{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Merge 12 months of sales data into a single csv file "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sales_April_2019.csv\n",
      "Sales_August_2019.csv\n",
      "Sales_December_2019.csv\n",
      "Sales_February_2019.csv\n",
      "Sales_January_2019.csv\n",
      "Sales_July_2019.csv\n",
      "Sales_June_2019.csv\n",
      "Sales_March_2019.csv\n",
      "Sales_May_2019.csv\n",
      "Sales_November_2019.csv\n",
      "Sales_October_2019.csv\n",
      "Sales_September_2019.csv\n"
     ]
    }
   ],
   "source": [
    "files =[file for file in os.listdir(\"F:/EDA_projects/Sales_Analysis/SalesAnalysis/Sales_Data\")]\n",
    "for file in files:\n",
    "    print(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(186850, 6)"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = \"F:/EDA_projects/Sales_Analysis/SalesAnalysis/Sales_Data\"\n",
    "\n",
    "#blank dataframe\n",
    "all_data = pd.DataFrame()\n",
    "\n",
    "for file in files:\n",
    "    current_df = pd.read_csv(path+\"/\"+file)\n",
    "    all_data = pd.concat([all_data, current_df])\n",
    "    \n",
    "all_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### convert it into dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data.to_csv('F:/EDA_projects/Sales_Analysis/SalesAnalysis/Sales_Data/all_data.csv',index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Data cleaning and formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "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": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order ID            585\n",
       "Product             585\n",
       "Quantity Ordered    585\n",
       "Price Each          585\n",
       "Order Date          585\n",
       "Purchase Address    585\n",
       "dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(186305, 6)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data = all_data.dropna(how='all')\n",
    "all_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What is the best month for sale?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'04'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'04/19/19 08:46'.split('/')[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "def month(x):\n",
    "    return x.split('/')[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### add month col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Month']=all_data['Order Date'].apply(month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "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",
       "Month               object\n",
       "dtype: object"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "invalid literal for int() with base 10: 'Order Date'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-188a0bea4a2e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Month'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mall_data\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Month'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m   5535\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5536\u001b[0m             \u001b[1;31m# else, only a single dtype is given\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5537\u001b[1;33m             \u001b[0mnew_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5538\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5539\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    565\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m\"raise\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    566\u001b[0m     ) -> \"BlockManager\":\n\u001b[1;32m--> 567\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"astype\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    568\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    569\u001b[0m     def convert(\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\managers.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, f, align_keys, **kwargs)\u001b[0m\n\u001b[0;32m    394\u001b[0m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mb\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    395\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 396\u001b[1;33m                 \u001b[0mapplied\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mb\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    397\u001b[0m             \u001b[0mresult_blocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_extend_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mapplied\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mresult_blocks\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    398\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals\\blocks.py\u001b[0m in \u001b[0;36mastype\u001b[1;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[0;32m    588\u001b[0m             \u001b[0mvals1d\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    589\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 590\u001b[1;33m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvals1d\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    591\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    592\u001b[0m                 \u001b[1;31m# e.g. astype_nansafe can fail on object-dtype of strings\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\dtypes\\cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[1;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[0;32m    964\u001b[0m         \u001b[1;31m# work around NumPy brokenness, #1987\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    965\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0missubdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtype\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minteger\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 966\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype_intsafe\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mravel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    967\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m         \u001b[1;31m# if we have a datetime/timedelta array of objects\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.astype_intsafe\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: invalid literal for int() with base 10: 'Order Date'"
     ]
    }
   ],
   "source": [
    "all_data['Month']=all_data['Month'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['04', '05', 'Order Date', '08', '09', '12', '01', '02', '03', '07',\n",
       "       '06', '11', '10'], dtype=object)"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['Month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "185950"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filter=all_data['Month']=='Order Date'\n",
    "len(all_data[~filter])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data=all_data[~filter]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(185950, 7)"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "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",
       "    </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",
       "      <td>04</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",
       "      <td>04</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",
       "      <td>04</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",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>04</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",
       "2   176559  Bose SoundSport Headphones                1      99.99   \n",
       "3   176560                Google Phone                1        600   \n",
       "4   176560            Wired Headphones                1      11.99   \n",
       "5   176561            Wired Headphones                1      11.99   \n",
       "\n",
       "       Order Date                      Purchase Address Month  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001    04  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215    04  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001    04  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001    04  \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001    04  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Month']=all_data['Month'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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",
       "Month                int32\n",
       "dtype: object"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Price Each']=all_data['Price Each'].astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Quantity Ordered']=all_data['Quantity Ordered'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "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>sales</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",
       "      <td>4</td>\n",
       "      <td>23.90</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",
       "      <td>4</td>\n",
       "      <td>99.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</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",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>176561</td>\n",
       "      <td>Wired Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>11.99</td>\n",
       "      <td>04/30/19 09:27</td>\n",
       "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
       "      <td>4</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   176558        USB-C Charging Cable                 2       11.95   \n",
       "2   176559  Bose SoundSport Headphones                 1       99.99   \n",
       "3   176560                Google Phone                 1      600.00   \n",
       "4   176560            Wired Headphones                 1       11.99   \n",
       "5   176561            Wired Headphones                 1       11.99   \n",
       "\n",
       "       Order Date                      Purchase Address  Month   sales  \n",
