Sales Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e3ab0638",
   "metadata": {},
   "source": [
    "# Data Analysis Steps"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0583ea4",
   "metadata": {},
   "source": [
    "### 1. Define the question/s"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1ab2289",
   "metadata": {},
   "source": [
    "1. What is the yearly sales trend?\n",
    "2. Which product sold the most in 2016?\n",
    "3. Among the three product categories, which one had the lowest sale in 2018?\n",
    "4. What is the yearl sales trend of the three customer groups?\n",
    "5. Are we getting more corporate customers?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43863ba2",
   "metadata": {},
   "source": [
    "### 2. Collect the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d72a402d",
   "metadata": {},
   "source": [
    "The *_train_* dataset holds the sales data of an online office supplies and furniture shop from 2015 to 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bac15f2",
   "metadata": {},
   "source": [
    " ### 3. Clean the Data   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59a7862f",
   "metadata": {},
   "source": [
    "#### Import packages / libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e5af1844",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt #continuation import for visualization\n",
    "import seaborn as sns # for visualization and correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fe0f3e15",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "\n",
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       "    }\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>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>12/06/2017</td>\n",
       "      <td>16/06/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
       "3       4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display the data from file\n",
    "train = pd.read_csv('train.csv')\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bed9e9e",
   "metadata": {},
   "source": [
    "#### Basic methods for investigating / viewing a DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0e0748b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Row ID         9800 non-null   int64  \n",
      " 1   Order ID       9800 non-null   object \n",
      " 2   Order Date     9800 non-null   object \n",
      " 3   Ship Date      9800 non-null   object \n",
      " 4   Ship Mode      9800 non-null   object \n",
      " 5   Customer ID    9800 non-null   object \n",
      " 6   Customer Name  9800 non-null   object \n",
      " 7   Segment        9800 non-null   object \n",
      " 8   Country        9800 non-null   object \n",
      " 9   City           9800 non-null   object \n",
      " 10  State          9800 non-null   object \n",
      " 11  Postal Code    9789 non-null   float64\n",
      " 12  Region         9800 non-null   object \n",
      " 13  Product ID     9800 non-null   object \n",
      " 14  Category       9800 non-null   object \n",
      " 15  Sub-Category   9800 non-null   object \n",
      " 16  Product Name   9800 non-null   object \n",
      " 17  Sales          9800 non-null   float64\n",
      "dtypes: float64(2), int64(1), object(15)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cdfb9acd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Row ID            0\n",
       "Order ID          0\n",
       "Order Date        0\n",
       "Ship Date         0\n",
       "Ship Mode         0\n",
       "Customer ID       0\n",
       "Customer Name     0\n",
       "Segment           0\n",
       "Country           0\n",
       "City              0\n",
       "State             0\n",
       "Postal Code      11\n",
       "Region            0\n",
       "Product ID        0\n",
       "Category          0\n",
       "Sub-Category      0\n",
       "Product Name      0\n",
       "Sales             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# display how many null is in the dataframe\n",
    "train.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b22b671c",
   "metadata": {},
   "source": [
    "Postal Code column have 11 null values. We need to check the data if what happen on that column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef2538e7",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
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       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2234</th>\n",
       "      <td>2235</td>\n",
       "      <td>CA-2018-104066</td>\n",
       "      <td>05/12/2018</td>\n",
       "      <td>10/12/2018</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>QJ-19255</td>\n",
       "      <td>Quincy Jones</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10001013</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Logitech ClearChat Comfort/USB Headset H390</td>\n",
       "      <td>205.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5274</th>\n",
       "      <td>5275</td>\n",
       "      <td>CA-2016-162887</td>\n",
       "      <td>07/11/2016</td>\n",
       "      <td>09/11/2016</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SV-20785</td>\n",
       "      <td>Stewart Visinsky</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10000595</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Safco Contoured Stacking Chairs</td>\n",
       "      <td>715.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8798</th>\n",
       "      <td>8799</td>\n",
       "      <td>US-2017-150140</td>\n",
       "      <td>06/04/2017</td>\n",
       "      <td>10/04/2017</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>VM-21685</td>\n",
       "      <td>Valerie Mitchum</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-PH-10002555</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Nortel Meridian M5316 Digital phone</td>\n",
       "      <td>1294.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9146</th>\n",
       "      <td>9147</td>\n",
       "      <td>US-2017-165505</td>\n",
       "      <td>23/01/2017</td>\n",
       "      <td>27/01/2017</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CB-12535</td>\n",
       "      <td>Claudia Bergmann</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>TEC-AC-10002926</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Accessories</td>\n",
       "      <td>Logitech Wireless Marathon Mouse M705</td>\n",
       "      <td>99.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9147</th>\n",
       "      <td>9148</td>\n",
       "      <td>US-2017-165505</td>\n",
       "      <td>23/01/2017</td>\n",
       "      <td>27/01/2017</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CB-12535</td>\n",
       "      <td>Claudia Bergmann</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-AR-10003477</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Art</td>\n",
       "      <td>4009 Highlighters</td>\n",
       "      <td>8.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9148</th>\n",
       "      <td>9149</td>\n",
       "      <td>US-2017-165505</td>\n",
       "      <td>23/01/2017</td>\n",
       "      <td>27/01/2017</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>CB-12535</td>\n",
       "      <td>Claudia Bergmann</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-ST-10001526</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Iceberg Mobile Mega Data/Printer Cart</td>\n",
       "      <td>1564.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9386</th>\n",
       "      <td>9387</td>\n",
       "      <td>US-2018-127292</td>\n",
       "      <td>19/01/2018</td>\n",
       "      <td>23/01/2018</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RM-19375</td>\n",
       "      <td>Raymond Messe</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10000157</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 191</td>\n",
       "      <td>79.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9387</th>\n",
       "      <td>9388</td>\n",
       "      <td>US-2018-127292</td>\n",
       "      <td>19/01/2018</td>\n",
       "      <td>23/01/2018</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RM-19375</td>\n",
       "      <td>Raymond Messe</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-PA-10001970</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1881</td>\n",
       "      <td>12.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9388</th>\n",
       "      <td>9389</td>\n",
       "      <td>US-2018-127292</td>\n",
       "      <td>19/01/2018</td>\n",
       "      <td>23/01/2018</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RM-19375</td>\n",
       "      <td>Raymond Messe</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-AP-10000828</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Appliances</td>\n",
       "      <td>Avanti 4.4 Cu. Ft. Refrigerator</td>\n",
       "      <td>542.94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9389</th>\n",
       "      <td>9390</td>\n",
       "      <td>US-2018-127292</td>\n",
       "      <td>19/01/2018</td>\n",
       "      <td>23/01/2018</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>RM-19375</td>\n",
       "      <td>Raymond Messe</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>OFF-EN-10001509</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Envelopes</td>\n",
       "      <td>Poly String Tie Envelopes</td>\n",
