Exploratory Data Analysis and Data Cleaning.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "75466b4a-f4d9-4fef-b20b-14853a582086",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis and Data Cleaning <a name=\"Top\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b711908-60ec-43e7-b197-f3c80dc40892",
   "metadata": {
    "tags": []
   },
   "source": [
    "# TABLE OF CONTENT\n",
    "\n",
    "[ABOUT THIS DATASET](#About)  \n",
    "1. [IMPORT LIBRARIES](#ImportLibraries)  \n",
    "2. [DATA EXPLORATION AND DATA CLEANING](#DataExplDataClean)  \n",
    "    2.1 [Data types](#DataTypes_df1)      \n",
    "    2.2 [Missing values](#Missing_df1)       \n",
    "    2.3 [Uniform Values](#UniformValues_df1)     \n",
    "    2.4 [Remove Duplicates](#RemDupl_df1)    \n",
    "    2.5 [Numeric data](#NumData_df1)    \n",
    "    2.6 [Categorical data](#CatData_df1)    \n",
    "3. [DATA ANALYSIS](#DataAnalysis) \n",
    "4. [FINAL CONCLUSIONS](#Conclusions) <a name=\"Conclusions\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3859d23a-dadf-4493-be7d-5b05ede7a165",
   "metadata": {
    "tags": []
   },
   "source": [
    "# ABOUT THIS DATASET <a name=\"About\"></a>\n",
    "\n",
    "This dataset provides detailed insights into Amazon sales data, including SKU Code, Design Number, Stock, Category and Size, to help optimize product profitability. It covers a 90 days time frame, from 31 March 2022 to 29 June 2022.\n",
    "\n",
    "**Attributes:**\n",
    "* **Order ID**: Order Identification Number. (String)\n",
    "* **Date**: Date of the sale. (Date)\n",
    "* **Status**: Status of the sale. (String)\n",
    "* **Fulfilment**: Method of fulfilment. (String)\n",
    "* **Sales Channel**: Method of selling. (String)\n",
    "* **Ship Service Level**:  Method of Shipping Service. (String)  \n",
    "* **Style**: Style of the product. (String)\n",
    "* **SKU**: Stock Keeping Unit. (String)\n",
    "* **Category**: Type of product. (String)\n",
    "* **Size**: Size of the product. (String)\n",
    "* **ASIN**: Amazon Standard Identification Number. (String)\n",
    "* **Courier Status**: Status of the courier. (String)\n",
    "* **Qty**: Quantity of the product. (Integer)\n",
    "* **Currency**: The currency used for the sale (INR). (String)\n",
    "* **Amount**: Amount of the sale. (Float)\n",
    "* **Ship City**: Destination City of the shipment. (String)\n",
    "* **Ship State**: Destination State of the shipment. (String)\n",
    "* **Ship Postal Code**: Destination Postal Code of the shipment. (String)\n",
    "* **Ship Country**: Destination Country of the shipment. (String)\n",
    "* **Promotion IDS**: Promotion code of the sale. (String)\n",
    "* **B2B**: Business to business sale. (Boolean)\n",
    "\n",
    " \n",
    "**Questions to be answered:**\n",
    "\n",
    "1. Which categories have sold most?  [[&#8599;]](#AQ1)    \n",
    "2. Which are the 20 Best-Selling Products?   [[&#8599;]](#AQ2)    \n",
    "3. Which are the 20 Best-Selling Products by Revenue?   [[&#8599;]](#AQ3)    \n",
    "4. Which are the 20 cities that made the most orders?   [[&#8599;]](#AQ4)    \n",
    "5. Which are the 20 cities that more income generate?    [[&#8599;]](#AQ5)    \n",
    "6. How many orders are fulfilled by Amazon and how many by the Merchant?    [[&#8599;]](#AQ6)    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4f01d6b-ece3-4e8e-9ee7-69487c93f41a",
   "metadata": {},
   "source": [
    "# 1. IMPORT LIBRARIES [&#129081;](#Top)<a name=\"ImportLibraries\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9844ba18-dd2f-47d9-87c1-8793b69defb0",
   "metadata": {},
   "source": [
    "**LIBRARIES:**\n",
    "\n",
    "* Library **pandas**: to handle data in tabular representation.\n",
    "* Library **numpy**: for linear algebra routines.\n",
    "* Library **missingno**: to graphically display missing values in the data.\n",
    "* Library **re**: to work with regular expressions.\n",
    "* Library **matplotlib, seaborn**: to visualize data in in plots, graphs and charts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "5d7bff6f-9d12-4b9c-af77-d463e7d258e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import missingno as msno\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import matplotlib.ticker as ticker\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fe1de520-31d8-42a9-b474-4fe9b2275b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "from plotly.subplots import make_subplots\n",
    "import plotly.graph_objects as go\n",
    "import plotly.figure_factory as ff"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "973d6ed9-8aba-4b3c-a5dd-1351fe0a58d6",
   "metadata": {
    "tags": []
   },
   "source": [
    "# 2 DATA DESCRIPTION AND DATA CLEANING [&#129081;](#Top)<a name=\"DataExplDataClean\"></a>\n",
    "In this phase, will be performed the exploratory data analysis: data types, missing values, string standardisation, duplicates, measures of the central trend, and relationship detection between fields."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4076caf8-1828-4879-8582-d9fe7bab1b93",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2.1 Data types [&#129081;](#Top)<a name=\"DataTypes_df1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94e1dbc6-d2e5-4db2-923b-9b2bd13e1d2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load data\n",
    "df = pd.read_csv(\"Data in\\\\Amazon Sale Report.csv\", low_memory=False) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c806a3f9-ce47-47af-9112-bc0164e47469",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>Order ID</th>\n",
       "      <th>Date</th>\n",
       "      <th>Status</th>\n",
       "      <th>Fulfilment</th>\n",
       "      <th>Sales Channel</th>\n",
       "      <th>ship-service-level</th>\n",
       "      <th>Style</th>\n",
       "      <th>SKU</th>\n",
       "      <th>Category</th>\n",
       "      <th>...</th>\n",
       "      <th>currency</th>\n",
       "      <th>Amount</th>\n",
       "      <th>ship-city</th>\n",
       "      <th>ship-state</th>\n",
       "      <th>ship-postal-code</th>\n",
       "      <th>ship-country</th>\n",
       "      <th>promotion-ids</th>\n",
       "      <th>B2B</th>\n",
       "      <th>fulfilled-by</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>405-8078784-5731545</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>SET389</td>\n",
       "      <td>SET389-KR-NP-S</td>\n",
       "      <td>Set</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>647.62</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>400081.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>171-9198151-1101146</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Shipped - Delivered to Buyer</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>JNE3781</td>\n",
       "      <td>JNE3781-KR-XXXL</td>\n",
       "      <td>kurta</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>406.00</td>\n",
       "      <td>BENGALURU</td>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>560085.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>Amazon PLCC Free-Financing Universal Merchant ...</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>404-0687676-7273146</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>JNE3371</td>\n",
       "      <td>JNE3371-KR-XL</td>\n",
       "      <td>kurta</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>329.00</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>410210.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>IN Core Free Shipping 2015/04/08 23-48-5-108</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>403-9615377-8133951</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Standard</td>\n",
       "      <td>J0341</td>\n",
       "      <td>J0341-DR-L</td>\n",
       "      <td>Western Dress</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>753.33</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>PUDUCHERRY</td>\n",
       "      <td>605008.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>Easy Ship</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>407-1069790-7240320</td>\n",
       "      <td>04-30-22</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>JNE3671</td>\n",
       "      <td>JNE3671-TU-XXXL</td>\n",
       "      <td>Top</td>\n",
       "      <td>...</td>\n",
       "      <td>INR</td>\n",
       "      <td>574.00</td>\n",
       "      <td>CHENNAI</td>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>600073.0</td>\n",
       "      <td>IN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index             Order ID      Date                        Status  \\\n",
       "0      0  405-8078784-5731545  04-30-22                     Cancelled   \n",
       "1      1  171-9198151-1101146  04-30-22  Shipped - Delivered to Buyer   \n",
       "2      2  404-0687676-7273146  04-30-22                       Shipped   \n",
       "3      3  403-9615377-8133951  04-30-22                     Cancelled   \n",
       "4      4  407-1069790-7240320  04-30-22                       Shipped   \n",
       "\n",
       "  Fulfilment Sales Channel  ship-service-level    Style              SKU  \\\n",
       "0   Merchant      Amazon.in           Standard   SET389   SET389-KR-NP-S   \n",
       "1   Merchant      Amazon.in           Standard  JNE3781  JNE3781-KR-XXXL   \n",
       "2     Amazon      Amazon.in          Expedited  JNE3371    JNE3371-KR-XL   \n",
       "3   Merchant      Amazon.in           Standard    J0341       J0341-DR-L   \n",
       "4     Amazon      Amazon.in          Expedited  JNE3671  JNE3671-TU-XXXL   \n",
       "\n",
       "        Category  ... currency  Amount    ship-city   ship-state  \\\n",
       "0            Set  ...      INR  647.62       MUMBAI  MAHARASHTRA   \n",
       "1          kurta  ...      INR  406.00    BENGALURU    KARNATAKA   \n",
       "2          kurta  ...      INR  329.00  NAVI MUMBAI  MAHARASHTRA   \n",
       "3  Western Dress  ...      INR  753.33   PUDUCHERRY   PUDUCHERRY   \n",
       "4            Top  ...      INR  574.00      CHENNAI   TAMIL NADU   \n",
       "\n",
       "  ship-postal-code  ship-country  \\\n",
       "0         400081.0            IN   \n",
       "1         560085.0            IN   \n",
       "2         410210.0            IN   \n",
       "3         605008.0            IN   \n",
       "4         600073.0            IN   \n",
       "\n",
       "                                       promotion-ids    B2B  fulfilled-by  \\\n",
       "0                                                NaN  False     Easy Ship   \n",
       "1  Amazon PLCC Free-Financing Universal Merchant ...  False     Easy Ship   \n",
       "2       IN Core Free Shipping 2015/04/08 23-48-5-108   True           NaN   \n",
       "3                                                NaN  False     Easy Ship   \n",
       "4                                                NaN  False           NaN   \n",
       "\n",
       "  Unnamed: 22  \n",
       "0         NaN  \n",
       "1         NaN  \n",
       "2         NaN  \n",
       "3         NaN  \n",
       "4         NaN  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # Display the first 5 lines of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ee5e7d-c4de-400a-baf1-ef21c98e92f2",
   "metadata": {},
   "source": [
    "* Check the dimension of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a7a8b66-b51b-4dec-adae-65fe65b23e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_sizes = {'start': df.shape}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "52424d5b-db89-4b5e-ac91-c51db9cadf8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Raw data contains 128975 records and 24 columns.\n"
     ]
    }
   ],
   "source": [
    "print(f\"Raw data contains {df_sizes['start'][0]} records and {df_sizes['start'][1]} columns.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d4e8ddf0-8a4d-4e60-9327-7b71e6c9a3a4",
   "metadata": {},
   "source": [
    "* Standardize the column names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3ca0f0ee-c38b-472b-8de8-3fddc2f13cbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['index', 'Order ID', 'Date', 'Status', 'Fulfilment', 'Sales Channel ',\n",
       "       'ship-service-level', 'Style', 'SKU', 'Category', 'Size', 'ASIN',\n",
       "       'Courier Status', 'Qty', 'currency', 'Amount', 'ship-city',\n",
       "       'ship-state', 'ship-postal-code', 'ship-country', 'promotion-ids',\n",
       "       'B2B', 'fulfilled-by', 'Unnamed: 22'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fc6fa62e-bedf-478c-8c99-2ff4f7aace3a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df = df.rename(columns={'Order ID': 'Order_ID', 'Sales Channel ': 'Sales_channel',\n",
    "        'ship-service-level': 'ship_service_level', 'ship-city': 'ship_city', \n",
    "        'ship-state': 'ship_state', 'ship-postal-code': 'ship_postal_code', 'ship-country': 'ship_country', \n",
    "        'promotion-ids': 'promotion_ids','Courier Status': 'Courier_Status', 'currency': 'Currency', \n",
    "        'fulfilled-by':'fulfilled_by'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d185ef38-6980-4297-a1fc-e9921923a12d",
   "metadata": {},
   "source": [
    "* Check columns data type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ca54a7a8-5ef5-4e0b-b1ed-7f5cdda06faf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 128975 entries, 0 to 128974\n",
      "Data columns (total 24 columns):\n",
      " #   Column              Non-Null Count   Dtype  \n",
      "---  ------              --------------   -----  \n",
      " 0   index               128975 non-null  int64  \n",
      " 1   Order_ID            128975 non-null  object \n",
      " 2   Date                128975 non-null  object \n",
      " 3   Status              128975 non-null  object \n",
      " 4   Fulfilment          128975 non-null  object \n",
      " 5   Sales_channel       128975 non-null  object \n",
      " 6   ship_service_level  128975 non-null  object \n",
      " 7   Style               128975 non-null  object \n",
      " 8   SKU                 128975 non-null  object \n",
      " 9   Category            128975 non-null  object \n",
      " 10  Size                128975 non-null  object \n",
      " 11  ASIN                128975 non-null  object \n",
      " 12  Courier_Status      122103 non-null  object \n",
      " 13  Qty                 128975 non-null  int64  \n",
      " 14  Currency            121180 non-null  object \n",
      " 15  Amount              121180 non-null  float64\n",
      " 16  ship_city           128942 non-null  object \n",
      " 17  ship_state          128942 non-null  object \n",
      " 18  ship_postal_code    128942 non-null  float64\n",
      " 19  ship_country        128942 non-null  object \n",
      " 20  promotion_ids       79822 non-null   object \n",
      " 21  B2B                 128975 non-null  bool   \n",
      " 22  fulfilled_by        39277 non-null   object \n",
      " 23  Unnamed: 22         79925 non-null   object \n",
      "dtypes: bool(1), float64(2), int64(2), object(19)\n",
      "memory usage: 22.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6adb0a08-f476-47fc-9069-5bce5f571d0f",
   "metadata": {},
   "source": [
    "The dataset contains 24 columns, of which 4 containig numeric values:\n",
    "* index  (int)\n",
    "* Qty (int)\n",
    "* Amount (float)\n",
    "* ship-postal-code (float), later it will be converted to integer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4588a49-a75d-4752-b3c8-d5ac7a2cc337",
   "metadata": {},
   "source": [
    "* Set the column 'index' as the index of the dataframe. With this operation the number of columns becomes 22."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ebbccb93-9274-4a57-b580-5ce90f391c76",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('index', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26ce465b-ba00-4c1b-83d9-50d13cafed24",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2.2 Missing values [&#129081;](#Top)<a name=\"Missing_df1\"></a>\n",
    "### Evaluate how many values are missing for each attribute and how they are distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d3cffba2-851e-41b6-8bf3-748b4673014e",
   "metadata": {},
   "source": [
    "* Create a separate dataframe containing the informations about missing and unique values for each column, to better see the correlations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "19bafb49-9679-4810-8e03-49c250a02d36",
   "metadata": {},
   "outputs": [],
   "source": [
    "def missing_evaluation(dataframe):\n",
    "    '''\n",
    "    missing_evaluation(dataframe)\n",
    "    \n",
    "    Returns a dataframe containing the informations about missing and unique values \n",
    "    for each column on a dataframe passed as a parameter\n",
    "\n",
    "    Parameters: \n",
    "    dataframe: dataframe to analyze\n",
    "    '''\n",
    "    \n",
    "    # Column 'qt_missing': Number of NaN values\n",
    "    df_missing_stats = pd.DataFrame(data=dataframe.isna().sum(), index=dataframe.columns, columns=['qt_missing'])\n",
    "    # Column 'nr_unique_values': Number of unique values\n",
    "    df_missing_stats['qt_unique_values'] = pd.DataFrame(data=dataframe.nunique(), index=dataframe.columns)\n",
    "    # Column 'unique_values': unique values of the attributes\n",
    "    df_missing_stats['unique_values'] = pd.DataFrame(data=dataframe.apply(pd.unique), index=dataframe.columns)\n",
    "    # Column 'perc_missing': percentage of missing values\n",
    "    df_missing_stats['perc_missing'] = pd.DataFrame(data=dataframe.isnull().mean())\n",
    "    return df_missing_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1e9c9ffb-ad1c-43d4-88de-e427228b62c6",
   "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>qt_missing</th>\n",
       "      <th>qt_unique_values</th>\n",
       "      <th>unique_values</th>\n",
       "      <th>perc_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Order_ID</th>\n",
       "      <td>0</td>\n",
       "      <td>120378</td>\n",
       "      <td>[405-8078784-5731545, 171-9198151-1101146, 404...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>[04-30-22, 04-29-22, 04-28-22, 04-27-22, 04-26...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>[Cancelled, Shipped - Delivered to Buyer, Ship...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fulfilment</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[Merchant, Amazon]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_channel</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[Amazon.in, Non-Amazon]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_service_level</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[Standard, Expedited]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Style</th>\n",
       "      <td>0</td>\n",
       "      <td>1377</td>\n",
       "      <td>[SET389, JNE3781, JNE3371, J0341, JNE3671, SET...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKU</th>\n",
       "      <td>0</td>\n",
       "      <td>7195</td>\n",
       "      <td>[SET389-KR-NP-S, JNE3781-KR-XXXL, JNE3371-KR-X...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>[Set, kurta, Western Dress, Top, Ethnic Dress,...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>[S, 3XL, XL, L, XXL, XS, 6XL, M, 4XL, 5XL, Free]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIN</th>\n",
       "      <td>0</td>\n",
       "      <td>7190</td>\n",
       "      <td>[B09KXVBD7Z, B09K3WFS32, B07WV4JV4D, B099NRCT7...</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Courier_Status</th>\n",
       "      <td>6872</td>\n",
       "      <td>3</td>\n",
       "      <td>[nan, Shipped, Cancelled, Unshipped]</td>\n",
       "      <td>0.053282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qty</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>[0, 1, 2, 15, 3, 9, 13, 5, 4, 8]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Currency</th>\n",
       "      <td>7795</td>\n",
       "      <td>1</td>\n",
       "      <td>[INR, nan]</td>\n",
       "      <td>0.060438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>7795</td>\n",
       "      <td>1410</td>\n",
       "      <td>[647.62, 406.0, 329.0, 753.33, 574.0, 824.0, 6...</td>\n",
       "      <td>0.060438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_city</th>\n",
       "      <td>33</td>\n",
       "      <td>8955</td>\n",
       "      <td>[MUMBAI, BENGALURU, NAVI MUMBAI, PUDUCHERRY, C...</td>\n",
       "      <td>0.000256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_state</th>\n",
       "      <td>33</td>\n",
       "      <td>69</td>\n",
       "      <td>[MAHARASHTRA, KARNATAKA, PUDUCHERRY, TAMIL NAD...</td>\n",
       "      <td>0.000256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_postal_code</th>\n",
       "      <td>33</td>\n",
       "      <td>9459</td>\n",
       "      <td>[400081.0, 560085.0, 410210.0, 605008.0, 60007...</td>\n",
       "      <td>0.000256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_country</th>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>[IN, nan]</td>\n",
       "      <td>0.000256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion_ids</th>\n",
       "      <td>49153</td>\n",
       "      <td>5787</td>\n",
       "      <td>[nan, Amazon PLCC Free-Financing Universal Mer...</td>\n",
       "      <td>0.381105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2B</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[False, True]</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fulfilled_by</th>\n",
       "      <td>89698</td>\n",
       "      <td>1</td>\n",
       "      <td>[Easy Ship, nan]</td>\n",
       "      <td>0.695468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <td>49050</td>\n",
       "      <td>1</td>\n",
       "      <td>[nan, False]</td>\n",
       "      <td>0.380306</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    qt_missing  qt_unique_values  \\\n",
       "Order_ID                     0            120378   \n",
       "Date                         0                91   \n",
       "Status                       0                13   \n",
       "Fulfilment                   0                 2   \n",
       "Sales_channel                0                 2   \n",
       "ship_service_level           0                 2   \n",
       "Style                        0              1377   \n",
