Analysis of sale.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x800 with 0 Axes>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(20,8))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product Quantity Ordered Price Each  \\\n",
       "0   141234                    iPhone                1        700   \n",
       "1   141235  Lightning Charging Cable                1      14.95   \n",
       "2   141236          Wired Headphones                2      11.99   \n",
       "3   141237          27in FHD Monitor                1     149.99   \n",
       "4   141238          Wired Headphones                1      11.99   \n",
       "\n",
       "       Order Date                       Purchase Address  \n",
       "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215  \n",
       "1  01/28/19 14:15       185 Maple St, Portland, OR 97035  \n",
       "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016  \n",
       "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001  \n",
       "4  01/25/19 11:59          387 10th St, Austin, TX 73301  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('Sales_January_2019.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>150502</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>02/18/19 01:35</td>\n",
       "      <td>866 Spruce St, Portland, ME 04101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>150503</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/13/19 07:24</td>\n",
       "      <td>18 13th St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>150504</td>\n",
       "      <td>27in 4K Gaming Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>389.99</td>\n",
       "      <td>02/18/19 09:46</td>\n",
       "      <td>52 6th St, New York City, NY 10001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>150505</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>02/02/19 16:47</td>\n",
       "      <td>129 Cherry St, Atlanta, GA 30301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>150506</td>\n",
       "      <td>AA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>3.84</td>\n",
       "      <td>02/28/19 20:32</td>\n",
       "      <td>548 Lincoln St, Seattle, WA 98101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product Quantity Ordered Price Each  \\\n",
       "0   150502                    iPhone                1        700   \n",
       "1   150503     AA Batteries (4-pack)                1       3.84   \n",
       "2   150504    27in 4K Gaming Monitor                1     389.99   \n",
       "3   150505  Lightning Charging Cable                1      14.95   \n",
       "4   150506     AA Batteries (4-pack)                2       3.84   \n",
       "\n",
       "       Order Date                     Purchase Address  \n",
       "0  02/18/19 01:35    866 Spruce St, Portland, ME 04101  \n",
       "1  02/13/19 07:24  18 13th St, San Francisco, CA 94016  \n",
       "2  02/18/19 09:46   52 6th St, New York City, NY 10001  \n",
       "3  02/02/19 16:47     129 Cherry St, Atlanta, GA 30301  \n",
       "4  02/28/19 20:32    548 Lincoln St, Seattle, WA 98101  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1=pd.read_csv('Sales_February_2019.csv')\n",
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>162009</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>03/28/19 20:59</td>\n",
       "      <td>942 Church St, Austin, TX 73301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162009</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>03/28/19 20:59</td>\n",
       "      <td>942 Church St, Austin, TX 73301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162009</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>03/28/19 20:59</td>\n",
       "      <td>942 Church St, Austin, TX 73301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162010</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>03/17/19 05:39</td>\n",
       "      <td>261 10th St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162011</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>03/10/19 00:01</td>\n",
       "      <td>764 13th St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product Quantity Ordered Price Each  \\\n",
       "0   162009                      iPhone                1        700   \n",
       "1   162009    Lightning Charging Cable                1      14.95   \n",
       "2   162009            Wired Headphones                2      11.99   \n",
       "3   162010  Bose SoundSport Headphones                1      99.99   \n",
       "4   162011      34in Ultrawide Monitor                1     379.99   \n",
       "\n",
       "       Order Date                      Purchase Address  \n",
       "0  03/28/19 20:59       942 Church St, Austin, TX 73301  \n",
       "1  03/28/19 20:59       942 Church St, Austin, TX 73301  \n",
       "2  03/28/19 20:59       942 Church St, Austin, TX 73301  \n",
       "3  03/17/19 05:39  261 10th St, San Francisco, CA 94016  \n",
       "4  03/10/19 00:01  764 13th St, San Francisco, CA 94016  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.read_csv('Sales_March_2019.csv')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>176558</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>2</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/19/19 08:46</td>\n",
       "      <td>917 1st St, Dallas, TX 75001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176559</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 22:30</td>\n",
       "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>176560</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                     Product Quantity Ordered Price Each  \\\n",
       "0   176558        USB-C Charging Cable                2      11.95   \n",
       "1      NaN                         NaN              NaN        NaN   \n",
       "2   176559  Bose SoundSport Headphones                1      99.99   \n",
       "3   176560                Google Phone                1        600   \n",
       "4   176560            Wired Headphones                1      11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001  \n",
       "1             NaN                                   NaN  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3=pd.read_csv('Sales_April_2019.csv')\n",
    "df3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=pd.read_csv('Sales_May_2019.csv')\n",
    "df5=pd.read_csv('Sales_June_2019.csv')\n",
    "df6=pd.read_csv('Sales_July_2019.csv')\n",
    "df7=pd.read_csv('Sales_August_2019.csv')\n",
    "df8=pd.read_csv('Sales_September_2019.csv')\n",
    "df9=pd.read_csv('Sales_October_2019.csv')\n",
    "df10=pd.read_csv('Sales_November_2019.csv')\n",
    "df11=pd.read_csv('Sales_December_2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(9723, 6)\n",
      "(12036, 6)\n",
      "(15226, 6)\n",
      "(18383, 6)\n",
      "(16635, 6)\n",
      "(13622, 6)\n",
      "(14371, 6)\n",
      "(12011, 6)\n",
      "(11686, 6)\n",
      "(20379, 6)\n",
      "(17661, 6)\n",
      "(25117, 6)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "print(df1.shape)\n",
    "print(df2.shape)\n",
    "print(df3.shape)\n",
    "print(df4.shape)\n",
    "print(df5.shape)\n",
    "print(df6.shape)\n",
    "print(df7.shape)\n",
    "print(df8.shape)\n",
    "print(df9.shape)\n",
    "print(df10.shape)\n",
    "print(df11.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Marging the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product Quantity Ordered Price Each  \\\n",
       "0   141234                    iPhone                1        700   \n",
       "1   141235  Lightning Charging Cable                1      14.95   \n",
       "2   141236          Wired Headphones                2      11.99   \n",
       "3   141237          27in FHD Monitor                1     149.99   \n",
       "4   141238          Wired Headphones                1      11.99   \n",
       "\n",
       "       Order Date                       Purchase Address  \n",
       "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215  \n",
       "1  01/28/19 14:15       185 Maple St, Portland, OR 97035  \n",
       "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016  \n",
       "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001  \n",
       "4  01/25/19 11:59          387 10th St, Austin, TX 73301  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### for yearly sale we have to merge the data into 1 dataframe\n",
    "\n",
    "Sale=pd.concat([df,df1,df2,df3,df4,df5,df6,df7,df8,df9,df10,df11])\n",
    "Sale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(186850, 6)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1121100"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>186305</td>\n",
       "      <td>186305</td>\n",
       "      <td>186305</td>\n",
       "      <td>186305</td>\n",
       "      <td>186305</td>\n",
       "      <td>186305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>178438</td>\n",
       "      <td>20</td>\n",
       "      <td>10</td>\n",
       "      <td>24</td>\n",
       "      <td>142396</td>\n",
       "      <td>140788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Order ID</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>Order Date</td>\n",
       "      <td>Purchase Address</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>355</td>\n",
       "      <td>21903</td>\n",
       "      <td>168552</td>\n",
       "      <td>21903</td>\n",
       "      <td>355</td>\n",
       "      <td>355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Order ID               Product Quantity Ordered Price Each  \\\n",
       "count     186305                186305           186305     186305   \n",
       "unique    178438                    20               10         24   \n",
       "top     Order ID  USB-C Charging Cable                1      11.95   \n",
       "freq         355                 21903           168552      21903   \n",
       "\n",
       "        Order Date  Purchase Address  \n",
       "count       186305            186305  \n",
       "unique      142396            140788  \n",
       "top     Order Date  Purchase Address  \n",
       "freq           355               355  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            object\n",
       "Product             object\n",
       "Quantity Ordered    object\n",
       "Price Each          object\n",
       "Order Date          object\n",
       "Purchase Address    object\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CHECKING DATA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 186850 entries, 0 to 25116\n",
      "Data columns (total 6 columns):\n",
      "Order ID            186305 non-null object\n",
      "Product             186305 non-null object\n",
      "Quantity Ordered    186305 non-null object\n",
      "Price Each          186305 non-null object\n",
      "Order Date          186305 non-null object\n",
      "Purchase Address    186305 non-null object\n",
      "dtypes: object(6)\n",
      "memory usage: 10.0+ MB\n"
     ]
    }
   ],
   "source": [
    "Sale.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            545\n",
       "Product             545\n",
       "Quantity Ordered    545\n",
       "Price Each          545\n",
       "Order Date          545\n",
       "Purchase Address    545\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>664</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>678</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>876</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1299</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Order ID Product Quantity Ordered Price Each Order Date Purchase Address\n",
       "664       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "678       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "797       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "876       NaN     NaN              NaN        NaN        NaN              NaN\n",
       "1299      NaN     NaN              NaN        NaN        NaN              NaN"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "##3 checking the nan value in dataframe\n",
    "Sale[Sale.isnull().any(axis=1)].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SO ALL NULL VALUES ARE IN SAME ROW SO WE DONT HAVE TO TREAT THIS NULL WE CAN DROP THE NAN ROW"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "186305"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "186850-545  ## we required this much of row only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DATA PREPROCESSING   (CLEANING + NEW COLUMN IF REQUIRED)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product Quantity Ordered Price Each  \\\n",
       "0   141234                    iPhone                1        700   \n",
       "1   141235  Lightning Charging Cable                1      14.95   \n",
       "2   141236          Wired Headphones                2      11.99   \n",
       "3   141237          27in FHD Monitor                1     149.99   \n",
       "4   141238          Wired Headphones                1      11.99   \n",
       "\n",
       "       Order Date                       Purchase Address  \n",
       "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215  \n",
       "1  01/28/19 14:15       185 Maple St, Portland, OR 97035  \n",
       "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016  \n",
       "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001  \n",
       "4  01/25/19 11:59          387 10th St, Austin, TX 73301  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale=Sale.dropna(how='all')\n",
    "Sale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(186305, 6)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.shape  # so all row which contaion nan are drop from data set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## we will create new column which contain total price and the month column for monthly sale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sale['Quantity Ordered']=Sale['Quantity Ordered'].astype('int32')"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Sale[Sale['Order ID']==1069 ]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Sale['Quantity Ordered'] = pd.to_numeric(Sale['Quantity Ordered'])\n",
    "Sale['Price Each'] = pd.to_numeric(Sale['Price Each'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "their is issue in some order id so we have to treat order id or date first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142396"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale['Order Date'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sale['Month'] = Sale['Order Date'].str[0:2]  ## to create new column for month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Sale['Month']=Sale['Month'].astype('int')\n",
    "\n",
    "# ERROR COMES SHOW :- invalid literal for int() with base 10: 'Or'\n",
    "#SO WE HAVE TO TREAT THE MONTH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(185950, 7)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale = Sale[Sale['Order Date'].str[0:2]!='Or']\n",
    "Sale.shape\n",
    "\n",
    "## from not wual to or then all values comes whivh contaion non or value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sale['Month']=Sale['Month'].astype('int')  # now the codes run now we can treat price column also"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sale['Quantity Ordered'] = pd.to_numeric(Sale['Quantity Ordered'])\n",
    "Sale['Price Each'] = pd.to_numeric(Sale['Price Each'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sale['Total_Price']=Sale['Quantity Ordered'].astype('int') * Sale['Price Each'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sale['Total_Price']=Sale['Total_Price'].astype('float')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "      <td>1</td>\n",
       "      <td>23.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   141234                    iPhone                 1      700.00   \n",
       "1   141235  Lightning Charging Cable                 1       14.95   \n",
       "2   141236          Wired Headphones                 2       11.99   \n",
       "3   141237          27in FHD Monitor                 1      149.99   \n",
       "4   141238          Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                       Purchase Address  Month  Total_Price  \n",
       "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215      1       700.00  \n",
       "1  01/28/19 14:15       185 Maple St, Portland, OR 97035      1        14.95  \n",
       "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016      1        23.98  \n",
       "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001      1       149.99  \n",
       "4  01/25/19 11:59          387 10th St, Austin, TX 73301      1        11.99  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### we will crate address column for proper result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "### have to write function for creating city\n",
    "\n",
    "def city(address):\n",
    "    return address.split(\",\")[1].strip(\" \")\n",
    "Sale['City']=Sale['Purchase Address'].apply(lambda x : city(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Boston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Portland</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "      <td>1</td>\n",
       "      <td>23.98</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Austin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   141234                    iPhone                 1      700.00   \n",
       "1   141235  Lightning Charging Cable                 1       14.95   \n",
       "2   141236          Wired Headphones                 2       11.99   \n",
       "3   141237          27in FHD Monitor                 1      149.99   \n",
       "4   141238          Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                       Purchase Address  Month  Total_Price  \\\n",
       "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215      1       700.00   \n",
       "1  01/28/19 14:15       185 Maple St, Portland, OR 97035      1        14.95   \n",
       "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016      1        23.98   \n",
       "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001      1       149.99   \n",
       "4  01/25/19 11:59          387 10th St, Austin, TX 73301      1        11.99   \n",
       "\n",
       "            City  \n",
       "0         Boston  \n",
       "1       Portland  \n",
       "2  San Francisco  \n",
       "3    Los Angeles  \n",
       "4         Austin  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Boston', 'Portland', 'San Francisco', 'Los Angeles', 'Austin',\n",
       "       'Atlanta', 'Seattle', 'New York City', 'Dallas'], dtype=object)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.City.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "### we will follow same process for the state column \n",
    "\n",
    "\n",
    "def code(address):\n",
    "    return address.split(\",\")[2].strip(\" \")[0:2]\n",
    "Sale['State']=Sale['Purchase Address'].apply(lambda x : code(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "      <td>1</td>\n",
       "      <td>23.98</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   141234                    iPhone                 1      700.00   \n",
       "1   141235  Lightning Charging Cable                 1       14.95   \n",
       "2   141236          Wired Headphones                 2       11.99   \n",
       "3   141237          27in FHD Monitor                 1      149.99   \n",
       "4   141238          Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                       Purchase Address  Month  Total_Price  \\\n",
       "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215      1       700.00   \n",
       "1  01/28/19 14:15       185 Maple St, Portland, OR 97035      1        14.95   \n",
       "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016      1        23.98   \n",
       "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001      1       149.99   \n",
       "4  01/25/19 11:59          387 10th St, Austin, TX 73301      1        11.99   \n",
       "\n",
       "            City State  \n",
       "0         Boston    MA  \n",
       "1       Portland    OR  \n",
       "2  San Francisco    CA  \n",
       "3    Los Angeles    CA  \n",
       "4         Austin    TX  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "Sale['Hrs'] = pd.to_datetime(Sale['Order Date']).dt.hour\n",
    "Sale['Mins'] = pd.to_datetime(Sale['Order Date']).dt.minute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "      <td>1</td>\n",
       "      <td>23.98</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "      <td>11</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   141234                    iPhone                 1      700.00   \n",
       "1   141235  Lightning Charging Cable                 1       14.95   \n",
       "2   141236          Wired Headphones                 2       11.99   \n",
       "3   141237          27in FHD Monitor                 1      149.99   \n",
       "4   141238          Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                       Purchase Address  Month  Total_Price  \\\n",
       "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215      1       700.00   \n",
       "1  01/28/19 14:15       185 Maple St, Portland, OR 97035      1        14.95   \n",
       "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016      1        23.98   \n",
       "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001      1       149.99   \n",
       "4  01/25/19 11:59          387 10th St, Austin, TX 73301      1        11.99   \n",
       "\n",
       "            City State  Hrs  Mins  \n",
       "0         Boston    MA   21    25  \n",
       "1       Portland    OR   14    15  \n",
       "2  San Francisco    CA   13    33  \n",
       "3    Los Angeles    CA   20    33  \n",
       "4         Austin    TX   11    59  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5219</th>\n",
       "      <td>181544</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>04/22/19 12:48</td>\n",
       "      <td>731 11th St, New York City, NY 10001</td>\n",
       "      <td>4</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>12</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6776</th>\n",
       "      <td>200528</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>05/13/19 13:40</td>\n",
       "      <td>643 4th St, Boston, MA 02215</td>\n",
       "      <td>5</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4717</th>\n",
       "      <td>181069</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>04/27/19 21:01</td>\n",
       "      <td>668 Park St, San Francisco, CA 94016</td>\n",
       "      <td>4</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>385</th>\n",
       "      <td>210292</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>06/08/19 09:00</td>\n",
       "      <td>953 Ridge St, San Francisco, CA 94016</td>\n",
       "      <td>6</td>\n",
       "      <td>3400.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8791</th>\n",
       "      <td>149611</td>\n",
       "      <td>ThinkPad Laptop</td>\n",
       "      <td>2</td>\n",
       "      <td>999.99</td>\n",
       "      <td>01/31/19 17:47</td>\n",
       "      <td>850 6th St, New York City, NY 10001</td>\n",
       "      <td>1</td>\n",
