EDA.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "XkMOFWOzVWsT"
      },
      "outputs": [],
      "source": [
        "import numpy as np, pandas as pd, seaborn as sns, matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.preprocessing import StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 615
        },
        "id": "0iW_OK7e9gLL",
        "outputId": "4e558982-0c96-43c8-898f-981a0750ad46"
      },
      "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>Unnamed: 0</th>\n",
              "      <th>Menu Item</th>\n",
              "      <th>Modifiers</th>\n",
              "      <th>Price</th>\n",
              "      <th>Qty</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Net</th>\n",
              "      <th>Tax</th>\n",
              "      <th>Total</th>\n",
              "      <th>Voided?</th>\n",
              "      <th>Reason</th>\n",
              "      <th>Refund Qty</th>\n",
              "      <th>Refund</th>\n",
              "      <th>Last 4 Card Digits</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Tip</th>\n",
              "      <th>Gratuity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>Margarita Rock Flight</td>\n",
              "      <td>Salt</td>\n",
              "      <td>$24.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>$24.00</td>\n",
              "      <td>$2.250</td>\n",
              "      <td>$26.25</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>3108.0</td>\n",
              "      <td>2023-12-08 18:46:00</td>\n",
              "      <td>$6.86</td>\n",
              "      <td>$45.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>Margarita Rock Flight</td>\n",
              "      <td>Salt</td>\n",
              "      <td>$24.00</td>\n",
              "      <td>1.0</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>$24.00</td>\n",
              "      <td>$2.250</td>\n",
              "      <td>$26.25</td>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>3108.0</td>\n",
              "      <td>2023-12-08 18:46:00</td>\n",
              "      <td>$6.86</td>\n",
              "      <td>$45.71</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0              Menu Item Modifiers   Price  Qty Discount     Net  \\\n",
              "0           0  Margarita Rock Flight      Salt  $24.00  1.0    $0.00  $24.00   \n",
              "1           1  Margarita Rock Flight      Salt  $24.00  1.0    $0.00  $24.00   \n",
              "\n",
              "      Tax   Total Voided? Reason Refund Qty Refund  Last 4 Card Digits  \\\n",
              "0  $2.250  $26.25   False    NaN          0  $0.00              3108.0   \n",
              "1  $2.250  $26.25   False    NaN          0  $0.00              3108.0   \n",
              "\n",
              "            Order Date    Tip Gratuity  \n",
              "0  2023-12-08 18:46:00  $6.86   $45.71  \n",
              "1  2023-12-08 18:46:00  $6.86   $45.71  "
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_csv(\"data/Marketing_data.csv\")\n",
        "df.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6xGQfHOeVpPo"
      },
      "source": [
        "## Plot - 1\n",
        "- Analysis shows customers who only came once mostly bought drinks only."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "XNKSciwu9gCa",
        "outputId": "30cc1ff4-c24c-46ce-f621-e91023d675d5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1200x800 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "item_data = pd.read_excel('data/Campbell Menu Data - 2.xlsx', index_col=0)\n",
        "\n",
        "item_data.rename(columns={\"itemName\": \"Menu Item\"}, inplace=True)\n",
        "ungrouped_data = pd.read_csv(\"data/Marketing_data.csv\")\n",
        "category_dict = item_data.groupby('Category')['Menu Item'].apply(list).to_dict()\n",
        "itemp = item_data.groupby('Menu Item')['itemPrice'].apply(list).to_dict()\n",
        "\n",
        "for category, items in category_dict.items():\n",
        "    ungrouped_data.loc[ungrouped_data['Menu Item'].isin(items), 'Category'] = category\n",
        "\n",
        "for item, price in itemp.items():\n",
        "  ungrouped_data.loc[ungrouped_data['Menu Item'] == item, 'ItemPrice'] = price[0]\n",
        "df=ungrouped_data\n",
        "\n",
        "nique_people = df['Last 4 Card Digits'].unique()\n",
        "\n",
        "people_came_once = df['Last 4 Card Digits'].value_counts()[df['Last 4 Card Digits'].value_counts() == 1].index\n",
        "\n",
        "unique_orders = df[df['Last 4 Card Digits'].isin(people_came_once)]['Category'].value_counts()\n",
        "\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "plt.barh(unique_orders.index, unique_orders.values, color='skyblue') \n",
