthday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5304</th>\n",
       "      <td>729681</td>\n",
       "      <td>Female</td>\n",
       "      <td>Rossana Padovesi</td>\n",
       "      <td>Polvica</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80035</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1981-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5316</th>\n",
       "      <td>732289</td>\n",
       "      <td>Male</td>\n",
       "      <td>Indro Piccio</td>\n",
       "      <td>Varcaturo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80014</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1949-02-24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5372</th>\n",
       "      <td>742042</td>\n",
       "      <td>Male</td>\n",
       "      <td>Amaranto Loggia</td>\n",
       "      <td>Casaferro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80034</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1936-03-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5377</th>\n",
       "      <td>742886</td>\n",
       "      <td>Female</td>\n",
       "      <td>Edmonda Capon</td>\n",
       "      <td>Terzigno</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80040</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1963-08-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5378</th>\n",
       "      <td>743343</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ambra Sagese</td>\n",
       "      <td>Pomigliano D'Arco</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80038</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1961-01-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5485</th>\n",
       "      <td>759705</td>\n",
       "      <td>Male</td>\n",
       "      <td>Callisto Lo Duca</td>\n",
       "      <td>Casilli</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80047</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1976-08-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5525</th>\n",
       "      <td>765589</td>\n",
       "      <td>Male</td>\n",
       "      <td>Michelino Lucchesi</td>\n",
       "      <td>Pompei Scavi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80045</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1947-11-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5531</th>\n",
       "      <td>766410</td>\n",
       "      <td>Male</td>\n",
       "      <td>Adelmio Beneventi</td>\n",
       "      <td>Licola</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80078</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1940-01-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5631</th>\n",
       "      <td>781667</td>\n",
       "      <td>Female</td>\n",
       "      <td>Ilda Manna</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80134</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>1977-05-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>789177</td>\n",
       "      <td>Male</td>\n",
       "      <td>Calogero Folliero</td>\n",
       "      <td>Mariglianella</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Napoli</td>\n",
       "      <td>80030</td>\n",
       "      <td>Italy</td>\n",
       "      <td>Europe</td>\n",
       "      <td>2000-03-03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CustomerKey  Gender                Name               City State Code  \\\n",
       "5304       729681  Female    Rossana Padovesi            Polvica        NaN   \n",
       "5316       732289    Male        Indro Piccio          Varcaturo        NaN   \n",
       "5372       742042    Male     Amaranto Loggia          Casaferro        NaN   \n",
       "5377       742886  Female       Edmonda Capon           Terzigno        NaN   \n",
       "5378       743343  Female        Ambra Sagese  Pomigliano D'Arco        NaN   \n",
       "5485       759705    Male    Callisto Lo Duca            Casilli        NaN   \n",
       "5525       765589    Male  Michelino Lucchesi       Pompei Scavi        NaN   \n",
       "5531       766410    Male   Adelmio Beneventi             Licola        NaN   \n",
       "5631       781667  Female          Ilda Manna             Napoli        NaN   \n",
       "5695       789177    Male   Calogero Folliero      Mariglianella        NaN   \n",
       "\n",
       "       State Zip Code Country Continent   Birthday  \n",
       "5304  Napoli    80035   Italy    Europe 1981-04-18  \n",
       "5316  Napoli    80014   Italy    Europe 1949-02-24  \n",
       "5372  Napoli    80034   Italy    Europe 1936-03-14  \n",
       "5377  Napoli    80040   Italy    Europe 1963-08-06  \n",
       "5378  Napoli    80038   Italy    Europe 1961-01-05  \n",
       "5485  Napoli    80047   Italy    Europe 1976-08-28  \n",
       "5525  Napoli    80045   Italy    Europe 1947-11-13  \n",
       "5531  Napoli    80078   Italy    Europe 1940-01-13  \n",
       "5631  Napoli    80134   Italy    Europe 1977-05-08  \n",
       "5695  Napoli    80030   Italy    Europe 2000-03-03  "
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#seeing what the null values are\n",
    "df_customers[df_customers.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "8ed0cc4b-67ed-4913-ab5c-5e42b98d9b25",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing State Code null values for Naploli as NA as it is the state code for Napoli \n",
    "df_customers.fillna('NA',inplace=True)\n",
    "\n",
    "#removing spaces in column names as it is better for sql\n",
    "df_customers.columns = df_customers.columns.str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "73410f9f-70b0-4065-9905-d077a04152b7",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicated values\n",
    "df_customers.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bf28d47c-2263-48d7-a2d1-d5d3c64f2712",
   "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>Date</th>\n",
       "      <th>Currency</th>\n",
       "      <th>Exchange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>USD</td>\n",
       "      <td>1.0000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>CAD</td>\n",
       "      <td>1.1583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>AUD</td>\n",
       "      <td>1.2214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>EUR</td>\n",
       "      <td>0.8237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>GBP</td>\n",
       "      <td>0.6415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Date Currency  Exchange\n",
       "0  1/1/2015      USD    1.0000\n",
       "1  1/1/2015      CAD    1.1583\n",
       "2  1/1/2015      AUD    1.2214\n",
       "3  1/1/2015      EUR    0.8237\n",
       "4  1/1/2015      GBP    0.6415"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#File exchange rates\n",
    "\n",
    "df_exchange=pd.DataFrame()\n",
    "df_exchange=pd.read_csv(list_of_csvs[1],encoding='ascii')\n",
    "df_exchange.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c90d9ee9-cd92-48df-b96b-5322c8491688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 11215 entries, 0 to 11214\n",
      "Data columns (total 3 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   Date      11215 non-null  object \n",
      " 1   Currency  11215 non-null  object \n",
      " 2   Exchange  11215 non-null  float64\n",
      "dtypes: float64(1), object(2)\n",
      "memory usage: 263.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df_exchange.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7129f18a-c448-4638-8747-ae75d07afe2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Converting Date to date format\n",
    "df_exchange['Date'] = pd.to_datetime(df_exchange['Date'], format='%m/%d/%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "207aab48-cd40-4f8e-b888-dc1ffea0eae1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        0\n",
       "Currency    0\n",
       "Exchange    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking null values\n",
    "df_exchange.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dfc69b49-bfe7-4f08-9d46-3bbbe2407ce7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for duplicates\n",
    "df_exchange.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4094b901-968a-41af-bed7-3d1b19db5715",
   "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>ProductKey</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Color</th>\n",
       "      <th>Unit Cost USD</th>\n",
       "      <th>Unit Price USD</th>\n",
       "      <th>SubcategoryKey</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>CategoryKey</th>\n",
       "      <th>Category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Contoso 512MB MP3 Player E51 Silver</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>Silver</td>\n",
       "      <td>$6.62</td>\n",
       "      <td>$12.99</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Contoso 512MB MP3 Player E51 Blue</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>Blue</td>\n",
       "      <td>$6.62</td>\n",
       "      <td>$12.99</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Contoso 1G MP3 Player E100 White</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>White</td>\n",
       "      <td>$7.40</td>\n",
       "      <td>$14.52</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Contoso 2G MP3 Player E200 Silver</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>Silver</td>\n",
       "      <td>$11.00</td>\n",
       "      <td>$21.57</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Contoso 2G MP3 Player E200 Red</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>Red</td>\n",
       "      <td>$11.00</td>\n",
       "      <td>$21.57</td>\n",
       "      <td>101</td>\n",
       "      <td>MP4&amp;MP3</td>\n",
       "      <td>1</td>\n",
       "      <td>Audio</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ProductKey                         Product Name    Brand   Color  \\\n",
       "0           1  Contoso 512MB MP3 Player E51 Silver  Contoso  Silver   \n",
       "1           2    Contoso 512MB MP3 Player E51 Blue  Contoso    Blue   \n",
       "2           3     Contoso 1G MP3 Player E100 White  Contoso   White   \n",
       "3           4    Contoso 2G MP3 Player E200 Silver  Contoso  Silver   \n",
       "4           5       Contoso 2G MP3 Player E200 Red  Contoso     Red   \n",
       "\n",
       "  Unit Cost USD Unit Price USD  SubcategoryKey Subcategory  CategoryKey  \\\n",
       "0        $6.62         $12.99              101     MP4&MP3            1   \n",
       "1        $6.62         $12.99              101     MP4&MP3            1   \n",
       "2        $7.40         $14.52              101     MP4&MP3            1   \n",
       "3       $11.00         $21.57              101     MP4&MP3            1   \n",
       "4       $11.00         $21.57              101     MP4&MP3            1   \n",
       "\n",
       "  Category  \n",
       "0    Audio  \n",
       "1    Audio  \n",
       "2    Audio  \n",
       "3    Audio  \n",
       "4    Audio  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#File Products\n",
    "\n",
    "df_products=pd.DataFrame()\n",
    "df_products=pd.read_csv(list_of_csvs[2],encoding='ascii')\n",
    "\n",
    "df_products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c74944da-c2a1-4f0c-aedf-7710b2f21174",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2517 entries, 0 to 2516\n",
      "Data columns (total 10 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   ProductKey      2517 non-null   int64 \n",
      " 1   Product Name    2517 non-null   object\n",
      " 2   Brand           2517 non-null   object\n",
      " 3   Color           2517 non-null   object\n",
      " 4   Unit Cost USD   2517 non-null   object\n",
      " 5   Unit Price USD  2517 non-null   object\n",
      " 6   SubcategoryKey  2517 non-null   int64 \n",
      " 7   Subcategory     2517 non-null   object\n",
      " 8   CategoryKey     2517 non-null   int64 \n",
      " 9   Category        2517 non-null   object\n",
      "dtypes: int64(3), object(7)\n",
      "memory usage: 196.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_products.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "a61622e7-b66e-47b7-ab2b-aac38d128a50",
   "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>ProductKey</th>\n",
       "      <th>UnitCostUSD</th>\n",
       "      <th>UnitPriceUSD</th>\n",
       "      <th>SubcategoryKey</th>\n",
       "      <th>CategoryKey</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>2517.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1259.000000</td>\n",
       "      <td>147.655562</td>\n",
       "      <td>356.830131</td>\n",
       "      <td>491.810091</td>\n",
       "      <td>4.878824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>726.739637</td>\n",
       "      <td>184.373683</td>\n",
       "      <td>494.054962</td>\n",
       "      <td>229.887134</td>\n",
       "      <td>2.299170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.950000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>630.000000</td>\n",
       "      <td>32.250000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>305.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1259.000000</td>\n",
       "      <td>86.670000</td>\n",
       "      <td>199.990000</td>\n",
       "      <td>406.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1888.000000</td>\n",
       "      <td>183.950000</td>\n",
       "      <td>410.000000</td>\n",
       "      <td>801.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2517.000000</td>\n",
       "      <td>1060.220000</td>\n",
       "      <td>3199.990000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ProductKey  UnitCostUSD  UnitPriceUSD  SubcategoryKey  CategoryKey\n",
       "count  2517.000000  2517.000000   2517.000000     2517.000000  2517.000000\n",
       "mean   1259.000000   147.655562    356.830131      491.810091     4.878824\n",
       "std     726.739637   184.373683    494.054962      229.887134     2.299170\n",
       "min       1.000000     0.480000      0.950000      101.000000     1.000000\n",
       "25%     630.000000    32.250000     69.000000      305.000000     3.000000\n",
       "50%    1259.000000    86.670000    199.990000      406.000000     4.000000\n",
       "75%    1888.000000   183.950000    410.000000      801.000000     8.000000\n",
       "max    2517.000000  1060.220000   3199.990000      808.000000     8.000000"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_products.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a87446b4-8cc9-4ac6-bcea-580ff6dcae20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ProductKey        0\n",
       "Product Name      0\n",
       "Brand             0\n",
       "Color             0\n",
       "Unit Cost USD     0\n",
       "Unit Price USD    0\n",
       "SubcategoryKey    0\n",
       "Subcategory       0\n",
       "CategoryKey       0\n",
       "Category          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null values\n",
    "df_products.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "930d65ff-ce69-4ca0-87d8-541bde61c9aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "np.int64(0)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for duplicate values\n",
    "df_products.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "65b30eeb-deb4-4765-bf2d-bca847e5902c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Convert Unit Cost USD and Unit Price USD to float while removing $ and , for computational purposes\n",
    "df_products['Unit Cost USD'] = df_products['Unit Cost USD'].str.replace(r'[\\$,]', '', regex=True).astype(float)\n",
    "df_products['Unit Price USD'] = df_products['Unit Price USD'].str.replace(r'[\\$,]', '', regex=True).astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19cc4f13-7cf5-4bff-a1a0-21bae84b90d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing spaces in column name\n",
    "df_products.columns = df_products.columns.str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "640a2b49-234b-442e-b3df-2153dbc3e759",
   "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>Order Number</th>\n",
       "      <th>Line Item</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Delivery Date</th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>StoreKey</th>\n",
       "      <th>ProductKey</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Currency Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>366000</td>\n",
       "      <td>1</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>NaN</td>\n",
       "      <td>265598</td>\n",
       "      <td>10</td>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>366001</td>\n",
       "      <td>1</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>1048</td>\n",
       "      <td>2</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>366001</td>\n",
       "      <td>2</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/13/2016</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>USD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>366002</td>\n",
       "      <td>1</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>1106</td>\n",
       "      <td>7</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>366002</td>\n",
       "      <td>2</td>\n",
       "      <td>1/1/2016</td>\n",
       "      <td>1/12/2016</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order Number  Line Item Order Date Delivery Date  CustomerKey  StoreKey  \\\n",
       "0        366000          1   1/1/2016           NaN       265598        10   \n",
       "1        366001          1   1/1/2016     1/13/2016      1269051         0   \n",
       "2        366001          2   1/1/2016     1/13/2016      1269051         0   \n",
       "3        366002          1   1/1/2016     1/12/2016       266019         0   \n",
       "4        366002          2   1/1/2016     1/12/2016       266019         0   \n",
