Final_project_panda.ipynb

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        "<a href=\"https://colab.research.google.com/github/Piyushvishnoi/Python_Pandas_Project/blob/main/Final_project_panda.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "# Step 1: Install required libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Step 2: Download the Chipotle dataset\n",
        "data_read = pd.read_csv(\"/chipotle.tsv\", sep=\"\\t\")\n",
        "data=pd.DataFrame(data_read)"
      ]
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        "# Step 3: Display basic information\n",
        "print(\" First 5 Rows of Dataset:\")\n",
        "display(data.head())\n",
        "\n",
        "print(\"\\n Dataset Summary:\")\n",
        "print(data.info())\n",
        "\n",
        "print(\"\\n Dataset Shape (Rows, Columns):\", data.shape)"
      ],
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          "text": [
            " First 5 Rows of Dataset:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   order_id  quantity                              item_name  \\\n",
              "0         1         1           Chips and Fresh Tomato Salsa   \n",
              "1         1         1                                   Izze   \n",
              "2         1         1                       Nantucket Nectar   \n",
              "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
              "4         2         2                           Chicken Bowl   \n",
              "\n",
              "                                  choice_description item_price  \n",
              "0                                                NaN     $2.39   \n",
              "1                                       [Clementine]     $3.39   \n",
              "2                                            [Apple]     $3.39   \n",
              "3                                                NaN     $2.39   \n",
              "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98   "
            ],
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              "      <td>Chicken Bowl</td>\n",
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              "summary": "{\n  \"name\": \"print(\\\"\\\\n Dataset Shape (Rows, Columns):\\\", data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"order_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 2,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Izze\",\n          \"Chicken Bowl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"choice_description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"[Clementine]\",\n          \"[Apple]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_price\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"$2.39 \",\n          \"$3.39 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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            "\n",
            " Dataset Summary:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4622 entries, 0 to 4621\n",
            "Data columns (total 5 columns):\n",
            " #   Column              Non-Null Count  Dtype \n",
            "---  ------              --------------  ----- \n",
            " 0   order_id            4622 non-null   int64 \n",
            " 1   quantity            4622 non-null   int64 \n",
            " 2   item_name           4622 non-null   object\n",
            " 3   choice_description  3376 non-null   object\n",
            " 4   item_price          4622 non-null   object\n",
            "dtypes: int64(2), object(3)\n",
            "memory usage: 180.7+ KB\n",
            "None\n",
            "\n",
            " Dataset Shape (Rows, Columns): (4622, 5)\n"
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      "cell_type": "code",
      "source": [
        "# Step 4: Basic Data Analysis\n",
        "print(\"\\n🔹 Summary Statistics for Numerical Columns:\")\n",
        "print(data.describe())\n",
        "\n",
        "print(\"\\n🔹 Unique Items Ordered:\")\n",
        "print(data[\"item_name\"].nunique())\n",
        "\n",
        "print(\"\\n🔹 Most Ordered Items:\")\n",
        "print(data[\"item_name\"].value_counts().head(10))"
      ],
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      "execution_count": 8,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 Summary Statistics for Numerical Columns:\n",
            "          order_id     quantity\n",
            "count  4622.000000  4622.000000\n",
            "mean    927.254868     1.075725\n",
            "std     528.890796     0.410186\n",
            "min       1.000000     1.000000\n",
            "25%     477.250000     1.000000\n",
            "50%     926.000000     1.000000\n",
            "75%    1393.000000     1.000000\n",
            "max    1834.000000    15.000000\n",
            "\n",
            "🔹 Unique Items Ordered:\n",
            "50\n",
            "\n",
            "🔹 Most Ordered Items:\n",
            "item_name\n",
            "Chicken Bowl                    726\n",
            "Chicken Burrito                 553\n",
            "Chips and Guacamole             479\n",
            "Steak Burrito                   368\n",
            "Canned Soft Drink               301\n",
            "Steak Bowl                      211\n",
            "Chips                           211\n",
            "Bottled Water                   162\n",
            "Chicken Soft Tacos              115\n",
            "Chips and Fresh Tomato Salsa    110\n",
            "Name: count, dtype: int64\n"
          ]
        }
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      "cell_type": "code",
      "source": [
        "# Step 5: Convert item_price column to float for analysis\n",
        "#data[\"item_price\"] = data[\"item_price\"].str.replace(\"$\", \"\").astype(float)    #to replace dollar sign with no space\n",
        "print(data.info())\n",
        "print(\"\\n🔹 Average Item Price:\", data[\"item_price\"].mean())\n",
        "print(\"\\n🔹 Most Expensive Item Ordered:\")\n",
        "#The idmax() function in pandas is used to find the index of the first occurrence of the maximum value in a Series or DataFrame.\n",
        "print(data.loc[data[\"item_price\"].idxmax()])"
      ],
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      "execution_count": 12,
      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4622 entries, 0 to 4621\n",
            "Data columns (total 5 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   order_id            4622 non-null   int64  \n",
            " 1   quantity            4622 non-null   int64  \n",
            " 2   item_name           4622 non-null   object \n",
            " 3   choice_description  3376 non-null   object \n",
            " 4   item_price          4622 non-null   float64\n",
            "dtypes: float64(1), int64(2), object(2)\n",
            "memory usage: 180.7+ KB\n",
            "None\n",
            "\n",
            "🔹 Average Item Price: 7.464335785374297\n",
            "\n",
            "🔹 Most Expensive Item Ordered:\n",
            "order_id                                      1443\n",
            "quantity                                        15\n",
            "item_name             Chips and Fresh Tomato Salsa\n",
            "choice_description                             NaN\n",
            "item_price                                   44.25\n",
            "Name: 3598, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Sorting and Filtering\n",
        "print(\"\\n🔹 Top 5 Most Expensive Orders:\")\n",
        "display(data.sort_values(by=\"item_price\", ascending=False).head())\n",
        "\n",
        "print(\"\\n🔹 Orders with Quantity More Than 2:\")\n",
        "display(data[data[\"quantity\"] > 2].head())"
      ],
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          "text": [
            "\n",
            "🔹 Top 5 Most Expensive Orders:\n"
          ]
        },
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          "data": {
            "text/plain": [
              "      order_id  quantity                     item_name  \\\n",
              "3598      1443        15  Chips and Fresh Tomato Salsa   \n",
              "3480      1398         3                 Carnitas Bowl   \n",
              "1254       511         4               Chicken Burrito   \n",
              "3602      1443         4               Chicken Burrito   \n",
              "3601      1443         3                Veggie Burrito   \n",
              "\n",
              "                                     choice_description  item_price  \n",
              "3598                                                NaN       44.25  \n",
              "3480  [Roasted Chili Corn Salsa, [Fajita Vegetables,...       35.25  \n",
              "1254  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       35.00  \n",