       "      <td>1999.98</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>17</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Order ID             Product  Quantity Ordered  Price Each  \\\n",
       "5219   181544  Macbook Pro Laptop                 2     1700.00   \n",
       "6776   200528  Macbook Pro Laptop                 2     1700.00   \n",
       "4717   181069  Macbook Pro Laptop                 2     1700.00   \n",
       "385    210292  Macbook Pro Laptop                 2     1700.00   \n",
       "8791   149611     ThinkPad Laptop                 2      999.99   \n",
       "\n",
       "          Order Date                       Purchase Address  Month  \\\n",
       "5219  04/22/19 12:48   731 11th St, New York City, NY 10001      4   \n",
       "6776  05/13/19 13:40           643 4th St, Boston, MA 02215      5   \n",
       "4717  04/27/19 21:01   668 Park St, San Francisco, CA 94016      4   \n",
       "385   06/08/19 09:00  953 Ridge St, San Francisco, CA 94016      6   \n",
       "8791  01/31/19 17:47    850 6th St, New York City, NY 10001      1   \n",
       "\n",
       "      Total_Price           City State  Hrs  Mins  \n",
       "5219      3400.00  New York City    NY   12    48  \n",
       "6776      3400.00         Boston    MA   13    40  \n",
       "4717      3400.00  San Francisco    CA   21     1  \n",
       "385       3400.00  San Francisco    CA    9     0  \n",
       "8791      1999.98  New York City    NY   17    47  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## HIGHEST PRICE OF THE SALE\n",
    "Sale.sort_values('Total_Price',ascending=False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>13217</th>\n",
       "      <td>271971</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>10/24/19 18:50</td>\n",
       "      <td>897 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>10</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>18</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13375</th>\n",
       "      <td>206812</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>05/03/19 18:29</td>\n",
       "      <td>624 Center St, San Francisco, CA 94016</td>\n",
       "      <td>5</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>18</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13382</th>\n",
       "      <td>206819</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>05/24/19 18:12</td>\n",
       "      <td>61 West St, Seattle, WA 98101</td>\n",
       "      <td>5</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>18</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12957</th>\n",
       "      <td>308032</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/30/19 08:47</td>\n",
       "      <td>699 Sunset St, Los Angeles, CA 90001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>8</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13386</th>\n",
       "      <td>206823</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>05/08/19 18:26</td>\n",
       "      <td>126 Johnson St, Portland, OR 97035</td>\n",
       "      <td>5</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>18</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Order ID                 Product  Quantity Ordered  Price Each  \\\n",
       "13217   271971  AAA Batteries (4-pack)                 1        2.99   \n",
       "13375   206812  AAA Batteries (4-pack)                 1        2.99   \n",
       "13382   206819  AAA Batteries (4-pack)                 1        2.99   \n",
       "12957   308032  AAA Batteries (4-pack)                 1        2.99   \n",
       "13386   206823  AAA Batteries (4-pack)                 1        2.99   \n",
       "\n",
       "           Order Date                        Purchase Address  Month  \\\n",
       "13217  10/24/19 18:50    897 Spruce St, Los Angeles, CA 90001     10   \n",
       "13375  05/03/19 18:29  624 Center St, San Francisco, CA 94016      5   \n",
       "13382  05/24/19 18:12           61 West St, Seattle, WA 98101      5   \n",
       "12957  12/30/19 08:47    699 Sunset St, Los Angeles, CA 90001     12   \n",
       "13386  05/08/19 18:26      126 Johnson St, Portland, OR 97035      5   \n",
       "\n",
       "       Total_Price           City State  Hrs  Mins  \n",
       "13217         2.99    Los Angeles    CA   18    50  \n",
       "13375         2.99  San Francisco    CA   18    29  \n",
       "13382         2.99        Seattle    WA   18    12  \n",
       "12957         2.99    Los Angeles    CA    8    47  \n",
       "13386         2.99       Portland    OR   18    26  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## LOWEST PRICE OF THE SALE\n",
    "Sale.sort_values('Total_Price',ascending=True).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1700.0"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## maximum pric of individual itam\n",
    "Sale['Price Each'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Sale['Product'][Sale['Price Each'].max()]  ## name of product which have highest price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10903</td>\n",
       "      <td>1.811768e+06</td>\n",
       "      <td>1.822257e+06</td>\n",
       "      <td>139485</td>\n",
       "      <td>282440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13449</td>\n",
       "      <td>2.188885e+06</td>\n",
       "      <td>2.202022e+06</td>\n",
       "      <td>172669</td>\n",
       "      <td>354885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>17005</td>\n",
       "      <td>2.791208e+06</td>\n",
       "      <td>2.807100e+06</td>\n",
       "      <td>218969</td>\n",
       "      <td>447559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20558</td>\n",
       "      <td>3.367671e+06</td>\n",
       "      <td>3.390670e+06</td>\n",
       "      <td>262259</td>\n",
       "      <td>544186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>18667</td>\n",
       "      <td>3.135125e+06</td>\n",
       "      <td>3.152607e+06</td>\n",
       "      <td>238780</td>\n",
       "      <td>487899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15253</td>\n",
       "      <td>2.562026e+06</td>\n",
       "      <td>2.577802e+06</td>\n",
       "      <td>195528</td>\n",
       "      <td>402436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16072</td>\n",
       "      <td>2.632540e+06</td>\n",
       "      <td>2.647776e+06</td>\n",
       "      <td>206169</td>\n",
       "      <td>417349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>13448</td>\n",
       "      <td>2.230345e+06</td>\n",
       "      <td>2.244468e+06</td>\n",
       "      <td>172289</td>\n",
       "      <td>353857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>13109</td>\n",
       "      <td>2.084992e+06</td>\n",
       "      <td>2.097560e+06</td>\n",
       "      <td>168513</td>\n",
       "      <td>341698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>22703</td>\n",
       "      <td>3.715555e+06</td>\n",
       "      <td>3.736727e+06</td>\n",
       "      <td>290650</td>\n",
       "      <td>598437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>19798</td>\n",
       "      <td>3.180601e+06</td>\n",
       "      <td>3.199603e+06</td>\n",
       "      <td>254865</td>\n",
       "      <td>518231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>28114</td>\n",
       "      <td>4.588415e+06</td>\n",
       "      <td>4.613443e+06</td>\n",
       "      <td>359978</td>\n",
       "      <td>733082</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity Ordered    Price Each   Total_Price     Hrs    Mins\n",
       "Month                                                              \n",
       "1                 10903  1.811768e+06  1.822257e+06  139485  282440\n",
       "2                 13449  2.188885e+06  2.202022e+06  172669  354885\n",
       "3                 17005  2.791208e+06  2.807100e+06  218969  447559\n",
       "4                 20558  3.367671e+06  3.390670e+06  262259  544186\n",
       "5                 18667  3.135125e+06  3.152607e+06  238780  487899\n",
       "6                 15253  2.562026e+06  2.577802e+06  195528  402436\n",
       "7                 16072  2.632540e+06  2.647776e+06  206169  417349\n",
       "8                 13448  2.230345e+06  2.244468e+06  172289  353857\n",
       "9                 13109  2.084992e+06  2.097560e+06  168513  341698\n",
       "10                22703  3.715555e+06  3.736727e+06  290650  598437\n",
       "11                19798  3.180601e+06  3.199603e+06  254865  518231\n",
       "12                28114  4.588415e+06  4.613443e+06  359978  733082"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "### sales as per the month\n",
    "\n",
    "Sale.groupby('Month').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.122979</td>\n",
       "      <td>186.607105</td>\n",
       "      <td>187.687376</td>\n",
       "      <td>14.366567</td>\n",
       "      <td>29.090535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.123090</td>\n",
       "      <td>182.787868</td>\n",
       "      <td>183.884962</td>\n",
       "      <td>14.419123</td>\n",
       "      <td>29.635491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.122220</td>\n",
       "      <td>184.201665</td>\n",
       "      <td>185.250471</td>\n",
       "      <td>14.450538</td>\n",
       "      <td>29.535999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.124679</td>\n",
       "      <td>184.237158</td>\n",
       "      <td>185.495390</td>\n",
       "      <td>14.347557</td>\n",
       "      <td>29.771103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.126826</td>\n",
       "      <td>189.250581</td>\n",
       "      <td>190.305852</td>\n",
       "      <td>14.413860</td>\n",
       "      <td>29.451829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.125350</td>\n",
       "      <td>189.023580</td>\n",
       "      <td>190.187565</td>\n",
       "      <td>14.425852</td>\n",
       "      <td>29.691309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.124467</td>\n",
       "      <td>184.183835</td>\n",
       "      <td>185.249826</td>\n",
       "      <td>14.424474</td>\n",
       "      <td>29.199538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.124321</td>\n",
       "      <td>186.468140</td>\n",
       "      <td>187.648849</td>\n",
       "      <td>14.404230</td>\n",
       "      <td>29.584232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.128044</td>\n",
       "      <td>179.415893</td>\n",
       "      <td>180.497387</td>\n",
       "      <td>14.500731</td>\n",
       "      <td>29.403494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1.119367</td>\n",
       "      <td>183.194696</td>\n",
       "      <td>184.238580</td>\n",
       "      <td>14.330441</td>\n",
       "      <td>29.505818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1.126615</td>\n",
       "      <td>180.993608</td>\n",
       "      <td>182.074956</td>\n",
       "      <td>14.503215</td>\n",
       "      <td>29.490184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1.125280</td>\n",
       "      <td>183.654155</td>\n",
       "      <td>184.655913</td>\n",
       "      <td>14.408341</td>\n",
       "      <td>29.342059</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quantity Ordered  Price Each  Total_Price        Hrs       Mins\n",
       "Month                                                                 \n",
       "1              1.122979  186.607105   187.687376  14.366567  29.090535\n",
       "2              1.123090  182.787868   183.884962  14.419123  29.635491\n",
       "3              1.122220  184.201665   185.250471  14.450538  29.535999\n",
       "4              1.124679  184.237158   185.495390  14.347557  29.771103\n",
       "5              1.126826  189.250581   190.305852  14.413860  29.451829\n",
       "6              1.125350  189.023580   190.187565  14.425852  29.691309\n",
       "7              1.124467  184.183835   185.249826  14.424474  29.199538\n",
       "8              1.124321  186.468140   187.648849  14.404230  29.584232\n",
       "9              1.128044  179.415893   180.497387  14.500731  29.403494\n",
       "10             1.119367  183.194696   184.238580  14.330441  29.505818\n",
       "11             1.126615  180.993608   182.074956  14.503215  29.490184\n",
       "12             1.125280  183.654155   184.655913  14.408341  29.342059"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.groupby('Month').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Sale['Month'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Sale['State'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,8))\n",
    "sns.countplot(Sale['City'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Atlanta</th>\n",
       "      <th>GA</th>\n",
       "      <td>16602</td>\n",
       "      <td>2.779908e+06</td>\n",
       "      <td>104794</td>\n",
       "      <td>2.795499e+06</td>\n",
       "      <td>214264</td>\n",
       "      <td>442932</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Austin</th>\n",
       "      <th>TX</th>\n",
       "      <td>11153</td>\n",
       "      <td>1.809874e+06</td>\n",
       "      <td>69829</td>\n",
       "      <td>1.819582e+06</td>\n",
       "      <td>141946</td>\n",
       "      <td>289060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Boston</th>\n",
       "      <th>MA</th>\n",
       "      <td>22528</td>\n",
       "      <td>3.637410e+06</td>\n",
       "      <td>141112</td>\n",
       "      <td>3.661642e+06</td>\n",
       "      <td>288225</td>\n",
       "      <td>590442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dallas</th>\n",
       "      <th>TX</th>\n",
       "      <td>16730</td>\n",
       "      <td>2.752628e+06</td>\n",
       "      <td>104620</td>\n",
       "      <td>2.767975e+06</td>\n",
       "      <td>214390</td>\n",
       "      <td>435155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>CA</th>\n",
       "      <td>33289</td>\n",
       "      <td>5.421435e+06</td>\n",
       "      <td>208325</td>\n",
       "      <td>5.452571e+06</td>\n",
       "      <td>427444</td>\n",
       "      <td>866638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New York City</th>\n",
       "      <th>NY</th>\n",
       "      <td>27932</td>\n",
       "      <td>4.635371e+06</td>\n",
       "      <td>175741</td>\n",
       "      <td>4.664317e+06</td>\n",
       "      <td>357696</td>\n",
       "      <td>733598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Portland</th>\n",
       "      <th>ME</th>\n",
       "      <td>2750</td>\n",
       "      <td>4.471893e+05</td>\n",
       "      <td>17144</td>\n",
       "      <td>4.497583e+05</td>\n",
       "      <td>35211</td>\n",
       "      <td>72856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>11303</td>\n",
       "      <td>1.860558e+06</td>\n",
       "      <td>70621</td>\n",
       "      <td>1.870732e+06</td>\n",
       "      <td>144421</td>\n",
       "      <td>295533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <th>CA</th>\n",
       "      <td>50239</td>\n",
       "      <td>8.211462e+06</td>\n",
       "      <td>315520</td>\n",
       "      <td>8.262204e+06</td>\n",
       "      <td>643265</td>\n",
       "      <td>1319477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <th>WA</th>\n",
       "      <td>16553</td>\n",
       "      <td>2.733296e+06</td>\n",
       "      <td>104941</td>\n",
       "      <td>2.747755e+06</td>\n",
       "      <td>213292</td>\n",
       "      <td>436368</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Quantity Ordered    Price Each   Month   Total_Price  \\\n",
       "City          State                                                         \n",
       "Atlanta       GA                16602  2.779908e+06  104794  2.795499e+06   \n",
       "Austin        TX                11153  1.809874e+06   69829  1.819582e+06   \n",
       "Boston        MA                22528  3.637410e+06  141112  3.661642e+06   \n",
       "Dallas        TX                16730  2.752628e+06  104620  2.767975e+06   \n",
       "Los Angeles   CA                33289  5.421435e+06  208325  5.452571e+06   \n",
       "New York City NY                27932  4.635371e+06  175741  4.664317e+06   \n",
       "Portland      ME                 2750  4.471893e+05   17144  4.497583e+05   \n",
       "              OR                11303  1.860558e+06   70621  1.870732e+06   \n",
       "San Francisco CA                50239  8.211462e+06  315520  8.262204e+06   \n",
       "Seattle       WA                16553  2.733296e+06  104941  2.747755e+06   \n",
       "\n",
       "                        Hrs     Mins  \n",
       "City          State                   \n",
       "Atlanta       GA     214264   442932  \n",
       "Austin        TX     141946   289060  \n",
       "Boston        MA     288225   590442  \n",
       "Dallas        TX     214390   435155  \n",
       "Los Angeles   CA     427444   866638  \n",
       "New York City NY     357696   733598  \n",
       "Portland      ME      35211    72856  \n",
       "              OR     144421   295533  \n",
       "San Francisco CA     643265  1319477  \n",
       "Seattle       WA     213292   436368  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.groupby(['City','State']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hrs</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4428</td>\n",
       "      <td>709296.70</td>\n",
       "      <td>27554</td>\n",
       "      <td>713721.27</td>\n",
       "      <td>103596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2619</td>\n",
       "      <td>458490.00</td>\n",
       "      <td>16657</td>\n",
       "      <td>460866.88</td>\n",
       "      <td>61807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1398</td>\n",
       "      <td>233833.64</td>\n",
       "      <td>8507</td>\n",
       "      <td>234851.44</td>\n",
       "      <td>32508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>928</td>\n",
       "      <td>144726.42</td>\n",
       "      <td>5904</td>\n",
       "      <td>145757.89</td>\n",
       "      <td>23044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>937</td>\n",
       "      <td>162058.18</td>\n",
       "      <td>6148</td>\n",
       "      <td>162661.01</td>\n",
       "      <td>26876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1493</td>\n",
       "      <td>229621.21</td>\n",
       "      <td>9301</td>\n",
       "      <td>230679.82</td>\n",
       "      <td>42877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2810</td>\n",
       "      <td>445000.11</td>\n",
       "      <td>17539</td>\n",
       "      <td>448113.00</td>\n",
       "      <td>80343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4556</td>\n",
       "      <td>740568.11</td>\n",
       "      <td>28850</td>\n",
       "      <td>744854.12</td>\n",
       "      <td>127447</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7002</td>\n",
       "      <td>1185970.62</td>\n",
       "      <td>43626</td>\n",
       "      <td>1192348.97</td>\n",
       "      <td>198135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9816</td>\n",
       "      <td>1628498.49</td>\n",
       "      <td>60981</td>\n",
       "      <td>1639030.58</td>\n",
       "      <td>268253</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12308</td>\n",
       "      <td>1932665.62</td>\n",
       "      <td>76928</td>\n",
       "      <td>1944286.77</td>\n",
       "      <td>331941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14005</td>\n",
       "      <td>2288855.18</td>\n",
       "      <td>87654</td>\n",
       "      <td>2300610.24</td>\n",
       "      <td>368952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>14202</td>\n",
       "      <td>2299876.68</td>\n",
       "      <td>89161</td>\n",
       "      <td>2316821.34</td>\n",
       "      <td>368679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>13685</td>\n",
       "      <td>2139743.86</td>\n",
       "      <td>85808</td>\n",
       "      <td>2155389.80</td>\n",
       "      <td>351045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>12362</td>\n",
       "      <td>2072194.77</td>\n",
       "      <td>77836</td>\n",
       "      <td>2083672.73</td>\n",
       "      <td>322563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11391</td>\n",
       "      <td>1931174.99</td>\n",
       "      <td>72060</td>\n",
       "      <td>1941549.60</td>\n",
       "      <td>299893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11662</td>\n",
       "      <td>1892454.54</td>\n",
       "      <td>72939</td>\n",
       "      <td>1904601.31</td>\n",
       "      <td>310132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>12229</td>\n",
       "      <td>2116777.02</td>\n",
       "      <td>77454</td>\n",
       "      <td>2129361.61</td>\n",
       "      <td>323428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>13802</td>\n",
       "      <td>2207696.93</td>\n",
       "      <td>86421</td>\n",
       "      <td>2219348.30</td>\n",
       "      <td>371688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>14470</td>\n",
       "      <td>2398588.31</td>\n",
       "      <td>91389</td>\n",
       "      <td>2412938.54</td>\n",
       "      <td>381042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13768</td>\n",
       "      <td>2268185.16</td>\n",
       "      <td>86375</td>\n",
       "      <td>2281716.24</td>\n",
       "      <td>354086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12244</td>\n",
       "      <td>2030763.83</td>\n",
       "      <td>77103</td>\n",
       "      <td>2042000.86</td>\n",
       "      <td>312062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>9899</td>\n",
       "      <td>1599464.44</td>\n",
       "      <td>62088</td>\n",
       "      <td>1607549.21</td>\n",
       "      <td>248503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>7065</td>\n",
       "      <td>1172625.87</td>\n",
       "      <td>44364</td>\n",
       "      <td>1179304.44</td>\n",
       "      <td>173159</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Quantity Ordered  Price Each  Month  Total_Price    Mins\n",
       "Hrs                                                          \n",
       "0                4428   709296.70  27554    713721.27  103596\n",
       "1                2619   458490.00  16657    460866.88   61807\n",
       "2                1398   233833.64   8507    234851.44   32508\n",
       "3                 928   144726.42   5904    145757.89   23044\n",
       "4                 937   162058.18   6148    162661.01   26876\n",
       "5                1493   229621.21   9301    230679.82   42877\n",
       "6                2810   445000.11  17539    448113.00   80343\n",
       "7                4556   740568.11  28850    744854.12  127447\n",
       "8                7002  1185970.62  43626   1192348.97  198135\n",
       "9                9816  1628498.49  60981   1639030.58  268253\n",
       "10              12308  1932665.62  76928   1944286.77  331941\n",
       "11              14005  2288855.18  87654   2300610.24  368952\n",
       "12              14202  2299876.68  89161   2316821.34  368679\n",
       "13              13685  2139743.86  85808   2155389.80  351045\n",
       "14              12362  2072194.77  77836   2083672.73  322563\n",
       "15              11391  1931174.99  72060   1941549.60  299893\n",
       "16              11662  1892454.54  72939   1904601.31  310132\n",
       "17              12229  2116777.02  77454   2129361.61  323428\n",
