eComm_01.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "import os\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "\n",
    "import plotly.express as px\n",
    "\n",
    "import calendar\n",
    "import datetime as dt\n",
    "\n",
    "from datetime import timedelta\n",
    "\n",
    "from urllib.request import urlopen\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "\n",
    "import contextily as ctx\n",
    "import geojson \n",
    "\n",
    "from dash import Dash, html, dcc\n",
    "from jupyter_dash import JupyterDash\n",
    "from dash.dependencies import Input, Output\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "plt.rcParams['figure.dpi'] = 100   \n",
    "plt.rcParams['savefig.dpi'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the following blog and video\n",
    "\n",
    "https://macxima.medium.com/python-how-to-read-multiple-excel-sheets-or-tabs-4d412185c18\n",
    "\n",
    "https://www.youtube.com/watch?v=fdPV-3K-8pc\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "products\n",
      "         ASIN                                              title product_type\n",
      "0  B07GWKDLGT  Nikon D3500 W/ AF-P DX NIKKOR 18-55mm f/3.5-5....  dslr camera\n",
      "1  B01MTLH408  Manfrotto Element Aluminum 5-Section Monopod, ...  dslr camera\n",
      "stock\n",
      "  StockCode        ASIN\n",
      "0     21703  B07GWKDLGT\n",
      "1     40001  B01MTLH408\n",
      "assessment\n",
      "   rating  review_count        ASIN\n",
      "0     4.6          1399  B07GWKDLGT\n",
      "1     4.6           289  B01MTLH408\n",
      "invoices\n",
      "  InvoiceNo        ASIN  Quantity   price  total_sale invoice_date  \\\n",
      "0  78536597  B07GWKDLGT         4  496.95     1987.80   2018-12-01   \n",
      "1  78536597  B01MTLH408         4   39.99      159.96   2018-12-01   \n",
      "\n",
      "   invoice_time CustomerID  \n",
      "0            17      18011  \n",
      "1            17      18011  \n",
      "customers\n",
      "  CustomerID  Country\n",
      "0      18011  Germany\n",
      "1      18011  Germany\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'    # Create SQL database and for each loop create a new table within the database. \\n    conn = sqlite3.connect(\"sales original.db\")\\n    vars()[table_name].to_sql(sqlite_table, conn, if_exists = \\'replace\\', index=True, index_label = \"id_\" + sqlite_table[0:4]) # Index = True creates a Primary key called index in the table \\n    conn.close()'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#path of the excel file to get all the sheets from the excel file into xls sheet name list\n",
    "xls = pd.ExcelFile(\"sales original.xlsx\")\n",
    "\n",
    "#set the file name to read the excel sheets\n",
    "file_name = \"sales original.xlsx\"\n",
    "\n",
    "#For loop to get the sheet name from the excel file\n",
    "for table_name in xls.sheet_names: \n",
    "    \n",
    "    # Declare a variable to store the different sheet names\n",
    "    sqlite_table = table_name\n",
    "    print(sqlite_table)\n",
    "\n",
    "    # Dynamically create dataframes for each sheet in the excel file.\n",
    "    vars()[table_name] = pd.DataFrame()\n",
    "\n",
    "    # Read the excel sheet into a Pandas dataframe\n",
    "    vars()[table_name] = pd.read_excel(file_name, sheet_name = table_name, index_col=None)\n",
    "    print(vars()[table_name].head(2))\n",
    "    \n",
    "\"\"\"    # Create SQL database and for each loop create a new table within the database. \n",
    "    conn = sqlite3.connect(\"sales original.db\")\n",
    "    vars()[table_name].to_sql(sqlite_table, conn, if_exists = 'replace', index=True, index_label = \"id_\" + sqlite_table[0:4]) # Index = True creates a Primary key called index in the table \n",
    "    conn.close()\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# del merged_tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Concact the useful columns for further analysis. \n",
    "# This means take the following df's and add columns to make a super DF. From: invoices\n",
    "\n",
    "\n",
    "merged_tables = pd.concat([invoices, products, customers, assessment, stock], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 554417 entries, 0 to 554416\n",
      "Data columns (total 18 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   InvoiceNo     554417 non-null  object        \n",
      " 1   ASIN          554417 non-null  object        \n",
      " 2   Quantity      554417 non-null  int64         \n",
      " 3   price         554417 non-null  float64       \n",
      " 4   total_sale    554417 non-null  float64       \n",
      " 5   invoice_date  554417 non-null  datetime64[ns]\n",
      " 6   invoice_time  554417 non-null  int64         \n",
      " 7   CustomerID    554417 non-null  object        \n",
      " 8   ASIN          554417 non-null  object        \n",
      " 9   title         554417 non-null  object        \n",
      " 10  product_type  554417 non-null  object        \n",
      " 11  CustomerID    554417 non-null  object        \n",
      " 12  Country       554417 non-null  object        \n",
      " 13  rating        554417 non-null  float64       \n",
      " 14  review_count  554417 non-null  int64         \n",
      " 15  ASIN          554417 non-null  object        \n",
      " 16  StockCode     554417 non-null  object        \n",
      " 17  ASIN          554417 non-null  object        \n",
      "dtypes: datetime64[ns](1), float64(3), int64(3), object(11)\n",
      "memory usage: 76.1+ MB\n"
     ]
    }
   ],
   "source": [
    "merged_tables.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_tables.to_csv('merged_ecommerce.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# merged_tables = pd.read_csv('merged_ecommerce.csv')  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['InvoiceNo', 'ASIN', 'Quantity', 'price', 'total_sale', 'invoice_date',\n",
       "       'invoice_time', 'CustomerID', 'ASIN', 'title', 'product_type',\n",
       "       'CustomerID', 'Country', 'rating', 'review_count', 'ASIN', 'StockCode',\n",
       "       'ASIN'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False,  True,\n",
       "       False, False,  True, False, False, False,  True, False,  True])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>InvoiceNo</th>\n",
       "      <th>ASIN</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>price</th>\n",
       "      <th>total_sale</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>invoice_time</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>ASIN</th>\n",
       "      <th>title</th>\n",
       "      <th>product_type</th>\n",
       "      <th>CustomerID</th>\n",
       "      <th>Country</th>\n",
       "      <th>rating</th>\n",
       "      <th>review_count</th>\n",
       "      <th>ASIN</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>ASIN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B07GWKDLGT</td>\n",
       "      <td>4</td>\n",
       "      <td>496.95</td>\n",
       "      <td>1987.80</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>17</td>\n",
       "      <td>18011</td>\n",
       "      <td>B07GWKDLGT</td>\n",
       "      <td>Nikon D3500 W/ AF-P DX NIKKOR 18-55mm f/3.5-5....</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>18011</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1399</td>\n",
       "      <td>B07GWKDLGT</td>\n",
       "      <td>21703</td>\n",
       "      <td>B07GWKDLGT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B01MTLH408</td>\n",
       "      <td>4</td>\n",
       "      <td>39.99</td>\n",
       "      <td>159.96</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>17</td>\n",
       "      <td>18011</td>\n",
       "      <td>B01MTLH408</td>\n",
       "      <td>Manfrotto Element Aluminum 5-Section Monopod, ...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>18011</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.6</td>\n",
       "      <td>289</td>\n",
       "      <td>B01MTLH408</td>\n",
       "      <td>40001</td>\n",
       "      <td>B01MTLH408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B0064FS7HI</td>\n",
       "      <td>1</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.99</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>17</td>\n",
       "      <td>18011</td>\n",
       "      <td>B0064FS7HI</td>\n",
       "      <td>STK LP-E8 Battery for Canon Rebel T5i, T3i, T2...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>18011</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.4</td>\n",
       "      <td>333</td>\n",
       "      <td>B0064FS7HI</td>\n",
       "      <td>85034A</td>\n",
       "      <td>B0064FS7HI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B013JHU5YG</td>\n",
       "      <td>1</td>\n",
       "      <td>199.99</td>\n",
       "      <td>199.99</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>17</td>\n",
       "      <td>18011</td>\n",
       "      <td>B013JHU5YG</td>\n",
       "      <td>Lowepro Whistler BP 350 AW (Grey) . Profession...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>18011</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.3</td>\n",
       "      <td>86</td>\n",
       "      <td>B013JHU5YG</td>\n",
       "      <td>72798C</td>\n",
       "      <td>B013JHU5YG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B06XWN9Q99</td>\n",
       "      <td>1</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.49</td>\n",
       "      <td>2018-12-01</td>\n",
       "      <td>17</td>\n",
       "      <td>18011</td>\n",
       "      <td>B06XWN9Q99</td>\n",
       "      <td>Samsung (MB-ME32GA/AM) 32GB 95MB/s (U1) microS...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>18011</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.7</td>\n",
       "      <td>15717</td>\n",
       "      <td>B06XWN9Q99</td>\n",
       "      <td>20726</td>\n",
       "      <td>B06XWN9Q99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo        ASIN  Quantity   price  total_sale invoice_date  \\\n",
       "0  78536597  B07GWKDLGT         4  496.95     1987.80   2018-12-01   \n",
       "1  78536597  B01MTLH408         4   39.99      159.96   2018-12-01   \n",
       "2  78536597  B0064FS7HI         1   12.99       12.99   2018-12-01   \n",
       "3  78536597  B013JHU5YG         1  199.99      199.99   2018-12-01   \n",
       "4  78536597  B06XWN9Q99         1    7.49        7.49   2018-12-01   \n",
       "\n",
       "   invoice_time CustomerID        ASIN  \\\n",
       "0            17      18011  B07GWKDLGT   \n",
       "1            17      18011  B01MTLH408   \n",
       "2            17      18011  B0064FS7HI   \n",
       "3            17      18011  B013JHU5YG   \n",
       "4            17      18011  B06XWN9Q99   \n",
       "\n",
       "                                               title product_type CustomerID  \\\n",
       "0  Nikon D3500 W/ AF-P DX NIKKOR 18-55mm f/3.5-5....  dslr camera      18011   \n",
       "1  Manfrotto Element Aluminum 5-Section Monopod, ...  dslr camera      18011   \n",
       "2  STK LP-E8 Battery for Canon Rebel T5i, T3i, T2...  dslr camera      18011   \n",
       "3  Lowepro Whistler BP 350 AW (Grey) . Profession...  dslr camera      18011   \n",
       "4  Samsung (MB-ME32GA/AM) 32GB 95MB/s (U1) microS...  dslr camera      18011   \n",
       "\n",
       "   Country  rating  review_count        ASIN StockCode        ASIN  \n",
       "0  Germany     4.6          1399  B07GWKDLGT     21703  B07GWKDLGT  \n",
       "1  Germany     4.6           289  B01MTLH408     40001  B01MTLH408  \n",
       "2  Germany     4.4           333  B0064FS7HI    85034A  B0064FS7HI  \n",
       "3  Germany     4.3            86  B013JHU5YG    72798C  B013JHU5YG  \n",
       "4  Germany     4.7         15717  B06XWN9Q99     20726  B06XWN9Q99  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(merged_tables.columns)\n",
    "display(merged_tables.columns.duplicated())\n",
    "display(merged_tables.head())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 554417 entries, 0 to 554416\n",
      "Data columns (total 18 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   InvoiceNo     554417 non-null  object        \n",
      " 1   Product_Id    554417 non-null  object        \n",
      " 2   Quantity      554417 non-null  int64         \n",
      " 3   Price         554417 non-null  float64       \n",
      " 4   Total_Sales   554417 non-null  float64       \n",
      " 5   Customer_Id   554417 non-null  object        \n",
      " 6   Product_Name  554417 non-null  object        \n",
      " 7   Product_Type  554417 non-null  object        \n",
      " 8   Country       554417 non-null  object        \n",
      " 9   Rating        554417 non-null  float64       \n",
      " 10  Review_Count  554417 non-null  int64         \n",
      " 11  StockCode     554417 non-null  object        \n",
      " 12  Hour          554417 non-null  int64         \n",
      " 13  Day           554417 non-null  object        \n",
      " 14  Month         554417 non-null  object        \n",
      " 15  Year          554417 non-null  int64         \n",
      " 16  Quarter       554417 non-null  int64         \n",
      " 17  Invoice_Date  554417 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(3), int64(5), object(9)\n",
      "memory usage: 76.1+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(554417, 18)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To check the corresponding iloc values of all the columns for easy reference.\n",
    "\n",
    "merged_tables.info()\n",
    "merged_tables.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Manually checking if all the values in each row of duplicate columns are same. For instance there are 4 ASIN columns in the \n",
    "# merged table so it is important to see if all the values in each row are equal. If any values in two columns that is compared is different then \"False\" is returned. If not \"True\"\n",
    "\n",
    "merged_tables.iloc[ : , 8].equals(merged_tables.iloc[ : , 15])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Deleting unneccesary columns after checking if the column values are equal from the above step. This code deletes duplicate columns based on the column name.\n",
    "\n",
    "merged_tables = merged_tables.loc[ : , ~merged_tables.columns.duplicated()].copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Rename columns for better user readability using df.rename()\n",
    "\n",
    "merged_tables.rename(columns={'price': 'Price', 'total_sale': 'Total_Sales', 'CustomerID' : 'Customer_Id',\n",
    "'title' : 'Product_Name', 'product_type' : 'Product_Type', 'rating' : 'Rating', 'review_count' : 'Review_Count', 'ASIN':'Product_Id'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a single date time column with the correct dtype and using the combination of values from the original invoice_date and invoice_time\n",
    "\n",
    "merged_tables[\"Invoice_DateTime\"] = merged_tables[\"invoice_date\"] + merged_tables[\"invoice_time\"].astype(\"timedelta64[h]\")\n",
    "\n",
    "# Drop the invoice_date and invoice_time from the table since we've created a new column with the combined date and time columns.\n",
    "\n",
    "merged_tables = merged_tables.drop([\"invoice_date\", \"invoice_time\"], axis = 1).copy()\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>InvoiceNo</th>\n",
       "      <th>Product_Id</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Product_Name</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Country</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review_Count</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Invoice_DateTime</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B07GWKDLGT</td>\n",
       "      <td>4</td>\n",
       "      <td>496.95</td>\n",
       "      <td>1987.80</td>\n",
       "      <td>18011</td>\n",
       "      <td>Nikon D3500 W/ AF-P DX NIKKOR 18-55mm f/3.5-5....</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1399</td>\n",
       "      <td>21703</td>\n",
       "      <td>2018-12-01 17:00:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B01MTLH408</td>\n",
       "      <td>4</td>\n",
       "      <td>39.99</td>\n",
       "      <td>159.96</td>\n",
       "      <td>18011</td>\n",
       "      <td>Manfrotto Element Aluminum 5-Section Monopod, ...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.6</td>\n",
       "      <td>289</td>\n",
       "      <td>40001</td>\n",
       "      <td>2018-12-01 17:00:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo  Product_Id  Quantity   Price  Total_Sales Customer_Id  \\\n",
       "0  78536597  B07GWKDLGT         4  496.95      1987.80       18011   \n",
       "1  78536597  B01MTLH408         4   39.99       159.96       18011   \n",
       "\n",
       "                                        Product_Name Product_Type  Country  \\\n",
       "0  Nikon D3500 W/ AF-P DX NIKKOR 18-55mm f/3.5-5....  dslr camera  Germany   \n",
       "1  Manfrotto Element Aluminum 5-Section Monopod, ...  dslr camera  Germany   \n",
       "\n",
       "   Rating  Review_Count StockCode    Invoice_DateTime  \n",
       "0     4.6          1399     21703 2018-12-01 17:00:00  \n",
       "1     4.6           289     40001 2018-12-01 17:00:00  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_tables.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Seperate the Invoice_DateTime coloumn to individal segments.\n",
    "merged_tables['Hour'] = merged_tables['Invoice_DateTime'].dt.hour\n",
    "merged_tables['Day_Number'] = merged_tables['Invoice_DateTime'].dt.dayofweek.astype(str)\n",
    "merged_tables['Day_Number']= merged_tables['Day_Number'].str.rjust(2, \"0\")\n",
    "merged_tables[\"Day_Name\"] = merged_tables[\"Invoice_DateTime\"].dt.day_name()\n",
    "merged_tables[\"Day\"] = merged_tables[\"Day_Number\"].astype(str) + \" \" + merged_tables[\"Day_Name\"].astype(str) \n",
    "merged_tables['Month_Number'] = merged_tables['Invoice_DateTime'].dt.month.astype(str)\n",
    "merged_tables['Month_Number']= merged_tables['Month_Number'].str.rjust(2, \"0\")\n",
    "merged_tables['Month_name'] = merged_tables['Invoice_DateTime'].dt.month_name()\n",
    "merged_tables[\"Month\"] = merged_tables[\"Month_Number\"].astype(str) + \" \" + merged_tables[\"Month_name\"].astype(str) \n",
    "merged_tables['Year'] = merged_tables['Invoice_DateTime'].dt.year\n",
    "merged_tables['Quarter'] = merged_tables['Invoice_DateTime'].dt.quarter\n",
    "merged_tables['Invoice_Date'] = merged_tables['Invoice_DateTime'].dt.date.astype(\"datetime64[ns]\")\n",
    "\n",
    "# list(calendar.day_abbr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "merged_tables = merged_tables.drop([\"Day_Number\", \"Day_Name\",'Month_Number', 'Month_name', \"Invoice_DateTime\"], axis = 1).copy()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Unique number of Product_Type: 6'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Unique number of Country: 37'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Unique number of Customer_Id: 4340'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Unique number of Product_Name: 4188'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Unique number of ASIN: 4134'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Unique invoices: 20534'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Display all the unique values in product type, countries, customer id, product name and ASIN key. \n",
    "# the unique number of ASIN should be equal to unique number of Product names\n",
    "display(\"Unique number of Product_Type: \" + str(merged_tables.Product_Type.nunique()))\n",
    "display(\"Unique number of Country: \" + str(merged_tables.Country.nunique()))\n",
    "display(\"Unique number of Customer_Id: \" + str(merged_tables.Customer_Id.nunique()))\n",
    "display(\"Unique number of Product_Name: \" + str(merged_tables.Product_Name.nunique()))\n",
    "display(\"Unique number of ASIN: \" + str(merged_tables.Product_Id.nunique()))\n",
    "display(\"Unique invoices: \" + str(merged_tables.InvoiceNo.nunique()))\n",
    "\n",
    "# display(merged_tables[merged_tables['Year'] == 2018].groupby(['InvoiceNo']).agg({'InvoiceNo':['nunique']}))\n",
    "# display(merged_tables[merged_tables['Year'] == 2019].groupby(['InvoiceNo']).agg({'InvoiceNo':['nunique']}))\n",
    "# display(merged_tables[merged_tables['Country'] == \"Lithuania\"].groupby(['InvoiceNo']).agg({'InvoiceNo':['nunique']}))\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "InvoiceNo       0\n",
       "Product_Id      0\n",
       "Quantity        0\n",
       "Price           0\n",
       "Total_Sales     0\n",
       "Customer_Id     0\n",
       "Product_Name    0\n",
       "Product_Type    0\n",
       "Country         0\n",
       "Rating          0\n",
       "Review_Count    0\n",
       "StockCode       0\n",
       "Hour            0\n",
       "Day             0\n",
       "Month           0\n",
       "Year            0\n",
       "Quarter         0\n",
       "Invoice_Date    0\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(merged_tables.isnull().sum())\n",
    "display(merged_tables.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Id</th>\n",
       "      <th>Product_Name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3675</th>\n",
       "      <td>B07Y693ND1</td>\n",
       "      <td>Razer DeathAdder Essential Gaming Mouse: 6400 ...</td>\n",
       "      <td>3188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3891</th>\n",
       "      <td>B081TJ7B88</td>\n",
       "      <td>Apple iPhone X, 256GB, Silver - Fully Unlocked...</td>\n",
       "      <td>2327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>B005EJH6Z4</td>\n",
       "      <td>AmazonBasics Wireless Computer Mouse with USB ...</td>\n",
       "      <td>2242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2052</th>\n",
       "      <td>B079JSZ1Z2</td>\n",
       "      <td>Samsung Galaxy S9 G960U 64GB Unlocked 4G LTE P...</td>\n",
       "      <td>2115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2872</th>\n",
       "      <td>B07NZX5BKH</td>\n",
       "      <td>Samsung Galaxy S10+ Plus 128GB+8GB RAM SM-G975...</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>B00KLPIHS6</td>\n",
       "      <td>Neewer V shape Dual Mount Bracket Hot Shoe for...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1711</th>\n",
       "      <td>B074JL6RY2</td>\n",
       "      <td>TracFoneÂ Samsung GalaxyÂ S7 4GÂ LTE Prepaid S...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>B00LG9JATG</td>\n",
       "      <td>JVC HASR185R On Ear Colorful Lightweight Folda...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3744</th>\n",
       "      <td>B07YWH1QMF</td>\n",
       "      <td>iVeze Keyboard Wrist Rest Pad and Mouse pad wi...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2294</th>\n",
       "      <td>B07F65MWT9</td>\n",
       "      <td>Galaxy J3 2018 Case, Samsung Galaxy J3 Eclipse...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4198 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Product_Id                                       Product_Name  count\n",
       "3675  B07Y693ND1  Razer DeathAdder Essential Gaming Mouse: 6400 ...   3188\n",
       "3891  B081TJ7B88  Apple iPhone X, 256GB, Silver - Fully Unlocked...   2327\n",
       "283   B005EJH6Z4  AmazonBasics Wireless Computer Mouse with USB ...   2242\n",
       "2052  B079JSZ1Z2  Samsung Galaxy S9 G960U 64GB Unlocked 4G LTE P...   2115\n",
       "2872  B07NZX5BKH  Samsung Galaxy S10+ Plus 128GB+8GB RAM SM-G975...   2019\n",
       "...          ...                                                ...    ...\n",
       "594   B00KLPIHS6  Neewer V shape Dual Mount Bracket Hot Shoe for...      1\n",
       "1711  B074JL6RY2  TracFoneÂ Samsung GalaxyÂ S7 4GÂ LTE Prepaid S...      1\n",
       "621   B00LG9JATG  JVC HASR185R On Ear Colorful Lightweight Folda...      1\n",
       "3744  B07YWH1QMF  iVeze Keyboard Wrist Rest Pad and Mouse pad wi...      1\n",
       "2294  B07F65MWT9  Galaxy J3 2018 Case, Samsung Galaxy J3 Eclipse...      1\n",
       "\n",
       "[4198 rows x 3 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here I want to check how many unique combo of ASIN and product names are present in the dataset. \n",
    "# I created a new df called unique_combo where I've grouped by ASIN and product name to show unique number of ASIN and product name combo.\n",
    "\n",
    "unique_combo = merged_tables.groupby(['Product_Id','Product_Name']).size().reset_index().rename(columns={0 : 'count'}).sort_values('count', ascending = False)\n",
    "display(unique_combo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Unique number of Product_Name: 4188'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Unique number of ASIN: 4134'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(\"Unique number of Product_Name: \" + str(unique_combo.Product_Name.nunique()))\n",
    "display(\"Unique number of ASIN: \" + str(unique_combo.Product_Id.nunique()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Id</th>\n",
       "      <th>Product_Name</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4139</th>\n",
       "      <td>Null</td>\n",
       "      <td>BONAOK Wireless Bluetooth Karaoke Microphone,3...</td>\n",
       "      <td>889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4167</th>\n",
       "      <td>Null</td>\n",
       "      <td>NPET T20 One-Handed Gaming Keyboard, RGB Backl...</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4141</th>\n",
       "      <td>Null</td>\n",
       "      <td>Braun FP3020 12 Cup Food Processor Ultra Quiet...</td>\n",
       "      <td>273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4178</th>\n",
       "      <td>Null</td>\n",
       "      <td>Sense Energy Monitor with Solar â€“ Track Elec...</td>\n",
       "      <td>254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4134</th>\n",
       "      <td>Null</td>\n",
       "      <td>(2020 Upgraded Ver.) [4CH] Crystal Vision CVT8...</td>\n",
       "      <td>131</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4177</th>\n",
       "      <td>Null</td>\n",
       "      <td>Samsung Galaxy S20 5G Factory Unlocked New And...</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4170</th>\n",
       "      <td>Null</td>\n",
       "      <td>Portable Monitor - 15.6-Inch USB-C Portable Di...</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4183</th>\n",
       "      <td>Null</td>\n",
       "      <td>UMIDIGI Power 3 6150mAh Monster Battery Unlock...</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4152</th>\n",
       "      <td>Null</td>\n",
       "      <td>INEVIFIT EROS Bluetooth Body Fat Scale Smart B...</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4188</th>\n",
       "      <td>Null</td>\n",
       "      <td>Vertical Wireless Mouse, Jelly Comb Ergonomic ...</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4182</th>\n",
       "      <td>Null</td>\n",
       "      <td>UMIDIGI A3S Unlocked Smartphone 2020, 5.7\" Dou...</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4168</th>\n",
       "      <td>Null</td>\n",
       "      <td>Osophter for LG V60 ThinQ Case,LG V60 Case Sho...</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4196</th>\n",
       "      <td>Null</td>\n",
       "      <td>X3 Hurricane Variable Speed 260mph+ Cordless R...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Product_Id                                       Product_Name  count\n",
       "4139       Null  BONAOK Wireless Bluetooth Karaoke Microphone,3...    889\n",
       "4167       Null  NPET T20 One-Handed Gaming Keyboard, RGB Backl...    376\n",
       "4141       Null  Braun FP3020 12 Cup Food Processor Ultra Quiet...    273\n",
       "4178       Null  Sense Energy Monitor with Solar â€“ Track Elec...    254\n",
       "4134       Null  (2020 Upgraded Ver.) [4CH] Crystal Vision CVT8...    131\n",
       "4177       Null  Samsung Galaxy S20 5G Factory Unlocked New And...    110\n",
       "4170       Null  Portable Monitor - 15.6-Inch USB-C Portable Di...     53\n",
       "4183       Null  UMIDIGI Power 3 6150mAh Monster Battery Unlock...     25\n",
       "4152       Null  INEVIFIT EROS Bluetooth Body Fat Scale Smart B...     21\n",
       "4188       Null  Vertical Wireless Mouse, Jelly Comb Ergonomic ...     21\n",
       "4182       Null  UMIDIGI A3S Unlocked Smartphone 2020, 5.7\" Dou...      3\n",
       "4168       Null  Osophter for LG V60 ThinQ Case,LG V60 Case Sho...      2\n",
       "4196       Null  X3 Hurricane Variable Speed 260mph+ Cordless R...      1"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create a filter to show all the rows that meet the conditions. \n",
    "\n",
    "unique_combo[(unique_combo['Product_Name']).str.contains('Ne',case=False) & (unique_combo['Product_Id'] == \"Null\") ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract date elements without creating a new column.\n",
    "\n",
    "\n",
    "# merged_tables[\"Weekday\"] = (str(merged_tables[\"Invoice_Date\"].dt.day_of_week) + merged_tables[\"Invoice_Date\"].dt.day_name())\n",
    "# merged_tables[\"Weekday\"]\n",
    "# \n",
    "# str(merged_tables[\"Invoice_Date\"].dt.day_of_week) \n",
    "# merged_tables[\"Invoice_DateTime\"].dt.day_of_week\n",
    "# merged_tables[\"Invoice_DateTime\"].dt.day_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete coloumn code\n",
    "# merged_tables = merged_tables.drop([\"Weekday\"], axis = 1).copy()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>InvoiceNo</th>\n",
       "      <th>Product_Id</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Price</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Product_Name</th>\n",
       "      <th>Product_Type</th>\n",
       "      <th>Country</th>\n",
       "      <th>Rating</th>\n",
       "      <th>Review_Count</th>\n",
       "      <th>StockCode</th>\n",
       "      <th>Hour</th>\n",
       "      <th>Day</th>\n",
       "      <th>Month</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Invoice_Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B07GWKDLGT</td>\n",
       "      <td>4</td>\n",
       "      <td>496.95</td>\n",
       "      <td>1987.80</td>\n",
       "      <td>18011</td>\n",
       "      <td>Nikon D3500 W/ AF-P DX NIKKOR 18-55mm f/3.5-5....</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.6</td>\n",
       "      <td>1399</td>\n",
       "      <td>21703</td>\n",
       "      <td>17</td>\n",
       "      <td>05 Saturday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B01MTLH408</td>\n",
       "      <td>4</td>\n",
       "      <td>39.99</td>\n",
       "      <td>159.96</td>\n",
       "      <td>18011</td>\n",
       "      <td>Manfrotto Element Aluminum 5-Section Monopod, ...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.6</td>\n",
       "      <td>289</td>\n",
       "      <td>40001</td>\n",
       "      <td>17</td>\n",
       "      <td>05 Saturday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B0064FS7HI</td>\n",
       "      <td>1</td>\n",
       "      <td>12.99</td>\n",
       "      <td>12.99</td>\n",
       "      <td>18011</td>\n",
       "      <td>STK LP-E8 Battery for Canon Rebel T5i, T3i, T2...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.4</td>\n",
       "      <td>333</td>\n",
       "      <td>85034A</td>\n",
       "      <td>17</td>\n",
       "      <td>05 Saturday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B013JHU5YG</td>\n",
       "      <td>1</td>\n",
       "      <td>199.99</td>\n",
       "      <td>199.99</td>\n",
       "      <td>18011</td>\n",
       "      <td>Lowepro Whistler BP 350 AW (Grey) . Profession...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.3</td>\n",
       "      <td>86</td>\n",
       "      <td>72798C</td>\n",
       "      <td>17</td>\n",
       "      <td>05 Saturday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-12-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>78536597</td>\n",
       "      <td>B06XWN9Q99</td>\n",
       "      <td>1</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.49</td>\n",
       "      <td>18011</td>\n",
       "      <td>Samsung (MB-ME32GA/AM) 32GB 95MB/s (U1) microS...</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>Germany</td>\n",
       "      <td>4.7</td>\n",
       "      <td>15717</td>\n",
       "      <td>20726</td>\n",
       "      <td>17</td>\n",
       "      <td>05 Saturday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>2018-12-01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  InvoiceNo  Product_Id  Quantity   Price  Total_Sales Customer_Id  \\\n",
       "0  78536597  B07GWKDLGT         4  496.95      1987.80       18011   \n",
       "1  78536597  B01MTLH408         4   39.99       159.96       18011   \n",
       "2  78536597  B0064FS7HI         1   12.99        12.99       18011   \n",
       "3  78536597  B013JHU5YG         1  199.99       199.99       18011   \n",
       "4  78536597  B06XWN9Q99         1    7.49         7.49       18011   \n",
       "\n",
       "                                        Product_Name Product_Type  Country  \\\n",
       "0  Nikon D3500 W/ AF-P DX NIKKOR 18-55mm f/3.5-5....  dslr camera  Germany   \n",
       "1  Manfrotto Element Aluminum 5-Section Monopod, ...  dslr camera  Germany   \n",
       "2  STK LP-E8 Battery for Canon Rebel T5i, T3i, T2...  dslr camera  Germany   \n",
       "3  Lowepro Whistler BP 350 AW (Grey) . Profession...  dslr camera  Germany   \n",
       "4  Samsung (MB-ME32GA/AM) 32GB 95MB/s (U1) microS...  dslr camera  Germany   \n",
       "\n",
       "   Rating  Review_Count StockCode  Hour          Day        Month  Year  \\\n",
       "0     4.6          1399     21703    17  05 Saturday  12 December  2018   \n",
       "1     4.6           289     40001    17  05 Saturday  12 December  2018   \n",
       "2     4.4           333    85034A    17  05 Saturday  12 December  2018   \n",
       "3     4.3            86    72798C    17  05 Saturday  12 December  2018   \n",
       "4     4.7         15717     20726    17  05 Saturday  12 December  2018   \n",
       "\n",
       "   Quarter Invoice_Date  \n",
       "0        4   2018-12-01  \n",
       "1        4   2018-12-01  \n",
       "2        4   2018-12-01  \n",
       "3        4   2018-12-01  \n",
       "4        4   2018-12-01  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_tables.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['dslr camera', 'keyboard', 'monitor', 'mouse', 'processor',\n",
       "       'smartphone'], dtype=object)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_tables['Product_Type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA\n",
    "\n",
    "1. What is the customer buying pattern? Per day of the week, weekly, monthly, quaterly, yearly, etc.\n",
    "\n",
    "2. Are there any specific days when the sales have been unusually high / low and what could be the possible reasons?\n",
    "\n",
    "3. Which countries and which customers made the highest number of orders? Are they the same with the highest spending?\n",
    "\n",
    "4. Can you try to make a map showing the 5 countries generating the max sales revenue, excluding Germany?\n",
    "\n",
    "5. Can we see the quarterly revenue behaviour?\n",
    "\n",
    "6. Can you create a plot showing the growth rate of new customers over the months?\n",
    "\n",
    "7. What do you think about the customers? Are they individuals or wholesalers? Why would you say so?\n",
    "\n",
    "8. Are there any issues with the dataset?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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>Country</th>\n",
       "      <th>Unique_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>18628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Unique_orders\n",
       "0         Germany          18628\n",
       "1  United Kingdom            475\n",
       "2          France            387\n",
       "3         Ireland            288\n",
       "4         Belgium             98"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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>Country</th>\n",
       "      <th>Unique_orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  Unique_orders\n",
       "0         Lebanon              1\n",
       "1    South Africa              1\n",
       "2          Brazil              1\n",
       "3    Saudi Arabia              1\n",
       "4  Czech Republic              2"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Show top and bottom 15 countries for number of unique orders.\n",
    "\n",
    "display(pd.DataFrame(merged_tables.groupby(['Country']).agg({'InvoiceNo':'nunique'}).sort_values(\"InvoiceNo\", ascending=False).head(5).reset_index().rename(columns={\"InvoiceNo\": \"Unique_orders\"})))\n",
    "display(pd.DataFrame(merged_tables.groupby(['Country']).agg({'InvoiceNo':'nunique'}).sort_values(\"InvoiceNo\", ascending=True).head(5).reset_index().rename(columns={\"InvoiceNo\": \"Unique_orders\"})))\n",
    "#display(pd.DataFrame(merged_tables[merged_tables['Year'] == 2019].groupby(['Country']).agg({'InvoiceNo':'nunique'}).sort_values(\"InvoiceNo\", ascending=False).head(15).reset_index().rename(columns={\"InvoiceNo\": \"Unique_orders\"})))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. What is the customer buying pattern? Per day of the week, weekly, monthly, quaterly, yearly, etc.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[134109, 128524, 101728, 96531, 71551, 21974]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product_Category</th>\n",
       "      <th>Product_Type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>smartphone</td>\n",
       "      <td>134109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>keyboard</td>\n",
       "      <td>128524</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mouse</td>\n",
       "      <td>101728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dslr camera</td>\n",
       "      <td>96531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>processor</td>\n",
       "      <td>71551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>monitor</td>\n",
       "      <td>21974</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Product_Category  Product_Type\n",
       "0       smartphone        134109\n",
       "1         keyboard        128524\n",
       "2            mouse        101728\n",
       "3      dslr camera         96531\n",
       "4        processor         71551\n",
       "5          monitor         21974"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Product_Type\n",
       "smartphone     134109\n",
       "keyboard       128524\n",
       "mouse          101728\n",
       "dslr camera     96531\n",
       "processor       71551\n",
       "monitor         21974\n",
       "dtype: int64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# merged_tables['Product_Type'] = pd.Categorical(merged_tables['Product_Type'],\n",
    "# categories=['dslr camera', 'keyboard','monitor', 'mouse','processor', 'smartphone'])\n",
    "\n",
    "prod_type_dict = list(merged_tables.value_counts(\"Product_Type\"))\n",
    "display(prod_type_dict)\n",
    "\n",
    "prod_type_sum = pd.DataFrame(merged_tables.Product_Type.value_counts().rename_axis('Product_Category').reset_index())\n",
    "display(prod_type_sum)\n",
    "\n",
    "#Make function to display values instead of percentage on pie chart\n",
    "label=[i for i in prod_type_sum.Product_Category]\n",
    "label=[i.capitalize() for i in label]\n",
    "\n",
    "def make_autopct(prod_type):\n",
    "    def my_autopct(pct):\n",
    "        total = sum(prod_type)\n",
    "        val = int(round(pct*total/100.0))\n",
    "        return '{p:.0f}% \\n({v:d})'.format(p=pct,v=val)\n",
    "    return my_autopct\n",
    "\n",
    "def func(type_of_product):\n",
    "  return \"{:1.1f}%\".format(type_of_product)\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "#labels = merged_tables[\"Product_Type\"].unique()\n",
    "sizes = merged_tables.value_counts(\"Product_Type\")\n",
    "display(sizes)\n",
    "#explode = (0, .05)  # only \"explode\" the 2nd slice \n",
    "sns.set(rc={'axes.facecolor':'white', 'figure.facecolor':'white'})\n",
    "sns.set_style(\"whitegrid\")\n",
    "fig1, ax1 = plt.subplots()\n",
    "ax1.pie(sizes, labels = label, autopct=make_autopct(prod_type_dict), center = (0,32342343231),\n",
    "        shadow=False, startangle=145, radius = 100 )\n",
    "#plt.title(\"Breakdown of product category\")\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "ax1.set_aspect('equal', anchor='C')\n",
    "#ax1.anchor('SE')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>index</th>\n",
       "      <th>Total_sold</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>dslr camera</td>\n",
       "      <td>InvoiceNo  Product_Id  Quantity    Pric...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>keyboard</td>\n",
       "      <td>InvoiceNo  Product_Id  Quantity  Price ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>monitor</td>\n",
       "      <td>InvoiceNo  Product_Id  Quantity   Price...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>mouse</td>\n",
       "      <td>InvoiceNo  Product_Id  Quantity  Price ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>processor</td>\n",
       "      <td>InvoiceNo  Product_Id  Quantity    Pric...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>smartphone</td>\n",
       "      <td>InvoiceNo  Product_Id  Quantity   Price...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index   Total_sold                                                  1\n",
       "0      0  dslr camera         InvoiceNo  Product_Id  Quantity    Pric...\n",
       "1      1     keyboard         InvoiceNo  Product_Id  Quantity  Price ...\n",
       "2      2      monitor         InvoiceNo  Product_Id  Quantity   Price...\n",
       "3      3        mouse         InvoiceNo  Product_Id  Quantity  Price ...\n",
       "4      4    processor         InvoiceNo  Product_Id  Quantity    Pric...\n",
       "5      5   smartphone         InvoiceNo  Product_Id  Quantity   Price..."