        "plt.xlabel('Frequency')\n",
        "plt.ylabel('Category')\n",
        "plt.title('Categories Ordered by People Who Came Once')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mynYqaUyVs0a"
      },
      "source": [
        "## Plot - 2\n",
        "- Special days with lowest amount of sales"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "09iLyHb39f_9",
        "outputId": "ef865736-4057-402e-d2f9-555a7d04a819"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
          ]
        },
        {
          "data": {
            "image/png": 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atWuX7OzstHLlSk2dOlU7duxQkSJF5Obmph9++IFA6inE6Xv4T1q2bJmWLFmixYsXa/jw4SpRooTs7e01efJkdevWTVFRUeaD8pRc9ZVXXtHNmzctvjwDSJ9p06Zp/vz5Wr58uRYtWqTAwEAlJibqnXfe0ezZsy362tnZqVixYnrttdd07ty57CkYeEKlHJyPGDFCs2bN0owZM3Tu3Dn5+/tr0qRJGjNmjE6ePGnubxiGSpcurZEjR2rLli1c+RLIoJR97ty5czp48KA2b96sX3/9VWFhYbp69aq6d++uP/74Q126dFFwcLB27Nih5cuXa9++fTKZTPriiy9kY2OjF154IZu3BHgypJ7aIfVo+n79+snOzk6//vqrJKlZs2aaP3++Dh48qA0bNmj16tUEUk8pvr0j26U+xz85OVlJSUmKi4tTz5495efnpzVr1qh169aaMGGCgoODtXTpUn3yySeKjIyUJHMIde7cOZ06dco8/xSA+zMMwxzmGoahmzdv6sqVKxo6dKgqV66slStXqnXr1po8ebJ69eql999/XwsXLjQ/PmWfu3Llik6ePKnExMRs2Q7gSXXu3Dlt3bpVY8eOVb169bR69WotXLhQ9evX18qVKzVq1CidOnVKkmRra6ukpCT5+PioWrVqunHjRjZXD/z3LV++XDdv3jTfHzVqlF577TXdvn1bzzzzjKS787ht2bJFUVFR6tWrl3bu3CnDMFSjRg35+vpq27Zt6tatm6ZPn6758+erUKFC2bU5wBPh0qVLku7+3dq+fbuqVq2qkJAQc7uvr6/y5Mmjn3/+2fyYXLlyqUiRIipYsKB5AnQ8fQilkO3uHdlka2urRo0aqVmzZoqMjNTQoUM1aNAgde7cWSVKlFChQoU0ceJEjR492vyY8PBwRUdHa/HixXJ1dbX2JgBPFJPJZP7PlclkkouLixo1aqRXXnlFZ86c0aBBg/TFF1+oS5cuat68uWJjY82XxU5x/PhxXbp0SSNGjDBPTgkgfQoWLKhevXrJ399fu3btUteuXfXll19q4cKFaty4sZYtW6YhQ4bo/Pnzku7+XZwxY4Z+//13VatWLZurB/7bRo4cqWnTpilXrlzmtiZNmuiff/7R77//bh7haxiGPDw8tHnzZsXExKhDhw46cuSI+TH58uVT7ty59fvvv6tixYpW3grgyXLgwAF5e3tr5cqVku6GTfnz59eHH36oli1b6ueff5arq6vGjx+vbdu2afHixZLSfg/E04k5pZCttm3bpuDgYK1YsUK2trZ6+eWX1alTJ1WoUEHS3S++r732mubMmaNq1aopPDxcQ4cOVZcuXVSxYkWLeTViY2Pl5OSUXZsCPBH279+vvXv3atWqVXrmmWf04osvqmPHjubl69ev16BBg7RkyRIVL15c+/bt09y5c1W+fHm1bdvWHEDFxcUpNjZWbm5u2bUpwBMh5RSh06dPy83NTXXr1pV0d242BwcH9e/fX2FhYZo5c6bs7e318ccfKyQkRFWrVtUPP/xgPmC/c+eOzp07p7Jly2bj1gBPhsTERNnZ2Wn//v3y9vaWu7u7zpw5o6pVq5r3rVKlSpnnuwkLC9OAAQM0a9Ysi2PLhIQE5ikFHuLgwYOqVauWevXqpa+++spi2bp167Rp0yaNHz9ederU0fPPP6+///5bnp6eGjlypBwcHLKpavyXEEoh28yaNUvDhw+Xn5+fPD09dfnyZYWGhurOnTtasmSJatasqYsXL6p06dJ677331Lx5c40cOVJxcXHauHGjTCaT+aADwMPNnTtX3377rfLly6dcuXLpzz//1M2bN+Xn56cVK1ZIkhYvXqw333xTGzZsULFixdS7d2/lyZPHPBkz+xyQfkuXLtWkSZN0/PhxhYWFKTExUa+++qq+/PJLlS9fXra2turUqZOuXLmiadOmyd3dXS1bttS7776rFi1ayGQyKTk5WcnJyex3QDqkDpHWrFmjd955R19++aVat26tAgUK6MSJE6pRo4ZeeukljRs3TiVLlkxzxeaUC+kAeLiDBw/Kz89Pffr0sQikQkND5efnZ76/d+9erVixQsuWLdPRo0dVvHhxHTp0SC4uLtlRNv5rDCAbTJo0yXBwcDDmzp1rxMTEmNtXrlxp1KxZ08ibN6+xa9cuwzAM45dffjFcXFyMMmXKGH5+fkZ8fLxhGIaRnJycLbUDT6LJkycbzs7OxowZM4xLly4ZhmEY4eHhxvDhw428efMaDRs2NPdt27atYTKZjBIlShgVKlQw73MA0m/KlClG/vz5jdGjRxs7duwwzp07Z8yYMcPw9vY2KlSoYOzYscMwDMMYO3asUapUKaNOnTpGhQoVjDJlyhgJCQmGYRhGUlJSdm4C8MRKObYMDAw0SpcubUycONG4evWqYRiGcfz4cSNfvnxGs2bNjOPHj2dnmcAT7fTp04bJZDKGDx9u0T5ixAjD09PTOHfunGEY//edLTEx0bh+/boxdOhQ49ixY1avF/9dhFKwul9++cUwmUzGli1bDMO4+4sqdcAUHBxsvPDCC0bz5s3NBxXh4eHGiRMnzAfoKQfsAB5u5syZhslkMkJCQtIsi4mJMb799lujQIECRlBQkLl9w4YNxvr1643ExETDMNjngIyYOnWqYWdnZyxdujTNssOHDxvFihUzatWqZW4bP368MWjQIKNfv37mfS1l3wOQMePHjzdefvll8/3OnTsbpUqVsgimTpw4YZhMJmPAgAHZVSbwxFu5cqVhMpmMgQMHmtu++uoro0CBAkZwcHCa/gwowINw+h6sKjIyUp07d9aOHTu0evVqVa5cWak/ginDpz/++GNNnjxZJ0+eVP78+S3WkZyczKR4QDqdO3dOjRs3lqOjo/744w9J/3dqgvH/T1mIjIw0nyq0ZcuWNOvgVAYg/bZu3aq6detq0qRJ6tq16337rFu3To0aNdLYsWP1wQcfpFnOabLAo9uzZ48aN26sKVOm6PXXX5ckde3aVZs2bdKAAQPUsmVLFShQQBcuXFDhwoXZ14BHFB8fr+XLlyswMFD9+/eXu7u7goKCNH/+fPn7+1v0jY6OZh5SPBDf7GFV+fPnV79+/fTKK6+oQ4cO2rRpk/lKYCaTSUlJSZKkN954Qzdu3NDFixfTrINACki/QoUKqXfv3nJ2dlarVq0UGxtrvsR8yrxs+fPnV5s2bXTs2DFdvXrVvB+mIJAC0q9kyZIqU6aMZs+ebb7K171eeuklPffccwoLC7vvcr4kA+lz7//Wk5OT5ePjo5dfflm///67uX3KlCmqX7++xo4dq5kzZyo6OlrPPPOM7OzslJiYaO2ygRzBwcFBAQEBmjFjhn788Uf17dtXK1eulL+/v8Wx5MCBA/XBBx8oISEhG6vFfxnf7mE1KQcOtWrVUs+ePeXr66vevXtr48aN5uUpgVPKZa9Lly6dbfUCTzrDMOTo6Kj27durc+fOOn/+vNq1a2cRTNnZ2SkpKUkHDx5U9erVVbBgQUIo4DEUKVJEGzZsUHR0tFq2bKnz58+bl6X8HXRxcZFhGFx1CHhEKV94U0bYR0ZGSrr7j8uCBQuqRYsWGjdunA4ePGh+zOTJk1W+fHnt3r1brq6u5nZCYODR2draKiAgQFOnTpWLi4uWLFlibpekYcOG6bvvvlOPHj24kiUeiFAKVmMymcwH5H5+furdu7d8fX3Vp08f89X0TCaToqKitG7dOlWvXl1OTk7ZXDXw5ErZ5xwdHfXuu++qW7duOnfunEUwJUlXr17VmTNn9PLLL2dzxUDOUKhQIQUHBysuLk4BAQHmYCrlC/SBAwdUoEAB1axZMzvLBJ5IjRs3VmhoqPn+xIkT1aZNG02bNs3c1q5dOzVu3Fjfffedbt++bQ6xFi5cqJ9//tnimBTA47G3t9drr72mKVOm6Mcff1Tv3r0l3Q2kRo4cqZ07d6patWrZXCX+ywilYFX/Fkxt27ZNkvTuu+8qPDxcI0eOlJR2aDaA9HtQMNW2bVslJydLkjp06KDo6GgNGDAgm6sFco7ChQtbBFNnz56VdPf0os8++0z58+fXK6+8ks1VAk+e5557zuILrpeXl0qWLKk+ffqoadOm+v7772UYhpo2baojR44oOjpatra25lOHbGxslJycbA6JAWTM/b6b2dnZqVWrVpoxY4amT5+ukiVL6ttvv9X27dv14osvZkOVeJIw0TmyRcoEy5IUGhqqcePG6fjx44qPj1diYqKOHDkie3t7JlgGMiD1fnXv/ZSf4+LiNG/ePE2ePFk+Pj6KjIzUhQsXdPjwYfY5IAtcvnxZ/v7+cnZ21qJFi9SzZ0+dPn1aBw8elL29PRfvANLp3n1l1KhRKlWqlFq0aCFJ+uuvvzRy5Ejt379fycnJGjBggDp06KBu3brphx9+yKaqgSdfyjHkgQMH5ObmJh8fnwf2TUpK0s8//6yvv/5ac+bMUaVKlaxYKZ5UHAUhS6SMwLhXSgZ6vxFTnp6eyps3rzmQSkxM5MsxkE6p/+v7zz//SJJFQHW/EVM7duzQP//8Yw6k2OeA9HvQ37l7FS5cWOvWrVN8fLx8fHx05swZcyCVmJhIIAWkU+p/skjSqlWrFBgYqDVr1ig2NlbPPfecJkyYoNWrV6t69eqaPn26kpOTdebMGUbdA48oJZBavny5WrRooR9++EExMTEP7G9ra6tWrVpp586dBFJIN0ZKIdOl/k/Wpk2bFB0drQoVKsjb21t2dnYWy1OP5Dh69KjKli0rGxsbLocNZEDqferrr7/WiRMn1L179/uev596xNT69ev16quvytbWln0OyIDU+9yUKVPk5eWl5s2b/+tjLl68qFGjRmnUqFHmQIp9Dsi4iIgIeXh4SLp7teZt27Zp1qxZeuWVV+To6Gjud+jQIR09elStWrWSnZ1dmtHEANJn9erVatmypcaPH6/mzZub9z8gsxBKIVOl/oM/YMAAzZ8/X7du3VKJEiX09ttvq2fPnsqVK9cDgykp7fBsAOkzcOBAzZgxQ1OmTFGVKlXk7e1tXna/U/lScMoekH6p959BgwZp3rx5ev/999WtWzflz58/XesgkALSL/Vx4Y8//qhdu3apb9++qlixoiSpRYsW2r59u2bNmqWGDRve9wpf7HNA+qXsL4Zh6NatW2rfvr3Kly+vIUOG6NatWwoLC9PChQv17LPPqk6dOnJ3d8/ukvGE45s/Mk3qA/Xt27drx44dWrp0qQ4fPqyaNWtq6dKlGjFihG7fvm2eZFJSmv9aEUgBGffrr79qwYIFCgkJ0euvvy5vb2/duHFDu3fvlnR3P3vQPkcgBaRfyv4zduxYTZ8+XatWrdLHH3+c7kBK4hL0QHqlDqQOHDigPXv2aNmyZZo8ebKOHj0qSVq+fLlq1aqlDh06KCQkxDyheWrsc0D6XL58WaNGjdKZM2dkMpnk4OCgv//+WxcvXtSNGzc0aNAgdejQQdOmTdPbb7+tKVOmZHfJyAH49o9Mk3KgvnjxYk2fPl3Vq1dXjRo19Mwzz2jMmDGqXbu2QkJC9PXXX5uDKQbqAY/myJEjioyMNN+/fv268ubNq4oVK+rEiRP66quvVKlSJTVq1EhvvvmmJAJfILPEx8fr4MGD6t+/vypWrKhTp05p4cKFevnll9WlSxdt2rQpu0sEcoSUv1v9+/dXq1atlCdPHjVo0EBTpkzRd999pyNHjkj6v2CqadOm2rt3b3aWDDzRQkNDtX79es2dO1dnzpyRg4ODunXrppUrV8rT01OXL19W586dderUKQ0cONA8ZyLwOPi3ATJVXFycZs2apU2bNqlGjRrmdgcHB33xxRf69NNPtWHDBsXExOjrr7+Wk5NTNlYLPHkMw1BkZKT8/f3Vpk0bDRo0SPnz55eXl5eSk5NVr149nT17VnXq1FHPnj1VunRpNW/eXNu2bdPLL7+c3eUDT6R7T3l1cHBQRESE9u3bp5IlS2rixImytbVVmTJltG3bNl2/fl116tQhCAYywebNmzVz5kytWbPGPFfiggUL1L17dyUnJ6tfv356/vnntXTpUn300Uf3nU8RwL9L+TsXEBAgOzs7/fTTT7p+/bqCgoLUqlUr1apVS6dPn5a/v795UMG1a9dUokQJ5mrDYyOUwmO590Dd0dFRc+fOVf/+/bV582aNGzdO3bt3l729vTmY6tWrl27fvm0xGSWA9DGZTCpQoIB+/vlnvf/++5Kkzz77TPXq1dNHH32knTt3qmvXrqpTp44KFy6skydPqlKlSsqbN2/2Fg48oVKfPpR6/rXx48erTZs26tevn7p06aJXX31V1apV08yZMzV79mzduXNHuXPnzs7SgRzBMAy5uLgoX7585i/DrVu3VmJiotq2batcuXKpS5cuKl++vL7++mtJzCEFZFTK97nffvtNtWvX1u3bt3Xz5k3lyZNHklSqVCmVKlVKkvTXX39p5syZWrBggbZt23bfedyAjGCiczyy1AfqFy9eVK5cuZSYmChPT09dv35dvXr10pkzZ9SmTRu999575gP5lMvOp1yinnQdyJiUfW/r1q1q1aqVVq9erSpVqkj6v6A4MTFRN27cUGBgoKKjo7Vp0yZGbQCPYezYsdq8ebOKFCkif39/vfbaa5KksLAweXl5Sbq7bzZq1EiFChXSzJkzs7Fa4Ml0v4vdbN26VY0aNdKaNWtUu3ZtxcbGysnJSdHR0SpXrpwSEhL0zjvvKCgoSLly5eK4EnhEu3fvVo0aNTRt2jR16NDB3J76+1poaKh++ukn7dixQ7/88osqVKiQXeUiByGUwiNJ/ctp6NChWrVqlSIjI+Xm5qaBAwfqnXfeUVRUlHr06KFz586pbdu26ty5s8V/rQikgPR70FUp//nnHxUqVMii7fbt21qwYIHmz5+vyMhI7dy5U/b29lzZEsiA1PvLl19+qTFjxqhly5Y6ceKELl++rPfff199+vSRJMXExCgkJETTpk3T33//rX379sne3p6/c0AGpN7npk2bptjYWPXo0UOS1KFDB61cuVKhoaF69tlnJUlXrlzRsGHDVKZMGfXp00dr167V//73v2yrH3iSHT16VDt27FBUVJQ+/PDDB/aLiIjQsWPHVKpUKRUtWtSKFSInY1wrHknKQfbw4cP1ww8/aOrUqYqOjtbhw4fVtm1bRUREqE+fPho/frx69+6tMWPGyNPTU6+//nqadQD4d6kP1ENCQvTPP//Izs5ODRo0UKFChZScnCyTyWTep+zs7JSUlKTatWtr8ODBsrOz41QGIINS9rn9+/crISFBS5cuVZ06dXT27Fn99NNPGjVqlGxtbdWrVy9dunRJISEhypUrl/bv388+B2SQYRjmfe7DDz/UggUL1L9/f124cEHPPPOMPv30U4WHh+vFF1/UiBEjlCtXLv3yyy+Kj4/XhAkTNHXqVAUHBxNKAY/g/PnzCgwM1MmTJ/XJJ59IsjxdPTUPDw95eHhYu0TkcIyUQrrdvHlTLi4u5vs3btxQs2bN1Lp1a3Xv3t3c/v3336tv375au3atGjZsqOvXr2v8+PH65JNPuPQ88BgGDhyoZcuWydXVVQUKFNC+ffsUGhqq5557ztwnZWTGg+bBAZB+a9asUYcOHZQrVy6tWbNGpUuXliRduHBBkydP1uzZs/XJJ5+oW7duioiIkLu7u/n0WQIpIOMmTJigoKAgrVq1SlWrVrVYFh0drREjRmjJkiVycnJS4cKFtXLlSjk4OKhq1apq3769eWQVgPSLjo7W5MmTNWnSJJUqVUrr1q2TxPEjrIfzOJAujRs31pAhQyza7ty5oyNHjpi/+BqGoeTkZHXv3l1NmzbV8uXLFR8fr3z58mnIkCGytbVVUlJSdpQPPPGmTZumWbNmaf78+dq3b59atmyp69ev6/Dhw+Y+qU8VSn2aHgcUwKNxd3dX06ZNFRYWpt27d5vbn3nmGXXr1s38JXjx4sXy8PAwz5VIIAWkT8r/xg3DUGJionbt2qVOnTqpatWqOnHihObMmaNatWrplVde0ZkzZ/T1119r27Zt2rVrl4KDg+Xg4KBPPvlE4eHhatSoUTZvDfBkcnNz03vvvac+ffro9OnT6tq1qyTx3Q1Ww1ET0mX06NEqUaKEJCkuLk6Ojo7y8PBQ48aNtWTJEjVq1EjPPPOMpLuXys6TJ4+uX78uBwcHi/Xw5Rh4NCdOnFC3bt1UtWpVLVu2TP3799fkyZMVEBCgGzduSJL5CikAMu5+c65VqVJFvXv3VmJiooKCguTg4KDWrVtLkry9vdWxY0d5e3tzajrwCFL/IyUiIkKenp4qUKCAFi5cKA8PDy1evFhubm6qVq2a9u3bp3fffVeHDx+Wp6enTCaTDh06pClTpmjhwoUKDg42H6cCeLCU/e7IkSM6efKknJ2d9fzzz8vb21tt27aVYRiaPn263nvvPU2ePNkcTPEdDlmJkVJ4qOTkZJUtW1aOjo4aM2aMGjVqpOvXr0uSXn31Vd28eVNjxozR5cuXZTKZFBsbq3/++YfJ74BMdP36dcXHx2vlypVq166dvv32W3Xp0kWGYejnn3/WxIkTlZCQkN1lAk+k1IHUrl27tGnTJu3cuVOSVK5cOX3wwQeqW7euhg0bpoULF5of5+Pjo65du/LfZOARpARSY8eOVa9evSRJb7zxhurWratRo0apadOm5osMfPDBBypatKju3LljfpyHh4caNGig0NBQVapUKdu2A3hSpARSS5cuVdOmTfXxxx9r6NChCggI0JEjR5QvXz61bdtWHTt21N69e/XOO+9IYlABsh5zSiHdEhIS9Mcff8jf31/169fX3Llz5ezsrLFjx2rBggWKiIjQiy++qPPnz+vOnTs6cOAApzAAGfSgK+SNGzdO06ZN09mzZzVixAjzvBmRkZFq06aNatSokeYUWwAPl3q0xscff6xly5YpKipKPj4+ev755zV16lRJ0r59+zR58mTt2LFDH374oQIDA7OzbOCJ1KVLF3Xq1Ek1atQwt3Xq1EmFCxfW8OHDzW3Xr19Xvnz5zPf9/f2VL18+/fLLLxbr4wqXQMZs3LhRLVu21FdffaVu3brp119/1RtvvKHChQtr+fLlqly5sq5fv65Jkybpt99+08KFC9Nc5RnIbIRSeKBNmzYpNjZWjRo1Uu/evVW4cGENGjRI+/fvl7+/v/z8/LRo0SI5Ojpq+/bt2rFjh06ePKlnnnmGK34Bj+Deq+wZhqG8efOqWrVqunXrlpo3b66jR49q1qxZqlSpkmJiYtSrVy9dvXpVoaGh7GvAY/jqq6/0/fffa8mSJapYsaKCgoI0atQoBQQEaNGiRZLuXolvxIgRcnBw0Lx587K5YuDJEh8fr1q1aiksLEzLly9XpUqVZDKZFBAQoCpVqmjw4MEW/W/duqUtW7ZozJgxunLlivbu3St7e3uCKOAR3bp1S/3795eXl5eGDRumf/75R9WrV5efn5+ioqJ05MgRBQcH64UXXlBUVJQMw7AIh4GsQiiF+4qIiFD79u11+/ZteXh4aMWKFdq9e7fKly8v6e5/jF999VW99NJLmj9/vnLnzp1mHZx/DDyagQMHavLkySpYsKAuXbqkkSNHqnfv3rp586YaNGigmzdv6ty5cypfvrxsbGy0adMm2dvbs88BGZA6BD5z5ow6deqkQYMG6dVXX9XatWvVqlUrvfPOO1qxYoXq1q2rn3/+WdLd+d2effbZ+45oBPDv7ty5o5YtW+rgwYP69ddfVblyZTVt2lQvvfSSPv74Y4vAae/evVqwYIEuX76sWbNm8c9O4BGl3q+2bt0qW1tbvfDCC6pfv76qVq2qSZMmacGCBXr77bfl6OioHTt2cEosrIrf6rgvDw8PDR8+XK1bt9a2bdv0ww8/mAOp5ORkVa5cWWvXrlWjRo0UGBioyZMnq0CBAhbr4MsxkD6pDxZOnjypNWvWaNOmTXJ0dNS6devUr18/xcTE6LPPPtPmzZt16NAhnT17Vj4+PqpcubJsbW05UAcywDAMc6i0fft21apVS+3atVPlypW1fft2derUSaNHj1bXrl2VkJCgmTNnKiIiQhs2bFDp0qUlPfhUWwAP5uzsrMWLF+uNN95Qs2bNtHnzZrm4uCg5OVnx8fEKDw+Xvb29ChYsKGdnZ/Xq1Uve3t4ymUz8nQMyKOX4MvXIwtq1a0uS1qxZI2dnZ3388ceSpCJFiujVV1/VM888c9/BBkBW4jc70kj5BZYrVy75+PioaNGiWrZsmYoVK6bGjRvLxsZGSUlJ5mCqSpUqevbZZzVixIjsLh14okRFRSlv3rzmg4UR/6+9+46v+ez/OP462UgQo4m9R4yIERS1m6K1YkeRRJW0qXUjiKBEiCJW1WgrhAhFCNHYMW6jasSsWRQRO/bI+P3hkfOToneiKg3v519NvuN80kdPz/V9n+v6XGPHEhsbS+PGjalSpQoA5cuXx8rKCm9vb0xNTRk6dCjVq1enevXqxvskJSVpoC6SRs+GwH5+fkRERBAZGYmHhwcAEyZMoEmTJnTt2hWAkiVL0rx5c3LkyJEqiFIgJZI2f15ulyVLFpYvX07z5s2pU6cOFhYWREZGEhYWxoULFzAzMyNr1qyUK1eOqKgo4z30OSeSdimz57dv386WLVu4desW5cuXx93dHYC4uDh++eUX4yYdP//8M3ny5GHChAlYW1tnYOXyLtL/3cUoZbCdMnBwcHBg/fr17Nixg8DAQCZMmABAs2bNjLOgnJyc+O233yhRokSG1S2SGbVq1Qp7e3tmzpwJPN1I4OrVq0yfPh0XF5dU53p5eWEwGOjTpw/379/H398/1XE9HIukXcpn3MGDB9m7dy/ffvttqt1iT5w4wY0bN7CysuLJkyfGPopffPEFoBlSIunx7Pvl/PnzmJiYULBgQbJkyUJkZCTu7u4sXryYH374gTp16nD//n3g6Wx7BwcH433UQ0okbe7fv0/WrFkxNTVl+fLluLu707ZtW+7evUtUVBRLly5l9erV1K1bl3r16lGjRg0cHR3ZuXMnu3btUiAlGUI9pQRIPWg4fPgwd+7cwd7enmLFigFPd2qYMmUKDx484KuvvqJ58+Z8/PHHNG3aFG9vb0A9pETS4/fff6dAgQJYWFjw4MEDsmTJQnx8PNOmTWPEiBHMnTvXOFMjxYQJE1i5ciVbt27VAF3kb/j2229ZsWIFCQkJLF++HFtbW+Nn2NKlSxk0aBB2dnYkJSVx7949426yarAs8mqGDh3K6tWrOX/+PD169MDNzY3KlSvz8OFDWrduzfHjxwkPD6dSpUqprtPYUiTtzp49S79+/Zg/fz6xsbF8/PHH9O/fHy8vL06ePEnNmjVxc3Nj2rRpABw4cICIiAju3r2Lp6cnZcuWzeC/QN5VCqUk1SDb19eXiIgIzp8/T40aNahWrRoBAQHA02Dqu+++Y/fu3eTIkYP79+/z22+/YW5unpHli2Q6zw6yp06dyvz584mKiiJPnjzcvXuXgIAAAgMDCQkJwc3NLdW1Ke9XPRyLvLpVq1bRo0cPHjx4QHh4OA0bNjQeu3HjBps2bWLt2rXkyJGDcePGYWZmpodjkXR49svOhQsXMmTIEAIDA7l8+TLTpk2jevXqfPXVV9SuXZvHjx/TsmVL1q1bx9GjR41920QkfcLCwujZsyfx8fHs2bMHd3d3jhw5wrlz5/jggw9o1qyZcYb+zp07ef/99wHNAJaMp+V7Ynyw9ff35/vvv2fRokVUrFiRQYMGMWPGDG7dusWMGTNo2LAhuXPn5vjx45w7d45+/fppJxSRV5DyYPvo0SMaNmxIQEAAXbp0ISQkhDx58uDr60tycjJdu3bFxMSEjh07Gq9VICWSPn8ebCcnJ9O8eXNCQ0Nxd3c37nSZsplHrly5aNu2LW3btjVeo885kfRJec/t2LGDmJgY/P396dSpEwCVK1emb9++TJ06FYDatWuzYsUKBg8eTMmSJTOsZpHMrnbt2tjb23Py5EmyZMmCvb09u3bton379jRt2pRvv/0WgJiYGEJCQrCxsaFChQoaU0qGUyT6Dnt2ktzRo0cJDw9n4cKFNGzYkJiYGJYsWYKLiwvr1q2jd+/eAFSqVIn27dszcOBA4zfHGqiLpM2mTZtYvnw5AL1792bs2LFUqFCB6OhoDh8+jJubG9evXydbtmwMGzYMHx8f3NzcWL9+far7aPAgkjbPBlKrV69mzpw5TJ06lRs3btCwYUO+//57du3aRVBQEIcOHTJel9L4NYU+50TSJzk5maNHj9K4cWOCgoK4cuWK8Vj9+vWZPHkyx48fZ/r06WzatAlLS0uCgoIwNTV97v0nIv9bUlIStra23Llzh+joaAoVKsSZM2eoVasWTZo0YdasWcYvRefNm8fRo0ext7cHNK6UjKdQ6h317EyLmJgYHBwc6NWrF5UrV2bLli106dKFKVOmEBYWRunSpZk9e/Zzy4gALWUQSaNr164xZcoUgoKCcHV1Zc6cObi6ugJQtmxZ1q1bx7Fjx+jUqZMxmBo8eDAzZsygQYMGGVy9SOaUEkgNGjSIvn37EhISwooVKyhYsCA7d+7ExcWF2bNns3nzZiZPnsy+ffsAfbaJ/F0Gg4Fy5coRFhZG3rx52bZtG0ePHjUer1+/PlOmTCE6Opro6OhU1+r9J5I2sbGxnDp1yjjRwNramlq1anH16lVy5MjBwoULjZt2bNmyhV9++YX+/fvz448/Mm3aNPLkyZPBf4HIU+op9Q56NpAaPHgwu3btIiwsDDs7OwwGA15eXpiZmTFp0iTMzc0ZOHAge/bsoVy5ckyfPl1rjkVe0ZEjR2jdujWnTp1i0qRJ9O3bF/j/HlPHjh3jo48+oly5csyfP5/33nvPeK2WD4m8mnnz5jFo0CDWrFlD1apVWbp0Ke3bt2fZsmW0bt0aeLoVduvWrfHz88PX1zeDKxbJfP6qJ81PP/1Ev379aNmyJV999VWqZsr79+/H0dFRQZRIOt29e5dKlSrx5MkTTE1NKVOmDC4uLoSGhmJtbU1ERATZs2fn559/5ssvv+TJkyfY2NiQPXt2Zs6ciZOTU0b/CSJGesJ5B6UEUr/99hs7d+4kICDAOH0Tnu4KliVLFszNzUlMTOTcuXN06dIFT09PDAaDmuGJpFNKEGxubk6pUqUoXLgwq1atolChQrRp0wZTU1MSEhJwcHBg3bp1ODo6MmbMGKZMmWK8hwIpkbT582fU2bNn8fT0NAZSnp6ezJw5k9atWxMfH4+5uTlNmzYlOjoaZ2fnDKxcJHN69j0XGhrK77//TmJiIm3atMHBwYF27dqRmJjIgAEDgKfL11OamVeuXBnQLnsi6WVtbU1UVBQGg4GNGzdy6dIl1qxZw6NHj4iNjSUqKoqmTZvStGlTdu/ezbVr17CwsCB37tzkzJkzo8sXSUUzpd5RY8eOJTo6GisrKxYsWICNjQ1JSUkATJ48mZCQEPLly8ft27e5desWMTExmJqaqsGySDq8LMD99ddfGTduHFevXqV37960adPGeOzJkydcunSJggULaoAukk4ves/16NEDg8FA69at6dChA4GBgXh5eQEwbdo04uLiGDFihHEnWT0ci7waHx8f5s6dS8OGDTlw4AD58uWjS5cudOvWDVNTUxYvXoyPjw+1atVi3LhxFC5