       "18              13802  2207696.93  86421   2219348.30  371688\n",
       "19              14470  2398588.31  91389   2412938.54  381042\n",
       "20              13768  2268185.16  86375   2281716.24  354086\n",
       "21              12244  2030763.83  77103   2042000.86  312062\n",
       "22               9899  1599464.44  62088   1607549.21  248503\n",
       "23               7065  1172625.87  44364   1179304.44  173159"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.groupby(['Hrs']).sum()  ## total price as per hrs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>141314</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/13/19 23:51</td>\n",
       "      <td>700 Jefferson St, New York City, NY 10001</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>23</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>467</th>\n",
       "      <td>141690</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/14/19 23:02</td>\n",
       "      <td>27 Jefferson St, Boston, MA 02215</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>23</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1769</th>\n",
       "      <td>142918</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/08/19 23:38</td>\n",
       "      <td>405 Lakeview St, New York City, NY 10001</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>23</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2535</th>\n",
       "      <td>143636</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/17/19 23:30</td>\n",
       "      <td>509 Washington St, New York City, NY 10001</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2660</th>\n",
       "      <td>143753</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/16/19 23:13</td>\n",
       "      <td>192 Church St, New York City, NY 10001</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>23</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2697</th>\n",
       "      <td>143790</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/08/19 23:16</td>\n",
       "      <td>823 13th St, Dallas, TX 75001</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>23</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3983</th>\n",
       "      <td>145012</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/25/19 23:42</td>\n",
       "      <td>204 River St, Boston, MA 02215</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>23</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4261</th>\n",
       "      <td>145272</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/14/19 23:20</td>\n",
       "      <td>369 North St, Los Angeles, CA 90001</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5226</th>\n",
       "      <td>146201</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/21/19 23:10</td>\n",
       "      <td>996 Cedar St, Seattle, WA 98101</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5668</th>\n",
       "      <td>146626</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/16/19 23:56</td>\n",
       "      <td>273 9th St, San Francisco, CA 94016</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5807</th>\n",
       "      <td>146762</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/19/19 23:59</td>\n",
       "      <td>135 Main St, San Francisco, CA 94016</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6631</th>\n",
       "      <td>147537</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/17/19 23:57</td>\n",
       "      <td>305 Elm St, Los Angeles, CA 90001</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7159</th>\n",
       "      <td>148046</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>01/13/19 23:32</td>\n",
       "      <td>105 West St, Dallas, TX 75001</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>23</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1213</th>\n",
       "      <td>151664</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/09/19 23:37</td>\n",
       "      <td>651 Main St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2744</th>\n",
       "      <td>153121</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/22/19 23:49</td>\n",
       "      <td>344 Highland St, Los Angeles, CA 90001</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2780</th>\n",
       "      <td>153154</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/27/19 23:12</td>\n",
       "      <td>986 1st St, New York City, NY 10001</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>23</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4207</th>\n",
       "      <td>154512</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/02/19 23:21</td>\n",
       "      <td>90 Park St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4534</th>\n",
       "      <td>154823</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/05/19 23:56</td>\n",
       "      <td>914 Dogwood St, Los Angeles, CA 90001</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5140</th>\n",
       "      <td>155403</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/15/19 23:11</td>\n",
       "      <td>74 Park St, Los Angeles, CA 90001</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5656</th>\n",
       "      <td>155896</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/11/19 23:33</td>\n",
       "      <td>389 Maple St, Portland, OR 97035</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>23</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5673</th>\n",
       "      <td>155912</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/09/19 23:10</td>\n",
       "      <td>111 12th St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6076</th>\n",
       "      <td>156292</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/27/19 23:40</td>\n",
       "      <td>109 Church St, Atlanta, GA 30301</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>23</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6918</th>\n",
       "      <td>157098</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/02/19 23:20</td>\n",
       "      <td>514 Cherry St, Austin, TX 73301</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8299</th>\n",
       "      <td>158415</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/10/19 23:41</td>\n",
       "      <td>903 Lincoln St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9430</th>\n",
       "      <td>159506</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/03/19 23:23</td>\n",
       "      <td>87 1st St, San Francisco, CA 94016</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9479</th>\n",
       "      <td>159553</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/25/19 23:56</td>\n",
       "      <td>939 Pine St, Los Angeles, CA 90001</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11634</th>\n",
       "      <td>161622</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>02/10/19 23:38</td>\n",
       "      <td>646 Cedar St, New York City, NY 10001</td>\n",
       "      <td>2</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>23</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2053</th>\n",
       "      <td>163964</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>03/05/19 23:06</td>\n",
       "      <td>725 Park St, Los Angeles, CA 90001</td>\n",
       "      <td>3</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2684</th>\n",
       "      <td>164563</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>03/25/19 23:46</td>\n",
       "      <td>122 Main St, Seattle, WA 98101</td>\n",
       "      <td>3</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>23</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3255</th>\n",
       "      <td>165104</td>\n",
       "      <td>Macbook Pro Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>03/29/19 23:58</td>\n",
       "      <td>437 North St, San Francisco, CA 94016</td>\n",
       "      <td>3</td>\n",
       "      <td>1700.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>23</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>247</th>\n",
       "      <td>295895</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/29/19 00:18</td>\n",
       "      <td>539 Forest St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1067</th>\n",
       "      <td>296681</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/10/19 00:18</td>\n",
       "      <td>786 6th St, Dallas, TX 75001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1429</th>\n",
       "      <td>297023</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/12/19 00:22</td>\n",
       "      <td>169 Hill St, New York City, NY 10001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>0</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2130</th>\n",
       "      <td>297686</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/27/19 00:25</td>\n",
       "      <td>183 Madison St, Portland, OR 97035</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>0</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2357</th>\n",
       "      <td>297904</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/03/19 00:07</td>\n",
       "      <td>655 Elm St, Dallas, TX 75001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2451</th>\n",
       "      <td>297995</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/25/19 00:50</td>\n",
       "      <td>210 Willow St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4359</th>\n",
       "      <td>299809</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/09/19 00:58</td>\n",
       "      <td>458 Cedar St, New York City, NY 10001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5126</th>\n",
       "      <td>300540</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/11/19 00:37</td>\n",
       "      <td>342 Hickory St, Portland, ME 04101</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Portland</td>\n",
       "      <td>ME</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5197</th>\n",
       "      <td>300608</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/06/19 00:54</td>\n",
       "      <td>660 13th St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6175</th>\n",
       "      <td>301551</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/29/19 00:40</td>\n",
       "      <td>351 Lakeview St, Los Angeles, CA 90001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6352</th>\n",
       "      <td>301724</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/14/19 00:46</td>\n",
       "      <td>373 Maple St, Portland, OR 97035</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6492</th>\n",
       "      <td>301858</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/24/19 00:04</td>\n",
       "      <td>194 Lakeview St, Dallas, TX 75001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>TX</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6548</th>\n",
       "      <td>301910</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/11/19 00:00</td>\n",
       "      <td>355 14th St, Portland, OR 97035</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6870</th>\n",
       "      <td>302220</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/22/19 00:42</td>\n",
       "      <td>59 Jefferson St, Atlanta, GA 30301</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7572</th>\n",
       "      <td>302886</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/09/19 00:37</td>\n",
       "      <td>394 South St, Atlanta, GA 30301</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>0</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10264</th>\n",
       "      <td>305454</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/24/19 00:15</td>\n",
       "      <td>891 Cedar St, Los Angeles, CA 90001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13370</th>\n",
       "      <td>308423</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/26/19 00:54</td>\n",
       "      <td>150 Lincoln St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16821</th>\n",
       "      <td>311739</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/28/19 00:26</td>\n",
       "      <td>757 Pine St, Seattle, WA 98101</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>WA</td>\n",
       "      <td>0</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16835</th>\n",
       "      <td>311752</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/25/19 00:23</td>\n",
       "      <td>561 Park St, New York City, NY 10001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>0</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16871</th>\n",
       "      <td>311787</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/08/19 00:30</td>\n",
       "      <td>455 7th St, Boston, MA 02215</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17679</th>\n",
       "      <td>312558</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/29/19 00:59</td>\n",
       "      <td>29 5th St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17815</th>\n",
       "      <td>312689</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/25/19 00:17</td>\n",
       "      <td>691 Willow St, Atlanta, GA 30301</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>GA</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18892</th>\n",
       "      <td>313714</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/06/19 00:36</td>\n",
       "      <td>734 Ridge St, New York City, NY 10001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>New York City</td>\n",
       "      <td>NY</td>\n",
       "      <td>0</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18977</th>\n",
       "      <td>313795</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/08/19 00:01</td>\n",
       "      <td>392 Ridge St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19064</th>\n",
       "      <td>313880</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/17/19 00:28</td>\n",
       "      <td>600 Chestnut St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19258</th>\n",
       "      <td>314065</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/15/19 00:44</td>\n",
       "      <td>35 2nd St, Boston, MA 02215</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>0</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20157</th>\n",
       "      <td>314925</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/19/19 00:31</td>\n",
       "      <td>188 West St, Boston, MA 02215</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20358</th>\n",
       "      <td>315117</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/31/19 00:13</td>\n",
       "      <td>424 Cedar St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20860</th>\n",
       "      <td>315592</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/08/19 00:09</td>\n",
       "      <td>245 Walnut St, San Francisco, CA 94016</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21896</th>\n",
       "      <td>316582</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>1</td>\n",
       "      <td>2.99</td>\n",
       "      <td>12/25/19 00:09</td>\n",
       "      <td>473 Ridge St, Los Angeles, CA 90001</td>\n",
       "      <td>12</td>\n",
       "      <td>2.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>185950 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Order ID                 Product  Quantity Ordered  Price Each  \\\n",
       "82      141314      Macbook Pro Laptop                 1     1700.00   \n",
       "467     141690      Macbook Pro Laptop                 1     1700.00   \n",
       "1769    142918      Macbook Pro Laptop                 1     1700.00   \n",
       "2535    143636      Macbook Pro Laptop                 1     1700.00   \n",
       "2660    143753      Macbook Pro Laptop                 1     1700.00   \n",
       "2697    143790      Macbook Pro Laptop                 1     1700.00   \n",
       "3983    145012      Macbook Pro Laptop                 1     1700.00   \n",
       "4261    145272      Macbook Pro Laptop                 1     1700.00   \n",
       "5226    146201      Macbook Pro Laptop                 1     1700.00   \n",
       "5668    146626      Macbook Pro Laptop                 1     1700.00   \n",
       "5807    146762      Macbook Pro Laptop                 1     1700.00   \n",
       "6631    147537      Macbook Pro Laptop                 1     1700.00   \n",
       "7159    148046      Macbook Pro Laptop                 1     1700.00   \n",
       "1213    151664      Macbook Pro Laptop                 1     1700.00   \n",
       "2744    153121      Macbook Pro Laptop                 1     1700.00   \n",
       "2780    153154      Macbook Pro Laptop                 1     1700.00   \n",
       "4207    154512      Macbook Pro Laptop                 1     1700.00   \n",
       "4534    154823      Macbook Pro Laptop                 1     1700.00   \n",
       "5140    155403      Macbook Pro Laptop                 1     1700.00   \n",
       "5656    155896      Macbook Pro Laptop                 1     1700.00   \n",
       "5673    155912      Macbook Pro Laptop                 1     1700.00   \n",
       "6076    156292      Macbook Pro Laptop                 1     1700.00   \n",
       "6918    157098      Macbook Pro Laptop                 1     1700.00   \n",
       "8299    158415      Macbook Pro Laptop                 1     1700.00   \n",
       "9430    159506      Macbook Pro Laptop                 1     1700.00   \n",
       "9479    159553      Macbook Pro Laptop                 1     1700.00   \n",
       "11634   161622      Macbook Pro Laptop                 1     1700.00   \n",
       "2053    163964      Macbook Pro Laptop                 1     1700.00   \n",
       "2684    164563      Macbook Pro Laptop                 1     1700.00   \n",
       "3255    165104      Macbook Pro Laptop                 1     1700.00   \n",
       "...        ...                     ...               ...         ...   \n",
       "247     295895  AAA Batteries (4-pack)                 1        2.99   \n",
       "1067    296681  AAA Batteries (4-pack)                 1        2.99   \n",
       "1429    297023  AAA Batteries (4-pack)                 1        2.99   \n",
       "2130    297686  AAA Batteries (4-pack)                 1        2.99   \n",
       "2357    297904  AAA Batteries (4-pack)                 1        2.99   \n",
       "2451    297995  AAA Batteries (4-pack)                 1        2.99   \n",
       "4359    299809  AAA Batteries (4-pack)                 1        2.99   \n",
       "5126    300540  AAA Batteries (4-pack)                 1        2.99   \n",
       "5197    300608  AAA Batteries (4-pack)                 1        2.99   \n",
       "6175    301551  AAA Batteries (4-pack)                 1        2.99   \n",
       "6352    301724  AAA Batteries (4-pack)                 1        2.99   \n",
       "6492    301858  AAA Batteries (4-pack)                 1        2.99   \n",
       "6548    301910  AAA Batteries (4-pack)                 1        2.99   \n",
       "6870    302220  AAA Batteries (4-pack)                 1        2.99   \n",
       "7572    302886  AAA Batteries (4-pack)                 1        2.99   \n",
       "10264   305454  AAA Batteries (4-pack)                 1        2.99   \n",
       "13370   308423  AAA Batteries (4-pack)                 1        2.99   \n",
       "16821   311739  AAA Batteries (4-pack)                 1        2.99   \n",
       "16835   311752  AAA Batteries (4-pack)                 1        2.99   \n",
       "16871   311787  AAA Batteries (4-pack)                 1        2.99   \n",
       "17679   312558  AAA Batteries (4-pack)                 1        2.99   \n",
       "17815   312689  AAA Batteries (4-pack)                 1        2.99   \n",
       "18892   313714  AAA Batteries (4-pack)                 1        2.99   \n",
       "18977   313795  AAA Batteries (4-pack)                 1        2.99   \n",
       "19064   313880  AAA Batteries (4-pack)                 1        2.99   \n",
       "19258   314065  AAA Batteries (4-pack)                 1        2.99   \n",
       "20157   314925  AAA Batteries (4-pack)                 1        2.99   \n",
       "20358   315117  AAA Batteries (4-pack)                 1        2.99   \n",
       "20860   315592  AAA Batteries (4-pack)                 1        2.99   \n",
       "21896   316582  AAA Batteries (4-pack)                 1        2.99   \n",
       "\n",
       "           Order Date                            Purchase Address  Month  \\\n",
       "82     01/13/19 23:51   700 Jefferson St, New York City, NY 10001      1   \n",
       "467    01/14/19 23:02           27 Jefferson St, Boston, MA 02215      1   \n",
       "1769   01/08/19 23:38    405 Lakeview St, New York City, NY 10001      1   \n",
       "2535   01/17/19 23:30  509 Washington St, New York City, NY 10001      1   \n",
       "2660   01/16/19 23:13      192 Church St, New York City, NY 10001      1   \n",
       "2697   01/08/19 23:16               823 13th St, Dallas, TX 75001      1   \n",
       "3983   01/25/19 23:42              204 River St, Boston, MA 02215      1   \n",
       "4261   01/14/19 23:20         369 North St, Los Angeles, CA 90001      1   \n",
       "5226   01/21/19 23:10             996 Cedar St, Seattle, WA 98101      1   \n",
       "5668   01/16/19 23:56         273 9th St, San Francisco, CA 94016      1   \n",
       "5807   01/19/19 23:59        135 Main St, San Francisco, CA 94016      1   \n",
       "6631   01/17/19 23:57           305 Elm St, Los Angeles, CA 90001      1   \n",
       "7159   01/13/19 23:32               105 West St, Dallas, TX 75001      1   \n",
       "1213   02/09/19 23:37        651 Main St, San Francisco, CA 94016      2   \n",
       "2744   02/22/19 23:49      344 Highland St, Los Angeles, CA 90001      2   \n",
       "2780   02/27/19 23:12         986 1st St, New York City, NY 10001      2   \n",
       "4207   02/02/19 23:21         90 Park St, San Francisco, CA 94016      2   \n",
       "4534   02/05/19 23:56       914 Dogwood St, Los Angeles, CA 90001      2   \n",
       "5140   02/15/19 23:11           74 Park St, Los Angeles, CA 90001      2   \n",
       "5656   02/11/19 23:33            389 Maple St, Portland, OR 97035      2   \n",
       "5673   02/09/19 23:10        111 12th St, San Francisco, CA 94016      2   \n",
       "6076   02/27/19 23:40            109 Church St, Atlanta, GA 30301      2   \n",
       "6918   02/02/19 23:20             514 Cherry St, Austin, TX 73301      2   \n",
       "8299   02/10/19 23:41     903 Lincoln St, San Francisco, CA 94016      2   \n",
       "9430   02/03/19 23:23          87 1st St, San Francisco, CA 94016      2   \n",
       "9479   02/25/19 23:56          939 Pine St, Los Angeles, CA 90001      2   \n",
       "11634  02/10/19 23:38       646 Cedar St, New York City, NY 10001      2   \n",
       "2053   03/05/19 23:06          725 Park St, Los Angeles, CA 90001      3   \n",
       "2684   03/25/19 23:46              122 Main St, Seattle, WA 98101      3   \n",
       "3255   03/29/19 23:58       437 North St, San Francisco, CA 94016      3   \n",
       "...               ...                                         ...    ...   \n",
       "247    12/29/19 00:18      539 Forest St, San Francisco, CA 94016     12   \n",
       "1067   12/10/19 00:18                786 6th St, Dallas, TX 75001     12   \n",
       "1429   12/12/19 00:22        169 Hill St, New York City, NY 10001     12   \n",
       "2130   12/27/19 00:25          183 Madison St, Portland, OR 97035     12   \n",