       "0  04/19/19 08:46          917 1st St, Dallas, TX 75001      4   23.90  \n",
       "2  04/07/19 22:30     682 Chestnut St, Boston, MA 02215      4   99.99  \n",
       "3  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4  600.00  \n",
       "4  04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   11.99  \n",
       "5  04/30/19 09:27     333 8th St, Los Angeles, CA 90001      4   11.99  "
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['sales']=all_data['Quantity Ordered']*all_data['Price Each']\n",
    "all_data.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Month\n",
       "1     1.822257e+06\n",
       "2     2.202022e+06\n",
       "3     2.807100e+06\n",
       "4     3.390670e+06\n",
       "5     3.152607e+06\n",
       "6     2.577802e+06\n",
       "7     2.647776e+06\n",
       "8     2.244468e+06\n",
       "9     2.097560e+06\n",
       "10    3.736727e+06\n",
       "11    3.199603e+06\n",
       "12    4.613443e+06\n",
       "Name: sales, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.groupby('Month')['sales'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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": [
    "months=range(1,13)\n",
    "plt.bar(months,all_data.groupby('Month')['sales'].sum())\n",
    "plt.xticks(months)\n",
    "plt.ylabel('Sales in USD ($)')\n",
    "plt.xlabel('Month number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Which city has max order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "' Dallas'"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'917 1st St, Dallas, TX 75001'.split(',')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [],
   "source": [
    "def city(x):\n",
    "    return x.split(',')[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['city']=all_data['Purchase Address'].apply(city)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "city\n",
       " Atlanta          14881\n",
       " Austin            9905\n",
       " Boston           19934\n",
       " Dallas           14820\n",
       " Los Angeles      29605\n",
       " New York City    24876\n",
       " Portland         12465\n",
       " San Francisco    44732\n",
       " Seattle          14732\n",
       "Name: city, dtype: int64"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.groupby('city')['city'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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.bar(all_data.groupby('city')['city'].count().index,all_data.groupby('city')['city'].count())\n",
    "plt.xticks(rotation='vertical')\n",
    "plt.ylabel('received orders')\n",
    "plt.xlabel('city names')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What time should we display advertisements to maximise for product purchase?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('O')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data['Order Date'][0].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_data['Hour'] = pd.to_datetime(all_data['Order Date']).dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "keys=[]\n",
    "hour=[]\n",
    "for key,hour_df in all_data.groupby('Hour'):\n",
    "    keys.append(key)\n",
    "    hour.append(len(hour_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xe5aa76f08>]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HYb6bTnGtYaC80gdf5TI4PozEyBC7S3EKZwTC1tyjXPP85/xp8S7G943i05+O47aR3V1+8rnW0isqhJTu4czdeNAtL0LTMFBeZ8ehMnYVlHP9UNedLrkttFUgVFbX8cTHO5j2wueUVlbz8u3D+Pvtw4ju2K5V3t+d3JQSz77iSjblHLG7lAumYaC8zrxNufj7ClcN7Gp3KU7XOBBufbVlgVBX7+DTHYVMenY1r3+2n++M7MaymeNIS3avif5a05UDuxAU4MvcDe53IFnDQHmVunoHC7Yc4rKkKMKDA+wuxxYnA6Gu/sICweEwbD90jNfW7OOuNzcw+LfL+P5bG2lvnS76u2kXufwNgdpacKAfVw3swsdb86l0syuSPe8Ea6XOYk1mCSUV1V5x4PhsTp5l9J1X13Lrq2t55wejvrWMMYZ9JZV8sfcwX2SVsHbfYY4crwWgZ2Qw04Z0ZXRiBBP6RXnsWULNcfPweN7bmMt/t+Zz0/B4u8s5bxoGyqt8uCmX8CB/xveNsrsU2/WN+WYg/GSQD3lHT/B5Vglf7j3MF3tLKCyrBqBraDsm9ItmdGJnRidGEBPqfccDztfQbuH0jAxm7saDGgZKuaJjJ2pZuqOQW4fHu82dzNpa40B47LMT1K9ZAUDn4AAuTuzMmF4RjE7sTLdOQW53nwe7iAg3p8Tzh0W7yCqqoFeUe5yxds6fCBF5Q0SKRCSjUVsnEVkmIpnW13CrXUTkORHJEpGtIjK00TrTreUzRWR6o/ZhIrLNWuc50T1OtZFF2/KpqXN4/RDR6frGNBxDuDTOj1lX92fJQ2PZ+KvLef47Q7l1RDe6dw7WILhA1w2Nw9dHeN+Nrkg+nz+P3gQmn9b2CLDcGNMbWG49B5gC9Lb+zQBegobwAGYBI4ERwKyTAWItM6PReqdvS6lWMW9THomRwQyMC7W7FJfTJ7oD30sO5M4xCfSN6aC//FsoskMglyVF8eGmPLe5T/I5w8AYsxooPa15KjDHejwHmNao/S3TYC0QJiJdgDRgmTGm1BhzBFgGTLZe62iM+dI0XKXxVqP3UqrV5Bw+zvrsUq4fFqe/6JRT3JwST0lFNSt3Fdldynlp7jGDaGNMPoAxJl9ETh6NiwUafy7KtdrO1p7bRHuTRGQGDZ8iiI6OJj09vVnFV1RUNHtdT+JN/bAgqwYBok/kkJ6e+43XvKkfzkb74ZTW6AtxGEIDhRcXbyGg2PUPuLf2AeSm/uQyzWhvkjHmFeAVgJSUFJOamtqMEiE9PZ3mrutJvKUfjDE8vj6d0b06cv2Ub59C6S39cC7aD6e0Vl/cWr2LV9fso//QUUS5+BXZzT2lotAa4sH6evJzUC7Q+FyqOODQOdrjmmhXqtV8deAIOaXHuW6IHjhWznVjShz1DsOHbjB5XXPDYCFw8oyg6cBHjdrvsM4qGgUcs4aTlgCTRCTcOnA8CVhivVYuIqOss4juaPReSrWKDzflEhTgy+QB3jtNgrJHYmQIw3uE874bTF53PqeWvgN8CfQVkVwRuRv4IzBRRDKBidZzgE+AfUAW8CrwIwBjTCnwBLDB+vdbqw3gh8Br1jp7gUWt860p1XBry4+35jN5QAzBgXpZjXK+G1Pi2VdSycYDrj153Tl/Oowxt57hpQlNLGuA+87wPm8AbzTRvhEYcK46lGqOT3cWUl5Vx/V6bYGyyZUXdeE3C7czd8NBhvdw3bu86WWYyqPN25RHl9B2jOrZ2e5SlJcKDvTj6kFd+e/WfCpcePI6DQPlsYrLq1m1p5hpQ2Lx9ZIbrCjXdGNKPCdq6/n4a9c9P0bDQHmsj7bkUe8wXncTG+V6hnYLo1dUCO+58PQUGgbKY83blMeguFCvuP+ucm0iwk0pcWzKOUpWUbnd5TRJw0B5pJ35ZezIL9NJ6ZTLuHZIHH4+4rJ3QdMwUB5p/uY8/HyEqwd5360tlWs6OXndPBedvE7DQHmcunoH8zfnMT4pik5eemtL5ZpuHh7P4coalu90vcnrNAyUx/ksq4Ti8mo9cKxczrg+kUR1CHTJ+xxoGCiPM29THmFB/oxP0ltbKtfi5+vD9cPiWLm7iMKyKrvL+QYNA+VRyqtqWbK9gKsHdtWbtCuXdFNKPA4DH3yVe+6FnUjDQHmUT7blU13n4DodIlIuKiEimBE9Ornc5HUaBsqjfLgpj54RwQyOD7O7FKXO6Kbh8WQfPs76/affRNI+GgbKYxwsbfjh0ltbKld3xUUxhAT6MdeFDiRrGCiPMX9zww1Epg3RISLl2oIC/JgyIIal2wuprqu3uxxAw0B5kI+25DEyoROxYe3tLkWpc5o8IIaK6jq+2HvY7lIADQPlITILy9lbXMmVA7vYXYpS52VMrwiCA3xZur3A7lIADQPlIRZlNPxApSXrrS2Ve2jn70tqUhTLdhRS77D/rCINA+URFmUUMKx7ONEd29ldilLnLS05hpKKGjbl2H9LTA0D5fYOHK5kZ34ZU/SG98rNjO8bSYCvD4sz7B8q0jBQbk+HiJS76tDOnzG9OrNke4HtF6BpGCi3tyijgItiQ4nvFGR3KUpdsLTkGHKPnGBHfpmtdWgYKLd26OgJvj54lMk6RKTc1OX9o/ERWLK90NY6NAyUWzs51qrHC5S7iggJJKV7J5bYfNygRWEgIj8Rke0ikiEi74hIOxFJEJF1IpIpInNFJMBaNtB6nmW93qPR+zxqte8WkbSWfUvKmyzOKCAppgM9I0PsLkWpZksbEMPuwnKySyptq6HZYSAiscCPgRRjzADAF7gF+BPwrDGmN3AEuNta5W7giDGmF/CstRwi0t9aLxmYDLwoIjr3sDqnovIqNhwo1SEi5fYm9Y8GYImNF6C1dJjID2gvIn5AEJAPXAZ8YL0+B5hmPZ5qPcd6fYI0zCY2FXjXGFNtjNkPZAEjWliX8gJLtxdiDEwZoFcdK/cW3ymI5K4d3TMMjDF5wFNADg0hcAz4CjhqjKmzFssFTs4aFgsctNats5bv3Li9iXWUOqPFGQX0jAimT7QOESn3l5Ycw6acoxTZdAc0v+auKCLhNPxVnwAcBd4HpjSx6MmTZ5uaU9icpb2pbc4AZgBER0eTnp5+YUVbKioqmr2uJ3HnfqioMXyx9zhXJPizatWqlr2XG/dDa9J+OMWOvuh83AHA8wvWcFk3f6duG1oQBsDlwH5jTDGAiMwDRgNhIuJn/fUfBxyyls8F4oFca1gpFCht1H5S43W+wRjzCvAKQEpKiklNTW1W4enp6TR3XU/izv3w3saDOMxW7rliJBfFhbbovdy5H1qT9sMpdvSFMYbXdq9if217UlNHOnXb0LJjBjnAKBEJssb+JwA7gJXADdYy04GPrMcLredYr68wDZfcLQRusc42SgB6A+tbUJfyAou25RMX3p4BsR3tLkWpViEiTEqO5su9hzl2vNbp22/JMYN1NBwI3gRss97rFeAXwEwRyaLhmMDr1iqvA52t9pnAI9b7bAfeoyFIFgP3GWNc424PyiWVVdXyWVYJk5Nj9I5myqOkJcdQ5zCs2O38C9BaMkyEMWYWMOu05n00cTaQMaYKuPEM7/Mk8GRLalHeY8XOImrrDVMu0lNKlWcZHBdGdMdAFmcUcO2QOKduW69AVm5nUUY+0R0DGRIfbncpSrUqHx9hUv8YVu0p5kSNcwdINAyUWzleU8eqPcWkJcfg46NDRMrzpCXHUFXrYHVmsVO3q2Gg3Er67mKqah161bHyWCN7diK0vb/TL0DTMFBuZVFGAZ2CAxjRo5PdpSjVJvx9fZjQL4rlO4uorXc4bbsaBsptVNXWs2JnIZP6R+Pnq7uu8lxpyTEcO1HLun2lTtum/kQpt/FZZgmVNfU6RKQ83tjekbTz93HqUJGGgXIbizIK6NDOj9GJEXaXolSbah/gy7g+kSzdUYDD4ZzbYWoYKLdQW+/g052FTOwXTYCf7rbK86Ulx1BYVs3XuUedsj39qVJu4cu9hzl2olaHiJTXmJAUjZ+PsNhJQ0UaBsotLMooICjAl7F9Iu0uRSmnCA3y5+LEztZ9O9p+qEjDQLm8eodh2Y4CxidF0c5fb4KnvMek5Bj2l1SSWVTR5tvSMFAub0N2KSUVNXrTe+V1/nc7zIy2HyrSMFAub3FGAYF+PozvG2V3KUo5VXTHdgztFsaSHRoGyss5HIbFGQWM6xNJcGCLJtlVyi2lJceQkVfGwdLjbbodDQPl0rbkHqWgrEqnq1ZeKy25Yd9fuqNt73GgYaBc2uKMAvx9hcuSou0uRSlb9IgIpm90hza/GlnDQLksYwyLMvIZ0yuC0PbOv0G4Uq4ibUAMG7NLKamobrNtaBgol7X9UBkHS0/oWUTK66UlR+Mw8GkbDhVpGCiXtTijAF8fYWJ/DQPl3fp36UhcePs2HSrSMFAua1FGPiMTOtEpOMDuUpSylYiQlhzD51mHKa+qbZNtaBgol5RZWM7e4kodIlLKMnlADDX1DtJ3t83tMDUMlEtalFGAyKnT6pTydkO7hRMREtBmE9dpGCiXtCijgGHdwonq2M7uUpRyCQ3Hz6JJ31VEVW19q7+/XtKpXE5mYTk788t4/Kr+dpeilEu5fmgckSGB1NQ7Wn3SxhZ9MhCRMBH5QER2ichOEblYRDqJyDIRybS+hlvLiog8JyJZIrJVRIY2ep/p1vKZIjK9pd+Ucm8LtuTh6yNcPair3aUo5VJSenRi5qS+dGzX+tfdtHSY6K/AYmNMEjAI2Ak8Aiw3xvQGllvPAaYAva1/M4CXAESkEzALGAmMAGadDBDlfRwOw4LNh7ikVwSRHQLtLkcpr9HsMBCRjsBY4HUAY0yNMeYoMBWYYy02B5hmPZ4KvGUarAXCRKQLkAYsM8aUGmOOAMuAyc2tS7m3jQeOkHf0BNcOibW7FKW8SkuOGfQEioF/iMgg4CvgQSDaGJMPYIzJF5GT8w7HAgcbrZ9rtZ2p/VtEZAYNnyqIjo4mPT29WYVXVFQ0e11P4or98I+MagJ9of3hPaSnZzplm67YD3bQfjjFG/uiJWHgBwwFHjDGrBORv3JqSKgp0kSbOUv7txuNeQV4BSAlJcWkpqZeUMEnpaen09x1PYmr9UNVbT0PpH/KlQNjSbt8sNO262r9YBfth1O8sS9acswgF8g1xqyznn9AQzgUWsM/WF+LGi0f32j9OODQWdqVl0nfXUR5VR3TdIhIKadrdhgYYwqAgyLS12qaAOwAFgInzwiaDnxkPV4I3GGdVTQKOGYNJy0BJolIuHXgeJLVprzM/M15RHYIZHRiZ7tLUcrrtPQ6gweAf4tIALAPuJOGgHlPRO4GcoAbrWU/Aa4AsoDj1rIYY0pF5Algg7Xcb40xpS2sS7mZo8drWLmrmNsv7o6fr14LqZSztSgMjDFbgJQmXprQxLIGuO8M7/MG8EZLalHu7ZNtBdTUO/QsIqVson+CKZcwf3MuvaJCSO7a0e5SlPJKGgbKdgdLj7Mh+wjXDolFpKmTy5RSbU3DQNnuoy15AEwdrNNPKGUXDQNlK2MM8zfnMSKhE3HhQXaXo5TX0jBQtsrIK2NvcaUeOFbKZhoGylbzN+cR4OvDFQO62F2KUl5Nw0DZpq7ewcKvD3FZUhShQa0/Ja9S6vxpGCjbfJZVQklFtU4/oZQL0DBQtlmwOY/Q9v6MT4q0uxSlvJ6GgbJFZXUdS7YXcuXALgT6te7t+5RSF07DQNli6Y4CTtTW61lESrkIDQNli/mbDxEX3p5h3fQOp0q5Ag0D5XRFZVV8llnMtMGx+Pjo9BNKuQINA+V0C78+hMPAtCE6/YRSrkLDQDndgi15XBQbSq+oDnaXopSyaBgop8osLCcjr0wPHCvlYjQMlFMt2JKHr49w9SAdIlLKlWgYKKdxOAwLNh/ikl4RRHYItLscpVQjGgbKaTZkl5J39IQOESnlgjQMlNMs2JJHUIAvk5Kj7S5FKXUaDQPlFFW19Xy8NZ+05BiCAvzsLkcpdRoNA+UU6buLKK+q0yEipVyUhoFyivmb84jsEMjoxM52l6KUaoKGgWpzR4/XsHJXMdcM6oqfr+5ySrmiFv9kioiviGwWkY+t5wkisk5EMkVkrogEWO2B1vMs6/Uejd7jUat9t4iktYiDQn0AAA+ISURBVLQm5Vr+uy2fmnqHDhEp5cJa48+0B4GdjZ7/CXjWGNMbOALcbbXfDRwxxvQCnrWWQ0T6A7cAycBk4EUR0QnuPciCzXn0igohuWtHu0tRSp1Bi8JAROKAK4HXrOcCXAZ8YC0yB5hmPZ5qPcd6fYK1/FTgXWNMtTFmP5AFjGhJXcp17C+pZEP2Ea4dEkvDf7dSyhW19By/2cDPgZMzjnUGjhpj6qznucDJsYFY4CCAMaZORI5Zy8cCaxu9Z+N1vkFEZgAzAKKjo0lPT7/gggsrHRyrOA7NWNfTVFRUNKsPL8TLX1cR4AOx1Tmkp+e26baayxn94A60H07xxr5odhiIyFVAkTHmKxFJPdncxKLmHK+dbZ1vNhrzCvAKQEpKiklNTW1qsTOqdxgu/dMKQnx8WXLTOK//SzU9PZ0L7cMLsTO/jLVL1nDvuESmTU5qs+20VFv3g7vQfjjFG/uiJcNEY4BrRCQbeJeG4aHZQJiInAyZOOCQ9TgXiAewXg8FShu3N7FOq/L1Ee5NTWTPEQerM0vaYhOqkaeW7KZDoB/3jk20uxSl1Dk0OwyMMY8aY+KMMT1oOAC8whhzG7ASuMFabDrwkfV4ofUc6/UVxhhjtd9inW2UAPQG1je3rnO5ZXg3OrcTnl66m4bNq7awMbuU5buKuGdcIqFB/naXo5Q6h7Y46fsXwEwRyaLhmMDrVvvrQGerfSbwCIAxZjvwHrADWAzcZ4ypb4O6AAjw82FaL3+25h5j6Y7CttqMVzPG8OfFu4nsEMidY3rYXY5S6jy0yiQxxph0IN16vI8mzgYyxlQBN55h/SeBJ1ujlvMxuqsfKwv8eWbpHib2i9b78Lay9D3FrM8u5YmpyToPkVJuwisvB/X1ER6a2IfdheX8Z2ubHJ7wWg6H4S+LdxPfqT03D+9mdzlKqfPklWEAcNVFXUiK6cDsTzOpq3fYXY7H+HhbPjvyy/jpxL4E+Hnt7qWU2/Han1YfH2HmxD7sL6lk3qY8u8vxCLX1Dp5ZupukmA5co7e1VMqteG0YAEzsH82guFD+ujyT6ro2O2btNd7fmEv24eM8PKmvHodRys14dRiICD+d1Je8oyd4d/1Bu8txa1W19fx1+R6GdQ9nQr8ou8tRSl0grw4DgEt7RzAioRPPr8ziRI1+OmiuOV9kU1hWzc/T+nr9ld1KuSOvDwMR4eFJfSkur+atL7PtLsctHTtRy4vpexnXJ5KRPfXmNUq5I68PA4ARCZ0Y2yeSv6/aS3lVrd3luJ1XV+/j2IlafpbW1+5SlFLNpGFgeXhSH44cr+WNz7LtLsWtFJdX88bn+7lqYBcGxIbaXY5Sqpk0DCwD48JIS47mtTX7OHq8xu5y3MYLK7OornPw00n6qUApd6Zh0MjMiX2pqKnj5dX77C7FLRwsPc6/1x3gppR4EiKC7S5HKdUCGgaN9LUulnrz82yKyqvsLsflPfvpHnxEeHBCb7tLUUq1kIbBaR66vA819Q5eXLnX7lJc2u6CcuZvzmP66B7EhLazuxylVAtpGJwmISKYG4bG8fa6HA4dPWF3OS7rqaW7CQnw44fj9MY1SnkCDYMm/PjyhmGPv63ItLkS17Qp5wjLdhQyY2xPwoMD7C5HKdUKNAyaEBvWnltHxPPexlyySyrtLselNNy4ZhcRIQHcdUmC3eUopVqJhsEZ3HdZL/x9hb8u108Hja3JLGHtvlLuH9+L4EC9cY1SnkLD4AyiOrRj+ugeLNiSx57CcrvLcQnGGP6yZHfDJ6eReuMapTyJhsFZ3Ds2keAAP55dtsfuUlzCwq8PsS3vGD+Z2IdAP1+7y1FKtSINg7MIDw7g7ksSWJRRQEbeMbvLsVVhWRWzFm5nUFwo1w6JtbscpVQr0zA4h7svTSAsyJ8/Ld6FMcbucmxhjOHh97+mqraeZ24ejK/euEYpj6NhcA4d2/nz4ITerMks4d/rcuwuxxb/XHuANZklPHZlfxIjQ+wuRynVBjQMzsP0i3swrk8kT3y8g90F3nUwOauogt9/spNxfSL5rh40VspjaRicBx8f4embBtGxvT/3v73Ja+6IVlvvYOZ7W2jn78tfbhiodzBTyoM1OwxEJF5EVorIThHZLiIPWu2dRGSZiGRaX8OtdhGR50QkS0S2isjQRu813Vo+U0Smt/zban0RIYE8e9Ngsoor+O3HO+wuxyn+tjyTrbnH+MO1FxHVUecfUsqTteSTQR3wU2NMP2AUcJ+I9AceAZYbY3oDy63nAFOA3ta/GcBL0BAewCxgJDACmHUyQFzNJb0juHdcIu+sz+G/W/PtLqdNbco5wvMrs7h+aBxTLupidzlKqTbW7DAwxuQbYzZZj8uBnUAsMBWYYy02B5hmPZ4KvGUarAXCRKQLkAYsM8aUGmOOAMuAyc2tq63NnNiHwfFhPDJvKwdLj9tdTpuorK5j5twtdAltz6xr+ttdjlLKCaQ1TpcUkR7AamAAkGOMCWv02hFjTLiIfAz80RjzmdW+HPgFkAq0M8b8zmr/P+CEMeapJrYzg4ZPFURHRw979913m1VvRUUFISHNPyum+LiDx784QWyID4+MaIefm55qeaZ+eDOjmlW5dfxiRDuSOnn+xWUt3R88hfbDKZ7cF+PHj//KGJNyenuLJ5cRkRDgQ+AhY0zZWQ4yNvWCOUv7txuNeQV4BSAlJcWkpqZecL0A6enpNHfdk9rFHuKBdzazubYLP0tLatF72aWpfli+s5D0xRu5Z2xP7r2inz2FOVlr7A+eQPvhFG/sixadTSQi/jQEwb+NMfOs5kJr+Afra5HVngvEN1o9Djh0lnaXdvWgrtycEs+L6Xv5IqvE7nJaxeGKan7x4VaSYjowc1Ifu8tRSjlRS84mEuB1YKcx5plGLy0ETp4RNB34qFH7HdZZRaOAY8aYfGAJMElEwq0Dx5OsNpc365r+9IwI5qG5WzhcUW13OS1ijOHRedsoO1HH7FsG69xDSnmZlnwyGAPcDlwmIlusf1cAfwQmikgmMNF6DvAJsA/IAl4FfgRgjCkFngA2WP9+a7W5vKAAP57/zlCOnqjl4fe/xuFw3+kq3t+Yy9IdhfwsrS9JMR3tLkcp5WTNPmZgHQg+0wGCCU0sb4D7zvBebwBvNLcWO/Xr0pFfXdmPxz/azj++yOZuN7zhS87h4/zmP9sZ1bOTW9avlGo5vQK5Fdw+qjuT+kfzx0U73W5203qHYeZ7W/AR4akbB+HjpmdGKaVaRsOgFYgIf75hIBEhgTzwzmYqquvsLum8vbx6LxsPHOG305KJCw+yuxyllE00DFpJWFAAs28ezIHDlTz+UYbd5ZyXA2X1PLtsD1de1IVpg/UeBUp5Mw2DVjSyZ2ceuKw38zblMX9zrt3lnFVVbT0vb60mPCiA300boJPQKeXlNAxa2QOX9WJEj078an4G+0sq7S6nSQXHqnjgnc0cqjD85cZBhAcH2F2SUspmGgatzM/Xh9m3DMbP14f7397kUvMXVdXW87flmYx/Kp1Ve4q5uW8A4/pE2l2WUsoFaBi0ga5h7XnmpkFkFlUw/ql0fjl/G4eOnrCtHmMMn2zLZ8LTq3h62R5S+0ayfOY4piT421aTUsq1tHhuItW0Cf2iWfWzVF5cuZd3N+TwwcZcbh0Rz4/G9yLaifcG2HGojN/8Zzvr9peSFNOBt38wktGJEQDsdVoVSilXp2HQhrqEtueJaQO4Z1xPXliZxb/X5fDuhoN8d1R37h2XSGSHwDbb9uGKap5etod31+cQ2t6f300bwC3D4/Hz1Q+DSqlv0zBwgrjwIP5w3UB+OK4Xz63I5B+f7+ftdTncMbo794xNpFMrHsCtrXfw1pcHmP3pHk7U1PO90Qk8OKE3oUE6JKSUOjMNAyfq1jmIp24cxI9SE3lueSavrN7Hv748wJ1jEvj+pQmEBbUsFNJ3F/HExzvYW1zJ2D6RPH5VP3pFdWil6pVSnkzDwAY9I0OYfcsQ7hvfi9nLM3l+ZRZzvsjm7ksTuOuSBDq2+/Zf8Q6HoabeQXWdg5o6BzX1DV+r6+opO1HH31ftZcWuIhIignnjeymM7xul1w4opc6bhoGNekd34IXvDOX+8WXM/nQPsz/N5LU1++kUHPC/X/Qnf/HX1p99RtQOgX48dkU/po/uQYCfHhdQSl0YDQMX0K9LR16+PYWMvGP888sDVNXVE+jnQ4CfDwG+vgT6+xDg2/A80PrX8Nj3f22D48PoHNJ2B6SVUp5Nw8CFDIgN5U83DLS7DKWUF9LxBKWUUhoGSimlNAyUUkqhYaCUUgoNA6WUUmgYKKWUQsNAKaUUGgZKKaUAMebs0xy4KhEpBg40c/UIoKQVy3FX2g8NtB8aaD+c4sl90d0Y861bHLptGLSEiGw0xqTYXYfdtB8aaD800H44xRv7QoeJlFJKaRgopZTy3jB4xe4CXIT2QwPthwbaD6d4XV945TEDpZRS3+StnwyUUko1omGglFLKu8JARCaLyG4RyRKRR+yux04iki0i20Rki4hstLseZxGRN0SkSEQyGrV1EpFlIpJpfQ23s0ZnOEM//FpE8qx9YouIXGFnjc4gIvEislJEdorIdhF50Gr3un3Ca8JARHyBF4ApQH/gVhHpb29VthtvjBnsZedTvwlMPq3tEWC5MaY3sNx67une5Nv9APCstU8MNsZ84uSa7FAH/NQY0w8YBdxn/V7wun3Ca8IAGAFkGWP2GWNqgHeBqTbXpJzMGLMaKD2teSowx3o8B5jm1KJscIZ+8DrGmHxjzCbrcTmwE4jFC/cJbwqDWOBgo+e5Vpu3MsBSEflKRGbYXYzNoo0x+dDwywGIsrkeO90vIlutYSSPHxppTER6AEOAdXjhPuFNYSBNtHnzebVjjDFDaRg2u09ExtpdkLLdS0AiMBjIB562txznEZEQ4EPgIWNMmd312MGbwiAXiG/0PA44ZFMttjPGHLK+FgHzaRhG81aFItIFwPpaZHM9tjDGFBpj6o0xDuBVvGSfEBF/GoLg38aYeVaz1+0T3hQGG4DeIpIgIgHALcBCm2uyhYgEi0iHk4+BSUDG2dfyaAuB6dbj6cBHNtZim5O//CzX4gX7hIgI8Dqw0xjzTKOXvG6f8KorkK1T5WYDvsAbxpgnbS7JFiLSk4ZPAwB+wNve0hci8g6QSsMUxYXALGAB8B7QDcgBbjTGePTB1TP0QyoNQ0QGyAbuOTlu7qlE5BJgDbANcFjNv6ThuIF37RPeFAZKKaWa5k3DREoppc5Aw0AppZSGgVJKKQ0DpZRSaBgopZRCw0AppRQaBkoppYD/B9/jUKmBAFAMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.grid()\n",
    "plt.plot(keys,hour)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### between 12pm and 7pm is probably the best time to advertise to maximise product purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### What product sold the most? &  Why?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe60459c48>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "all_data.groupby('Product')['Quantity Ordered'].sum().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Product\n",
       "20in Monitor                   109.99\n",
       "27in 4K Gaming Monitor         389.99\n",
       "27in FHD Monitor               149.99\n",
       "34in Ultrawide Monitor         379.99\n",
       "AA Batteries (4-pack)            3.84\n",
       "AAA Batteries (4-pack)           2.99\n",
       "Apple Airpods Headphones       150.00\n",
       "Bose SoundSport Headphones      99.99\n",
       "Flatscreen TV                  300.00\n",
       "Google Phone                   600.00\n",
       "LG Dryer                       600.00\n",
       "LG Washing Machine             600.00\n",
       "Lightning Charging Cable        14.95\n",
       "Macbook Pro Laptop            1700.00\n",
       "ThinkPad Laptop                999.99\n",
       "USB-C Charging Cable            11.95\n",
       "Vareebadd Phone                400.00\n",
       "Wired Headphones                11.99\n",
       "iPhone                         700.00\n",
       "Name: Price Each, dtype: float64"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.groupby('Product')['Price Each'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [],
   "source": [
    "products=all_data.groupby('Product')['Quantity Ordered'].sum().index\n",
    "quantity=all_data.groupby('Product')['Quantity Ordered'].sum()\n",
    "prices=all_data.groupby('Product')['Price Each'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '20in Monitor'),\n",
       " Text(0, 0, '27in 4K Gaming Monitor'),\n",
       " Text(0, 0, '27in FHD Monitor'),\n",
       " Text(0, 0, '34in Ultrawide Monitor'),\n",
       " Text(0, 0, 'AA Batteries (4-pack)'),\n",
       " Text(0, 0, 'AAA Batteries (4-pack)'),\n",
       " Text(0, 0, 'Apple Airpods Headphones'),\n",
       " Text(0, 0, 'Bose SoundSport Headphones'),\n",
       " Text(0, 0, 'Flatscreen TV'),\n",
       " Text(0, 0, 'Google Phone'),\n",
       " Text(0, 0, 'LG Dryer'),\n",
       " Text(0, 0, 'LG Washing Machine'),\n",
       " Text(0, 0, 'Lightning Charging Cable'),\n",
       " Text(0, 0, 'Macbook Pro Laptop'),\n",
       " Text(0, 0, 'ThinkPad Laptop'),\n",
       " Text(0, 0, 'USB-C Charging Cable'),\n",
       " Text(0, 0, 'Vareebadd Phone'),\n",
       " Text(0, 0, 'Wired Headphones'),\n",
       " Text(0, 0, 'iPhone')]"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 2880x1728 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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pme2c9IiskESkA9aI6deq+lVDh2YZ0zzGNx5UnaCqw1V1eEWF9xZ0nEKRVh27MDvtBK1bu0IqZyIpJBFphSmju1T1wWB4QWBuI3gO8raZC/QJifcG5uUY751l3HGcIiXNpNgMrVvD4MEeaVfO5FRIQdTczcC7qnpF6KNJwNjg9VjgodD4iWLsCSwLTHqPAweLSNcgmOFg4PHgs+Uismcw14mhczmOU4Q0hkICM9u9/jp4jG15EmWHtDdwAnCAiEwPHiOBi4GDRORD4KDgPVjY9hxgFnAj8HMAVV0MXAi8FjwuCMYAfgbcFMjMBh5N4bs5jtNIZBRSkm6x2Rg6FJYuhTlz0j2vUxrkdMSo6lSy+3kADsxyvAKn1XOuW4BbsoxXAoNyrcVxnOKgMXdIYH6kbbdN99xONETkFuBwYKGqDgrGUss7bQiv1OA4TmyqqkAEunVL97yDBlmSrAc2FJTb2DgXNM2803pxheQ4TmyqqmDTTa24apq0aQM77+wKqZCo6nPA4jrDqeSd5prbFZLjOLFJOyk2zKBB8P77jXNuB4CKTD5n8BgXQSatvNOGFxZl9Y7jOGEaUyH17w8TJ8KqVdC2bePMUeZUq+rwlM6VOL80jO+QHMeJzcKFjauQwCPtioy08k4bxBWS4zixaewdEsCsWY1zficvUsk7zTWJm+wcx4nFunWweLErpOaKiNwD7Ad0F5G5WLTcxcD9InIy8ClwTHD4ZCzkexYW9n0SWN6piGTyTmHDvNN6cYXkOE4svvzSKimknRSboWtXCyd3hVQYVHVUPR+lknfaEG6ycxwnFo2VFBumf39XSOWIKyTHcWLhCslpLFwhOY4Ti6ZSSJ9+CqtXN94cTvHhCslxnFg0lUKqqYGPP268OZziwxWS4zixyCikTTdtvDk80q48cYXkOE4sFi60SLhWrRpvDldI5YkrJMdxYtGYSbEZuneHTp1cIZUbrpAcx4lFUygkEY+0K0dcITmOE4uqqsZLig3jCqn8cIXkOE4smmKHBKaQPv4Y1q5t/Lmc4sAVkuM4kampsdJBTaWQqqstH8kpD1whOY4TmSVLrLhqUykkcLNdOeEKyXGcyDRFUmwGV0jlhyskx3EiszBoy9YUCmmLLaB9e1dI5YQrJMdxItOUOyQP/S4/vB+SUxDkfIkto+O1EVbixKEpFRKYQnr77aaZyyk8OXdIInKLiCwUkbdCY38Ukc9FZHrwGBn67FwRmSUi74vIIaHxQ4OxWSJyTmh8axF5RUQ+FJH7RKR1ml/QcZz0yCik7t2bZr7+/WHOHAukcJo/UUx2twGHZhm/UlWHBI/JACIyEDge2CmQuV5EWopIS+A64DBgIDAqOBbgkuBcA4AlwMlJvpDjOI1HVRV07gxt2jTNfP37w5o1MHdu08znFJacCklVnwNy9kIPOBK4V1VXq+pHWJ/13YPHLFWdo6prgHuBI0VEgAOAfwTytwNHxfwOjuM0EU2VFJvBI+3KiyRBDaeLyMzApNc1GOsFfBY6Zm4wVt/4psBSVa2uM+44ThHiCslpTPJVSDcA2wJDgPnA5cF4Nk+15jGeFREZJyKVIlJZXV1d32GO4zQSTa2QevaETTZxhdRUiMj2odiA6SLylYj8Op+4gXzIK8pOVReEFnMj8Ejwdi7QJ3Rob2Be8Drb+CKgi4hUBLuk8PHZ5p0ATABo3769h1w5ThNTVQW77dZ087VoAdtu6wqpqVDV97GNBoHv/3PgX8BJmK//L+Hj68QN9ASeFJHtVDWvMJS8dkgismXo7dFAJgJvEnC8iLQRka2BAcCrwGvAgCCirnXwBSapqgLPAD8I5McCD+WzJsdxGhlt+h0SeC5SATkQmK2qnzRwTH1xA3kRJez7HuAlYHsRmSsiJwOXisibIjIT2B84A0BV3wbuB94BHgNOU9V1we7ndOBx4F3g/uBYgN8BZ4rILMyndHO+X8ZxnEbkmy5UVxdGIc2ebYVdnVSoyLg+gse4eo47Hrgn9D5O3EB+C8t1gKqOyjJcr9JQ1YuAi7KMTwYmZxmfQwKN6jhOE7HCNFEhFNKqVTB/PvTykKc0qFbV4Q0dEFiyjgDODYZuAC7EfPwXYnEDPyZmHEAuvHSQ4zjRWFk4hQRutmtiDgNez8QLqOqCwNpVA9xI7SaiobiB2LhCchwnGsEOqSm6xYZxhVQQRhEy1+URN5AXXsvOcZxoFGiH1KcPtGpVXgpJ1YrLFgIRaQccBPw0NHypiAzBzHEfZz5T1bdFJBM3UE0QN5Dv3K6QHMeJRoF8SC1bwjbblJdC2n57GDUKzj+/6edW1ZVYgFl47IQGjs8aN5APbrJzHCcaK3vQoYMlqjY15RT6PX8+fPghdOlS6JU0Pb5DckoSb19RAFb0aPLdUYb+/eHZZwtrymoqpk2z5+ENxsE1T3yH5DhONFZsVlCF9PXXtR1rmzPTppnS3XXXQq+k6XGF5DhONFYWdocE5WG2q6yEHXaADh0KvZKmxxWS4zjRKLDJDspHIZWjuQ5cITmOEwWloDukfv0s2u7DDwszf1Mxbx588YUrJMdxnPpZ3RHWtWnypNgMrVrBVls1/x1SZaU9u0JyHMepjwIlxYYph9DvykpruTFkSKFXUhhcITmOk5sCJcWGySgkbcbR+5WVMHAgtGtX6JUUBldIjuPkpkh2SMuWwZdfFm4NjYmqhXyXq7kOPDHWcVJl6VKYPr2wa+jQoREuakWyQwLbJXXvXrh1NBZz51qe1bBhhV5J4XCF5Dgpctxx8MQThV4FvPACfOtbKZ5whUUzFItC2nPPwq2jsSj3gAZwheQ4qTFtmimjM86AI44ozBpWr4bDDoOnn05ZIa3sARUrad++cM6Nrbe2CgbNNbBh2jQLbd9ll0KvpHC4QnKclLjsMujUCcaPh86dC7eOQYPg+edTPumKHtC+CuiX8omj06YN9O3bfBVSZaX97tq2LfRKCocHNThOCsyeDQ88AKeeWlhlBDBiBLz4IlRXp3jSlT2gXVWKJ8yP5hr6rWoKqZz9R+AKyXFS4fLLoaICfvWrQq/EFNLXX8OMGSmedEUPaF/4yqbNVSF98olFD5az/whcITlOYhYuhFtvhRNOgJ49C70a2Gcfe546NcWTrsyY7ApL//524V6ypNArSZdybjkRxhWS4yTkmmssmOC3vy30Sozeva3MTqp+pBXFY7IDM5E2JyorbYe9886FXklhcYXkOAn4+mu47jo48khrO10sjBhhCimVqgZr2kF1u6LZIUHzM9tVVpoyKkQ33mLCFZLjJOCmm8x89LvfFXolGzJihJkSU6mOHeQgFcMOaZtt7Lk5KSSv0FCLKyTHyZO1a+GKK2DffYsvUXPECHtOxWwXVGkohh1Su3bQq1fzUkgffWQ3NcWikETkYxF5U0Smi0hlMNZNRKaIyIfBc9dgXETkahGZJSIzRWRokrlzKiQRuUVEForIW6Gx2IsTkbHB8R+KyNjQ+LDgy88KZCXJF3KcpuLee+Gzz+Dsswu9ko3Zfnsrr5OKQgrq2BXDDgmaX6RdpkJDkYV876+qQ1Q1oybPAZ5S1QHAU8F7gMOAAcFjHHBDkkmj7JBuAw6tMxZrcSLSDRgP7AHsDozPKLHgmHEhubpzOU7RoQqXXmqJjCNHFno1GyNi0XapRNoV0Q4JmqdCat3a/paKmCOB24PXtwNHhcYnqvEy0EVEtsx3kpwKSVWfAxYnXNwhwBRVXayqS4ApwKHBZ51U9SVVVWBi6FyOU7Q8+ii89ZZF1hXrnn7ECItGmz8/4YmKcIe0YAEsX17olaTDtGkweLBVomgiKkSkMvQYV+dzBZ4QkWmhzzZX1fkAwXOmVWMv4LOQ7NxgLL+F5Sm3weJEJNfiGhqfm2U8K8EPZxxA69at81y64yTnkkugTx8YNarQK6mfsB/p2GMTnGhFD2j5DbQpDg3QnEK/a2pMITXx31F1yBSXjb1VdV5wXZ8iIu81cGy227G8YzvTDmqob3Fxx7OiqhNUdbiqDq+o8DJ8TmF4+WV47jk480xrrV2s7LortG+fgh8pkxRbJDvB5hT6PXu29XgqJv+Rqs4LnhcC/8LcLAsyprjgOVO2Yy7QJyTeG5iX79z5KqS4i2tovHeWcccpWi69FLp2hZ/8pNAraZiKCthrrxQUUpEkxWbYdlt7bg4KqdgqNIhIexHpmHkNHAy8BUwCMsFoY4GHgteTgBODgLY9gWUZ61k+5KuQ4i7uceBgEekaBDMcDDwefLZcRPYMoutODJ3LcYqO99+Hf/8bTjvNGuEVO/vsAzNnWuPAvCmSskEZOnaEzTdvHgqpstJ8RzvtVOiVrGdzYKqIzABeBf6jqo8BFwMHiciHwEHBe4DJwBxgFnAj8PMkk+e0e4nIPcB+QHcRmYtFy10M3C8iJwOfAseEFjcyWNxK4CQAVV0sIhcCrwXHXaCqmUCJn2GRfG2BR4OH4xQlf/mLXUB+8YtCryQaI0ZYROBLL1mfpLxYsRl0SyPDNj3WR9r1yXloUVNZaf2PisX0q6pzgI06Mqnql8CBWcYVOC2t+XMqJFWtz90Wa3GqegtwS5bxSqC4Ax4dB4tWmzgRTj4ZNtss9/HFwJ57munu+eeTKKTi2iGBKaQnnwT2L/RK8qemBl5/3YryOoZXanCciFx1lfUYOuusQq8kOu3amcM8bz/S2k1gbYei8iGBKaTPPwfWlG43uw8/tND1YvEfFQOukBwnAsuWwQ03wA9+UOtULxVGjIBXX4VvvslDuMiSYjNkIu1Ysk1B15GETIUGV0i1uEJynAj8/e/w1VfFWSYoFyNGwJo18NpruY/diCJLis2wXiEt7t/gccVMZaW1K99xx0KvpHhwheQ4OVi9Gv76VzjwwOLKF4nK3nvbc15mu/U7pMJ3iw2zfpdawgpp2jQYMsR8fI7hCslxcnDnnRbQUGwtJqKy6aYwcGCede1WFqfJrmtX+16NoZBeew122AHmzs19bL6sW2cBDW6u2xBXSCnx5ZcWLfPOO4VeiZMmNTVw2WVW9eA73yn0avJnxAh44QW7EMZiRXGa7CAw2zWCQrr+ess3u+OO1E+9nvffhxUrSnPH3Zi4QkqJv/zF7qR/+tOUunTmwd13wyOPFGbu5sqkSXbxOPvs4i2iGoURI8wH9uabMQVX9oAWa2CTZY2yriQ0hkJauRL++U97fccdjfe/7AEN2XGFlAJffgnXXmuNw6ZOhfvvb/o1vP02jB1rCjH2XbCTFVUrorr11hZdV8rk3bBvxWbQblHR1LEL078/sKwvVKdXaPnhhy0U+9hj4d13Yfr01E69AdOmWUj+Djs0zvlLFVdIKXDFFbb9fvRRc1L+9rd2p9VUqMLpp5simjcPnn666eZuzkydaoVUzzqr9B3PfftadfL4Cqn4kmIzWKRdC1iydWrnvPNO6N3bbjBbtYK77krt1BtQWQlDh0LLlo1z/lLFFVJCFi+Ga66xO+idd7bkyc8+swKcTcU998B//wtXXgldusDtt+cUcSJwySXWdfWkkwq9knQYMcKUbCwz1MriKqwaJu3Q70WL4LHH4Ic/hB49rPHi3Xenb3GoroY33nD/UTZcISXkyitti//739v7ffe17f4ll8Cnnzb+/MuW2R388OG2SzruOHjwwebTvKxQvPUW/Oc/8MtfmmmlOTBihEULzpkTQ6jod0ikppDuv9+UxejR9n7MGPt5PfNMKqdfz3vvwapV7j/KRokbIgrLkiVw9dXw/e/b7ijDZZeZM/zss+HeezeWk/PjG+R1fPbb2vHjrXvmpEm2/R871pI4//GP5nNnXwguu8wU0c8T1S4uLsJ+pMjVJop4h7TppkCbpakppDvvtP/jwYPt/eGHQ6dONp5mhKUHNNSP75AS8Ne/WuTSH/6w4Xjfvpazct99KfSiaYAZM8xcOG4c7Labje25JwwY4Ga7JHz2mZlqTjkluOg1E3bcEbp1i/E3Wd0aVncuuqTYDCJAt1mpKKQ5c6wi+pgxtWObbGKm+AcfTNcnXFlprUu22y69czYXXCHlydKl5i/63vdq76jCnH22OUd/+cvGiXqrqbGePN26wZ/+VDsuAieeCM8+Cx9/nP685cCVV5qf5YwzCr2SdGnRwqo2REI250YAACAASURBVFZIK7vbc5Ga7IDUFNJdd9n/Tt1W4mPGmPn74YcTT7GeTEBDC7/6boT/SPLkqqvMf1N3d5ShXTsz+0yfDrds1HQjORMnWqLjJZeYUgqTKWffmIl9zZXFi2HCBLsw9etX6NWkz4gRVmX6iy8iHFzESbHr6TYLlm4F6/L3PqiaWe7b37ZIxDDf/ralc9x5Z7JlrmddBTNmuLmuPtyHlAfLlpm57qijrLlWfRx3HFx3Hfzv/8Ixx1gEXBosWWI7sL32gh/9aOPP+/WD/fYzpXXeeaWd0NnU3HCDhfCXYhHVKGT8SC+8EOHgIi0btAHdZoFWwNJ+sOnsvE5RWQkffJD9d96ihUXdXXmlReF1755wvVUD+eabWoWUpj+5OeA7pDy4+moz2dW3O8ogYjupRYvgwgvTm/+88ywZ9/rr69/2jx1rHTVfeim9eZs7n31mFTdGjtwwSKU5MXSoVZiOZLZbEXQhLPYdEiQy2911F7RubcFJ2RgzxqLvUkl4n2eayEO+s+MKKSZffWV3S0ccYfXNcjF0qHUYvfpqK0GTlGnT7C7+tNMsCbc+vv99MxtOnJh8znKgpsaiEteutZuI5krr1hb4Ek0hlcgOCfJWSNXVlsf33e/Wb8EYPBgGDUrJbDdvGJ06hULWnQ1whRSTa64xk1mu3VGYiy4y5ZDUSV5TY2HIm20GF1zQ8LEdO1rAxX335dmYrcy45hp46im72WjuF4t99glK4nzTseEDV/YAqYZNljTJuvKiwwJo9XXeCunJJ2Hhwg2j67IxZoxZG2LlcGVj3nCGDfOAhvrwH0sMli+3MkFs9zDDHxHk/GiPzW8Qxo+30kKTJ+c//803W+fPyy6L5o8aO9ZMi5Mm5T9nOfDOOxamf/jh8JOfFHo1jc+IEXZzw9y9Gj5wRQ9o9yW0KGKfRcLQ77vusv+lww5r+Lgf/rD2+LypbgULdinqgAYR6SMiz4jIuyLytoj8Khj/o4h8LiLTg8fIkMy5IjJLRN4XkUOSzO9BDTG49lqLwuL758eWPf10S1g94wzg2FZQsTaW/KJFcM45VgnixNnCiVGWUNMCOn7CcedN59hjD4+95nJgzRq7++3YEW66qTwCQPbay5Ko130yAvo/Uf+BRZwUuwHdZsHCQbHFvv7acozGjIE2bRo+tk8fCxS6884EgUILB8G6NsXuP6oGzlLV10WkIzBNRKYEn12pqn8JHywiA4HjgZ2AnsCTIrKdquaV7OI7pIgsXw6XX24Ob3pNiy3furWZgz74AHj19Njy555r0X3XXUf0ysstamCXO2DWoSxYEHvKsuCCC6yu2I03wuabF3o1TUOHDoH/89N9Gj5wRY+iTYrdgG6zYMk2dgMWg4cesoTXXOa6DKNH2//vtPj//sZ800TFvENS1fmq+nrwejnwLtCrAZEjgXtVdbWqfgTMAnbPd35XSBG57jqLbBs/Pv9zjBwZKLRnx8PXm0UX/GwPbroJfv1rc67GYpeJoBXcfXdMuTLgxRfhz3+GH//YQvjLiREjgM/3aLh1w8rirWO3Ad1mQU1rWNYn97Eh7rrLqqpkWrzn4gc/sBvLvIMb5g2HTZawzTZ5yqdHhYhUhh7jsh0kIlsBuwKvBEOni8hMEblFRLoGY72Az0Jic2lYgTWIK6QIfP21hQMfeijsnrfuN664AljbDp7+v2gCNS1g8nX07JmnMuzxHvR81UsJ1eHrry2BuF8/yykrN0aMAKrbwrwG7EcrSshkB7H8SAsWwBNP2K4naoBBly4WjXfPPRadF5t5w2HLacVgFq5W1eGhx4S6B4hIB+CfwK9V9SvgBmBbYAgwH7g8c2iW8+ftdEzkQxKRj4HlwDqCLyki3YD7gK2Aj4FjVXWJiAhwFTASWAn8KLM1FJGxwHnBaf9PVYvq8nn99cl3Rxm23x7Y42p46QwYfgP0fKNhgcpTYf4wrrjX/Bx5MeR2ZkzenRkzGk7kLSfOPBM++gieey7/n2spJzWu3xV8OgL6ZklWW1cB33QrnR0SmELa9qlIIvfdZyW9oprrMowebR1ln3zSblAjU90aFuwMe10BpFiptREQkVaYMrpLVR8EUNUFoc9vBDK9qecC4a1pb2BevnOnsUPaX1WHqGrGMnoO8JSqDgCeCt4DHAYMCB7jMI1LoMDGA3tgtsfxoe1gwVmxwnZHhxxi+Rup8O0LrAvnY1c1fC/xdQ946iLYZgrHHptgvkH30qqV5yRlePhh8xmdfbaFQJcjm20GbPoefDIi+wGZOnalsEPqOA8qVsXaId15p+XxDRwYb6qRI22nFDvabsHOZlbsWRlTsGkJNg43A++q6hWh8S1Dhx0NvBW8ngQcLyJtRGRr7Pr+ar7zN4bJ7kggs8O5HTgqND5RjZeBLsGXPASYoqqLVXUJMAWIc+/RqNxwA1RVpbM7Ws8mX8GB/2N3p283oGmmXGrmvcN+kWyb324xhx9u/0R5mRqaEQsXWmj3LrvA+fGDJZsX/Z6Hz/aGmix/XKWQFJuhhcYK/f7gA3jttfi7I7BovGOPtei8r7+OIRhUaCh2hQTsDZwAHFAnxPtSEXlTRGYC+wNnAKjq28D9wDvAY8Bp+UbYQXKFpMATIjIt5BjbXFXnB4udD2S89/U5v1J1iqXJypWW83PQQRYqmyq73gpbvA5PXAZr2m78+Sd7w4wfwbcuhx7JSzyceGKt3bxcUbVWHUuX2h1yrlDfZk/f5+GbrlC108afrSyBwqphYiik+ip7R2X0aLs2PPRQDKF5w6Htl9Dlk/wmbSJUdaqqiqoODixfQ1R1sqqeoKo7B+NHZK7xgcxFqrqtqm6vqo8mmT+pQtpbVYdi5rjTRGTfBo6tz/kV2SkmIuMykSHVTXCr/7e/2R11qrujDC1q4LBfwVd94YU6VR3XtYT/XA+dP4F9IwY/5GDkSOvtU87BDbfeaheRP/85j2jF5ki/oH5QNrNdKe2QIAj93jb7bi9MUNn7gAOgZ8/8ptpnH4vOi2W2mz/MdkeFD2goahIFNajqvOB5oYj8C/MBLRCRLVV1fmCSyyQy1Of8mgvsV2f8v/XMNwGYANC+fftG9Q6vXAmXXgoHHhg9LDQ2/abCoHvghd/BrrdAl2Cj+OrpsHAwHHc0tE6nM1jr1pZtPmGC7RDKjiVb8au/wP77W/h8KQckpEaXj6Hj52Y63v2GDT+LsEMqqp9ht1kWNbi8J3T+vP7j5u7BnDnw+9/nP1WLFrZLuvRSYKfNoEOOXK21m1hS7N6X5j9pmZD3DklE2geZvIhIe+BgzNE1CRgbHDYWyGxsJwEnirEnsCzY9j0OHCwiXYNghoODsYIyYYKZuBpldxTmO78D1PxFAF9tCc9cAP0fhR3+nepUJ54Iq1enVLW4lKhpAf+aSIsWcNttXkdsPYKZ7T4ZsbFNYkUPoMZKB5UCUUO/Z45hk02szmMSxowJGm++dVzugxcMhppWpeA/KjhJ/jU3B6aKyAwsquI/qvoYcDFwkIh8CBwUvAeYDMzBMnlvBH4OoKqLgQuB14LHBcFYwVi1yhrf7b9/bf+YRqPLZ7DPJfD28fDJPvDEX2BdazjsF6lv74cNs6iisjPbvfgb+HQE111nphYnRL/nYXlva3IXZmWmjl1NQZYVmygKaV0FvHU8RxwBnTolm27gwKDa/pujcx+cyfXaMt8SD+VD3iY7VZ0DbJTVoqpfAgdmGVfgtHrOdQvQCH1V8+PGG62j5r33NtGE37oMXj8Z/nk3fNUHvn1+3s3GGiLT3vycc4Bh2zbKHEXH/F3g6Qth4P2MHp0kdr6Z0jfwI326D3T9uHa8VJJiM3SaCy1XN6yQZh0Cq7rnFV2XjTFjYPpv9oBFA6D7h/UfOG84tFsInT+r/xgHKNPiqg3avte2gatnQ78P2e+/+6/3ZjWq/6D1Kjj4N/CP+6HLHNjn4twyeTJmjNXF0xknwgGNbY+MyRc7w+Rroc9LMPhO2Pyt3DINsbYNPHin5Xwd/jNEXCFtxGZvQZulZrbbJVQTZ8VmTRLQkJofqkUNdJ3TsEJ6czS0XcQhhyRt+2qMGgW/+U2NnXf/P9Z/4LzhHtAQEbem1+X1n8DyXrBfEyep7PQAHHguHHMstGq8Bka9esF3vgPMPCF3RFJTsmgA3PGE2dtfPAtueBNueANeOAu+yjMc6umLoGoQHPljaFdQK3Dx0kKh7wsW2BCmVCp9h2ko9PubjvDeUTDoPlo3UL4vDj17Als/DTPH1J/gvqathdX3dHNdFFwhhaluDVPPgb7PwVb/bdq5BRhxcV6VxOMydiywdOuNL0KFYmkfmPgkaAs4ZQ/4zZZw2OlQ8Q1M+Qtc8RncPoXbbrOOvZGYsz+8dBbsdh0MKHiMTHHT93lYtCOsCO0cVpRIYdUwGYWUTTm8d7RF4e2cpKFRFgbfaeHmc/fI/vmCXUBbekBDRFwhhXn9ZHPw7nd+s95eH3000Ho5zDix0EuB5ZvDxKdgTUc48SDo/gG0XwR7XAen7AW/GGCllpZuxUknwRZbBAmNH4w0J3U2VnWGf98Gm74PB52d/Rinln4hPxJYVOKqbqW5Q1rbAb7O0kdk5mgzh/fJUrcvCTs+aGWLZtbjmCqdCg1FgSukDNWtYeq50GeqbcObMe3aAQMfgLePyV4loqlY2dXMdMu3hNEjYYuZGx+z6SzY/3z45QBeeglOOgmmTAHu/g9cPg8mXw1zd9/wrvjRaywf5egTUsvjatb0rISW39QmyK7cFGhRmjsk2Nhs99WW8NGBtptJ+0Zzk+Ww/SR4+7jsN0jzhkOH+VZvz8mJK6QM08dahNt+f2zWu6P1DLkd1nQyU0YhWN0B7nwMvtwORh0JfV5u+Hix4rbXXQfz5gGjvms3DtNOgZtegWveh//+Hl45zfxj374Qer/WJF+l5KlYA71fqd0hlVrZoAz1KaS3jjez2eCUzXUZBt9pP7PZB2/82bxhFu5dDteUFCjLKLus7HwPtFwD20QrX1/y9H0eOn9sZrvBTdu9b9Uq4O6HYf5QOP5o2CbejrR1a2D7R+zxTSd493tmMvnvH4EW0OsVGPGnRlh5M6bv8+Y/Xd2+9MoGZej8CbRYu7FCmjkGer5m5uDGYNvHoe0im2e7ybXjq9ubb27gPxpn3maIK6QMbb6GXcsoY7SFWjfZ5//Xotg6NY1JYc0a67zJJ/vC90ebUknCJl/BrrfZY1kveP8IM6G0LPOy5nHpOxW0AubuaQVXoTTal4dpuc7KIYUV0sId4YuhcOivGm/eirUw6D544yTb+bcJyoB/McQDGmLiJrtyZpc77B9mZoRs8xTINESbPBn47k9h55Qzjzt/bjXZGqpl5mSnz4sg6yzyckWJmuxg49DvN0fb99rpvsadd+e7oLodvBsyga8PaPCQ76i4QipnNp0FfV6AGWMTNB2OSI1wyinwwANw+eXAsJsaeUInFpssh81nWGDDiqBjTKnUsQsTDv2uEbvZ2mYKdFyQUzQRfV6yKL5wtN38YVa8tuMXjTt3M8IVUrmzy+2WuJept9UYKPD4ldx6qxWrPfPMxpvKSUC/581kt7wnbLK4NM2e3WbB6i4WKfjZt2DZVhZ00NgINs9HB8LyLWwsU6HBiYwrpHJnp/st5Lcxc5KeOR9e+RVnnNEE1dOd/On7vJmdZh9SegENGcKRdjPHQKsVqVfNr5fBd5kJ/K3jzZe0aHtXSDFxhVTutF0GOzwEb/4Qqlulf/4XfgPP/QGG3sjll5OsFbvTuPSdas/L+pWm/whqFVLVTvD2saaM2qxomrm7f2DRfDPHwPxdgRZe4TsmrpAci7Zb1R1mHZbueSvHwZTLYKd74fBTXRkVOx0XQLeganWp7pC6fGxBDK+eDt90axpzXZjBd5rvKGNx8ICGWHjYt2N5FO2/sOTgHSalc86ZP4RHboDtHobvnVA6fXXKnb7Pw+IBpbtDqlgDnT+FL3a1lg/bTGnw8NS73g66Fx6/HKafBJ0+zd1N1tkA3yE5lr8x+C744HBY2S35+d47Av51uxWoPebY0nSOlyuZunalukOCWrPdoHvtb7sp6bAQtnmypPOPRORQEXlfRGaJyDlNObcrJMfYZSLUtDaHbBJmfwceuN/+GUcd2aitNJxGoN9z9tyxhHO5Mgqpqc11GTLzlqC5TkRaAtcBhwEDgVEiMrCp5neTnWNsMRM2n27dVd/4MaAgNSDBM8o+U6BFiyAw4dOnsx7DpyOg+3tWLDWTse6UDt3mwI++XdrO+MF3WuuSXgWqZbjjg/DRLbZDKz12B2YFHcERkXuBI4F3mmJyV0hOLQf9Dl77ufUlUgmeWwD2uk0bUIWaGmo/Cx+LwNZPwRGnQLslBf4yTt5s9VyhV5CMvi/ao1C0XgVHnVy4+XNTISJhe+IEVZ0QvO4FhHutzwXqafbUCAtrqomcEqD/E/aoh6dCzlw5f78mWJDjOI1AtaoOr+ezbFEejV3HZT3uQ3Icx3EyzAX6hN73BpqsmZMrJMdxHCfDa8AAEdlaRFoDxwMp5YLkxk12juM4DgCqWi0ipwOPAy2BW1T17aaa3xWS45QoqSd1Og6gqpOByTkPbARcITlOgXCF4jgbUjQ+pEJmBzuO4ziFpygUUqGzgx3HcZzCUxQKiVB2sKquATLZwY7jOE6ZIKqFt0mLyA+AQ1X1J8H7E4A9VPX0OseNA8YFb4cCq1JeSgWQtBJo0nOUu3wxrKHc5YthDaUuXyxrqMtKAFXtkfJ5U6FYghoiZQcH5S0mZDk2nUWIVDaQwdwk5yh3+WJYQ7nLF8MaSl2+WNZQahSLya6g2cGO4zhO4SkWhVTQ7GDHcRyn8BSFya7Q2cEh0jAHJj1HucsXwxrKXb4Y1lDq8sWyhpKiKIIaHMdxHKdYTHaO4zhOmeMKyXEcxykKylohifGjhPKHJJQ/O6H8j/KVLxZEZIvgua2IHCMimzXRvPuISPyCcumvQ0TkL0Wwjp4islvwul2h15MPIlIhIlsmkN9DRI4WkRYi0i/NtTm5KWuFpOZAG5lQ/ucJ5QeJyCYJ5PNefwYRGSIix4vIcSIyJM9zbCEim+e5hLuC54uA/sADMeceEjz3EJHfisgOEUV/ALwsIhNE5DARaRVn3jpraBn8DH8RvN4tqmzwe2yb8ELaV0T+JiJ3BfP/JKb8/wB/Af4uIi2Af+axht1F5IciErvltYiMFJGpIvJfEXleRA7P4xxjgUeBxwLFFPfv6FrgaOBcVa0Bbowp/6PgeTcRmRwk/DsxKIoouwLTXUTeBGZgybiqqifGOYGITAZeB2qwE/whhvguwGci8n5o/n1jyOe9fhH5DbAX8CHwEZagPEpE/gi8oKqX5ZDfDVPIWwKLgRYi0g34HLheVV+L+B3aZL6Lqp4pIodFlMtwOXAgcCHwLHAr9r0aRFV/DSAiu2IXovNE5FPgQVWNdTED7gzmHqWq14jIn4HvxJAfCjwnIgvI7+/gFuB07Oe+TkRGATfFkD9IVfcXkWdUtSZIv4iMiNwErMb+Dk8UkZ+q6o9jnOKPwH6qujLYnf0XeCTOGoCfqOqI4DtUi8imMeV3VNUDReSZ4H3LmPJjgNuw38PJwMPAP+KcQEQ6Az8DugL/AxymqnF/DiWLKyQ4KaH8FUmEVXWXhPMnWf8TqprVVCQigyPI7wWcoapL68h2AU7A8sui8FxwEbg02C2ujCiXoZ2ItAHaqOo9InJqHGFVfUNE3gZeAc7CdmpxFVIPVf2biBwbUy6zhpwKNActVfW9kAUyrvVjjYj0AjTY6a6OKd9PVQ/KvBGRJ2PKv4FdhFcCXYDpMeUBqkWkA/Yd2hLcIMbg68zuLrhJWRZTvoOI7At8rarzRWRFTHmwG5vrgP8Jbix+TXzFXLKUtckuYDFwLHY3chywJKb888BmwLeAzYGpcYRFpLeIXCcij4vI9SLSJ7fUBuS9flWdGaxhg4uoiPw281kO+atVdWkWE9nOqnpNjHX8D3Z3uUhVvwG+H1U24E7gIeBvgUL7KIqQiHQSkdEicj/wNDAc+LWqbhdzfoCFInIcZno7GpgfR1hEDhaRJ0XkzcDkFvnnF/C0iPwN6CkiVwFTYsr/HNtpdgX+CvwipvxcETlXrI3M74L3B4jIARHlBwPPisgr2E5z58B091yMNfwO25HsGDyfG+cLYLuaYzClOIbauplROQPbFV8Y/B0+GFMeoK2qPkZtDbuC+zibkrLPQxKRSVh18WnYBWmUqka2X4vIXcDbIflBqjoqhvyTwHigEtgNuEBVo/4TJ15/cI6rgBdV9T4RuQz4UlUvjiH/EHCOqr4rIkcCo1U18k4h8F8MAnYIvsN/VDWW2U5E9sRMhw8BfVT1kwgy04B7gH+p6uw482U51ybAT7CL4XvAjYFyjSo/Fdgf27XuLyJPx/k7CM4xKDO/qr4ZRzYpIjK+no9UVS9oyrUkQUQ6YTs0AFT10xiyic1tInI98AkwCrgd2FpVfxnnHKWMKySRZ1X126H3z8Wx3WeR3+B9BPmpqrpP6P0Lqrp3gvljrT8k91esF9U9qnprTNmOwETMzNIb+GngFI4q/0zIf7G/iDylqgfGkL8W+Bo4QFV3F5EnVPXgqPNGnSfC+XbGLmYCoKqR7+5F5FlMIT2J+cOejrM2EemJ7SzD8+dUBCLyPBsXMhbi+7Ay0ZJbAx+ratwd4s7An4BOmKnsvCi79EA2le8gIhOAvlgdzYx8ZD+YiDxMrbltXxF5UlXj+BEz5zmc2huLh+PKlzLuQ4LpInIjFpQwDIj0TxDicxH530B+ODFNNcAkEXk0mHcXzBEah7zXX+cfWYDtMJPTSVH+kUXkwpD8J5iJ41bMQR0nsCOp/yJvZ7SY02Ujs0gchRqc5xEsmCNTFFiBOOamS4GnsAvR48H7ODyE+TNj/f2q6oiY82RFRH4LHIDdlOwa7PDifIfrgTGq+omIbIWZYfdpUCIgre+A7UYOyn1YvbRV1cekNpUjtrktCOgQ4Augq4icqKoTE6yppCj7HRKAiAwHtgXmxIgMy8i2xCK0tgXmYBFa62LIb4rdGGwFfAysrhskEOEcea8/CSJS705QVZ+NcZ5tsUCC7TFz13lxTGiByfBPwMXAmcDvVfV7EeSWYM50YUPFrHmYy/6jqv8vjkwd+ZaYE7475kuL9Y8pIg+o6jEJ5h+G3Uh0Ar4CzlfVyhjyz4cVQ92dfwT5l4C9gwi/CuD5uIEeYnlD52D/C7OBS1U1kj8xkL8ReCt4KICqPh1DPrG5LbhJnEyo24Gq3h7nHCWNqpb1A5hS5/09MeXvqPP+qpjyT9d5f39Trj+QuSj0WoD/iyl/TB35Y+KuIeHvsDuWQ/MfzDHfPaLcMymu4W7gSswx/mPgxzHlnwL+Bnw7z/mfwvyIdwSPiTHlXwN6Bq97Aq/lMf+3gLbAiLg/W2A0FuV4X/B8Qh4/gxex7tOtg+cXY8qPr/P4Qx5rOBz4LfDdPH+Pk9L6myzFR9ma7ERkf8zEMEBEMrb2CuyfMYr8rljuyHARydiZK4BdI8ofDXwPGCgimS15BXaH2ujrr8P6O1FVVRH5Vkz5UwnCpAP59e+jICK/xxJ813cA1hg7FFVdFPwMMs7oqFUGIt89R+DxJMJqJsftgOMDE/CbwN2qOi3iKeLk/GTjQ2ojNJcAs2LK/wiLcvs9tjuJlcuHmfr2AnoAVViCdFwWYopURaQSWBRHWFXPT+gHS8PcViMi/2LDXVoc83dJU7YKCTOv1QDbYI5kAdZiZp8o1GChmYuDZwG+wf4xo/A05vf5FPh7aP4vIsonXX+Yb0TkO9gd5l7AmpjybUSkq6ouEUuMjVt54mBNkIeTzRlNhAu0xkvczHWu28UShfsDs1X11TxOUw2sw36v64BjReQ32kDUpogcpqqPYoEQdbkl14QhP2InLFR7VvAdIkeXBRyrqqeHzjuOeO0TrgluQhYE8hdhIdg5EZE7qP0Ob4kliu+cOVdUUvCDPU4dc1seXJlAtuQpZ4XUTVWfDezVmZ9DSywwIYrdeGFwEZpKlnbrEfiWqj4qInPYOKM/54WE5OsPcxKWs3Em5sMZG1P+bOAhiw+gBjNZxGFaEC4eviucE0M+qTM6MUGUYjvMbHaKiJygqpFzeUTkCcyHeBfwJw3sNyJyfg7RTPBFXb9lpL9JTRgQIJYEvSnwAxF5ELshaIkpk5wKSUROwm4edhbLOcrcUHwWYxnnxV13PRyhdfxgxAsuWaKqf064huexaMn+2E4zdgmnUqacFdJgzKFd1/GqRLugn4D9sdb9Z4h0d07thaS6znhU5ZZ0/bUCqguAX8eRqSP/IhA71DxEZ+Co4AHRf4YZPhaRX5GnMxrW5590pjZkOu4OYYiq7he8/nsQxh11bgGmapYwbVWtL78n8/njwfPtdcPO4yBW4eA4zOQbOWwc+Db2e9sKM9dldup/izKvWorBrSJyiqrGqh0XOscnsP7neACh74ClI0RlTWCufgO7sVsbcylpmNvuYMO8xjuxIImywKPsSOVilHT+vPNXAvlE6xeRMcBPsTykZdid3rAY8rtgu6ItQ2uIG6XWEqt4sVBjRCkGsnUv2hrxYpqRnwD0IxSyHdecF+Sg3Ett+P/xGiPqLvAjjtMYybR15DcKO4/5M3gM8/udhoVg76aqP40h3wdLedgcWKCqdW+0cslvhfmg8oqQC87xALbD/z5WJaGfqp4QQ75PljXESYzdKOpUY0SbBudIlNdY6pTzDgnI3/8Qkg875GOHDCfNX0m6/oBfYr6jpzDzYZyinGB3w2MCuVOIWV9PRH6Ilar5CNhGRK5V1Tujyid1RpOOyW80lof1S+xi9sOY8kmL7EocBZKFNqp6c+CIv0lEcobN1+EQzH/6CdBPRG6PueO5G9ulTweGYKbLuME1PVT1GBEZoarniVUxiYyqfiZWNaRLzoOzk4a5LWlei4tVTQAAIABJREFUY0lT9gqJ5BejRA55kl9I0riYrlQr5LgWM3fEbUGxWlVni0gLVZ0lInH9EqcDI9QqNLfCFHJkhZSCMzqxyU9VvxKRx7GyMWAKJvKNheZZZDcU4blMRK5kw+8QxReZYaFY+aM3ReQWoGPMpfwY2Ectj6glVtMxjkJKFCEXUC1WZPdzEfkD0CuOcAo3d2mY207A8hoHA+9i+XVlgyuk5BejpA75pBeSxBdT4M/Bxeh84FrgqpjyjwXyd4vIdCyPJA41mLnvs+A5bpXmpM7oudhdccYfF9sPl2SnKyJHAEcCW2B3xP/W6DXQMubNumHnsWzxqnpcsJZfYjck78eRB14Adg9+/7sAL4j1VUKjVb2oGyG3MJMOodHbwRwa3NSMw3ZsN8f8Dklv7nqr6ujg9eNx/Igh2mBRrvOxIJnRxPODlTSukJJfjJI65BPlr5DCxVRVHxdrDlcDnKqqscJWtbYQ69+DR1x+DlwrIl2xHJjTYsonckYHJr9McdZJWD2+uOS10w0UwCCsl9M87K7+HBHZRlWvziWvQRZ/sCudGuwwhIhld4Ld5cuq+nxwvhoRaY/9Thrsh1WH4cEjzFPY32MUE3bebVRE5ATgI1WdCqCqK8T6Sh2A7VpyyWfWl/TmLg1zWxqh4yWLBzWQrChkIJ+3Qz6QT5S/ksL6/4jVwXsTuzi+kSu6q478SKy6cTUW8nuxqv4n7jryJQVndF7FWQPZzM3Hd7C8l1g7XbH6ewdo6B8x+HuaEtMXuUFBWolY2FPqlPzJNd5YZIuQ04hJpQ18h0jli4KgGM3MGyJuYEi4jNhsrIp83ACdSap6RByZ5kTZ75CS+h+SOuQlef5KUv8JwP51Inuew0qnROWPbNztM7JCkuSVGpI6o5N0Cl2HXcweI49wa6A6rIwAAn9eXLNlOxFpraprAj9Kh4hy9V0w415Ik0Za3k+dCDmim6rqi+iLulO+GGtn3ztYw6S6v5OI5G1uk9pCxV6pocxJ6n9I5JAnQf5KQNL1g9nuR2Emr12C99tAZH9Y0m6fBanUECJJp9DJWHRd5mJ2o6rG6Xi7s9SWjsogwE4xzgFwCTBVRD4B+hC9YsdLInIucHmgzFoDvwFeijl/okhLkkXIfSAio1T1nsxAcKP4QUT5e7EqJW9g+XTfIX6DQkhmbnsS+7vNVF0pS1whpZAMRzKH/HIRGU2t3fnrmPJJ1w92N3dw8Mjwe6Jf2AcD/xWRxUA3YJEEJWkaCl3OKD0KX6nhZKxKdD6dQu/CoskewC5mfyNeHbc96hmPVX1AVf8tVvW8B1AV4w7/f7Hv+0iws/oG26HELUGVNNIySYTcr4HxIvIzaoM5XiR6sndnVc34y54QkadizB0mSaWGzzGzc+bG5mK1hPWyoux9SCn4HwZjDumMQ368qkbeIYgltY4LzX+jqka+Q0+6/jrnGqwRm6KlgYjU1whQNUJiasgZPYoEbQOCc20Q1KAROs4Gcht0dq3ry2kqRKQ3ltwc9sGkVqsvwvznYK3Px2JdU1+JE+QhIhWBlaE9FiH3iqp+3jir3Wjuj9nQtHZi5n0Uc1nI3DY4eI5tbgvM5GcBM7DqF6eq6vcjf4lmQtkrpEIhIn3rDlH7R9yklSLWLyCPttlpyYvV5MsEhkTK8peU2mYnDGr4Angi8xY4KPM+RrhyYgJT7wXYzdEfgJGqemZTzZ9lPceq6v0J5CP/DpIiCft6JZUPzlE3KKUgNzaFpmxNdoHTeDoQboYXudKCWFHUT9nQRBenUsPHWA+at+vKE8FMlnT99ZA01DQv27eI/IQ8svxVdX3h0SBsPRNpGPd7JAlqqGtyS6vQZ1xqVPUpERmvqk8GO5ZC8hMsUCFfmsyPElVp5JIPbqq+T6214sEYpxkW7JIyDM68b8js3dwoW4WEFZL8f1hDsaexEM2FMeRvwMrtfIG1j34q6p19wB5Y7tJOWEb2PzVGh06Sr38jVHVM4FR+Ps9THCMineOYHAMSZfkHYetDMVPJIBGJFbZOgqCGqKa9+giiA59T1aki8g7291QBPKuqv49xqhliyclPB4o1li9SROru5tZiHYjjJjnHImR23WCY2ooXcc5V98K9ljxTIfLkAcx3VYkFBz1AbX5iLs5U1VsC8x9AIiVZqpS9yS6IjDsIM3c8qaqx7izFcoCOwhTEI6p6eR5r2ANzhleq6ikxZfNev9Q29gszCus6G8V2nu1i8mfg3Dg+HBG5DKv7lcnyPwZraREpy182Lkj5XJy7ShHpjgU17Ig5lP+sqvmUrolNeK0i8oyq7h+8npJvoIaIdFPVxTFl7sF8oNMxX8jmWKO8tqpab8Sc1PYi2mAYM3/mDExowOy6wQ44CiLyb6xbbOY7ZHgjpnLPCxF5TFUPDb1/XFUPiSi7i6rOyGb+S7qDKyXKdocU3InvhymTLbDWybFMDCKyLdb1dQ/s7jxOy4HuWLmYA7HIpouwkNGo8onXj13822DVnZdiF5JDsNDTKNyFJdNOzSwLi7Lbh3jVIpJm+ecdti4iAlyRhr9HRHoCvVT1NRFpFzH8O7yrDhc0jWQ2FKs7l208blBDZw01AhSRR4Mw7KkNCZHQRBlX6eSgQlVHZt6IyGRVHSkiL2FRo1kJdpRZ78xjmr+Xicjfqf07XCZB4rTmSJJW1RnBc9kon2yUrULCijnOxsxt07E/yAODf+QoGfYzMUXyL2x3UwN0EpEDIu4O5mN/uM9gyXSDMbtx1MicROsHUNUjgyjBX2JVBq4CvtTo7S+2xiIE9wAmqpUg2iNOQEGwjv3jHJ+FvMPWVVVFZL6I7I6F3tcE47HC9wPT2yBgBxEZju34Dosg+rmI7KOqU1V1SXCufYneObgHVr7qP8FjeZx1h1grImcDM7G/xTXBTU+Dpr+kJsuUaScix1L7HTKdixtMhQjtSv+MRVlOw0zAUc1tGcI3lC/HlHUoY5OdiNTbFVWD+mA55BNFeKUQ2ZNo/VnOtz3WMbZf2OwQUbYVlgh5CBYyXV9uTV25tO5MExEKZsiUj4kdGJIxt4WeI0VJidXvuwYLOV+ERRrOA34R1ewm1lzv/2EKsDVwh1pb8zjrb41dgLcG5gAPqWrcVvYFRUS6YcEUme9wM2aG7KCqORW1iLwI7B3cpLQAXtAICdtZImbXU6iI2VKlbBWSYxeh+i46DX3WwPlaApvHjXLLdmcaxRcWUmgVmNmyCtsxzNcmbmom1nrix1gxz1HArWHzUQT5FtQmtdaIyL4xdqoEO7yjsSCZf2jEOnAh+XZYhYL15ZfyOEfsmo5SW0duI+LutIPzDWbD7xDnZzgO+x1+glX+uE1VcxYLltp8ul7YjUWmYvkX+foByxVXSGWMiNwMfImVifk4GN4Ka4zWJVeARWAvnwS8GDI3dQ3kv6uq/7+9Mw+TrKjS9/vRrQJqoygooDY0KCCrCyiLoCgCijAgyqCgIuiIqCgjOOACKIID44aCivBjEcUVGPYdGprFZlNAfywubMoAKotDg7J888eJW3U7u6rrxr3ZlV1d8T5PPpWZXRE3svrmjRsnvvOdjzQcR6s701r77wIH275HkSD6eeclZZ7lVN017SmdYXvrpu1Tu5WJfcBVCWHE52z/PqePnv4a5eFI+iZx8ZxNhI+H7HJywo4KZ425bG9yVtoKt5HK03EloJGnYy1SsAsRgr4OeBXwctu7Nz1+6qtT1dzUxxTghcBfmk6qtbanAO9yeBFOIZSzuWG/Sc1k3kOa9NjeTdIqwNbE3bGIC9rRtm9v0MUnicqoxynygEzsjZ1FhP+acjzhqTZ0Z5rRFkIQ8df0/G+Ec3kOz66epEkxtzgdafL519x2faBSk1V7aENhR5oJQiq62N5AlAzZ2Jmejh7O4flsbQI6X9IFLcbQtgTIcYywSmshDFkOWE9RE2pdYtVeyGDSr5AkvRX4CLExvBiZ+wcK3606TxDx61PnF/JSWKQcTJy4TxMXkesJ66FHM8e/LyHTXRf4hjPcwhcWOt6Zbk3Itqu/42G2z8ho/31C1HElkVu2XNO787SyqE8CVM/dQHquYduZud4G3mN75WafoDtJMt3K9ia1nwXs5HBefxmROrBRRvtjCcXhDcR5/CzPR24+Sh8/ol0JkOnp6d6EGKFapW1k+xMZx1+RcDyfQVwDvjqWyrMwN2VCkq4H3u6WyXPpi3Qrw7kPaxHuCxt7PnVNFHY1s+txekk7A6+3/bGM488C3gScnzbTO9n/DAIN3odNhAT/FUS47QyP0xejq7hlQY4j5/jq6OmY+liPuJj/0e3qgs0j9MkMO/bms831urDgKSG7KL3cRU30Utu7pefnKxIa3y/p7WO0W7V34rF90vzUc6PwVHpUlUInonX9D+nxYctprO61eLZwuGWvQtwlPwhkuVVo3nygaqV8jO2/jtCENM6B5p1IepXtG8izS+rtQ8D7bG/bsY9phEpwNUmrNRVVSFou3VC2dRipuFTS2QzLxhsLItI4diZurF5JuH08aDs3fDypWWzQA1gIeA1wpaTL0yPrJATukXSUpI9IOgq4O4Wf/jhGu9H+9rn/J4cRSaSvJOqx5NZCQsn3TNKWkmYrymrntN8y/VxF0pGa18JlLJ62fRFRrO5C5s6yb8J3iYKCixF5Ubm1fD6dfu5PqOS+ltkeIl/nXKJsw9lE+PCPdPNza4ykTXoeG6R9vbGo/tZv6Hk0KoEOEZsElmh4vNH4KZHovR+xUs1Rp+2Sfn6+55FbwuMAQmV3KvBB57s7fIL4DDcRn+GmzPaTnkm/QrK9Ycf2H0yS25UIqe816Z8+MEbTFdLk17uiWSbz+Gelu7pq/6VNqGlz4kL6XmAjYi/liIz2nyYuxvsTHnRHAOtltO/kw0b3WjzPTfseT9m+SlLjPbwaa9f2G26T9HHbe0jas0VfbdibEWxzFL5+o15Yq5CWuzsmvBq4TNJ9xP5Noz20Gq0L9DlVSM7dc+pFw/lcy5MWbZkqvTlJYfdE6mPdLuOZjEzaCUnSfrYP1QheXM6wkVHYxbyeEEWsKmmrJiex7dVyx9xz3H4pgwAWV5hr3m/7CUmPjdlibjpd0G1XhdQOVCQ3Pph5/HPThPajpHDK3X84lBCYHJz6aZNlf3mSHd9M5ALNTCvl3NVaW1rZ5tR+/zqi0uztwCpEwcnHgMNtz3dykLQCobj8vdt7AHYp0FeN43IiD+ovxA3afUTo7Mu2m/yf/pwwRN2OsNPKuakCODSdPwcB3yacTwo52J6UDyKBE2B67yOzn2uIRMhNq0fDdj8gioDN82jYvhrvN9PxX0EYvB7R4m+xMhGqeA5ht/LuzPbbpbG/IrX/Smb7txL+eTcRexnfavEZphLquDbnwuE9rz/Xsp8XA+sDL14Q5+wYx74YeDewWvp5cXr/sobtfwQsmZ4vCZycfl47RruDgTOAQ4ALgA+1GPsKRIhwGUKCvz3hCZjbz/eA5dPz5YGj08/ZDdtfkn7OTD/PbjGG16Xvw5Tca0l5ePKukIgy2+8kVjfPJ8xFrwZOy+znDtsntzh+Fd8W8eXPymFx8hCTtK7tvdLbt0n6aIuxPEFcSDcjKn5Oy2x/OmHSugVxZ/mLzPZfYFgp+JSkNXIaJyHIzsCykl4N/Nj2uxq0W5GYjN+iYefyqYTh7cGZY3gJEbJcBfidpENt353TR0d2IGxz3kSIKXZIQoGxxDUVqxKr/Dnp5yq250gaK3y6qe03wJB0/wLySoccTBiR3gQcCPzEDWphjcJriDw0iFX2urb/LKmJyS3A/WmFc1OKQGTlo2nuQo+nKtIJxqXI4KLCZJ6QjifUNCcTy/ppRHLo8cTFrSlLp3DHb9Nru0HIzzVTSkmPub1J5cwuyqDE/wM+BhyVJoSdgGMy2p9EOJ3vZPtbCiugt2S076oU3N32GxQ+ck9JekHDdtOJO/PnERv5EJPzZzOPD3HeHEDUwlkPOIG8xNRO2P6bpHMZts1Z02Gb09RsdQ/gaEnTiO/DnmmCGSsX6bmauwzJ0tVrNzMZ7jSh9XAQcKGkp4hz6Eupz+80aWx7xzSOjxN5SLdmHr9LoccCk3tCWtH2Lj3v3ZDi0Dm0ypdRWORAfHFUvxA7w/LF9hcUNZmmE0mxTV2i60yxfUsMAchX+i1j+7sKp+U2VErB1WmnFHwybUhb0hLMXcV3VByS65mSDrE9X0foBixu+4r0fFbaDxk3NIJtDg1vTtK5927b7xjhn8fq41SGJ/P6a9OsBEnXCQ0Y+gyr2x5JHfiThn1MJxKsq4qvhzG2WrZO60KPhWAyT0j/nb7ElwKPECukTYl4+Jgk8cI5RHinlyblH6p6P/XXjS1fJO1q+zj1ZPqrefmKOhcr/OCWV3ij5dq23C9pR0L6ux1hH9QYd1cKfobYkF49/dwvs/2Okrrmj5wu6RxipbouEcYcT1rZ5sCQXdISGs7nyWnbVZ3XdUKrxmFJa0pa3PbjLcdyMrAX8Gvi//CHhC9jU3YjJrQ5RJTlwy3HMWmZ1E4NiiJ56xNhjoeAa2w/0LDtFo76P62ywyVNs/1I7phr7ftaYVLSmqSKqbaz8idS3H13hiuufr/JRWEkhWNFk7Bnv5A0m7AMuogINR5j+wMt+nkRsVK90/Z9fR3k2MduZZtTa38Vw8q0NrLtTigSYW+pvX6D7dzk5F8TIoZbafEZFPZJ26XJbTHgNM/HbaXWbtSIQk60o1AmpDUIqXL9i/A627/M7Od5hDCiCrmN6V+lmsWPpGM97PaQhaQjCbeJK3L3oapVnlJVyzpNLmbVHbVSddae9k3+BpWH2DzCjiafRcM+cvOQeSG61PYbFYaeuwGn287KIekVNRBl0MdN1ND2xmhBocgJy3Eb/2/gMyl0vC3wXtttQ8BZ1G6MXkJ4QlblI+5zA8cPDZdBESHQ+BW0q6s12Zm0ITtJXyVOvifTJvgH0+roUDI2oyUdTThU/5nhkFvuvtJKmb9f57+IsMI+klYjwk1jKswS1QUjy8y0xi5EnL2qzgoZf4Ouwo5qMzy1v8TtK89W+SNfpH3+yPEMQNSgPtnmqKOfoKSv2/5Uer448fl3zBjCzsCJijyyl9DCOT3tI+7I3J+hSWJr1zLsQ+ddOg/LJNSSSTshAa91Mk5UGEP+TNI+LfpZ1e0MGGdI+iLxxameA3kuy8Sd3FpE/sbviAtiI1LIUcCb24TIbB+W2t9i+z9z2/dL2FENp8Pxqz2zK9KjDYMSNdRvCurk3hh18hMEZkv6BjEp/xA4vEmjnj3QO4l9l+MICXjuXmirxNZaCsXLiFXuc4j0h13JU5tCi/OwMMxknpCmKlVFtX1j2ow/iciyz+FkSXsxd+y+yWZsPcRyYeYx6+xL1AI6iwjb/SancYqX36uwP7qetGpqOiGk9mu03EzuKuyoQoUiBBVDocMmIcOe49fLR9Dk+D30ihoal7/ogvtkm0PyE5R0gO0LlfwNx6I2qf+EUGfOIs7tpk7fvef+qQ3bjcSzbB8r6X22j5G0fWb7VukPtZC3CGHQ0I1Azj5eYXJPSJ8ixAz3A9h+UNI2QNNwV8VWwP8wnP/RSB3URngwSj8bS3omMe6TJL3MdtM8nIr102OoW/IuyOsQprJZm8kdQmwV9VXBLbXXTVcH23YRltRJq8UTCFHD1wYgavg8sap5jHb7F239BHsn9b8AX6XhOeThAn1LEorV59Hesb5TYivt0x/qIe8uRQ4nPZNa1NAPJJ3pzHLXfT7+jwnJ+h8JU9QrbN/Rop9nEPlEfx7zlxcR+iEskfR64LGkeDyMUKpBWEBd2r/RjjmOy+t7ah37Wtr238b+zf6hjiXUe/pajFil3mK7qUtDNamvQDh2n0eIGg5pM4ZCOybzCqlfPCzp67SU2/aBD9lumo0/ImppvVNrvy6xdzCNsEr5D9tf7jKmAdBWWPIl4F/S8w0Ix/RnEuKIS7sPa/7UwpTXJXVa/TxsXK1UPZWTJWWtsBTJrF8iqr5OAQ50lBJpStcS6tVe8AeoVX8mYx/N9pdS+sNFwK22b+wynkI+ZULqzvldGkvalfgSVXYnuaGWGUmM0bY4HbS33qn4JnFRPiW13wxoPCFJ2tL2uYoCeZ8i/MzaWCDl0g9hyVQPl5w/yvZdAJLG67tVD1v+C8OTY66o4St0qJxMeP9tYfvvCvuh88jbG31a0qm0LKGeOAHYk3CsyCaJGj5GhPreJ2l327mihkIHyoTUEdsnJEXVsrSLfX+EKHfe1rrmu8Tq5hjgQ4QyKJdW1js1lPbgqvhv7nnVqZ6Sokrv5rXXJ9veqUHTfghLnpb0bNuPOpnspgvyuPiY9YoZklJxPUJckUPXysli+Lxpkwz69Q7HrriRSG5v+11qK2oYsRQM5EnnC2VC6kxSI72VsP3/PfAP8oxFLwJWl/Qbhu8Mc77QXYvTwbzWO40UVjVOSImNMyT9lJDt5tCqnpKkNxEb5y+vrW6mEnkoY9InYclBwJkpufJeYg/iven9cUPSRbbfTEi3lyVyecZ0+tZwcvFUonLy/emfGglTanyOqEtVJYge0HDc/SihXn2GacBdkn6X/in3M7QVNRyYfu5NVAy4jjBn3Sjj2AXKhNQPtrG9oYaz/RsZOdbYID0qchVuvcXpslwmEs+1vWX1QlLuF+m09JhBiCuezGzftkDeH4i78RnExA7h1v2VzOO3xvZlknYgFG5rE5PSu9y+UF1bqovnirZ3kTSrSaN+CSEcJegvGvMX52Vt4Abm9rODDC+7fn0GWno6ur+lYCY1RWXXkdpEdDZxITzS9lrjdGwB77d9fMd+htRm6fUpthvncIzQ/qduYPuSVkUjUu3FNOhDwDn1CXUyIul4YnV2AvBj4KImCdtJkHOKa75xaZW9vZPzQsPjdxU1VKHOpRjeC216DuwDXD3CZ3i97aYJuiISzJ8mIgXZooa0Sn8tETpcC7je8ykfX5iXMiF1RNI6RA7MDKKmzFm2z2vQ7pu299IIfmw5YYamF/9R2u5KbHyvRXyJqn2A2bbHdK1QJBNvD2zOsLhjKrB0kwkixd4hLqTLMewh9j/1PaEG/XyLSMi8juHE3sYKs0UFSVNtP5kurs9tkmMl6bKRzrfR3p9PP1fSI2qwvcFY7Wrt57Hgarr/MprkPVcKL+kMj1yCozGKUjAvA+5yu1Iwk5oSsmtJCi1NTfkn6xOlu39OXFTHpFra9yHcsIykmwjL/CoptZENkO3jgOMkfcL2EdX7kl7R8NgXE+4OdxHloiFCZo2+iNWGvKRTiOqeTykKquVWnH0OsGV6QDs/wQlNujHaR9KQ2pJmod/R7khz71S7ihpWyrkJ6WE0L8Zcj8ZHJX2HuW9schzTq/SHpYDNJX12AqY/DJQyIbXne0RG+o3p+anAMwi36Hmcl3uR9AngzD7cyX+gY3sIqfARtddfpoFjhe2HiTys9W0PVVmVdDLQROVWsRywXtoDWxd4cUZbbO+aJrJlgftttzWLnci0VVueli7ChxP7X8sD+xB7gjm0FTVUk+YdamfBBXCVpP2Ar9r+p8K55NPAVTkfADgn8/d7qdIfftEm/aFQJqQuTK/FmG90chWWNGa4LvFJ4E1piX8uEcfPqkOUOKZX8kzDyaAespN0GcN+bo3KJtRUbqu0UbnV2Im4CH6BEEW8J6expPcAH09tZ0j6tu2TMscw0WmltrT9dUlbEhPKckTI7Ge2z805eAdRw86Eqeo9hG1QVfE1p0DfZwlT1jNTCsbjwClkilv6kMJRpT9Ur8v1NZOyh9SS3o382vszG24mX2L7TQoPr60Ih+LVgItt79ugfTUZ7AKcmN6eCmzU5Pg9fX3I9vdz2qR204EViYtBPWR3c5P9i56+liPcEv6Ym5yZ9i82SfsnzwAuy9m/WBRI6QffIFbnexD7gONWsVTS24g8skrU8BXbZzVoN+L3aBBohBQO241TOCTtBmxDeDvOBs7uKjiabJQJqSWSDiJyjv6rFibYlyhDMGZ9FY1Qv0eR3b+Z7THdH3omg+8Rd3RtJ4O2dWTqfazc076x00JSSW1GOES/CrjEGeUsksR5J9t3J+XeybYnRQ6IpJ8RF7/ZwLUedo0Y73HMBt5oe066ybrU9voN2j3EvEm8lahh3CrWprFc2ZvCYbtRTackJHkrUf5lBnFjNd7S/wlPWVK25yCibHdvmKDpSuOTvW/YfpLmVkQrp5/HMvz/OIWQnTYNdVS0qiNTIenbxEbuRoTB65JAjvXPNp672N4sIKe+0keBb0t6PvAgYR8zWfgM8X++NfAFhdPGLcAvbX9vHMdxA1E1eQ4RemtafuKG3huzAVI5VcyRtAnwyqYNbVvSR21vS5SDKbSgTEgtcbgpHM1wqCq3/a/VrYT6aHsEObH3iq51ZNa2vUla9e2cVHM5/FPShsRF7bXESq8xjnpWOxCx//vSxD4pSKKYPyjy4F5DlBF5F3ExbTwhSer1jXuCSDw+1faolkK1tIVnADMl/QV4AfBAzufogqRdiBXJrNp7GwEzbP+gQfuNCeukvdLN5b8TYc9PtxjL2cxdVyzXj29SU0J2A0K1EurEF/iDth8YRExd4S7xfqIc+rOBVTLzNy4m7JJ+QagNP2177Yz2LyXu9GcQF8HD3DApMrXfnVAb3knUIzqhzZ7YRESRELsY8DAxoV8L/DZ3UpZ0LHArsbJZm8gH+w3hs7hNP8fcc9xpuSHmEfoYLQ9plu2NR2rT83vfINxSfk2cvxe6hR+epHn2bt2numeThbJC6jOSnmf7oQa/2qmEuqKIWu/dxFPA/ravaTxgoIqTJyn6usSFKYf3EuHCPQiFXFY59LT3cwjDoobcmkwfJC6cTyf59yyah04nOr8lVkPPB9YkCvTNIcrZ5/BSD9eDOl9hWPt+SWP64UFMaFX7tJ/yfdu7j9Wu62SUGG3ybTSp2P4khK8eEbb+nKS7COXrz8bWetXlAAAOL0lEQVRqrz748RWCMiG1JG0mv7cezpC0KvAD5q6+OhqdSqiPFHdPeyinAY1Udik8sSXwCLH3sw9xYfs2IaFu0sdSwEO2n0jChtuJO+vGSDoQeDWRg7KmpF9lhjquANZPeUzrAFcoldZ2nlHthMOphDmAomzIu4BfSHqp7aUzurpH0lGEwGBtogLwFBqeB8TqthqT07kwXtwmaScnt3UYSgW4LacT2zcoTI5/SYTtvkzsrY7FWnT04ysEJWTXEknvJFYEO9h+SNIWhEHoLvU9ofm0Xx+4w/b9tfemEMacP+4wrnnUe/P53TOIiWga4Qy9HxH6+VKTPiQdSkwkU4jcpb8CDwHrOK/A31xSeeXb1lwyyj95YZEULyhSDtF66bEs4Zoxm5B9Z9WUSufkSsAfclfZ6QbtPOJ82gDYyvYOOX20JQk5DgA2ZDhqcCXwRduPNWg/DXgHsC2hFL2A2Dtr5GUn6XCG/+Z35n+CQkWZkDqQNuIPJ76IGwL/2jBc149j915on0Wo3F5k+0MN+xiavCRdVeXuNJ3Uqth9Wo3caHvNnPa1fo4kwmw3ECucTYn9rEnpSZdDSj+oLoathQSSlgd2YG5z08bS/yT1/jDwCkLld4wzyof3E0kvsn1fxu/PBM4gJqHftzjeZoQYZz1iD/M+4BpC6dg0Ub5AmZBaI+lLxN3YakT+wXdIMevxUNZI6rVmeZwIlZ3lhv+pKcT1KWJT/KtEPRcRuVWvatD+EiJ/SESW/maprwszJ6TR6ifZ8zHYTMqwb9p+WNI2hNPDP4GvN4n9F4aRdA3wNcKpAcjfkFePy0GOMKWfDEIYVDv2dKIUyR7AMraXG8Q4JiplQmrJSIqaiomirBlhUhvC9pgF5mrCisorpXreOFSWNsD3dUYibK3tUGhP0i1EqOh/icTaMdVVhWEk/SwnzDpC+04uB/1E0oXjeez0PZpO3IzdRSgdr7d9z3iNYVGhiBpaMtKkI2lP20cOYjxtaDLpjNG+c0Jj2gBfQ9Lith/PbD4VhlSKd9p+ML1epIUMC4ilJV1HqPYgwzU+0bVQZd8YwES4LJGH9Q/gL0QOVnFpaEGZkFqiuesYVSuEV0raMWdDvgDEvtHdkm5luIRGk7/hL9Iq7UXAXjC0FzJqIueihqT9Ce++WZJ+S5T+mArMdF5xuK7lOlq7HHQlJcHuQwhyTiSMYpcAjnZG+Yi22N4zjeMZhEJxI8I5ZIrtdRf08RclSsiuJZL2Jk6+421fmt47x/ZW4zyOtYmk0PpmdKeLi6Rn2f5H99EteCQ9l3C7mJNeP4vwE3x4sCMbH3rClnWRygVuUF9I0la2z5E0zzmTczFXy0KV/UDSVUShyKWIvcxViNXKLNsbjsPxVyMEDesTeXwQSbazbZ84asPCPJQVUktsf01hqLq7pI8APxrQUE4gvNv+1KWTJH3dmpC/vpCoApvbxwpEYuHWblYxdlfbx9UEIkM0EYYkMYTT895/niwF+upJoXXLp6ZJmlV4s7eGVFNhTFWG/kFipfooSSE5jjzmcIi/V9JtldRbUm4IuC37EblLJwJ7u4XLQyEoE1IHUlLsUYryy7sQd0XjzY3ANW2+BIpaTNsSm9H/SyTlbuoMx+iUDLw9cXe4NnF3/M6Gza9PPy/seb/psv3A9HNv4Gqi0ueriJDJZOFPkja2Pau2h7YJzav2npd+niBpLcIYNacW0B2ExLmeDF3V1Rqvm4JXSjoxHXf1+vPxOLjtMQtyFppRQnYTnCTdfhHDVjFN91+Q9ARwHPDvtv+eG3JMexaziVj9lV1DlrkrrFq73sTaRjWpFgUU7hzfIorr/YXYYP8z8HHbf8vo50xilV3Jvt0kD0nSekSV1DWA/09US70260N0JEmtR6Qkqk4sygppgtNx03QGMQGcIOmvwAuV7Iwatj+UyLnYS9LLCaVRFh1XWBUzFS7Lle1NlkPBRCatinZOycnLAA84PP02Ie/vINv/1uL41xArJCS9Dvi+pGubJmf3iRcAt9p+VNKyRG6dgCPGcQyFPlBWSBOUrvsvI/T3QiJ893bgObbfmtF2CcITbxtgaeBXtkfNcaq169sKK4UfX0bIvxtn6S+qSDo/8//wR4TDwM2k86mJqKF23ryZSM4+m6iUOm4uDZKuAN6QJuLziRphDwCfsb3FeI2j0J2yQuqIpF0JldtTZCaFdmS0/ZdWOKpbHpuEAllJpWkT+VTgVEXV26aTSucVFgwpDd9P2v+Q1FlpOAnpVcQ1vVO9l7B8uoSQfq8NrJ3+D8arFtA/02T0AuDFtn8CIGnMys2FhYsyIXXnI0Tpg3FV1tj+dfo5lKDbNjE35U9sTijsliOSI3ONOXtVemeM1cZRPO0HtRXW3ZJOA37dZIVVoy9Kw4nISCtk4sYo1217TdtD5U8yLuYDcWPo4dF0Y7gByZ073RgtOdBRFbIpE1J3LiKUPb9hONSxwJ0CehJzIS5CWYm5kt5NTCBLEnfIr7D95owxdFbpwYgrrNwVZmul4SLAaCvkRitnSSsSk9dbNGzYO5UIwR08VvuFxCZrJ6IG11WE9BrCtfuQgY2o0Iqyh9QRzVv6YFxCdv1IzJV0O7ESOsr2dZLOtv22jPadVHr9oovScLKj8GR8IxF2Po64sXkCuNT2lYMbWXOSgKPOY8DtHifn/UL/KBPSBKZKzAU2IRJz98idENL+y/ZEXaM1gd2AK5s4NShKj29HXND+SmSpb5Sh0issJKSw7RLM7fgxELfuXDSvSfDiRD7az20fM4AhFVpSJqSWSPqm7b1GCJ0x3nfnKcy1C7Cq7f/o0M8MQnL9Nmcap3ZU6S1FyL2fD+xPFHc7M6P9PJvnTXJoCsOk5O6XEXlIlThnwgpDkgx+pu3eKq6FhZgyIU1gJL2G4Uqt7wDm2D5/wGNajBB5NBZFKCrXHgnsb3sTZZYP0HApEBHlpFe3/dGccU92mnrfTSSUCkgOehyF5hRRQ0skfQI40wOqaCrpWOIC/A8iIfLPwCOSdrD94XEeS1eV3hK2z5W0b9VlzvF7NtYvTbkohQbUhAx3SNqLufOQLh7YwDIYQWm4OBE+PmkwIyq0pUxI7fkk8KakNDsXOMX2TeN4/FUqexxJN9neIT3vFVksMLqq9GrcJukzRE2eTzG3L1qTcfyA4eKALyaKpBWaUa0g7iHyuOo5aBNiQmJeReHjwFdyrJMKCwclZNcSJat/SUsSiaDbEdUyL7a97/xb9+X4V9jeKD1/h+0z0vNLbb9xQR8/HauTSq+nr60JM8xbqs+S0bbyMnsaeMSTpPREobCoUSaklqhWe6b23lRgs/HYx5G0BnHxfqr23jOBLW2fvqCPXztmF5XeqPL4nHCRpJcQYoiVifLZh9q+u2n7Aiiqxb4UuJ2oJ3Q3IZ8+fDzPp8LkpkxILZG0TuWWUAhyVXojyHUrGjlN1/q5EDgAuJYolPbFcbJvWmRIXna7256TVv3HEjcXl9l+7WBHV5gslAmpA2mV8pTtW2rvvc72Lwc4rAmJovT4isAdtv88xq/3tp1le+Pa66FwZqEZaYW0te17JS0HnG57vfEMARcKRdTQEklfJdwBnkymjh+0/QBhGFruzjOQdCAR8rsZWFPSDZledqdLOoewEFqXBj56hXn4KHB08iR8GNhT0hRgvAxSC4WyQmqLakXg0j7KEcA+wH+WcFEemrfA3mW5ycWSXgRMJ1ZY9/d7jIVCYcFTVkjtmapUzM72jZK2I/Ie1hj0wCYgN0vaiShjsE56PQNgfnlekt4OXG77EWAaqTCbpINt3zwO457wLEyOI4VCWSG1RNL69NyNpxDHu2z/eHAjm3goajCNxHztayRdaXvD9PxqYC+ijPcxudZHhbmRtOlC4uRdmESUFVJLbM8e4b2ngDIZZWJ71ySZXxa43/aTDZv+E4YEEYtVYhIpy+ihMDKfBcqEVBhXyoRUGDiSdifKH9wJTJd0gu3vN2j6J0kHEYKIE1NfSwDPXFBjLRQKC44SsisMHElXEoasT6ew5yzbGzRoN4WoNDvH9iXpveWA6bavXqCDXkQYwQcOwoLpPbZzq84WCp0oK6TCwsAVwPqp0N46wBXJNXy+1XdTiPSsnvfuBe5dgGNd1OhUcbZQ6CdlhVQYOPMxhB2X6ruFQmHhoExIhYWKNvWUUrvlgRVsXyNpSdtzFswIC4XCgqKE7AoDJ9VTeguwDS3qKUnanzB2XU3Sa4FfEA7shUJhAlEmpMLA6GM9pc1TKZBLkjCiqOwKhQnIYoMeQGFS82Uil+gQ20cT1W/b8E9JKwBOFkJt+ykUCgOk7CEVBkqXekq1PlYmJrdVgVuAz9n+/QIYbqFQWICUCamw0JBbT2mE9iLqId1o+/F+j69QKCxYyoRUmPBIusj2m1OS57LAS2y/fdDjKhQKeZQ9pMKiQHUer2j734ClBjmYQqHQjjIhFRYF7pR0AXBeMml9atADKhQK+ZSQXWGRQNJU20+mfaRpth8e9JgKhUIeZYVUmPBI2gyYKWkmcDkhbCgUChOMskIqTHiSW/gWtv8uaRpwXhO38EKhsHBRVkiFRQEBlSv4qO7ghUJh4aZYBxUWBT4PnCvJxOR0wIDHUygUWlBCdoVCoVBYKCghu8KERdKqkr4h6SBJa0g6XdIlkt426LEVCoV8ygqpMGGRdDmwD/A84FhgXeBR4ELbGw5ybIVCIZ+yh1SYyDxh+2oASb+z/UB6XnzsCoUJSJmQChOZGZK+SAgZpteerzTYYRUKhTaUkF1hwiJp09H+zfbM8RxLoVDoTpmQCoVCobBQUFR2hUKhUFgoKBNSoVAoFBYKyoRUKBQKhYWCMiEVCoVCYaHg/wAb7kPq0IjFNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40,24))\n",