       "      <td>2.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9741</th>\n",
       "      <td>9742</td>\n",
       "      <td>CA-2016-117086</td>\n",
       "      <td>08/11/2016</td>\n",
       "      <td>12/11/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>QJ-19255</td>\n",
       "      <td>Quincy Jones</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>NaN</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-BO-10004834</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Riverside Palais Royal Lawyers Bookcase, Royal...</td>\n",
       "      <td>4404.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Row ID        Order ID  Order Date   Ship Date       Ship Mode  \\\n",
       "2234    2235  CA-2018-104066  05/12/2018  10/12/2018  Standard Class   \n",
       "5274    5275  CA-2016-162887  07/11/2016  09/11/2016    Second Class   \n",
       "8798    8799  US-2017-150140  06/04/2017  10/04/2017  Standard Class   \n",
       "9146    9147  US-2017-165505  23/01/2017  27/01/2017  Standard Class   \n",
       "9147    9148  US-2017-165505  23/01/2017  27/01/2017  Standard Class   \n",
       "9148    9149  US-2017-165505  23/01/2017  27/01/2017  Standard Class   \n",
       "9386    9387  US-2018-127292  19/01/2018  23/01/2018  Standard Class   \n",
       "9387    9388  US-2018-127292  19/01/2018  23/01/2018  Standard Class   \n",
       "9388    9389  US-2018-127292  19/01/2018  23/01/2018  Standard Class   \n",
       "9389    9390  US-2018-127292  19/01/2018  23/01/2018  Standard Class   \n",
       "9741    9742  CA-2016-117086  08/11/2016  12/11/2016  Standard Class   \n",
       "\n",
       "     Customer ID     Customer Name      Segment        Country        City  \\\n",
       "2234    QJ-19255      Quincy Jones    Corporate  United States  Burlington   \n",
       "5274    SV-20785  Stewart Visinsky     Consumer  United States  Burlington   \n",
       "8798    VM-21685   Valerie Mitchum  Home Office  United States  Burlington   \n",
       "9146    CB-12535  Claudia Bergmann    Corporate  United States  Burlington   \n",
       "9147    CB-12535  Claudia Bergmann    Corporate  United States  Burlington   \n",
       "9148    CB-12535  Claudia Bergmann    Corporate  United States  Burlington   \n",
       "9386    RM-19375     Raymond Messe     Consumer  United States  Burlington   \n",
       "9387    RM-19375     Raymond Messe     Consumer  United States  Burlington   \n",
       "9388    RM-19375     Raymond Messe     Consumer  United States  Burlington   \n",
       "9389    RM-19375     Raymond Messe     Consumer  United States  Burlington   \n",
       "9741    QJ-19255      Quincy Jones    Corporate  United States  Burlington   \n",
       "\n",
       "        State  Postal Code Region       Product ID         Category  \\\n",
       "2234  Vermont          NaN   East  TEC-AC-10001013       Technology   \n",
       "5274  Vermont          NaN   East  FUR-CH-10000595        Furniture   \n",
       "8798  Vermont          NaN   East  TEC-PH-10002555       Technology   \n",
       "9146  Vermont          NaN   East  TEC-AC-10002926       Technology   \n",
       "9147  Vermont          NaN   East  OFF-AR-10003477  Office Supplies   \n",
       "9148  Vermont          NaN   East  OFF-ST-10001526  Office Supplies   \n",
       "9386  Vermont          NaN   East  OFF-PA-10000157  Office Supplies   \n",
       "9387  Vermont          NaN   East  OFF-PA-10001970  Office Supplies   \n",
       "9388  Vermont          NaN   East  OFF-AP-10000828  Office Supplies   \n",
       "9389  Vermont          NaN   East  OFF-EN-10001509  Office Supplies   \n",
       "9741  Vermont          NaN   East  FUR-BO-10004834        Furniture   \n",
       "\n",
       "     Sub-Category                                       Product Name    Sales  \n",
       "2234  Accessories        Logitech ClearChat Comfort/USB Headset H390   205.03  \n",
       "5274       Chairs                    Safco Contoured Stacking Chairs   715.20  \n",
       "8798       Phones                Nortel Meridian M5316 Digital phone  1294.75  \n",
       "9146  Accessories              Logitech Wireless Marathon Mouse M705    99.98  \n",
       "9147          Art                                  4009 Highlighters     8.04  \n",
       "9148      Storage              Iceberg Mobile Mega Data/Printer Cart  1564.29  \n",
       "9386        Paper                                          Xerox 191    79.92  \n",
       "9387        Paper                                         Xerox 1881    12.28  \n",
       "9388   Appliances                    Avanti 4.4 Cu. Ft. Refrigerator   542.94  \n",
       "9389    Envelopes                          Poly String Tie Envelopes     2.04  \n",
       "9741    Bookcases  Riverside Palais Royal Lawyers Bookcase, Royal...  4404.90  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the data in postalcode  column\n",
    "train[train['Postal Code'].isna()] \n",
    "# The isna() function is used to detect missing values"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c364c78c",
   "metadata": {},
   "source": [
    "We can see that the postal code have 'Nan' which are the null values. We need to fill the null values with non null values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b234ce67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Row ID         9800 non-null   int64  \n",
      " 1   Order ID       9800 non-null   object \n",
      " 2   Order Date     9800 non-null   object \n",
      " 3   Ship Date      9800 non-null   object \n",
      " 4   Ship Mode      9800 non-null   object \n",
      " 5   Customer ID    9800 non-null   object \n",
      " 6   Customer Name  9800 non-null   object \n",
      " 7   Segment        9800 non-null   object \n",
      " 8   Country        9800 non-null   object \n",
      " 9   City           9800 non-null   object \n",
      " 10  State          9800 non-null   object \n",
      " 11  Postal Code    9800 non-null   object \n",
      " 12  Region         9800 non-null   object \n",
      " 13  Product ID     9800 non-null   object \n",
      " 14  Category       9800 non-null   object \n",
      " 15  Sub-Category   9800 non-null   object \n",
      " 16  Product Name   9800 non-null   object \n",
      " 17  Sales          9800 non-null   float64\n",
      "dtypes: float64(1), int64(1), object(16)\n",
      "memory usage: 1.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Row ID           0\n",
       "Order ID         0\n",
       "Order Date       0\n",
       "Ship Date        0\n",
       "Ship Mode        0\n",
       "Customer ID      0\n",
       "Customer Name    0\n",
       "Segment          0\n",
       "Country          0\n",
       "City             0\n",
       "State            0\n",
       "Postal Code      0\n",
       "Region           0\n",
       "Product ID       0\n",
       "Category         0\n",
       "Sub-Category     0\n",
       "Product Name     0\n",
       "Sales            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train['Postal Code'] = train['Postal Code'].fillna('054041') #054041 is the postal code in that city\n",
    "\n",
    "train.info() # we need to check if the information is correct\n",
    "\n",
    "train.isnull().sum() # Shows if there still null values."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44c55747",
   "metadata": {},
   "source": [
    "The postal Code now has 0 null values. We are able to cleaned the data correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f01ce306",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Row ID</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>12/06/2017</td>\n",
       "      <td>16/06/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
       "0       1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "1       2  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "2       3  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
       "3       4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "4       5  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "  Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0     42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1     42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2     90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3     33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4     33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5454ce6b",
   "metadata": {},
   "source": [
    "we need to remove the Row ID column for a better file viewing. row id is not relevant to our data analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9541694",
   "metadata": {},
   "source": [
    "in order to remove / delete the column we will use del method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ac3ca5f2",
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>08/11/2017</td>\n",
       "      <td>11/11/2017</td>\n",
       "      <td>Second Class</td>\n",
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       "      <td>Claire Gute</td>\n",
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       "      <td>United States</td>\n",
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       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
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       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>12/06/2017</td>\n",