              "3602  [Fresh Tomato Salsa, [Rice, Black Beans, Chees...       35.00  \n",
              "3601  [Fresh Tomato Salsa, [Fajita Vegetables, Rice,...       33.75  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(data[data[\\\"quantity\\\"] > 2]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"order_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 412,\n        \"min\": 511,\n        \"max\": 1443,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1443,\n          1398,\n          511\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 3,\n        \"max\": 15,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          15,\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Carnitas Bowl\",\n          \"Veggie Burrito\",\n          \"Chips and Fresh Tomato Salsa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"choice_description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"[Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Lettuce]]\",\n          \"[Fresh Tomato Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole]]\",\n          \"[Roasted Chili Corn Salsa, [Fajita Vegetables, Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.288793536648739,\n        \"min\": 33.75,\n        \"max\": 44.25,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          35.25,\n          33.75,\n          44.25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 Orders with Quantity More Than 2:\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "     order_id  quantity          item_name  \\\n",
              "409       178         3       Chicken Bowl   \n",
              "445       193         3               Bowl   \n",
              "689       284         3  Canned Soft Drink   \n",
              "818       338         3      Bottled Water   \n",
              "850       350         3  Canned Soft Drink   \n",
              "\n",
              "                                    choice_description  item_price  \n",
              "409  [[Fresh Tomato Salsa (Mild), Tomatillo-Green C...       32.94  \n",
              "445  [Braised Carnitas, Pinto Beans, [Sour Cream, C...       22.20  \n",
              "689                                        [Diet Coke]        3.75  \n",
              "818                                                NaN        3.27  \n",
              "850                                           [Sprite]        3.75  "
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              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(data[data[\\\"quantity\\\"] > 2]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"order_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80,\n        \"min\": 178,\n        \"max\": 350,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          193,\n          350,\n          284\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bowl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"choice_description\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"[Braised Carnitas, Pinto Beans, [Sour Cream, Cheese, Cilantro-Lime Rice]]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.673659714940987,\n        \"min\": 3.27,\n        \"max\": 32.94,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          22.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 7: Dropping a Column (Example)\n",
        "data_cleaned = data.drop(columns=[\"choice_description\"])\n",
        "print(\"\\n🔹 Columns after dropping 'choice_description':\", data_cleaned.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XKRNs8JXiyt4",
        "outputId": "3ceb5a30-ca82-4167-87ac-52c164843476"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "🔹 Columns after dropping 'choice_description': Index(['order_id', 'quantity', 'item_name', 'item_price'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 8: Save the cleaned dataset (optional)\n",
        "data_cleaned.to_csv(\"chipotle_cleaned.csv\", index=False)"
      ],
      "metadata": {
        "id": "6RSJBYx-i2df"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q14AqnFWj6qQ",
        "outputId": "59c3c67f-d4ea-4a3b-9e10-ccc45c1bdd91"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['order_id', 'quantity', 'item_name', 'choice_description',\n",
              "       'item_price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#to check how many null values in a column\n",
        "data.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "Tzi1yiNtkIIu",
        "outputId": "5a7cd1be-c3fe-4531-c512-c46588f77bea"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id                 0\n",
              "quantity                 0\n",
              "item_name                0\n",
              "choice_description    1246\n",
              "item_price               0\n",
              "dtype: int64"
            ],
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              "</div><br><label><b>dtype:</b> int64</label>"
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for null values\n",
        "null_values = data.isnull().sum()\n",
        "print(\"Null values in each column:\")\n",
        "print(null_values)\n",
        "\n",
        "# Create a copy to avoid modifying the original data\n",
        "data_copy = data.copy()\n",
        "\n",
        "# Fill NA values in the copied dataset\n",
        "data_copy.fillna('NA', inplace=True)\n",
        "# Display processed data\n",
        "print(data_copy.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h7D7wdFukd4R",
        "outputId": "fc3876e7-a657-4185-c374-16047f7aa523"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Null values in each column:\n",
            "order_id                 0\n",
            "quantity                 0\n",
            "item_name                0\n",
            "choice_description    1246\n",
            "item_price               0\n",
            "dtype: int64\n",
            "   order_id  quantity                              item_name  \\\n",
            "0         1         1           Chips and Fresh Tomato Salsa   \n",
            "1         1         1                                   Izze   \n",
            "2         1         1                       Nantucket Nectar   \n",
            "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
            "4         2         2                           Chicken Bowl   \n",
            "\n",
            "                                  choice_description  item_price  \n",
            "0                                                 NA        2.39  \n",
            "1                                       [Clementine]        3.39  \n",
            "2                                            [Apple]        3.39  \n",
            "3                                                 NA        2.39  \n",
            "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Example of concatenation (modify as needed)\n",
        "# Assuming there are at least two columns to concatenate\n",
        "if data_copy.shape[1] > 1:\n",
        "    data_copy['concatenated'] = data_copy.iloc[:, 0].astype(str) + ' ' + data_copy.iloc[:, 1].astype(str)  # Selecting first two columns, converting to string, and concatenating with a space\n",
        "\n",
        "# Display processed data\n",
        "print(data_copy.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7-ZCx7A8mmAQ",
        "outputId": "a26c7c4f-bf7a-4a86-b1ad-6c53817c1dad"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "   order_id  quantity                              item_name  \\\n",
            "0         1         1           Chips and Fresh Tomato Salsa   \n",
            "1         1         1                                   Izze   \n",
            "2         1         1                       Nantucket Nectar   \n",
            "3         1         1  Chips and Tomatillo-Green Chili Salsa   \n",
            "4         2         2                           Chicken Bowl   \n",
            "\n",
            "                                  choice_description  item_price concatenated  \n",
            "0                                                 NA        2.39          1 1  \n",
            "1                                       [Clementine]        3.39          1 1  \n",
            "2                                            [Apple]        3.39          1 1  \n",
            "3                                                 NA        2.39          1 1  \n",
            "4  [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98          2 2  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_copy.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0j4rPA_poypz",
        "outputId": "8bd1212c-3cda-4d2c-a705-9b8ca7696dbb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      order_id  quantity           item_name  \\\n",
              "4617      1833         1       Steak Burrito   \n",
              "4618      1833         1       Steak Burrito   \n",