cOKNLFnmrPH78GDMzM7p27cq+ffvw8fGhTZs2WFtbZ3RpIn9J013eESmBU4rSpUuzfv16tm/fztmzZ4GnvTdMTEzo1KkTXbt2JUeOHJQvX579+/cbG08qkBJJm+TkZOPDcUhICOPHj2fmzJkkJSVRrVo1+vbtS968eZkxYwZLliwBoEmTJkyZMoUiRYqo2atIOm3ZsoVt27YBT4Oo/v37A+Di4sL+/ftxdXUlICDAGEjdvn2btWvXkpiYaAykQP1sRF7FjBkzCAsL4+effyYsLIzRo0ezZcsWZs2axffff09iYiIdOnRg5MiRPHjwgIIFC2Z0ySJvHVNTU0xMTFiwYAGOjo5MmjSJ0NBQHjx4kNGlifwlzZR6xxw4cIBSpUqRLVs2NmzYwEcffYS7uztjxoxJtYTvz9TPRiTtnp1ROGDAAIKDgylcuDC3bt3C3t6ebdu2YWpqyvbt2/nuu+/Ytm0b1tbWPHnyhKNHj6Z6QBaRv5acnMzdu3dxdnamcOHC5MqVi6ioKLZs2UKlSpW4desWn332GTExMfj6+tK8eXMuXbqEj48PcXFx7N69GzMzM80EFnlFDx8+JDAwkJw5c9KnTx+WL19O9+7dGTJkCBs2bODMmTP4+Pjg4eGRaiypdhAir+5ln1nPzvZt1qwZN2/eJCoqihw5crzpEkXSTKHUO2TFihUMGDCAgQMH0qVLF7JmzcqqVato1aoVXl5e+Pn5YWdnB6QeKGigLvJqbty4gZeXF8OGDaNo0aLs378fLy8vTE1NjTMQjxw5wqlTp/j999/x9vbGzMxMIbDIK7h58yYVKlQgLi6O2bNn4+npaTx27do1PD09+f333zl16hSOjo5YWVmxYcMGY/9EzZASSZs/jwuTk5M5fvw4tra23L59mxYtWtCzZ0/69u3Lrl27cHFxoWDBgowePZo2bdpoXCnyN5w7d458+fJhYWHx0nOe/Uy7ePEiBQoUeFPlibwSfT3xDmnatClOTk7MnTuXhQsXcv/+fZo3b054eDgzZ85kzJgxxMbGAqT65koDB5H0mzlzJjVq1ODOnTsUKFAAGxsbPvjgA3788UcSExOpWrUqiYmJlC9fnpYtW9K3b1/MzMxITExUICWSTo8fP+bKlSvY29tTokQJli1bxqZNm4zH8+TJw6JFiwgPD2fhwoV8//33bN68GXNzcxISEhRIiaTRn1s5PHnyBIPBQIkSJbCzs2P//v1YWVnRoUMH4OmXM02aNKFdu3bG3S41rhR5Nb/99htdu3Zl165dwPPtWVI82wJCgZRkBgql3lIv+p+UpaUloaGhFClShNmzZxuDqRYtWhAeHs706dNZtGhRBlQr8nZJTEzE1taWrFmzcujQIXLlygU8HYhXr16duXPnkpSURKFChfjzZFU9HIukzbOfcxYWFpQpU4a9e/eyceNG/vjjD8aNG8fmzZuN52TLlo2SJUvi6upKxYoVMTExISkpSSGwSBocP34c+P/PqAkTJtChQwc6derE7t27jcvOHzx4wKNHj9i7dy/Xrl1j5syZlClThq+//hoTExP1ShT5GwoVKsStW7f49ttvgaeTCF626EnjSclMFEq9pVJmOs2fP5/ly5cbB+8WFhaEhIRQvHhxJkyYwOLFi40zprZu3Urv3r0zsmyRTOnPIbCpqSktWrTA39+fhIQEmjRpYjxmMBhwdnZmxowZNGzY8KXfconIyz27xHzx4sV8/fXX+Pr6snv3bgoWLMiKFSuIi4tjwoQJrFu3jqSkJOrWrcuIESNS3Uf9bET+t8mTJ+Pg4MB///tfAEaOHMn48ePJnj07N27coFatWsYNOxo3boytrS3e3t44OTnxxx9/MHz4cODpMj89KIukXUrgdPHiRa5cuUK2bNlYtGgR69evZ+rUqYBmHsrbQT2l3jIp6/STkpJ4/PgxVatWxdramhEjRtCkSZNUA/Dy5ctjaWlJly5d8PLywsrKClBTc5H0ePbheNOmTcTFxZEzZ04qV66Mvb09q1evpk+fPjg4OLB69Wrjdc/21FA/G5FXM3DgQH766ScqV65MtmzZCA0NZcGCBbi5uXHmzBk6duzIw4cPefz4Mebm5uzdu/cv+3CIyPPOnDnDN998w4IFC1i7di3r1q2jcePG1KlThwcPHvD1118zceJEgoOD6dy5M5cuXWLfvn08ePAAV1dXTE1NNbYUeUV79uyhQYMGtG/fnu7du1O7dm1Gjx7Nnj17GDVqFE5OThldosjfpk+Ht8izD7mXL18mf/78bN26FVdXV8aMGUNycjJNmjQxPvw6OjqyZcsWTpw4gaWlpfE+GjSIpF1KIOXj48OiRYsoUqQIV69eJV++fPj6+tKsWTMSExMZOHAgLVq0ICIiAkj9zZYCKZG0OXLkCOXLlwdg2bJlhIaGsmLFCpydnYmMjCQ0NNR4bvHixVm2bBnr16/n/v379OrVSxsJiKTTwoULOXXqFEOHDuXOnTs0bNiQggUL0qxZMwCyZMnC6NGjAfDw8MBgMODm5kb+/PmN91CvRJH0S3muS0xM5L333uO3336jZcuWjBo1iooVK7Jq1Sq2bt2Kk5OTvtyUTE/z1t8SSUlJxofclStX0rlzZ/bv30/u3LlZvnw5BoOBgIAAIiMjefz4MQBZs2Zl8eLFfPvttxgMhpeuSRaR5z277O6HH34gJCSExYsXs23bNjw8PNi9ezePHj3CxMSEpk2bMnHiRDZv3szAgQMzsGqRzOvkyZNUrFjROOPwwoULuLi44OzszNKlS+nYsSMzZ87Ezc2N+Ph4zp07R6FChfD09DTubKmHY5G0mz17Nl26dKFmzZoUKlSISZMm8fnnn3PmzBnjxjhJSUmYm5vj7+/PwIED+fTTT9mwYUOq++hhWST97ty5AzydRPDJJ59QoUIFVq1aRXBwML/88gsPHz5kxIgRHDt2TO8xyfQUSr0Fnl0+tHHjRpYtW8aBAwcYNWoUBw4cIHfu3KxcuRIrKyu+/vprXFxc+OCDD9i1axe1a9c2NnvVmmSR/y06OhogVcPWmJgY2rVrx/vvv8+yZcsICAhg0qRJfPzxx9y7d4/4+HiaNWvGmjVrGDduXAZWL5J52dvb4+LiQkREBMnJyTx48IAbN26wdOlSPD09GT9+PJ9//jnw9MuZsWPHGgf1KTRwF0mbkJAQvL29Wb16NR999BEA7733Hr6+vnTt2hU3Nzd27NhhbLRsZmbGyJEjmTFjBvXr18/Y4kUyuYMHD/L+++/z008/kTVrVoYPH05ERAQnTpwgKiqK/PnzU6xYMeLj4xk1apQ2EJBMT6HUWyAlkOrfvz/e3t7kyZOHxo0bs2vXLkaMGMHevXuNM6Y6dOhA2bJlqVy5MjExMcZASs1eRf63wMBAvvzyS0JCQoCnD7jJycncu3ePSpUq8d///hd3d3fGjx9Pr169SExMJDQ0lMjISExNTfnggw9SbdMrImlnY2NDvXr1WL58OVevXqVx48acP3+eLl268PXXX+Pl5QXA3bt3WbJkCRYWFlhbW2dw1SKZT3BwMN26daN+/frGZXoJCQkA2NnZ8c033+Dq6oqLiws7duwwzrY3NzdPtUxWRNInZRZ+QkICjRs35tNPP+Xzzz/n3LlzLFu2jCVLlnD27Fm8vb2ZO3cuPXv2ZPjw4frCRTI9NTp/S2zfvp127dqxbNkyatWqBcC8efP44YcfsLW1ZfTo0Tg6Oj4XQKm3hkjanT9/nr59+3Lz5k3c3d3p1q0bAN988w0+Pj6YmZkREhJChw4dALh9+zaurq7UqVOHkSNHZmDlIplPSj+NlGFKyiYeFStWpFGjRkydOpUBAwawePFivLy8aNmyJbdv32bUqFFcvnyZPXv2YGZmlqrfooj8tTlz5tCrVy88PT1Zs2YNbdu2ZcqUKUDqMeO1a9fo378/K1euZOXKlZodJfI3pHxO3bt3j2zZshl7RK1du5agoCDu3LmDnZ0ddnZ2lCxZkv/85z8ZXbLIa6VQ6i2xdetWWrduzaZNm6hUqZLx97Nnz6ZPnz64uLgwatSoVMdEJO1SAt2LFy/i7e3NjRs3cHd3x8PDA3ja4HXp0qXs2LGDfPnycffuXXr16sX169fZuXOnwl+RdLp16xY5c+Y0/pyQkIDBYMDPz4/169ezZcsWsmbNSq9evdi/fz979uyhRo0a2NjYEBkZibm5uZq/iqTD5MmT6d+/P5GRkTRt2pRZs2YxbNgw3NzcXhpMubu78/Dhw+f6SIlI2qQEUlFRUcyePZtbt25haWlJUFAQZcuW5dy5c2zdupXAwECOHj0KwLFjxyhTpkwGVy7y+iiUekvs3r0bNzc3Jk6cSKtWrVLNiHJ0dMTS0pKyZcsyduxYChYsmMHVimROLwqmPD096datG8ePH2fQoEGsW7eOfPnyYWtri5WVFdHR0Xo4FkmnqKgohg0bhoeHB+3ateO9994zHjt9+jSOjo74+fkxePBgAK5cucLp06cpUKAABQsWxMTERDOBRdJpy5YtxMbG0rFjRwDi4+NZvHgxvr6+Lw2m4uPjsbGxURsIkb8hIiKCjh07MnjwYMqUKcOsWbPYs2cPBw4coESJEgDcu3eP3r17s379ejZt2kTJkiUzuGqR10ehVCY2bdo0rly5YtyKt1WrVuzfv5/w8HCqVKkCQGxsLP369aNChQr8+OOPzJgxgyZNmmRk2SKZyst6rv3xxx989dVX3Lhxg549e9K5c2cAIiMjefToETlz5qR+/fp6OBZ5BUeOHCEoKIiFCxdSsWJFnJ2d8fPzw9raGmtra3x8fNi+fTsLFiygWLFiz12vXokir+7ZJa+3b98mLCzsuWDqyZMnmJubG6/Re07k1dy9e5dWrVrx4Ycf4uPjw4ULF6hbty4ffvghs2bNeu78mzdvYmtrmwGVivxzFEplUnfv3sXf35/g4GB69+7N0KFDAahfvz6nTp2iR48e5M+fn7CwMMzNzYmKiqJs2bJ89NFHxgGFiPy1Z2c37dmzh7i4OBwcHMiVKxe2tracP3+e3r17c+PGDbp3727sMfWye4hI+pw4cYLg4GCWL1/OvXv3cHFxwdvbm6tXr9KtWzd++ukn6tSpowdikX9QSjA1bNgwOnfuTFBQUEaXJJJppfSNSvncunbtGtWrV2ft2rXY2tri5OTExx9/bAykQkJCaNWqFTY2Nhlcucg/RyO4TCJlN4YU1tbW9O7dG29vb7777jv8/f2Bp9vVt2jRgk2bNhEUFETWrFkJDw8HIG/evFp/LJJGycnJxjBpyJAhdOrUiR49etC+fXtGjBjB2bNnKVy4MFOnTiV37tzMmzePGTNmPHcfBVIir6506dKMHj2amJgYevbsyfnz56lWrRpr1qwhLi6OoUOH8uDBAwVSIv+g7Nmz07FjR8aMGcOUKVP05abIK7py5QpFixZlyZIlmJiYkJycTJ48eahYsSKLFi2iatWqNG/enOnTpwNw9epVwsPDWbNmTQZXLvLP0nqSTCJlwH3o0CEqVqwIQP78+fH09CQpKYmZM2diYmLC0KFDmTFjBvHx8RgMBrJnzw7A8OHDOXXqFC4uLhn2N4hkFocPH6ZChQoABAQEMG/ePEJDQ6lfvz49e/YkNDSUa9euMXr0aEqUKMHUqVPp3LkzR48e1U5fIq+ZiYkJlpaWDBs2jIEDBxIREUFoaCgWFhYAWFlZZXCFIm+/7NmzG/u7ffLJJxldjkimZGJiQosWLejSpQuWlpa0bNmSJ0+eUKJECSZOnEitWrX47rvvjOdPmjSJEydOGHdWF3lbafleJrJ27Vq6dOnCuHHj8PT0NP7+4sWLTJgwgblz5+Lv74+3t7fx2KlTpxg+fDjR0dFERkZSuXLljChdJNM4efIkZcqUISIigrp169KqVSu++OIL2rZtS1RUFO3bt+eTTz5h//79VK1aFX9/f4oWLUpcXBx58+Y1fvOlYErk9fnzeyo+Pp7Y2FhKlSqFqamplu+JvGHqlSjyaq5cucKYMWOYNm0ay5Yto3Xr1ty8eZNOnTpx9epV6tSpQ+nSpdm7dy/Lly8nOjoaJyenjC5b5B+lEVwmkj9/ftq0acOkSZMIDg42/r5AgQK0bt2ahIQEevfunepYoUKF6Nq1K1u3blUgJZIG9vb2uLi4EBERgZmZGf369aNevXrs2rULT09Pxo8fT2hoKDVr1mTVqlX06tWLP/74Azs7O0xMTEhKSlIgJfKa/fk9lSNHDsqWLYupqSmJiYkKpETeMAVSImlz7949bt++bfz5vffeY/DgwXz55Ze0adOGZcuWYWtry8KFC2nYsCExMTEEBwfz6NEjtm/frkBK3gn6RPmXetG3vhUrVuTLL7/E1NSUcePGkZSUZJwxlStXLlxdXfnkk09o06aN8RpLS0vttieSDjY2NtSrV4+JEycyZswYmjdvDkBgYCCNGzeme/fuABQrVgxHR0cqV65MgQIFjNfr4VjkzVLfNhER+Tc6efIk7du3x9ramh49ehi/+MyXLx8TJkwgOTmZdu3asXjxYtq1a8fYsWMxMTHhyZMnmJqaKvyVd4b+S/8XejaQWrt2Lbdu3QKgQ4cOVKhQgV69emEwGPD39+fixYs0adKEkSNHYmdnR7t27TAYDNrxSySNUpYFpaxkNhgM+Pj4sGDBAoYPH25c23/79m3++OMP7t27R86cOTl48CAeHh54eHhgMBi0fEhEREREgKfPc8HBwcTExGBlZcWtW7e4f/8+uXLlonr16nh6euLh4UHu3Lnp0KED2bNn56OPPiI5ORlLS8uMLl/kjVJPqX+ZZ/tmDBkyhCVLlmBpaYmFhQU5cuRg3bp1WFpacvz4cZYuXcqYMWMoXLgwOXPmZNu2bZibm6ufjUg63Lp1i5w5cxp/TkhIwGAw4Ofnx+bNm1mxYgV2dnZMnz6defPmGQOoe/fucejQIczMzPSeExEREZFULl++TGBgIKdPn6ZkyZJ8+eWXLFy4kG3btnHw4EFy5cpF8eLF2bt3L1euXCE6Opq6detmdNkib5xCqX+pCRMmMHHiRFauXEn16tWZOnUqffv2pVq1amzcuBEbGxuSk5O5ePEiN27coEKFCpiYmKjxpEg6REVFMWzYMDw8PIy7CqU4ffo0jo6OjBw5koEDBwIwe/ZsTp8+TUJCAoGBgZiZmWlWooiIiIi80KVLlwgICOCXX37Bw8MDLy8vAHbv3s2lS5eYPXs2Fy9e5PDhwxw+fJhy5cplcMUib55CqX+hCxcu4OPjQ9u2bWndujWRkZG4ubnRu3dvlixZQq5cuVi3bh02NjaprtPyIZH0OXLkCEFBQSxcuJCKFSvi7OyMn58f1tbWWFtb4+Pjw/bt21mwYAHFihV77nqFwCIiIiLyV2JjYwkICGD37t20atWKoUOHGo89efKEpKQk4uPjU305KvIuUYLxL1SwYEFcXV2pWbMmv/76K1988QWBgYGMHj0ad3d3du/ejZOTE/fv3091nQIpkfQpX74833//PTExMTRu3JiNGzfi7OxMnz592L9/P40aNeLMmTNcvHgRgMTExFTXK5ASERERkb+SL18+fH19qVGjBhEREYwbN854zGAwYGlpqUBK3mlKMTJYUlLSC3/fpk0b8uXLx9atW6lcuTKffvop8HS7+q5du9K4cWM1wRN5TUqXLs3o0aOJiYmhZ8+enD9/nmrVqrFmzRri4uIYOnQoDx480DI9EREREUk3e3t7fH19cXZ2ZvXq1YwYMQLQF5wioFAqQyUnJxtnN82bN4/+/fszd+5cTpw4YTzn1KlTHDx4EGtra+7fv09ERASlS5dm1qxZmJqaPjdzQ0RejYmJCZaWlgwbNozVq1cTFhbGuXPnsLCwAMDKyiqDKxQRERGRzColmCpVqhQ7duzg+vXrGV2SyL+CekplkGd36/Lz82PatGk4Oztz4MAB6tevz+eff86HH37IoUOHaNKkCQaDwbhD2IEDB5Sqi/wD/ryLXnx8PLGxsZQqVQpTU1P1bRMRERGRvyUuLg4AOzu7DK5E5N9ByUYGePbBd9++fZw6dYqff/6Z999/n02bNjF27FgmT56MiYkJjRo1Yv369SxevJjs2bPTp08f7fgl8g95NpACyJEjBzly5ADQe05ERERE/jaFUSKpaaZUBpo/fz6hoaEkJiayfPly4256mzdvJiAgAAsLC/r370+jRo1SXaeHYxERERERERHJ7LQO5Q3auHEj/v7+xp8TExP5/fffOXjwIIcPHzb+vkGDBgwdOpTExER8fX3ZvXt3qvsokBIRERERERGRzE7L996QR48esWTJEnbu3ImFhQWDBg3Cw8MDW1tbRowYwbRp07CwsKBq1arA02Dq0aNHrF27Fmdn5wyuXkRERERERETk9dLyvTfo0qVLjB8/nl27dtGiRQuGDh0KwKJFi5g0aRIODg707duXKlWqPHetGiyLiIiIiIiIyNtEodQbdvnyZcaMGcOePXueC6aCgoIoX748PXv2pGbNmhlcqYiIiIiIiIjIP0dTb94we3t7fH19cXZ2JiIigoCAAAA6depE//792bx5M+vXr8/gKkVERERERERE/lmaKZVBnp0x1bJlS4YMGQLA+vXradiwoZqZi4iIiIiIiMhbTaFUBrp8+TIBAQHs3buXunXrMnbsWOOxxMREBVMiIiIiIiIi8tbS8r0MZG9vz9ChQylRogTXr1/n2XxQgZSIiIiIiIiIvM00U+pf4MaNG+TMmRMTExOSk5MxGAwZXZKIiIiIiIiIyD9KodS/SFJSEiYmmrwmIiIiIiIiIm8/hVIiIiIiIiIiIvLGaVqOiIiIiIiIiIi8cQqlRERERERERETkjVMoJSIiIiIiIiIib5xCKREREREREREReeMUSomIiIiIiIiIyBunUEpERERERERERN44hVIiIiIigMFgYMWKFa/1niNHjsTJyem539nZ2Rlfz93dnVatWr3W1xURERHJDAzJycnJGV2EiIiIyD/p6tWrDB8+nMjISOLi4rC1taVSpUoMHz6c2rVrA3D58mVsbW2xtLR8ba979+5dHj16RO7cuQE4duwY5cqVIzw8nJo1a2Jra8vDhw9JTk4mZ86cr+11/5f69evj5OTE5MmT/+d5W7ZsAcDCwoI8efJQpUoVPDw8cHV1fQOVioiIyNvMLKMLEBEREfmntWnThsePHzNv3jyKFy9OXFwcGzdu5Pr168Zz7O3tX/vrWltbY21tbfz59OnTALRs2RKDwQDwWkOwf0KPHj0YNWoUCQkJXLhwgfDwcDp27Ii7uzuzZ8/O6PJEREQkE9PyPREREXmr3bp1i23bthEYGEiDBg0oUqQI1atXZ8iQIbRo0cJ43p+X7+3YsQMnJyesrKyoVq0aK1aswGAwcODAAQCio6MxGAxs3LiRatWqkTVrVmrVqsXx48eN93h2+d7IkSNp3rw5ACYmJsZQ6s/L95KSkhg/fjwlS5bE0tKSwoULM2bMGONxHx8fSpcuTdasWSlevDh+fn48efLkudcMCQmhaNGi5MiRg44dO3Lnzh3j623ZsoUpU6ZgMBgwGAycPXv2pf/+smbNir29PQULFqRmzZoEBgYya9Ys5syZw4YNG9JU19mzZzExMeHXX39Nde/JkydTpEgRkpKSuHnzJp07dyZv3rxkyZKFUqVKMXfu3JfWJSIiIpmfQikRERF5q6XMVlqxYgWPHj1K0zW3b9+mefPmVKxYkX379jF69Gh8fHxeeK6vry8TJ07k119/xczMDE9PzxeeN2DAAGPIEhsbS2xs7AvPGzJkCOPGjcPPz4+jR48SGhqKnZ2d8biNjQ3BwcEcPXqUKVOmMGfOHIKCglLd4/Tp06xYsYLVq1ezevVqtmzZwrhx4wCYMmUK77//Pj169DDWUahQoTT9e0nRrVs3bG1tWb58eZrqKlq0KI0bN34uZJo7dy7u7u6YmJgY/96ff/6ZY8eO8d1335EnT5501SUiIiKZi5bviYiIyFvNzMyM4OBgevTowcyZM6lSpQr16tWjY8eOODo6vvCa0NBQDAYDc+bMwcrKinLlynHx4kV69Ojx3LljxoyhXr16AAwePJiPP/6Yhw8fYmVlleo8a2trY9+oly0VvHPnDlOmTGH69Ol069YNgBIlSlCnTh3jOcOGDTP+c9GiRRkwYABhYWEMGjTI+PukpCSCg4OxsbEBoEuXLmzcuJExY8aQI0cOLCwsjDOgXoWJiQmlS5dONcPqf9X12Wef0atXLyZNmoSlpSX79u3j0KFDrFy5EoDz589TuXJlqlWrZryHiIiIvN00U0pERETeem3atOHSpUtERETQpEkToqOjqVKlCsHBwS88//jx4zg6OqYKlqpXr/7Cc58NtvLlywfAlStXXqnOY8eO8ejRIxo1avTScxYvXkzt2rWxt7fH2tqaYcOGcf78+VTnFC1a1BhIpdT1qjW9THJysnEJYlrqatWqFaampoSHhwMQHBxMgwYNjOGTl5cXYWFhODk5MWjQIHbs2PFa6xUREZF/H4VSIiIi8k6wsrLiww8/xM/Pjx07duDu7s6IESP+9n3Nzc2N/5wS0iQlJb3SvbJkyfKXx3fu3Ennzp1p1qwZq1evZv/+/fj6+vL48eOX1pRS16vW9CKJiYmcPHmSYsWKpbkuCwsLunbtyty5c3n8+DGhoaGpljo2bdqUc+fO0a9fPy5dukSjRo0YMGDAa6tZRERE/n0USomIiMg7qVy5cty7d++Fx8qUKcOhQ4dS9aDas2fPP15TqVKlyJIlCxs3bnzh8R07dlCkSBF8fX2pVq0apUqV4ty5c+l+HQsLCxITE1+5znnz5nHz5k3atGmTrro+++wzNmzYwIwZM0hISMDV1TXV8bx589KtWzcWLFjA5MmTtbufiIjIW049pUREROStdv36ddq1a4enpyeOjo7Y2Njw66+/Mn78eFq2bPnCa9zc3PD19eXzzz9n8ODBnD9/ngkTJgCkWrL2ullZWeHj48OgQYOwsLCgdu3aXL16lSNHjtC9e3dKlSrF+fPnCQsLw9nZmcjISONyuPQoWrQou3fv5uzZs1hbW5MrVy5MTF78XeX9+/e5fPkyCQkJXLhwgfDwcIKCgvDy8qJBgwYAaa7LwcGBmjVr4uPjg6enZ6qZYcOHD6dq1aqUL1+eR48esXr1ahwcHNL9t4mIiEjmoZlSIiIi8laztramRo0aBAUFUbduXSpUqICfnx89evRg+vTpL7wme/bsrFq1igMHDuDk5ISvry/Dhw8HeK6B+evm5+fHf/7zH4YPH46DgwMdOnQw9oNq0aIF/fr1w9vbGycnJ3bs2IGfn1+6X2PAgAGYmppSrlw58ubN+1xPqmfNmTOHfPnyUaJECVxdXTl69CiLFy9mxowZxnPSU1f37t15/Pjxc7sUWlhYMGTIEBwdHalbty6mpqaEhYWl+28TERGRzMOQnJycnNFFiIiIiPzbLVy4EA8PD+Lj4/9n7yd5udGjR/PTTz9x8ODBjC5FREREMpiW74mIiIi8wPz58ylevDgFChQgJiYGHx8f2rdvr0DqFd29e5ezZ88yffp0/P39M7ocERER+RfQ8j0RERGRF7h8+TKffvopDg4O9OvXj3bt2qnx9t/g7e1N1apVqV+//nNL90REROTdpOV7IiIiIiIiIiLyxmmmlIiIiIiIiIiIvHEKpURERERERERE5I1TKCUiIiIiIiIiIm+cQikREREREREREXnjFEqJiIiIiIiIiMgbp1BKRERERERERETeOIVSIiIiIiIiIiLyximUEhERERERERGRN06hlIiIiIiIiIiIvHH/B5R7h1184WJ6AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
        "df['Total'] = pd.to_numeric(df['Total'].replace('[\\$,]', '', regex=True), errors='coerce')\n",
        "\n",
        "significant_days = {\n",
        "    '12-25': 'Christmas',\n",
        "    '12-31': 'New Year\\'s Eve',\n",
        "    '01-01': 'New Year\\'s Day',\n",
        "    '02-14': 'Valentine\\'s Day',\n",
        "    '11-11': 'Veterans Day',\n",
        "    '11-28': 'Thanksgiving',\n",
        "    '01-20': 'Inauguration Day',\n",
        "}\n",
        "\n",
        "significant_data = df[df['Order Date'].dt.strftime('%m-%d').isin(significant_days.keys())]\n",
        "total_sales_comparison = significant_data.groupby(significant_data['Order Date'].dt.strftime('%m-%d'))['Total'].sum().reset_index()\n",
        "total_sales_comparison['Day Explanation'] = total_sales_comparison['Order Date'].map(significant_days)\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "sns.barplot(x='Day Explanation', y='Total', data=total_sales_comparison, palette='viridis', hue='Day Explanation', dodge=False)\n",
        "plt.title('Total Revenue Comparison on Significant Days')\n",
        "\n",
        "plt.xlabel('Significant Days')\n",
        "plt.ylabel('Total Revenue ($)')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.legend(title=None)\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "txfCIUXxVwt4"
      },
      "source": [
        "## Plot - 3"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [],
      "source": [
        "def assign_label(avg_spending):\n",
        "    if avg_spending < percentiles*0.5:\n",
        "        return 'Economy'\n",
        "    elif avg_spending > percentiles*1.5:\n",
        "        return 'Premium'\n",
        "    else:\n",
        "        return 'Standard'\n",
        "    \n",
        "def assign_time_label(hour):\n",
        "    if hour < 12:\n",
        "        return 'Morning'\n",
        "    elif hour < 18:\n",
        "        return 'Evening'\n",
        "    else:\n",
        "        return 'Night'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "cNS-ueYL9f6r"
      },
      "outputs": [],
      "source": [
        "customer_avg_spending = df.groupby('Last 4 Card Digits')['Total'].mean().reset_index()\n",
        "\n",
        "percentiles = customer_avg_spending.Total.mean()\n",
        "customer_avg_spending['Orderer Type'] = customer_avg_spending['Total'].apply(assign_label)\n",