       "      <td>16/06/2017</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>11/10/2016</td>\n",
       "      <td>18/10/2016</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
       "0  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
       "2  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
       "3  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "  Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0     42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1     42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2     90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3     33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4     33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# deleting a column\n",
    "del train['Row ID']\n",
    "\n",
    "train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19dceb95",
   "metadata": {},
   "source": [
    "#### Checking / Converting Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "3b507c9e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 17 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Order ID       9800 non-null   object \n",
      " 1   Order Date     9800 non-null   object \n",
      " 2   Ship Date      9800 non-null   object \n",
      " 3   Ship Mode      9800 non-null   object \n",
      " 4   Customer ID    9800 non-null   object \n",
      " 5   Customer Name  9800 non-null   object \n",
      " 6   Segment        9800 non-null   object \n",
      " 7   Country        9800 non-null   object \n",
      " 8   City           9800 non-null   object \n",
      " 9   State          9800 non-null   object \n",
      " 10  Postal Code    9800 non-null   object \n",
      " 11  Region         9800 non-null   object \n",
      " 12  Product ID     9800 non-null   object \n",
      " 13  Category       9800 non-null   object \n",
      " 14  Sub-Category   9800 non-null   object \n",
      " 15  Product Name   9800 non-null   object \n",
      " 16  Sales          9800 non-null   float64\n",
      "dtypes: float64(1), object(16)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afe25e37",
   "metadata": {},
   "source": [
    "Looking at Dtypes, The 'Order Date' and 'Shipping Date' data type is object where the data should be integer/ into date data type. therefore we need to convert it. 'Postal Code' data type also is object we also need to convert it into integers.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "bf01da47",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 17 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Order ID       9800 non-null   object        \n",
      " 1   Order Date     9800 non-null   datetime64[ns]\n",
      " 2   Ship Date      9800 non-null   datetime64[ns]\n",
      " 3   Ship Mode      9800 non-null   object        \n",
      " 4   Customer ID    9800 non-null   object        \n",
      " 5   Customer Name  9800 non-null   object        \n",
      " 6   Segment        9800 non-null   object        \n",
      " 7   Country        9800 non-null   object        \n",
      " 8   City           9800 non-null   object        \n",
      " 9   State          9800 non-null   object        \n",
      " 10  Postal Code    9800 non-null   object        \n",
      " 11  Region         9800 non-null   object        \n",
      " 12  Product ID     9800 non-null   object        \n",
      " 13  Category       9800 non-null   object        \n",
      " 14  Sub-Category   9800 non-null   object        \n",
      " 15  Product Name   9800 non-null   object        \n",
      " 16  Sales          9800 non-null   float64       \n",
      "dtypes: datetime64[ns](2), float64(1), object(14)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "#  Converting object to datetime\n",
    "train['Order Date'] = pd.to_datetime(train['Order Date'], format = '%d/%m/%Y')\n",
    "train['Ship Date'] = pd.to_datetime(train['Ship Date'], format = '%d/%m/%Y')\n",
    "# Pandas to_datetime() method helps to convert string Date time\n",
    "\n",
    "# Convert float to object/string \n",
    "train['Postal Code'] = train['Postal Code'].astype(str)\n",
    "# astype() method is used to cast a pandas object to a specified dtype\n",
    "\n",
    "train.info() #check the dtype if it is correct"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08370e3b",
   "metadata": {},
   "source": [
    "### We now corrected the data type for order data and shipping date also the postal code which convert it into string type."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd20aec3",
   "metadata": {},
   "source": [
    "# -----"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbb5a2a9",
   "metadata": {},
   "source": [
    "## Modeling the Data (Continuation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99078be",
   "metadata": {},
   "source": [
    "### 4. Analyze the Data\n",
    "\n",
    "### Q1. What is the yearly sales trend?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "39ace5c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "</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>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.9600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.9400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CA-2017-138688</td>\n",
       "      <td>2017-06-12</td>\n",
       "      <td>2017-06-16</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>DV-13045</td>\n",
       "      <td>Darrin Van Huff</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>California</td>\n",
       "      <td>90036.0</td>\n",
       "      <td>West</td>\n",
       "      <td>OFF-LA-10000240</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Labels</td>\n",
       "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
       "      <td>14.6200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-TA-10000577</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Tables</td>\n",
       "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
       "      <td>957.5775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>US-2016-108966</td>\n",
       "      <td>2016-10-11</td>\n",
       "      <td>2016-10-18</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>SO-20335</td>\n",
       "      <td>Sean O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Fort Lauderdale</td>\n",
       "      <td>Florida</td>\n",
       "      <td>33311.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10000760</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Eldon Fold 'N Roll Cart System</td>\n",
       "      <td>22.3680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "0  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "1  CA-2017-152156 2017-11-08 2017-11-11    Second Class    CG-12520   \n",
       "2  CA-2017-138688 2017-06-12 2017-06-16    Second Class    DV-13045   \n",
       "3  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "4  US-2016-108966 2016-10-11 2016-10-18  Standard Class    SO-20335   \n",
       "\n",
       "     Customer Name    Segment        Country             City       State  \\\n",
       "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
       "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
       "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
       "\n",
       "  Postal Code Region       Product ID         Category Sub-Category  \\\n",
       "0     42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
       "1     42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
       "2     90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
       "3     33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
       "4     33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
       "\n",
       "                                        Product Name     Sales  \n",
       "0                  Bush Somerset Collection Bookcase  261.9600  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
       "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
       "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
       "4                     Eldon Fold 'N Roll Cart System   22.3680  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e8327e62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Order ID       9800 non-null   object        \n",
      " 1   Order Date     9800 non-null   datetime64[ns]\n",
      " 2   Ship Date      9800 non-null   datetime64[ns]\n",
      " 3   Ship Mode      9800 non-null   object        \n",
      " 4   Customer ID    9800 non-null   object        \n",
      " 5   Customer Name  9800 non-null   object        \n",
      " 6   Segment        9800 non-null   object        \n",
      " 7   Country        9800 non-null   object        \n",
      " 8   City           9800 non-null   object        \n",
      " 9   State          9800 non-null   object        \n",
      " 10  Postal Code    9800 non-null   object        \n",
      " 11  Region         9800 non-null   object        \n",
      " 12  Product ID     9800 non-null   object        \n",
      " 13  Category       9800 non-null   object        \n",
      " 14  Sub-Category   9800 non-null   object        \n",
      " 15  Product Name   9800 non-null   object        \n",
      " 16  Sales          9800 non-null   float64       \n",
      " 17  Order Year     9800 non-null   int64         \n",
      "dtypes: datetime64[ns](2), float64(1), int64(1), object(14)\n",