       "2357   12/03/19 00:07                655 Elm St, Dallas, TX 75001     12   \n",
       "2451   12/25/19 00:50      210 Willow St, San Francisco, CA 94016     12   \n",
       "4359   12/09/19 00:58       458 Cedar St, New York City, NY 10001     12   \n",
       "5126   12/11/19 00:37          342 Hickory St, Portland, ME 04101     12   \n",
       "5197   12/06/19 00:54        660 13th St, San Francisco, CA 94016     12   \n",
       "6175   12/29/19 00:40      351 Lakeview St, Los Angeles, CA 90001     12   \n",
       "6352   12/14/19 00:46            373 Maple St, Portland, OR 97035     12   \n",
       "6492   12/24/19 00:04           194 Lakeview St, Dallas, TX 75001     12   \n",
       "6548   12/11/19 00:00             355 14th St, Portland, OR 97035     12   \n",
       "6870   12/22/19 00:42          59 Jefferson St, Atlanta, GA 30301     12   \n",
       "7572   12/09/19 00:37             394 South St, Atlanta, GA 30301     12   \n",
       "10264  12/24/19 00:15         891 Cedar St, Los Angeles, CA 90001     12   \n",
       "13370  12/26/19 00:54     150 Lincoln St, San Francisco, CA 94016     12   \n",
       "16821  12/28/19 00:26              757 Pine St, Seattle, WA 98101     12   \n",
       "16835  12/25/19 00:23        561 Park St, New York City, NY 10001     12   \n",
       "16871  12/08/19 00:30                455 7th St, Boston, MA 02215     12   \n",
       "17679  12/29/19 00:59          29 5th St, San Francisco, CA 94016     12   \n",
       "17815  12/25/19 00:17            691 Willow St, Atlanta, GA 30301     12   \n",
       "18892  12/06/19 00:36       734 Ridge St, New York City, NY 10001     12   \n",
       "18977  12/08/19 00:01       392 Ridge St, San Francisco, CA 94016     12   \n",
       "19064  12/17/19 00:28    600 Chestnut St, San Francisco, CA 94016     12   \n",
       "19258  12/15/19 00:44                 35 2nd St, Boston, MA 02215     12   \n",
       "20157  12/19/19 00:31               188 West St, Boston, MA 02215     12   \n",
       "20358  12/31/19 00:13       424 Cedar St, San Francisco, CA 94016     12   \n",
       "20860  12/08/19 00:09      245 Walnut St, San Francisco, CA 94016     12   \n",
       "21896  12/25/19 00:09         473 Ridge St, Los Angeles, CA 90001     12   \n",
       "\n",
       "       Total_Price           City State  Hrs  Mins  \n",
       "82         1700.00  New York City    NY   23    51  \n",
       "467        1700.00         Boston    MA   23     2  \n",
       "1769       1700.00  New York City    NY   23    38  \n",
       "2535       1700.00  New York City    NY   23    30  \n",
       "2660       1700.00  New York City    NY   23    13  \n",
       "2697       1700.00         Dallas    TX   23    16  \n",
       "3983       1700.00         Boston    MA   23    42  \n",
       "4261       1700.00    Los Angeles    CA   23    20  \n",
       "5226       1700.00        Seattle    WA   23    10  \n",
       "5668       1700.00  San Francisco    CA   23    56  \n",
       "5807       1700.00  San Francisco    CA   23    59  \n",
       "6631       1700.00    Los Angeles    CA   23    57  \n",
       "7159       1700.00         Dallas    TX   23    32  \n",
       "1213       1700.00  San Francisco    CA   23    37  \n",
       "2744       1700.00    Los Angeles    CA   23    49  \n",
       "2780       1700.00  New York City    NY   23    12  \n",
       "4207       1700.00  San Francisco    CA   23    21  \n",
       "4534       1700.00    Los Angeles    CA   23    56  \n",
       "5140       1700.00    Los Angeles    CA   23    11  \n",
       "5656       1700.00       Portland    OR   23    33  \n",
       "5673       1700.00  San Francisco    CA   23    10  \n",
       "6076       1700.00        Atlanta    GA   23    40  \n",
       "6918       1700.00         Austin    TX   23    20  \n",
       "8299       1700.00  San Francisco    CA   23    41  \n",
       "9430       1700.00  San Francisco    CA   23    23  \n",
       "9479       1700.00    Los Angeles    CA   23    56  \n",
       "11634      1700.00  New York City    NY   23    38  \n",
       "2053       1700.00    Los Angeles    CA   23     6  \n",
       "2684       1700.00        Seattle    WA   23    46  \n",
       "3255       1700.00  San Francisco    CA   23    58  \n",
       "...            ...            ...   ...  ...   ...  \n",
       "247           2.99  San Francisco    CA    0    18  \n",
       "1067          2.99         Dallas    TX    0    18  \n",
       "1429          2.99  New York City    NY    0    22  \n",
       "2130          2.99       Portland    OR    0    25  \n",
       "2357          2.99         Dallas    TX    0     7  \n",
       "2451          2.99  San Francisco    CA    0    50  \n",
       "4359          2.99  New York City    NY    0    58  \n",
       "5126          2.99       Portland    ME    0    37  \n",
       "5197          2.99  San Francisco    CA    0    54  \n",
       "6175          2.99    Los Angeles    CA    0    40  \n",
       "6352          2.99       Portland    OR    0    46  \n",
       "6492          2.99         Dallas    TX    0     4  \n",
       "6548          2.99       Portland    OR    0     0  \n",
       "6870          2.99        Atlanta    GA    0    42  \n",
       "7572          2.99        Atlanta    GA    0    37  \n",
       "10264         2.99    Los Angeles    CA    0    15  \n",
       "13370         2.99  San Francisco    CA    0    54  \n",
       "16821         2.99        Seattle    WA    0    26  \n",
       "16835         2.99  New York City    NY    0    23  \n",
       "16871         2.99         Boston    MA    0    30  \n",
       "17679         2.99  San Francisco    CA    0    59  \n",
       "17815         2.99        Atlanta    GA    0    17  \n",
       "18892         2.99  New York City    NY    0    36  \n",
       "18977         2.99  San Francisco    CA    0     1  \n",
       "19064         2.99  San Francisco    CA    0    28  \n",
       "19258         2.99         Boston    MA    0    44  \n",
       "20157         2.99         Boston    MA    0    31  \n",
       "20358         2.99  San Francisco    CA    0    13  \n",
       "20860         2.99  San Francisco    CA    0     9  \n",
       "21896         2.99    Los Angeles    CA    0     9  \n",
       "\n",
       "[185950 rows x 12 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Sale.sort_values(['Hrs','Total_Price'],ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(Sale['Hrs'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(Sale['Hrs'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "keys = [pair for pair, df in Sale.groupby(['Hrs'])]\n",
    "\n",
    "plt.plot(keys, Sale.groupby(['Hrs']).count()['Total_Price'])\n",
    "plt.xticks(keys)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "from above all three diagram we can say order place is high between 10:00am to 21:00pm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(Sale['Price Each'],Sale['Total_Price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "sns.boxenplot(Sale['Month'],Sale['Total_Price'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "### lets check duplicate orders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>City</th>\n",
       "      <th>Atlanta</th>\n",
       "      <th>Austin</th>\n",
       "      <th>Boston</th>\n",
       "      <th>Dallas</th>\n",
       "      <th>Los Angeles</th>\n",
       "      <th>New York City</th>\n",
       "      <th>Portland</th>\n",
       "      <th>San Francisco</th>\n",
       "      <th>Seattle</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>785</td>\n",
       "      <td>529</td>\n",
       "      <td>1051</td>\n",
       "      <td>784</td>\n",
       "      <td>1529</td>\n",
       "      <td>1313</td>\n",
       "      <td>625</td>\n",
       "      <td>2366</td>\n",
       "      <td>727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>936</td>\n",
       "      <td>625</td>\n",
       "      <td>1255</td>\n",
       "      <td>984</td>\n",
       "      <td>1900</td>\n",
       "      <td>1609</td>\n",
       "      <td>834</td>\n",
       "      <td>2905</td>\n",
       "      <td>927</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1245</td>\n",
       "      <td>841</td>\n",
       "      <td>1645</td>\n",
       "      <td>1201</td>\n",
       "      <td>2456</td>\n",
       "      <td>1977</td>\n",
       "      <td>985</td>\n",
       "      <td>3603</td>\n",
       "      <td>1200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1472</td>\n",
       "      <td>973</td>\n",
       "      <td>1917</td>\n",
       "      <td>1349</td>\n",
       "      <td>3025</td>\n",
       "      <td>2439</td>\n",
       "      <td>1235</td>\n",
       "      <td>4426</td>\n",
       "      <td>1443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1329</td>\n",
       "      <td>838</td>\n",
       "      <td>1806</td>\n",
       "      <td>1421</td>\n",
       "      <td>2568</td>\n",
       "      <td>2267</td>\n",
       "      <td>1167</td>\n",
       "      <td>3928</td>\n",
       "      <td>1242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1107</td>\n",
       "      <td>715</td>\n",
       "      <td>1390</td>\n",
       "      <td>1063</td>\n",
       "      <td>2203</td>\n",
       "      <td>1775</td>\n",
       "      <td>942</td>\n",
       "      <td>3303</td>\n",
       "      <td>1056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1146</td>\n",
       "      <td>763</td>\n",
       "      <td>1553</td>\n",
       "      <td>1102</td>\n",
       "      <td>2230</td>\n",
       "      <td>1930</td>\n",
       "      <td>919</td>\n",
       "      <td>3531</td>\n",
       "      <td>1119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>941</td>\n",
       "      <td>649</td>\n",
       "      <td>1244</td>\n",
       "      <td>968</td>\n",
       "      <td>1871</td>\n",
       "      <td>1629</td>\n",
       "      <td>822</td>\n",
       "      <td>2832</td>\n",
       "      <td>1005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>915</td>\n",
       "      <td>663</td>\n",
       "      <td>1284</td>\n",
       "      <td>934</td>\n",
       "      <td>1882</td>\n",
       "      <td>1533</td>\n",
       "      <td>739</td>\n",
       "      <td>2733</td>\n",
       "      <td>938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1602</td>\n",
       "      <td>1092</td>\n",
       "      <td>2175</td>\n",
       "      <td>1639</td>\n",
       "      <td>3296</td>\n",
       "      <td>2647</td>\n",
       "      <td>1384</td>\n",
       "      <td>4771</td>\n",
       "      <td>1676</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1414</td>\n",
       "      <td>869</td>\n",
       "      <td>1919</td>\n",
       "      <td>1368</td>\n",
       "      <td>2746</td>\n",
       "      <td>2355</td>\n",
       "      <td>1166</td>\n",
       "      <td>4315</td>\n",
       "      <td>1421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1989</td>\n",
       "      <td>1348</td>\n",
       "      <td>2695</td>\n",
       "      <td>2007</td>\n",
       "      <td>3899</td>\n",
       "      <td>3402</td>\n",
       "      <td>1647</td>\n",
       "      <td>6019</td>\n",
       "      <td>1978</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "City   Atlanta  Austin  Boston  Dallas  Los Angeles  New York City  Portland  \\\n",
       "Month                                                                          \n",
       "1          785     529    1051     784         1529           1313       625   \n",
       "2          936     625    1255     984         1900           1609       834   \n",
       "3         1245     841    1645    1201         2456           1977       985   \n",
       "4         1472     973    1917    1349         3025           2439      1235   \n",
       "5         1329     838    1806    1421         2568           2267      1167   \n",
       "6         1107     715    1390    1063         2203           1775       942   \n",
       "7         1146     763    1553    1102         2230           1930       919   \n",
       "8          941     649    1244     968         1871           1629       822   \n",
       "9          915     663    1284     934         1882           1533       739   \n",
       "10        1602    1092    2175    1639         3296           2647      1384   \n",
       "11        1414     869    1919    1368         2746           2355      1166   \n",
       "12        1989    1348    2695    2007         3899           3402      1647   \n",
       "\n",
       "City   San Francisco  Seattle  \n",
       "Month                          \n",
       "1               2366      727  \n",
       "2               2905      927  \n",
       "3               3603     1200  \n",
       "4               4426     1443  \n",
       "5               3928     1242  \n",
       "6               3303     1056  \n",
       "7               3531     1119  \n",
       "8               2832     1005  \n",
       "9               2733      938  \n",
       "10              4771     1676  \n",
       "11              4315     1421  \n",
       "12              6019     1978  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(Sale['Month'],Sale['City'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What product sold the most? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAFoCAYAAAC155dTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydd7hkVZW33x+0CI6SJIiABGFUUGi0CWZFJY0j6CcjIEGUwThiFphREHXEiBEUJTSiIIgIklpyVKGRbKJBkBaEZggGVNLv+2Pt4lZX39v37DrFrb591/s89VTVubX22VW36qy9V5RtkiRJkqnNEsOeQJIkSTJ8UhkkSZIkqQySJEmSVAZJkiQJqQySJEkSYNqwJ9AvK620ktdee+1hTyNJkmTSsNJKKzFr1qxZtrfp/dukVQZrr702s2fPHvY0kiRJJhWSVhrteJqJkiRJklQGSZIkSSqDJEmShFQGSZIkCakMkiRJElIZJEmSJKQySJIkSUhlkCRJkpDKIEmSJGESZyAnw0VStUw2UkqSRZfcGSRJkiSpDJIkSZIGykDS0pIul3SNpBskfaIcX0fSLyTdKOkHkpYqx59Yns8pf1+7a6z9yvHfStq66/g25dgcSfsO/m0mSZIkC6PJzuCfwJa2NwamA9tI2gL4LHCI7fWBe4G3lde/DbjX9nrAIeV1SNoA2AnYENgGOFTSkpKWBL4BbAtsAOxcXpskSZJMEOMqAwd/LU+fUG4GtgR+WI7PBHYoj7cvzyl/f5XC27g9cLztf9r+PTAH2Kzc5ti+2faDwPHltUmSJMkE0chnUFbwVwN3AWcDNwH32X64vGQusHp5vDpwG0D5+/3AU7uP98iMdTxJkiSZIBopA9uP2J4OrEGs5J8z2svK/Wgxh+7j+AJI2lvSbEmz582bN/7EkyRJkkZURRPZvg+4ANgCWF5SJ09hDeD28ngusCZA+ftywD3dx3tkxjo+2vkPtz3D9oyVV165ZupJkiTJQmgSTbSypOXL42WAVwO/Bs4H3lhetgdwSnl8anlO+ft5jmyjU4GdSrTROsD6wOXAFcD6JTppKcLJfOog3lySJEnSjCYZyKsBM0vUzxLACbZPk/Qr4HhJnwKuAo4orz8C+K6kOcSOYCcA2zdIOgH4FfAw8G7bjwBIeg8wC1gSONL2DQN7h0mSJMm4aLKWCJgxY4Znz5497GlMWbIcRZJMTiRdaXtG7/HMQE6SJElSGSRJkiSpDJIkSRJSGSRJkiSkMkiSJElIZZAkSZKQyiBJkiQhlUGSJElCKoMkSZKEVAZJkiQJzWoTJYshWU4iSZJucmeQJEmSpDJIkiRJUhkkSZIkpDJIkiRJSGWQJEmSkMogSZIkIZVBkiRJQiqDJEmShFQGSZIkCakMkiRJElIZJEmSJKQySJIkSUhlkCRJkpDKIEmSJKGBMpC0pqTzJf1a0g2S9inHD5T0R0lXl9t2XTL7SZoj6beStu46vk05NkfSvl3H15H0C0k3SvqBpKUG/UaTJEmSsWmyM3gY+KDt5wBbAO+WtEH52yG2p5fbGQDlbzsBGwLbAIdKWlLSksA3gG2BDYCdu8b5bBlrfeBe4G0Den9JkiRJA8ZVBrbvsP3L8vgvwK+B1Rcisj1wvO1/2v49MAfYrNzm2L7Z9oPA8cD2ii4rWwI/LPIzgR36fUNJkiRJPVU+A0lrA5sAvyiH3iPpWklHSlqhHFsduK1LbG45NtbxpwL32X645/ho599b0mxJs+fNm1cz9SRJkmQhNFYGkp4MnAS8z/afgcOAZwLTgTuAL3ZeOoq4+zi+4EH7cNszbM9YeeWVm049SZIkGYdGPZAlPYFQBN+z/SMA23d2/f3bwGnl6VxgzS7xNYDby+PRjt8NLC9pWtkddL8+SZIkmQCaRBMJOAL4te0vdR1fretlrweuL49PBXaS9ERJ6wDrA5cDVwDrl8ihpQgn86mOLuvnA28s8nsAp7R7W0mSJEkNTXYGLwZ2A66TdHU5tj8RDTSdMOncArwdwPYNkk4AfkVEIr3b9iMAkt4DzAKWBI60fUMZ76PA8ZI+BVxFKJ8kSZJkglAszCcfM2bM8OzZs4c9jUlLbPjq6P6utJVPkmQ4SLrS9oze45mBnCRJkjRzICdJkiyK5A51cOTOIEmSJEllkCRJkqQySJIkSUhlkCRJkpAO5CSZsqTzNekmdwZJkiRJKoMkSZIklUGSJElCKoMkSZKEVAZJkiQJqQySJEkSUhkkSZIkpDJIkiRJSGWQJEmSkMogSZIkIZVBkiRJQiqDJEmShFQGSZIkCakMkiRJElIZJEmSJKQySJIkSUhlkCRJktBAGUhaU9L5kn4t6QZJ+5TjK0o6W9KN5X6FclySvippjqRrJT2/a6w9yutvlLRH1/EXSLquyHxV/bRgSpIkmWAkVd8WVZrsDB4GPmj7OcAWwLslbQDsC5xre33g3PIcYFtg/XLbGzgMQnkABwCbA5sBB3QUSHnN3l1y27R/a0mSJElTxlUGtu+w/cvy+C/Ar4HVge2BmeVlM4EdyuPtgWMc/BxYXtJqwNbA2bbvsX0vcDawTfnbsrZ/5miwekzXWEmSJMkEUOUzkLQ2sAnwC2BV23dAKAxglfKy1YHbusTmlmMLOz53lOOjnX9vSbMlzZ43b17N1JMkSZKF0FgZSHoycBLwPtt/XthLRznmPo4veNA+3PYM2zNWXnnl8aacJEmSNKSRMpD0BEIRfM/2j8rhO4uJh3J/Vzk+F1izS3wN4PZxjq8xyvEkSZJkgmgSTSTgCODXtr/U9adTgU5E0B7AKV3Hdy9RRVsA9xcz0ixgK0krFMfxVsCs8re/SNqinGv3rrGSJEmSCWBag9e8GNgNuE7S1eXY/sDBwAmS3gb8Adix/O0MYDtgDvAAsCeA7XskfRK4orzuINv3lMfvBI4GlgHOLLckSZJkglAE8Ew+ZsyY4dmzZw97GpOWfuKdu78rbeWT4bM4/A+H/R6Gff5+kHSl7Rm9xzMDOUmSJEllkCRJkqQySJIkSUhlkCRJkpDKIEmSJCGVQZIkSUIqgyRJkoRUBkmSJAmpDJIkSRJSGSRJkiSkMkiSJElIZZAkSZKQyiBJkiQhlUGSJElCKoMkSZKEVAZJkiQJqQySJEkSUhkkSZIkpDJIkiRJSGWQJEmSkMogSZIkIZVBkiRJQiqDJEmSBJg27AkkUxNJ1TK2H4eZJEkCDXYGko6UdJek67uOHSjpj5KuLrftuv62n6Q5kn4raeuu49uUY3Mk7dt1fB1Jv5B0o6QfSFpqkG8wSZIkGZ8mZqKjgW1GOX6I7enldgaApA2AnYANi8yhkpaUtCTwDWBbYANg5/JagM+WsdYH7gXe1uYNJUmSJPWMqwxsXwTc03C87YHjbf/T9u+BOcBm5TbH9s22HwSOB7ZX2Aq2BH5Y5GcCO1S+hyRJkqQlbRzI75F0bTEjrVCOrQ7c1vWaueXYWMefCtxn++Ge40mSJMkE0q8yOAx4JjAduAP4Yjk+mlfQfRwfFUl7S5otafa8efPqZpwkSZKMSV/KwPadth+x/SjwbcIMBLGyX7PrpWsAty/k+N3A8pKm9Rwf67yH255he8bKK6/cz9STJEmSUehLGUharevp64FOpNGpwE6SnihpHWB94HLgCmD9Ejm0FOFkPtURK3g+8MYivwdwSj9zSpIkSfpn3DwDSccBrwBWkjQXOAB4haTphEnnFuDtALZvkHQC8CvgYeDdth8p47wHmAUsCRxp+4Zyio8Cx0v6FHAVcMTA3l2SJEnSCE3WRJ4ZM2Z49uzZw57GpKVt0tew5ZP2LA7/g2G/h2Gfvx8kXWl7Ru/xLEeRJEmSpDJIkiRJUhkkSZIkpDJIkiRJSGWQJEmSkCWsk0nKZIziSJJFmdwZJEmSJKkMkiRJklQGSZIkCakMkiRJElIZJEmSJKQySJIkSUhlkCRJkpDKIEmSJCGVQZIkSUIqgyRJkoRUBkmSJAmpDJIkSRJSGSRJkiSkMkiSJElIZZAkSZKQyiBJkiQhlUGSJElCKoMkSZKEVAZJkiQJDZSBpCMl3SXp+q5jK0o6W9KN5X6FclySvippjqRrJT2/S2aP8vobJe3RdfwFkq4rMl9VP81tkyRJklY02RkcDWzTc2xf4Fzb6wPnlucA2wLrl9vewGEQygM4ANgc2Aw4oKNAymv27pLrPVeSJEnyODOuMrB9EXBPz+HtgZnl8Uxgh67jxzj4ObC8pNWArYGzbd9j+17gbGCb8rdlbf/MtoFjusZKkiRJJoh+fQar2r4DoNyvUo6vDtzW9bq55djCjs8d5fioSNpb0mxJs+fNm9fn1JMkSZJeBu1AHs3e7z6Oj4rtw23PsD1j5ZVX7nOKSZIkSS/9KoM7i4mHcn9XOT4XWLPrdWsAt49zfI1RjidJkiQTSL/K4FSgExG0B3BK1/HdS1TRFsD9xYw0C9hK0grFcbwVMKv87S+StihRRLt3jZUkSZJMENPGe4Gk44BXACtJmktEBR0MnCDpbcAfgB3Ly88AtgPmAA8AewLYvkfSJ4EryusOst1xSr+TiFhaBjiz3JIkSZIJRBHEM/mYMWOGZ8+ePexpTFr6Sefo/q5Mdvlk8fgMh/0ehn3+fpB0pe0ZvcczAzlJkiRJZZAkSZKkMkiSJElIZZAkSZKQyiBJkiQhlUGSJElCKoMkSZKEVAZJkiQJqQySJEkSUhkkSZIkpDJIkiRJaFCoLhk8k7GeSZIkize5M0iSJElSGSRJkiSpDJIkSRLSZ5AkSTI0FiX/Ye4MkiRJklQGSZIkSSqDJEmShPQZ9MWiZOdLkiQZBKkMJiGpjJIkGTRpJkqSJElSGSRJkiSpDJIkSRJaKgNJt0i6TtLVkmaXYytKOlvSjeV+hXJckr4qaY6kayU9v2ucPcrrb5S0R7u3lCQTg6TqW5IsqgxiZ/BK29NtzyjP9wXOtb0+cG55DrAtsH657Q0cBqE8gAOAzYHNgAM6CiRJkiSZGB4PM9H2wMzyeCawQ9fxYxz8HFhe0mrA1sDZtu+xfS9wNrDN4zCvJEmSZAzaKgMDP5V0paS9y7FVbd8BUO5XKcdXB27rkp1bjo11PEmSJJkg2uYZvNj27ZJWAc6W9JuFvHY0g6kXcnzBAULh7A3wjGc8o3auSZIkyRi02hnYvr3c3wWcTNj87yzmH8r9XeXlc4E1u8TXAG5fyPHRzne47Rm2Z6y88sptpp5McdL52578DBcv+lYGkv5F0lM6j4GtgOuBU4FORNAewCnl8anA7iWqaAvg/mJGmgVsJWmF4jjeqhxLkiRJJog2ZqJVgZOLtp8GfN/2WZKuAE6Q9DbgD8CO5fVnANsBc4AHgD0BbN8j6ZPAFeV1B9m+p8W8kiRJkkr6Vga2bwY2HuX4/wGvGuW4gXePMdaRwJH9ziVJkiRpx5QsVJeF3pJk0SB/i4sOWY4iSZIkSWWQJEmSpDJIkiRJSGWQJEmSkMogSZIkIZVBkiRJQiqDJEmShFQGSZIkCakMkiRJElIZJEmSJKQySJIkSUhlkCRJkpDKIEmSJGGKVi1NkiSBrJraTe4MkiRJklQGSZIkSSqDJEmShFQGSZIkCakMkiRJElIZJEmSJKQySJIkSUhlkCRJkpBJZ0kyacmEqWSQpDJIkiGRF/NkUWKRMRNJ2kbSbyXNkbTvsOeTJEkylVgklIGkJYFvANsCGwA7S9pguLNKkiSZOiwSygDYDJhj+2bbDwLHA9sPeU5JkiRThkXFZ7A6cFvX87nA5r0vkrQ3sHd5+ldJvx3wPFYC7h7tDxX23VHHmOzyFWPkZzj8z3Cx/R9UjDFs+THHmED50Rj1PcGiowxGe3cLeMpsHw4c/rhNQppte8Ywx5jq8ovCHKa6/KIwh8kuv6jMoYZFxUw0F1iz6/kawO1DmkuSJMmUY1FRBlcA60taR9JSwE7AqUOeU5IkyZRhkTAT2X5Y0nuAWcCSwJG2bxjCVAZhgmo7xlSXXxTmMNXlF4U5THb5RWUOjVEmsSRJkiSLipkoSZIkGSKpDJIkSZKprQwUvKWl/NYt5T/SUv4t/covKkh6WrlfRtKOklaZoPPuKmn5iTjXOPOQpC8MYJynS9q0PH5SH/KbS3q9pCUkrdV2PsNgAJ/BNEmrDX5miz5TWhk4HCbbtZR/V0v550pauoV83/PvRtLTJK3ah9wzJb1f0jclHSbpfZLWqxzme+X+08B6wImVc5he7leW9GFJz24ouhLwQ0lnSnpnm4uApCUl7STpv8rjTZvKlv/jMi3Pvz/wBeBbkpYATqqU/zrwemA/248C366UX17SRyUdXu5XqJEvY2wmaRdJCyScNpRv+xnsAZwJnFWUQu338MvlfldJV0n6bI38sFkkoomGzEqSrgOuIRLdbHv3mgEknQH8EniUGODjFeIbA7cpsqk7539ZhXzf8y8XrHcBqwH3AEtIWhH4I3Co7SvGkT8C+D/gfOAMInlwHeDtkla0/baG7+GJnfdi+wOStm0o1+GLwKuATwIXAkcBLxxPyPaXgS9LWgl4HXEReQpwhu3PV87h2HLunW1/TdJngFdXyD8fuEjSnfT3PXiN7VdKOt/2oyVEu4bn2H6VpPPL8yUr5X8IfAs4mfhOnwRs2VRY0neAfxLf490lvd32Wyvn0PYz2Mv2S4v8w5KeWin/vHK/je1NJP2sUh5JywHvBFYA9ge2tX1a7Tj9kMoA9mwp/6U2wrY3bnn+NvN/IfB+2/d1Hyymk92I/I+F8c5SS6qb30jqhAg35aJyEfpc2SU9UCEL8CRJTwSeaPs4Se+oEbZ9t6SzgGWANwFbAbXKYGXb35T0H5VynTmMq7zG4UFJqwMuO7x/Vsr/tbMil7QJcH/t+W13VtK/k1R7IV/L9ms6TySdUykP7T+DhyU9ucgvQ1ncVbCEpI8DN3XGq5SHWFR8A9jf9iOS3gdMiDKY0maiwj3AfxBa+E3AvZXyFwOrAC8CVgUuqRGWtIakb0iaJelQSWuOLzUffc/f9ldt3zeKWeV5tr/WQP5BAEmHdI6Vi/lxth+qmMf+wK7A3bb/Afy/prKFY4FTgG+W8/++iZCk50j6b0kXET/AvwI7dF+UKrhL0psIc8/rgTtqhCVtJekcSdcVM9O4n38P7yJ2SCsAXwb+q1L+bcCOhCLelZEaYE15uHyHD5Z0Znl+kKSDGsrPlbSfopT9R8vzLSU13l3Q/jP4KLHDeU65369S/g3E7/+gsjjZv1IeYBnbZzGiSFoVIqphyucZSDqVqJJ6JTCD2Oa/tkL+e8ANXfLPtb1zhfw5wAHAbGBT4CDbNdvrVvMvY5wC7Gv715K2B95su/EKV9IuROXZAwj7/+dtX1ghvz/wXODZ5T2cbrvKVCRpC8LcdQqwpu1bG8icBBwHnGn7bzXnG2WspYG9iAvJb4BvF8XWVP4S4JXAT4up47ya70G/FNv6qBTfQdNxXr6Qccb9Lkg6YGxxN1UoQ6X4rg4AliV2l/va/nTlGIcCtwI7AzOBdWy/d9BzHY00E8Fytr9fHv9W0tsr5dew/ebyeJakxhfBwtK2Ly2PLykrihrazh9iJXiMpKuJulA7NRHqupAcT6xgLgH2AK6uPH8rW29xfv4V2NL2yZK+TfwYx2NF2z+snOuo2P5H+d9fQ3wWmwEXVQzxSLlZkqhcEUr6GBFM8PeuOTVRJucSPgoxUhyy87hGGV1M7OjWI8wkJ9l+pKmw7U8oosrWAW6x3XhnJeliFixsKSr9LpL2BN5C/B8686r5DL4C7AD8qJh4tiSCIhpj+12SXkssqubYPmQ8mUGRygCuLhePXwIvAK6tlP+jpP8u8jOoNA8Ap5Zt9bWE4+0nlfJ9z1/SJxn5Ed1KmAaOAg4EmjjBuy8kEOVxv0j9haStrbdf5+fSktZhlAuv7ZtrJiDpNMLx3imwaOqUweeIz/M5RFmWz9WcH9iqH7+D7VfWyozBd5l/h3wssbpthKQPE9+Zq4FNys6o0Wdg+6X10x2VdwIvGcUP1hTZvldS5zdVfX1VhMMK+BOwgqTdbR/T53zqzj3VzUQAkmYAzwRuHi+CZhTZJYmQvGcCN1NWBRXyTyW+NGsDtwD/7HXoNhijr/m33doPCknPJFZQzyJMLP9j+6aFS80nfwrwv8DBwAeAj9l+QwO5O4hQwl5l4NpIFkmn2/63Gpke+SUJh+VKhO+k6oepCGs8H7ieouBrFJqk7Qgb98OEMj3Y9ukV8hfafvlYzxvIX9x9UZd0ie2XNJUvMi8gFjLLAn8GPmF7doX8/xK73BsY+QxrTGVvI6LSNgYuJ6LSjm4qX8a4mIjMe6xqs+2ZNWP0je0pfQPO7nl+XKX8d3uef6VS/rye5ydM5PyLzI5dj9X9vKH86T3yp03w/3AlIr78dGJnslJDufMHOIfvA4cQjti3Am+tlD8X+Cbw8j7Pf1TP7chK+cuBJ5XHTwIu7+P9/zfRuvZjwPF9vP8XERFdL+3nf0NEvz29PH46cEWl/Pk9t/P6mMNTCd/fU/v8P546qO9k7W3KmokkvZLYlq7fFfEwjfgSNZHfhIgNn9EVRjcN2KSh/OuJ6IMNJHW2gdOIVc3jPv8e3kFJ9LLtEppZk3DzL50HRf4pNSdvYe/uvPbu8hl0sombZp5Wx4EvhFlthB1mrn8Fdipmx+uA79u+sqF82xDpq4gonAeIz7HW77MbsUPeiNjd/W+l/FuIaJ6PET6Hqlyfwo2MRNPdC8ypEXZLk5mkjQif2fLxVLg+V+JRSScz/w6vJm+pb6asMiBMOo8C6wLnECvahwhTQxMeJbbU95R7Af8gvtRNOI+w8/+BSNbpnP9PDeXbzr+bJ0pawWHvXBGozYi+UdKngMuI3IUbK+X7snd3kHQ48Axia91xfo77I3SEtA4E2zMVSXzrATfZvryPYR4mnJePlvv/kPQhLyQ6TdJXbO/T40Rt7DztknsCcKGk/wNWBOZVzv1o27t1zwvYp0L+P2y/p0t+bxqWcO56D8sSIalziP/DHyrOT3H4fpIRU9mBtmvyHWYC7yZ8R/0yYQ7jXqasz0DSJravkvQqeiIRbJ/XQH4123cUe3ev/Li2Wknb2j6z2Bl75Y98vOffM9aLGFEijxIJL5dVyAvYHvhX4LfEVrfxF2sA9u6z3V9uwMAo7+FJjDhQ/2G7cZy7pJ8SPqPvARd1Pj9Jn7A9Vtjl0OnaIX+IkUS9acCuDZXR8oRp5VhgF0KRLUlkwE/o/1TSZcDWtv8iaVlgVs0iRdJMIou5cY7NKGMsQYuorDZM5Z3BRsTWuNdJZWLVPh67EREf/zOKfJOtYccx1Zul2PQi2nb+IwJx4a8pfdArb0VJjP8jfswvpS6SZjkiJG+HzpA0+ww73CJpH+ZXJrUK8XmU7X2Rr5k/wHTbryiPv1UTYlyU6SUeJZ6+qSKQtBVh7luOkfdQk6+yBvB2wszYkW/6PW6zQ3458X9fmzARdXa432w69w6K7OE3Mf97qMlRECO/y9rsYwjH8R/KzqScvqqkCLSMymrDlN0ZdFNWAd0/oqrt5QDO3+pC1Hb+kjYGPkwkbfVzIfl6Of+LCVPRk9wgmqdnjCWJTO67aldCWjBhyTUXgdHCQisvIkj6CRGJ0gkx3skV0UXFb7S3KxLVeuR/CbzWdl+9w4vyOogwk3wc2M72ByrH2JyIarvJ9i8qZdckwrJXBe60XV3KQVFS5ETCVHMosKntxnk3kl5NJI11wqU/afuntfNoQ9uorDZM5Z0B0L+9uUu+2/nZsdXWXEhbxae3nX/hm0Ti2XeA/6S+3tFGtl+mSBrbVdKPaoQVGcz/RZSRWFfS120f21TeLRKWOlOouWiMwZuJPI33Etv7XSrl2xYsvJT6/IxuHrV9rqQDbJ8jad8aYbUvNLc1sZu4FVhL0kzbVZVTidpUR5TY/O9IqlqQFP9APzWRgMd2V/sxYuL5jO3bKodpm7fUN1NeGRDp3m1sk62cn7S/ELWdP0Ruw02SlrA9R1JtEs/DxdZ5n6TdiR9DDe8BXuqoFPkEQhk2VgZqkbBUuF9RX6nbzDSu36Yb239WFOjrlG7emAql7j4LFnY5T6cBl0m6a2TIKmVyjaKkxrmK5L2/Vk5lLbcrNPdWIuHr0bJLvITKMtpEfailgeskHQnURrXtSUQDPWYiqlnYAUczf2mZmdQlX8L8UVm/pj4qq29SGbS3N1+pqOfTl/OT9hei1vZyon770sD3FSUpqrb4xKp4CSKDcxfqwwIfJUxUt5X7Wnvt69yTsERdBm+rsNByzr53eJJeRzjgn0asBH/shmWLPaDsW9vvKw8/oSiSV1uwca6k/Qg/1sbl+ZZl7Cbfx0uBzcr3b2Pg0rLAwA0Tv2y/CUDSe4HpRDBDDW0zkNuWloEo5/4g8T14EvHbmpAM5FQGMJew13ccsbUO2LbOz7YXorbzx3Ynkuhb5VbLPCJnorM9vqFS/l3A1xUNUe4lbL41PFgioq4itta10RzHEhU7VwYOo2GuSA997fDKheu5hK3+dmB1YF9J69r+asU4R7j0jygO6W/b3quB3CFE1vzFXYc3JP6f72/+TrgFWAroNKb5PRFI0PT7OKPcuumUO1no6rrsDH/eeQ9ld/EvxPdq3FLkGqmxdQ7wbEl9ZSAzf2mZ6dSXloG4HsyXgTxRpAMZaGlvbuX8LPKt4tMHMP+2pQhOJhzH1xCruhfb3mHhUoOjOB8/SnFeAp+rcaJLOo5oTLO77RdJOsd2o8Y0Gkk4fDVwJ5U7vGKS2bI7FLd8n86u9D2d766kqd7nC5G7aDRz0ljHF0XUU8pivOOjvO585q+x1aHK/1fGWhVYi/gt3jXe60eRP9X262rlBsGU3xm0tTe3dX5q/vj0/5S0m+vi09vayyHqubzC9gOKQlkXEKUdmrKMRzqD/bREZTRG7TOQb1OUwe63n3GbxjSPEBeSs1jwYtKEh7sVAYCj4mWtqexuSXsxkvj3fw3lxloN1tZG2pUITd2AaIxzj+3elf7C5NtEtI21AGu0MHNUzF2D2JXdZPvuhuedD7XIQNZI0cjMQB4ibe3NrZyftIhPL7SdP7QvRXC/pG8xYi++v7Nibuj/GEoGchdtGuzQ7J0AACAASURBVNOcQUQRrcFIH4OaTm3P00g5kg4iTDU17FHm8Z4yj6Z+mx9LOowwp9xBxOh/GPhx5fnfSyihc4ld0ncq5dtEtP2s+Cu+aPtBRQn0D9Gw3Igie35jogTIppJO6COSCdplIJ9DfG871QQmnFQG7e3NbZ2ff5H0ZkZCyWqjONrOHyJy4QJJ9xClCO7uRKk0NBWc0fX4501PKmnd8rCtE75tRNVbicY0vyQu6v9ZIfs9IurlRCJx75vUOdDHav7em8y4UMqu7jDCXCmieN+4pjLbh0jappxvNUKhnujotlXDA2VH8xChUKZXyreJaPtvQhGeVpy2/wB+RPPSLC/vLKg6JjrqI5kgfAVXuL8M5D8Sps7OouJg23f2MU7fTHmfwQDszRsRzr+O8/MA241X1oqEsb27zv9t2437z7ad/yBRZe11SUeN8Sc32V5rpCXizoQi6SuiqlwA5nMgu2EpcPV0JJN0ru1XNT33oFDkBWxFdIu7ibi4VpnrWp5/a8LvMgP4COGUPrpCfl+iVeUeRFTPL1qGXDemRDB1J9h9qfO8yfdI89dGehojtbkah/cqWq9+kPC7vRx4h+3a9q+tmPLKYFhIekbvIUYuZMO6mLdqtdhGXtI0RpzwjbJPNaBWiS0dyH8COlmqAl7TeW67n8qbfSHpsjL3C2y/QtIPOqGWw0DSp23/dwv5/7B9wiDntJBzDeR71HIO8y0ihrGomLJmouKguxrobiTTOINY0s3ENrzbLFSTgXwLUX+9Owyzsb277fzHGrZPuQ5njP+SUU4ajs+3UJl9avsTXWOsxkhEVW1YXhsHcq+Zp8q8M0A6sfEPSHoZ4cgdJpu2lN8LmBBl0P09aoOktYB9mX+X/vuG4i8ou4MOG3WeT1RU15RVBkRBq38jmmmcB5xcGQp2GOEw+xPRhP3cpivawuZEbsKGRKbhSa7oykT7+Y/GjpKWqzFTdWP7C5LebfsblaKtsk8lHUhUzrweeK6kq1xX6bNvB7LtWyvOswAlCuoi25dI+hXxfZoGXGj7YxVD7VPs5R8kzCwfqpxHb8TKQ0SZ9JPdfxLWhKLIfu/mIaL7X20SZb8cB7yPWKRNJ/xJL2oo+wHbR5aoIoid6oQy5c1EJQLoNUSRrnNs19ZkeRpxUX8T0eHri33MYXPC8Tjbdo3zstX8u2zu3XwG2K/SVgoju4oNgetrVjOSPg+cRPyINibs9x+BZkk/WrC4V1WMvCL7ei+i/3AnIqivgnG1dM+1OzdADctyj2JufIxK39cRRMbu1URAwfOIXetLFhb3rrGb0T/V9ri7E0nfHUN+S9urN51/Ges4wm/XeQ+rEgmRy7h9858m5/8x8HrbViSy/Xhhn12P7Ma2r9EorWg9QS1op+zOoKxAX0FcyJ8G/IDKbamil8EbiFX+9VRoc0krESUIXkVEP3yaCjPLIOZPrFyuI1biED/CFYls5iYO2JOJH93Rti8o8zrT9raV8+g7+7RwvaSdGQltvb4TqTReVJIkAYcPwr4v6enA6ravkPSkhiGm3bvJ7sJqSzY87S20MDd2saZLBjORK3K27T0kLbTyqtuXwxikWW05dzUCKt/FHUu49ZgUn8Goq+JKn8GyxHfvOkKZ3tUJGx7v+2X7mnI/4TuCDlNWGQB3EXa9U4iVhIFXlUSRJpmj1xIX8ZOJVf2jwLKStmwYyXIHcfE6n7D3bkTYCZsmmbSaf2EdIpJpc+AY27Mkbd70B2D7SyWmey9Fq8zvNzxv7zit2g0SSXtblVuHj9HgglhWcXdI2owILX20HK8KES7mnucS5QxmEDudJkrxj5JeYvsS2/eWsV5G8453bc2NHeZKOpQIj9yIqKC6JJFM+bjR1szWw0OSPsLIe3iwvIfxwrUvKPe7Eb+pK4mSJOtXnv9x3308nkxZM5GkPcb6m+2ZDeRbRSCMth3sGmDc1UHb+feM9QTii7w1sIbtsWLfFzbGNOLH9KympiqNlAFYgDZRTbWUecBISYJqJ3zHxNN13ygaRFGP6WtEjP/dRETV7cB/2b6ncg59mxuL/GbEAuFmNwytXZQoC5MdKO8BOKXG3yHpp7a36nre1FQ35qrfFaHWw2bKKoNkQcoqatWm0TgdO2ft30Z57WeAU4kV2fOBHZoolC5lMo0wlc0jcgXu8AQ1BOmayyxiF/JdIu/hKNvbVcgvQcx9XnGkv8wNmhyNYm48AzijoYmqe5ynA29k/iZJtQ1+2vaAboWilMqr6SpLUnMxLn6Th4kd+3SiP8K4q/2uhdnWwF8Y2VksZ7u2r8XQSGUwhVGUkDgVuKzLRLECEQHx77bfMY78B4nuZrcRtmuI9oVrEm0cv9RwHpcRxe06jrdLXdd79pvAp2zPVdSY+ZjrOlyd7tKVrPgQfmL7tU3li9wzCb/Pswgn9P/YvqlmjJ7x5lulLuR1DzG/ufGxH3RDc2NnnCuIZKvHFgI19mv12QN6DIW+CqHQq0IqizN7voqffeySNwXWBX5fq9AkndG9AOjTfzY0prLPIIkwuF2AoxRx+iZ8Gaczf0bmqNj+oqQvEU7b9YkV5fnANa5bZRxN1Je5lagxdHSFLMTFp1OY7R7gBZXy/9J5UBRSVVOUIncTsFOt3AAYVJbxLbaPayHfV42truipbwK7dCv0PuZwr+3P1AqNYeZ5tqRnV5p5HtRIT4fp9FcaZmhM+Z2B5m8kvgT1bSv7is9W1Fv/FPGleZS4kP6SKGfxt8r5f4QIo5sOfLnJimxRo5ioVgLudn0P5NcSyT6dz/FzthvXkpf0baL8dKfi52pu0AugyHZCKx/LIO88brKy1Ui1yvkOExfGZzZ7B+2RdC5hXvlVOeSaCCu17wE9myj4+Pdi7rnIFVVPyxg/Jj7LqoqfgzLzFN/bDsTOotpnMWxSGUQj8X9zH30Ainy/8dlfBy7vXnkoygBvYfs9Fee/BHgl8NPiuGxVUmIYlJXg24kCZx17dW0f5zbnF2F3/1fCxPOTyp1Nm3O3CiQY4DzWGuX8jSN91L7GViuFXsZoFaM/CDNPMRd2f48btz4dNmkminZ7bbR3X/HZRNTNfBd928cuLEpoDB4pN5eL2lDK33aQtLLteZVi3yOS5j4JfJzobVBzzja18AG2tv1jSesR5rF7gYvHkemdQ284b2eH+B3bY/YWmMgL/mhI2tb2mYQDupdGIcrle/f1mp1EL442n41afY5y/k1sX0Xz3IyxaGXmKQu85Qg/2mWED2XSKIMlxn/JYs8LiEbiF5db7T9vrqRDJb2jxGk3jc8e67Ov/Z98jkjQ2oBomVfbywBFCWMkrSfpGyXOvUb++HL/fuBYRVZpDY/aPpdo9HIOscOq4ZtEI/IliNVpozr2XXRKN+xPRAM1cnz38Feiwc3BhBPzUeI7MFHF1s6XdF7XbZakw4qCWxidfIpHRrk1ouyi7pC0maRpkpbQSCvJpvPfWNKxks7tvIcK8c735aU9t5eMKTE6OwJzCB/YTUR0VQ0b2d4NuNX2rpWyQ2fK7wxsN60dMpb8W7vis4/qis9+yziiqxfF07uSX7ny/KdLOoMRe3s/5o0PERey/YmaQF+lrtBYZ87Pt711iQ6q4RpFSYjzSnRJbU+HNrXwAZ6iKOvwiO2fSWrss+liI9vvLY9/J+m/bL9TUm0/5375BVHDv1PSYyciK30msVIdFdudHtzn9/zpofJ5Nk2+26zcHhuaZtnjHfpubtOJGHLLgnO2Hypm4z8Rv8stqFvZP1yU4H3FKT2eIl6kmLLKQNJ+tj+jUWqjVDrOnk58aZYDnlW23ePGZ9t+du2ce857FKMkbKmi1V4XbS+GDxQzyS+LyaCmYB+231ceHihpRcJMU8NZRZl8X1GbvjbG/TOEM/9TZZzGDXq6uFjSaYTzckPgwrJDrN2l9MvLXHIzijP2K7Y/qAg9bcKRhFK/gZj/PcATJR1j+5sLE5S0OmHv77tlJO0VOpKuJMKabyQuxLcRrVQ/b/vUBvJtzTxvJnan7ySi9CashPkgmLLKgBF7aNvaKKcQZoVGCVYdRlNCHRoqowPL/QeIi1cnAmLMVeBCOJh2F8M3EL6TmxVZoG8bT6CbUSKivkr0lW6E7YMVGdCn2v5WzbkLL+7+zCXV7kyw/TFF0cJnEBFdnXISVYUPW3CMpJ8RZdXXAGYWZdS0feVfiPajncqxJxH/118Qq/ZR0eBaRvYq9H4qjf6WiEjq9PI+gvguXkTk04zHRrZfpsgi31XSjyrP/wCxy16B6L62DfUtZIeH7Sl5I5xN/4/o/fod4AuEjXBa5Tgn9nn+tcptbWL12Hm+VuU4Fy7secVnsRNxAV4S2LRSfiuid+t1Rf5rlfKXAE8Azi/Pz6uU34NoVXhNOX+j/0n57F9FOAy3LLetOvOonMMawKFEY5tDCeU4jO/0KsCSfcj+nDANLk2Yey5r8n0CLu45f9X/rsitXs65Usv3fyURFgwRTHBFeXxBQ/nziJX9ycSq/trK8/+EUAAXlefnTPR3oM1tKu8MjiYKWh0H3E9UHHx1OV7j/FmxbE+r4rPdFbYn6e/uv2DXhcVn0CnO1U/0wrFExdWdbX9NUR6iJpnp44yEtz4iqbaxStuIqL1sv7Ss6B6R9NSGcmsRTsblCYcjRARJPx26jiac2LOJi+pM6mzmrdD8+TIq5sKa8+9CrGo7dX12LTuE8fItnqL5S6Gv2HnuZmXQu3cWmyk6tPWzs4AwzxyuCHO9H3h3eQ9NM7HbmnmWsX2WolgeDDmyr5aprAzWdnj+u7lKkURUQ1/x8F3RFoqnIxdBV1TMtP3xYp5Yi/nNEzW06fQFC17M+42Ieg79RUQ9LOnJ5fzLMH/3uTFxhHVeKOl/3V8T826Wtn1peXyJotHMRHIw8FrXd3nrhIa+y/a7RvnzjaMc6+ZkRhRp93PTrAz6yz2AZvTlPbzC9r+P8uemC6R5hGlsPSKa6IaFv3wBfifpo4RCfH8f8kNlKiuDU4rD7wLgz8TO4OXEVm9c1D4+u1Ovv/t5J4u1SdvNPW0fpZ4MVjUvgd1N352+Cq0u5m4fEfVR4Ifl/D8E9quUf5OktxPhufcTZQ1qS1qcKulMYoc2nWY26kFyKfDPfgRtW9IyklZzZfKl27eMbLWz6JqHJT1X0tLuvzHRiYTjeDaxWzmRyChuOod3KZLnvgfMsX1In/MYClM6A1lR8XEzwkxwH2FjbJQwJWlrR/3/BZLE3KwE9rK2/1w75y75gXVG0gA6fZWVWdXFfABO9IEg6XKiDMW5hHnsO7bf0sc4qxI7tFtt3znQSY5/7suApxJ9LqBhOYwu+Z8R/787if9JlXy/aIDN6CVdQ2T//pY+3oOks2xv0/V8lu2tG8iNuXirUWjDZqorgw2JcMrfdB3b3JU9UyUtT0QQdMw8C+2uVWQeKxsh6QiPZDFXIekbxKrw0lq/Q2clqNIVrJuG76FVeK5GSiCI8N08VuityXvR6C0XO/I1F4ELbL9C0tlE9Mmptqc3lS9jrEHkaaxHJC59xvZtNWMsTqguRwFFUbju3+FLbdeabPtCUsfUuzWxKOx0zHuq7XFNpz0KbQ/Cf9SpT1Wl0IbJlFUGkr5IhDI+TKyo3mp7nipr+0g6nAgnvJ2RL8C4foQeZdB3PSFJ6xAlp18IPJswcezYUPYjtj+n+XMWat7DqrbvVHT2mm9H1YdialVTSV39g/uQ3ZpwoG9KlLU42fZRlWOcw/wO5IPavJ+K8w4qX6ZVfShJh9h+f3m8NDDT9psq5E8BPmr7N5K2B97c5ELcM8aTiV7k3e+hSaOpQTaK6vt7OGymss9ghksDFEkbASdK+nAf4zzL/TVSWVfSQcSXtvMYqKtDTxTGex5RhnkOcTFqRFEEAn5j+7MV5+zId0whn+reXjdlUE70znRanP/scn9pufXDsBzIg8qXaVUfCrhc0dPggDLW5yvldyVyJa4mwnT7KQf+Q8LO/3oivLdRFn3ngl8c2DsSyXeHEXk7tUza1fVUVgbTJC1l+0Hb1xbH6bFE9mUNx0nah/nL5jaxE3avRs6pPGc3HyFq+Z9OmIqqIhiK423Dlo6320sUxZWM9BBu8hm0daJ3zFsinN+PmbuamLl6zt9dgpom5++h14FcVXGzX7oU8kNE3szyjIQ01pgoHrV9rqQDbJ8jqWnr0o5C/QERRXYJ8d1ulGzVEwBxK1Fb6igiqbI2EOKJto+QtLvt70h6Q6V8XyHWXbsyARtIOoaRHfakyUKeymaizYiGHnd1HVsS2NH28RXjnELUMumE9E24nVCR9bsjESf+DNtN4+w78m0db71OwAn5DIp5azSamrlaOfFHGW+YDuRBdCrbt9xeDvzVo4dp9sp197Hujqt3EzPZaAEQXQPUBkL8gFBEXyB2yuu5hK02lD/H9qs7JsvO8wZyC5T/7lBrLh0mU1YZDApJp7myReKAz388ERb7eyIs7lLbtwxhHlsQWZ+nAmtMhh/BIJz45X3/vUR2fY6IyAE4xvYFg5vtuPM4samvqMFYK9q+ZxBjVZzzSUSY9mM7G/fZTL7sVqYT5s/GvaAlfZ8oL/M+Ijz6DV4wF2mxZSqbiQbF/ZIOYX4zUaM68APiP23/pc0AkqYTtt5liXIM+9r+dIX814lKo1vaPlnROWzc/r2LGOv0KfdJRmLRX0hksS4FfJ3IYXlc6TJR9JUJ3zXOfB3/JDVa2XfJb0l8Fg8TZSkOdJQjb8osevoX11J8f2+hq2shdUmhbyVCrH9J+C3+s9+5TEZSGbTnp22EJe1JfIEfYcTOWGOvXrc4vvtt7ALwFeKC9iNHOYctiebuTXmO7VcVkwFUNhmRtI0jjX894P3ADzwxHaIG4cSf5pE2pYfa/gOAonDeRNDWcdzhYFp0/CMKHW5t+y+KchCzqPOF9dW/uIeZwLuBP/Yp/xBwN5Fr03EgX7FQicWIVAYtsT2zRI6sAn3VInkH0SKz33IIfdeB70K275XUsRnWfi/+KmlzAEmbEFm8NbTqp6DoLvearufH2d65geggnPiPSvoX239zaShfLoZtu241omOOk/QW20dL2pRwvh5FOGSb0rbjnxgpA1IbCQbxOZ5MZf/iHq4lEkf7/S3160Du+E2mAU8jwqxXAe6o8b0Nm1QGLSlRF1sRMf43ESUBaoq8nQs8R9INjPwIan5MrevAE+WOTyFWxycQF5Ia3kY4Hh8gFFPt9rqvfgqSXklE/azftaqfRjjDx6XWQTkGnwBOK+aaO4gKnG8uxyeSXYlkp/cQpo6fEKGWC0UjiXvTiI5/fWUwEzuUs8qCQoTZsYa+Szd0vYdlgT9ImlP+VPse+qrR1ckrkPRNYBfbc0vexsdqxhk2qQza8zrbL9JIFusPKuVfWG4dajtEDaIO/I/LbV3CEV3VnAbYw3andSSS9gYOr5Dvt7nMzcQqdF1CqUJs9Q+uOHcrbF8k6Y1EXP5GhELY0f03eemXJyvalf7VkVXeSKHWRNuMM865jPwPGqMB9C8e1HugfY2uGUSYN0RzoNr6VkMllUF7OlvrB8qPsap8c5tsxZKk9aeSH/CtcuuHE4uf4f/KuCcATdLwlyeyt9+oaAQi4ke9Iw2UQdkNQDjsflker0IkDI2L7Vsl/QHYb0Cr/L5wNLyv7fs8aN5PKKRPFoXaqDFLCX74kbtKP5Td5RtcMoobjtOvA3kjovxD7wW9adVTis/s56O8hy1s1yS/tXUgHwicLelR4rcw0bvDVmRoaUskbUw4nNYl6qCf7pG+sguT+4rtfTRKfZ3KGP8TXJm23yX7eqJk72sYcYRPA1Z0g4xiRdmAHYiGHmcSP4AHgVm2x70YdeUJrE44wK8jsqn/1O0DaDDO14hkp+6ktyZJZ4sVxQHfHUgwrhNe0kWjfd/GOr6QcS6jx4Fs+4XjyfWMsSylHwNAxxnfQO7i0XYHYx0fYwwRJTRaJYlJegJhbuo7KmpY5M6gT8rqa1qJL9+M6BD1Q+KCNi629yn3bbe4K0u6jujy1UkYa/qFPo9YBf2BkZX8Q0QS3bjYPoUoBb57d0x4UTJN5Pcsr/8RML1EMnVaLtbwZEIhdRRYbUjhpEf99+8dazVYu0ps5UDWKDW+aP4/fKTy+ALYtqQ7ym/5l4wsKmqK7e1B+G5WkfR84PhB5X5MBKkM+udbwBeJCIZvEU09nkA4U8csfNVB0nuB0wawgn1Lv4K27yfyJDaz/Vh3L0nHAU2icToJPnsWB2rHTLQX8Xk0ZTWif+7VRLLQ0ypksb1nUSKrAHfZbnwRWIzot3/vjyUdRtQSuoNwvn+Y5r2TO7R1IK9Tsxvs4WeS9gO+aPtBRUb+h4h2sjVsVm4dav13/XbcWyRIZdA/a9m+tjy+1qX8gqRxTUSF9wGvVHQpO4uw2zbaVfTwnd6wSppfyDvROOv1E41TVkJvIS7gnbpCDxLJQzXsTFyAPk44sHepEZa0C9G/+fdERNTXbR9bOYfJzsNFMd8naXeilPa42D5E0jbExXw1YmV+ou2zak7ewoHcudjeov5qfEG0Kd2biOp6IvAPwmdSFUjQxn9X6Kvj3qJC+gz6RGOUXJZ0oRtUMS2rh1cq0vC3JSotPptoKP6RhUvPdyHfDeiYaKYBL25y/jLGWkRT+O7on4eA611Rs0fSprZbJedIWo3IAv59beJTsVe/zPbDxWZ7Ua29erJTPr+7CYf+LsT3qFGxuAGdfzsiT6TjQD7Y9ukN5I5k9HwIe+JrfG1MLEr6SuAsJqaDiF4IvySc6JMmaS13Bv1zsaT9gS90bU0/AlQ15HDUTjkJOEmRtdr0y9cdVnkO8eWtCqssCUu3En2An0nsCKYRK/2aDOAHJR1L/z+iDxPv+2pgk6Ioa0pqP1rOfVu5n1QrsjZIOhG4vNz+7OiB/aUhTOVAogfxA2WBcwFRSXc81nZF34THmb4TOIsDeoMmgReLKqkM+ucThG28d2vatJn3+3oP2H6Y5uUtnlnuj2Dk/7gkEetc1WqvhfOxQ9ss6Nd1O9IlXQLUKIN3AV+XtAJwL1GSYKrwUeJ//lrg48U88RvgctvfnMB5XEV0+3uAKDbXdFfyfEm937VOWZaJzt7tO4GzOKC3IxL/JiWpDPqkRBkcTl1yVbf8NWrXdnOsL2rj+Owu+nU+dmibBf2gpBcRF5QZxA6nMY5+FG8kHMh3FqU6JSgBCDdLOoNIctqMyPPYgFDSjZDUW/rhIWL3ebLtMctUdIVGP4HYYXZMVY16iQNXtbXVS9qNMC9e0nXsxcC6tmvyP9omcK7UIrJv6KTPYEhoQG03BzSX84gSGicRUUAfsr1Rhfy+wJeJKKp3Ar+w/fYK+TWJFe66xAXoc01jzIv8XoQj+1ain8BM2013aJMaSUcTFTrvJ5TpbOBXtQpR0hFEP4uriUSw5wE3EHWzXjfIOfec9/wBKIOx8gwusf2SBvIvIUq/t7oYapS+Bp4Epdw7pDIYMJKWt31fg9c95mhWlN79KuG8+mwTZaD5m4p0eATYv9ZpNWznY5nD0xlxIFcl7BQH8ktsP1pCTC+ZKg5kSR8hdgHTgLuIKptX2J6zUMEFx/mp7a26np9t+zUVARGP9YMo9vNv296rgVzrBkNjKZSmikbR2OeFxIr+ZOAcVxS7k7St7TMlLeD78MSWs29Fmon6pDju3ty9hZb0LKIswWZjCo7Qqu3mGF/+FYj48MY9mSUtB9xn+6HiRL6RWBE2kX0WsRO4HziBqDH0FGJlf2bFHA4Enk+EFT5X0tWuq1h5KbBZ2dpvDFxawiz76aU8qbD9uc7jEte+IxGMsKbtFSuGmivpUCJvZiPgtqJYf99QvrvlqMt3aVzaKoLC7yTt7FI1Fh4LN/5dwzm8r8hsQkT1/Y+izMmPbJ/YYIhHeu4nJbkz6BNJ/4+4EL7R9n2StiaKre3W7QNYiPxA2m6OMm7jbbeiRO/zCcfzbURtovuAjd0gc7LYiz9MOAyPIKKQ/kasrF5UMef5Vp+qL4Vw/hh/8kSb3CaakiOwabmtQmSTX044kKt6QpTv5DrAzX3sLk8kehhcRqyyt7X9xpox+qU4zQ8AXsTIbvky4CDbf68caymiPMsHiY59/9pA5vOMfOaTxizUSyqDFhSn5+eJH8GLgJ2amIgGdO7ei9wTiWigVW03KrDVsbWWVfS1tp9bjjfdXne3jew2e1X5PSR9g6gtdBWxsn850cd2StYYqkHSJxi5EDV12o42ztOBNzJ/baDGcf4lnHRv4F+JaKbvuKLl5CCRtKorelAraiL9O7A9EV59NuE4v3ahgiPyWxKBD5sSPqs7CXPdL9ygTtmiQiqDPpH0SWIV8myin8FhlCiYShNHv+fvTff/B2HeOb2pI6ysqLckfvznlsdLECv7JsrgFiLhTYwkvwnY1XbjNpLqs7F9iYD5iu37Jb2OyGB+EDik4fY+KUi6gshPeMxf4/qG9PM1eaoJAhgkfSxGLiT6P5xs+6aW516LqB77TqJg3WptxptIUhn0iaQx7fK1P6Jh0eWE7nRo6zxuZF4ZxGdQnI0fcV2SWUf2MXOSpN8Q5om/Auc3iSJJRpB0YhPT4ELkF2jyZLumydPAkHTORJ67LMzWIhZSfyAiun5pe+5EzWEQpAO5T0a72El6t+1vDGM+/dA2pG8QSq84GzeUtLSjL0MN0+CxaKxbbd9bni/WTuPHiRUlXQn8qjy362Lk2zZ5GhhDUEKrEHkW/ySi8uaV+0lFKoM+0fx9CDor6w0kvanG+ZkA4Se4TdJvGUnWafIZnlR2N6sC+8Bjtu82vXwnFYqSKBfZvkTSr4jy49OAC23XtF1sWxKiVZOnNigSzD5MRLUdQxTdWwY4fCJCO22/u8zjCUQk1ouJjPglbU9/vM8/KNJM1CeSPkD844+2fUE5dqbtbSd4HhsRCVfdjr+h1XopZp+XuKvr1ON8vqcQWdwPlOdPBJZ2F9owQwAACjJJREFUlOde7OkxlT3m+O/kCTSQH0iMvPps8jQIJP2MaNK0HOH7Wo9YpV9SE9XW4vzPJpzHmxERdRA5C5e7q8/Hok7uDPrE9pdKGNpekt4BfH9IU5lJ1OL5Y9uBJK1OxFm/1hUFt8qK6DVERMZqhKlhXGUgaU/bR3U54x+jiRO+OJ5dHvf+eVEpfvZ4051p/Iaux017CndMar0x8k2DEDqtS+8ldmh/o0SCTSB/d1S6vUPS7zrhpJJqzY79sh9RuuIY4AM1CWuLEqkMWlASzg5VdGnajVgNTDTXEhmnfX0BS+LYG4hVzUbEqu7/NZT9D0IBPIkIr/1X26+qOH2n73Fvr9ym29UDy/0HgJ8TbS83IbbpU4U/SnqJ7Uu6fCYvo3m3ulnlfqak5xE5Iwto1oVwCxFG2Z2oWNuprC0bSOpEsj2n+/FEnNz2uM2sJgNpJprklKzbVYFO+YGm9naKjflywrZ6Wa2ZS9KNRHXTQ21fKekM29tVvoXu8frdmfQmrTUqobA4oMg6/xqxI7ubcGbeDvyX7XsqxjmN2F12QkvdJM9A0qZEH+wNgV8DJ9meXfUmWqJRagJ1mMxJYBNNKoMpjKLa43ZESNwZRPZ0VSRG8Vm8gchkfi7R9vMy2/9sKD/azuRS23+rmMNBRNJPp5TClZXO00lPSRxcGZjnqNH0spoMZEmn2/63lnPYnKiUOrtp4uMgUPQb/q3tv0laBXg/sTP4qidhY/phkcpgktLW3t4z1jJEM/nXASsCV9uu7WGLpHUJE9N2DZPWWu1MesZ6GtFQ/daa7NPFFfUUnmvw+u8TmbPdbSfHdSBLWonI3H0Vkfh4BnDGRGYfS7oUeGlRgj8lSqPMAz5qe+uJmsdkJ30GLZG0JxHN8wgVCVsDYCx7ezXF4XYycHJxin+0z6H+Cfyd5qGdnyF2JvtIWp+I1a6m7E72oNi7JQ01omqS0hv503SVeAdRRuR84v++EbBR+R887pn4hQeLIngq8DTbPwCQ9D8TdP7FglQG7XkHEUo5oREEtq8p948lfvWb9FaigV5N7Aw60UBNZUcz87xhoUIFR+OR73btTG6T9GPgmsqdycAiqiYbo+0MiUVJo6qhXTzX9oe7xm16IR1KlnEPfyuLshcCJwIoWsg+aaizmmSkmaglkv4XOJ6Ipuhsrx/3DNiepDeIC8AGwA0VDuTeaKA31UQDDdLM0zXmNGBL203bfyJpJrDXZA3pa0PbkiCS1iYUxxeISp0Qi8T92maoTxQl12R3ouXmMbYfKSGv022fOtzZTR5SGbREC5ZPnhAz0SCS3tpGAw3CAT0I2kRUTXWKMnkFYeo8ilhUPARcYPuy4c2sOSWUtpu/Azd6gioILy6kMpjEdJLegJcRSW/vrF2Zt40GKmMMxAGdDI9iKlyG+TPZh1J1tBYtWMF3aSLf5Ie2vzOEKU1KUhn0iaSv2N5nFHMNE70qLaaV3YBn2d63xThV0UBjjLEUEcXxyQqZ5QhfwwrA/kRjlNMq5BdwVDaJkU9GKImTzyDyDDqBEJPWCV9CbS/0KL2Rk9FJB3Kf2N6n3A/tyybpBYx0KLsXOK/NeI5GMp8vt5p59O2ALhwLfIPo3/yIpPcBjZUB0LGNi2jkPiGZp4sZ6zSpZTRZKNFFw57GpCKVQZ9Iei9wmofUiUvSEcTF759EstHtwJ8lvdH23hM0h7blKDosY/ssRXN3qCuH0OsovaDEmicN0EjHvFsk7cP8eQatFhcTxSgRVUsTBeOOHc6MJiepDPrnfcArS7LTWUTz7Osm8PzreaTN5HUu/WZHcWg/nnyacEB/uTigd+hznN9J+ihRU//9zF/nZlwkfZeRxjxPIxqMJM3o7GznEnka3U2BJoUyYMFcm38AB9eU40jSZ9A3KuWCFb1ftyVq6jwbOM/2RxYuPZDzX2r7xeXxv9v+SXl8ge1XPN7n75pHawd0Gee1hHnnN533UiHbqU3zKPBnT5Hy1UkySFIZ9IlGaRrfT4x8i/NvSFw4H+k6thSwzbBiq/soRzFmCG6NiULSGoTj+ZlEy8XP2L6tqXwCii5nawI3Ev0AbiNCND+fsfpTg1QGfSJp404WcNIfo4QEdmhUMbNrnHOAA4jes5sCB01QSZDFhlKbaC/bD5Td7hHELu8i2zOGO7tkIkifQZ/Yvqaszh+x/ZvOcUmb2/7FEKc2abD9ic5jRbvKtYFb+qg0ubTtS8vjSxTdzpI6nkXkGDxQ7tcriuGvw51WMlGkMugTSV8ksl4fLgWy3mp7HlF8LVelFUg6kPA5XA88V9JVlUlrp0o6kyhhPR2o8jkkALwLOFzSskQv4XdLWhKYqGJzyZBJM1GfqKuBSnGifpVoyv3ZNFHUoQWb0zzW17dijFWBtYidxV2DnmOSLO7kzqB/pklayvaDtq+V9HoirnnDYU9sEnK9pJ2JUsgbl+frwmOJcKMi6d+Ai23/GViW0tRE0qdsXz8B8570LEqZ9MlwyZ1Bn0jajJ5VaNlW72j7+OHNbPKhaGw/GgstiSDpMtsvKo9/DuxDtH78zmSpuLmoIunlTaqeJosPuTPoE9uXj3LsEaKcdVKB7T1LWO4qwF22H24o+iA85nxeouO4zzIEA+G/GSnzkUwBUhkkQ0fSXkQJ5VuBtSTNtP3tBqJ/lPQJwvl8TBlrGWCpx2uuSbK4kmaiZOhIuozoFvdoMbVdYvuFDeSWJEpnP2D7/HJsNWAt2z9/XCe9mLCQTmm72K7tlpZMYnJnkCwKXApsVprUbAxcWkoQL7RrXDHLnd5z7A6iL2/SjLF6aLfurZ1MLnJnkAydhRTXm5CucUmSpDJIFjHKjuAlti+qlHs6sLrtKyQ9yfYDj88Mk2TxJM1EydAZozlOY2UgaX+iYuqzJc0ATiIqySZJ0pBUBsnQGGBznNeUcuLnFyd0RhMlSSVLDHsCyZTm00SuwP/aPpzo2tYPD0paHXApS9HvOEkyZUmfQTJUBtEcR9IzCcXyLOA3wP/YvulxmG6SLLakMkgWGWqb44wiL6KfwbW2/zHo+SXJ4kwqg2TSI+lc268qCVSrAGvY/rdhzytJJhPpM0gWBzrf47Vtv51ozpIkSQWpDJLFgVslnQ3MKgXvHhlPIEmS+UkzUbJYIGma7YeL32BZ2/cPe05JMpnInUEy6ZG0JXChpAuBiwkncpIkFeTOIJn0lKqnW9v+S+nhO6tJ1dMkSUbInUGyOCCgU910zCqnSZKMTZajSBYHPgacJcmEYjhgyPNJkklHmomSJEmSNBMlkxdJz5L0ZUmfkLShpFMlnS9pu2HPLUkmG7kzSCYtki4GPgwsDxwBTAf+Bpxj+0XDnFuSTDbSZ5BMZh7q9DqWNMf2vPI46xIlSSWpDJLJzLqSDiKcxmt1PV5nuNNKkslHmomSSYukl4/1N9sXTuRckmSyk8ogSZIkyWiiJEmSJJVBkiRJQiqDJEn+f3t1IAAAAAAgaH/qRUoiSAYAVAOBOQqxV5J3UwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "product_group = Sale.groupby('Product')\n",
    "quantity_ordered = product_group.sum()['Quantity Ordered']\n",
    "\n",
    "keys = [pair for pair, df in product_group]\n",
    "plt.bar(keys, quantity_ordered,color='black')\n",
    "plt.xticks(keys, rotation='vertical', size=8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20in Monitor</th>\n",
       "      <td>4129</td>\n",
       "      <td>451068.99</td>\n",
       "      <td>29336</td>\n",
       "      <td>454148.71</td>\n",
       "      <td>58764</td>\n",
       "      <td>122252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27in 4K Gaming Monitor</th>\n",
       "      <td>6244</td>\n",
       "      <td>2429637.70</td>\n",
       "      <td>44440</td>\n",
       "      <td>2435097.56</td>\n",
       "      <td>90916</td>\n",
       "      <td>184331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27in FHD Monitor</th>\n",
       "      <td>7550</td>\n",
       "      <td>1125974.93</td>\n",
       "      <td>52558</td>\n",
       "      <td>1132424.50</td>\n",
       "      <td>107540</td>\n",
       "      <td>219948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34in Ultrawide Monitor</th>\n",
       "      <td>6199</td>\n",
       "      <td>2348718.19</td>\n",
       "      <td>43304</td>\n",
       "      <td>2355558.01</td>\n",
       "      <td>89076</td>\n",
       "      <td>183480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AA Batteries (4-pack)</th>\n",
       "      <td>27635</td>\n",
       "      <td>79015.68</td>\n",
       "      <td>145558</td>\n",
       "      <td>106118.40</td>\n",
       "      <td>298342</td>\n",
       "      <td>609039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AAA Batteries (4-pack)</th>\n",
       "      <td>31017</td>\n",
       "      <td>61716.59</td>\n",
       "      <td>146370</td>\n",
       "      <td>92740.83</td>\n",
       "      <td>297332</td>\n",
       "      <td>612113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Apple Airpods Headphones</th>\n",
       "      <td>15661</td>\n",
       "      <td>2332350.00</td>\n",
       "      <td>109477</td>\n",
       "      <td>2349150.00</td>\n",
       "      <td>223304</td>\n",
       "      <td>455570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bose SoundSport Headphones</th>\n",
       "      <td>13457</td>\n",
       "      <td>1332366.75</td>\n",
       "      <td>94113</td>\n",
       "      <td>1345565.43</td>\n",
       "      <td>192445</td>\n",
       "      <td>392603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Flatscreen TV</th>\n",
       "      <td>4819</td>\n",
       "      <td>1440000.00</td>\n",
       "      <td>34224</td>\n",
       "      <td>1445700.00</td>\n",
       "      <td>68815</td>\n",
       "      <td>142789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Google Phone</th>\n",
       "      <td>5532</td>\n",
       "      <td>3315000.00</td>\n",
       "      <td>38305</td>\n",
       "      <td>3319200.00</td>\n",
       "      <td>79479</td>\n",
       "      <td>162773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG Dryer</th>\n",
       "      <td>646</td>\n",
       "      <td>387600.00</td>\n",
       "      <td>4383</td>\n",
       "      <td>387600.00</td>\n",
       "      <td>9326</td>\n",
       "      <td>19043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LG Washing Machine</th>\n",
       "      <td>666</td>\n",
       "      <td>399600.00</td>\n",
       "      <td>4523</td>\n",
       "      <td>399600.00</td>\n",
       "      <td>9785</td>\n",
       "      <td>19462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lightning Charging Cable</th>\n",
       "      <td>23217</td>\n",
       "      <td>323787.10</td>\n",
       "      <td>153092</td>\n",
       "      <td>347094.15</td>\n",
       "      <td>312529</td>\n",
       "      <td>634442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Macbook Pro Laptop</th>\n",
       "      <td>4728</td>\n",
       "      <td>8030800.00</td>\n",
       "      <td>33548</td>\n",
       "      <td>8037600.00</td>\n",
       "      <td>68261</td>\n",
       "      <td>137574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ThinkPad Laptop</th>\n",
       "      <td>4130</td>\n",
       "      <td>4127958.72</td>\n",
       "      <td>28950</td>\n",
       "      <td>4129958.70</td>\n",
       "      <td>59746</td>\n",
       "      <td>121508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USB-C Charging Cable</th>\n",
       "      <td>23975</td>\n",
       "      <td>261740.85</td>\n",
       "      <td>154819</td>\n",
       "      <td>286501.25</td>\n",
       "      <td>314645</td>\n",
       "      <td>647586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vareebadd Phone</th>\n",
       "      <td>2068</td>\n",
       "      <td>826000.00</td>\n",
       "      <td>14309</td>\n",
       "      <td>827200.00</td>\n",
       "      <td>29472</td>\n",
       "      <td>61835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wired Headphones</th>\n",
       "      <td>20557</td>\n",
       "      <td>226395.18</td>\n",
       "      <td>133397</td>\n",
       "      <td>246478.43</td>\n",
       "      <td>271720</td>\n",
       "      <td>554023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iPhone</th>\n",
       "      <td>6849</td>\n",
       "      <td>4789400.00</td>\n",
       "      <td>47941</td>\n",
       "      <td>4794300.00</td>\n",
       "      <td>98657</td>\n",
       "      <td>201688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            Quantity Ordered  Price Each   Month  Total_Price  \\\n",
       "Product                                                                         \n",
       "20in Monitor                            4129   451068.99   29336    454148.71   \n",
       "27in 4K Gaming Monitor                  6244  2429637.70   44440   2435097.56   \n",
       "27in FHD Monitor                        7550  1125974.93   52558   1132424.50   \n",
       "34in Ultrawide Monitor                  6199  2348718.19   43304   2355558.01   \n",
       "AA Batteries (4-pack)                  27635    79015.68  145558    106118.40   \n",
       "AAA Batteries (4-pack)                 31017    61716.59  146370     92740.83   \n",
       "Apple Airpods Headphones               15661  2332350.00  109477   2349150.00   \n",
       "Bose SoundSport Headphones             13457  1332366.75   94113   1345565.43   \n",
       "Flatscreen TV                           4819  1440000.00   34224   1445700.00   \n",
       "Google Phone                            5532  3315000.00   38305   3319200.00   \n",
       "LG Dryer                                 646   387600.00    4383    387600.00   \n",
       "LG Washing Machine                       666   399600.00    4523    399600.00   \n",
       "Lightning Charging Cable               23217   323787.10  153092    347094.15   \n",
       "Macbook Pro Laptop                      4728  8030800.00   33548   8037600.00   \n",
       "ThinkPad Laptop                         4130  4127958.72   28950   4129958.70   \n",
       "USB-C Charging Cable                   23975   261740.85  154819    286501.25   \n",
       "Vareebadd Phone                         2068   826000.00   14309    827200.00   \n",
       "Wired Headphones                       20557   226395.18  133397    246478.43   \n",
       "iPhone                                  6849  4789400.00   47941   4794300.00   \n",
       "\n",
       "                               Hrs    Mins  \n",
       "Product                                     \n",
       "20in Monitor                 58764  122252  \n",
       "27in 4K Gaming Monitor       90916  184331  \n",
       "27in FHD Monitor            107540  219948  \n",
       "34in Ultrawide Monitor       89076  183480  \n",
       "AA Batteries (4-pack)       298342  609039  \n",
       "AAA Batteries (4-pack)      297332  612113  \n",
       "Apple Airpods Headphones    223304  455570  \n",
       "Bose SoundSport Headphones  192445  392603  \n",
       "Flatscreen TV                68815  142789  \n",
       "Google Phone                 79479  162773  \n",
       "LG Dryer                      9326   19043  \n",
       "LG Washing Machine            9785   19462  \n",
       "Lightning Charging Cable    312529  634442  \n",
       "Macbook Pro Laptop           68261  137574  \n",
       "ThinkPad Laptop              59746  121508  \n",
       "USB-C Charging Cable        314645  647586  \n",
       "Vareebadd Phone              29472   61835  \n",