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     },
     "execution_count": 65,
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   "source": [
    "testing = pd.DataFrame(merged_tables.groupby(\"Product_Type\")).reset_index().rename({0:\"Total_sold\"}, axis=1)\n"
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  {
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           "bgcolor": "#E5ECF6",
           "caxis": {
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          },
          "title": {
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        "title": {
         "text": "Units sold per Product type"
        },
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         "anchor": "y",
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   ],
   "source": [
    "sizes = pd.DataFrame(merged_tables.value_counts(\"Product_Type\")).reset_index().rename({0:\"Total_sold\"}, axis=1)\n",
    "\n",
    "\n",
    "display(sizes)\n",
    "display(px.pie(sizes, values='Total_sold', names='Product_Type', hole=.3, title=\"Units sold per Product type\"))\n",
    "\n",
    "fig = px.bar(sizes, x=\"Product_Type\", y=\"Total_sold\",  title=\"Units sold per Product type\")\n",
    "fig.show()"
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  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Country</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Average_Units_Sold</th>\n",
       "      <th>Total_Units_Sold</th>\n",
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       "      <th>395</th>\n",
       "      <td>14911</td>\n",
       "      <td>[Ireland]</td>\n",
       "      <td>201</td>\n",
       "      <td>1565.6</td>\n",
       "      <td>9340224.3</td>\n",
       "      <td>14.0</td>\n",
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       "      <td>94.8</td>\n",
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       "      <th>387</th>\n",
       "      <td>14156</td>\n",
       "      <td>[Ireland]</td>\n",
       "      <td>55</td>\n",
       "      <td>3887.2</td>\n",
       "      <td>5725778.9</td>\n",
       "      <td>41.1</td>\n",
       "      <td>60602</td>\n",
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       "      <th>176</th>\n",
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       "      <td>32</td>\n",
       "      <td>960.2</td>\n",
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       "      <td>31</td>\n",
       "      <td>938.0</td>\n",
       "      <td>487763.4</td>\n",
       "      <td>11.1</td>\n",
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       "    <tr>\n",
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      "text/plain": [
       "    Customer_Id           Country Total_Orders Average_Sale Total_Revenue  \\\n",
       "                           unique                                           \n",
       "395       14911         [Ireland]          201       1565.6     9340224.3   \n",
       "394       14646     [Netherlands]           73       9997.6    21664738.3   \n",
       "387       14156         [Ireland]           55       3887.2     5725778.9   \n",
       "176       12569  [United Kingdom]           32        960.2      160355.4   \n",
       "272       12682          [France]           31        938.0      487763.4   \n",
       "99        12471  [United Kingdom]           28       1888.4      885666.3   \n",
       "303       12720  [United Kingdom]           25       1146.9      406008.3   \n",
       "271       12681          [France]           22        962.1      630162.6   \n",
       "54        12415       [Australia]           20      12509.3     9244349.2   \n",
       "222       12621  [United Kingdom]           20       1430.4      676571.0   \n",
       "\n",
       "    Average_Units_Sold Total_Units_Sold  \n",
       "                                         \n",
       "395               14.0            83711  \n",
       "394               94.8           205535  \n",
       "387               41.1            60602  \n",
       "176               11.4             1911  \n",
       "272               11.1             5781  \n",
       "99                18.5             8699  \n",
       "303               13.6             4819  \n",
       "271               12.1             7899  \n",
       "54               108.4            80119  \n",
       "222               16.6             7873  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cust_count = merged_tables[merged_tables['Year'] == 2019] \n",
    "\n",
    "\n",
    "cust_count = merged_tables[merged_tables['Country'] != \"Germany\"].groupby(['Customer_Id']).agg({'Country':'unique', 'InvoiceNo':'nunique', 'Total_Sales':['mean', 'sum'], \n",
    "                                                 'Quantity': ['mean', 'sum']}).round(1).reset_index()#.rename(columns={0 : 'count'}).sort_values('count', ascending = False)\n",
    "cust_count['Total_Orders']=cust_count['InvoiceNo']['nunique']\n",
    "cust_count['Average_Sale']=cust_count['Total_Sales']['mean']\n",
    "cust_count['Total_Revenue']=cust_count['Total_Sales']['sum']\n",
    "cust_count['Country'] = cust_count['Country']['unique']\n",
    "cust_count['Average_Units_Sold'] = cust_count['Quantity']['mean']\n",
    "cust_count['Total_Units_Sold'] = cust_count['Quantity']['sum']\n",
    "cust_count.head()\n",
    "\n",
    "cust_count = cust_count.sort_values('Total_Orders', ascending = False)\n",
    "cust_count.drop(418, axis = 0,inplace = True) # drop row where Customer_Id = Null\n",
    "cust_count = cust_count[['Customer_Id', 'Country', 'Total_Orders', \"Average_Sale\", 'Total_Revenue',\"Average_Units_Sold\", 'Total_Units_Sold']]\n",
    "\n",
    "\n",
    "cust_count.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 418 entries, 395 to 209\n",
      "Data columns (total 7 columns):\n",
      " #   Column                  Non-Null Count  Dtype  \n",
      "---  ------                  --------------  -----  \n",
      " 0   (Customer_Id, )         418 non-null    object \n",
      " 1   (Country, unique)       418 non-null    object \n",
      " 2   (Total_Orders, )        418 non-null    int64  \n",
      " 3   (Average_Sale, )        418 non-null    float64\n",
      " 4   (Total_Revenue, )       418 non-null    float64\n",
      " 5   (Average_Units_Sold, )  418 non-null    float64\n",
      " 6   (Total_Units_Sold, )    418 non-null    int64  \n",
      "dtypes: float64(3), int64(2), object(2)\n",
      "memory usage: 26.1+ KB\n"
     ]
    }
   ],
   "source": [
    "cust_count.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# cust_count = px.bar(sizes, x=\"Customer_Id\", y=\"Total_Orders\",  title=\"Units sold per Product type\")\n",
    "# fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18962"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Customers</th>\n",
       "      <th>Average_Units_Sold</th>\n",
       "      <th>Total_Units_Sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00 Monday</td>\n",
       "      <td>3047</td>\n",
       "      <td>1166.2</td>\n",
       "      <td>88330069.7</td>\n",
       "      <td>1497</td>\n",
       "      <td>12.0</td>\n",
       "      <td>906148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01 Tuesday</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>02 Wednesday</td>\n",
       "      <td>2052</td>\n",
       "      <td>784.5</td>\n",
       "      <td>48485380.6</td>\n",
       "      <td>1181</td>\n",
       "      <td>7.4</td>\n",
       "      <td>459617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>03 Thursday</td>\n",
       "      <td>2993</td>\n",
       "      <td>1004.4</td>\n",
       "      <td>89819435.1</td>\n",
       "      <td>1525</td>\n",
       "      <td>10.0</td>\n",
       "      <td>894923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>04 Friday</td>\n",
       "      <td>3430</td>\n",
       "      <td>1174.9</td>\n",
       "      <td>114301948.9</td>\n",
       "      <td>1649</td>\n",
       "      <td>11.9</td>\n",
       "      <td>1154448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>05 Saturday</td>\n",
       "      <td>3477</td>\n",
       "      <td>1181.5</td>\n",
       "      <td>105295667.4</td>\n",
       "      <td>1652</td>\n",
       "      <td>11.6</td>\n",
       "      <td>1034159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>06 Sunday</td>\n",
       "      <td>3963</td>\n",
       "      <td>1320.6</td>\n",
       "      <td>129034351.3</td>\n",
       "      <td>1901</td>\n",
       "      <td>12.3</td>\n",
       "      <td>1200525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Day Total_Orders Average_Sale Total_Revenue Unique_Customers  \\\n",
       "                                                                           \n",
       "0     00 Monday         3047       1166.2    88330069.7             1497   \n",
       "1    01 Tuesday            0          0.0           0.0                0   \n",
       "2  02 Wednesday         2052        784.5    48485380.6             1181   \n",
       "3   03 Thursday         2993       1004.4    89819435.1             1525   \n",
       "4     04 Friday         3430       1174.9   114301948.9             1649   \n",
       "5   05 Saturday         3477       1181.5   105295667.4             1652   \n",
       "6     06 Sunday         3963       1320.6   129034351.3             1901   \n",
       "\n",
       "  Average_Units_Sold Total_Units_Sold  \n",
       "                                       \n",
       "0               12.0           906148  \n",
       "1                0.0                0  \n",
       "2                7.4           459617  \n",
       "3               10.0           894923  \n",
       "4               11.9          1154448  \n",
       "5               11.6          1034159  \n",
       "6               12.3          1200525  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by day of week\n",
    "df_dow = merged_tables[merged_tables['Year'] == 2019].groupby(['Day']).agg({'InvoiceNo':'nunique', 'Total_Sales':['mean', 'sum'], \n",
    "                                                'Customer_Id':'nunique', 'Quantity': ['mean', 'sum']}).round(1)\n",
    "\n",
    "df_dow['Total_Orders']=df_dow['InvoiceNo']['nunique']\n",
    "df_dow['Average_Sale']=df_dow['Total_Sales']['mean']\n",
    "df_dow['Total_Revenue']=df_dow['Total_Sales']['sum']\n",
    "df_dow['Unique_Customers']= df_dow['Customer_Id']['nunique']\n",
    "df_dow['Average_Units_Sold'] = df_dow['Quantity']['mean']\n",
    "df_dow['Total_Units_Sold'] = df_dow['Quantity']['sum']\n",
    "\n",
    "\n",
    "df_dow = df_dow[['Total_Orders', \"Average_Sale\", 'Total_Revenue', 'Unique_Customers',\"Average_Units_Sold\", 'Total_Units_Sold']]\n",
    "df_dow = df_dow.reset_index()\n",
    "# Tuesday data is missing, so add zero values.\n",
    "df_dow.loc['6'] = [\"01 Tuesday\",0,0,0,0,0,0]\n",
    "\n",
    "df_dow = df_dow.sort_values(['Day'])\n",
    "df_dow.index = [0,1,2,3,4,5,6]\n",
    "df_dow = df_dow[['Day', 'Total_Orders', \"Average_Sale\", 'Total_Revenue', 'Unique_Customers',\"Average_Units_Sold\", 'Total_Units_Sold']]\n",
    "display(df_dow[\"Total_Orders\"].sum())\n",
    "df_dow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Hour</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Customers</th>\n",
       "      <th>Average_Units_Sold</th>\n",
       "      <th>Total_Units_Sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>279.0</td>\n",
       "      <td>279.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "      <td>3657.5</td>\n",
       "      <td>1411814.1</td>\n",
       "      <td>26</td>\n",
       "      <td>40.9</td>\n",
       "      <td>15773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8</td>\n",
       "      <td>540</td>\n",
       "      <td>1899.8</td>\n",
       "      <td>16959489.2</td>\n",
       "      <td>415</td>\n",
       "      <td>18.2</td>\n",
       "      <td>162761</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Hour Total_Orders Average_Sale Total_Revenue Unique_Customers  \\\n",
       "                                                                  \n",
       "0    6            1        279.0         279.0                1   \n",
       "1    7           27       3657.5     1411814.1               26   \n",
       "2    8          540       1899.8    16959489.2              415   \n",
       "\n",
       "  Average_Units_Sold Total_Units_Sold  \n",
       "                                       \n",
       "0                1.0                1  \n",
       "1               40.9            15773  \n",
       "2               18.2           162761  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by Hour\n",
    "df_Hour = merged_tables[merged_tables['Year'] == 2019].groupby(['Hour']).agg({'InvoiceNo':'nunique', 'Total_Sales':['mean', 'sum'], \n",
    "                                                'Customer_Id':'nunique', 'Quantity': ['mean', 'sum']}).round(1)\n",
    "df_Hour['Total_Orders']=df_Hour['InvoiceNo']['nunique']\n",
    "df_Hour['Average_Sale']=df_Hour['Total_Sales']['mean']\n",
    "df_Hour['Total_Revenue']=df_Hour['Total_Sales']['sum']\n",
    "df_Hour['Unique_Customers']= df_Hour['Customer_Id']['nunique']\n",
    "df_Hour['Average_Units_Sold'] = df_Hour['Quantity']['mean']\n",
    "df_Hour['Total_Units_Sold'] = df_Hour['Quantity']['sum']\n",
    "\n",
    "df_Hour = df_Hour[['Total_Orders', 'Average_Sale', 'Total_Revenue', 'Unique_Customers', 'Average_Units_Sold', 'Total_Units_Sold']]\n",
    "df_Hour = df_Hour.reset_index()\n",
    "df_Hour.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Customers</th>\n",
       "      <th>Average_Units_Sold</th>\n",
       "      <th>Total_Units_Sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01 January</td>\n",
       "      <td>1100</td>\n",
       "      <td>1063.1</td>\n",
       "      <td>38126912.3</td>\n",
       "      <td>742</td>\n",
       "      <td>11.4</td>\n",
       "      <td>407747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>02 February</td>\n",
       "      <td>1108</td>\n",
       "      <td>1032.9</td>\n",
       "      <td>29315803.0</td>\n",
       "      <td>759</td>\n",
       "      <td>10.6</td>\n",
       "      <td>300844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Month Total_Orders Average_Sale Total_Revenue Unique_Customers  \\\n",
       "                                                                          \n",
       "0   01 January         1100       1063.1    38126912.3              742   \n",
       "1  02 February         1108       1032.9    29315803.0              759   \n",
       "\n",
       "  Average_Units_Sold Total_Units_Sold  \n",
       "                                       \n",
       "0               11.4           407747  \n",
       "1               10.6           300844  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by Month\n",
    "df_Month = merged_tables[merged_tables['Year'] == 2019].groupby(['Month']).agg({'InvoiceNo':'nunique', 'Total_Sales':['mean', 'sum'], \n",
    "                                                'Customer_Id':'nunique', 'Quantity': ['mean', 'sum']}).round(1)\n",
    "df_Month['Total_Orders']=df_Month['InvoiceNo']['nunique']\n",
    "df_Month['Average_Sale']=df_Month['Total_Sales']['mean']\n",
    "df_Month['Total_Revenue']=df_Month['Total_Sales']['sum']\n",
    "df_Month['Unique_Customers']= df_Month['Customer_Id']['nunique']\n",
    "df_Month['Average_Units_Sold'] = df_Month['Quantity']['mean']\n",
    "df_Month['Total_Units_Sold'] = df_Month['Quantity']['sum']\n",
    "\n",
    "df_Month = df_Month[['Total_Orders', 'Average_Sale', 'Total_Revenue', 'Unique_Customers', 'Average_Units_Sold', 'Total_Units_Sold']].sort_values(\"Month\")\n",
    "df_Month = df_Month.reset_index()\n",
    "df_Month.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Invoice_Date</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Customers</th>\n",
       "      <th>Average_Units_Sold</th>\n",
       "      <th>Total_Units_Sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>39</td>\n",
       "      <td>830.3</td>\n",
       "      <td>1002201.6</td>\n",
       "      <td>35</td>\n",
       "      <td>7.7</td>\n",
       "      <td>9256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2019-01-05</td>\n",
       "      <td>55</td>\n",
       "      <td>1023.9</td>\n",
       "      <td>1786655.0</td>\n",
       "      <td>46</td>\n",
       "      <td>11.8</td>\n",
       "      <td>20504</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Invoice_Date Total_Orders Average_Sale Total_Revenue Unique_Customers  \\\n",
       "                                                                          \n",
       "0   2019-01-04           39        830.3     1002201.6               35   \n",
       "1   2019-01-05           55       1023.9     1786655.0               46   \n",
       "\n",
       "  Average_Units_Sold Total_Units_Sold  \n",
       "                                       \n",
       "0                7.7             9256  \n",
       "1               11.8            20504  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by Invoice_Date\n",
    "df_date = merged_tables[merged_tables['Year'] == 2019].groupby(['Invoice_Date']).agg({'InvoiceNo':'nunique', 'Total_Sales':['mean', 'sum'], \n",
    "                                                'Customer_Id':'nunique', 'Quantity': ['mean', 'sum']}).round(1)\n",
    "df_date['Total_Orders']=df_date['InvoiceNo']['nunique']\n",
    "df_date['Average_Sale']=df_date['Total_Sales']['mean']\n",
    "df_date['Total_Revenue']=df_date['Total_Sales']['sum']\n",
    "df_date['Unique_Customers']= df_date['Customer_Id']['nunique']\n",
    "df_date['Average_Units_Sold'] = df_date['Quantity']['mean']\n",
    "df_date['Total_Units_Sold'] = df_date['Quantity']['sum']\n",
    "\n",
    "df_date = df_date[['Total_Orders', 'Average_Sale', 'Total_Revenue', 'Unique_Customers', 'Average_Units_Sold', 'Total_Units_Sold']]\n",
    "df_date = df_date.reset_index()\n",
    "df_date.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <th>Country</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Customers</th>\n",
       "      <th>Average_Units_Sold</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>17167</td>\n",
       "      <td>1028.9</td>\n",
       "      <td>480882971.2</td>\n",
       "      <td>3815</td>\n",
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       "      <td>28012.1</td>\n",
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       "    <tr>\n",
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       "      <td>Netherlands</td>\n",
       "      <td>91</td>\n",
       "      <td>8969.7</td>\n",
       "      <td>21302953.4</td>\n",
       "      <td>8</td>\n",
       "      <td>85.1</td>\n",
       "      <td>202159</td>\n",