       "SKU                          0              7195   \n",
       "Category                     0                 9   \n",
       "Size                         0                11   \n",
       "ASIN                         0              7190   \n",
       "Courier_Status            6872                 3   \n",
       "Qty                          0                10   \n",
       "Currency                  7795                 1   \n",
       "Amount                    7795              1410   \n",
       "ship_city                   33              8955   \n",
       "ship_state                  33                69   \n",
       "ship_postal_code            33              9459   \n",
       "ship_country                33                 1   \n",
       "promotion_ids            49153              5787   \n",
       "B2B                          0                 2   \n",
       "fulfilled_by             89698                 1   \n",
       "Unnamed: 22              49050                 1   \n",
       "\n",
       "                                                        unique_values  \\\n",
       "Order_ID            [405-8078784-5731545, 171-9198151-1101146, 404...   \n",
       "Date                [04-30-22, 04-29-22, 04-28-22, 04-27-22, 04-26...   \n",
       "Status              [Cancelled, Shipped - Delivered to Buyer, Ship...   \n",
       "Fulfilment                                         [Merchant, Amazon]   \n",
       "Sales_channel                                 [Amazon.in, Non-Amazon]   \n",
       "ship_service_level                              [Standard, Expedited]   \n",
       "Style               [SET389, JNE3781, JNE3371, J0341, JNE3671, SET...   \n",
       "SKU                 [SET389-KR-NP-S, JNE3781-KR-XXXL, JNE3371-KR-X...   \n",
       "Category            [Set, kurta, Western Dress, Top, Ethnic Dress,...   \n",
       "Size                 [S, 3XL, XL, L, XXL, XS, 6XL, M, 4XL, 5XL, Free]   \n",
       "ASIN                [B09KXVBD7Z, B09K3WFS32, B07WV4JV4D, B099NRCT7...   \n",
       "Courier_Status                   [nan, Shipped, Cancelled, Unshipped]   \n",
       "Qty                                  [0, 1, 2, 15, 3, 9, 13, 5, 4, 8]   \n",
       "Currency                                                   [INR, nan]   \n",
       "Amount              [647.62, 406.0, 329.0, 753.33, 574.0, 824.0, 6...   \n",
       "ship_city           [MUMBAI, BENGALURU, NAVI MUMBAI, PUDUCHERRY, C...   \n",
       "ship_state          [MAHARASHTRA, KARNATAKA, PUDUCHERRY, TAMIL NAD...   \n",
       "ship_postal_code    [400081.0, 560085.0, 410210.0, 605008.0, 60007...   \n",
       "ship_country                                                [IN, nan]   \n",
       "promotion_ids       [nan, Amazon PLCC Free-Financing Universal Mer...   \n",
       "B2B                                                     [False, True]   \n",
       "fulfilled_by                                         [Easy Ship, nan]   \n",
       "Unnamed: 22                                              [nan, False]   \n",
       "\n",
       "                    perc_missing  \n",
       "Order_ID                0.000000  \n",
       "Date                    0.000000  \n",
       "Status                  0.000000  \n",
       "Fulfilment              0.000000  \n",
       "Sales_channel           0.000000  \n",
       "ship_service_level      0.000000  \n",
       "Style                   0.000000  \n",
       "SKU                     0.000000  \n",
       "Category                0.000000  \n",
       "Size                    0.000000  \n",
       "ASIN                    0.000000  \n",
       "Courier_Status          0.053282  \n",
       "Qty                     0.000000  \n",
       "Currency                0.060438  \n",
       "Amount                  0.060438  \n",
       "ship_city               0.000256  \n",
       "ship_state              0.000256  \n",
       "ship_postal_code        0.000256  \n",
       "ship_country            0.000256  \n",
       "promotion_ids           0.381105  \n",
       "B2B                     0.000000  \n",
       "fulfilled_by            0.695468  \n",
       "Unnamed: 22             0.380306  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_stats = missing_evaluation(df)\n",
    "df_missing_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "1db9398c-38f8-4ebc-852f-6a53de684379",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the distribution of missing values with missingno matrix\n",
    "msno.matrix(df);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "08655e4b-8527-4e7f-ba2b-568fd4fa8f8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the heatmap to identify correlations of the nullity between each of the different columns.\n",
    "msno.heatmap(df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bfc5ae4-76f4-41b2-9af1-d676680c5d66",
   "metadata": {},
   "source": [
    "Worth noting that:\n",
    "* When one of the columns **'Amount'** and **'Currency'** is null, the other is also null (7795 raws with null values).\n",
    "* When one of columns **'ship_city', 'ship_state', 'ship_postal_code', 'ship_country'** has a null value, the others are also null (33 raws with null values)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1de5c07-9917-4e3b-9b77-9733a0551c08",
   "metadata": {},
   "source": [
    "### Fill in (or eliminate) the missing values with a case-by-case assessment.\n",
    "After the exploration of the missing values, it's possible to proceed fill (or clean) each column, performing an individual assessment."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19bd5409-d817-41ce-98a8-f4f448813f53",
   "metadata": {},
   "source": [
    "* **Courier_Status**: missing values filled with 'Unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4ff6139-d3f0-4b0b-ac8d-bfa58a4b7784",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace NaN values in column 'Courier_Status' with the value 'Unknown'\n",
    "df['Courier_Status'].fillna('Unknown', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb3f3ae8-1bdb-46bd-ae48-23ac11cb7813",
   "metadata": {},
   "source": [
    "* Check nullity correlation on **Amount** and **Currency** columns\n",
    "* Fill missing values in **Amount** with zero\n",
    "* Fill missing values in **Currency** with 'INR'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ec4f4868-bb6b-4701-bdc9-4ca823f24479",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_idx_amount_null = set(df[df['Amount'].isnull()].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "7f496b77-94d9-4f0f-b833-37039f69efde",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "set2_idx_currency_null = set(df[df['Currency'].isnull()].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b6fc90dd-3f0b-4baa-9d7a-79261b6c77e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question: Columns 'Amount' and 'Currency' have 1 to 1 nullity correlation?\n",
      "Answer: True\n"
     ]
    }
   ],
   "source": [
    "print(f\"Question: Columns 'Amount' and 'Currency' have 1 to 1 nullity correlation?\\nAnswer: {set_idx_amount_null == set2_idx_currency_null}\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "34deb741-87b1-42c0-b5ac-1c7dc94ceb55",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Amount'].fillna(0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b09d0e87-dd02-46d2-87b0-25cdbd448a4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Currency'].fillna('INR', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31f964c6-4787-4925-b5e0-b7215408fd83",
   "metadata": {},
   "source": [
    "* **promotion_ids**: missing values filled with 'No Promo'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "39e1edd5-fb93-46f5-9967-96e5ccb7f8e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replace NaN values in column 'promotion_ids' with the value 'Unknown'\n",
    "df['promotion_ids'].fillna('No Promo', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ed0502b-0336-45cf-be47-80b8ba87d4af",
   "metadata": {},
   "source": [
    "* **ship_city** and **ship_state**: missing values filled with 'Unknown'\n",
    "* **ship_postal_code**: missing values filled with zero\n",
    "* **ship_country**: missing values filled with 'Unknown'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2db42836-6a99-48b8-91be-97069908c8e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ship_city'].fillna('Unknown', inplace = True)\n",
    "df['ship_state'].fillna('Unknown', inplace = True)\n",
    "df['ship_postal_code'].fillna(0, inplace = True)\n",
    "df['ship_country'].fillna('Unknown', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bfc02d55-d2cc-48b5-91c2-b02e5bb6d13b",
   "metadata": {},
   "source": [
    "* **fulfilled_by**: missing values filled with 'FBA' (Fulfilled by Amazon)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ec70a0a3-ad1a-4f3d-93da-049eadf65613",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['fulfilled_by'].fillna('FBA', inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38aa5e49-c62e-4278-9d29-4113975d04fe",
   "metadata": {},
   "source": [
    "* **Unnamed: 22** will be deleted because it isn't relevant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cdfc810e-bd71-4e0e-b02e-5d3e0670331d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['Unnamed: 22'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43832392-1276-4d39-ac80-9b149182b914",
   "metadata": {},
   "source": [
    "### Check that none of the missing values are left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4ace799-fe1a-404f-ba36-f4f327b4da5f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "90d181ed-024c-453a-969f-9d0f3a04d403",
   "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>qt_missing</th>\n",
       "      <th>qt_unique_values</th>\n",
       "      <th>unique_values</th>\n",
       "      <th>perc_missing</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Order_ID</th>\n",
       "      <td>0</td>\n",
       "      <td>120378</td>\n",
       "      <td>[405-8078784-5731545, 171-9198151-1101146, 404...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <td>0</td>\n",
       "      <td>91</td>\n",
       "      <td>[04-30-22, 04-29-22, 04-28-22, 04-27-22, 04-26...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>[Cancelled, Shipped - Delivered to Buyer, Ship...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fulfilment</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[Merchant, Amazon]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_channel</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[Amazon.in, Non-Amazon]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_service_level</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[Standard, Expedited]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Style</th>\n",
       "      <td>0</td>\n",
       "      <td>1377</td>\n",
       "      <td>[SET389, JNE3781, JNE3371, J0341, JNE3671, SET...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKU</th>\n",
       "      <td>0</td>\n",
       "      <td>7195</td>\n",
       "      <td>[SET389-KR-NP-S, JNE3781-KR-XXXL, JNE3371-KR-X...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>[Set, kurta, Western Dress, Top, Ethnic Dress,...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size</th>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>[S, 3XL, XL, L, XXL, XS, 6XL, M, 4XL, 5XL, Free]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIN</th>\n",
       "      <td>0</td>\n",
       "      <td>7190</td>\n",
       "      <td>[B09KXVBD7Z, B09K3WFS32, B07WV4JV4D, B099NRCT7...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Courier_Status</th>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>[Unknown, Shipped, Cancelled, Unshipped]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Qty</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>[0, 1, 2, 15, 3, 9, 13, 5, 4, 8]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Currency</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>[INR]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Amount</th>\n",
       "      <td>0</td>\n",
       "      <td>1410</td>\n",
       "      <td>[647.62, 406.0, 329.0, 753.33, 574.0, 824.0, 6...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_city</th>\n",
       "      <td>0</td>\n",
       "      <td>8956</td>\n",
       "      <td>[MUMBAI, BENGALURU, NAVI MUMBAI, PUDUCHERRY, C...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_state</th>\n",
       "      <td>0</td>\n",
       "      <td>70</td>\n",
       "      <td>[MAHARASHTRA, KARNATAKA, PUDUCHERRY, TAMIL NAD...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_postal_code</th>\n",
       "      <td>0</td>\n",
       "      <td>9460</td>\n",
       "      <td>[400081.0, 560085.0, 410210.0, 605008.0, 60007...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_country</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[IN, Unknown]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion_ids</th>\n",
       "      <td>0</td>\n",
       "      <td>5788</td>\n",
       "      <td>[No Promo, Amazon PLCC Free-Financing Universa...</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B2B</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[False, True]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fulfilled_by</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>[Easy Ship, FBA]</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    qt_missing  qt_unique_values  \\\n",
       "Order_ID                     0            120378   \n",
       "Date                         0                91   \n",
       "Status                       0                13   \n",
       "Fulfilment                   0                 2   \n",
       "Sales_channel                0                 2   \n",
       "ship_service_level           0                 2   \n",
       "Style                        0              1377   \n",
       "SKU                          0              7195   \n",
       "Category                     0                 9   \n",
       "Size                         0                11   \n",
       "ASIN                         0              7190   \n",
       "Courier_Status               0                 4   \n",
       "Qty                          0                10   \n",
       "Currency                     0                 1   \n",
       "Amount                       0              1410   \n",
       "ship_city                    0              8956   \n",
       "ship_state                   0                70   \n",
       "ship_postal_code             0              9460   \n",
       "ship_country                 0                 2   \n",
       "promotion_ids                0              5788   \n",
       "B2B                          0                 2   \n",
       "fulfilled_by                 0                 2   \n",
       "\n",
       "                                                        unique_values  \\\n",
       "Order_ID            [405-8078784-5731545, 171-9198151-1101146, 404...   \n",
       "Date                [04-30-22, 04-29-22, 04-28-22, 04-27-22, 04-26...   \n",
       "Status              [Cancelled, Shipped - Delivered to Buyer, Ship...   \n",
       "Fulfilment                                         [Merchant, Amazon]   \n",
       "Sales_channel                                 [Amazon.in, Non-Amazon]   \n",
       "ship_service_level                              [Standard, Expedited]   \n",
       "Style               [SET389, JNE3781, JNE3371, J0341, JNE3671, SET...   \n",
       "SKU                 [SET389-KR-NP-S, JNE3781-KR-XXXL, JNE3371-KR-X...   \n",
       "Category            [Set, kurta, Western Dress, Top, Ethnic Dress,...   \n",
       "Size                 [S, 3XL, XL, L, XXL, XS, 6XL, M, 4XL, 5XL, Free]   \n",
       "ASIN                [B09KXVBD7Z, B09K3WFS32, B07WV4JV4D, B099NRCT7...   \n",
       "Courier_Status               [Unknown, Shipped, Cancelled, Unshipped]   \n",
       "Qty                                  [0, 1, 2, 15, 3, 9, 13, 5, 4, 8]   \n",
       "Currency                                                        [INR]   \n",
       "Amount              [647.62, 406.0, 329.0, 753.33, 574.0, 824.0, 6...   \n",
       "ship_city           [MUMBAI, BENGALURU, NAVI MUMBAI, PUDUCHERRY, C...   \n",
       "ship_state          [MAHARASHTRA, KARNATAKA, PUDUCHERRY, TAMIL NAD...   \n",
       "ship_postal_code    [400081.0, 560085.0, 410210.0, 605008.0, 60007...   \n",
       "ship_country                                            [IN, Unknown]   \n",
       "promotion_ids       [No Promo, Amazon PLCC Free-Financing Universa...   \n",
       "B2B                                                     [False, True]   \n",
       "fulfilled_by                                         [Easy Ship, FBA]   \n",
       "\n",
       "                    perc_missing  \n",
       "Order_ID                     0.0  \n",
       "Date                         0.0  \n",
       "Status                       0.0  \n",
       "Fulfilment                   0.0  \n",
       "Sales_channel                0.0  \n",
       "ship_service_level           0.0  \n",
       "Style                        0.0  \n",
       "SKU                          0.0  \n",
       "Category                     0.0  \n",
       "Size                         0.0  \n",
       "ASIN                         0.0  \n",
       "Courier_Status               0.0  \n",
       "Qty                          0.0  \n",
       "Currency                     0.0  \n",
       "Amount                       0.0  \n",
       "ship_city                    0.0  \n",
       "ship_state                   0.0  \n",
       "ship_postal_code             0.0  \n",
       "ship_country                 0.0  \n",
       "promotion_ids                0.0  \n",
       "B2B                          0.0  \n",
       "fulfilled_by                 0.0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_missing_stats = missing_evaluation(df)\n",
    "df_missing_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "063175fa-3f08-4708-92a1-1254a57e6e76",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2.3 Uniform the values in each column [&#129081;](#Top)<a name=\"UniformValues_df1\"></a>\n",
    "To make values consistent, such as eliminating duplicates and having unique values, it is first of all necessary to standardize the style of the strings.\n",
    "It's a procedure in which the intervention to apply for each column must be evaluated individually."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce3beca3-0d87-4a2a-9950-d5ed1dfee74f",
   "metadata": {},
   "source": [
    "* **Order_ID**: it's proper that it's a string, remove extra white spaces from the beginning and end, and set the characters in UPPER case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e7039883-3d7d-4537-ba00-281023aeca2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Order_ID'] = df['Order_ID'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "22fedc4d-d5f3-4707-ad90-f46d09c842ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Order_ID'] = df['Order_ID'].str.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "60c7b468-33fc-41cc-bc33-1ae23af07ad2",
   "metadata": {},
   "source": [
    "* **Date**: it's in string format, set it to to dtype datetime. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0c948db5-65c0-460d-b743-575f20fe1bea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set column 'Date' to data type datetime\n",
    "df['Date'] = pd.to_datetime(df['Date'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19c3d449-f339-4c8e-b265-bcbe2800f027",
   "metadata": {},
   "source": [
    "* **Status**: it's proper that it's a string, remove extra white spaces from the beginning and end.   \n",
    "Then check if there are the same 'Status' values but with different spellings.  \n",
    "As verified above, it contains 13 values, so they can be checked visually one by one, avoiding apply without distinction Capitalize or UPPER methods, keeping the contents legible at a glance within possible limits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "e0ba6c63-1c9c-4195-8128-286f1c99b6b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Status'] = df['Status'].str.strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8c49c480-e01d-49aa-b77c-5f63537dca95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'Cancelled'\n",
      "'Pending'\n",
      "'Pending - Waiting for Pick Up'\n",
      "'Shipped'\n",
      "'Shipped - Damaged'\n",
      "'Shipped - Delivered to Buyer'\n",
      "'Shipped - Lost in Transit'\n",
      "'Shipped - Out for Delivery'\n",
      "'Shipped - Picked Up'\n",
      "'Shipped - Rejected by Buyer'\n",
      "'Shipped - Returned to Seller'\n",
      "'Shipped - Returning to Seller'\n",
      "'Shipping'\n"
     ]
    }
   ],
   "source": [
    "# Check column 'Status', result: it doesn't need intervention\n",
    "for value in df.Status.sort_values().unique():\n",
    "    print(f\"'{value}'\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1874a7a-0683-4538-914f-9165db6c7478",
   "metadata": {},
   "source": [
    "* **Fulfilment**: contains 2 unique values ('Merchant' and 'Amazon'), and are already written in the correct form\n",
    "* **Sales_channel**: contains 2 unique values ('Amazon.in' and 'Non-Amazon'), and are already written in the correct form\n",
    "* **ship_service_level**: contains 2 unique values('Standard' and 'Expedited'), and are already written in the correct form"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aedc578a-ce63-41a2-8b4d-ac6858d66f29",
   "metadata": {},
   "source": [
    "* **Style** and **SKU**: it's proper that they are strings, remove extra white spaces from the beginning and end, and set the characters in UPPER case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "84550d1f-71ec-4a25-963a-70ffb293c60d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Style'] = df['Style'].str.strip()\n",
    "df['Style'] = df['Style'].str.upper()\n",
    "df['SKU'] = df['SKU'].str.strip()\n",
    "df['SKU'] = df['SKU'].str.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e89a3f5d-5d3f-49ab-8129-70585e8a1b3e",
   "metadata": {},
   "source": [
    "* **Category**: it's proper that it's a string, remove extra white spaces from the beginning and end, and set the characters in UPPER case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "975c5bf0-aeb3-4ed8-8ff0-369f19dc0f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Category'] = df['Category'].str.strip()\n",