       "Wired Headphones            271720  554023  \n",
       "iPhone                       98657  201688  "
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product_group = Sale.groupby('Product')\n",
    "product_group.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31017"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "quantity_ordered.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LINEAR REGRESSION"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF=Sale.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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 ID</th>\n",
       "      <th>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Purchase Address</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>141234</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>01/22/19 21:25</td>\n",
       "      <td>944 Walnut St, Boston, MA 02215</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>141235</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>01/28/19 14:15</td>\n",
       "      <td>185 Maple St, Portland, OR 97035</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>141236</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/17/19 13:33</td>\n",
       "      <td>538 Adams St, San Francisco, CA 94016</td>\n",
       "      <td>1</td>\n",
       "      <td>23.98</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>141237</td>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>01/05/19 20:33</td>\n",
       "      <td>738 10th St, Los Angeles, CA 90001</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>141238</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>01/25/19 11:59</td>\n",
       "      <td>387 10th St, Austin, TX 73301</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "      <td>11</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order ID                   Product  Quantity Ordered  Price Each  \\\n",
       "0   141234                    iPhone                 1      700.00   \n",
       "1   141235  Lightning Charging Cable                 1       14.95   \n",
       "2   141236          Wired Headphones                 2       11.99   \n",
       "3   141237          27in FHD Monitor                 1      149.99   \n",
       "4   141238          Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                       Purchase Address  Month  Total_Price  \\\n",
       "0  01/22/19 21:25        944 Walnut St, Boston, MA 02215      1       700.00   \n",
       "1  01/28/19 14:15       185 Maple St, Portland, OR 97035      1        14.95   \n",
       "2  01/17/19 13:33  538 Adams St, San Francisco, CA 94016      1        23.98   \n",
       "3  01/05/19 20:33     738 10th St, Los Angeles, CA 90001      1       149.99   \n",
       "4  01/25/19 11:59          387 10th St, Austin, TX 73301      1        11.99   \n",
       "\n",
       "            City State  Hrs  Mins  \n",
       "0         Boston    MA   21    25  \n",
       "1       Portland    OR   14    15  \n",
       "2  San Francisco    CA   13    33  \n",
       "3    Los Angeles    CA   20    33  \n",
       "4         Austin    TX   11    59  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF.drop(['Order ID','Order Date','Purchase Address'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Boston</td>\n",
       "      <td>MA</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Portland</td>\n",
       "      <td>OR</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>1</td>\n",
       "      <td>23.98</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>CA</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27in FHD Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>CA</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Austin</td>\n",
       "      <td>TX</td>\n",
       "      <td>11</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Product  Quantity Ordered  Price Each  Month  Total_Price  \\\n",
       "0                    iPhone                 1      700.00      1       700.00   \n",
       "1  Lightning Charging Cable                 1       14.95      1        14.95   \n",
       "2          Wired Headphones                 2       11.99      1        23.98   \n",
       "3          27in FHD Monitor                 1      149.99      1       149.99   \n",
       "4          Wired Headphones                 1       11.99      1        11.99   \n",
       "\n",
       "            City State  Hrs  Mins  \n",
       "0         Boston    MA   21    25  \n",
       "1       Portland    OR   14    15  \n",
       "2  San Francisco    CA   13    33  \n",
       "3    Los Angeles    CA   20    33  \n",
       "4         Austin    TX   11    59  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder\n",
    "lr=LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "DF['Product']=lr.fit_transform(DF['Product'])\n",
    "DF['City']=lr.fit_transform(DF['City'])\n",
    "DF['State']=lr.fit_transform(DF['State'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "### converted all categorical into numerical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "#DF=DF.drop(['Product'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>11.99</td>\n",
       "      <td>1</td>\n",
       "      <td>23.98</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>1</td>\n",
       "      <td>149.99</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>11</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Product  Quantity Ordered  Price Each  Month  Total_Price  City  State  \\\n",
       "0       18                 1      700.00      1       700.00     2      2   \n",
       "1       12                 1       14.95      1        14.95     6      5   \n",
       "2       17                 2       11.99      1        23.98     7      0   \n",
       "3        2                 1      149.99      1       149.99     4      0   \n",
       "4       17                 1       11.99      1        11.99     1      6   \n",
       "\n",
       "   Hrs  Mins  \n",
       "0   21    25  \n",
       "1   14    15  \n",
       "2   13    33  \n",
       "3   20    33  \n",
       "4   11    59  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "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>Product</th>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <th>Price Each</th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Price</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Hrs</th>\n",
       "      <th>Mins</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Product</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.114337</td>\n",
       "      <td>0.136151</td>\n",
       "      <td>-0.002564</td>\n",
       "      <td>0.135899</td>\n",
       "      <td>-0.000715</td>\n",
       "      <td>-0.003963</td>\n",
       "      <td>-0.002811</td>\n",
       "      <td>-0.003338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Quantity Ordered</th>\n",
       "      <td>-0.114337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.148272</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>-0.139417</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>0.002704</td>\n",
       "      <td>-0.002218</td>\n",
       "      <td>0.001225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Price Each</th>\n",
       "      <td>0.136151</td>\n",
       "      <td>-0.148272</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003375</td>\n",
       "      <td>0.999203</td>\n",
       "      <td>-0.000096</td>\n",
       "      <td>0.002176</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>-0.002163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <td>-0.002564</td>\n",
       "      <td>0.000791</td>\n",
       "      <td>-0.003375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.003466</td>\n",
       "      <td>0.002044</td>\n",
       "      <td>0.003840</td>\n",
       "      <td>0.001731</td>\n",
       "      <td>-0.002075</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Price</th>\n",
       "      <td>0.135899</td>\n",
       "      <td>-0.139417</td>\n",
       "      <td>0.999203</td>\n",
       "      <td>-0.003466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.000089</td>\n",
       "      <td>0.002078</td>\n",
       "      <td>0.001668</td>\n",
       "      <td>-0.002162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>City</th>\n",
       "      <td>-0.000715</td>\n",
       "      <td>-0.000017</td>\n",
       "      <td>-0.000096</td>\n",
       "      <td>0.002044</td>\n",
       "      <td>-0.000089</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.022792</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>0.000076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <td>-0.003963</td>\n",
       "      <td>0.002704</td>\n",
       "      <td>0.002176</td>\n",
       "      <td>0.003840</td>\n",
       "      <td>0.002078</td>\n",
       "      <td>-0.022792</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001890</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Hrs</th>\n",
       "      <td>-0.002811</td>\n",
       "      <td>-0.002218</td>\n",
       "      <td>0.001721</td>\n",
       "      <td>0.001731</td>\n",
       "      <td>0.001668</td>\n",
       "      <td>-0.000317</td>\n",
       "      <td>0.001890</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.015345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mins</th>\n",
       "      <td>-0.003338</td>\n",
       "      <td>0.001225</td>\n",
       "      <td>-0.002163</td>\n",
       "      <td>-0.002075</td>\n",
       "      <td>-0.002162</td>\n",
       "      <td>0.000076</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>-0.015345</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Product  Quantity Ordered  Price Each     Month  \\\n",
       "Product           1.000000         -0.114337    0.136151 -0.002564   \n",
       "Quantity Ordered -0.114337          1.000000   -0.148272  0.000791   \n",
       "Price Each        0.136151         -0.148272    1.000000 -0.003375   \n",
       "Month            -0.002564          0.000791   -0.003375  1.000000   \n",
       "Total_Price       0.135899         -0.139417    0.999203 -0.003466   \n",
       "City             -0.000715         -0.000017   -0.000096  0.002044   \n",
       "State            -0.003963          0.002704    0.002176  0.003840   \n",
       "Hrs              -0.002811         -0.002218    0.001721  0.001731   \n",
       "Mins             -0.003338          0.001225   -0.002163 -0.002075   \n",
       "\n",
       "                  Total_Price      City     State       Hrs      Mins  \n",
       "Product              0.135899 -0.000715 -0.003963 -0.002811 -0.003338  \n",
       "Quantity Ordered    -0.139417 -0.000017  0.002704 -0.002218  0.001225  \n",
       "Price Each           0.999203 -0.000096  0.002176  0.001721 -0.002163  \n",
       "Month               -0.003466  0.002044  0.003840  0.001731 -0.002075  \n",
       "Total_Price          1.000000 -0.000089  0.002078  0.001668 -0.002162  \n",
       "City                -0.000089  1.000000 -0.022792 -0.000317  0.000076  \n",
       "State                0.002078 -0.022792  1.000000  0.001890  0.000030  \n",
       "Hrs                  0.001668 -0.000317  0.001890  1.000000 -0.015345  \n",
       "Mins                -0.002162  0.000076  0.000030 -0.015345  1.000000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DF.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaoAAAFKCAYAAABMyxt0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nOydd1gVx9eA36HYESxUMbF3DdbYxQoqRqJGxRaIxpgEE03ssaCxJkajMdGYGLFFIepPFLGjggUFsWKPGkVQwAZ2gfn+uJfLpRchkM95n2cf7s6emXNmZtmzU3ZGSClRKBQKhaKwYlDQBigUCoVCkRnKUSkUCoWiUKMclUKhUCgKNcpRKRQKhaJQoxyVQqFQKAo1ylEpFAqFolCjHJVCoVAo8gwhxB9CiCghxLkMrgshxGIhxFUhxBkhRKOs0lSOSqFQKBR5iSfgmMn1rkB17TEcWJpVgspRKRQKhSLPkFIGAPczEekJrJYaggAzIYR1ZmkqR6VQKBSKf5MKwC2983BtWIYY5as5ilzxKuZagaxrVeatjv+6zg8ssuyezhdOPb9TIHrtilkViN6Cyq8i/zl557B43TRy8swpYl71EzRddkksl1Iuz4G69OzNVL9yVAqFQvGmk5iQbVGtU8qJY0pNOFBR79wWiMgsgur6UygUijcdmZj94/XZCgzRzv5rDjySUkZmFkG1qBQKheJNJzFPHBAAQoj1gD1QXggRDkwDjAGklMsAP6AbcBV4CrhllaZyVAqFQvGGI/OmpaRNS7pkcV0Cn+ckTeWoFAqF4k0nD1tU+YFyVAqFQvGmk4ctqvxATab4f8Lk2Qto270/zoNG5Ev638+fxumz+wk6toN37OqmKzPNYwwXLx/mTlTKlVNatWrGoSPbeBh7BWfnrtnSV6+dHbP3LWbugSV0+/T9NNdrNKuDh+/3/H7VmyZdm6e5XqxUcRYELWfQ9GEZ6hg3cxQ+R73w8l9Frfo10pWp3aAm3vtX43PUi3EzR+nCS5uZsNTrR3yObGCp14+YmJoA0LVXF7z8V+HlvwrPbcuoUaeaLk6p0qX47JcxzN63mFl7F1G1UbLO/MjvgGkfMffAEmbsWJBn+cuo3Jq0asSGvZ66I+iGP/aObQDo91FvfI56cfLOYXyP/5Wn5Z1EHbtahNwOoJOTfYY2vq6uzOp24PB+bDy4lv3n/Th0dQ/e+1fnmV57h9Z4+a9iw15P1u1agV2zBgBY21qybtcKgFNAGJD7f/6EV9k/CoA3wlEJIRKEEKeEEOeEEH8JIUq8RlquQoglrxHXJre6M8O5W2eWLZiZH0nTxcGeqtUq8U799ox0n8iPi9LX47d9L+3aOqcJv3XrNp8MH4u319Zs6RMGBgye8TELXWfxTedRvPtea2yq2aaQuRcRze9jlhDkE5huGr2+duHSsfMZ6mjdsQVvVbGlZ4t+zBzzHZPmjUlXbtK8McwcM4+eLfrxVhVbWnXQOAm3kYM5HhhCz5b9OR4YgtvIQQBE3Ixg2Pvu9OvwIb8t9GTy/HG6tMbNHMW5gyeZ1PELpnb9moir4fmW3wb2jbCsbM0Ee3c8Jy3Ns/xlVG4hh0Pp38mV/p1cGd5nJM+fvSDo4HEATh0/w+8/ruL5s+cMchyWp+UNYGBgwJeTP+PogeOZ2vi6ujKqW3Or8rgM68OSOb8Sduo8h/2Psn9HQJ7pPRZ4gn4dPqR/J1c8Rs1m6g8TAIi+ew/XHiMA7IB3gQlA7p4viYnZPwqAN8JRAc+klHZSynrAS1K9eWinSf4bZeFKbm+kLGhiVx/T0iZZC+YCJ6fOrF+3GYDg4FOYmpbG0so8jVxw8Cnu3olOE37z5m3Czl0kMZs3eRW7akT9c4foW3dJeBXP8W2HaNilaQqZe+HRhF/8B824bErerleF0uVNORd4OkMd7Rxa4+u9E4CzoWGYlDahvEW5FDLlLcpRslRJzpwIA8DXe6eulWDv0IZt3jsA2Oa9g/aObQE4HXKOuEdxAJw5EYaltQUAJUuVoFHzdwjw2gdAwqt4nsU+zbf8NuzSlCObDwJw7eSVPMtfdsqtk1N7DvsH8fzZCwAunbtCg8Z1efb0WZ6XN0D/oX3Yt/0A92MeZNvGvKxbAENDQ9p3bYvfxt0UK16Mk8fO5JnepHIDKF6imO4eiH8Vz6uXuhZOUV7jeS5lYraPguBNcVT6BALVhBCVhBAXhBC/AKFARSGEixDirLblNS8pghDCTQhxWQhxEGilF+4phOijd/5Y7/c4bVqnhRBztXJNgHXa1l3xfyOzeYG1jSXh4cmfOUTcjsTGJv9WWChjWZb7ETG68/uR9yljWS6TGMkIIeg/+UO8Z6/OVM7C2pw7EVG687uRUVhYm6eRiYrUl4nWyZQzL0NM1D0AYqLuUba8WRodzgOcOOwfBECFtyvw4N5Dhs53x2P797jN/ZQixYvmW37NUqWZV/nLTrk5OHdi55Y9aXQlxCdkGi839phbladDt7ZsXLUlRTr/Zt1G34lh9dL1dO/twPg5o3kc+4Sgg8fzVG/7rm3ZHPgni9fOZ/ro2bpwSxsLgDNoliSaRxYfzmaIalEVHoQQRmhW7j2rDaqJZnHEhsArNBXdAU1TuqkQwlm7WOJ0NA6qM1AnG3q6As7Au1LKd4DvpJQbgRBgoLZ19yzTRAoRQqRd8SS9N/s8VJhrfR0GO3Jmfyj3I+9loSIbOtJZ6CW7djRp1QhnFycWzfwFACMjQ2rVr8H+tbvw6D6WF89e0D1pLCof8ptf+csq3fIW5aheuwpH9x/7V+wZ++2XLPp2aYrW+r9dtyamJtg7tiH4cChfuU6keIlidOvdJU/17t8RQK82A/jKbQKfjf9YF35X45AbANWADwHLbGUijRH/6ge/OeZNmfVXXAhxSvs7EFiBpgvuH+3qvQBNgQNSymgAIcQ6IKl/QT/cC0h/lDSZTsBKKeVTACllZisJo013ONr1s375YSbDhmT6KUK+M/yTwbi69QfgxIkz2NomL25sU8GayMi7+ab7wZ17lLUprzsva12Wh1FZFiEAVRvVoEbT2nQY7EjREsUwMjbi+dPnbJy3lg6DHWnn0gmAkNCzWNkkd91YWlsQfScmRVpREdFYWOvLmOtk7kU/oLxFOWKi7lHeohz3Yx7q5KrXrsrUHybgPuBrHj2IBTQPlKjIaK6dugJAsN9RnaPKq/yWKmNCkWJFadK9Baf2BKdIM6/ydzciKtNy6/xeB/z9AoiPT6CvWy96DXwPgLBTF6jXsHae21PnnVrM/XU6JUqVoHRpE7r17kLw4dB/rW77uvXiw88GULJUCfZtP4i5ZXn8/Q7yTtP6eao3idCg09hWqoBZWVMe3n+kfykCzYSKNsDGNBGzooAmSWSXN6VFlTRGZSelHCmlfKkNf6Ink9nCjhm98sSjLUOheY0ropdWjpocUsrlUsomUsomBe2kAJb/uoaWzbvTsnl3fLftxmVgLwCaNrUjNjYu3bGovOL66atYVLKmvK0FhsZGNOvRmpN7QrJn96hFjGk1grGtP8Vr9mqObD7IxnlrAfBfs5Np3cYwrdsY9u8MwKmvZsuc+o3q8jjusa7bJYmYqHs8ffKU+o00sxyd+jpycNchAA7uPkSPvpoZjD36duXALs0kB6sKlsz/YzZT3Gdw81ryAtH3ou9z53YUVlU0Q5R1WtUn4kp4nuZ37bQV7F+7i1FNhhK6+zgte7UDoErD6nmWv4O7D2Vabo7vd2bnlr0AeK/crJtgsX9nAMVLFM/z8nZq9gHdm/ahfe1u+G7cycQR0/jzN+9/rW69V25m/PCpxETd57D/UZz6OtKsTRNePH+RZ3orVkpeWLxW/RoYGxvz8P4jLKzNKVos6ZFDGTS9PpfIDYW86+9NaVFlh2PAIiFEeeAB4AL8BBzXhpcDYoEPgKRR6xtAY8AbzR4rxtrw3cBUIcSfUsqnQoiy2lZVHJAvMx7GTptL8MkzPHwYS0fnQXw2dDC9ezjkSdq7du7HwaE9Z84d4NnTZ4wYkTyT7UjQdlo27w7AtzMn0Lffe5QoUZxLV46wytOL2bMW0ahxA9ZvWIaZmSldu3Xkm8mjaNokY9sSExJZN/V3vl49BQNDAwK9/Ym4cgvn0f25cfYqp/aGULlBVdx/HU9J05LYdWyC8+j+TO4yKsM0U3No71Fad2zB1iBvnj97jseo5H7/DXs96d/JFYDZ4+czfdE3FC1WlMP+QRzadxSAlT+tYd7yb3Ee4ETk7buM+3gyAMO/csOsTGkmztXM+EpISGCgw1AA5n2zkLk/TsbI2JjoW3dZMWZJvuX3zP5QGrRvxLyDP/Py2QvGfzkjT/KXWblZV7TCysaCE0dOprDFZWgfPvx8IEWKFmHvmW08fvyEz/qNzhN7CkPdnjt5nr2++3GfOIIy5c0wNjYmMvwO076clSd6OzrZ4/RBV+JfxfPi+QvGfzIVgMrVK/GVhztonkcCmE/ysEbOKOTfUYl8HWsoJAghHkspS6UKqwT4amcCJoUNACaiqXQ/KeU4bbibNjwSzTcLhlJKdyGEJeCDplW1DxiZpEcIMQEYgmaWoZ+UcpIQojcwG3gGtMhonEpt85H/qG0+FP9fyIttPl6c2ZXtZ07RBg6vrS+nvBGO6r+GclT5j3JUiv8v5IWjen7aL9vPnGLvdPvXHZXq+lMoFIo3nULe9acclUKhULzpJMQXtAWZohyVQqFQvOnkYIffgkA5KoVCoXjTUV1/CoVCoSjUqP2oFAqFQlGoUS0qhUKhUBRqVItKkVMK4nsmgAc39/3rOovbtPnXdQI0KFe5QPSujQjKWigfqF9A+S0ICurb0PQWw/2vIAv5Wn/KUSkUCsWbjmpRKRQKhaJQo8aoFAqFQlGoUS0qhUKhUBRqVItKoVAoFIUatYSSQqFQKAo1hbzr703Z4ff/Dd/Pn8bps/sJOraDd+zqpiszzWMMFy8f5k7UuRThrVo149CRbTyMvYKzc9c8sWfy7AW07d4f50Ej8iQ9fRYumMHF84cIPbGHhnb10pX5dsZ4rv8dzMP7l1OEDxncl8jbZwgJ3k1I8G4+cnPBoYs9YecCuHj+EK7ug9KkZVzEmLnLpuNzZAOrti/H2jZ5Sw63kYPwObKBzYF/0sK+mS68Zft32Rz4Jz5HNqRIc8WWn1m/ZyXr96xk18kt/LBSs3mfmZkpf/31O6En9nDksC9169ZMkd8LWeR3xozxXPs7mAep8ptEr17defXyNo0bNQCgZs2qBAZsJeiGP4NHZLxzdEb2ZpfSZib8smEhWw6v55cNCzExNaFl+3fxC9lEyO0Adp3cwvo9K/l4tKsuTk7L29LGgl83LmZTwFr+OrAGl2Ef6OTnLpuus3978Eb8w7bjc9QLL/9V1KpfI12bazeoiff+1fgc9WLczORNKEubmbDU60d8jmxgqdePmJhq9jrt2qsLXv6r8PJfhee2ZdSoU01n1/JNP+EXsomgf/zZd843z+6vaQsmsvfsNrz3r06R1qgpn7EpcB1e+zwB/geYZVpBWVHId/jN0lEJIWyFED5CiCtCiGtCiCVCiKJ5bYgQwl4I0VLvfIQQYoj2t6sQwiaH6RURQvwohPhba7uPEMI2m3FdhRBLcpaDbNvlKYTok5u4XRzsqVqtEu/Ub89I94n8uGhmunJ+2/fSrq1zmvBbt27zyfCxeHttzY36dHHu1pllC9K343Xo6tiB6tUqU6tOaz79dDw/L5mTrpyv7x5atOqe7jXvv7bSpGkXmjTtgucqLxYvmoVTj0HUf6c9js6dqFyjUgp5ZxcnYh/F0bNlf9Yt9+LLyZ8CULlGJRx6dqKP/WDcB3zNhDlfY2BggIGBAeNnf8XIgWPo3W5QijSHOn+OS2c3XDq7cebEOfz9AgCYMH4kp0+H0ahxZ9w++pIFP2h23nV07EC1apWprc3vkgzyu913Dy0zyG+pUiVx//wjjh0L1YXdv/+Q0aOnsGbZhvQLWktG9mYXN/dBHD90AudWLhw/dIKPRg5m/OyvWDRzKUf8g3hw7yGTPp/Obws9dXFyWt4J8QksnL6E3m0H8WH34fR17aUr7wkjpunsv3D2EnGP4ujZoh8zx3zHpHlj0rV50rwxzBwzj54t+vFWFVtadWiuycvIwRwPDKFny/4cDwzBbaTG6UTcjGDY++706/Ahvy30ZPJ8zU7XCfEJLJyxhFev4hnkMJTYh3G816/ra99fANu8/XAf8HUa24MCgulrP4R+HV0BLqPZ2DX3yMTsHwVApo5KaL5g2wxskVJWB6oDxYHv8sEWe0DnqKSUy6SUSa8RrkCOHBWanXRNgBpa27cAm0Wqr/KEhtdqWQoh/pUuVCenzqxftxmA4OBTmJqWxtLKPI1ccPAp7t6JThN+8+Ztws5dJDEP34qa2NXHtLRJnqWXRI8eDqxZtxGAY8dDMTUzxcrKIo3cseOh3LkTlWV6zZo25O+/b3D9+k1evXrFLp+92Du0TiFj79gaX+8dAOzzPUDTNo014Q6t2eWzl1cvXxFxK5LwG+HUa1ibeg1rE34jnNs3I4h/FZ9umiVKFqdpq8Yc2KF58NeuXYP9/ocAuHTpb95+2xYLi/K818OBta+Z3+ke45j/w1KeP3+uC4uOvkfIidPEv8reGERqe4sVL8a0BRNZs+M3/tz9B+1S5S+Jdg5tdGXn672DLj07EH4jnJi795BS5kl5x0Td4+JZTUvy6ZNnXL9yAwur8mlsadGuGd6emv+Ts6FhmJQ2obxFuRQy5S3KUbJUSc6cCNPavBN7xzZa/W3YprVrm/cO2ju2BeB0yDniHsUBcOZEGJbWmvqJibqHsZER4TfCuXrxOtev3OBU8NnXzi9AaNBpHj2ITZPHoIPBJCToVjwPArL1Ep4h//EWVQfguZRyJYCUMgEYDQwRQpRK3fIQQvgKIey1v5cKIUKEEGFCiOl6MjeEENOFEKFCiLNCiFrabeFHAKOFEKeEEG2EEB5CiDHa1kcTYJ32WnchxP/00usshNisb7QQogTgBozW2ow2Dy+ADkKISkKIC0KIX4BQoKIQwk0IcVkIcRBopZeWuRBikxAiWHu00oZ7CCGWCyF2A6uFEIZCiO+1MmeEEJ9o5YS2FXpeCLEdSPv0ySbWNpaEh0fqziNuR2JjUzA7xuY3FWysCL8VoTu/HR5JhRzmtdf73Qg9sQevDcup36A2t8KT04uKjMYilZM3tzLnToTGCSQkJPA49glmZU2xsDLnbkSyc7gbEY25lblG/nZUpmm279aO44dCePL4KQBnzp7H2bkbAE2b2PH227bYVrDG5jXza2dXF9uK1vj57c12nPRIbe+wUUMIPnyCwV0/ZnifLxg15XOKFS+WJl458zLERN0DNA9u0zKmurKp37gefYa8T58hzlSpkbxCRk7LWx9rWytq1q/BudDzKcIbNX+HhIREwk5dTI4fGYWFdcr4FtbmREXq6YiM1smkzkvZ8ml71ZwHOHHYP3mVEQtrzb2gs+tE2GvfXzngI2BHTiKkIY9bVEIIRyHEJSHEVSHEhHSuvyWE2C+EOKl9XnbLLL2sHFVd4ESK/EgZC9wAqmUR9xspZROgAdBOCNFA71qMlLIRsBQYI6W8ASwDFkop7aSUgXr6NgIhwEAppR3gB9QWQiTVpBuwMpXuasBNra36hGjzBFATWC2lbAi8BKajcVCdgTp6cRZp7WoK9AZ+17vWGOgppRwADAUeaeWaAh8LISoD72t11Qc+Rq/VmFPSW6KloJaLyW9eN6++2/dQtXpzGjXuzL59gXz+qVuW6WWkM91wMghPlaajcyd2bkl2Ht99t4QyZUwJCd7N559/xKlT54hPSHit/AohmP+9B+PGzciWfGaktrd5u2a4ug9i/Z6V/LbpJ4oUK4K1rWW207t49hLdm/bhl3nLuRx2lQV6Y185Le8kipcozvwVs/hh6iKdQ03CwbkT0Xdi0k03BemsdpTd8m7SqhHOLk4smvmLXnoCIyMjnV0vXrx87fsrOwz9cghAPLAuWxEyIiE++0cWCCEMgZ+BrmiepS5CiDqpxCYD3trnb3/gFzIhK0clIN0Sy86iVn2FEKHASTTOQd/QpBbQCaBSNtLSITW1vwYYJIQwA1qQ9m0iM7uTwv+RUia9Er0LHJBSRkspXwJeenE6AUuEEKeArUBpIURSX9dWKeUz7e8uaFqap4BjQDk0XaVtgfVSygQpZQTgn16+hBDDtS3QkFfxcbrw4Z8M5kjQdo4EbScyMgpbW2vdNZsK1kRG3k23nP6LfDriQ93kh4jIO9hWTO7trWBrTUQO8nr//gNevnwJwO8r1lGpUkUq2ianZ2FtTvTdlA+0qMgorGw0DV5DQ0NKlS7Jowex3I2MwtImuSFsaWNOzJ0YjXwFiwzTNC1Tmrp2tTm096guLC7uMcM+/oomTbtw7NgJGjSog+fKxUS+Rn5NTEpRt24t9u7ZyJXLQbRs2ZTAwK2EnQvA2jr7TiU9e4UQjB32jW78p3uT3ly/8g8eCyeyfs9KFq/9HoB70Q903WvlLcrx6MEjrCpY8OTxU549fYaFtTlnTpzDyNgIs7KmuSpvACMjQ+avmInf5t0pxtH6uvZiw96VvD+wB5fOXdalC2BpbZHGeUVFRGNhrS9jrpNJnZf7MQ91ctVrV2XqDxMY7TohRZfcvbv3aNO5hc6uvLi/ssLpA0fadGoJMJD0n3fZJ2+7/poBV6WU17TP0w1Az1QyEiit/W0KRJAJWTmqMDTdbjqEEKUBS+ASGk+un0YxrUxlYAzQUUrZANiedE3LC+3fBHI3RX4lMAhwAf6SUqZ281eBt/UcShKNgKS+gieprmVU0QZAC21Lz05KWUFKmeRJ9NMQwEg9ucpSyt1ZpJ2sXMrlUsomUsomxkbJZi//dQ0tm3enZfPu+G7bjcvAXgA0bWpHbGxcumNR/1WWLlulm/ywdesuBg/UzDl5t1kjYh/FZmssKgn98Z0ePboQFnaJatUqU6lSRYyNjXHo2YmDuw6niHNw12Gc+mpmQ3Z0sif4UKgu3KFnJ4yLGGNT0ZqKlSty7uQFwk5dpGLlithUtMbI2ChNmp16tCdw7xFevnipCzM1LY2xsTEAL1++wtt7K40ad8Jn6y4G5TK/sbFxWNvUp3qN5lSv0ZwjR4Jp0+Y96tZrm6MXmfTsPXrgGP0/Sp77U7NedQA8Rs/BpbMbXwwaC0DA7kO6snPq25U9W/2pWLkide1q68rm9j+RCAMDHt5/lKvyBpi6YCLXr/zDul/13yXB23Mzi2ct43TwWfw278apryMA9RvV5XHcY11XXhIxUfd4+uQp9RvV1drsyMFdmrHDg7sP0UNrV4++XTmwS9PBY1XBkvl/zGaK+wxuXruVIr3eQ3piZGTEfr+AdO+F3OY3I1q2fxdX94GMcp0A8DRT4eyQt46qAqBfQOHaMH080DQ2wtH0ko3MLMGsHNU+oITe7DtD4AdgibYlcQOwE0IYCCEqovGkoPGUT4BHQghLNE3ArIhDM/khy2valkkEmuajZ2phKeUTYBWwQGsz2jyUIP0WzTHAXghRTghhDHygd2034J50IoSwy8DGXcCn2vgIIWoIIUoCAUB/7RiWNdA+g/hZsmvnfm5cv8WZcwdY8vMcRo+aort2JGi77ve3Mydw6coRSpQozqUrR5j0zZcANGrcgEtXjvB+r24s+mkWwSG7cmuKjrHT5jLwk9HcuBlOR+dBbNr2+mkC+O3Yx7XrN7l04TDLln2H+8hJumshwbt1v+fO+YYb10IoUaI4N66FMHXKVwCMdP+I06f8ORGyh5GfD+WjYaP4ctRk/Lb/ybkzB9izzZ9rl68zYuxQ2nbRDEluWe+LaRlTfI5sYNAn/fhp1jIArl2+zp5t/mw8uJYlf/7A3EkLSExMJCEhgXmTFvDz+gVsClinSzMJh56d2LUl5ZhR7VrVOX1qP2fPHsTBsT2jv5oKwI4d+7h+/SYXtfkdmUF+58z5huva/F6/FsIUbX4zwtLSnOvXQhj4ST+GjRrCjhObKVmqBACL135PecvkSQbp2fvbQk+MjI3w8l+F9/7VfDZuWLp6Vi5ZS/O2TdhyeD3N2zbhj5/WMG/SAhat+Y7DV/dQtnwZBg7vy/HAkFyXt12zBjh94EjTVo10U9GTZuoBdOnZkZ1b9nJo71HC/4lga5A3U34Yz5wJP+hkNuz11P2ePX4+UxdMYGuQN7duRHBon6YlufKnNbzbrik+RzbwbrumrPxpDQDDv3LDrExpJs4dw4a9nqzbtQIAu2YN6N7HkUcPY9kcuI5DV3dzKezKa99fALN/8cDTdxlvV32LHSc209NFM+Nz/KzRlChZgqUbFgKcQjN0knukzPah3/ujPYanSi29HrfUL+sugKeU0hboBqzJbFKbyKpfVuuAfgZqA+aAl5RSN1EAWAvYAefQtLQ8pJQHhBCeaLrUrqFpQW2VUnoKIW4ATaSUMUKIJsB8KaW9EKIGsBFIRONdOwKPpZTzhRC90czie4amdfNMCNEfGCWlTL5TU9pdFJivLYRE4CLwmZTylnbyhq+Usp6evBuaKZ6RaCreUErpLoQor5d/IyBASjlCCOGRZJ82vgEwE+iBpqKiAWcgFvgJzcSUpI9f1mrH3tKlVInKBTLwpLb5yH/O3ruetVA+oLb5yH/SG2v6NwiNPPTaip+tn5btQivuMj1TfUKIFmj8gIP2fCKAlHKOnkwY4CilvKU9vwY0l1Km242QpaNKZUBLYD3QS0p5Iiv5/ERoZhuelFKuKEg78gPlqPIf5aj+/6IcVc55tm5K9h3VwG+zclRGaF7KOwK3gWBggJQyTE9mB5pGj6cQojaa3rsKMoPKy9H4kJTyCPB2TuLkB0KIE2i6FtN+CadQKBSKnJGHa/1JKeOFEO5ohkMMgT+klGFCiBlAiJRyK5pn929CiNFougVdM3JS8B9d609K2bigbVAoFIr/N+RxK1RK6YdmkoR+2FS93+fR+141K/6TjkqhUCgUeUghX5RWOSqFQqF401GOSqFQKBSFGrVxokKhUCgKMzI+IWuhAkQ5KoVCoXjTUS0qRU75wKJRgegtiG+ankUEZi2UDzSrN7hA9D4toPy+W4pKjw8AACAASURBVH9IgegtCArqe6b/9ALRiYXbduWoFAqF4k1HTaZQKBQKRaFGOSqFQqFQFGoKebelclQKhULxpqNm/SkUCoWiUKNm/SkUCoWiUKNm/SkUCoWiMCML+WSKrHb4VRQi6rWzY/a+xcw9sIRun76f5nqNZnXw8P2e369606Rr2v0ki5UqzoKg5Qyanv4urZmxcMEMLp4/ROiJPTS0q5euzLczxnP972Ae3r+cInzI4L5E3j5DSPBuQoJ385GbS471p2by7AW07d4f50Ejcp3GuJmj8DnqhZf/KmrVr5GuTO0GNfHevxqfo16MmzlKF17azISlXj/ic2QDS71+xMRUswG1vUNrvPxX6XaAtWvWIGWCQmBU5i0MSpYju7xWXoUBhiaWeO3zZLXfcqrWzHhfqs8nDOd/h9azKWAt/Yf2yVAuPUqbmfDLhoVsObyeXzYsxMTUhJbt38UvZBMhtwPYdXIL6/es5OPRrro4xkWMmbtsOj5HNrBq+3Ksba1019xGDsLnyAY2B/5JC3vNxuGWNhb8unExmwLW8teBNbgMS96Ie9SUz9gUuA6vfZ78sXUpW45swOfIBlzdB6WxNad6AaYtmMjes9vw3r86RVpzl03X7Ta8PXgj/mHb8/ye6tqrC17+q/DyX4XntmXUqFNNVx7LN/0EcAEIA77MtJIyI1Fm/ygA/lOOSgiRIIQ4JYQ4J4T4SwhRIgM5PyGE2WvqshdCPNLqSzo65SIdDyHEmNexBUAYGDB4xscsdJ3FN51H8e57rbGpZptC5l5ENL+PWUKQT/oflfb62oVLx87nWHdXxw5Ur1aZWnVa8+mn4/l5yZx05Xx999CiVfd0r3n/tZUmTbvQpGkX/li5Psc2pMa5W2eWLZiZ6/itO7bgrSq29GzRj5ljvmPSvPSraNK8McwcM4+eLfrxVhVb3dbnbiMHczwwhJ4t+3M8MAS3kZoH4rHAE/Tr8CH9O7niMWo2U3+YkCI9gxJlka+e58jW18mrQXEzZPwL+nV0ZeoXMxn7bfrPsvf6dcPSxoJebQbQu+2gNFvSZ4Wb+yCOHzqBcysXjh86wUcjBzN+9lcsmrmUI/5BPLj3kEmfT+e3hZ7J+XJxIvZRHD1b9mfdci++nPwpAJVrVMKhZyf62A/GfcDXTJjzNQYGBiTEJ7Bw+hJ6tx3Eh92H09e1F5VrVAIgKCCYvvZDcOn8EVVqVOJ44Al6txuEo3MnnUxu9QJs8/bDfUDa7e8mjJiGS2c3XDq7ceHsJeIexeX5PRVxM4Jh77vTr8OH/LbQk8nzxwGQEJ/AAo+fQLP7eHPgc6BOjiouCZmY/aMA+E85KuCZlNJOu4X8SyDFK6bQYCCl7CalfJgH+gK1+pKOnP335iFV7KoR9c8dom/dJeFVPMe3HaJhl6YpZO6FRxN+8Z90v5B/u14VSpc35Vzg6Rzr7tHDgTXrNgJw7HgopmamWFlZpJE7djyUO3fS3Uk6z2liVx/T0ia5jt/OoTW+3jsBOBsahklpE8pbpGzllLcoR8lSJTlzQrMxqa/3TuwdNat32Du0YZv3DgC2ee+gvWNbAJ49faaLX7xEsRR1UbtBTYSBIYmvnubI1tfJqzAqgnylsenG1ZtYV7SmbPkyaeT6fOjMbwtW6ux9cE/z71OseDGmLZjImh2/8efuP2jn0DpdPe0c2uCrLQ9f7x106dmB8BvhxNy9h5SSXT57sU8V196xtS7OPt8DNG2j2WbO3qE1u3z28urlKyJuRRJ+I5x6DWsTE3WPi2c1rfWnT55x/coNLKzKAxB0MJiEhATqNaxN5K1ISpQqTvyr+DzRCxAadJpHD2IzLesW7Zrh7bkZyNt76nTIOeIexQFw5kQYltaa/z398gDi0LSsKmRqZEbEJ2T/KAD+a45Kn0CgmhCikhDighDiFyAUqCiEuCGEKA8ghBgihDgjhDgthFijDTMXQmwSQgRrj2xv4KWNv0UIcUIIESaEGK4X7iiECNXq0t/XvY4Q4oAQ4poQ4ovcZLaMZVnuR8Tozu9H3qeMZfa6j4QQ9J/8Id6zV2ctnA4VbKwIvxWhO78dHkkFG6tMYqSl1/vdCD2xB68Ny7G1tcmVHXmJhbU5dyKSnerdyCgsrM3TyERF6stE62TKmZchJuoeoHlglC2f3IBv37UtmwP/ZPHa+UwfPRvQ1MFXHu4kPLmXb3lKDxn/AlG0JAB17WpjbWuJpU3alwzbtyvQpWdH1u78nZ/WzadiZU1rfdioIQQfPsHgrh8zvM8XjJryOcWKF0sTP3V5mJYx5c5tTdnVb1yPPkPep88QZ6rUSO56NLdKroOEhAQexz7BrKwpFlbm3NWvm4hozK1S1o21rRU169fgXGjKHgJzK3NKm5lyxD8IgKjIaCxSxX0dvRnRqPk7JCQkEnbqYnL8PLynknAe4MRhbd5SUQloCBzLlsGpUV1/eY8QwgjoCpzVBtUEVkspG0op/9GTqwt8A3SQUr5Dch/uImChlLIp0Bv4PQNVbVJ1/VXVhn+k3WW4CfCFEKKcEMIc+A3ordX1gV46tQAHoBkwTQhhnItMpwnK7tpiHQY7cmZ/KPcjc/eQTG/ttJysa+a7fQ9VqzenUePO7NsXyMoVP+bKjrwkW3lKZ8m47OR7/44AerUZwFduE/hs/McA9HXrxaF9RyHx330jTXz2ECEMWb9nJf2H9ubSuSvEp/NWXKSoMS+ev2SQ4zD+t24rHgsnAtC8XTNc3Qexfs9Kftv0E0WKFcHa1jLb+i+evUT3pn34Zd5yLoddZcHK2bprGdVBuuEkl3vxEsWZv2IWP0xdxJPHKVunHbu3Q8pE/DbtTpGmPrnVmxkOzp2IvhOTJjyv7imAJq0a4ezixKKZv6S+VArYBIwCMm/2ZUQh7/r7r836Ky6EOKX9HQisAGyAf6SU6b1mdAA2SiljAKSU97XhndC0cpLkSgshTKSUcaniB0opndJJ9wshRNJshopAdcAcCJBSXk+lC2C7lPIF8EIIEQVYAuH6CWpbZsMBWpRtSE2TlIPeD+7co6xNed15WeuyPIy6T3ao2qgGNZrWpsNgR4qWKIaRsRHPnz5n47y1Gcb5dMSHDB06EICQkFPYVkxuBVWwtSYi8m62dAPcv/9A9/v3FeuYM3tStuPmJQbFSmNQTNOFFn3nHFZ6LQtLa4s0D5qoiGgsrPVlzHUy96IfUN6iHDFR9yhvUY77MWl7mkODTmNbqQJmZU1p0LgeDd9tgFGZciAMAAFSkvg0e3WY23zGx94h4XE0Lp3dAPA9/hcRNyPSxLkbGc2+7QcA8PcLYNpCTR0JIRg77Bv++ftWCnmPhROpWa8G0Xdj+GLQ2DTl8ejBI6wqWOgciYW1OWdOnKN6naqYlTXl4f1HREVGYWVjQVRkNIaGhpQqXZJHD2K5GxmVotVnaWNOjLbcjYwMmb9iJn6bd+PvF5DCJqcPHKlWqwq3rif/a1lYmxN9N1W95kJvRvR17UWvQT2oUrMye7b659s9Vb12Vab+MAH3AV+n6II0MjIEjZNaB2zO1NjMKOTT0/9rLapneuNFI6WUL7XhTzKQF5DuK5EB0EIvrQrpOKn0ExTCHo2ja6FtOZ0EimWiC+CF3u8E0nlBkFIul1I2kVI2Se2kAK6fvopFJWvK21pgaGxEsx6tObknJDsms3zUIsa0GsHY1p/iNXs1RzYfzNRJASxdtko3+WHr1l0MHqiZBfZus0bEPorN0ViU/nhWjx5duHjxarbj5iWJz2OJf3ib+Ie32b8zAKe+jgDUb1SXx3GPdd0uScRE3ePpk6fUb1QXAKe+jhzcdQiAg7sP0aNvVwB69O3KgV2aCSwVKyUPEdSqXwNjY2Me3n/EN59Pp1uT3sQ/uEXCk3skvojLFyeVOp/6S+O8P7AHoUGn07RCAA7sCKRZa81YTeMWDbl5TeOYjh44Rv+PkmcA1qxXHQCP0XNw6ezGF4PGAhCw+xBO2vJw6tuVPVv9qVi5InXtamNkbIRDz07c/icSYWDAw/uPADi467AuTkcne4IPherCHXp2wriIMTYVralYuSLnTl4AYOqCiVy/8g/rfvVKYX/L9u/i6j6QEf1HU+HtCthUtNbpPbjrcArZ3OjNCG/PzSyetYzTwWfx27w7X+4pqwqWzP9jNlPcZ+jqJYlpmpbvBWBBpoZmgUxMzPZREIj/0tL0QojHUspSqcIqAb7aCRZJYTfQdMtZAv9D41TuCSHKSinvCyH+BE5KKb/XyttJKU+lStceGJO6RSWE6AkMk1L2EELUAk4Bjmimh4YCbaWU1/V0eQCPpZTztfHPAU5SyhsZ5dOtUu90K6WBfSNcprphYGhAoLc/vj9vwnl0f26cvcqpvSFUblAV91/HU9K0JK9evOJR9EMmdxmVIo1WfdpTuX5V1k5L29u5JiLdvm8AFi+ahUMXe54+e8awYV9xIvQMACHBu2nStAsAc+d8Q/9+72NjY0lExF3+WPknM75dwKyZE3By6kJ8fAIP7j/k85ETuHTpbyD323yMnTaX4JNnePgwlnJlzfhs6GB693DIdvxm9QYzYc5XtGzfnOfPnuMxajbnT2vGFzbs9aR/J1cA6rxTi+mLvqFosaIc9g9i3iTN88C0TGnmLf8W6wqWRN6+y7iPJxP7MA5X94E4fdCV+FfxvHj+goUzfubU8TM6vcfPrUEULYUwKkpiNserXievwqgohiYW3LgewfXLN5j+1VzdwPzitd8z4+u5xNy9R6nSpZj181SsKljy7MkzZo2fz5XzVylarAhjZnxJgyb1EEIQeSuSL4eMT6PHtExp5v06A6sKlty5fZdxw6dQv1Fdpi/6BpPSpXj0IJbI8Dtcu3KD/TsCCNh9mCJFi/DtT1OoVa86jx7GMnGEB7e1rb2hXw7hvf7dSYhPYP60xRzxD8KuWQP+8PmFK+evkqhtASyZ8yuH/YPwObIB4yLGPHoQS8lSJTDVttq2btjOikWrGTF2KOdPX8yVXoDZv3jQuKUdZmXNuB99n2XzV+CzfjsAHj9O4mxoGBtXbcmXe2rqDxPo2L0dkeGaXoyEhAQGOgzFrlkDVm5dCpohkCQPMgnwy9bNocfjse9n2xGU+v5///o+Kv+vHZWUMkYI8SEwFk1L5qSU0lU70eJnNNM6jdB02aWeQWgP+ADX9YJnAtuALWhm11xC0+XnIaU8IIToCsxG02KLklJ2zktHld9k5qjyizdtP6rj59YUiN43aT+qgqKgnqUn7xx+bcfxeEzP7Duq+T7/uqP6T41RpXZS2rAbQL1UYZX0fq8CVqW6HgP0y0LXAcA0g8tdM4izA9iRKswj1Xn6X8sqFApFQVHIx6j+U45KoVAoFHmPVI5KoVAoFIUa5agUCoVCUagp5IvSKkelUCgUbzrxylEpFAqFohBT2Gd/K0elUCgUbzpqjEqRU049v1MgehuUy3ivovziTfueqaDym946dgqFjjx2VEIIRzRrqhoCv0sp56Yj0xfwQLOiz2kp5YCM0lOOSqFQKN5w8nJ6uhDCEM2CCp3RrGkaLITYKqU8rydTHZgItJJSPhBCpF3SX4//2lp/CoVCochr8nabj2bAVSnlNe16rBuAnqlkPgZ+llI+AJBSZrp4qHJUCoVC8YYj42W2j2xQAdBfPTectBs61gBqCCEOCyGCtF2FGaK6/hQKheJNJwddf/pbEmlZLqVcri+STrTUCozQbI9kD9gCgUKIehntzK4clUKhULzp5OAzKq1TWp6JSDiaffqSsAVSb4IWDgRJKV8B14UQl9A4ruD0ElRdfwqFQvGGIxNlto9sEAxUF0JUFkIUAfoDW1PJbAHaA2h3s6gBXMsoQdWiUigUijedPFyYQkoZL4RwB3ahmZ7+h5QyTAgxAwiRUm7VXusihDiPZgumsVLKDDdoU46qkDJu5ihadWzB82fPmfblLC6evZxGpnaDmskbsO07yneTfwSgtJkJ8379FpuKVkTcusO44VOIexRH115dcHXXbC//7Mkztm/cxYCP+2JgaIDfpl1Uq1WFqrWqgJRMHz2HC2cv8+3iydRuUJOHD2KZ8MlUIsM133i5jRyEs4sTCQmJfD/lR44eOA5odlodM+NLDA0N+N+fvngu0ewkvGLLz5QoWQKAsuXLcO7Ueb52m4SJqQnrdv2OpY0FiQmJTPliJnu37X/tvNo7tObT8R8jEyUJCQl8P2VRig0MEQIjs4okvnyS7Q0MASbPXkDA4eOULWPGlrXLsh1Pn/yo28zyG3I7gKsXNC+rd27f5a9V/0u3jpIwLmKco3q3tLFgxuLJlLcoS2KiZPParaz//a8M74Xc6ilStAi//28JRYoUwdDIkH2++1k2/w8AmrVuzJdTP8NAGGBobEjxYsVITEzME70A0xZMpE3nltyPeUDf9sl7e81dNp23q74FgIlpKYqXKE5c7ON8/b+dPX4+l89rdsnW7vAbpT1yvYVQNidJZD89Kf1ItYGjlHKq3m8JfKU9suSN7/oTQkghxBq9cyMhRLQQwjeX6ZkJIT7TO7fPaVqtO7bgrSq29GzRj5ljvmPSvDHpyk2aN4aZY+bRs0U/3qpiS6sOzQFwGzmY44Eh9GzZn+OBIbiNHARAxM0Ihr3vTr8OH/L7j6v4esYXuA/4mt5tB+Iy7AMun79K7zYD6dfRlWtX/sHZxYnYR3H0bNmfdcu9+HLypwBUrlEJh56d6GM/GPcBXzNhztcYGBhgYGDA+NlfMXLgGHq3G4Sjcycq16gEwFDnz3Hp7IZLZzfOnDiHv18AAFN/mIBMlLz7VnumjZrFtB8m5ElejwWeoF+HD+nfyRWPUbOZmipdgxJlka+e56RaAHDu1pllC2bmOF4S+VW3meX3xfMXurL/+qNJGdaRLo85rPeE+AQWTl9C77aD+LD7cPq69qJqrcp5rufli5d80udL+ndyxaWTKy3aN9dt6T5x7hgmfz6DgQ5DKVe+LBfOXsozvQDbvP1wH/B1mnqaMGKarmwvnL1E3KO4fP2//W2hJ5Pnj9Oltc3LDzQ7jL8WMjH7R0Hwxjsq4AlQTwhRXHveGbj9GumZAZ9lKZUJ7Rxa4+u9E4CzoWGYlDahvEW5FDLlLcpRslRJzpwIA8DXeyf2jm0AsHdowzZvzf6N27x30N6xLQCnQ87ptiGPj08A4PbNCIoWLQJAgjYs/lU8j2MfY+/YGl9tOvt8D9C0TWNt+q3Z5bOXVy9fEXErkvAb4dRrWJt6DWsTfiOc2zcjiH8Vzy6fvdg7tE5hd4mSxWnaqjEHdmgcVV27Wvht3g3Abh9/ihYvSrVaVV47r8+ePtPFL16iWIq1zGo3qIkwMCTx1dOsqiINTezqY1raJMfxksivus0sv/pkp45yWu8xUfd0LYenT55x/coNmrZqnOd69PNpZGyEkbGhLp9SSkqWKkm9hrWJfRTHzWu38lRvaNBpHj2ITbdMk2jRrhnenpuB/Pu/PXMiDEvr5G9jQ4NOA9zP1LDskJiDowBQjkrDDqC79rcLsD7pghCirBBiixDijHa+fwNtuIcQ4g8hxAEhxDUhxBfaKHOBqkKIU0KI77VhpYQQG4UQF4UQ60QW69lYWJtzJyL5+7e7kVFYWJunkYmK1JeJ1smUMy9DTJSmOysm6h5ly5ul0dGtdxcib0UCUOHtCsQ9jMPpA0f+3P0HU+aPp1jxYphbJduRkJDA49gnmJU1xcLKnLv69kVEY25lrpG/nRweFRmNhVVKu9t3a8fxQyE8eaxxEq9exVO52tsA1G1YG0NDQ2rUrZ4neW3ftS2bA/9k8dr5TB89G9AsJfSVhzsJOejuy0vys27Tyy9AkaJFWLvzd1b5/op917ZZ1lFO610fa1sratavQdyjuHzRY2BgwPo9K9l7dhvHDoZw7qRmsYNvx8xl8drv+XH1d5QpZ8bKn9bmS/4yolHzd0hISCTs1MXk+Pnwf+s8wInD/kHZsiknqBbVf4MNQH8hRDGgAXBM79p04KSUsgEwCVitd60W4IDmS+xpQghjYALwt5TSTko5VivXEBgF1AGqAK0yMyY9P5bmDTkdV5fdFZCbtGpEs9aNCTt1AQAjI0OsK1px9cI1BnT5iGfPnuM2clCGdqQbTgbhqWxydO7Ezi17defhN25TolQJNuz1pP9HfXj65JmuZacjl3ndvyOAXm0G8JXbBD4b/zEAfd16cWjfUUhMyCJ2/pCfdZtefgG6Ne7NIMdhTPpsOu/160ZJkxKZpp3Tek+ieInizF8xix+mLuLF85dZ5iE3ehITE3Hp7IZjo17UbVibqjU161MOHN6PLwaNZfb4+dz8+xZfeYzM8/xlhoNzJ6LvxKSbbkrlaePm5P/W2cWJRTN/yZZ8jlAtqsKPlPIMUAlNa8ov1eXWwBqtnD9QTghhqr22XUr5QkoZg2Yw0zIDFcellOFSykTglFZXCtatW/fnxYsXn168ePHprfBbWNkkN+8trS3S/BNERURjYa0vY66TuRf9QNflUN6iHPdjkr+hq167KlN/mMAPHj9RplwZAO5GRPEk7gkXz2m6b/b57qdW/RpERUbp7DA0NKRU6ZI8ehDL3cgoLPXtszEn5k6MRr5CcriFtTnRd5PtNi1Tmrp2tbG0tmD9npWs37OSO7fv4rdxF/07uTJl5LcUK1aUC2eS30pfJ69JhAadxrZSBczKmtKgcT36ufXGqExFDEuWw6CoCQYlyqaJk5cYFCuNkVkFjMwqEH0nJt/qNgn9/AK6Orh9M4KwUxd4u8pbOtnUdQTkuN5B87Izf8VM/Dbvxt8vIMt7Ibd6kngc+5gTR07Ssn1zzMqZUb1ONc6dPE9UZBQvXrzknab18kVvavq69mLD3pW8P7AHl85dzvf/29GuE7LsgswNqkX132ErMB+9bj8tmX1l/UIvLIGMZ1FmKTdw4MABtWrVKlGrVq0SR/cG49RXMz5av1FdHsc91nUJJBETdY+nT57qBpOd+jpycNchAA7uPkSPvl0B6NG3Kwd2BQJgVcGS+X/MZor7DPy3H+StKrbYvGXNo4exGBkbc+XC3wA0a92E65dvcHDXYZy06XR0sif4UKgm/V2HcejZCeMixthUtKZi5YqcO3mBsFMXqVi5IjYVrTEyNsKhZycO7jqss7lTj/YE7j3C+t//0g1ABwUE06NfNwDcJ37C47jH3Lwe/tp5rVgpecWWWvVrYGxszMP7j/jm8+l0a9Kb+Ae3SHhyj8QXcSQ+ff0u/sxIfB5L/MPbxD+8zf6dAflStxnl18TUBOMixgCYlTXl7SoVMStrmmEdATmud4CpCyZy/co/rPvVCyDLeyE3eszKmVGqdCkAihYrwrttm3Dj6j/EPYyjVOmSvFWlImGnLlKlxttEht/N0/xlhLfnZhbPWsbp4LP4bd6d7/+3N6/dIj9IjM/+URCo6enJ/AE8klKeFULY64UHAAOBb7XhMVLK2EyGmeKA3I+2A4f2HqV1xxZsDfLm+bPneIxKHm/YsNeT/p1cAZg9fn7yNFf/IE2XFrDypzXMW/4tzgOciLx9l3EfTwZg+FdumJUpzcS5mtlIiQmJ/LJ+AQaGhmzd4MeIMUOZtmAit29GMHLgWF48f8G3P03B58gGHj2MZeIIDwCuXb7Onm3+bDy4loT4BOZOWkCidivreZMW8PP6BRgYGrB1w3auXb6us92hZ6c0U4Xv3r5L/UZ1OH7zAC+ev2C028Q8yWtHJ3ucPuhK/Kt4Xjx/wfhPppIXjJ02l+CTZ3j4MJaOzoP4bOhgevdwyHb8/KrbjPJbpfrbfPP9OGSiRBgIVi5ZS8zde2nqaMTYoZw/fZGA3YfZst43R/Vu16wBTh84cuX8VdbvWQnAkjm/pnsvvI4ec4tyTF/0DYaGBggDA/Zs9Sdw7xEAZo75ju9/n4lMlMTcvU+lam+xKWBdnugFmP2LB41b2mFW1owdJzazbP4KfNZvB6BLz47s3LL3X/u/TUhIYKDDUADmLPUAOAqUR7PawzRgRbZvyCRk4d4GRhT2nR3zGyHEYyllqVRh9sAYKaWTEKIssBKoDDwFhkspzwghPIDHUsr52jjnACcp5Q0hxJ9oxrp2ANuT0tLKLUHz0ZtnRjY1tGpVIJVSEHsWFdT9p/ajUuQ1BXUvn7xz+LUr905b+2wbbxVw4F+/md74FlVqJ6UNOwAc0P6+T9ol6pFSeqQ6r6f3O/UGYAf0rrm/hrkKhUKR58jEwv0i88Y7KoVCoXjTKahJEtlFOSqFQqF4w0lMUC0qhUKhUBRiVNefQqFQKAo1hX1OnXJUCoVC8YajWlQKhUKhKNQoR6XIMXbFrApE79qIvF/sMiueRgT+6zqh4L5nKqjvt96tPyRrIcUbi+r6UygUCkWhJjGhcK+mpxyVQqFQvOGo76gUCoVCUahJLORr/SlHpVAoFG84UjkqhUKhUBRm1Kw/hUKhUBRq1Kw/hUKhUBRqEgr5rL/CbZ0iBfXa2TF732LmHlhCt0/fT3O9RrM6ePh+z+9XvWnStXma68VKFWdB0HIGTR+WY90LF8zgwvlDhJ7YQ0O7eunKzJgxnmt/B/Pg/uV0r/fq1Z1XL2/TuFGDHOtPzeTZC2jbvT/Og0bkOo1xM0fhc9QLL/9V1KpfI12Z2g1q4r1/NT5HvRg3c5QuvLSZCUu9fsTnyAaWev2Iialmr0x7h9Z4+a9iw15P1u1agV2z5LyG3A7QbUlvaGKZbTtfK69CYFjakg17PfnrwBre0+6mnJou73XAa59G5svJn+ZYjU1Fa1ZtX86Ww+uZu2w6RsZGtGz/LrtP+RAcfpBdJ7ewfs9KnAc46eIYFzFm7rLp+BzZwKrty7G2Tf5+0G3kIHyObGBz4J+0sG8GgKWNBb9uXMymgLX8dWANLsM+0Ml3cmrPXwfWEHI7gH4f9WZz4J/4HNmAq/ugNLbmVC/AtAUT2Xt2G977V6dIa+6y6azfs5L1HgZwEgAAIABJREFUe1ayPXgj/mHb8/ye6tqrC17+q/DyX4XntmXUqFNNVx7LN/0EcAEIA77MvJYyRkqR7aMgUI7qP4IwMGDwjI9Z6DqLbzqP4t33WmNTzTaFzL2IaH4fs4Qgn/Q/ou31tQuXjp3PsW5Hxw5Uq1aZ2nVa8+mn41myZE66ctt999CyVfd0r5UqVRL3zz/i2LH/Y++8w6o4tgD+Gy5goUq5FMGW2HvviIiCiuKzgiVCjMZEjUax94YlliSmWGI0icaSYo29txhb7CFREyNKR2kCCpd5f9zrlXJBUFDec398+33s7plzZmZn79mZObtzocD2DdGtU3uWL5nz3OlbtWtOuUou+Dbvw5yghUxaEGRQbtKCIOYELcC3eR/KVXKhpYf2ASBwxADOHD+Hbws/zhw/R+AI7Q/ib8fP08djIH6eAcwYFcy0xRP0uh6lPtIvSa9JjHwpZTUqaYVMT8PPM4DBPUbw4fThGJtkHUixKmPJyGnDeLf3KHq5D8DG3oYmrRoWyM4HU95j/cpNdGvpT0J8Iv/p24XxwaNZt2IjP3+3jQexcUwaNpOt3+98Wi5/HxLiE/Ft4cf6lZv0DrJilQp4+XrS030Aw/uOYcK8MRgZGaFJ17B05mf0cOvPwM5D6B3QnYpVKgBw68+/CRo0iQunLxEwrB8j+gXRo01/vLt56mWe1y7Ajs27GN53TI5yTxg6Hf/2gfi3D+SPK3+SGJ9Y6G0q7E4Y7/xnOH08BrJq6VqmLBoHgCZdw5IZywCqA82AYUCNAl04HVLmf3sVFHtHJYSwFUJc1G0RQoh7mfZNDcjbCCGe+egphDAWQsTlcf5NIUSKzs51IcTnwsAyqUIIlRCiyD+vUKnem0T9G0F0aCSatHTO7DhB/Q6Ns8jE3o3mbsi/BlcaLV+rEpZ2Vlw9fqnAtrt28WLd+h8B+O3MBaysrXB0VOeQ++3MBSIiogzqmDljHIsWf0lqamqB7RuiUb3aWFlaPHf6Nl6t2Ll5DwBXLlzDwtICO7VtFhk7tS1m5mZcPn8NgJ2b9+Du3RoAd6/W7Ni8G4Adm3fT1tsNgJTkFH36UqVLFsqqry9aVnTNtnTpUiTEJaBJ12Q5XbacM3duhRIXq70dzhw/h0dndwCsba356Ks5fLd7Fd/tXkXdxrUNmmjcqgEHdx4BYOfm3fj09ubu7bs8iI0jI0Oyd9sB3L1aZUnj7t2Knbo6PLjzCI1ba52ju1cr9m47QNrjNMJCw7l7+y616lcnJiqWkCva3nrywxT+uXEbtaMdAP/c+Jd/b4ViZl6ayPAo7t0JIz0tvVDsAlw4fYn4Bwl5VnPzNk3YvPZnoHDb1KVzV0mMTwTg8vlrODhp773M9QEkou1Zlc0zk7mQIUW+t1dBsXdUUspYKWU9KWU9YDmw9Mm+lPKxgSQ2wPOPB2XlT53dukA9oEvmk0IIYymlRkrZupDs5UoZBxvuh8Xo9++H36eMg20eKZ4ihMBvykA2B3/7bGEDODs7cjc0TL9/7244ZZ3z/5mnevVq4uLqxK5dB57LflGgdrInIuypU40Mj0LtZJ9DJio8s0y0XsbWvgwxUbGA9gfDxs5aL9e2oxs/H/+eT9ctYuaHwfrjpiVMUVmVRWXljDAtXSTlyk5GajxCZcrei1vZfPgbPpr6SQ7nGXr7HhXeLIeTiyMqlQp379Y4Omt/DMfOGsn6lZsZ0HEwY9+ZwrTF43PYsLaxIik+CY1G6wAjw6Oxc7Aj4p627jw6t6HXwP/Qra8PDs5PH3DsHZ9eA41GQ1LCQ6xtrFA72hOZ+dqERWPvmPXaOLk4UrV2Fa5eyDpCYFrClNjo+/r9qPBo1NnSvojd3GjQrC4aTQbXLoY8TV+IbeoJ3fr6cPKQwU+dVQDqA7/lK8PZKO5Df//TwRRCiHHAk4+YrZBSLgPmA1WFEBeBPUAwsBWwRlveSVLKnYb05YaUMk0I8SvwphDCE5gAxAA1hRD1gRgppbUuT5MAfyAD2CmlnCyEqAx8BtgBD4F3pJSGJ3JyL6yhfOUrqccAby4fvsD98NgCmXxq+vltCyFY9NEMBr3z4XPZLiryVSYD92R+yn149zEO7z5Gg2Z1eX/8YIb21s5DdGrYg18OLgUjY4ytnEhPfwwZ6c+V//wiTEojNY/xqtcN1wpl+WLTUn5vd4mHScl6mcT4ROZNWMz8FbOQGRlcOneVsuWdAWjq1ohKmYbOzMzNKG1WiuSHKZmMGKwoAI7tP8merQfo0NWDrn6dmfXJZN7tNVKXzPA1MHicp/VeqnQpFq2ey+Jpn2QpR25kv2bPazcvvLp5Eh0Rk+N4YbUpgEYtG9DN34e3fXPMIZoDPwGjgLy7fbmgUcLTiwYhRBOgH9AEUAFnhBBH0TqRN3U9IYQQJoCvlDJRCKEGTgIFclRCCDPAA3jyONkMqCGlvCOEMM4k1wXoCDSRUqYIIWx0p1aidU63hBAt0TqtDtlsDAGGADS3qU9Vi4pZ8vAgIhYbZzv9vo2TDXFR98kPbzSoQpXG1fEY4E2J0iUxNjEmNTmVHxesyzXNe0MHMmhQPwDOnbuIi6uz/lxZFyfCwvM3x2JhYU7NmtU4sF87dOjoaM/PP6+he/dAzl+4nC8dhYVRSUuMSmqH0KIjrup7DQAOTuocPzRRYdGonTLL2OtlYqMfYKe2JSYqFju1Lfdjco4iXzh9CZcKZbG2sSLufjzRkTr9GenItFSEcQnk48J3VJnLKTMyyEh+AGh7TmF3wqnwZnmuXfwjS5pj+09ybP9JALr370qGrnckjAQBXd7lUWrWwYvPNyzGxs6G65dCmB20AHMrc1QqFRqNBgcne2KiYnEsq9YPl6md7Pnt2FkGDuun1xEVHoWjs5qo8GhUKhXmlmbEP0ggMjwqS8/LwdmeGF29GxurWLR6Drt+3sehXcdylP3xo8fY2tvo99VO9k/r/QXs5kbvgO5079+FSlUrsn/7oSJrU5Wrv8G0xRMY3ndMliFIY2MVaJ3UeuDnPDObB8X9hd9iP/SXB62Bn6SUyVLKRLS9plYG5ASwQAhxGdgHuAoh7AzIGeJJz+w4sEVKuV93/Fcp5R0D8p7A11LKFAAp5X0hhDVax/aTTtfngHP2hFLKlVLKRlLKRtmdFMA/l26iruCEnYsalYkxTbq04vf95/JViJWjPiGo5VDGtnqPTcHfcurno3k6KYAvl39Do8YdaNS4A9u276V/v54ANG3SgIT4hFznorKTkJCIk3NtKldpRuUqzfjttwuvxEkBZKQm6IMZDu85hk9vbwBqN6hJUmKSftjlCTFRsSQ/TKZ2g5oA+PT25ujeEwAc3XeCLr07AtCld0eO7NVOU7pWeDpFUK12FUxMTIi7H4+FlQUmpibaE8IIYVISqTE0cl245ZSaNIRpKQBs7MpQ/o1y3LsTliNNGVvtMJOFlQW9Bv6HLbqgh9NHztInsIderkpNbcTZMP8x+LcPZHbQAgDOnfyddj7uAPj07sgvP+7FtaIrtepXx9jEGC9fT5IfpnD7xr96XUf3nsRHV4ftfNw5e+KC/riXrycmpiY4uzrhWtGVq79rHeu0JRP558a/rF+xyWDZHyYl4+jsgLOrk97u0b0ns8g8j93c2Lz2Zz6du5xLZ6+w6+d9RdKmHMs6sOjrYKYOn8Wdv0Oz6Ju+dCJo56aW5JnRZ1DYc1RCCG8hxJ9CiJtCiAl5yPUUQkghRKO89P3P9qgw2Ik2yFuAFdBASpkuhLgLlMxn2idzVNl5mEeesvfjBdqhQUN68k2GJoP1075izLdTMVIZcXzzIcJuhNLtQz9uX7nJxQPnqFjnDYavGI+ZlRn12jWi24d+TOkw6tnKn8Hu3Qfp6O1ByB8nSUlJ4Z13RuvPnTu7j0aNtZ3DefMm49fnP5QuXYp//j7H12u+Z/bsF7p/cmXs9Pmc/f0ycXEJtOvWn/cHDaBHF698pz9x4FdatWvO9tObSU1JZcaop3NJGw+sxc8zAIDg8YuY+clkSpQswclDpzlx8FcA1iz7jgUrZ9Otrw/h9yIZN3gKoP3h8+nVkfS0dB6lPmL8u9MAqFS5PJM/GoexdVlAoEmOA01akZc1I+UBKnN7Nh36BiEEn879krj78QBs2L8G//aBWhuzR1Gl5hsArFyyVv+D+NHUj5kQPJpNB9eiMlZx4fQlgscvymHn0zlfMm/5DIaNH0zI1RtsWb+De/+GsWz9YkqblSL+QQJtO7rxx5U/cevQkmP7TrJ1w05mL5vKtlMbiY9LYOLQGQD8/dc/7N9xiB+PrkOTrmH+pCVkZGRQr0kdfHp5c+P6TTbsXwPAZ/NWcPLQadp2dGPcnFGUsbUmNfURPxz5lpio+2zf+At///UPQ8cO4vqlkOeyCxD8xQwatqiHtY01u8//zPJFq9m24RcAOvi2Y8/WA0XWpoaMDsS6jCUT52ujCDUaDf28BunqoyNoR3su6kxNAnblq3FkojCD+YQQKrQP5O2Bu8BZIcR2KeX1bHIWwAfkY15NFEZU0stCCDEDSJJSLtIN/a0AWqAb+gP6AFHAKSnlG7o0YwAXKeWHQoj26HpVQASZ5pYM2HoT+DG7g9HNUQ2XUnbT7Rs/0SOE8EE7PNjhydCfrld1BpgnpdwihDACakspcw2/C6zQ45VcFGU9qqJHWY/q/5dX9Vv6e8TJFx63O+WU/9+cFuE/5WlPCNEcmCGl9NLtTwSQUs7LJvcxcAAIAoKklLkOEf3PDv1JKc8AG4CzwGngSynlFSllJHBOCHFFCDEf+A5oIYQ4B/QCbhRhnnaiDeA4pxvmexJB4AcMFUJcQvtink8uKhQUFBReOoUc9VcWyDxGeZdsYfO6IDTX/Aa2/U8N/UkpZ2TbXwgsNCDXJ9uhprmoNNib0um4iTYkPfvxA2ifAp7sp2fWI6WcC8zNluZvIP/jUgoKCgovEU2+Z1KyBn7pWCmlXJlZxEAyfY9NN6q0FAjIr83/KUeloKCgoFD4ZBRg1FLnlFbmIXIX7fTKE1yAzBE8FkAt4IjulQBHYLsQomtuw3+vvaMSQtQD1mY7nCylbPEKsqOgoKDw0skoQI8qH5wFKgshKgL30E599H1yUkoZj/adUgCEEEd4xhzVa++opJQXMTDEp6CgoPC6IAvRUemiq4cDe9EGun0tpbwmhJgFnJNSbi+oztfeUSkoKCi87mQUsj4p5S6yhclLKaflIuv+LH2Ko1JQUFB4zSnMHlVRoDiqYsjF1IhXYre2bc4vYhQ1r+r9HkPfdXsZvKry/nbl+T5I/KLUqeH30m2WNDJ56Tbh1bWpwqBovzj54iiOSkFBQeE1R+lRKSgoKCgUa4r5x9MVR6WgoKDwulPI4emFjuKoFBQUFF5zivsXXxVHpaCgoPCak17MA0EUR6WgoKDwmqP0qBQUFBQUijWF/cJvYaM4quKDAD4BOm069A3TR84l5MpfOYSq16n6dNG1g7+ycMrHAFhaW7BgxWycXR0JC41g3JCpJMYnAjBuzihatmtOakqqXm+jlg0ImvkBAKamJpR/sxwZmgyWBa/g8aNH9B3cG9eKLnjU7KxfaG/11s8pbVYa0K4We/XidcYETsp3AS2tLZi/fBbOro5EhkeTmpyKY1k1xiYmqJ3suXv7HgCHdh1l1dK1+nTDJgzB06ctGRkafvhmKxtX//hcNsNCIxj/7jQS4xNp2Lw+S9bOI+xOOKXNS2NhaUZifBJbvt/J2s+yrn5sYmrC7E+nUL1OVeIeJDDh3WmE39W+6xY4oj/d/H3QaDL4aOrH/HrkDA7OamZ9OgU7tQ0ZGZKf121nx6ZdzPlsGo5lHVAZq/juyw1s35RzfbsOXT0YNPItjFQqThw4xSdzvsx3WQGcXZ2Yt3wmVtYWhFz5iykjZpOelo4oYY7KzBYytG/MaFISkI8S89Q1JXgJx06ewaaMNVvXLS9QPgwxae4Y3DxbkJqSyqQRs7h+5c8cMjXqVGPep9MoUaoExw6cInjyYgCCpo+gbYfWpKWlEXr7HpM+mEViQhK169dg5mJtG3RwUqNJT+dBbFyR3z9OLg4sWh1MyVIlKVvOibj78cTdj8elvDMRYVEYqYzI0GRwbN9JlgUvp2Hz+gTN+gBjE2Pi7scxuPuIAtWdoXYGsPPMDwBXAA3a16HyXCk3N4p71N//7HpUrwohhKMQYqMQ4pYQ4roQYpcQwk0I8aPufD0hRKfnUN0RqAxUnhO0kEkLggwKTVoQxJygBfg270O5Si609GgGQOCIAZw5fg7fFn6cOX6OwBH9AWjVrjnlKrng27wPmfWeO3kBP88A/DwDGBUwnuSkZNav1C7vffHsFYb2HkVYaHgW24O6DcO/fSD+7QO5fP4qh3YdK1ABA4f358yJ83Rr6Y+RkRHmlub0aRfA159+R9qjx3rdmZ1U1z6dcHBW0711X3q49Wfv1gO56n+WzTMnzhM4vL/+3MXfLtHPaxAAAzoOoUeb/nh386RilQpZdHTz9yEhPhHfFn6sX7mJkVPeA6BilQp4+XrS030Aw/uOYcK8MRgZGaFJ17B05mf0cOvPwM5D6B3QnXfHDuLvv27j5xnA4B4j+HD6cIxNsj4nWpWxZOS0YbzbexS93AdgY29Dk1YNC1TeD6a8x/qVm+jW0p+E+ES6+T9d+izjUdLTJeqf4aQAunVqz/IlcwpkPzfc2rWgfCVXvJv2YPqYeUxbON6g3PSF45keNA/vpj0oX8mV1h7NATh19Axd3fzp5t6P27fuMGRkAAA3Qm7Rq/1APp77BX9dv4GRkRHB4xcV+f0THRlLQJeh9HDrR9sandFoNIx8axypqY9YtXQNPVr3w799IPWa1KadjzsT54/mw4AJ9HIfwLjBUwtUd7m1s0y0Rfu90udyUqCN+svv9ipQHFUBENpXz7cAR6SUb0gpa6Bd+llKKXvqxOoBz+OofIFvAXnlwjUsLC2wU9tmEbBT22Jmbsbl89cA2Ll5D+7erQFw92rNjs27AdixeTdtvd0AaOPVip2b9wCQm95GLRpwbP8pUlMeAfDn1Rv6HoMhSpuVonHLhhzZrXVUJUuVZPqSiXy3exXf7/uaNl6tDKZr49Wanbo8pqel41jWAYCIe5GUKFUCG7syOdL0HNiNVUvW6FdPfRAb99w2d27era+vJ9SqX527t+9y704Y6Wnp7N12APdsuty9W+l1HNx5hMattc7D3asVe7cdIO1xGmGh4dy9fZda9asTExWrf5pPfpjCPzduY2ZemtLm2t5o6dKlSIhLQJOuyWKnbDln7twKJU5XxjPHz+HR2R0Aa1trPvpqDt/tXsV3u1dRt3Ftg+Vt3KoBB3ce0Ze3bcfWBuXyQ6N6tbGytHju9Jnx6OjGts3aHuSl81extLLAPls7tFfbYm5hxsVzVwDYtnkX7Tq1AeDUkd/QaDT69A7OagBSUx6h0Wjw6OjGwT3HkEiuXrhe5PdPelo6aY/TADAtYYIQgvpN6xD6z132bTsEaNv4H1f+om1HNw7tOkbEvUjgaRsG6NSjA9/uWsmG/WuYvHBsdgeky5vhdlaYyAJsrwLFURWMtkCalFI/DqL7+nqoEOKqEMIUmAX0EUJcFEL0EULcEELYg3bBMCHETSGEnQHdWVbFjAyPQu1kn0VA7WRPVHgUT2Wi9TK29mWIiYoFICYqFhs7a32aiLDMaXLq9ermyZ6t+/NfCZ3acObEOR4mJQPwzqi3OHvyPAM6DmZIzw8YNXUYJUuVzJEucx6vXbyud0wV3ixPyVIlWb31C5atX0SlKk8/5eRSviwdfNuxbs9XLFu/CNeKLs9tU1svT51h7Ya1CP5yBpWqVNTbjAqPRu2YtX7sHZ/WoUajISnhIdY2Vqgd7YnMXLdh0dhnS+vk4kjV2lX4YsEqKlYuz96LW9l8+Bs+mvpJjqXLQ2/fo8Kb5XBycUSlUuHu3RpH3Q/y2FkjWb9yMwM6DmbsO1OYtjhnj8Taxoqk+CT9D3pkeNb8GJUww9i6LCoLNRipcqQvShwc1USERer3I8KiUDups8iondREZm7fYVE4OGaVAeju34XjB0/p9+s0qIlPdy9GTxnGzLEL0Gg0L+X+cXBWs+nQN+w+v4W1n6+nmVuTLD1+c0tz3Nq3JO1xGpZWFqz8aRnr966mcy9vACpWLk+Hru14u+t7+LcPRKPJoGOPDjnKm1c707WhfcB5si5mWCDSRf63V4EyR1UwaqFtEAaRUj4WQkwDGkkphwMIIaoB/YCPAU/gkpQyxkDyHE0g+w+ZoV53DpnsSg2EnWZOY6e2pXL1Svx6+Ddq1s3fU5p3N0+2fL9Dv9+sTRPcOrRiwFB/AExLmuLk4sA/N/7NVceaZevo/64/G/av4fbNfwm58hczPpyH2tGOJWuC6dZSp6uECY9SH9Pf+x08OrkxY+lEBnUb9lw2MxNy5U86N+5JS49mdOvrk8Vm9jrNrQ4NHs/0zFmqdCkWrZ7L4mmfULdRbf66doN3e36Aa4WyfLFpKb+3u6R39gCJ8YnMm7CY+StmITMyuHTuKmXLOwPQ1K0RlTINSZqZm1HarBTJD1MyZ9RgPgHk42TSHyUBYFTSApW5Gk1CeA75osJQ9HPOen62zLujAtFoNOz4cY/+2OUL1zj36+/s/Hkvgz8YyJnDZw2mLez7JzIsij4eA7F3sGPpN/NwKe/CsmDtM6xKpWLelzPYuPoHXCu4UKNuNd7tNZKSpUqwdsdyrpy/RpNWDalepyrf7f4KgBIlS/Ag5kH+8qBrZ4Fd32PfxW0NADWwHwgBCjYmjxL1pwBfA9vQOqq3gTWZzg0DBgOcPXs28auvvvp25cqV910sKuHgpCY6Iqs/iwqLzvIU6uBkr5eJjX6AndqWmKhY7NS23I/RDi9EhkXpn8q1abR6ewd2p3u/rljbWnPq8BnSsw1D5YZVGUtq1qvOmLefBlEIIRj7zmT+vRWaRXbG0olUrVWF6MgYPug/lseP0/jh8Lekp2uYPiqYsNBw/NsHAtpJ4bA7Ydy4fpOJ88dgbWNF3P14IsOjOfjLEQAO7TrG9KWTCmQzZ71ofwieOIio8CiEAGMTY20vycme6Mhs9R6urcOo8GhUKhXmlmbEP0ggMjxKPwQF4OBsT4zuehgbq/hu9yrMLc0Y/GEgCfGJLP9I+4MUevseYXfCqfBmea5d/COLrWP7T3Js/0kAuvfvSoaudySMBAFd3uVR6uMs8p9vWIyNnQ3XL4UwO2gB5lbmqFQqNBoNDk72xDwpi3wa15WRmohx6azDYkWBUUlLfj6kDUy5+vt1HJ0d9OccndVER0RnkY8Mi8Ihc/t2VhMV+VTGt09n3Du0IrDH+wD0fbsnPft30+vPyJCkJKfwZrWiv38yEx0ZQ1JiMpFhUfr2NeWjcdz5O5TvV/1AwPD+xN2PJzUlldSUVC6cvkSVGm+CEOz4YTefBa/Ioq9tRzeGjNbeF7OC5ufZzmIiY/XFQzst0YTncFRKMMX/F9eAAs1uSylDgUghhAfQFNid6fTnaOe06jVu3Hj+ihUr/pRSNm7bqh1JiUn6oYgnxETFkvwwmdoNagLg09ubo3tPAHB03wm69O4IQJfeHTmy97j+uE9v7VBD7QY19Xo3r/kZP88AIsOi2LphZ77L49mlLccPnOLxo6c/mL8e+Q2/t3vq96vWqgzAjA/n4d8+kA/6jwXglx/28MtP+/BvH4i7d2uO636Q+w/148Jpbe+iZr3qCCMjfaThkd3H9QEFDZvX587foQWyeWzfCXx09eLTuyNHdfVia28DwLWLIbxRtRLGJsYkJT7Ey9eTo3tPZinz0b0n9Tra+bhz9sQF/XEvX09MTE1wdnXCtaIrV3/XOp5pSyZy+ugZvOv/B//2gdy++S9NWmnnum3sylD+jXLcuxNGdsrYaoecLKws6DXwP2z5XnttTh85S5/AHnq5KjXfBGCY/xj82wcyO2gBAOdO/k47H3d9eY/s0bYPxNOhPmFaGqnJ6vCKgozUBLp79Ke7R38O7j6Kb2/t1G3dhrVITEgiOlv7jo6K5WFSMnUb1gLAt3cnDunmQVu1bcY7wwfw/oAx+rnU77/+kREB4+jVfiAHdx+lz1v/oeKb5bEqY1nk94/ayZ4SJU0B7bWqVa86B3YeBuD98YMxtzRj0bRPtTr2Hqd+0zqoVCpKlipBrQY1+OfGbc6cOI9nZ3f9Nbe0tsDJxYHDu4/pA4v+uPRnru2sZKmSlDYr9aR4ZkAH4OpzXasCbK8C8ayur8JTdMEUp4GvpJSrdMcaA6WBz6WUtYQQPYCuUsqBmdL1AJYB30kpDYc7aQcmPgO8b/xxq9KMUcFcvxQCwMYDa/HzDACgRt1qT8NrD51mwaQlgLans2DlbJzKOhB+L5Jxg6eQEKeN7JowbzQt2jYjNSWVzHqdXB1Zu305/bwGsW7PV5hZmCEztE0xJTkVG/syaDQZHPrlCJOHzQJg5U/LWPvZOk4d/k2f8RIlTQmaNZI6jWohhCA8NJyRb+UsplUZSxasmIVjWQeSEpKwLGNJWlo6jx89xtTUlLTHaTxKfUSGlIx9ZzIxkbGYW5oz93NtWHfKwxTmjl/Ejes3n8tmxL1Ixg2ZSkJcIn0Cu9Nz4H/QpGswNTWhZOmSpKWls33jL6z+5FuGjh3E9UshHNt3EtMSpsxeNpVqtSoTH5fAxKEz9E5m0Mi36OrXGU26hkXTP+XUodPUa1KHr7d9wY3rN8nI0N5f3365AZ/e3tipbRFCsPazdez6aR8AG/av0fcsg7+YQZWab2jresla9m07CGjnnyYEj6Zi5fKojFVcOH2J4PGLcpS3bDln5i2fgZW1JSFXbzBl+CzSHqdx9tYOjEzNAInMyEDzMAY0abk0RS1jp8/n7O+XiYtLwNbGmvcHDaBHF68802Qn8zIfU+ePpZVHc1KTU5k0cjbXLmmd+s+H1tHdQxtlV7NudX14+vGDp5hhIsq/AAAgAElEQVQzUVvGPb/9hKmpKXEPtA8wl85fZebY+XTt1ZHBIwaSlp6Og6M9mowM4mLjKOr7p6lbY0bPGA5SIoyMKFvOGe/63ShtXpo9F7YQExVLepqG+AfxbFrzE5ZWFnT160RGhmTr9zv4ftUPgPZ1hMARAzAyEqSna5g/cQlXLlzLUY+G2lnZcs4s/jqYKjXfvIx2dOx7YG6BLpCOFS798+0I3r277qX3vxRHVUCEEM5oh/EaAqnAbWAUsEXnqGzQLsFsAsyTUm4SQpgAsUATKWXIs2zUd2z5Si7K//J6Ogp5o6xHVfS8qvvnQviJFza83DX/jmpo6Mt3VMocVQGRUoYBvQ2cqqU7fx9onO1cXbRBFM90UgoKCgovG2XhxNccIcQE4D20kX8KCgoKxY7iPq6mOKoiRko5H5j/qvOhoKCgkBvFPepPcVQKCgoKrznKR2kVFBQUFIo1iqNSUFBQUCjWaJShPwUFBQWF4ozSo1JQUFBQKNYoUX8KCgqvnFfx4i3A5esbX7rNprXfeuk2/9fJKOauSnFUCgoKCq85ytCfgoKCgkKxpnj3p5SvpysoKCi89hT2wolCCG8hxJ+6hWInGDg/WghxXQhxWQhxUAhRPi99iqNSUFBQeM3JQOZ7exZCCBXaJYw6AjUAfyFEjWxiv6NdYLYO8COwMC+diqNSUFBQeM2RBdjyQRPgppTybynlY2Aj4JvFnpSHpZRPlrc+DbjkpVBxVAoKCgqvOYW8cGJZIPPS23d1x3JjEFkXlM2BEkzxP8C4OaNo2a45qSmpTB85l5Arf+WQqV6n6tMF4Q7+ysIpHwPaVUMXrJiNs6sjYaERjBsylcT4RH26GvWq8e0vK1nz6Xd4dvFApTJiy/c7WfvZuiz6TUxNmP3pFKrXqUrcgwQmvDuN8LsRAASO6E83fx80mgw+mvoxvx45g4OzmlmfTsFObUNGhuTnddvZ8JV2sbj5y2dS/o1yANg52GJhaU5kWFSR2x019X1ad2hJ+uM0Hj5MwcauDAIKxa5pCVO+2vIZpqamqIxVHNx5mOWLvgagSauGjJz2PkbCCJWJilIlS5KRkVHk5fX0acu7QW9TsXJ5ensF6hcqBJg0dwxuni1ITUll0ohZXL/yZ442VaNONf0ihscOnCJ48mIAgqaPoG2H1qSlpRF6+x6TPphFYkIStevXYObiSYB2babPP1qVQ2d+mBK8hGMnz2BTxpqt65YXOH2Ltk0JmjWy0NoywPQlE2ndvgX3Yx7Qu+3T8PfKNd5k8oIgSpmVIjw0gsnDZpKWlp5rW3iR/M/4eBINm9cjKeGhNk+j5vLXtZsFrh9DFCQ8XQgxBBiS6dBKKeXKzCIGkhk0IIToDzQC2uRlU+lR5YIQYrIQ4ppusu+iEKKpEGKUEKJ0PtLmSy4/tGrXnHKVXPBt3oc5QQuZtCDIoNykBUHMCVqAb/M+lKvkQkuPZgAEjhjAmePn8G3hx5nj5wgc0V+fxsjIiJFT3ufXI2f4T78ujOgXRI82/fHu5knFKhWy6O/m70NCfCK+LfxYv3ITI6e8B0DFKhXw8vWkp/sAhvcdw4R5YzAyMkKTrmHpzM/o4dafgZ2H0Dugu17nhKHT8W8fSD+vQZiamrJ57ZaXYvf0sbP0dn8L//ZvU6lKBc4cP19odh8/esy7PUfi5xmAv2cAzds20y95PnF+EFOGzaKf1yBs7Wz448qfL6W8t/78m6BBk7hw+lIWG27tWlC+kiveTXswfcw8pi00vOj09IXjmR40D++mPShfyZXWHs0BOHX0DF3d/Onm3o/bt+4wZGQAADdCbtGr/UC6e/RnSJ8PmPFRjjn0fNGtU3uWL5nzXGkBxgePLtS2DLBj8y6G9x2Tw9a0xeP5NHg5fTwGcnj3Md56v2+ebeFZGBkZ5Zn/j2d9oV+mvrCcFICmAJuUcqWUslGmbWU2dXcB10z7LkBYdptCCE9gMtoV0R/llT/FURlACNEc8AEa6Cb7PNF2ZUehXXb+WeRX7pm08WrFzs17ALhy4RoWlhbYqW2zyNipbTEzN+Pyee0S1js378HduzUA7l6t2bFZ26vesXk3bb3d9On8BvXk4C9HkFISE3Wfe3fCSE9LZ++2A7h7tcpiw927FTt1eg7uPELj1g11+luxd9sB0h6nERYazt3bd6lVvzoxUbH6nl/ywxT+uXEbtaNdFp216lfHtIQJP3yz5aXYPX30LBqNhlr1qxMeGk5p81KFZhcgJTkFAGMTY4xNVDxZPVtKiZm5GbXqVychPpE7f4e+lPL+c+Nf/r0VSnY8OrqxbfMuQLuku6WVBfbZ2pS92hZzCzMunrsCwLbNu2jXSfvQe+rIb2g0Gn16B2c1AKkpj/THTUuWQD5n0HOjerWxsrR4rrTCuAR3b98t1LYMcOH0JeIfJOSwV/6Nclz49SKgfRBq11lbR4baQvU6VVn18zLW713N5xsW57iPQXtPPCv/RUFhBlMAZ4HKQoiKQghTwA/YnllACFEfWIHWSUU9S6HiqAzjBMQ88fJSyhigJ+AMHBZCHAYQQnwphDin63nN1B37wIBcByHEr0KIC0KIH4QQ5vnNiNrJnoiwp9cxMjwKtZN9Dpmo8Mwy0XoZW/syxETFAhATFYuNnTUA9o52eHRy48dvtlKyVEniHsTr00eFR6N2zGrD3vFpPjQaDUkJD7G2sULtaE9k5vyFRWOfLa2TiyNVa1fh6oXrWY43dWtM8sMUQv+5+1Lt2jvaY2ltxalDpwvVrpGRERv2r+HAlR38dvQcV3/X2p0dNJ9P133Ex98upIytNWuWrXup5c2Og6OaiLBI/X5EWBRqJ3UWGbWTmsjMbSosCgfHrDIA3f27cPzgKf1+nQY12XFsI9uOfs/MsQvyzEeRYGRMxL2n+S7sOs7OrZC/aaNzJJ5d2uLg7KDNRra2EHLlT8bNGcXYwVPp5zWIbRt+YdiEITn02Tva55n/YROGsOngWsbMHIGJqUl+a+WZFGYwhZQyHRgO7AX+ADZLKa8JIWYJIbrqxD4CzIEfdCNW23NRByiOKjf2Aa5CiL+EEF8IIdpIKT9F231tK6Vsq5ObLKVsBNQB2ggh6mSXE0LYAVMATyllA+AcMDq/GREi53Dvkyf1p0I50+WQycbY2SP5ZPaXZGQYnh7Nnj63fBg8nqk5lypdikWr57J42ic8TErOIlevSW3