       "\n",
       "   ProductKey  Quantity Currency Code  \n",
       "0        1304         1           CAD  \n",
       "1        1048         2           USD  \n",
       "2        2007         1           USD  \n",
       "3        1106         7           CAD  \n",
       "4         373         1           CAD  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#File Sales\n",
    "\n",
    "df_sales=pd.DataFrame()\n",
    "df_sales=pd.read_csv(list_of_csvs[3],encoding='ascii')\n",
    "df_sales.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ea93ffe8-a5ae-4a4f-907a-e9a60e63a0d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing spaces in column name\n",
    "df_sales.columns = df_sales.columns.str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "8fd955b6-549c-4a92-bbf8-e54c838e735a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62884 entries, 0 to 62883\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count  Dtype \n",
      "---  ------        --------------  ----- \n",
      " 0   OrderNumber   62884 non-null  int64 \n",
      " 1   LineItem      62884 non-null  int64 \n",
      " 2   OrderDate     62884 non-null  object\n",
      " 3   DeliveryDate  13165 non-null  object\n",
      " 4   CustomerKey   62884 non-null  int64 \n",
      " 5   StoreKey      62884 non-null  int64 \n",
      " 6   ProductKey    62884 non-null  int64 \n",
      " 7   Quantity      62884 non-null  int64 \n",
      " 8   CurrencyCode  62884 non-null  object\n",
      "dtypes: int64(6), object(3)\n",
      "memory usage: 4.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df_sales.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "640f2ffd-98b1-4da8-9476-b97fef4cdba7",
   "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>LineItem</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>DeliveryDate</th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>StoreKey</th>\n",
       "      <th>ProductKey</th>\n",
       "      <th>Quantity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.288400e+04</td>\n",
       "      <td>62884.000000</td>\n",
       "      <td>62884</td>\n",
       "      <td>62884</td>\n",
       "      <td>6.288400e+04</td>\n",
       "      <td>62884.000000</td>\n",
       "      <td>62884.000000</td>\n",
       "      <td>62884.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.430905e+06</td>\n",
       "      <td>2.164207</td>\n",
       "      <td>2018-11-30 21:21:56.939126016</td>\n",
       "      <td>2018-12-01 20:07:20.582660352</td>\n",
       "      <td>1.180797e+06</td>\n",
       "      <td>31.802144</td>\n",
       "      <td>1125.859344</td>\n",
       "      <td>3.144790</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>3.660000e+05</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>2016-01-01 00:00:00</td>\n",
       "      <td>3.010000e+02</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.121017e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2018-01-25 00:00:00</td>\n",
       "      <td>2018-01-27 00:00:00</td>\n",
       "      <td>6.808580e+05</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>437.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.498016e+06</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2019-02-06 00:00:00</td>\n",
       "      <td>2019-02-07 00:00:00</td>\n",
       "      <td>1.261200e+06</td>\n",
       "      <td>37.000000</td>\n",
       "      <td>1358.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.788010e+06</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2019-11-23 00:00:00</td>\n",
       "      <td>2019-11-23 00:00:00</td>\n",
       "      <td>1.686496e+06</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.243032e+06</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2021-02-20 00:00:00</td>\n",
       "      <td>2021-02-27 00:00:00</td>\n",
       "      <td>2.099937e+06</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>2517.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.532963e+05</td>\n",
       "      <td>1.365170</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.859634e+05</td>\n",
       "      <td>22.978188</td>\n",
       "      <td>709.244010</td>\n",
       "      <td>2.256371</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        OrderNumber      LineItem                      OrderDate  \\\n",
       "count  6.288400e+04  62884.000000                          62884   \n",
       "mean   1.430905e+06      2.164207  2018-11-30 21:21:56.939126016   \n",
       "min    3.660000e+05      1.000000            2016-01-01 00:00:00   \n",
       "25%    1.121017e+06      1.000000            2018-01-25 00:00:00   \n",
       "50%    1.498016e+06      2.000000            2019-02-06 00:00:00   \n",
       "75%    1.788010e+06      3.000000            2019-11-23 00:00:00   \n",
       "max    2.243032e+06      7.000000            2021-02-20 00:00:00   \n",
       "std    4.532963e+05      1.365170                            NaN   \n",
       "\n",
       "                        DeliveryDate   CustomerKey      StoreKey  \\\n",
       "count                          62884  6.288400e+04  62884.000000   \n",
       "mean   2018-12-01 20:07:20.582660352  1.180797e+06     31.802144   \n",
       "min              2016-01-01 00:00:00  3.010000e+02      0.000000   \n",
       "25%              2018-01-27 00:00:00  6.808580e+05      8.000000   \n",
       "50%              2019-02-07 00:00:00  1.261200e+06     37.000000   \n",
       "75%              2019-11-23 00:00:00  1.686496e+06     53.000000   \n",
       "max              2021-02-27 00:00:00  2.099937e+06     66.000000   \n",
       "std                              NaN  5.859634e+05     22.978188   \n",
       "\n",
       "         ProductKey      Quantity  \n",
       "count  62884.000000  62884.000000  \n",
       "mean    1125.859344      3.144790  \n",
       "min        1.000000      1.000000  \n",
       "25%      437.000000      1.000000  \n",
       "50%     1358.000000      2.000000  \n",
       "75%     1650.000000      4.000000  \n",
       "max     2517.000000     10.000000  \n",
       "std      709.244010      2.256371  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sales.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f5fd343d-d55f-486e-9292-041f7295f344",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OrderNumber         0\n",
       "LineItem            0\n",
       "OrderDate           0\n",
       "DeliveryDate    49719\n",
       "CustomerKey         0\n",
       "StoreKey            0\n",
       "ProductKey          0\n",
       "Quantity            0\n",
       "CurrencyCode        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking for null \n",
    "df_sales.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "186467d1-d402-4317-986e-80f11f272311",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Changin Delivery Date and Order Date to Datetime format for analysis purpose\n",
    "df_sales['DeliveryDate'] = pd.to_datetime(df_sales['DeliveryDate'], errors='coerce')\n",
    "df_sales['OrderDate'] = pd.to_datetime(df_sales['OrderDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4fdaf910-e60c-4ba0-8cfd-bb78701c0be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "#filling null delivery date values to same day as order date as it was bought in store, doing this only for non-online orders or can ask the company team in real world case\n",
    "df_sales.loc[\n",
    "    df_sales['DeliveryDate'].isna() & (df_sales['StoreKey'] != 0), \n",
    "    'DeliveryDate'\n",
    "] = df_sales['OrderDate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c98866e1-1231-436e-a3a5-ec484d5ed4a4",
   "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>StoreKey</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>Square Meters</th>\n",
       "      <th>Open Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>595.0</td>\n",
       "      <td>1/1/2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>665.0</td>\n",
       "      <td>1/12/2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1/7/2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>1/1/2010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>12/9/2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StoreKey    Country                         State  Square Meters  Open Date\n",
       "0         1  Australia  Australian Capital Territory          595.0   1/1/2008\n",
       "1         2  Australia            Northern Territory          665.0  1/12/2008\n",
       "2         3  Australia               South Australia         2000.0   1/7/2012\n",
       "3         4  Australia                      Tasmania         2000.0   1/1/2010\n",
       "4         5  Australia                      Victoria         2000.0  12/9/2015"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#File Stores\n",
    "\n",
    "df_stores=pd.DataFrame()\n",
    "df_stores=pd.read_csv(list_of_csvs[4],encoding='utf-8')\n",
    "\n",
    "df_stores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "21cc37e6-bab7-48d7-a300-9b0ebc343ab8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 67 entries, 0 to 66\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   StoreKey       67 non-null     int64  \n",
      " 1   Country        67 non-null     object \n",
      " 2   State          67 non-null     object \n",
      " 3   Square Meters  66 non-null     float64\n",
      " 4   Open Date      67 non-null     object \n",
      "dtypes: float64(1), int64(1), object(3)\n",
      "memory usage: 2.7+ KB\n"
     ]
    }
   ],
   "source": [
    "df_stores.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "e6fc3427-6cae-489d-8686-41aa74a4b04e",
   "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>StoreKey</th>\n",
       "      <th>Square Meters</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>67.000000</td>\n",
       "      <td>66.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>1402.196970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>19.485037</td>\n",
       "      <td>576.404058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>245.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>16.500000</td>\n",
       "      <td>1108.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>33.000000</td>\n",
       "      <td>1347.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>49.500000</td>\n",
       "      <td>2000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>66.000000</td>\n",
       "      <td>2105.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        StoreKey  Square Meters\n",
       "count  67.000000      66.000000\n",
       "mean   33.000000    1402.196970\n",
       "std    19.485037     576.404058\n",
       "min     0.000000     245.000000\n",
       "25%    16.500000    1108.750000\n",
       "50%    33.000000    1347.500000\n",
       "75%    49.500000    2000.000000\n",
       "max    66.000000    2105.000000"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stores.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "4b791e1c-b978-417a-80d2-f00ab7666c24",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StoreKey         0\n",
       "Country          0\n",
       "State            0\n",
       "Square Meters    1\n",
       "Open Date        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking null values\n",
    "df_stores.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "318c6578-e0dc-4f7c-876a-6bca1a604dad",
   "metadata": {},
   "outputs": [],
   "source": [
    "#null values are for online as there is no square meters for it\n",
    "#repalcing with 0\n",
    "df_stores.fillna(0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "717a519f-fe59-4bfd-b498-acdf7cd26110",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replacing spaces in column name\n",
    "df_stores.columns = df_stores.columns.str.replace(' ', '')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "1a6e4c36-5e3d-48d8-9523-6451a5d00733",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_stores['OpenDate'] = pd.to_datetime(df_stores['OpenDate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d5ddd0af-90cd-4ab9-be92-c0ed43c17a52",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Further EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "103a1d36-73f6-47f0-9643-c414b8f401b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating birthday, age range, and store age\n",
    "\n",
    "from datetime import datetime\n",
    "def calculate_age(birthday):\n",
    "    today = datetime.now()\n",
    "    age = today.year - birthday.year - ((today.month, today.day) < (birthday.month, birthday.day))\n",
    "    return age\n",
    "    \n",
    "#Creating copy as we would need original celaning data to input into sql, table wise\n",
    "\n",
    "df_customers_copy=df_customers\n",
    "df_customers_copy['Age'] = df_customers_copy['Birthday'].apply(calculate_age)\n",
    "\n",
    "def agerange(age):\n",
    "    if age < 10:\n",
    "        return 'Under 10'\n",
    "    elif age < 20:\n",
    "        return '10-19'\n",
    "    elif age < 30:\n",
    "        return '20-29'\n",
    "    elif age < 40:\n",
    "        return '30-39'\n",
    "    elif age < 50:\n",
    "        return '40-49'\n",
    "    elif age < 60:\n",
    "        return '50-59'\n",
    "    else:\n",
    "        return '60 and above'\n",
    "\n",
    "df_customers_copy['AgeRange'] = df_customers_copy['Age'].apply(agerange)\n",
    "\n",
    "df_stores_copy=df_stores\n",
    "\n",
    "df_stores_copy['StoreAge']=df_stores_copy['OpenDate'].apply(calculate_age)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "53724219-6ed8-47f0-a08e-792225d94335",
   "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>CustomerKey</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>City_customer</th>\n",
       "      <th>StateCode_customer</th>\n",
       "      <th>State_customer</th>\n",
       "      <th>ZipCode_customer</th>\n",
       "      <th>Country_customer</th>\n",
       "      <th>Continent_customer</th>\n",
       "      <th>Birthday</th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeRange</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>301</td>\n",
       "      <td>Female</td>\n",
       "      <td>Lilly Harding</td>\n",
       "      <td>WANDEARAH EAST</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5523</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1939-07-03</td>\n",
       "      <td>85</td>\n",
       "      <td>60 and above</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>325</td>\n",
       "      <td>Female</td>\n",
       "      <td>Madison Hull</td>\n",
       "      <td>MOUNT BUDD</td>\n",
       "      <td>WA</td>\n",
       "      <td>Western Australia</td>\n",
       "      <td>6522</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1979-09-27</td>\n",
       "      <td>44</td>\n",
       "      <td>40-49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>554</td>\n",
       "      <td>Female</td>\n",
       "      <td>Claire Ferres</td>\n",
       "      <td>WINJALLOK</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3380</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1947-05-26</td>\n",
       "      <td>77</td>\n",
       "      <td>60 and above</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>786</td>\n",
       "      <td>Male</td>\n",
       "      <td>Jai Poltpalingada</td>\n",
       "      <td>MIDDLE RIVER</td>\n",
       "      <td>SA</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>5223</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1957-09-17</td>\n",
       "      <td>66</td>\n",
       "      <td>60 and above</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1042</td>\n",
       "      <td>Male</td>\n",
       "      <td>Aidan Pankhurst</td>\n",
       "      <td>TAWONGA SOUTH</td>\n",
       "      <td>VIC</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>3698</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australia</td>\n",
       "      <td>1965-11-19</td>\n",
       "      <td>58</td>\n",
       "      <td>50-59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   CustomerKey  Gender               Name   City_customer StateCode_customer  \\\n",
       "0          301  Female      Lilly Harding  WANDEARAH EAST                 SA   \n",
       "1          325  Female       Madison Hull      MOUNT BUDD                 WA   \n",
       "2          554  Female      Claire Ferres       WINJALLOK                VIC   \n",
       "3          786    Male  Jai Poltpalingada    MIDDLE RIVER                 SA   \n",
       "4         1042    Male    Aidan Pankhurst   TAWONGA SOUTH                VIC   \n",
       "\n",
       "      State_customer ZipCode_customer Country_customer Continent_customer  \\\n",
       "0    South Australia             5523        Australia          Australia   \n",
       "1  Western Australia             6522        Australia          Australia   \n",
       "2           Victoria             3380        Australia          Australia   \n",
       "3    South Australia             5223        Australia          Australia   \n",
       "4           Victoria             3698        Australia          Australia   \n",
       "\n",
       "    Birthday  Age      AgeRange  \n",
       "0 1939-07-03   85  60 and above  \n",
       "1 1979-09-27   44         40-49  \n",
       "2 1947-05-26   77  60 and above  \n",
       "3 1957-09-17   66  60 and above  \n",
       "4 1965-11-19   58         50-59  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_customers_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "9731429d-e0d7-4690-8fe3-5a124261e5e2",
   "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>StoreKey</th>\n",
       "      <th>Country_store</th>\n",
       "      <th>State_store</th>\n",
       "      <th>SquareMeters</th>\n",
       "      <th>OpenDate</th>\n",
       "      <th>StoreAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Australian Capital Territory</td>\n",