       "      <td>234098.4</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "       Country Total_Orders Average_Sale Total_Revenue Unique_Customers  \\\n",
       "                                                                          \n",
       "0      Germany        17167       1028.9   480882971.2             3815   \n",
       "1  Netherlands           91       8969.7    21302953.4                8   \n",
       "\n",
       "  Average_Units_Sold Total_Units_Sold Average_Order_Value  \n",
       "                                                           \n",
       "0               10.1          4731881             28012.1  \n",
       "1               85.1           202159            234098.4  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by Country\n",
    "df_country = merged_tables[merged_tables['Year'] == 2019].groupby(['Country']).agg({'InvoiceNo':'nunique', 'Total_Sales':['mean', 'sum'], \n",
    "                                                'Customer_Id':'nunique', 'Quantity': ['mean', 'sum']}).round(1)\n",
    "df_country['Total_Orders']=df_country['InvoiceNo']['nunique']\n",
    "df_country['Average_Sale']=df_country['Total_Sales']['mean']\n",
    "df_country['Total_Revenue']=df_country['Total_Sales']['sum']\n",
    "df_country['Unique_Customers']= df_country['Customer_Id']['nunique']\n",
    "df_country['Average_Units_Sold'] = df_country['Quantity']['mean']\n",
    "df_country['Total_Units_Sold'] = df_country['Quantity']['sum']\n",
    "\n",
    "df_country = df_country[['Total_Orders', 'Average_Sale', 'Total_Revenue', 'Unique_Customers', 'Average_Units_Sold', 'Total_Units_Sold']]\n",
    "df_country.sort_values(by = ['Total_Revenue', 'Total_Orders', 'Unique_Customers'],ascending = False, inplace = True)\n",
    "#df_country['revenue_per_customer'] = df_country['Total_Revenue'] / df_country['Unique_Customers']\n",
    "df_country['Average_Order_Value'] = (df_country['Total_Revenue'] / df_country['Total_Orders']).round(1)\n",
    "df_country = df_country.reset_index()\n",
    "df_country.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Products</th>\n",
       "      <th>Average_Unit_Price</th>\n",
       "      <th>Total_Units_Sold</th>\n",
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       "      <th>0</th>\n",
       "      <td>14646</td>\n",
       "      <td>73</td>\n",
       "      <td>21664738.3</td>\n",
       "      <td>707</td>\n",
       "      <td>96.9</td>\n",
       "      <td>205535</td>\n",
       "      <td>Netherlands</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>14911</td>\n",
       "      <td>201</td>\n",
       "      <td>9340224.3</td>\n",
       "      <td>1790</td>\n",
       "      <td>97.5</td>\n",
       "      <td>83711</td>\n",
       "      <td>Ireland</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Customer_Id  Total_Orders  Total_Revenue  Unique_Products  \\\n",
       "0       14646            73     21664738.3              707   \n",
       "1       14911           201      9340224.3             1790   \n",
       "\n",
       "   Average_Unit_Price  Total_Units_Sold      Country  \n",
       "0                96.9            205535  Netherlands  \n",
       "1                97.5             83711      Ireland  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Group by customer\n",
    "\n",
    "df_customer = merged_tables.groupby(['Customer_Id']).agg({'InvoiceNo':'nunique', 'Total_Sales':'sum', \n",
    "                                                    'Product_Id': 'nunique', 'Price':'mean',\n",
    "                                                     'Quantity': ['mean', 'sum']}).round(1)\n",
    "\n",
    "df_customer['Total_Orders'] = df_customer['InvoiceNo']['nunique']\n",
    "df_customer['Total_Revenue'] = df_customer['Total_Sales']['sum']\n",
    "df_customer['Unique_Products'] = df_customer['Product_Id']['nunique']\n",
    "df_customer['Average_Unit_Price'] = df_customer['Price']['mean']\n",
    "df_customer['Total_Units_Sold'] = df_customer['Quantity']['sum']\n",
    "\n",
    "\n",
    "df_customer = df_customer[['Total_Orders','Total_Revenue','Unique_Products','Average_Unit_Price','Total_Units_Sold']].reset_index().sort_values(by='Total_Revenue',ascending=False)\n",
    "df_customer.drop(4339, axis = 0,inplace = True) # drop row where Customer_Id = Null\n",
    "df_customer.columns = [tup[0] for tup in df_customer.columns]\n",
    "df_customer.columns\n",
    "# We also want to add the customer Country to df_customer. Do a left join.\n",
    "dummy = merged_tables[['Customer_Id', 'Country']].drop_duplicates()\n",
    "df_customer = pd.merge(df_customer, dummy, on = 'Customer_Id', how = 'left', copy = False)\n",
    "df_customer.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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     "output_type": "display_data"
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       "      <th>Month</th>\n",
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       "      <th>Total_Revenue</th>\n",
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       "      <td>124</td>\n",
       "      <td>656.4</td>\n",
       "      <td>2267133.8</td>\n",
       "      <td>108</td>\n",
       "      <td>6.4</td>\n",
       "      <td>22086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>03 Thursday</td>\n",
       "      <td>01 January</td>\n",
       "      <td>213</td>\n",
       "      <td>699.3</td>\n",
       "      <td>5458648.4</td>\n",
       "      <td>163</td>\n",
       "      <td>7.5</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>04 Friday</td>\n",
       "      <td>01 January</td>\n",
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       "      <td>2011.0</td>\n",
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       "      <td>162</td>\n",
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       "      <th>4</th>\n",
       "      <td>05 Saturday</td>\n",
       "      <td>01 January</td>\n",
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       "      <th>67</th>\n",
       "      <td>02 Wednesday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>68</td>\n",
       "      <td>592.3</td>\n",
       "      <td>1249856.7</td>\n",
       "      <td>59</td>\n",
       "      <td>6.0</td>\n",
       "      <td>12744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>03 Thursday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>132</td>\n",
       "      <td>799.3</td>\n",
       "      <td>4406557.6</td>\n",
       "      <td>106</td>\n",
       "      <td>8.4</td>\n",
       "      <td>46490</td>\n",
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       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>04 Friday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>116</td>\n",
       "      <td>1019.2</td>\n",
       "      <td>3438948.4</td>\n",
       "      <td>104</td>\n",
       "      <td>9.5</td>\n",
       "      <td>31926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>05 Saturday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>120</td>\n",
       "      <td>1694.4</td>\n",
       "      <td>4239346.6</td>\n",
       "      <td>95</td>\n",
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       "      <td>46171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>06 Sunday</td>\n",
       "      <td>12 December</td>\n",
       "      <td>244</td>\n",
       "      <td>924.6</td>\n",
       "      <td>7372363.3</td>\n",
       "      <td>203</td>\n",
       "      <td>8.2</td>\n",
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       "</table>\n",
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       "</div>"
      ],
      "text/plain": [
       "             Day        Month Total_Orders Average_Sale Total_Revenue  \\\n",
       "                                                                        \n",
       "0      00 Monday   01 January          179       1225.3     7236550.8   \n",
       "1   02 Wednesday   01 January          124        656.4     2267133.8   \n",
       "2    03 Thursday   01 January          213        699.3     5458648.4   \n",
       "3      04 Friday   01 January          204       2011.0    11728432.2   \n",
       "4    05 Saturday   01 January          193        912.9     5839731.0   \n",
       "..           ...          ...          ...          ...           ...   \n",
       "67  02 Wednesday  12 December           68        592.3     1249856.7   \n",
       "68   03 Thursday  12 December          132        799.3     4406557.6   \n",
       "69     04 Friday  12 December          116       1019.2     3438948.4   \n",
       "70   05 Saturday  12 December          120       1694.4     4239346.6   \n",
       "71     06 Sunday  12 December          244        924.6     7372363.3   \n",
       "\n",
       "   Unique_Customers Average_Units_Sold Total_Units_Sold  \n",
       "                                                         \n",
       "0               144               11.5            67892  \n",
       "1               108                6.4            22086  \n",
       "2               163                7.5            58704  \n",
       "3               162               23.7           138023  \n",
       "4               153                9.6            61727  \n",
       "..              ...                ...              ...  \n",
       "67               59                6.0            12744  \n",
       "68              106                8.4            46490  \n",
       "69              104                9.5            31926  \n",
       "70               95               18.5            46171  \n",
       "71              203                8.2            65654  \n",
       "\n",
       "[72 rows x 8 columns]"
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     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by day of week\n",
    "df_month_week = merged_tables[merged_tables['Year'] == 2019].groupby(['Month','Day']).agg({'InvoiceNo':'nunique', 'Total_Sales':['mean', 'sum'], \n",
    "                                                'Customer_Id':'nunique', 'Quantity': ['mean', 'sum']}).round(1)\n",
    "\n",
    "df_month_week['Total_Orders']=df_month_week['InvoiceNo']['nunique']\n",
    "df_month_week['Average_Sale']=df_month_week['Total_Sales']['mean']\n",
    "df_month_week['Total_Revenue']=df_month_week['Total_Sales']['sum']\n",
    "df_month_week['Unique_Customers']= df_month_week['Customer_Id']['nunique']\n",
    "df_month_week['Average_Units_Sold'] = df_month_week['Quantity']['mean']\n",
    "df_month_week['Total_Units_Sold'] = df_month_week['Quantity']['sum']\n",
    "\n",
    "\n",
    "df_month_week = df_month_week[['Total_Orders', \"Average_Sale\", 'Total_Revenue', 'Unique_Customers',\"Average_Units_Sold\", 'Total_Units_Sold']]\n",
    "df_month_week = df_month_week.reset_index()\n",
    "# Tuesday data is missing, so add zero values.\n",
    "#df_month_week.loc['6'] = [\"01 Tuesday\",0,0,0,0,0,0]\n",
    "\n",
    "df_month_week = df_month_week.sort_values(['Month','Day'])\n",
    "#df_month_week.index = [0,1,2,3,4,5,6]\n",
    "df_month_week = df_month_week[['Day', 'Month', 'Total_Orders', \"Average_Sale\", 'Total_Revenue', 'Unique_Customers',\"Average_Units_Sold\", 'Total_Units_Sold']]\n",
    "display(df_month_week[\"Total_Orders\"].sum())\n",
    "df_month_week"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
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        "title": {
         "text": "Average sale per weekday"
        }
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "\n",
    "display(px.pie(df_dow, values='Total_Orders', names='Day', hole=.3, title=\"Number of unique orders by weekday\"))\n",
    "display(px.pie(df_dow, values='Average_Sale', names='Day', hole=.3, title=\"Average sale per weekday\"))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Customers</th>\n",
       "      <th>Average_Units_Sold</th>\n",
       "      <th>Total_Units_Sold</th>\n",
       "      <th>Average_Order_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Germany</td>\n",
       "      <td>17167</td>\n",
       "      <td>1028.9</td>\n",
       "      <td>480882971.2</td>\n",
       "      <td>3815</td>\n",
       "      <td>10.1</td>\n",
       "      <td>4731881</td>\n",
       "      <td>28012.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>445</td>\n",
       "      <td>1267.5</td>\n",
       "      <td>11787916.0</td>\n",
       "      <td>101</td>\n",
       "      <td>13.6</td>\n",
       "      <td>126310</td>\n",
       "      <td>26489.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>France</td>\n",
       "      <td>366</td>\n",
       "      <td>1376.4</td>\n",
       "      <td>11095393.0</td>\n",
       "      <td>85</td>\n",
       "      <td>13.8</td>\n",
       "      <td>111131</td>\n",
       "      <td>30315.3</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>271</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>15390867.6</td>\n",
       "      <td>4</td>\n",
       "      <td>18.7</td>\n",
       "      <td>147902</td>\n",
       "      <td>56792.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>93</td>\n",
       "      <td>1192.5</td>\n",
       "      <td>2280107.1</td>\n",
       "      <td>25</td>\n",
       "      <td>11.5</td>\n",
       "      <td>21917</td>\n",
       "      <td>24517.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>91</td>\n",
       "      <td>8969.7</td>\n",
       "      <td>21302953.4</td>\n",
       "      <td>8</td>\n",
       "      <td>85.1</td>\n",
       "      <td>202159</td>\n",
       "      <td>234098.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Spain</td>\n",
       "      <td>84</td>\n",
       "      <td>1087.4</td>\n",
       "      <td>2691363.7</td>\n",
       "      <td>29</td>\n",
       "      <td>11.8</td>\n",
       "      <td>29128</td>\n",
       "      <td>32040.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Australia</td>\n",
       "      <td>53</td>\n",
       "      <td>8156.6</td>\n",
       "      <td>9804245.9</td>\n",
       "      <td>9</td>\n",
       "      <td>72.0</td>\n",
       "      <td>86520</td>\n",
       "      <td>184985.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Portugal</td>\n",
       "      <td>50</td>\n",
       "      <td>1115.5</td>\n",
       "      <td>1622999.0</td>\n",
       "      <td>18</td>\n",
       "      <td>11.2</td>\n",
       "      <td>16270</td>\n",
       "      <td>32460.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>48</td>\n",
       "      <td>1379.3</td>\n",
       "      <td>2717170.4</td>\n",
       "      <td>22</td>\n",
       "      <td>15.8</td>\n",
       "      <td>31169</td>\n",
       "      <td>56607.7</td>\n",
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       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Finland</td>\n",
       "      <td>40</td>\n",
       "      <td>1532.8</td>\n",
       "      <td>1033078.0</td>\n",
       "      <td>12</td>\n",
       "      <td>15.4</td>\n",
       "      <td>10395</td>\n",
       "      <td>25827.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Italy</td>\n",
       "      <td>33</td>\n",
       "      <td>1306.9</td>\n",
       "      <td>948826.1</td>\n",
       "      <td>12</td>\n",
       "      <td>11.1</td>\n",
       "      <td>8032</td>\n",
       "      <td>28752.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sweden</td>\n",
       "      <td>32</td>\n",
       "      <td>9418.1</td>\n",
       "      <td>3936760.6</td>\n",
       "      <td>8</td>\n",
       "      <td>78.1</td>\n",
       "      <td>32662</td>\n",
       "      <td>123023.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Norway</td>\n",
       "      <td>31</td>\n",
       "      <td>1528.3</td>\n",
       "      <td>1432056.1</td>\n",
       "      <td>10</td>\n",
       "      <td>17.3</td>\n",
       "      <td>16225</td>\n",
       "      <td>46195.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Poland</td>\n",
       "      <td>18</td>\n",
       "      <td>1144.5</td>\n",
       "      <td>382271.7</td>\n",
       "      <td>6</td>\n",
       "      <td>11.4</td>\n",
       "      <td>3799</td>\n",
       "      <td>21237.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Denmark</td>\n",
       "      <td>17</td>\n",
       "      <td>1675.7</td>\n",
       "      <td>625032.2</td>\n",
       "      <td>9</td>\n",
       "      <td>22.1</td>\n",
       "      <td>8239</td>\n",
       "      <td>36766.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Japan</td>\n",
       "      <td>16</td>\n",
       "      <td>10251.6</td>\n",
       "      <td>2747436.0</td>\n",
       "      <td>8</td>\n",
       "      <td>84.2</td>\n",
       "      <td>22555</td>\n",
       "      <td>171714.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Austria</td>\n",
       "      <td>16</td>\n",
       "      <td>1127.0</td>\n",
       "      <td>444046.1</td>\n",
       "      <td>10</td>\n",
       "      <td>12.7</td>\n",
       "      <td>5015</td>\n",
       "      <td>27752.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Cyprus</td>\n",
       "      <td>14</td>\n",
       "      <td>1402.4</td>\n",
       "      <td>776928.1</td>\n",
       "      <td>8</td>\n",
       "      <td>10.6</td>\n",
       "      <td>5860</td>\n",
       "      <td>55494.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Unknown</td>\n",
       "      <td>13</td>\n",
       "      <td>698.4</td>\n",
       "      <td>322679.4</td>\n",
       "      <td>5</td>\n",
       "      <td>7.5</td>\n",
       "      <td>3450</td>\n",
       "      <td>24821.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Hong Kong</td>\n",
       "      <td>10</td>\n",
       "      <td>1504.6</td>\n",
       "      <td>443845.1</td>\n",
       "      <td>1</td>\n",
       "      <td>16.8</td>\n",
       "      <td>4961</td>\n",
       "      <td>44384.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Israel</td>\n",
       "      <td>8</td>\n",
       "      <td>1696.5</td>\n",
       "      <td>517444.5</td>\n",
       "      <td>4</td>\n",
       "      <td>15.0</td>\n",
       "      <td>4574</td>\n",
       "      <td>64680.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Singapore</td>\n",
       "      <td>7</td>\n",
       "      <td>3164.0</td>\n",
       "      <td>753034.7</td>\n",
       "      <td>1</td>\n",
       "      <td>23.7</td>\n",
       "      <td>5629</td>\n",
       "      <td>107576.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>6</td>\n",
       "      <td>1152.1</td>\n",
       "      <td>178581.6</td>\n",
       "      <td>1</td>\n",
       "      <td>14.2</td>\n",
       "      <td>2199</td>\n",
       "      <td>29763.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>United States</td>\n",
       "      <td>5</td>\n",
       "      <td>1404.8</td>\n",
       "      <td>272538.2</td>\n",