      "memory usage: 1.3+ MB\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
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       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
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       "      <td>South</td>\n",
       "      <td>FUR-BO-10001798</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Bookcases</td>\n",
       "      <td>Bush Somerset Collection Bookcase</td>\n",
       "      <td>261.96</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>CA-2017-152156</td>\n",
       "      <td>2017-11-08</td>\n",
       "      <td>2017-11-11</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>CG-12520</td>\n",
       "      <td>Claire Gute</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Henderson</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>42420.0</td>\n",
       "      <td>South</td>\n",
       "      <td>FUR-CH-10000454</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
       "      <td>731.94</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Order ID Order Date  Ship Date     Ship Mode Customer ID  \\\n",
       "0  CA-2017-152156 2017-11-08 2017-11-11  Second Class    CG-12520   \n",
       "1  CA-2017-152156 2017-11-08 2017-11-11  Second Class    CG-12520   \n",
       "\n",
       "  Customer Name   Segment        Country       City     State Postal Code  \\\n",
       "0   Claire Gute  Consumer  United States  Henderson  Kentucky     42420.0   \n",
       "1   Claire Gute  Consumer  United States  Henderson  Kentucky     42420.0   \n",
       "\n",
       "  Region       Product ID   Category Sub-Category  \\\n",
       "0  South  FUR-BO-10001798  Furniture    Bookcases   \n",
       "1  South  FUR-CH-10000454  Furniture       Chairs   \n",
       "\n",
       "                                        Product Name   Sales  Order Year  \n",
       "0                  Bush Somerset Collection Bookcase  261.96        2017  \n",
       "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.94        2017  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Order Year column\n",
    "train['Order Year'] = train['Order Date'].dt.year\n",
    "\n",
    "train.info()\n",
    "train.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f0e190df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9800 entries, 0 to 9799\n",
      "Data columns (total 18 columns):\n",
      " #   Column         Non-Null Count  Dtype         \n",
      "---  ------         --------------  -----         \n",
      " 0   Order ID       9800 non-null   object        \n",
      " 1   Order Date     9800 non-null   datetime64[ns]\n",
      " 2   Ship Date      9800 non-null   datetime64[ns]\n",
      " 3   Ship Mode      9800 non-null   object        \n",
      " 4   Customer ID    9800 non-null   object        \n",
      " 5   Customer Name  9800 non-null   object        \n",
      " 6   Segment        9800 non-null   object        \n",
      " 7   Country        9800 non-null   object        \n",
      " 8   City           9800 non-null   object        \n",
      " 9   State          9800 non-null   object        \n",
      " 10  Postal Code    9800 non-null   object        \n",
      " 11  Region         9800 non-null   object        \n",
      " 12  Product ID     9800 non-null   object        \n",
      " 13  Category       9800 non-null   object        \n",
      " 14  Sub-Category   9800 non-null   object        \n",
      " 15  Product Name   9800 non-null   object        \n",
      " 16  Sales          9800 non-null   float64       \n",
      " 17  Order Year     9800 non-null   object        \n",
      "dtypes: datetime64[ns](2), float64(1), object(15)\n",
      "memory usage: 1.3+ MB\n"
     ]
    }
   ],
   "source": [
    "# convert order year int64 data type into object\n",
    "train['Order Year'] = train['Order Year'].astype(str)\n",
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9cc624b0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Year</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>479856.2081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>459436.0054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>600192.5500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>722052.0192</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Year        Sales\n",
       "0       2015  479856.2081\n",
       "1       2016  459436.0054\n",
       "2       2017  600192.5500\n",
       "3       2018  722052.0192"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sum of sales by year \n",
    "\n",
    "y_sales = train.groupby('Order Year', as_index = False).agg({'Sales': 'sum'})\n",
    "\n",
    "y_sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5de96663",
   "metadata": {},
   "source": [
    "#### Q1 Ans. There's an increase of sales from 2015 to 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c5530ed",
   "metadata": {},
   "source": [
    "# -"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f0270c0",
   "metadata": {},
   "source": [
    "### Q2. Which product sold the most in 2018?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9047d5cf",
   "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>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Order Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>CA-2018-114412</td>\n",
       "      <td>2018-04-15</td>\n",
       "      <td>2018-04-20</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>AA-10480</td>\n",
       "      <td>Andrew Allen</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Concord</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>28027.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-PA-10002365</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Xerox 1967</td>\n",
       "      <td>15.552</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>US-2018-156909</td>\n",
       "      <td>2018-07-16</td>\n",
       "      <td>2018-07-18</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>SF-20065</td>\n",
       "      <td>Sandra Flanagan</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>19140.0</td>\n",
       "      <td>East</td>\n",
       "      <td>FUR-CH-10002774</td>\n",
       "      <td>Furniture</td>\n",
       "      <td>Chairs</td>\n",
       "      <td>Global Deluxe Stacking Chair, Gray</td>\n",
       "      <td>71.372</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>CA-2018-107727</td>\n",
       "      <td>2018-10-19</td>\n",
       "      <td>2018-10-23</td>\n",
       "      <td>Second Class</td>\n",
       "      <td>MA-17560</td>\n",
       "      <td>Matt Abelman</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>United States</td>\n",
       "      <td>Houston</td>\n",
       "      <td>Texas</td>\n",
       "      <td>77095.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>OFF-PA-10000249</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Paper</td>\n",
       "      <td>Easy-staple paper</td>\n",
       "      <td>29.472</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>CA-2018-120999</td>\n",
       "      <td>2018-09-10</td>\n",
       "      <td>2018-09-15</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>LC-16930</td>\n",
       "      <td>Linda Cazamias</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Naperville</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>60540.0</td>\n",
       "      <td>Central</td>\n",
       "      <td>TEC-PH-10004093</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Phones</td>\n",
       "      <td>Panasonic Kx-TS550</td>\n",
       "      <td>147.168</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>CA-2018-139619</td>\n",
       "      <td>2018-09-19</td>\n",
       "      <td>2018-09-23</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>ES-14080</td>\n",
       "      <td>Erin Smith</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>United States</td>\n",
       "      <td>Melbourne</td>\n",
       "      <td>Florida</td>\n",
       "      <td>32935.0</td>\n",
       "      <td>South</td>\n",
       "      <td>OFF-ST-10003282</td>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>Storage</td>\n",
       "      <td>Advantus 10-Drawer Portable Organizer, Chrome ...</td>\n",
       "      <td>95.616</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
       "12  CA-2018-114412 2018-04-15 2018-04-20  Standard Class    AA-10480   \n",
       "23  US-2018-156909 2018-07-16 2018-07-18    Second Class    SF-20065   \n",
       "34  CA-2018-107727 2018-10-19 2018-10-23    Second Class    MA-17560   \n",
       "41  CA-2018-120999 2018-09-10 2018-09-15  Standard Class    LC-16930   \n",
       "43  CA-2018-139619 2018-09-19 2018-09-23  Standard Class    ES-14080   \n",
       "\n",
       "      Customer Name      Segment        Country          City           State  \\\n",
       "12     Andrew Allen     Consumer  United States       Concord  North Carolina   \n",
       "23  Sandra Flanagan     Consumer  United States  Philadelphia    Pennsylvania   \n",
       "34     Matt Abelman  Home Office  United States       Houston           Texas   \n",
       "41   Linda Cazamias    Corporate  United States    Naperville        Illinois   \n",
       "43       Erin Smith    Corporate  United States     Melbourne         Florida   \n",
       "\n",
       "   Postal Code   Region       Product ID         Category Sub-Category  \\\n",
       "12     28027.0    South  OFF-PA-10002365  Office Supplies        Paper   \n",