       "      <td>595.0</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Northern Territory</td>\n",
       "      <td>665.0</td>\n",
       "      <td>2008-01-12</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Australia</td>\n",
       "      <td>South Australia</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2012-01-07</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Tasmania</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>2015-12-09</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   StoreKey Country_store                   State_store  SquareMeters  \\\n",
       "0         1     Australia  Australian Capital Territory         595.0   \n",
       "1         2     Australia            Northern Territory         665.0   \n",
       "2         3     Australia               South Australia        2000.0   \n",
       "3         4     Australia                      Tasmania        2000.0   \n",
       "4         5     Australia                      Victoria        2000.0   \n",
       "\n",
       "    OpenDate  StoreAge  \n",
       "0 2008-01-01        16  \n",
       "1 2008-01-12        16  \n",
       "2 2012-01-07        12  \n",
       "3 2010-01-01        14  \n",
       "4 2015-12-09         8  "
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_stores_copy.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5475dfea-3648-490e-aabd-854b7f5b59c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renaming columns in customers and stores as they have some location column names\n",
    "df_customers_copy.rename(columns={\n",
    "    'City': 'City_customer',\n",
    "    'StateCode': 'StateCode_customer',\n",
    "    'State': 'State_customer',\n",
    "    'ZipCode': 'ZipCode_customer',\n",
    "    'Country': 'Country_customer',\n",
    "    'Continent': 'Continent_customer'\n",
    "}, inplace=True)\n",
    "\n",
    "df_stores_copy.rename(columns={'Country':'Country_store','State':'State_store'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b8e38d56-084c-484d-bec9-ab418847bb21",
   "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>LineItem</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>DeliveryDate</th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>StoreKey</th>\n",
       "      <th>ProductKey</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>CurrencyCode</th>\n",
       "      <th>Country_store</th>\n",
       "      <th>State_store</th>\n",
       "      <th>SquareMeters</th>\n",
       "      <th>OpenDate</th>\n",
       "      <th>StoreAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>366000</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>265598</td>\n",
       "      <td>10</td>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Nunavut</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>2015-04-04</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>366001</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>1048</td>\n",
       "      <td>2</td>\n",
       "      <td>USD</td>\n",
       "      <td>Online</td>\n",
       "      <td>Online</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>366001</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-13</td>\n",
       "      <td>1269051</td>\n",
       "      <td>0</td>\n",
       "      <td>2007</td>\n",
       "      <td>1</td>\n",
       "      <td>USD</td>\n",
       "      <td>Online</td>\n",
       "      <td>Online</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>366002</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>1106</td>\n",
       "      <td>7</td>\n",
       "      <td>CAD</td>\n",
       "      <td>Online</td>\n",
       "      <td>Online</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>366002</td>\n",
       "      <td>2</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-12</td>\n",
       "      <td>266019</td>\n",
       "      <td>0</td>\n",
       "      <td>373</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "      <td>Online</td>\n",
       "      <td>Online</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OrderNumber  LineItem  OrderDate DeliveryDate  CustomerKey  StoreKey  \\\n",
       "0       366000         1 2016-01-01   2016-01-01       265598        10   \n",
       "1       366001         1 2016-01-01   2016-01-13      1269051         0   \n",
       "2       366001         2 2016-01-01   2016-01-13      1269051         0   \n",
       "3       366002         1 2016-01-01   2016-01-12       266019         0   \n",
       "4       366002         2 2016-01-01   2016-01-12       266019         0   \n",
       "\n",
       "   ProductKey  Quantity CurrencyCode Country_store State_store  SquareMeters  \\\n",
       "0        1304         1          CAD        Canada     Nunavut        1210.0   \n",
       "1        1048         2          USD        Online      Online           0.0   \n",
       "2        2007         1          USD        Online      Online           0.0   \n",
       "3        1106         7          CAD        Online      Online           0.0   \n",
       "4         373         1          CAD        Online      Online           0.0   \n",
       "\n",
       "    OpenDate  StoreAge  \n",
       "0 2015-04-04         9  \n",
       "1 2010-01-01        14  \n",
       "2 2010-01-01        14  \n",
       "3 2010-01-01        14  \n",
       "4 2010-01-01        14  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Merging Data\n",
    "\n",
    "df_SalesandStoreMerged = pd.merge(df_sales, df_stores_copy, on='StoreKey', how='left')\n",
    "df_SalesandStoreMerged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01556f88-b3f4-4e49-a6da-5a8f32ce33d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_SalesStoresProductsMerged = pd.merge(df_SalesandStoreMerged, df_products, on='ProductKey', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f6e0feee-3708-4988-9e3a-caa80c839089",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_SalesStoresProductsCustomersMerged = pd.merge(df_SalesStoresProductsMerged, df_customers_copy, on='CustomerKey', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bce29850-f704-45d6-b0a2-eb88aa3096cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_finalMerged = pd.merge(df_SalesStoresProductsCustomersMerged, df_exchange,\n",
    "                     left_on=['OrderDate','CurrencyCode'],\n",
    "                     right_on=['Date','Currency'],\n",
    "                     how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b1cf2b16-fcca-4a14-a37b-b582d231d054",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_finalMerged.drop(columns=['Date', 'Currency'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6a1cd9dc-ce1d-47a1-9472-8d24c7bd1866",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 62884 entries, 0 to 62883\n",
      "Data columns (total 35 columns):\n",
      " #   Column              Non-Null Count  Dtype         \n",
      "---  ------              --------------  -----         \n",
      " 0   OrderNumber         62884 non-null  int64         \n",
      " 1   LineItem            62884 non-null  int64         \n",
      " 2   OrderDate           62884 non-null  datetime64[ns]\n",
      " 3   DeliveryDate        62884 non-null  datetime64[ns]\n",
      " 4   CustomerKey         62884 non-null  int64         \n",
      " 5   StoreKey            62884 non-null  int64         \n",
      " 6   ProductKey          62884 non-null  int64         \n",
      " 7   Quantity            62884 non-null  int64         \n",
      " 8   CurrencyCode        62884 non-null  object        \n",
      " 9   Country_store       62884 non-null  object        \n",
      " 10  State_store         62884 non-null  object        \n",
      " 11  SquareMeters        62884 non-null  float64       \n",
      " 12  OpenDate            62884 non-null  datetime64[ns]\n",
      " 13  StoreAge            62884 non-null  int64         \n",
      " 14  ProductName         62884 non-null  object        \n",
      " 15  Brand               62884 non-null  object        \n",
      " 16  Color               62884 non-null  object        \n",
      " 17  UnitCostUSD         62884 non-null  float64       \n",
      " 18  UnitPriceUSD        62884 non-null  float64       \n",
      " 19  SubcategoryKey      62884 non-null  int64         \n",
      " 20  Subcategory         62884 non-null  object        \n",
      " 21  CategoryKey         62884 non-null  int64         \n",
      " 22  Category            62884 non-null  object        \n",
      " 23  Gender              62884 non-null  object        \n",
      " 24  Name                62884 non-null  object        \n",
      " 25  City_customer       62884 non-null  object        \n",
      " 26  StateCode_customer  62884 non-null  object        \n",
      " 27  State_customer      62884 non-null  object        \n",
      " 28  ZipCode_customer    62884 non-null  object        \n",
      " 29  Country_customer    62884 non-null  object        \n",
      " 30  Continent_customer  62884 non-null  object        \n",
      " 31  Birthday            62884 non-null  datetime64[ns]\n",
      " 32  Age                 62884 non-null  int64         \n",
      " 33  AgeRange            62884 non-null  object        \n",
      " 34  Exchange            62884 non-null  float64       \n",
      "dtypes: datetime64[ns](4), float64(4), int64(10), object(17)\n",
      "memory usage: 16.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df_finalMerged.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "53f73181-3461-49bb-af99-604f93beaba7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculating Reveunue Per Customer USD and Profit Per CustomerUSD\n",
    "df_finalMerged['RevenueUSD'] = df_finalMerged['Quantity'] * df_finalMerged['UnitPriceUSD']\n",
    "df_finalMerged['ProfitUSD'] = df_finalMerged['RevenueUSD']-(df_finalMerged['Quantity'] * df_finalMerged['UnitCostUSD'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "aa5db6af-9794-42fd-9392-2cc616616e86",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Creating a Frequency Column to the data\n",
    "\n",
    "# Count the number of orders per customer\n",
    "order_counts = df_finalMerged.groupby('CustomerKey')['OrderNumber'].count()\n",
    "\n",
    "# function to Categorize frequency\n",
    "def categorize_frequency(count):\n",
    "    if count >= 1 and count <= 4:\n",
    "        return 'Occasional'\n",
    "    elif count >= 5 and count <= 10:\n",
    "        return 'Moderate'\n",
    "    elif count > 10:\n",
    "        return 'Frequent'\n",
    "    else:\n",
    "        return 'Unknown'\n",
    "\n",
    "# Apply categorization to get a Series of categories\n",
    "order_counts_category = order_counts.apply(categorize_frequency)\n",
    "\n",
    "# Merge this frequency information back into the original DataFrame\n",
    "# Reset index to merge\n",
    "order_counts_category = order_counts_category.reset_index(name='Frequency')\n",
    "\n",
    "# Merge with the original DataFrame\n",
    "df_finalMerged = df_finalMerged.merge(order_counts_category, on='CustomerKey', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "da34af60-60f2-4236-985c-bfa75aab3b61",
   "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>LineItem</th>\n",
       "      <th>OrderDate</th>\n",
       "      <th>DeliveryDate</th>\n",
       "      <th>CustomerKey</th>\n",
       "      <th>StoreKey</th>\n",
       "      <th>ProductKey</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>CurrencyCode</th>\n",
       "      <th>Country_store</th>\n",
       "      <th>State_store</th>\n",
       "      <th>SquareMeters</th>\n",
       "      <th>OpenDate</th>\n",
       "      <th>StoreAge</th>\n",
       "      <th>ProductName</th>\n",
       "      <th>Brand</th>\n",
       "      <th>Color</th>\n",
       "      <th>UnitCostUSD</th>\n",
       "      <th>UnitPriceUSD</th>\n",
       "      <th>SubcategoryKey</th>\n",
       "      <th>Subcategory</th>\n",
       "      <th>CategoryKey</th>\n",
       "      <th>Category</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Name</th>\n",
       "      <th>City_customer</th>\n",
       "      <th>StateCode_customer</th>\n",
       "      <th>State_customer</th>\n",
       "      <th>ZipCode_customer</th>\n",
       "      <th>Country_customer</th>\n",
       "      <th>Continent_customer</th>\n",
       "      <th>Birthday</th>\n",
       "      <th>Age</th>\n",
       "      <th>AgeRange</th>\n",
       "      <th>Exchange</th>\n",
       "      <th>RevenueUSD</th>\n",
       "      <th>ProfitUSD</th>\n",
       "      <th>Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>366000</td>\n",
       "      <td>1</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>2016-01-01</td>\n",
       "      <td>265598</td>\n",
       "      <td>10</td>\n",
       "      <td>1304</td>\n",
       "      <td>1</td>\n",
       "      <td>CAD</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Nunavut</td>\n",
       "      <td>1210.0</td>\n",
       "      <td>2015-04-04</td>\n",
       "      <td>9</td>\n",
       "      <td>Contoso Lens Adapter M450 White</td>\n",
       "      <td>Contoso</td>\n",
       "      <td>White</td>\n",
       "      <td>31.27</td>\n",
       "      <td>68.0</td>\n",
       "      <td>406</td>\n",
       "      <td>Cameras &amp; Camcorders Accessories</td>\n",
       "      <td>4</td>\n",
       "      <td>Cameras and camcorders</td>\n",
       "      <td>Male</td>\n",
       "      <td>Tyler Vaught</td>\n",
       "      <td>London</td>\n",
       "      <td>ON</td>\n",
       "      <td>Ontario</td>\n",
       "      <td>N5W 5K6</td>\n",
       "      <td>Canada</td>\n",
       "      <td>North America</td>\n",
       "      <td>1971-03-23</td>\n",
       "      <td>53</td>\n",
       "      <td>50-59</td>\n",
       "      <td>1.3884</td>\n",
       "      <td>68.0</td>\n",
       "      <td>36.73</td>\n",
       "      <td>Moderate</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OrderNumber  LineItem  OrderDate DeliveryDate  CustomerKey  StoreKey  \\\n",
       "0       366000         1 2016-01-01   2016-01-01       265598        10   \n",
       "\n",
       "   ProductKey  Quantity CurrencyCode Country_store State_store  SquareMeters  \\\n",
       "0        1304         1          CAD        Canada     Nunavut        1210.0   \n",
       "\n",
       "    OpenDate  StoreAge                      ProductName    Brand  Color  \\\n",
       "0 2015-04-04         9  Contoso Lens Adapter M450 White  Contoso  White   \n",
       "\n",
       "   UnitCostUSD  UnitPriceUSD  SubcategoryKey  \\\n",
       "0        31.27          68.0             406   \n",
       "\n",
       "                        Subcategory  CategoryKey                Category  \\\n",
       "0  Cameras & Camcorders Accessories            4  Cameras and camcorders   \n",
       "\n",
       "  Gender          Name City_customer StateCode_customer State_customer  \\\n",
       "0   Male  Tyler Vaught        London                 ON        Ontario   \n",
       "\n",
       "  ZipCode_customer Country_customer Continent_customer   Birthday  Age  \\\n",
       "0          N5W 5K6           Canada      North America 1971-03-23   53   \n",
       "\n",
       "  AgeRange  Exchange  RevenueUSD  ProfitUSD Frequency  \n",
       "0    50-59    1.3884        68.0      36.73  Moderate  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', None)\n",