       "      <td>4</td>\n",
       "      <td>13.4</td>\n",
       "      <td>2607</td>\n",
       "      <td>54507.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Canada</td>\n",
       "      <td>5</td>\n",
       "      <td>1460.0</td>\n",
       "      <td>227755.6</td>\n",
       "      <td>4</td>\n",
       "      <td>18.2</td>\n",
       "      <td>2839</td>\n",
       "      <td>45551.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Greece</td>\n",
       "      <td>5</td>\n",
       "      <td>920.7</td>\n",
       "      <td>138109.2</td>\n",
       "      <td>4</td>\n",
       "      <td>11.0</td>\n",
       "      <td>1651</td>\n",
       "      <td>27621.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Malta</td>\n",
       "      <td>5</td>\n",
       "      <td>1135.6</td>\n",
       "      <td>134001.1</td>\n",
       "      <td>2</td>\n",
       "      <td>8.8</td>\n",
       "      <td>1036</td>\n",
       "      <td>26800.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>European Community</td>\n",
       "      <td>3</td>\n",
       "      <td>1014.5</td>\n",
       "      <td>61885.1</td>\n",
       "      <td>1</td>\n",
       "      <td>8.5</td>\n",
       "      <td>520</td>\n",
       "      <td>20628.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Czech Republic</td>\n",
       "      <td>2</td>\n",
       "      <td>3541.9</td>\n",
       "      <td>88547.9</td>\n",
       "      <td>1</td>\n",
       "      <td>27.3</td>\n",
       "      <td>682</td>\n",
       "      <td>44274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>United Arab Emirates</td>\n",
       "      <td>2</td>\n",
       "      <td>1161.1</td>\n",
       "      <td>82435.6</td>\n",
       "      <td>2</td>\n",
       "      <td>14.6</td>\n",
       "      <td>1035</td>\n",
       "      <td>41217.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Bahrain</td>\n",
       "      <td>2</td>\n",
       "      <td>1059.0</td>\n",
       "      <td>19061.5</td>\n",
       "      <td>2</td>\n",
       "      <td>14.9</td>\n",
       "      <td>268</td>\n",
       "      <td>9530.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Brazil</td>\n",
       "      <td>1</td>\n",
       "      <td>1411.9</td>\n",
       "      <td>45180.1</td>\n",
       "      <td>1</td>\n",
       "      <td>11.1</td>\n",
       "      <td>356</td>\n",
       "      <td>45180.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Lebanon</td>\n",
       "      <td>1</td>\n",
       "      <td>952.4</td>\n",
       "      <td>44764.2</td>\n",
       "      <td>1</td>\n",
       "      <td>8.6</td>\n",
       "      <td>402</td>\n",
       "      <td>44764.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>South Africa</td>\n",
       "      <td>1</td>\n",
       "      <td>461.4</td>\n",
       "      <td>27220.4</td>\n",
       "      <td>1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>362</td>\n",
       "      <td>27220.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Saudi Arabia</td>\n",
       "      <td>1</td>\n",
       "      <td>816.3</td>\n",
       "      <td>7346.6</td>\n",
       "      <td>1</td>\n",
       "      <td>8.9</td>\n",
       "      <td>80</td>\n",
       "      <td>7346.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Country Total_Orders Average_Sale Total_Revenue  \\\n",
       "                                                                   \n",
       "0                Germany        17167       1028.9   480882971.2   \n",
       "3         United Kingdom          445       1267.5    11787916.0   \n",
       "4                 France          366       1376.4    11095393.0   \n",
       "2                Ireland          271       1947.0    15390867.6   \n",
       "10               Belgium           93       1192.5     2280107.1   \n",
       "1            Netherlands           91       8969.7    21302953.4   \n",
       "9                  Spain           84       1087.4     2691363.7   \n",
       "5              Australia           53       8156.6     9804245.9   \n",
       "11              Portugal           50       1115.5     1622999.0   \n",
       "8            Switzerland           48       1379.3     2717170.4   \n",
       "13               Finland           40       1532.8     1033078.0   \n",
       "14                 Italy           33       1306.9      948826.1   \n",
       "6                 Sweden           32       9418.1     3936760.6   \n",
       "12                Norway           31       1528.3     1432056.1   \n",
       "21                Poland           18       1144.5      382271.7   \n",
       "17               Denmark           17       1675.7      625032.2   \n",
       "7                  Japan           16      10251.6     2747436.0   \n",
       "19               Austria           16       1127.0      444046.1   \n",
       "15                Cyprus           14       1402.4      776928.1   \n",
       "22               Unknown           13        698.4      322679.4   \n",
       "20             Hong Kong           10       1504.6      443845.1   \n",
       "18                Israel            8       1696.5      517444.5   \n",
       "16             Singapore            7       3164.0      753034.7   \n",
       "25               Iceland            6       1152.1      178581.6   \n",
       "23         United States            5       1404.8      272538.2   \n",
       "24                Canada            5       1460.0      227755.6   \n",
       "26                Greece            5        920.7      138109.2   \n",
       "27                 Malta            5       1135.6      134001.1   \n",
       "30    European Community            3       1014.5       61885.1   \n",
       "28        Czech Republic            2       3541.9       88547.9   \n",
       "29  United Arab Emirates            2       1161.1       82435.6   \n",
       "34               Bahrain            2       1059.0       19061.5   \n",
       "31                Brazil            1       1411.9       45180.1   \n",
       "32               Lebanon            1        952.4       44764.2   \n",
       "33          South Africa            1        461.4       27220.4   \n",
       "35          Saudi Arabia            1        816.3        7346.6   \n",
       "\n",
       "   Unique_Customers Average_Units_Sold Total_Units_Sold Average_Order_Value  \n",
       "                                                                             \n",
       "0              3815               10.1          4731881             28012.1  \n",
       "3               101               13.6           126310             26489.7  \n",
       "4                85               13.8           111131             30315.3  \n",
       "2                 4               18.7           147902             56792.9  \n",
       "10               25               11.5            21917             24517.3  \n",
       "1                 8               85.1           202159            234098.4  \n",
       "9                29               11.8            29128             32040.0  \n",
       "5                 9               72.0            86520            184985.8  \n",
       "11               18               11.2            16270             32460.0  \n",
       "8                22               15.8            31169             56607.7  \n",
       "13               12               15.4            10395             25827.0  \n",
       "14               12               11.1             8032             28752.3  \n",
       "6                 8               78.1            32662            123023.8  \n",
       "12               10               17.3            16225             46195.4  \n",
       "21                6               11.4             3799             21237.3  \n",
       "17                9               22.1             8239             36766.6  \n",
       "7                 8               84.2            22555            171714.8  \n",
       "19               10               12.7             5015             27752.9  \n",
       "15                8               10.6             5860             55494.9  \n",
       "22                5                7.5             3450             24821.5  \n",
       "20                1               16.8             4961             44384.5  \n",
       "18                4               15.0             4574             64680.6  \n",
       "16                1               23.7             5629            107576.4  \n",
       "25                1               14.2             2199             29763.6  \n",
       "23                4               13.4             2607             54507.6  \n",
       "24                4               18.2             2839             45551.1  \n",
       "26                4               11.0             1651             27621.8  \n",
       "27                2                8.8             1036             26800.2  \n",
       "30                1                8.5              520             20628.4  \n",
       "28                1               27.3              682             44274.0  \n",
       "29                2               14.6             1035             41217.8  \n",
       "34                2               14.9              268              9530.8  \n",
       "31                1               11.1              356             45180.1  \n",
       "32                1                8.6              402             44764.2  \n",
       "33                1                6.1              362             27220.4  \n",
       "35                1                8.9               80              7346.6  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Customers</th>\n",
       "      <th>Average_Units_Sold</th>\n",
       "      <th>Total_Units_Sold</th>\n",
       "      <th>Average_Order_Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>445</td>\n",
       "      <td>1267.5</td>\n",
       "      <td>11787916.0</td>\n",
       "      <td>101</td>\n",
       "      <td>13.6</td>\n",
       "      <td>126310</td>\n",
       "      <td>26489.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>France</td>\n",
       "      <td>366</td>\n",
       "      <td>1376.4</td>\n",
       "      <td>11095393.0</td>\n",
       "      <td>85</td>\n",
       "      <td>13.8</td>\n",
       "      <td>111131</td>\n",
       "      <td>30315.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ireland</td>\n",
       "      <td>271</td>\n",
       "      <td>1947.0</td>\n",
       "      <td>15390867.6</td>\n",
       "      <td>4</td>\n",
       "      <td>18.7</td>\n",
       "      <td>147902</td>\n",
       "      <td>56792.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Belgium</td>\n",
       "      <td>93</td>\n",
       "      <td>1192.5</td>\n",
       "      <td>2280107.1</td>\n",
       "      <td>25</td>\n",
       "      <td>11.5</td>\n",
       "      <td>21917</td>\n",
       "      <td>24517.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Netherlands</td>\n",
       "      <td>91</td>\n",
       "      <td>8969.7</td>\n",
       "      <td>21302953.4</td>\n",
       "      <td>8</td>\n",
       "      <td>85.1</td>\n",
       "      <td>202159</td>\n",
       "      <td>234098.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country Total_Orders Average_Sale Total_Revenue Unique_Customers  \\\n",
       "                                                                             \n",
       "1  United Kingdom          445       1267.5    11787916.0              101   \n",
       "2          France          366       1376.4    11095393.0               85   \n",
       "3         Ireland          271       1947.0    15390867.6                4   \n",
       "4         Belgium           93       1192.5     2280107.1               25   \n",
       "5     Netherlands           91       8969.7    21302953.4                8   \n",
       "\n",
       "  Average_Units_Sold Total_Units_Sold Average_Order_Value  \n",
       "                                                           \n",
       "1               13.6           126310             26489.7  \n",
       "2               13.8           111131             30315.3  \n",
       "3               18.7           147902             56792.9  \n",
       "4               11.5            21917             24517.3  \n",
       "5               85.1           202159            234098.4  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "display(df_country.sort_values(by='Total_Orders', ascending=False))\n",
    "df_top5_Count = df_country.sort_values(by='Total_Orders', ascending=False).reset_index(drop=True)\n",
    "df_top5_Count = df_top5_Count[1:6]\n",
    "df_top5_Count\n",
    "\n",
    "# \"Total_Orders\", \"Total_Revenue\", \"Unique_Customers\", \"Total_Units_Sold\", \"Average_Order_Value\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Country', ylabel='Total_Orders'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(x=\"Country\", y=\"Total_Orders\",data=df_top5_Count, label=\"Total_Orders\")\n",
    "sns.lineplot(x=\"Country\", y=\"Average_Sale\",data=df_top5_Count, label=\"Average_Sale\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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     "metadata": {},
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   ],
   "source": [
    "fig = px.sunburst(df_country, path=[\"Total_Orders\", \"Total_Revenue\", \"Unique_Customers\", \"Total_Units_Sold\", \"Average_Order_Value\"], values='Total_Orders',\n",
    "                  color='Country', hover_data=['Total_Orders'],\n",
    "                  color_continuous_scale='RdBu',\n",
    "                  color_continuous_midpoint=np.average(df_country['Total_Orders'], weights=df_country['Unique_Customers']))\n",
    "fig.show()\n",
    "\n",
    "fig = px.sunburst(df_country, path=['Country', 'Total_Orders', 'Total_Revenue'])\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Unique number of Country in merged: ['Germany' 'Ireland' 'Norway' 'Australia' 'France' 'United Kingdom'\\n 'Netherlands' 'Italy' 'Portugal' 'Poland' 'Switzerland' 'Belgium' 'Spain'\\n 'Japan' 'Lithuania' 'Iceland' 'Denmark' 'Cyprus' 'Sweden' 'Finland'\\n 'Austria' 'Bahrain' 'Israel' 'Hong Kong' 'Greece' 'Singapore' 'Lebanon'\\n 'United Arab Emirates' 'Saudi Arabia' 'Czech Republic' 'Canada' 'Unknown'\\n 'Brazil' 'United States' 'European Community' 'Malta' 'South Africa']\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "\"Unique number of Country in coutnry: ['Germany' 'Netherlands' 'Ireland' 'United Kingdom' 'France' 'Australia'\\n 'Sweden' 'Japan' 'Switzerland' 'Spain' 'Belgium' 'Portugal' 'Norway'\\n 'Finland' 'Italy' 'Cyprus' 'Singapore' 'Denmark' 'Israel' 'Austria'\\n 'Hong Kong' 'Poland' 'Unknown' 'United States' 'Canada' 'Iceland'\\n 'Greece' 'Malta' 'Czech Republic' 'United Arab Emirates'\\n 'European Community' 'Brazil' 'Lebanon' 'South Africa' 'Bahrain'\\n 'Saudi Arabia']\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 37 entries, 0 to 36\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   0       37 non-null     object\n",
      "dtypes: object(1)\n",
      "memory usage: 424.0+ bytes\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Unique number of Country:'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 36 entries, 0 to 35\n",
      "Data columns (total 1 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   0       36 non-null     object\n",
      "dtypes: object(1)\n",
      "memory usage: 416.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "display(\"Unique number of Country in merged: \" + str(merged_tables.Country.unique()))\n",
    "\n",
    "display(\"Unique number of Country in coutnry: \" + str(df_country.Country.unique()))\n",
    "display(\"Unique number of Country: \" + str(merged_tables.Country.nunique()) == str(df_country.Country.unique()))\n",
    "\n",
    "new_df = pd.merge(df_country, merged_tables, on='Country', how='outer')\n",
    "\n",
    "merged_tables.loc[~merged_tables['Country'].isin(df_country['Country']), 'Country']\n",
    "\n",
    "\n",
    "x = str(merged_tables.Country.nunique()) == str(df_country.Country.unique())\n",
    "#print(df_country['Country'].isin(merged_tables['Country']))\n",
    "\n",
    "original = pd.DataFrame(merged_tables.Country.unique())\n",
    "original.info()\n",
    "\n",
    "display(\"Unique number of Country:\")\n",
    "new = pd.DataFrame(df_country.Country.unique())\n",
    "new.info()\n",
    "\n",
    "#original['0'] = np.where(original['0'] == new['0'], 'True', 'False')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7967     Lithuania\n",
       "7968     Lithuania\n",
       "7969     Lithuania\n",
       "7970     Lithuania\n",
       "7971     Lithuania\n",
       "7972     Lithuania\n",
       "8602     Lithuania\n",
       "8603     Lithuania\n",
       "8604     Lithuania\n",
       "8605     Lithuania\n",
       "8606     Lithuania\n",
       "8607     Lithuania\n",
       "8608     Lithuania\n",
       "8609     Lithuania\n",
       "8832     Lithuania\n",
       "9215     Lithuania\n",
       "9216     Lithuania\n",
       "9217     Lithuania\n",
       "9218     Lithuania\n",
       "9219     Lithuania\n",
       "9220     Lithuania\n",
       "9221     Lithuania\n",
       "9222     Lithuania\n",
       "9630     Lithuania\n",
       "9631     Lithuania\n",
       "9632     Lithuania\n",
       "9633     Lithuania\n",
       "9634     Lithuania\n",
       "9635     Lithuania\n",
       "10237    Lithuania\n",
       "10238    Lithuania\n",
       "10239    Lithuania\n",
       "10240    Lithuania\n",
       "10241    Lithuania\n",
       "10242    Lithuania\n",
       "10243    Lithuania\n",
       "19602    Lithuania\n",
       "Name: Country, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merged_tables.loc[~merged_tables['Country'].isin(df_country['Country']), 'Country']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#del df_country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
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       "      <th>InvoiceNo</th>\n",
       "      <th>Total_Sales</th>\n",
       "      <th>Customer_Id</th>\n",
       "      <th>Quantity</th>\n",
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      "text/plain": [
       "                                        InvoiceNo  Total_Sales  Customer_Id  \\\n",
       "unique   [78537086, 78537081, 78537090, 78537827]          NaN          NaN   \n",
       "mean                                          NaN       1660.8          NaN   \n",
       "sum                                           NaN      61451.1          NaN   \n",
       "nunique                                       NaN          NaN          1.0   \n",
       "\n",
       "         Quantity  \n",
       "unique        NaN  \n",