              "4619      1834         1  Chicken Salad Bowl   \n",
              "4620      1834         1  Chicken Salad Bowl   \n",
              "4621      1834         1  Chicken Salad Bowl   \n",
              "\n",
              "                                     choice_description item_price  \\\n",
              "4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...    $11.75    \n",
              "4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...    $11.75    \n",
              "4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...    $11.25    \n",
              "4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...     $8.75    \n",
              "4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...     $8.75    \n",
              "\n",
              "     concatenated  \n",
              "4617       1833 1  \n",
              "4618       1833 1  \n",
              "4619       1834 1  \n",
              "4620       1834 1  \n",
              "4621       1834 1  "
            ],
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-3f21edf4-b0ba-4cf9-aae4-511f6a6be4cf button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-3f21edf4-b0ba-4cf9-aae4-511f6a6be4cf');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-275db4c2-f4f7-4d3f-8eac-78f6edc74140\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-275db4c2-f4f7-4d3f-8eac-78f6edc74140')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-275db4c2-f4f7-4d3f-8eac-78f6edc74140 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_copy.fillna(0,inplace=False)  #to have value 0 in place of NaN values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "Oi9nb9VxFdwj",
        "outputId": "fccd9eca-c003-42eb-fbc4-d458152c4a71"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      order_id  quantity                              item_name  \\\n",
              "0            1         1           Chips and Fresh Tomato Salsa   \n",
              "1            1         1                                   Izze   \n",
              "2            1         1                       Nantucket Nectar   \n",
              "3            1         1  Chips and Tomatillo-Green Chili Salsa   \n",
              "4            2         2                           Chicken Bowl   \n",
              "...        ...       ...                                    ...   \n",
              "4617      1833         1                          Steak Burrito   \n",
              "4618      1833         1                          Steak Burrito   \n",
              "4619      1834         1                     Chicken Salad Bowl   \n",
              "4620      1834         1                     Chicken Salad Bowl   \n",
              "4621      1834         1                     Chicken Salad Bowl   \n",
              "\n",
              "                                     choice_description item_price  \\\n",
              "0                                                    NA     $2.39    \n",
              "1                                          [Clementine]     $3.39    \n",
              "2                                               [Apple]     $3.39    \n",
              "3                                                    NA     $2.39    \n",
              "4     [Tomatillo-Red Chili Salsa (Hot), [Black Beans...    $16.98    \n",
              "...                                                 ...        ...   \n",
              "4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...    $11.75    \n",
              "4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...    $11.75    \n",
              "4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...    $11.25    \n",
              "4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...     $8.75    \n",
              "4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...     $8.75    \n",
              "\n",
              "     concatenated  \n",
              "0             1 1  \n",
              "1             1 1  \n",
              "2             1 1  \n",
              "3             1 1  \n",
              "4             2 2  \n",
              "...           ...  \n",
              "4617       1833 1  \n",
              "4618       1833 1  \n",
              "4619       1834 1  \n",
              "4620       1834 1  \n",
              "4621       1834 1  \n",
              "\n",
              "[4622 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-76fbf70a-2cd4-4f6f-ba68-737304872e71\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>quantity</th>\n",
              "      <th>item_name</th>\n",
              "      <th>choice_description</th>\n",
              "      <th>item_price</th>\n",
              "      <th>concatenated</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Chips and Fresh Tomato Salsa</td>\n",
              "      <td>NA</td>\n",
              "      <td>$2.39</td>\n",
              "      <td>1 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Izze</td>\n",
              "      <td>[Clementine]</td>\n",
              "      <td>$3.39</td>\n",
              "      <td>1 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Nantucket Nectar</td>\n",
              "      <td>[Apple]</td>\n",
              "      <td>$3.39</td>\n",
              "      <td>1 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Chips and Tomatillo-Green Chili Salsa</td>\n",
              "      <td>NA</td>\n",
              "      <td>$2.39</td>\n",
              "      <td>1 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>Chicken Bowl</td>\n",
              "      <td>[Tomatillo-Red Chili Salsa (Hot), [Black Beans...</td>\n",
              "      <td>$16.98</td>\n",
              "      <td>2 2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4617</th>\n",
              "      <td>1833</td>\n",
              "      <td>1</td>\n",
              "      <td>Steak Burrito</td>\n",
              "      <td>[Fresh Tomato Salsa, [Rice, Black Beans, Sour ...</td>\n",
              "      <td>$11.75</td>\n",
              "      <td>1833 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4618</th>\n",
              "      <td>1833</td>\n",
              "      <td>1</td>\n",
              "      <td>Steak Burrito</td>\n",
              "      <td>[Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...</td>\n",
              "      <td>$11.75</td>\n",
              "      <td>1833 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4619</th>\n",
              "      <td>1834</td>\n",
              "      <td>1</td>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
              "      <td>$11.25</td>\n",
              "      <td>1834 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4620</th>\n",
              "      <td>1834</td>\n",
              "      <td>1</td>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Lettu...</td>\n",
              "      <td>$8.75</td>\n",
              "      <td>1834 1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4621</th>\n",
              "      <td>1834</td>\n",
              "      <td>1</td>\n",
              "      <td>Chicken Salad Bowl</td>\n",
              "      <td>[Fresh Tomato Salsa, [Fajita Vegetables, Pinto...</td>\n",
              "      <td>$8.75</td>\n",
              "      <td>1834 1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4622 rows × 6 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-76fbf70a-2cd4-4f6f-ba68-737304872e71')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-76fbf70a-2cd4-4f6f-ba68-737304872e71 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-76fbf70a-2cd4-4f6f-ba68-737304872e71');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-37304734-abb5-48fc-93ac-f90a9e3b68ed\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-37304734-abb5-48fc-93ac-f90a9e3b68ed')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-37304734-abb5-48fc-93ac-f90a9e3b68ed button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data_copy\",\n  \"rows\": 4622,\n  \"fields\": [\n    {\n      \"column\": \"order_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 528,\n        \"min\": 1,\n        \"max\": 1834,\n        \"num_unique_values\": 1834,\n        \"samples\": [\n          644,\n          333,\n          991\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 15,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          8,\n          2,\n          15\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"Barbacoa Burrito\",\n          \"Crispy Tacos\",\n          \"Chips and Roasted Chili Corn Salsa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"choice_description\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1044,\n        \"samples\": [\n          \"[Fresh Tomato Salsa, [Rice, Pinto Beans, Lettuce]]\",\n          \"[Adobo-Marinated and Grilled Chicken, [Sour Cream, Cheese, Cilantro-Lime Rice]]\",\n          \"[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Guacamole, Lettuce]]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_price\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 78,\n        \"samples\": [\n          \"$23.50 \",\n          \"$2.39 \",\n          \"$7.40 \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"concatenated\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2004,\n        \"samples\": [\n          \"538 1\",\n          \"1176 1\",\n          \"1693 1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_copy.item_price.mean()\n",