    "df_finalMerged.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "3d8c41cc-0732-489e-a460-1238557e4b86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAMWCAYAAACKoqSLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/TGe4hAAAACXBIWXMAAA9hAAAPYQGoP6dpAAC08UlEQVR4nOzdeZxWZf0//tewDAgKuLAqAu4b4FaEKyaKghammUqu5BbmgqLxccP4FO5LrvkxRUvTNKVSQxF3JU0UERcUQ6kUMBdGUEDg/v3Rj/vrBCqjM87ReT4fj/vhnOu6zjnvc+bgzLzmmuuuKJVKpQAAAAAAUAiN6rsAAAAAAAD+H6EtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQBAA9W1a9cccsgh9V3GJ6qoqMiIESPq/DwPPvhgKioq8uCDD5bb+vTpk80226zOz50kr732WioqKjJ69Ogv5Xwf92VeJwAAK05oCwBQx6ZPn55jjjkmG2ywQVq0aJEWLVpkk002yZAhQzJ58uT6Lu9L0bVr11RUVKSioiKNGjVKmzZt0r179xxxxBF54oknau08N910Uy6++OJaO15tKnJtX4b58+fnoosuSq9evdK6des0b948G2ywQY455pi8/PLL9V1ekuTxxx/PiBEj8t5779V3KQBAA1dRKpVK9V0EAMDX1Z133pkf/OAHadKkSQYNGpSePXumUaNGeemll3L77bfn9ddfz/Tp09OlS5cvvbauXbumT58+X8oMz65du2bVVVfNiSeemCR5//338+KLL+bWW2/NzJkzc8IJJ+TCCy+sts/8+fPTpEmTNGnSZIXPs8cee2TKlCl57bXXVnifJUuWZOHChamsrEyjRv+Z09CnT5/8+9//zpQpU1b4OJ+3tlKplAULFqRp06Zp3LhxrZ1vRdTFdS7Pv//97+y2226ZOHFi9thjj/Tt2zcrr7xypk6dmptvvjkzZ87MwoUL67SGFXH++edn2LBhmT59erp27Vrf5QAADdiKfwcMAECNvPrqq9lvv/3SpUuXjB8/Ph07dqzWf8455+SKK64oB4VfZYsWLcqSJUtSWVn5iWPWXHPN/PCHP6zWds455+SAAw7IRRddlPXXXz9HH310ua958+Z1Vm/yn1B4aVBb1+f6NBUVFfV6/i/DIYcckmeeeSa33XZb9t5772p9I0eOzKmnnlpPlX1+S8P+r/vnDgCoH1/9nxAAAArq3HPPzbx583LdddctE9gmSZMmTXLsscemc+fO1dpfeuml7LPPPllttdXSvHnzbL311vnTn/5Ubczo0aNTUVGRxx57LEOHDk3btm3TsmXL7LXXXnnrrbeqjS2VSvnf//3frLXWWmnRokV22mmnPP/888ut+b333svxxx+fzp07p1mzZllvvfVyzjnnZMmSJeUxS9dgPf/883PxxRdn3XXXTbNmzfLCCy/U+B6ttNJK+c1vfpPVVlstP//5z/PxPwL77zVt33///Rx//PHp2rVrmjVrlnbt2mWXXXbJ008/neQ/s0bvuuuuvP766+WlGJbOlly6bu3NN9+c0047LWuuuWZatGiRqqqq5a5pu9TEiROzzTbbZKWVVkq3bt1y1VVXVetf+nn479mz/33MT6vtk9a0vf/++7P99tunZcuWadOmTb773e/mxRdfrDZmxIgRqaioyLRp03LIIYekTZs2ad26dQ499NB88MEHK/ZJ+IzrnDt3blq2bJnjjjtumf3++c9/pnHjxhk1atQnHvuJJ57IXXfdlcGDBy8T2CZJs2bNcv7559f42g855JDlzoZdek8+rqKiIsccc0zGjBmTzTbbLM2aNcumm26asWPHVttv2LBhSZJu3bqVP09LP7dLj3HjjTdm0003TbNmzfKXv/wlXbt2zXe/+91l6pg/f35at26dI4888hPvDQDAJzHTFgCgjtx5551Zb7310qtXrxXe5/nnn8+2226bNddcMz/96U/TsmXL/P73v8/AgQPzhz/8IXvttVe18T/5yU+y6qqr5swzz8xrr72Wiy++OMccc0xuueWW8pgzzjgj//u//5v+/funf//+efrpp7Prrrsu8+foH3zwQXbcccf861//ypFHHpm11147jz/+eIYPH54333xzmfVYr7vuusyfPz9HHHFEmjVrltVWW63mNynJyiuvnL322iu//vWv88ILL2TTTTdd7rijjjoqt912W4455phssskmefvtt/Poo4/mxRdfzJZbbplTTz01c+bMyT//+c9cdNFF5WN/3MiRI1NZWZmTTjopCxYs+NSZwe+++2769++ffffdN/vvv39+//vf5+ijj05lZWUOO+ywGl3jitT2cffdd1923333rLPOOhkxYkQ+/PDDXHrppdl2223z9NNPLxNW7rvvvunWrVtGjRqVp59+Otdcc03atWuXc8455zNr+6zrXPr5ueWWW3LhhRdWW8Lhd7/7XUqlUgYNGvSJx1/6C4cDDzzwM2v5PNe+oh599NHcfvvt+fGPf5xVVlklv/zlL7P33ntnxowZWX311fO9730vL7/8cn73u9/loosuyhprrJEkadu2bfkY999/f37/+9/nmGOOyRprrJFu3brlhz/8Yc4999y888471f4N/PnPf05VVdUys8sBAFZICQCAWjdnzpxSktLAgQOX6Xv33XdLb731Vvn1wQcflPt23nnnUvfu3Uvz588vty1ZsqS0zTbblNZff/1y23XXXVdKUurbt29pyZIl5fYTTjih1Lhx49J7771XKpVKpdmzZ5cqKytLAwYMqDbuf/7nf0pJSgcffHC5beTIkaWWLVuWXn755Wr1/vSnPy01bty4NGPGjFKpVCpNnz69lKTUqlWr0uzZs1fofnTp0qU0YMCAT+y/6KKLSklKf/zjH8ttSUpnnnlmebt169alIUOGfOp5BgwYUOrSpcsy7Q888EApSWmdddapdr8/3vfAAw+U23bcccdSktIFF1xQbluwYEFp8803L7Vr1660cOHCUqn0/z4P06dP/8xjflJtS+/nddddV25bep6333673Pbss8+WGjVqVDrooIPKbWeeeWYpSemwww6rdsy99tqrtPrqqy9zrv+2otd5zz33lJKU/vKXv1Tbv0ePHqUdd9zxU8+x1157lZKU3n333c+sp1Ra8Ws/+OCDl3s/l96Tj0tSqqysLE2bNq3aMZOULr300nLbeeedt9zP59JjNGrUqPT8889Xa586dWopSenKK6+s1v6d73yn1LVr12r/7gAAVpTlEQAA6kBVVVWS5c+m7NOnT9q2bVt+XX755UmSd955J/fff3/23XffvP/++/n3v/+df//733n77bfTr1+/vPLKK/nXv/5V7VhHHHFEtT8F33777bN48eK8/vrrSf4za3HhwoX5yU9+Um3c8ccfv0xdt956a7bffvusuuqq5XP/+9//Tt++fbN48eI8/PDD1cbvvffe1WYhfhFL79P777//iWPatGmTJ554Im+88cbnPs/BBx+clVZaaYXGNmnSpNqftldWVubII4/M7NmzM3HixM9dw2d58803M2nSpBxyyCHVZm726NEju+yyS+6+++5l9jnqqKOqbW+//fZ5++23y8/hp1mR6+zbt286deqUG2+8sTxuypQpmTx58mfOJF1awyqrrPKZtXyea19Rffv2zbrrrlvtmK1atcrf//73FT7GjjvumE022aRa2wYbbJBevXpVuzfvvPNO/vKXv2TQoEHLLNUAALAihLYAAHVgaUA1d+7cZfp+9atfZdy4cfntb39brX3atGkplUo5/fTTq4W6bdu2zZlnnpkkmT17drV91l577Wrbq666apL//Ml7knJ4u/7661cb17Zt2/LYpV555ZWMHTt2mXP37dt3uefu1q3bZ9yFFbf0Pn1asHfuuedmypQp6dy5c775zW9mxIgRNQrckprV3KlTp7Rs2bJa2wYbbJAky6xhW5uWfs423HDDZfo23njj/Pvf/868efOqtX/Wc/BpVuQ6GzVqlEGDBmXMmDHltXJvvPHGNG/ePN///vc/9fitWrVK8umB/FKf59pX1H/fo+Q/92lF7tFSn/T8HHTQQXnsscfK9d9666356KOPVnhJCACA/2ZNWwCAOtC6det07NgxU6ZMWaZv6Rq3/x38LX2zr5NOOin9+vVb7nHXW2+9atsfX1/040ofe0OvFbVkyZLssssuOfnkk5fbvzTIW2pFZ6yuiKX36b+v7+P23XffbL/99rnjjjty77335rzzzss555yT22+/PbvvvvsKnac2a07yibMoFy9eXKvn+Sy1+Rx8koMOOijnnXdexowZk/333z833XRT9thjj7Ru3fpT99too42SJM8991y23377Wqunpve+Nu7RJz0/++23X0444YTceOON+Z//+Z/89re/zdZbb73c8BkAYEUIbQEA6siAAQNyzTXX5Mknn8w3v/nNzxy/zjrrJEmaNm1ant36RXXp0iXJf2bRLj1+krz11lvLzDBcd911M3fu3Fo794qaO3du7rjjjnTu3Dkbb7zxp47t2LFjfvzjH+fHP/5xZs+enS233DI///nPy6Ftbf4p+htvvJF58+ZVm4X68ssvJ0n5zbCWzmh97733qu27dMblx61obUs/Z1OnTl2m76WXXsoaa6yxzMzYL2JFrjNJNttss2yxxRa58cYbs9Zaa2XGjBm59NJLP/P4e+65Z0aNGpXf/va3nxna1uTaV1111WXue7L8e7+iPu/zs9pqq2XAgAG58cYbM2jQoDz22GPLvHEfAEBNWB4BAKCOnHzyyWnRokUOO+ywzJo1a5n+/57h165du/Tp0ye/+tWv8uabby4z/q233qpxDX379k3Tpk1z6aWXVjvf8gKlfffdNxMmTMg999yzTN97772XRYsW1fj8n+XDDz/MgQcemHfeeSennnrqp86enDNnTrW2du3apVOnTlmwYEG5rWXLlsuM+7wWLVqUX/3qV+XthQsX5le/+lXatm2brbbaKknKa6R+fL3fxYsX5+qrr17meCtaW8eOHbP55pvn+uuvrxZKTpkyJffee2/69+//eS9puVbkOpc68MADc++99+biiy/O6quvvkIznHv37p3ddtst11xzTcaMGbNM/8KFC3PSSSclqdm1r7vuupkzZ04mT55cbnvzzTdzxx13rOilL2NpILy8MPizHHjggXnhhRcybNiwNG7cOPvtt9/nrgMAwExbAIA6sv766+emm27K/vvvnw033DCDBg1Kz549UyqVMn369Nx0001p1KhR1lprrfI+l19+ebbbbrt07949hx9+eNZZZ53MmjUrEyZMyD//+c88++yzNaqhbdu2OemkkzJq1Kjsscce6d+/f5555pn85S9/yRprrFFt7LBhw/KnP/0pe+yxRw455JBstdVWmTdvXp577rncdtttee2115bZpyb+9a9/ldfxnTt3bl544YXceuutmTlzZk488cRqb4b1395///2stdZa2WeffdKzZ8+svPLKue+++/K3v/0tF1xwQXncVlttlVtuuSVDhw7NN77xjay88srZc889P1e9nTp1yjnnnJPXXnstG2ywQW655ZZMmjQpV199dZo2bZok2XTTTfOtb30rw4cPzzvvvJPVVlstN99883ID7prUdt5552X33XdP7969M3jw4Hz44Ye59NJL07p164wYMeJzXc8Xuc6lDjjggJx88sm54447cvTRRy/T/0luuOGG7Lrrrvne976XPffcMzvvvHNatmyZV155JTfffHPefPPNnH/++TW69v322y+nnHJK9tprrxx77LH54IMPcuWVV2aDDTbI008//bnuxdKQ+tRTT81+++2Xpk2bZs8991yhmc0DBgzI6quvnltvvTW777572rVr97lqAABIkpQAAKhT06ZNKx199NGl9dZbr9S8efPSSiutVNpoo41KRx11VGnSpEnLjH/11VdLBx10UKlDhw6lpk2bltZcc83SHnvsUbrtttvKY6677rpSktLf/va3avs+8MADpSSlBx54oNy2ePHi0llnnVXq2LFjaaWVVir16dOnNGXKlFKXLl1KBx98cLX933///dLw4cNL6623XqmysrK0xhprlLbZZpvS+eefX1q4cGGpVCqVpk+fXkpSOu+881b4HnTp0qWUpJSkVFFRUWrVqlVp0003LR1++OGlJ554Yrn7JCmdeeaZpVKpVFqwYEFp2LBhpZ49e5ZWWWWVUsuWLUs9e/YsXXHFFdX2mTt3bumAAw4otWnTppSk1KVLl2r35dZbb13mPMu7ZzvuuGNp0003LT311FOl3r17l5o3b17q0qVL6bLLLltm/1dffbXUt2/fUrNmzUrt27cv/c///E9p3Lhxyxzzk2pbej+vu+66ase97777Sttuu21ppZVWKrVq1aq05557ll544YVqY84888xSktJbb71VrX3p8zF9+vTl3tvPc51L9e/fv5Sk9Pjjj3/qsf/bBx98UDr//PNL3/jGN0orr7xyqbKysrT++uuXfvKTn5SmTZtW42svlUqle++9t7TZZpuVKisrSxtuuGHpt7/9bfmefFyS0pAhQ5bZf3n/BkaOHFlac801S40aNap2Dz/pGB/34x//uJSkdNNNN63AHQEA+GQVpVItvjsBAADwtbbXXnvlueeey7Rp0+q7lMI54YQT8utf/zozZ85MixYt6rscAOArzJq2AADACnnzzTdz11135cADD6zvUgpn/vz5+e1vf5u9995bYAsAfGHWtAUAAD7V9OnT89hjj+Waa65J06ZNP3X94YZm9uzZue+++3Lbbbfl7bffznHHHVffJQEAXwNCWwAA4FM99NBDOfTQQ7P22mvn+uuvT4cOHeq7pMJ44YUXMmjQoLRr1y6//OUvs/nmm9d3SQDA14A1bQEAAAAACsSatgAAAAAABSK0BQAAAAAoEGva1pIlS5bkjTfeyCqrrJKKior6LgcAAAAAKJhSqZT3338/nTp1SqNGnzyfVmhbS95444107ty5vssAAAAAAAruH//4R9Zaa61P7Bfa1pJVVlklyX9ueKtWreq5GgAAAACgaKqqqtK5c+dylvhJhLa1ZOmSCK1atRLaAgAAAACf6LOWV/VGZAAAAAAABSK0BQAAAAAoEKEtAAAAAECBWNP2S7Z48eJ89NFH9V1Gg1ZZWZlGjfy+AgAAAIBiEtp+SUqlUmbOnJn33nuvvktp8Bo1apRu3bqlsrKyvksBAAAAgGUIbb8kSwPbdu3apUWLFp/5DnHUjSVLluSNN97Im2++mbXXXtvnAQAAAIDCEdp+CRYvXlwObFdfffX6LqfBa9u2bd54440sWrQoTZs2re9yAAAAAKAaC3t+CZauYduiRYt6roQk5WURFi9eXM+VAAAAAMCyhLZfIn+KXww+DwAAAAAUmdAWAAAAAKBAhLbUqj59+uT444+v7zIAAAAA4CtLaPs1NHPmzBx33HFZb7310rx587Rv3z7bbrttrrzyynzwwQf1XR4AAAAA8Cma1HcB1K6///3v2XbbbdOmTZv84he/SPfu3dOsWbM899xzufrqq7PmmmvmO9/5Tn2X+YkWL16cioqKNGrk9wkAAAAANEySsa+ZH//4x2nSpEmeeuqp7Lvvvtl4442zzjrr5Lvf/W7uuuuu7LnnnkmS9957Lz/60Y/Stm3btGrVKt/+9rfz7LPPlo8zYsSIbL755vnNb36Trl27pnXr1tlvv/3y/vvvl8fMmzcvBx10UFZeeeV07NgxF1xwwTL1LFiwICeddFLWXHPNtGzZMr169cqDDz5Y7h89enTatGmTP/3pT9lkk03SrFmzzJgxo+5uEAAAAAAUnND2a+Ttt9/OvffemyFDhqRly5bLHVNRUZEk+f73v5/Zs2fnL3/5SyZOnJgtt9wyO++8c955553y2FdffTVjxozJnXfemTvvvDMPPfRQzj777HL/sGHD8tBDD+WPf/xj7r333jz44IN5+umnq53vmGOOyYQJE3LzzTdn8uTJ+f73v5/ddtstr7zySnnMBx98kHPOOSfXXHNNnn/++bRr1642bwsAAAAAfKVYHuFrZNq0aSmVStlwww2rta+xxhqZP39+kmTIkCHZc8898+STT2b27Nlp1qxZkuT888/PmDFjctttt+WII45IkixZsiSjR4/OKquskiQ58MADM378+Pz85z/P3Llz8+tf/zq//e1vs/POOydJrr/++qy11lrl886YMSPXXXddZsyYkU6dOiVJTjrppIwdOzbXXXddfvGLXyRJPvroo1xxxRXp2bNnHd4dAAAAAPhqENo2AE8++WSWLFmSQYMGZcGCBXn22Wczd+7crL766tXGffjhh3n11VfL2127di0HtknSsWPHzJ49O8l/ZuEuXLgwvXr1Kvevttpq1QLj5557LosXL84GG2xQ7TwLFiyodu7Kysr06NGjdi4WAAAAAL7i6nV5hIcffjh77rlnOnXqlIqKiowZM6Zaf0VFxXJf5513XnlM165dl+n/+J/wJ8nkyZOz/fbbp3nz5uncuXPOPffcZWq59dZbs9FGG6V58+bp3r177r777jq55rq03nrrpaKiIlOnTq3Wvs4662S99dbLSiutlCSZO3duOnbsmEmTJlV7TZ06NcOGDSvv17Rp02rHqaioyJIlS1a4nrlz56Zx48aZOHFitfO8+OKLueSSS8rjVlpppfKyDQAAAADQ0NVraDtv3rz07Nkzl19++XL733zzzWqva6+9NhUVFdl7772rjfvZz35WbdxPfvKTcl9VVVV23XXXdOnSJRMnTsx5552XESNG5Oqrry6Pefzxx7P//vtn8ODBeeaZZzJw4MAMHDgwU6ZMqZsLryOrr756dtlll1x22WWZN2/eJ47bcsstM3PmzDRp0iTrrbdetdcaa6yxQudad91107Rp0zzxxBPltnfffTcvv/xyeXuLLbbI4sWLM3v27GXO06FDh89/oQAAAADwNVavyyPsvvvu2X333T+x/7+DvT/+8Y/Zaaedss4661RrX2WVVT4xBLzxxhuzcOHCXHvttamsrMymm26aSZMm5cILLyyv3XrJJZdkt912K88yHTlyZMaNG5fLLrssV1111Re5xC/dFVdckW233TZbb711RowYkR49eqRRo0b529/+lpdeeilbbbVV+vbtm969e2fgwIE599xzs8EGG+SNN97IXXfdlb322itbb731Z55n5ZVXzuDBgzNs2LCsvvrqadeuXU499dQ0avT/fg+wwQYbZNCgQTnooINywQUXZIsttshbb72V8ePHp0ePHhkwYEBd3goAAAAA+Eqq15m2NTFr1qzcddddGTx48DJ9Z599dlZfffVsscUWOe+887Jo0aJy34QJE7LDDjuksrKy3NavX79MnTo17777bnlM3759qx2zX79+mTBhQh1dTd1Zd91188wzz6Rv374ZPnx4evbsma233jqXXnppTjrppIwcOTIVFRW5++67s8MOO+TQQw/NBhtskP322y+vv/562rdvv8LnOu+887L99ttnzz33TN++fbPddttlq622qjbmuuuuy0EHHZQTTzwxG264YQYOHJi//e1vWXvttWv70gEAAADga6GiVCqV6ruI5D/rpd5xxx0ZOHDgcvvPPffcnH322XnjjTfSvHnzcvuFF16YLbfcMquttloef/zxDB8+PIceemguvPDCJMmuu+6abt265Ve/+lV5nxdeeCGbbrppXnjhhWy88caprKzM9ddfn/3337885oorrshZZ52VWbNmLbeeBQsWZMGCBeXtqqqqdO7cOXPmzEmrVq2qjZ0/f36mT5+ebt26Vaud+uHzAQAAAEB9qKqqSuvWrZebIX5cvS6PUBPXXnttBg0atEzINnTo0PLHPXr0SGVlZY488siMGjUqzZo1q7N6Ro0albPOOqvOjg8AAAAANExfieURHnnkkUydOjU/+tGPPnNsr169smjRorz22mtJ/rMu7n/Pll26vXQd3E8a82lvljV8+PDMmTOn/PrHP/5Rk0sCAAAAAFiur0Ro++tf/zpbbbVVevbs+ZljJ02alEaNGqVdu3ZJkt69e+fhhx/ORx99VB4zbty4bLjhhll11VXLY8aPH1/tOOPGjUvv3r0/8TzNmjVLq1atqr0AAAAAAL6oeg1t586dm0mTJmXSpElJkunTp2fSpEmZMWNGeUxVVVVuvfXW5c6ynTBhQi6++OI8++yz+fvf/54bb7wxJ5xwQn74wx+WA9kDDjgglZWVGTx4cJ5//vnccsstueSSS6otq3Dcccdl7NixueCCC/LSSy9lxIgReeqpp3LMMcfU7Q0AAAAAAPgv9bqm7VNPPZWddtqpvL00SD344IMzevToJMnNN9+cUqlU7U3ClmrWrFluvvnmjBgxIgsWLEi3bt1ywgknVAtkW7dunXvvvTdDhgzJVlttlTXWWCNnnHFGjjjiiPKYbbbZJjfddFNOO+20/M///E/WX3/9jBkzJptttlkdXTkAAAAAwPJVlEqlUn0X8XXwae/8Nn/+/EyfPj3dunVb5o3U+PL5fAAAAAB8Pnc/MeOzB33N9e+19ufe99MyxI/7SqxpCwAAAADQUAhtAQAAAAAKRGgLAAAAAFAgQlu+crp27ZqLL764vssAAAAAgDrRpL4LaOi2GnbDl3auiecdVON9DjnkkFx//fXLtL/yyitZb731aqMsAAAAAOBjhLZ8pt122y3XXXddtba2bdvWUzUAAAAA8PVmeQQ+U7NmzdKhQ4dqr8aNG+ePf/xjttxyyzRv3jzrrLNOzjrrrCxatKi8X0VFRX71q19ljz32SIsWLbLxxhtnwoQJmTZtWvr06ZOWLVtmm222yauvvlre59VXX813v/vdtG/fPiuvvHK+8Y1v5L777vvU+t5777386Ec/Stu2bdOqVat8+9vfzrPPPltn9wMAAAAA6pLQls/lkUceyUEHHZTjjjsuL7zwQn71q19l9OjR+fnPf15t3MiRI3PQQQdl0qRJ2WijjXLAAQfkyCOPzPDhw/PUU0+lVCrlmGOOKY+fO3du+vfvn/Hjx+eZZ57Jbrvtlj333DMzZsz4xFq+//3vZ/bs2fnLX/6SiRMnZsstt8zOO++cd955p86uHwAAAADqiuUR+Ex33nlnVl555fL27rvvnnfffTc//elPc/DBBydJ1llnnYwcOTInn3xyzjzzzPLYQw89NPvuu2+S5JRTTknv3r1z+umnp1+/fkmS4447Loceemh5fM+ePdOzZ8/y9siRI3PHHXfkT3/6U7Vwd6lHH300Tz75ZGbPnp1mzZolSc4///yMGTMmt912W4444ohavBMAAAAAUPeEtnymnXbaKVdeeWV5u2XLlunRo0cee+yxajNrFy9enPnz5+eDDz5IixYtkiQ9evQo97dv3z5J0r1792pt8+fPT1VVVVq1apW5c+dmxIgRueuuu/Lmm29m0aJF+fDDDz9xpu2zzz6buXPnZvXVV6/W/uGHH1ZbdgEAAAAAviqEtnymli1bZr311qvWNnfu3Jx11ln53ve+t8z45s2blz9u2rRp+eOKiopPbFuyZEmS5KSTTsq4ceNy/vnnZ7311stKK62UffbZJwsXLlxubXPnzk3Hjh3z4IMPLtPXpk2bFbtAAAAAACgQoS2fy5ZbbpmpU6cuE+Z+UY899lgOOeSQ7LXXXkn+E8q+9tprn1rHzJkz06RJk3Tt2rVWawEAAACA+iC05XM544wzsscee2TttdfOPvvsk0aNGuXZZ5/NlClT8r//+7+f+7jrr79+br/99uy5556pqKjI6aefXp6Fuzx9+/ZN7969M3DgwJx77rnZYIMN8sYbb+Suu+7KXnvtla233vpz1wIAAAAA9aFRfRfAV1O/fv1y55135t577803vvGNfOtb38pFF12ULl26fKHjXnjhhVl11VWzzTbbZM8990y/fv2y5ZZbfuL4ioqK3H333dlhhx1y6KGHZoMNNsh+++2X119/vbyGLgAAAAB8lVSUSqVSfRfxdVBVVZXWrVtnzpw5adWqVbW++fPnZ/r06enWrVu19V6pHz4fAAAAAJ/P3U8s/83iG5L+vdb+3Pt+Wob4cWbaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQli/Na6+9loqKikyaNKm+SwEAAACAwmpS3wU0dHc/MeNLO1f/XmvXeJ9DDjkk119/fY488shcddVV1fqGDBmSK664IgcffHBGjx5dS1UCAAAAQMNmpi2fqXPnzrn55pvz4Ycfltvmz5+fm266KWuvXfMgGAAAAAD4ZEJbPtOWW26Zzp075/bbby+33X777Vl77bWzxRZblNvGjh2b7bbbLm3atMnqq6+ePfbYI6+++uqnHnvKlCnZfffds/LKK6d9+/Y58MAD8+9//7vOrgUAAAAAik5oywo57LDDct1115W3r7322hx66KHVxsybNy9Dhw7NU089lfHjx6dRo0bZa6+9smTJkuUe87333su3v/3tbLHFFnnqqacyduzYzJo1K/vuu2+dXgsAAAAAFJk1bVkhP/zhDzN8+PC8/vrrSZLHHnssN998cx588MHymL333rvaPtdee23atm2bF154IZttttkyx7zsssuyxRZb5Be/+EW1fTp37pyXX345G2ywQd1cDAAAAAAUmNCWFdK2bdsMGDAgo0ePTqlUyoABA7LGGmtUG/PKK6/kjDPOyBNPPJF///vf5Rm2M2bMWG5o++yzz+aBBx7IyiuvvEzfq6++KrQFAAAAoEES2rLCDjvssBxzzDFJkssvv3yZ/j333DNdunTJ//3f/6VTp05ZsmRJNttssyxcuHC5x5s7d2723HPPnHPOOcv0dezYsXaLBwAAAPiCthp2Q32XUO9G7tOnvktoEIS2rLDddtstCxcuTEVFRfr161et7+23387UqVPzf//3f9l+++2TJI8++uinHm/LLbfMH/7wh3Tt2jVNmngUAQAAACDxRmTUQOPGjfPiiy/mhRdeSOPGjav1rbrqqll99dVz9dVXZ9q0abn//vszdOjQTz3ekCFD8s4772T//ffP3/72t7z66qu55557cuihh2bx4sV1eSkAAAAAUFhCW2qkVatWadWq1TLtjRo1ys0335yJEydms802ywknnJDzzjvvU4/VqVOnPPbYY1m8eHF23XXXdO/ePccff3zatGmTRo08mgAAAAA0TP4mvZ7177V2fZfwqUaPHv2p/WPGjCl/3Ldv37zwwgvV+kulUvnjrl27VttOkvXXXz+33377F64TAAAAAL4uTGcEAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIELbL9F/vwkX9cPnAQAAAIAiE9p+CZo2bZok+eCDD+q5EpJk4cKFSZLGjRvXcyUAAAAAsKwm9V1AQ9C4ceO0adMms2fPTpK0aNEiFRUV9VxVw7RkyZK89dZbadGiRZo08fgDAAAAUDxSqy9Jhw4dkqQc3FJ/GjVqlLXXXltwDgAAAEAhCW2/JBUVFenYsWPatWuXjz76qL7LadAqKyvTqJGVQQAAAAAoJqHtl6xx48bWUgUAAAAAPpHphgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAqnX0Pbhhx/OnnvumU6dOqWioiJjxoyp1n/IIYekoqKi2mu33XarNuadd97JoEGD0qpVq7Rp0yaDBw/O3Llzq42ZPHlytt9++zRv3jydO3fOueeeu0wtt956azbaaKM0b9483bt3z913313r1wsAAAAA8FnqNbSdN29eevbsmcsvv/wTx+y222558803y6/f/e531foHDRqU559/PuPGjcudd96Zhx9+OEcccUS5v6qqKrvuumu6dOmSiRMn5rzzzsuIESNy9dVXl8c8/vjj2X///TN48OA888wzGThwYAYOHJgpU6bU/kUDAAAAAHyKJvV58t133z277777p45p1qxZOnTosNy+F198MWPHjs3f/va3bL311kmSSy+9NP3798/555+fTp065cYbb8zChQtz7bXXprKyMptuumkmTZqUCy+8sBzuXnLJJdltt90ybNiwJMnIkSMzbty4XHbZZbnqqqtq8YoBAAAAAD5d4de0ffDBB9OuXbtsuOGGOfroo/P222+X+yZMmJA2bdqUA9sk6du3bxo1apQnnniiPGaHHXZIZWVleUy/fv0yderUvPvuu+Uxffv2rXbefv36ZcKECZ9Y14IFC1JVVVXtBQAAAADwRRU6tN1tt91yww03ZPz48TnnnHPy0EMPZffdd8/ixYuTJDNnzky7du2q7dOkSZOsttpqmTlzZnlM+/btq41Zuv1ZY5b2L8+oUaPSunXr8qtz585f7GIBAAAAAFLPyyN8lv3226/8cffu3dOjR4+su+66efDBB7PzzjvXY2XJ8OHDM3To0PJ2VVWV4BYAAAAA+MIKPdP2v62zzjpZY401Mm3atCRJhw4dMnv27GpjFi1alHfeeae8Dm6HDh0ya9asamOWbn/WmE9aSzf5z1q7rVq1qvYCAAAAAPiivlKh7T//+c+8/fbb6dixY5Kkd+/eee+99zJx4sTymPvvvz9LlixJr169ymMefvjhfPTRR+Ux48aNy4YbbphVV121PGb8+PHVzjVu3Lj07t27ri8JAAAAAKCaeg1t586dm0mTJmXSpElJkunTp2fSpEmZMWNG5s6dm2HDhuWvf/1rXnvttYwfPz7f/e53s95666Vfv35Jko033ji77bZbDj/88Dz55JN57LHHcswxx2S//fZLp06dkiQHHHBAKisrM3jw4Dz//PO55ZZbcskll1Rb2uC4447L2LFjc8EFF+Sll17KiBEj8tRTT+WYY4750u8JAAAAANCw1Wto+9RTT2WLLbbIFltskSQZOnRotthii5xxxhlp3LhxJk+enO985zvZYIMNMnjw4Gy11VZ55JFH0qxZs/Ixbrzxxmy00UbZeeed079//2y33Xa5+uqry/2tW7fOvffem+nTp2errbbKiSeemDPOOCNHHHFEecw222yTm266KVdffXV69uyZ2267LWPGjMlmm2325d0MAAAAAIAkFaVSqVTfRXwdVFVVpXXr1pkzZ471bQEAAAC+hrYadkN9l1DvRu7Tp75LqHf9e639ufdd0QzxK7WmLQAAAADA153QFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFUq+h7cMPP5w999wznTp1SkVFRcaMGVPu++ijj3LKKaeke/fuadmyZTp16pSDDjoob7zxRrVjdO3aNRUVFdVeZ599drUxkydPzvbbb5/mzZunc+fOOffcc5ep5dZbb81GG22U5s2bp3v37rn77rvr5JoBAAAAAD5NvYa28+bNS8+ePXP55Zcv0/fBBx/k6aefzumnn56nn346t99+e6ZOnZrvfOc7y4z92c9+ljfffLP8+slPflLuq6qqyq677pouXbpk4sSJOe+88zJixIhcffXV5TGPP/549t9//wwePDjPPPNMBg4cmIEDB2bKlCl1c+EAAAAAAJ+gSX2efPfdd8/uu+++3L7WrVtn3Lhx1douu+yyfPOb38yMGTOy9tprl9tXWWWVdOjQYbnHufHGG7Nw4cJce+21qayszKabbppJkyblwgsvzBFHHJEkueSSS7Lbbrtl2LBhSZKRI0dm3Lhxueyyy3LVVVfVxqUCAAAAAKyQr9SatnPmzElFRUXatGlTrf3ss8/O6quvni222CLnnXdeFi1aVO6bMGFCdthhh1RWVpbb+vXrl6lTp+bdd98tj+nbt2+1Y/br1y8TJkyou4sBAAAAAFiOep1pWxPz58/PKaeckv333z+tWrUqtx977LHZcssts9pqq+Xxxx/P8OHD8+abb+bCCy9MksycOTPdunWrdqz27duX+1ZdddXMnDmz3PbxMTNnzvzEehYsWJAFCxaUt6uqqr7wNQIAAAAAfCVC248++ij77rtvSqVSrrzyymp9Q4cOLX/co0ePVFZW5sgjj8yoUaPSrFmzOqtp1KhROeuss+rs+AAAAABAw1T45RGWBravv/56xo0bV22W7fL06tUrixYtymuvvZYk6dChQ2bNmlVtzNLtpevgftKYT1onN0mGDx+eOXPmlF//+Mc/anppAAAAAADLKHRouzSwfeWVV3Lfffdl9dVX/8x9Jk2alEaNGqVdu3ZJkt69e+fhhx/ORx99VB4zbty4bLjhhll11VXLY8aPH1/tOOPGjUvv3r0/8TzNmjVLq1atqr0AAAAAAL6oel0eYe7cuZk2bVp5e/r06Zk0aVJWW221dOzYMfvss0+efvrp3HnnnVm8eHF5jdnVVlstlZWVmTBhQp544onstNNOWWWVVTJhwoSccMIJ+eEPf1gOZA844ICcddZZGTx4cE455ZRMmTIll1xySS666KLyeY877rjsuOOOueCCCzJgwIDcfPPNeeqpp3L11Vd/uTcEAAAAAGjwKkqlUqm+Tv7ggw9mp512Wqb94IMPzogRI5Z5A7GlHnjggfTp0ydPP/10fvzjH+ell17KggUL0q1btxx44IEZOnRotfVsJ0+enCFDhuRvf/tb1lhjjfzkJz/JKaecUu2Yt956a0477bS89tprWX/99XPuueemf//+K3wtVVVVad26debMmWPWLQAAAMDX0FbDbqjvEurdyH361HcJ9a5/r7U/974rmiHWa2j7dSK0BQAAAPh6E9oKbZMvJ7Qt9Jq2AAAAAAANjdAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACiQGoe2Y8eOzaOPPlrevvzyy7P55pvngAMOyLvvvlurxQEAAAAANDQ1Dm2HDRuWqqqqJMlzzz2XE088Mf3798/06dMzdOjQWi8QAAAAAKAhaVLTHaZPn55NNtkkSfKHP/whe+yxR37xi1/k6aefTv/+/Wu9QAAAAACAhqTGM20rKyvzwQcfJEnuu+++7LrrrkmS1VZbrTwDFwAAAACAz6fGM2233XbbDB06NNtuu22efPLJ3HLLLUmSl19+OWuttVatFwgAAAAA0JDUeKbt5ZdfnqZNm+a2227LlVdemTXXXDNJ8pe//CW77bZbrRcIAAAAANCQ1Gim7aJFi/Lggw/m//7v/9KhQ4dqfRdddFGtFgYAAAAA0BDVaKZtkyZNctRRR2XBggV1VQ8AAAAAQINW4+URvvnNb+aZZ56pi1oAAAAAABq8Gr8R2Y9//OOceOKJ+ec//5mtttoqLVu2rNbfo0ePWisOAAAAAKChqXFou99++yVJjj322HJbRUVFSqVSKioqsnjx4tqrDgAAAACggalxaDt9+vS6qAMAAAAAgHyO0LZLly51UQcAAAAAAPkcb0SWJL/5zW+y7bbbplOnTnn99deTJBdffHH++Mc/1mpxAAAAAAANTY1D2yuvvDJDhw5N//79895775XXsG3Tpk0uvvji2q4PAAAAAKBBqXFoe+mll+b//u//cuqpp6Zx48bl9q233jrPPfdcrRYHAAAAANDQ1Di0nT59erbYYotl2ps1a5Z58+bVSlEAAAAAAA1VjUPbbt26ZdKkScu0jx07NhtvvHFt1AQAAAAA0GA1qekOQ4cOzZAhQzJ//vyUSqU8+eST+d3vfpdRo0blmmuuqYsaAQAAAAAajBqHtj/60Y+y0kor5bTTTssHH3yQAw44IJ06dcoll1yS/fbbry5qBAAAAABoMGoc2ibJoEGDMmjQoHzwwQeZO3du2rVrV9t1AQAAAAA0SJ8rtF2qRYsWadGiRW3VAgAAAADQ4NU4tH377bdzxhln5IEHHsjs2bOzZMmSav3vvPNOrRUHAAAAANDQ1Di0PfDAAzNt2rQMHjw47du3T0VFRV3UBQAAAADQINU4tH3kkUfy6KOPpmfPnnVRDwAAAABAg9aopjtstNFG+fDDD+uiFgAAAACABq/Goe0VV1yRU089NQ899FDefvvtVFVVVXsBAAAAAPD51Xh5hDZt2qSqqirf/va3q7WXSqVUVFRk8eLFtVYcAAAAAEBDU+PQdtCgQWnatGluuukmb0QGAAAAAFDLahzaTpkyJc8880w23HDDuqgHAAAAAKBBq/GatltvvXX+8Y9/1EUtAAAAAAANXo1n2v7kJz/Jcccdl2HDhqV79+5p2rRptf4ePXrUWnEAAAAAAA1NjUPbH/zgB0mSww47rNxWUVHhjcgAAAAAAGpBjUPb6dOn10UdAAAAAADkc4S2Xbp0qYs6AAAAAADI5whtk+TVV1/NxRdfnBdffDFJsskmm+S4447LuuuuW6vFAQAAAAA0NI1qusM999yTTTbZJE8++WR69OiRHj165Iknnsimm26acePG1UWNAAAAAAANRo1n2v70pz/NCSeckLPPPnuZ9lNOOSW77LJLrRUHAAAAANDQ1Him7YsvvpjBgwcv037YYYflhRdeqJWiAAAAAAAaqhqHtm3bts2kSZOWaZ80aVLatWtXGzUBAAAAADRYNQ5tDz/88BxxxBE555xz8sgjj+SRRx7J2WefnSOPPDKHH354jY718MMPZ88990ynTp1SUVGRMWPGVOsvlUo544wz0rFjx6y00krp27dvXnnllWpj3nnnnQwaNCitWrVKmzZtMnjw4MydO7famMmTJ2f77bdP8+bN07lz55x77rnL1HLrrbdmo402SvPmzdO9e/fcfffdNboWAAAAAIDaUOPQ9vTTT88ZZ5yRSy+9NDvuuGN23HHHXHbZZRkxYkROO+20Gh1r3rx56dmzZy6//PLl9p977rn55S9/mauuuipPPPFEWrZsmX79+mX+/PnlMYMGDcrzzz+fcePG5c4778zDDz+cI444otxfVVWVXXfdNV26dMnEiRNz3nnnZcSIEbn66qvLYx5//PHsv//+GTx4cJ555pkMHDgwAwcOzJQpU2p4dwAAAAAAvpiKUqlU+rw7v//++0mSVVZZ5YsXUlGRO+64IwMHDkzyn1m2nTp1yoknnpiTTjopSTJnzpy0b98+o0ePzn777ZcXX3wxm2yySf72t79l6623TpKMHTs2/fv3zz//+c906tQpV155ZU499dTMnDkzlZWVSf7zpmljxozJSy+9lCT5wQ9+kHnz5uXOO+8s1/Otb30rm2++ea666qoVqr+qqiqtW7fOnDlz0qpVqy98PwAAAAAolq2G3VDfJdS7kfv0qe8S6l3/Xmt/7n1XNEOs8Uzbb3/723nvvfeS/CesXRrYVlVV5dvf/vbnq3Y5pk+fnpkzZ6Zv377lttatW6dXr16ZMGFCkmTChAlp06ZNObBNkr59+6ZRo0Z54oknymN22GGHcmCbJP369cvUqVPz7rvvlsd8/DxLxyw9z/IsWLAgVVVV1V4AAAAAAF9UjUPbBx98MAsXLlymff78+XnkkUdqpagkmTlzZpKkffv21drbt29f7ps5c+Yyb37WpEmTrLbaatXGLO8YHz/HJ41Z2r88o0aNSuvWrcuvzp071/QSAQAAAACW0WRFB06ePLn88QsvvFAt0Fy8eHHGjh2bNddcs3arK7Dhw4dn6NCh5e2qqirBLQAAAADwha1waLv55punoqIiFRUVy10GYaWVVsqll15aa4V16NAhSTJr1qx07Nix3D5r1qxsvvnm5TGzZ8+utt+iRYvyzjvvlPfv0KFDZs2aVW3M0u3PGrO0f3maNWuWZs2afY4rAwAAAAD4ZCu8PML06dPz6quvplQq5cknn8z06dPLr3/961+pqqrKYYcdVmuFdevWLR06dMj48ePLbVVVVXniiSfSu3fvJEnv3r3z3nvvZeLEieUx999/f5YsWZJevXqVxzz88MP56KOPymPGjRuXDTfcMKuuump5zMfPs3TM0vMAAAAAAHxZVnimbZcuXZIkS5YsqbWTz507N9OmTStvT58+PZMmTcpqq62WtddeO8cff3z+93//N+uvv366deuW008/PZ06dcrAgQOTJBtvvHF22223HH744bnqqqvy0Ucf5Zhjjsl+++2XTp06JUkOOOCAnHXWWRk8eHBOOeWUTJkyJZdcckkuuuii8nmPO+647LjjjrngggsyYMCA3HzzzXnqqady9dVX19q1AgAAAACsiBq/Edn111+fu+66q7x98sknp02bNtlmm23y+uuv1+hYTz31VLbYYotsscUWSZKhQ4dmiy22yBlnnFE+9k9+8pMcccQR+cY3vpG5c+dm7Nixad68efkYN954YzbaaKPsvPPO6d+/f7bbbrtqYWvr1q1z7733Zvr06dlqq61y4okn5owzzsgRRxxRHrPNNtvkpptuytVXX52ePXvmtttuy5gxY7LZZpvV9PYAAAAAAHwhFaVSqVSTHTbccMNceeWV+fa3v50JEyZk5513zsUXX5w777wzTZo0ye23315XtRZaVVVVWrdunTlz5qRVq1b1XQ4AAAAAtWyrYTfUdwn1buQ+feq7hHrXv9fan3vfFc0QV3h5hKX+8Y9/ZL311kuSjBkzJvvss0+OOOKIbLvttunTp8/nLhgAAAAAgM+xPMLKK6+ct99+O0ly7733ZpdddkmSNG/ePB9++GHtVgcAAAAA0MDUeKbtLrvskh/96EfZYost8vLLL6d///5Jkueffz5du3at7foAAAAAABqUGs+0vfzyy9O7d++89dZb+cMf/pDVV189STJx4sTsv//+tV4gAAAAAEBDUuOZtm3atMlll122TPtZZ51VKwUBAAAAADRkNQ5tH3744U/t32GHHT53MQAAAAAADV2NQ9s+ffos01ZRUVH+ePHixV+oIAAAAACAhqzGa9q+++671V6zZ8/O2LFj841vfCP33ntvXdQIAAAAANBg1HimbevWrZdp22WXXVJZWZmhQ4dm4sSJtVIYAAAAAEBDVOOZtp+kffv2mTp1am0dDgAAAACgQarxTNvJkydX2y6VSnnzzTdz9tlnZ/PNN6+tugAAAAAAGqQah7abb755KioqUiqVqrV/61vfyrXXXltrhQEAAAAANEQ1Dm2nT59ebbtRo0Zp27ZtmjdvXmtFAQAAAAA0VDUObbt06VIXdQAAAAAAkBq8Edn999+fTTbZJFVVVcv0zZkzJ5tuumkeeeSRWi0OAAAAAKChWeHQ9uKLL87hhx+eVq1aLdPXunXrHHnkkbnwwgtrtTgAAAAAgIZmhUPbZ599Nrvtttsn9u+6666ZOHFirRQFAAAAANBQrXBoO2vWrDRt2vQT+5s0aZK33nqrVooCAAAAAGioVji0XXPNNTNlypRP7J88eXI6duxYK0UBAAAAADRUKxza9u/fP6effnrmz5+/TN+HH36YM888M3vssUetFgcAAAAA0NA0WdGBp512Wm6//fZssMEGOeaYY7LhhhsmSV566aVcfvnlWbx4cU499dQ6KxQAAAAAoCFY4dC2ffv2efzxx3P00Udn+PDhKZVKSZKKior069cvl19+edq3b19nhQIAAAAANAQrHNomSZcuXXL33Xfn3XffzbRp01IqlbL++utn1VVXrav6AAAAAAAalBqFtkutuuqq+cY3vlHbtQAAAAAANHgr/EZkAAAAAADUPaEtAAAAAECBCG0BAAAAAApkhULbLbfcMu+++26S5Gc/+1k++OCDOi0KAAAAAKChWqHQ9sUXX8y8efOSJGeddVbmzp1bp0UBAAAAADRUTVZk0Oabb55DDz002223XUqlUs4///ysvPLKyx17xhln1GqBAAAAAAANyQqFtqNHj86ZZ56ZO++8MxUVFfnLX/6SJk2W3bWiokJoCwAAAADwBaxQaLvhhhvm5ptvTpI0atQo48ePT7t27eq0MAAAAACAhmiFQtuPW7JkSV3UAQAAAABAPkdomySvvvpqLr744rz44otJkk022STHHXdc1l133VotDgAAAACgoWlU0x3uueeebLLJJnnyySfTo0eP9OjRI0888UQ23XTTjBs3ri5qBAAAAABoMGo80/anP/1pTjjhhJx99tnLtJ9yyinZZZddaq04AAAAAICGpsYzbV988cUMHjx4mfbDDjssL7zwQq0UBQAAAADQUNU4tG3btm0mTZq0TPukSZPSrl272qgJAAAAAKDBqvHyCIcffniOOOKI/P3vf88222yTJHnsscdyzjnnZOjQobVeIAAAAABAQ1Lj0Pb000/PKquskgsuuCDDhw9PknTq1CkjRozIscceW+sFAgAAAAA0JDUObSsqKnLCCSfkhBNOyPvvv58kWWWVVWq9MAAAAACAhqjGoe3HCWsBAAAAAGpXjd+IDAAAAACAuiO0BQAAAAAoEKEtAAAAAECB1Ci0/eijj7LzzjvnlVdeqat6AAAAAAAatBqFtk2bNs3kyZPrqhYAAAAAgAavxssj/PCHP8yvf/3ruqgFAAAAAKDBa1LTHRYtWpRrr7029913X7baaqu0bNmyWv+FF15Ya8UBAAAAADQ0NQ5tp0yZki233DJJ8vLLL1frq6ioqJ2qAAAAAAAaqBqHtg888EBd1AEAAAAAQD7HmrZLTZs2Lffcc08+/PDDJEmpVKq1ogAAAAAAGqoah7Zvv/12dt5552ywwQbp379/3nzzzSTJ4MGDc+KJJ9Z6gQAAAAAADUmNQ9sTTjghTZs2zYwZM9KiRYty+w9+8IOMHTu2VosDAAAAAGhoarym7b333pt77rkna621VrX29ddfP6+//nqtFQYAAAAA0BDVeKbtvHnzqs2wXeqdd95Js2bNaqUoAAAAAICGqsah7fbbb58bbrihvF1RUZElS5bk3HPPzU477VSrxQEAAAAANDQ1Xh7h3HPPzc4775ynnnoqCxcuzMknn5znn38+77zzTh577LG6qBEAAAAAoMGo8UzbzTbbLC+//HK22267fPe73828efPyve99L88880zWXXfduqgRAAAAAKDBqPFM2yRp3bp1Tj311NquBQAAAACgwftcoe27776bX//613nxxReTJJtsskkOPfTQrLbaarVaHAAAAABAQ1Pj5REefvjhdO3aNb/85S/z7rvv5t13380vf/nLdOvWLQ8//HBd1AgAAAAA0GDUeKbtkCFD8oMf/CBXXnllGjdunCRZvHhxfvzjH2fIkCF57rnnar1IAAAAAICGosYzbadNm5YTTzyxHNgmSePGjTN06NBMmzatVosDAAAAAGhoahzabrnlluW1bD/uxRdfTM+ePWulKAAAAACAhmqFlkeYPHly+eNjjz02xx13XKZNm5ZvfetbSZK//vWvufzyy3P22WfXTZUAAAAAAA3ECoW2m2++eSoqKlIqlcptJ5988jLjDjjggPzgBz+oveoAAAAAABqYFQptp0+fXtd1AAAAAACQFQxtu3TpUtd1AAAAAACQFQxt/9sbb7yRRx99NLNnz86SJUuq9R177LG1UhgAAAAAQENU49B29OjROfLII1NZWZnVV189FRUV5b6KigqhLQAAAADAF1Dj0Pb000/PGWeckeHDh6dRo0Z1URMAAAAAQINV49T1gw8+yH777SewBQAAAACoAzVOXgcPHpxbb721LmoBAAAAAGjwarw8wqhRo7LHHntk7Nix6d69e5o2bVqt/8ILL6y14gAAAAAAGpoaz7QdNWpU7rnnnsyaNSvPPfdcnnnmmfJr0qRJtV5g165dU1FRscxryJAhSZI+ffos03fUUUdVO8aMGTMyYMCAtGjRIu3atcuwYcOyaNGiamMefPDBbLnllmnWrFnWW2+9jB49utavBQAAAADgs9R4pu0FF1yQa6+9NoccckgdlLOsv/3tb1m8eHF5e8qUKdlll13y/e9/v9x2+OGH52c/+1l5u0WLFuWPFy9enAEDBqRDhw55/PHH8+abb+aggw5K06ZN84tf/CJJMn369AwYMCBHHXVUbrzxxowfPz4/+tGP0rFjx/Tr1+9LuEoAAAAAgP+ocWjbrFmzbLvttnVRy3K1bdu22vbZZ5+dddddNzvuuGO5rUWLFunQocNy97/33nvzwgsv5L777kv79u2z+eabZ+TIkTnllFMyYsSIVFZW5qqrrkq3bt1ywQUXJEk23njjPProo7nooouEtgAAAADAl6rGyyMcd9xxufTSS+uils+0cOHC/Pa3v81hhx2WioqKcvuNN96YNdZYI5tttlmGDx+eDz74oNw3YcKEdO/ePe3bty+39evXL1VVVXn++efLY/r27VvtXP369cuECRPq+IoAAAAAAKqr8UzbJ598Mvfff3/uvPPObLrppsu8Edntt99ea8X9tzFjxuS9996rtjTDAQcckC5duqRTp06ZPHlyTjnllEydOrVcx8yZM6sFtknK2zNnzvzUMVVVVfnwww+z0korLVPLggULsmDBgvJ2VVVVrVwjAAAAANCw1Ti0bdOmTb73ve/VRS2f6de//nV23333dOrUqdx2xBFHlD/u3r17OnbsmJ133jmvvvpq1l133TqrZdSoUTnrrLPq7PgAAAAAQMNU49D2uuuuq4s6PtPrr7+e++677zNn8vbq1StJMm3atKy77rrp0KFDnnzyyWpjZs2alSTldXA7dOhQbvv4mFatWi13lm2SDB8+PEOHDi1vV1VVpXPnzjW7KAAAAACA/1LjNW3ry3XXXZd27dplwIABnzpu0qRJSZKOHTsmSXr37p3nnnsus2fPLo8ZN25cWrVqlU022aQ8Zvz48dWOM27cuPTu3fsTz9OsWbO0atWq2gsAAAAA4Iuq8Uzbbt26VXsTsP/297///QsVtDxLlizJddddl4MPPjhNmvy/kl999dXcdNNN6d+/f1ZfffVMnjw5J5xwQnbYYYf06NEjSbLrrrtmk002yYEHHphzzz03M2fOzGmnnZYhQ4akWbNmSZKjjjoql112WU4++eQcdthhuf/++/P73/8+d911V61fCwAAAADAp6lxaHv88cdX2/7oo4/yzDPPZOzYsRk2bFht1VXNfffdlxkzZuSwww6r1l5ZWZn77rsvF198cebNm5fOnTtn7733zmmnnVYe07hx49x55505+uij07t377Rs2TIHH3xwfvazn5XHdOvWLXfddVdOOOGEXHLJJVlrrbVyzTXXpF+/fnVyPQAAAAAAn6SiVCqVauNAl19+eZ566ql6W/O2vlVVVaV169aZM2eOpRIAAAAAvoa2GnZDfZdQ70bu06e+S6h3/Xut/bn3XdEMsdbWtN19993zhz/8obYOBwAAAADQINVaaHvbbbdltdVWq63DAQAAAAA0SDVe03aLLbao9kZkpVIpM2fOzFtvvZUrrriiVosDAAAAAGhoahzaDhw4sNp2o0aN0rZt2/Tp0ycbbbRRbdUFAAAAANAg1Ti0PfPMM+uiDgAAAAAAUotr2gIAAAAA8MWt8EzbRo0aVVvLdnkqKiqyaNGiL1wUAAAAAEBDtcKh7R133PGJfRMmTMgvf/nLLFmypFaKAgAAAABoqFY4tP3ud7+7TNvUqVPz05/+NH/+858zaNCg/OxnP6vV4gAAAAAAGprPtabtG2+8kcMPPzzdu3fPokWLMmnSpFx//fXp0qVLbdcHAAAAANCg1Ci0nTNnTk455ZSst956ef755zN+/Pj8+c9/zmabbVZX9QEAAAAANCgrvDzCueeem3POOScdOnTI7373u+UulwAAAAAAwBezwqHtT3/606y00kpZb731cv311+f6669f7rjbb7+91ooDAAAAAGhoVji0Peigg1JRUVGXtQAAAAAANHgrHNqOHj26DssAAAAAACCp4RuRAQAAAABQt4S2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAAqk0KHtiBEjUlFRUe210UYblfvnz5+fIUOGZPXVV8/KK6+cvffeO7Nmzap2jBkzZmTAgAFp0aJF2rVrl2HDhmXRokXVxjz44IPZcsst06xZs6y33noZPXr0l3F5AAAAAADLKHRomySbbrpp3nzzzfLr0UcfLfedcMIJ+fOf/5xbb701Dz30UN54441873vfK/cvXrw4AwYMyMKFC/P444/n+uuvz+jRo3PGGWeUx0yfPj0DBgzITjvtlEmTJuX444/Pj370o9xzzz1f6nUCAAAAACRJk/ou4LM0adIkHTp0WKZ9zpw5+fWvf52bbrop3/72t5Mk1113XTbeeOP89a9/zbe+9a3ce++9eeGFF3Lfffelffv22XzzzTNy5MiccsopGTFiRCorK3PVVVelW7duueCCC5IkG2+8cR599NFcdNFF6dev35d6rQAAAAAAhZ9p+8orr6RTp05ZZ511MmjQoMyYMSNJMnHixHz00Ufp27dveexGG22UtddeOxMmTEiSTJgwId27d0/79u3LY/r165eqqqo8//zz5TEfP8bSMUuP8UkWLFiQqqqqai8AAAAAgC+q0KFtr169Mnr06IwdOzZXXnllpk+fnu233z7vv/9+Zs6cmcrKyrRp06baPu3bt8/MmTOTJDNnzqwW2C7tX9r3aWOqqqry4YcffmJto0aNSuvWrcuvzp07f9HLBQAAAAAo9vIIu+++e/njHj16pFevXunSpUt+//vfZ6WVVqrHypLhw4dn6NCh5e2qqirBLQAAAADwhRV6pu1/a9OmTTbYYINMmzYtHTp0yMKFC/Pee+9VGzNr1qzyGrgdOnTIrFmzlulf2vdpY1q1avWpwXCzZs3SqlWrai8AAAAAgC/qKxXazp07N6+++mo6duyYrbbaKk2bNs348ePL/VOnTs2MGTPSu3fvJEnv3r3z3HPPZfbs2eUx48aNS6tWrbLJJpuUx3z8GEvHLD0GAAAAAMCXqdCh7UknnZSHHnoor732Wh5//PHstddeady4cfbff/+0bt06gwcPztChQ/PAAw9k4sSJOfTQQ9O7d+9861vfSpLsuuuu2WSTTXLggQfm2WefzT333JPTTjstQ4YMSbNmzZIkRx11VP7+97/n5JNPzksvvZQrrrgiv//973PCCSfU56UDAAAAAA1Uode0/ec//5n9998/b7/9dtq2bZvtttsuf/3rX9O2bdskyUUXXZRGjRpl7733zoIFC9KvX79cccUV5f0bN26cO++8M0cffXR69+6dli1b5uCDD87Pfvaz8phu3brlrrvuygknnJBLLrkka621Vq655pr069fvS79eAAAAAICKUqlUqu8ivg6qqqrSunXrzJkzx/q2AAAAAF9DWw27ob5LqHcj9+lT3yXUu/691v7c+65ohljo5REAAAAAABoaoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAF0qS+CwAAAADgs939xIz6LqHe9e+1dn2XAF8KM20BAAAAAApEaAsAAAAAUCCWRwAAAAAKb6thN9R3CfVu5D596rsE4Etipi0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABdKkvgsAACiqrYbdUN8l1LuJ5x1U3yUAAECDY6YtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAXSpL4LAACguO5+YkZ9l1Cv+vdau75LAACgATLTFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIF4IzKA/9LQ33Qn8cY7AAAAUJ+EtkA1Ww27ob5LqHcj9+lT3yUAAAAADZjlEQAAAAAACqTQoe2oUaPyjW98I6usskratWuXgQMHZurUqdXG9OnTJxUVFdVeRx11VLUxM2bMyIABA9KiRYu0a9cuw4YNy6JFi6qNefDBB7PlllumWbNmWW+99TJ69Oi6vjwAAAAAgGUUOrR96KGHMmTIkPz1r3/NuHHj8tFHH2XXXXfNvHnzqo07/PDD8+abb5Zf5557brlv8eLFGTBgQBYuXJjHH388119/fUaPHp0zzjijPGb69OkZMGBAdtppp0yaNCnHH398fvSjH+Wee+750q4VAAAAACAp+Jq2Y8eOrbY9evTotGvXLhMnTswOO+xQbm/RokU6dOiw3GPce++9eeGFF3Lfffelffv22XzzzTNy5MiccsopGTFiRCorK3PVVVelW7duueCCC5IkG2+8cR599NFcdNFF6devX91dIAAAAADAfyn0TNv/NmfOnCTJaqutVq39xhtvzBprrJHNNtssw4cPzwcffFDumzBhQrp375727duX2/r165eqqqo8//zz5TF9+/atdsx+/fplwoQJdXUpAAAAAADLVeiZth+3ZMmSHH/88dl2222z2WabldsPOOCAdOnSJZ06dcrkyZNzyimnZOrUqbn99tuTJDNnzqwW2CYpb8+cOfNTx1RVVeXDDz/MSiuttEw9CxYsyIIFC8rbVVVVtXOhAAAAAECD9pUJbYcMGZIpU6bk0UcfrdZ+xBFHlD/u3r17OnbsmJ133jmvvvpq1l133TqrZ9SoUTnrrLPq7PgAAAAAQMP0lVge4Zhjjsmdd96ZBx54IGuttdanju3Vq1eSZNq0aUmSDh06ZNasWdXGLN1eug7uJ41p1arVcmfZJsnw4cMzZ86c8usf//hHzS8MAAAAAOC/FDq0LZVKOeaYY3LHHXfk/vvvT7du3T5zn0mTJiVJOnbsmCTp3bt3nnvuucyePbs8Zty4cWnVqlU22WST8pjx48dXO864cePSu3fvTzxPs2bN0qpVq2ovAAAAAIAvqtCh7ZAhQ/Lb3/42N910U1ZZZZXMnDkzM2fOzIcffpgkefXVVzNy5MhMnDgxr732Wv70pz/loIMOyg477JAePXokSXbddddssskmOfDAA/Pss8/mnnvuyWmnnZYhQ4akWbNmSZKjjjoqf//733PyySfnpZdeyhVXXJHf//73OeGEE+rt2gEAAACAhqnQoe2VV16ZOXPmpE+fPunYsWP5dcsttyRJKisrc99992XXXXfNRhttlBNPPDF77713/vznP5eP0bhx49x5551p3LhxevfunR/+8Ic56KCD8rOf/aw8plu3brnrrrsybty49OzZMxdccEGuueaa9OvX70u/ZgAAAACgYSv0G5GVSqVP7e/cuXMeeuihzzxOly5dcvfdd3/qmD59+uSZZ56pUX0AAAAAALWt0DNtAQAAAAAaGqEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBNKnvAvh/thp2Q32XUO8mnndQfZcAAAAAAPXKTFsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACiQJvVdAHzc3U/MqO8S6l3/XmvXdwkAAIXS0L9H9P0hADQ8ZtoCAAAAABSI0BYAAAAAoEAsjwAAAAW21bAb6ruEejdynz71XQIAwJfKTFsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQKxpCwAAAJ/h7idm1HcJ9a5/r7XruwSABsNMWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBArGkLAADAp9pq2A31XUK9G7lPn/ouAYAGxExbAAAAAIACEdoCAAAAABSI0BYAAAAAoECEtgAAAAAABeKNyAAopIb+hicTzzuovksAAACgnphpCwAAAABQIEJbAAAAAIACEdoCAAAAABSINW0BoIDufmJGfZdQ7/r3Wru+SwAAAKgXZtoCAAAAABSI0BYAAAAAoECEtgAAAAAABSK0BQAAAAAoEKEtAAAAAECBCG0BAAAAAApEaAsAAAAAUCBCWwAAAACAAhHaAgAAAAAUiNAWAAAAAKBAhLYAAAAAAAUitAUAAAAAKBChLQAAAABAgQhtAQAAAAAKRGgLAAAAAFAgQlsAAAAAgAIR2gIAAAAAFIjQFgAAAACgQIS2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIELb/3L55Zena9euad68eXr16pUnn3yyvksCAAAAABoQoe3H3HLLLRk6dGjOPPPMPP300+nZs2f69euX2bNn13dpAAAAAEADIbT9mAsvvDCHH354Dj300GyyySa56qqr0qJFi1x77bX1XRoAAAAA0EA0qe8CimLhwoWZOHFihg8fXm5r1KhR+vbtmwkTJiwzfsGCBVmwYEF5e86cOUmSqqqqz13D4gUffu59vy4+mPd+fZdQ777IM1QbPIeew6T+n8PEs+g59BwWRUN/Fj2HxeA59BwWQUN/DpP6fxY9h57DxHNYBJ7DL/YcLt23VCp96riK0meNaCDeeOONrLnmmnn88cfTu3fvcvvJJ5+chx56KE888US18SNGjMhZZ531ZZcJAAAAAHzF/eMf/8haa631if1m2n5Ow4cPz9ChQ8vbS5YsyTvvvJPVV189FRUV9VjZV1dVVVU6d+6cf/zjH2nVqlV9l0MD5TmkCDyHFIVnkSLwHFIEnkOKwHNIEXgOv7hSqZT3338/nTp1+tRxQtv/3xprrJHGjRtn1qxZ1dpnzZqVDh06LDO+WbNmadasWbW2Nm3a1GWJDUarVq38w6feeQ4pAs8hReFZpAg8hxSB55Ai8BxSBJ7DL6Z169afOcYbkf3/Kisrs9VWW2X8+PHltiVLlmT8+PHVlksAAAAAAKhLZtp+zNChQ3PwwQdn6623zje/+c1cfPHFmTdvXg499ND6Lg0AAAAAaCCEth/zgx/8IG+99VbOOOOMzJw5M5tvvnnGjh2b9u3b13dpDUKzZs1y5plnLrPsBHyZPIcUgeeQovAsUgSeQ4rAc0gReA4pAs/hl6eiVCqV6rsIAAAAAAD+w5q2AAAAAAAFIrQFAAAAACgQoS0AAAAAQIEIbQEAAAAACkRoCwAAAABQIEJbAADqzJIlS+q7BL6GSqVSfZcA0OAs72u6r/PUtaVf8xctWlRtuyEQ2vK15AsH9a0hfSEB+DSNGv3n283x48dn/vz5/v9IraioqEiSvP/++/VcCdSOpf9vnD9/fj1XAsu3ZMmS8tf0hx9+OA8++GD+8Y9/lNugLpRKpVRUVGTcuHH5yU9+knfeeaf8PUBD4F8XX3lLv8H597//nbfffjsLFizwhYMvzdLn71//+lf+/ve/5/XXX0/ynx8mBRPUtaXPWKlUykcffbTcPqhvpVIpL774YnbZZZc89thjDeobberWDTfckJ/+9KdZvHixX9jzlbY0lLj//vtz0UUX5aWXXqrvkmAZS3/GHj58ePbYY48cdthh6dGjR+688856royvs4qKivzhD3/Ivvvum5YtW+bVV19N8p//bzaEn3ckW3ylLf0G584778zuu++eHXfcMRtvvHHuueeefPDBB/VdHl9zS5+/P/7xjxk4cGB22GGHHHTQQTn11FOTCG6pW0ufv7Fjx+bggw/Otttum1NPPTWPPPJIkgjGKIyKiopsvPHGOeCAA3LDDTdk3rx59V0SXxOTJ0/OuHHj0rhx4zRq1MjXXL6Sln49/8Mf/pDvfOc7Wbx4cRYvXlzfZUHZxycJPP/887n33nszduzYjBkzJocddli+973v5cYbb6znKvm6euaZZ3LkkUfmnHPOyfnnn59vfOMbSf7zlzYN4ecdoS1fSUtnUywNbA844IB873vfy80335ztttsuhx56aG655RbBLXWqoqIid999dwYNGpQDDzwwd911V3bdddeMGjUqxx57bHmMHyKpC0t/YbDPPvukbdu2Ofroo3Prrbdm2LBhmTJlSn2XRwP23zMel25vt912eeyxx8p/zm5mJDXx8a+lS5+dkSNHVvtvQ/jhja+fioqKPPHEEzn66KNz2WWX5bTTTsumm26aJHn77bfL43w/SX1YsmRJ+f+tCxYsSJIMGDAg22yzTXr06JELLrggJ554Yg499NDcdNNN9VkqX1NTpkzJpptumiOOOCLvvvtubr755uyxxx7ZbLPNcsEFF5TXuf26EtrylfL4448n+X9/mvGvf/0rF1xwQc4444wMHz48q666ah5//PGsssoqOeqoo3LTTTdZ64xaM2XKlHz44Yfl7TfeeCOXXHJJfvGLX+TYY4/NmmuumV/96lfp06dPbrzxxgwZMiSJ4JbaVyqV8tZbb+Xss8/Oz3/+81xwwQU58MAD895772WbbbbJZpttVt8l0oAt/Rr917/+Nf/617/K20cddVRatWqV4cOHVxsHK+LjgezSZ6dx48b57ne/m4kTJy6zRAwU1ahRo3L++edXa5s8eXLWX3/9HHLIIZk/f37uuOOOfOc738mAAQPyi1/8IolfSlA/lv7/dsSIEfnOd76T3XffPY8++mhmzZpVHjNq1KiceOKJ+dGPfpRrrrmmvkrla+TjPzt36NAhjzzySM4888x85zvfyU033ZS11lorhx9+eIYNG5YXX3yxHiute75b5ivjd7/7XU4//fRqv3FevHhx9tprrxx22GGZNWtWdt5553z729/O1KlT853vfCcjRozIb37zGzNu+UJKpVLuvffe9OjRI7///e/LbxDRvn377LTTTunXr19mzpyZHXfcMQMGDMgdd9yRfffdN1deeWUOPfTQJL7RpnZVVFSksrIyixYtyg9+8INMnz49Xbp0ycCBA3PhhRcmSR566KG89dZb9VwpDdW9996bAw88MNtuu21+/etfZ9KkSUmSY489Nq+99lqmT5+exMwxPt1ZZ52VsWPHlrcvuuii7LXXXnnqqadSVVWVysrK7L///vnLX/6SP/zhD/VYKay4hQsX5uSTT86VV15ZbltppZXy3nvv5cwzz8yAAQNy3XXXpVmzZtl9993zi1/8Ik888UQ9VkxD9PG/hrn66qtzxRVX5Jvf/GZ22GGHPPjgg8tMjho1alQOPfTQ/OY3v6mPcvmaWPp94Zw5c7JgwYLMmTMnu+yyS84999yMGTMmm2++ec4888xceeWVOfXUU7P11lt//ZfdKsFXxIsvvlh6/fXXS6VSqTRjxoxy+9K2n/70p6UBAwaU5syZUyqVSqUTTzyx1KZNm1L79u1L77777pdeL18/Rx99dGnllVcuXX/99aX333+/VCqVSosWLSqVSqXS+eefX/r/2rvPuKiurY/jv6GJiB1iwa5YYleMvYO9gL1iiaiIvaKJXaPYRbGiYMWuYC/Ye8OuWBAF1GBDpLf9vPAyj9zk3sRcZRDW903imTPjmnxOzjnzP3uv3bJlS/X69WullFILFixQlSpVUuXLl1chISE6q1mkH0lJSUoppRITE5VSSgUHB6siRYqotWvXKktLS+Xg4KA9Hh8/fqzs7OyUr6+vzuoVGUvycfm5y5cvq5kzZ6oSJUqon376SY0aNUqdOHFCZc2aVa1atUoHVYrvyc2bN1WNGjVUkyZNtOeyzZs3q0qVKikrKytVp04ddfToURUeHq5mzpyp2rVrp70GC5FWJV/L586dq/T09JSbm5tSSqnQ0FA1aNAgVatWLTVw4EB1/vx5pZRS/v7+ysrKSt29e1dnNYuM7dq1a2rIkCHK29tbu23atGlKT09PLV68WPubKFnyMS7El0o+dvbt26eaNGmifvrpJ1W9enXtPUBkZGSK/cePH69KlCihXr58meq1piYDXYfGQvyVLVu20KhRI0qXLg3A7du3cXBwoFu3bgwdOpRChQoBEBgYSJ48eTA1NQU+PaU5ePAgJUqUIEeOHLoqX6QD8fHxGBoasmzZMgwNDXFyctJOyUw+3m7fvs379+8xMzMDPrXu6NChA8OHDydLliy6LF+kA+pfi5ScOnWK06dPM3jwYCwsLLC3t8fBwQEbGxtWrVql3d/Dw4OAgABKliypw6pFRpGUlKSdPnnr1i0iIyOpWbMm1apVo1q1arRp04ZHjx4xduxYAgICiIiIYNGiRTRt2lR7DRfi31WoUIHp06fj6urKrFmztKNqk0fW7t69m169elGlShXevHlDdHQ0r1+/xszMLMUxKURaof610rlGo8HBwYHQ0FBGjBiBoaEhDg4OLF68mKioKLJly6Z9z6ZNm4iKiiJXrlw6rFxkVOfOncPa2hojIyN++ukn7faJEyeilGLkyJHo6elhb2+vPW6T28LJLEPxpZLXK+rYsSPTp0+nYsWKbNy4EWtra65fv06lSpWAT7O5tmzZwt69ezly5Ah58+bVbeHfmNzNiDTt3r17zJgxgx49evDu3TvgU/+yIkWKsGPHjhQhRb58+di9eze//fYbvXv3ZvXq1ZiZmWlDNCH+KQODT8+3Lly4QLNmzUhKSmLMmDHs3r1b23qjRYsWBAcH0717d/r06YO7uzsdO3aUwFZ8FcmrSrdt25bw8HAePXoEQJcuXejatSt+fn6sXLmS1atXM2TIEFxdXVm3bh0FChTQceUiI0gOx8aMGUPr1q1p1KgRNjY2+Pj4kJiYSLly5bCzs+POnTt0794dR0dHHj9+zN27dwFZkEz8kfrX9Ehra2uGDh2KkZERU6dO5ciRIwA0b96cVatW4eXlRZMmTXjz5g23bt3i119/lcBWpFkajQY9PT127txJrVq1CA4OJnPmzNoFyAwMDLTB16FDhxg6dChLlixh06ZN6T6UEGlT7dq1mT17NkopTp06RXBwsPa1SZMmMWXKFIYOHao9NyeTwFb8E3Fxcaxdu5Zff/2V0aNHU7p0ac6dO4eDg4M2sI2KiiIgIIDIyEhOnjxJ5cqVdVt0atDdIF8h/lpiYqLatGmTqlevnmrevLkKDQ1VSil179491bt3b1WzZk21bNky7f59+vRRVatWVXXr1lU3btzQVdkiHfLx8VEGBgZq1qxZavz48apVq1bKxMRErVu3TsXHx6s3b94oV1dXVa9ePdWyZUs5/sRXdfPmTfXDDz+kON99/tqECRNU/vz5lZWVlbK1tVW3bt3SQZUio/m8JYK3t7cqU6aMOnjwoDp79qyqW7euqlOnjtq8efOftk7o27ev+umnn1R8fHxqliy+I8ntXpRS6vDhw6ply5bK2tpaHTt27A/7vnv3Tk2fPl3VqlVLBQYGpmaZQnyRmzdvKlNTU7Vq1Sr1/v179fTpU/XLL79oWyUkJSWpiIgI5ezsrOzs7NSdO3d0XbLIIP7sWp1s1qxZysLCQk2dOvUPbd88PDzkWi7+sc+v9W/fvlXFixdXp0+fVu/evVMWFhaqf//+2tdXr16tXrx4oeLj4//QliM90yglK0CItEn9a1pFUlIS27ZtY+nSpWTNmpX169djbm7O/fv3mTNnDv7+/vTs2RNHR0cA3r17R+bMmcmcObOOv4FIL6Kjo2nWrBmVK1dm0aJF2u0DBw5k/fr1rFy5km7duqGvrw98egJoYmKio2pFerRlyxYWL17MkSNHyJo1KwAJCQnaUeAAb968IXfu3MTExMj5T6Sqffv2cebMGczMzBgzZgwAb9++xd7eng8fPjB48GA6deqEnp6ett3M3r17mT17NgcOHCB79uw6/gYirfh8lKz6t+m1hw4dYunSpcTGxjJ+/HgaNWoE/H8Lo7CwMEqWLMkvv/zCsGHDdFK/EH/l2LFjDBkyhFOnTvHDDz9ot0+YMIG5c+eyevVqevfuTVRUFPHx8XJ+FKni83PvmjVruHLlCoaGhpQuXRonJycAZsyYwYoVK+jfvz/9+vUjf/78KT7j3+9Lhfgzycfax48fMTQ0xNjYmBMnTtCwYUMAfv75Z7Jly8aOHTto1aoVS5YswcDAgA8fPuDg4IC1tTUODg4ZajS3zB0SaZZGoyExMRE9PT06deqEk5MTHz9+xN7entevX1OmTBnGjh1LqVKl8PLy0q6YnitXLgksxFelr69PbGysdmpafHw8ACtWrKBu3bpMnDgRT09PIiIiACSwFV/dx48fefXqFdHR0dptyTfGJ06c0PZT1mg0GBsb66pMkQGFhYUxePBg5s6dq23bAZA7d27Wr19P9uzZWb58OZ6eniilMDQ0BODMmTM8evRIWiMIrc9Dg7Vr1+Lo6MjQoUPZvHkzAM2aNcPJyYlMmTIxe/ZsTpw4AYChoSGJiYnkyJGDGjVq8PHjR2RMikjL/P39+fDhAwCJiYkA9OjRAyMjI/r27cuqVaswMTGRwFakmuRz77hx43B2diYhIYH79+/j4uJCixYtAPj1118ZNGgQa9euZcGCBbx58ybFZ0hgK/4OPT09goODadOmDadPn8bLy4vGjRtz4MABACwtLVm/fj2WlpbMnz9fe1y5uLhw48YNmjRpkqECW5DQVqRxySMXk4NbR0fHPw1u8+TJw+HDhwkLC9NtwSJdMjIyokiRImzbtg349AMxObi1tLQkNDSUyZMna2+8hfja8ufPz9u3bzl+/PgfQq7NmzdrAzGQPmIideXIkYMzZ85Qo0YNLl++zNGjR7WvJQe3MTExXLlyRXtsRkREYGRkxIEDB8iZM6euShdpzOehwfjx49HX1+fFixfMnTuXX3/9FfjUy3bw4MFkzpyZUaNGcf36deDT/aKPjw/79u2jXbt2ch4UaULydfnzhwj169encePGDB8+nMDAQO1vndy5c9OpUyd+++036tatq5N6Rcbz+T3lxYsX2bx5Mzt27MDd3Z0DBw7g7u7O7du3ad++PfBpRHjPnj158uQJuXPn1lXZ4jtnbm6ORqPByckJe3t73N3dtQ8HnJ2dsbOz4+XLl/Tr149JkybRo0cPli9fzrZt2yhSpIhui9cBaY8g0pzk6XDXr1/n2rVrGBsbU7p0aapVq0ZiYiJbtmxh+fLlZM2alQ0bNmBmZoa/vz/ZsmUjX758ui5ffOc+n46ZmJiovZm+cuUKP//8M2XKlGHr1q3a/UePHk2bNm0oXbp0imluQvwTycefn58fr169wtDQEGtrawD69u3L9u3bWb58OXXq1MHExIQFCxbg4eHB2bNnsbS01HH1Ir37bws8PX36FDs7O3744QecnZ21U9cBwsPDMTU1TfHez8+vQiRbs2YNs2fPZvPmzVSrVo3NmzfTp08f8ubNi52dnbZF0Z49ezh37hwuLi4pjqvAwMAM+YNOpD3J1/MzZ85w6tQpMmfOTOfOnSlQoAC7d+/G1dUVIyMj5s6dS5YsWVi7di0HDhzg7Nmzsoit+Ob69u3LqFGjKFu2rPba7u3tjaOjI/fu3SNHjhzAp9mF3t7e/Prrr7i7u1OnTh3g/4/vf29jI8RfSb7/O3z4MK1btyZfvnwsX76cxo0bkylTJu1+8+bN49q1awQHB1O+fHkGDx7Mjz/+qMPKdUdCW5GmJJ/4d+3ahZOTE4ULF8bAwIC3b98ya9YsbG1ttcHt6tWriYuLY+/evfKkT3wVycffsWPH2LVrF0+ePKFjx47Y2tqSO3duvLy8mDVrFgAtWrQgMDAQHx8fbt26JYGZ+Gp27NiBg4MD2bJlQylFrVq12LJlCwCDBg1i586dAFhYWPDmzRu8vb0zxsqpQqc+D2yXLVvG3bt3CQ4OZvDgwVSsWJEffviBJ0+e0L59e/LkycP48eNp0KDBf/wMIeCPfWtnzZpFVFQU06dPx9vbmz59+jB+/Hjevn3LypUrcXJyYsaMGSk+IykpCaWUPAQQac6BAwdo06YNjRo14tSpU1SvXp1x48bRsmVLfHx8WL58OYcPH6ZEiRJ8+PCBQ4cOyfVcfHNv376lRYsWPH/+nFOnTlGyZEkA7t27R5MmTVi6dCm2trba/QMCAqhatSpr1qyhXbt22u0S2Ir/xaVLl/j9999ZvXo1ISEhjB8/njZt2qQIbgFiYmLIlClTxj7WUm3JMyH+plOnTilzc3O1fPlypZRSJ06cUJkzZ1ZZsmRRGzduVEp9WmVwzZo1qlmzZurZs2e6LFekM7t371bZsmVT9vb2auzYsSpr1qzK0dFRPXz4UCml1K1bt1SvXr1U06ZNVZs2bdTNmzd1XLFID5KSkpRSSkVGRipra2u1fv169fDhQ7VlyxaVN29e1axZM+2+Z86cUXv27FF79+5VwcHBuipZZFDjxo1T5ubmavTo0crOzk79+OOPatKkSSooKEgppdTjx49VlSpVVOXKldW1a9d0XK1Iy168eKH99wMHDiilPt3fBQYGquDgYFWuXDk1d+5cpZRSfn5+Knfu3MrExES7TYi0KPl6/urVK9WrVy+1evVqpZRSoaGhqnHjxqpOnTrK29tbu/+ZM2fUpUuX5HouUk1SUpIKDg5WzZs3V+bm5srf318ppVRISIiytrZWHTt2VOfPn9fu/+bNG1WpUqUUx60QXyr53KiUUomJidp/j4mJUU2bNlWVK1dWO3fuVLGxsUoppTw8PFK7xDRLRtqKNEUpxS+//EJ8fDxz584lODiYOnXqUKdOHYyNjfHy8mLz5s20bduWxMREIiMjyZYtm67LFunEzZs3sbOzY/z48Tg4OACQPXt2NBoNLVu2ZNKkSZQqVQr4NLInKSlJmu6Lr+bkyZMsWbKELFmyMG/ePH744QcSEhLw9fWlV69eVKpUiUOHDum