    "fig,ax1 = plt.subplots()\n",
    "ax2=ax1.twinx()\n",
    "ax1.bar(products, quantity, color='g')\n",
    "ax2.plot(products, prices, 'b-')\n",
    "ax1.set_xticklabels(products, rotation='vertical', size=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### The top selling product is 'AAA Batteries'. The top selling products seem to have a correlation with the price of the product. The cheaper the product higher the quantity ordered and vice versa."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(197579, 10)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What products are most often sold together?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### note: keep orders that have same order Id,are sold mostly together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "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>sales</th>\n",
       "      <th>city</th>\n",
       "      <th>Hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</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",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>176574</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/03/19 19:42</td>\n",
       "      <td>20 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>176574</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/03/19 19:42</td>\n",
       "      <td>20 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>176585</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 11:31</td>\n",
       "      <td>823 Highland St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>176585</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 11:31</td>\n",
       "      <td>823 Highland St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>176586</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>2.99</td>\n",
       "      <td>04/10/19 17:00</td>\n",
       "      <td>365 Center St, San Francisco, CA 94016</td>\n",
       "      <td>4</td>\n",
       "      <td>5.98</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>176586</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/10/19 17:00</td>\n",
       "      <td>365 Center St, San Francisco, CA 94016</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119</th>\n",
       "      <td>176672</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>04/12/19 11:07</td>\n",
       "      <td>778 Maple St, New York City, NY 10001</td>\n",
       "      <td>4</td>\n",
       "      <td>14.95</td>\n",
       "      <td>New York City</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>176672</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/12/19 11:07</td>\n",
       "      <td>778 Maple St, New York City, NY 10001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>New York City</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>176681</td>\n",
       "      <td>Apple Airpods Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>150.00</td>\n",
       "      <td>04/20/19 10:39</td>\n",
       "      <td>331 Cherry St, Seattle, WA 98101</td>\n",
       "      <td>4</td>\n",
       "      <td>150.00</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>176681</td>\n",
       "      <td>ThinkPad Laptop</td>\n",
       "      <td>1</td>\n",
       "      <td>999.99</td>\n",
       "      <td>04/20/19 10:39</td>\n",
       "      <td>331 Cherry St, Seattle, WA 98101</td>\n",
       "      <td>4</td>\n",
       "      <td>999.99</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>176689</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/24/19 17:15</td>\n",
       "      <td>659 Lincoln St, New York City, NY 10001</td>\n",
       "      <td>4</td>\n",
       "      <td>99.99</td>\n",
       "      <td>New York City</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>176689</td>\n",
       "      <td>AAA Batteries (4-pack)</td>\n",
       "      <td>2</td>\n",
       "      <td>2.99</td>\n",
       "      <td>04/24/19 17:15</td>\n",
       "      <td>659 Lincoln St, New York City, NY 10001</td>\n",
       "      <td>4</td>\n",
       "      <td>5.98</td>\n",
       "      <td>New York City</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>176739</td>\n",
       "      <td>34in Ultrawide Monitor</td>\n",
       "      <td>1</td>\n",
       "      <td>379.99</td>\n",
       "      <td>04/05/19 17:38</td>\n",
       "      <td>730 6th St, Austin, TX 73301</td>\n",
       "      <td>4</td>\n",
       "      <td>379.99</td>\n",
       "      <td>Austin</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>176739</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/05/19 17:38</td>\n",
       "      <td>730 6th St, Austin, TX 73301</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Austin</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>225</th>\n",
       "      <td>176774</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>04/25/19 15:06</td>\n",
       "      <td>372 Church St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>176774</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/25/19 15:06</td>\n",
       "      <td>372 Church St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>233</th>\n",
       "      <td>176781</td>\n",
       "      <td>iPhone</td>\n",
       "      <td>1</td>\n",
       "      <td>700.00</td>\n",
       "      <td>04/03/19 07:37</td>\n",
       "      <td>976 Hickory St, Dallas, TX 75001</td>\n",
       "      <td>4</td>\n",
       "      <td>700.00</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>176781</td>\n",
       "      <td>Lightning Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>14.95</td>\n",
       "      <td>04/03/19 07:37</td>\n",
       "      <td>976 Hickory St, Dallas, TX 75001</td>\n",
       "      <td>4</td>\n",
       "      <td>14.95</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "3     176560                Google Phone                 1      600.00   \n",
       "4     176560            Wired Headphones                 1       11.99   \n",
       "18    176574                Google Phone                 1      600.00   \n",
       "19    176574        USB-C Charging Cable                 1       11.95   \n",
       "30    176585  Bose SoundSport Headphones                 1       99.99   \n",
       "31    176585  Bose SoundSport Headphones                 1       99.99   \n",
       "32    176586      AAA Batteries (4-pack)                 2        2.99   \n",
       "33    176586                Google Phone                 1      600.00   \n",
       "119   176672    Lightning Charging Cable                 1       14.95   \n",
       "120   176672        USB-C Charging Cable                 1       11.95   \n",
       "129   176681    Apple Airpods Headphones                 1      150.00   \n",
       "130   176681             ThinkPad Laptop                 1      999.99   \n",
       "138   176689  Bose SoundSport Headphones                 1       99.99   \n",
       "139   176689      AAA Batteries (4-pack)                 2        2.99   \n",
       "189   176739      34in Ultrawide Monitor                 1      379.99   \n",
       "190   176739                Google Phone                 1      600.00   \n",
       "225   176774    Lightning Charging Cable                 1       14.95   \n",
       "226   176774        USB-C Charging Cable                 1       11.95   \n",
       "233   176781                      iPhone                 1      700.00   \n",
       "234   176781    Lightning Charging Cable                 1       14.95   \n",
       "\n",
       "         Order Date                         Purchase Address  Month   sales  \\\n",
       "3    04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001      4  600.00   \n",
       "4    04/12/19 14:38     669 Spruce St, Los Angeles, CA 90001      4   11.99   \n",
       "18   04/03/19 19:42        20 Hill St, Los Angeles, CA 90001      4  600.00   \n",
       "19   04/03/19 19:42        20 Hill St, Los Angeles, CA 90001      4   11.95   \n",
       "30   04/07/19 11:31        823 Highland St, Boston, MA 02215      4   99.99   \n",
       "31   04/07/19 11:31        823 Highland St, Boston, MA 02215      4   99.99   \n",
       "32   04/10/19 17:00   365 Center St, San Francisco, CA 94016      4    5.98   \n",
       "33   04/10/19 17:00   365 Center St, San Francisco, CA 94016      4  600.00   \n",
       "119  04/12/19 11:07    778 Maple St, New York City, NY 10001      4   14.95   \n",
       "120  04/12/19 11:07    778 Maple St, New York City, NY 10001      4   11.95   \n",
       "129  04/20/19 10:39         331 Cherry St, Seattle, WA 98101      4  150.00   \n",
       "130  04/20/19 10:39         331 Cherry St, Seattle, WA 98101      4  999.99   \n",
       "138  04/24/19 17:15  659 Lincoln St, New York City, NY 10001      4   99.99   \n",
       "139  04/24/19 17:15  659 Lincoln St, New York City, NY 10001      4    5.98   \n",
       "189  04/05/19 17:38             730 6th St, Austin, TX 73301      4  379.99   \n",
       "190  04/05/19 17:38             730 6th St, Austin, TX 73301      4  600.00   \n",
       "225  04/25/19 15:06     372 Church St, Los Angeles, CA 90001      4   14.95   \n",