        "#data_copy.item_price\n",
        "#item_price_mean=data_copy.item_price.mean()\n",
        "#print(item_price_mean)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hbVUZcBRGXOc",
        "outputId": "4c09e73a-9280-413f-b3ee-641e51887f1d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7.464335785374297"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bRA6MOHj6whf",
        "outputId": "d0a16518-f143-4811-f3cf-b5a7f84a723f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['order_id', 'quantity', 'item_name', 'choice_description',\n",
              "       'item_price'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Remove the dollar sign and strip extra spaces\n",
        "data_copy['item_price'] = data_copy['item_price'].str.replace('$', '', regex=False).str.strip()\n",
        "\n",
        "# Step 2: Convert to float\n",
        "data_copy['item_price'] = data_copy['item_price'].astype(float)\n",
        "\n",
        "# Check the result\n",
        "print(data_copy)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qfr2yDwX7wD-",
        "outputId": "a77d64a0-189a-47e8-9e5b-3e3e24d03b7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      order_id  quantity                              item_name  \\\n",
            "0            1         1           Chips and Fresh Tomato Salsa   \n",
            "1            1         1                                   Izze   \n",
            "2            1         1                       Nantucket Nectar   \n",
            "3            1         1  Chips and Tomatillo-Green Chili Salsa   \n",
            "4            2         2                           Chicken Bowl   \n",
            "...        ...       ...                                    ...   \n",
            "4617      1833         1                          Steak Burrito   \n",
            "4618      1833         1                          Steak Burrito   \n",
            "4619      1834         1                     Chicken Salad Bowl   \n",
            "4620      1834         1                     Chicken Salad Bowl   \n",
            "4621      1834         1                     Chicken Salad Bowl   \n",
            "\n",
            "                                     choice_description  item_price  \n",
            "0                                                    NA        2.39  \n",
            "1                                          [Clementine]        3.39  \n",
            "2                                               [Apple]        3.39  \n",
            "3                                                    NA        2.39  \n",
            "4     [Tomatillo-Red Chili Salsa (Hot), [Black Beans...       16.98  \n",
            "...                                                 ...         ...  \n",
            "4617  [Fresh Tomato Salsa, [Rice, Black Beans, Sour ...       11.75  \n",
            "4618  [Fresh Tomato Salsa, [Rice, Sour Cream, Cheese...       11.75  \n",
            "4619  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...       11.25  \n",
            "4620  [Fresh Tomato Salsa, [Fajita Vegetables, Lettu...        8.75  \n",
            "4621  [Fresh Tomato Salsa, [Fajita Vegetables, Pinto...        8.75  \n",
            "\n",
            "[4622 rows x 5 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Plot histogram\n",
        "data_copy['item_price'].plot(kind='hist', bins=20, edgecolor='black')\n",
        "plt.xlabel('Item Price')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Item Prices')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "niL2d7tz_R2j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "outputId": "e238d094-21d0-47fa-bb6e-8afd022ec16f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Filter rows where quantity > 2:\n",
        "filtered_data = data_copy[data_copy['quantity'] > 2]\n",
        "print(\"Orders with Quantity > 2:\")\n",
        "display(filtered_data.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "hdhf-wyyJOdd",
        "outputId": "1a2e8736-57c3-4d30-c5ea-842e75ff67b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Orders with Quantity > 2:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "     order_id  quantity          item_name  item_price\n",
              "409       178         3       Chicken Bowl       32.94\n",
              "445       193         3               Bowl       22.20\n",
              "689       284         3  Canned Soft Drink        3.75\n",
              "818       338         3      Bottled Water        3.27\n",
              "850       350         3  Canned Soft Drink        3.75"
            ],
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              "      <td>22.20</td>\n",
              "    </tr>\n",
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              "      <th>689</th>\n",
              "      <td>284</td>\n",
              "      <td>3</td>\n",
              "      <td>Canned Soft Drink</td>\n",
              "      <td>3.75</td>\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                    [key], {});\n",
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              "\n",
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              "\n",
              "\n",
              "<div id=\"df-e7b28b00-c010-4bbc-8c65-1e2080cf0a7d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e7b28b00-c010-4bbc-8c65-1e2080cf0a7d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e7b28b00-c010-4bbc-8c65-1e2080cf0a7d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(filtered_data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"order_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 80,\n        \"min\": 178,\n        \"max\": 350,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          193,\n          350,\n          284\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"quantity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Bowl\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"item_price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.673659714940987,\n        \"min\": 3.27,\n        \"max\": 32.94,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          22.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Drop the choice_description column:\n",
        "data_copy = data_copy.drop(columns=['choice_description'])\n",
        "print(\"Columns after dropping 'choice_description':\", data_copy.columns)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1rqQjWKJjx1",
        "outputId": "b9395168-5cba-4339-a0f6-e4e756e4007e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns after dropping 'choice_description': Index(['order_id', 'quantity', 'item_name', 'item_price'], dtype='object')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculate the average item_price:\n",
        "#data_copy['item_price'] = data_copy['item_price'].str.replace('$', '').astype(float)\n",
        "avg_price = data_copy['item_price'].mean()\n",
        "print(\"Average Item Price:\", avg_price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SSiWyQfzJt0d",
        "outputId": "efa93813-232c-46dd-9c7b-d4dc75a83121"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Item Price: 7.464335785374297\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Find the most frequently ordered item:\n",
        "most_ordered = data_copy['item_name'].value_counts().head(10)\n",
        "print(\"Most Ordered Items:\")\n",
        "print(most_ordered)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UG4bQdxPKaNb",
        "outputId": "3569cb28-93c3-4814-c499-a3eef9edcb89"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most Ordered Items:\n",
            "item_name\n",
            "Chicken Bowl                    726\n",
            "Chicken Burrito                 553\n",
            "Chips and Guacamole             479\n",
            "Steak Burrito                   368\n",
            "Canned Soft Drink               301\n",
            "Steak Bowl                      211\n",
            "Chips                           211\n",
            "Bottled Water                   162\n",
            "Chicken Soft Tacos              115\n",
            "Chips and Fresh Tomato Salsa    110\n",
            "Name: count, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Bar chart for top 10 most ordered items:\n",