6TJGBrVu3jqlTp7Jz504qV67MqVOnaNiwIaVKlcLOzo4hQ4aQL18+/P39mTVrFmvXrpWRteJPHT9+HBcXF6ZPn46XlxdLlizh2bNnWFhYAHDkyBEGDx7M8ePHKVCgAFevXmXOnDm0a9eOjh07yqhakaYlt+4ICwvD1dWVSpUqAfDy5Uvs7e2JiYlh+PDh0n9ZpLqEhATtb5dbt27Rv39/3r59y4EDB7C0tOT06dMMHTqUfPnyUbt2bSpWrIirqytv3rzh6tWrcu4V/4j616hsX19fDh8+zIMHD+jXrx/ly5enaNGixMbG0rZtW96+fYudnR3h4eHMmTOHBw8eaEeCZ2RyJy10Lvm5wcmTJ7lz5w6Ojo60adOGyMhIOnTogI2NDRs3bqRnz57ExcVhZ2fHli1b0NfXl8BWfFUfPnyge/fuODg48Pz5c4oUKULfvn3ZtGkTXl5ezJ8/n0uXLgGfFkyRwFZ8LYmJiQQEBHD27FlOnDih7Y9sYGBA48aNWbduHXfu3NH2EhMitSUvVuLo6EjlypXZvXs3tra2rFy5ktatW7N06VLc3NwIDAykVKlSeHp6oqen94eF84QIDw/HwsKCjx8/0rVrV9avX8/du3exsLDQLuiZNWtWYmNj2bp1KyEhIUyePJmsWbPSuXNn9PX1ZeFPkaaZmZnx4MEDzp8/z507d7Tb8+XLx8aNG8maNSvTpk3D29tbh1WKjCj5t8vEiRMZNWoUenp6PHnyhPr163P//n3q1avH8uXLKVKkCCtXrmTatGkYGRlx+fJlOfeKf0yj0bB7927s7Ox4/fo1pqamjB07lgULFnD79m0yZcqEj48PJUuW5PDhwxw8eJDr169LYJtMp+N8hfiX48ePq6xZs6pt27Zpt50/f15ZWVlpp6XfuXNH2draqilTpqgHDx7oqlSRjoWGhqr79++r+Ph41a5dO9W7d28VFRWllFKqSpUqSl9fXw0ZMkTFxMTouFKRXnw+Vejdu3dq3bp1KmvWrKpPnz4p9ouPj1d79+5VpUqVUs+fP0/tMkUG9PmxmSwoKEi9evVKBQcHq8qVK6v58+crpT6dO3/44QdVoEABtWzZsv/4fiFOnTql6tWrp4KCgtTMmTOVvr6+qlu3rjp79myK/UJDQ9XQoUNVvnz5lIWFhapataqKi4tTSsmxJdK25OMzICBAVaxYUTVq1EidPHkyxT4vXrxQ7dq1U4GBgbooUWRwK1asUKampurcuXPq+fPn6vjx46pBgwbK3Nxc3b9/XymlVFxcnPr48aMKDQ3VHtPx8fG6LFt8x65evaqKFCmi3N3dlVKfWiKYmJiowoULq379+qm7d+8qpT61SHr16pV6//69DqtNe2SkrdC5ly9fcujQIX755Rc6duyo3R4VFcW1a9d4/vw5AJs3b0YpxYgRI7RT1IX4p9S/RniHhYURFxeHUgpzc3NKly5NTEwMwcHB1KpVi8yZMxMXF4eVlRVubm4MHTr0Dw3ShfhSycdfZGQkSini4uLImTMnbdq0wdXVlf3799O/f3/t/gYGBjRr1oxr165RsGBBXZUtMoikpCTtlN3IyEiioqIAKFCgAHny5OH58+d8/PhRu9BYSEgIzZs3Z8yYMdrjVqb8ij+TJ08elFI4OTnx6tUrDh8+jJ6eHrNmzeLIkSPa/czNzZk4cSKHDx/G3d2dS5cuYWhoSEJCghxbIk1Jvp6HhoYSGBhIfHw8CQkJFC1alK1btxIaGsrs2bM5deqU9j358uVj27ZtFC5cWFdliwzs0aNHtG7dmlq1alGwYEEaNmzIqlWrKFq0KM2aNePJkycYGhpiamqKubk5Go0GpZTMMBRf5POZVq9fv8bOzo6ff/6ZwMBASpcuTZ8+fRg7diwbN27E1dWVGzduoK+vT548eciRI4fuCk+DJLQVqU591kb5wYMH1KlTh+3bt5M7d+4Ur1eoUIHu3bvTunVrfvrpJ1xdXZk6daq0RBBfhUajwcfHB2tra2xsbOjbty9xcXHApxvvN2/e8PjxY86fP8+MGTPw9fWlS5culChRQseVi++d+ldfp8OHD9OtWzesra3p3r07r169IkeOHNjZ2eHi4oKPjw+Ojo7a9xkYGJAlSxYdVi4yiuQ+tNOmTcPGxoY2bdqwcuVK7esRERFoNBrOnz+Pn58fEydORCnF0KFDZfqk+K9KlSrFmjVrAHj48CHFixdn+fLlfPjwAVdXV44dO6bd99ixY5QvX55mzZppjysJDURaknw99/b2pkmTJtSvX5/q1avj7u7O77//TqlSpdixYwfBwcHMmzcvxfEtvUFFavizFkUJCQlcvXo1xTZLS0t69OjB8+fPKVOmjHbQVDJ5WCb+SvKxFhERQWxsLHp6evj6+vL7779Tr149nJycSEhIYPjw4TRs2JBFixYxaNAgLC0t2bVrF+vWrdP+FhcpSWgrUsXnF4zkk35wcDClS5emZcuWBAYGcuHCBd6/f6993dzcnEmTJrFixQrat2/P9evXqVixok7qF+lH8kMBPz8/OnfuTMuWLalatSo3b97EysqK2NhYihUrxvjx41m5ciX29vasXbuWbdu2kT17dh1XL9KD5B94HTp0oFy5crRr147Q0FDq1KnDo0ePyJ49O3Z2dsyZMwd3d3eGDx+u65JFBrRixQpWrlxJ8+bNKVy4MEOGDOHXX38FwMbGhtq1azN//nxat25NaGgo7u7uwKdzrIQR4r+xtLRk7ty5KKUYMGAA+vr6rF69mvDwcObOnYurqyutWrVi5MiRKe4f5bgSupZ8D5l8XGo0Gg4cOEDPnj3p2rUr586do1KlSsyZM4elS5fy8uVLbXDr5+fHypUriY6O1uVXEBlM8kPY8+fPEx8fD0C7du3InDkzs2fPJiYmRrtv0aJF6d27N2PHjtUuCinE36Wnp0dISAhVq1bl2rVreHl5YWNjw/Xr1zExMaF48eKEhYXx9OlTGjdujIGBAWFhYVSoUIEhQ4YwfPhwjIyMdP010iSN+nzYoxDf0JMnT5g9ezarV69m9+7dTJ06lYMHD5IvXz6GDx/Orl27GDduHD169JBwTHxT169fJzQ0lJs3bzJu3DgSExPx8/Pj559/JjY2lhs3bmBsbIyfnx/6+vqYm5uTL18+XZct0okHDx7QtWtXHBwcGDRoEEFBQdSpU4ePHz9iaGjI6dOnKVWqFO/fv+fgwYNYWVlJI37xzSUlJWl/3AG4u7uTM2dO2rdvT1RUFFu2bGHgwIGMGDECFxcXAK5cuUJiYiLVqlVDX18/xarUQvyVR48eMXjwYACWLFmCnp4eEyZM4PHjx5ibm7Nv3z4MDQ21oxmF0LXr169TpUoV7Z9fvXpFt27daNq0KePGjePdu3dUrVoVU1NToqOj6datG05OTuTJk4dHjx6hr69PsWLFdPgNREbx+TX9zp07VKhQgYULFzJs2DCioqIYN24cN27coG7duowcOZKoqCicnJwoXLgwS5cuBT4tkisPy8SXatmyJZcuXeL9+/esXr2avn37ah94BQYGYmdnR9u2bbG1tcXb2xsfHx9OnDgh+c9/k6oddEWGlZSUpHbs2KGyZ8+uGjRooDQajdq4cWOKfQYOHKiKFy+uli1bpsLCwrTvE+Jrev36tSpTpozSaDRq/Pjx2u1JSUnq6tWrqkKFCqps2bLaBciE+BqSz2WxsbEqICBADR8+XCUkJKigoCBlaWmp+vXrp+7du6dKliypSpUqpe7du5fifUJ8S58fZ1u3blUeHh6qevXqysPDQ7s9NjZWrV27VhkZGSlnZ+c/fEZCQkJqlCrSmYcPH6omTZqopk2bahciCQ0NVYmJiUopWfhGpB2nTp1SGo1GLV26VLstLCxMeXp6qsDAQPX777+rkiVLqgEDBiillOrQoYOysLBQw4cPVy9evNBV2SID+vya7uLiombMmKEyZcqkDA0N1W+//aaUUio8PFyNGzdOVaxYUenr6ytLS0tVvnx57YKPQvwdydfqiIgI9ebNG6WUUmfPnlUajUZly5ZNnTx5UsXGxqZ4z7Rp01TRokVVoUKFVP78+dW1a9dSve7vjYS2IlWNHDlSaTQaVatWLe226Oho7b8PHDhQlSpVSs2fP199+PBBFyWKdC42Nlbt3LlTVa1aVVWpUiXFa0lJSeratWuqUKFCqnr16jqqUKRX27dvV506dVJJSUnq8ePHSimlevfurTp06KC9obG1tVUajUYVL15cxcbGSmgrvrnPj7EJEyYoQ0NDVa1aNWVkZKR69OihIiIitK/HxsYqDw8PpdFo1MqVK3VRrkiHHj58qJo3b66qVq2qbt68qd2e/GNQiLQgODhYTZgwQeXMmVO5ublpt4eGhiqllJo6dapq3bq1dtXzqVOnqgIFCqhmzZpp9xEiNU2ZMkWZmZkpHx8ftWXLFuXs7Kz09PTU9OnTlVJKxcXFqffv36tdu3apo0ePah++ysMy8XckX6P9/f1V9+7d1dKlS9Xr169VQECAOnr0qOrYsaMyMzNT+/bt+0Nwe/v2bXXmzBkVFBSki9K/OzKHTXxz6l/T2pKSkihSpAiOjo7s37+frl274uXlhbGxMdHR0WTOnJnly5fTp08f1q9fT9++fXVdukiHjIyMaN68OUZGRgwbNgwbGxuOHj0KfOpNVrlyZby9vWXBO/E/i4iIwNTUFPg0DXjq1KkMGTIEgOLFixMdHc3Dhw/p0qWLtodT3rx52bt3L1WqVJG+TiJVJE879/f359KlS1y8eJF8+fJx/vx5unXrRu7cuXFxcSFTpkwYGRnRrVs3zM3Nadq0qY4rF+mFpaUl8+fPx93dnXLlymm3f96uQwhds7CwYNiwYRgaGjJhwgSUUjg5OWFubg58Wh09Li5O2yImPDycGTNm0KJFC+0+QqSWiIgIjhw5wtixY2ndujUAnTt31rYlzJQpEyNGjNAugJtMFnwUf0dy+41bt27RtGlTmjRpQsGCBcmdOzdmZmYULVoUa2trbG1t6dWrFxs2bKBx48YYGRmxbt06mjZtmuJ6L/476WkrvqnkwPb8+fOEh4dTvXp1cuTIwZYtWxg3bhy1a9fGy8tLu//jx48pUaIEoaGh/PDDDzqsXKQHycfftWvX8PPzQ6PRULt2bUqXLk1MTAzHjh1j9OjRFCpUiCNHjui6XJGOXLt2jU6dOnHz5k2ePHnC1q1bCQkJwd3dHY1Go70hbtWqFQEBAbi5ubF792727NnD2bNnKVSokI6/gUjvPu935+Liwr59+8idOzebNm0iS5YsAHh7e9OpUycGDRrE7NmzyZQpU4rPkB624lv49/7KQuha8jF579493r17h6+vL/Pnz8fFxQVHR0cAJk+ejLe3NzVq1CAmJoYdO3Zw8+ZNihcvruPqRUYUFhZGuXLlGD58OKNHj0Z9mmFNYmIinTp1wtvbW9vjVknfcPEPBAYGUq9ePbp3787MmTNTXLc/v463adOGy5cvM27cOJ49e4arqyv379+nVKlSuir9uyN3ROKbSb4A7Nq1i5YtW3LhwgXCw8PRaDS0bduWOXPmcPbsWTp37kx0dDSTJk2ia9euhIWFSWAr/mefH39t2rRhxYoVrF+/nrp163L27FmMjY2xtrZm3rx5vHjxgp9++knXJYt04ubNmzRs2JBWrVphamrK5MmTcXV15e7duxgaGmJgYEBCQgIAU6dOJXfu3PTq1QtfX1+8vb0lsBXfnFJKezPt5+dHq1atuHjxIufPnycwMFC7X9u2bdm2bRurVq1i4MCB2pWnk0lgK74FCWxFWqOnp8fevXupXr06+vr69O3bl+HDhzNu3Djc3NyAT9fz2rVrExwcTFBQEOfPn5fAVqSKpKSkP2zLkSMH7dq1w8PDg/v376PRaNBoNBgaGlK8eHEaNGjAiBEj2LFjhwS24h/x9vbmxx9/ZNKkSdrrdnBwML6+vixZsoStW7cC4OPjQ5MmTdi2bRsnT57k+vXrEth+Kd10ZRAZha+vr8qaNavy8PBQMTExKV5LSkpSu3fvVnny5FGFCxdWefLkUZcvX9ZRpeJ792e9P0+ePKnMzMzUqlWrlFJKXblyRWk0GpU5c2a1f/9+pdSnnso7d+5U1apVU8+ePUvVmkX6c/PmTWViYqImTJig3RYTE6M6duyoSpQooRYtWvSHPo3x8fHK399fvX37NrXLFRnQ58efs7Oz0mg0Kj4+Xl24cEEZGhqqnj17quDg4BTv2bJli6pfv770GBVCZEhhYWHql19+UXPnztVue/nypZo4caLKmjWrcnV11W6Pj4+XxWxFqvn8uuzn56fOnz+v7R964cIFZW1trdq2basePnyolPr0u6dt27Zq7969ytHRUVWpUkW9e/dO1lAQX2z06NGqadOm2l7IXl5eys7OTv3www+qYMGCytjYWI0dO1a7/4sXL7SLzYsvI+0RxDc1fvx4AgMD8fLyIioqihs3buDh4UG2bNlo1qwZNjY2vHjxgosXL2JlZSUjzMQ/kjwF4/Xr1zx79gwAKysrpkyZglKKqVOnEhISQq1atWjcuDGJiYls3bqVQ4cO0aBBA2JjY4mPj9f2HxXinwgKCqJKlSo0atRI+3QZYN26dfj6+vLx40fevHlDnz59tD27ExMT0dfX11XJIgO7c+cOa9asoW3btjRo0ACAU6dOYWNjQ/fu3ZkxYwYWFhZ/eJ9MXRdCZCQ3btygQYMGWFhYMH36dNq1a6d97dWrVyxbtgw3NzfGjx/P6NGjdVipyMjGjBnD1q1bef36NdWqVWPs2LG0atWKvXv34urqyrVr16hevTrPnz9HT0+P27dv89tvv7Fnzx4uXrwo13XxxTw8PHBwcGD48OGEhIRw5MgRevbsSbt27ahSpQqLFi1i3bp17N+/n5IlS+q63O+azGsTX5X6rCdOfHw879694/nz5xw+fBhPT0/ev3/P+/fvyZUrF9evX6d8+fLkz58/xQ2QEF/i8z5j/fv3J2vWrGTOnJldu3bRunVr4uLi+PjxI+3bt6dZs2asXLmSc+fOsWHDBho1asShQ4do0qTJH3o1CvGlEhMTKVq0KDExMZw7d47atWsza9YsZsyYwYULF7CwsGDAgAF4enqip6dH7969JbAVOrF7924GDx6MqakpQ4cOJSkpiaSkJOrXr8+xY8ewsbFBT0+PyZMn/+FhqvywE0JkJGXLlqVVq1Zs3ryZkJCQFK/lzZuXQYMGERkZiaurK/369SN79uwy3Vx8c58/QN23bx8HDx7E3d2dXLly4ezszPTp04mMjKRz585UqlSJ/fv3c+fOHWrVqsW4ceMAePbsGYUKFSI2NhZjY2M5bsUX6dOnD7///jv79+8HYPPmzVSrVo1cuXIBn86PBgYGmJmZ6bLMdEFG2oqv7ujRo5iYmFC7dm3u3r2Lra0tsbGx1K1bl65du9KqVSu2bduGi4sLx48fJ3v27LouWXynkh8S3L17lzp16jBo0CAGDBiAhYVFijDs8uXLODk5sWHDBkqXLs3du3eZNm0ahQsXpk+fPpQpU0aH30KkJ48ePWLo0KEYGRmRJ08evL292bBhA02aNAE+jcoZNmwY9+/fZ9y4cXTv3l3HFYuM6ODBg6xcuZKDBw9y9OhR6tWrR2JiIkopDAwMOHPmDPXr12f69On88ssvui5XCCFSjfqTRZmUUnTr1o2DBw/i7e1N/fr1U7z++++/o6enh7m5eWqWKjKg169fpzjOvL29OX/+PDlz5sTZ2RmA8PBw7O3tefHiBSNGjKBDhw4YGhpq3xMaGsrs2bPx8PDg7NmzlC1bNtW/h/h+/Nk58XORkZEYGhpiZGSUYvvo0aN58OABXl5eZM2a9VuXma7JSFvxVcXGxuLp6YmXlxenTp2ibt26XLhwgXfv3qUYFu/n54epqSnyzED8LzQaDe/evWPgwIHY29szc+ZM7WufP4F++/Yt165d0y7+tGXLFiIiIpgyZQomJiY6qV2kT5aWlixevJjBgwezceNGpk+frg1sExISyJs3LwsXLmT8+PHUqVNHx9WKjODP2hk0b96cnDlz8vHjR/r378+6deuoXr06SUlJJCQkULduXfz8/OSHnBAiQ0kOJy5dusSlS5eIioqiTJkytG3bFi8vLzp27Ej79u3ZvXs3devW1b4vT548OqxaZBR9+vShfPnyjBw5kqSkJCIiIhg1ahQBAQH06tVLu1+2bNlYv3499vb2uLm58eHDBxwcHNDX1+fNmzesWLGCK1eucOLECbnOi//K1dWV4sWL07Rp0z8sPpt8vsySJUuK7e/fv8fFxYW1a9dy+vRpCWy/AhlpK76Kz5/APH/+nEmTJuHl5YWvr2+KYOLkyZMcOnSI5cuXc/r0aSpWrKirkkU6ce/ePdq0acPatWupU6fOH8IJpRQJCQl07tyZPXv2YGVlxf379zl79qwcf+KbefLkCYMGDUJfX58JEyZoz4Px8fEYGhpKL1uRKj4PbLdu3cqrV694/fo1ffv2pVixYly5coWZM2cSFBTE8uXL+emnn0hMTATQHp8JCQl/uFEXQoj0aufOnfTr1w9ra2siIyN59OgRjRs3ZsWKFSQkJNC9e3dOnz7Nxo0bady4sa7LFRmIp6cnXbp0wdjYmA8fPpA9e3ZevnxJly5dePfuHXPmzKFZs2ba3+Th4eG0bNmSH3/8kZUrV2o/5+XLlxgaGsq0dfGXatasyePHj/Hy8qJBgwZ/eT84fvx4Hj9+zM2bN9m2bRuVKlVKnULTOQltxVcRFRWFiYmJNrwNDg5m/PjxbN++nZMnT1KjRg1evHjBsGHDePbsGe7u7lSoUEHXZYt0YPPmzfTq1Yu4uDg0Gs2fjiqLiorixIkTxMfH8/TpU1q1aoWlpaWOKhYZRXKrBKUUEydOpHbt2rouSWRQY8eOZePGjTRq1Ij79+8TERHB6NGjcXBw4OTJk7i6uhIUFMTChQtlBLgQIsPy9/enSZMmODs74+joyO3bt6lTpw69e/dm8eLF2v2aNm3K48ePuXPnDpkzZ9ZhxSKjCAkJIW/evOjr6+Pu7s61a9cYO3YsRYsWJSQkBFtbW7JmzYqzs7N2hhd8+g1kbGyMnp6eLCQq/rbPB+S1bNmS69evs27dOho0aPCHNgifW79+Pf7+/vz8888UK1YstcpN9+T/WvE/u379OpaWlty/fx+NRoNSigIFCvDbb7/RunVrGjVqxPXr18mfPz8LFy5k7969EtiKr6ZIkSIYGBiwa9cu4M8XyfH09GTx4sXY2toyYsQICWxFqrC0tMTV1RVDQ0NGjx7NxYsXdV2SyIC2bt2Kl5cXBw8eZOPGjUybNo1Hjx5pR9g0aNCAkSNHkjlzZtauXavjaoUQQneCgoIwNzfH0dGRZ8+e0apVK7p27aoNbK9cuQLAoUOHOH36tAS2IlXs2rWLli1bEhkZCXxaQOzcuXOsWLGCwMBALCws2L17N+Hh4cyePZtjx45p32tiYiKBrfhiGo2GuLg4APbv30/RokUZM2YMJ0+e1LYb/DP29vZMmTJFAtuvTP7PFX9bUlJSij8nT6FUSlGyZElatWqFv7+/drRjwYIFcXR0JCYmBisrKy5cuECBAgWk75P4qgoXLqzt3fTs2TPt9s8nETx58oQqVapID2WR6iwtLZk7dy4FChQgf/78ui5HZAD/fp578eIFderUoWLFinh5edGtWzfc3Nyws7Pj48ePBAcHU6dOHRYvXoy7u7uOqhZCCN0zNjbGzMyMGzduULduXZo1a4abmxsAV69eZePGjQQEBKDRaLCwsNBxtSKjCAkJoVChQmTLlg2A6dOn0717d44ePYqbmxuBgYEUKFCAPXv2EBkZyYgRI7QPGJJJYCu+hFIKIyMjtmzZQocOHciVKxd3795lwIABnDhx4r8Gt58veie+Dvm/V/xtenp6PHjwgF9++YVnz55ph8xXrVqVBQsWUKpUKWxsbHjw4IH2wpA/f346duzI4MGDyZEjhw6rF+mVhYUFy5cv5/Dhw0ycOJF79+4Bn54QRkVFMWHCBHbu3Enfvn3/68qXQnwrpUuXZtOmTRQqVEjXpYh07vTp0yxYsIAFCxbw8uVLAIKDgzExMeHatWv079+f2bNn4+joCHxqL+Pp6UlCQgKVK1fWjsYRQoj0KvnB1p89yM+RIwc3btygWrVqtGjRgpUrV2r7e2/cuBF/f39y5syZqvUK8fLlS+21OXnQ1Lhx4+jUqRO+vr4sXbpUG9xu376d6tWrU6VKFV2WLL5zGo2GCxcu8PPPP9OiRQvmzZvHrVu3KFKkCL179/7L4FZ8XbKyhPjb4uPjsbe35+rVq2zfvp22bdtiZWVF586dqVy5MgsXLmTYsGE0btwYHx8fihQpwtatW4mNjWXWrFl/WFlQiK/F1taWxYsXM3jwYK5cuULNmjUxNjYmJCSEixcvcujQIUqWLKnrMkUG9t/6PwnxNaxfv56ZM2fSokULypQpQ758+QDo0aMHTZs2xcPDgw0bNtC9e3cAoqOj8fb2pkSJEikWlpDROEKI9Cp5inhcXBxGRkZcuHCB27dvY2xsjJ2dHeXKlWPx4sV07doVMzMz/Pz8MDY2Zs2aNaxbt44zZ85IaCtSxcuXL7XX8bCwMO3AE319fe1x7OzsDMD27dvR09Ojf//+lChRQjtrRha9Ff+LBw8eYGlpSadOnTA1NQXgxIkTNGjQgAEDBrBy5Urq168vv3FSgYS24m8zNDSkY8eOdO3alXLlynHu3DkcHR3x8fGhQYMG9OvXj8WLFzNz5kyqVatGmTJlCA4O5vTp0xLYim9KT0+PAQMGULlyZebOnYufnx9Zs2alVq1azJs3T3rYCiHStQ0bNjBw4EA2bNhAq1atyJQpEwALFy7EzMyMMWPGsHz5coKDg3n9+jWBgYFMnjyZFy9e4OPjA6RcdEIIIdKbNWvWMH/+fPz8/MiUKRPbtm2jX79+FChQgOjoaBYsWMCxY8fo3Lkzb9++ZerUqbi7u2Nubo6enh4nTpygXLlyuv4aIgM4f/48o0aNYuTIkXTs2BEjIyPy5s0L/H8QGxMTg7GxMc7OziilWLJkCYUKFWLw4MHa67kEtuKfSD5+Pn78SGhoqDawjY6OJnPmzMyZM4eaNWvSq1cvNm3aRMOGDXVccfqnUdLkUXyBkydP0rZtW3x9fbGysuLly5esWrWK2bNnU7VqVXr16kXDhg35/fffefPmDRUrVqRIkSK6LltkIPJUWQiRkdy/f5/OnTvj5OTEgAEDtNs7duzIzp07adu2LfXq1SMhIYG5c+eSmJio7S+/f/9+DA0N5bwphEjXlFIcPHiQsWPHkjNnTvbv38+YMWOoXbs27dq1w8/PD2dnZ4KCgvDz8yN37tw8fPiQsLAwMmfOTP78+cmdO7euv4bIIM6ePctvv/1GdHQ048aNY//+/WTLlo2ZM2f+6f4JCQns2bMHOzs7uZaLryY0NJSKFSvSrl07bW9vAD8/P+bOnUt4eDgLFiyQ2aypQEJb8cXGjBnDy5cvcXd3x9jYmC5dunDz5k2qVavGs2fPuHDhAvPnz2fIkCG6LlVkQJ+PFpORY0KI9O7IkSMMGDCAgwcPUrJkSfT09HBycuLIkSMsWrSIRYsWkT17drp160ajRo24ffs2ZmZmlCpVCj09PRISElK0RxBCiPQoMTGREydOMGrUKPT09LCwsGDevHmULl0agNu3b9O/f39CQkK0wa0QunLx4kXmzJlDXFwcV69eBaBQoUIkJCSgr6+v7W37/v17OnXqhIuLCyCDV8SXS/69fOXKFS5dukRSUhKWlpY0b96cVatWsWDBAho2bIirqythYWEsXryYgIAA1q9fL/ePqURCW/HFduzYwYIFCzh79iz9+/dn3759+Pr6UrZsWfz9/Tl8+DCNGzembNmyui5VCCGESNdmzpzJwoULefPmjXbby5cvtSNq7927R//+/YmPj2f//v2YmZlp90vuiyeEEOnNfzq/nTlzhgkTJnD16lWePHlC/vz5taHF7du3cXJyws/Pj+fPn0v/WpHqPh9wcvbsWRYsWMC5c+fIkycPgwYNIiQkhPj4eLJnz05SUhIxMTFMnjxZwjPxP9m5cycDBw7EysoKY2NjDh06xKxZs3BwcMDLy4tp06YRHR1Nrly5ePPmDUePHpXF7lKRhLbiH6lfvz5nz54lb968HDhwgIoVK+q6JCGEECLD2bp1K3379mXPnj3Y2NikeC05tJgzZw6nTp1i+/btmJiY6KhSIYRIXcHBwTx9+pS6deuyYcMG/Pz8WLBgAUePHmXo0KGYmppy/vx5DA0Nte+5ceMG48aNw83NjRIlSuiwepER/NXD00uXLjFv3jzevHnDlClTqF+//p/uJ7NmxD917949rK2t+fXXXxk0aBB3796lcuXK9O/fn6VLl5KYmEhERAS7d+8me/bsVKpUiaJFi+q67AxFhleIL5Kc8Y8bN44SJUrg5uZGxYoVkexfCCGESH3VqlXDwMCAlStX8uzZsxSv6enp8fHjR86cOUOpUqUksBVCZAhKKeLi4ujevTsTJ05k2rRp9OrVS9t7sVGjRixdupT4+Hjq1atHbGys9r2VKlVi7969EtiKb+7nn39m//79JCQk/Md9qlevzvDhw8mZMydTpkzRLh767ySwFV8qKSkJgKCgIEqWLMmgQYN49uwZzZo1o1+/fixduhT4FOpmz56d3r17Y2dnJ4GtDkhoK75I8nSNqlWrkpSUxLVr11JsF0IIIUTqKVasGCtWrGDfvn1MmDCBGzduaF979uwZ7du3JygoiDlz5gDIQ1YhRLqn0WgwMjLCx8eHkJAQpkyZgrOzMwMHDgRAX1+fBg0aMG/ePGJiYrCxsUkR3BoZGemqdJGB3Lp1i4EDB3Ly5Mn/GtzWrl2b0aNHY2Zmxi+//MLZs2dTsUqRHiQHtMn/BIiKigI+3RcmJCRw5coV6tWrR4sWLViyZAkAFy5cYMmSJQQFBaV+0UJLQlvxj+TJk4fJkyezcOFCLl++rOtyhBBCiAyrU6dOLF26lO3bt9O6dWuaN2+OjY0NnTt3Jjw8nCtXrmBgYEBiYqI8ZBVCZAixsbFoNBoMDAwoUKAAt27d4sSJE9rX9fX1adiwIfPmzePp06e0adNGh9WKjCQ5OLty5QoVK1akZ8+eHD9+nLi4uP/4nlq1auHk5ETr1q2pWbNmapUq0gk9PT0ePXrEnj17ANi+fTt16tQhPDycfPnyERERgY2NDdbW1qxcuVK7mN22bdt4+fIlWbNm1WH1Qnrain8sJCSEHj16sGHDBgoUKKDrcoQQQogM7caNG7i7u/Pw4UMKFSpElSpVGDBgAPr6+tLvTgiRIcXFxfH69WuaN29O3rx5cXZ2plGjRin2uXTpEmZmZhQvXlxHVYqM5PM+tlFRUdSoUQN9fX1cXFxo1KjR37pWJyYmaoM1If6OoUOHsnTpUoYPH46rqytr1qyhV69eACxatIiRI0cyY8YMWrduTebMmVm5ciVr1qzh9OnTlCtXTsfVZ2wS2or/SUxMDMbGxrouQwghhBD/gfy4E0JkBEopNBoN169fx8/Pj/j4eGrUqEGlSpV48uQJ7dq1I3/+/IwePZrGjRvj7OxMQkIC8+bN03XpIgMaMWIEAQEBhIWFcePGDXLkyIG7uzsNGzaUh6ziq9i4cSMVKlSgQoUKADRu3JhTp07Rv39/li1blmLfSZMmsWPHDp4/f06pUqWIjY1l48aNVKpUSQeVi89JaCuEEEIIkU4khxZCCJER7dy5k6FDh1KsWDFMTU05fPgwnp6e2NvbExAQQOfOnUlMTCR79uxcv36dQ4cOyXRzkerWrl3LyJEjOX78OHnz5kUpRc+ePfH398fT05MGDRpgaGio6zLFd0opxePHj7G1teXgwYMUKlQI+BTaRkdHc/v2bVavXo2trW2KAXj+/v68ePGCXLlykT9/fszNzXX1FcRn5BGOEEIIIUQ6IYGtECIj+HyKeXL7lxs3buDo6MiMGTPo378/T58+pXjx4jx69IjExESKFSvGjh072LRpE+Hh4SxbtowyZcro+JuIjCgkJAQrKysqVaqERqNBo9Fw/Phx6tWrh5OTE25ubtSvX18WxRP/iEajwdLSkkuXLmFqasqNGzfInTs3vr6+APTp04d+/foBYGdnR6ZMmQAwNzenVKlSOqtb/DlZiEwIIYQQQgghxHdDT0+PZ8+eoZTSTiUPCQmhZs2a2sC2Xr16DBgwgOnTp6Ovr8+LFy8oXLgwzs7OzJo1SwJbkeqSFyGLiIjg+fPn6OnpodFoiImJAWDUqFE8fvyYjh07cvPmTV2WKtIBExMT3r17R5s2bRg6dChXrlwBwMPDg86dO+Pg4MCuXbv48OEDM2fOpEmTJsTGxiKT8dMWCW2FEEIIIYQQQnw3YmNj6dKlC8WKFdMGDC9evCAkJIR79+7RsGFDWrRogZubGwCHDx9m4sSJvHv3ThuUCfGtJYe0yZJHh/fv35/w8HCGDh0KoJ2iniVLFkaMGEHv3r2pUqVK6hYr0hWlFHp6euTKlQt3d3fu3r3L4sWLtcHtmjVr6Nq1KwMGDKBp06bMmzePFStWkClTJjk/pjHS01YIIYQQQgghxHdDKcW5c+dwdHTEwMCA69ev8+rVKzp06MC9e/do27Ytnp6e2j7fo0aN4unTp3h4eJA9e3Zdly8ygM9beGzatIlbt26hp6eHlZUV7du3Z9myZSxevJi6desyffp0wsLCGDlyJD/++CPz588HZCFR8eWSz3lRUVGYmJhoj6Fjx47h4OBA7dq1GTZsGNWqVQNg8+bNREdHU79+fUqUKKHj6sWfkZ62QgghhBBCCCHSrM8DMPjUs7FWrVqsXr2a3r17U716dS5fvkzr1q25f/8+ZcqUITQ0lKioKFasWIGnpyenT5+WwFakmuTjdezYsXh5edGoUSNMTU3p2LEjy5cvp2fPnpiamjJlyhR27txJ1qxZMTc3x8fHR/sZEtiKL6XRaDhw4ADLli3D0NAQa2trevTogbW1NatWraJ///4ADB8+HCsrK7p166bjisVfkZG2QgghhBBCCCHSpOTA9tWrVwQGBlKjRg3ta/Hx8fj5+dGlSxcsLCw4c+YMzs7O7Nu3j8ePH1OxYkU+fPiAl5cXlStX1uG3EBnRwYMHGTBgAFu3bqVmzZrs2LGDTp06sWLFCm14lpCQwMmTJzExMaF69ero6+trF9cT4kudP3+ehg0b4uTkxK1bt4iMjKREiRIsXryYXLlycfToUZycnChTpgzTp0+nQoUKui5Z/AUJbYUQQgghhBBCpFlBQUFUrlyZd+/eUb9+fWrWrIm1tTVWVlZky5aNK1eu8PPPP5MtWzbOnj3L69evOX78OJaWluTPn5+8efPq+iuIDCD5AUPyP1esWMH+/fvZu3cvu3btolevXsyfP5/+/fvz4cMHHjx4QPXq1VN8hrREEH9XciuE5OPt0aNH+Pj4oNFoGDlyJElJSSxfvpzNmzdTuHBhli5dSq5cuThw4AATJkzgwIED5M+fX9dfQ/wFWYhMCCGEEEIIIUSalZSURMGCBSlZsiQRERG8ePGCli1bUr9+fezt7Xn69CkTJ07k1atX2NjYYGZmRufOnalSpYoEtiJVxMfHa1sivH37FoBs2bJhYmLCpk2b6NWrF3PnztWOsD116hTr1q0jNDQ0xedIYCv+SvK4y+joaOBTKw5/f3/69evHokWLtG1g9PT06N+/P927dycwMJDhw4fz9u1bWrRowfnz5yWw/U5IaCuEEEIIIYQQIs0qXLgw27dv58cff8TCwgJHR0f8/f0ZN24cAQEBzJ8/n969e2NiYoKvry/t2rUD/j/cEOJb2rVrFzt37gRg2LBh2NraEh8fT/78+bl69Sp9+/Zl+vTpDBw4EIDIyEhWrFiBUgpzc3Ndli6+QxqNht9//53y5ctreyDny5eP6tWro5Ri//79JCUlAWBoaMiAAQOwt7fn6tWrODs7k5SUhLGxsS6/gvgC0h5BCCGEEEIIIUSa5+/vz7Bhw0hKSmLmzJnaFdDDwsLYu3cvDx484ODBg6xZs0Z62IpU06tXLzZs2EDLli05d+4cp0+fply5cgAsWLCA0aNHM23aNGrUqIGxsTHTpk0jNDSUq1evYmBgoJ3mLsTfFRgYyPjx4/H19WXt2rW0atWKiIgI5s2bh7e3NzY2NsyYMQMjIyPgU+/kdevW0bhxY4oUKaLb4sUXkdBWCCGEEEIIIcR34dGjRwwZMgSA8ePHU79+/RSvyyJOIjVMmzaNTp06Ubp0aQDKli3Lw4cP+e233xgzZswf9t23bx83b96katWq5MiRA29vbwwNDaWHrfjHAgICmD17Ntu3b2fDhg20atWKjx8/4uLiwrFjx6hbty4zZ87UBrfi+yShrRBCCCGEEEKI78ajR48YOnQoSikmTZpErVq1dF2SyECuXbvGr7/+yt69ezEwMCA+Pp4OHTqglOLEiROsXr0aOzs7MmXKpH3Pq1evCA0NJXfu3OTPnx+NRiMPGMTfkrzQWLLPj5snT57g4uLCtm3b2LhxY4rg9uTJk1SoUIFFixZJcPsdk9BWCCGEEEIIIcR35dGjR4wcOZI3b96wcOFCatSooeuSRAaS3NJg165dVKpUiWLFigHQp08ftm/fjru7e4rgNiQkBAsLC+37/z2IE+K/CQoK4vLly7Rv3x4gxQjt5ODWx8eHTZs20bhxYyIjI5k0aRK3bt1i06ZN/PDDD7osX/wPJLQVQgghhBBCCPHdefDgARMnTmT+/PkUKlRI1+WIDEQpxfPnzylfvjwtWrRg1KhR2h7LP//8M9u3b2fJkiU0aNCAwYMHA7B3717pXyu+WEJCAvb29jx48ICxY8fSpUsXIGVwe//+faZOnUpoaChbt27F3NycqKgoIiMjZbG775yEtkIIIYQQQgghvktxcXEy9Vekij8LXI8dO4ajoyPVq1dn2LBh2uB2wIAB7NixA3Nzc4yNjbly5QqGhoa6KFukA48fP2b06NGEh4fTr18/unXrBqQMbnfs2MHgwYM5d+4cxYsX12W54iuS0FYIIYQQQgghhBDiP/i8ncHr16/JmTMnSikMDQ05duwYDg4O1K5dO0Vwe/jwYRISEmjWrBn6+vrSw1b8T54+fcqQIUOIiorCwcGBrl27AhAfH4+hoSG3bt2iR48e7Nq1ixIlSui4WvG1SBMVIYQQQgghhBBCiP8gObCdMWMGLVq0oFGjRixYsICwsDCsra1ZtWoV586dw9XVlatXrwLQtGlTWrZsib6+PomJiRLYiv9J0aJFWbJkCSYmJqxevZp169YBaEdwb9q0CRMTE8zMzHRZpvjKZKStEEIIIYQQQgghxL/5vCXC2rVrGTt2LNOnT+fEiROEhIRQtGhRXF1dyZUrF8eOHWPgwIGUKlWKhQsXUrJkSR1XL9Kjp0+fMmrUKEJCQqhRowa1atXizJkzbN++naNHj1KhQgVdlyi+IglthRBCCCGEEEIIIf7l3/vXHj9+nKNHj1K1alU6dOgAgJubG5s2baJIkSIsXbqUXLlysW/fPjw9Pdm2bZt2dK4QX1twcDBr1qxh165d6OvrU7BgQX777TfKli2r69LEVyahrRBCCCGEEEIIIcS/vHr1irx58wJw4sQJhg4dyuvXr9mwYQM2NjYAJCQksGrVKjZt2kSxYsVYuHBhiqnpn/fBFeJbSEpKIjo6Gn19fYyNjXVdjvgG5AwihBBCCCGEEEIIAVy9epXChQuzb98+AKpWrUrbtm3R09PD09OT2NhYAAwMDBgwYAA9e/bk0qVLLF26FPg0SheQwFZ8U0op9PT0yJIliwS26ZiMtBVCCCGEEEIIIYQAHj9+zLRp0zh06BBr166lVatWREREMHfuXA4cOEDDhg2ZMWMGRkZGwKcRt3v37qVNmzbo6+vruHohRHoioa0QQgghhBBCCCHEvwQEBDB79my2b9/Ohg0baNWqFR8/fsTFxYVjx45Rr169FMFtssTERAluhRBfjYS2QgghhBBCCCGEyPA+D12fPHmCi4sL27ZtY