    "df['Category'] = df['Category'].str.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3437ccd7-e01b-447c-8918-6a964025d1b0",
   "metadata": {},
   "source": [
    "* **Size**: contains 11 unique values, and as can be seen from the table above they are already written in the correct form."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf1075b6-f01b-44bb-bfbf-6df08867ad50",
   "metadata": {},
   "source": [
    "* **ASIN**: it's proper that it's a string, remove extra white spaces from the beginning and end, and set the characters in UPPER case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "2cac975a-e52c-44ea-9f93-f74f62ef65a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ASIN'] = df['ASIN'].str.strip()\n",
    "df['ASIN'] = df['ASIN'].str.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0969e0a-630a-4738-8988-a124b816a8f2",
   "metadata": {},
   "source": [
    "* **Courier_Status**: contains 4 unique values (Shipped, Cancelled, Unshipped, Unknown), and are already written in the correct form.   \n",
    "* **Qty**: is a integer number, with max value of 15 units. It doesn't nedd intervention. \n",
    "* **Currency**: contains 1 unique value (INR), and is already written in the correct form.  \n",
    "* **Amount**: is a float number. With the measurements made so far it doesn't seem to need intervention."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "486be256-7e9e-4f8f-b812-6d06e42023f8",
   "metadata": {},
   "source": [
    "* **ship_city** and **ship_state**: it’s proper that they are strings, remove extra white spaces from the beginning and end, and uniform all the characters to UPPER case. Then check the accuracy of geographic names. And normalize different istances of the same state and city."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "483df4e7-7d01-482e-9cb5-a4bfbc16d12e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ship_city'] = df['ship_city'].str.strip()\n",
    "df['ship_city'] = df['ship_city'].str.upper()\n",
    "df['ship_state'] = df['ship_state'].str.strip()\n",
    "df['ship_state'] = df['ship_state'].str.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e07d119f-f502-4164-9ad1-c3423f968126",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['ship_state'].isin(['AR']),'ship_state'] = 'ARUNACHAL PRADESH'\n",
    "df.loc[df['ship_state'].isin(['NEW DELHI']),'ship_state'] = 'DELHI'\n",
    "df.loc[df['ship_state'].isin(['NL']),'ship_state'] = 'NAGALAND'\n",
    "df.loc[df['ship_state'].isin(['ORISSA']),'ship_state'] = 'ODISHA'\n",
    "df.loc[df['ship_state'].isin(['PONDICHERRY']),'ship_state'] = 'PUDUCHERRY'\n",
    "df.loc[df['ship_state'].isin(['PUNJAB/MOHALI/ZIRAKPUR','PB']),'ship_state'] = 'PUNJAB'\n",
    "df.loc[df['ship_state'].isin(['RAJSHTHAN','RAJSTHAN','RJ']),'ship_state'] = 'RAJASTHAN'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "c4bc1eec-8dda-4135-9e18-01e5f585b30c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ship_city'] = df['ship_city'].replace(['PUDUCHERRY','PONDYCHERRY', 'PUDUCHERRY 605001', 'PUDUCHERRY 605003'],'PUDUCHERRY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d074a288-51b4-4c0d-95e4-6abed532b7fb",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "df['ship_city'] = df['ship_city'].replace(['NORTH DELHI', 'NORTH WEST DELHI','NEW DELHI (NORTH)'],'NEW DELHI (NORTH)')\n",
    "df['ship_city'] = df['ship_city'].replace(['SOUTH  DELHI', 'SOUTH DELHI'],'NEW DELHI (SOUTH)')\n",
    "df['ship_city'] = df['ship_city'].replace(['SOUTH EAST DELHI'],'NEW DELHI (SOUTH EAST)')\n",
    "df['ship_city'] = df['ship_city'].replace(['SOUTH WEST DELHI','SOUTH-WEST DELHI'],'NEW DELHI (SOUTH WEST)')\n",
    "df['ship_city'] = df['ship_city'].replace(['WEST DELHI'],'NEW DELHI (WEST)')\n",
    "df['ship_city'] = df['ship_city'].replace(['CENTRAL DELHI','CITY'],'NEW DELHI (CENTRAL)')\n",
    "df['ship_city'] = df['ship_city'].replace(['EAST DELHI'],'NEW DELHI (EAST)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "58e9b772-8fa2-43b6-9106-f4bba400c2d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = r'^NEW DELHI(?!.*\\()'\n",
    "df['ship_city'] = df['ship_city'].apply(lambda x: \"NEW DELHI\" if re.match(pattern, x) else x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "10747453-e071-4a2e-a462-6973cfd207a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ship_city'] = df['ship_city'].replace(['N.DELHI','NEW DELH','DELHI','DELHI -86','DELHI 110085','DELHI CANTT','DELHI-92.','DELHIQ', 'NEW-DELHI',\n",
    " 'NEWDELHI'],'NEW DELHI')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "9696b77f-204c-4ed9-9207-421f844ab27e",
   "metadata": {},
   "outputs": [],
   "source": [
    "pattern = r'^BANGALOR(?!.*\\()'\n",
    "df['ship_city'] = df['ship_city'].apply(lambda x: 'BANGALORE' if re.match(pattern, x) else x)\n",
    "df['ship_city'] = df['ship_city'].apply(lambda x: 'BANGALORE' if re.match(r'^BENGALUR(?!.*\\()', x) else x)\n",
    "df['ship_city'] = df['ship_city'].replace(['BENGALOORU','BENGOLOOR', 'BANGALURU'],'BANGALORE')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d5b6090-aacf-4c1b-9a68-5cb898ecedcc",
   "metadata": {},
   "source": [
    "Note: since this notebook is for demonstration purposes only, I normalized all State names, and only the cities of Puducherry, New Delhi and Bangalore."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b770ab71-e9d0-4a52-be74-890a14199097",
   "metadata": {},
   "source": [
    "* **ship_postal_code**: it's currently a float, but postal code is a categorical data, so it needs to be converted in string. Remember that all missing values have been replaced with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "aa912907-43fc-44f8-9f83-c5b55e816a50",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ship_postal_code'] = df['ship_postal_code'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c4c8e544-a2b1-4069-be4e-a20b63486df9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 128975 entries, 0 to 128974\n",
      "Data columns (total 22 columns):\n",
      " #   Column              Non-Null Count   Dtype         \n",
      "---  ------              --------------   -----         \n",
      " 0   Order_ID            128975 non-null  object        \n",
      " 1   Date                128975 non-null  datetime64[ns]\n",
      " 2   Status              128975 non-null  object        \n",
      " 3   Fulfilment          128975 non-null  object        \n",
      " 4   Sales_channel       128975 non-null  object        \n",
      " 5   ship_service_level  128975 non-null  object        \n",
      " 6   Style               128975 non-null  object        \n",
      " 7   SKU                 128975 non-null  object        \n",
      " 8   Category            128975 non-null  object        \n",
      " 9   Size                128975 non-null  object        \n",
      " 10  ASIN                128975 non-null  object        \n",
      " 11  Courier_Status      128975 non-null  object        \n",
      " 12  Qty                 128975 non-null  int64         \n",
      " 13  Currency            128975 non-null  object        \n",
      " 14  Amount              128975 non-null  float64       \n",
      " 15  ship_city           128975 non-null  object        \n",
      " 16  ship_state          128975 non-null  object        \n",
      " 17  ship_postal_code    128975 non-null  object        \n",
      " 18  ship_country        128975 non-null  object        \n",
      " 19  promotion_ids       128975 non-null  object        \n",
      " 20  B2B                 128975 non-null  bool          \n",
      " 21  fulfilled_by        128975 non-null  object        \n",
      "dtypes: bool(1), datetime64[ns](1), float64(1), int64(1), object(18)\n",
      "memory usage: 21.8+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01e78b05-db80-42c3-a362-bd898062469a",
   "metadata": {},
   "source": [
    "* **promotion_ids**: it's proper that it is a string, remove extra white spaces from the beginning and end, and set the characters in UPPER case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "535dc38c-a104-4eb9-a264-786c00aa1fa5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['promotion_ids'] = df['promotion_ids'].str.strip()\n",
    "df['promotion_ids'] = df['promotion_ids'].str.upper()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ea7d439-78bc-4b2d-8daf-a22479499db7",
   "metadata": {},
   "source": [
    "* **B2B**: boolean, True when the sale is Business to Business, False when is Business to Consumer. It doesn't needs intervention.\n",
    "* **fulfilled_by**: contains 2 unique values (Easy Ship, FBA). It doesn't needs intervention."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca53037b-8b66-4c7a-954b-ed6d96489057",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 2.4 Remove Duplicates [&#129081;](#Top)<a name=\"RemDupl_df1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a0c4a73-9f6f-4985-8192-0fe8fb0508e4",
   "metadata": {},
   "source": [
    "* Remove duplicate raws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "19465ae5-4698-46d3-be81-9589069ab396",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop_duplicates(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d5faa4a4-b393-49b5-bc4b-8bd0d77d7d56",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Deleted 6 raws, 128969 remain.\n"
     ]
    }
   ],
   "source": [
    "df_sizes.update({'after_remove_duplicates': df.shape})\n",
    "print(f\"Deleted {df_sizes['start'][0] - df_sizes['after_remove_duplicates'][0] } raws, {df_sizes['after_remove_duplicates'][0]} remain.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8424bb4f-2aaf-422a-9805-e80197ab0553",
   "metadata": {},
   "source": [
    "* Remove **fulfilled_by** column, bacause it doesn't add information to data. In fact it contains the same information as the column **Fulfilment**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "ceada4b8-de3b-4342-99d1-393ea8ef8b38",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Question: When the value in column 'Fulfilment' is 'Merchant', the value in column 'fulfilled_by' is 'Easy Ship'?\n",
      "Answer:   True\n",
      "\n",
      "Question: When the value in column 'Fulfilment' is 'Amazon', the value in column 'fulfilled_by' is 'FBA'?\n",
      "Answer:   True\n"
     ]
    }
   ],
   "source": [
    "print(f\"\"\"\n",
    "Question: When the value in column 'Fulfilment' is 'Merchant', the value in column 'fulfilled_by' is 'Easy Ship'?\n",
    "Answer:   {set(df.query(\"Fulfilment == 'Merchant'\").index) == set(df.query(\"fulfilled_by == 'Easy Ship'\").index)}\"\"\") \n",
    "print(f\"\"\"\n",
    "Question: When the value in column 'Fulfilment' is 'Amazon', the value in column 'fulfilled_by' is 'FBA'?\n",
    "Answer:   {set(df.query(\"Fulfilment == 'Merchant'\").index) == set(df.query(\"fulfilled_by == 'Easy Ship'\").index)}\"\"\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4cdaacbc-3f1e-4675-9216-d5e06347b6cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns = ['fulfilled_by'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d295f8a-f349-49d0-94aa-070130c60767",
   "metadata": {},
   "source": [
    "## 2.5 Numeric data [&#129081;](#Top)<a name=\"NumData\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "a5d52e1e-8cce-4441-8f6b-45df47811814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>128969</td>\n",
       "      <td>128969.00000</td>\n",
       "      <td>128969.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2022-05-12 11:50:02.758802176</td>\n",
       "      <td>0.90445</td>\n",
       "      <td>609.371580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2022-03-31 00:00:00</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2022-04-20 00:00:00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>413.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2022-05-10 00:00:00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>583.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2022-06-04 00:00:00</td>\n",
       "      <td>1.00000</td>\n",
       "      <td>771.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2022-06-29 00:00:00</td>\n",
       "      <td>15.00000</td>\n",
       "      <td>5584.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.31333</td>\n",
       "      <td>313.335444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Date           Qty         Amount\n",
       "count                         128969  128969.00000  128969.000000\n",
       "mean   2022-05-12 11:50:02.758802176       0.90445     609.371580\n",
       "min              2022-03-31 00:00:00       0.00000       0.000000\n",
       "25%              2022-04-20 00:00:00       1.00000     413.000000\n",
       "50%              2022-05-10 00:00:00       1.00000     583.000000\n",
       "75%              2022-06-04 00:00:00       1.00000     771.000000\n",
       "max              2022-06-29 00:00:00      15.00000    5584.000000\n",
       "std                              NaN       0.31333     313.335444"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(datetime_is_numeric=True) # only numerical Attributes (including Date)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "a4a120b3-7f12-4ea1-80d6-c3211568614d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timedelta('90 days 00:00:00')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Date'].max() - df['Date'].min()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e2efd084-fbd5-4fed-9e05-b6d6678ae663",
   "metadata": {},
   "source": [
    "The start date of the dataset is 31 March 2022, the end date is 29 June 2022, over a 90 day period."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb9da1e-f7b8-46e7-aef6-d51c9365d2db",
   "metadata": {},
   "source": [
    "## 2.6 Categorical data [&#129081;](#Top)<a name=\"CatData\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "87b58da4-501c-495f-b21f-8aa97d74e832",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Order_ID</th>\n",
       "      <td>128969</td>\n",
       "      <td>120378</td>\n",
       "      <td>403-4984515-8861958</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Status</th>\n",
       "      <td>128969</td>\n",
       "      <td>13</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>77801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fulfilment</th>\n",
       "      <td>128969</td>\n",
       "      <td>2</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>89692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sales_channel</th>\n",
       "      <td>128969</td>\n",
       "      <td>2</td>\n",
       "      <td>Amazon.in</td>\n",
       "      <td>128845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_service_level</th>\n",
       "      <td>128969</td>\n",
       "      <td>2</td>\n",
       "      <td>Expedited</td>\n",
       "      <td>88609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Style</th>\n",
       "      <td>128969</td>\n",
       "      <td>1377</td>\n",
       "      <td>JNE3797</td>\n",
       "      <td>4224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SKU</th>\n",
       "      <td>128969</td>\n",
       "      <td>7195</td>\n",
       "      <td>JNE3797-KR-L</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Category</th>\n",
       "      <td>128969</td>\n",
       "      <td>9</td>\n",
       "      <td>SET</td>\n",
       "      <td>50281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Size</th>\n",
       "      <td>128969</td>\n",
       "      <td>11</td>\n",
       "      <td>M</td>\n",
       "      <td>22709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ASIN</th>\n",
       "      <td>128969</td>\n",
       "      <td>7190</td>\n",
       "      <td>B09SDXFFQ1</td>\n",
       "      <td>773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Courier_Status</th>\n",
       "      <td>128969</td>\n",
       "      <td>4</td>\n",
       "      <td>Shipped</td>\n",
       "      <td>109484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Currency</th>\n",
       "      <td>128969</td>\n",
       "      <td>1</td>\n",
       "      <td>INR</td>\n",
       "      <td>128969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_city</th>\n",
       "      <td>128969</td>\n",
       "      <td>7201</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>13342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_state</th>\n",
       "      <td>128969</td>\n",
       "      <td>38</td>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>22259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_postal_code</th>\n",
       "      <td>128969</td>\n",
       "      <td>9460</td>\n",
       "      <td>201301.0</td>\n",
       "      <td>1006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ship_country</th>\n",
       "      <td>128969</td>\n",
       "      <td>2</td>\n",
       "      <td>IN</td>\n",
       "      <td>128936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promotion_ids</th>\n",
       "      <td>128969</td>\n",
       "      <td>5788</td>\n",
       "      <td>NO PROMO</td>\n",
       "      <td>49150</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     count  unique                  top    freq\n",
       "Order_ID            128969  120378  403-4984515-8861958      12\n",
       "Status              128969      13              Shipped   77801\n",
       "Fulfilment          128969       2               Amazon   89692\n",
       "Sales_channel       128969       2            Amazon.in  128845\n",
       "ship_service_level  128969       2            Expedited   88609\n",
       "Style               128969    1377              JNE3797    4224\n",
       "SKU                 128969    7195         JNE3797-KR-L     773\n",
       "Category            128969       9                  SET   50281\n",
       "Size                128969      11                    M   22709\n",
       "ASIN                128969    7190           B09SDXFFQ1     773\n",
       "Courier_Status      128969       4              Shipped  109484\n",
       "Currency            128969       1                  INR  128969\n",
       "ship_city           128969    7201            BANGALORE   13342\n",
       "ship_state          128969      38          MAHARASHTRA   22259\n",
       "ship_postal_code    128969    9460             201301.0    1006\n",
       "ship_country        128969       2                   IN  128936\n",
       "promotion_ids       128969    5788             NO PROMO   49150"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include='O').T # only numpy.object data type Categorical Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "876519c0-f5dc-4d98-a7fc-53a296ebf748",
   "metadata": {},
   "source": [
    "* Add the column **Month**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3c68ccf3-e8b6-4f27-a03b-6c188ba8d561",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 3, 5, 6], dtype=int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['month'] = df['Date'].dt.month\n",
    "df['month'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "3c01aa6d-6bb4-4915-a642-c6e6c28c7542",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['month'].replace([3,4,5,6],['March','April', 'May', 'June'], inplace = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e183f170-9531-49af-a9a4-942cf5d70977",
   "metadata": {},
   "source": [
    "* Save the cleaned dataframe in a csv file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "9a55ba43-a40e-4f04-80ea-91b626bba60b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"Data out\\\\new_df_Amazon_sales.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe5ccab2-20eb-41a8-9181-25dad9d351f6",
   "metadata": {},
   "source": [
    "# 3. DATA ANALYSIS <a name=\"DataAnalysis\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24668653-067a-4432-88e6-b1709b8cb507",
   "metadata": {},
   "source": [
    "## Answer to Q1: Which categories have sold most? [&#129081;](#Top)<a name=\"AQ1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5b697941-1c39-4f99-a5ca-e5523b5516ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SET</td>\n",
       "      <td>39202022.03</td>\n",
       "      <td>45287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>KURTA</td>\n",
       "      <td>21299013.70</td>\n",
       "      <td>45044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>11216072.69</td>\n",
       "      <td>13943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TOP</td>\n",
       "      <td>5347792.30</td>\n",
       "      <td>9903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ETHNIC DRESS</td>\n",
       "      <td>791217.66</td>\n",
       "      <td>1053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BLOUSE</td>\n",
       "      <td>458408.18</td>\n",
       "      <td>863</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>BOTTOM</td>\n",
       "      <td>150667.98</td>\n",
       "      <td>398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SAREE</td>\n",
       "      <td>123933.76</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DUPATTA</td>\n",
       "      <td>915.00</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Category       Amount    Qty\n",
       "0            SET  39202022.03  45287\n",
       "1          KURTA  21299013.70  45044\n",
       "2  WESTERN DRESS  11216072.69  13943\n",
       "3            TOP   5347792.30   9903\n",
       "4   ETHNIC DRESS    791217.66   1053\n",
       "5         BLOUSE    458408.18    863\n",
       "6         BOTTOM    150667.98    398\n",
       "7          SAREE    123933.76    152\n",
       "8        DUPATTA       915.00      3"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Which categories have sold most?\n",
    "df_q1 = df[['Amount', 'Qty']].groupby(df['Category']).sum()\\\n",
    "                                .sort_values('Amount', ascending=False)\\\n",
    "                                .reset_index()\n",
    "df_q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "9cb84bf4-2af0-4688-925e-fe8f0e7f5d16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# define font sizes\n",
    "_title_size = 18\n",
    "_axis_label_size = 12\n",
    "_label_size = 10\n",
    "_axis_label_pad = 10\n",
    "_legend_title_size = 12\n",
    "_legend_content_size = 12\n",
    "_padding_title = 20\n",
    "_fweight = 'normal'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "612dce8c-f94e-45d9-9811-bf83b90498ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "ax_q1 = sns.barplot(y = 'Amount', x='Category', palette = ['#3182bd'], data = df_q1)\n",