       "23     19140.0     East  FUR-CH-10002774        Furniture       Chairs   \n",
       "34     77095.0  Central  OFF-PA-10000249  Office Supplies        Paper   \n",
       "41     60540.0  Central  TEC-PH-10004093       Technology       Phones   \n",
       "43     32935.0    South  OFF-ST-10003282  Office Supplies      Storage   \n",
       "\n",
       "                                         Product Name    Sales Order Year  \n",
       "12                                         Xerox 1967   15.552       2018  \n",
       "23                 Global Deluxe Stacking Chair, Gray   71.372       2018  \n",
       "34                                  Easy-staple paper   29.472       2018  \n",
       "41                                 Panasonic Kx-TS550  147.168       2018  \n",
       "43  Advantus 10-Drawer Portable Organizer, Chrome ...   95.616       2018  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sales_2018 = train[train['Order Year'] == '2018']\n",
    "sales_2018.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e7890bc4",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\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>Order Date</th>\n",
       "      <th>Ship Date</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Postal Code</th>\n",
       "      <th>Region</th>\n",
       "      <th>Product ID</th>\n",
       "      <th>Category</th>\n",
       "      <th>Sub-Category</th>\n",
       "      <th>Product Name</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Order Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8153</th>\n",
       "      <td>CA-2018-140151</td>\n",
       "      <td>2018-03-23</td>\n",
       "      <td>2018-03-25</td>\n",
       "      <td>First Class</td>\n",
       "      <td>RB-19360</td>\n",
       "      <td>Raymond Buch</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>98115.0</td>\n",
       "      <td>West</td>\n",
       "      <td>TEC-CO-10004722</td>\n",
       "      <td>Technology</td>\n",
       "      <td>Copiers</td>\n",
       "      <td>Canon imageCLASS 2200 Advanced Copier</td>\n",
       "      <td>13999.96</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Order ID Order Date  Ship Date    Ship Mode Customer ID  \\\n",
       "8153  CA-2018-140151 2018-03-23 2018-03-25  First Class    RB-19360   \n",
       "\n",
       "     Customer Name   Segment        Country     City       State Postal Code  \\\n",
       "8153  Raymond Buch  Consumer  United States  Seattle  Washington     98115.0   \n",
       "\n",
       "     Region       Product ID    Category Sub-Category  \\\n",
       "8153   West  TEC-CO-10004722  Technology      Copiers   \n",
       "\n",
       "                               Product Name     Sales Order Year  \n",
       "8153  Canon imageCLASS 2200 Advanced Copier  13999.96       2018  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product = sales_2018.groupby('Product Name', as_index = False).agg({'Sales':'sum'}) # create series for prod name and sales\n",
    "p = sales_2018.sort_values('Sales', ascending = False) #sort the Sales column\n",
    "p.head(1) # shows first row"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f4b2570",
   "metadata": {},
   "source": [
    " #### Q2 Ans. The product that sold the most in 2018 is Canon imageCLASS 2200 Advanced Copier with 14000 sales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8eb7222d",
   "metadata": {},
   "source": [
    "# -"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67db69f5",
   "metadata": {},
   "source": [
    "### Q3. Among the three product categories, which one had the lowest sale in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "eb28b3a2",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Furniture</th>\n",
       "      <td>212313.7872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Office Supplies</th>\n",
       "      <td>240367.5410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Technology</th>\n",
       "      <td>269370.6910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       Sales\n",
       "Category                    \n",
       "Furniture        212313.7872\n",
       "Office Supplies  240367.5410\n",
       "Technology       269370.6910"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psales = sales_2018.groupby('Category', as_index = True).agg({'Sales':'sum'})\n",
    "psales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ee0ab11",
   "metadata": {},
   "source": [
    "# -"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f190a45",
   "metadata": {},
   "source": [
    "### Q4. What is the yearly sales trend of the three groups?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97b26574",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Year</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>262956.8006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>265356.2933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>291142.9652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>328604.4719</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Year        Sales\n",
       "0       2015  262956.8006\n",
       "1       2016  265356.2933\n",
       "2       2017  291142.9652\n",
       "3       2018  328604.4719"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# convert the segment into consumer\n",
    "cons_segment = train[train['Segment'] == 'Consumer']\n",
    "consumer = cons_segment.groupby('Order Year', as_index = False).agg({'Sales':'sum'})\n",
    "consumer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d7cb69fc",
   "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 Year</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>89101.9118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>74404.1132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>104072.2670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>157403.8849</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Year        Sales\n",
       "0       2015   89101.9118\n",
       "1       2016   74404.1132\n",
       "2       2017  104072.2670\n",
       "3       2018  157403.8849"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "home_segment = train[train['Segment'] == 'Home Office']\n",
    "home = home_segment.groupby('Order Year', as_index = False).agg({'Sales':'sum'})\n",
    "home"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "844b9f02",
   "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 Year</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>127797.4957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>119675.5989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>204977.3178</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>236043.6624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Order Year        Sales\n",
       "0       2015  127797.4957\n",
       "1       2016  119675.5989\n",
       "2       2017  204977.3178\n",
       "3       2018  236043.6624"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corp_segment = train[train['Segment'] == 'Corporate']\n",
    "corporate = corp_segment.groupby('Order Year', as_index = False).agg({'Sales':'sum'})\n",
    "corporate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46fd3445",
   "metadata": {},
   "source": [
    "# -"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d04459f",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38519a27",
   "metadata": {},
   "source": [
    "### Q5. Are we getting more complex customers?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad6adbf2",
   "metadata": {},
   "source": [
    "in this section we need to import matplotlib and seaborn for better understanding of the data using visualization."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db8420bf",
   "metadata": {},
   "source": [
    "#### We will use Line Chart in this section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8092a09f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='Order Year', y='Sales', data = y_sales, marker= 'o')\n",
    "plt.xlabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3046ff40",
   "metadata": {},
   "source": [
    "#### Q5 Ans. We can say that the number of corporate customers is increasing yearly"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbca76b4",
   "metadata": {},
   "source": [
    "### Which products sold the most in 2018?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63d22654",
   "metadata": {},
   "source": [