+PGjdrgds6cORw/fpxy5crh5uaGgYGBjqsWQqRXcnYRQgghhBBCCCFEhnTy5EmCgoLo2bMn+vr62uC2ePHiODs7AzBgwAA2bNhAo0aNcHZ25uPHj0RFRcmoWiHENyUjbYUQQgghhBBCCJGhKKWIjo6mbdu2REREMGzYMLp06QKkHHF79+5dJk+eTFJSEp6enmTLlo2YmBgyZcqERqNBKYVGo9HlVxFCpFOynKEQQgghhBBCCCEyFI1Gg4mJCStWrCBPnjysWrWKzZs3A2hH3AKULVuWxo0bc+HCBWJjYwEwNjaWwFYI8c1JaCuEEEIIIYQQQogMqXjx4ixcuBATExPc3d3x8vICPgW3cXFx2n0sLS3R00sZoUhgK4T4liS0FUIIIYQQQgghRIZVtGhRlixZgomJCatXr8bDwwMAIyMjYmJiWLx4MXnz5iVXrlw6rlQIkZFIT1shhBBCCCGEEEJkeE+fPmXs2LEEBARQunRpqlSpwpEjR3j79i0XL17EwMBAWiIIIVKNhLZCCCGEEEIIIYQQwIsXL9i+fTvbtm3DzMyMQoUKsXDhQgwMDEhISMDAwEDXJQohMggJbYUQQgghhBBCCCH+i8TERPT19XVdhhAiA5GetkIIIYQQQgghhBD/8u9j25RSEtgKIVKdjLQVQgghhBBCCCGEEEKINERG2gohhBBCCCGEEEIIIUQaIqGtEEIIIYQQQgghhBBCpCES2gohhBBCCCGEEEIIIUQaIqGtEEIIIYQQQgghhBBCpCES2gohhBBCCCGEEEIIIUQaIqGtEEIIIYQQQgghhBBCpCES2gohhBBCCCGEEEIIIUQaIqGtEEIIIYQQQgghhBBCpCES2gohhBBCiAzr1atXDBkyhGLFipEpUyYKFixI69at8fX1TdU6NBoNe/bsSdW/UwghhBBCpF0Gui5ACCGEEEIIXQgMDKR27drkyJGDuXPnUr58eeLj4zl8+DBOTk48ePBA1yWmEBcXh5GRka7LEEIIIYQQqUBG2gohhBBCiAxp0KBBaDQaLl++TPv27SlZsiRly5Zl5MiRXLx4EYDnz5/Ttm1bTE1NyZYtG506deL333/Xfkbv3r2xtbVN8bnDhw+nQYMG2j83aNCAoUOHMnbsWHLlykXevHmZMmWK9vUiRYoAYGdnh0aj0f55ypQpVKpUCXd3d4oWLYqxsTHr168nd+7cxMbGpvg7bW1t6dmz51f7byOEEEIIIXRLQlshhBBCCJHhvHv3jkOHDuHk5ESWLFn+8HqOHDlISkqibdu2vHv3jlOnTnH06FECAgLo3LnzF/9969atI0uWLFy6dIk5c+Ywbdo0jh49CsCVK1cA8PDw4OXLl9o/Azx+/JidO3eya9cubty4QceOHUlMTMTHx0e7T2hoKPv376dv375fXJcQQgghhEibpD2CEEIIIYTIcB4/foxSitKlS//HfXx9fbl9+zZPnz6lYMGCAKxfv56yZcty5coVqlWr9rf/vgoVKjB58mQALC0tWbp0Kb6+vtjY2GBubg58Corz5s2b4n1xcXGsX79euw9At27d8PDwoGPHjgBs3LiRQoUKpRjdK4QQQgghvm8y0lYIIYQQQmQ4Sqm/3Of+/fsULFhQG9gC/Pjjj+TIkYP79+9/0d9XoUKFFH/Oly8foaGhf/m+woULpwhsARwcHDhy5AghISEAeHp60rt3bzQazRfVJIQQQggh0i4ZaSuEEEIIITIcS0tLNBrN/7zYmJ6e3h8C4Pj4+D/sZ2homOLPGo2GpKSkv/z8P2vdULlyZSpWrMj69etp0qQJd+/eZf/+/V9YuRBCCCGESMtkpK0QQgghhMhwcuXKRdOmTXFzcyMyMvIPr4eFhVGmTBmCgoIICgrSbr937x5hYWH8+OOPAJibm/Py5csU771x48YX12NoaEhiYuLf3r9fv354enri4eGBtbV1itHAQgghhBDi+yehrRBCCCGEyJDc3NxITEzkp59+YufOnTx69Ij79+/j6upKzZo1sba2pnz58nTv3p3r169z+fJl7O3tqV+/PlZWVgA0atSIq1evsn79eh49esTkyZO5c+fOF9dSpEgRfH19efXqFe/fv//L/bt160ZwcDCrV6+WBciEEEIIIdIhCW2FEEIIIUSGVKxYMa5fv07Dhg0ZNWoU5cqVw8bGBl9fX5YvX45Go8Hb25ucOXNSr149rK2tKVasGFu3btV+RtOmTZk4cSJjx46lWrVqfPz4EXt7+y+uZf78+Rw9epSCBQtSuXLlv9w/e/bstG/fHlNTU2xtbb/47xNCCCGEEGmbRv2dVRiEEEIIIYQQaUrjxo0pW7Ysrq6uui5FCCGEEEJ8ZRLaCiGEEEII8R15//49J0+epEOHDty7d49SpUrpuiQhhBBCCPGVGei6ACGEEEIIIcTfV7lyZd6/f4+Li4sEtkIIIYQQ6ZSMtBVCCCGEEEIIIYQQQog0RBYiE0IIIYQQQgghhBBCiDREQlshhBBCCCGEEEIIIYRIQyS0FUIIIYQQQgghhBBCiDREQlshhBBCCCGEEEIIIYRIQyS0FUIIIYQQQgghhBBCiDREQlshhBBCCCGEEEIIIYRIQyS0FUIIIYQQQgghhBBCiDREQlshhBBCCCGEEEIIIYRIQyS0FUIIIYQQQgghhBBCiDTk/wCz4KZl2YOJhAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting total customers gender wise split in country\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Count occurrences of each gender within each country\n",
    "gender_counts = df_finalMerged.groupby(['Country_customer', 'Gender']).size().reset_index(name='Count')\n",
    "\n",
    "# Plotting gender distribution within each country\n",
    "plt.figure(figsize=(14, 8))\n",
    "sns.barplot(data=gender_counts, x='Country_customer', y='Count', hue='Gender',palette='tab20')\n",
    "plt.title('Gender Distribution by Country')\n",
    "plt.xlabel('Country')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797b746d-bdb0-4f19-bd97-059841be1333",
   "metadata": {},
   "source": [
    "There is no notable variation in customer distribution across different gender categories. However, the United States has the highest number of customers compared to other countries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "bd7c49f0-89ba-4dbd-8c18-c28fd31708e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finding top 10 City with most customers\n",
    "\n",
    "customer_counts = df_finalMerged['City_customer'].value_counts()\n",
    "\n",
    "#Geting the top 10 cities\n",
    "top_10_cities = customer_counts.head(10)\n",
    "\n",
    "#Plotting the top 10 cities with the most customers\n",
    "plt.figure(figsize=(10, 6))\n",
    "top_10_cities.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Top 10 Cities with Most Customers')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()  # Adjusts plot to fit labels\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a24a837b-661a-467f-b440-b5dcca7c035f",
   "metadata": {},
   "source": [
    "Based on the analysis, it is evident that several cities in the United States rank among the top ten cities with the highest number of customers. Notably, Toronto leads the list, followed by Montreal and Calgary, indicating a significant interest in our products within these Canadian cities as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "39081a6e-eda2-42ff-997c-e87fdb2c3bb4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\16132\\AppData\\Local\\Temp\\ipykernel_7676\\2873521230.py:16: FutureWarning: \n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "  sns.barplot(data=top10_cities_profits, x='City_customer', y='ProfitUSD', palette='viridis')\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating profits by City\n",
    "city_profits = df_finalMerged.groupby(['City_customer'])['ProfitUSD'].sum().sort_values(ascending=False)\n",
    "\n",
    "# Convert to DataFrame for better readability\n",
    "city_profits_df = city_profits.reset_index()\n",
    "\n",
    "top10_cities_profits = city_profits_df.head(10)\n",
    "\n",
    "# Set plot style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create a figure and axis\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create a barplot\n",
    "sns.barplot(data=top10_cities_profits, x='City_customer', y='ProfitUSD', palette='viridis')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Total Profit (USD)')\n",
    "plt.title('Top 10 Profitable Cities')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "9c65fecd-1c2b-49f2-bfac-085590b4b94e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 10 regions statewise for most number of Customers\n",
    "\n",
    "customer_counts = df_finalMerged['State_customer'].value_counts()\n",
    "\n",
    "#Geting the top 10 states\n",
    "top_10_states = customer_counts.head(10)\n",
    "\n",
    "#Plotting the top 10 cities with the most customers\n",
    "plt.figure(figsize=(10, 6))\n",
    "top_10_states.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('State')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Top 10 States with Most Customers')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()  # Adjusts plot to fit labels\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "18d8a924-71a0-4628-9029-44217e82c68c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\16132\\AppData\\Local\\Temp\\ipykernel_12396\\2753805304.py:16: FutureWarning:\n",
      "\n",
      "\n",
      "\n",
      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
      "\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculating profits by State\n",
    "state_profits = df_finalMerged.groupby(['State_customer'])['ProfitUSD'].sum().sort_values(ascending=False)\n",
    "\n",
    "# Convert to DataFrame for better readability\n",
    "state_profits_df = state_profits.reset_index()\n",
    "\n",
    "top10_states_profits = state_profits_df.head(10)\n",
    "\n",
    "# Set plot style\n",
    "sns.set(style=\"whitegrid\")\n",
    "\n",
    "# Create a figure and axis\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Create a barplot\n",
    "sns.barplot(data=top10_states_profits, x='State_customer', y='ProfitUSD', palette='viridis')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('City')\n",
    "plt.ylabel('Total Profit (USD)')\n",
    "plt.title('Top 10 Profitable States')\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fb73235-4177-4c80-8371-b1db919fe585",
   "metadata": {},
   "source": [
    "This shows strong customer bases in major U.S. states, as well as significant interest from Canada and Germany. It provides valuable insights into market concentration and potential areas for targeted engagement and expansion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "4f629d72-bc4e-46ea-92c0-b4bdd0b2f857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Seeing Age group as well\n",
    "\n",
    "ageRange_counts = df_finalMerged['AgeRange'].value_counts()\n",
    "\n",
    "#Plotting distribution of customers by Age\n",
    "plt.figure(figsize=(10, 6))\n",
    "ageRange_counts.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Age Range')\n",
    "plt.ylabel('Number of Customers')\n",
    "plt.title('Distribution of Customers by Age')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()  # Adjusts plot to fit labels\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "fddc203a-d1ee-4765-943b-dc7425d485e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgeRange\n",
       "60 and above    14180428.57\n",
       "30-39            5054852.78\n",
       "50-59            4886498.39\n",
       "40-49            4835403.98\n",
       "20-29            3705504.66\n",
       "Name: ProfitUSD, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating profits by AgeRange\n",
    "agerange_profits = df_finalMerged.groupby(['AgeRange'])['ProfitUSD'].sum().sort_values(ascending=False)\n",
    "agerange_profits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "361de648-e0f8-4b5f-86ab-43838fd70825",
   "metadata": {},
   "source": [
    "The analysis reveals that the majority of customers are aged 60 and above. This demographic trend suggests a significant interest and engagement from older adults, which may influence product preferences and marketing strategies. Targeting this age group with tailored offerings and communications could enhance customer satisfaction and retention."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "f2ed9e4b-192f-4e1b-b297-026bbe7e537b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frequency\n",
       "Moderate      29903\n",
       "Frequent      17288\n",
       "Occasional    15693\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Seeing Frequency group as well \n",
    "\n",
    "df_finalMerged['Frequency'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "45aead0e-82ea-4e1b-a843-fc9df7bb71e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frequency\n",
       "Moderate      15766046.80\n",
       "Frequent       8849080.35\n",
       "Occasional     8047561.23\n",
       "Name: ProfitUSD, dtype: float64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculating Profits by Frequency Range\n",
    "freq_profits = df_finalMerged.groupby(['Frequency'])['ProfitUSD'].sum().sort_values(ascending=False)\n",
    "freq_profits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a4ded0a-613b-405b-9665-be5a6b886648",
   "metadata": {},
   "source": [
    "Understanding the frequency of customer visits enables us to tailor our advertising strategies more effectively. By categorizing customers based on their engagement levels, we can create targeted marketing campaigns that resonate with different segments, thereby enhancing customer engagement and optimizing our advertising efforts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "f105472f-6e66-4a71-b720-09b2c093d84a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CurrencyCode\n",
       "USD    33767\n",
       "EUR    12621\n",
       "GBP     8140\n",
       "CAD     5415\n",
       "AUD     2941\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Seeing top currencies used as well\n",
    "df_finalMerged['CurrencyCode'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebea7959-2a02-485c-a4ff-128abac649cb",
   "metadata": {},
   "source": [
    "We can infer that most customers use USD, with significant numbers also using EUR and GBP indicating their region as well. Also, there is notable interest from Canadian and Australian markets, indicating a diverse international customer base."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "b4597fed-f044-4c9e-ac7f-1c8100482983",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correlation between RevenueUSD and ExchangeRateOnThatDate by Currency:\n",
      "  Currency  Correlation\n",
      "0      AUD     0.007797\n",
      "1      CAD    -0.015094\n",
      "2      EUR    -0.018534\n",
      "3      GBP    -0.002628\n",
      "4      USD          NaN\n"
     ]
    }
   ],
   "source": [
    "#Seeing if there is any co-relation between currency exchange rates and revenue\n",
    "\n",
    "# Initialize an empty list to store correlation results\n",
    "correlation_results = []\n",
    "\n",
    "# Group by Currency and calculate correlation for each group\n",
    "for currency, group in df_finalMerged.groupby('CurrencyCode'):\n",
    "    correlation = group[['RevenueUSD', 'Exchange']].corr().iloc[0, 1]\n",
    "    correlation_results.append({\n",
    "        'Currency': currency,\n",
    "        'Correlation': correlation\n",
    "    })\n",
    "\n",
    "# Convert results to a DataFrame\n",
    "correlation_results_df = pd.DataFrame(correlation_results)\n",
    "\n",
    "# Display the correlation results\n",
    "print(\"Correlation between RevenueUSD and ExchangeRateOnThatDate by Currency:\")\n",
    "print(correlation_results_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7363cfa2-5d05-4246-931d-f6449d328a9e",
   "metadata": {},
   "source": [
    "The correlations for all currencies are extremely low, indicating that changes in exchange rates for AUD, CAD, EUR, and GBP have minimal to no effect on revenue in USD."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "70a8066c-2c46-47df-850e-9b738ca462f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProductName\n",
      "Adventure Works Desktop PC2.30 MD230 Black    162\n",
      "Adventure Works Desktop PC2.30 MD230 White    158\n",
      "WWI Desktop PC1.80 E1800 White                158\n",
      "WWI Desktop PC1.60 E1600 Black                158\n",
      "WWI Desktop PC1.80 E1801 Black                157\n",
      "Adventure Works Desktop PC1.80 ED180 White    156\n",
      "WWI Desktop PC1.60 E1600 Silver               156\n",
      "WWI Desktop PC1.80 E1800 Silver               156\n",
      "Adventure Works Desktop PC1.60 ED160 Black    155\n",
      "WWI Desktop PC2.33 X2330 Black                154\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#Finding top 10 Products Sold\n",
    "\n",
    "product_counts = df_finalMerged['ProductName'].value_counts()\n",
    "\n",
    "print(product_counts.nlargest(10))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "956d1a98-ea93-479e-9584-eed5eda74006",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ProductName\n",
      "WWI Desktop PC2.33 X2330 Black                 337986.00\n",
      "Adventure Works Desktop PC2.33 XD233 Silver    311663.95\n",
      "Adventure Works Desktop PC2.33 XD233 Brown     310368.05\n",
      "Adventure Works Desktop PC2.33 XD233 Black     299352.90\n",
      "Adventure Works Desktop PC2.33 XD233 White     292225.45\n",
      "WWI Desktop PC2.33 X2330 White                 283908.24\n",
      "WWI Desktop PC2.33 X2330 Brown                 282679.20\n",
      "Adventure Works 52\" LCD HDTV X590 White        263727.12\n",
      "Adventure Works 52\" LCD HDTV X590 Black        250152.93\n",
      "WWI Desktop PC2.33 X2330 Silver                240891.84\n",
      "Name: ProfitUSD, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "#Finding top 10 Profitable Products\n",
    "product_profits = df_finalMerged.groupby(['ProductName'])['ProfitUSD'].sum().sort_values(ascending=False)\n",
    "\n",
    "print(product_profits.nlargest(10))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f34764c-d4c4-4229-abf2-4909b6279a22",
   "metadata": {},
   "source": [
    "The top products purchased by customers are Computer Products.\n",
    "We can further look into top brands as category as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "16019dc7-c296-41fd-a935-71cc470d2f3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Finding top Categories sold\n",
    "\n",
    "category_counts = df_finalMerged['Category'].value_counts()\n",
    "\n",
    "#Plotting the top categories with the most customers\n",
    "plt.figure(figsize=(10, 6))\n",
    "category_counts.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Category')\n",
    "plt.ylabel('Number of Sales')\n",
    "plt.title('Distribution of sales by Categories')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()  # Adjusts plot to fit labels\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "6b234549-19ca-481d-8865-23c187ebe03e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Category\n",
       "Computers                        11277447.90\n",
       "Home Appliances                   6296338.85\n",
       "Cameras and camcorders            3919800.99\n",
       "TV and Video                      3536694.39\n",
       "Cell phones                       3498626.54\n",
       "Music, Movies and Audio Books     1909259.17\n",
       "Audio                             1827851.77\n",
       "Games and Toys                     396668.77\n",
       "Name: ProfitUSD, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating profits by Category\n",
    "category_profits = df_finalMerged.groupby(['Category'])['ProfitUSD'].sum().sort_values(ascending=False)\n",
    "\n",
    "category_profits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be4eea8-d821-4139-8358-e70ed37ad00f",
   "metadata": {},
   "source": [
    "This distribution indicates a strong demand for both technology, entertainment, and recreational-related products while also showing demand for household items.\n",
    "Understanding these preferences can guide inventory management, targeted marketing efforts, and product development strategies to better align with customer interests and enhance engagement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "id": "5a2e7810-68ce-4802-b08f-deb8da31ba37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of subcategories sold\n",