    "plt.title('Revenue by Categories', fontsize= _title_size)\n",
    "\n",
    "formatter = ticker.StrMethodFormatter(\"{x:,.0f}.\")\n",
    "ax_q1.yaxis.set_major_formatter(formatter)\n",
    "ax_q1.xaxis.labelpad = _axis_label_pad\n",
    "ax_q1.yaxis.labelpad = _axis_label_pad\n",
    "\n",
    "for i, bar in enumerate(ax_q1.containers[0].patches):\n",
    "    ax_q1.text(bar.get_x() + bar.get_width()/2., bar.get_height(),\n",
    "        '{:,.0f}'.format(bar.get_height()), ha='center', va='bottom')\n",
    "plt.xlabel(\"Category\", weight = _fweight, fontsize=_axis_label_size)\n",
    "plt.ylabel(\"Sales\", weight = _fweight, fontsize=_axis_label_size)\n",
    "plt.savefig('img\\\\Revenue_by_categories.jpg', dpi=300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20c9218e-8aac-4d2e-8b98-180edcf6ba64",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Answer Q2: Which are the 20 Best-Selling Products? [&#129081;](#Top)<a name=\"AQ2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "24316ac9-d2f1-423c-9b71-453f28936684",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>ASIN</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDXFFQ1</td>\n",
       "      <td>661</td>\n",
       "      <td>524581.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDY8DCT</td>\n",
       "      <td>561</td>\n",
       "      <td>454290.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDYQ3WG</td>\n",
       "      <td>503</td>\n",
       "      <td>407302.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KURTA</td>\n",
       "      <td>B081WSCKPQ</td>\n",
       "      <td>485</td>\n",
       "      <td>194645.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XNJG8B1</td>\n",
       "      <td>467</td>\n",
       "      <td>526536.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XNJ19QH</td>\n",
       "      <td>421</td>\n",
       "      <td>479937.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDXRYBG</td>\n",
       "      <td>415</td>\n",
       "      <td>332155.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>KURTA</td>\n",
       "      <td>B081WX4G4Q</td>\n",
       "      <td>399</td>\n",
       "      <td>169808.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDY9SQ6</td>\n",
       "      <td>386</td>\n",
       "      <td>303616.70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XQBF1G4</td>\n",
       "      <td>373</td>\n",
       "      <td>284058.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDXSQ33</td>\n",
       "      <td>359</td>\n",
       "      <td>275966.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDY4VDC</td>\n",
       "      <td>353</td>\n",
       "      <td>276375.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XQ98B2Q</td>\n",
       "      <td>350</td>\n",
       "      <td>267030.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XQ8MCKP</td>\n",
       "      <td>348</td>\n",
       "      <td>258716.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>KURTA</td>\n",
       "      <td>B081WVMMCY</td>\n",
       "      <td>344</td>\n",
       "      <td>146626.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KURTA</td>\n",
       "      <td>B08KRXV1QR</td>\n",
       "      <td>291</td>\n",
       "      <td>115806.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08B3Z2YY3</td>\n",
       "      <td>289</td>\n",
       "      <td>250171.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>SET</td>\n",
       "      <td>B0894X27FC</td>\n",
       "      <td>281</td>\n",
       "      <td>193079.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XNDL1DL</td>\n",
       "      <td>272</td>\n",
       "      <td>305616.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B099NQQ79L</td>\n",
       "      <td>268</td>\n",
       "      <td>235151.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Category        ASIN  Qty     Amount\n",
       "0   WESTERN DRESS  B09SDXFFQ1  661  524581.77\n",
       "1   WESTERN DRESS  B09SDY8DCT  561  454290.16\n",
       "2   WESTERN DRESS  B09SDYQ3WG  503  407302.57\n",
       "3           KURTA  B081WSCKPQ  485  194645.29\n",
       "4             SET  B08XNJG8B1  467  526536.20\n",
       "5             SET  B08XNJ19QH  421  479937.14\n",
       "6   WESTERN DRESS  B09SDXRYBG  415  332155.24\n",
       "7           KURTA  B081WX4G4Q  399  169808.87\n",
       "8   WESTERN DRESS  B09SDY9SQ6  386  303616.70\n",
       "9             SET  B08XQBF1G4  373  284058.96\n",
       "10  WESTERN DRESS  B09SDXSQ33  359  275966.88\n",
       "11  WESTERN DRESS  B09SDY4VDC  353  276375.80\n",
       "12            SET  B08XQ98B2Q  350  267030.48\n",
       "13            SET  B08XQ8MCKP  348  258716.00\n",
       "14          KURTA  B081WVMMCY  344  146626.29\n",
       "15          KURTA  B08KRXV1QR  291  115806.00\n",
       "16            SET  B08B3Z2YY3  289  250171.98\n",
       "17            SET  B0894X27FC  281  193079.79\n",
       "18            SET  B08XNDL1DL  272  305616.95\n",
       "19  WESTERN DRESS  B099NQQ79L  268  235151.42"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q2 = df.groupby(['Category', 'ASIN'])[['Qty','Amount']].sum()\\\n",
    "                                     .sort_values('Qty', ascending=False)\\\n",
    "                                     .head(20)\\\n",
    "                                     .reset_index()\n",
    "df_q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "7a6dc578-2cb1-4a92-8110-71b2f43d5336",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_dict = {'WESTERN DRESS': '#4BD9EC', 'KURTA': '#758BFD', 'SET': '#313E5B'} \n",
    "col_list = [colors_dict.get(cat) for cat in df_q2['Category']]\n",
    "\n",
    "plt.figure(figsize = (12, 6))\n",
    "ax_q2 = sns.barplot(y = 'Qty', x='ASIN', palette = col_list, data = df_q2)\n",
    "plt.title('20 Best-Selling Products by Quantity', fontsize=_title_size)\n",
    "\n",
    "formatter = ticker.StrMethodFormatter(\"{x:,.0f}\")\n",
    "ax_q2.yaxis.set_major_formatter(formatter)\n",
    "ax_q2.xaxis.labelpad = _axis_label_pad\n",
    "ax_q2.yaxis.labelpad = _axis_label_pad\n",
    "ax_q2.margins(0.01, 0.2)\n",
    "\n",
    "for i, bar in enumerate(ax_q2.containers[0].patches):\n",
    "    ax_q2.text(bar.get_x() + bar.get_width()/2., bar.get_height(),\n",
    "        '{:,.0f}'.format(bar.get_height()), ha='center', va='bottom')\n",
    "plt.xticks(rotation=90, ha='center')\n",
    "plt.xlabel(\"Product\", weight = _fweight, fontsize= _axis_label_size)\n",
    "plt.ylabel(\"Qty\", weight = _fweight, fontsize= _axis_label_size)\n",
    "\n",
    "legend_entries = []\n",
    "for elem in df_q2['Category'].unique():\n",
    "    legend_entries.append(mpatches.Patch(color=colors_dict.get(elem), label=elem))\n",
    "plt.legend(handles=legend_entries, title='Category:', title_fontsize= _legend_title_size, fontsize=_legend_content_size)\n",
    "plt.savefig('img\\\\20_Best_Selling_Products_by_Quantity.jpg', dpi=300, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c317094d-2a0f-42f1-be0a-83b74fc8ffb7",
   "metadata": {},
   "source": [
    "## Answer to Q3: Which are the 20 Best-Selling Products by Revenue? [&#129081;](#Top)<a name=\"AQ3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "96ca532e-56fa-4370-bc0e-dc3e036b408b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>ASIN</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XNJG8B1</td>\n",
       "      <td>526536.20</td>\n",
       "      <td>467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDXFFQ1</td>\n",
       "      <td>524581.77</td>\n",
       "      <td>661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XNJ19QH</td>\n",
       "      <td>479937.14</td>\n",
       "      <td>421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDY8DCT</td>\n",
       "      <td>454290.16</td>\n",
       "      <td>561</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDYQ3WG</td>\n",
       "      <td>407302.57</td>\n",
       "      <td>503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDXRYBG</td>\n",
       "      <td>332155.24</td>\n",
       "      <td>415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XNDL1DL</td>\n",
       "      <td>305616.95</td>\n",
       "      <td>272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDY9SQ6</td>\n",
       "      <td>303616.70</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XQBF1G4</td>\n",
       "      <td>284058.96</td>\n",
       "      <td>373</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDY4VDC</td>\n",
       "      <td>276375.80</td>\n",
       "      <td>353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B09SDXSQ33</td>\n",
       "      <td>275966.88</td>\n",
       "      <td>359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XQ98B2Q</td>\n",
       "      <td>267030.48</td>\n",
       "      <td>350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XNJ8P3S</td>\n",
       "      <td>259032.58</td>\n",
       "      <td>221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XQ8MCKP</td>\n",
       "      <td>258716.00</td>\n",
       "      <td>348</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08B3Z2YY3</td>\n",
       "      <td>250171.98</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B099NQQ79L</td>\n",
       "      <td>235151.42</td>\n",
       "      <td>268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>SET</td>\n",
       "      <td>B08XNHXSFZ</td>\n",
       "      <td>221741.86</td>\n",
       "      <td>197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B099NRCT7B</td>\n",
       "      <td>217865.04</td>\n",
       "      <td>257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>WESTERN DRESS</td>\n",
       "      <td>B099NR7612</td>\n",
       "      <td>214934.45</td>\n",
       "      <td>244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>SET</td>\n",
       "      <td>B0983F3BLZ</td>\n",
       "      <td>201821.14</td>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Category        ASIN     Amount  Qty\n",
       "0             SET  B08XNJG8B1  526536.20  467\n",
       "1   WESTERN DRESS  B09SDXFFQ1  524581.77  661\n",
       "2             SET  B08XNJ19QH  479937.14  421\n",
       "3   WESTERN DRESS  B09SDY8DCT  454290.16  561\n",
       "4   WESTERN DRESS  B09SDYQ3WG  407302.57  503\n",
       "5   WESTERN DRESS  B09SDXRYBG  332155.24  415\n",
       "6             SET  B08XNDL1DL  305616.95  272\n",
       "7   WESTERN DRESS  B09SDY9SQ6  303616.70  386\n",
       "8             SET  B08XQBF1G4  284058.96  373\n",
       "9   WESTERN DRESS  B09SDY4VDC  276375.80  353\n",
       "10  WESTERN DRESS  B09SDXSQ33  275966.88  359\n",
       "11            SET  B08XQ98B2Q  267030.48  350\n",
       "12            SET  B08XNJ8P3S  259032.58  221\n",
       "13            SET  B08XQ8MCKP  258716.00  348\n",
       "14            SET  B08B3Z2YY3  250171.98  289\n",
       "15  WESTERN DRESS  B099NQQ79L  235151.42  268\n",
       "16            SET  B08XNHXSFZ  221741.86  197\n",
       "17  WESTERN DRESS  B099NRCT7B  217865.04  257\n",
       "18  WESTERN DRESS  B099NR7612  214934.45  244\n",
       "19            SET  B0983F3BLZ  201821.14  138"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q3 = df.groupby(['Category', 'ASIN'])[['Amount', 'Qty']].sum()\\\n",
    "                                     .sort_values('Amount', ascending=False)\\\n",
    "                                     .head(20)\\\n",
    "                                     .reset_index()\n",
    "df_q3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "9a578d63-c453-46bd-b9a0-a72df869e274",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors_dict = {'WESTERN DRESS': '#4BD9EC', 'KURTA': '#758BFD', 'SET': '#313E5B'} \n",
    "col_list = [colors_dict.get(cat) for cat in df_q3['Category']]\n",
    "\n",
    "plt.figure(figsize = (12, 6))\n",
    "ax_q3 = sns.barplot(y = 'Amount', x='ASIN', palette = col_list, data = df_q3)\n",
    "plt.title('20 Best-Selling Products by Revenue', fontsize=_title_size)\n",
    "\n",
    "formatter = ticker.StrMethodFormatter(\"{x:,.0f}\")\n",
    "ax_q3.yaxis.set_major_formatter(formatter)\n",
    "ax_q3.xaxis.labelpad = _axis_label_pad\n",
    "ax_q3.yaxis.labelpad = _axis_label_pad\n",
    "\n",
    "for i, bar in enumerate(ax_q3.containers[0].patches):\n",
    "    ax_q3.text(bar.get_x() + bar.get_width()*0.8, bar.get_height(),\n",
    "        '{:,.0f}'.format(bar.get_height()), ha='center', va='bottom', rotation=45)\n",
    "    \n",
    "plt.xticks(rotation=90, ha='center')\n",
    "plt.xlabel(\"Product\", weight = _fweight, fontsize= _axis_label_size)\n",
    "plt.ylabel(\"Sales\", weight = _fweight, fontsize= _axis_label_size)\n",
    "\n",
    "legend_entries = []\n",
    "for elem in df_q3['Category'].unique():\n",
    "    legend_entries.append(mpatches.Patch(color=colors_dict.get(elem), label=elem))\n",
    "plt.legend(handles=legend_entries, title='Category:', title_fontsize= _legend_title_size, fontsize=_legend_content_size)\n",
    "ax_q3.margins(0.01, 0.2)\n",
    "plt.savefig('img\\\\20_Best_Selling_Products_by_Revenue.jpg', dpi=300, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bc7700e-5ef4-489a-9842-16aa77be6eb4",
   "metadata": {},
   "source": [
    "## Answer Q4: Which are the 20 cities that made the most orders? [&#129081;](#Top)<a name=\"AQ4\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "416c41b9-d591-469d-b7a9-32d38ab7c545",
   "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>ship_state</th>\n",
       "      <th>ship_city</th>\n",
       "      <th>Order_ID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>KARNATAKA</td>\n",
       "      <td>BANGALORE</td>\n",
       "      <td>13342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TELANGANA</td>\n",
       "      <td>HYDERABAD</td>\n",
       "      <td>9092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>MUMBAI</td>\n",
       "      <td>7122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DELHI</td>\n",
       "      <td>NEW DELHI</td>\n",
       "      <td>6753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>CHENNAI</td>\n",
       "      <td>6284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>PUNE</td>\n",
       "      <td>4616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>WEST BENGAL</td>\n",
       "      <td>KOLKATA</td>\n",
       "      <td>2844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HARYANA</td>\n",
       "      <td>GURUGRAM</td>\n",
       "      <td>1954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>THANE</td>\n",
       "      <td>1877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>LUCKNOW</td>\n",
       "      <td>1627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>NOIDA</td>\n",
       "      <td>1627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>UTTAR PRADESH</td>\n",
       "      <td>GHAZIABAD</td>\n",
       "      <td>1484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>GUJARAT</td>\n",
       "      <td>AHMEDABAD</td>\n",
       "      <td>1444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>MAHARASHTRA</td>\n",
       "      <td>NAVI MUMBAI</td>\n",
       "      <td>1403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>RAJASTHAN</td>\n",
       "      <td>JAIPUR</td>\n",
       "      <td>1062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>TAMIL NADU</td>\n",
       "      <td>COIMBATORE</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>ANDHRA PRADESH</td>\n",
       "      <td>VISAKHAPATNAM</td>\n",
       "      <td>858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HARYANA</td>\n",
       "      <td>FARIDABAD</td>\n",
       "      <td>820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>BIHAR</td>\n",
       "      <td>PATNA</td>\n",
       "      <td>816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>ODISHA</td>\n",
       "      <td>BHUBANESWAR</td>\n",
       "      <td>780</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ship_state      ship_city  Order_ID\n",
       "0        KARNATAKA      BANGALORE     13342\n",
       "1        TELANGANA      HYDERABAD      9092\n",
       "2      MAHARASHTRA         MUMBAI      7122\n",
       "3            DELHI      NEW DELHI      6753\n",
       "4       TAMIL NADU        CHENNAI      6284\n",
       "5      MAHARASHTRA           PUNE      4616\n",
       "6      WEST BENGAL        KOLKATA      2844\n",
       "7          HARYANA       GURUGRAM      1954\n",
       "8      MAHARASHTRA          THANE      1877\n",
       "9    UTTAR PRADESH        LUCKNOW      1627\n",
       "10   UTTAR PRADESH          NOIDA      1627\n",
       "11   UTTAR PRADESH      GHAZIABAD      1484\n",
       "12         GUJARAT      AHMEDABAD      1444\n",
       "13     MAHARASHTRA    NAVI MUMBAI      1403\n",
       "14       RAJASTHAN         JAIPUR      1062\n",
       "15      TAMIL NADU     COIMBATORE       891\n",
       "16  ANDHRA PRADESH  VISAKHAPATNAM       858\n",
       "17         HARYANA      FARIDABAD       820\n",
       "18           BIHAR          PATNA       816\n",
       "19          ODISHA    BHUBANESWAR       780"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_q4 = df.groupby(['ship_state','ship_city'])[['Order_ID']].count()\\\n",
    "                                    .sort_values('Order_ID', ascending=False)\\\n",
    "                                    .head(20)\\\n",
    "                                    .reset_index()\n",
    "df_q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66a6757e-62b8-4d7b-a336-faaffbf42328",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee91e719-006c-4c80-a90f-b4338b087b8a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "c49f7c39-4769-4a63-b353-72e496d79dd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "\n",
    "plt.title('First 20 Cities by Nr. of Orders', fontsize=_title_size)\n",
    "\n",
    "palette_colors = sns.color_palette('Set2',len(df_q4['ship_state'].unique()))\n",
    "palette_dict = {state: color for state, color in zip(list(df_q4['ship_state'].unique()), palette_colors)}\n",
    "col_list = [palette_dict.get(state) for state, city in zip(df_q4['ship_state'],df_q4['ship_city'])]\n",
    "\n",
    "ax_q4 = sns.barplot(y = 'Order_ID', x='ship_city', palette = col_list, data = df_q4)\n",
    "\n",
    "formatter = ticker.StrMethodFormatter(\"{x:,.0f}\")\n",
    "ax_q4.yaxis.set_major_formatter(formatter)\n",
    "ax_q4.yaxis.labelpad = _axis_label_pad\n",
    "\n",
    "for i, bar in enumerate(ax_q4.containers[0].patches):\n",
    "    ax_q4.text(bar.get_x() + bar.get_width()*0.7, bar.get_height(),\n",
    "        '{:,.0f}'.format(bar.get_height()), ha='center', va='bottom', rotation=45)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"City\", weight = _fweight, fontsize= _axis_label_size)\n",
    "plt.ylabel(\"Nr. of Orders\", weight = _fweight, fontsize= _axis_label_size)\n",
    "\n",
    "legend_entries = []\n",
    "for elem in df_q4['ship_state'].unique():\n",
    "    legend_entries.append(mpatches.Patch(color=palette_dict.get(elem), label=elem))\n",
    "plt.legend(handles=legend_entries, title='State:', title_fontsize= _legend_title_size-1, fontsize=_legend_content_size-1)\n",
    "\n",
    "ax_q4.margins(0.01, 0.2)\n",
    "plt.savefig('img\\\\First_20_Cities_by_Nr_of_Orders.jpg', dpi=300, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc90ba05-9c2a-469d-9dc3-df329e5c3038",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "79cb3eb2-8d14-42e4-a25c-29fb49fd7d68",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "3fd8043b-edae-477e-85ad-44855185b114",
   "metadata": {},
   "source": [
    "## Answer Q5: Which are the 20 cities that more income generate? [&#129081;](#Top)<a name=\"AQ5\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "4d82ad5d-6400-42f1-ad1a-40b00f2b5c99",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_q5 = df.groupby(['ship_state','ship_city'])[['Amount','Qty']].sum()\\\n",
    "                                    .sort_values('Amount', ascending=False)\\\n",
    "                                     .head(20)\\\n",