    "#### We will use Horizontal Barchart in this section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b9d63d5c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jOCBJa5Fm3DcFxis9wvL7efFZQDdyaoekW3LbbwKnA2Pz67RcBvDzvC+eI11Q/LWsva6kWfXS7DsR8T7wICk//2jgx5LGkoL0ouGktJR78hdzIeWMHyTpEVLOeM1Z6rzdAOC8vL/uJt35+QvwRN4Hk0kXMJ1JOfzPklKsLqL6xeJFpIuYsXn7+0nfs/iAdPFxfT4Oc4GLC9stLenRPO5jK9Q7EDgs93UKsHet8ZmZ2cKj9P+5mdWzHJzOioiQtB+wf0Q44LKacvpYQ0S80V5t9OjRIwYNGtRe1ZuZLZIGD27OMy6qkzQuIhqaWs858maLhz7A+fkpO28Dh3Zsd8zMzKy9OZA3Wwzk5787d9laJH8/wMzM6pRz5M3MzMzM6pADeTMzMzOzOuRA3szMzMysDvmpNWZm1m4aGhqisbGxo7thZlZXmvvUGs/Im5mZmZnVIQfyZmZmZmZ1yIG8mZmZmVkdciBvZmZmZlaH/AehzMys3UyfPp0hQ4Z0dDfMFjuDBw/u6C7YIsAz8mZmZmZmdciBvJmZmZlZHXIgb2ZmZmZWhxzIm5mZmZnVIQfyZmZmZmZ1yIG8mZmZmVkdciBvZmZmZlaHHMiblZG0uqRhkp6X9ISkOyRt2MQ20yStXKH8CEkH5vcbS5og6TFJ6zWzLxvm9p+T9KSkv0tarYlt3mtO3YX1z5X0y8LnkyRdUGUsk/IYHpS0aS7vKWlWHteTksZIOqhKW/0lzSisW/FByJIaJJ3bRL+7S/pRS8ZaoY6b8niey/2akF/bF9bpIemGZtTV0v0+3zmT981tLRtB1bovl7RvlfKpeYzjJW1XZfvTJO3aRBv9i/vJzMwWPv9BKLMCSQJuAq6IiP1yWS9gNeCZltYXERcXPn4T+GdENOuveEjqAtwO/DQibs1lOwOrAK+2tC81nAxMkHQ1EMD3gd4V1rumNB5J3wD+COyRlz0fEb3zsnWBGyUtERGXVahnVETsJWnZ3O5tETGutFBS54hoBBqb6Hd34EfAhc0daD6+ioi5ABGxTy7vDxwXEXuVrd85IqYDCwTFdez4iLhB0u7An4EtiwsldYqIU5pRT3/gPeDhtu+imZk1h2fkzea3M/BRMQCPiAkRMap8xlTS+ZIOLmx7fJ6NHiNp/bzOqZKOk/Q14Bjg+5JGSLpK0t6Fuq7OwXHRd4HRpSA+92VEREyWdLCk8wvb35aD0dLnP+QZ13slrZLL1pP0L0njJI2StHGu8x3gJOB84ALglIh4u3zH5PVKliUF/QuIiBeAnwJHVVpeWO99YBywXt5PQyUNB64s7uu87FJJIyW9IKlU7xl52wmSzsrrHi9prKSJkobksp559v9CYDywdq1+5X17vaRbgeF5+8mFZf/M+/HpGncUFuhHS0haNo95bL57sXdhLKPysR1fmhFXcr7SHaTbgVWb0cwDQOk8nSbpFEkPAt8pzujnZUNye5OU7iz1BI4Ajs37v19Lx2hmZp+eA3mz+W1OCi5b452I6EsKiM8pLoiIO4CLgbMjYmfgL8AhAJJWALYH7mijviwLjI+IrYH7gVKwORQ4MiL6AMdRmMmOiGuBzwHLR8RV1SqW9GNJzwO/o3agPh7YuFYnJa0EbAtMyUV9gL0j4rsVVt8Y+ArQFxgsaUngRNKdgF4RcXyeYd4gr9ML6CNpp7z9RsCVEdE7Il6s1a9sO+CgiNilwrK+wMDcxnckNZSNq1Y/yo3IgfAE0jlRchJwX0RsQ7q4PCvfwXgN2C0f2wFAKf1onzzGLYAfkM6npnwdmFT4/EFE7BgRwyqs+0Zu8yLSnYtpzDufe0XEqLJ9cLikRkmNM2fObEZXzMysNRzIm7Wdaws/K+Yel0TE/cD6klYF9gf+EREft1E/5gLX5fd/A3aU1I0U3F2fg8Y/A2uUNpC0FrA60COvW63fF0TEesDPSSk51ajGsn6SHgOGA2dERCmQvyUiZlXZ5vaImB0Rb5CC2UrfE9g9vx5j3oXEBnnZixHxSI0+lbs7It6ssex/ua83Aju2oB/lds6BcC9SSlOxjhPzsRoJdAE+DywJXCJpEnA9sGlefyfg2oiYk1OB7qsxtrNyvYcDhxXKr6u8OpDGCenCsmeN9QCIiKER0RARDV27dm1qdTMzayXnyJvNbwrV86E/Zv6L3y5ly6PK+2quIs3s7gccWqUvX2plX8r7tQTwdg4YK/kTcCqwCWkG//ga9QEMI83OVtMbeLLKslHluejZ+zXqm114P4fK/3YJ+G1E/Hm+wpQGUqvuSmqtX35syz9X7EcLCfh2RDw9X6F0Kun7EVuRjukHNfpRzfERUenLu83Z/9X2vZmZdQDPyJvN7z5gaUk/KBVI2kbSl4AXgU0lLZ3TYb5ctu2Aws/RzWjrclLePIVZ6aJrgO0l7Vnoyx6StgCmAb0kLSFpbVIaR8kSzLsY+S7wYM5vnyrpO7keSdoqv/8qKaf6SuB0YB/lJ9IUSSrOKu8JPFtpUDlw/j1wXo2xt4V3geUKn+8CDi3dUZC0Zr7j0dZ2k7SipGVIX2B+qGx5W/TjLuBIScp1lL58vALwSv6y7veATrn8AWA/SZ0krUFKx2lv5fvfzMwWMs+smBVEREjaBzhH0omkGc9pwDER8ZKkvwMTSUHsY2WbLy3pUVIgvX8z2npV0pPAzVWWz5K0V+7LOcBHue2jgcnAVFKO82RSCkfJ+8BmksYBM5h3gTEQuEjSyaQUjWGSnibl8+8bEQG8L+kEUp5/eX74T5QeSfgR8BZQfMTkejldpgspwDuvyhNr2kxE/E/SQ/mLqHfmPPlNgNE5/n0POIA0i9yWHiTdTVmf9CSf+Z6uExHDq/TjtRa0cTrpuEzMwfw0YC/S9xr+kS/IRjBvFv0m0vGaRHq60v2tGVgL3QrckL+Ie2R5nryZmbU/pf+7zWxhk9SVFHhtHREzOro/1jSlpxQ1RMRPOrov9aJHjx4xaNCgju6G2WJn8OBmPcnY6pSkcRHR0NR6Tq0x6wB5Zvsp0sy1g3gzMzNrMafWmHWAiLiH9BQSqyMRcTnpuw1mZmYdzjPyZmZmZmZ1yIG8mZmZmVkdciBvZmZmZlaH/NQaMzNrNw0NDdHY2Nj0imZm9gk/tcbMzMzMbDHmQN7MzMzMrA45kDczMzMzq0MO5M3MzMzM6pD/IJSZmbWb6dOnM2TIkI7uhtW5wYMHd3QXzBZJnpE3MzMzM6tDDuTNzMzMzOqQA3kzMzMzszrkQN7MzMzMrA45kDczMzMzq0MO5M3MzMzM6pADeTMzMzOzOuRA3hY7klaTdI2kFySNkzRa0j55WX9JMyRNkDRR0j2SVi1se6CkyZKmSHpC0nEV6j9V0n9yHc9KulHSpu0wjmmSJuV2Jknau7Ds4RbW1V/SbVXKZ0h6TNKTkgaXLf+8pPeK+0FSn9yf5ySdK0m5fGlJ1+XyRyX1rNKXOXlMpdeJuXykpIaWjCtv10vS12osb5B0bgvqGynp6dy3JyUdXlh2h6TuLairp6TJrelHE/VulPtZ6uPQVtTRU9J326I/ZmbWMRzI22IlB5U3Aw9ExLoR0QfYD1irsNqoiOgVEVsCY4Ef522/ChwD7B4RmwFbAzOqNHV2rmMD4DrgPkmrtMOQdo6IXsC+wCdBYERs34ZtjIqI3kADcICkPoVlZwN3lq1/EXA4sEF+7ZHLDwPeioj183ZnVmlvVt53pdcZn7L/vYCKgbykzhHRGBFHtbDOgXm/7wCcKWkpgIj4WkS83ZpOtrIf1ZzLvHNwE+C8VtTRE3Agb2ZWxxzI2+JmF+DDiLi4VBARL0bEAoFODvqXA97KRf8HHBcR0/N2H0TEJU01GBHXAcPJQZGkL+cZ7kmSLpW0dC7fRtLDkh6XNEbScpIOlnR+oU+3SepfoZnlC/1E0nv5Z/88M3uDpKckXV2YId8jlz0IfKsZ43gfGAesl7f/JvACMKXQ7hrA8hExOiICuBL4Zl68N3BFfn8D8OVSX1pK0u75Tsp4SddL6pbLy/fhCsBpwIA8Oz0g3zEZKmk4cGXxboSkbpIuy8dmoqRvN9GVbsD7wJy8/TRJK+fZ7CclXaJ092a4pGXyOn1y/0aTLxJzebEfp+ZzY6TSnaOjCuv9Mh+3uyVdqwp3hYA1gJdLHyJiUt52lKRehboekrSlpC8V7oA8Jmk54AygXy47VlInSWdJGpv3zaBCv++X9HdJz0g6Q9LAvP8nSVqvOcfUzMzangN5W9xsBoxvYp1+kiYA/wZ2BS7N5ZuTAtnWGA9sLKkLcDkwICK2ADoDP8wzutcBR0fEVrndWc2od0ROzbgfOLnKOr1JdxI2BdYFdsj9uAT4OtAPWL2phiStBGwLTJG0LPBzYEjZamtSCCDz+zULy14CiIiPSXczVqrQ1DKaP7VmQFk/Vs5j3TUitgYagZ9W2YfvA6cA1+XZ6etyNX2AvSOifMb5l8CMiNgi35G5r8ruuFrSROBp4PSImFNhnQ2AC/Ldm7eB0kXBZcBREbFdlbpLNga+AvQFBktaUim16NukY/ot0l2SSs4m3QW6Mwfh3XP5X4CDASRtCCwdEROB44Af57sM/Ujn3onMuzt1NumOyoyI2AbYBviBpHVyvVsBRwNbAN8DNoyIvrm9I8s7J+lwSY2SGmfOnNnEbjAzs9ZyIG+LNUkX5NnRsYXiUvCyNino+l1bNJV/bgRMjYhn8ucrgJ1y+SsRMRYgIt7JwW5Tdo6IzUkB1PmlmekyYyLi5YiYC0wgpUxsnPvxbJ45/1uNNvpJeox0V+GMiJhCCuDPjoj3qoyzKJqxrKg8tea6suXbki5KHsoXXAcBX6Bl+/CWiKh0obQrcMEnnYt4q8I6kFJrtgQ+Dxwn6QsV1pkaERPy+3FAz3yHoHtE3J/Lr6pSP8DtETE7It4AXgNWA3YE/hkRsyLiXeDWShtGxGXAJsD1QH/gEaU7P9cDe0laEjiUdFEJ8BDwxzzz373KftsdODDv80dJF2Eb5GVjI+KViJgNPE86VwAmkc638v4NjYiGiGjo2rVrjV1gZmafRueO7oBZG5vCvJlRIuLHeYa3scr6twD/KGzbh+qztLX0zm1USyURlYPaj5n/grpLpY0j4nlJr5IC3DFli2cX3s9h3u91pfYqGRURe5WVfRHYV9LvgO7AXEkfkPZV8fsGawHT8/uXgbWBlyV1BlYA3mxmH4oE3B0R+89XKG1J88f0fo26m1sHEfG6pPGk/fFi2eLy/b5MC+uvdNyanYqUU8AuBS7Nd202j4hxku4mpTn9P/KMfkScIel20ncJHpG0a4UqBRwZEXfNV5hSvYp9nVv4PBf/P2Jm1mE8I2+Lm/uALpJ+WCirNSW4I2mGEeC3wO8krQ6fPIWlyS8n5jzr3YFrgadIM7Pr58XfI6XFPAX0kLRN3ma5HOxOA3pJWkLS2qQ0i0ptrAqsw4LBZDVPAesU8pf3r7VyuYjoFxE9I6IncA7wm4g4PyJeAd6VtK0kAQcC/8yb3UKaPYf05dz78t2AlnqElB60PoCkrjlNpNo+fJf0XYfmGA78pPRB0udqrSypK+ki7fla65XkL8LOkLRjLhrYzH6VPAh8XVKXfPdlzyr92iPPupPP15WA/+TFfyF9GXZsRLyZ11kvIiZFxJmkC86NWXC/3UVKAyvVu2FOsTIzs0WUZ1JssRIRofQlzbMlnQC8Tpqd/XlhtVKOvEh53N/P294haTXgnhykBvPy58sdK+kAYFlgMrBLRLwOIOkQ4PocZI4FLo6ID3Mu+HlKX4qcRUrzeAiYSkpRmMyC+f0jJM0BlgROjIhXm7kfPlB6bOLtkt4gBYibN2fbZvghKWVjGdITbUpPtfkrcJWk50gz8ftV2X6ZvP9L/hURJxb6/rqkg4Frc7oIwMkR8UyVfTgCODHX+dsm+v4r4II8gz2HlEJ0Y4X1rpY0C1gauDwiWvLdiUNIs+QzScFxs0XEWEm3AI+TLtoaqfzkpN2BP+W7JADHR8R/cx3jJL1DShsrOUbSzqQxP0E6ZnOBjyU9TjqefyKlyYzP5//rzPsis5mZLYLUugkzMzNrD5K6RcR7+W7AA8DhEdHUF7iL2/cARgIb5+9NdKgePXrEoEGDOrobVucGDx7c9EpmixFJ4yKiyb+t4tQaM7NFy9B8d2E88I8WBvEHkr6oetKiEMSbmVn7cmqNmdkipMIjM1uy7ZWkZ/ubmdlngGfkzczMzMzqkAN5MzMzM7M65EDezMzMzKwO+ak1ZmbWbhoaGqKxsdrfYzMzs0r81BozMzMzs8WYA3kzMzMzszrkQN7MzMzMrA45kDczMzMzq0MO5M3MzMzM6pD/squZmbWb6dOnM2TIkI7uhtWJwYMHd3QXzOqKZ+TNzMzMzOqQA3kzMzMzszrkQN7MzMzMrA45kDczMzMzq0MO5M3MzMzM6pADeTMzMzOzOuRA3lpNUhdJYyQ9LmmKpCGFZZdLmpqXPSPpSklrVqlnL0mP5XWfkDSoFX3pL2n7TzOeVrT5VUmNkp6U9JSk3zexfn9Jt32K9kr7dIKk8ZK2q7LeaZJ2bUZf2mx/SVpb0oi8L6ZIOroVdYyU1FChvLOk30h6No99gqST2qbnLSepp6TvFj43SDq3HdoZKenfklQou1nSe23dVo0+zDdWMzNbtDiQt09jNrBLRGwF9AL2kLRtYfnxedlGwGPACElLFSuQtCQwFPh6Xrc3MLIVfekPLLRAXtLmwPnAARGxCbA58MJCaPr4iOgFnAj8uUK/OkXEKRFxTxP19KeF+0tSrb878THws7wvtgV+LGnTltRfw6+AHsAWeez9gCXbqO6KmhhrT+CT4DYiGiPiqHbqytvADrlP3YE12qmdanpSGKuZmS1aHMhbq0VSmh1cMr+iynpnA/8Fvlq2eDnSHyb7X153dkQ8LWm5PPu8JICk5SVNk7SkpKPyzP1EScMk9QSOAI7Ns7X9JK0i6R+SxuZXKRg6VdIVkobn+r4l6XeSJkn6V6G9MwptVJppPwH4dUQ8lfv9cURcmLe9XNK+pRXLZlCXl3RTrvtiSUvkdXaXNDrPtF8vqVsTu/8BYP287TRJp0h6EPhOsf28bEiud5KkjVuxv4ZKGg5cWa0zEfFKRIzP798FngTWzHWMlHSm0t2bZyT1y+XL5OM3UdJ1wDLl9UrqCvwAODIiPijVHxGnFtY5INc9QdKfJXUq7XdJv1a60/OIpNVyebPGmmejR+V9N17z7mCcAfTL7R2rwp0WSSvmWfOJuc0tC3VfmvfFC5KaG/gPA/bL778F3FgYdzdJ9xaO7d6FZb9Uukt0t6RrJR2Xy9fL5/m4PLaNc/nlks6V9HDuX+n8LR9rtX1iZmYdwIG8fSqSOkmaALwG3B0Rj9ZYfTywcbEgIt4EbgFezAHHQElL5GBwJLBnXnU/4B8R8RFpNrp3RGwJHBER04CLgbMjoldEjAL+lD9vA3wb+Euh2fVyvXsDfwNGRMQWwCxgT0krAvsAm+U2flVhLJsD45reQwvoC/wM2CL341uSVgZOBnaNiK2BRuCnTdTzdWBS4fMHEbFjRAyrsO4bud6LgONasb/6AHtHRLNmZvOFQm+geC50joi+wDFA6U83/hCYmffxr3M75dYH/p3Ph0ptbQIMAHbIs/VzgIF58bLAI/lOzwOkCwJaMNbXgN3yvhsAlNJnTgRG5X13dlmXhgCP5TH9gvkvfjYGvkI6BwaXLhqbcC+wU7442Q+4rrDsA2Cf3L+dgT8oacjj6k0K/ovpSkNJF0V9gOOACwvL1gB2BPYiBfCVxlptn8xH0uFKaWeNM2fObMYwzcysNWrdPjZrUkTMAXop3fa/SdLmETG5yuqqVBgR35e0BbArKbjYDTiYFGCdANwMHMK8QGwicLWkm/OySnYFNtW89OLlJS2X398ZER9JmgR0Av6VyyeRUgluIwVJf5F0e/7cVsZExAsAkq4lBU4fAJsCD+X+LgWMrrL9WZJOBl4HDiuUX1dlfZg3izuOFNhVUmt/3RIRs2rU/wmlOwn/AI6JiHeq9KFnfr8TORCMiImSJjaj/kOAo4GVSKlBXyYF32Nz35chBZsAHzLv2I0jnVctGeuSwPmSepEuEDZsqn+k4/ntPKb7JK0kaYW87PaImA3MlvQasBrwchP1zQEeJAXNy0TEtEK/BfxG0k7AXNIdkNVyH/5ZGoekW/PPbqR9dn2hjqULbd0cEXOBJ0p3Lypo1j6JiKGkiwZ69OixwF06MzNrGw7krU1ExNuSRgJ7ANUC+d6kGcZK208CJkm6CpgKHBwRD+Vb+V8COhUuEPYkBYHfAH4pabMKVS4BbFcegOYAZnZuc66kjyKiFGjMJc0cfyypLylI3A/4CbBLWf1TSAHk4xXa/ji3j1KDxe8FlAc1QQrI7o6I/SvUVe74iLihQvn7NbaZnX/OofrvfK39Vavu4rpLkoL4qyPixrLF1frQVJD3HPB5ScvllJrLgMskTSZdhAm4IiL+r8K2xWNbbLe5Yz0WeBXYKm/zQRN9hcoXq6U+zC6U1ToW5YYBNwGnlpUPBFYB+uQL02lAlyp9gDSGt/Odi0qK/atWR2v2iZmZtROn1lir5Vzj7vn9MqSZzqcqrKecE7wG82a/S8u6SepfKOoFvFj4fCVwLXBZXn8JYO2IGEGare8OdAPeJeXblwwnBeCldnq1YFzdgBUi4g5SKkilbc8CfiFpw1K/JJXSYaYxL01kb+b/YmZfSevkcQwgzbY+AuwgqZTz3rVUbztq1f6StKakBS7G8gXLX4EnI+KPzezDA+Q0GKUvD29ZvkJEzMz1ni+pS163E/Muju4F9pW0al62oqQvNNFuc8+NFYBX8iz190gXDrDgvqs2pv6ktKZ3qqxbav9eVXmiUzYK+C3p96C8f6/lIH5noDTuB4GvKz1Vqhs5PS33Y6qk7+R2JWmrWn1jwbFW2ydmZtYBHMjbp7EG6Uk0E4GxpFnlYhrKWZIeB54BtgF2jogPy+oQcIKkp3Ou/RBSWk3J1cDnmBfEdAL+ltNiHiPlOr8N3Arsk7+U1w84CmhQ+tLhE6QvdzbXcsBteVz3k2Yh5xMRE0lB/rWSniTdhSg9UeQS4EuSxgBfZP5Z3tGk/OPJpDsPN0XE63nM1+Y2H6HsuwTtoLX7aw3SHYdyO5ACu1007xGRX2uiDxcB3fKYTwDGVFnvJOAVYLKkx0iB7RXA9Ih4gvT9guG5nrtp+skuzR3rhcBBkh4hpZCUjuNE4GOlL9GWnxunluomHeeDanUkX9CtD7xZbZ38ZfHfR8QbZYuuzm01ki4eSl+8Hkv63snjpJSmRmBG3mYgcFj+vZxCutCspXys1faJmZl1AM2782y26FF6esbeEfG9ju6LgaSfkL58ektH92VxkO9EHBoRTX25uaX1douI95Se+vMAcHjpqUILW48ePWLQoBb/aQj7jBo8eHDTK5l9BkgaFxEL/G2Vcs6Rt0WWpPNIj6tsambXFpKIOL+j+7A4yd/7aNMgPhuq9Bz/LqTvEHRIEG9mZu3LgbwtsiLiyI7ug1k9au6jQs3MrL45R97MzMzMrA45kDczMzMzq0MO5M3MzMzM6pCfWmNmZu2moaEhGhsbO7obZmZ1pblPrfGMvJmZmZlZHXIgb2ZmZmZWhxzIm5mZmZnVIQfyZmZmZmZ1yIG8mZmZmVkd8l92NTOzdjN9+nSGDBnS0d2wRdjgwYM7ugtmdcsz8mZmZmZmdciBvJmZmZlZHXIgb2ZmZmZWhxzIm5mZmZnVIQfyZmZmZmZ1yIG8mZmZmVkdciBvZmZmZlaHmgzkJc2RNKHw6llj3csl7Zvfj5TU0IZ9bTZJ/SXNkPSYpCclDS4rnyBpoqR7JK1aYdkESacU6ttD0tOSnpN0YqF8RUl3S3o2//xchb70lDSrbB8emJdNk7RyK8e3fY3l3yj2sxn1TZM0SdLjkoZLWr2lfWpG/TXH2cQxu60VbXaX9KPC5x6SbmhhHcdJekrS5LxvDmxpPyrU2azx5N+fp/P58qSkwwvL7pDUvQVt9pQ0Ob9vkHRuqzq/YL2nSgpJ6xfKjs1lLf7dl3SwpPOrLGvRmFuruH/Kf8+K/741UUdIuqrwubOk11tzHrdWtX2Zy1/P59UUSTdI6pqXHdHSc7wj/503M7PmzcjPiohehde09u5UGxkVEb2BBuAASX0K5b0iYktgLPDjsm1K4zwNQFIn4ALgq8CmwP6SNs3rnwjcGxEbAPfmz5U8X7YPr/yUY+sPVAzkJXWOiFsi4owW1rlzRGwFNAK/+JT9