       "mean         18.3  \n",
       "sum         676.0  \n",
       "nunique       NaN  "
      ]
     },
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       "                                        InvoiceNo  Total_Sales  Customer_Id  \\\n",
       "unique   [78537086, 78537081, 78537090, 78537827]          NaN          NaN   \n",
       "mean                                          NaN       1660.8          NaN   \n",
       "sum                                           NaN      61451.1          NaN   \n",
       "nunique                                       NaN          NaN          1.0   \n",
       "\n",
       "         Quantity num_orders  total_sale_mean  total_sale  \\\n",
       "unique        NaN   78537086           1660.8     61451.1   \n",
       "mean         18.3   78537081           1660.8     61451.1   \n",
       "sum         676.0   78537090           1660.8     61451.1   \n",
       "nunique       NaN   78537827           1660.8     61451.1   \n",
       "\n",
       "         num_unique_customers  Quantity_mean  Quantity_sum  \n",
       "unique                    1.0           18.3         676.0  \n",
       "mean                      1.0           18.3         676.0  \n",
       "sum                       1.0           18.3         676.0  \n",
       "nunique                   1.0           18.3         676.0  "
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "check =merged_tables[merged_tables['Country'] == \"Lithuania\"].agg({'InvoiceNo':'unique', 'Total_Sales':['mean', 'sum'], \n",
    "                                                'Customer_Id':'nunique', 'Quantity': ['mean', 'sum']}).round(1)\n",
    "display(check)\n",
    "\n",
    "check['num_orders']=check['InvoiceNo']['unique']\n",
    "check['total_sale_mean']=check['Total_Sales']['mean']\n",
    "check['total_sale']=check['Total_Sales']['sum']\n",
    "check['num_unique_customers']=check['Customer_Id']['nunique']\n",
    "check['Quantity_mean']=check['Quantity']['mean']\n",
    "check['Quantity_sum']=check['Quantity']['sum']\n",
    "display(check)\n",
    "\n",
    "# check = check[['num_orders', 'total_sale_mean', 'total_sale', 'num_unique_customers','Quantity_mean','Quantity_sum']].round(1)\n",
    "# check.sort_values(by = ['num_orders', 'total_revenue', 'num_unique_customers','total_sale_mean'],ascending = False, inplace = True)\n",
    "# display(check)\n",
    "# df_country['num_orders']=df_country['InvoiceNo']['nunique']\n",
    "# df_country['total_sale_mean']=df_country['Total_Sales']['mean']\n",
    "# df_country['total_revenue']=df_country['Total_Sales']['sum']\n",
    "# df_country['median']=df_country['Total_Sales']['median']\n",
    "# df_country['num_unique_customers']= df_country['Customer_Id']['nunique']\n",
    "# df_country['units_sold_mean'] = df_country['Quantity']['mean']\n",
    "# df_country['total_units_sold'] = df_country['Quantity']['sum']\n",
    "# \n",
    "# df_country = df_country[['num_orders', 'total_revenue', 'num_unique_customers', 'total_units_sold','total_sale_mean','median']].round(1)\n",
    "# df_country.sort_values(by = ['num_orders', 'total_revenue', 'num_unique_customers','total_sale_mean'],ascending = False, inplace = True)\n",
    "# df_country['revenue_per_customer'] = (df_country['total_revenue'] / df_country['num_unique_customers']).round(1)\n",
    "# df_country['revenue_per_order'] = (df_country['total_revenue'] / df_country['num_orders']).round(1)\n",
    "# df_country = df_country.reset_index()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Day</th>\n",
       "      <th>Total_Orders</th>\n",
       "      <th>Average_Sale</th>\n",
       "      <th>Total_Revenue</th>\n",
       "      <th>Unique_Customers</th>\n",
       "      <th>Average_Units_Sold</th>\n",
       "      <th>Total_Units_Sold</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00 Monday</td>\n",
       "      <td>3047</td>\n",
       "      <td>1166.2</td>\n",
       "      <td>88330069.7</td>\n",
       "      <td>1497</td>\n",
       "      <td>12.0</td>\n",
       "      <td>906148</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Day Total_Orders Average_Sale Total_Revenue Unique_Customers  \\\n",
       "                                                                        \n",
       "0  00 Monday         3047       1166.2    88330069.7             1497   \n",
       "\n",
       "  Average_Units_Sold Total_Units_Sold  \n",
       "                                       \n",
       "0               12.0           906148  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_dow.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mLHNwcCB37twEBAQYy/766y969uzJ5MmTY2W//v7+ODs7m5Tt378fZ2dnrl69CoDBYGDOnDmULVuWfPnyUaNGDdasWWOyzbx58yhXrhx58+alTJkyTJ8+3eQih6VLl+Lt7Y2bmxsdOnTg33//Ndn+zJkzdOjQgaJFi5I3b168vb1ZsGAB8HzJOm/evKxatcpkm3HjxvHdd9998NhDQ0MBsLOzMylPkiQJAGFhYR/ctoiIiHy4t09pxUM3b94EIG3atCblqVOn5saNG8bHvr6+AMYg9jlMnDiRtWvXMnDgQLJly0ZAQACDBw/m4cOHNGrUiD///JOZM2cyadIksmbNypEjR+jVqxcZMmSgRo0arF+/nqFDh9K3b1+KFy/Oli1bmDhxonGsT548oUWLFhQrVozff/8dKysrVqxYwciRIylSpAi5cuWidOnSrFq1ipo1awLPz29cu3YtP/zwwwePKzo6GgALC4tXPm9paZafK0RERL54Zhn2njx5AoCNjY1Jua2tbYxZsM/p8ePH/Prrr4wZM4Zvv/0WgEyZMnHt2jXmzZtHo0aNuHz5Mra2tmTIkIF06dKRLl06UqdOTbp06QD47bffqFy5Mo0aNQKgTZs2HDlyhKCgIOD52Js2bcr3339vnGXr1KkTs2bN4vTp0+TKlYvatWvToUMHbt26RZo0adi3bx93796latWqHzw2BwcHIOYM3qNHj4CYM34iIiLyeZhl2Htx1WdERITJFaDh4eEkSpQorrrF2bNnCQ8Pp3fv3vTp08dYHhkZSUREBE+fPqV69eqsWLGC8uXL4+zsTIkSJfD29jaGveDgYKpUqWLSrru7uzHspUiRgu+//54NGzYQFBTEpUuX+Oeff4D/n30rWbIkjo6OrF69mjZt2rBy5UrKlClD8uTJP3hsmTJlIkGCBFy6dMmk/MXj7Nmzf3DbIiIi8uHMMuy9WNIMCQkhU6ZMxvKQkBBcXFw++f4NBoNxOTMyMtKkHGDSpEl88803MbazsbEhYcKErF69msOHD7Nnzx52795tvNikU6dOJu28YG1tbfz3nTt3qFevHsmTJ6ds2bJ4eHjg6upKqVKljHUSJEhAzZo1Wbt2LY0bN2br1q0ffd6ira0thQoVYsuWLbRq1co4/k2bNuHg4ICbm9tHtS8iIiIfxixPpHJxccHOzo79+/cby0JDQzl16hSFChX6ZPt9EboePnxoLHt5puubb77BysqK69evkzlzZuPPjh07mDdvHpaWlqxevZrFixdTsGBBunTpwrJly6hbty4bNmwAIFeuXBw8eNBkv8ePHzf+e+3atTx48IAlS5bQoUMHvL29jUvXL4fE2rVrExwcjJ+fH3Z2dnh6en70+Nu3b8/Ro0f58ccf2bFjB5MmTWLevHm0bdtW99gTERGJI2YZ9mxsbGjcuDHjxo1j27ZtBAUF0bVrV5ycnPD29v5k+82fPz+WlpZMmjSJK1eusH37dubPn2983t7engYNGjBp0iRWrVrFlStXWLlyJWPHjiVlypTA86Xm0aNHs2rVKq5evUpgYCAHDhzA3d0deH6O3pYtW5g7dy4XL15k4cKFbNq0ybgPJycnnjx5wsaNG7l+/Tq7d++mW7duACb3GMyaNSsFChRg+vTp1KxZkwQJEnz0+D08PJg6dSoXLlygY8eOrF27ll69en3UhR8iIiLyccxyGRegS5cuREZG0r9/f54+fUrhwoWZN29ejIs2YlPGjBkZOnQoM2fOZNmyZeTJk4e+ffvSvn17Y50+ffqQIkUKpkyZQkhICE5OTnTq1Ik2bdoAUK9ePf799198fX25ceMGSZMmpUKFCvTo0QOA0qVLM378eKZOncrkyZPJnz8/LVu2ZN26dQBUrFiRkydPMnr0aMLCwkifPj1169Zl27ZtHDt2jIYNGxr7UqtWLQ4dOvRRt1z5L29v708aqEVEROT9mO3XpcnbTZs2jT179rB48eK47oqIiIh8ImY7syevFxgYyMWLF1mwYAFDhw6N6+6IiIjIJ6Sw9xX666+/WLRoEbVr16ZSpUpx3R0RERH5hLSMKyIiImLGzPJqXBERERF5TmFPRERExIwp7ImIiIiYMYU9ERERETOmsCciIiJixhT2RERERMyYwp6IiIiIGVPYExERETFjCnsiIiIiZkxhT0RERMSMKeyJiIiImDGFPREREREzprAnIiIiYsYU9kRERETMmMKeiIiIiBlT2BMRERExYwp7IiIiImZMYU9iRXR0dFx3QUReor9JEXnBwmAwGOK6E2IeZu34jev/3orrboh89dIlTUPbUk3juhsi8oWwiusOiPm4/u8tLt29GtfdEBERkZdoGVdERETEjCnsiYiIiJgxhT0RERERM6awJyIiImLGFPZEREREzJjCnoiIiIgZU9gTETFTBoOBpUuXUq1aNdzd3SlbtiwjRowgLCzMWOf8+fO0adOGggULUrRoUfr27UtoaKhJO+PGjcPZ2TnGz+zZs411wsLCGDNmDN7e3uTPn5+qVauyaNEi3dxZ5Aug++yJiJipuXPnMnHiRFq1aoWHhweXLl1i8uTJnDlzhl9++YWHDx/SvHlzUqdOzZgxY7h79y5jx47l5s2bzJ8/39jOP//8g4eHBz/99JNJ+2nTpjX+u3v37hw5coQuXbrwzTff8PfffzNixAgePHhAx44dP9eQReQV4nxmLzo6milTpuDl5UW+fPlo2bIlly5dMqnz559/Urt2bdzd3SlTpgyjR4/m6dOnr2xvwYIF5M6dm0ePHpmUT5w4EWdnZ6ZNm2ZSfu/ePVxcXFi9evUH9f/q1as4Ozuzf//+D9o+tpQpU4apU6fGaR9E5MsRHR3N7NmzqV+/Pt27d6d48eI0bNiQQYMGsW/fPk6cOMHixYsJDQ1l9uzZlC1blnr16jFu3Dj27NlDYGCgsa2goCAKFSpE/vz5TX7SpEkDwMmTJ9m+fTuDBw+mUaNGeHh40LVrV+rXr8/cuXPRFzWJxK04D3u+vr4sWbKE4cOHs3TpUiwsLGjdujUREREABAYG0qlTJypUqMCqVasYPHgwGzduZMiQIa9sr3jx4kRFRXHs2DGT8l27dpE2bVp27txpUh4YGIjBYKB48eKfZoAiInEgLCyM6tWrU7VqVZPyrFmzAnDlyhV2795NwYIFSZEihfF5Ly8vkiRJYnytvHPnDnfu3MHFxeWN+6tfvz4eHh4mZVmyZOHx48fcvXs3NoYkIh8oTsNeREQE8+fPp3PnzpQqVQoXFxcmTpzIrVu32LJlCwBLliyhWLFitGnThsyZM1OyZEm6du3KmjVrjIHwZTly5CBVqlQcPHjQWHbv3j1OnTpFu3btOH78OPfv3zc+FxAQgLOzM6lSpfr0AxYR+UwcHBwYMGAABQsWNCnfvHkz8Py18ty5c8bw94KlpSUZMmTg4sWLAJw6dQqAbdu28e2335InTx5q1qzJjh07jNvkyZOHoUOHkixZMpO2tmzZgqOjo0mYFJHPL07DXlBQEI8ePaJYsWLGMgcHB3Lnzk1AQAAALVu2pFevXjG2jYyMNDnJ+GXFihXj8OHDxse7d+8mVapU1K5dm8SJE7Nnzx7jc4GBgZQoUcL4eMWKFVSqVAk3NzcqVarEggULTE4wDg4OpmnTpuTPn58KFSrw999/m+zbx8eHnj17Mnr0aDw8PMiXLx8dOnTg9u3bxjq3bt2ia9euFCpUiKJFi9KuXTvjCyvA3bt36dKlC0WLFsXNzY0GDRpw4MAB4/MPHz6kd+/eFCpUCA8PD3799dcYv4MVK1ZQs2ZN3NzcyJ8/P02aNOHkyZMA/Prrr7i7u/PkyRNj/ejoaEqWLMlvv/32yt+piMR/hw4dYs6cOZQrV44cOXIQGhpKkiRJYtRLkiSJ8fU1KCgIeP6hefjw4UybNg1HR0fatWvHrl27XruvX375hYCAANq2bYulZZwvIol81eL0L/DmzZuA6Um+AKlTp+bGjRsA5M6d22T5ICIigl9++YU8efK89tNi8eLFOXLkiDGk7d69G09PT6ytrSlatKjxBSosLIygoCA8PT0BWLp0KaNHj6Zjx46sX7+en376iTlz5jBu3DgA48nMdnZ2LF++nIEDB+Lr6xtj/xs3buTBgwf4+fkxbdo0Dh48yMSJEwF4/PgxTZo0ISoqCj8/PxYuXEjy5MmpV68et27dAmDw4ME8ffoUPz8/1q5dS9asWenQoQOPHz8G4KeffuLYsWPMnDmT+fPn89dff3Ht2jXj/rds2cKgQYNo3rw5GzduZMGCBTx9+pR+/foBUL16dZ49e2b8hA+wd+9e7t27F2PJR0TMQ2BgIG3atCFTpkyMGDHCWG5hYRGjrsFgMJZXqVKF2bNnM2PGDEqUKMG3337LzJkzyZo1K1OmTHnlvhYsWMDo0aOpWrUqTZs2/TQDEpF3Fqdh78XMko2NjUm5ra0t4eHhMepHRkbSq1cvzp49y6BBg17bbvHixQkLCyM4OBiDwcCePXvw8vICwNPTk927dwNw8OBBrK2tKVSoEPD8/MG2bdtStWpVMmbMSIUKFejatSt+fn6Eh4ezfv16njx5wujRo8mRIwclSpSgb9++MfZvZ2fH0KFDyZYtG15eXtSoUcO4rLx+/Xru37/P+PHjcXFxIWfOnIwYMQI7OzuWLVsGwOXLl0maNCmZMmUic+bM9OvXjylTppAgQQLOnz/P7t27GThwIIUKFSJXrlyMHz/e5HeYLFkyhg8fTs2aNUmfPj358uWjbt26nD59GoAUKVJQpkwZ1qxZY9xm5cqVlClTRsstImZo/fr1tGjRgnTp0vHrr78al1vt7OxeuULy+PFj7O3tAUifPj2lSpUymZ2ztramRIkSxteUF6Kjo/n5558ZOXIkVatWZfTo0a8MkyLyecXprVcSJkwIPJ+te/FvgPDwcBIlSmRSNywsjJ9++on9+/czZcoU8uXL99p2nZycyJIlC4cOHSI6Opp79+4ZL8Dw8vJiyJAhnD17loCAAAoVKoStrS337t3j5s2bTJ482eSK3ejoaMLDw7l69SrBwcFkyZLF+CII4O7uHmP/mTNnxtra2vjY3t6eZ8+eAc/PfwkLC6NIkSIm24SHh3Pu3DkAOnXqRM+ePdmyZQuFChXC09OTypUrY2trS3BwMACurq7GbVOmTEnGjBmNjwsXLkyKFCnw9fXl0qVLXLhwgX/++cdkObp27dq0a9eOW7dukSRJErZu3crkyZNf+zsVkfhp7ty5jBs3jsKFC+Pr62vy+pU1a1YuX75sUj86OpqrV69Svnx5ALZv305ERITx8Qvh4eEm5+hFRETQrVs3tmzZQrNmzejTp4+CnsgXIk7D3ovl25CQEDJlymQsDwkJMVm6DQkJoXXr1ly9epU5c+aYnOP3Oi+WcsPCwnB1dTW+KGXMmJFMmTIREBBAYGAg3t7eAMYg1KdPn1demfuir/+9hYCVVcxf4X9nKl8WHR1N1qxZmTFjRoznEidODIC3tze7du1i165d7N27l7lz5zJ58mTjzN/L/X1VP9avX0+vXr2oWrUqbm5u1KlTh+DgYIYOHWqs4+npSapUqVi/fj3JkiXD3t7eOPspIuZhyZIljB07lkqVKjFmzJgYr00lSpRg3rx53Lt3zzirv2vXLh49emQ8l3nDhg1s27aNokWLkjRpUuD5zN/27dtNPrT6+PiwdetW+vTpQ/PmzT/PAEXkncTpMq6Liwt2dnYm96gLDQ3l1KlTxqXVf//9l2bNmnHv3j1+//33dwp68DzsHT9+nP379xvPyXvB09OTw4cPc+LECeMLmqOjI46Ojly+fJnMmTMbf06ePMmkSZMAyJUrFxcuXODevXvGto4fP/5eY86ZMyfXr1/H3t7euI/06dMzfvx4AgICiIiIYNSoUVy5coXKlSszfPhwtmzZgqWlJdu3byd37tzA8xOtX/6dvfzpfObMmdSpU4fRo0fTqFEjChcuzJUrV4D/D6sJEiSgZs2abN68mc2bN1OjRg0SJEjwXmMRkS/X7du3GTVqFOnTp6dx48acOnWKI0eOGH/u3bvH999/j62tLS1atGDLli0sX76cnj17UrJkSeOqxQ8//EBkZCRt2rRh27ZtbN68mWbNmvH48WO6dOkCwNatW1m/fj3ffvst+fPnN9nPkSNHXnnnBBH5fOJ0Zs/GxobGjRszbtw4UqRIQfr06Rk7dixOTk7GGbcXwWfu3LmkSJHC5KrWFClSvDagFC1alMuXL3Pjxo0Yd2/39PSkR48eJEuWzDiDaGFhwQ8//MCECRNIly4dpUqVIjg4mCFDhlC6dGlsbGyoUqUKM2bMoHv37vTu3ZvQ0FBGjhz5XmOuXr06s2fPplOnTvTq1Qt7e3tmzpzJjh076Ny5MzY2Nhw9epTAwEAGDBhAypQp2bFjB48ePcLd3Z1MmTJRsWJFhg4dio2NDSlTpmTChAkmL6Zp06bl0KFDnDx5Ent7e/7880/8/PyA50sttra2wPOl3Dlz5mBtbU3Pnj3faxwi8mXbsWMHT58+5dq1azRq1CjG86NGjaJWrVr89ttvjBw5kh49epAkSRIqVqxocgeEnDlz4ufnx6RJk+jbty8REREULlyYESNGGFdkXlzs9eeff/Lnn3/G2Ne2bdvIkCHDJxqpiLyNhSGOb20eFRXFhAkT8Pf35+nTpxQuXJiBAweSIUMGoqOjyZ8//ysv1oC3v4DUqVOHS5cu8ffff5uEwrCwMIoVK0blypUZM2aMyTaLFi1i4cKFXL16FUdHRypXrkzXrl2Nyx9Xr15l6NChBAQEkDRpUn788Ud8fHz47bffKFq0KD4+Ply7do2FCxca25w6dSorV640vgheuXKFMWPGsG/fPqKiosiVKxfdunUzzmbeunWLUaNGsX//fh4+fMg333xD27ZtqVKlCgBPnz5lzJgxrF+/nujoaOrXr8+BAwfw8vKic+fOXLlyhYEDB3LkyBFsbGxwcXGhfv36dO3alYULF5osvTRs2JDo6GiWLl36PoftlQatGculu1c/uh0R+TiZHTMwpLo+wInIc3Ee9iTuGAwGypcvT5s2bahbt+5Ht6ewJ/JlUNgTkZfF6TKuxI1nz57x559/8vfffxMWFmacMRQRERHzo7D3FbK2tmb48OEAjB071ngVsIiIiJgfhb2v1Ju+5khERETMh76wUERERMSMKeyJiIiImDGFPREREREzprAnIiIiYsYU9kRERETMmMKeiIiIiBlT2BMRERExY7rPnsSadEnTxHUXRAT9LYqIKX03rsSK6OhoLC01USzypdDfpIi8oLAnIiIiYsb0sU9ERETEjCnsiYiIiJgxhT0RERERM6awJyIiImLGFPZEREREzJjCnoiIiIgZU9gTERERMWMKexIrDNHRcd0FEZF4Ra+b8rnopsoSa47MmEXY9Rtx3Q0RkS+eXbq05G/fNq67IV8JfTeuxJqw6zcIvXQprrshIiIiL9EyroiIiIgZU9gTERERMWMfFPYCAwNjux8iIiIi8gl8UNhr3Lgx5cuXZ+bMmdy4oRPyRURERL5UHxT2lixZgoeHB7/88gtly5alZcuWrF+/noiIiNjun4iIiIh8hI+69UpERARbt25l1apV7N27l0SJElG5cmVq166Nm5tbbPZT4oHdAwbralwRkXfgkDkznsMGx3U35CvxURdo2NjYULlyZWbPns26devIlSsXS5cupX79+lSvXp21a9fGVj9FRERE5AN8VNh78uQJq1evpmXLllSpUoVTp05Rv359ZsyYQYECBfDx8WH06NGx1VcRERGzYjAYWLp0KdWqVcPd3Z2yZcsyYsQIwsLCjHX++usv6tSpg6urK15eXgwfPtzkeYBz587Rrl073N3dKVKkCJ06deL8+fMmde7du0f//v3x9PTE3d2dZs2acezYsc8yTolbH7SMu3fvXlavXs2WLVt48uQJhQoVonbt2lSsWJGECRMa6w0ePJg1a9Zw6NChWO20fJm0jCsi8m5eLOPOmTOHiRMn0qpVKzw8PLh06RKTJ0/GxcWFX375ha1bt9K5c2eKFClCs2bNiIyMZMaMGSRIkIClS5diZWXFlStXqF27Nvb29nTq1AlHR0dWrFjBvn37WLFiBRkzZiQ6Oprvv/+ea9eu0b17dxwdHfn11185dOgQK1euJEuWLHH9K5FP6IO+QaNly5akSZOGJk2aULt2bTJlyvTKetmyZcPT0/OjOvg20dHRTJs2jeXLlxMaGkrBggUZNGgQmTNnNtZZv349M2fO5NKlS6ROnZp69erRunVrLCwsYrTn4+PDypUr37jP06dP06RJE9KnT8/PP/8c62N6X19SX0RE5N1ER0cze/Zs6tevT/fu3QEoXrw4yZIl46effuLEiRNMnTqV7NmzM3fuXGxsbAAoVKgQ5cqVw9/fn3r16rFgwQKePHliDHYAXl5e1K9fn0mTJjF+/Hj279/P4cOHmT17NqVKlTK2U6xYMVasWGHcv5inDwp7s2bNwsvLC0vLN68CN2nShCZNmnxQx96Vr68vS5YsYdSoUaRJk4axY8fSunVr1q1bh42NDTt27KBXr1707duXkiVL8s8//9C7d29sbGxo3rx5jPb69etn8j+9p6cnffv2pXLlyp90HCIi8nUJCwujevXqMd5fsmbNCsCVK1c4f/48jRs3NgY9AEdHR7755hv++usv6tWrx/nz58mRI4cx6AFYWFhQsGBBli9fDkC+fPlYvHixycWT1tbWWFhY6E4aX4EPCnsvPhXcuXOHZ8+e8WIlODo6midPnhAYGEjDhg1jr5evERERwfz58+nZs6exTxMnTsTLy4stW7ZQpUoVbt++TevWrWnUqBEAGTNmZPXq1ezdu/eVYc/e3h57e/sYZalSpfrk4xERka+Hg4MDAwYMiFG+efNmAHLkyEHy5Mm5du2ayfPPnj3jxo0bxpCWPHlygoODefbsGdbW1sZ6V65c4eHDhzx48IBkyZJRoEABACIjI7ly5QrTpk3DYDBQq1atTzVE+UJ80AUaQUFBVK5cGS8vL8qUKUPZsmUpW7Ys3t7eVK9enWHDhsV2P1/bj0ePHlGsWDFjmYODA7lz5yYgIACAOnXq8NNPPwEQFRXFzp07OXDgACVKlPjo/T969Ii+fftSqFAhChYsiI+PD48fPwZg//79ODs7c/XqVWP9q1ev4uzszP79+4HnS8adOnWiZcuWFChQgFmzZvHkyRP69etHiRIlcHV1pWbNmsY/fHgecEeOHImHhweFChVi/PjxREdHm/Trzz//pEGDBri7u+Pq6kqdOnXYu3cvAFu3bsXFxSXGi0e9evUYNWrUR/9ORETkwx06dIg5c+ZQrlw5cuTIQa1atdi8eTOzZ8/m3r17XL9+nX79+hEWFsaTJ08AqFWrFrdv36Z3795cuXKF+/fv8+uvv7Jr1y4AY70XBg8eTMWKFVm3bh2tWrUiZ86cn32c8nl9UNgbM2YMoaGh9O7dmyJFiuDp6cmAAQMoVaoUFhYW/Pbbb7Hdz1e6efMmAGnTpjUpT506dYxv9rh+/Tqurq60bt0aNze3WJl53Lx5MylTpsTf358xY8awYcMG5syZ815tbNmyheLFi7NixQqqV6/O5MmTOX36NLNnz2bDhg2ULFmSrl27GkPj8OHD2bBhAz///DOLFy/m+vXrJl9fd+LECTp27Ej58uVZs2YNy5cvx9HRkR49ehAREUHp0qVxdHRk9erVxm0uXLjA0aNH+e677z76dyIiIh8mMDCQNm3akClTJkaMGAFA586dad26NVOmTMHDw4Py5cuTJEkSypUrR6JEiQAoUaIEY8eOZe/evZQrV45ixYqxfft22rZtC2Cs90K9evVYuHAhHTt2ZPbs2QwcOPDzDlQ+uw9axj169Cg+Pj7UrVuXxIkTs3r1ar7//nu+//57unTpwsKFCylUqFBs9zWGF59WXj6XAcDW1pZ///3XpMzBwYE//viDy5cvM3z4cHr16sWkSZM+av+urq5069YNgEyZMlGiRAlOnDjxXm0kTZqUH374wfj48uXL2NnZkSlTJuzt7fnxxx8pVKgQSZMmJSwsDH9/fwYNGmRcth45cqRxphAgQYIE9O/f37hsDdC0aVNatmzJ3bt3SZs2LdWrV2f16tV06NABgFWrVpEnTx5cXFw++HchIiIfbv369fj4+JA1a1bmzZtHsmTJALCysqJHjx507tyZK1eukDp1ahwcHGjcuLGxDkD16tWpWrUqly9fJlGiRKRJk4YpU6ZgaWkZ49SkF+ftFSlSBIPBwIwZM+jYsSNOTk6fa7jymX3QzF5ERITxBNJvvvmG06dPG5+rVasWR44ciZXOvc2L27z89+TS8PDwGJ9k7OzsyJ07NxUrVqRv375s3LgxxlLm+3rxO3ghadKkPH369L3aePmqYYDWrVvzzz//4OHhQZMmTZgzZw5Zs2bF3t6eCxcu8OzZM1xdXY31bW1tyZUrl/Fxrly5+Pbbb5kzZw59+/bl+++/p1OnTsDzZWyA2rVrc/HiRY4ePYrBYGDNmjXUrl37vfotIiKxY+7cuXTv3p38+fOzaNEik3PEDxw4wK5du7C1tSV79uw4ODgQGRnJ6dOnyZ07N/D8HnurVq3C0tKSLFmykCZNGgBOnjyJi4sLCRIk4MyZM6xYsSLGvl1dXTEYDMaVMjFPHxT20qVLx5UrV4DnYSUsLMy4zGhjYxNjVu1TebF8GxISYlIeEhJi/IQSGBjI8ePHTZ7PkSPHK7d7XwkSJHhrnZdvYxgZGRnj+ZfvSwjg7u7Ojh07mDx5Ms7Ozvzxxx9UrFiRffv2vXYfVlb/P0EbEBBAhQoVOHr0KDlz5qRjx46MHTvWpH727NnJly8fa9as4cCBA9y5c4cqVaq8dSwiIhK7lixZwtixY6lYsSLz5s2LMQv3v//9jwEDBvDs2TNj2YoVKwgNDcXb2xuAM2fO0Lt3b86dO2esc/bsWXbv3k25cuWA5ytyffv2jXHf2127dmFjY8M333zzqYYoX4APCnvly5dn3Lhx/O9//yNVqlR88803TJw4kdOnTzN//nyTy78/JRcXF+zs7EyWMUNDQzl16pRxGXn+/PkxLjw4evQoVlZWn/Qmki+uiHr5LueX3uGGw1OmTOHgwYOULVuW/v37s2nTJjJmzMimTZvIli0btra2HDx40Fg/MjKSoKAg4+N58+ZRtGhRpk2bRvPmzSlRooTx/MWXg2ft2rXZvHkzmzdvpmzZsibLASIi8undvn2bUaNGkT59eho3bsypU6c4cuSI8efevXs0aNCAO3fu4OPjw759+/j1118ZNmwYVapUMb7PlSpVikyZMtGjRw+2b9/O+vXradWqFRkyZKBZs2YAVK5cmWzZstG9e3dWrlzJ7t27GTJkCIsXL6ZTp044ODjE5a9CPrEPOmevU6dOXLp0iRUrVlCxYkX69OlDp06d2LBhAwkSJGDChAmx3c9XsrGxoXHjxowbN44UKVKQPn16xo4di5OTk/ETT8uWLWnatClTpkyhRo0anDx5krFjx9K0aVOSJ0/+yfqWM2dOkiRJwowZM+jevTshISFMnDjxlTdyftmlS5dYs2YNw4YNI1OmTBw5coTr16/j7u5O4sSJady4MVOmTCFVqlRky5aN+fPnc+vWLeP2adOmZevWrQQGBuLk5MT+/fuZPHkyYLrcXaVKFUaNGsUff/zB1KlTP80vQUREXmvHjh08ffqUa9eumZxn/cKoUaOoVasWs2bNYvz48bRr146UKVPSrl0748UX8PwCjLlz5zJy5Eh69OiBra0tJUuWpFu3btjZ2QGQOHFiFixYwMSJE5k4cSL3798nW7Zs/Pzzz9SsWfNzDVniyAeFPVtbWyZNmsSDBw+4d+8enp6erFu3jhMnTpAnT57XfqPGp9ClSxciIyPp378/T58+pXDhwsybN8/kTuOzZs1i0qRJzJs3jxQpUtCyZUtat279SftlZ2fHuHHjGD9+PFWqVCFr1qz06dPH5GKMVxkyZAijR4+mZ8+ePHjwgPTp09OjRw9q1KgBQPfu3bG1tWXo0KE8evSISpUqUaZMGeP2Xbp04c6dO7Rr1w54vmQ7cuRIevbsybFjx8iWLZuxf+XKlYu129CIiMj7qVOnDnXq1HlrvRIlSrz1dTpz5szMmjXrjXVSpUrFyJEj36uPYh7e+7tx161bx5IlSzh69KjxHLSECRNSoEABGjZsaDw/QL58TZs2xd3dna5du8ZKe/puXBGRd/Piu3FFPod3ntmLjo6mR48ebNiwgdSpU1O5cmVSpkwJwK1btzhw4ACdO3emRo0a+o7WL9zWrVv5559/OHz4MKNHj47r7oiIiMgn9M5h7/fff+d///sfPj4+NG3aNMb34kZHR7N48WJGjhyJl5eXru78gs2ZM4eLFy8ybNiwGDekFhEREfPyzmHP39+f+vXrv/L7ZAEsLS1p1KgRZ8+eZdmyZQp7X7ClS5fGdRdERETkM3nnW69cvHjR+K0Nb+Ll5cX58+c/qlMiIiIiEjveOew9efKEpEmTvrVe8uTJuXfv3kd1SkRERERixzuHPYPB8E7fGGFpaUl0dPRHdUpEREREYscHfYOGiIiIiMQP73VT5cGDBxvvxv06L389mIiIiIjErXcOe4ULFwZMv1/1VZIkSWL8vj4RERERiVvvHPYWLlz4KfshZsAune7ZJyLyLvR6KZ/Te39dmsirGKKjsbDUKaAiIu9Kr5vyuSjsiYiIiJgxfaQQERERMWMKeyIiIiJmTGFPRERExIwp7ImIiIiYMYU9ERERETOmsCciIiJixhT2RERERMyYwp7Eiuho3a5RRORro9f++EE3VZZYs3rZfu7cfhjX3RARkc8gZSp7atQrGtfdkHfwzt+NK/I2d24/5Nb1B3HdDREREXmJlnFFREREzJjCnoiIiIgZU9gTERERMWMKeyIiIiJmTGFPRERExIwp7ImIiIiYMYU9ERERETOmsCciIiIfLCoqitmzZ+Pt7Y2bmxvVq1dn9erVr62/YMECnJ2duXr1qkl5UFAQP/zwA4UKFaJo0aL07t2bkJCQGNv/8ssvlCtXDldXV2rUqMHWrVtjfUzmRmFPREREPtiECROYMmUKdevWZdasWRQvXpxevXqxdu3aGHUvXrzIhAkTYpTfvHmTZs2aERYWxrhx4xg8eDCHDx+mRYsWREZGGuvNnTuXsWPH8t133zFt2jQyZ85Mly5dCAgI+KRjjO8U9l4jOjqaKVOm4OXlRb58+WjZsiWXLl16bf3+/ftTpkyZN7Y5depUnJ2dX/lTo0aN127n7++Ps7PzG9t2dnbG39//zYMSERGJRY8ePcLPz49mzZrRpk0bPDw88PHxoUiRIvj5+ZnUjYqKwsfHh2TJksVoZ8mSJTx58oSZM2dSunRpKlWqxLBhwzh79iz79u0D4OnTp8yaNYvmzZvTsWNHSpUqxeTJk3F1dWX69OmfY7jxlr4u7TV8fX1ZsmQJo0aNIk2aNIwdO5bWrVuzbt06bGxsTOpu3bqV5cuXkz59+re26+TkxB9//BGj3Mrq9YeicuXKeHl5vf8gREREPiFbW1uWLl1KypQpTcqtra0JCwszKZs3bx537tyhTZs2DB061OS5pk2bUrZsWZMgaG1tDUBERAQAR48eJTQ0lPLlyxvrWFhY4O3tzYQJE3j69CkJEyaMzeGZDYW9V4iIiGD+/Pn07NmTUqVKATBx4kS8vLzYsmULVapUMdYNCQlhwIABFClShGvXrr217QQJEpAqVar36k/ChAn1P7CIiHxxrKyscHFxAcBgMHDnzh38/f3Zu3cvw4YNM9Y7c+YM06ZNY+7cuTHO1QNIkSIFKVKkACA8PJxTp04xdOhQsmTJgqenJwDnzp0DIEuWLCbbZs6cmaioKC5fvkzOnDk/xTDjPS3jvkJQUBCPHj2iWLFixjIHBwdy585tcl6AwWDAx8eHGjVqUKRIkVjZ99SpU2nQoAHdunWjQIECDBkyJMYy7s2bN2nfvj3u7u6ULl2a9evXm7RhMBiYO3culSpVIm/evBQsWJC2bdty5coVAEaMGEG5cuVMtnn48CFubm5s27YtVsYhIiJfl7Vr1+Lp6cmECRMoWbIklStXBiAyMpLevXtTt27dd3qvrFatGg0aNOD8+fP06dMHW1tb4Pn7FICdnZ1J/SRJkgDEmEmU/6ew9wo3b94EIG3atCblqVOn5saNG8bHv/76K7dv36Zbt26xuv/Dhw/j6OjI6tWradasmclzkZGR/PDDD9y/fx8/Pz8mTpzInDlzTOosWLCAWbNm0bNnTzZt2oSvry8XLlzg559/BqBOnTpcuXKFwMBA4zYbNmzAzs7OOJMpIiLyPvLly4efnx/Dhg3j1KlTNGjQgPDwcGbOnEloaCjdu3d/p3YGDRrE/Pnz8fb2pn379sYre6Ojo19Z32AwAGBpqUjzOlrGfYUnT54AxDg3z9bWln///Rd4Pvs3bdo0Fi1aFKPem1y/fh13d/cY5YGBgSRIkMD4uEuXLtjb2wNw6NAhY/m+ffs4c+YMW7ZsIVOmTACMGjWKmjVrGutkypSJn3/+2XjBSPr06alUqZJxBtDZ2Zk8efKwZs0aChUqBMDKlSupUaPGG88dFBEReZ3MmTOTOXNmChcuTMaMGWnevDnz589n5syZzJkzBxsbGyIjI42hLTo6mqioKJP3PoASJUoY/3vr1i2mT59OjRo1cHBwAJ5fFJI0aVJj/cePHwMY3zMlJr2zv8KL8+MiIiJMzpULDw8nUaJEhIeH06NHD9q3b288V+FdpU6dmoULF8Yof/l/dkdHx9f+TxscHEzSpEmNQQ8gV65cJEqUyPi4TJkyHD16lClTpnDp0iXOnTvHmTNnSJMmjbFO7dq1mTRpEv379+fGjRscPnw4xgmzIiIib3L37l127txJyZIlcXR0NJa7uroCzy92fPbsGc2bN4+xrbe3N0WKFGHhwoXs27ePiIiIGKtLefPmZdGiRQBkzZoVgEuXLuHm5masc+nSJWxsbMiYMWNsD89sKOy9wovl25CQEJNQFRISgouLC0ePHjWebPricu9nz54RGRmJu7s7Q4YMoXr16q9s28rKisyZM79x/2+7GOPFlPV/231hzpw5TJ06lVq1alGkSBGaNGnCtm3bTM7tq1atGqNHj+avv/4iODgYV1dXndgqIiLv5fHjx/j4+NC1a1fatWtnLN+1axfw/BzxFyHthe3btzNt2jRmzJhhvNhi5cqV7Nixg23bthnPyYuMjGTfvn3GSRV3d3cSJ07Mpk2bjGHPYDCwZcsWihQp8l6rbF8bhb1XcHFxwc7Ojv379xvDXmhoKKdOnaJx48a4ubmxefNmk20WLlzI5s2bWbhwocmnm9iWO3duQkNDOXPmDDly5ADgwoULxhNXAWbMmEGnTp1o06aNsWzevHkmIdHBwQFvb282b95McHAwDRs2/GR9FhER85QxY0Zq1qzJ9OnTsbS0xNXVlRMnTjBjxgw8PT2pVq0aFhYWJtucOXMGgJw5c5IhQwYAfvjhBzZv3kybNm344YcfMBgMLFy4kHPnzjF//nwAEiVKRMuWLZk+fTrW1ta4u7uzYsUKTp48yYIFCz7vwOMZhb1XsLGxoXHjxowbN44UKVKQPn16xo4di5OTE97e3tjY2MSYnUuaNOk7zdp9rKJFi5IvXz569erFoEGDSJAgAcOHDzc5MTVt2rTs2bOHMmXKYGlpyerVq9m8eXOM+yDVrl2b9u3bYzAYqFq16iftt4iImKdhw4aRJUsWVqxYwdSpU0mVKhVNmzalQ4cOMYLe6+TMmZNFixYxYcIE+vTpQ0REBO7u7vj5+ZE/f35jvU6dOpEgQQKWLVvG/PnzyZ49O76+vhQsWPATjc48KOy9RpcuXYiMjKR///48ffqUwoULM2/evDifJra0tGTWrFkMHz6cli1bkjBhQtq2bWty36IxY8YwdOhQateuTZIkSciXLx9Dhgxh8ODBXL161fhJysPDg+TJk1OgQAHjia8iIiLvw8bGhvbt29O+fft3ql+rVi1q1aoVozxPnjzMmzfvjdtaWFjQoUMHOnTo8EF9/VpZGF51Aph8FR4/foynpyfTpk2jePHiH93evOlbuXX9wcd3TEREvnhp0iWjVcdyb68ocU4ze1+hf//9l7///puNGzeSLl06PDw84rpLIiIi8oko7H2FIiMj6devHylSpGDSpEnvfE6FiIiIxD8Ke18hR0dHk2/PEBEREfOl7xYRERERMWMKeyIiIiJmTGFPRERExIwp7ImIiIiYMYU9ERERETOmsCciIiJixnTrFYk1KVPZx3UXRETkM9Frfvyhr0uTWBEdbcDSUjdnFhH5mui1P35Q2BMRERExYzpnT0RERMSMKeyJiIiImDGFPREREREzprAnIiIiYsYU9kRERETMmMKeiIiIiBlT2BMRERExYwp7Eiuio6PjugsiIvIF0PvBl0c3VZZYs2HpPO6F3IjrboiISBxJkTotleu3iutuyH/ou3El1twLuUHI9Stx3Q0RERF5iZZxRURERMyYwp6IiIiIGVPYExERETFjCnsiIiIiZkxhT0RERMSMKeyJiIiImDGFPREREREzpvvsiYiISKyLiopi3rx5LF++nFu3bpElSxZatWpFjRo1Xll/wYIFjBw5km3btpEhQwYAmjRpwoEDB167j9OnTzN16lSmTZv22joLFy6kSJEiHzeYeE5hT0RERGLdhAkTWLBgAV26dMHV1ZUdO3bQq1cvLC0tqVatmkndixcvMmHChBhtDBo0iLCwMJOyy5cv07t3b+rVqwdA3bp18fLyMqkTERFBt27dSJUqFW5ubrE8svjHrMJedHQ006ZNY/ny5YSGhlKwYEEGDRpE5syZjXX69OmDv7+/yXZp0qRh586dr2332LFj+Pr6cujQIZ48eUK6dOkoX748bdu2xc7O7p37d+bMGa5du0bp0qXfe2xvsn//fpo2bWryaUhERCSuPHr0CD8/P5o1a0abNm0A8PDw4OTJk/j5+ZmEvaioKHx8fEiWLBk3b940aSd79uwmjyMjIxk2bBguLi7069cPACcnJ5ycnEzqjRw5kkePHrF48WISJkz4KYYYr5jVOXu+vr4sWbKE4cOHs3TpUiwsLGjdujURERHGOqdPn6Zdu3bs3r3b+LNq1arXtnnmzBmaNGlC1qxZ+e2339iwYQM9evRg/fr1dOjQ4b3617ZtW44fP/6hwxMREYkXbG1tWbp0KS1atDApt7a2NnlPBpg3bx537twxhsI3WbJkCadOnWLIkCHY2Ni8sk5QUBALFy6kU6dOZMyY8cMHYUbMZmYvIiKC+fPn07NnT0qVKgXAxIkT8fLyYsuWLVSpUoWoqCjOnj1Lhw4dSJUq1Tu16+/vT6ZMmejdu7exLGPGjCRMmJAffviBoKAgXFxcPsmYRERE4iMrKyvje6PBYODOnTv4+/uzd+9ehg0bZqx35swZpk2bxty5c7l69eob23z06BFTp06lRo0ab1yaHTNmDBkzZqRZs2axMxgzYDYze0FBQTx69IhixYoZyxwcHMidOzcBAQHA83MCwsPDyZYt2zu3a2FhwbVr1wgODjYp9/DwYP369WTNmhV4HjbHjx9PuXLlyJs3L0WLFqVbt27cv38fgDJlynDt2jWmTZtGkyZNAHB2do6xpFymTBmmTp0KPA+aZcqUYcSIERQqVIh27doBEBgYSN26dXFzc6NmzZqcPn3apI3Q0FAGDRpEqVKlyJMnDyVKlGDQoEE8ffoUgJo1a9KnTx+TbXbu3EnevHm5d+/eO/9uRERE3mbt2rV4enoyYcIESpYsSeXKlYHnS7K9e/embt2673QBxR9//EFoaCht27Z9bZ2goCD27NlD69atsbIym/msj2Y2Ye/FOn/atGlNylOnTs2NGzcACA4OxsLCggULFlCmTBnKlSvHsGHDePjw4WvbrV+/PtbW1lSvXp369eszfvx4du7cSVRUFNmzZ8fW1hZ4/kli3bp1jBgxgk2bNjF69Gj27NnDjBkzgOf/kzo5OdGyZUtjmHsX165d49atW6xcuZLu3btz5coVWrZsSa5cuVi5ciXt27dn+vTpJtv07t2bY8eOMWXKFDZt2mQ8T3Hp0qUA1KpVi02bNhnDH8Dq1av59ttvSZEixTv3TURE5G3y5cuHn58fw4YN49SpUzRo0IDw8HBmzpxJaGgo3bt3f6d2fv/9d8qUKWOcZHkVPz8/HB0dX3vF79fKbMLekydPAGKs4dva2hIeHg48ny62tLQkffr0zJw5k969e7Njxw46dOhAdHT0K9vNnDkza9asoUWLFty/f5/Zs2fTunVrPD09WbZsmbGeq6sro0ePpmjRoqRPn57SpUvj6elpnHVLkSIFCRIkIHHixCRLluy9xtahQwcyZsxIjhw5WLZsGSlTpmTQoEFky5aNChUq0L59e5P6JUqUYNSoUeTLl48MGTJQtWpV8uTJY+xL9erVefbsGVu3bgUgLCyMrVu3UqtWrffql4iIyNtkzpyZwoULU69ePcaOHUtwcDDz589n5syZDBs2DBsbGyIjI43vw9HR0URFRZm0ERQUxMWLF2NcxfuyqKgotm7dSsWKFV97Pt/XymzmOF9cbRMREWFy5U14eDiJEiUCoHPnzjRv3hwHBwcAcubMSapUqahfvz7Hjx8nX758r2w7TZo09O7dm969e3Pjxg327NnD77//zoABA0iTJg2lSpWiRo0a7Nu3jwkTJnDx4kXOnTvH+fPnKVSo0EePLUuWLMZ/BwcHkzt3bhIkSGAsK1CggEn977//nj///JPVq1dz+fJlgoODuXLlirGdZMmSUaZMGVatWkXVqlXZuHEj9vb2MS5dFxER+RB3795l586dlCxZEkdHR2O5q6sr8PyCymfPntG8efMY23p7e1OkSBEWLlxoLPvrr79IlCjRG+9mceTIEe7fv0+lSpVibRzmwmxm9l4s34aEhJiUh4SEGC/JtrCwMAa9F3LmzAkQ43LvF8aOHcu+fftM9lOnTh2WLFmCk5MTO3bsAGDw4MH8+OOPPH36lNKlSzNu3DiqVKny1n4bDAaTx8+ePYtR57+Xjf93m5fPSzAYDLRr145hw4aRIEECKlSowMyZM2MEwtq1a7N3717u3LnDmjVrqF69us5vEBGRWPH48WN8fHxYvny5SfmuXbsAGDFiBH/88YfJT6dOnQCYMWMGQ4YMMdnu6NGj5M6d+423UTl27BhWVla6r94rmM27u4uLC3Z2duzfv59MmTIBzy9UOHXqFI0bNwage/fuPHjwgHnz5hm3e3ErlP/ey+eFvXv3EhwcjIeHh0m5jY0NCRMmxNHRkfv377N48WImTpxoPPEU4Pz58yROnPi1fba2tjY5XzAsLOytF0jkypULf39/IiIijNPUL9/O5dSpU+zYsYNly5YZZyqfPXvG5cuXTS5B9/T0JFWqVCxfvpyDBw8yaNCgN+5XRETkXWXMmJGaNWsyffp0LC0tcXV15cSJE8yYMQNPT0+qVauGhYWFyTZnzpwBnk/C/PeescHBwXh6er5xn8HBwWTIkMF4Lr38P7MJezY2NjRu3Jhx48aRIkUK0qdPz9ixY3FycsLb2xuAqlWr0r59e2bMmEGVKlW4cOECQ4cOpWrVqq+9Qrdr1660b9+eH3/8kcaNG5MuXTquX7/OsmXLePToEfXr18fe3h57e3u2bdtGnjx5ePr0KX5+fpw8edJkaThJkiRcvHiRO3fukDJlStzd3Vm6dCmFCxfG2tqaSZMmvXV2rWHDhixatIi+ffvSvn17Ll++bPI1MSlTpsTKyoqNGzeSIkUKHjx4wMyZM7l9+7bJvY0sLS2pWbMmM2fOJG/evK8NuyIiIh9i2LBhZMmShRUrVjB16lRSpUpF06ZN6dChQ4yg9zZ3796NsTL3X3fu3CFp0qQf02WzZWH475pgPBYVFcWECRPw9/fn6dOnFC5cmIEDB5p8Qti0aRMzZ87k/Pnz2NvbU61aNX766ac3fhI4dOgQc+bM4ejRo4SGhpI0aVI8PT3p3Lmzse09e/bw888/c+nSJZImTUrRokXJkSMHM2fOZM+ePSROnJhly5YxevRo0qdPz5o1azh37hxDhgzh8OHDpEiRghYtWrB9+3YKFixI586d8ff3p0+fPjFurXLq1ClGjhzJsWPHSJs2Lc2aNWPIkCHGb9BYu3YtU6dO5caNG6RKlYrSpUtjbW3Ntm3b2LJli/GP7NKlS5QvX54hQ4bQoEGDj/79+00dTsj1Kx/djoiIxE+p02Wkcef+cd0N+Q+zCnvyfgICAmjdujW7du3C3t7+o9tT2BMR+bop7H2ZzGYZV97duXPnCA4OZubMmXz33XexEvRERETky2Q2V+PKu7t48SJ9+vQhadKkdO3aNa67IyIiIp+QZva+QmXLluXIkSNx3Q0RERH5DDSzJyIiImLGFPZEREREzJjCnoiIiIgZU9gTERERMWMKeyIiIiJmTGFPRERExIwp7ImIiIiYMd1nT2JNitRp47oLIiISh/Q+8GXSd+NKrIiOjsbSUhPFIiJfO70ffHkU9kRERETMmKK3iIiIiBlT2BMRERExYwp7IiIiImZMYU9ERETEjCnsiYiIiJgxhT0RERERM6awJ7HCEK07+IiIiPmKz+9zus+exJpLW04Rfv9xXHdDREQkVtkmT0xm79xx3Y0Ppq9Lk1gTfv8xT+6ExXU3RERE5CVaxhURERExYwp7IiIiImZMYU9ERETEjCnsiYiIiJgxhT0RERERM6awJyIiImLGFPZEREREzJjCnoiIiMg7ioqKYvbs2Xh7e+Pm5kb16tVZvXq1SZ2//vqLOnXq4OrqipeXF8OHDycszPQ+tBEREUyYMIHSpUsb29mwYYNJnXv37tG/f388PT1xd3enWbNmHDt27L37rLAnIiIi8o4mTJjAlClTqFu3LrNmzaJ48eL06tWLtWvXArBlyxbat29P4sSJmTRpEv379ycwMJBmzZoRGRlpbKdHjx74+fnRpk0bZs6cSa5cuejWrRs7d+4EIDo6mg4dOrBjxw569OjBlClTsLKyolmzZly8ePG9+vzVfYNGdHQ006ZNY/ny5YSGhlKwYEEGDRpE5syZjXVCQkL4+eef2blzJwkSJMDT05N+/fqRIkWK17Z77NgxfH19OXToEE+ePCFdunSUL1+etm3bYmdn90nHNHXqVFauXMmff/75SfcjIiLyNXv06BF+fn40a9aMNm3aAODh4cHJkyfx8/OjWrVqTJ06lezZszN37lxsbGwAKFSoEOXKlcPf35969eoREBDApk2bmD17NqVKlTK2c+nSJXbu3EnJkiXZv38/hw8fNqlTqFAhihUrxooVK+jevfs79/urm9nz9fVlyZIlDB8+nKVLl2JhYUHr1q2JiIgAnk+rtmzZkitXrvDLL78wa9YsTp06Re/evV/b5pkzZ2jSpAlZs2blt99+Y8OGDfTo0YP169fToUOHzzU0ERER+YRsbW1ZunQpLVq0MCm3trY25ojz58/j6elpDHoAjo6OfPPNN/z1118AbNq0iYwZMxpDHICFhQVLliyhf//+AOTLl4/FixdTokQJk/1YWFgY9/WuvqqZvYiICObPn0/Pnj2Nv+CJEyfi5eXFli1bqFKlCuvWrePatWts2bKFlClTAtC3b1+GDBlCWFjYK2fp/P39yZQpk0kgzJgxIwkTJuSHH34gKCgIFxeXzzNIERER+SSsrKyM7+cGg4E7d+7g7+/P3r17GTZsGADJkyfn2rVrJts9e/aMGzduGENaUFAQOXPmZO3atfj6+nLp0iUyZcpE165dqVChAgCJEyemQIECAERGRnLlyhWmTZuGwWCgVq1a79Xvr2pmLygoiEePHlGsWDFjmYODA7lz5yYgIACAXbt2UaxYMWPQA/Dy8mLr1q2vXY61sLDg2rVrBAcHm5R7eHiwfv16smbNCoCPjw9NmjQxqTN16lTKlCljfOzs7MyyZcto0aIFbm5ueHl5MWvWLJNtli5dajwxtEOHDvz7778mz585c4YOHTpQtGhR8ubNi7e3NwsWLACen+yZN29eVq1aZbLNuHHj+O677177uxMREZH/t3btWjw9PZkwYQIlS5akcuXKANSqVYvNmzcze/Zs7t27x/Xr1+nXrx9hYWE8efIEeP5efOzYMcaNG0fbtm2ZM2cO2bJl48cff2THjh0x9jV48GAqVqzIunXraNWqFTlz5nyvvn5VYe/mzZsApE2b1qQ8derU3LhxA4CLFy+SIUMGpk+fjre3N99++y0DBgwgNDT0te3Wr18fa2trqlevTv369Rk/fjw7d+4kKiqK7NmzY2tr+179HDNmDDVr1mT16tXUrl2bCRMmEBgYCMD69esZOnQozZs3Z/Xq1eTPn59FixYZt33y5AktWrQgceLE/P7776xfv55KlSoxcuRI/vnnH1KkSEHp0qVNwl50dDRr1659708KIiIiX6t8+fLh5+fHsGHDOHXqFA0aNCA8PJzOnTvTunVrpkyZgoeHB+XLlydJkiSUK1eORIkSAc9n+m7fvo2vry81a9akRIkSTJkyhezZs+Pr6xtjX/Xq1WPhwoV07NiR2bNnM3DgwPfq61cV9l4k6pfX0eH5Gnx4eDgAYWFhrFq1itOnTzN+/HiGDh3KwYMH6dChAwaD4ZXtZs6cmTVr1tCiRQvu37/P7Nmzad26NZ6enixbtuy9+/ndd99Ro0YNsmbNyk8//UTSpEk5ePAgAL/99huVK1emUaNGZM2alTZt2vDtt9+ajLFp06YMHjyYbNmykTlzZjp16gTA6dOnAahduzb79+/n1q1bAOzbt4+7d+9StWrV9+6riIjI1yhz5swULlyYevXqMXbsWIKDg9m0aRNWVlb06NGDgwcPsn79evbu3cugQYMICQkhWbJkACRJkoRUqVKRJ08eY3sJEiTAw8ODf/75J8a+3NzcKFKkCF26dKF169YsX77cOIH1Lr6qsJcwYUKAGCc2hoeHG9O2tbU1iRMnZvz48cZl1J9//pmAgACOHz/+2rbTpElD79692bx5M9u3b2fEiBFkzJiRAQMGvHJK9k2yZctm8tjOzo5nz54BEBwcjKurq8nz7u7uxn+nSJGC77//ng0bNjB06FBatWpF6dKlgeczeAAlS5bE0dHReF+glStXUqZMGZInT/5e/RQREfma3L17l5UrV3L37l2T8hfvyzdv3uTAgQPs2rULW1tbsmfPjoODA5GRkZw+fZrcuXMDz4Pis2fPYkwiRUZGGrPKmTNnWLFiRYw+uLq6YjAYFPZe58XybUhIiEl5SEgITk5OADg5OZE1a1asra2Nz+fIkQOAq1evvrLdsWPHsm/fPpP91KlThyVLluDk5GQS9l51YP/rvzOP/93uv2283Nc7d+5QvXp1li5dSsqUKWnQoAH+/v4m9RMkSEDNmjVZu3Ytjx8/ZuvWrdSuXfuVYxMREZHnHj9+jI+PD8uXLzcp37VrF/D8vPv//e9/DBgwwDhJA7BixQpCQ0Px9vYGoFSpUjx48IA9e/YY60RERLBr1y4KFiwIwNGjR+nbty+HDh2KsS8bGxu++eabd+73V3U1rouLC3Z2duzfv59MmTIBEBoayqlTp2jcuDHw/B42v/32G0+fPjWm6xcXXrx8L76X7d27l+DgYDw8PEzKbWxsSJgwIY6OjsDzUPbw4UOTOpcuXXqvMeTKlYuDBw/SrFkzY9nLM45r167lwYMHbNq0yRgCXyzfvhwSa9euzZw5c/Dz88POzg5PT8/36oeIiMjXJmPGjNSsWZPp06djaWmJq6srJ06cYMaMGXh6elKyZEnSpk3LsmXL8PHxoU6dOpw+fZpx48ZRpUoVChUqBEC1atXw8/OjR48edO/enTRp0vDbb79x8+ZNJk+eDEDlypWZP38+3bt3p0uXLqRKlYpt27axePFiunbtioODwzv3+6ua2bOxsaFx48aMGzeObdu2ERQURNeuXXFycjKm7QYNGpAgQQK6d+9OcHAwBw8epH///hQtWtRkbf1lXbt2Ze/evfz4448EBARw7do1AgIC6NmzJ48ePaJ+/foAFChQgNOnT+Pv78/Vq1fx8/N77yXeNm3asGXLFubOncvFixdZuHAhmzZtMj7v5OTEkydP2LhxI9evX2f37t1069YNMF2+zpo1KwUKFGD69OnUrFmTBAkSvFc/REREvkbDhg2jQ4cOrFixgjZt2rB48WKaNm3KjBkzsLCwIGfOnMyaNYsLFy7Qrl07Fi5cSLt27Rg9erSxDWtra3755RcqVKjAxIkT6dy5M48ePeLXX381Zo3EiROzYMECPDw8mDhxIu3bt+fw4cP8/PPPtG3b9r36bGF43VUHZioqKooJEybg7+/P06dPKVy4MAMHDiRDhgzGOhcvXmTUqFHs378fGxsbypUrR58+fbC3t39tu4cOHWLOnDkcPXqU0NBQkiZNiqenJ507dza2HRkZyZgxY1i9ejURERGULFkSd3d3fvvtN+O3Xzg7OzNq1CiTK2PLlCnDd999R+fOnQHYsGEDU6dO5erVq+TPnx93d3fWrVvHn3/+icFgYPz48axcuZKwsDDSp09P3bp12bZtGxkzZmTUqFHGdpcvX07//v3ZsGFDjPMEP0TwskCe3Al7e0UREZF4JFFKO3LWKxTX3fhgX13Yk/83bdo09uzZw+LFi2OlPYU9ERExR/E97H1V5+zJc4GBgVy8eJEFCxYwdOjQuO6OiIiIfEIKe1+hv/76i0WLFlG7dm0qVaoU190RERGRT0jLuBJrtIwrIiLmKL4v435VV+OKiIiIfG0U9kRERETMmMKeiIiIiBlT2BMRERExYwp7IiIiImZMYU9ERETEjCnsiYiIiJgx3VRZYo1t8sRx3QUREZFYF9/f33RTZYkVhmgDFpYWcd0NERGRTyI+v88p7ImIiIiYMZ2zJyIiImLGFPZEREREzJjCnoiIiIgZU9gTERERMWMKeyIiIiJmTGFPRERExIwp7ImIiIiYMYU9ERERETOmsCciIiJixhT2RERERMyYwp6IiIiIGVPYExERETFjCnsiIiIiZkxhT0RERMSMKeyJiIiImDGFPflg0dHRTJkyBS8vL/Lly0fLli25dOlSXHfrnVy7dg1nZ+cYP8uXLwfgn3/+oXHjxuTPn5/SpUszb948k+2/5LH7+vrSpEkTk7LYGM/b2vhcXjW+Pn36xDiWJUuWND7/JY/vwYMHDBw4kJIlS1KgQAEaNmxIYGDgO/frSx4bvH188fnYAdy9e5eePXtSrFgx3N3dadOmDWfPnn3nvsX38cX34/eyCxcu4O7ujr+//zv3Ld6MzyDygaZOnWrw8PAwbN++3fDPP/8YWrZsafD29jaEh4fHddfeatu2bQZXV1fDrVu3DCEhIcafJ0+eGO7du2coWrSooV+/foazZ88a/vjjD4Orq6vhjz/+MG7/pY79l19+MTg7OxsaN25sLIuN8bxLG3E1PoPBYPjuu+8MEyZMMDmWd+/ejRfja9GihaF69eqGgIAAw7lz5wzDhg0zuLm5Gc6ePWsWx+5N4zMY4vexMxgMhrp16xrq169vOHbsmOHs2bOGzp07G0qUKGF4/PixWRy/N43PYIj/x++FiIgIQ61atQw5c+Y0rFix4p37Fl/Gp7AnHyQ8PNzg7u5u+P33341l//77r8HNzc2wbt26OOzZu5kxY4ahevXqr3xu5syZBi8vL8OzZ8+MZePHjzdUqFDBYDB8mWO/efOmoVWrVob8+fMbKlasaBKGYmM8b2vjU3vT+CIjIw2urq6GLVu2vHLbL3l8Fy9eNOTMmdNw8OBBY1l0dLTB29vbMGnSpHh/7N42vvh87AyG52/kXbt2NQQHBxvL/vnnH0POnDkNR48ejffH723ji+/H72Xjx483NGnSxCTsxffj9zIt48oHCQoK4tGjRxQrVsxY5uDgQO7cuQkICIjDnr2b06dPkz179lc+FxgYSOHChbGysjKWFStWjAsXLnD37t0vcuwnT54kadKkrFmzhnz58pk8FxvjeVsbn9qbxnfx4kXCw8PJli3bK7f9kseXPHlyZs+eTd68eY1lFhYWGAwG/v3333h/7N42vvh87OD5+CZMmECOHDkAuHPnDvPmzcPJyYns2bObxfF70/ji+/F7ISAggKVLlzJ69GiT8vh+/F6msCcf5ObNmwCkTZvWpDx16tTcuHEjLrr0XoKDg7l79y7ff/89xYsXp2HDhuzatQt4PjYnJyeT+qlTpwbg+vXrX+TYy5Qpw/jx48mYMWOM52JjPG9r41N70/iCg4OxsLBgwYIFlClThnLlyjFs2DAePnxo7Dt8meNzcHCgVKlS2NjYGMs2btzI5cuX8fT0jPfH7m3ji8/H7r8GDBhAiRIl+N///seIESNInDhxvD9+L3vV+Mzh+IWGhtKrVy/69+8fo5/mdPwU9uSDPHnyBMDkRRzA1taW8PDwuOjSO4uIiODixYuEhYXx008/MXv2bFxdXWndujX79u3j6dOnrxwXQHh4eLwbe2yM521txKUzZ85gaWlJ+vTpmTlzJr1792bHjh106NCB6OjoeDW+gwcP0rdvX8qWLUuZMmXM7tj9d3zmdOyaNWvGihUrqF69Oh07duTkyZNmdfxeNT5zOH6DBw8mf/78VKtWLcZz5nT8rN5eRSSmhAkTAs+D04t/w/P/eRMlShRX3XonNjY2BAQEYGVlZfwjzJs3L+fOnWPevHkkTJiQiIgIk21e/FEmTpw43o09NsbztjbiUufOnWnevDkODg4A5MyZk1SpUlG/fn2OHz8eb8a3detWevToQb58+ZgwYcI79Su+jA1ePT5zOXaA8bSQYcOGceTIEfz8/Mzq+L1qfCNHjozXx2/VqlUEBgaydu3aVz5vTsdPM3vyQV5MW4eEhJiUh4SExJiy/hIlTpw4xqetnDlzcuvWLZycnF45LoA0adLEu7HHxnje1kZcsrCwML7ZvJAzZ07g+RJKfBifn58fnTt3pmTJksyZM8f4xmEux+5144vvx+7u3busW7eOqKgoY5mlpSXZsmUz9jE+H7+3jS++H78VK1Zw9+5dSpcujbu7O+7u7gAMGjSIKlWqxPvj9zKFPfkgLi4u2NnZsX//fmNZaGgop06dolChQnHYs7cLCgrC3d3d5F5fACdOnCB79uwULlyYgwcPmrzA7du3j6xZs+Lo6Bjvxh4b43lbG3Gpe/futGrVyqTs+PHjwPPZiC99fL///jvDhg2jUaNGTJo0yeRDiDkcuzeNL74fu5CQELp3786BAweMZc+ePePUqVNky5Yt3h+/t40vvh+/cePGsWHDBlatWmX8AejSpQuzZ8+O98fPxGe99lfMyoQJEwxFihQxbN261Xh/ofLly8f5vebeJioqylC3bl1D1apVDQEBAYazZ88aRo4cacibN68hKCjIcOfOHUPhwoUNvXv3Npw5c8awYsUKg6urq8Hf39/Yxpc89t69e5vcmiQ2xvMubcTV+P7880+Ds7OzwdfX13Dp0iXD9u3bDWXKlDF069btix/f+fPnDXny5DF07NjR5D5lISEhhtDQ0Hh/7N42vvh87AyG57eRadmypaFChQqGgIAAw+nTpw1du3Y1FC5c2HDt2rV4f/zeNr74fvxe5eVbr8T34/cyhT35YJGRkYYxY8YYihUrZsifP7+hdevWhitXrsR1t97J3bt3DX369DGUKFHC4Orqaqhfv74hICDA+PzRo0cN9erVM+TNm9fw7bffGhYuXGiy/Zc89v+GIYMhdsbztjY+l1eN73//+5+hZs2aBjc3N0OJEiUMP//8s+Hp06fG57/U8c2YMcOQM2fOV/707t37nfr1pY7tXccXX4/dC6GhoYZBgwYZSpQoYXBzczO0bNnS5L508fn4GQxvH198P37/9XLYe5e+xZfxWRgMBsPnm0cUERERkc9J5+yJiIiImDGFPREREREzprAnIiIiYsYU9kRERETMmMKeiIiIiBlT2BMRERExYwp7IiIiImZMYU9E5DPT7U1F5HNS2BMReQc+Pj44Ozu/8adMmTJvbCMiIoJRo0axdu3a99q3v78/zs7OXL169b37HRERwcKFC6lbty4FChSgQIECfPfdd8yfP58nT568d3sv7N+/H2dnZ5PvBRWRL5NVXHdARCQ+6NChAw0aNDA+9vX15dSpU0ybNs1YZmNj88Y2QkJC+PXXXxk1atQn6+fLHj58SOvWrQkKCqJhw4Z06dIFCwsLAgMDmTFjBitXrmTOnDk4OTl9lv6ISNxQ2BMReQeZMmUiU6ZMxscpUqTAxsaG/Pnzx12n3qJfv34EBwezePFicuXKZSz39PSkRo0aNGzYkB49erBw4UIsLCzisKci8ilpGVdEJJYcP36cVq1aUbRoUQoUKEC7du04c+YMAFevXqVs2bIA9OnTx2TJd/ny5dSqVYv8+fPj5uZGjRo12LBhw0f15cyZM2zatIk2bdqYBL0XsmbNyo8//khAQAB///038Hy5OHfu3CxfvhxPT09Klixp7P+SJUuoUKECbm5uNG7cmOvXr8do8/r163Tr1o0iRYqQL18+mjVrxqlTp4zPX716FWdnZ3755RcqVapEkSJF8Pf3Jzw8nCFDhlCyZEny5s1LxYoVmT9//keNX0T+n8KeiEgs+Pvvv2nYsCHR0dGMGDGC4cOHc+PGDRo0aMC5c+dInTq1ccm3ffv2xn8vWrSIgQMHUrZsWWbNmsXYsWOxtramZ8+erwxU72rXrl0AxoD5KpUrV8bCwoJt27YZy6Kiopg5cybDhw/np59+Inv27Pj5+TFo0CC8vLzw9fUlX758DBgwwKSte/fu0aBBA06ePMmAAQMYP3480dHRNGrUiHPnzpnUnThxIq1atWL48OEUK1aMESNGsGPHDnr37s28efMoW7Yso0ePxt/f/4PHLyL/T8u4IiKxYPz48WTMmJG5c+eSIEEC4Plyqbe3N1OnTmXSpEnGGbZMmTKRO3duAK5cuULLli3p2LGjsa0MGTJQq1YtDh06RLp06T6oPy8u5siQIcNr6yRNmpSkSZNy7do1k/J27dpRunRp4PmVw76+vlSoUIH+/fsbxxUWFsaSJUuM2yxYsIAHDx6wePFi0qdPD0DJkiWpXLkykydPZsqUKca65cuXp06dOsbHBw4coHjx4lSpUgWAokWLkjhxYpInT/5BYxcRUwp7IiIf6fHjxxw/fpyOHTsagx6Ag4MD3377LTt27Hjttj4+PsDziykuXrzIxYsX2bdvHwDPnj374D69uL2LldWbX+atrKxi3AomZ86cxn+fP3+eu3fvxpghrFSpkknY27dvH7ly5SJNmjRERkYCYGlpScmSJVmzZs1r24fn4W7JkiXcunWLb7/9llKlSpmEXxH5OAp7IiIf6eHDhxgMBlKmTBnjuZQpU/Lw4cPXbnv58mUGDhzI33//jZWVFd988w3Ozs7Ax92P78Xs2rVr18iSJcsr64SFhXHv3j1j3RccHR2N//7333+B5xekvCxVqlQmjx88eMClS5fIkyfPK/f18m1e/vt76tevH05OTqxZs4YhQ4YA4O7uzsCBA40zoCLy4RT2REQ+kr29PRYWFty5cyfGc7dv3yZZsmSv3C46Opo2bdpgbW3NsmXLyJ07N1ZWVpw9ezbGbNj7KlOmDGPHjmXTpk20bdv2lXW2bNlCdHT0G8/re7GUevfuXZPyBw8emDy2t7enSJEi9OrV65XtvOm2NDY2NrRv35727dtz/fp1/vrrL3x9fenevTsbN2587XYi8m50gYaIyEdKnDgxefPmZcOGDURFRRnLHz58yPbt2ylYsCCAyRIvwP3797lw4QJ16tTBzc3NuOS6c+dO4HkY/FDffPMNVatWZebMmSZXxL5w5coVxo0bh7u7O8WKFXttO1myZCFt2rT873//Myn/66+/TB4XKVKECxcukDVrVlxdXY0/a9asYfny5THG/sLTp0+pUKGC8erbdOnS0ahRI6pUqcLNmzffd9gi8gqa2RMRiQXdu3enVatW/PDDDzRu3Jhnz54xe/ZsIiIi6NSpE/B89guen9+WLVs28uXLR/r06Vm0aBFOTk44ODiwe/duFixYAPBR33ABMHjwYEJCQvj+++9p1KgRxYsXx9LSksOHD7NgwQJSpkzJhAkTsLR8/ed+CwsLevToQffu3enfvz8VK1bkyJEjLF682KRe8+bNWb16Nc2bN6dly5YkT56cDRs2sGzZMvr06fPa9hMmTEiePHmYNm0a1tbWODs7c+HCBVauXEmFChU+avwi8pzCnohILPDw8OCXX35hypQpdOvWDRsbGwoVKsTo0aPJkSMHAHZ2drRo0YKlS5eyfft29uzZg6+vLyNGjMDHxwcbGxuyZ8/OjBkzGDlyJIGBgTRp0uSD+2Rvb88vv/zCihUrWLlyJcuWLSMqKoosWbLQunVrGjVqRKJEid7aTtWqVbG0tMTX15fVq1eTM2dOhg4dSrdu3Yx10qRJw5IlSxg/fjyDBw8mPDycLFmyMGLECJMrb19l6NChTJo0ifnz53P79m0cHR2pU6cOP/744wePXUT+n4VB38gtIiIiYrY0syciEo+8uK3Jm1hYWLz2HDkR+fpoZk9EJB55cVuWNylSpAgLFy78DL0RkfhAYU9EJB45fvz4W+skSZKEb7755jP0RkTiA4U9ERERETOm++yJiIiImDGFPREREREzprAnIiIiYsYU9kRERETMmMKeiIiIiBlT2BMRERExYwp7IiIiImZMYU9ERETEjP0f5rXau2jpAdUAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total orders per day\n",
    "ax = sns.barplot(data=df_dow, x=\"Total_Orders\", y=\"Day\", orient=\"h\")\n",
    "ax.bar_label(ax.containers[0])\n",
    "ax.grid(False)\n",
    "sns.despine(bottom = True, left = True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Revenue per day\n",
    "ax = sns.barplot(data=df_dow, x=df_dow[\"Total_Revenue\"].round(1), y=\"Day\", orient=\"h\")\n",
    "ax.bar_label(ax.containers[0], fmt = '%d')\n",
    "ax.grid(False)\n",
    "sns.despine(bottom = True, left = True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total orders per Month\n",
    "ax = sns.barplot(data=df_Month, x=\"Total_Orders\", y=\"Month\", orient=\"h\")\n",
    "ax.bar_label(ax.containers[0])\n",
    "ax.grid(False)\n",
    "sns.despine(bottom = True, left = True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Revenue per Month\n",
    "ax = sns.barplot(data=df_Month, x=df_Month[\"Total_Revenue\"].round(1), y=\"Month\", orient=\"h\")\n",
    "ax.bar_label(ax.containers[0], fmt = '%d')\n",
    "ax.grid(False)\n",