       "226  04/25/19 15:06     372 Church St, Los Angeles, CA 90001      4   11.95   \n",
       "233  04/03/19 07:37         976 Hickory St, Dallas, TX 75001      4  700.00   \n",
       "234  04/03/19 07:37         976 Hickory St, Dallas, TX 75001      4   14.95   \n",
       "\n",
       "               city  Hour  \n",
       "3       Los Angeles    14  \n",
       "4       Los Angeles    14  \n",
       "18      Los Angeles    19  \n",
       "19      Los Angeles    19  \n",
       "30           Boston    11  \n",
       "31           Boston    11  \n",
       "32    San Francisco    17  \n",
       "33    San Francisco    17  \n",
       "119   New York City    11  \n",
       "120   New York City    11  \n",
       "129         Seattle    10  \n",
       "130         Seattle    10  \n",
       "138   New York City    17  \n",
       "139   New York City    17  \n",
       "189          Austin    17  \n",
       "190          Austin    17  \n",
       "225     Los Angeles    15  \n",
       "226     Los Angeles    15  \n",
       "233          Dallas     7  \n",
       "234          Dallas     7  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=all_data[all_data['Order ID'].duplicated(keep=False)]\n",
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\mcr\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "#create grouped col \n",
    "df['Grouped'] = df.groupby('Order ID')['Product'].transform(lambda x: ','.join(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "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>sales</th>\n",
       "      <th>city</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Grouped</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>176560</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/12/19 14:38</td>\n",
       "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "      <td>Google Phone,Wired Headphones</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",
       "      <td>4</td>\n",
       "      <td>11.99</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>14</td>\n",
       "      <td>Google Phone,Wired Headphones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>176574</td>\n",
       "      <td>Google Phone</td>\n",
       "      <td>1</td>\n",
       "      <td>600.00</td>\n",
       "      <td>04/03/19 19:42</td>\n",
       "      <td>20 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>600.00</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19</td>\n",
       "      <td>Google Phone,USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>176574</td>\n",
       "      <td>USB-C Charging Cable</td>\n",
       "      <td>1</td>\n",
       "      <td>11.95</td>\n",
       "      <td>04/03/19 19:42</td>\n",
       "      <td>20 Hill St, Los Angeles, CA 90001</td>\n",
       "      <td>4</td>\n",
       "      <td>11.95</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>19</td>\n",
       "      <td>Google Phone,USB-C Charging Cable</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>176585</td>\n",
       "      <td>Bose SoundSport Headphones</td>\n",
       "      <td>1</td>\n",
       "      <td>99.99</td>\n",
       "      <td>04/07/19 11:31</td>\n",
       "      <td>823 Highland St, Boston, MA 02215</td>\n",
       "      <td>4</td>\n",
       "      <td>99.99</td>\n",
       "      <td>Boston</td>\n",
       "      <td>11</td>\n",
       "      <td>Bose SoundSport Headphones,Bose SoundSport Hea...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order ID                     Product  Quantity Ordered  Price Each  \\\n",
       "3    176560                Google Phone                 1      600.00   \n",
       "4    176560            Wired Headphones                 1       11.99   \n",
       "18   176574                Google Phone                 1      600.00   \n",
       "19   176574        USB-C Charging Cable                 1       11.95   \n",
       "30   176585  Bose SoundSport Headphones                 1       99.99   \n",
       "\n",
       "        Order Date                      Purchase Address  Month   sales  \\\n",
       "3   04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4  600.00   \n",
       "4   04/12/19 14:38  669 Spruce St, Los Angeles, CA 90001      4   11.99   \n",
       "18  04/03/19 19:42     20 Hill St, Los Angeles, CA 90001      4  600.00   \n",
       "19  04/03/19 19:42     20 Hill St, Los Angeles, CA 90001      4   11.95   \n",
       "30  04/07/19 11:31     823 Highland St, Boston, MA 02215      4   99.99   \n",
       "\n",
       "            city  Hour                                            Grouped  \n",
       "3    Los Angeles    14                      Google Phone,Wired Headphones  \n",
       "4    Los Angeles    14                      Google Phone,Wired Headphones  \n",
       "18   Los Angeles    19                  Google Phone,USB-C Charging Cable  \n",
       "19   Los Angeles    19                  Google Phone,USB-C Charging Cable  \n",
       "30        Boston    11  Bose SoundSport Headphones,Bose SoundSport Hea...  "
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14649, 11)"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets drop out all duplicate Order ID\n",
    "df2 = df.drop_duplicates(subset=['Order ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0xe60fac2c8>"
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2['Grouped'].value_counts()[0:5].plot.pie()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "values=df2['Grouped'].value_counts()[0:5]\n",
    "labels=df['Grouped'].value_counts()[0:5].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "trace=go.Pie(labels=labels, values=values,\n",
    "               hoverinfo='label+percent', textinfo='value', \n",
    "               textfont=dict(size=25),\n",
    "              pull=[0, 0, 0,0.2, 0]\n",
    "               )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "linkText": "Export to plot.ly",
        "plotlyServerURL": "https://plot.ly",
        "showLink": false
       },
       "data": [
        {
         "hoverinfo": "label+percent",
         "labels": [
          "iPhone,Lightning Charging Cable",
          "Google Phone,USB-C Charging Cable",
          "iPhone,Wired Headphones",
          "Vareebadd Phone,USB-C Charging Cable",
          "Google Phone,Wired Headphones"
         ],
         "pull": [
          0,
          0,
          0,
          0.2,
          0
         ],
         "textfont": {
          "size": 25
         },
         "textinfo": "value",
         "type": "pie",
         "values": [
          882,
          856,
          361,
          312,
          303
         ]
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
              0.3333333333333333,
              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
              0.2222222222222222,
              "#7201a8"
             ],
             [
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              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmap"
           }
          ],
          "heatmapgl": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
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             ],
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              "#9c179e"
             ],
             [
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              "#bd3786"
             ],
             [
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             ],
             [
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              "#ed7953"
             ],
             [
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              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "heatmapgl"
           }
          ],
          "histogram": [
           {
            "marker": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
             }
            },
            "type": "histogram"
           }
          ],
          "histogram2d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
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              "#46039f"
             ],
             [
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              "#7201a8"
             ],
             [
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              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
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              "#ed7953"
             ],
             [
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              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2d"
           }
          ],
          "histogram2dcontour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
              0,
              "#0d0887"
             ],
             [
              0.1111111111111111,
              "#46039f"
             ],
             [
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              "#7201a8"
             ],
             [
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              "#9c179e"
             ],
             [
              0.4444444444444444,
              "#bd3786"
             ],
             [
              0.5555555555555556,
              "#d8576b"
             ],
             [
              0.6666666666666666,
              "#ed7953"
             ],
             [
              0.7777777777777778,
              "#fb9f3a"
             ],
             [
              0.8888888888888888,
              "#fdca26"
             ],
             [
              1,
              "#f0f921"
             ]
            ],
            "type": "histogram2dcontour"
           }
          ],
          "mesh3d": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "mesh3d"
           }
          ],
          "parcoords": [
           {
            "line": {
             "colorbar": {
              "outlinewidth": 0,
              "ticks": ""
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