a61qx6w1ugOfBPIRMT0imgzCSiQdAewG9I2IzYGdAH2K/rTGwIjoBewAnClpKYCI+FpEvN2aCiOiMSKOarsuMgnYr/B5X+CJNqwf+HRjbmE7xf3Tnyq/Z014H9hc0jL5827Af9qge23luvzv0GbAh8AAgIi4uA3+bTIzs4WoVak1kvpIul/SOEl3SVqjifX3zzO+kyWdmcv+n6Q/5vdHS3ohv19P0oO12pF0lKQnlGbVh9VqOyLeB8YB65X1ScBywFtNDLcv8FxEvBARHwLDgL3zsr2BK/L7K4BvNlFXVZIOkDQmz5T9OV9AlO4GjM8zwvcq3RE5Ajg2r9svzxT+UdIIUsD3yWycpNUk3ZS3f1w1ZvKzB4D1i7O4uZ7jJJ2a34+UdGbu7zOS+uXyTpJ+n4/1RElHFuo9Mo9jkqSNa3WgxjHrK+lhpVn7hyVtlMs3K+y7iZI2AM4A1stlZ2n+Wela/Sz5BfCjiHgn92lGRFyRt/9y7sMkSZdKWjqXT5P0G0mjJTVK2jqft8/nC4OS5fMxeULSxZKa+j3sRgoO5xTaWTmP6UlJl+TZ1eGl4DH/7jwuaTSFi1UV7ggozahfmo/nC5KOKqz3S6W7EXdLulbScVX6djP590HSusAM4PVCPRflfTFF0pBC+Tb5GD6ej91yeVEPSf9Susv1u8L6zRnzennbcZJGVTrP8jHrruR/mnd37CpJu5b2jyr8nuUqdsr9fkG1Z+fvBPbM7/cHri30odp5fLCkG6uM/73C+30lXZ7ff13So7mueyStVqNP5fuiM7As+d/AfD4cl99X+x1fRtKw/HtzHbBM1QbMzKzdNSeQX0bzUkJukrQkcB6wb0T0AS4Ffl1tY0k9gDOBXYBewDaSvkkKGEv/OfYD/idpTWBHYFQT7ZwI9M6z6sUAqVL7KwHbAlNKbUmaAPwb2DXXW7JdDizulLRZLlsTeKmwzsu5DGC1iHgFIP9ctUo31tP8qTX9igslbUKaFdshz8DOAQZKWgW4BPh2ni3/Tr4jcjFwdp5VG5Wr2RDYNSJ+Vtb2ucD9efutC/uhmr1Is6xN6RwRfYFjgNLf1z4cWId5x+bqwvpvRMTWwEVAtaAQqHjMSp4Cdsqz9qcAv8nlRwB/yvuugXSMTmTenZDjy+qp1U9yULlcRDxfoW9dgMuBARGxBdAZ+GFhlZciYjtgVF5v3zyW0wrr9AV+BmxBulj5VpVdcbWkicDTwOkRMafCOhsAF+TZ1beBb+fyy4Cjcl9q2Rj4Su7TYElLKqVKfBvonftWK3XiHeAlSZuTAtbrypafFBENwJbAlyRtqXRn4Trg6Hxe7grMyuv3Iv0ubAEMkLR2C8Y8FDgy/3txHHBhhW0fIt3h2Ax4gXn/Bm0LPFJaqcbv2Rqkf6P2Il0sVjMM2C+fL1sCjxaWVTuPmzv+ogeBbXNdw4ATmlifXO8E0l2CFYFbq6xX6Xf8h8DM/Hvza6DiXTNJh+cLuMaZM2c2o0tmZtYanZuxzqwcIAGQ/8PeHLhbEkAn4JUa228DjIyI1/P2V5P+E7tZUrccNK0NXENKX+gH3AhsVKOdiaQg52bSjGAl/SQ9BswFzoiIKZL6k9I39sp9+TnwO1IgOB74QkS8J+lrud4NqJxOETXGW8nzxX1YwZdJ/yGOzWNdBniNFFw8EBFTASLizRp1XF8l0NsFODBvP4c0Y1rJCElzSPv2ZFJqSi035p/jgJ75/a7AxRHxcYX+FtevFrhWO2YlKwBXKM24B7BkLh8NnCRpLeDGiHg278dqavUT0jGvdow3AqZGxDP58xWkGe9z8udb8s9JQLeIeBd4V9IHmpfjPSYiSnegriUFhpXy9wdGRGO+oHtY0r8i4sWydaZGxIT8fhzQU9IKQPeIuD+XX0VKDavk9oiYDcyW9BqwWu7PPyNiVu5jtUCvZBgpveYrpHP5kMKy/6eU39+ZFARvStq3r0TEWIDSXY98zO6NiBn58xPAF5j/QrramLuR0mCuLxz7pSv0dRTp35kXSReVh+cJhDfz734TQ+XmiJgLPFFr9jsiJuZZ/f2BO8oWVzuPoXnjL1oLuE7pbuVSwNSmBkBKrfmJ0mAvAI6n8kVJpd/xnUiTA6UxTqzUQEQMJV1Y0aNHj5b+e2lmZs3UmtQaAVMK+d5bRMTuTaxfzWjSf/pPk/6D7QdsR5o1q9XOnqT/gPoA4/It4nKjIqJ3RPSJiIurtH8L6T8mIuKdiHgvv78DWFLpC5ovky40StYCpuf3r2peus8apOC7NQRcURjrRhFxKrUDynLvt7Ltkp1z2wfmXOSPmf/86FK2/uz8cw7zLghr9bfS+uWaOmanAyNyzvrXS32KiGuAb5Bmde+StEuV+ktq7tccWL6vlCpSadtaSuOcW3hf+lwad3nbNY9xvggeD3yxRnswb9+25Lyptn1L3Ap8D/h3KSgHkLQOaWb8y3kG93bSMWvOeVLsT3PWWQJ4O+b/LsomFbYt3QnsB4wkpQHtS/r3pzmKbTe1n24Bfk8hrSareB5XqL84/uL+Kq5/HnB+vjs0iAV/T6uKiCAdu52qrFLtd9aBuZnZIqI1gfzTwCqStgPIt+I3q7H+o6Rb6isr5X3vD5RmCh8g/Uf/APAYsDMwO89IVWxHKZ947YgYQbqN3J2UQ9waOwLP5/pXzzNUSOpL2jf/I30hdgNJ6+SUgP2YN+t6C3BQfn8Q8M9W9uNeYF/Ne4LOipK+QLrQ+VIOiJC0Yl7/XVJ+f3Pr/mHevpOk5Zu53avAqpJWUsoB36sZ2wwHjihdWBX621ZWYN6XBg8uFeaA+4WIOJd0TLak9j5qTj9/C1xQ2l+Sls8zy0+RZoBLT2r5HvPO5+bqm8+nJUhpFA/WWlnpqSK9yedqU/KF2AxJO+aigS3s34PA1yV1yTPde9ZaOc/c/5wFU+yWJ11gzsiz16W7Ak+RcuG3gZTKVOVivNnyBcRUSd/JdUrSVhXWewlYGdgg3xV5kPRvUKVAviW/Z5VcCpwWEeWpahXP4ya8KmmTfM7sU6WugxbcrEmf/BvYTA+Qz6d8d3bLVrRpZmZtpMWBfKQvfO5L+lLl48AEajzZIeeO/x8wAngcGB8RpYB3FGm2+4Gc9vESOaip0U4n4G+SJpGC/7OjZU+z6KeUp/44KQgr5ZTvC0zO5ecC+0XyMfAT4C7gSeDvEVHK3T4D2E3Ss6QnU1TLmS3PkZ/vqSER8QQpnWV4vlV9N7BGnok9HLgx96uUf3wrsI8q5NtXcDSwc95f40i5wU2KiI9Ied2PAreRgq+m/IX03YOJub/fbU5bLfA74LeSHiKdByUDSMduAinn+8qI+B/wkNIXrM9qRT8vIp2zY5W+JHs/KTf4A9JdpOvzPp1LyqVuidGkc2UyKRXipirrXZ3HNA64PCLGtaCNQ0gXIqOZl3/eLDnl5RbS7+uNpCcZVUvJKm0zLCLGl5U9TvodnUIKah/K5aUnpZyX9//dtGAmuYaBwGG5zinM+1J6uUeBUmrUKNJ3XipdTLXk92wBEfFyRPypwqJq53EtJ5J+D+9j/lTGU0nn4ijgjWbWNSCPaSLpAvH0Zm4H6feiW972BGBMC7Y1M7M2pnR31cxsHkndcs54V9Is7OHlgbpZc/To0SMGDRrU0d2wRdjgwYObXsnsM0bSuEgPi6jpU93ONrPF1lClv5fQhfT9DQfxZmZmixgH8ma2gIho67QoMzMza2Ot+oNQZmZmZmbWsRzIm5mZmZnVIQfyZmZmZmZ1yE+tMTOzdtPQ0BCNjY0d3Q0zs7rS3KfWeEbezMzMzKwOOZA3MzMzM6tDDuTNzMzMzOqQA3kzMzMzszrkPwhlZmbtZvr06QwZMqSju2EdaPDgwR3dBbPFlmfkzczMzMzqkAN5MzMzM7M65EDezMzMzKwOOZA3MzMzM6tDDuTNzMzMzOqQA3kzMzMzszrkQN7MzMzMrA45kLfPFEmrSxom6XlJT0i6Q9KGTWwzTdLKFcqPkHRgfr+xpAmSHpO0XjP7smFu/zlJT0r6u6TVmtjmvebUXVj/XEm/LHw+SdIFVcYyKY/hQUmb5vKekmblcT0paYykg6q01V/SjMK6FR8eLalB0rlN9Lu7pB+1ZKxV6hkp6WlJEyU9Jel8Sd0/ZX0NFcoPlnR+C+rpL+m2srLLJe3b2r6V1VXxPJE0Jx/jyZKul9S1ynoPN6ONY6ptb2ZmC4cDefvMkCTgJmBkRKwXEZsCvwBqBs/VRMTFEXFl/vhN4J8R0Tsinm9GX7oAtwMXRcT6EbEJcBGwSmv6UsPJwCGS1pW0DvB94KQK610TEVtERC/gd8AfC8uez+PaBNgPOFbSIVXaGxURvYEG4ABJfYoLJXWOiMaIOKqJfncHWhTIK6n0b9rAiNgS2BKYDfyzJfUuZmZFRK+I2Bz4EDiiuFBSJ4CI2L4ZdR0DOJA3M+tADuTts2Rn4KOIuLhUEBETImJU+Qxpnrk9uLDt8Xk2eoyk9fM6p0o6TtLXSEHN9yWNkHSVpL0LdV0t6RtlffkuMDoibi30ZURETC6f3ZV0m6T+hc9/kDRe0r2SVsll60n6l6RxkkZJ2jjX+Q4pcD8fuAA4JSLeLt8xeb2SZYGotAMj4gXgp0DNQDwi3gfGAevl/TRU0nDgyuK+zssuzTPdL0gq1XtG3naCpLPyusdLGptn14fksp559v9CYDywdo0+fQicAHxe0lZ5+5/m2enJko4p1Dm5tF0+xqcWqjpA0sN5m77l7UhaRdI/cl/HStqh1r6qRFIfSffn43mXpDVy+Q9ynY/nNrrm8nUkjc7LTm9mM6OA9fPxGCHpGmBSru+9/LN/PjY35DsaV+cLpqOAHsAISSNaOj4zM2sbDuTts2RzUnDZGu9ERF9SQHxOcUFE3AFcDJwdETsDfwEOAZC0ArA9cEcb9WVZYHxEbA3cD5TSV4YCR0ZEH+A44MJC/64FPgcsHxFXVatY0o8lPU+aka8VqI8HNq7VSUkrAdsCU3JRH2DviPhuhdU3Br4C9AUGS1oSOJF0J6BXRBwvaXdgg7xOL6CPpJ3y9hsBV+a7Bi/W6ldEzAEeBzbOdwsOAb6Y+/oDSb1rbZ8tm2esfwRcWmH5n0jnwjbAt0nnQyX98oXKBEkTgG8A5PGfB+ybj+elwK/zNjdGxDYRsRXwJHBYoc2Lcpv/bWoAkjoDXyUH7qT9elK+S1WuN+lCdVNgXWCHiDgXmA7snM/58voPl9QoqXHmzJlNdcfMzFqpc0d3wKxOXFv4eXatFSPifkkXSFoV+Bbwj4j4uI36MRe4Lr//G3CjpG6ki4XrJZXWW7r0RtJawOpASOoWERXzpyPiAuACSd8lpeRUzIUHVKUcUnD6WO7nGRExRdJ3gFsiYlaVbW6PiNnAbEmvUTnVaff8eix/7kYK7P8NvBgRj9ToU7X+7wjclO8eIOlGoB9wSxPbXwsQEQ9IWl4L5tzvCmxaOBbLS1ouIt4tW29UROz1Saeky/PbjUgXenfnOjoBr+Rlm0v6FSn1qBtwVy7fgXTRAHAVcGaVvi+TLxogzcj/lXTujImIqVW2GRMRL+c+TgB6Ag9WWReAiBhKurikR48eFe/umJnZp+dA3j5LpgDVvkz4MfPfoepStjyqvK/mKmAgKaf80Cp9+VIr+1LeryWAt3N+eyV/Ak4FNiHN4B9foz6AYaR8/Wp6k2aDK5kvOC14v0Z9swvv51D53yUBv42IP89XKPVsou75K0k54FuQ+r9GldVaci5U+rwEsF2NC5cmuwlMiYjtKiy7HPhmRDyulPrVv0Y/KplVfp7ki4VPe3zMzKwDOLXGPkvuA5aW9INSgaRtJH0JeJE0i7p0Tof5ctm2Awo/RzejrctJ6QhExJQKy68Btpe0Z6Eve0jaApgG9JK0hKS1SWkPJUsw72Lku8CDOb99ap75Ln3ps5QD/lVgVeBK4HRgH+Un0hRJ2qDwcU/g2UqDyoHz70mpH+3pXWC5wue7gEPz3QckrZnveDRbTln5LfBSREwEHgC+KamrpGWBfUiz1K8Cq0paSdLSQPmFyYBc347AjIiYUbZ8OPCTQru9WtJP4GlgFUnblfotabO8bDnglTyWgYVtHiJdNFJW3p7Kj5GZmS1knlmxz4yICEn7AOdIOhH4gBQ0HxMRL0n6OzCRFMQ+Vrb50pIeJQXS+zejrVclPQncXGX5LEl75b6cA3yU2z4amAxMJeUvTyblpJe8D2wmaRwwg3kXGAOBiySdDCwJDJP0NCmff9+ICOB9SSeQ8vx3KevSTyTtmvvxFvOn1ayX02W6kIK38yLisqb2wacREf+T9JDSl07vzHnymwCj8wzye8ABpBniplwtaTYp3egeYO/cxviczjImr/eXiHgMQNJpwKOk4/BUWX1vKT2ecXkq3205ipSiNJH0b+wDlD0dppaI+FDpMZTn5ovKzqTjOAX4Ze7Xi6TzoxRIHw1cI+lo4B/NbetTGgrcKemVSnnyZmbW/pT+fzeztpSfJjIJ2LrCjK3ZZ0aPHj1i0KBBHd0N60CDB1f8kxJmVoOkcRGxwN8tKefUGrM2lme2nyLNXDuINzMzs3bh1BqzNhYR9wCf7+h+mJmZ2eLNM/JmZmZmZnXIgbyZmZmZWR1yIG9mZmZmVof81BozM2s3DQ0N0djY2NHdMDOrK35qjZmZmZnZYsyBvJmZmZlZHXIgb2ZmZmZWhxzIm5mZmZnVIf9BKDMzazfTp09nyJAhHd0NW4gGDx7c0V0w+8zwjLyZmZmZWR1yIG9mZmZmVoccyJuZmZmZ1SEH8mZmZmZmdciBvJmZmZlZHXIgb2ZmZmZWhxzIm5mZmZnVIQfy1mYkhaSrCp87S3pd0m0trKenpO8WPjdIOrcF218j6YeFz1+UNFHSAn83Ibc1uYX9W13SMEnPS3pC0h2SNpTUv6VjrdaXT1tXE+1I0smSnpX0jKQRkjZrj7ZaQ9JISU9LmpBfN+TyUyUd14r6ukv6URPrPNyC+prdj/K2y8/t1pK0gqQr8zn4fH6/wqet18zM6osDeWtL7wObS1omf94N+E9LKsjBdk/gk2AnIhoj4qgWVHMscLykVSQtAZwP/CgiPm5JX6r0T8BNwMiIWC8iNgV+Aaz2aeteiH4MbA9sFREbAr8FbpHUZWF3JF9UVPp3aGBE9MqvfT9lM92BioG8pE4AEbH9p2yjuW33pHBuN0epj2X+CryQz8H1gKnAX1rZx0+l0gWymZktHA7kra3dCeyZ3+8PXFtaIKmvpIclPZZ/bpTLD5Z0vaRbgeHAGUC/PBt7bHF2Os+GXppnbV+QtECAHxGvAr8HfgccAUwEXpY0StL4/FogcJPUSdJZksbmGfxBFca3M/BRRFxcaG9CRIzKH7tJukHSU5KuzoE/kk7J9U6WNLRQ3kfS45JGkwLsBUhaNo95bN53e+fyzSSNyftpoqQN8rq35zonSxpQocqfA0dGxMzc/+HAw8DAXO97kv6Q99O9klbJ5etJ+pekcXlfbpzLL5d0bj6mL0j6JPCWdHxhfw7JZT0lPSnpQmA8sHalcddSoy+rSbopj//xfJzPANbL++msfD6NkHQNMKk05kLdJ0ialLc/owV9WmCs5W2z4Lld8Zyr1MdCO+sDfYDTC8WnAQ15v/SX9EDeD09Iulj5YknS7pJG52N7vaRuuXyapCG5fFJhf1Y798p/Z83MrAN4JsXa2jDgFKXAe0vgUqBfXvYUsFNEfCxpV+A3wLfzsu2ALSPiTUn9geMiYi9IQU1ZGxuTAurlgKclXRQRH5WtczFwENAfaABmAbtFxAeSNiBdYDSUbXMYMCMitpG0NPCQpOERMbWwzubAuBrj7w1sBkwHHgJ2AB4Ezo+I0/J4rgL2Am4FLiMF1ffnQK+Sk4D7IuJQSd2BMZLuIV2k/Ckirpa0FNAJ+BowPSL2zG3Nl24haXlg2Yh4vqyNxtxvgGWB8RHxM0mnAIOBnwBDgSMi4llJXwQuBHbJ26wB7Eg6NrcAN0jaHdgA6AuINOu/E/BvYCPgkIiolvJytaRZ+f3dEXF82fJqfTkXuD8i9lGaye4GnAhsHhG98j7on/u0edmxRdJXgW8CX4yImZJWrNK/+dQYa6W2i+f24VQ453K1FfsIbApMiIg5pYKImCNpAukYvpO33RR4EfgX8C1JI4GTgV0j4n1JPwd+SroIAHgjIrZWSgU6Dvg+1c89KPzOVtgfhwOHA6ywgjN+zMzaiwN5a1MRMVFST9Js/B1li1cArsiBdABLFpbdXSkgqOL2iJgNzJb0Gimt5eWyfsyV9GegISL+lwPa8yX1AuYAG1aod3dgy8KM8gqk4Kw8kKplTES8DJADq56kQH5nSScAXYEVgSmSHgC6R8T9edurgK9W6dc3NC8vuwvweWA0cJKktYAbc1A7Cfi9pDOB2wp3Cpoi0jEBmAtcl9//Dbgxz9xuD1yvdDMBYOnC9jdHxFzgCUmlNKPd8+ux/LkbaX/+G3gxIh6p0Z+BEdFYsaO1+7ILcCCk4BaYIelzFaoZUyFABtgVuKxwt6K552StsTa1XaVz7sMafSweq2rlYyLiBQBJ15Iusj4gBfcP5f22FOkcKrkx/xwHfKvQv0rnHtT4nY2IoaSLLXr06FGpr2Zm1gYcyFt7uIWU2tIfWKlQfjowIs+W9gRGFpa934L6Zxfez6H6eTw3vyDlzb8KbEVKKfugwvoizY7fVaPtKUCtnO0F+qaUe34h6aLiJUmnkgKiagFZpX59OyKeLit/UtKjpFSmuyR9PyLuk9SHNDP/23xHoTTjSkS8I+l9SeuWAr1sa+B+KgvSPnu7NLPcxLhV+PnbiPjzfINJx74lx7tcU31pjmrtN/eYVNqu2lib2m6Bcy7P3Ffr4xSgt6Ql8sUTOXVmK+BJYC0WHEPktu6OiP2r1Fs6hsXfqYrnXr4L8mmOoZmZtQHnyFt7uBQ4LSImlZWvwLwvvx5cY/t3SWkzbWkF4JUc+HyPlIZS7i7gh5KWBFB6Es2yZevcBywt6QelAknbSPpSjbZLXyJ9I88m7wsQEW+TZox3zMsHVtn+LuBI6ZO8+t7557qkLzyeS7p42lJSD2BmRPyNdDG1dYX6zgLOVf5Sck5z2hG4Ji9fgnkXK98FHoyId4Cpkr6Tt5GkrWqMudTvQwt52GtKWrWJbZrURF/uBX6YyzvlVKKWnE/Dc5+75jqalVpD9bGWt13+uTnn3Hwi4jnSzP/JheKTSelQz+XPfSWtkwP8AaS7Qo8AOyjl2COpq6RKd6bKx7XAuWdmZosGB/LW5iLi5Yj4U4VFvyPNEj9E5UC6ZCLwsdKXDY9to25dCBwk6RFSWk2l2cS/AE8A45UeA/lnymb7IyKAfYDdlB77NwU4lZQTX1EO2C8hfWnxZmBsYfEhwAVKX3adtcDGyemkNKSJuV+lLzkOACbnFJ6NgSuBLUh5zBNI+c2/qlDfebkPkyQ9DfwS2DsiSu2/D2wmaRwpVaU0oz8QOEzS46