    "\n",
    "subcategory_counts = df_finalMerged['Subcategory'].value_counts()\n",
    "\n",
    "#Plotting subcategories and sales\n",
    "plt.figure(figsize=(10, 6))\n",
    "subcategory_counts.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('SubCategory')\n",
    "plt.ylabel('Number of Sales')\n",
    "plt.title('Distribution of sales by SubCategories')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()  # Adjusts plot to fit labels\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "4d0f78c8-e2e8-4441-9816-a07165756a8b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Subcategory\n",
       "Desktops                            5629155.33\n",
       "Televisions                         2631908.38\n",
       "Projectors & Screens                2357342.37\n",
       "Water Heaters                       2039982.30\n",
       "Camcorders                          2018119.70\n",
       "Movie DVD                           1909259.17\n",
       "Laptops                             1793677.71\n",
       "Touch Screen Phones                 1738593.81\n",
       "Smart phones & PDAs                 1590479.02\n",
       "Refrigerators                       1316366.73\n",
       "Washers & Dryers                    1197517.99\n",
       "Digital SLR Cameras                 1170056.98\n",
       "Monitors                             950021.49\n",
       "Bluetooth Headphones                 806865.02\n",
       "Recording Pen                        741086.69\n",
       "Coffee Machines                      692610.86\n",
       "Microwaves                           650954.38\n",
       "Digital Cameras                      510597.56\n",
       "Printers, Scanners & Fax             428520.56\n",
       "Home Theater System                  398020.77\n",
       "Download Games                       344731.16\n",
       "Car Video                            331773.07\n",
       "MP4&MP3                              279900.06\n",
       "Cameras & Camcorders Accessories     221026.75\n",
       "Air Conditioners                     188442.49\n",
       "VCD & DVD                            174992.17\n",
       "Lamps                                156987.12\n",
       "Computers Accessories                118730.44\n",
       "Home & Office Phones                  93627.20\n",
       "Cell phones Accessories               75926.51\n",
       "Fans                                  53476.98\n",
       "Boxed Games                           51937.61\n",
       "Name: ProfitUSD, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating profits by SubCategory\n",
    "subcategory_profits = df_finalMerged.groupby(['Subcategory'])['ProfitUSD'].sum().sort_values(ascending=False)\n",
    "subcategory_profits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4e46529-e6c9-4849-9a09-a57cd19de7b6",
   "metadata": {},
   "source": [
    "By analyzing the top and least-performing subcategories, we gain a clear understanding of where the strengths and opportunities lie. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1a4932f4-4a4a-485e-9179-4cee882eeacd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Distribution of brands sold\n",
    "\n",
    "brand_counts = df_finalMerged['Brand'].value_counts()\n",
    "#Plotting brands and sales\n",
    "plt.figure(figsize=(10, 6))\n",
    "brand_counts.plot(kind='bar', color='skyblue')\n",
    "plt.xlabel('Brands')\n",
    "plt.ylabel('Number of Sales')\n",
    "plt.title('Sales by brands')\n",
    "plt.xticks(rotation=45, ha='right')\n",
    "plt.tight_layout()  # Adjusts plot to fit labels\n",
    "\n",
    "# Show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a2fd21fc-dabf-4db6-ac28-ff77bec0c83b",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Brand\n",
       "Adventure Works         6937318.88\n",
       "Contoso                 6321209.14\n",
       "Wide World Importers    5367028.30\n",
       "Fabrikam                4061475.11\n",
       "The Phone Company       3057762.90\n",
       "Proseware               1935287.10\n",
       "Litware                 1553765.19\n",
       "Southridge Video        1522876.84\n",
       "A. Datum                 883502.31\n",
       "Northwind Traders        649413.31\n",
       "Tailspin Toys            373049.30\n",
       "Name: ProfitUSD, dtype: float64"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating profits by Brands\n",
    "brand_profits = df_finalMerged.groupby(['Brand'])['ProfitUSD'].sum().sort_values(ascending=False)\n",
    "brand_profits"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d940ab-163f-4059-9f90-4224b83ac63b",
   "metadata": {},
   "source": [
    "Likewise with brands, by analyzing the top and least-performing brands, we gain a clear understanding of where the strengths and opportunities lie. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "3ab65c95-a5bf-40c0-96c1-a28bf59a4d1e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              SquareMeters  StoreAge  RevenueUSD\n",
      "SquareMeters      1.000000  0.281306    0.001338\n",
      "StoreAge          0.281306  1.000000   -0.004737\n",
      "RevenueUSD        0.001338 -0.004737    1.000000\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Can see if store size and age have any co-relation to revenue\n",
    "\n",
    "filtered_df = df_finalMerged[(df_finalMerged['SquareMeters'] != 0) & (df_finalMerged['StoreAge'] != 0)]\n",
    "\n",
    "correlation_matrix = filtered_df[['SquareMeters', 'StoreAge', 'RevenueUSD']].corr()\n",
    "\n",
    "print(correlation_matrix)\n",
    "\n",
    "plt.figure(figsize=(10, 7))\n",
    "\n",
    "# Create a heatmap with the correlation matrix\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "\n",
    "# Add titles and labels\n",
    "plt.title('Correlation Heatmap of Store Squarefeet,Age and Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a58a9c58-8873-4152-adb7-1fc703b5ff1c",
   "metadata": {},
   "source": [
    "Clearly there is no co-relation between the RevenueUSD and StoreAge and StoreSquarefeet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "05d357a8-8e05-4f5c-b9db-545b2f3c3f9f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   StoreKey  Country_store            State_store   ProfitUSD\n",
      "0         0         Online                 Online  6672692.64\n",
      "1        55  United States                 Nevada   830551.75\n",
      "2        50  United States                 Kansas   819659.12\n",
      "3        54  United States               Nebraska   810300.06\n",
      "4        57  United States             New Mexico   784095.76\n",
      "5         9         Canada  Northwest Territories   774757.55\n",
      "6        61  United States         South Carolina   764997.20\n",
      "7        45  United States            Connecticut   761271.82\n",
      "8        59  United States                 Oregon   754702.32\n",
      "9        64  United States          Washington DC   745283.99\n"
     ]
    }
   ],
   "source": [
    "# Can see Store with Most Revenue and it's Location\n",
    "\n",
    "# Group by StoreKey,Country,State and sum the ProfitUSD\n",
    "store_profit = df_finalMerged.groupby(['StoreKey', 'Country_store','State_store'])['ProfitUSD'].sum()\n",
    "\n",
    "# Sort the stores by profit in descending order\n",
    "top_stores = store_profit.sort_values(ascending=False).head(50)\n",
    "\n",
    "# Convert to DataFrame for better readability\n",
    "top_stores_df = top_stores.reset_index()\n",
    "\n",
    "# Display results\n",
    "print(top_stores_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee5dce64-b80f-41f4-a5db-283e9535a6c5",
   "metadata": {},
   "source": [
    "Online Store is the highest performer by itself by a significant margin, with a profit of $6,672,692.64. This indicates that the online store has the highest profitability compared to physical stores.\n",
    "It also shows that majority of profits made from physical stores are located in USA with Canada being in the picture as well especially from the Northwest Territories.\n",
    "But it is important to note that the profits made from Online are the highest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "6185c8ca-6c36-4da1-a975-be5ae734b1a9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Revenue Per Day: $26792.28\n",
      "Average Revenue Per Week: $160329.93\n",
      "Average Revenue Per Month: $672552.73\n",
      "Average Revenue Per Year: $9292579.93\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\16132\\AppData\\Local\\Temp\\ipykernel_7676\\2790816349.py:18: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  monthly_revenue = df_finalMerged.resample('M', on='OrderDate')['RevenueUSD'].sum().reset_index()\n",
      "C:\\Users\\16132\\AppData\\Local\\Temp\\ipykernel_7676\\2790816349.py:25: FutureWarning: 'Y' is deprecated and will be removed in a future version, please use 'YE' instead.\n",
      "  yearly_revenue = df_finalMerged.resample('Y', on='OrderDate')['RevenueUSD'].sum().reset_index()\n"
     ]
    }
   ],
   "source": [
    "#Find average profits by day,week,month,year\n",
    "\n",
    "# Group by 'Order Date' and calculate total revenue per day\n",
    "daily_revenue = df_finalMerged.groupby('OrderDate')['RevenueUSD'].sum().reset_index()\n",
    "\n",
    "# Calculate the average revenue per day\n",
    "average_daily_revenue = daily_revenue['RevenueUSD'].median()\n",
    "print(f'Average Revenue Per Day: ${average_daily_revenue:.2f}')\n",
    "\n",
    "# Resample by week and calculate total revenue per month\n",
    "weekly_revenue = df_finalMerged.resample('W', on='OrderDate')['RevenueUSD'].sum().reset_index()\n",
    "\n",
    "# Calculate the average revenue per week\n",
    "average_weekly_revenue = weekly_revenue['RevenueUSD'].median()\n",
    "print(f'Average Revenue Per Week: ${average_weekly_revenue:.2f}')\n",
    "\n",
    "# Resample by month and calculate total revenue per month\n",
    "monthly_revenue = df_finalMerged.resample('M', on='OrderDate')['RevenueUSD'].sum().reset_index()\n",
    "\n",
    "# Calculate the average revenue per month, taking median as it may have outliers\n",
    "average_monthly_revenue = monthly_revenue['RevenueUSD'].median()\n",
    "print(f'Average Revenue Per Month: ${average_monthly_revenue:.2f}')\n",
    "\n",
    "# Resample by year and calculate total revenue per year\n",
    "yearly_revenue = df_finalMerged.resample('Y', on='OrderDate')['RevenueUSD'].sum().reset_index()\n",
    "\n",
    "# Calculate the average revenue per year\n",
    "average_yearly_revenue = yearly_revenue['RevenueUSD'].mean()\n",
    "print(f'Average Revenue Per Year: ${average_yearly_revenue:.2f}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a5651da0-fb5f-4f81-9aa0-fa0803affa1a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\16132\\AppData\\Local\\Temp\\ipykernel_12396\\1516916552.py:2: FutureWarning:\n",
      "\n",
      "'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Resample by month and calculate total revenue\n",
    "monthly_revenue = df_finalMerged.resample('M', on='OrderDate')['RevenueUSD'].sum().reset_index()\n",
    "\n",
    "# Monthly Plot\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(monthly_revenue['OrderDate'], monthly_revenue['RevenueUSD'], linestyle='-', color='r')\n",
    "plt.title('Monthly RevenueUSD')\n",
    "plt.xlabel('Order Date')\n",
    "plt.ylabel('RevenueUSD')\n",
    "plt.grid(True)\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1f88e7ef-8cf3-49a6-9df3-6f80f4e3729d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Revenue for Month 1: $6,759,981.20\n",
      "Total Revenue for Month 2: $7,842,476.23\n",
      "Total Revenue for Month 3: $2,625,522.85\n",
      "Total Revenue for Month 4: $607,334.05\n",
      "Total Revenue for Month 5: $4,757,983.80\n",
      "Total Revenue for Month 6: $4,293,036.54\n",
      "Total Revenue for Month 7: $3,852,415.81\n",
      "Total Revenue for Month 8: $4,085,169.32\n",
      "Total Revenue for Month 9: $4,363,863.61\n",
      "Total Revenue for Month 10: $4,315,027.44\n",
      "Total Revenue for Month 11: $4,756,059.88\n",
      "Total Revenue for Month 12: $7,496,608.86\n"
     ]
    }
   ],
   "source": [
    "#We want to calculate revenue for each month to see trends\n",
    "\n",
    "# Extract month from the OrderDate\n",
    "df_finalMerged['Month'] = df_finalMerged['OrderDate'].dt.month\n",
    "\n",
    "# Filter data for April (Month 4)\n",
    "for i in range(1,13):\n",
    "    month_data = df_finalMerged[df_finalMerged['Month'] == i]\n",
    "\n",
    "    # Calculate total revenue for the month\n",
    "    total_revenue_month = month_data['RevenueUSD'].sum()\n",
    "\n",
    "    print(f'Total Revenue for Month {i}: ${total_revenue_month:,.2f}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379f1dc5-797d-45d9-9b9f-82bcdaa94add",
   "metadata": {},
   "source": [
    "Can infer that lowest performing month is April with the top 3 being December, January, and February "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c317887-2373-4b33-ac4a-61b99351e184",
   "metadata": {},
   "source": [
    "## Write to Mysql "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "681d285e-1fb0-4f10-94de-725c9d9d1dc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine, text\n",
    "# Database connection parameters\n",
    "user = 'root'\n",
    "password = 'expert789'\n",
    "host = 'localhost'  # or your MySQL host\n",
    "database = 'DataSpark'\n",
    "\n",
    "# Create SQLAlchemy engine\n",
    "engine = create_engine(f'mysql+mysqlconnector://{user}:{password}@{host}/{database}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "482ea557-f985-42a9-9f2d-c857f95a856c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining the SQL command to create the table Customers\n",
    "create_table_query1 = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS Customers (\n",
    "    CustomerKey INT PRIMARY KEY,\n",
    "    Gender VARCHAR(10),\n",
    "    Name VARCHAR(255),\n",
    "    City VARCHAR(255),\n",
    "    StateCode VARCHAR(255),\n",
    "    State VARCHAR(255),\n",
    "    ZipCode VARCHAR(20),\n",
    "    Country VARCHAR(100),\n",
    "    Continent VARCHAR(100),\n",
    "    Birthday DATE,\n",
    "    Age INT,\n",
    "    AgeRange VARCHAR(20)\n",
    ")\n",
    "\"\"\"\n",
    "\n",
    "# Execute the create table command\n",
    "with engine.connect() as connection:\n",
    "    connection.execute(text(create_table_query1))\n",
    "\n",
    "#Write the Customer contents into Mysql\n",
    "df_customers.to_sql('Customers', con=engine, if_exists='append', index=False)\n",
    "\n",
    "\n",
    "# Defining the SQL command to create the table ExchangeRates\n",
    "create_table_query2 = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS ExchangeRates (\n",
    "    Date DATE,\n",
    "    Currency VARCHAR(3),\n",
    "    Exchange FLOAT,\n",
    "    PRIMARY KEY (Date, Currency)\n",
    ")\n",
    "\"\"\"\n",
    "\n",
    "# Execute the create table command\n",
    "with engine.connect() as connection:\n",
    "    connection.execute(text(create_table_query2))\n",
    "    \n",
    "#Write the ExchangeRates contents into Mysql\n",
    "df_exchange.to_sql('ExchangeRates', con=engine, if_exists='append', index=False)\n",
    "\n",
    "\n",
    "# Defining the SQL command to create the table Products\n",
    "create_table_query3 = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS Products (\n",
    "    ProductKey INT PRIMARY KEY,\n",
    "    ProductName VARCHAR(255),\n",
    "    Brand VARCHAR(100),\n",
    "    Color VARCHAR(50),\n",
    "    UnitCostUSD FLOAT,\n",
    "    UnitPriceUSD FLOAT,\n",
    "    SubcategoryKey INT,\n",
    "    Subcategory VARCHAR(255),\n",
    "    CategoryKey INT,\n",
    "    Category VARCHAR(255)\n",
    ")\n",
    "\"\"\"\n",
    "\n",
    "# Execute the create table command\n",
    "with engine.connect() as connection:\n",
    "    connection.execute(text(create_table_query3))\n",
    "    \n",
    "#Write the Products contents into Mysql\n",
    "df_products.to_sql('Products', con=engine, if_exists='append', index=False)\n",
    "\n",
    "\n",
    "# Defining the SQL command to create the table Sales\n",
    "create_table_query4 = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS Sales (\n",
    "    OrderNumber INT,\n",
    "    LineItem INT,\n",
    "    OrderDate DATE,\n",
    "    DeliveryDate DATE,\n",
    "    CustomerKey INT,\n",
    "    StoreKey INT,\n",
    "    ProductKey INT,\n",
    "    Quantity INT,\n",
    "    CurrencyCode VARCHAR(10),\n",
    "    PRIMARY KEY (OrderNumber, LineItem)\n",
    ")\n",
    "\"\"\"\n",
    "\n",
    "# Execute the create table command\n",
    "with engine.connect() as connection:\n",
    "    connection.execute(text(create_table_query4))\n",
    "\n",
    "#Write the Sales contents into Mysql\n",
    "df_sales.to_sql('Sales', con=engine, if_exists='append', index=False)\n",
    "\n",
    "\n",
    "# Defining the SQL command to create the table Stores\n",
    "create_table_query5 = \"\"\"\n",
    "CREATE TABLE IF NOT EXISTS Stores (\n",
    "    StoreKey INT PRIMARY KEY,\n",
    "    Country VARCHAR(100),\n",
    "    State VARCHAR(100),\n",
    "    SquareMeters FLOAT,\n",
    "    OpenDate DATE\n",
    ")\n",
    "\"\"\"\n",
    "\n",
    "# Execute the create table command\n",
    "with engine.connect() as connection:\n",
    "    connection.execute(text(create_table_query5))\n",
    "\n",
    "#Write the Stores contents into Mysql\n",
    "df_stores.to_sql('Stores', con=engine, if_exists='append', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "31985447-080a-47ec-89a3-799a39f9ee6b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60c98d04-844d-4d73-bec8-22548d8e32de",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "832d4757-e223-4225-93a0-f241f8855a29",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}