    "                                    .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "ea341053-486c-4e85-a54f-7719eeb8ba63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8VocghBBCiBzor7/+AqBbt25YWVmRL18+WrZsiY2NDQMGDODXX3+lQIECODo6AvDTTz9Rs2ZNHBwcKFOmDJMnT9bnaty4MQCOjo4UKFCAX3/9FQCj0Yi7uzuFCxemVatW/PPPP8/1NeYkkvAL3fjx4/n444/58MMPAciTJw+apmVxVEIIIYTIaV5++WWsrKzo1asXW7ZsISwsDAB3d3e+/fZb6tevT0REBOHh4QDkz5+fxYsXEx4ezk8//cT8+fMJDAwEYO/evQCEh4cTERFB/fr1CQwM5NNPP2Xt2rXcunWLV199lW7duunnb9euHZ999tlzfc3ZmST8AgBfX19OnTqFu7s758+fZ+zYsYBsFSaEEEKIZ+fg4MC+fftQStGvXz+KFSvGm2++yY0bN1Ls7+XlRbVq1ciTJw/Vq1enW7du7NmzJ9X5FyxYwLhx43B3d8fa2poPPviA4OBgfZV/06ZNvP/++5ny2nIiSfgFGzZsIDIyknXr1uHt7c2AAQP0T9xCCCGEEOnh7u5OQEAAoaGhnDhxgqtXrzJ8+PAU+x46dIimTZtSrFgxChUqxLfffsvt27dTnfuff/5h2LBhODo64ujoSJEiRdA0jStXrmTSq8nZJOEXVK9enSFDhujPy5cvz6FDh7h06ZJFPynvEUIIIUR6uLm54evry4kTJ1KsHujevTtvvvkmly9f5t69ewwYMEDPO1LqX6ZMGRYsWEB4eLj+ePToEQ0aNMj015ITScL/Atu3bx/Xrl3DxcVFvyAmPj5evyimTJkyAHz33XeAlPcIIYQQIm3OnDnD9OnTCQ0NBeDy5cusWLGCevXqUbx4cUJDQ4mOjtb7P3jwgCJFimBnZ8dvv/3G8uXL9WPFihUjT548XLhwQW8bMGAA06ZN4+TJkwDcu3eP1atXP6dXl/PItpwvqO7du3P9+nXs7Ozw8PCgXbt2eHl5kSdPHgoUKICrqysREREMHDgQe3t7+vfvn9UhCyGEECKHKFiwIIcOHeLrr78mPDwcR0dH2rVrx5dffomdnR1VqlShRIkS5MmTh9u3bzNv3jxGjRrF4MGDadKkCV26dNHLi+3t7Rk/fjwNGzYkJiaGrVu30qFDByIiIujatSv//PMPhQoV4rXXXsPb2xuA1q1b8+qrr/LBBx9k4buQfSgp08hcnp6e2pEjR7I6DAt79uxh4sSJ7N27l6NHj3L06FF+/PFHRowYQYsWLYiLi6NRo0bcuHGD1157jQULFgCmkh5Z5RdCCCEyzunTp3F3d8/qMEQOk9rPjVLqd03TPJO2ywr/C8jKygonJycAatasiaurK1ZWVixYsIBixYpRo0YNnJ2deeWVV5gzZw5gKvXJk0cqwIQQQgghchrJ4F4gd+7cAaBRo0Y8ePCAUaNGAVC4cGFee+01KlWqxNmzZwH49NNPJdkXQgghhMgFJIt7Qfj6+vLBBx9w7NgxwHQh7qVLl/Q72ZUqVYpKlSrx888/A1ClShXAVMYjyb4QQgghRM4lmdwL4vHjxzx69IjNmzcTHBxM+fLlef/99wkODqZ79+78888/rF27Vi/1SSA1+0IIIYQQOZsk/LlcwkXZ5cuXRylFZGQkGzdu5NKlSxQoUIAlS5ZgbW3N119/TbFixZgyZYrFOCGEEEIIkbPJRbsviM6dO3P48GHatWvH/Pnzad++PTVr1sTPz4/Fixdb9JWafSGEEEKI3EOyulwq6d3pbGxs+PHHHylVqhTnzp3j1q1buLi4cObMmWTjJNkXQgghhMg9ZIU/l0pae1+1alXq1q2Lt7c3d+/eZeXKlaxatYqTJ0/i5uaW6jghhBBCPD//O7iW+zFRGT6vg40dX9brmOHzipxBlnJzmTFjxjB//vwUj8XHxxMVFcX27dtp0KABgwYNolOnTs85QiGEEEKkJjOS/WeZ19XVlRMnTgDw8OFDWrVqRe/evYmLiyMsLAw7OzuGDx9uMSYgIABHR0cMBgMeHh60bduW69ev68e9vLxwdnYmIiLCom3Tpk0W83Tp0gVnZ2diYmIAqFu3rj6ntbU1BoMBg8FA7969AdPNp5RSzJw502KeyZMnM3r0aP35d999R4UKFfj7779TPVduJwl/LjJw4ECWLl1KSEiIRXtcXBwAU6ZMYePGjVhZWREXF0elSpUAuUBXCCGEEJbu3btHy5YtcXNzw2g0YmVlxbJly6hfvz4rVqwgOjraon+LFi0IDg7m5MmTFCxYkI8++sjiuL29PdOnT0/1fHfv3mXnzp1UqFCBjRs3AnDo0CGCg4PZvHkzjo6OBAcHExwcjL+/PwB+fn40a9YMo9GY6rxffvklM2fOZO/evVSoUCHVc+V2kvDnEv379+fRo0cEBQWxdu1aAgMD9WNWVlZcunSJMWPG6J+urays9ONSxiOEEEKIBDdv3sTLy4tmzZoxa9YsPU8wGo1MmDCBatWqsWHDhhTHKqVo0qQJly5dsmgfN24c8+bN4/bt2ymOW7p0KW3btmXQoEFPTOATxMbGsmzZMhYsWMCjR484cuRIsj4TJkxg5cqV7N27FxcXl3SfKzeQhD8XOHDgAOHh4QQEBPDyyy8zduxYAgMDk/1PFRUVler/oEIIIYQQYCp3eeONN/j444/1tmPHjnH37l2aNWtG7969U02Uo6Oj2bx5M2+//bZFu4uLCz4+PkydOjXFcf7+/vTu3ZtOnTrx66+/cvXq1SfGuGnTJipVqkTFihXx9fVNFk9AQACBgYHs3r2bokWL/qdz5QaS8OcCDRo0YNWqVYCpfOeVV17h/v37Fgn/Sy+9RO3atYmMjMyqMIUQQgiRA7Rp04aVK1dy7do1vc3Pzw8fHx+UUnTs2JGDBw9y5coV/fjOnTsxGAwUK1aMe/fu0aVLl2Tzjhs3juXLlydb/T969ChhYWE0bdqUfPny0bFjx2RbhidlNBrx9fUFwMfHh5UrVxIV9e91CnXq1OHOnTts3br1P58rN5CEPwcLCAhg3rx53LhxAzAl+1ZWVtSsWZMyZcrQv39/vX4fwNvbm/bt22dRtEIIIYTICcaMGYOvry9eXl5cu3aN6Oholi9fjtFoxNXVFXd3d2JiYli0aJE+JqGGPzQ0lJiYGCZNmpRs3iJFijBkyBAmT55s0e7n50d4eDjlypXD1dWVTZs26XX6Kblx4wbbt29n8uTJuLq68uqrr/Lw4UPWrl2r9/Hw8GDbtm0MGzZMXxRNz7lyC0n4c6gBAwawdu1aDh06RNOmTYmMjMTKyorY2FgApk2bRvny5dm7dy9gujDXzs6OYsWKZWXYQgghhMgBxo0bR69evfDy8mLFihW4ubkRGhpKSEgIISEhbN++PcVEuWDBgixYsIC5c+da7NSTYMSIEWzbtk3fMefx48esWLGCw4cP63Nfu3YNpRT79u1LMbZFixbRuXNnLl26pI8xGo3JynqqV6/Otm3bGDp0KKtWrUrXuXILSfhzoKlTp/LPP/+wYcMGFi1aRLVq1fjtt9+IiIjQd9yxtrbm4cOHXL58GZALc4UQQoicwMHGLtvM+8EHH+Dj44Ovry+tW7e2OFa/fn3i4+P1hcXEqlevTpcuXfj888+THcufPz/jxo0jNDQUgMDAQCpWrKjvHJige/fuqV4nEBAQQI8ePSza3nrrLT2RTxpLQtKfnnPlFkq2ZMxcnp6eWkpXjv8XFy9epEyZMlhbW/P5558zZcoUOnfuTGRkJN26daNDhw4APHjwgIIFC2bouYUQQgiRcU6fPo27u3tWhyFymNR+bpRSv2ua5pm0XVb4c6CyZctibW1NTEwMhQoV4uTJk/j7+9O4cWNmzJihX7SSkOzLhzohhBBCiBeXJPw5xOzZs/n111/RNI08eUz/2WxsbBgwYAAvvfQSAIMHD8bJyYnw8HCLsVLOI4QQQgjx4pKEPwcYMGAAw4cPZ926dRw6dEhfsU+6ct+9e3eKFClCiRIlsiJMIYQQQgiRDUnCn83dvXuX6tWrc+jQIWxtbVm5cqWe9CuliI2N5dq1a/Tt2xdra2v8/PwAKeMRQgghhBAm1lkdgHiyIkWK0LlzZ5ydnXF3d2fy5MmsWrWK2NhYGjVqhLW1NSVLluTdd9+ldu3aAMTHx+tlP0IIIYQQ4sUmCX8O4OzsDECBAgWYNGkSkyZNYvPmzdjZ2fHxxx/zySef6Ml+4hp/IYQQQuQscd+OgIf3M35iewesBszI+HlFjiCZYTYUHBxMWFgY0dHRFu3x8fEULFiQr7/+mri4ONq2bYutrS01atTQ+8gFukIIIUQOlhnJfhrnrVu3LgaDAQ8PD6ytrTEYDBgMBpo2bWrx3GAw0KJFCwBCQkIoWrRoqnPGxcXh4uJC+/btLdqDgoJQSlns1R8UFISn5787Sj548IBhw4ZRsWJFqlWrRo0aNejVq5e+h3+CLl264OzsTExMjEW7UirZ/QOUUkRERDw1vtxGVvizmQEDBnDq1CnKli2Lr68vzZs3Jz4+HqWUxcr9gQMH8PLyYuXKlYCU8QghhBDivzl06BBgSuI9PT0JDg5O8fmz2LJlC6VKleKXX37hxo0bFC9eXD9WsmRJZsyYwbvvvoujo6PFOE3TaNu2LVWrVuXEiRPY2dkRFxfH999/z8WLFyldujRgutZx586dVK5cmY0bN9KxY0eLec6cOcOePXto0qTJM8eXm0iGmI1MnTqVmzdvsnfvXkqVKsWxY8cA9IT/9u3bADx69Ih+/fpJsi+EEEKIbM1oNDJgwAA6dOjAkiVLLI6VKlUKb2/vFO/Iu2vXLkJCQpg1axZ2dqa7BFtZWTFgwABeffVVvd/SpUtp27YtgwYNSvFuuZMnT2bcuHHpii83kSwxm4iPj+f27dv6V0/29vasX7+e/v37M2vWLP7++28WLVrEpk2byJcvHz4+Pvo4SfaFEEIIkZnCw8MtSnoGDBjw1DG3bt3i559/pkuXLvTu3TvFhHzixIkYjUauXbtm0f7HH39Qq1YtbGxsnngOf39/evfuTadOnfj111+5evWqxfFOnToRGxvL+vXr0xVfbiGZYjaRJ08eWrVqxahRo+jVqxdfffUV8+bNo2nTpoSGhrJz506io6M5cuRIsnFCCCGEEJnJ0dGR4OBg/fHtt98+dcySJUt44403KFiwIA0bNiQ2NpaDBw9a9HF2dqZfv3588sknT5xr586dGAwGKlSowFdffQXA0aNHCQsLo2nTpuTLl4+OHTuyePHiZGOnTZvG+PHjiY+Pf+b4cgup4c9GXn/9dX799VcOHjxI1apVqVKlClWqVOGvv/4iJiaGIUOG8N577xEREUH+/PnlAl0hhBBCZFv+/v7cunULV1dXAO7du4fRaKRevXoW/f73v//h5ubGK6+8orfVrFmTuXPnEhsbi7W1NS1atCA4OJjRo0frF936+fkRHh5OuXLlAHj8+DEODg68//77FvM3b96cEiVKsHTp0nTFlxvI8nA2U6VKFerWrcuvv/7K/v37ATh+/Dh3796lQIEC+Pn5UaBAAUn2hRBCCJFt/fbbb9y7d49r164REhJCSEgIJ06cYPXq1Tx8+NCib6FChRg1ahRTpkzR21q0aEHp0qUZMWIEUVFRenvC2MePH7NixQoOHz6sz3/t2jWUUuzbty9ZPJ999hmTJ09OV3y5gST82VClSpVo0KAB7777Li1atMDGxoYPP/wQAGtr+VJGCCGEyLXsHbLlvElr+A0Gg34sLCyM0qVL648WLVrg7+9Pt27dLBYoXVxcqFmzJmvWrEk2/+DBg4mNjdWfK6XYsmULYFoMrV69Og0bNiQqKoqePXsSGBhIxYoVqVSpksU83bt3T7EW39PTk1q1aunPnzW+nE5pmpbVMeRqnp6eWtK6+7SIjY3l/Pnz3Lx5k8aNGwNyga4QQgiR25w+fRp3d/esDkPkMKn93Cilftc0zTNpuywXZ1PW1ta4ubnh5uYGyB10hRBCCCFE+kjCnw2N2pL8CvPMML21z3M5jxBCCCGEyDpZvmSslBqhlDqplDqhlFqhlLJLcjyvUmqlUuq8UuqQUso10bFeSqlz5kevRO3lzH3PmcfapnLuceZ5zyqlWiVqf0Upddx8bLYyF3g9KRYhhBBCCCGyoyxN+JVSLsBQwFPTtKqAFdA1Sbd3gDBN0yoCM4DPzWOLAJOAukAdYJJSqrB5zOfADE3TKgFh5jmSntvDfK4qwOvAPKWUlfnwfKA/UMn8eP1JsQghhBBCCJFdZfkKP6ayonxKKWvAHria5PhbwCLz39cAzc0r7q2AHZqm3dU0LQzYAbxuPtbM3Bfz2PYpnPct4AdN0x5rmnYROA/UUUqVBBw0TftVM13RvDjR+NRiEUIIIYQQIlvK0oRf07QrwFfAJeAacE/TtO1JurkAl839Y4F7gFPidrNQc5sTEG7um7g9qdTGu5j/nrT9SbFYUEr1V0odUUoduXXrVmovXwghhBBCiEyXpRftmktw3gLKAeHAaqVUT03TEt8KLaUVdC0d7clOn47xaZpb07TvgO/AtC1nCmOEEEIIIZL5dmUwD6Nin97xGdnbWTPgbUOGzytyhqwu6WkBXNQ07ZamaTHAWqBBkj6hQBkAc9lPIeBu4naz0pjKgW4Djua+iduTSm18qPnvSdufFIsQQgghxH+WGcn+s8zr6upKyZIliYuL09v8/f1RSjFnzhy9bcyYMdja2pK0ksHV1ZUTJ05YtHl6ehIUFGTR1qVLF5ydnYmJibFoV0pRvXp1atSoQfXq1dmwYYPF8bCwMOzs7Bg+fHiy9m7dulG1alWqVauGwWBg9+7dAAQEBNC5c2eL/ps2bcLLy4s7d+7oNxKrWLEi9vb2+vPx48cTFBSkt1WtWpUmTZpw5swZi7k2b96MUorAwMCU39RsIKsT/ktAPaWUvbkWvjlwWik1WCk12NxnA5CwA09nYLe5tn4b0FIpVdj8TUFLYJv52M/mvpjHrgdQStVRSi1ONG9X88475TBdnPubpmnXgAdKqXrmmHwSxj8hFiGEEEKIXKFkyZJs27ZNf75o0SJeeeUV/XlsbCxLly6lUaNGLFmy5Jnnv3v3Ljt37qRChQps3Lgx2fEDBw5w7NgxPv/8c3r06GFxB95ly5ZRv359VqxYQXR0tN4+YcIESpcuzfHjxzl+/Di7du2iYsWKT43FycmJ4OBggoODWbhwIR4eHvrzqVOnAuhtJ06coF69eowYMcJiDqPRSLNmzVK8w292kdU1/IcwXfz6B3DcHM93gBtwx9zND3BSSp0HRgLvm8feBT4BDpsfH5vbAMYCI81jnMxzALwEPDKPPwmsAk4BW4FBmqYlfJwdCCzEdCHv38CWJ8UihBBCCJFb+Pr6EhAQAMDFixd5+PAhVatW1Y9v3ryZChUq8PHHH6cryV26dClt27Zl0KBBTxzv5eVFREQEYWFhepvRaGTChAlUq1bNYvU/NDSUUqVKkbCXipOTEy+99NIzx/Y0Xl5eXLp0SX9+584ddu3axfLly9m/fz/Xr1/P8HNmhCy/8ZamaZMwba+pM+9vP9J8PArwTmWsEUj2k6Jp2gVMW3UmVReYm6jfVGBqCuOPAFVTaE81FiGEEEKI3KBp06bMmzePsLAwAgIC8PHx4ciRI/pxo9FI7969adSoEdHR0fz222/UqfNv2tW5c2fs7P69rdJff/1lMb+/vz/Tp0+nfv36DBs2jKtXr1KqVKlkcaxbt45mzZpRrFgxAI4dO8bdu3dp1qwZ169fx2g06qU6Q4cOpXPnzqxYsYIGDRrw5ptv0qxZM32unTt3YjAY9Of3799/5g8E8fHxrF+/nq5d/91BfsmSJbzxxhsUL16cjh07snjxYsaMGfNM8z4PWV3SkyJN09ppmhb99J7PPO//NE37M6PnFUIIIYTILZRSdOnShR9++IGVK1fSrVs3/djNmzcJCgrC29u0/tmrV69kq/Rr1qzRy2KCg4Px8PDQjx09epSwsDCaNm1Kvnz59CQ5sQYNGlCuXDn69u3L55//e8sjPz8/fHx8UErRsWNHDh48yJUrVwBo3rw5ly5dYsKECdja2uLt7c1nn32mj23RooVFTLNnz07z+3Hq1CkMBgPFixcnKCiIQYMG6cf8/f3x9fUFTN+MZNeynixf4RdCCCGEENmLr68vdevWpUmTJjg5/bsD+eLFi4mNjaVatWqAqZ4/MjKSGTNmkC9fvqfO6+fnR3h4OOXKlQPg8ePHODg48P77/1ZJHzhwgAIFCvDVV1/RqVMnzp49S548eVi+fDl2dnb6B4SYmBgWLVrEBx98AEDBggV58803efPNN3nllVeYOnWqxbzp5eHhwZEjR4iOjqZr164MHDiQlStXcuTIEU6dOkWfPn30vlevXuXAgQM0aJB0D5qslS1X+IUQQgghRNYpX748U6dOZeLEiRbt/v7+BAYGEhISQkhICKGhodSpU4cff/zxqXM+fvyYFStWcPjwYX38tWvXUEqxb9++ZP1HjRpF8eLFWbBgAYGBgbi5uREaGqqP3b59O/7+/gDs2LGD+/fvA6BpGkePHtU/VGQUW1tb5s+fz5YtWwgODsZoNDJ27Fg9npCQED766KNsucovK/xCCCGEENmEvZ11pu3D/6z69+9v8fzOnTuEhYVZ1MYD9OjRA6PRSM+ePZ84X2BgIBUrVqRSpUoW7d27d8doNNKoUSOLdqUUX331FV27dqVy5cr06NHD4nj9+vWJj49n7969/Pnnn4wcOZKEzRMrVapksY1oRilevDijR4/mo48+Ys+ePezfvz/Za6levTqzZs0if/78GX7+9FKyq2Tm8vT01BJf6JIWo7YsfnqnDDC9tc9zOY8QQgghUnb69Gnc3d2zOgyRw6T2c6OU+l3TNM+k7VLSI4QQQgghRC4mCb8QQgghhBC5mCT8QgghhBBC5GKS8AshhBBCCJGLScIvhBBCCCFELibbcgohhBBCZBM35x4i/mFMhs+bx94G50F1M3xekTPICr8QQgghRDaRGcn+s8zr6uqKm5sbNWrUoGLFirz11lscOHAAgICAABwdHTEYDPoj4U62AQEBdO7cOdl8QUFBeHpa7hJ54sQJXF1dLc554sSJdL4ykRaywi+EEEIIIXRr1qyhatWqAKxdu5Y2bdqwbds2AFq0aMGaNWuyMjyRDrLCL4QQQgghUtSxY0cGDBjAV199ldWhiP9AVviFEEIIIUSq6taty4YNG2jbti07d+7EYDDoxwYPHkzfvn2fOP7UqVMWY6KiojIpUpEaSfiFEEIIIUSqNE3T/56ekh4PDw+OHDmiPz9x4gTt2rXLsPjE00lJjxBCCCGESNXhw4f1mn6RM8kKvxBCCCGESNH69euZP38+W7du5cyZM1kdjkgnSfiFEEIIIbKJPPY2mbYPf1p17tyZvHnzEhkZiYeHB5s3b6ZevXqcOXMmWQ2/p6cnCxcuBGDz5s2ULl1aP9a7d2+aN2+eYa9BpJ9KXJclMp6np6eWuG4tLUZtWZxJ0Via3trnuZxHCCGEECk7ffo07u7uWR2GyGFS+7lRSv2uaZpn0nap4RdCCCGEECIXk4RfCCGEEEKIXEwSfiGEEEIIIXIxSfiFEEIIIYTIxSThF0IIIYQQIheThF8IIYQQQohcTPbhF0IIIYTIJtad70tU3L0Mn9fOqhAdKi7M8HlFziAr/EIIIYQQ2URmJPtpnbdu3boYDAY8PDywtrbGYDBgMBjo3bs3YNr7XSnFzJkzLcZNnjwZpRQ//fST3hYREUGBAgXw9Px3S3ilFBEREQB4eXmxadOmJ8YTEhKCUoqBAwdatBUtWtSiX1hYGHZ2dgwfPtyiPSAgAEdHR2rWrIm7uzs1atTgo48+4tGjRynGlKBo0aKEhIQ8MbacRhJ+IYQQQgjBoUOHCA4OZvPmzTg6OhIcHExwcDD+/v4A+Pn50axZM4xGY7KxNWvWZNGiRfrz1atX4+bm9p9jKlCgAIGBgZw/fz7VPsuWLaN+/fqsWLGC6Ohoi2MtWrTg6NGjnD59mh07dvD777/z9ttv/+e4chpJ+IUQQgghxBPFxsaybNkyFixYwKNHjzhy5IjF8aZNm/Lnn38SFhYGwKJFi/D19f3P582bNy+jR49m4sSJqfYxGo1MmDCBatWqsWHDhlT7OTs7s2jRInbu3MnJkyf/c2w5iST8QgghhBDiiTZt2kSlSpWoWLEivr6+yVb5lVJ06dKFH374gYsXL/Lw4UOqVq2aIeceNGgQBw4cIDg4ONmxY8eOcffuXZo1a0bv3r1T/PYhscKFC1OpUiVJ+IUQQgghhEjMaDTqK/Y+Pj6sXLmSqKgoiz6+vr4sXryYgIAAfHx8MuzcdnZ2TJw4kQ8++CDZMT8/P3x8fFBK0bFjRw4ePMiVK1eeOJ+maU89p1Iq3fFmR5LwCyGEEEKIVN24cYPt27czefJkXF1defXVV3n48CFr16616Fe+fHlsbW35/vvv6datW4bG0Lt3by5cuMDevXv1tujoaJYvX47RaMTV1RV3d3diYmIsriVIKiwsjPPnz+vfPhQrVow7d+7ox2NjY7l37x7FihXL0PizmiT8QgghhBAiVYsWLaJz585cunSJkJAQQkJCMBqNKZbPfPbZZ3zxxRc4OTllaAxWVlZMmTKFSZMm6W2BgYG4ubkRGhqqx7V9+3b9IuOkbt26RZ8+fWjRogUeHh4AvPbaayxYsEDv891331GvXj3s7e0zNP6sJgm/yLbi4+OzOgQhhBDiubKzKpTt5g0ICKBHjx4WbW+99RaHDx9Otn1l/fr16dmzZ5rm9fX1pXTp0vrjzz//fGL/zp07W2zJ6e/vnyyu+vXrEx8fr38TsHPnTmrWrImbmxstWrSgRo0arFy5Uu8/c+ZMQkJCqF69OgaDgS1btrBkyZI0xZ+TqLTUMYn08/T01JJeyf40o7YszqRoLE1vnXH1dRlh2LBhFClShJiYGKZMmZLV4QghhBCZ7vTp07i7u2d1GCKHSe3nRin1u6ZpnknbZYVfZAujR4/m3LlzNGnShLNnz9KgQQNu374NpO3iGiGEEEIIkTJJ+EW2EB4ezuDBg/Hy8mL16tXUqFGDrl278vjxY5RSkvQLIYQQQqSTJPwiW6hSpQqnTp0iNjYWgPnz5+Pi4kKbNm2A3Lc9lhBCCCHE8yIJv8gyhw8f5tq1a2iaRrNmzVi9ejWbN2/Wjy9atIgSJUpw5syZLIxSCCGEECJns87qAMSLqXfv3oSGhhITE8Nbb73FiBEjeP/99xk3bhyxsbE0bNiQ4sWLExISot+mWwghhBBCPDtJ+MVzN3ToUO7du8eOHTvYvHkzc+fOpW3btnTo0AEbGxsWLVrE+vXruXjxImXLlqV+/fpZHbIQQgjxfPzdHOLuPL3fs7Jyggq7Mn5ekSNISY94ruLi4ihatCgff/wxAG3atKFMmTLs3LkTgHbt2vH111/zwQcfMGrUKJYvXw7InvxCCCFeEJmR7Kdx3gkTJjBw4ED9+aZNm1BKcfLkSb2tXbt2+Pn5Aabr6xL2r094REdHAzBv3jyqVatGjRo1cHNzY/To0dy5c0fvV7FiRezt7fXn48ePTxaPl5cX5cuXx2Aw4OHhQadOnbh37x4AQUFBFuMNBgM+Pj76MaUUn3/+uT5XUFAQnp7/7lb54MEDhg0bRsWKFfU4e/XqRWhoqEUMXbp0wdnZmZiYGIt2pRQRERFPfU+zC1nhF8/NyZMnqVKlCmPHjkUpRXx8PHny5MHR0ZFbt27p/ezt7SlTpgyVK1cG0PsJIYQQIvN4eXkxZMgQ/fmePXuoW7cuQUFBVKlShbi4OPbt28fs2bP1PgcOHKBAgQIW8xw+fJgZM2Zw+PBhHB0diYuL4+TJkzg5OREcHAyYEvDRo0fztHsVzZ49m3bt2qFpGl27duXbb79l7NixAHh4eKQ6vmTJksyYMYN3330XR0dHi2OaptG2bVuqVq3KiRMnsLOzIy4uju+//56LFy9SunRpAO7evcvOnTupXLkyGzdupGPHjml6H7MjyaLEczFo0CAWLVoEQN68ebG1tdWPlS1bFjs7OwD69OnDL7/8YjFWkn0hhBAi8zVs2JCLFy9y48YNwJTwT5gwgaCgIACOHj2Kg4MD5cuXf+I8oaGhFCpUSP8gYGVlRfXq1f9TbLGxsTx8+JDChQunqX+pUqXw9va2WOVPsGvXLkJCQpg1a5aef1hZWTFgwABeffVVvd/SpUtp27YtgwYNwmg0/qf4s5pkUiLT9enTh8jISL744guL9oREvlixYpw5cwYfHx9iYmJo3759FkQphBBCvNjy5ctH7dq1CQoK4sGDB0RGRtK6dWuLVfmmTZtajGnQoIFeUvPmm28C0LJlS6ytrSlbtizdu3fnu+++4+HDh+mKaejQoRgMBooXL87t27fp1auXfuzUqVMWJT0J5cIJJk6ciNFo5Nq1axbtf/zxB7Vq1cLGxuaJ5/b396d379506tSJX3/9latXr6brNWQHkvCLTLV3714CAgIYNGgQACtXrmThwoXMnz+fR48eAabEf9GiRZQsWZIlS5YAcnddIYQQIis0bdqUoKAg9u3bR6NGjbCysqJSpUqcPHmSoKAgvLy8LPofOHCA4OBggoOD2bBhAwD58+fn119/JTAwEE9PTxYuXEj9+vX1+v5nMXv2bIKDg7l16xZVq1bVy3nAVNKTcO7g4GA+/PBDi7HOzs7069ePTz755Inn2LlzJwaDgQoVKvDVV18Bpm8zwsLCaNq0Kfny5aNjx44sXrz4mePPLiThF5mqcePGLFiwgBEjRjBw4EC++eYb4uPj8ff356OPPgJMNYPvv/++/rVbfHy83GhLCCGEyAJeXl4EBQWxZ88emjRpAph+l+/evZt9+/YlW+FPjVKK2rVrM3LkSPbt28c///zDiRMn0h2XlZUVHTt2ZMeOHc807n//+x/r1q3j77//1ttq1qzJ0aNH9Zt9tmjRguDgYDp06KBfiOvn50d4eDjlypXD1dWVTZs24e/vn+74s5ok/CLT9evXj65du7JlyxYWL15M//792bRpE6tWreK3336jSJEifPrpp4BcoCuEEEJkpQYNGhASEsKPP/6or+Y3btyYb775BkdHR1xdXZ86x5kzZyyS+7NnzxIdHa1fDJteP//8My+//PIzjSlUqBCjRo1iypQpeluLFi0oXbo0I0aMICoqSm9PKDt6/PgxK1as4PDhw4SEhBASEsK1a9dQSrFv377/9BqyiuzSIzKNpmn6Sv3gwYPx9vamePHigOlrtmrVqukXyySQZF8IIcQLzcop8/bhTwM7Ozvq1q3LlStXKFWqFAC1a9fmypUreHt7J+vfoEEDi9/dmzdv5uHDhwwfPpybN29iZ2eHlZUVS5cuxdnZ+ZnDHjp0KBMmTCAmJoaXXnqJb7/9Vj+WUMOfoFSpUmzevDnZHIMHD2bWrFn6c6UUW7ZsYfz48VSpUoX8+fNTsGBBKleuTM+ePQkMDKRixYpUqlTJYp7u3btjNBpp1KgRAJUrV9bznPz583P27Nlnfn3Pi5Ja6czl6empPW3LqaRGbXk+NWLTW/tk+Jy3bt2iWLFi+vO4uDisrKwAyw8A3t7eFChQIEd/PSaEEEL8V6dPn8bd3T2rwxA5TGo/N0qp3zVN80zaLiv8IsMMHTqUS5cuYTAYcHBwYOTIkVhZWRETE2NxJfzXX39NwYIF9S2uEn8QEEIIIYQQGUvqJ0SGmDx5Mv/88w/z58/ntddeIzAwkP/9738A2NjYcOnSJd5//30ePXpEnz599GRfLtAVQgghhMhckvCLDBETE8OgQYMoWbIkDRs2pGbNmuzatUvfPitPnjxERkYSGBio3/FO0zSp2RdCCCGEyGSSbYn/5LfffgOgSJEiDBs2jJMnT3L79m2uXLnCxx9/TFhYGNevX8fFxYU6dero210BsrIvhBBCCPEcSA2/SLdevXqxdetWQkNDGTVqFBEREXTv3p0yZcrw6quv0q5dO+bOnUtISAglSpSgS5cuPHjwIKvDFkIIIYR4oUjCL9Jl8ODBgGk7rpEjR/LNN98wadIkBg4ciI2NDYULFwZMN8qIiopC0zTs7OySbcMphBBCCCEylyT84plNmDCBCxcusHnzZk6ePMn06dO5fPkyZcqUoWjRouTJk4ewsDD69+9Pvnz5kt2GWwghhBApu/6bkfiYRxk+bx6bfJSo0yfD5xU5Q5bX8CulHJVSa5RSZ5RSp5VS9ZMcz6uUWqmUOq+UOqSUck10rJdS6pz50StRezlz33PmsbapnHuced6zSqlWidpfUUodNx+brczF5k+K5UXSunVr/cYWRYsWJSQkhB9++AH498ZZV69epVGjRqxevRowXaArhBBCiCfLjGT/WeZ1dXW1uEsugKenJ0FBQfrzLl264OzsTExMjEU/pRTVq1enRo0aVK9enQ0bNhAVFUWVKlXYsmWL3u/OnTuUKVOGY8eOATBv3jyUUgQHB1vM5+XlhbOzs8X1f15eXmzatMmiX2rxiH9lecIPzAK2aprmBtQATic5/g4QpmlaRWAG8DmAUqoIMAmoC9QBJimlCpvHfA7M0DStEhBmnsOCUsoD6ApUAV4H5imlrMyH5wP9gUrmx+tPiuVFsWHDBsLDw2nYsCFg2pmnePHiTJ06lR9//JE///xT71ulShWGDRsGyNabQgghRG5x9+5ddu7cSYUKFdi4cWOy4wcOHODYsWN8/vnn9OjRA2trawICAhg4cCD37t0DTGXB7777LjVq1ADAaDTSrFkzfcvuxOzt7Zk+fXq64xEmWZrwK6UcgMaAH4CmadGapoUn6fYWsMj89zVAc/OKeytgh6ZpdzVNCwN2AK+bjzUz98U8tn0Kp38L+EHTtMeapl0EzgN1lFIlAQdN037VTMvSixONTy2WXK9fv36sW7dO31ITTPvrx8XFUaVKFTw9PfVbSsfFxVmMla03hRBCiNxh6dKltG3blkGDBqWYoCfw8vIiIiKCsLAwateuTc+ePRk+fDiBgYGcP3+e999/H4Djx49z69YtjEYjK1as4PHjxxbzjBs3jnnz5nH79u3/FM+LLqszsfLALcBfKXVUKbVQKZU/SR8X4DKApmmxwD3AKXG7Wai5zQkIN/dN3J5UauNdzH9P2v6kWCwopforpY4opY7cunUr9VefQ/Tp04fLly/j7+8PmBL62FjT22tlZYWDgwM1atRgyJAhPHz4ECsrqydNJ4QQQohsrHPnzhgMBv1x6tQp/Zi/vz+9e/emU6dO/Prrr1y9ejXFOdatW0ezZs0oVqwYAB9++CF//PEH7777LosWLcLa2nQZqZ+fH7169aJs2bIYDAbWr19vMY+Liws+Pj5MnTo1xfOkNZ4XXVYn/NZALWC+pmk1gUjg/SR9UlpB19LRnlR6xqdpbk3TvtM0zVPTNM+EH/ScKiQkhAcPHlCrVi0Ali1bxqBBg+jWrRvLly/X+/Xr149p06Zhb2+fVaEKIYQQIgOsWbOG4OBg/eHh4QHA0aNHCQsLo2nTpuTLl4+OHTuyePFii7ENGjSgXLly9O3bl88//7fy2dbWlqFDh9KgQQN9vujoaJYvX06vXqbLMH19fVNcpR83bhzLly/n0qVLFu1piUeYZPUuPaFAqKZph8zP15A84Q8FygChSilroBBw19zulahfaSAIuA04KqWszavwpYGUPu4lzJt4/FVze+kU2p8US67l6urKsGHD2LFjB3Xr1iU2NpapU6dy//593n33XRwdHWnTpg0AvXv3Bkw1+1LGI4QQQuQufn5+hIeHU65cOQAeP36Mg4ODXp4Dphr+AgUK8NVXX9GpUyfOnj2rb8ltZWVlUQWwYcMG7t+/T/PmzQFT/nDjxg19578ERYoUYciQIUyePPmZ4xEmWZqVaZp2HbislKpsbmoOnFJKDVZKDTa3bQASduDpDOw219ZvA1oqpQqbL9ZtCWwzH/vZ3Bfz2PUASqk6SqnFiebtat55pxymi3N/0zTtGvBAKVXPXJ/vkzD+CbHkSgllO40aNaJFixbUr1+fNWvW8Prrr9OlSxc++OCDFGvqJNkXQgghcpfHjx+zYsUKDh8+TEhICCEhIVy7dg2lFPv27UvWf9SoURQvXpwFCxakOqfRaGTOnDn6fJcuXcLX15dFixYl6ztixAi2bdvG33//na54XnTZITMbAixTSv0JGIBPATfgjvm4H+CklDoPjMT8DYCmaXeBT4DD5sfH5jaAscBI8xgn8xwALwGPzONPAquAU8BWYJCmaQlXmw4EFmK6kPdvIGEvqRRjyW3WrVsHoNfXAbz66qtMnDhR/xQNsHv3biIjI597fEIIIURulccmX7acNzw8nIoVK1KpUiWL9u7du6dYhqOU4quvvuLzzz8nKioq2fGrV6+yZ88eOnfubNHeo0cP/P39k23nnT9/fsaNG0doqOkyy8DAwGeK50WnsuMCtVJqE9BR07ToDJ73S2CJpml/PrVzBvH09NSOHDnyTGNGbXk+9WfTW/skaxs4cCAXLlxg27ZtaJqGUoq4uDiLr+DCw8Pp168fhQoVYuHChc8lViGEECI3On36NO7u7lkdhshhUvu5UUr9rmmaZ9L27LDCn4ymae0yOtk3z/u/55ns5zTvvPMOERERbNu2DUD/RJ6Q7N+5Y/rS5eDBg7i7u+vJfnx8fBZEK4QQQggh0iKrL9oV2cSNGzfYuHEjH374IQDffvstf/75JzExMbRr145atWqxbNkyatWqxeuvv87rr5vuRSYX6AohhBBCZG+SqQkAihcvzo4dO5g/fz5t27Zl+fLl+Pj44OLiwvbt2/n7779RSrF//36LcZLsCyGEEEJkb5KtCV2NGjVYsWIFUVFRLFiwgHr16jF58mTu3r3L7du3GThwIBcvXiQiIiLZxTRCCCGEECJ7kpIeYaF69eps2rSJfPn+vZr/0aNH3L9/HwcHB4xGo8XuPUIIIYQQInuTzE0kk5DsR0VF4e3tjbOzM3369AGw2K1HCCGEEBlr+/btREdn+L4l2Nra0rJlywyfV+QMUtIjUhQfH88ff/xB+fLl8fPz09tM9yITQgghRGbIjGT/WeZVShEREWHRVrRoUUJCQujQoQMGgwGDwYBSiurVq+t/T2gvUaIEzs7O+vOEe/uMGTMGW1tbbt26ZTG3q6srbm5u1KhRw2IHwJR4eXlRvnx5DAYDbm5uzJo1C4CQkBCsra0xGAxUr14dT09PDhw4YDH29OnTKKWYOXOmRfvkyZNxdnamZs2avPzyy9SuXZvZs2cTFxen90n8WhMeCe/nvHnzqFatGjVq1MDNzY3Ro0en6b183mSFX6QoT548NGjQgAYNGgCyG48QQgjxoktI3sGUzB44cIACBQpY9Jk8eTIRERF89dVXeltsbCxLly6lUaNGLFmyhJEjR1qMWbNmDVWrVuXEiRPUqlWLNm3aUKpUqRRjmD17Nu3atePy5ctUrVoVLy8vChUqhKOjI8HBwQDMnz+fd955h9OnT+vj/Pz8aNasGUajkeHDh1vM6ePjo8d74cIFevbsyfnz55k9e7beJ6XXevjwYWbMmMHhw4dxdHQkLi6OkydPPuVdzBqS8Itkbs4f81zO4zzwi+dyHiGEEEJknc2bN1OhQgU+/vhjBgwYkCzhT1C1alUKFy7MlStXUk34E5QpU4bKlSvz119/Ubt2bYtjXl5eFivtsbGxLFu2jF9++YXWrVtz5MgRPD2T3ZsKgPLly2M0GqlWrRqffPIJhQoVSjWG0NBQChUqpH8QsLKyonr16k+MO6vIkq0QQgghhMg0RqOR3r1706hRI6Kjo/ntt99S7Ld//36KFi1KjRo1njrnqVOnOHPmTIoJ9rp16+jatav+fNOmTVSqVImKFSvi6+uL0Wh84txubm7Y29tz9uxZva1BgwZ6Oc+bb74JQMuWLbG2tqZs2bJ0796d7777jocPH1rMlXicwWAgPDz8qa8tM8gKvxCpkDImIYQQwiS91/DdvHmToKAglixZAkCvXr0wGo3UqVNH79O5c2c0TePvv/9m3bp12Nrapjrf0KFDmTBhAnZ2dixYsIDKlSsTEhJCeHg4BoOBW7duERsby8GDB/UxRqMRX19fwFS+YzAY+Prrr7Gzs0vz60ippCd//vz8+uuvHDlyhF9++YWFCxcyd+5cDh8+rL+GpOOKFi2a5nNmpDQl/Eqpu5qmFUmh/aamac4ZH5YQz9+wYcMoUqQIMTExTJkyRZJ9IYQQL5xixYpx584dPUmNjY3l3r17FCtWLF3zLV68mNjYWKpVq6bPFxkZyYwZM/RdARNq+FevXk3Pnj3566+/KF68eIrzJdTwJ5VQwx8XF8eIESPo2rUrhw4d4saNG2zfvp3g4GA+/vhjAB4+fMjatWvp3r17iuc4e/YsDx8+xM3N7amvTylF7dq1qV27NoMHD8bZ2Vm/FiE7SWtGY5O0QSllA8gejSJXGD16NOfOnaNJkyacPXuWBg0acPv2bYAsu8mY3NxMCCHE8/baa6+xYMEC/fl3331HvXr1sLe3T9d8/v7+BAYGEhISQkhICKGhodSpU4cff/wxWV9vb29atmzJZ599lu74rays+OKLL7h27Rrr169n0aJFdO7cmUuXLukxGI3GVMt6QkJCeOeddxg4cCAODg5PPNeZM2c4ceKE/vzs2bNER0dTunTpdMefWZ64wq+U+gXQADul1N4kh0sDB5KPEiLnCQ8PZ/DgwXh5eeHl5cXAgQPp2rUrP/30E3nz5kXTtOe2JWlUVBR2dnYopaSsSAghXjC2traZtg9/WsycOZNhw4ZRvXp18uTJQ5kyZfRynGd16NAhwsLCaNasmUV7jx49MBqN9OzZM9mYadOm8corrzB27FhKlCiRrvPa2dkxZcoUPvroI6Kiopg+fbrF8bfeeosBAwbo22MuXryYXbt28fDhQxwcHOjRowdDhgyxGNOgQQOL38ebN2/m4cOHDB8+nJs3b2JnZ4eVlRVLly7F2Tn7Fb+oJ60iKqV6AQqYDwxIdEgDbgC7NU2LydQIczhPT0/tyJEjzzRm1JbFmRSNpemtfVJsf5F26YmNjcXa2poZM2YQHx/PsGHD9DsJ9+rVi9DQUHbt2vXc4unXrx/R0dHcvHmTLVu2PLfzCiGEyBqnT5/G3d09q8MQOUxqPzdKqd81TUu2BdETlw41TVukaVoAUNP894THYk3TtkmyL3K6hOS+Xr16rFq1is2bN+vHFi1aRIkSJThz5sxziWXw4MGEh4czc+ZM8ubNy/r16/VjUt4jhBBCiPRK00W7mqadUUq1BAxAgSTHPsyEuITIVN26daNcuXJ8+umnANSvX59x48bxwQcfEBsbS8OGDSlevDghISGEhYVlejxRUVGEhYUxadIkChcujIuLC4cOHWLHjh18+umnODg4SHmPEEIIIdIlrbv0zAG6AD8DiTcYlWVHkeNMmTKFs2fPsn//fmJiYvjyyy8BaN++PTY2NgQEBLB+/XouXrxI2bJlqV+/fqbHFBUVRd68eVm2bBkRERFs2bKF+fPns3jxYl5//XUOHDggyb4QQggh0iWt+/B3Awyapl3OzGCEeB4qVarE5s2byZ8/P1WrVkXTNP2W2m3btqV69eo8fPiQM2fO8NZbbwGZtyf/vHnz6NWrF46Ojvj6+hISEsKmTZuYNm0arVq1olWrVrRp04Y1a9bQuXPnDD+/EEIIIXK/tCb8d4DwTIxDiOfm7bff5sGDBxQsWJDDhw9Tp04d4uPj+frrr/U+lStXpnLlykDmJfurVq1i8ODB7NixgyVLltC4cWMaN25MfHw8jx8/tuibN2/eDD+/EEIIIV4MaU34pwPLlFLTMO3Oo9M07UKGRyVEBps8eTKxsbE4OTkxYsQIChYsSHR0NM7Ozhw4cIDGjRtjY2PD1atXady4Mf369dPHZlYpTaNGjRg8eDCaptGuXTt27NiBjY0NVapUoWvXrvz9998cOHCAl156iTfeeCNTYhBCCCFE7pfWhH+++c+ktzbTkJtviWyuX79+3Lhxg4EDB+Lr60tcXByjR4/G1taWmJgYSpUqxeHDh3FycqJLly4WyX5miYuLI0+ePFy8eJFZs2Yxd+5c3nrrLapUqcKYMWP47rvvuHHjBs7OzgwaNAjIvG8ahBBCZB//O7iW+zFRGT6vg40dX9brmOHzipwhTdmDpml5UnlIsi+ytY0bN3Lz5k02bNhA69at2bx5M/v37ycuLg5N07CxsSE+Pp6pU6fi7e3NDz/8AJiS68xw6tQpwHQnwBIlSlCnTh0eP37M9OnTuXTpEt9//z1WVlY0b96c7t27S7IvhBAvmMxI9p913piYGCZPnkzlypWpUqUKBoOBzp07c+rUKSZPnszo0aMt+s+ZMwdfX18AgoKC8PS03AZ+3rx5KKUIDg62aPfy8qJ8+fIYDAYqV67MlClTksVSp04dDAYDAHfu3MFgMGAwGKhYsSL29vb68/Hjx6f59b2I0rrCL0SOVKVKFT755BPA9A+Yg4MD586dIywsjKJFiwKmkp02bdrodwLMrOR60KBB5M+fny+++EI/h42NDZs3b2bRokUULlyYxo0b8/rrr/Pzzz+TP39+fawk+0IIIZ6X3r178/DhQw4dOoSjoyOaprFmzRpOnz6drvmMRiPNmjXDaDQye/Zsi2OzZ8+mXbt2XLt2DXd3d1577TXq1q0LwMmTJ7lx4wa2trb88ccf1KpVS//QEBQUxOjRo3nWm5u+qNK6LecvpLIFp6ZpjTM0IiEyUPny5fVblNvY2FCpUiVKlSqlJ/urV6/G29tbT/Y1TcuU5LpPnz7Ex8czd+5c4N8EvmvXrrRv356CBQsSFBSElZUVAQEBFsm+EEII8bycO3eOdevWERoaiqOjIwBKKby9vQE4fvz4M813/Phxbt26xY8//oinpydffvllihtRlCxZksqVK3Pp0iU94ffz88PHx4e8efNiNBqpVavWf3txL7C0rvAvTPK8BPAOsDRjwxEi4ySsotva2lq0K6W4cuUKI0aMIG/evPo/YgnHMtrevXsJCAjg0KFDAKxcuZIHDx4QGxtLp06d+PDDD3njjTewsrIiPj5e/1pU07RMiUcIIYRIzdGjR6lUqRKFCxfOkPn8/Pzo1asXZcuWxWAwsH79erp06ZKs319//cWdO3fw8vICTN/KL1u2jAMHDmBra0vNmjWZPn267FqXTmmt4V+U5PE50AZ4LXPDE+LZXL16lcjISCB5GUxsbCwxMTHcuXOH1q1bU6JECZYsWQKYkuvM0rhxYxYsWMCIESMYOHAg33zzDXFxcfj5+TF37lw6duyIjY0NsbGxFjFLsi+EECKrnTp1CoPBwMsvv8ywYcNS/d2UUnt0dDTLly+nV69eAPj6+mI0Gi36DB06lCpVquDu7s6IESMoVqwYYLoGr3LlylSoUIEyZcpQs2ZN1q1bl8Gv7sXxX2r4rwDVMyoQIf6rwYMHc+HCBcqVK0ezZs3o1KkT8O9Kf8LjpZdewsnJSa8jfB4XxPbr14/Hjx/z1VdfsXv3bsqXL0+HDh2oV68ebdu2pXbt2lhbyyU1QgghslbNmjU5d+4c4eHhODo64uHhQXBwMHPmzOHIkSNUrlyZkJAQizG3b9/G2dk52VwbNmzg/v37NG/eHDD9vr1x4waXL1+mTJkywL81/Pv37+e1116jcePGVKtWDaPRyKlTp3B1dQUgMjISo9FI165dM/X151ZpynKUUn2SPAYDPwEHMzc8IdJm0KBB3Lhxg++//x5HR0du3rypH8uTJw+XLl1i7NixREVF8emnn/L9998Dz3f3m8GDB3Po0CHKly8PgLOzM9WqVZOvJ4UQQmQblSpV4q233qJfv37cu3dPb0/49rxp06Zs27aN0NBQAO7evcvKlStp2bJlsrmMRiNz5swhJCSEkJAQLl26hK+vL4sWLUrWt2HDhgwaNIgPP/yQa9eusXfvXi5evKiPvXz5MkeOHOHSpUuZ9Mpzt7RmOv+X5PE6cADonklxCZFmp06dwtramtWrV+Pi4sLjx49ZtmwZX3zxBd9++y1gulPtw4cPWb9+PW5ubkDmXaC7ZcsWHj58mOKxxCsg3t7eFClShOrV5YsyIYQQJg42dlk+b0BAAG5ubtSuXZsqVarQqFEjfv/9d4YOHYq7uzszZszgrbfewmAw0LRpU4YMGaKv4sfGxmJnZ8fVq1fZs2cPnTt3tpi7R48e+Pv7p1hKO378eH755RcWL15M69atKViwoH7Mzs6O9u3bExAQkL434AWnMrN2WYCnp6f2rFtGjdqyOJOisTS9tU+K7Tfnj3ku53ce+EWGzfX48WPy5s3L1q1bmTBhAtOmTePBgwcsW7aMt99+my5durBo0SKio6Mz9cZa/fr1486dO6xdu9aiPS4uDisrK/1C3K+//poTJ07otYxyga4QQryYTp8+jbu7e1aHkWG+/PJLTp06hb+/f1aHkqul9nOjlPpd0zTPpO1pLhpWSlUCugEumOr3V2iadu4/xCrEf7Jq1Sqio6Np3749NjY2ADRo0IAtW7boF/0cPXqUR48eAfD222/z4MGDTItn0KBBXL58ma1btwLw8OFD7O3tAdONti5dusTcuXOZPHkyffr00bc7k5tqCSGEyA2aN29ORESEviGGyD7Sug//G8AyYBPwD1AZOKKU+j9N0zZkYnxCpKhXr16Eh4fj4uLCzp07qV+/Ph06dEh20VBwcLDeZmdnh51d5nxVev78eU6dOkX37qYqN39/f/bs2YODgwPlypVjxIgR2NraEhkZSWBgIN26dQMyr6woo8g3D0IIIdJq165dWR2CSEVaV/g/Bd7SNO3nhAallBcwB5CEXzxXhw8f5vr162zbtg2Ar776ihkzZhAVFUXPnj1xcnLi8uXLDB06FGdnZ4YMGZLpMVWsWJGxY8eyevVqtm/fzoULF/jss8+4efMmGzZs4Mcff6RTp07Url2biIgIfVx2TaajoqKws7NDKSXfQAghhBA5XFoT/tLAL0na9pnbhXiuErby2r17N82aNaNr167s3buX8+fPc/HiRZycnDhx4gQlS5Zk3rx5QOaVzaxYsYIHDx7QtWtXXn/9dQoWLMh3333HqlWrqFChAgAnTpzQL+LN7LKijNCvXz+io6O5efMmW7ZskWRfCCGEyOHS+ps8GBiVpG2kuV2I5+LRo0fcv3+fEiVK0LRpU/z9/RkzZox+UW6ZMmX49NNPAWjdunWmJ/tdu3YlICCAvXv34uvry/z586levToLFy7Uk30wJfwJW5vZ2dnp1xdkR4MHDyY8PJyZM2eSN29e1q9frx+TC/yFEEKInCmtK/wDgY1KqWHAZaAMEAm8mVmBCZHY4MGDuXnzJrGxsQwZMoQRI0awb98+Tp48yfDhw/H29ubmzZtcuXIl2djMSPavX79OVFSUXla0du1a9u3bx/fff0/Pnj1xdnbmzp079O/fn2LFijF48OAMjyGjRUVFERYWxqRJkyhcuDAuLi4cOnSIHTt28Omnn+Lg4CDlPUIIkcnivh0BD+9n/MT2DlgNmJHx84ocIU2/uTVNOwO4A28D04EugIemaaczMTYhAHjnnXe4efMmH330ER07duTrr78mLCyM5s2bM3ToULy9vYmLi2PAgAHExsY+l5icnJy4evUq3333HQAdO3akRYsWXL58mWPHjgGmC4ZLlSqlb70ZHx//XGJLj7i4OOLi4sibNy/Lli1jyJAhbNmyhSZNmhAWFsbrr78OZM6HJyGEEIlkRrL/DPO6urri5uZGjRo1cHd3Z+HChfqxuLg4XFxcaN++vcWYoKAgPD0td4KcN28eSimCg4Mt2n/++Wfq1q2LwWDA3d2dZs2aER8fT4cOHTAYDBgMBpRSVK9eHYPBQKtWrQDTNW+Jr4EDKFq0aLK7/tapUweDwWDRFhISglKKgQMHWrQVLVo0Te9JbpDm396apsVqmvaLpmmrNE3bp2laTGYGJgTA8ePHsbGxYdWqVbi7u9O8eXNsbW31xF7TNOLj4xk2bBhWVlbMnTtXb88Mjx49Ijw8HBsbG8aPH8+pU6cICgoCoE2bNri4uDBt2jTAtD3ZN998A2T/rTetrKzInz8/ffv2xd3dnRs3bjBt2jRatWrFsmXLcHR0ZM2aNVkdphBCiOdgzZo1HDt2jNWrV/Pee+9x9epVwHRjyVKlSvHLL79w48aNJ85hNBpp1qyZvugFpptyderUie+//57g4GBOnz7N9OnTUUqxbt06goOD9Q8IBw4cIDg4WP8mPS1OnjzJjRs3iIyM5I8//rA4VqBAAQIDAzl//nya58tN0pSBKKVqKKV2K6XuKqWizY8YpVR0ZgcoXmzVqlWjf//+xMXFER8fT8mSJbGxsdFX0ZVS5MmTh88//5zVq1cDpuQ6M3a/GTx4ML169aJPnz5s27aNli1b4uzszJYtW/S990ePHo2Dg0Oyfwiza7LfrVs3PvjgA/15gwYN8PHxoU2bNkRHW/7vnTdv3ucdnhBCiCxUtWpVChcurJfLGo1GBgwYQIcOHZ641/7x48e5desWRqORFStW8PjxYwAePHhAZGQkxYsX1/vWrFkzw35n+/n54ePjQ69evSw+aIDpd9jo0aOZOHFihpwrp0lrDf8K4EdgKPAo88IR4l+xsbFYW1tTq1Yt4N9V+7i4OP3vgwYN4r333qNKlSp6n8xIrt955x0ePHjARx99xO+//86cOXOoV68e3t7e/PTTT3z77bf89NNPnD9/nkKFCln8Y5ZdTZkyhbNnz7J//35iYmL48ssv9WOurq707duX8+fPc+DAAV566SXeeOONLIxWCCHE87Z//36KFi1KjRo1uHXrFj///DOLFi3Czc2Nfv36MXr06BTH+fn50atXL8qWLYvBYGD9+vV06dKFwoUL069fPypVqkSTJk1o2LAhPXr00Hffe5oGDRpY/I4PDw/X/x4TE8OyZcs4cOAAtra21KxZk+nTp1ssVg0aNIjKlSsTHBys3/zyRZHWzKgE8KGmaSc0Tfs78SMzgxMvpoMHDwJgbW2dYt37K6+8grW1Nf379yc6OlpP9iFz9rVPqazIxsaG+/fvU6lSJfr27cs333zDSy+9RIcOHfjhhx+A7L+rTaVKldi8eTMnT55k1apVFv9we3l5sXDhQipXrkz79u3x8/MDsvd1CEIIITJG586dqVy5Mk2aNOGzzz7D1taWJUuW8MYbb1CwYEEaNmxIbGys/vs6sejoaJYvX06vXr0A8PX1tVhtnzNnDsHBwbz11lscPnyYqlWrcu7cuTTFlVDmk/BInLRv3LiRypUrU6FCBcqUKUPNmjVZt26dxXg7OzsmTpxo8c32iyKtCf8ioHtmBiIEwPjx4y0SzDx58hAXFwf8m8xHRkbSpk0brKys+P7774HMTa5TKiuytbXVy4oKFChAmTJl+N///kf//v2BzCsrykhvv/02Dg4OFCxYkMOHD7NmzRpGjhypH/fw8KB79+4MGjQIyP7XIQghhMgYa9as4ezZs6xYsYKePXty48YN/P392b59O66urri6uuolO0lt2LCB+/fv07x5c1xdXRk3bhw///wzly9f1vuUL1+evn378uOPP9KgQQM2btz4n2M2Go2cOnVKjy84ODjF+Hr37s2FCxfYu3fvfz5nTpLWkp7PgF+VUh8AFsXJmqY1y/CoxAurWrVqtGjRgl27dhEeHs6oUaOwsrIC4NatWxQrVozSpUvTv39/5s+fD2RuIvqksqIEgwYNYtCgQXh4eOht2TUxHj9+PHnz5qVYsWL07t0be3t7NE3D2dmZAwcO0LhxY2xsbLh69SqNGzemX79++tjs+pqEEEJkDm9vb1atWkXXrl25d+8e165d0xezrly5QtWqVZk5c6bFGKPRyJw5c+jbt6/e9u6777Jo0SKGDx/OgQMHeO2111BKER4ezsWLFylXrtx/ivPatWvs3buXK1euULBgQcC01XSpUqW4dOmSRV8rKyumTJnC//73v/90zpwmrb/B1wAXgfnAsiQPITKMk5MTcXFxDBo0iIMHD+Ln58eaNWu4dOkSc+fOZdeuXbz77rt8++23QOYl+89aVpQ42c+uRowYwenTp2nXrh2bNm1iyJAhnDhxAqUUcXFxlCpVisOHD/Pll18SExNjkewLIYR4TuwdstW806ZN4+DBg7Ru3drim2sXFxdq1qzJmjVriI2Nxc7OjqtXr7Jnzx46d+5sMUePHj3w9/dH0zTmzp2rb/uZUMPfoUOH//TSFi9eTOvWrfVkH0zlO+3btycgICBZ/86dO79QW3JC2lf4DYCTpmmyK4/IVK+99hobN26kdu3ajBo1im7dulG4cGH++OMPHBwc2Lt3L82bNwcy7wLd8ePH4+fnx9SpU3nnnXf0siIrK6tkZUXvvvuu/k2DpmnZtoznzp07nD17lm+++YYKFSowf/58GjVqhJOTEwMHDqRs2bLExsYydepUvL299esQpIxHCCGer6y+OVbSfe0rVqzIo0cp79eye/duAL788ksqVapEqVKliIyMTNavcePG/P236bLPxHdwT01KZboptd2+fRuAsWPHpjhP4pKehL4JDh8+/NQ4cpO0/ib/Bcj+S5giR9M0jcePH3Pt2jX+/vtvVq9ejYODA2XKlMFoNOLr68vFixeJiIjI1OQ6cVnR9OnTASzKioAUy4qya7IP4OjoyMsvv8yBAwd48OABJUuW5JVXXuHMmTMsXrwYMH2b0bZtW1auXAlIsi+EEOLpmjdvzpo1axg3blxWhyKeIK0r/BeB7UqpdSSv4f8ww6MSLySlFHnz5qVTp054e3vj5OTEvn37OHr0KIcPH6ZIkSIYjUasrdP6Y5s+CWVFgwcPZubMmfj5+VGoUCHq1KmD0Wjk1Vdf5d1339X754TE2MrKipo1a7Jnzx7Wrl3L9evXee+992jTpg1vvfUWffv2pWTJkjRt2hTIvG9PhBBC5C67du3K6hBEGqQ1c7IHfgJsgcSbpVpleETihdeoUSM6derExIkTsba2pnbt2jRu3Bh4PheOZoeyooyU8G1Ir169qF27NhERETx69IgmTZoQHR2Ni4sL+fPntxiTnb+tEEIIIcSzSVPCr2la78TPlVLVAR9kq06RCUqXLs1HH30EmHbJyZcvn34ss5NrTdOIjo5+YlnR8OHDiYiIIH/+/Nk2MZ47dy61a9emTp06KKX0pD/xxcVxcXG0b98eFxcXHBwy6SIxIYQQQmS5NNdGKKWKYUrwewE1MNX1D8ukuIQAyPTynaSyS1nRf9G9e3cePHhAjx499ERfKaVvMZrQtnHjRooVK2ZxL4Ps+gFGCCGEEOn3xKxFKWUDvAn4Aq2A88AKwBXoomnazUyOT7yg5i3d91zO817PRim2Z3VZUXrt37+fu3fvsnXrVsC0N7G1tTXFihXD2tqakJAQ5s2bx4cffkj79u1p3749kDOuQxBCCCFE+jxtmfIGEA8EAJM0TfsDQCn1XibHJUSWysqyov+iePHiFClSBICvvvqKLVu2ULBgQaysrPjxxx8pWLAgDx8+ZP369fTo0UMfl51fkxBCvEi+XRnMw6jYDJ/X3s6aAW8bMnxekTM87bf8n4AjUBeorZQqnOkRCZHNZOfynaTy5MnD/fv3Wbt2LRcuXGDhwoUsW7aM27dvM3z4cJycnKhTpw4PHz7M6lCFEEKkIDOS/WeZ19XVFTc3NwwGA25ubvTr14+YmBgCAgL0G2qFhIQku3FVREREsrLQuLg4XFxc9G+TEwQFBWFvb4/BYKBq1ao0adKEM2fOpP/Fiad6YiajaZqXUqospgt0RwOzlVLbgfyAzXOIT4gsc37+nudynooDm/yn8StWrCAyMpIuXbpQvnx5OnfujK+vL97e3vrtyr/++mvWrl0LQJcuXXjw4MF/jlsIIUTutGbNGqpWrUpcXByvvvqq/vvjWW3ZsoVSpUrxyy+/cOPGDYoXL64f8/Dw4MiRI4DpxlkjRoxgy5YtGRK/SO6p3+NrmvaPpmmfaJpWCWgOXMNU5nNMKfVFZgcohEhd165dCQgIICgoCF9fX+bPn4+vry/ffPMNGzduJCgoiNDQUKZNm6bfKdHOzo5ixYplceRCCCGyu6ioKKKioihcOH0FHkajkQEDBtChQweWLFmSaj8vLy8uXbqU3jBFGjxTrYKmafuAfUqpoUAHTCv/QogscO3aNaKioti2bRsAa9euZe/evXz99deMHDkSW1tb1q5dy507dyhWrBhff/11FkcshBAiJ+jcuTN2dnb8/ffftGzZkpYtWxIQEGDRJzw8HIPBoD+Pj4+3OH7r1i1+/vlnFi1apJcGjR49Otm54uPjWb9+PV27ds2MlyLM0lWcrGlaFKbdelZkbDhCiLRycnLi2rVrfPfdd/Tv35+OHTtiZ2fH1q1b2blzJ926ddPrJhMuOpbdeIQQQjxNQklPVFQUnTp1YubMmTg6Olr0cXR0JDg4WH8eERFBwYIF9edLlizhjTfeoGDBgjRs2JDY2FgOHjxIvXr1ADh16hQGg4ErV67g5OTEgQMHnsdLe2Fli9/8SikrpdRRpdSmFI7lVUqtVEqdV0odUkq5JjrWSyl1zvzolai9nLnvOfNY21TOO84871mlVKtE7a8opY6bj81W5qtQnhSLEM/Lp59+yrFjx7C1tWXChAmcOXOGoKAgANq0acNLL73ElClTAFOin5Ds54S7AgshhMg+7OzsaNeuHTt27Hjmsf7+/mzfvh1XV1dcXV25desWRqNRP+7h4UFwcDBXrlzBw8ODgQMHZmToIons8tt/GHA6lWPvAGGaplUEZgCfAyiligCTMO0gVAeYlGgXoc+BGebrDsLMc1hQSnkAXYEqwOvAPKWUlfnwfKA/UMn8eP1JsQjxvPTo0YPDhw9TpkwZAAwGA0WLFuWnn37SL3YaPXo0jo6O3Lp1y2JsTrypVtKviIUQQjw/8fHx7Nmzh5dffvmZxv3222/cu3ePa9euERISQkhICCdOnGD16tXJdomztbVl/vz5bNmyxeIbA5GxsjzhV0qVBtoCC1Pp8hawyPz3NUBz84p7K2CHpml3NU0LA3YAr5uPNTP3xTy2fSrz/qBp2mNN0y5iuqlYHaVUScBB07RfNU3TgMWJxqcWixCZ7ujRo9y7d49169ZRpEgRbt++TZEiRRgwYAAuLi589913DB48mNatW+f4C3OnT58OyP0BhBAvHnu7zNkK+lnm7dy5s75lZnx8PB9++OEzncvf359u3bpZLDS5uLhQs2ZN1qxZk6x/8eLFGT16tH7/G5HxssMG4zOBMUDBVI67AJcBNE2LVUrdA5wSt5uFmtucgHBN02KTtKc078EUxseY/560/Umx3E48sVKqP6ZvCHjppZdSeVlCPBtN07hz5w4AM2fOZMeOHVy4cIEhQ4bQtWtXOnbsyMqVK6levTr9+/fXx+S0z6RDhw5lzpw5aJqW4gVeQgiRm2X1zbFCQkJSbPf19cXX1xcw7dV/+7ZF6kOBAgUwrZPC/PnzU5xj9+7d+t8TtuRM8KwfKsSzydKEXynVDripadrvSimv1Lql0Kalo/2/zvukMZYNmvYd8B2Ap6dnSucW4plVq1aN+vXrs2zZMn7++WdWrFjB+fPnGTFiBPHx8QwePJj//e9/ev+ceIHuu+++S1hYGIsXL+aPP/7Q23PiBxchhBAiu8jqbKAh8KZSKgT4AWimlFqapE8oUAZAKWUNFALuJm43Kw1cxbTa7mjum7g9qdTGh5r/nrT9SbEIkelsbGxwcnJi+fLlFCxYEHt7e2rVqsX48eP566+/iI21vItiTkv2586dS0REBKtWraJTp05s3LiRBQsWADnz+gMhhBAiu8jSjEDTtHGappXWNM0V0wW0uzVN66mUGqyUGmzutgFI2IGns7mPBmwDWiqlCpsv1m0JbDMf+9ncF/PY9QBKqTpKqcWJ5u1q3nmnHKaLc3/TNO0a8EApVc9cn++TMP4JsQiR4a5cuULSH6/x48dTp04d7t69y+rVq7lx4wYLFiygUKFCWFtnhwq99Gvbti3Lli0DTLsLzZw5k59//pkrV65kcWRCCCFEzpZdMwQ3YL/5737AEqXUeUyr6V0BNE27q5T6BDhs7vexpmkJq+1jgR+UUlOAo+Y5AF4CHpnHn1RKrQJOAbHAIE3T4sz9BgIBQD5gi/mRaixCZLQBAwZw9epVHB0d+eCDD3BzcyM6OhpbW1smTZpEQEAAp0+fZtWqVZQqVYpPPvkkq0NOt127dlGuXDkcHByAf0uRKlWqxKNHj7hw4QIuLi45skRJCCGEyA6yTcKvaVoQEGR+6gqMNLdHAd6pjDECxhTaL2DaqjOpusDcRP2mAlNTGH8EqJpCe6qxCJFR+vTpQ0REBGvWrKFbt25s27YNNzc3bG1Nt5OIjIzUL5y6ceMGxYsXB3Jmzf4777zDP//8Q4ECBWjatCmDBw/Wy3defvllGjZsyNChQ9m7d6/FDV2EEEIIkXbZJuFPTNO0dpk07/+e3kuIrHP58mU0TWP58uVYW1tTuXJl9u7dy82bN2ncuDEeHh4sXbqUqlWr8sYbb+hbb+bEm2oNHDiQ+/fvs3PnTtavX8+WLVuIiYnByspKfy3Dhw/nt99+448//qBJkyZZHLEQQmS+m3MPEf8wJsPnzWNvg/Oguhk+r8gZclaGIEQuV6ZMGfz8/LC2tmb79u0sXLiQMWPG4ODgQGBgIGfPniVPnjz6DjYJiXFOvKjV3d2dJUuWAKZbrO/ZswdfX1+mTp2qb9dmbW1N//79JdkXQrwwMiPZf9Z5w8LCsLOzY/jw4RbtAQEBKKVYuXKlRVvnzqbLJkNCQrC2tsZgMFCtWjXc3Nzo168foaH/7nbu6+vLnDlzLOYdPXo0kydPBiAoKAh7e3v9PgBNmjThzJkzFv03b96MUorAwMBUX0PiWKpXr079+vX1G3tNnjwZZ2dnDAYDbm5udO/encjISIvxY8aMwdbWNtlNLF1dXXFzc6NGjRpUrFiRt956iwMHDli8H46OjhgMBv3x/vvv6+9rt27dqFq1KtWqVcNgMOhblSZ+HxNs2rQJLy+vVF/js5CEX4hsJiGJb9myJadOnaJu3bqMHTuWiIgI7t69y8CBA/n777+JiIhIdlFvTpBw99yhQ4diZ2fHuXPn2LFjBzt37mTMmDGAqa4/4VuLli1bAuTI1yqEEDnRsmXLqF+/PitWrCA6OtriWNmyZZk4cWKyneESODo6EhwczPHjx/nzzz8pWbIkDRo04N69e2k+v4eHB8HBwZw4cYJ69eoxYsQIi+NGo5FmzZphNCar6k4xlj///JOuXbvSp08f/ZiPj49+7PLlyxYfQmJjY1m6dCmNGjXSF6YSW7NmDceOHeP8+fP06tWLNm3acOjQIf14ixYtCA4O1h+fffYZABMmTKB06dIcP36c48ePs2vXLipWrJjm9+W/kIRfiGwoIbl1cnLS2yIjI7l//z4ODg4YjUYKFCiQ41b2x44dy7hx45g5c6Z+e/VKlSqxYcMGXFxcqFWrFpUrV+by5cvJXltOe61CCJFTGY1GJkyYQLVq1diwYYPFMU9PTypXroyfn18qo/9la2vLxx9/jIuLC0uXJt11PW28vLy4dOmS/vzOnTvs2rWL5cuXs3//fq5fv56meV577TXOnj2bYowNGjSwOMfmzZupUKECH3/88VM/VHTs2JEBAwbw1VdfPTWG0NBQSpUqpf8+c3Jyem43aJWEX4hsKOEfA6UUkZGRvPnmmxQuXJi+ffsCYGVllZXhpcuYMWP4448/aNOmDYGBgUyZMkVfESlQoIDeb/369RQuXDirwhRCiBfasWPHuHv3Ls2aNaN3794pJryffvopU6ZM4dGjR2mas06dOpw8eVJ//tlnn1mUvCxevDjFcfHx8axfv56uXf/dFHHJkiW88cYbFC9enI4dO6Y6NqnVq1dTs2bNZO0PHjxgz549dOrUSW8zGo307t2bRo0aER0dzW+//fbEuevWrWvx+nbu3Gnx+hYuXAiYvtn++OOPqVOnDsOHD7e483BK44YOHZqm15YWkvALkY3FxMSwf/9+KlSooK+mxMfH57jV7kePHnHlyhUmT55MkyZNWLVqFY8fPyYwMJDTp08DcPPmTVq1akXBggX1bUaljEcIIZ4vPz8/fHx8UErRsWNHDh48mOx+KNWqVaNJkyZ88803aZoz6b/l77//vkXJi4+Pj8XxU6dOYTAYKF68OEFBQQwaNEg/5u/vr+9U5+vr+8QV+PDwcAwGAzVq1CA4OJiAgAD92OLFi6lRowbFixenbNmyNG3aFDD9LgoKCsLb27QpY69evZ66yp/09SUt6UlYrGvevDmXLl1iwoQJ2Nra4u3trZf7pDRu9uzZTzzvs5CEX4hszMbGhhYtWjBjxgwgZ269CaYbadWtW5dVq1Zx/fp1nJ2def/997l79y7Lly8HTN9atGvXju+//x7ImR9shBAiJ4uOjmb58uUYjUZcXV1xd3cnJiaGRYsWJev7ySef8PXXXxMeHv7UeQ8fPkzVqsl2O09VQg3/lStX8PDwYODAgQAcOXKEU6dO0adPH1xdXenRowcXLlywuGg2sYQa/mPHjrFu3TqLenkfHx+OHTvGhQsX+P333/n2228B0weB2NhYqlWrhqurK/Pnz2flypVP/DbjWV5fwYIFefPNN/niiy+YN2+e/jsws2XLbTmFELDkUP/ncp7/q/vdczlP3bp1OXfuHHv37uW1116jWLFiTJo0CS8vL7y9valevTpDhgwBcu4HGyGEyMkCAwNxc3Nj3759etuvv/6Kj48PH3zwgUXfcuXK0blzZ2bNmsUrr7yS4nzR0dFMmzaN0NBQevTo8czx2NraMn/+fCpVqkRwcDBGo5GxY8cyZcoUvc+0adMwGo00aNDgmecHKFGiBLNmzaJfv3707t0bf39/AgMDadGihd6nVatW/Pjjj/Ts2TPZ+PXr1zN//ny2bt361HPt2LGDunXr4uDggKZpHD16lHLlyqUr7mclCb8QItNomqav0ifUOO7atYvHjx/TrFkzXFxcqFChQrLkXpJ9IcSLKo+9Tabtw/80/v7+yRLz+vXrEx8fz969e5P1nzhxokWZDPxbRhMbG0tMTAyvvvoqBw4coFChQumKu3jx4owePZqPPvqIPXv2sH//fovj3bt3p3r16syaNYv8+fOn6xxt27bFzc2Nzz//nLCwMJo1a2ZxvEePHhiNRj3h79y5M3nz5iUyMhIPDw82b95MvXr19P4JtfgJPD09WbhwIX/++ScjR47US4AqVaqUbIvSzKKkRjZzeXp6agl7iqfVqC1puwDlv5re2ifF9pvzxzyX8zsP/CLVY/OW7kv1WEZ6r2ejVI+dn7/nucRQcWDKe8zn5BX+4OBgDAaDRV1jQuK/dOlSjh8/zo4dOyhevDjW1tZs3Lgxw2MQQoic4PTp07i7u2d1GCKHSe3nRin1u6ZpnknbZYVfCJGhvvzyS8aOHcv69et54403ANNKf1xcHFZWVvoKSfv27YmIiOC1117T+0jNvhBCCJHx5HtzIUSGKl++PHXq1MHHx0f/qlfTNKysrLh48SJjxozhwYMH1K9fX5J9IYQQ4jmQFX4hRIZISNqrVKlC//79qVWrFk2aNKFo0aLExMTQoUMHlFJERUWxYcMGizpRSfaFEEKIzCMr/EKIDJGQtJcqVYqVK1diMBjYsWMHnTt31rdUK126NK+88op+l10hhBBCZD5J+IUQ/8mqVatYvnw5ERERRERE4ODggKenJ6GhoezevRsPDw8ePXrEDz/8gLW1NW+//Tbt27fP6rCFEEKIF4aU9Agh0q1Xr16Eh4fj4uLC9u3bqV+/Pr6+vri7u1OpUiVq1qzJH3/8wa+//oq/vz9du3bFzs4OOzu7rA5dCCGEeGFIwi+ESJfffvuN69evs23bNgC++uorpk+fTlRUFE2bNuWzzz7jvffeA0z7ONevXz8rwxVCiBxh3fm+RMXdy/B57awK0aHiwgyfV+QMUtIjhEiXMmXKALB7924AunbtipubGxcuXCA6Opphw4ZhY2NDbGxsVoYphBA5SmYk+8867+PHjxk1ahQVKlTAzc2NmjVrEhgYCEBQUBD29vbUrFmTKlWqUKVKFUaOHElYWJg+3svLi02bNgHwzz//0K5dO6pXr061atXw9PTkxIkTAEyePJnRo0dbnHvOnDn4+vpatG3evBmllB6DeHaS8Ashnomvry9r1qyhZMmSNG3alICAAMaMGcPbb79Nly5dKFWqFNOmTQNMO/dYW8sXiUIIkZO89957hIaGcvLkSc6cOcOSJUsYPHiwfrddDw8Pjh49ysmTJzl48CAPHjygefPmxMXFpThXq1at+PPPPzl+/Djr16/H2dn5meIxGo00a9YMo9GYIa/vRSS/iYUQaTZ8+HB2796NUop27drRs2dP/vrrL06cOMHw4cPx9vbm5s2bXL16FZDtNoUQIqf5559/WLlyJZcuXdKvt6patSrjx4/no48+YuLEiRb9CxYsyLx586hQoQJbt26lbdu2FsdDQ0MpXbq0/tzFxeWZ4rlz5w67du3izJkzeHh4cP36dUqUKJHOV/fikhV+IUSaDBgwgGvXrrFp0yYePnzIuXPnKF26NM2aNWPo0KF4e3sTFxfHgAEDpIxHCCFyqOPHj1OxYkWKFCli0V6vXj2OHTuW4hgbGxtq1qzJyZMnkx0bM2YMPj4+NGnShLFjx3L48GGL44sXL8ZgMOiPzz77zOL4kiVLeOONNyhevDgdO3Zk8eLF//EVvpgk4RdCPNWOHTuwsrJi5cqVVK9enRIlSjBw4EBiYmIAU+lOfHw8I0aMwMrKirlz5+rtQgghco70/rud2rgePXoQEhLCoEGDiIyMpGnTpqxYsUI/7uPjQ3BwsP54//33Lcb7+/vrNf2+vr5S1pNOkvALIZ7qtdde05N4gE8++YSKFSuyf/9+wPQPfZ48efjss89YvXo1APHx8VLSI4QQOUy1atU4f/48d+/etWg/ePAg1atXT3FMTEwMwcHBVK1aNcXjTk5OdOnShTlz5jBx4kSLhP9Jjhw5wqlTp+jTpw+urq706NGDCxcucODAgWd7UUISfiHEkyW+CCthBcfOzg57e3vWrFkDQJ48eYiPj8fe3l7vlyeP/PMihBA5jaurK97e3gwcOJCoqCgATpw4wdSpU5k0aVKy/hEREQwZMoSiRYvSqlWrZMd/+uknfZ64uDj+/PNPypUrl6ZYjEYjY8eOJSQkRH989NFHssqfDvIbWQiRooTVHSsrK70tYcXe1taWSZMmsWvXLoukP2k/IYQQz8bOqlCWzzt//nxKlSqFh4cHbm5u9OzZk1mzZtGkSRMATp06hcFgoEqVKtSpU4d8+fKxa9cui98XCYKCgqhZsybVqlWjWrVqWFlZ8fHHHz81hqioKH744Qd69Ohh0d69e3dWr15NZGRkml+PkF16hBApeO+997h+/Tr29vaMHj0ag8FAfHy8ntRrmkbx4sXp3LlzituwCSGESJ/scHMsOzs7ZsyYwYwZM5Id8/Ly4uHDh08cHxQUpP/9yy+/5Msvv0yx3+TJk5O1DR48WP970rIigLJly3LvXubcqyA3kxV+IYSF/v37c+PGDYxGIw4ODsyePRv4dwU/LCxMX8Hv3bs3b7/9dpbFKoQQQoink4RfCKE7d+4ckZGRfP/99zg6OjJ79mxOnDjB8ePHAdN+yHPnzmX9+vUAeh2m7MYjhBBCZF+S8AshdBUrVmTq1KkULlyYx48fY21tjb29PXfu3AEgX758aJqm77WcsNIvNftCCJF+smginkV6fl4k4RdC6JRSlC1bFqUUtra2gGnHhnz58gGwYMECGjVqxNmzZ3nw4IH8khJCiP/Izs6OO3fuyL+nIk00TePOnTv6XZDTSi7aFUJYSLpaX6hQIf766y/mz5/P48ePGTFiBF5eXrKqL4QQGaB06dKEhoZy69atrA5F5BB2dnaULl36mcZIwi+ESJGmaXpS379/f4YMGcIXX3yR7Fhu1LNnT7y8vOjbt29WhyKEyOVsbGzSvC+9EOklCb8QIkUJu/LUrl2bsLAwPdlPvD1nbjRs2DAOHjxInjx5aNu2LSVLlszqkIQQQoj/JPf+1hZCZIiuXbuyePFiIPcn+wMHDuT69ev89NNPREdHExISAphetxBCCJFTyQq/ECJV85bu0/+emWU87/VslCnzPougoCBsbW1ZuXIlAG5ubgwaNIg9e/ZQsGDBLI5OCCGESL/cu1QnhMhQublmH0x3j5w5cyZg+nAzduxYXnnlFQ4cOADIKr8QQoicSxJ+IcQLbcSIEfrt45VSxMXFoZQib968FCpUiOXLlwPk6lImIYQQuZv8BhNCvLD++ecftmzZwjfffMP06dMBsLKyIiYmhjx58vDxxx/z559/EhAQkLWBCiGEEP+B1PALIV5YZcuWpV+/fri5ufHFF1+glGLkyJH6ar69vT2dO3fWb0ImhBBC5ESywi+EeCHFxcUBcPnyZS5dusS8efNYvXo19evXZ+rUqXo/X19funfvnlVhCiGEEP+ZJPxCiBfK/fv3iYqKwsrKCoCOHTtia2tLlSpVKF26NGfPnrVY0XdxcQGQ294LIYTIsSThF0K8MLp06cI777xDvXr12L17N5GRkZQqVYqtW7fSvn17bt26xdatWzEajWzcuNFibG7fpUgIIUTuJTX8QogXgr+/P5GRkfz0008sWLCABQsWcO7cOdq2bUuFChU4efIkO3bswMbGhi1btlChQoWsDlkIIYTIEJLwCyFeCI8fP6Zw4cIAvPvuu7z00kusXr0aR0dH+vTpg6urKzY2NsTGxurJfmbebEwIIYR4XqSkRwjxQmjbti12dnYEBQUB0Lp1a9q1a8fs2bOxtbXF1taW+Ph4rK3/XQeRZF8IIURuIAm/EOKFULhwYUqVKsXOnTs5dOgQYLpgt0GDBmzevBnIXjfX+vHHH7l+/XpWhyGEECIXyD6/3YQQIpPEx8dToEABBg4cyKNHj9iwYQMLFiwAIDg4ONvts9+/f38CAwMpUaJEVocihBAiF5CEXwiR6+zevZsLFy5w7do1wLRyHx8fT8mSJRk9ejRVqlRhy5YttGrViuLFi9O3b98sjvhfffv25e+//2bJkiUAREdHEx0drR+X7UGFEEI8K7loVwiRq7zzzjtcuHCBIkWKUK5cOUaMGIGLi4terlOoUCG6d+9O9+7dCQkJwdXVFTB9C5DVJT2XL18mOjqamjVrArB06VJ27tzJ/fv3adasGYMHD5brCoQQQjwzSfiFELnG6NGjCQsL4+eff+bIkSPMmTOHK1eu6DfPevDgASNHjqRevXq88847erKvaVqWJ/sAZcqUYciQIWzdupU6deoQExPD119/TUxMDP/3f/+Hk5MT3bp1y+owhRBC5DCS8Ashcg1nZ2e6du0KgKenJ0WKFOGnn36iTp06AOTNm5fWrVtz7NgxoqOj9dr9rF4179WrF82aNaNXr17Url2b+Ph4IiMj6d+/P+XLlwdg0qRJREREZGmcQgghcqasX9ISQoj/6MaNGwCMGTOGqlWr6nXu5cqVIzIyUu/34MEDPD09iY+Pzza18O+99x4PHjygV69eelvdunUZP368nuwD/PTTTxavJbN99NFH/Pbbb8/tfEIIITKPJPxCiBxtyJAh9O7dm1GjRhEWFoadnZ2ezJcoUYL4+HgAfHx82LBhAy+99BLjxo0jb968WRk2AF999RVBQUEsX74cgL1797Jjxw6Cg4Oxs7MD4O7du3h7e1OyZEmGDx/+XOLq0aMHwcHBVKxY8bmcLztL+FnKLh8QhRAiPaSkRwiRY40cOZKrV68yZ84cunfvzr59+3jjjTf0enwHBwfOnDlDjx49sLa2pnfv3gDky5cvK8PWeXl5sWXLFjZu3Mj58+fZuHEjJUqUoFChQlSuXJkxY8Zw+PBhqlevzsSJE4HMv7j41KlT3L9/n40bNwJw/fp1YmNjKV26dKadMzW3bt0iLi4uy7YnvXfvHkopHBwcUErJnZeFEDmWJPxCiBwpLCyMy5cv8+GHH1K+fHkaNGjATz/9xPHjx6lXrx7NmjWjaNGibN26lf/97398/vnnANkiaTt69CjFihXD09OTadOmMWjQIPLkycPWrVspXLgwGzZsYPXq1cTHx9OqVStatWoFPL+dhBJu+DVr1iy2bNnC5cuX6dixI6NGjcLR0THTzw8wYMAAbt++TXh4OL6+vvTs2fO5nDdB//79efDgAQANGzaUHZKEEDmaJPxCiBwnNjaWwoULU6NGDfr06UPDhg0JCAjghx9+YPv27axbt44yZcrwyiuv8P777/Ppp58C2WPrzf/7v//jxo0bhISE4O/vT8OGDVm8eDHR0dEULlwYgKZNmzJ37lyuXLlC2bJl9bHPI3YPDw9effVVlixZwp49e1izZg03b95kwIABzJs3jw8++CDTY+jfvz937tzh+++/Z+PGjVy5csXieGZ/aJs0aRI3b95k9erV/P3339SoUYOwsDD9W5bs8KFRCCGehdTwCyFylOHDh/PNN98A0K9fP8aPH09cXBwBAQG0adOGKVOmcPnyZY4ePQqQrZL9KVOmEBkZyfbt2xk4cCDr16/n4cOHuLu7U6NGDcD0YWbgwIG4urpaJPuZacGCBSxevJg///wTTdOoWrUqixcvxsrKCoDy5cszZcoULl26RGxsbKbGcvr0aUqXLs2PP/5IkSJFOHr0KOvXr2fkyJH63ZEzO9l2cHCgZ8+e2NjY4ObmxqhRo5gzZw6zZs16LucXQoiMJiv8Qogco3fv3ty5c4cpU6YAULx4cTp27Eh0dDT79++ncePGFC1alLi4OKKioizGZnWyD6aV4Vq1agGmHYN+//13WrduTb9+/WjRogVOTk706NEDe3t7PbnN7NXkt99+m8jISJydndm/fz+Ojo5MmzYNTdP44Ycf+PHHH3n11Vf54osvqFChAtbWmftrw93dnQ8//BCAX375hRMnTvDxxx+TP39+2rZtS/78+Z9LeY+/vz9Vq1YlPj6e0NBQZs2axWeffcarr76q/zcUQoicIksTfqVUGWAxUAKIB77TNG1Wkj55zX1eAe4Ab2uaFmI+1guYYO46RdO0Reb2csAPQJH/b+/M420qvz/+Xq555hoyZCYhIkmkZEpmbhlDpsyzMuYrkpR8VaRUIpUhQ4aQCoVQGTIkoiRzhjJe3Gv9/nj2Oc497r3V73v2PhfP+/Xycvbe59y17r777L2e51nrs4DNQGtVvUwQIjIY6ADEAr1U9TNn/z3ANCANsBToraqamC8Wi8Vdzpw5Q3R0NIsWLQLgm2++4cKFC5QpU4aSJUvyzTff8OSTTxIbG0vmzJlp06ZNmD2+hi9oL126NG+//TZNmzZl48aNbNiwga+//pp58+YRGRnJo48+yn/+8x9KliwJuL8qcfbsWWJjY1myZAkA27Zt46233qJPnz68+uqrZM+enR07djBw4EBy587tah3E0KFDSZUqFblz5+bJJ58kefLkFC1alHnz5pEpUyYAxowZ41ovgnfffZf06dNz77330r9/f/744w9GjBjB6dOnqV69Os2bN2fjxo1WrcdisdyQhHuGPwbor6qbRSQDsElEPlfVHwPe0wE4rapFRKQ5MBZoJiJZgf8A5QF1PrtIVU877/mvqs4SkTednzE50LCIlACaAyWB3MAXIlJMVWOd9z4FbMAE/LWBZQn54saJsVgscUmfPj1nzpxh1apV7N69m6lTp1K4cGEOHjzIK6+8Qr9+/di8eTN//fWXX40nKaTxwLUUkBo1alCiRAlWrVpF7ty5yZUrF82aNePQoUOsWLGCRx991B/se9H998qVK/z+++/Mnz+fJk2aUKpUKXr37s2kSZOYMmUKnTt3pkGDBpw+fdpfX+DGOW3dujVnz57loYce4uuvv6Z+/frkzJnzOnWeJUuWUK1atZDaBmjZsiUXL16kcOHCbNiwwb/KISIcOXKEXLlyAbBz504OHTrEPffcE3IfLBaLxU3C+iRU1SOqutl5fRbYBeQJeltDYLrzei5QXczT8xHgc1U95QT5nwO1nWPVnPfifLZRPOYbArNU9ZKq/grsBSqISC4go6quVzOV837A5xPyxWKxuIgv+K1duzbr1q1j8+bNLF68mJkzZ1K/fn26d+9Orly5aNKkSZIL9n1cvXqVdOnSUbRoUR555BGSJ0/ON998A8CGDRvImTNnnPe7eWuZMWMG586dI2vWrAwZMoQXX3yRNWvWkCxZMvLnz0/FihXZtGmT//2+YN+NQcjPP//MmTNn+OSTT+jbty/nzp1j+fLlLF261J+WdfDgQR5//HFuu+02+vbtG1L7+/bt4/z58yxYsIBx48ZRrlw55syZQ58+fYiOjiZXrlycOXOGunXrkiVLFho0aBBS+xaLxeIFSeZpKCIFgLLAxqBDeYDfAVQ1BvgLiAzc73DQ2RcJ/Om8N3B/MAl9Po/zOnh/Yr4E/y5Picj3IvL9H3/8keDvbLFYEseXPuELfqtVq8amTZvYtGkTP/5oFgKfeeYZ8ufPz19//RXns+EO9pctW8avv/7K4cOH/f74fp/06dNz5coVxo8fT6VKlUifPj2DBg3yxK9u3brx2WefkT59egAaNmxIx44dGT16NKtWrSJVqlQ0atSIvXv3cuDAgTifdWMQkiJFCtavX8/MmTMZPXo027dvZ+vWrcydO5cePXoQExPD5s2bufPOO3n77bcB/M3UQkFERARbt25l7dq1ANx777089NBDXL161Z8+duTIER5//HFmz54NeNeEy6YPWSyWUBHulB4ARCQ9MA/oo6pngg/H8xH9f+y/zuz/4/P/6Ger6hRgCkD58uXtHdti+X8iIsTGxhIREYGqUrJkSUaPHs3IkSP9qT1ffvklqVOnJkeOHOF210+bNm3Yv38/BQsWJDo6mvbt2/u19AEyZ87MhAkTOHz4MHv37uWhhx4C3F+VaN68OarqD1xjYmIQEZ566inSpElDhw4d6Nq1K8uWLaNAgQLky5fPNV/OnTtH8uTJKVCgAFOmTGHJkiXs3LmTlStXcvvtt7Nr1y5eeOEFTpw4QYMGDfwz66E6R7169aJu3bo88sgjDB48mO7du9O8eXPmzZvHU089BcD69etp2rQpd9xxB3fccUdI7SfGoUOHyJMnj3+AZWVALRbL/0rYA34RSYEJ9j9U1fnxvOUgcDtwUESSA5mAU87+qgHvywusBk4AmUUkuTMLnxc4nMjPDfz8YWd/3nj2J+aLxWIJIe3atSMmJoYZM2YQERHhD/pjY2MpUaIEL730Ej/++CMbN26kbNmyfm34pBAYbdiwgb179/LNN99w9OhR1qxZQ//+/bly5Qr16tXjzJkz9OnThwcffJAnn3ySPHny+H13M5A8f/48x44d89t777332LhxIz/++CPPPfccrVu3plChQuzZs4fGjRvTs2dPv1+hPqetW7cmIiKCffv2MXLkSBo1akS9evVo2bIl+/bt4/bbb6do0aL89ttvHDhwIE4ufyjOUZ8+fZg+fTrr1q2jcuXKdOnShYIFC3Lo0CGGDBlCkyZNOHjwIBs3buTSpUukSpUqpPYT48knn+T48eOkS5eOqKgoqlevTvbs2cNybSe1tDiLxfL/J6zfZCf//V1gl6qOD9jfQ0R6OJuLgLbO68eAlU5u/WdALRHJIiJZgFrAZ86xVc57cT670Pm5FUTk/YCf21xEUjmqPkWBb1X1CHBWRCo6/rXxfT4RXywWS4gYOXIkP/74IxcvXqR169aASbuIiYkhIiKCQ4cOERMTQ+3atfnPf/7jD/avXr0a9mAfTL57wYIFAbjtttt4/PHHGTVqFKNGjWLjxo1kzJiR+vXrs3//fq5cueL/nNu+p0uXjnnz5qGq5MuXj3fffZeuXbvSqFEjhgwZwubNm6lcuTLt2rXzB/tunNMXXniB6Ohopk2bRqdOnejVqxcTJkxARGjWrBnPPPMM77//Pi1atODOO++kQoUKIbXfvn17jh49ypEjR6hcuTLr168H4JFHHqF9+/Y0adKE2NhYunXrRtq0aeME+27z7rvvcuDAAZYuXcrDDz/Mtm3bePnllzl27Jin1/bq1asBM7gJZfqUxWIJH+EeulcGWgPVRGSr868OUBwjewlmQBApInuBfsAgAFU9BYwCvnP+jXT2AQwE+jmfiXR+BkA+4KLz+Z3AHOBHYDnQ3VHoAegKvIMp5N2HUehJ0BeLxRI6WrVqxaRJk5g+fToXLlzwa64nT56cCxcuMGvWLGbPns358+fjBCPhnon01evccccdHDlyhPbt2/uP1a9fn8cff9xfpFu8eHFiYmI8D6ayZs3Ka6+9Rq1atXjzzTcpU6YM/fr1o0qVKnz11VfXvd+Nc3rp0iUqV64MmNSnBx54gC+++ILFixfTpEkTevTowTfffEOJEiXi9CIIBevXrydr1qzMmjWLtGnToqq8++67/uOqSkxMDAMHDiRXrlz+Bm9ezetcuXKFe++9F4CuXbtSr149kidPzuTJk12TIw1mxIgRREVF+ZuMJUuWjNjY2L/5lMViSeqEW6VnraqKqpZW1budf0uBApg0H1Q1WlUfV9UiqlpBVX8J+PxUZ38RVX0vYP8vznuLOJ+95By6D5gU8L7RqlpYVe9Q1WUB+79X1VLOsR6+WfzEfLFYLKGhcOHClCtXjnTp0jFlyhSio6Np2bIlAGnTpiVbtmxs2bKF8+fPhz3I99GzZ0/atm3rV5D59NNP+fXXX+nevTtgBislSpRg+/btgGku5dOd95rIyEgmTZpEqVKl/Pv27NlDxowZXbXrC5oLFizI4cOHWbp0KYcOHeLIkSPcfffdjBkzhqNHj9KmTRsmTZrEc889B4R2laFcuXKMGzfOv/3yyy/z66+/Mm3aNMCssiRPnpz+/fv7BxterhxVr16dVatWsXjxYkSESpUqUaNGDQ4dOsSpU95kj957773Uq1ePrVu3MmLECAB/x2WvF7TtArrFEjqSxtMyCFWtF1+jrBD83KdVdVuof67FYgktvkDeF5yKCK1bt6Zhw4ZkzJiRqKgoLl8O+S3i/0W/fv04fPgwEydOZMOGDSxYsIA0adIwa9Ystm/fzmOPPcaaNWuYPHlynKA6TZo0YfPZN9C4cOECDRo0IEeOHHTo0MFVm76guXbt2qRJk4YZM2bQrl07qlatyvPPP899993H7t27gWsBJoRulUFV4wywYmNjSZ06Ne3bt+fXX38FTHCvqn7dfS96Ibz33nvMmjWLjRs3UrRoUbp168aCBQtYsWIFYJSpLly4wKeffuqqHz4yZszI2bNn6dy5M3v27GHChAnMmDGD06dPe2IfTBO2lStXIiI26LdYQkTYi3YtFoslIVSVnDlzMnnyZLJnz06DBg1o3LhxuN3yc+bMGX7//XeGDx9OoUKFuP/++/nss8/YuXMndevW5euvv2bo0KGsWLGCfPnyMWHCBMD94uKoqCgKFiwYZzY7kJMnTxIZGcnmzZu55557+M9//gO4X6QZGxvLbbfdxuDBg4mIiODYsWPkzWs0En7++WcuXbr0Nz/h37N//34KFChw3fn2DSoqVKhAo0aNeOihh65r6uX2zH7nzp355ZdfuP/+++nduzfLli0jKiqK6Ohopk6dyu+//06HDh04c+YMKVOmdM0PX1E8QJUqVZg/fz5ly5Zl8ODBNGrUiNSpU/vradymW7duLFq0iMWLFzNu3Dhq1aqVJIrxLZYbHRvwWyyWJIvvIf/MM88QFRXFRx99BCQd9ZCMGTNSqlQp2rdvT+XKlZk2bRozZ87k888/Z8qUKQwZMoTRo0fH+Yzbvr/22mvs3buXPXv2kDZtWkaOHBnn+Llz5xg0aBAPPfQQTzzxBA888IBrfn322WeUKFGCiIgIcufO7c8HT5UqFSJCnjx5OH/+PHXr1qVQoUJxpEtDQY8ePdi/fz/vv/8+WbNmjXPMF0SWLVuWDh068O2337rSxTchXnvtNQ4dOsTnn38OmNWsBQsWMGrUKJo0aULhwoXp168fS5cuJUWKFK6twHTv3p2HHnqIOnXqkD59emJjYzl69Cjbtm1j1qxZZMyYkaJFizJy5EiGDx/uig8+Lly4QLZs2fjqq6/Yvn07/fv355VXXqFWrVpJ5jtvsdyo2IDfYrEkeYYOHcrttxsV3aTw4P/mm2/IkCEDxYsXZ8iQIZQoUYLVq1czbdo06tat6w+mv/vuO7/f4E2KyB133MGKFSuIiIigWrVqXL16leeff94f4KZIkYJHH32UrVu3cvnyZf/Mcaj9at++PYcOHaJ06dIcPnyYDh06UK1aNZIlS4aIcOrUKbJmzUpMTAyNGjWiT58+QOj+vt27d2fDhg3ky5ePP//8k6xZs8b52adOnSIy0vRN7NatG9myZfufbf4bSpUqRfXq1f3buXPnZvny5QDkyJGDmjVrsn79ei5fvuwfrIT62r98+TKLFy/m6tWrpE6dmipVqpAlSxaaNm1Khw4dyJw5M2vXrmXXrl1s2LAhZHYTIm3atAwaNIhkyZJRuHBhLl68SP/+/Rk7dix16tQBIDo6mtSpU7vui8Vys2EDfovFkmQ5/K15yEcAh4+4lwqTu8LSf/zerl278uOPP5InTx4uXbrERx99xOOPP46qsm7dOh566CEiIyOJjY29TlnFi7SERx55hAsXLpA2bVqWLVtGnTp1uHr1Ki+88AJg1ITuu+8+tmzZ4lp+9LJlyzh69CifffYZJ06cYPz48XTv3p3XX3+dGjVqcObMGQYOHEilSpVo165dyIP9du3acenSJTZt2sTAgQPp0KEDX3zxhT9t5dy5cwwePJiKFSvSvn17f7DvRerIxIkTqVKlCg8//DDR0dH+/RUrVmTmzJn+7d27d/ubffl8C2Wwr6pERERQtmxZzp8/z6JFi0iePDl16tQhS5YstGjRggEDBpAiRQruuusuypcv7/9cqM/Re++9R5o0aShcuDD33nuv/7ps0aIFsbGxDBs2jNSpU7N06VIaNmxIlSpVQmo/PjZs2EChQoXIkCFDWOttLJZQYdfHLBbLDUO483gHDRrEkSNHWLVqFa+++ipZs2b1F1cWLVqUS5cu0bZtWx599FHSp09PmzZtPPFr5syZ/PXXX4AJmtOmTUtsbCx58uRh8eLFfP755wwfPpwmTZrw4YcfkidPHgYPHuyaStCFCxf8KwfZsmXjnnvu4d5772XkyJHs2LGDDBkyUKdOHX777bc4xdehCmiLFy/uT//q168f+fLlY+3atYAJWFOmTMmjjz7KgQMH4th3+/pq2bIln332Gfny5UNE/IGkqhIdHc1ff/2FqtKgQQMmTpwY57Oh9k1EiIiIoGnTpnTu3JnatWuzcOFC6tevz6+//srgwYNJkSIFMTExca6TUPvRuXNnPvroI3788Ufq1avHxo0b/TauXr3KE088waBBg6hRowa//PKLJ8H+U089xdNPP02vXr1YuXKl6/YsFi+wAb/FYrH8A86dO0fOnDmZOHEiyZIlI3v27KRLl84vtVm2bFm6dOlC+/btadq0qX+21m2t/ZYtW/L000/Tu3dv/vzzT3+zJF+zsnz58rF06VKef/55UqdOzcCBAwF3VIIOHToEQKNGjTh27BitW7dmw4YNjBs3jmbNmlGzZk0OHDiAiHDnnXcSExMT0lWG8+fPA/h/x9jYWDJnzkzatGlZuND0TxQRUqZMScmSJUNuPzHWrVvHqVOnWLx4MVmyZOHIkSP+3g0+n06fPk2DBg24/fbb/T0AQs2OHTv45ZdritLR0dHMnj2bxx57jN27d7N27VoyZcrkbwqXPLl7iQATJ0701zGMHDmSoUOHsmTJEiDuisacOXNo3rw58+fP9x9zi5dffpk//viDNWvWUKpUKbZs2QIQp0mexXIjYgN+i8Vi+QekT5+etm3b+nPBwaRhnD171v+eO+64gyZNmtCuXTvA/XqDvXv3cubMGWbOnEmePHno06ePP+iPjY0lefLkXL16lUmTJl1X9BzqmdqnnnqKTp06sW7dOiIiIli+fLk/R7xVq1bUrVuXdOnSsXXrVsDMwoeyF8GAAQPo3bs3zz//vH+fiJAqVSqGDBnC4sWL/cEkQLFixTzthZAzZ05/Lv64ceN44okn6NSpE1FRUQDkz5+fEydOkDdvXiZNMu1iQj1Y7Nq1Kz179mTIkCH+FYRGjRqRKVMm+vTpg4jw3HPP8fHHH/v/Tm5x9epV7rjjDsaMGePflytXLjZt2gRcW0n4888/efjhh129dn2oKidPnuT+++/371u2bBlt27Zl7NixHDt2zBW7FosX2Bx+i8ViSYTAnOVgpZfLly/7g4DHHnuMGjVq0KVLF/9xtwt0ixQpwrRp08iYMSO5cuXizTffpHfv3owbN47s2bP7BxwNGzb0N1FyaxCSOnVqsmbNysyZM4mOjqZ69erMnj07zntWrVpFzZo1/duhWmV46qmnOHnyJMOHD6dWrVqUK1eOOnXq+H/P22+/nS5durB161YeeeQRUqRIEVL7/4QUKVJw9uxZFixYwC+//MI777xDjhw5qFOnDj179uS1117jpZdeonnz5kDo/07Dhw/nyJEjLFq0iEWLFvl19TNnzsw333zDmTNn+O677wAzGPN1/HWDEydOkC1bNmrWrBmni29CdQy+BnZuDqB9ReQNGjSgXr16/PDDDyxatIgtW7awY8cO1qxZw6effhqng7bFciNhZ/gtFoslEUQkTlASSN68ebl48SIdOnQgMjIyTrDvJoHFwNmyZSNlypQUKVKELl26EBkZybBhwzh//jyTJ0/mjz/+oGzZsoA7KkG+Wej8+fOTJk0a7rrrLubNm8eWLVv8AeTp06d56qmnyJw5M7169Qqp/dWrV3PixAnmzZtHmTJlaNq0Kd999x3vv/8+v//+u/99BQoU4MqVK/5g32vy589P7dq1adeuHZcvX6ZgwYKkS5eO8ePHkz59ekTEtWAfzOCza9euZMiQgV27djFp0iQ6duzI6NGj+fLLL/1/K4BatWoB7qTO9O7dm9atW9OzZ0/mzJkTp8laYB1D/fr1r6tjcCvYf/rpp1mwYAEAlSpV4vvvv6dOnToMGDCAIkWK0KhRI3LlyuVv0OYVvhS5cGIbnxGnuP5Gxgb8FovFEg/t2rXzNxuKiIiIN+jPlSsXs2fPJl26dLz11luA+zn7HTp0YMKECXGCWd9DuUCBAgwaNIhMmTIRGRnJypUryZ49u/99bqRC+IKwxx9/nNtvv52mTZtSsmRJWrVqxdixYwHIkiULjz/+ODNmzABCe46qVq3Khx9+CMBHH33Ee++9R/Hixfn000959dVX/YOjqKgonnvuuZDZ/TtOnToFXOveC0YqdPTo0SxZsoSvvvqKgwcPMmbMmOsajrkR2ObMmZNWrVrRsWNHpkyZwgcffECfPn1Ys2aN/+8UfI27kfZ19OhRXnvtNe644w42bNjAxYsXAXMNp0uXzl/HkC9fPtfqGALp0qWLv8GZj0KFClGxYkV++OEHv1Tqt99+62o9QzBPPvkknTp14vHHH2fWrFn