    "sns.despine(bottom = True, left = True)\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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X0qlTJ/7++2+9l35WlLm5OdOnT+f111/H1taW5cuXk5ubyyuvvAJorv9fs2YNo0ePZvz48bi6urJ7925WrFjBiBEj7imX3I+DgwMvvfQSX3zxBebm5vTv35/z58+zaNEiPD09efzxx8vc1+jRo9m0aROjR4/mtddew9HRkY0bN7Jnzx7mzJmDiUnJnyeef/551q9fzwsvvMCECRMoLCzks88+09kXLy8vHn30Ud555x0uXLhAhw4dSEpKYuHChbRo0QI3N7cS++/Rowf//e9/efvtt3n00UfJz89n5cqVODg4EBQUdN/9+vrrr7GysqJTp078/vvv/PXXX3z66aeVjkmfV199lT/++IMXXniBF198kYyMDBYuXKgzDn5+fnz//ffMnTuXvn37kpaWRnh4OFevXtV+4i/WpUsX2rRpw759+0qc0Srm6enJsGHD+OKLL8jNzaV79+7ExcXxxRdf0L17d0JCQsq1L6GhoaxatQr4J1np3r07H3/8MYBOQjFq1Ch++uknRo0axZgxY3B0dGTLli2sW7eOadOmafsLDAzklVde4ZVXXsHDw4MjR47w+eefExwcrPfeKI0bN2by5Mm8//77/PTTT/ecDyNqN0koRK3h4ODArFmzeO2113SWjxs3jmvXrrFq1Sry8/Pp06cPs2fP5uWXXzbI6zZs2JApU6Ywf/58rly5gr+/P2vWrKFNmzYA2NjY8O233/Lpp58yb948bty4QfPmzXnjjTcYM2ZMuV9vwoQJNG7cmDVr1vDDDz/g4ODAww8/zKRJk7C2ti5zP87Oznz//fd8+umnzJ49m/z8fHx8fFiyZAn9+/e/77aOjo58//33zJ49m7fffpsGDRrw4osv3nOPgY8++ohly5axdu1aUlNTcXJy4pFHHmHSpEn3nZnp3bs38+fPZ9WqVdoTMbt06cI333yjPTG2JNOnT+fHH39k2bJltGnThsWLF/PQQw9VOiZ93NzcWLNmDXPnzmXy5Mk4OTnx1ltvMXfuXG2bYcOGcf78eSIjI/nuu+9o2rQpoaGh/Otf/+Kdd94hMTERT09Pbfs+ffpw5coVBgwYUOrrz549m9atWxMZGUl4eDhNmjRh5MiRvPrqq/dNCPXp0qULdnZ2NG7cWHseSrt27WjYsCGOjo46l5w2bdqUtWvX8umnnzJr1izy8vJwc3Nj9uzZPPHEEwCYmJiwfPlyFi1axLJly0hPT6dp06aMGjVK5/Lcuz399NP8+OOPzJkzh5CQELkpWx2iUhRFMXYQQghRmr179/Lcc8/xzTffaKfsaxtFURgyZAjdu3fnnXfeMXY4QhiUzFAIIUQVu3nzJl9//TVHjx4lOTn5vndJFaK2koRCCCGqmJWVFWvXrqWoqIjZs2fTqlUrY4ckhMFJyUMIIYQQlSaXjQohhBCi0iShEEIIIUSlSUIhhBBCiEqr8ydlHjx4EEVRynVzISGEEEJobvWuUqkICAgotW2dn6FQFAVDn3eqKApqtdrg/dYFMjb6ybiUTMZGPxmXksnY6FcV41Ke99A6P0NRPDPRsWNHg/WZnZ1NXFwcnp6epT7+uL6RsdFPxqVkMjb6ybiUTMZGv6oYlzsfcFeaOj9DIYQQQoiqJwmFEEIIISpNEgohhBBCVJokFEIIIYSotAonFElJSQQEBLBhwwbtsri4OEaMGEGnTp3o06cP4eHhOtsUFRWxePFiQkJC8Pf3Z8yYMaSkpOi0Ka0PIYQQQtQ8FUoo8vPzefPNN8nOztYuy8jIYPTo0bi5uREZGcmECRNYtGgRkZGR2jZLlixh7dq1fPjhh0RERKBSqRg7dixqtbrMfQghhBCi5qnQZaOff/45DRo00Fm2bt06LCwsmDVrFmZmZnh4eJCSksKKFSsICwtDrVazatUqpkyZQmhoKAALFy4kJCSErVu3MmjQoFL7EEIIIUTNVO4ZipiYGCIiIvj44491lsfGxhIYGIiZ2T85SlBQEElJSaSnpxMfH8+tW7cICgrSrre3t6d9+/bExMSUqQ8hhBBC3KuwSOF40jWOJmdzPOkahUXVf9Ovcs1QZGVlMXXqVGbOnImrq6vOutTUVLy8vHSWNWnSBICLFy+SmpoKcM92TZo04dKlS2Xqw8nJqTzhaimKolOeqaycnBydf8U/ZGz0k3EpmYyNfjIuJZOx0bX3+GW+3nKSa1l5AETuvkYj+2OMesSb7r5NK9W3oiioVKoytS1XQjFr1iw6derEkCFD7lmXm5uLhYWFzjJLS0sA8vLytP/x+tpkZmaWqY+Kys/PJy4ursLblyQ5OdngfdYVMjb6ybiUTMZGPxmXksnYwIlzOayLuncG/1pWHgvWHuHJECfat7Su1Gvc/b5ckjInFBs3biQ2NpbNmzfrXW9lZaU9ubJYcRJgY2ODlZUVAGq1Wvt9cRtra+sy9VFR5ubmeHp6Vnj7u+Xk5JCcnIybm5s2dqEhY6OfjEvJZGz0k3EpmYyNRlGRwuKfo+7bZtuRmwx7IAATk7LNMtwtMTGxzG3LnFBERkaSnp5Onz59dJa/9957hIeH06xZM9LS0nTWFf/ctGlTCgoKtMtatWql08bHxwcAFxeX+/ZRUSqVqkru925tbS33kS+BjI1+Mi4lk7HRT8alZPV9bI4mXtWWOUqSnplHUmoOHT0bV+g1ylrugHIkFPPnzyc3N1dn2YMPPsjEiRN55JFH+OWXX1i7di2FhYWYmpoCEB0djbu7O05OTtjZ2WFra8vevXu1CUVWVhYnTpxgxIgRAAQGBt63DyGEEEJATl4Bv+9NKb0hcC0rt/RGBlDmqzyaNm1K69atdb4AnJycaN68OWFhYdy8eZMZM2aQmJjIhg0bWL16NePGjQM0NZgRI0Ywf/58tm3bRnx8PJMnT8bFxYUBAwYAlNqHEEIIUZ/dzFbz/e8neeHD3/n7wPkybdPI3qr0RgZgsMeXOzk5sXLlSmbPns2wYcNwdnZm6tSpDBs2TNtm4sSJFBQUMHPmTHJzcwkMDCQ8PFx7wkdZ+hBCCCHqm4wbufy0/TRbdieRk1cIgIuTDTez87mZk1/ido0drGnfpnpm+CuVUJw8eVLnZz8/PyIiIkpsb2pqypQpU5gyZUqJbUrrQwghhKgv0q5ls+HvRLbuTUFdUASAm6s9w/u3pZd/c/Yeu8RHq2NK3H7s0A6YVvCEzPIy2AyFEEIIIQzj3OUbrP/zFNsPnNfepMq7tSNPPuBFYLum2pMle/o1Y9rzgSzfeJT0zH/OlWjsYM3YoR3o6des2mKWhEIIIYSoIU6fv84P206x++hFlNs3u+zU1pnhD7Slo0djvVdd9PRrRvcOrhyIu8Dx+CR8fdzp3K55tc1MFJOEQgghhDCy42fS+WFbAvvj/7l1QndfF558wAuvVo6lbm9qosLXvREmuZdp596o2pMJkIRCCCGEMApFUTh48grrtiVw/IzmbpcmKugd0IIn+rWltau9kSMsH0kohBBCiGpUVKSw59glftiWQOJ5zaMnzExN6B/YkrC+bXFt3KCUHmomSSiEEEKIalBQWMSOg+dZ/+cpzl2+CYClhSkDe7jxWKgHTg1r923EJaEQQgghqpA6v5A/Ys4S+Vciadc0T75uYG3O4GB3hgS3oaGtpZEjNAxJKIQQQogqkJ2bz2/RyWzcfpqMG5pnbjjYWjI01INHerphY2Vu5AgNSxIKIYQQwoBuZKvZHHWGzVFntHexbOxgTVhfTwZ0b42luamRI6waklAIIYQQBnAtK5eN20/z6+4kctWa22M3d27AE/28CO3cAnOzMj8+q1aShEIIIYSohNT0W2z4K5E/Ys6Sf/v22G2aNWT4A23p0bGZUe4JYQySUAghhBAVcO7yDX7YlsD2gxcoun177HZujXjyAS+6+DTRe1fLukwSCiGEEKIcEs9dZ922BPYcu6S9PXZn7yYM798W3zZO9S6RKCYJhRBCCFEKRVE4fiaddX8kcDDhinZ5j46uDO/flrYtS789dl0nCYUQQghRAkVR2B+fxro/EohLvgaAiYmK0IDmPNGvLa1catftsauSJBRCCCHEXQqLFKKPXuSHP05x5uI/t8ce0K0Vj/f1xMWpdt4euypJQiGEEELcVlBYxN/7NbfHvnBFc3tsKwtTBvZ057FQDxrZWxk5wppLEgohhBD1Xl5+IX/sTSHy70SuZOQAYGttzpCQNgwOboN9AwsjR1jzSUIhhBCi3srOzWfL7mR+2n6a6zdv3x7bzpJhoR483KPu3R67KklCIYQQot7JvJnH5p1n+HlnErdu3x67iaM1Yf3a8kBgKyzq6O2xq5IkFEIIIeqN9Mwcze2xo5PJu3177BZNbBnevy29A1pgZlq3b49dlSShEEIIUeelpt9i/Z+n2BZzjoJCze2xPVo0ZHh/L3p0cMWkntweuypJQiGEEKLOSrmUxfo/T7Hj4Hlu3x0b3zZOPNnfiwBv53p7V8uqUO6EIj09nblz5xIVFUVeXh6BgYFMnToVT09PAKZNm8aGDRt0tmnatCk7duwAoKioiC+++IIffviBrKwsunTpwnvvvUfr1q217ePi4pg9ezbHjh3DwcGBkSNH8sILL1RmP4UQQtQjCWcz+GFbAnuOpWqXdfFpwvD+Xvi2cTJiZHVXuROKl19+GRMTE1asWIGNjQ2LFi1i1KhRbN26FWtra06ePMn48eMZMWKEdhtT039OblmyZAlr167lo48+omnTpsybN4+xY8fy888/Y2FhQUZGBqNHj+aBBx7g/fff59ChQ7z//vs4ODgQFhZmmL0WQghRKxUWKRxPusbx5GyKrK7RuZ219mmeiqJw7LTm9tiHTmluj61SQc+OzRjevy0eLRyMGHndV66EIiMjgxYtWvDyyy/Ttm1bAF555RWGDh3KqVOn8PX1JTExkVdeeQVnZ+d7tler1axatYopU6YQGhoKwMKFCwkJCWHr1q0MGjSIdevWYWFhwaxZszAzM8PDw4OUlBRWrFghCYUQQtRju49cZPnGo6Rn5gIQufsaTg2PM/axDpibmfLDHwnEp2QAmttj9+ncgif6taVlUztjhl1vlCuhcHR0ZMGCBdqfr169Snh4OC4uLnh6epKcnExeXh4eHh56t4+Pj+fWrVsEBQVpl9nb29O+fXtiYmIYNGgQsbGxBAYGYmb2T2hBQUEsW7aM9PR0nJxkqkoIIeqb3Ucu8tHqmHuWp2fmMnd1rPZnczMTHuzemmF9PGnayKY6Q6z3KnxS5jvvvKOdTfjqq6+wsbEhISEBlUrF6tWr2bFjByYmJoSGhjJp0iTs7OxITdXUslxdXXX6atKkCZcuXQIgNTUVLy+ve9YDXLx4sUIJhaIoZGdnV2Q39crJydH5V/xDxkY/GZeSydjcq6hI4VBCKonJ2eSoLtHJy6VeX4VQVKSw7McjpbYb0qs1g3u1xsHOEsCgf/drg6r4XVIUpcwnrlY4oXj++ed56qmn+P7773n11Vf57rvvOHXqFCYmJjRv3pylS5eSkpLCxx9/TEJCAqtXr9bupIWF7i1MLS0tyczUPHwlNzdX73qAvLy8CsWan59PXFxchba9n+TkZIP3WVfI2Ogn41IyGRuNE+dy+G3/dbKyNfdIYPc17G3ieLiLA+1bWhs3uEoqKlLIL1RQFyio84s0/2q/bv+c/8/3+bfXXbuZz7Usdan9N7K6xaXzZ7hUDftSkxn6d+nu9+SSVDihKL6q44MPPuDQoUOsWbOGOXPmMGrUKOztNY9z9fLywtnZmaeeeoqjR49iZaV5qIpardZ+D5pEwdpa84tiZWWFWq174BQnEjY2FZu+Mjc318ZrCDk5OSQnJ+Pm5qaNW2jI2Ogn41IyGZt/7D1+mXVR934Sz8ouZF1UOq8/7Ud336ZVHoeiKOQXFJGrLtR85RWQm6/5Pi/v9jJ1gXZ9XnG7O5brW6bOL6rSuBs2cqFdO9fSG9ZRVfG7lJiYWOa25Uoo0tPTiY6OZuDAgdorN0xMTPDw8CAtLQ2VSqVNJooVly9SU1O1pY60tDRatWqlbZOWloaPjw8ALi4upKWl6fRR/HPTphX7RVKpVBVORu7H2tq6SvqtC2Rs9JNxKVl9H5vCIoXVvybct803v52idxc37VUNoHk6Zm6e5g07J69A8+adV0iOukC7PDev4PbPd3xfnCgUb1e87e3lxfdsqAomKrCyNMPKwgxrS1Pt91YWmu+tLcywsjTF2tIMSwtTMrJy+WVXcqn9ujRuWK+PoWKG/F0qz306ypVQpKWl8cYbb+Dk5ESPHj0ATTnhxIkT9OvXjzfeeIPr168THh6u3ebo0aOAZkajZcuW2NrasnfvXm1CkZWVxYkTJ7SXmQYGBrJ27VoKCwu1SUt0dDTu7u5yQqYQos46cSZde/VCSa5ez2HsnK0AmsQgr1B718eqYmlhqn2D13nTt/zneysLM6yLv7e843udBEHzs7WlGeZmJuV6oyosUthzLPW+49PYwZr2cn8JoypXQuHj40NwcDDvv/8+H374Ifb29ixdupSsrCxGjRrFyZMnefnll/nqq68YNGgQSUlJ/Oc//2Hw4MHaKz9GjBjB/PnzadSoEc2bN2fevHm4uLgwYMAAAMLCwli5ciUzZszgxRdf5MiRI6xevZr333/f8HsvhBA1xLWs+ycTxYofrX03M1OTOz7tm97+9H/7k39xMmBpqn1zt74jGSheb235TxtLC1MsLcx0ZkOMxdRExUuPddR7lUexsUM71IhY67NyJRQqlYrPPvuMTz/9lEmTJnHjxg26du3Kt99+S7NmzWjWrBmLFi1i6dKlLF26FDs7O4YMGcKkSZO0fUycOJGCggJmzpxJbm4ugYGBhIeHa0/6cHJyYuXKlcyePZthw4bh7OzM1KlTGTZsmEF3XAghagJFUTh6+ipbdieVqf2Lj3agfZtGt2cLNCUDSwvNp/66rKdfM6Y9H6hzHwrQzEyMHdqBnn7NjBidAFApilKFlTLjKy65dOzY0WB9ZmdnExcXR7t27aRedxcZG/1kXEpWX8cmV13A9gPn2Rx1hpTUG2XaprGDNStnDKjXn8QLixQOxF3geHwSvj7udG7XvF6Px52q4nepPO+h8nAwIYSoRmnXstmyO4n/7UnhZk4+oDlPoV/XlrRwtmXFT8dK3Fam9TXlD1/3RpjkXqade6N6Px41iSQUQghRxRRF4diZdDZHnWHvsUvaKyiaNrJhcLA7D3Rrja21OaCZhZBpfVEbSUIhhBBVJC+/kB0HzrN55xmSLmZpl/u3bcyQ4DZ0be9yzyfsnn7N6N7BVab1Ra0jCYUQQhjYlYwcbVnjRrbmRn0W5pqyxuBe7rR2tb/v9jKtL2ojSSiEEMIAFEXhRNI1NkedIfrYJYpu1zWaOFozqFcbBnRvhZ1N2W5hLERtJAmFEEJUgjq/kB0Hz7M5KokzFzO1y/08GzM4uA3dfO8tawhRF0lCIYQQFXD1+j9ljaxb/5Q1+nZpweDgNriVUtYQoq6RhEIIIcpIURTikq/x884kdh25qC1rODtaM6inOwO6t8a+gZQ1RP0kCYUQQpRCnV9I1KELbN55htPn/ylrdPBwYkhwG7r7umBqWrfvVClEaSShEEKIEqRn5vDr7mR+25NM5s3bZQ0zE0I7t2BISBvcmzU0coRC1BySUAghxB0UReFkSgabo86w68hFCm+XNRo3tOKRXu482L01DW0tjRylEDWPJBRCCAHkFxQSdegim3eeIfHcde1y3zaaskZQBylrCHE/klAIIeq1a1m52rLG9Rt5AJibmRAa0ILBwe54tHAwboBC1BKSUAgh6qWTKdfYHJXEriMXKCjUlDWcGlrxSE93HgqSsoYQ5SUJhRCi3sgvKGLXYc3VGglnr2uXt3NrxJCQNvTo6IqZlDWEqBBJKIQQdV5GVi6/RSfza3QyGbfLGmamJvQOaM6Q4DZ4tnQwboBC1AGSUAgh6qyEsxls3nmGnYf+KWs0sre8XdZww8FOyhpCGIokFEKIOiW/oIjdRzRXa5xMydAu92nteLus0QxzMylrCGFoklAIIeqEjBu5/G9PCr/uTuJaVnFZQ0VIp+YMDm6DVytHI0coRN0mCUU5FRYpHE+6xvHkbIqsrtG5nbU8SVAII0o8d53NO8+w4+AFCgqLAHC0s2RgT3ceDmqNo72VkSMUon6QhKIcdh+5yPKNR0nPzAUgcvc1nBoe56XHOtLTr5mRoxOi/igoLCL6yCU27zxDXPI17XLvVo4MDmlDLz8pawhR3SShKKPdRy7y0eqYe5anZ+by0eoYpj0fKEmFEFUs82Yev+1JZsuuZK5laRJ7M1MVwf7NGRzsjnfrRkaOUIj6q9wJRXp6OnPnziUqKoq8vDwCAwOZOnUqnp6eAMTFxTF79myOHTuGg4MDI0eO5IUXXtBuX1RUxBdffMEPP/xAVlYWXbp04b333qN169baNqX1Ud0KixSWbzx63zYrfjpG9w6uUv4QogqcPv9PWSO/QFPWcLCzZGAPNx7u4UYjKWsIYXTlnhN8+eWXOXfuHCtWrGD9+vVYWVkxatQocnJyyMjIYPTo0bi5uREZGcmECRNYtGgRkZGR2u2XLFnC2rVr+fDDD4mIiEClUjF27FjUas2T/MrSR3U7cSZdW+YoydXrOZw4k15NEQlRNxSfk3Q0OZvjSde0D+ICKCwsYufhC7z1RRSTFm5nW8w58guK8GzpwOv/6syqmQP410M+kkwIUUOUa4YiIyODFi1a8PLLL9O2bVsAXnnlFYYOHcqpU6eIjo7GwsKCWbNmYWZmhoeHBykpKaxYsYKwsDDUajWrVq1iypQphIaGArBw4UJCQkLYunUrgwYNYt26dfftwxiKp1YN1U4IUfI5SSMe9iHjRh5bdiVx9fY6UxMVvfybMSSkDd6tHFGpZCZQiJqmXAmFo6MjCxYs0P589epVwsPDcXFxwdPTk88//5zAwEDMzP7pNigoiGXLlpGens6FCxe4desWQUFB2vX29va0b9+emJgYBg0aRGxs7H37cHJyqsz+VkhZPwHJJyUhyuZ+5yQtijik/bmhrQUP93BjYA83nBpaV2OEQojyqvBJme+88452NuGrr77CxsaG1NRUvLy8dNo1adIEgIsXL5KamgqAq6vrPW0uXboEUGofFUkoFEUhOzu73NsVc3exppG9pfbadn2cGlri7mJdqdepC3JycnT+FRoyLv8oKlJY9uOR+7YxNVExdmg7enV0wcLcFKjc73BtJMdMyWRs9KuKcVEUpcwzghVOKJ5//nmeeuopvv/+e1599VW+++47cnNzsbCw0Glnaam5tW1eXp52J/W1yczMBCi1j4rIz88nLi6uQtsWe8DflnVRJb9+fz9bTp6Mr9Rr1CXJycnGDqFGknGBpMu5903OQXNuRU5WGqcTs6opqppLjpmSydjoZ+hxufs9uSQVTiiKr+r44IMPOHToEGvWrMHKykp7cmWx4iTAxsYGKytNSUCtVmu/L25jba2Zziytj4owNzfXxltR7dpBi+aX+XrLSZ0/hioV/Ht4R3p0dKlU/3VFTk4OycnJuLm5af9PhYzLna7lXwKultquYSMX2rVzLbVdXSXHTMlkbPSrinFJTEwsc9tyJRTp6elER0czcOBATE1NATAxMcHDw4O0tDRcXFxIS0vT2ab456ZNm1JQUKBd1qpVK502Pj4+AKX2UREqlarCycid+ga607uLGwfiLnA07gy/HcgiJ6+QRg62Bum/LrG2tpYx0UPGBRo52JapnUvjhvV+rECOmfuRsdHPkONSnhOgy3XZaFpaGm+88Qb79u3TLsvPz+fEiRN4eHgQGBjI/v37KSws1K6Pjo7G3d0dJycnfHx8sLW1Ze/evdr1WVlZnDhxgq5duwKU2oexmZqo8HVvhL97A3renpXYdeSikaMSona4ma1m7e8nS23X2MGa9m2M//suhCi7ciUUPj4+BAcH8/777xMbG0tCQgJvvfUWWVlZjBo1irCwMG7evMmMGTNITExkw4YNrF69mnHjxgGaOsyIESOYP38+27ZtIz4+nsmTJ+Pi4sKAAQMASu2jJunRQTNjsvvIJe0zBIQQ+l3LymXakl3Ep2RgaWF637Zjh3aQm8QJUcuUq+ShUqn47LPP+PTTT5k0aRI3btyga9eufPvttzRrprnt9MqVK5k9ezbDhg3D2dmZqVOnMmzYMG0fEydOpKCggJkzZ5Kbm0tgYCDh4eHakz6cnJxK7aOmaO/miH0DC7JuqTmaeJUA7ybGDkmIGik1/RbvLNtNano2jewtef+lnly8clPnPhSgmZkYO7SD3MZeiFqo3Cdl2tnZMWvWLGbNmqV3vZ+fHxERESVub2pqypQpU5gyZUqJbUrro6YwNTWhp18zfotOZufhi5JQCKFH8qUs3l22m4wbebg6NeA/43rg4tQAN1d7undw5UDcBY7HJ+Hr407nds1lZkKIWkoex1dJwbc/SUUflbKHEHeLS7rG21/uJONGHm6u9nz8WjAuTg2064vPSeroZoOveyNJJoSoxSShqKQOHk7YN7DgRram7CGE0Ngff5mZy3ZzKyefdm6N+OiVXjjK3WSFqLMkoaik4rIHwM7DcrWHEABRBy/w4aq9qPML6eLThP+M64GtTdlujiOEqJ0koTAAKXsI8Y8tu5OY920sBYUKvQOaM2N0d6wsKnwPPSFELSEJhQF08HCioa2m7HFEyh6inlIUhYg/TvJV5BEUBR7p6cYb/+qCuZn8mRGiPpDfdAMwNTWhR0fNLMUuKXuIeqioSCF803HW/Kp5ns3TA7wZ/7gfJnKSpRD1hiQUBhLsX1z2uChlD1GvFBYWsSjiID/tOA1obkr17MM+5bplrxCi9pOEwkA6tCkue+RL2UPUG3n5hXy0OoY/Y89hYqJi8jOdebS3h7HDEkIYgSQUBmJqakJPKXuIeiQ7N59ZK6LZezwVCzMTZozqRr+uLY0dlhDCSCShMKBeUvYQ9cT1G3lM/2oXx06nY2Nlxvsv9aCbr4uxwxJCGJEkFAYkZQ9RH6Rdy+btL6M4fT6ThrYWzHm5Fx08Ghs7LCGEkUlCYUB3lj12Hrpg5GiEMLxzl2/w1hdRXLhyC2dHaz5+LQSPFg7GDksIUQNIQmFgxWWPPcfkJleibkk4m8FbX+zkamYuLZva8slrITR3tjV2WEKIGkISCgPr0MYJB1tLTdnjlJQ9RN1wOOEKM5fu4ka2mrYtHfjolWAaO1gbOywhRA0iCYWBaW5y5QrAzsNS9hC1X/TRi8xauYecvEL82zbmw/E9aWhraeywhBA1jCQUVSC4k5Q9RN2wdW8Kc1fHUFBYRI+Orrz3YhA2VubGDksIUQNJQlEFfNs0lrKHqPU2/JXI4nWHKFJgQLdWvPVcIOZmpsYOSwhRQ0lCUQVMTVT08JOyh6idFEXh65+P89+fjwMQ1teTCU92wlSeyyGEuA9JKKpIsFztIWqhwiKFL9cfJvKvRABGDWrPqMG+8lwOIUSpJKGoIlL2ELVNfkEh8/4vlv/tScFEBa8N70RYv7bGDksIUUtIQlFFpOwhapOcvAL+E76XXUcuYmZqwtTnAnkoqLWxwxJC1CKSUFShfx5pLmUPUXNl3VLzztLdHEq4gpWFKe+92J1efs2MHZYQopaRhKIKFZc9bubkc/jUFWOHI8Q90jNzePvLnZw8m4GdjTmzX+5FJ68mxg5LCFELlSuhuH79Ou+++y69e/emc+fOPPPMM8TGxmrXT5s2DW9vb52v3r17a9cXFRWxePFiQkJC8Pf3Z8yYMaSkpOi8RlxcHCNGjKBTp0706dOH8PDwSu6i8dxZ9pBHmoua5uKVm0z9PIpzl2/g1NCKua8G49XK0dhhCSFqqXIlFK+//jqHDx9mwYIFrF+/Hl9fX1544QVOnz4NwMmTJxk/fjw7d+7Ufm3cuFG7/ZIlS1i7di0ffvghERERqFQqxo4di1qtBiAjI4PRo0fj5uZGZGQkEyZMYNGiRURGRhpuj6tZiH9zQMoeomY5cyGTt77YSVpGDs0aN+CT10Jo5WJv7LCEELVYmROKlJQUdu3axXvvvUfXrl1p06YNM2bMoGnTpvz8888UFhaSmJhIx44dcXZ21n41atQIALVazapVq5gwYQKhoaH4+PiwcOFCLl++zNatWwFYt24dFhYWzJo1Cw8PD8LCwhg1ahQrVqyomr2vBu3bOOFgJ2UPUXMcP5POtCU7uX4zjzbNGjL3tWCaNLIxdlhCiFquzAmFo6Mjy5cvp0OHDtplKpUKRVHIzMwkOTmZvLw8PDw89G4fHx/PrVu3CAoK0i6zt7enffv2xMTEABAbG0tgYCBmZmbaNkFBQSQlJZGenl7unasJTE1U9OwoZQ9RM+w7kcq7y3aTnVuAbxsn5rzSC0c7K2OHJYSoA8xKb6Jhb29PaGiozrJff/2Vs2fPEhwcTEJCAiqVitWrV7Njxw5MTEwIDQ1l0qRJ2NnZkZqaCoCrq6tOH02aNOHSpUsApKam4uXldc96gIsXL+Lk5FT+PURz57/s7OwKbatPTk6Ozr+l6erjxJbdyew+epFRj7TFzLTungtb3rGpL2rCuEQdvsSSDccpKlLo7N2YyU/5oVLyyc7ON1pMUDPGpiaScSmZjI1+VTEuiqKU+cZ2ZU4o7rZ//36mT59O//796devH4sXL8bExITmzZuzdOlSUlJS+Pjjj0lISGD16tXaHbSwsNDpx9LSkszMTAByc3P1rgfIy8uraKjk5+cTFxdX4e1LkpycXLaGRQoNrEy4lVPAlr8P07ZZ3f9EWOaxqWeMNS57T97k1/3XAfBzs2FQgCWnExOMEktJ5JjRT8alZDI2+hl6XO5+Xy5JhRKKP/74gzfffBN/f38WLFgAwIQJExg1ahT29poTu7y8vHB2duapp57i6NGjWFlp3kTVarX2e9AkCtbW1gBYWVlpT9C8cz2AjU3Fa7zm5uZ4enpWePu75eTkkJycjJubmzb20vQ6o+L3fee5kGXBo/3bGSyWmqYiY1MfGGtcFEVh/V9ntMnEwB6teO5hL0xq0HM55JjRT8alZDI2+lXFuCQmJpa5bbkTijVr1jB79mwGDBjA/PnztZmLSqXSJhPFissXqamp2lJHWloarVq10rZJS0vDx8cHABcXF9LS0nT6KP65adOm5Q1VS6VSVSohKYm1tXWZ++3TpTW/7ztPbNwVzJ+ywtys7pY9oHxjU59U57gUFSms2HiUn3clAfDswz489YBXjX0uhxwz+sm4lEzGRj9Djkt5/l6U613tu+++44MPPuDZZ5/ls88+05kGeeONN3jhhRd02h89ehQAT09PfHx8sLW1Ze/evdr1WVlZnDhxgq5duwIQGBjI/v37KSws1LaJjo7G3d29wudP1BRytYeoTgWFRSz47gA/70pCpYLxwzry9ADvGptMCCFqvzInFElJScyZM4cBAwYwbtw40tPTuXLlCleuXOHGjRsMHjyYXbt28dVXX3H27Fm2b9/O9OnTGTx4MB4eHlhYWDBixAjmz5/Ptm3biI+PZ/Lkybi4uDBgwAAAwsLCuHnzJjNmzCAxMZENGzawevVqxo0bV2UDUF3kag9RXXLVBcz+7z62HzyPqYmKN/7VhUHBbYwdlhCijitzyeN///sf+fn5bN26VXvfiGLDhg1j7ty5LFq0iKVLl7J06VLs7OwYMmQIkyZN0rabOHEiBQUFzJw5k9zcXAIDAwkPD9fOdDg5ObFy5Upmz57NsGHDcHZ2ZurUqQwbNswwe2tkwZ2as2V3MtHHLvFKgX+dL3uI6nczJ58PwvdwIukaFuamTHs+kK7tKl4uFEKIsipzQjF+/HjGjx9/3zYPPfQQDz30UInrTU1NmTJlClOmTCmxjZ+fHxEREWUNq1Zp764pe1y/kcfhU1fkD70wqIysXN5bEU3SxSwaWJnxzgtB+Lap3aVCIUTtIR+Rq5GpiUr7FEcpewhDSk2/xVtf7CTpYhYOdpZ89GqwJBNCiGolCUU161X8SPNjl8gvkGd7iMpLuZTFW19EcSn9Fk0b2fDJayG4N2to7LCEEPWMJBTVrLjscUuu9hAGEJ9yjbe/3Mm1rDxau9jx8WvBuDZuYOywhBD1kCQU1ezOssfOwxeMHI2ozQ6cTGPm0t3czMnHp7UjH70ajFNDucmPEMI4JKEwguKyx55jqVL2EBUSdegCH4TvIU9dSICXMx+M64mdTdlujyuEEFVBEgojaO/uhKOUPUQF/RadzLw1sRQUKgT7N+OdF4KwsqzwY3mEEMIgJKEwAlMTFT2l7CHKSVEUftiWwJfrD6Mo8HAPN94c0VXuZyKEqBHkL5GRBEvZQ5SDoiis2nycb7Zonpr75ANevBLmh2kNesiXEKJ+k4TCSNpJ2UOUUWFhEYsiDrJx+2kAXnjUl5ED28lzOYQQNYokFEYiV3uIslDnFzL3mxi2xZzDxETFv58K4LFQT2OHJYQQ95CEwojkag9xP9m5+by/cg97jqVibmbCtOcDeaBbK2OHJYQQeklCYURS9hAlybyZx4yvdnEk8SrWlmbMGhtEUAdXY4clhBAlkoTCiO4se0QdkrKH0LiSkcNbX+wk8Xwm9g0smPNyL/w8nY0dlhBC3JckFEZWXPbYK8/2EMC5yzeY+kUUF67cpLGDNXNfDcazpYOxwxJCiFJJQmFk7dydaGRvya3cAg4lpBk7HGFEp85l8PaXO7l6PYfmzrZ8/FowLZvaGTssIYQoE0kojMzUREXPjsVXe8gjzeurI4lXmPHVLrJuqfFs6cDHrwXTxNHG2GEJIUSZSUJRAwR3ag5I2aO+ij56iVkr9pCTV4ifZ2Nmj+9JQ1tLY4clhBDlIglFDdDOrZGUPeqpP/adZe7qfeQXFBHUwYX3XgzCxsrc2GEJIUS5SUJRA5joPNtDyh71xcbtiSyKOEiRAg8EtuLt5wKxMDc1dlhCCFEhklDUEMH+d5Y9Co0cjTCkwiKF40nXOJqczfGkaxQUFvHNlhOEbzoOwGOhHkx8qhOmpvLrKISoveSZxzVEcdnjWlYehxKuENjexdghCQPYfeQiyzceJT0zF4DI3dewtDhEnlqTND73SDue6NdWnsshhKj15CNRDSFlj7pn95GLfLQ6RptMFCtOJh7u0Zrh/b0kmRBC1AmSUNQgUvaoOwqLFJZvPHrfNrFxaRQWKdUUkRBCVK1yJRTXr1/n3XffpXfv3nTu3JlnnnmG2NhY7fq4uDhGjBhBp06d6NOnD+Hh4TrbFxUVsXjxYkJCQvD392fMmDGkpKTotCmtj7rszqs9DibIsz1qsxNn0u+Zmbjb1es5nDiTXk0RCSFE1SpXQvH6669z+PBhFixYwPr16/H19eWFF17g9OnTZGRkMHr0aNzc3IiMjGTChAksWrSIyMhI7fZLlixh7dq1fPjhh0RERKBSqRg7dixqtRqgTH3UZXeWPXZJ2aNWu5Z1/2SivO2EEKKmK/NJmSkpKezatYvvv/+ezp07AzBjxgx27NjBzz//jJWVFRYWFsyaNQszMzM8PDxISUlhxYoVhIWFoVarWbVqFVOmTCE0NBSAhQsXEhISwtatWxk0aBDr1q27bx/1QbB/c37emaQte5ibyWWEtVEjeyuDthNCiJquzDMUjo6OLF++nA4dOmiXqVQqFEUhMzOT2NhYAgMDMTP7J0cJCgoiKSmJ9PR04uPjuXXrFkFBQdr19vb2tG/fnpiYGIBS+6gPNGUPKyl71HLt2zjh1PD+yUJjB2vat3GqpoiEEKJqlXmGwt7eXjuzUOzXX3/l7NmzBAcHs3DhQry8vHTWN2nSBICLFy+SmpoKgKur6z1tLl26BEBqaup9+3ByqtgfX0VRyM7OrtC2+uTk5Oj8a2jd2zvz655zbN9/lg5u9lXyGlWlqsemNnl+oBcL1h4pcf1zD7clL1fGSY4Z/WRcSiZjo19VjIuiKGW+Eq3C96HYv38/06dPp3///vTr14+PPvoICwsLnTaWlprnEeTl5Wl3UF+bzMxMAHJzc+/bR0Xl5+cTFxdX4e1LkpycbPA+AVxsNfu693gqvb1NMDOtfZcVVtXY1CY3MtR6l9vbmPJwFwfsTa4RF3etmqOqueSY0U/GpWQyNvoZelzufl8uSYUSij/++IM333wTf39/FixYAICVlZX25MpixUmAjY0NVlaa6V+1Wq39vriNtbV1mfqoKHNzczw9PSu8/d1ycnJITk7Gzc1NG7sheXsrbNwbRcaNPNTmznT0djb4a1SVqh6b2mTzmoMABPu50KtjYxKTLuLp3oxOXi6YmNS+JLGqyDGjn4xLyWRs9KuKcUlMTCxz23InFGvWrGH27NkMGDCA+fPnazMXFxcX0tJ0H2xV/HPTpk0pKCjQLmvVqpVOGx8fnzL1UVEqlapSCUlJrK2tq6Rf0DyBdHPUGWLirhIS0LpKXqMqVeXY1AaJ56+z/+RVVCr418PtcbIzxVq5Tjsf13o9LvdT34+Zksi4lEzGRj9Djkt5brxXrstGv/vuOz744AOeffZZPvvsM51pkMDAQPbv309h4T83ZIqOjsbd3R0nJyd8fHywtbVl79692vVZWVmcOHGCrl27lqmP+qTX7ctH9x5PlZtc1UIRW08CENKpOS2b2hk5GiGEqHplTiiSkpKYM2cOAwYMYNy4caSnp3PlyhWuXLnCjRs3CAsL4+bNm8yYMYPExEQ2bNjA6tWrGTduHKCpwYwYMYL58+ezbds24uPjmTx5Mi4uLgwYMACg1D7qk+KrPbJzCzh4Uq72qE2SLmay51gqKhU8PcDb2OEIIUS1KHPJ43//+x/5+fls3bqVrVu36qwbNmwYc+fOZeXKlcyePZthw4bh7OzM1KlTGTZsmLbdxIkTKSgoYObMmeTm5hIYGEh4eLh2psPJyanUPuoLExMVvfybsTnqDDsPX6CbrzwsrLb4/nfN7ESwv8xOCCHqjzInFOPHj2f8+PH3bePn50dERESJ601NTZkyZQpTpkypcB/1SfDthKK47CE3uar5ki5mEn30EioVPDXAq/QNhBCijpCHg9VgPq0b4dRQyh61ScTWBAB6+jWjtUvtuoeIEEJUhiQUNZiJiUp7cubOwxeMHI0oTcqlLHYd0TyDRc6dEELUN5JQ1HC9/OVqj9pi7e0rO3r6ueLmKrMTQoj6RRKKGk7KHrXD2VSZnRBC1G+SUNRwd5Y9oqTsUWNF/JGAokBQBxfcmzU0djhCCFHtJKGoBYrLHvuOp6LOl7JHTXPu8g2iDmmSPZmdEELUV5JQ1AK6ZY+00jcQ1Wrd7dmJ7r4ueLRwMHY4QghhFJJQ1AI6V3vcrtOLmuHClZvsOHgekNkJIUT9JglFLRHs3xyQskdNE7H1JEUKBLZvimdLB2OHI4QQRiMJRS3h3dpRyh41zMUrN9l+QDM78cyDMjshhKjfJKGoJYqf7QFS9qgpIv5IoEiBru2a0ralo7HDEUIIo5KEohYJ9tOUPfYek7KHsV26eou/DxSfOyHP7BBCCEkoapHiskdOnpQ9jO2HbQkUFSl09m6Cd+tGxg5HCCGMThKKWkSn7HFYyh7Gkpp+iz9jzwFy7oQQQhSThKKW0ZY95GoPo/lh2ykKixQ6eTnj4yazE0IIAZJQ1DrerR1pfLvscUDKHtXu8rVstsWcBWR2Qggh7iQJRS1jYqKi5+2yxy4pe1S7H7YlUFik4N+2Me3dnYwdjhBC1BiSUNRCIf5S9jCGtIw7Zyd8jByNEELULJJQ1EJeraTsYQzrt52ioFDBz7Mxvm1kdkIIIe4kCUUtpLnaQzNLIWWP6nElI4et+1IAeFrOnRBCiHtIQlFLBd8+j0LKHtUj8i/N7IRvGyc6ejQ2djhCCFHjSEJRS0nZo/qkZ+bwvz2a2Qm5skMIIfSrVEKxZMkSRo4cqbNs2rRpeHt763z17t1bu76oqIjFixcTEhKCv78/Y8aMISUlRaePuLg4RowYQadOnejTpw/h4eGVCbNOurPssfOQlD2q0vo/T1FQWER790b4ecrshBBC6FPhhOLrr79m8eLF9yw/efIk48ePZ+fOndqvjRs3atcvWbKEtWvX8uGHHxIREYFKpWLs2LGo1WoAMjIyGD16NG5ubkRGRjJhwgQWLVpEZGRkRUOts4rLHvtOSNmjqtw9O6FSqYwckRBC1EzlTiguX77Miy++yKJFi3B3d9dZV1hYSGJiIh07dsTZ2Vn71aiR5m6CarWaVatWMWHCBEJDQ/Hx8WHhwoVcvnyZrVu3ArBu3TosLCyYNWsWHh4ehIWFMWrUKFasWGGA3a1bvFo50tjBWsoeVWjDX4nkFxTRzq0R/m2djR2OEELUWOVOKI4fP07Dhg3ZtGkT/v7+OuuSk5PJy8vDw8ND77bx8fHcunWLoKAg7TJ7e3vat29PTEwMALGxsQQGBmJmZqZtExQURFJSEunp6eUNt04zMVHRy+/2sz2k7GFw17Jy+S06GdBc2SGzE0IIUTKz0pvo6tevH/369dO7LiEhAZVKxerVq9mxYwcmJiaEhoYyadIk7OzsSE1NBcDV1VVnuyZNmnDp0iUAUlNT8fLyumc9wMWLF3FyKv/1/4qikJ2dXe7tSpKTk6PzrzF19WnETztOs/f4Ja5n3sDC3NSo8dSksamsdVtPoi4oom3Lhni3aFCpY6gujYuhydjoJ+NSMhkb/apiXBRFKfOHqXInFPdz6tQpTExMaN68OUuXLiUlJYWPP/6YhIQEVq9erd1JCwsLne0sLS3JzMwEIDc3V+96gLy8vArFlZ+fT1xcXIW2vZ/k5GSD91leiqJgb2NKVnYhP/91mHYtrY0dElAzxqYybuYU8r+9mgS4m4c58fHxBum3to9LVZKx0U/GpWQyNvoZelzufk8uiUETigkTJjBq1Cjs7e0B8PLywtnZmaeeeoqjR49iZWUFaM6lKP4eNImCtbXmjdDKykp7guad6wFsbGwqFJe5uTmenp4V2lafnJwckpOTcXNz08ZtTCEppvyy+yznM815/MF2Ro2lpo1NRa35LYGCQgWP5vYM6dep0uWOujIuVUHGRj8Zl5LJ2OhXFeOSmJhY5rYGTShUKpU2mShWXL5ITU3VljrS0tJo1aqVtk1aWho+PppnI7i4uJCWpnuCYfHPTZs2rXBcFU1G7sfa2rpK+i2vPl1b88vusxw4eQVTc0ssjVz2gJozNhVx/UYev8ecB+DZh9vRoEEDg/Vdm8elqsnY6CfjUjIZG/0MOS7l+TBl0BtbvfHGG7zwwgs6y44ePQqAp6cnPj4+2NrasnfvXu36rKwsTpw4QdeuXQEIDAxk//79FBb+cxlkdHQ07u7uFTp/oj7w1l7tUciBeLnao7I2bk8kT12IZ0sHurarWBIrhBD1jUETisGDB7Nr1y6++uorzp49y/bt25k+fTqDBw/Gw8MDCwsLRowYwfz589m2bRvx8fFMnjwZFxcXBgwYAEBYWBg3b95kxowZJCYmsmHDBlavXs24ceMMGWqdolKptPek2Hn4gpGjqd0yb+bxy64kAJ4ZIFd2CCFEWRm05NG3b18WLVrE0qVLWbp0KXZ2dgwZMoRJkyZp20ycOJGCggJmzpxJbm4ugYGBhIeHa0/6cHJyYuXKlcyePZthw4bh7OzM1KlTGTZsmCFDrXN6+Tdj4/bTxJxIJS+/sEaUPWqjjdtPk6suxKNFQwLby+yEEEKUVaUSirlz596z7KGHHuKhhx4qcRtTU1OmTJnClClTSmzj5+dHREREZUKrd7xbOeLsaM2VjBwOxKfRo6Nr6RsJHVm31Pyy6wwAT8vshBBClIs8HKyOUKnuuMmVlD0qZOP2RHLyCmnTrCHdfV2MHY4QQtQqklDUIcXnURSXPUTZ3chW8/NOzbkTTz/oJbMTQghRTpJQ1CFet8semqs9Lhs7nFrlpx2nyckrwM3Vnu6+Ui4SQojykoSiDtEte8izPcrqZraazVH/nDthYiKzE0IIUV6SUNQx2keaH5eyR1ltijpDdm4BrV3s5GRWIYSoIEko6pjiskeuWsoeZXEzJ59NO04D8JTMTgghRIVJQlHH6JQ95JHmpdocdYZbuQW0bGqnHTchhBDlJwlFHaQte8jVHvd1Kyefn27PTjw9wEtmJ4QQohIkoaiDpOxRNj/vPMOtnHxaNLGll39zY4cjhBC1miQUdZCUPUqXnZvPxu3/nDthKrMTQghRKZJQ1FEhnTSfuKXsod8vu5K4mZNPc+cG2rESQghRcZJQ1FFtWzrQ5HbZY3+clD3ulJNXwI9/a2YnnnxAZieEEMIQJKGoo1Qqlfa8gF1ykysdv+xK4ka2GtfGDQgNkNkJIYQwBEko6jC52uNemtmJRACeesALU1P5FRBCCEOQv6Z1mJQ97vXr7iSybqlxdWpAn84tjB2OEELUGZJQ1GF3lj3k2R6Qm1fAhtuzE08+0FZmJ4QQwoDkL2odJ480/8ev0clk3lTTtJENfbq0NHY4QghRp0hCUcdJ2UMjV33n7IQXZjI7IYQQBiV/Ves4KXto/G9PCtdv5NHE0Zp+XWV2QgghDE0SinrgzrJHrrrAyNFUv7z8QiL/PAXA8P4yOyGEEFVB/rLWA21bOtCkkY2m7BGfZuxwqt3/9iSTcSMPZ0dr+ge2MnY4QghRJ0lCUQ/c+WyP+naTK3V+IZF/as6dGN6vLeZmcsgLIURVkL+u9cSdN7mqT2WP3/emcC0rl8YNrXigm8xOCCFEValUQrFkyRJGjhypsywuLo4RI0bQqVMn+vTpQ3h4uM76oqIiFi9eTEhICP7+/owZM4aUlJRy9SHKr7jskVePyh75BYWsv33uxBP9vTA3MzVyREIIUXdVOKH4+uuvWbx4sc6yjIwMRo8ejZubG5GRkUyYMIFFixYRGRmpbbNkyRLWrl3Lhx9+SEREBCqVirFjx6JWq8vchyg/lUpFsPaR5heMHE31+H3vWdIzc3FqaMWD3WV2QgghqpJZeTe4fPkyM2bMYP/+/bi7u+usW7duHRYWFsyaNQszMzM8PDxISUlhxYoVhIWFoVarWbVqFVOmTCE0NBSAhQsXEhISwtatWxk0aFCpfYiK6+XfjA1/JxITd5lcdQFWFuX+76818gsKWb8tAYAn+rWV2QkhhKhi5Z6hOH78OA0bNmTTpk34+/vrrIuNjSUwMBAzs3/eqIKCgkhKSiI9PZ34+Hhu3bpFUFCQdr29vT3t27cnJiamTH2IiqtPZY8/Ys5xNTOXRvaWPNi9tbHDEUKIOq/cH1H79etHv3799K5LTU3Fy8tLZ1mTJk0AuHjxIqmpqQC4urre0+bSpUtl6sPJyam8IaMoCtnZ2eXeriQ5OTk6/9Ym3ds7s3lnCtv3nyXA08Hg/deEsSkoKGLd1pMADAl2oyA/j4J8o4UD1IxxqalkbPSTcSmZjI1+VTEuiqKgUqnK1Nagc965ublYWFjoLLO0tAQgLy9Pu5P62mRmZpapj4rIz88nLi6uQtveT3JyssH7rGouDTTnqsTGpXH46HEsqugySmOOzf7Em1zNzMXWyoQWtjer5P++omrjMVNdZGz0k3EpmYyNfoYel7vfk0ti0ITCyspKe3JlseIkwMbGBisrKwDUarX2++I21tbWZeqjIszNzfH09KzQtvrk5OSQnJyMm5ubNu7awkdR2Lh3J1eu55Kjaox/u6YG7d/YY1NQWMSXW3YB8HhfT/w61oxyh7HHpSaTsdFPxqVkMjb6VcW4JCYmlrmtQRMKFxcX0tJ0a/PFPzdt2pSCggLtslatWum08fHxKVMfFaFSqSqcjNyPtbV1lfRb1UI6tdCcnBl/lX7d3EvfoAKMNTa/703hyvVcHOwsGdK7bY078bS2HjPVQcZGPxmXksnY6GfIcSlruQMMfGOrwMBA9u/fT2HhP4/Jjo6Oxt3dHScnJ3x8fLC1tWXv3r3a9VlZWZw4cYKuXbuWqQ9RecGdbj/b4/bVHnVFQWER6/7QXNnxeB/PGpdMCCFEXWbQhCIsLIybN28yY8YMEhMT2bBhA6tXr2bcuHGApg4zYsQI5s+fz7Zt24iPj2fy5Mm4uLgwYMCAMvUhKs+zxR1Xe8TVnas9/t5/jsvXsmloa8HAHm7GDkcIIeoVgyYUTk5OrFy5kqSkJIYNG8YXX3zB1KlTGTZsmLbNxIkTeeKJJ5g5cybPPPMMpqamhIeHa0/6KEsfonJUKhUht2/FHXW4btzkqrCwiHV/aO6K+XgfT6wsZXZCCCGqU6X+6s6dO/eeZX5+fkRERJS4jampKVOmTGHKlCkltimtD1F5vfybEflXIrF15CZX2w+e51L6LewbWPBIz6o5L0QIIUTJ5OFg9ZRnCwea1pGyR2FhERFbNedODJPZCSGEMApJKOoplUqlfQJpbS977Dh0gYtXb2FnY84jPd2MHY4QQtRLklDUY8H+zQE0ZY+82nm1R2GRop2deCzUExsrcyNHJIQQ9ZMkFPWYR4uG2rJHbPxlY4dTIVGHLnDhyk1src0ZHCznTgghhLFIQlGP3Vn22Hn4opGjKT/N7ITmmR2PhXrI7IQQQhiRJBT1XHHZI+ZE7St77Dp8gfNpN2lgbc7g4DbGDkcIIeo1SSjqueKyhzq/dpU9iooU1t4+d2Jobw8aWMvshBBCGJMkFPVcbS177D56kXOXb9DAyowhITI7IYQQxiYJhah1ZY+iIoW1v2vOnXi0twe2MjshhBBGJwmFwKNFQ1ycak/ZI/rYJVJSb2BjZcajMjshhBA1giQUApVKRS+/22WPQzW77HHn7MSQ4DbY2lgYOSIhhBAgCYW4LbjT7bJHDb/J1d7jl0i+lIW1pRlDQz2MHY4QQojbJKEQAHg0/6fsERNXM8seiqKw9nfNlR2Dg92xk9kJIYSoMSShEIBu2WNXDb3aY+/xVM5czMTa0pTHQj2NHY4QQog7SEIhtGpy2UNRFNbevivmoF5tsG8gsxNCCFGTSEIhtGpy2SMm7jKnz2diZWHKY3LuhBBC1DiSUAgtzU2uNLMUNansoSgK3/9ePDvhTkNbSyNHJIQQ4m6SUAgdvW7fNbMmlT32x6eReO46lhZy7oQQQtRUklAIHR7NG+Lq1KDGlD00sxPxAAzs4YaDncxOCCFETSQJhdChUqm0sxQ7D18wcjRw4GQaCWevY2FuyuN9ZXZCCCFqKkkoxD2KHxYWG5dGjhHLHneeOzGwhxuOdlZGi0UIIcT9SUIh7tHmjrJH7AnjlT0OJlzhZEoGFmYmhMnshBBC1GgGTyguXLiAt7f3PV8//PADAHFxcYwYMYJOnTrRp08fwsPDdbYvKipi8eLFhISE4O/vz5gxY0hJSTF0mOI+dMoeR4xT9tDcFVMzO/FwDzcc7WV2QgghajKDJxQnT57E0tKSqKgodu7cqf0aMmQIGRkZjB49Gjc3NyIjI5kwYQKLFi0iMjJSu/2SJUtYu3YtH374IREREahUKsaOHYtarTZ0qOI+tGWPE5eNUvY4cuoqccnXMDczkXMnhBCiFjB4QpGQkIC7uztNmjTB2dlZ+2VlZcW6deuwsLBg1qxZeHh4EBYWxqhRo1ixYgUAarWaVatWMWHCBEJDQ/Hx8WHhwoVcvnyZrVu3GjpUcR/askdBUbWXPRRF4fvbd8V8KKg1Tg2tq/X1hRBClF+VzFB4eur/RBkbG0tgYCBmZmbaZUFBQSQlJZGenk58fDy3bt0iKChIu97e3p727dsTExNj6FDFfahUKoI7GafscfT0VY6fScfM1IQn+rWt1tcWQghRMWalNymfhIQEnJ2d+de//kVycjKtW7fmlVdeISQkhNTUVLy8vHTaN2nSBICLFy+SmpoKgKur6z1tLl26VOGYFEUhOzu7wtvfLScnR+ffuqqLVyN+2AYxJy5zLSMLK8vSDxdDjM23v8YB0K9LM6zNDft/Zyz15ZipCBkb/WRcSiZjo19VjIuiKKhUqjK1NWhCoVarSU5OxtramqlTp2JjY8OmTZsYO3Ys//3vf8nNzcXCQvehTpaWmhsV5eXlaQdBX5vMzMwKx5Wfn09cXFyFty9JcnKywfusSRRFoZGtGdduFrD5r8N0aG1T5m0rOjbJl/M4kZyBiQn4uhZUyf+bMdX1Y6YyZGz0k3EpmYyNfoYel7vfk0ti0ITCwsKCmJgYzMzMtAF06NCB06dPEx4ejpWV1T0nV+bl5QFgY2ODlZXmTH61Wq39vriNtXXF6+jm5uYllmEqIicnh+TkZNzc3CoVV23Q+4I5G3ckczbDjOEPtyu1fWXH5ofoWAD6d2lBUNfSX6+2qE/HTHnJ2Ogn41IyGRv9qmJcEhMTy9zW4CUPG5t7P8V6eXmxc+dOXFxcSEtL01lX/HPTpk0pKCjQLmvVqpVOGx8fnwrHpFKp9MZVWdbW1lXSb03St6sbG3ckcyjhKipTC6zLUPaAio3N8TPpHE/KwMxUxdMPtauTY1sfjpmKkrHRT8alZDI2+hlyXMpa7gADn5QZHx9PQEAAsbGxOsuPHTuGp6cngYGB7N+/n8LCQu266Oho3N3dcXJywsfHB1tbW/bu3atdn5WVxYkTJ+jatashQxVl5N7MHtfGmqs9Yk6kVulrFd93on9gK5o4yh8JIYSoTQyaUHh5edG2bVvef/99YmNjOX36NB999BGHDh1i/PjxhIWFcfPmTWbMmEFiYiIbNmxg9erVjBs3DtCUTEaMGMH8+fPZtm0b8fHxTJ48GRcXFwYMGGDIUEUZaR5pXvxsj6p7pHlc0jUOnbqCqYmK4f29St9ACCFEjWLQkoeJiQlLly5l/vz5TJo0iaysLNq3b89///tfvL29AVi5ciWzZ89m2LBhODs7M3XqVIYNG6btY+LEiRQUFDBz5kxyc3MJDAwkPDy8zCeFCMML9m/OD9tOsT9Oc5OrspY9yqP4iaL9A1vRtJHMTgghRG1j8HeGRo0aMWfOnBLX+/n5ERERUeJ6U1NTpkyZwpQpUwwdmqig4rLHpau3iDmRSu+AFgbtPz7lGgcTrmBiomJ4f7nvhBBC1EbycDBRqqouexQ/UbRfl5a4ODUweP9CCCGqniQUokyC/ZsDaMsehpJwNoMD8WmYmKh48gE5d0IIIWorSShEmbg3s6dZFVztUTw70adzC1wby+yEEELUVpJQiDLReaS5gcoeCWcziI27jIkKnpLZCSGEqNUkoRBlFtLpn7JHdm5+pfuL2JoAQGjnFjRztq10f0IIIYxHEgpRZm6ud5Y9KvdI88Tz19l3IlUzOzHA20ARCiGEMBZJKESZ3Vn22HWkcmWP4rti9g5oQXOZnRBCiFpPEgpRLsVlj9hKlD3OXMhk7/FUVCrkyg4hhKgjJKEQ5VJc9sivRNlj7VbN7ERIp+a0bGpnyPCEEEIYiSQUolxUKhXBt2cpKlL2SLqYSfTRS6jkyg4hhKhTJKEQ5VZ818yKlD2KZyd6+TWjlYu9wWMTQghhHJJQiHJzc7WnuXP5yx7Jl7LYfeQSAE/LlR1CCFGnSEIhyk1ztYem7LHz8IUybxdxx+xEa1eZnRBCiLpEEgpRIcVlj/3xaWUqe5xNzdKec/H0gzI7IYQQdY0kFKJC7ix77CtD2SNiawKKAj06uuImsxNCCFHnSEIhKuTOsseuUsoe5y7fIOp2m2dkdkIIIeokSShEhZW17FE8OxHUwQX3Zg2rKzwhhBDVSBIKUWFlKXucT7tB1KHzgDyzQwgh6jJJKESFqVQqgkspe0T8kUCRAt3au+DZwqEaoxNCCFGdJKEQldLrPmWPC1dusuOAZnZCzp0QQoi6TRIKUSmasoet3rLHutuzE13bNcWzpYNxAhRCCFEtJKEQlaIpe2hmKXYe+qfskXotm79ldkIIIeoNSShEpRU/LOzAyTSycwsA+HF7EkVFCl18muDVytGY4QkhhKgGNTKhKCoqYvHixYSEhODv78+YMWNISUkxdliiBK1d7LRlj5+ikoiOv8H2g5q7YsrshBBC1A81MqFYsmQJa9eu5cMPPyQiIgKVSsXYsWNRq9XGDk3ooVKpcHO1A2DjjmT+dyATRQFzMxPSM3ONHJ0QQojqUOMSCrVazapVq5gwYQKhoaH4+PiwcOFCLl++zNatW40dntBj95GL7Lr9FNE75RcU8dHqGHbffoaHEEKIuqvGJRTx8fHcunWLoKAg7TJ7e3vat29PTEyMESMT+hQWKSzfePS+bVb8dIzCIqWaIhJCCGEMZsYO4G6pqakAuLq66ixv0qQJly7d+ym4LBRFITs7u9KxFcvJydH5tz47nnSt1LLG1es5HIi7gK97o2qKquaRY6ZkMjb6ybiUTMZGv6oYF0VRUKlUZWpb4xKK4oGwsLDQWW5paUlmZmaF+szPzycuLq7Ssd0tOTnZ4H3WNseTy5aoHY9PwiS39KeS1nVyzJRMxkY/GZeSydjoZ+hxufv9uCQ1LqGwsrICNOdSFH8PkJeXh7W1dYX6NDc3x9PT0yDxgSbpSU5Oxs3NrcIx1RVFVteI3H2t1Ha+Pu60q+czFHLM6Cdjo5+MS8lkbPSrinFJTEwsc9sal1AUlzrS0tJo1aqVdnlaWho+Pj4V6lOlUmFjY2OQ+O5kbW1dJf3WJp3bWePU8Ph9yx6NHazp3K45piZlmzary+SYKZmMjX4yLiWTsdHPkONS1nIH1MCTMn18fLC1tWXv3r3aZVlZWZw4cYKuXbsaMTKhj6mJipce63jfNmOHdpBkQggh6rgal1BYWFgwYsQI5s+fz7Zt24iPj2fy5Mm4uLgwYMAAY4cn9Ojp14xpzwfi1NBKZ3ljB2umPR9IT79mRopMCCFEdalxJQ+AiRMnUlBQwMyZM8nNzSUwMJDw8PAynxgiql9Pv2Z07+DKgbgLHI9PwtfHXcocQghRj9TIhMLU1JQpU6YwZcoUY4ciysHURIWveyNMci/Tzr2RJBNCCFGP1LiShxBCCCFqH0kohBBCCFFpklAIIYQQotIkoRBCCCFEpUlCIYQQQohKk4RCCCGEEJWmUhSlTj9X+sCBAyiKYtB7WCiKQn5+Pubm5uW6LWl9IGOjn4xLyWRs9JNxKZmMjX5VMS5qtRqVSkXnzp1LbVsj70NhSFVxsKlUKrnJVglkbPSTcSmZjI1+Mi4lk7HRryrGRaVSlfl9tM7PUAghhBCi6sk5FEIIIYSoNEkohBBCCFFpklAIIYQQotIkoRBCCCFEpUlCIYQQQohKk4RCCCGEEJUmCYUQQgghKk0SCiGEEEJUmiQUQgghhKg0SSiEEEIIUWmSUAghhBCi0iShEEIIIUSlSUIB9OvXD29vb/773//qXf/uu+/i7e3N559/Xs2RGd/bb7+Nt7f3fb/qk1dffZUnn3zynuXPPPMM3t7e7Nu3T2f5b7/9hre3N6mpqeV+rQ0bNtSa8S3LcTJy5EjefvttY4da44wcObLEMZs9e7bebd5++21GjhxZzZFWn82bN/PUU08REBBAQEAAYWFhrF27tlx9/PXXXyQmJlZRhNXLEONRHer848vLytzcnN9++43Ro0frLC8oKOD333+vkseg1wYzZszgjTfe0P4cHBzM9OnTeeSRR4wYlfH07NmTjz76iNzcXKysrAC4ceMGR44cwdXVlR07dtCtWzdt+9jYWNq0aYOLi4uxQq4WZTlOXn/9dWOEVisMHDiQGTNm3LPc2tpab/sZM2ZQWFhY1WEZxfr16/nwww+ZPn06gYGBKIpCdHQ0s2fP5urVq7z22mul9nHhwgXGjx/PN998g6enZzVEXXUMMR7VRRKK23r06EFUVBSXLl3C1dVVu3zPnj3Y2NiU+Itd19nZ2WFnZ3fPMmdnZyNFZFw9evQgPz+fo0ePEhgYCMDu3buxt7dn+PDh/P7777z55pva9jExMfTq1ctY4VYbOU4qx8rKqlxjdfdY1yXfffcdTzzxhM5MYJs2bUhNTeWbb74p0xuooihVGWK1MsR4VBcpedzm5+dHs2bN+O2333SWb9myhYEDB+rMUBw8eJDnnnuOLl260L17d6ZPn05mZqZ2fb9+/Vi+fDkTJkwgICCA7t27M2fOHAoKCqptf6qLvmn5vXv34u3tzfnz5wHNL/eKFSvo378//v7+DB06lE2bNhkj3Eornm04cOCAdllUVBQ9e/YkJCSE+Ph40tLSAMjKyiIhIYHg4GDUajXz5s0jJCSEgIAAnnzySXbu3KnT99atWxkyZAh+fn6MGDGCixcvVuu+VYdbt24xffp0unbtSpcuXXj77bfJzs4G7j1uAM6fP4+3tzd79+4FNFP9r732GmPGjKFz584sW7bMKPtRnUaOHMn06dMZPnw4Xbt2ZePGjXW65GFiYsKBAwd0/qYCjB07loiICABSU1N588036dmzJ76+voSGhrJw4UKKioo4f/48/fv3B+C5556r9aXqsoxHv3797tnPO0uMxb9b27dvZ/DgwXTo0IFBgwbx119/GTZWg/ZWyw0cOFAnoVCr1fzxxx8MGjRIu+zIkSOMHDkST09PIiIiWLx4MUeOHGHMmDEUFRVp233++ecEBgby448/MmHCBL755ht+/vnnat2fmmLhwoV89913zJw5k82bN/Pcc88xa9Ysvv32W2OHViE9evTg4MGD2p937txJSEgIHTp0wMHBgaioKAD279+Pqakp3bp1Y9q0aURFRTFv3jx+/PFHBg4cyPjx4/n7778BOHDgABMmTODBBx/kp59+YujQoSxfvtwYu1elfv/9dxo3bsyGDRv45JNP2LJlCytWrChXH1u3bqVnz55ERkby6KOPVlGkNcuGDRt47rnn+P777wkNDTV2OFVq7NixxMXF0bt3b1566SWWL1/OkSNHsLOzw93dHYBx48Zx7do1wsPD+e2333jxxRdZunQpf/75J66urvzwww+A5u/wmDFjjLk7lVaW8SirefPmMWPGDDZs2EDLli158803uXXrlsFilZLHHQYOHEh4eLi27LFr1y4cHR1p3769ts2qVavw9vbm3XffBcDT05NPP/2URx99lKioKO0ve0hICM899xwAbm5urF+/ngMHDvDYY49V+34ZU3Z2Nl9//TWffPIJffv2BaBVq1ZcuHCB8PBwnn32WSNHWH49evRgzpw5KIrC6dOnSU1NpVevXpiYmGhLZ2FhYcTExBAQEMCVK1f4+eefWb9+PR07dgRg9OjRxMfHEx4eTp8+fVizZg2dO3dmwoQJALi7u5OQkMA333xjzF01uI4dO2rPpWjVqhW9evXi2LFj5eqjYcOGvPjii1URntFs3ryZ//3vfzrLAgICWLVqFQDt2rVjyJAhxgit2j300ENERETwf//3f+zcuZPt27cDmr+jc+bMwdfXl6FDh/LQQw/RvHlzQPNpfPny5Zw8eZIHHniARo0aAZpjpUGDBkbbF0MobTy6dOlS5r4mTZpEjx49tN8PHTqUhIQEAgICDBKrJBR36NChAy1bttSenLllyxYGDx6s0yYhIeGemri3tzf29vacPHlSm1B4eHjotLGzsyM/P79qd6AGSkxMJC8vj7feeotp06ZplxcUFKBWq3VObqwtevTowfXr1zlz5gw7d+7Ex8dHW/8ODg5mwYIFgOaEzH79+nHixAkAbYJZLD8/H3t7e0D/cRUQEFDnEoq7P1E1bNiQCxculKuP1q1bGzKkGqFfv346594AOr8XdXGf78fPz4958+ahKAoJCQls376db775hrFjx7J161ZGjBjBb7/9xurVq0lJSdGWGu+cJa5LShuPsmrTpo32e1tbWwCDvi9JQnGX4rLHv/71L7Zt26adOiumKIreKz6KioowNzfX/mxhYXFPm7p0otDd7hyXO88VKd7nzz77TOdgLqZvnGq6Jk2a4OnpycGDB9m5cyfBwcHadcHBwcyYMYNjx45x4sQJ3nnnHc6dOwfAt99+e8+nJROTf6qOdx8fdx5PdYWpqWmpbe4cB33nHdW2BLQsGjRocN+koS7usz6pqamsWLGCl156iaZNm6JSqbSX0Pbv359HHnmEqKgovvnmG3Jychg4cCBDhw7lnXfeqZWznaUpy3jExMQA9/790JcoVPX7kpxDcZeBAwdy+PBh1q9fT8uWLe+ZafDy8iI2NlZnWXx8PDdv3rynbX1Q/KZ348YN7bKUlBTt923atMHMzIyLFy/SunVr7df27dsJDw/XeUOtTYrPo9i/f79OQuHi4oKnpydr166lQYMG+Pr60rZtWwDS0tJ0xmDDhg1ERkYCmintO0/0BDh69Gj17VANUHws3bx5U7vszmNJ1H0WFhZEREToPWm7+BP1+fPnOX78OP/3f//HxIkTeeSRR7C1tSU9PV375lhXLvMvy3g0btwYc3Nznb/BxSenVrfa+de8CrVr147WrVuzYMECnZMxi40aNYr4+Hj+85//cPr0afbt28ebb75J+/bttbWp+qRTp06YmJjw2Wefce7cOf7++29t3Rc0pZ6nn36azz77jI0bN3Lu3Dl+/PFH5s2bR+PGjY0YeeX06NGDLVu2ANC5c2eddcHBwfzyyy/07NkTExMT2rZtS9++fXnvvffYtm0b586dIzw8nGXLltGyZUsAxowZQ3x8PB9//DFJSUls2rSp1p60WlFeXl40aNCAr776ipSUFGJiYli4cGGdeXMQpWvUqBEvvvgin332GQsXLiQuLo5z587x119/8dprr9G9e3d69+4NwKZNm7hw4QKxsbG88sor5Ofno1arAbCxsQE0pcQ732hrm7KMR9euXencuTNbtmwhJiaGpKQkZs2aZZT9lpKHHgMHDuSrr77Se/OmgIAAVqxYwaJFi3jsscewtbXlgQce4I033qiTU9SladmyJf/5z39YunQp69atw9fXl+nTp/Pyyy9r20ybNo1GjRqxePFi0tLScHFx4bXXXuOll14yYuSV0717d9RqNSEhIfdMIwYHB/P111/rnBOxcOFCFi5cyHvvvUdmZiYtW7bkgw8+ICwsDNAksitWrGDevHmsWbOGtm3bMn78eObPn1+t+2VMtra2zJ8/n08//ZRBgwbh7u7OtGnT6twJmOL+Jk2ahJubG+vWrePbb78lNzcXV1dXHnnkEcaNG4eNjQ3Tpk3j66+/5rPPPqNp06Y88sgjuLq6cvjwYQAcHR0JCwvjk08+ISUlhZkzZxp5ryqutPEAmDx5MpmZmYwdOxZra2uGDx/OI488Uu1ldpVSlwv7QgghhKgWUvIQQgghRKVJQiGEEEKISpOEQgghhBCVJgmFEEIIISpNEgohhBBCVJokFEIIIYSoNEkohBBCCFFpklAIIYQQotIkoRBCCCFEpUlCIYQQQohKk4RCCCGEEJX2//L6KQrimdTOAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()\n",
    "ax.plot(df_dow['Day'], df_dow['Total_Orders'], marker = 'o')\n",
    "ax.set_title('Number of orders per day of week')\n",
    "ax.set_xticks(list(range(7)))\n",
    "ax.set_xticklabels(labels = ['Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Total orders per day\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "data = df_Month[\"Average_Sale\"]\n",
    "pal = sns.color_palette(\"Greens_d\", len(data))\n",
    "sns.barplot(data=df_Month, x=\"Average_Sale\", y=\"Month\", orient=\"h\", palette=np.array(pal[::-1]))\n",
    "\n",
    "# ax.bar_label(ax.containers[0])\n",
    "# ax.grid(False)\n",
    "sns.despine(bottom = True, left = True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/elysium/Documents/00_Code_Academy/Code_Academy Tasks/E-Commerce/eComm_01.ipynb Cell 57\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/elysium/Documents/00_Code_Academy/Code_Academy%20Tasks/E-Commerce/eComm_01.ipynb#Y111sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m g\u001b[39m=\u001b[39m sns\u001b[39m.\u001b[39mlineplot(x \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mMonth\u001b[39m\u001b[39m\"\u001b[39m, y \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mAverage_Sale\u001b[39m\u001b[39m\"\u001b[39m, data\u001b[39m=\u001b[39mdf_Month)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/elysium/Documents/00_Code_Academy/Code_Academy%20Tasks/E-Commerce/eComm_01.ipynb#Y111sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m g\u001b[39m.\u001b[39mset_xticklabels([\u001b[39m'\u001b[39m\u001b[39mJan\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mFeb\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mMar\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mApr\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mMay\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mJun\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mJul\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mAug\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mSep\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mOct\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mNov\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mDec\u001b[39m\u001b[39m'\u001b[39m])\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/elysium/Documents/00_Code_Academy/Code_Academy%20Tasks/E-Commerce/eComm_01.ipynb#Y111sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m ax\u001b[39m.\u001b[39mbar_label(ax\u001b[39m.\u001b[39;49mcontainers[\u001b[39m0\u001b[39;49m])\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/elysium/Documents/00_Code_Academy/Code_Academy%20Tasks/E-Commerce/eComm_01.ipynb#Y111sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m ax\u001b[39m.\u001b[39mgrid(\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/elysium/Documents/00_Code_Academy/Code_Academy%20Tasks/E-Commerce/eComm_01.ipynb#Y111sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m sns\u001b[39m.\u001b[39mdespine(bottom \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m, left \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m)\n",
      "\u001b[0;31mIndexError\u001b[0m: list index out of range"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g= sns.lineplot(x = \"Month\", y = \"Average_Sale\", data=df_Month)\n",
    "g.set_xticklabels(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
    "ax.bar_label(ax.containers[0])\n",
    "ax.grid(False)\n",
    "sns.despine(bottom = True, left = True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 554417 entries, 0 to 554416\n",
      "Data columns (total 18 columns):\n",
      " #   Column        Non-Null Count   Dtype         \n",
      "---  ------        --------------   -----         \n",
      " 0   InvoiceNo     554417 non-null  object        \n",
      " 1   Product_Id    554417 non-null  object        \n",
      " 2   Quantity      554417 non-null  int64         \n",
      " 3   Price         554417 non-null  float64       \n",
      " 4   Total_Sales   554417 non-null  float64       \n",
      " 5   Customer_Id   554417 non-null  object        \n",
      " 6   Product_Name  554417 non-null  object        \n",
      " 7   Product_Type  554417 non-null  object        \n",
      " 8   Country       554417 non-null  object        \n",
      " 9   Rating        554417 non-null  float64       \n",
      " 10  Review_Count  554417 non-null  int64         \n",
      " 11  StockCode     554417 non-null  object        \n",
      " 12  Hour          554417 non-null  int64         \n",
      " 13  Day           554417 non-null  object        \n",
      " 14  Month         554417 non-null  object        \n",
      " 15  Year          554417 non-null  int64         \n",
      " 16  Quarter       554417 non-null  int64         \n",
      " 17  Invoice_Date  554417 non-null  datetime64[ns]\n",
      "dtypes: datetime64[ns](1), float64(3), int64(5), object(9)\n",
      "memory usage: 76.1+ MB\n"
     ]
    }
   ],
   "source": [
    "merged_tables.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 4340 entries, 4339 to 825\n",
      "Data columns (total 5 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Customer_Id    4340 non-null   object \n",
      " 1   InvoiceNo      4340 non-null   int64  \n",
      " 2   Total_Sales    4340 non-null   float64\n",
      " 3   Product_Id     4340 non-null   int64  \n",
      " 4   Revenue_Order  4340 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(1)\n",
      "memory usage: 203.4+ KB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customer_info = merged_tables.groupby('Customer_Id').agg({'InvoiceNo':'nunique', 'Total_Sales':'sum', 'Product_Id':'nunique'}).reset_index().sort_values(by = 'Total_Sales', ascending = False)\n",
    "customer_info['Revenue_Order'] = customer_info['Total_Sales'] / customer_info['InvoiceNo']\n",
    "customer_info[customer_info['InvoiceNo']==1]\n",
    "display(customer_info.info())\n",
    "customer_country = merged_tables[['Customer_Id','Country']].drop_duplicates()\n",
    "customer_info = pd.merge(customer_info, customer_country, how = 'left', on = 'Customer_Id')\n",
    "customer_info = customer_info[customer_info['Customer_Id']!= 'Null']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "one-time buyers: 40.06299496000001 M euros \n",
      " repeat customers: 515.33243304 M euros \n",
      " anon buyers: 0.0 M euros\n"
     ]
    }
   ],
   "source": [
    "one_time = customer_info[customer_info['InvoiceNo']==1]['Total_Sales'].sum()\n",
    "repeat = customer_info[(customer_info['InvoiceNo']>1) & (customer_info['Customer_Id']!= 'Null')]['Total_Sales'].sum()\n",
    "anon = customer_info[customer_info['Customer_Id'] == 'Null']['Total_Sales'].sum()\n",
    "print(f'one-time buyers: {one_time/1000000} M euros \\n repeat customers: {repeat/1000000} M euros \\n anon buyers: {anon/1000000} M euros')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "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>customer_type</th>\n",
       "      <th>total_revenue (M EUR)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>one-time buyers</td>\n",
       "      <td>40.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>repeat customers</td>\n",
       "      <td>515.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>anon. buyers</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customer_type  total_revenue (M EUR)\n",
       "0   one-time buyers                  40.06\n",
       "1  repeat customers                 515.33\n",
       "2      anon. buyers                   0.00"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rev_per_customer_type = pd.DataFrame({'total_revenue (M EUR)': [round(one_time/1000000,2),round(repeat/1000000,2), round(anon/1000000,2)]}, index = ['one-time buyers', 'repeat customers', 'anon. buyers']).reset_index().rename(columns = {'index':'customer_type'})\n",
    "rev_per_customer_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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