RZ4b2rjTmPezjp4mB0Tvm5geYH1Fdr3uMn76mwvFpfjialMU0ipYhsFhH/I6WTTFb17yGU+vwv0kVRY96H1R4zebKkl0uvamOt0Hb5ud3kOVfFYcCGkp6T9DzpnD6ssHw06Yu1k0mpYTdFxOukC+hrJU0kBfYbN9FOtXPPzMwWAUpxiZlZIum9iOjW0f2w1lHZF2o7Wo8ePWLQoEoPgLLF1eDBgzu6C2Z1T9K4iCh/KMcCPCNvZmZmZlaH/GVXM5uPZ+PrW0SMZP4vkpuZ2WLKM/JmZmZmZnXIgbyZmZmZWR1yIG9mZmZmVof81BozM2s3DQ0N0dhY8Y/0mplZFX5qjZmZmZnZYsyBvJmZmZlZHXIgb2ZmZmZWhxzIm5mZmZnVIf9BKDMzazfTp09nyJAhHd0NaweDBw/u6C6YfeZ5Rt7MzMzMrA45kDczMzMzq0MO5M3MzMzM6pADeTMzMzOzOuRA3szMzMysDjmQNzMzMzOrQw7kzczMzMzqkAN5W+xIeq/s88GSzi98PlzSU/k1RtKOhWUjJTUWPjdIGlmlnTmSJuTXLRWWn1fsi5JzJT0naaKkrQvL9pD0dF52YpX2Nsr9myDpSUlDm7lLinU83NJtyra/ILf/hKRZhfHv24I63mvGOqtLGibp+dzWHZI2lNRf0m0t7PNpknZtyTZl21+bj9exrdz+YEmv5/00RdINkrrmZUdIOrC1fWtFX3pKmlxWtkXhOL4paWp+f0+VOi4vHe98PjYsjL6bmdmC/Aeh7DNF0l7AIGDHiHgjB9M3S+obEf/Nq60q6asRcWcT1c2KiF5V2mkAupcVfxXYIL++CFwEfFFSJ+ACYDfgZWCspFsi4omy7c8Fzo6If+Y2tmh6xJ/0p1NEzImI7Zu7TSUR8eNcX0/gtmrj/zQkCbgJuCIi9stlvYDVWlNfRJxSpZ1OETGnib6sDmwfEV9obnuSOkfEx2XF10XET/Lya4ABwGURcXFz620vETEJ6AUpSCcd1xs6sk9mZtY8npG3z5qfA8dHxBsAETEeuAL4cWGds4CTW9tADszPAk4oW7Q3cGUkjwDdJa0B9AWei4gXIuJDYFhet9wapECf3PdJpfYknSVpbJ45HpTL+0sakQPH0rrFOwTHF7YZksuWlXS7pMclTZY0oBnj7SlplKTx+bV9Ll9D0gN5dneypH5l260sabSkPcuq3Bn4qBjkRsSEiBiVP3bLs9pPSbo6B/5IOiWPZ7KkoYXy4gzytLzeg8B3JB2VZ/wnShpWYXjDSRd2EyT1k9RL0iN5/ZskfS7XO1LSbyTdDxxdY191BpYF3sqfT5V0XH6/Ta53dD6ekyts303SvXk/T5K0d+EYPCnpkjzrP1zSMnlZn3w8RzP/eV5Ttf1pZmaLDgfytjhappAqMAE4rbBsM2Bc2fqNubxkNDBb0s5NtNNFUmMO7L5ZKP8JcEtEvFK2/prAS4XPL+eyauXlzgbuk3SnpGMldc/lhwEzImIbYBvgB5LWycv6AidFxKbFiiTtTroz0Jc0G9tH0k7AHsD0iNgqIjYH/tXEPgB4DdgtIrYmzTSfm8u/C9yVZ+23AiYU2l8NuB04JSJuL6tvcxY8RkW9gWOATYF1gR1y+fkRsU3u9zLAXlW2/yAidoyIYcCJQO+I2BI4osK63wCej4he+ULiSuDnef1JQPFv1HePiC9FxB8q1DMgn4v/AVYEbq2wzmXAERGxHVDtTsEHwD55X+8M/KEQYG8AXBARmwFvA98u1HtUrrclmrs/F6CUvtYoqXHmzJktbNbMzJrLgbwtjmblwKtXDiIrplYUCIiysl/R9Kz85yOigRSwniNpPUk9gO8A51Vpp1zUKJ+/IOIyYBPgeqA/8IikpYHdgQNzoPgosBIpqAMYExFTK9S/e349BowHNs7bTAJ2lXSmpH4RMaPSwMssCVwiaVLuW+miYSxwiKRTgS0i4t3C+vcCJ0TE3c2ov9yYiHg5IuaSLg565vKdJT2a+7EL81+cFV1XeD8RuFrSAUB5Osx8JK1ACtbvz0VXADtVqXeBNvO5uDppHx9fVnd3YLmIKH2H4Zpq3QB+I2kicA/pgq+UcjQ1Iibk9+OAnhX6fFWNPpZr7v5cQEQMjYiGiGjo2rVrC5o0M7OWcCBvnzVPAH3KyrbO5Z+IiPuALsC21SqKiOn55wvASNJMcW9gfeA5SdOArpKey5u8DKxdqGItYHqN8optRsSlEbE3KfDcnBTcHVm4eFknIobnTd6v0n0Bvy1ss35E/DUiniHtn0nAbyU1dREEcCzwKmnWvQFYKvf1AVKg+x/gKs37UufHpEDzK1Xqm8KCx6hoduH9HKCzpC7AhcC+EbEFcAnp+FVS3Cd7kr6f0AcYl1NfWqvavv5ERARpNn6nskXNTVsZCKwC9MkXBq8yb5wL7BcqX6Q2qYX708zMOogDefus+R1wpqSV4JMvUR5MClrK/ZoF89zJ230uz4YjaWVSescTEXF7RKweET0joicwMyLWz5vdQpo5l6RtSekwr5BmrjeQtI6kpYD98rrlbe4hacn8fnXSzPt/gLuAHxaWbShp2Sb2w13AoZK65W3WlLRqvqMwMyL+BvyedJHTlBWAV/IM+feATrnOLwCvRcQlwF8LdQVwKLCxKj+h5z5gaUk/KIx9G0lfqtGHUpD5Rh5Tk0/RkbQEsHZEjCAd5+5At2rr57sTb2lerv/3gPurrV/DjsDzZXW/BbybzwtI50AlK5D26Uc59avml3Aj4m1ghuY9mWlgM/vY4v1pZmYLn59aY58pEXGLpDWBhyUF8C5wQIV8diLiDkmvV6lqE+DPkuaSLojPqPCUmXJ3AF8DngNmAofkdj6W9BNScN0JuDQiplTYfnfgT5I+yJ+Pj4j/SvoLKb1kfM6Xfh34Zq2ORMRwSZsAo3OK9XvAAaS7CWflcX0E/LCJMUG6CPqHpO8AI5g3M90fOF7SR7n+Tx6zGBFzJO0H3CrpnYi4sLAsJO1DSlc6kZQXPo2UF1/puwNExNuSLiHdSZhGujhqSifgbzn9RKQnAr3dxDYHARcrPT7yBfIxbIYBOZhegnQH5uAK6xxGSlF6n3SHp1Ja09WkfdZISit6qhltHwJcKmkm6RxrUiv3p5mZLWRKd3rNzKwjSeoWEe/l9ycCa0RE1Sfg1IsePXrEoEGDOrob1g4GDx7c9Epm1iqSxuXv4dXkGXkzs0XDnpL+j/Tv8otUnrU3MzP7hAN5M7NFQERcR+0n35iZmc3HX3Y1MzMzM6tDDuTNzMzMzOqQA3kzMzMzszrkp9aYmVm7aWhoiMbGxo7uhplZXWnuU2s8I29mZmZmVoccyJuZmZmZ1SGn1piZWbuR9C7wdEf3ox2sDLzR0Z1oB4vjuBbHMcHiOa7FcUzQunF9ISJWaWolP0fezMza09PNyfOsN5IaPa76sDiOCRbPcS2OY4L2HZdTa8zMzMzM6pADeTMzMzOzOuRA3szM2tPQju5AO/G46sfiOCZYPMe1OI4J2nFc/rKrmZmZmVkd8oy8mZmZmVkdciBvZmZmZlaHHMibmVm7kLSHpKclPSfpxI7uTy2S1pY0QtKTkqZIOjqXryjpbknP5p+fK2zzf3lsT0v6SqG8j6RJedm5ktQRYyr0p5OkxyTdlj8vDmPqLukGSU/lY7bdYjKuY/P5N1nStZK61Nu4JF0q6TVJkwtlbTYGSUtLui6XPyqpZweO66x8Dk6UdJOk7gt9XBHhl19++eWXX236AjoBzwPrAksBjwObdnS/avR3DWDr/H454BlgU+B3wIm5/ETgzPx+0zympYF18lg75WVjgO0AAXcCX+3gsf0UuAa4LX9eHMZ0BfD9/H4poHu9jwtYE5gKLJM//x04uN7GBewEbA1MLpS12RiAHwEX5/f7Add14Lh2Bzrn92d2xLg8I29mZu2hL/BcRLwQER8Cw4C9O7hPVUXEKxExPr9/F3iSFFjtTQoayT+/md/vDQyLiNkRMRV4DugraQ1g+YgYHel/5CsL2yx0ktYC9gT+Uiiu9zEtTwqq/goQER9GxNvU+biyzsAykjoDXYHp1Nm4IuIB4M2y4rYcQ7GuG4AvL4w7DpXGFRHDI+Lj/PERYK1CHxfKuBzIm5lZe1gTeKnw+eVctsjLt7R7A48Cq0XEK5CCfWDVvFq18a2Z35eXd5RzgBOAuYWyeh/TusDrwGU5ZegvkpalzscVEf8Bfg/8G3gFmBERw6nzcWVtOYZPtslB9AxgpXbrefMdSpphh4U4LgfyZmbWHirNJC3yzzuW1A34B3BMRLxTa9UKZVGjfKGTtBfwWkSMa+4mFcoWqTFlnUkpDhdFRG/gfVK6RjV1Ma6cN743KRWjB7CspANqbVKhbJEbVxNaM4ZFbnySTgI+Bq4uFVVYrV3G5UDezMzaw8vA2oXPa5HSBBZZkpYkBfFXR8SNufjVfDuc/PO1XF5tfC8z7/Z6sbwj7AB8Q9I0UmrTLpL+Rn2PCVJ/Xo6IR/PnG0iBfb2Pa1dgakS8HhEfATcC21P/44K2HcMn2+QUpBVYMJVnoZF0ELAXMDCny8BCHJcDeTMzaw9jgQ0krSNpKdKXt27p4D5VlXNR/wo8GRF/LCy6BTgovz8I+GehfL/8pIl1gA2AMTlt4F1J2+Y6Dyxss1BFxP9FxFoR0ZO0/++LiAOo4zEBRMR/gZckbZSLvgw8QZ2Pi5RSs62krrk/XyZ9V6PexwVtO4ZiXfuSzuuOuuu1B/Bz4BsRMbOwaOGNa2F809cvv/zyy6/P3gv4GunpL88DJ3V0f5ro646k29gTgQn59TVSjuq9wLP554qFbU7KY3uawlNBgAZgcl52PvmvqHfw+Poz76k1dT8moBfQmI/XzcDnFpNxDQGeyn26ivTUk7oaF3AtKcf/I9Is82FtOQagC3A96QukY4B1O3Bcz5Hy2kv/Zly8sMdV2tjMzMzMzOqIU2vMzMzMzOqQA3kzMzMzszrkQN7MzMzMrA45kDczMzMzq0MO5M3MzMzM6pADeTMzM1ssSDpJ0hRJEyVNkPTFGuteLmnfhdk/s7bWuaM7YGZmZvZpSdqO9Bc2t46I2ZJWBpbq4G6ZtSvPyJuZmdniYA3gjYiYDRARb0TEdEmnSBorabKkofkvas5HUh9J90saJ+kuSWvk8qMkPZFn+Ict5PGYNcl/EMrMzMzqnqRuwINAV+Ae4LqIuF/SihHxZl7nKuDvEXGrpMuB24B/AvcDe0fE65IGAF+JiEMlTQfWyTP83SPi7Q4YmllVTq0xMzOzuhcR70nqA/QDdgauk3Qi8K6kE0gB/orAFODWwqYbAZsDd+fJ+k7AK3nZROBqSTcDNy+EYZi1iAN5MzMzWyxExBxgJDBS0iRgELAl0BARL0k6FehStpmAKRGxXYUq9wR2Ar4B/FLSZhHxcXv136ylnCNvZmZmdU/SRpI2KBT1Ap7O79/IqTeVnlLzNLBK/rIskpaUtJmkJYC1I2IEcALQHejWXv03aw3PyJuZmdnioBtwnqTuwMfAc8DhwNvAJGAaMLZ8o4j4MD+G8lxJK5Bio3OAZ4C/5TIBZztH3hY1/rKrmZmZmVkdcmqNmZmZmVkdciBvZmZmZlaHHMibmZmZmdUhB/JmZmZmZnXIgbyZmZmZWR1yIG9mZmZmVoccyJuZmZmZ1aH/D76AXEdgDI11AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p_top = p[(p['Sales']>=4416.174)]\n",
    "\n",
    "sns.barplot(y=\"Product Name\", x=\"Sales\", data=p_top, ci = False, orient = 'h', color = 'grey')\n",
    "plt.title('Top Products in 2018')\n",
    "plt.ylabel('')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c7833ef",
   "metadata": {},
   "source": [
    "### Among the three product categories, which one had the lowest sale in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "085c9789",
   "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>Category</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Furniture</td>\n",
       "      <td>212313.7872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Office Supplies</td>\n",
       "      <td>240367.5410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Technology</td>\n",
       "      <td>269370.6910</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Category        Sales\n",
       "0        Furniture  212313.7872\n",
       "1  Office Supplies  240367.5410\n",
       "2       Technology  269370.6910"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "psales = sales_2018.groupby('Category', as_index = False).agg({'Sales':'sum'})\n",
    "psales"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a949c499",
   "metadata": {},
   "source": [
    "#### We will use Vertical Barchart in this section"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "afc6bf75",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x=\"Category\", y=\"Sales\", data= psales, palette = 'Blues')\n",
    "plt.title('Sales by Product Category')\n",
    "plt.xlabel('')\n",
    "plt.ylabel('')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb183c87",
   "metadata": {},
   "source": [
    "#### Q3 Ans. The Furniture Category had the lowest sale in 2018"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9458497e",
   "metadata": {},
   "source": [
    "### Among the three product categories, which one had the lowest sale in 2018"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6fdae524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x='Order Year', y='Sales', data = consumer, marker = \"o\")\n",
    "sns.lineplot(x='Order Year', y='Sales', data = home, marker = \"o\")\n",
    "sns.lineplot(x='Order Year', y='Sales', data = corporate, marker = \"o\")\n",
    "\n",
    "plt.legend(['consumer', 'home', 'corporate'], fontsize = 8)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d96f8f",
   "metadata": {},
   "source": [
    "#### Q4 Ans. All of the three customer groups exhibited an upward trend starting 2016, but the Corporate customers had a quite a big leap in 2017"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25deb33f",
   "metadata": {},
   "source": [
    "# Data Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30260f6c",
   "metadata": {},
   "source": [
    "1. What is the yearly sales trend?\n",
    "#### Q1 Ans. There's an increase of sales from 2015 to 2018\n",
    "2. Which product sold the most in 2016?\n",
    " #### Q2 Ans. The product that sold the most in 2018 is Canon imageCLASS 2200 Advanced Copier with 14000 sales\n",
    "3. Among the three product categories, which one had the lowest sale in 2018?\n",
    "#### Q3 Ans. The Furniture Category had the lowest sale in 2018\n",
    "4. What is the yearl sales trend of the three customer groups?\n",
    "#### Q4 Ans. All of the three customer groups exhibited an upward trend starting 2016, but the Corporate customers had a quite a big leap in 2017\n",
    "5. Are we getting more corporate customers?\n",
    "#### Q5 Ans. We can say that the number of corporate customers is increasing yearly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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