        "top_items = data_copy['item_name'].value_counts().head(10)\n",
        "top_items.plot(kind='bar', color='skyblue')\n",
        "plt.xlabel('Item Name')\n",
        "plt.ylabel('Number of Orders')\n",
        "plt.title('Top 10 Most Ordered Items')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 664
        },
        "id": "1Wwjp8ljKu7q",
        "outputId": "8de9661d-f8b2-4122-e399-de49fda1a2fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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G4PTp0/jtt98QGRmJdu3awdTUFAcPHsSKFSvg5eWl8W2RXr16Ye7cuejatStOnjyJwoULY/ny5VJxkEVPTw9//vknmjVrhrJly6J79+4oUqQI7t69i/DwcFhZWSEsLOyd71WqVClMmDABI0eOxK1bt9C6dWtYWlri5s2b2LhxI3r37o2hQ4fC0NAQEyZMQJ8+fdCgQQN8/fXXuHnzJpYsWfLBY3ZMTU3h7e2NNWvWwMPDA3Z2dihXrhzKlSuHkJAQ1K5dG+XLl0evXr1QsmRJxMfHIzIyEnfu3JG+Dz/Wt99+iydPnqBBgwYoWrQoYmJiMGfOHPj4+HySVcnpMyDbPDAiHZbTlOjnz5+LQYMGCRcXF2FoaCjc3d3F1KlT1abiCvF66nlQUJBYsWKFcHd3F8bGxqJSpUrSVOb3CQwMlKavv/1YsmSJ2rlPnjwRPXv2FPb29sLMzEzUq1dPHD9+XKP3eXPqeW5ymnouhBCnTp0S/v7+wsLCQpiZmYn69euLw4cPq50zYcIEUa1aNWFjYyNMTU2Fp6en+PXXX0Vqaqp0Tnp6uujfv79wcHAQKpVKo2noGRkZYsmSJaJWrVrCyspKmJiYiLJly4px48aJpKSkbOe/PRX7TTExMaJVq1bCzMxMFCpUSAwcOFBs375dbep5ltOnT4u2bdsKe3t7YWxsLIoXLy7at28v9uzZI52TNfX84cOHOb7fP//8I2rXri3Mzc2Fubm58PT0FEFBQeLKlStq582bN0+4ubkJY2NjUbVqVbF//35Rr169D5p6LoQQhw8fFlWqVBFGRkbZpqFfv35ddO3aVTg7OwtDQ0NRpEgR0aJFC7F+/XrpnKyp529/b+V2vYGBgcLc3Fx6vn79etGkSRPh6OgojIyMhKurq+jTp4+4f//+e6+HSBMqId7qMyWiAqVSqRAUFJTtlhcRERUMjtkhIiIiRWOxQ0RERIrGYoeIiIgUjbOxiD4xDpMjIvq02LNDREREisaeHbxeFv3evXuwtLTM04qtREREJB8hBJ4/fw4XFxe1leLfxmIHwL1797JtVEdERES64fbt2yhatGiux1ns4P92QL59+7a0lD0RERFpt8TERBQrVkxtT8KcsNjB/22EZ2VlxWKHiIhIx7xvCAoHKBMREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaAZyB9BVk08/KvD3GFGpUIG/BxERkdKxZ4eIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIoma7FTokQJqFSqbI+goCAAwKtXrxAUFAR7e3tYWFigXbt2iI+PV3uN2NhYBAQEwMzMDI6Ojhg2bBjS09PluBwiIiLSQrIWO8ePH8f9+/elx65duwAAX331FQBg0KBBCAsLw7p16xAREYF79+6hbdu20tdnZGQgICAAqampOHz4MJYuXYrQ0FCMHj1alushIiIi7aMSQgi5Q2T5/vvvsWXLFkRHRyMxMREODg5YtWoV/ve//wEALl++DC8vL0RGRqJGjRrYtm0bWrRogXv37sHJyQkAsGDBAgwfPhwPHz6EkZGRRu+bmJgIa2trPHv2DFZWVhp9DbeLICIikpemv7+1ZsxOamoqVqxYgR49ekClUuHkyZNIS0tDo0aNpHM8PT3h6uqKyMhIAEBkZCTKly8vFToA4O/vj8TERFy4cCHX90pJSUFiYqLag4iIiJRJa4qdTZs2ISEhAd26dQMAxMXFwcjICDY2NmrnOTk5IS4uTjrnzUIn63jWsdxMmjQJ1tbW0qNYsWL5dyFERESkVbSm2Fm8eDGaNWsGFxeXAn+vkSNH4tmzZ9Lj9u3bBf6eREREJA8DuQMAQExMDHbv3o0NGzZIbc7OzkhNTUVCQoJa7058fDycnZ2lc44dO6b2WlmztbLOyYmxsTGMjY3z8QqIiIhIW2lFz86SJUvg6OiIgIAAqa1KlSowNDTEnj17pLYrV64gNjYWvr6+AABfX1+cO3cODx48kM7ZtWsXrKys4O3t/ekugIiIiLSW7D07mZmZWLJkCQIDA2Fg8H9xrK2t0bNnTwwePBh2dnawsrJC//794evrixo1agAAmjRpAm9vb3zzzTeYMmUK4uLi8PPPPyMoKIg9N0RERARAC4qd3bt3IzY2Fj169Mh2bMaMGdDT00O7du2QkpICf39/zJs3Tzqur6+PLVu2oG/fvvD19YW5uTkCAwMxfvz4T3kJREREpMW0ap0duXCdHSIiIt2jc+vsEBERERUEFjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIome7Fz9+5ddOnSBfb29jA1NUX58uVx4sQJ6bgQAqNHj0bhwoVhamqKRo0aITo6Wu01njx5gs6dO8PKygo2Njbo2bMnkpKSPvWlEBERkRaStdh5+vQpatWqBUNDQ2zbtg0XL17EtGnTYGtrK50zZcoUzJ49GwsWLMDRo0dhbm4Of39/vHr1Sjqnc+fOuHDhAnbt2oUtW7Zg//796N27txyXRERERFpGJYQQcr35iBEjcOjQIRw4cCDH40IIuLi4YMiQIRg6dCgA4NmzZ3ByckJoaCg6dOiAS5cuwdvbG8ePH0fVqlUBANu3b0fz5s1x584duLi4ZHvdlJQUpKSkSM8TExNRrFgxPHv2DFZWVhpln3z6UV4vN89GVCpU4O9BRESkqxITE2Ftbf3e39+y9uz8+++/qFq1Kr766is4OjqiUqVK+OOPP6TjN2/eRFxcHBo1aiS1WVtbo3r16oiMjAQAREZGwsbGRip0AKBRo0bQ09PD0aNHc3zfSZMmwdraWnoUK1asgK6QiIiI5CZrsXPjxg3Mnz8f7u7u2LFjB/r27YsBAwZg6dKlAIC4uDgAgJOTk9rXOTk5Scfi4uLg6OiodtzAwAB2dnbSOW8bOXIknj17Jj1u376d35dGREREWsJAzjfPzMxE1apVMXHiRABApUqVcP78eSxYsACBgYEF9r7GxsYwNjYusNcnIiIi7SFrz07hwoXh7e2t1ubl5YXY2FgAgLOzMwAgPj5e7Zz4+HjpmLOzMx48eKB2PD09HU+ePJHOISIios+XrMVOrVq1cOXKFbW2q1evonjx4gAANzc3ODs7Y8+ePdLxxMREHD16FL6+vgAAX19fJCQk4OTJk9I5e/fuRWZmJqpXr/4JroKIiIi0may3sQYNGoSaNWti4sSJaN++PY4dO4ZFixZh0aJFAACVSoXvv/8eEyZMgLu7O9zc3DBq1Ci4uLigdevWAF73BDVt2hS9evXCggULkJaWhuDgYHTo0CHHmVhERET0eZG12Pniiy+wceNGjBw5EuPHj4ebmxtmzpyJzp07S+f88MMPSE5ORu/evZGQkIDatWtj+/btMDExkc5ZuXIlgoOD0bBhQ+jp6aFdu3aYPXu2HJdEREREWkbWdXa0habz9N/EdXaIiIjkpRPr7BAREREVNBY7REREpGgsdoiIiEjRWOwQERGRorHYISIiIkVjsUNERESKxmKHiIiIFI3FDhERESkaix0iIiJSNBY7REREpGgsdoiIiEjRWOwQERGRorHYISIiIkVjsUNERESKxmKHiIiIFM1A7gAkr8mnHxXo64+oVKhAX5+IiOh92LNDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoshY7Y8eOhUqlUnt4enpKx1+9eoWgoCDY29vDwsIC7dq1Q3x8vNprxMbGIiAgAGZmZnB0dMSwYcOQnp7+qS+FiIiItFSei52lS5fiv//+k57/8MMPsLGxQc2aNRETE5PnAGXLlsX9+/elx8GDB6VjgwYNQlhYGNatW4eIiAjcu3cPbdu2lY5nZGQgICAAqampOHz4MJYuXYrQ0FCMHj06zzmIiIhImfJc7EycOBGmpqYAgMjISISEhGDKlCkoVKgQBg0alOcABgYGcHZ2lh6FChUCADx79gyLFy/G9OnT0aBBA1SpUgVLlizB4cOHceTIEQDAzp07cfHiRaxYsQI+Pj5o1qwZfvnlF4SEhCA1NTXPWYiIiEh58lzs3L59G6VLlwYAbNq0Ce3atUPv3r0xadIkHDhwIM8BoqOj4eLigpIlS6Jz586IjY0FAJw8eRJpaWlo1KiRdK6npydcXV0RGRkJ4HWxVb58eTg5OUnn+Pv7IzExERcuXMj1PVNSUpCYmKj2ICIiImXKc7FjYWGBx48fA3jds9K4cWMAgImJCV6+fJmn16pevTpCQ0Oxfft2zJ8/Hzdv3kSdOnXw/PlzxMXFwcjICDY2Nmpf4+TkhLi4OABAXFycWqGTdTzrWG4mTZoEa2tr6VGsWLE85SYiIiLdYZDXL2jcuDG+/fZbVKpUCVevXkXz5s0BABcuXECJEiXy9FrNmjWT/lyhQgVUr14dxYsXx9q1a6VbZQVh5MiRGDx4sPQ8MTGRBQ8REZFC5blnJyQkBDVr1sTDhw/xzz//wN7eHsDr204dO3b8qDA2Njbw8PDAtWvX4OzsjNTUVCQkJKidEx8fD2dnZwCAs7NzttlZWc+zzsmJsbExrKys1B5ERESkTHkqdtLT0zF79mwMHz4cmzdvRtOmTaVj48aNw08//fRRYZKSknD9+nUULlwYVapUgaGhIfbs2SMdv3LlCmJjY+Hr6wsA8PX1xblz5/DgwQPpnF27dsHKygre3t4flYWIiIiUIU/FjoGBAaZMmZJv69gMHToUERERuHXrFg4fPow2bdpAX18fHTt2hLW1NXr27InBgwcjPDwcJ0+eRPfu3eHr64saNWoAAJo0aQJvb2988803iIqKwo4dO/Dzzz8jKCgIxsbG+ZKRiIiIdFuex+w0bNgQEREReR6fk5M7d+6gY8eOePz4MRwcHFC7dm0cOXIEDg4OAIAZM2ZAT08P7dq1Q0pKCvz9/TFv3jzp6/X19bFlyxb07dsXvr6+MDc3R2BgIMaPH//R2YiIiEgZ8lzsNGvWDCNGjMC5c+dQpUoVmJubqx1v1aqVxq+1evXqdx43MTFBSEgIQkJCcj2nePHi2Lp1q8bvSURERJ+XPBc7/fr1AwBMnz492zGVSoWMjIyPT0VERESUT/Jc7GRmZhZEDiIiIqIC8VEbgb569Sq/chAREREViDz37GRkZGDixIlYsGAB4uPjcfXqVZQsWRKjRo1CiRIl0LNnz4LISZSryacfFfh7jKhUqMDfg4iICkaee3Z+/fVXhIaGYsqUKTAyMpLay5Urhz///DNfwxERERF9rDwXO8uWLcOiRYvQuXNn6OvrS+0VK1bE5cuX8zUcERER0cfKc7Fz9+5dadfzN2VmZiItLS1fQhERERHllzwXO97e3jhw4EC29vXr16NSpUr5EoqIiIgov+R5gPLo0aMRGBiIu3fvIjMzExs2bMCVK1ewbNkybNmypSAyEhEREX2wPPfsfPnllwgLC8Pu3bthbm6O0aNH49KlSwgLC0Pjxo0LIiMRERHRB8tzzw4A1KlTB7t27crvLERERET57qMWFSQiIiLSdhr17Nja2kKlUmn0gk+ePPmoQERERET5SaNiZ+bMmdKfHz9+jAkTJsDf3x++vr4AgMjISOzYsQOjRo0qkJBEREREH0qjYicwMFD6c7t27TB+/HgEBwdLbQMGDMDcuXOxe/duDBo0KP9TEhEREX2gPI/Z2bFjB5o2bZqtvWnTpti9e3e+hCIiIiLKL3kuduzt7bF58+Zs7Zs3b4a9vX2+hCIiIiLKL3meej5u3Dh8++232LdvH6pXrw4AOHr0KLZv344//vgj3wMSERERfYw8FzvdunWDl5cXZs+ejQ0bNgAAvLy8cPDgQan4ISIiItIWeSp20tLS0KdPH4waNQorV64sqExERERE+SZPY3YMDQ3xzz//FFQWIiIionyX5wHKrVu3xqZNmwogChEREVH+y/OYHXd3d4wfPx6HDh1ClSpVYG5urnZ8wIAB+RaOiIiI6GPludhZvHgxbGxscPLkSZw8eVLtmEqlYrFDREREWiXPxc7NmzcLIgcRERFRgfjgXc8fPXqER48e5WcWIiIionyXp2InISEBQUFBKFSoEJycnODk5IRChQohODgYCQkJBRSRiIiI6MNpfBvryZMn8PX1xd27d9G5c2d4eXkBAC5evIjQ0FDs2bMHhw8fhq2tbYGFJSIiIsorjYud8ePHw8jICNevX4eTk1O2Y02aNMH48eMxY8aMfA9JRERE9KE0vo21adMm/P7779kKHQBwdnbGlClTsHHjxnwNR0RERPSxNC527t+/j7Jly+Z6vFy5coiLi8uXUERERET5ReNip1ChQrh161aux2/evAk7O7v8yERERESUbzQudvz9/fHTTz8hNTU127GUlBSMGjUKTZs2zddwRERERB8rTwOUq1atCnd3dwQFBcHT0xNCCFy6dAnz5s1DSkoKli9fXpBZiYiIiPJM42KnaNGiiIyMRL9+/TBy5EgIIQC83iKicePGmDt3LooVK1ZgQYmIiIg+RJ62i3Bzc8O2bdvw9OlTREdHAwBKly7NsTpERESktfK8NxYA2Nraolq1avmdhYiIiCjfffDeWPlt8uTJUKlU+P7776W2V69eISgoCPb29rCwsEC7du0QHx+v9nWxsbEICAiAmZkZHB0dMWzYMKSnp3/i9ERERKSttKLYOX78OBYuXIgKFSqotQ8aNAhhYWFYt24dIiIicO/ePbRt21Y6npGRgYCAAKSmpuLw4cNYunQpQkNDMXr06E99CURERKSlZC92kpKS0LlzZ/zxxx9q+2o9e/YMixcvxvTp09GgQQNUqVIFS5YsweHDh3HkyBEAwM6dO3Hx4kWsWLECPj4+aNasGX755ReEhITkOEWeiIiIPj8aFTuVK1fG06dPAbyegv7ixYt8CxAUFISAgAA0atRIrf3kyZNIS0tTa/f09ISrqysiIyMBAJGRkShfvrzaFhb+/v5ITEzEhQsXcn3PlJQUJCYmqj2IiIhImTQqdi5duoTk5GQAwLhx45CUlJQvb7569WqcOnUKkyZNynYsLi4ORkZGsLGxUWt3cnKStqWIi4vLtldX1vN3bV0xadIkWFtbSw9OmSciIlIujWZj+fj4oHv37qhduzaEEPj9999hYWGR47majpe5ffs2Bg4ciF27dsHExETzxPlg5MiRGDx4sPQ8MTGRBQ8REZFCaVTshIaGYsyYMdiyZQtUKhW2bdsGA4PsX6pSqTQudk6ePIkHDx6gcuXKUltGRgb279+PuXPnYseOHUhNTUVCQoJa7058fDycnZ0BvN5t/dixY2qvmzVbK+ucnBgbG8PY2FijnERERKTbNCp2ypQpg9WrVwMA9PT0sGfPHjg6On7UGzds2BDnzp1Ta+vevTs8PT0xfPhwFCtWDIaGhtizZw/atWsHALhy5QpiY2Ph6+sLAPD19cWvv/6KBw8eSHl27doFKysreHt7f1Q+IiIiUoY8LyqYmZmZL29saWmJcuXKqbWZm5vD3t5eau/ZsycGDx4MOzs7WFlZoX///vD19UWNGjUAAE2aNIG3tze++eYbTJkyBXFxcfj5558RFBTEnhsiIiIC8IErKF+/fh0zZ87EpUuXAADe3t4YOHAgSpUqla/hZsyYAT09PbRr1w4pKSnw9/fHvHnzpOP6+vrYsmUL+vbtC19fX5ibmyMwMBDjx4/P1xxERESku/Jc7OzYsQOtWrWCj48PatWqBQA4dOgQypYti7CwMDRu3PiDw+zbt0/tuYmJCUJCQhASEpLr1xQvXhxbt2794PckIiIiZctzsTNixAgMGjQIkydPztY+fPjwjyp2iIiIiPJbnldQvnTpEnr27JmtvUePHrh48WK+hCIiIiLKL3kudhwcHHDmzJls7WfOnPnoGVpERERE+S3Pt7F69eqF3r1748aNG6hZsyaA12N2fvvtN7WF+oiIiIi0QZ6LnVGjRsHS0hLTpk3DyJEjAQAuLi4YO3YsBgwYkO8BiYiIiD5GnosdlUqFQYMGYdCgQXj+/DmA12vmEBEREWmjD1pnJwuLHCIiItJ2eR6gTERERKRLWOwQERGRon3UbSwiyj+TTz8q8PcYUalQgb8HEZG2yVPPTlpaGho2bIjo6OiCykNERESUr/JU7BgaGuLs2bMFlYWIiIgo3+V5zE6XLl2wePHigshCRERElO/yPGYnPT0df/31F3bv3o0qVarA3Nxc7fj06dPzLRwRERHRx8pzsXP+/HlUrlwZAHD16lW1YyqVKn9SEREREeWTPBc74eHhBZGDiIiIqEB88Do7165dw44dO/Dy5UsAgBAi30IRERER5Zc8FzuPHz9Gw4YN4eHhgebNm+P+/fsAgJ49e2LIkCH5HpCIiIjoY+S52Bk0aBAMDQ0RGxsLMzMzqf3rr7/G9u3b8zUcERER0cfK85idnTt3YseOHShatKhau7u7O2JiYvItGBEREVF+yHPPTnJyslqPTpYnT57A2Ng4X0IRERER5Zc8Fzt16tTBsmXLpOcqlQqZmZmYMmUK6tevn6/hiIiIiD5Wnm9jTZkyBQ0bNsSJEyeQmpqKH374ARcuXMCTJ09w6NChgshIRERE9MHy3LNTrlw5XL16FbVr18aXX36J5ORktG3bFqdPn0apUqUKIiMRERHRB8tzzw4AWFtb46effsrvLERERET57oOKnadPn2Lx4sW4dOkSAMDb2xvdu3eHnZ1dvoYjIiIi+lh5vo21f/9+lChRArNnz8bTp0/x9OlTzJ49G25ubti/f39BZCQiIiL6YHnu2QkKCsLXX3+N+fPnQ19fHwCQkZGBfv36ISgoCOfOncv3kEREREQfKs89O9euXcOQIUOkQgcA9PX1MXjwYFy7di1fwxERERF9rDwXO5UrV5bG6rzp0qVLqFixYr6EIiIiIsovGt3GOnv2rPTnAQMGYODAgbh27Rpq1KgBADhy5AhCQkIwefLkgklJRERE9IE0KnZ8fHygUqkghJDafvjhh2znderUCV9//XX+pSMiIiL6SBoVOzdv3izoHEREREQFQqNip3jx4gWdg4iIiKhAfNCigvfu3cPBgwfx4MEDZGZmqh0bMGBAvgQjIiIiyg95LnZCQ0PRp08fGBkZwd7eHiqVSjqmUqlY7BAREZFWyXOxM2rUKIwePRojR46Enl6eZ64TERERfVJ5rlZevHiBDh06sNAhIiIinZDniqVnz55Yt25dvrz5/PnzUaFCBVhZWcHKygq+vr7Ytm2bdPzVq1cICgqCvb09LCws0K5dO8THx6u9RmxsLAICAmBmZgZHR0cMGzYM6enp+ZKPiIiIdF+eb2NNmjQJLVq0wPbt21G+fHkYGhqqHZ8+fbrGr1W0aFFMnjwZ7u7uEEJg6dKl+PLLL3H69GmULVsWgwYNwn///Yd169bB2toawcHBaNu2LQ4dOgTg9Z5cAQEBcHZ2xuHDh3H//n107doVhoaGmDhxYl4vjYiIiBTog4qdHTt2oEyZMgCQbYByXrRs2VLt+a+//or58+fjyJEjKFq0KBYvXoxVq1ahQYMGAIAlS5bAy8sLR44cQY0aNbBz505cvHgRu3fvhpOTE3x8fPDLL79g+PDhGDt2LIyMjPJ6eURERKQweS52pk2bhr/++gvdunXL1yAZGRlYt24dkpOT4evri5MnTyItLQ2NGjWSzvH09ISrqysiIyNRo0YNREZGonz58nBycpLO8ff3R9++fXHhwgVUqlQpx/dKSUlBSkqK9DwxMTFfr4XoczX59KMCf48RlQoV+HsQkbLkecyOsbExatWqlW8Bzp07BwsLCxgbG+O7777Dxo0b4e3tjbi4OBgZGcHGxkbtfCcnJ8TFxQEA4uLi1AqdrONZx3IzadIkWFtbS49ixYrl2/UQERGRdslzsTNw4EDMmTMn3wKUKVMGZ86cwdGjR9G3b18EBgbi4sWL+fb6ORk5ciSePXsmPW7fvl2g70dERETyyfNtrGPHjmHv3r3YsmULypYtm22A8oYNG/L0ekZGRihdujQAoEqVKjh+/DhmzZqFr7/+GqmpqUhISFDr3YmPj4ezszMAwNnZGceOHVN7vazZWlnn5MTY2BjGxsZ5yklERES6Kc89OzY2Nmjbti3q1auHQoUKqd0Osra2/uhAmZmZSElJQZUqVWBoaIg9e/ZIx65cuYLY2Fj4+voCAHx9fXHu3Dk8ePBAOmfXrl2wsrKCt7f3R2chIiIi3Zfnnp0lS5bk25uPHDkSzZo1g6urK54/f45Vq1Zh37592LFjB6ytrdGzZ08MHjwYdnZ2sLKyQv/+/eHr64saNWoAAJo0aQJvb2988803mDJlCuLi4vDzzz8jKCiIPTdEREQE4AM3As0vDx48QNeuXXH//n1YW1ujQoUK2LFjBxo3bgwAmDFjBvT09NCuXTukpKTA398f8+bNk75eX18fW7ZsQd++feHr6wtzc3MEBgZi/Pjxcl0SERERaZk8Fztubm7vXE/nxo0bGr/W4sWL33ncxMQEISEhCAkJyfWc4sWLY+vWrRq/JxEREX1e8lzsfP/992rP09LScPr0aWzfvh3Dhg3Lr1xERERE+SLPxc7AgQNzbA8JCcGJEyc+OhARERFRfsq3rcubNWuGf/75J79ejoiIiChf5Fuxs379etjZ2eXXyxERERHlizzfxqpUqZLaAGUhBOLi4vDw4UO1mVJERERE2iDPxU7r1q3Vnuvp6cHBwQF+fn7w9PTMr1xERERE+SLPxc6YMWMKIgcRERFRgci3MTtERERE2kjjnh09Pb13LiYIACqVCunp6R8dioiIiCi/aFzsbNy4MddjkZGRmD17NjIzM/MlFBEREVF+0bjY+fLLL7O1XblyBSNGjEBYWBg6d+7MPamIiIhI63zQmJ179+6hV69eKF++PNLT03HmzBksXboUxYsXz+98RERERB8lT8XOs2fPMHz4cJQuXRoXLlzAnj17EBYWhnLlyhVUPiIiIqKPovFtrClTpuC3336Ds7Mz/v777xxvaxERERFpG42LnREjRsDU1BSlS5fG0qVLsXTp0hzP27BhQ76FIyIiIvpYGhc7Xbt2fe/UcyIiIiJto3GxExoaWoAxiIiIiAoGV1AmIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUjcUOERERKRqLHSIiIlI0FjtERESkaCx2iIiISNFY7BAREZGisdghIiIiRWOxQ0RERIrGYoeIiIgUzUDuAERE2mby6UcF/h4jKhUq8Pco6Ov4FNdAlB9k7dmZNGkSvvjiC1haWsLR0RGtW7fGlStX1M559eoVgoKCYG9vDwsLC7Rr1w7x8fFq58TGxiIgIABmZmZwdHTEsGHDkJ6e/ikvhYiIiLSUrMVOREQEgoKCcOTIEezatQtpaWlo0qQJkpOTpXMGDRqEsLAwrFu3DhEREbh37x7atm0rHc/IyEBAQABSU1Nx+PBhLF26FKGhoRg9erQcl0RERERaRtbbWNu3b1d7HhoaCkdHR5w8eRJ169bFs2fPsHjxYqxatQoNGjQAACxZsgReXl44cuQIatSogZ07d+LixYvYvXs3nJyc4OPjg19++QXDhw/H2LFjYWRkJMelERERkZbQqgHKz549AwDY2dkBAE6ePIm0tDQ0atRIOsfT0xOurq6IjIwEAERGRqJ8+fJwcnKSzvH390diYiIuXLiQ4/ukpKQgMTFR7UFERETKpDXFTmZmJr7//nvUqlUL5cqVAwDExcXByMgINjY2auc6OTkhLi5OOufNQifreNaxnEyaNAnW1tbSo1ixYvl8NURERKQttKbYCQoKwvnz57F69eoCf6+RI0fi2bNn0uP27dsF/p5EREQkD62Yeh4cHIwtW7Zg//79KFq0qNTu7OyM1NRUJCQkqPXuxMfHw9nZWTrn2LFjaq+XNVsr65y3GRsbw9jYOJ+vgoiIiLSRrD07QggEBwdj48aN2Lt3L9zc3NSOV6lSBYaGhtizZ4/UduXKFcTGxsLX1xcA4Ovri3PnzuHBgwfSObt27YKVlRW8vb0/zYUQERGR1pK1ZycoKAirVq3C5s2bYWlpKY2xsba2hqmpKaytrdGzZ08MHjwYdnZ2sLKyQv/+/eHr64saNWoAAJo0aQJvb2988803mDJlCuLi4vDzzz8jKCiIvTdEREQkb7Ezf/58AICfn59a+5IlS9CtWzcAwIwZM6Cnp4d27dohJSUF/v7+mDdvnnSuvr4+tmzZgr59+8LX1xfm5uYIDAzE+PHjP9VlEBERkRaTtdgRQrz3HBMTE4SEhCAkJCTXc4oXL46tW7fmZzQiIiJSCK2ZjUVERERUEFjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFk3VvLCIioneZfPpRgb/HiEqFCvw9SF7s2SEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoBnIHICIiUrrJpx8V+HuMqFSowN9DV7Fnh4iIiBSNxQ4REREpGm9jERER0Xvp8q04WXt29u/fj5YtW8LFxQUqlQqbNm1SOy6EwOjRo1G4cGGYmpqiUaNGiI6OVjvnyZMn6Ny5M6ysrGBjY4OePXsiKSnpE14FERERaTNZi53k5GRUrFgRISEhOR6fMmUKZs+ejQULFuDo0aMwNzeHv78/Xr16JZ3TuXNnXLhwAbt27cKWLVuwf/9+9O7d+1NdAhEREWk5WW9jNWvWDM2aNcvxmBACM2fOxM8//4wvv/wSALBs2TI4OTlh06ZN6NChAy5duoTt27fj+PHjqFq1KgBgzpw5aN68OX7//Xe4uLjk+NopKSlISUmRnicmJubzlREREZG20NoByjdv3kRcXBwaNWoktVlbW6N69eqIjIwEAERGRsLGxkYqdACgUaNG0NPTw9GjR3N97UmTJsHa2lp6FCtWrOAuhIiIiGSltcVOXFwcAMDJyUmt3cnJSToWFxcHR0dHteMGBgaws7OTzsnJyJEj8ezZM+lx+/btfE5PRERE2uKznI1lbGwMY2NjuWMQERHRJ6C1PTvOzs4AgPj4eLX2+Ph46ZizszMePHigdjw9PR1PnjyRziEiIqLPm9YWO25ubnB2dsaePXuktsTERBw9ehS+vr4AAF9fXyQkJODkyZPSOXv37kVmZiaqV6/+yTMTERGR9pH1NlZSUhKuXbsmPb958ybOnDkDOzs7uLq64vvvv8eECRPg7u4ONzc3jBo1Ci4uLmjdujUAwMvLC02bNkWvXr2wYMECpKWlITg4GB06dMh1JhYRERF9XmQtdk6cOIH69etLzwcPHgwACAwMRGhoKH744QckJyejd+/eSEhIQO3atbF9+3aYmJhIX7Ny5UoEBwejYcOG0NPTQ7t27TB79uxPfi1ERESknWQtdvz8/CCEyPW4SqXC+PHjMX78+FzPsbOzw6pVqwoiHhERESmA1o7ZISIiIsoPLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREisZih4iIiBSNxQ4REREpGosdIiIiUjQWO0RERKRoLHaIiIhI0VjsEBERkaKx2CEiIiJFY7FDREREiqaYYickJAQlSpSAiYkJqlevjmPHjskdiYiIiLSAIoqdNWvWYPDgwRgzZgxOnTqFihUrwt/fHw8ePJA7GhEREclMEcXO9OnT0atXL3Tv3h3e3t5YsGABzMzM8Ndff8kdjYiIiGRmIHeAj5WamoqTJ09i5MiRUpuenh4aNWqEyMjIHL8mJSUFKSkp0vNnz54BABITEzV+31dJzz8wseYSE40K/D0K+jqUcA0Ar0NTSrgGgNehKSVcA8Dr0JQ2XkPW720hxLtPFDru7t27AoA4fPiwWvuwYcNEtWrVcvyaMWPGCAB88MEHH3zwwYcCHrdv335nraDzPTsfYuTIkRg8eLD0PDMzE0+ePIG9vT1UKlW+v19iYiKKFSuG27dvw8rKKt9f/1PhdWgPJVwDoIzrUMI1ALwObaKEawA+zXUIIfD8+XO4uLi88zydL3YKFSoEfX19xMfHq7XHx8fD2dk5x68xNjaGsbGxWpuNjU1BRZRYWVnp9DduFl6H9lDCNQDKuA4lXAPA69AmSrgGoOCvw9ra+r3n6PwAZSMjI1SpUgV79uyR2jIzM7Fnzx74+vrKmIyIiIi0gc737ADA4MGDERgYiKpVq6JatWqYOXMmkpOT0b17d7mjERERkcwUUex8/fXXePjwIUaPHo24uDj4+Phg+/btcHJykjsagNe3zcaMGZPt1pmu4XVoDyVcA6CM61DCNQC8Dm2ihGsAtOs6VEK8b74WERERke7S+TE7RERERO/CYoeIiIgUjcUOERERKRqLHSIiIlI0FjtE9MncuXMn12NHjhz5hEmI6HPC2Vj55N9//9X43FatWhVgkvwVERGB33//HZcuXQIAeHt7Y9iwYahTp47MyfImPT0d+/btw/Xr19GpUydYWlri3r17sLKygoWFhdzxNJKRkYFNmzZJ/xZly5ZFq1atoK+vL3MyzXl7e+PgwYOws7NTaz906BACAgKQkJAgTzDSSWlpaTA1NcWZM2dQrlw5ueN8sNu3b0OlUqFo0aIAgGPHjmHVqlXw9vZG7969ZU6XN8nJyYiIiEBsbCxSU1PVjg0YMECmVIDObwSqLVQqlUYPPT09uaNqbPny5cLAwEC0b99ezJo1S8yaNUu0b99eGBoaipUrV8odT2O3bt0Snp6ewszMTOjr64vr168LIYQYMGCA6NOnj8zpNBMdHS08PDyEmZmZqFSpkqhUqZIwMzMTZcqUEdeuXZM7nsa6d+8uqlSpIhITE6W2iIgIYWVlJaZPny5jsnezsbERtra2Gj10ycmTJ8XZs2el55s2bRJffvmlGDlypEhJSZExmebc3NzEmTNn5I7xUWrXri2WLVsmhBDi/v37wsrKSvj6+opChQqJcePGyZxOc6dOnRLOzs7CyspK6OvrCwcHB6FSqYS5ublwc3OTNRt7dihXXl5e6N27NwYNGqTWPn36dPzxxx9SD4O2a926NSwtLbF48WLY29sjKioKJUuWxL59+9CrVy9ER0fLHfG9mjdvDiEEVq5cKfWKPH78GF26dIGenh7+++8/mRNqJjMzE//73//w5MkT7NixA4cPH0arVq0wYcIEDBw4UO54uVq6dKnG5wYGBhZgkvz1xRdfYMSIEWjXrh1u3LiBsmXLok2bNjh+/DgCAgIwc+ZMuSO+1+LFi7FhwwYsX748W4+hrrC1tcWRI0dQpkwZzJ49G2vWrMGhQ4ewc+dOfPfdd7hx44bcETXi5+cHDw8PLFiwANbW1oiKioKhoSG6dOmCgQMHom3btvKFk7XUUqiXL1/KHSFfGBkZiejo6Gzt0dHRwtjYWIZEH8bOzk5cvnxZCCGEhYWF1LNz8+ZNYWpqKmc0jZmZmal9As9y5swZYW5uLkOiD5eSkiIaNWokatasKSwsLMScOXPkjvTZsrKyknoGJ0+eLJo0aSKEEOLgwYOiaNGickbTmI+Pj7CwsBDGxsbCw8ND6vnMeugCc3NzcfPmTSGEEC1bthSTJ08WQggRExMjTExMZEyWN9bW1tL/tdbW1uLixYtCCCGOHDkiypQpI2c0oYjtIrSNjY0NqlWrhnr16sHPzw81a9aEqamp3LHyrFixYtizZw9Kly6t1r57924UK1ZMplR5l5mZiYyMjGztd+7cgaWlpQyJ8s7Y2BjPnz/P1p6UlAQjIyMZEmnu7Nmz2drGjh2Ljh07okuXLqhbt650ToUKFT51vDzr2rUr6tevj7p166JUqVJyx/koQghkZmYCeP1z3aJFCwCvf/YfPXokZzSNtW7dWu4IH61s2bJYsGABAgICsGvXLvzyyy8AgHv37sHe3l7mdJozNDSEnt7reU+Ojo6IjY2Fl5cXrK2tcfv2bXnDyVpqKdSBAwfEr7/+Kho3bizMzc2FsbGxqFWrlvjxxx/Fzp075Y6nsXnz5gkjIyPx3XffiWXLlolly5aJPn36CGNjY7FgwQK542msffv2olevXkKI1z07N27cEM+fPxcNGjQQ3bp1kzmdZr755htRtmxZceTIEZGZmSkyMzNFZGSkKFeunAgMDJQ73jtljVV7e+za23/WlfFsPXv2FO7u7kKlUomiRYuKzp07iz/++ENcvXpV7mh5Vr9+fdG1a1exbNkyYWhoKPXk7tu3TxQvXlzecJ+R8PBwYWNjI/T09ET37t2l9pEjR4o2bdrImCxvGjduLI3n/Pbbb0W1atXEihUrhL+/v6hWrZqs2Thmp4Clp6fj+PHjWLhwIVauXJlrL4O22rhxI6ZNmyaNz/Hy8sKwYcPw5ZdfypxMc3fu3IG/vz+EEIiOjkbVqlURHR2NQoUKYf/+/XB0dJQ74nslJCQgMDAQYWFhMDQ0BPD6e6tVq1ZYsmQJbGxs5A34DjExMRqfW7x48QJMkr/u3r2L/fv3IyIiAhEREbh69SoKFy78zun12ubs2bPo3LkzYmNjMXjwYIwZMwYA0L9/fzx+/BirVq2SOaFmEhISsH79ely/fh3Dhg2DnZ0dTp06BScnJxQpUkTueBrJyMhAYmIibG1tpbZbt27BzMxMJ/6PAoATJ07g+fPnqF+/Ph48eICuXbvi8OHDcHd3x19//YWKFSvKlo3FTgG5evUq9u3bJz1SUlJQt25d+Pn5afVATKVKT0/H6tWrcfbsWSQlJaFy5cro3Lmzzt1evHbtmlrh+fYtRvp0Xrx4gYMHDyI8PBz79u3DqVOn4O3tjdOnT8sd7aO9evUK+vr6UmGtzc6ePYtGjRrB2toat27dwpUrV1CyZEn8/PPPiI2NxbJly+SOqLGHDx/iypUrAIAyZcrAwcFB5kTKwWKnABQpUgQvX76En58f/Pz8UK9ePVSoUAEqlUruaHlSsmRJHD9+PNs944SEBFSuXFlnZggowfjx4zF06FCYmZmptb98+RJTp07F6NGjZUqWd9HR0QgPD8eDBw+k8SJZdOE6fvzxR+zbtw+nT5+Gl5eXNDavbt26ap/KdcmJEyfUiuiqVavKnEhzjRo1QuXKlTFlyhRYWlpKsy0PHz6MTp064datW3JHfK/k5GT0798fy5Ytk34m9PX10bVrV8yZMyfbz722evnyJYQQUt6YmBhs3LgR3t7eaNKkiazZWOwUAB8fH1y+fBmVK1eWCp7atWvrzDdsFj09PcTFxWXrQo2Pj4erqytSUlJkSvZ+SlvkUV9fH/fv38/2b/H48WM4OjrqzK3RP/74A3379kWhQoXg7Oys9gFApVLh1KlTMqbTjJ6eHhwcHDBo0CC0bdsWHh4eckf6YHfu3EHHjh1x6NAh6VZoQkICatasidWrV0uL3Gkza2trnDp1CqVKlVIrdmJiYlCmTBm8evVK7ojv1adPH