u/nvvpdtt17kNUmaw66d9hWo3IyMD//aBeDfoTs361XmjakUA+g3pwwf9xxI8fhF3boUyesaIl1re7BhQZSAfz5Z5d1QgGo2GHT/u0R+7fOEaXdz86N0hgMEfDMTwdMXLpbDq2BAzR8+jd2B31u9djZlZadIepwE520L5N8rxRrVKfLlxKRv2r2HQqIGonXM6wbzu98+CV9C9dV/6dxyMpbUlAcMKb9HwQg6mQEq5S0pZRUr5hpRyru7YNCnldt3/nlJKByllPd3WNS99SjCFAaSUSUKIhkBroC2wydBLa0Bv3cSiMdpeWA3gcjaZZrrjJ3WN0BT4NbuizBOUk0dPY0Cgdi7p2sU/cHR++iTr4KQmOiImS9qosOgsT8QOTvZ6mdjoB9ipbYmJisVObcv9mDgAatStxvwVMwGwsSuDSqXC3bs1R/YcR+1kT3RkNhvhUTg6q4kKj0alUmFuaUb8gwQiw6P0Qz8ADs72xOhsGxurWLR6Drt+3sehXcey6FOpVNSoW52bIbf0x16GXZ9e3rxZrZK+F1eYdp+QlJDE+VO/06JtM2JjHlC5xptc/f06RkaCR48eU7dxrZdW3id4d23H7KWTAbj6+3UcdU/+AI7OaqIjorPIR4ZF4ZC5TTmriYp8KuPbpzPuHVoR2ON9g/b+vnGblOQUhLEVMv2xQZkiISMdx7JP813Y1zY7t2/eYZif9rmzXCVXWnk2z3L+SVto28mNv//8h4AuQ7Ocd3BW8/E32p7nj99t5ca1m7nm/8nISNrjNLZv3MVb7xXe9xufd5j2ZaH0qHJBSqmRUh6RUk5H243tkfm8EKIiEAS0081j/QKUNKBKAPszPTnUkFIOMmBPP0H5y/oD+HkG4OcZwOE9x/Dp7Q1A7QY1SUpM0jfYJ8RExZL8MFk/YevT25uje08AcHTfCbr07ghAl94dObL3uFamSS86N+5J58Y92b/jMAnxifx17SbGJsZ4+XpydO/JLDaO7j2Jj05POx93zp64oD/u5euJiakJzq5OuFZ05erv2uiyaUsm8s+Nf1m/YlOOSmnq1oibIbdwcHbA2dXppdht0bYpAcP7MdTvQ8qWL1uodq1trTG31I7olihpSlO3Rty++S+JcYmYW5pRrpIr1y6GUKlKecLvRr60en7Cnu0H6e7Rn+4e/Tm4+yi+vTsBULdhLRITkojO1qaio2J5mJRM3YZap+rbuxOHdmudYKu2zXhn+ADeHzCG1JSnc+BlyzmjUqkAcHZxpOKb5ZGa9FzzVBTI9Ee4VnQt9DaVG2VstUPpQgjeGTWQn77dZrAt/HXtJmVsranTUHuPGhurqFSlIpFhUfrgiJ++3ca1iyG55j/znFbbjq25GfJPIdSYlsLuURU24llDRK8jQoiqQIaU8oZufw5gjTaEsquU8h8hRF3gW6A+YI+2JzVeSrlWCHElk5w9cB7wkFLe1EUDukgpc8aY66jv2DLLRZkwbzQt2jYjNSWVGaOCuX4pBICNB9bi5xkAaHtI+vD0Q6dZMGkJAFZlLFmwcjZOZR0IvxfJuMFTSIhLzGJv5ieTiY6IxtPHAyOVEds3/sLqT75l6NhBXL8UwrF9JzEtYcrsZVOpVqsy8XEJTBw6g3t3tIE8g0a+RVe/zmjSNSya/imnDp2mXpM6fL3tC25cv0lGhm7oYt4KTurmhWZ8PIkrF64RcTeSoFkjX4rdbac2YmJqQvyDBMzMS2NlY0Xc/fhCsVu5+hvM/GQyKpURwsiI/dsPsWrpWgDadnRj6NhByAzt8FJp81JkZMgiL2/bjm6MmzOKMrbWJCQkEnL1BoP7fADA1PljaeXRnNTkVCaNnK0PXf/50Dq6e2h78zXrVteHpx8/eIo5ExcBsOe3nzA1NdXPa146f5WZY+fTtVdHBo8YSFp6OjIjgy8Wr2bp4vdya+a5Mnb6fM7+fpm4uARsbax5f9AAenTxynf60SO/KNQ2BRD8xQwatqiHtY0196Pvs3zRarZt+AX/d3rRO6A7AId2HWVZ8PJc20KVmm8ybs4ozC3MURmr+H7VZras35Ej/y09muXIP8CKHz7B2tYaIQR/XbvB3HGLSElO4UL4iRceX32vQu98O4Ivb29+6eO5iqMygG7Ybxla55QO3EQ7LOcPDAPCdfNPa4GmwN/AI2C7zlGNyCbnASwASuhMTHkyVmuI7I7qZWFofFzh/4MUzUscfsuEssxH0VMYjurdCr3y/Zuz4vYPL/2HQpmjMoCU8jzQwsCpZbrtiVxALumzyx0CGhduLhUUFBQKB2WZDwUFBQWFYk1xD6ZQHJWCgoLCa47So1JQUFBQKNYoPSoFBQUFhWJNejEPqlMclYKCgsJrTvF2U4qjUlBQUHjtKcgyH68CxVEpKCgovOYoc1QK/zO8ipe/lZeMXw4ljQrvS9sF4VW8fPvblW9fuk2A+jX7vhK7hYES9aegoKCgUKzRFHNXpTgqBQUFhdec4u2mFEeloKCg8NpT3L/5qjgqBQUFhdccJepPQUFBQaFYowz9KSgoKCgUa5TwdIXnYtycUbRs15zUlFSmj5xLyJWc6yxWr1P16WKJB39l4ZSPAbC0tmDBitk4uzoSFhrBuCFTSYxPpGP3DgQM7wdAysMUgscv4q/rNwHoN6QP73w4kNJmpXmUksqwvmO4cv5akdl0cFYze9lUXCuUxca+DA8Tk/lu+UbWfrYuiz0TUxNmfzqF6nWqEvcggQnvTiP8bgQAgSP6083fB40mg4+mfsyvR84AMH3JRFq3b8H9mAf0bvs0PHr+8pmUf6McAHYOtlhYmhMZFsWW73f+X9rdcnIDTmUdkFKSEJfA/Zg4/UKbz3s9wXDbdHJxYNHqYFQqFcYmxmxc/SNhoeGMm/shakc7UpJTeRAbx7F9J1kWvLzQy1q5xptMXhBEKbNSqCwd0CRGQQHnXaYEL+HYyTPYlLFm67rlBUpriIlzR9O6XXNSUx4x+YPZ/HHlzxwyNepUZc6nUylZsgTHD/7KvMnaBU87dPHg/aB3qFSlAv7eb3NNt1iqs6sT249v4PatOwBcPn/1hfMJoJHFu0+lLEX/AgghkrLtBwghPntRva3aNadcJRd8m/dhTtBCJi0IMig3aUEQc4IW4Nu8D+UqudDSoxkAgSMGcOb4OXxb+HHm+DkCR2hXbA27E8Y7/xlOH4+BrFq6limLxgFg72hHwPB+/HEphKbl3Ll+OYTgz6cXqU1Nuoalsz4jLS2d/l6DSIhLpGufjlSsUiGLvW7+PiTEJ+Lbwo/1Kzcxcop2xdiKVSrg5etJT/cBDO87hgnzxmBkpG3OOzbvYnjfMTnyPmHodPzbB9LPaxCmpqZsXruFHm36493N8//ObkuPZty9fY9mFTwY0uMDhBAc2nX0ha9nbm0zOjKWgC5D8fMMYEDHwQSO6M/khWMZEziR0YETiYmMZdL7M6jXpDYtdDYKs46nLR7Pp8HL6eMxkIxHyRiVss4h8yy6dWrP8iVzCpzOEK3bNadcRVc6NevFjKB5TF04zqDc1IXjmBk0n07NelGuoiutPJoDcDPkb0a9PYHzv17MkSb033v0bPcWPdu9xaxxCwslv8V9KXrFUb0EhBAF6rm28WrFzs17ALhy4RoWlhbYqW2zyNipbTEzN+Oyrtezc/Me3L1bA+Du1Zodm3cDsGPzbtp6uwFw6dxV/VPx5fPXcHBS6/WVNivF/h2HUalUPEp9jImpSZHajImKxcTYmLu373Iz5B/+uXGbi2ev4O7VKotNd+9W7NTpPbjzCI1bN9TZa8XebQdIe5xGWGg4d2/fpVb96gBcOH2J+AcJudZvrfrVMS1hwg/fbCE9LZ292w7839l1927Nzh+etiFrG2t+O3Y2i8zzXM/c2mZ6Wjppj9MAMC1hgmkJU8JCI/j7r9v8duwce7cdoFW75vxx5S8cnOwLvY7Lv1GOC7ofdZmWjJGpWa7XITca1auNlaVFgdMZoq23G9t/2AVo272FpXmu99Olc9pe0fYfduHRUVvPf9+4re81vQxkAf5eBYqjKiKEEGuFEEuEEIeBBUKINkKIi7rtdyFErneE2smeiLAo/X5keBRq3c2dWSYqPLNMtF7G1r4MMVGxgNYh2NjlfLrs1teHk4dOAxAdEUN4aATj5oxi/+VtJCU85PatO0VqU1/Oe1E4uThStXYVrp6/htoxq017x6d1odFoSEp4iLWNFWpHeyIz11FYNPbZ0uZGU7fGJD9MIfSfuwBEhUf/39lVO9rp5Rs0q8uj1EekpaVnlXmO65lX23RwVrPp0DfsPr+FY/tP6sv5pKxlyznj1r4lZ46fL9SyAtwK+Zs2OqdvZGoORq92VsNB17afEBkepXfQmWUiw6OfyoTllDFE2XLO/HDgG9Zs+YIGTesWSn4zkPneXgXKHNWLUUoIkblvbgNsz7RfBfCUUmqEEDuAYVLKk0IIcyA1N6WGPiuU4z0HA18eyu+7EI1aNqCbvw9v+2qHWiysLLBV2zJuyFR+PfwbC1fNwU5tU6Q2tfoExsbGLFo9l8XTPqFU6VI59OVWFwaP5/MmqtekNnf/vZdD5/+V3UzyXt08iY2+XyjXM6+2GRkWRR+Pgdg72PHV1s+4euH603RGRjRr04TvV23i3p2wPHU9Tx3PHD2PsXNGMWR0oK7srzY4QBio3Pxd67z1RkfG0L6BL/EPEqhRpyqfrl0IYAnk3pXPB8X9PSqlR/VipEgp6z3ZgGnZzv8gpdTo/j8JLBFCfABYSymzPN6uX7/++5CQkOSQkJDk0LuhODo/HZZzcFITHRGTRXFUWDRqp8wy9nqZ2OgH+mEGO7Ut92Pi9HKVq7/BtMUT+DBgAl7dPNl4YC0bD6wh7n4cZmalSU/XcGjXUewd7YvEZuZhm9jIWFq3b86un/dxaNcx1E72REdmsxkepa8LlUqFuaUZ8Q8StE+omevI2Z6YbPk1hEqlokbd6jx+9Fh/7P/FrpW1JRv2r2HD/jVER8bg4KxGpVLh0akNwsioUK5nZFjUM9tmdGQMt2/e4Y2qlfTH/N7uQXRENN+v+qFQypqd2zfvMMxvNP28BpHx6CFSk5anfFHgF9iDHw9+y48HvyUqMgbHslnrKSpbGSKy9aAcnNVERUSTF2mP0/T30PXLfxJ6+x5oH4hfiOLeo1IcVdHy8Mk/Usr5wDtAKeC0EKJaZsF+/fr1rVatWulq1aqV/vXAWXx6ewNQu0FNkhKT9MMwT4iJiiX5YTK1G9QEwKe3N0f3ngDg6L4TdOndEYAuvTtyZO9xABzLOrDo62CmDp/Fnb9D2bzmZ/w8Axg/ZBolSpagq18nADr4epKU+LBIbGamx1u+GBsbc3jXMYxNjPHy9eTo3pNZZI7uPYmPTm87H3fOnrigP+7l64mJqQnOrk64VnTl6u9/PONyQFO3RtwMuYWDswPOrk7/V3bj4xLwbx+If/tAjuw+jk8vb5q6NSI6Ipr4B/GFcj2P7jthsG2qnewpUdIU0PbQy1dyxdrGCmdXJ4ZPfBfXii7MGZt14r8w67iMrXZoUgiBqrQ1GamJecoXBRvX/KQPcji0+yhde2nvpzoN87iHk5Kp01Bb/117deLwnmN52ihja60PLHEp70y5Si4Af79o3jUyI9/bq0AU9y5fcUYIkSSlNM+0HwA0klIOF0KsBXZKKX/UnXtDSnlL9/9WYK2UcqshvfUdW8oJ80bTom0zUlNSmTEqmOu68NSNB9bqQ4xr1K32NLT40GkWTNKGtlqVsWTBytk4lXUg/F4k4wZPISEukWmLJ9CucxvC70YC2nmBfl6DABg6dhD+g3pRqnRJUlJSGdEviMu6Sd6isFmvSR3WbP+Se3fCsHewAwF7tx5k+si5DB07iOuXQji27ySmJUyZvWwq1WpVJj4ugYlDZ+iHjgaNfIuufp3RpGtYNP1TTunmv4K/mEHDFvWwtrHmfvR9li9azbYNvwAw4+NJXLlwjYi7kQTNGomRyojtG39h9Sff/t/ZnRA8ms69vHiYmMyHARMKpQ0BGGqbTd0aM3rGcO3YlRBs+vonosKjGR88GpfyztyPeUB0RAy2ahsO7DzMwskfF2pZ/d/pRe+A7gC4OFmQkXzf8E2bB2Onz+fs75eJi0vA1saa9wcNoEcXr3ynz/719Mnzgmjl0YyUlFSmjpyjDzH/8eC39GynDa2vWbdalvD04EmLAWjXsQ0Tg8dgY2tNYkISIVf/4l2/UXh2bsvwcYPRaDRoNBl8/tEqPv9u0QsvQdC6bLt8O4Lj9w6+9CUPFEf1AhTQUS0D2gIa4DoQIKV8ZEhvfceWr81FUZb5eDm8qvv8VVzf122Zj6uRp1+4kluW9ch3Azl579BLv6hKMMULkNlJ6fbXAmt1/wdkOzfiZeVLQUFBoSAU92/9KXNUCgoKCq85Usp8b/lBCOEthPhTCHFTCDHBwPkSQohNuvO/CSEq5KVPcVQKCgoKrzkaMvK9PQshhAr4HOgI1AD8hRA1sokNAh5IKd8ElgIL8tKpOCoFBQWF15xC7lE1AW5KKf+WUj4GNgK+2WR8gW90//8ItBN5TGgqjkpBQUHhNacg71EJIYYIIc5l2oZkU1cWyPwuyl3dMYMyundK4wFbckEJplBQUFB4zSlIVKiUciWwMg8RQz2j7AbyI6NHcVQKCgoKrzmFHPV3F3DNtO8ChOUic1f30W4rINeX35ShPwUFBYXXnEL+evpZoLIQoqIQwhTwI+s3UNHtD9T93xM4JPPo1ik9KgU9r+LlTOWF85fD6/Ri9at68fb3a9+/EruFQWF+GklKmS6EGA7sBVTA11LKa0KIWcA5KeV2YDXwnRDiJtqelF9eOhVHpaCgoPCak1HID4xSyl3ArmzHpmX6PxXolV99iqNSUFBQeM15VQsi5hfFUSkoKCi85hR2j6qwURyVgoKCwmuO0qNSUFBQUCjWKD0qBQUFBYViTYZ+IfLiieKoFBQUFF5zivsyH4qjKqaMmzOKlu2ak5qSyvSRcwm58lcOmep1qj5dnfXgryyc8jEAltYWLFgxG2dXR8JCIxg3ZCqJ8Ym4e7XivfGDkRkSjUbDR1M/4eKZyzi5OPDl5k9wKuuAEILjB08R9PbkLLZMTE2Y/ekUqtepStyDBCa8O43wuxEABI7oTzd/HzSaDD6a+jG/HjkDwPQlE2ndvgX3Yx7Qu+1bel2jpr5P6w4tSX+cRujte9yPeUCT1o0Ktawdu3cgYHg/AFIephA8fhF/Xb+Jg7Oa2cumYmtvg419GWSGJDoypsjtAkxfOhG39i0xMjIiIT6xyMv76NFjFn8djFNZB1QmxpgYG3Nk73GCBmW9tg2b1ydo1gcYmxgTdz+Owd0LtnRabtd/55kfeJiUjMyQuFYsS+g/91AZqzi48zDLF32db/0t2jYlaNZIVCojtny/k7WfrQO0qyY3bF6PpISH2vodNZe/rt3MkX7i3NG0btec1JRHTP5gNn9c+TOHTI06VbOstDtvsnal4w5dPHg/6B0qVamAv/fb+lV6nV2d2H58A7dv3QHg8vmrBaixp0wJXsKxk2ewKWPN1nXLn0tHYVDc32dUvkzxDIQQUgjxXaZ9YyFEtBBip26/q6H1Vl6EVu2aU66SC77N+zAnaCGTFgQZlJu0IIg5QQvwbd6HcpVcaOnRDIDAEQM4c/wcvi38OHP8HIEj+gPw2/Hz9PEYiJ9nADNGBTNtsTbbsdEPUKmM6OHWH886XWndrgUNmtfLYqubvw8J8UQci1gAAB96SURBVIn4tvBj/cpNjJzyHgAVq1TAy9eTnu4DGN53DBPmjcHI6L/tnXeYFVXSh9+aYQiSc5KoIAoSxIAISFJEESMqBgRZMy6sgUVXV3Rd9HNBFHUNrIBpUcyAIEo2sQgoUVAEA0EGEBiiMEN9f5y+w507wwDj7e47l3qfh4fu0+F3um9PV59z6lS5x2rCuEn0u/quXPWeM/srrmjfiys79SYrK4tWZ58W92td9/M6/nRJP67seD0jh4/h/qEDAcjKzOKJwU8z/KFn+HbRCjK2ZjBqxKu+6wJMeHMSI4ePIa1oWiDX+9MPP3NLj/70veR2zqrfmS2/baVh4wbUa1g3W6dUmVLc+9id/KX3IHq0v46BNz6QZ30ORn6/P8DNl/+Zqzr3pvPJ3bmqc296du7NmR1acfIpjQ/r/CkpKfx1yJ3ccc3dXHb2tZx3cecc9X/y4X/T85w+9DynT55Gqm2nM6ldrxbnt+rB4Lsf5YHHB+baB+CBxwfy0N2PcX6rHtSuV4s2Hc8EYOXyVQy4YRDzv/wm1zG//LSWyzv14vJOvXh44OOHdT2xXHz+OTz/xCMFOjaeHElQ2jAwQ3VodgJNRKSEt34OsDayUVXHq+pj8RQ8u0sbJo77CIDFC5ZSukxpKlXJGVi4UpWKlCxVkkXzlwIwcdxHtD+vLQDtu7RlwrjJAEwYN5kO57UDYPeu3dnHlzimePZXVKMmDfh51RrW/ryOlBRh9+49tG5/eg699ue1YaJ3zmkTZ3Ja25aeVhumfDCVfXv3se6X9az5cQ1NWpwIwII5C9m2JSPX9c2Z9RVZWa5PvFTpkmz8dXPcr3XhvCVs37YdgEXzl1K1ehUANqVvZvni7zi7SxvGj/2Q1d//xNbftvmuG7kfJ7dsnL09yOs9vW1Lflm9hpXf/kCVapWytbpecg7TJ83m17UbANiyeWv2tvMvO5dXJr3I2E9G87fH78lhgCLk9/tHE3n2iqQVoUhaKqrKiU1PYOS7T/P6lJd4duywXPcBoEmLE1nzo3s2M/dlMuWDqbTv0ibXfgejw3ntGP/WpOz7UrpMqYPe74XzXKto/FuT6NjV3ddV3/+Y3Wryg1Obn0zZMqV9O//hEu/EifHGDNXhMRm4wFvuCYyNbBCR3iLyjLc8RkRGiMgXIrJKRC73yquLyGwR+UZElohI2/zEqlSvzK/r0rPXN6xPp0r1yrn2SV8fvc/G7H0qVi7PpnT38t+UvpkKlcpl79ehazve/fS/jHhtKA/9ZUj2uTK2ZvDmtDFMmv8us6Z8RslSJXPoVa52oE5ZWVnsyNhJuQplqVKtMhui67puI5Wr5axrfjRs3ICvPp/ny7VGuPjqbnw+fU6uc+7LzOSEJg1YsmBpYLqVKlcgMzMz8OvtclFnvpw5lxNObsiSBcuyy+scV4syZUvz4jvOYFzQ4zwA6jWow7ndO3FD91vpeU4fsrL20/Wyc3Np5ff7qyrPvvEEr095icuuu4ixn4xm6uIJ/G/WPJYvXsHARwZwz40PcE2Xvnww9kNuHxSbLcJ77tYeOH/6+o1UiXq+bh90E29OG8NdD91BWtG0XMdXrZ7z+A3r06kac7+rVq/MhvUbo64h9z55UbN2Dd6a+jKj3/s3p5zR7JD7JzJZuv+w/4WBjVEdHm8Af/e6+5oCo4CDGZvqQBugES7w4tvA1cAUVf2nl/3ymNiDvJwuNwFM/Wh6rpPm+pLJI3Tb4XztzJg8mxmTZ3NKq2bc9tcbueWKASDC7l17uLJTbypVrcio9//NvC8WxNYvT708yw+ze6Bv/16oKl/O/Cr/6yjgtQKcetYpXNyzGzdcdGuO8iJFinD7oJsY+vcR7NyxKzBdDnIf/dQtklaEs7u0If3XjQz7+1PZ1wuQmprKiU1P4OYe/SleohhjJjzP4vlLOb1NS05segKvTv4PAMWKF2PLpi259PL7/ft0v5VNGzZTvmI5nnvzSR6/fzjfLfuBYaOGUOe42hzXqD7PvTEcgJTUlGwDfMjze/fimSEvsCl9M2lF07j/XwPpffs1jBw+JufxedzM2HuZt0auohxs3LCJc065iG1bMjip6QmMGPM4yK5DH5igJPoYlRmqw0BVF4lIXVxralL+e/O+qu4HlolIVa/sK2CUiKR523N1eKtqGt7v8e5r46lW40CXUdXqVdj466Yc+6ev20iV6tH7VM7eZ/PGLVSqUpFN6ZupVKUiv23aSiwL5izk2Lo1KVehLOnr0qnq6W3asJmMbdspWqxYTr316VSrUYX09RtJTU2lVJmSbNuS4b5Qo+taozKbYuoayxW9L+X626+mXIWyfDx+um/X2uDE4/j7sEH0u/outm3J4Io+l3LpNd0RgbLlyzLv8wVMnzQrMF2A1St/pHa9YwO53gjtzjkLcF2F0yfNzqG1Yf1Gtv62jT2797Bn9x4WzFlIw5OOBxEmvDWZZ4a8kGP/Dl3bcdOdfQB4+O7H8v39N21whmfL5q3MmDybxs1PYsGchcz/4ms6nN+OVStW0/vCW3Kcv2qNKjz5sstK/var7/P90pVUq3ng/FWqV2bjBu/8nmHbt3cf49+YRK9bXVzTq/pcxuXXuoSyS775NsfxVatXIT3mfv8a04KqWqMK6b9uJD/27d3Htr37AFi2aAW//LiWSo2roJl78z0uUUl0rz/r+jt8xgNDier2Owi/Ry0LgKrOBtrhxrZeFZFeeRz3LNAcaD7jo9l0u8J1wZx8SmN2bN+R62tzU/pmdu3clT0o3e2K85g15TMAZn38GRde0RWAC6/oyswpnwJQq+6BJJuNTm5IWloaW3/bRvqGTdQ5rhY1alWnfKXyHN+oPlMnzsihN2vK53TzztmpW3u++mxBdnmXizqTVjSNGrWqU6teLZZ8/W2+N2jNT2vZvWs3F5zeg6kTZ/hyrdVqVmXoqCE80O9hfl7lko2OG/0uV3XuzXfLVrJs4QrKVSwbqO5VnXvz5cy5lC5bOhDdCHcO7sfyxSt4/YU3c/0Ws6Z8SoszmpKamkrxEsVocspJrP7+R+Z+Np/OF7SnfEXXtVimXGmqH1uVGZNnZzsvfLtwxUF//+IlinNMyRLZdWvd8Qx+WLGKYsWLcka7U/lu6UrKVyxH05buGosUSaV+w3psWJeeff53XvmApd8sp1Y992wWSStCl4s6M2vK5wA5xpo6dG3LyuWrAXhj9DvZTg7TJ8+ie4/zAWjaMp/7vWNXdl269zifGR/lNOixlK9YLnvM7tg6Nahd/1g0KzPfYxKZRB+jkkRv8oWNiOxQ1VIicixwmao+JSLtgbtVtZuI9AZOVdV+IjIGmKiqb8ccWwdY64W/HwDUVdUBB9NsUe0sHfTonbTu0Io9u/cweMAQlnlusW9MHcNVnXsDcFKzRgdcmKfP4f/ucy61ZcuX4f9e/AfVa1Zl/doNDLzxfjK2bqd3v2vo1qMrmfsy+X3P7wx/+Fm+mbuIM9qdxgNDB1K5aiUQ4fNpX3LXDfdxyz19WbZwObM//pyixYryj6cfoFGTBmzbmsG9twxm7c8uF1rf/r3oftUFZGVmMfTBEXzhjY8M+fdgWrZuTrkK5fht4288P/QlPhj7IR988QZpRdOyv/pTi6RSrHixuF7r34cNotMFZ7N+jXMSyMrK4poufWl+elNGj3+O75atpHLVSpQsfQybNmzmnj/d76suwKPPDaZl6xZUqFQBVWVz+mbu7HOvr7qnt2nJC2+P4Iflq8jMdA4sX89dxA8rVvHOKx8A0OvWnnS/6nz271fe/+8E/jvyLQDO7d6RPndcR0qKkJmZxWP3PsHiBUtzPa95/f41a9dg2Cg3BlqiZHGKlyjO1s1bkZQUPhk/nZHDx9Cw8fEMfGQApUqXIrVIKv8dOY73Xp+Q6/xndWzF3Q/3JyU1hfFvfMhLT70CwAtvPUW5iuUQEb5b+j3/HDiU3bt2s3d/ToPxt0fvpk3HVuzevYcH+j+S7WL+9rRXuLyT+2Zs3KxRDvf0IfcNA6BT17O5d8hdVKhYju0ZO1i+5DtuvmoAnS/oQL+BN5KVlUVW1n6e/ddInhx+W666H4p7HnyMr75exNatGVSsUI7b+l7HZRd2OaJzpFWq/4dzuFQo3eCwDcFv278PPGeMGapDEDE2MWXtOTJDdT1wD7AP2AH0UtXVB9NsUe2sUH4Uy0eVvBxN+ahiDVVQhJWPKh6Gqnyp4w/7D3HLjpVmqAwzVEb8MUPlP4XZUJUpWf+w/xAzdq4K/GEyZwrDMIyjHAtKaxiGYSQ0lubDMAzDSGisRWUYhmEkNIk+VmyGyjAM4yhnf0ihkQ4XM1SGYRhHOdaiMgzDMBKaxDZTNo8q6RCRm1T1RdNNPt2j6VqPNt2wrrWwYLH+ko/cuRJMN1l0j6ZrPdp0w7rWQoEZKsMwDCOhMUNlGIZhJDRmqJKPsPq5TTc5NU03eTULDeZMYRiGYSQ01qIyDMMwEhozVIZhGEZCYxN+kwARKaaqvx+qLE5al+a3XVXfjbdmHnVIBaoS9fyq6s9+64aBiBwPPAtUU9VmItIUuEBVH/VZt4Kq/uanhmEcLjZGlQSIyAJVPeVQZXHSGu0tVgFaA9O99Q7ATFXN15DFQf8O4EFgAxAJUKaq2tRP3Sj91kBdchrJV3zUmwncBzyrqi3EZUBcoqqN/dL0dL8HvgFGA5M1oBeFiBwD3AXUVtUbRaQBcIKqTvRZtySwW1X3i0hDoBHuuvf5qHkW8I2q7hSRa4FTgKdU9Se/NAsr1qIqxIhINaAmUEJEWgCRzJtlgGP80FTVPp72ROAkVV3vrVfHffn7TX/ci2tzAFo5EJFXgeNwL/Asr1gB3wwVUFJVv4hk6FVVFRHfXp5RNAQ6AzcAT4vIm8AYVf3OZ93RwHzgTG99DfAW4KuhAmYDbUWkPDANmAdcCVzjo+ZzQDMRaQYMBF7CPUtn+6hZKDFDVbjpAvQGjgWGccBQZeC+wv2kbsRIeWzAvdz85hdgWwA6eXEqzjgH2Q2xWUTq4YVjE5GLgV/9FvWu8RPgExHpALwG3CYiC4FBqvqlT9LHqeqVItLTq8duiVhpfxFV3SUifYGnVfVxEfnaZ81M78PjIlxL6iURud5nzUKJGapCjKq+DLwsIpep6jsBy88UkSnAWNxL9Cpghl9iInKnt7jK0/4QyB6DU9Un/NKOYglQDVh/qB3jSD/cl3YjEfnJ0+7pt6iIVASuBa7DfYTcAYwHmuNaOPV8kt4rIiU4YJiPI+p39hERkTNxLai+Xpnf78ftInIv7j6388Ze03zWLJSYoUoOWorINFXdCuB1X9ylqvf7Jaiq/UTkEqCdV/Siqr7nlx5Q2vv/Z+9fUe+f74jIBNyLszSwTETmktNIdvdLW1VXAh1FpCzuq3+rX1oxfAm8ClysqmuiyueJyPM+6g4GPgJqicjrwFlAHx/1IvQH7gXeU9WlIlIfHz+8PK4Ergb6quqvIlIb+JfPmoUSc6ZIAkTka1VtEVPmizNFjEYdoIGqTvUGwVNVdbufmmEgIvmOGajqLB+1/wEMi/kIGaCqD/ql6elcoarjYsp6qOpbfup6OhWBVriu7DmquslnvVTgMVW9x08do+DYPKrkIFVEikVWvK6TYvns/4cRkRuBt4EXvKKawPt+anq6n4hIuaj18l4XpG+o6izPGJ0fWY4u81Mb6BbdilLVLcCFPmsCDMqj7F6/Rb2egc2q+qGqTlTVTSIyzU9NVc0CWvqpkRcicqmIfC8i20QkQ0S2i0hG0PUoDFjXX3LwGjDNcx1XnKfWyz5r3g6cDvwPQFW/F5EqPmsCVI59cQekC3AO8NeYsq55lMWTVBEpqqp7AUSkOD52eYpIV5zxrSkiI6I2lQEyfdQtjvNUreS1GqM9WGv4pRvF1yIyHjf+tjNS6PO8wMeBC1X1Wx81kgIzVEmA56G0GOiE+wP/h6r62soAflfVvRGHLBEpQjCJQrNEpHZkgq/X/eirrojcCtwG1BeRRVGbSgNf+KkNvIHzvBuFu86+wOs+6q3DuWZ3x7mJR9gO/MVH3ZuBATijNJ+cHqxBTHuoAGwGOkaVKeCnodpgRurwsDEqo0CIyOPAVqAXziPsNmCZqv7NZ93zcJGmI+NC7YCb/DTMniNDeeBRcnaJbQ8ieoOIXMiBj5CPVfXDADSLqKpvLah8dO9Q1aeD1g0DEXkK50X6Pjmdc3yP7lLYMEOVBIjIdg60KoriXFx3qmoZHzVTcF/35+JeoFOA/wQxx0hEKnFgsP1LvwfbY7STOnyTiIxT1Su8Fnqu3zKICCAi0gQ4CSgepevLpGoReZp8WuSq+mc/dD3t0XkUq6re4JdmYcUMVRLiTQo9XVV9mfTrvaxfVtVr/Tj/YeiXBxqQ80U2OwDdfjj3ad/DN4nILFU9W0S2kPNFKp5mhXhrerptcde2JmZTHWCd5y7vGyLyINAeZ6gm4cYAP1PVy33Si55g+xAuPFc23lxFI2TMUCUpIjJHVVv5eP4puIHgvX5pHET3T7g5L8fiQhm1wrWqOuZ7YHy0VwJnBBG+SURSvLhzqXlt9zzV/NCdCNynqotiyk8FHlRVXz0OvZZcM+BrdUF4q+Ja6r57OuY1zcMnnYHeuHKerTk/W3GFFXOmSAIkZ0TzFFyoH7+/QH4EPvc8paK9pPyOENEfOA03v6aDiDTCfQkHQWDhm6KM1AJVbRaEpkfdWCPl1WeeiNQNQD8SGDZTRMoA6UD9AHQhGGcggIgDxbyA9Ao9ZqiSg+ivzUycEbnIZ8113r8UDkSNCII9qrpHRCKpTJaLyAkBaQcavklVs0RkmYjUVNW1fmjkQfF8tpUIQH+eN09uJM77bwcwNwDdwFDVCd7/1q14mJihSgLUi2gesOZD4NIjqOrOQ+0fR9Z4L7L3cW7bW3AGMwgCD98EVAK+FZEvydly9SudylcicqOqjowuFBesdf5Bjokbqnqbt/i8iHwElMmrhRcvYhyRjomacBsZC4y7Q5LXC3FQ/AzJVVixMapCTMgeS2figqWWUtXa4lIV3Bz1ovEdL7RRWeCjIMfKRKQ07iW2IwCtTnmVq6ov0Rq8MaH3gL0cMEyn4gzzJarqa+R2LzJFp0OVFWZEZCOuG3ksbsJ8jujwfobkKqxYi6pwE+njPgvnJfWmt94D/79+n8SlGRkPoKoLRaRd/ocUHBHpqKrTveV6qro68gftjdEFkVm4CS5QawVvfRPQS1WX+qTXDTgeWOyXYYpFVTcArcWl9mjiFX8Yufd+kQCRKYKkGi7KSU9cUNoPgbF+PUfJgLWokgARmQGcq142UhFJw00M7eCj5v9U9YxoTykRWejXwL9EBdmVmIC7set+ISJfAH9T1RneentgiKq29kHraaAFLop5R+AdVR0Sb51EQUT6cyAyRfR43HZgpKo+E0rFfMaL0dkTFzX94aNlsvORYi2q5KAGzqEhEiWhFP5/hf4iLi27ikhR4M8c8GbyAznIcl7rflEyYqQAVHWmuBTmftABaK6qmZ7GLCBpDRUuFNU44HJVfdqb33QZzjHov2FWzA88A3UBzkjVBUYQQK9AYcUMVXLwGC6oZuQlejZuYqqf3AI8hYuavgb4GBeo1i/0IMt5rfvFKhF5ANf9By7h3WqftPZGQhip6k4vEkgy8wLQ2TNS7XDhqu7AJWp8EfBlwm8YiMjLuG7VycBDqrok5ColPNb1lySISDXgDNxLe67fg95BIyJbgdm41lNbbxlvvY2qlg+gDuVxc7baeLqzgcHqUm/EW2sXsDyyCpzgrUe80Xzv6gyS6G5jEXkW2Kiqg731b1S1eZj1iycisp8DHpx5RR3xLfRZYcVaVMnD6bgXOLiHf4IfIiF6GkbPCxsasy123Rc8gxRU1ICTA9JJFFKjAuF2Am6K2pZU7ylVTfbWcdxJqgfgaEVEHsNFa4ikf/iziLRWVT8S3YXiaRimy24Y815U9YfD2U9EPlPVNvHWD4GxwCzPk3I38CmAiBxPQNFAjMTFuv6SAC9HUnNV3e+tp+JipfkW6ToMT8OwSOR5L0HFpwsCEWkFVMc9Rzu9soa4uXoLQq2cESrWokoeynHA669sAHpheBqGRSLPe0maL01VnZNH2Xdh1MVILMxQJQePcsDrT3DJBP3o9osmDE/DbIIM3eRFKv8I+Chq3stMEbF5L4YRANb1V8gREcGlvMjEjVMJ8D8/vf6iNPfhPA3xWzNKuzXwHwIO3ZTHvJfxwKgAg8UerF5J0/VnGAfDDFUSICLzVbVlsmt6uv/DzakZHxURY4mqNsn/yD+kGT3v5Y1EmvciIs1UdWHY9TAMP7Guv+RgjoicpqpfJbkmAKr6i2vUZeNLEsEorsPNe2mI86iMlPsZYTs2s2+sZgXcghkpI+kxQ5UcdABuEZEfcS/UyMvMN6+/kDQh+NBNYc17qRSCpmEkJNb1lwSISJ28ylX1p2TS9HQr4UI3dcYZx4+B/hpAevgwEZEKRCU1VNWgcnAZRuiYoSrEeKkRbsFLBQG8FIkPF4B2B6AxrntqWXSwViN+iMgFwHCc88pmXGzF71S1UagVM4wAsVAehZuXcUntFgNdgWF+C4pITc+hYTBQH2ckB4vIXBGpGYD+y16G38h6eREZ5bduiPwTFwVkharWwuUAmxlqjQwjYGyMqnBzkqqeDCAiLwFzA9B8BnhOVcdEF4pIL+Df5IzJ5wdNVXVrZEVVt4hIMrtnZ6rqRhFJERFR1U9E5J9hV8owgsRaVIWbfZGFoLr8cMZxTGyhqr4CBNEdleJFMQeyx26S+YNrm5eP6jPgFREZBuwPuU6GESjJ/Ad+NNBMRDK8ZQFKeOt+pgtIzavQy5eU57Y4Mwz4QkTe9tZ74LrHkpWLgT247Le9cOGxuoVaI8MIGHOmMI4IERmOi+s3ICpwaEncgP8eH9N8RNfhJFx6dgGmqeoyvzXDQkSGqOp9hyozjGTGDJVxRHhR0h8FegM/4bz+6uAcO+5T1b0+6ZZR1Qyvqy8XqvpbXuWFHRFZEJskMTrJoGEcDZihMgqEiJTAefwJsFJVd/msN1FVu4nIavLOilrfT/2gEZGbcVMPGgIrojaVBuapas9QKmYYIWCGyig0eMFwa6nqz2HXxW88h5GKuNbroKhN21U1PZxaGUY4mKEyChVhBcMNExFpAkSy+H6aIHmwDCMwzD3dKGzMEZHTwq5EUIjI7cA4oLb3b5yI+JrSxDASDWtRGQVCRN4BRgGTVTWweT0isgw4AfiRYIPhhoKILAJaq+oOb70U8EWyXq9h5IXNozIKynNAH2CEiLwFjFHV5QHodg1AI5EQoiZ2e8tykH0NIykxQ2UUCFWdCkwVkbK4rLefiMgvwEjgNVXdl+8JjpAwA/CGgYgU8a7vVVx35zvepktwUwEM46jBuv6MAiMiFYFrcYkF1wGv4wb9T1bV9nHWehPXmvgU16r6SVX7x1MjkYieP+WNybXFtaRmh5Gs0jDCxAyVUSBE5F1cbL9Xcd1+66O2zVPVU+OstzgqAG8RYG7sRNhkQkS+VtVkDrZrGIeNdf0ZBeU/qjopukBEiqnq7/E2Uh45AvDGpKJPRiqLyJ0H26iqTwRZGcMIEzNURkF5BJgUU/Yl4FcrJ4wAvGGSioupmPQW2TAOhRkq44gQkWq4LLMlvDxQkRdpGeAYv3RVNYjI7InEelV9OOxKGEYiYIbKOFK64ALSHgtEdz9tByyid/ywlpRheJgzhVEgROQyVX3n0HsaBUFEKiRrRHjDOFLMUBlHhIhcq6qvichd5IxiDtggv2EY8ce6/owjpaT3f6k8ttlXj2EYccdaVEaBEJGzVPXzQ5UZhmH8UcxQGQXiIJlnc5UZhmH8UazrzzgiRORMoDW5J6SWwc39MQzDiCtmqIwjpShufKoILi16hAzg8lBqZBhGUmNdf0aBEJE6qvpT2PUwDCP5sRaVUVCKiciLQF2iniNV7RhajQzDSEqsRWUUCBFZCDwPzAeyIuWqOj+0ShmGkZSYoTIKhIjMV9WWYdfDMIzkxwyVUSBEZDCQDrwH/B4pt7A/hmHEGzNURoEQkdV5FKuq1g+8MoZhJDVmqAzDMIyExrz+jAIjIk2Ak4DikTJVfSW8GhmGkYxYi8ooECLyINAeZ6gmAV2Bz1TVJv0ahhFXUsKugFFouRzoBPyqqn2AZkCxcKtkGEYyYobKKCi7VXU/kCkiZXAegOZIYRhG3LExKqOgzBORcsBI3KTfHcDccKtkGEYyYmNUxh9GROoCZVR1UchVMQwjCTFDZRQIEWmXV7mqzg66LoZhJDdmqIwCISITolaLA6cD8y0orWEY8cbGqIwCoaoXRq+LSC3g8ZCqYxhGEmNef0a8WAM0CbsShmEkH9aiMgqEiDwNRPqNU4DmwMLwamQYRrJiY1RGgRCR66NWM4EfVfXzsOpjGEbyYobKKBAicgxwvLe6QlV/z29/wzCMgmJjVMYRISJpIvIk8AswGngZWCUig7ztLcKsn2EYyYe1qIwjQkRGAMcAf1HV7V5ZGWAoLiX9eapaL8QqGoaRZJihMo4IEVkJNNCYB0dEUoFNQFdVnRNK5QzDSEqs6884UvbHGikAVc0CNpqRMgwj3pihMo6UZSLSK7ZQRK4Fvg2hPoZhJDnW9WccESJSE3gX2I2Lmq7AaUAJ4BJVXRti9QzDSELMUBkFQkQ6Ao0BAZaq6rSQq2QYRpJihsowDMNIaGyMyjAMw0hozFAZhmEYCY0ZKsMwDCOhMUNlGIZhJDRmqAzDMIyE5v8B0V1oxa/Amv4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(DF.corr(),annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=DF.drop(['Total_Price','Price Each'],axis=1)\n",
    "y=DF['Price Each']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train,X_test,y_train,y_test =train_test_split(X,y,test_size=0.3,random_state=42 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "LR=LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "231.05121404373068"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7.51119663e+00, -1.01444703e+02, -1.73654160e-01, -4.21534364e-01,\n",
       "        5.50831008e-01,  1.02379251e-01, -1.58399562e-02])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LR.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([179.73408941, 257.79109874, 257.71483535, ..., 265.51883547,\n",
       "       241.85981014, 166.93396442])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=LR.predict(X_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "216.2820713180953"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from  sklearn.metrics import mean_absolute_error\n",
    "\n",
    "mean_absolute_error(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing Accuracy\n",
      "0.035622529335086806\n"
     ]
    }
   ],
   "source": [
    "print(\"Testing Accuracy\")\n",
    "print(LR.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>       <td>Price Each</td>    <th>  R-squared:         </th>  <td>   0.036</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th>  <td>   0.036</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>  <td>   3510.</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Sat, 15 Feb 2020</td> <th>  Prob (F-statistic):</th>   <td>  0.00</td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>19:04:23</td>     <th>  Log-Likelihood:    </th> <td>-1.3403e+06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>185950</td>      <th>  AIC:               </th>  <td>2.681e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>185947</td>      <th>  BIC:               </th>  <td>2.681e+06</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>      <td> </td>     \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "          <td></td>            <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>const</th>            <td>  229.8246</td> <td>    2.563</td> <td>   89.674</td> <td> 0.000</td> <td>  224.801</td> <td>  234.848</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Product</th>          <td>    7.4642</td> <td>    0.142</td> <td>   52.707</td> <td> 0.000</td> <td>    7.187</td> <td>    7.742</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Quantity Ordered</th> <td> -101.0407</td> <td>    1.722</td> <td>  -58.680</td> <td> 0.000</td> <td> -104.416</td> <td>  -97.666</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>104545.486</td> <th>  Durbin-Watson:     </th>  <td>   2.023</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th>   <td> 0.000</td>   <th>  Jarque-Bera (JB):  </th> <td>787235.853</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>            <td> 2.689</td>   <th>  Prob(JB):          </th>  <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>        <td>11.526</td>   <th>  Cond. No.          </th>  <td>    41.8</td> \n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:             Price Each   R-squared:                       0.036\n",
       "Model:                            OLS   Adj. R-squared:                  0.036\n",
       "Method:                 Least Squares   F-statistic:                     3510.\n",
       "Date:                Sat, 15 Feb 2020   Prob (F-statistic):               0.00\n",
       "Time:                        19:04:23   Log-Likelihood:            -1.3403e+06\n",
       "No. Observations:              185950   AIC:                         2.681e+06\n",
       "Df Residuals:                  185947   BIC:                         2.681e+06\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "====================================================================================\n",
       "                       coef    std err          t      P>|t|      [0.025      0.975]\n",
       "------------------------------------------------------------------------------------\n",
       "const              229.8246      2.563     89.674      0.000     224.801     234.848\n",
       "Product              7.4642      0.142     52.707      0.000       7.187       7.742\n",
       "Quantity Ordered  -101.0407      1.722    -58.680      0.000    -104.416     -97.666\n",
       "==============================================================================\n",
       "Omnibus:                   104545.486   Durbin-Watson:                   2.023\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):           787235.853\n",
       "Skew:                           2.689   Prob(JB):                         0.00\n",
       "Kurtosis:                      11.526   Cond. No.                         41.8\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "X=DF.drop(['Total_Price','Price Each','City','Mins','State','Month','Hrs'],axis=1)\n",
    "y=DF['Price Each']\n",
    "\n",
    "\n",
    "Xc=sm.add_constant(X)\n",
    "model=sm.OLS(y,Xc).fit()\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}