        "data=customer_avg_spending\n",
        "\n",
        "data1 = df.merge(customer_avg_spending[['Last 4 Card Digits', 'Orderer Type']], on='Last 4 Card Digits', how='left')\n",
        "df    = data1[['Last 4 Card Digits', 'Order Date', 'Menu Item', 'Qty', 'Total', 'Tip', 'Orderer Type']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "n93mAacF9f4D",
        "outputId": "eeb26c14-bf94-4098-c9c3-7945629032e8"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_15190/924893113.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
            "/tmp/ipykernel_15190/924893113.py:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Order Hour'] = df['Order Date'].dt.hour\n",
            "/tmp/ipykernel_15190/924893113.py:4: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  df['Time of Day'] = df['Order Hour'].apply(assign_time_label)\n"
          ]
        },
        {
          "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>Orderer Type</th>\n",
              "      <th>Time of Day_x</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Economy</td>\n",
              "      <td>Evening</td>\n",
              "      <td>1495.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Economy</td>\n",
              "      <td>Morning</td>\n",
              "      <td>7.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Economy</td>\n",
              "      <td>Night</td>\n",
              "      <td>1295.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Premium</td>\n",
              "      <td>Evening</td>\n",
              "      <td>4069.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Premium</td>\n",
              "      <td>Morning</td>\n",
              "      <td>431.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Premium</td>\n",
              "      <td>Night</td>\n",
              "      <td>5760.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Standard</td>\n",
              "      <td>Evening</td>\n",
              "      <td>97686.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Standard</td>\n",
              "      <td>Morning</td>\n",
              "      <td>5027.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Standard</td>\n",
              "      <td>Night</td>\n",
              "      <td>115880.83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Orderer Type Time of Day_x      Total\n",
              "0      Economy       Evening    1495.10\n",
              "1      Economy       Morning       7.63\n",
              "2      Economy         Night    1295.06\n",
              "3      Premium       Evening    4069.93\n",
              "4      Premium       Morning     431.95\n",
              "5      Premium         Night    5760.44\n",
              "6     Standard       Evening   97686.65\n",
              "7     Standard       Morning    5027.42\n",
              "8     Standard         Night  115880.83"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
        "df['Order Hour'] = df['Order Date'].dt.hour\n",
        "\n",
        "df['Time of Day'] = df['Order Hour'].apply(assign_time_label)\n",
        "person_max_label = df.groupby('Last 4 Card Digits')['Time of Day'].max().reset_index()\n",
        "data1 = df.merge(person_max_label[['Last 4 Card Digits', 'Time of Day']], on='Last 4 Card Digits', how='left')\n",
        "data1.drop(columns='Time of Day_y', inplace=True, errors='ignore')\n",
        "\n",
        "revenue_by_ordertype = data1.groupby(['Orderer Type',\"Time of Day_x\"])['Total'].sum().reset_index()\n",
        "revenue_by_ordertype"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 573
        },
        "id": "KZip2xfn9fwi",
        "outputId": "bcec5110-ada3-436d-9d64-794fcecb3420"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "data = {\n",
        "    'Orderer Type': ['Economy', 'Economy', 'Economy', 'Premium', 'Premium', 'Premium', 'Standard', 'Standard', 'Standard'],\n",
        "    'Time of Day_x': ['Evening', 'Morning', 'Night', 'Evening', 'Morning', 'Night', 'Evening', 'Morning', 'Night'],\n",
        "    'Total': [1495.10, 7.63, 1295.06, 4069.93, 431.95, 5760.44, 97686.65, 5027.42, 115880.83]\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(data)\n",
        "sns.set(style=\"whitegrid\")\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(x=\"Time of Day_x\", y=\"Total\", hue=\"Orderer Type\", data=df)\n",
        "\n",
        "plt.xlabel(\"Time of Day\")\n",
        "plt.ylabel(\"Total Revenue\")\n",
        "plt.title(\"Total Revenue by Orderer Type in Different Times of the Day\")\n",
        "\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "ZEaRcf-ZUG5T"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.12"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}