+eg+vSCrdjjds2MDx48f914nXjBgxIk5dSTgI1TmwAb/FYrEEMXLkSH788UcuXrwYJ+iPiYkB4MiRI+zevZusWbMydOhQXnvtNcD9nP3//ve/LFmyhJ9//plPP/2UgwcPAtcCs+TJk5MjRw7WrFlDs2bNmDdvHuDOg+qrr75i586d/mD10qVLrFu3jixZsrB582YuXbpElixZWL9+PYA/lSdUqwyrV6/220+TJg1Xr16lYsWK/PzzzzRr1oxJkybx+eefs3nz5v/Z1r+lRYsW/joOX98B3yCne/fuvPTSS8ybN4+BAweSPXt2xo8f77pPXbt2ZenSpdSpU4eRI0dyzz33UKpUKTp06OC/fgJn20PNTz/9xKlTp5g6dSpFixalSZMmrFq1ih07dgDmGs6VK5frdQyBvP3227z33ntMmzYNMNfU2rVr2bx5M4ULF+bhhx/m6aefpnr16iRPntzfkdpt3n33XQ4cOMDSpUt5+OGH2bZtGy+//LJnNQTdunVj+vTp9OnThxUrVoQt6A+3WlLXrl0ZPXq0//oIh0pcfOfg//u3sAG/xWKxBNGqVSsmTZrE9OnTuXDhAk888QRgAuoLFy7w0UcfMXfuXHLmzMmoUaMA94P92NhYKlSowObNm+nYsSNr1qxh4cKF/qDf58PmzZspV64c06dP9+8L9YOqTZs2DBo0iFdeeYUHH3yQ48ePU7RoUapWrUr16tXZt28fS5YsoVixYvz4449xPhsKX9q0acPgwYP99k+cOEGyZMkoWLAguXLlAkyqU4ECBf5nW/+W3r17c/r0aX7//Xc+/vhj/37ftXH+/HnatGnDK6+8wjvvvMPkyZMBdwNb38+uUKECWbNmZdq0af70lNmzZ/slXd2kePHijB07loiICC5dukTu3LkpXbp0nKJ3EWH8+PFxzomb36kqVarQpUsXRowYwYsvvsjQoUOZNWsWdevWZfny5fTs2ZNFixYxevToOEXDbnPlyhV/DUXXrl2pV68eyZMnZ/Lkydf19gg158+fJzIykq+++oqRI0fSv39/f9Dvxe/u46WXXgqrWlLbtm25dOkSV65c4erVq/4VXC9JSDHKN/H0bwN/G/BbLBZLEIULF6ZcuXKkS5eOKVOmEB0dTcuWLQHTDTRbtmxs3bqV48eP+z/jdoFuREQE5cuXJ2fOnFSpUoUOHTqwYcMGFixY4A+arly5Qrly5eLMjobary+//JJffvmF9evXM3XqVCpXrkyDBg04ePAgBQoUIGfOnHz55ZfceeedtGrVKk6qhFv269Wrx2+//eYPSq5evcpjjz1GlixZePDBB0NqPzF69uzJb7/9xvLly+nRowc//fQTcC1I/OOPP3jmmWf46aefSJEihb9XQahrK5544gneeecd/3bgz65atSq1a9fmvvvu8/eLGD16dMhsx4cvMClcuDCpU6f2KyMlS5bMXzw8aNAgduzYwWOPPQa4G+yfPHkSMIOQfv36cfDgQd58801mzZrFxIkTeeONN+jcuTN//PEHBQsWpGLFiv7fw+1BPZjVsNWrV7N48WJEhEqVKlGjRg0OHTrkTxVzg82bN5MuXToGDx5M3rx5adSoEUOGDKF///4sXbrU/7u7ndOuqpw6dSpsakk7duwgZcqUTJ06FYC6dev6B8herXT8E8Wofzt5YgN+i8ViiQffwy0yMpJJkyYhIrRu3ZqGDRuSMWNGHnvssThNm9wi8AGTKlUqfx5xtWrVaNOmDT/88AMfffQR99xzjz8XO/h3CCV33HEHhQoV4sCBAwCMHz+eqlWr0rhxY7/0p6/mIXfu3Nf9Dm7Yr1atGvXq1ePChQtcvXqVN954g8yZM/uX4r14SB86dIgyZcrwySefACYP/J133uG7777z/x2yZ89OmjRp/H0CfCk0oVyBeeqppzh79iwtWrSI9/j58+cZMWIEK1as4MUXX3SlriKY4N/PF9jmyZOH06dP06lTJ06dOkWpUqX873ErsO7duzdPPPEEPXr0YO7cueTPn5/nn3+elStXcvvttwNQr1497rvvPv+ALKHfI5R0796defPm8eeff1K4cGG6d+/OggUL/I39qlWrxoULF/j0009dsd+2bVvKly/P1q1bSZs2rX9Q1qJFCwYOHMiwYcNYuXIlAwYMiFPgHWq2bNmCiFC3bl3GjBnjrwWaPn06jRs35vTp066dAx+lSpXi7bff9m9XrVqVGTNmsGTJEk/Sek6dOoWI0KBBA1588cVEz8G/ubfZgN9isVgSQVXJmTMnkydPZs6cOaRMmZLGjRvTrFkz8ubN67r9xFSCatasSdu2bRkwYAAlS5Zk+PDhrvuTKlUqrly5Eien9sUXX6R06dI8/fTTqKq/8Vfg7+Cm/RdeeIGKFSsydOhQkidPTvPmzf0z3G7qtgeSJ08eOnbsCJgl96pVq9K1a1cmTZrE2bNn/QH1uHHjqFy5MidOnAi5DzExMURHRzN//nzSpUvHmjVr+OKLL/jtt9+4dOkS586do3///vz000/cfffd3HXXXYA7M9ezZ89m1qxZ1xUkw7WBTtasWencuTMZM2ZkypQpfl/cIrBouHjx4qxdu5ZLly6RP39+ChYs6H9fmzZtyJgxI5kyZXLNl0B8/SpWrVrF119/zcWLF6lXrx7ly5fnvffe81/LZ86cuW4QEgq6du3K5cuXefbZZ/nss8/8q2Tgbbfjtm3bcs8997B161aqVKnCpk2bPFVLmjJlCvPmzfPX/agqqkrJkiUZNWoUs2bNcuV7G8i/VYz6N/c2G/BbLBZLIvhuqM888wxRUVH+vGy381n/iUoQwCeffELVqlV5//33XfMrMAjLnj07PXv25JVXXuH999/359R26dKFVKlSISIhDx7/if2nnnrKH0hmy5bN/zm3U6369etHnz596N+/P4cPHwauBbQPP/wwFy9e5Ny5cyRLlsyfe/vMM8/4fQwVsbGxREdHs337dr799ls++OADBg0axIQJExg1ahQff/wx6dOnJ126dP4VBt+5caPG4/3332fChAn+4mW49nf05aFny5aN2rVr88orrwDuDs7iKxr+6quv2LZtGyKCiHDy5El69+5NypQp/UG226tDqkpERARly5bl/PnzLFy4kNWrV5M1a1Zq1qxJx44dmTBhAlFRUaRIkSLkKXKjRo3i2LFjzJw5k/vvv5/ly5cTGxtLsmTJPO12HDzoiI2NpVChQlSuXJlt27a5rpbUrVs3Fi5cyKlTp2jZsiVTp04lJibGfz2WL1+eK1euuJpS5bZilA34LRaL5R8wdOjQOIV7bgaS/0QlyJcf3qVLFxYvXuyqX4GrDKpKpUqVmDhxIpMmTWLs2LHMnTuXUaNGuVb8+U/tBxaA+j7nJh07duTAgQPUq1ePP//8k23btsWxe9999wEmmAFclXUUEdKnT0/v3r354IMPmDt3Ll988QVLlizh3nvv9QcLr7zyimsrDAAffPABx44d49NPP2XDhg2ICDt27CA2NhZV5c8///TXMbRp08afmuD2d+qfFA1HRkbSpk0bVwve4yMiIoKmTZvSuXNnHn30URYsWED9+vX54osvqF69Ohs2bODtt9/2B9uhGtTHxsZSsWJFv5pX7dq1SZs2LQMGDACuXcdnz56lWrVqrnU7Dh50LFu2zP87RkZGUq1aNZ555hnX1JK2bNnCrl27WLRoEZ06dWLIkCH07NkzTuPA0qVLExER4b82Qs2UKVNcV4yynXYtFoslEfauee/a6/1xO++GkiJVrs2EtmrVijp16nDnnXfSpk0bnnjiCT744IM4KkGXL1+mX79+FC5cGHBnNrtdu3bExMQwY8YM/ypDsmTJiI2N5aGHHmLq1KmsXr2alStXUqxYMcaNG+f3JRTnKNz2E2Px4sXExsYyd+5cAKZPn+5vAlalShXy588PGB1vX7DtBk8//TSpU6cmderUtGrVioYNG7Jz505++OEHNm7cSNWqVWnbti0LFizg119/pWDBgjzzzDOu+VOgQAEyZcrE7t27WbhwIStXruTQoUMUKlSIDh06ULlyZdKkScPTTz/tH6iCu0XvvuvB910JtBlYNNy8eXPuuece/2fcXh0KvE4vX77M7Nmzee2115g4cSLbtm3jySef5NKlS6RPnz7OZ0Lh17Zt2/yrCGAKcVOnTs2QIUOYNm0ahw8fJnfu3MTGxpIhQwa6desGhH5gFhMTQ/ny5Xn22WcBM+h4/fXXGTBgAK+++ioZMmSgU6dO1KtXjyNHjvgLqEPpR44cOciXLx/79u2jWLFi1KpVi4ceeoixY8eSK1cuqlevDuAf8LhBtWrV6Nq1KyNGjCBz5swsXryYsmXLMm/ePN577z169uxJvXr1OHbs2P/7HNgZfovFYvkXeDHj909UgrZs2cJff/3lWlpGQqsMsbGxREREcPDgQTJkyED37t154403/MF2qGb/wm0/Mc6cOUPt2rV54403AJg5cybff/89OXPmZO/evTRq1MjfJbVYsWL07dsXCH0aROfOndm/fz+PPvooqVKlombNmpw9e5ZnnnmGhg0b8sYbbzBz5kyeeOKJ6/LU3SA2NpbixYuTP39+nn32WaZNm8bu3btZvXo12bJl48033wTMKkOVKlVcz4f28U+Lhu++++4EPxNKRowYwcGDB+NIXTZs2JCsWbPSp08fRIQRI0Ywd+5c/6pRKP3q2rUrvXv3ZsCAAX5Fr9SpUwPmet23bx9z5swBru/NEMpgf/PmzSRPnpzatWsD+EUQhgwZwoULF/ySwxEREeTPnz/kakmHDx/m4sWL5M6dm3Tp0jF27FiGDh1K48aN6du3LyNGjGD69OnExMT4V1d99kPFxIkT2bRpE0WKFKF79+4cPnzYNcUoG/BbLBZLEuTvVIKioqJcVQn6u14Es2bN4sMPP+T8+fP+oCWUs6Lhtp8QXbt2pWPHjgwaNIgVK1YQHR1N0aJFWbNmjT9IKFeuXJxgxUeog0ifIlGlSpXo3LkzmTJlomrVqly4cIHx48fTuXNnfvzxR8qXL+/XEXcjJ71fv3707duXQYMGISKMGTOGl156ibvuusufMjNkyBD++usvjh49CrhTxxBIUiwaBujTpw8jR46kWbNmHD582H+9pkmThuXLl7Nx40ZWrVpFr169aNeunV+PP1QMHz6cI0eOsGjRIurXr3/d75s9e3ZGjhzJu+++y969e0NqO5CXX36Z8uXL+6VHVZUUKVIA1wYdvtWz4EFHKL5HPXr0oGPHjvTt25eVK1f61b4KFy5M7969qVmzJkWLFiVz5swkT548TjpeqL7HLVu25LPPPqNQoUIAFC1alJEjR7JixQpXFKNswG+xWCxJmHCpBP3dKkP27NnZsmUL58+fd2WVIdz242PMmDEcPXqUDz74gCJFijBt2jS/4ktg8Prbb7+52sxqy5YtHD58mJ07d/pzis+ePUutWrWIiorimWee4cKFC1SvXp3nnnuOQYMGAe6sfvjqGOrWrcuJEyf4/vvvSZ48OQUKFKBAgQIsXLiQ77//nm7dupErVy5uu+22kNqPj6RYNAymD0PevHn5888/eeCBB2jcuLG/0DtNmjSsWrWKtWvX+t9fq1atOH6HgmTJktG1a1cyZMjArl27mDRpEp07d6ZXr17+9xQuXJgRI0ZQpEiRkNkNplChQlSoUIE2bdowbdq0ON183R50dO/enWPHjvH2228TGRnJjz/+6E+Ja9++Pc2bNwfg2Wefda3uZt26dZw6dYrFixeTJUsWf4+FAgUKUKxYMf/7QqkYZQN+i8ViScKESyUIwr/KEG77gagqR48epVGjRqRMmZKuXbuSOnVqfvjhB9555x1iYmL4888/admyJQULFvQHa6GmS5cufpWiJ554gtdff50nn3ySunXrkjNnTp599ll/egbETcEI9erHkiVLUFXmzp1LjRo1iImJYdWqVXzwwQccP36c+vXrs3PnToYNG0ZkZKSrqww+kmrRMJhAtnXr1mTMmJGxY8dy//3306RJE39PiTRp0lw3mw2hHcjmzJmTVq1a0bFjR6ZMmcIHH3xAz549+fnnn/19PHLlykVUVBTg3t+qVKlSPPXUU6xatYrevXuzZMkSFi1aBJiUq6JFi7oy6Pjxxx9Jnjw5H3/8MXny5OHSpUvMnj2bsWPH+js8X7lyhTFjxpAyZUrGjx8PhP485MyZk6xZswJGqrdNmza0a9fO33guOjo65IpRtmjXYrFYkjBvfGBm/G6/syZZInPyxgdrXQlOuj3xQILHAlcZsmfPToMGDWjcuHFI7SdGuO2DCboqV67M/PnzyZ07N1WqVOH06dPUqlWL3bt3IyKMGzeODBkyxAlsQxmsjR49muPHj7NmzRr69+9P1qxZ2bZtG7/88gtp0qShePHiABw/fpyjR4+6OkO7a9cuSpcuTeXKlQGTQvP9999Trlw59uzZw6uvvsqKFSuYOHEiZ86c8c9Quh1Y58+fP8kVDQeSM2dO/+sJEybQq1cvWrVqxfz58xk+fDiPPfaYv0jUDbp27co999zDwYMHKV++vL9IuW3btv6BRyBurXjkzp2bOXPm0L59ez7//HMefPBBMmfOTKNGjUiWLBk5c+aMM+gIlR8lSpTw1/t89tlnrF69mjFjxnD27Fk+/PBDsmTJQvPmzenQoQODBw8G3LlmU6RIwdmzZ1mwYAG//PIL77zzDjly5KBOnTr06dOHCRMm0LJlS7/SVyh8sDP8FovFcgOQJdIECl7kqQcTzlWGpGDfR+3atXnwwQd5+umniYqK4pFHHqFfv37s3LmTHTt2MHDgQH+wH+r0kKtXr3LixAkeffRRRISMGTMyceJEBg0axKpVq8iVKxcAjz76KDly5HA12O/SpQudOnVi2LBhbNq0CTDpMWvXrqVv376MHDmSUqVKsWfPHkTEH+y7ee3OmTOHv/76i7x581KoUKEkVTQM11+rvu3XXnuN++67j5w5c3L69GlXg32fzQoVKpA1a1amTZvmb+A0e/ZsV9PQAvEp/5QvX55Dhw6xcuVKSpQowcWLF5kzZ85135tQDzp8tQKVK1dm+fLl1KxZkyZNmlCiRAl/zUeOHDkA967Z/PnzU7t2bdq1a8fly5cpWLAg6dKlY/z48aRNmxa4JusbKh/sDL/FYrHcQHihEhTMkiVLAPMAyp49O0uWLHFl1qtevXqJHh86dKi/mM2LFIxgMmbMSLdu3WjatClXrlzxB9kZMmTg4sWLZMiQAXAnSEiWLBmPPPIITZs2Ze3atcyfP58NGzawbds2NmzYwMKFC8mQIQNly5blhRde8PsR6uvFt8qwdu1aBg4cyNatW6lRowYPP/xwnN/5999/vy6AdOva9RV016xZk4IFCzJs2DD++OMPBg4cyNmzZ8mYMSNDhgyhTZs2HD16lNtuu81VaVIf69atIyYmhoceesjfyMp3DgLP1eHDh3nssceYNWsWELq/29mzZ8mQIYP/5wXarFq1KrVr1+a+++7jnnvuIVu2bIwePfp/tpkYvu+sL20pX758FCtWjLvvvpvNmzezfv163nvvPZo2beqqHz5f0qdPH0fydOvWrf5A34cbhfa+Rm/du3cHTB+Cr776isKFCzNmzBjy5cvnig824LdYLBbLPyJ79uyA96sMvgEHwA8//OCazv7fDTh8+Ap0fTn7kZGRfrk8cC+wrV27NuvXr2fDhg2UKlWKkiVLUrJkSX7++WdOnDhBmzZt/KlObgyIAlcZwBRPL168mD179lC0aFE6d+5M6tSpadu2rat1DIHMnj2bU6dOsXTpUv++FClSULBgQQoVKsSiRYu49957GT9+vGdFwwCdOnXi6NGj7N27l3LlyvHhhx/6G8hFRERw7tw5UqdOzbFjx6hduzZt2rQBQvd36969OzExMQwdOpR8+fJd9505f/48I0aMoFGjRogIZcqUCal9H999952/wZfv5/rOQf369Tl//rw/8L3//vu5//77Q2YbzPWhqjRu3JhUqVL59wf+jgcOHKB3797kyJGDnj17htQ+mIHE5cuXqVChAsmSJePq1av+e2j37t3JkCED8+bN4+TJk2TPnt1fNxBqbMBvsVgsln9FOFYZkpJ9H+fOneOee+5h1KhRgDerDiVLlkRVGT58OOvWraNy5cps27YtjoY8uJOT/nerDPPnz2fPnj2kT5+et99+G3C/CZqq+pucvfrqq/z000/s2rWL119/nRo1ajB//nwWLlxI0aJFef311z3xqXfv3hw9epTFixdz+fJlnnzySQ4dOkSePHmIiIjgxIkTjBgxgh49elC8ePGQB/ujR49m8eLFPPnkk7z22mv06tUrTtB//PhxRowYQa9evbj77rtdk7Vt0qQJZ8+e5eTJk1SoUIFOnTpRpkwZv/JNhgwZ6NGjBylSpODKlSv+VJtQ0aZNG06ePMnJkydZtGiRv3GW7zycO3eOdOnSsXv3bm677TZ/0W4ov8cdO3bk8OHD/Prrr9x///1MnTrVv9oDZuDVpk0bWrRoQUxMDGnSpAm5Dz5sDr/FYrFYLP8P8ubN62mw76No0aJ+7f0aNWqQIkUKhg0b5olt3yrDgw8+yPDhwylZsiQtWrQgMjKS8+fPM3r0aH+w76bM5aRJk9i/fz/FihUjXbp0rFy5kk8++YSePXtSt25doqKiKFKkCG+88QazZ8/2B/tuS29evHiRmjVr+hVnxowZw6pVq/zqSkePHiVbtmykTJnyurSiUF0/derUYd68eTRu3JgUKVIwYcIEDhw44P+9c+TIQbp06fz23ZC13bBhA+fPn+fzzz9n48aNpE+fnvfff59169YRGxvL8ePHGTx4MPv27QOu19r/X/knak0DBw5k9+7d1KxZ05Vgf8CAAfzxxx8sXbqUH374gR9++IEPPvgAMOf6jz/+8CtGpUiRwq+179YKqp3ht1gsFovlbwhMK3KTf5JWlCpVKvr06UO9evU4fvw4Dz74IODdoCOhVYYyZcr4g0Y30746dOjA/v376dixI2nTpmX37t389NNPNGzYkBIlSlCiRAl/mk+3bt08KRr2kTp1av/f8NChQxw7dow1a9aQP39+mjVrxtChQ3n33XcZP348L730EidOnAhpA7KYmBjKli3rn8W+cuUKCxcuZPz48Tz33HNkypSJS5cu8fLLL7ti30e2bNk4duwY27dv56677mL06NGMGTOG+fPnU7p0aXLkyEGKFCkYOHAgCxcuDPnfpUCBAn+r1pQ6dWrX1JrOnDlD8eLF6d+/PwApU6akRYsWREdH+9+TPXt2UqdO7T8HvkGPWwNSG/BbLBaLxZLE6b/s/QSPLVy2P2RpKq882uYfvS9wleG2224je/bsPPvss/7jbgUtq1at4sCBA3z55ZeAKaT29STYs2cPu3fv5o477mDnzp3XKRW5ObPfr18//4Cif//+5M6dm9tuu40JEyb4Z25feuklxo4dS3R0NKlTpw5p4XB89sEo8sTExLBy5Uqef/55Pv/8c9q3b0+vXr1cLVwuVKgQjRs35ptvviFbtmzkypWLwYMH07RpU0aNGsX48eNdG/SA6dabL18+nn32WXbs2MHu3bvJmDEjzzzzDG+++SaVK1fmlVdecc1+xowZad68eZwBRJYsWdi5cydwLa3ITR+CsSk9FovFYrHc4Hhd1+BbZZg7dy7Dhw9n5syZgPtSqdmzZ6dUqVKAkbR85JFHmDJlCtmyZePw4cPMnDmTGjVqkCNHDjp06OCqLz4Cuw3/+eefbNu2DTBpKoF56UOHDiV9+vRxGqO5ad9HpUqVqFOnDjNnzqREiRJxuuqGkhUrVnD+/HnAzJSXL1+eTZs2sXz5cvbv30+qVKl46aWXiI6O5sqVK4CR2g1VoNunTx/69evHqFGjuHLlCuPGjeOFF16gVKlSnD17FoAhQ4bw119/cfTo0ZDbB3j66acZNmwYY8aM4Y8//iBt2rT+3/XKlSv+RoH169dnxowZrviQEHaG32KxWCwWy9/iawJ3PcnY8cHakK0yJNYELlOmTCxfvpwsWbKwZ88epk2bxrFjx1i1ahVnz56lV69ePProoyFtWJQYn376KTExMcydOxeA6dOns3r1ak6cOEGVKlXInz8/Bw4cYMCAAaRPn57//ve/QOgKh/+JfYAXX3yRcuXK+QtXQ31emjdvzpw5c3jjjTdo2bIlGTNmpG7dukRHR7Nq1Sq+/fZbatasyTvvvEOBAgVCXqDbt29ffvnlF3r06MHatWt54IEHWLhwIaVLl6ZgwYIsXLiQChUquKrW1LlzZ06dOkXfvn1Zv349NWrUYNmyZRQrVgyAMmXKsGvXLtq1a8dtt91G69atQ+5DYtgZfovFYrFYLP8zXqwy3H777bz22mssX76clClTUrBgQSpWrMjtt99OREQEWbNmDXnDooRYtmwZdevW5Y033gBM86/vv/+enDlzsnfvXho1asTRo0dRVZo0acLUqVOB0BUO/xP7hw8fBuDll1/2FxKHOtj/6aefyJ07NwsWLGDhwoXMmDGDM2fOABAVFUWnTp2oUqUKS5YsoWzZsn5/fUo1oSBZsmR06dKFWrVqMXLkSPr06UOtWrXYv38/devWZefOnQwbNozIyMg4nbBDyeXLl3nzzTepVKkSTz31FJGRkdSqVYs9e/b43/P666+TKlUq3nnnHcDb5oF2ht9isVgsFssNw8MPP8zvv//OgAEDqF69Oq1atWLx4sWe5ux37dqVvXv38uijj/qlFLNly8a6devImjUrYPTdDxw4QIUKFfwz7aEKtv+J/d9//50DBw6QO3fukNv3sXjxYurXr0/Xrl0pWrQouXLlYtCgQQC0aNGCrFmzUqZMGcqUKUNUVJRfCz9UfsybN4+oqCiyZMnCunXrqFOnDmCkUa9evUr79u1ZsmQJlStX5ty5c/4C7lCeh/Xr13P//fdz8OBB3njjDZ599lnOnj1LjRo1iI2NZdiwYUydOpXs2bMzYcIEf0qV1/1M7Ay/xWKxWCyWG4bkyZPTvn17PvnkE95++206dOhAypQp/Z1iQz1zG0yHDh04d+4cn3/+OWCkOAGqVavmD7bBBPynT5+O89lQBHj/1P5vv/3mn2kPpX0fXbt29cudFipUCDBFwmPGjGHu3LksXLiQ6OhohgwZwsmTJ/3BfqgC3a5du/pn61u3bs3s2bN58cUXAdPcq2fPnhQpUoTDhw8TERHhilrT0KFDadiwIYsXL+bDDz9k2rRptG3blvr165MzZ06GDh1KmjRpSJYsGUWKFPEH+27Lw8aHneG3WCwWi8Vyw/Hggw+ydOlSkiVL5i+EdTtn/9ixYyxevJjhw4cD8Oabb7Jt2zYuX75MvXr1eOSRR1BVOnbsSMGCBXnkkUfCat+tbscdOnTg8uXLrFixAoBLly6RNm1aVJX77ruPiRMn0rt3b0aOHMn9999PZGSk/7OhCHSD7efPn58VK1ZQuXJl0qRJ4+/6vH37dvbt2xdn9SeUgfZdd91FzZo1mTFjBufOnWPPnj3s27ePS5cucddddwFw/PhxDh486M/lB3ca4/0dNuC3WCwWi8VyQ5I2bVr/ay9SJHLmzMnnn39Oy5YtWbZsGWfPnuWll15i+fLlfPbZZ+TMmZMlS5aQPn16pkyZ4vcrVEFmuO1DwoOOK1euUKdOHerUqUPJkiU5dOgQDzzwgF+NJlR+xGd/06ZN5M6dm379+vHRRx+xZ88eNm/eTLFixUI+6AokMjKSq1ev0qtXL1555RUuXbpEhgwZqF27NufPn+fxxx8nR44ccYL9cGEDfovFYrFYLEme45Pd040PJEfXlxI9XqZMGWbOnEnfvn156623uPPOO6lYsSKtWrXi0KFDjBw50t9EyY0Vh3DbT2zQsWLFCvLmzUvy5Ml57LHHXOlEnZD9xYsXc+7cOUaNGkXRokX5+eef/Sscbq381KxZk8WLF1OhQgUGDhxIixYtyJQpE1FRUSxdupS7776bF154AQj9wOvfYnP4LRaLxWKxWP4FpUuXZsmSJdx5553+fefPn+fs2bP+YNvNFYdw2/cNOqKjo3nrrbeoWLEiI0aM4OzZs+zatYuyZcu6EuwnZn/06NHs2bOHI0eOxElncivYV1UuXbrEkSNH2LdvHx9//DEZM2akYMGCvPXWW9SpU8cf7IcjZz8YO8NvsVgsFovF8i/xqeNER0f7UzfatWvnP+52gBdu+75Bh88PgHPnzl1XNO32oCfQ/sWLF6/7vd2yLyKkSpWKqKgoHn/8cSIjI1m7di1btmxh06ZNnvjwbwi/BxaLxWKxWCw3IFevXmXz5s0UKlSId99917/vVrEfOOioX78+kZGRtG3bNqz227Rp45l9gAceeICoqCi+/PJLMmTIwL333kvfvn0B9xWj/g12ht9isVgsFovl/0GyZMmoVKkSlSpVAtxXCUpq9n02fYOOV1991XM/wm0/b968PPfccwDExMTEWXEIdxpPIDbgt1gsFovFYvkHvPHBWk/sdHvigXj3713znif2i1Rpl+CxvZO/um5fDqBn8SbsnfxVyILtIl0finf/jI1PXb8zAsq3NMeuXlWSJfvfA+3W9035159JnjzphtVJ1zOLxWKxWCwWyw1FuPPVQxHs/x3xDjpc4P8z6EgIm8NvsVgsFovFYrHcxNiA32KxWCwWi8ViuYmxAb/FYrFYLBaLxXITYwN+i8VisVgsFovlJsYG/BaLxWKxWCwWy02MDfgtFovFYrFYLJabGBvwWywWi8VisVgsNzE24LdYLBaLxWKxWG5ibMBvsVgsFovFYrHcxNiA32KxWCwWi8ViuYmxAb/FYrFYLBaLxXITYwN+i8VisVgsFovlJsYG/P8SEaktIrtFZK+IDAq3PxaLxWKxWCwWS2LYgP9fICIRwCTgUaAE0EJESoTXK4vFYrFYLBaLJWFswP/vqADsVdVfVPUyMAtoGGafLBaLxWKxWCyWBBFVDbcPNwwi8hhQW1U7OtutgftUtUfQ+54CnnI27wB2e+BeNuCEB3aSqn3rQ9KwnxR8CLf9pOBDuO0nBR/CbT8p+BBu+0nBh3DbTwo+hNt+UvAh3Pa99CG/qmYP3pncA8M3ExLPvutGTKo6BZjivjvXEJHvVbW8lzaTkn3rQ9KwnxR8CLf9pOBDuO0nBR/CbT8p+BBu+0nBh3DbTwo+hNt+UvAh3PaTgg82peffcRC4PWA7L3A4TL5YLBaLxWKxWCx/iw34/x3fAUVFpKCIpASaA4vC7JPFYrFYLBaLxZIgNqXnX6CqMSLSA/gMiACmqurOMLvlw9MUoiRoH6wPScE+hN+HcNuH8PsQbvsQfh/CbR/C70O47UP4fQi3fQi/D+G2D+H3Idz2Icw+2KJdi8VisVgsFovlJsam9FgsFovFYrFYLDcxNuC3WCwWi8VisVhuYmzAb7FYLBaLxWKx3MTYgN9isVhuUETkiYDXlYOO9bj+ExbLrUfwd8NiuRWxAf8NiIgUD3idKuhYRe89uvUQkcUisiihf+H2zyuSasApIpVFZJIHdsol9s9t+0C/gNevBx1r74F9AESkj4jcKyJJSvlNRFKLyOPh9uNWQETOisiZeP6dFZEzHtiPEJEWIjJAREo5++qJyDfARLft/x1e3ZMsiSMi+T2wUS3gdcGgY03ctp8QVqXnBkRENqtqueDX8W276MMdwFOAb/CxC3hbVXe7bduxn+iXRlXnu2z/ob+x/5Wb9h0f+iV2XFXHe+BD2K/FAHt3Ay2BpsCvwHxVDQ6CQ23zKrAT+MO3K+Cwqmq16z8VUvtbVLVs8Ov4tl32YxxQCXM/2AZ8A6wD1qvqKS98CPAlAqgFtAAeAdao6mMe2I3EXH+B98SZqnrSbdtJwX64EZFpmMaY3wL3Ab8B9wODVPWTMPl0Nx7fkxy7DwM9gTucXbuAiaq62gPbYX8uOX7cD+QBvlbV4yJSGhgEVFHV2xP/9P9sO8k8FwNJUrMxln+MJPA6vu3QGzdfpPnAWxhdWQHKAqtEpImqbnDbB6B+IsfU8c81vAjo/wEZEjnm1Ug+3NdiMUwDvBbASWA2ZiLjYbdtO/QHooCLwCxggaqe88g2xP07B//NPZvNUdUBAE5DwvKY4L898LaI/KmqJdz2QUQexARXdTFBX2WgoKpe8MD2ncBKTI+WLZhr/15giIhUU9Wfbmb7ifiVDmgEtFTVui6bKw+UVtWrIpIaOAEUUdWjLtuNQ7jvSSJSF7OiMRJ4DnMtlAOmikgPVV3qsgvjgK3AMuASHjwHghGRl4F6jh8DRWQJ0A14AW9WPsP6XEwIG/DfmIT7IT8caBE0W/CJiKwE/gM86rYDqtouoWMiktNt+yKynUTOtaqWdtsHVX0uoWMicq/b9n1uJPA6vm03+AlYA9RX1b0AItLXA7sAqOp/gf86y7YtgC9F5DfgBVXd6oELxUVkG+YhUth5jbNdyAP7waQBMgKZnH+Hge1uGxWRg8ABYDLwtKqeFZFfvQj2HUYBvVV1TpBfUcBozKDwZrYfaDMlUAcz+KoNzAPe9MD0ZVW9CqCq0SKyx+tg3yGs9yTgaaCRqv4QsG+riHyPSftzO+Avhxnw1AU2ATOBL9XbdJK6QFnnOsiCuQ+VVtWfPbIf7udivNiUnhsQETmOmU0UoJnzGme7qaq6GvA6N9JiCRzbrap3xHfMZZ8yYR5qLYE7VTWPy/Z8eYACfIp5wPlR1d/ctJ+ATyW4NrP0l6qW98DmBWAvTsDpvMbZLqSq6Vy23xjzO1cClmO+C++oasFEP+iOLyUdX1oDzwQHXy7ZTDQf1avrUESmACWBs8BGYAOwQVVPe2T/VcxM8nbgI2AhsF1VPRn0JHbf8+KeGG77jp2aXEujWoWZ2X5dVQu4bdux77sXQdz7kWDS61yfhHH8COs9SUR+UtXi//aYS75UwlwTNYCBqupJfZuIbFLVewK2t6rq3V7Yduz9CXyNufaqOK9xth9Q1Sxe+RKIneG/MXk64PX3QceCt93gbCLHzntgHwARSQM0wAT55TApLo249uVyjcBASkQuhSPAd2znx9xQWwAxQH6gvKru98iFOz2yEy+qugBYEJA60BfIKSKTMek1K9y0LyKFMA/3hsDvmIf7aFWNdtOuj3Bdd/GQD0gF/AwcAg4Cf3plXFV7i0gf4GHMd+FlIKOINAWWepBmldh9z4t7Yrjtg0knWoMJaH4F/0DMK8J6L/IR7nsSSeNaQESyY1J978LcD457ZRuz2hk4uCgQuK2qDVy23zDg9bigY8HbnmFn+G8yRCS/20FAwArDdYfwYIXB8eFD4EFghePLSmBvmGZ2w1KE46hPZML8/rNU9WcnjcHzcxAuRGSaqj4ZtC8r8DjQzIOi2auYItWFwBmClmvdLlATkV+DbErAtqpqYTftB/kimFn+Ss6/UsApTOHuf7zyw/ElBSa1sDlQS1WzuWzvIBDf31qAPh4UCYbVvuNDWcz5fgz4BXNfGq6qrquiJHUC7knN3c7lD5hdvu4QHswui0g7TOZBamAuMEdVvQz2k4SoRkKISGVVXRcW2zbgvzEJcwV628SOq+p0N+07PvyAuYG9D8xW1d9F5BcPl/ADA/wPMasM/mIcVd3sgQ8LMTMoi4CPVPUbL8+B40NYA85wKh449keQcE6mqupIl+1HBu1KhlEEGQBsVlXPcrcDfMqLKZithCmci1TVzB7ZzgwUdTb3qOpfIpJGVS+6bDfRAU1i9TY3g/1gxEj0tsCkWW7FzGxPcdnmWa7PnT6BSS8aqLeOWlFYg11nEmQ7pqYGrp8EcXt2PUFE5HbMoOtll+1EYO7DeYDlqrpDROoBQ4A06pF62nV+2YD/xiOoAr0IEFiB/pZX6QTx+JUaU6j0sUf2imMC7WaY5cLiwF1eFGqJyKpEDqvbM8sBfvhqF1pgroXMwCOq+q1H9sMacIrIT5jfPV7lAy8GXgkhIveq6nce2UqGqR14GnNfeEFVf/TCtmO/FybArwxcwZHkdP7f7iumdNF+SoxiWCOM/KFg0tsWAF1U9bKb9v/GN8+ug6Rm37kua2KCrASFFly0nwV4Eqikqp70YwgaePjuS4pJoU6pqmFJpfYw2E1Ss+sikg2zutICE4AvUEdVzEWb00hiErFgA/4bEhH5ESgXxgr0QF/Conkdjx/lMcH/Y8BBVa3ktQ8BvlRUb6RJg+3mxAx+mgO3e7GMH2A7LAGn83D9jvgDfs8GXgH+eFo47aSutMfkCa8FxqjqPjdtJuDHeBztfVU9Egb7IzFFml1U9ayzLwMwCfhNVZ/12B/PC+iTgn0xjddiVVWdAPM+YJ+qbvHCfiJ+hW0l0LkOuwGdMcFmfw9tex7sJuKLJwMOx1YGoDEmJiiGGfg3U9W8btt27O8gCUjEXueXDfhvPMJdge7YjE/zupB6J4OXkF8CPBjmHL0DqpovXPYdH1yv5XDshDXgFA+bSyXiQ9gKp53c7RhgAteW0P2oyw3o4vHnYUwevwI/qmpiK2GhtLsDqBB8/xGR9Bi1oFIe+BDWAvokYL8TMBY4h5EJfRrYjEk7nKqqY73wIx6/UgCb1COVngC7mYE+QBuMctR/vUgrCnewG+RLWAYcInIRE5cMA9Y6A1AvU36TTLOtQKxKz41JYAW64HEFuoRf89qXPtQMOA0sBp7ByF/twzxswoknjTVE5D0SyR8HOnjgxq/EDTjLiEgZvxMeB5yBeJHKEFQ4/ZheK5ze76bdAL7A/K3LOP8Ccb0BnQ8RyY0JLKIx2tsCNBWRsUBjVT3ksgtX47v/qOo5EXF9Vivc10G47Tv0wayyZMB0ds2vqidEJC1mFc7VgF/i776eBfOcmOum7SA/smEa8jUDpmL04P/yyj4mvTU42G3slfEEBhyFPB5wDMGscE0GPhKR2R7ahmv9UYA4PVI8lYgNxgb8NyYNg7a9lnmah8mVbQbEOsWjXi8VvY/JFU6HubnuwHQXfACYhqlxCBdenYsl8ezLh3nwRnjkQ7gDzoGBG8GpDJjum27yB5AXyAlkx8hSetnh9smEjokHDegCmARMVtVpQT60Ad7g+ntWqFEnvTG+wbar9QMOYb0OkoB9MI2vTgOnRWSvqp4AUNULIuJFDUVw93XFdLp9VVU/9cC+j98wf4/3gAtAB7Pw7DjlsnIX4Q92wzrggDgNEQthngWfALlFZCBmlWGPyy4kCYnYYGxKzw2MM8tdBHNj2+dlsa6TOuPTvK6D6a7ZAW80rxGRHapayskZPaiqtwUc+0FVg4PPUNtfTPwPVAGqqcsNp+LxpxDmRv8g8F/g3XAWKnpJEkhlCGvhdAK+eNKALsBuuBtP7ccE9gnVcri+lB/u6yAJ2PcV0CcDPuCacpkAH6iqJ0GQiGTzDTbCgSQRxaSAYLc5RrnqP3gQ7IrpKtwcMxn3EaYB2+depdMk4tddmPPRTD2SKxbTgd2X4rhLVX/xwm6C/tiA/8bDCXJfwORO/4a5webFzCgMVdUrHvvjqea1Y9OfExeOfLmkokQgIncCQzF5si9jHqwxXth27PcL2uWTwlurTvMdl+2vwwQ2SaIXgYjkwHwPPCuclkQa0KnL6jgBPuxV1SLx7E+Gkce87tjNTDiug3DbF5HVJLKqoO7rz9fDPAOvYAZ/TVX1GzdtJuBHD1Wd6LXdxHCC3ZaYc+JVsBuWAYdj+ySm2/c3GKWwbz1OOc4IvINZYd6KGfSWwaQ7dlDVM175EscvG/DfeIjIfzEP9b56TZEiIya156Kq9g6jb65rXjt2fM2/BJNa5GsE5knzLxHJmNCXVkTyqep1BZQu+PAx5oYyDpgDxAYeV9VTHvgQ32xWVoxi0whVja9BWyjth70XQQJ+ZQG6q+rzLttJEg3onHtSekyTp/POvnSY1aZoVe3lsv1EB/gaXnnW/BrGjsjhtu8VTo50U1X9SUTuA15S1UQnZlzyI0kUaAbi1BWc1DAFfF4POJx4qCLXmgDeg2kG51MSm+Oy/WnAfmCkb9LFyYp4FqPW08ZN+wn6ZQP+Gw8R+RkoFvzlFSOR+ZOqFo3/kyGzXxQzq3wK093xba4VzHZQ1e/dtO/4ENbmX0ErDF+qavX4jrnsw34Cmlz5dvu2wxn0iuks+YVH5yFsqQxipOaeBXJj8kQ/whSNtwZmuj34ljA3oAvwIwUwBqN57gsu8wHTgSFup5dJmPtiOAFVd4yIwFTMapvvnthfVffezPYdH+IrmvWjLhfwh2Ol95/4EQb7FYEXMc/nUcAMIBsmE6CNqi4Pl2/hwpl8aIepbyuoqq7WuInIzwnFYYkdcxsb8N+AiMgeVS32b4+F0P5aTICRESPH2AejlFMFeF5V73PTflJAAuQgJUgaMnj7ViUc58FJZWiGCf5dT2VwAs2vME2magPVgZ2Y1TdPNJcljA3o4vElDWbQJZiVhrDK9HqFiKwAvsesvFbHpJb47omtVLXqzWzf8eEqJn1hq29XwGFV1fYu2z+ImYDy0S9wW90vlvX5EYMp1r3ukHFDM7ps/3tMPVcmTDO6R1V1g3OfmOn2PVnC3H3d8SE312b373V2b8Kk+ax3e8UroRRH55gN+C3/HBH5BJivqu8H7X8Cs2TmtiynX/c/+MIWj3oCSIAMaXx4cA7CWkPg2ElU69+LtKKEEJFqwDAPZlZrqurnCRybo6pNXbYfp0BcRI4B+VT1kpt2E/EnLA3oxPTlSBBV/doDH3JgZrn9fQCASap63APbP6hqGWfZ/jcN6MPhxT0x3PYdO40xg84iwEJMcOn6ykKA/aRSLBvWCZ+g5/MuDSiW9sI3CXP3dceHq5geEP8FPnZ7hTEe+9NxJMIDMzFE5FlMdkZrL/3xYWU5b0y6A/NFpD1m1KqYUWwajP6t2wQWAgbnsXtSJIhpU/07MBPYCN5o3weQwylYlYDXONvZPfLhU8zfPs5MmmM/Bx5Ic4rIdq4v1MuK6f7sRZ7iJBHpqwGye06h6LuYFSjXkbhykEeBtM4Ssid1FIE46XTfi0h/TG6/VzwdnzuYQrW8uHwtikhlTDrVNMzqo2AKmL8VkVaqus5N+zj1M6qqIhKsEOPFPTHc9lHVBcAC59pvCLziBH9D1QMRA68C+v8FEUmh7otqBP69g+vpXJ/hVae5mFzffb2uetB93aEyJkZoDPRz0l/XO/++92BCpifmGbRXRLZizntZYAve9MeJFzvDfwPjzKKWxDzcdqrqlyISparzXLZ7Adjr2C3svMbZLqQeSFI69Qo1MakbpTHB70xV3em2bcd+kphNCkRECmB06WsAr6nq6x7YzB+0SzHFYefdtu3YLwAsx+SJzxcjVTsXMxBt6/bDVcIsBykir5O4MoqrxbIJISIPYOp8sgCjVXWxy/Y2AF1VdUvQ/ruBt9xOMxSRP4GvMddBFec1zvYDqprlZrYf5EsEJr2tOVAKGKSqn3lg97XEjnv1XRCRIar6QsC2T8K6JVBf3ReUiAXOY/72abiWXiRAalVN4bL9sHZfT8CnApg+Db2BvKqa2mV7+VT1gIgUBkpwLUYL73mwAf/NhYgcCFzOdclGcJAXB7fz44IRkVSYwP9lTFW864FuUkKuFVHfB7wCTPdgFslnOzXQBbOMvx2j/++ZLKjjQ17gM+B1zIzSRlUNlgu9KZG4xevPYaTv/KjLxevx+FMdU8SswAsJpVu5YPdHVS3xb4+F0H5YZXrDbd/xwdeXpQKmId8s9UDAIcB+WIUcghGjFNQSM8ucFbMyv0hNc7KbFqeWIrD7ehzUo+7rTs2CL4+/MmbyYT1GpcfVZqXhLtxOCBvw32SIyO9uFyomYrsy0FJVu3tkLxVQF/OQKYCRZpyqqoc8sv8w0ANTJAmmnfxEVV3tkf1SmEC/JPASZoUjNvFPhdyH2Rjd6zWYXgy/qYeysHJNjjEXJpXjc8y5ANyXY3RmM9Oo02xOjEJGSufwFnVkc70gnLnDIlIXcy3+hSncdzuFJtj+LqBScDAlRi3qG1UtHv8nXffrdqC5qr58s9t38qa3YWZ1laCVpzCuNqXGzKx/7JG90Zic9QOYlNMFmDSSsPQGcXxKh+nN0VJV67ps671EDqu6XLzt+HACOIKR4fRJcXpZTxLWOo6EsDn8Nx+ejuCcJfOWmBvcr4BXo/fpmOXiZcBzqrrDC7sB9usCE4GRzj9fzvBUMY1Xlnrgxg+YOoZPMbNqFSRuC3cvHrAlVPUuABF5F9NS3UteCXi9DcgZsE8BV4uGgbEYZRzfIGMmsANIjSkaG+iy/UDCOXuzGDgInAQGBl6H4H4RPaY4b4WIDMCcdzDa22OdY54hRiLzccxERB5MwHcr2G9PeK9BP85AvBbmHDyCmZDwJOAHngJ2A5OBJaoaLSKenxcRSQnUwTyfawPzgDc9ML3Yq1n8RCisqn+F0X6exFLMwjX4tQH/DUgChZJggk5X8wMd+8Uw+ZktMA/42ZjVIlc7KQbRGpOnWAzoFRBgeCJ9hilEaqSqPwTs2ypGEu11wIuAvwPhf8D6U4dUNSY40HObxK45Z7bdbapzTfYN4E9Vre/k7a7xwH5Swcvv/nWo6hQROYzRHS/p7N6JWW1wtX4AQEQyYFI3WmLuSQsw9Ux53badFOwDqOo0r2wlhBi1qJaYld9vMakcBdVbedjbuDbYmCBGujeNiCT3It1RRHy1bY8AqzA6/BVUtZ3bth2G4dHEXyI0FZHVajqvC6Y3RRSmGdaTbq/8YoqlN7ls419jU3puQMKdQ+8s3a7BNNna6+wLe3dTLxGRnxJKE0jsmFd4+HDxFYhB3CIxrwZeifnmRT1LsCxnLVVd4bz2Qo7xPNc6LKclboGeZ+dfEpdHHauqXq50eI6IXMQEmMOAtY5ajmf3xHDbd3xYTNwJCAVOAKtU9QMP7B/EpNFMBj5R1bMi8muYU2lSA/UwAfgDwJeq2tJlm77n85Oq+quzz8trMez56yKyAyirqldEpCXQHzMIKwv8R1WruGw/7OcgPuwM/w1IQgG9L4ceUxzkJlGYGf5VIrIcmEX8KiWu4eTmBqKY2VWvRrCJqdB4pVCzVlUfcF7P0Ljavt9iUoxcRV3uWPg/4sU1mVJEMvhy9QOC/UyYtB632ZNEckUTkkedipnxdBURGZ7IYVXVUS67MARzT5wMfOTUtnhJuO0DxFcImRV4QkRKqeogl+3Pw+SpNwNiRWQhYVgBDRIy2IapK5srIhnxRjb7Hsy18IWI/IJ5Pnt5ny4uItvi2e+bhCjtgQ8xAcIV9YD31ciFfiEiLyXyuVARr+6/13WO19m3M/w3NvHl0HulUhNQCNQCkys9HVjgC3pctu3r5hcY1GXA6P12VNX9Ltv/k2vSd3EO4ZEMnsTt9hvc/CtJFg15iUcz/P0wMqhd1Gl05qzATcbM5r2S2OdDYD9JzCTJ9fKoaTA5017Jo/aPZ3c6TNpbpKqmd9N+gB+FMPfD5kBRjGrSAlXdcyvYT8CnCGCT26tdji2fBGYLTP56Jsw18KmvsN4DH4KFDParah8vbMfjS2XMuYjCPBsXqOoUl23uxJz7eHE7A8HxYTMmres08BtQTR3JbglqRuaBL3cTphjtOl9swH/jkUAO/QBVTTTVx2WfsmK6e7ZUD9q4J+JHE+ApVa3tsp2kIIMX9m6/4SaeNAL/IcxN3oueEF0wM6w+W+eAF1V1sge2DwLjEzquqgkec8GXJCGP6uSz98YEenOAV9SDbrvx+HEXzoNeVQvfavaDfPGk22+QzRSYYtUWQE1V9aQhoohsDxAySA58G+57sbPaVhOj2ORqLn9SmGwSkXrAW5iVjcWq2snZ/xDwjLqvVJTkYjSwAf8NSVLIoXdmbbKo6glnOyXwJNAvCeSv3yrB7i+Y3MRkmB4EA3yHgJfC/ZD3gqQw8ArwJT3mnuqlFOcRzGpCvOlL6lEDOAmzPKrjQ1agH9AKs9r4qt7kmufBOEG+XyZYPVQviyfNEoz2eRugiKq2ctn+Wa4f/Pu+F1eAPZiuv1+67EfYJ1/EdDhuSVzJ6JlOWovbtieqao+gfYVxVp5UtZTbPjg2kwMZAu8BTlaCuL3akxRitHj9sgH/jYeINMaMHithltFnAe94VZwkIs0xo+fzwM/ACIwSwHfAKC8e7on4lh5TtHZ3uHzwCklc7xi3Z3KSEk7ebBHMA3+fqkZ7ZDd4BttXqLjWVzDnsv0kMbgVo0SSEKqqrsqjisjLQBNgCjDJq/SNAPvxBZvgUfG0UzOyELgdkzcuwF2YdIZGqnrGTfuOD8FploqZ3VyFUUty3YdEfIvAyDh/6HbAGW4hAxG5E1iJWW3b4tgti5nhf1hVd7tpP8CPXJh6ipZAaWAMJp1luwe2n1HVl5zXj2tADwYReUFVh7hsP6wxWoJ+2YD/xiVcOfROBXwjVd3rzOytx4zcPdObjifQAjOb1ADT/Optr3yxhA9nFucFjAb4b5jVjrzAe5jZPLdzx/8Tz+6sGEm8Eao6y2X7YV8+Two4M2qXMB0+Ax9qYVeL8gIxmt+XMekKV519yYAXMY3heobTv6SCiHRW1bfC7YebiMhcYI6qzgnaH4VJuY1y2X4nTEySF5NSNwdY6GWwm1TSXcNZ5xivPzbgvzlwllMfB5p5MJsW/AXyXIYynkDLN5v0tUczCHcDP2gYv0AJDHr8eJm/HS5E5L+YYu2+vlQaRw1jHHBRPez6G+RXVuALtx8sIpJVVU+5aeMf+vEE5nkyI2h/J+C8qn4UHs+8QUQ6A9PjW1kSkd6q+qrL9n8ESmuQFK8zIN7uRZGiUz+VIBr+Zky3BCKyW1Xv+LfHQmj/MmYSsL+qfu/s8zrl2D8REjwpEq5JEi9jtISwspw3ASKSB1Ocsgwjg+c2OYKCzfSB214Eml7lJifCO0BBRw1gHaZ99waPl60zBLzujEmzutWoBxQLHHip6hkR6Qr8hCne9BxVPeUohrhux20b/5D+wIPx7J+NSem4qQN+YALQW0RaqurWoGNtAVcDfuBycLAP/mZ4l1y27WMuRglmq7MdeP0r4W/GdKsQbsno3JjAdryI5MTM8KfwwG4gwf0gEjrmCSKSFiiESWkK23PaBvw3ICIyGEihqiOdXeuBvzBfqmmYZVw3eZu4wWbwtuuIyKLEjqtqAzftq2p550tcAZOn1wuYISJHgXWq2s1N+44P/kGPiDRKAoOgcKDxrbKoaqyEoZ29DxGphpGEu1WIiK9Y2Rl8ef2wDwc/Ac8AnzhFi4Ga9F70g0gtImXjsSVAKg/sg5F+bIbJ116IKRLd65FtyzWCJ+R8COC6UpEj5DEZmOwodzUHjovILkw6i6v58w5lROQMTg2F8xpn2/X+KCLSAHgNOIVphjcJOAYUEJGBqjrdbR/i9cum9Nx4OLPKVVT1vLO9RVXLOoVJX6nTjOlmRkT+AH4HZgIbCXrQqbfqLOmAipg27m2AZF5X4yeV4k2vEZFPMLMm7wftfwIjR+jqwE9EtnP9jFFW4DBGf36Xm/aTCs7DvLzvnhSwPwPwndcpf17j+/6JSDbM6l8G4AlVPeLFd1NEVpPIzKWqPuym/SBf0gENMcF/JKaWxrP78a1OAnVFfryYGBKRRhgRhe2q+pmz7w5Mrd9NPzElIj9gVjkyYVY4S6vqLyKSA9Of5a5w+GVn+G9Qgh6srzr7YsU0vHEVEZmjqk2d12NVdWDAsRWqWsttHzDdO2tiimFaAp9iZpR2emAbMe26KwF3Y4oFv8MMPB5Q1aNe+GABTFfp+SLSHtiECXruxShjeNHVsl7QtgIngwPfW4B3gbki0lWdpndimnFNco7dEjizm43E9GbYKCJ9PbJb1Qs7/5BozIrzGSAf3nSctjiEO6AWkTeAkpg011EiUkFVRznqQDd9sO9wVZ1mdyLyq6r+AqCqx0XkutQ7r7AB/41JehFJ4VMgUdVpACKSCvBCjaJowOuawMCAbU+am6hqLEbuarnze7cAVovISPWmi90UzDL+m5hCYc87WQbNLheRoHbm6k0L83Ajqnqfk0JTErPSs0xVvxSR+sAhN41rAl0jRSQz0F1VR7tpP6mgquNE5BzwlRhpXPCwAVkSIHiF8U1n1v1DzHXprvEwyxA6dnwdbisAX2D6IHzvtl1LXBzFpgRR1V4uu/AgUMaZgEyL0aMf5bLNOMg1mdzgOpLkQEpVdTv2TSYiWTCqcVed1z5fkrlsO0FsSs8NiIi8gJnh7qGqF5x96YCJwFFVHeyy/aQieZUK0z67BVAAWARMVVVXgzzHdgRQBjPLXwm4AziCqadYr6orPfChKJATk9oUSH7g8K2QPysiu4FHfLPKAfvbAcPU5eZjInI78CymUO0TTHHqKExq10fhUgkKJxKGBmThxpnF/Dae/SmAKHVfnjXs92Qx0qjbgLWY4CpOcOFBoGkBRKRtYsfdzh8PZ0yQiE8ZgG4YcYsFqtrfZXv7gavEX7+jXqf8+rAz/DcmzwKjgQMi8hvmorods3T+rAf20zoFYskwBTG+YjFfkxHXEZHpmEYqy4Dn1MOOkuBfYdjs/JvoqBE8BvQFRmJUk9zmv8CQ4FlmEcnuHKvvgQ/hpi/wuYjUUdWfwV/U3hJItAtviHgf+AqYB9QGNgA7gbtupdSueIoEVUQ8a0AWbhII9v3dRTGNd9xEEngd37Zb3DKN/pIyiQX0IpLfAxeKB6w2C1DY2fb1xPBs5dlZae2DMwED3KsedBtW1QJu2/j/YGf4b2CcfP0izuZeVb3okd3Eump6UiDmzCb58qQ9b7QjIqW5NrtfCUiJmd3/BqPS4/pStojs0AS6RorI9nAVBnmNiFTHSJI2AjpicvjraUBLdRdt/6CqZQK2jwH5VNUrKcQkQQKFgp41IEsqSJi6iyaFGX5L0kFE7gfyYNJNjzvPq0EYsY/bXbad6KAioTTIEPuQDSMV3AwjVf66qv7ltt0A+4l+31R1s1e+BGID/hsQEYlP79qPqn7tlS+3KhJXf/8bL25i8fiwV1WL/NtjNyMi8gAmpeYbjDrPdQ2QXLL7A1CVa7OoqwK3Neno5IcF8agBWbiRMHcXFZFYzASIb5X1gu8QkFpVbwVpVAsgIi9jxAS2YiYEl2DSWV4A3vLq3hiPX5UxnX67e2DrPPAHpuN6fHLBrvYKciYkdzo+QFAtgdrGW5Z/wdPx7FNMTnlePEgnEZFIzCyWT25vFyZn+VYJcB5OaMZARPKp6gEPfPhORDqp6ttB9jtgFGtueoKKs1IB1TGaz56s9GBk1zYR94bum71RTLOVWxb1qAFZEmASZoWvpV7rLurZbJqqepFCaLkxqAuUVdVop1j0MEYW8mevHRHTkb4l0BT4Fe+ar73MtZX/4B5BXnwv+2P6UlzEpPMtUNVzHthNFDvDfxPgzG4OBbIAo1V1scv27gRWAp8BWzDBTlmMYk81Vf3JTftJgaAl9C9VtXp8x1z2ISewALjMtQC/PCa9qPGtlENuSZo46knDwjWj5RVOCsHjmFl+X3fRJ91OnwiwnzWx47fQRMwtj4hsUtV7Ara3qurdHtovhqlbaQGcxHTbHqCqXtQP/C0icq+qfueRrYKY89AQ+A14Qa/vxO0ZNuC/gXFyl5/FjFhfUNXPPbI7F5ijqnOC9kdhZriivPAjnIjT7Cz4dXzbHvjyMKaAGWCnFwpBFkM8uZoKnFDVYOWkmxpJvAFZm1thEsCHXOsu2gJIiwfdRZ0UgoOAT+M7OIXA9ZUmEXmdxJt/WZUeDxCRP4HAtN4HA7fV/WaEVzFSnB18SnEi8ku4lGkc+yW49p38S1XLe2i7pGO7NfBMcNzkJTal5wZEROpiZvT/wnQxXOexC3ep6mPBO1V1niMZeiugCbyOb9tdR1RXYXLHLd7zSjz7sopISqBFOGdzPCbBBmQi0gfTs+KWQFUPAuOAcc5sZwsPzL6OqR1Zh+k+vla9n82zmvtJg4ZB2/Hdo9wkChPgrhKR5ZiUFs/T+pzi4RbOvxiMXHX5YAlnl2wXwpyDhhjZ7FmY7Iuw1E/4/bIz/DceAbM5PxBPcOnBCD7BlJVbRRFCRA4C4zE3sr7Oa5ztPl4t5VuSJiJSHhivqokW2N8KiMgBVc0Xbj/cRESaJHZcVV3PXXZqJapyrfnVCmCyV7KoIvIYsCTcQY0lfpyeIc1V9WWP7KXDKKe1AKoB0zGrXSs8sP0Npr5qFjBLVX8W0/HWqyJ6X0+KhZiO08E9KVwtGk4IO8N/Y+K67OXfkCMe3W0wwa4nnXaTAG9zrRgo8DXAO967Y0lKqOr3cq3j7K3OrVC0Wz/odWAdleJBsaIzo79KRLZgZhdHAT9j7k9e0Ap4w5nVnQmsUNOvxBImgmpL8mBqvjxBVc9jOk1/6NSYPI6RBnU94Meo4+TF1NNkx3wPvJzdHhlgL8k8B+wM/02EVyP4BDS3/ajqc27at1iSOk5B9dLA4rlblVthhj8Qr2t4HJvpMOkDzTABznxgtte1JCKSEWiMGXCUwcxwzrRS0d7hdJVtjFHHKYYJ8pupal6P7CeJAnIRyYRJL2qBkSfNjOnKfl2TvFsFG/Df4MQ3glfVAS7brKiqG9y0kdQRkeGJHFZVHeWZM5awkUChYlZMM7bebitmJRUC5FGvOwSkUdVbZjU5HGmNju74z5iZ9b1cn0LglRxioE+RmO7j3YCsNs3RG0TkIvAtMAynlsPLolkR+ZVrUsm5MIX7vlU+TwrI4/EpB2YQ2hy43e1rUUReS+x4uArYb5mb8M1EAiP4Ql6N4IHJIvItpuLcs+51SYzz8exLB3QAIjHL6Zabn+BCxasYKbq+qvpHPO+/KVHVYK1ri7d8jAmyinOtN4oPT1KKAnH035tgVhyyAvO8tH+LMwQT2E4GPhKR2V4aD8yTD8dqV3yo6nHgNeC1v+sEHCKSZB8cO8N/A5IERvDJgF6YmZtRqjrDC7tJFWcA1hsT7M8BXnFuMJabHBFpBORR1UnO9reYlArFDIjnhtE9i0eIyGKuzaoHyiD6GsC5KqSQFHDug40wq83lgEWYoslVYVAMuuVxlGJaYIL/osB/MBkAezz0ISwiHk7mQ3fgNDAV04irCrAP6O+TC/XIl/SYe0B8k4SeYgP+GxAR6Yv5EqcDPsI0tvjc66UyR9t2PZCMa0t4qu53N00SOLmK/TDFatOBV1X1dHi9sniJiKzD1M387mxvxXT7TQe8pwEN2Sw3LyLyUDy7fQ9XUdWvXLYfn4jCNUc8UAURkROYZoyzgOWqeiXgWE5VPea2D5b4O72LyF2Y4L+Zqhb20JdwBfwrMKuvGTD34/cwhfRVgFaqWtUDH7oCgzHPAoBzwFhVfcNt2wlhU3puQFT1v8B/A0bwnwC5RWQgHo3gRaQDpuJ+KDDpVpvBEZGXMUvWUzB9CcLeNtsSFlIGFUauVdWTwEmnkNJya5AZyJvASs9AD+wHplR1Bt7ywGYw+VT1gm8joGiyJXAnpsbM4j6fYFZYEJF5qhqlqtuB7Zh0H1cJGnxep+jnkSRlTlUd4kjV/hYgZPKTiHR327iIDMPUcVVV1V+cfYWAV0Ukq6o+77YP8fp1i8VpNy3OCL4l0NTtEbyjcbsf6KeqR920lVRxdHYvYRp6BH6JbqlVjlsdEdmrqkUSOLbPy9k0S/hISis94cybFpE0QAPMs6gcZiDSCPhaVa+Gw6dbjcS6wHtkPzEVP1XVkR744F9ZCF5l8GLVQUR2A2WCe1I4348fVLWYm/YTws7w3zwcAYao6mAPbP1HVT/3wE6SRVWThdsHS5Jgo4h0UtU4Wuci0hlTZ2O5NUhKKz1hmcUTkQ8x9QsrgInASmCvqq4Ohz+3MIl1gfeCd9V0m74OEakf334XKCQiizATcL7XONueNN8KDvadfRedycKwYAP+GxARqQi8CJzCqMHMALIByUSkjaoud9mFBol9ccMlOWWxhIG+wCci0hLY7Oy7B0iFmdm03BpkCdxQ1R4Bm7dKM8JSmCLJXcBPqhorIjaFwHvKiMgZHElc5zV4t/r8pYg8oqr7A3eKSDuM0IgXUsUNA16PCzoWvO0GB0Wkuqp+GbhTRKpjJmfDgg34b0wmYnLxMmFmUR5V1Q0iUhyjw+x2wB8oRfgcpvrfYrnlcNSYKolINaCks/tTVV0ZRrcs3hPWlR4R2c612dwiIrLNdwi4qqpl3PZBVcs4z6CWwBcichzIICK33aqpn+FAVSPC7EJf4HMRqaOqPwOIyGDMdRFfcXvIcbtI/h/QC1goImsxEp0K3AtUJu5gxFNsDv8NiIhsVdW7nde7VPXOgGOe5uwlFZ1di8ViCRdOY59PMHU91630uK1Qk4C2uAB5Mameddy0Hx8iUh4T5D0GHFTVSl77YAkPzkz2W5hVzo6YYLeeVyp2QQNgnNcngFXAuPjSbUJsvwhwG6ZPUknMd3EnpjneIVXd56b9BP2yAf+NR7gLUhLyxWKxWG5lglZ6doZjpUdE7sYRcAB+Beap6kSv/QjwR4AHk8Csq8VDROQBzCD4G4yYiKtBdpDt+AbAWYG2QDpV7eSy/SWYgfa2oP3lMTWQXtUyxPXLBvw3HiISi+n0KkAawCeFJkBqVU3hoS824LdYLJYwIiLFML1ZWmA6Pc8GBqiqF11FfT4MT+y4F+oslvAjIme51pcnFXAFiCWJKNh5kZUgIjtUtVQCx7ar6l1u2k8Im8N/AxLuHL2ALzRA2jAUBVksFovlGj8Ba4D66nQRdRo0ekl8nUTTYTqQRwI24L8FUNUMf/+usOKFwl7qRI6l8cB+vNiA3/KvuQG+0BaLxXIrEYWZ4V8lIssx3W7FSwdU9RXfaxHJAPQG2jm+vJLQ5yw3P440bSOgparW9cBefFkHWYAngK/dtg98l0ARfwdMEW9YsCk9FovFYrHcBAQEVi2AasB0TPf1FR7Zzwr0A1o5tl/1qlDTkrQQkZRAHUw9SW1gHjBfVV2X5RSRVUG7FJPqthqYoqpXXLafE1gAXOZagF8eSAk0DpdqlQ34LRaLxWK5yXCC78eBZqpazQN7LwNNgCnAJFU957ZNS9JDRGpiBpyPYFRxZgOvq2qBcPoVDkTkYUx/CghTEX8cf2zAb7FYLBaL5X/B6SB6CYghriSire26hXCugzXAk6r6q7PvF1Ut5KEP/RI7rqrjvfIlKWFz+C0Wi8VisfxPqKoXxZCWpM89mHqSL0TkF0wNh9dCI4F1hp0xPQFueewMv8VisVgsFoslpIhIZUx6TxSwFVNPMsVjH2xzUAc7IrdYLBaLxWKxhBRVXaeqPYA8wH+B+8PhRhhsJklsSo/FYrFYLBaL5X9GRNICV3xKOCJyB0at5zdVbRdW525xbEqPxWKxWCwWi+V/RkS+Bjqo6s8iUgT4FvgQKAF8q6qDPfBhO9dm9osAe32HMAXkpd32ISliA36LxWKxWCwWy/+MiGxX1buc16OArKra3dHl3+Q75rIP+RM7rqq/ue1DUsSm9FgsFovFYrFYQkHgLHI14GUAVb3sSHa670A8Ab2IZANO6i08y22Ldi0Wi8VisVgsoWCbiIwTkb6YdJoVACKS2SsHRKSiiKwWkfkiUlZEdgA7gGMiUtsrP5IaNuC3WCwWi8VisYSCTsAJoABQS1UvOPtLAOM88mEi8AIwE1gJdFTV24AHgTEe+ZDksDn8FovFYrFYLJabAhHZqqp3O693qeqdAcduWV1+m8NvsVgsFovFYvmfCVLIuQ6PFHICawUuBrvggf0kiQ34LRaLxWKxWCyh4AAmneYQ4Quuy4jIGYwMZxrnNc526jD5FHZswG+xWCwWi8ViCQUrMLn6uYDZwExV3eqlA6oa4aW9GwWbw2+xWCwWi8ViCRmOFn5z519qTAHtLFXdE1bHbmFswG+xWCwWi8VicQURKQtMBUrb2ffwYWU5LRaLxWKxWCwhQ0RSiEh9EfkQWAbsAaLC7NYtjZ3ht1gsFovFYrH8z4hITaAFUBf4FpgFfKKq58PqmMUG/BaLxWKxWCyW/x0RWQV8BMxT1VPh9sdyDRvwWywWi8VisVgsNzE2h99isVgsFovFYrmJsQG/xWKxWCwWi8VyE2MDfovFYrEkCURkiIi8E24/LBaL5WbD5vBbLBaLxVNEpCXQDygOnAW2AqNVdW3AewoAvwIpVDUmDG5aLBbLTYOd4bdYLBaLZ4hIP2AC8AKQE8gHvAE0DKNbFovFclNjZ/gtFovF4gkikgk4BLRT1Y/jOT4CKKKqT4jIAeB2wKffXRdYADykqtud9+cAfgPyqeofHvwKFovFckNiZ/gtFovF4hX3A6kxgfvf8aDzf2ZVTa+qX2Ga+DwR8J4WwBc22LdYLJbEsQG/xWKxWLwiEjjxP+TkTwdaiojv2dUamBESzywWi+UmJnm4HbBYLBbLLcNJIJuIJP//BP2qulFEzgMPicgRoAiwKNROWiwWy82GneG3WCwWi1esB6KBRv/gvQkVmE3HpPW0BuaqanRoXLNYLJabFzvDb7FYLBZPUNW/RGQ4MElEYoAVwBWgBvAwcCHg7X8AV4FCwJ6A/TOAbRg5z9Ze+G2xWCw3OnaG32KxWCyeoarjMRr8wzBB/e9AD+CToPddAEYD60TkTxGp6Ow/CGzGrACs8c5zi8ViuXGxspwWi8ViuaEQkanAYVUdFm5fLBaL5UbApvRYLBaL5YbB6cDbBCgbZlcsFovlhsGm9FgsFovlhkBERgE7gJdV9ddw+2OxWCw3Cjalx2KxWCwWi8ViuYmxM/wWi8VisVgsFstNjA34LRaLxWKxWCyWmxgb8FssFovFYrFYLDcxNuC3WCwWi8VisVhuYmzAb7FYLBaLxWKx3MT8H+OscXSkO0e0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "plt.title('First 20 Cities by Revenue', fontsize=_title_size)\n",
    "\n",
    "palette_colors = sns.color_palette('Set2',len(df_q5['ship_state'].unique()))\n",
    "palette_dict = {state: color for state, color in zip(list(df_q5['ship_state'].unique()), palette_colors)}\n",
    "col_list = [palette_dict.get(state) for state, city in zip(df_q5['ship_state'],df_q5['ship_city'])]\n",
    "\n",
    "ax_q5 = sns.barplot(y = 'Amount', x='ship_city', palette = col_list, data = df_q5)\n",
    "\n",
    "formatter = ticker.StrMethodFormatter(\"{x:,.0f}\")\n",
    "ax_q5.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "ax_q5.yaxis.labelpad = _axis_label_pad\n",
    "\n",
    "for i, bar in enumerate(ax_q5.containers[0].patches):\n",
    "    ax_q5.text(bar.get_x() + bar.get_width()*0.7, bar.get_height(),\n",
    "        '{:,.0f}'.format(bar.get_height()), ha='center', va='bottom', rotation=45)\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"City\", weight = _fweight, fontsize= _axis_label_size)\n",
    "plt.ylabel(\"Amount\", weight = _fweight, fontsize= _axis_label_size)\n",
    "\n",
    "legend_entries = []\n",
    "for elem in df_q5['ship_state'].unique():\n",
    "    legend_entries.append(mpatches.Patch(color=palette_dict.get(elem), label=elem))\n",
    "plt.legend(handles=legend_entries, title='State:', title_fontsize= _legend_title_size, fontsize=_legend_content_size-1)\n",
    "\n",
    "ax_q5.margins(0.02, 0.2)\n",
    "plt.savefig('img\\\\First_20_Cities_by_Revenue.jpg', dpi=300, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2f5847c-cd1b-4e7d-bae2-dc046e236048",
   "metadata": {},
   "source": [
    "## Answer Q6: How many orders are fulfilled by Amazon and how many by the Merchant? [&#129081;](#Top)<a name=\"AQ6\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "cf490dbe-7657-42ba-9805-79a125eea6b7",
   "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>Nr. of Orders</th>\n",
       "      <th>% of Orders</th>\n",
       "      <th>Tot. Sales</th>\n",
       "      <th>% of Tot. Sales</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fulfilment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Amazon</th>\n",
       "      <td>89692</td>\n",
       "      <td>0.695454</td>\n",
       "      <td>54319516.0</td>\n",
       "      <td>0.691176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merchant</th>\n",
       "      <td>39277</td>\n",
       "      <td>0.304546</td>\n",
       "      <td>24270527.3</td>\n",
       "      <td>0.308824</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Nr. of Orders  % of Orders  Tot. Sales  % of Tot. Sales\n",
       "Fulfilment                                                         \n",
       "Amazon              89692     0.695454  54319516.0         0.691176\n",
       "Merchant            39277     0.304546  24270527.3         0.308824"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# How are the order volume and revenues distributed on the Fulfilment Method?\n",
    "df_fulfilment_stats = pd.DataFrame(data= df.groupby(['Fulfilment'])['Order_ID'].count())\n",
    "df_fulfilment_stats = df_fulfilment_stats.rename(columns={'Order_ID': 'Nr. of Orders'})\n",
    "df_fulfilment_stats['% of Orders'] = [ x / (df_fulfilment_stats['Nr. of Orders'].sum()) for x in df_fulfilment_stats['Nr. of Orders'] ]\n",
    "df_fulfilment_stats['Tot. Sales'] = df.groupby(['Fulfilment'])[['Amount']].sum()\n",
    "df_fulfilment_stats['% of Tot. Sales'] = [ x / (df_fulfilment_stats['Tot. Sales'].sum()) for x in df_fulfilment_stats['Tot. Sales'] ]\n",
    "df_fulfilment_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "d75d9c47-311e-47ae-b138-d3ca0e285a3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8, 6))\n",
    "fig_x2 = df['Fulfilment'].value_counts().plot.pie(autopct=\"%1.1f%%\", colors = ('#FF9900','#146EB4'), \n",
    "                                                  textprops={'fontsize': 14})\n",
    "fig_x2.set(ylabel=None)\n",
    "plt.legend(title = 'Fulfilment', title_fontsize= _legend_title_size, fontsize=_legend_content_size, bbox_to_anchor=(1,0.8),\n",
    "                          bbox_transform=plt.gcf().transFigure)\n",
    "plt.title('Fulfilment Orders Overview', fontsize= _title_size);\n",
    "plt.savefig('img\\\\Fulfilment_Orders_Overview.jpg', dpi=300, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3c83c559-a9ea-46bb-88a2-a0486c003428",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shipment_stats = df.query(\"Courier_Status != 'Unknown'\").groupby(['Courier_Status','Fulfilment'])[['Amount','Qty']].sum()\\\n",
    "                                    .sort_values('Amount', ascending=False)\\\n",
    "                                    .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "098077c5-6655-407e-9a3f-60cae8b7291f",
   "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>Courier_Status</th>\n",
       "      <th>Fulfilment</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Qty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>50329299.0</td>\n",
       "      <td>77858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shipped</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>20726730.0</td>\n",
       "      <td>32035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Unshipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>3990217.0</td>\n",
       "      <td>6226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Unshipped</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>353625.0</td>\n",
       "      <td>527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Courier_Status Fulfilment      Amount    Qty\n",
       "0        Shipped     Amazon  50329299.0  77858\n",
       "1        Shipped   Merchant  20726730.0  32035\n",
       "2      Unshipped     Amazon   3990217.0   6226\n",
       "3      Unshipped   Merchant    353625.0    527\n",
       "4      Cancelled     Amazon         0.0      0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shipment_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "7b974bd0-fb93-4012-bf67-98d3be8c3f30",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_shipment_stats['Nr. of Orders'] = df.groupby(['Courier_Status','Fulfilment'])[['Order_ID']].count()\\\n",
    "                                    .sort_values('Order_ID', ascending=False)\\\n",
    "                                    .reset_index()['Order_ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "70caceaa-c329-4c41-a270-b1737c65010b",
   "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>Courier_Status</th>\n",
       "      <th>Fulfilment</th>\n",
       "      <th>Amount</th>\n",
       "      <th>Qty</th>\n",
       "      <th>Nr. of Orders</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Shipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>50329299.0</td>\n",
       "      <td>77858</td>\n",
       "      <td>77603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Shipped</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>20726730.0</td>\n",
       "      <td>32035</td>\n",
       "      <td>31881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Unshipped</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>3990217.0</td>\n",
       "      <td>6226</td>\n",
       "      <td>6872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Unshipped</td>\n",
       "      <td>Merchant</td>\n",
       "      <td>353625.0</td>\n",
       "      <td>527</td>\n",
       "      <td>6157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Cancelled</td>\n",
       "      <td>Amazon</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Courier_Status Fulfilment      Amount    Qty  Nr. of Orders\n",
       "0        Shipped     Amazon  50329299.0  77858          77603\n",
       "1        Shipped   Merchant  20726730.0  32035          31881\n",
       "2      Unshipped     Amazon   3990217.0   6226           6872\n",
       "3      Unshipped   Merchant    353625.0    527           6157\n",
       "4      Cancelled     Amazon         0.0      0           5932"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_shipment_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "163cea7a-bff1-44bf-ac39-68942abf777c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (12, 6))\n",
    "\n",
    "ax6 = sns.countplot(x = 'Courier_Status', hue = 'Fulfilment', palette = ['#146EB4','#FF9900'], data = df.query(\"Courier_Status != 'Unknown'\"));\n",
    "plt.title('Shipping State Overview', fontsize=_title_size);\n",
    "ax6.xaxis.labelpad = _axis_label_pad\n",
    "ax6.yaxis.labelpad = _axis_label_pad\n",
    "\n",
    "for i in ax6.containers:\n",
    "    ax6.bar_label(i,)\n",
    "plt.xlabel(\"Courier Status\", weight = _fweight, fontsize= _axis_label_size)\n",
    "plt.ylabel(\"Nr. of Orders\", weight = _fweight, fontsize= _axis_label_size)\n",
    "plt.legend(title='Fulfilment:', title_fontsize= _legend_title_size, fontsize=_legend_content_size)\n",
    "plt.savefig('img\\\\Shipping_State_Overview.jpg', dpi=300, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d0b484d8-6988-49fe-a46e-03c043ebd8f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_q7 = df.groupby(['ship_city', 'Fulfilment'])[['Amount','Qty']].sum()\\\n",
    "                                    .sort_values('Amount', ascending=False)\\\n",
    "                                    .reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "781c0320-cf21-4bf3-a4e4-dc0f27484e31",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 20 top cities\n",
    "cities = []\n",
    "i=0\n",
    "while len(cities) < 20:\n",
    "    if df_q7['ship_city'][i] not in cities:\n",
    "        cities.append(df_q7['ship_city'][i])\n",
    "    i+=1\n",
    "\n",
    "# Amount by City, grouped by fulfilment \n",
    "fulfil_stat_amz =[]\n",
    "fulfil_stat_merc =[]\n",
    "for city in cities:\n",
    "    fulfil_stat_amz.append(df_q7.query(\"ship_city == '\"+city+\"' and Fulfilment == 'Amazon'\").values[0][2])\n",
    "    fulfil_stat_merc.append(df_q7.query(\"ship_city == '\"+city+\"' and Fulfilment == 'Merchant'\").values[0][2])\n",
    "\n",
    "# Arrays for bar-plot\n",
    "fulfil_stat_amz = np.array(fulfil_stat_amz)\n",
    "fulfil_stat_merc = np.array(fulfil_stat_merc)\n",
    "total= np.add(fulfil_stat_amz,fulfil_stat_merc)\n",
    "\n",
    "fulfil_dict = {\n",
    "        'Amazon': (fulfil_stat_amz, np.array([amz/tot for tot, amz in zip(total, fulfil_stat_amz)])),\n",
    "        'Merchant': (fulfil_stat_merc, np.array([merc/tot for tot, merc in zip(total, fulfil_stat_merc)]))\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "f5571592-9823-429b-9260-4cd306822532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cities = tuple(cities)\n",
    "ax_q7_width = 0.9\n",
    "color_bars = ['#FF9900','#146EB4']\n",
    "color_bar_labels= ['#000000','#FFFFFF']\n",
    "fig_q7, ax_q7 = plt.subplots(1,1);\n",
    "ax_q7.set_title('First 20 Cities by Revenue - Fulfilment detail', fontsize=_title_size)\n",
    "fig_q7.set_size_inches(18.5, 10.5);\n",
    "\n",
    "bottom = np.zeros(len(cities))\n",
    "ax_q7.plot([1, 2])\n",
    "\n",
    "for i, fulfil in enumerate(fulfil_dict.items()):\n",
    "    p = ax_q7.bar(x= cities, height=fulfil[1][0], width=ax_q7_width, label=fulfil[0], bottom=bottom, color=color_bars[i])\n",
    "    bottom += fulfil[1][0]\n",
    "    ax_q7.bar_label(p, labels=[f\"{x:.0%}\" for x in fulfil[1][1]], label_type='center',color=color_bar_labels[i] )\n",
    "\n",
    "formatter = ticker.StrMethodFormatter(\"{x:,.0f}\")\n",
    "ax_q7.yaxis.set_major_formatter(formatter)\n",
    "\n",
    "ax_q7.yaxis.labelpad = _axis_label_pad\n",
    "\n",
    "\n",
    "plt.xticks(rotation=90)\n",
    "plt.xlabel(\"City\", weight = _fweight, fontsize= _axis_label_size)\n",
    "plt.ylabel(\"Amount\", weight = _fweight, fontsize= _axis_label_size)\n",
    "\n",
    "ax_q7.legend(title='Fulfilment:', title_fontsize= _legend_title_size, fontsize=_legend_content_size)\n",
    "ax_q7.margins(0.02, 0.2)\n",
    "\n",
    "plt.savefig('img\\\\First_20_Cities_by_Revenue_Fulfilment_hue.jpg', dpi=300, bbox_inches='tight', pad_inches=0.1)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b4c46e50-6432-4a82-9860-a6e80a3e5a3f",
   "metadata": {},
   "source": [
    "The Fulfilment ratio between Amazon and Merchant is evenly distributed even when we consider the individual destination cities."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "103020fb-8714-48c6-b5b5-043ea3fbbf30",
   "metadata": {},
   "source": [
    "## FINAL CONCLUSIONS [&#129081;](#Top)<a name=\"Conclusions\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90038740-4f48-4ca8-bb43-ce6b0b5fdfc1",
   "metadata": {},
   "source": [
    "In the course of the analysis:\n",
    "* It was established that Western Dress, Kurta and Set are the categories which have sold most.\n",
    "* It was verified that the 20 Best-Selling Products by quantity belongs to the categories Western Dress, Kurta and Set.  \n",
    "* On the other hand, the 20 Best-Selling Products by revenue belongs only to the categories Western Dress and Set.  \n",
    "* Also it was performed a fulfilment and shipping state overview, discovering an uniformly distributed fulfillment method even among the dectionation cities."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f5450df5-a457-4f1a-aeab-9de257c2d6a2",
   "metadata": {
    "tags": []
   },
   "source": [
    "   \n",
    "---   \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "145bd9f1-829d-4b98-bb9a-8a752993f23f",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "\n",
    "\n",
    "### Data Source\n",
    "The data for this project can be found at [data.world](https://data.world/anilsharma87), thanks to [ANil](https://data.world/anilsharma87).\n",
    "\n",
    "### Author \n",
    "[Barbara Callegari](https://numberslab.net)  \n",
    "To learn more about the author visit her [LinkedIn Profile](https://www.linkedin.com/in/barbaracallegari)\n",
    "\n",
    "### Licence\n",
    "All rights reserved 2023. All code is **created and owned by Barbara Callegari**.   \n",
    "**If you use his code, please give her a skill endorsement in Python and Data Analysis on LinkedIn**.   \n",
    "Visit her at https://www.linkedin.com/in/barbaracallegari"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
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 },
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