+zevRtz585FrVq1AAAHDx7EgAED0LhxY8yfP1/mhJpp0qQJ2rZti++++w4JCQkoU6YMjIyM8OjRI0yfPh19+/aVLRtnYxWAM2fOICEhQbqf/+OPP+LixYvw8fFB/fr18euvv8od8Z3eLBR27NgBa2tr6XlGRgb27NmDEiVKyJBMc5rO0FCpVDpRKAghcuwZjIqK0qm1RSZMmIBff/0Vw4cPlzvKBzt9+jQiIiKwb98+TJs2DUZGRlLvTtY6I7ri22+/RVpaGi5duoQyZcoAAK5cuYLu3bvj22+/xfbt22VO+H7GxsZITEzM1n716lWduQ30zz//YP369fDz85PamjdvDlNTU7Rv315nip1Tp05hxowZAID169fD2dkZp0+fxj///IPRo0fLWuxwNlYBe/TokVi/fr345ptvhIGBgU7MOMlpxkzWw8jISHh4eIiwsDC5Y34Wslbu1dPTy7aKr5WVldDT0xP9+vWTO6bGLC0tpXWOlOLMmTMiMDBQZ36+32RiYiJOnTqVrf3EiRM6swZVz549RevWrUVqaqo02zImJkZUqlRJDBw4UO54GjE1NZXWpHnT+fPnhZmZmQyJPoypqamIiYkRQgjx1VdfibFjxwohhIiNjZX9+4k9OwVgw4YN0sDkixcvws7ODrVr18a0adNQr149ueO9V9Y9Yzc3Nxw/fhyFChWSOdHna+bMmRBCoEePHhg3bpxaL5uRkRFKlCgBX19fGRPmzVdffSWtCqurhBA4ffq09DN+8OBBJCYmokKFCjrx8/2mYsWKIS0tLVt7RkYGXFxcZEiUd9OmTcP//vc/ODo64uXLl6hXrx7i4uLg6+ur9b3oWXx9fTFmzBgsW7YMJiYmAF6Pfxk3bpxO/XyXLl0amzZtQps2bbBjxw5p9f0HDx7AyspK1mwcs1MAHB0dpZlX9erVQ/ny5eWO9NnT9Q1NIyIiULNmTZ2YHfMukyZNwvTp0xEQEIDy5ctnux5ZNwrUkK2tLZKSklCxYkXp9lWdOnW0evp/bjZv3oyJEyciJCREGpR84sQJ9O/fH8OHD9epBfsOHTqEqKgoabZlo0aN5I6ksfPnz8Pf3x8pKSnS9OyoqCiYmJhgx44dKFu2rMwJNbN+/Xp06tQJGRkZaNiwIXbu3Ang9c/9/v37sW3bNtmysdghNbNnz0bv3r1hYmKC2bNnv/NcXfjFBAArVqxA9+7d0bZtW2nw36FDh7Bx40aEhoaiU6dOMifMWWJiovRpKKcxCW+S+1OTptzc3HI9plKpdGKG33///Yc6derozN/5u9ja2uLFixdIT0+HgcHrjv6sP5ubm6ud++TJEzkivteyZcvw9ddfw9jYWK09NTUVq1evRteuXWVKljcvXrzAypUrcfnyZQCvZ8Xp4vIYcXFxuH//PipWrCitpnzs2DFYWVnB09NTtlwsdgpIRkYGNm3apNaT8OWXX0JfX1/mZO/m5uaGEydOwN7eHiVKlMh1uryu/GICdHdD0zdnYOnp6eX4byH+/8BlXRhkrURZCwjqwqylnChhg1OlzFSkgsVipwBcu3YNzZs3x927d9VmOBQrVgz//fefzu+no2uMjY1x4cKFbAvwXbt2DeXKldPaqakRERGoVasWDAwMEBER8c5zdW2siC7LzMzEhAkTMG3aNCQlJQEALC0tMWTIEPz000/Sp1n6NPT09BAfH59t5lVUVBTq16+vtT1Sb5o0aRKcnJzQo0cPtfa//voLDx8+1OrZi3nZyXzDhg0FmOTdOEC5AAwYMAClSpXCkSNHpGnBjx8/RpcuXTBgwAD8999/Mid8v7S0NHh6emLLli3w8vKSO85H0dUNTbMKmPT0dERERKBHjx462YMwePBg/PLLLzA3N8fgwYPfee706dM/UaoP99NPP2Hx4sWYPHmy2pooY8eOxatXr7R+UKxSbo9WqlQJKpUKKpUKDRs2lG7DAa971m/evImmTZvKmFBzCxcuzHFrjrJly6JDhw5aXey8OWlCm7HYKQARERFqhQ4A2Nvbq/3nqO0MDQ21tscjr4YMGYIBAwbgzJkzqFmzJoDXY3ZCQ0Mxa9YsmdO9n4GBAaZOnaozYw/edvr0aWnGz6lTp955a1QXLF26FH/++afaYpQVKlRAkSJF0K9fP60vdmxtbaXbPjY2Njp7ezRr8PSZM2fg7+8PCwsL6VjWTMV27drJlC5v4uLiULhw4WztDg4OuH//vgyJNLdkyRK5I2iExU4BMDY2xvPnz7O1JyUlwcjISIZEHyYoKAi//fYb/vzzT7VPTbqmb9++cHZ2xrRp07B27VoAr8fxrFmzRmc2NG3QoAEiIiK0fjHHnISHh0t/3rdvn3xB8smTJ09yHGjp6empE7dM9u7dK30Qe/PfRtdkbVpaokQJfP3119KUbV1UrFgxHDp0KNsA/kOHDunMEgDajmN2CkDXrl1x6tQpLF68GNWqVQMAHD16FL169UKVKlUQGhoqb0ANtWnTBnv27IGFhQXKly+fbXaGnPdfPzcLFizAuHHj0LlzZ1SpUiXbv4UubHmRlpYGU1NTnDlzBuXKlZM7zgerXr06qlevnm22Yv/+/XH8+HEcOXJEpmSkq6ZMmYIpU6Zg6tSpaNCgAQBgz549+OGHHzBkyBCMHDlS5oSaW79+PdauXYvY2FikpqaqHZNzOxjd/biuxWbPno3AwED4+vpK64ikp6ejVatWOnHbJIuNjY3OdANrKikpKdvmk9o8LiFLv379AOQ8pkXbbzdkMTQ0hKurq05kfZcpU6YgICAAu3fvlhZ8i4yMxO3bt7F161aZ0+VdQkICjh07luPGrLpw6zQjIwMzZszI9ResLvS2DRs2DI8fP0a/fv2k/CYmJhg+fLhOFTqzZ8/GTz/9hG7dumHz5s3o3r07rl+/juPHjyMoKEjWbOzZKUDXrl1T20n47QGy2iw9PR2rVq1CkyZN4OzsLHecj3Lz5k0EBwdj3759auOQdGFcgtIsXrwYGzZswPLly3VqT6+33bt3DyEhIWprovTr10/nbjmEhYWhc+fOSEpKgpWVVbaNWXWhUBg9ejT+/PNPDBkyBD///DN++ukn3Lp1C5s2bcLo0aN1Zj0w4PWHsUuXLsHU1BTu7u7Z1g7Sdp6enhgzZgw6duyotinr6NGj8eTJE8ydO1e2bCx2KFdmZma4dOkSihcvLneUj1KrVi0IITBw4EA4OTllG5Cp7dO2lXL7B3g9g+batWtIS0tD8eLFs92O04Vdz5XEw8MDzZs3x8SJE2FmZiZ3nA9SqlQpzJ49GwEBAbC0tMSZM2ektiNHjuQ4y4kKxpu/MxwdHbFr1y5UrFgR0dHRqFGjBh4/fixbNt7GymfR0dE4e/YsKleuDDc3N/z333/47bff8PLlS7Ru3Ro//vijzsw6qVatGk6fPq3zxU5UVBROnjwprXmka5Ry+wfQfDd6XZGcnIw1a9bg5cuXaNKkCdzd3eWOlCd3797FgAEDdLbQAV7PZMraksfCwgLPnj0DALRo0QKjRo2SM1qenDhxItdbcboyPtLZ2RlPnjxB8eLF4erqiiNHjqBixYq4efMmZO9X+aTbjirchg0bhIGBgTAyMhLGxsZi6dKlwsTERDRt2lQEBAQIAwMDMXnyZLljamzNmjWiZMmSYs6cOeLw4cMiKipK7aEr/Pz8xK5du+SO8VH+/PNP0bx5c/H48WO5o3y2YmJiRN26dYWFhYVo1KiRiImJER4eHkKlUgmVSiXMzMxERESE3DHzpE2bNmLNmjVyx/goHh4e4siRI0IIIWrVqiUmTZokhBBi9erVwsHBQc5oGvv777+FoaGhaNGihTAyMhItWrQQHh4ewtraWnTr1k3ueBrr2bOntNP53LlzhampqWjUqJGwsbERPXr0kDUbb2Plo6pVq8Lf3x8TJkxAaGgogoKCMHHiRHz//fcAgEWLFmHGjBlauz3B23JaCValUuncWJfr16/ju+++Q5cuXVCuXLlsm09WqFBBpmSaU8rtHyEETp48iVu3bkGlUsHNzU1aHE7btW/fHrdv30ZwcDDWrl2Lq1evolSpUli8eDH09PTQt29fPHnyBHv37pU76jv9+++/0p8fPnyI8ePHo3v37jluzKoLs/xGjBgBKysr/Pjjj1izZg26dOmCEiVKIDY2FoMGDcLkyZPljvheFSpUQJ8+fRAUFCSNdXFzc0OfPn1QuHBhjBs3Tu6IGsnMzERmZqa0VMnq1atx+PBhuLu7o0+fPrIuvcJiJx+9eb84MzMTRkZGauMsbt26BW9vb7x48ULmpJqJiYl553Fdub115MgRdOrUCbdu3ZLadK1oe99/dllrjmiz8PBw9OzZEzExMVKXdlbB89dff6Fu3boyJ3w3Z2dn/Pvvv6hWrRqePHmCQoUK4dChQ9KMrKioKDRs2BCPHj2SOem7abqdha78bLwtMjISkZGRcHd3R8uWLeWOoxFzc3NcuHABJUqUgL29Pfbt24fy5cvj0qVLaNCggdYvLKgLOGYnHyUnJ8PS0hLA6/9QTE1N1e6Fm5qaIiUlRa54eaYrxcz79OjRA5UqVcLff/+d4wBlXaALxcy7XLt2DS1atED16tUxY8YMeHp6QgiBixcvYvbs2WjevDnOnj2LkiVLyh01Vw8ePJB+Juzs7GBmZgYnJyfpuLOzM54+fSpXPI29Pb1cVxUvXhwNGjRAgwYN4OfnJ2394uvrKxWgusLW1lZaiLZIkSI4f/48ypcvj4SEBJ34cPzo0SMkJyer/c64cOECfv/9dyQnJ6N169bo1KmTjAlZ7OSrrH1acnuua5YtW/bO47qwBgfwuofq33//1amp/0ozc+ZM1KhRA3v27FFr9/T0RJs2bdCoUSPMmDEDc+bMkSmhZt7++dZVe/fuRXBwMI4cOZJtnalnz56hZs2aWLBgAerUqSNTwvfr3r079u3bh969eyM1NRVubm6oX78+GjZsCD8/P51YMqNHjx6YNWsW6tati127dqF8+fL46quvMHDgQOzduxe7du1Cw4YN5Y75Xv3794eLiwumTZsG4PUHgzp16sDFxQWlSpVCt27dkJGRgW+++Ua2jLyNlY/09PRgbW0t/SeYkJAAKysrqdtYCIHExESd6Rq2tbVVe56WloYXL17AyMgIZmZmOrEGBwC0bNkS3bp10+kFEvX09N75y1Xbv6fKlSuHSZMm5XpbISwsDCNHjsT58+c/cTLN6enpoXfv3lJvbUhICLp06SJthPjixQv88ccfWv9vAbwei1O/fn0MGjQox+OzZ89GeHg4Nm7c+ImT5V1KSgoOHTqEiIgI7Nu3D0ePHkVaWho8PDzQoEEDhISEyB0xV/r6+rh//z4MDAzw6tUruLi4IDMzE1OmTJHGuvz888/Z/i/WNm5ubggNDZWW8fj999+xYMECXL58GQYGBvj999+xfv16WVcXZ7GTj5YuXarReYGBgQWcpOBER0ejb9++GDZsGPz9/eWOo5FFixZhwoQJ6NGjh84Owty8ebPa87S0NJw+fRpLly7FuHHj0LNnT5mSacbKygpnz57NdW+vmzdvokKFCjnuKact/Pz8NOrN0YX9pooXL47t27fDy8srx+OXL19GkyZNEBsb+4mTfbynT59i2rRpmDNnDpKSkrS6+NTT00NcXBwcHR3ljvJRTE1NcfnyZek2VvPmzVGuXDlMmTIFAHD16lX4+vrKus4Oix3KsxMnTqBLly7S6rHa7l0DMnV1EGaWVatWYc2aNdmKIW3zvv/U4+Pj4eLiotP/FrrExMQE58+fz/XW7rVr11C+fHm8fPnyEyfLu9TUVERGRmLfvn1Sz06RIkVQt25d1KtXT6tvt+vp6SE6OhoODg7vPE/bt7RxcnLCzp07UbFiRQBAoUKFsHDhQqk3PTo6GpUqVUJSUpJsGTlmh/LMwMAA9+7dkzuGxpQyIDMnNWrUQO/eveWOoZGLFy8iLi4ux2PaPoNJabIGweZW7Jw9exaFCxf+xKnyZvz48VJxU7x4cdStWxe9e/fGypUrdWrbDg8Pj1yP6cqM0Ro1amD27Nn4448/sGHDBjx//lza0BR43bOTNYBcLix2KFdvrscBvP7Bu3//PubOnYtatWrJlIqyvHz5ErNnz0aRIkXkjqKRhg0b5riK6pvLANCn0bx5c4waNQpNmzaFiYmJ2rGXL19izJgxaNGihUzpNDN27Fi4urpi2rRp+Oqrr2Bvby93pA+yfv16nd4nDgB++eUXNGzYECtWrEB6ejp+/PFHtXFGq1evln1bHt7Goly9fftHpVLBwcEBDRo0wLRp07T+k9+bkpOTERERkeNS7LqwUaCtra1aMSCEwPPnz2FmZoYVK1Zo/bij963ZlEUpyx1ou/j4eFSuXBn6+voIDg6WtlK5fPkyQkJCkJGRgVOnTqlNrdc2O3bsQHh4OPbt24fTp0/Dw8MDfn5+qFevHurVq/feW0PaQCljdoDXvbOHDh2Cs7Mzqlevrnbsv//+g7e3N9zc3GRKx2KHPgOnT59G8+bN8eLFCyQnJ8POzg6PHj2CmZkZHB0dcePGDbkjvtfbg9/19PTg4OCA6tWra/1MDdJOMTEx6Nu3L3bs2KG2yKO/vz9CQkJk/cWUV8+fP8eBAwcQERGB8PBwREVFoXTp0qhfv76sO22/j5KKHW3HYocUz8/PDx4eHliwYAGsra0RFRUFQ0NDdOnSBQMHDkTbtm3ljkg6JC0tLduMviyPHj1CoUKFPnGij/P06VNcu3YNQgi4u7vrdPGckZGBY8eO4d9//8W8efO0fjaWm5sbTpw4obO34HQJi50CkJGRgdDQUOzZswcPHjzINkBW2/fOAZS1e7uNjQ2OHj2KMmXKwMbGBpGRkfDy8sLRo0cRGBioM7PKsuj6Ttu6rl27dli/fn227//4+Hg0bNhQq9cKUprMzEycOHFCup116NAhJCcno2jRoqhfvz7q16+v00t9UP7hAOUCMHDgQISGhiIgIADlypXTmaIgy8aNG9G+fXtpIbtFixahT58+8PPzg5WVFcaOHQsDAwMMHz5c7qgaMTQ0lMYfOTo6IjY2Fl5eXrC2tsbt27dlTvdusbGx+Oabb3Dq1CnUqFEDixcvRuPGjREdHQ3g9foW27Zt0/p9pZQkNjYW3377LRYvXiy1xcXFoX79+ihbtqyMyT4vzZo1w+HDh/H8+XO4uLigfv36mDFjBurXr6/V246QTD7BzuqfHXt7e/Hff//JHeODValSRfz4448iMzNT/PXXX8LU1FTMmDFDOr5w4ULh6ekpX8A8aty4sVi5cqUQQohvv/1WVKtWTaxYsUL4+/uLatWqyZzu3b766itRo0YNsWLFCtGqVSvh6ekpAgICRFxcnHjw4IFo166dqF+/vtwxPysPHjwQnp6eYtCgQUIIIe7evSs8PDzEV199JTIyMmRO9/no0KGDWLhwobh69arcUUgH8DZWAXBxccG+ffveuX6CNlPa7u0nTpzA8+fPUb9+fTx48ABdu3aVlmJfvHgxfHx85I6YK6XstJ2lQYMG2LBhA2xsbNTaExMT0bp1a524xQsAt2/fRu3atdGuXTts2bIFlStXxsqVK6Gvry93NCJZZWRkYNOmTbh06RIAoGzZsmjVqpXsPxssdgrAtGnTcOPGDcydO1fnbmEB2WcIWFpaIioqSuoa5mq3n46enh7u378vTQG2sLBQ2x1c1/4tcpt98uDBAxQpUgRpaWkyJcu7q1evok6dOmjcuDGWL1+ukz/rpB2WLVuGr7/+GsbGxmrtqampWL16tVavAv2ma9euISAgAHfu3JGWM7hy5QqKFSuG//77D6VKlZItG4udAtCmTRuEh4fDzs4OZcuWzTZzY8OGDTIl04y+vj7i4uKkdSqsrKwQFRUlTUXVtV+wN2/eRHp6eraBvNHR0TA0NMx1vyZtoJTC8+zZswAAHx8f7N27V20RtYyMDGzfvh0LFy7ErVu3ZEr4bm+vc5TlxYsXMDY2VvvUqisb5JL2yNoQ9O0PAY8fP4ajo6PW/3xnad68OYQQWLlypfQz/vjxY3Tp0gV6enr477//ZMvGAcoFwMbGBm3atJE7xgcTQsDDw0P6zz0pKQmVKlVS271dl3Tr1g09evTIVuwcPXoUf/75J/bt2ydPMA2NHj1a2mk7NTUVv/76q9pO27rAx8cHKpUKKpVKbRn5LKamppgzZ44MyTQzc+ZMuSOQgolcVhC/c+eO9LOuCyIiInDkyBG1DzP29vaYPHmy7Kvus9gpAEuWLJE7wkfR9fxvO336dI4/aDVq1EBwcLAMiTRXt25dXLlyRXpes2bNbIsgavtMrMTERClzyZIlcezYMbXVbY2MjODo6Cj7Pf134fRlKgiVKlWSPgQ0bNgQBgb/9ys5IyMDN2/eRNOmTWVMmDfGxsZ4/vx5tvakpCQYGRnJkOj/sNgpIOnp6di3bx+uX7+OTp06wdLSEvfu3YOVlRUsLCzkjvdOSvuPXaVS5fgD+OzZM63vHtb2XidN2NraSl309erVQ+nSpbMNUNZVr169yrb9iLbvUK0EiYmJGp+rzf8erVu3BgCcOXMG/v7+ar8bjIyMUKJECWnncF3QokUL9O7dG4sXL0a1atUAvO5B/+6772Tf0oZjdgpATEwMmjZtitjYWKSkpODq1asoWbIkBg4ciJSUFCxYsEDuiJ+Vli1bwtTUFH///bfUe5CRkYGvv/4aycnJ2LZtm8wJlc3a2hpHjhyBl5dXtvFguig5ORnDhw/H2rVr8fjx42zHtb2AVoKsNcA0oa3/Hm3btkVoaCisrKywdOlSdOjQIdsAZV2TkJCAwMBAhIWFSWNV09PT0apVKyxZskTWDzksdgpA69atYWlpicWLF8Pe3l4aULpv3z706tVLWhCOPo2LFy+ibt26sLGxQZ06dQAABw4cQGJiIvbu3StNqaeC0a5dOxw6dAheXl6IiIhAzZo1c+3S1oWp50FBQQgPD8cvv/yCb775BiEhIbh79y4WLlyIyZMno3PnznJHVLyIiAjpz7du3cKIESPQrVs3aUmGyMhILF26FJMmTdLanmojIyPExMSgcOHCuQ5Q1lXXrl2Tpp57eXmhdOnSMidisVMg7O3tcfjwYZQpU0Zt9oyurU+jJPfu3cPcuXMRFRUFU1NTVKhQAcHBwWoD6ahgvHz5EkuXLsX169cxbdo09OrVSxpw/bYZM2Z84nR55+rqimXLlkkrip86dQqlS5fG8uXL8ffff2Pr1q1yR/ysNGzYEN9++y06duyo1r5q1SosWrRIa28FV6hQAZUrV0b9+vXRvXt3zJ49O9dbbroy9Xz8+PEYOnRotp/vly9fYurUqRg9erRMyVjsFAhbW1scOnQI3t7easXOwYMH0a5dO8THx8sdkUgW9evXx8aNG3V6zI6FhQUuXrwIV1dXFC1aFBs2bEC1atVw8+ZNlC9fHklJSXJH/KyYmZkhKioq22zLq1evwsfHR2s/XB4+fBiDBw/G9evX8eTJE1haWuZ4a06lUunMcgbaPIVeT7Z3VrAmTZqoTVVVqVRISkrCmDFj0Lx5c/mCfeZevHiBy5cv4+zZs2oPXfCuxfZ0ZfVkAAgPD5cKHSGEzi1jALyeUXbz5k0AgKenJ9auXQsACAsL0+kiTlcVK1YMf/zxR7b2P//8E8WKFZMhkWZq1qyJI0eO4OHDhxBC4OrVq3j69Gm2h64UOkDuU+ijoqJk70Vnz04BuHPnDvz9/SGEQHR0NKpWrYro6GgUKlQI+/fv15n7skrYvR0AHj58iO7du+c6EFlbBzC+SUk7bS9btgxTp06Vxq55eHhg2LBh+Oabb2ROppkZM2ZAX18fAwYMwO7du9GyZUsIIZCWlobp06dj4MCBckf8rGzduhXt2rVD6dKlUb16dQDAsWPHEB0djX/++UcnPmDGxMTA1dVVZ1fhzlp089mzZ7CyslK7joyMDCQlJeG7775DSEiIbBlZ7BSQ9PR0rFmzBlFRUUhKSkLlypXRuXNnmJqayh1NY8HBwdLu7YULF872g6gL4ysAoHPnzoiJicHMmTPh5+eHjRs3Ij4+HhMmTMC0adMQEBAgd8T3+uKLL1ChQoVcd9pev369jOk0N336dIwaNQrBwcHS2kcHDx5ESEgIJkyYgEGDBsmcMO9iYmJw8uRJlC5dGhUqVJA7zmfp9u3bmD9/Pi5fvgzg9aDY7777Tqt7dt6WkJCAxYsXSwN7vb290bNnT51YVHDp0qUQQqBHjx6YOXOmWuasKfRZg8dl80m2G/3MrFq1KtdjQ4cO/YRJPo6u796exdnZWRw9elQIIYSlpaW4cuWKEEKIzZs3i1q1askZTWNK2Wm7RIkSYunSpdnaQ0NDRYkSJWRI9HFevnwpdwRSgOPHjws7OztRpEgR0aZNG9GmTRtRtGhRYW9vL06ePCl3PI3t27dPpKamyh0jRyx2CoC1tbXYunVrtvbvv/9eODs7y5DowxQuXFgqDHSZpaWluHnzphBCCFdXV3Hw4EEhhBA3btwQpqamMibLm9jYWOHq6ioGDRok3N3dxddffy3S09PljpUnxsbGIjo6Olv71atXhbGxsQyJ8i49PV2MHz9euLi4CH19fXH9+nUhhBA///yz+PPPP2VO93nav3+/6Ny5s/D19RV37twRQgixbNkyceDAAZmTaaZ27dqiW7duIi0tTWpLS0sTgYGBok6dOjIm+3AvX74Uz549U3vIiQOUC8DKlSvRsWNHHDx4UGrr378/1q5di/DwcBmT5c2QIUMwa9YsnRxE+qYyZcpIWy5UrFgRCxcuxN27d7FgwQIULlxY5nSaK1asGHbt2oWVK1eiWrVqaosk6orSpUtLA3rftGbNmmyzabTVr7/+itDQUEyZMkVtvaBy5crhzz//lDHZ5+mff/6Bv78/TE1NcerUKaSkpAB4vUL6xIkTZU6nmRMnTmD48OFq20UYGBjghx9+wIkTJ2RMljcvXrxAcHAwHB0dYW5uDltbW7WHrGQttRRs5cqVwtbWVpw4cUL07dtXuLi46FwvSevWrYW1tbVwc3MTLVq0kLpXsx66Yvny5WLJkiVCCCFOnDghChUqJPT09ISJiYlYvXq1vOHewcbGRtja2mZ7GBsbCysrK7U2XbF+/Xqhr68v/P39xfjx48X48eOFv7+/MDAwEBs2bJA7nkZKlSoldu/eLYQQwsLCQurZuXTpkrCxsZEz2mfJx8dHujX65r/HqVOnhJOTk5zRNObo6Ch27NiRrX379u3C0dFRhkQfpl+/fsLLy0usX79emJqair/++kv88ssvomjRomLFihWyZuPeWAWkU6dOSEhIQK1ateDg4ICIiAitWEUyL3R99/YsXbp0kf5cpUoVxMTE4PLly3B1dUWhQoVkTPZuStxpu127djh69ChmzJiBTZs2AXg9mPTYsWOoVKmSvOE0dPfu3Rx/ljMzM9+5RAAVjCtXruS4Ga61tTUSEhI+faAP8PXXX6Nnz574/fffUbNmTQDAoUOHMGzYsGyLJWqzsLAwacHN7t27o06dOihdujSKFy+OlStXyrq6OIudfDJ48OAc2x0cHFC5cmXMmzdPaps+ffqnivVRlLb7eRYzMzNUrlxZ7hjvpa3L3H+sKlWqYMWKFXLH+GDe3t44cOAAihcvrta+fv16nSnYlMTZ2RnXrl1DiRIl1NoPHjyIkiVLyhMqj37//XeoVCp07doV6enpAABDQ0P07dsXkydPljmd5p48eSL9nVtZWUlrBNWuXRt9+/aVMxqLnfxy+vTpHNtLly6NxMRE6biuraOgy7u3Z+nRo8c7j//111+fKEn+0MWdttPT05GRkaG20WF8fDwWLFiA5ORktGrVCrVr15YxoeZGjx6NwMBA3L17F5mZmdiwYQOuXLmCZcuWYcuWLXLH++z06tULAwcOxF9//QWVSoV79+4hMjISQ4cOxahRo+SOpxEjIyPMmjULkyZNwvXr1wEApUqVynVbFW2VteCmq6urtOBmtWrVtGLBTa6zQ7lSyu7tb9+KS0tLw/nz55GQkIAGDRpgw4YNMiXTnK7vtN29e3cYGRlh4cKFAIDnz5+jbNmyePXqFQoXLoyLFy9i8+bNOrEAHPB6I9nx48erraM1evRoNGnSRO5onx0hBCZOnIhJkyZJW0MYGxtj6NCh+OWXX2RO92FiYmKQnJwMT09P6OnpzjwirV5wU9YRQwqVkJAgHj9+nK398ePHsk+/y4svv/xSdOnSRaSkpKgN/AsPDxelS5eWOd3HycjIEL179xa//fab3FE0os0D/zTh7u6uNgBz7ty5wsXFRSQkJAghhPjhhx+En5+fXPFIAVJSUsSFCxfE0aNHxfPnz+WOo5HFixeLadOmqbX16tVL6OnpCT09PeHl5SViY2NlSvfxbt26Jf755x8RFRUldxSus1MQmjZtKkJCQrK1z58/XzRr1kyGRB/Gzs5OXL58WQihPsvh5s2bOrU+TW4uX76sM+seFStWTISHhwshXq8blLVWzbJly3Tie8rMzEzcuHFDet6mTRvRv39/6fmFCxeEg4ODHNHyzM3NTTx69Chb+9OnT4Wbm5sMiUhXVa9eXfz111/S823btgkDAwOxYsUKcfLkSeHr6yt69uwpY0Ll4JidAnD06NEcByH7+fnhp59+kiHRh8nMzMzx9sidO3dgaWkpQ6L8df36dWkwoLbT5oF/mjAxMcHLly+l50eOHMHUqVPVjuvKbuG3bt3K8eciJSUFd+/elSHR56dt27Yan6vNt6mz9k7MsnnzZnz55ZfSrKWJEyeie/fucsX7IMePH0d4eHiO+ynKOTmHxU4BSElJyfGXaFpamtp/+Noua/f2RYsWAdDd3dvfniknhMD9+/fx33//6cyMJ20e+KcJHx8fLF++HJMmTcKBAwcQHx+PBg0aSMevX78OFxcXGRO+37///iv9eceOHWr7/2RkZGDPnj3ZZgRRwdCF/aI08fLlS7XJBYcPH0bPnj2l5yVLlkRcXJwc0T7IxIkT8fPPP6NMmTJwcnJSm5Aj++QcubuWlMjPz08EBwdna+/Xr5+oXbu2DIk+zO3bt4W3t7fw8vISBgYGokaNGsLe3l6UKVNGxMfHyx1PY35+fmqPBg0aiK+//losXLhQbXl2bTZ9+nQxa9YsIYQQu3btEiYmJsLY2Fjo6emJmTNnypzu/fbt2ydMTU1FyZIlhampqejRo4fa8b59+4quXbvKlE4zKpVKqFQqoaenJ/0562FkZCQ8PDxEWFiY3DFJh3h6eop//vlHCCHEw4cPhb6+vjhx4oR0/OjRozqzMKIQrxdHzFrAVdtwNlYBOHToEBo1aoQvvvgCDRs2BADs2bMHx48fx86dO1GnTh2ZE2pOCbu3K5Eu7rR96dIl7Ny5E87Ozvjqq6/UZpksWrQI1apVg4+Pj3wBNeTm5objx49r9YKUn5OsGZVv93AmJiaidevW2Lt3rzzBNDB58mTMmjUL/fr1w969e/Hw4UOcP39eOj5z5kxs2bIFu3fvljGl5goXLoz9+/dr5dYvLHYKyJkzZzB16lScOXMGpqamqFChAkaOHKmV3wS5+fvvv3NdvXPYsGFqYy7o03n16hVMTEzkjkGkFfT09BAXFwdHR0e19gcPHqBIkSJavap1ZmYmxo4di7CwMDg7O2P69Onw8vKSjn/11Vdo2rSp2q0tbTZlyhTcu3dPK1d/Z7FDubKxscHff/+NZs2aqbUPGjQIq1evxv3792VKppnr16/j119/lRYNdHV1VRsEq6+vj4MHD6JMmTJyRdRYRkYGJk6ciAULFiA+Pl5a82jUqFEoUaKEzvxnqMsiIyPx+PFjtGjRQmpbtmwZxowZg+TkZLRu3Rpz5sxRWziRCs7Zs2cBvB4PtnfvXtjZ2UnHMjIysH37dixcuBC3bt2SKeHnJzMzEwEBAbh69Sq8vb1haGiodlzOweIcoJxPEhMTpYFmiYmJ7zxX21e7zZK1e/uWLVuk1W379++PDRs26MTu7XPmzIGTk5P0/OnTpxg9erT0CXDNmjWYMWOGTiyO+Ouvv2Lp0qWYMmUKevXqJbWXK1cOM2fOZLHzCYwfPx5+fn5SsXPu3Dn07NkT3bp1g5eXF6ZOnQoXFxeMHTtW3qCfCR8fH6hUKqhUKrXB7llMTU0xZ84cGZJ9vgYMGIDw8HDUr18f9vb28g9KfpOcA4aURE9PTxq0mzWI8e1HVrsu0eXd28uVKyeOHj0qPX9zrSAhXg+a1ZXFEbnTtvycnZ3F8ePHpec//vijqFWrlvR87dq1wsvLS45on6Vbt26JmzdvCpVKJY4fPy5u3bolPe7duyfS09PljvjZsbCwEFu2bJE7Ro7Ys5NP3uxG1YVeD03p8u7tt27dUpvO/O2336pNWS1RogTu3LkjR7Q8407b8nv69KlaT2FERITaLd4vvvgCt2/fliPaZylrI9bw8HD4+PjAwED911lGRgb279+f447oVDDs7OxQqlQpuWPkiMVOPqlXr16Of9Y1Stq9XU9PD/fu3UPRokUBvN635U3x8fHZ7ilrK+60LT8nJyfcvHkTxYoVQ2pqKk6dOoVx48ZJx58/f64z309K0qBBA9y/fz/bAOWEhATUr19f6/eNU5KxY8dizJgxWLJkidZtYspip4AkJCTg2LFjOa4i2bVrV5lSvZ+Sdm8vW7Ysdu/ejWrVquV4fMeOHShXrtwnTvVhdHmnbVtbW42/X7JWhtZGzZs3x4gRI/Dbb79h06ZNMDMzU1tG4uzZs1r7qVbJhBA5fn89fvwY5ubmMiT6fM2ePRvXr1+Hk5MTSpQoka34P3XqlEzJWOwUiLCwMHTu3BlJSUmwsrLKtoqkNhc7SroF1717d3z//feoWLEiAgIC1I6FhYVh8uTJWjlFMidffvklwsLCMH78eJibm2P06NGoXLkywsLC0LhxY7njvdObf8ePHz/GhAkT4O/vD19fXwCvZznt2LEDo0aNkimhZn755Re0bdsW9erVg4WFBZYuXQojIyPp+F9//cVdzz+hrC0jVCoVunXrpjYLLiMjA2fPnkXNmjXlipcnGRkZCA0NxZ49e3L8gKzNawW9qXXr1nJHyBWnnhcADw8PNG/eHBMnTtS6rry8ePbsGTIyMtSmdAKvP30bGBjoxKyyjh07Ys2aNfD09JSmmF+5cgVXrlxBu3btsHbtWpkTfl7atWuH+vXrIzg4WK197ty52L17NzZt2iRPsDx49uwZLCwsoK+vr9b+5MkTWFhYqBVAVHCy9oxaunQp2rdvr7bQqZGREUqUKIFevXrpxOKPwcHBCA0NRUBAAAoXLpytp+rtW/CUdyx2CoC5uTnOnTsnbdyoq5o1a4aWLVuiX79+au0LFizAv//+i61bt8qULG9Wr16N1atX4+rVqwAAd3d3dOzYER06dJA5meZKliyJ48ePw97eXq09ISEBlStXxo0bN2RKljcWFhY4c+ZMtsHW165dg4+Pj85sBkraY9y4cRg6dKhO37IqVKgQli1bplN7Dr7LyZMncenSJQCvhxNow7hC3sYqAP7+/jhx4oTOFztK2b29Q4cOOlXY5EQpO23b29tj8+bNGDJkiFr75s2bsxVyRJoYM2YMAODhw4e4cuUKAKBMmTJwcHCQM1aeGBkZ6cws13d58OABOnTogH379knbd2QNFF+9erWs/yYsdvLJmzsiBwQEYNiwYbh48SLKly+fbZBWq1atPnW8D6KU3dt1mdJ22h43bhy+/fZb7Nu3D9WrVwfwuqjevn07/vjjD5nTkS568eIFgoODsWzZMmmsi76+Prp27Yo5c+boxFCCIUOGYNasWZg7d65OTP7ITf/+/fH8+XNcuHBB2vbi4sWLCAwMxIABA/D333/Llo23sfLJm5savotKpdKZqZD169dHuXLlsq1CGhQUhLNnz+LAgQMyJft8ZH1fqVQqvP2jamhoiBIlSmDatGlqWxhou6NHj2L27NlSN7eXlxcGDBggFT9EedGnTx/s3r0bc+fORa1atQAABw8exIABA9C4cWPMnz9f5oTv16ZNG4SHh8POzg5ly5bVqm0W8sLa2hq7d+/GF198odZ+7NgxNGnSBAkJCfIEA3t28s3bo+eVYMKECWjUqBGioqJy3L2dCl7W95WSdtquXr06Vq5cKXcMUoh//vkH69evh5+fn9TWvHlzmJqaon379jpR7NjY2KBNmzZyx/homZmZOa41ZWhoKPvvSPbs0DspYfd20i7Xr1/HkiVLcOPGDcycOROOjo7Ytm0bXF1dUbZsWbnjkY4xMzPDyZMn1XYLB4ALFy6gWrVqSE5OlinZ5yM2NhZFixZFmzZtkJCQgL///ltavf7u3bvo3LkzbG1tsXHjRtkyanbvhTSyd+9eeHt757gR6LNnz1C2bFns379fhmQfzsfHBytXrsSFCxdw4sQJ/PXXXyx0PqHIyMhsiwYuW7YMbm5ucHR0RO/evZGSkiJTuryLiIhA+fLlcfToUfzzzz/S7KuoqChpoClRXvj6+mLMmDF49eqV1Pby5UuMGzdOWstJF6Snp2P37t1YuHAhnj9/DgC4d++eTsxQdHNzw6NHjzB37lwkJiaiRIkSKFWqFEqVKgU3NzckJibKvikre3byUatWrVC/fn0MGjQox+OzZ89GeHi4rNXt+yhl9/asBcc0oc33w5s1awY/Pz8MHz4cwOudtitXrqy203afPn10ZqdtX19ffPXVVxg8eDAsLS0RFRWFkiVL4tixY2jbtq3O7FVG2uP8+fPw9/dHSkoKKlasCOB18WxiYoIdO3boRG9hTEwMmjZtitjYWKSkpODq1asoWbIkBg4ciJSUFCxYsEDuiO+kp6eHuLg4ODo6QgiB3bt34/LlywBej8lr1KiRzAlZ7OSr4sWLY/v27dm6U7NcvnwZTZo0QWxs7CdOpjl9fX1pnxk9Pb0cZwZkLc+uzQOtsxYcA17n3bhxI6ytrVG1alUAr9eBSEhIQNu2bbFkyRK5Yr5X4cKFERYWJuX+6aefEBERgYMHDwIA1q1bhzFjxuDixYtyxtSYhYUFzp07Bzc3N7Vi59atW/D09FT7dE6kqRcvXmDlypVqv2A7d+6sttCgNmvdujUsLS2xePFi2NvbSz8X+/btQ69evRAdHS13xHfS09NDfHy8Vk/35wDlfPS+jSUNDAzw8OHDT5go75Sye/ubBczw4cPRvn17LFiwQFr1NiMjA/369dPq3ilAeTtt29jY4P79+3Bzc1NrP336NIoUKSJTKtJ1ZmZm6NWrl9wxPtiBAwdw+PDhbKtvlyhRQmfW0Ro1atR7p/nLuXk0i518VKRIEZw/fz7XxaHOnj2LwoULf+JUeaOU3dvf9Ndff+HgwYNqy/vr6+tj8ODBqFmzJqZOnSpjundT2k7bHTp0wPDhw7Fu3TqoVCpkZmbi0KFDGDp0qFbvGUfa6/Hjx9KClLdv38Yff/yBly9fomXLlqhbt67M6TSTmZmZY0/5nTt3YGlpKUOivDt37tw7t0qRe/0gFjv5qHnz5hg1ahSaNm0KExMTtWMvX77EmDFjdGo9FEB3d29/U3p6Oi5fviztjZXl8uXLsk+HfB+l7bQ9ceJEBAUFoVixYsjIyIC3tzcyMjLQqVMn/Pzzz3LHIx1y7tw5tGzZErdv34a7uztWr16Npk2bIjk5GXp6epgxYwbWr1+v1ZtTZmnSpAlmzpyJRYsWAXhdGCQlJWHMmDE6s4XExo0b4ejoKHeMXHHMTj6Kj49H5cqVoa+vj+DgYOmX6+XLlxESEoKMjAycOnVK7baENnvf7u1PnjyRMZ3mBg8ejGXLluHHH39EtWrVALxe2G7y5Mn45ptvZO1afZ9Hjx6hbdu2OHjwoLTT9pvrcTRs2BA1atTAr7/+KmPKvIuNjcX58+eRlJSESpUqcYYf5VmzZs1gYGCAESNGYPny5diyZQv8/f2llbj79++PkydP4siRIzInfb87d+7A398fQghER0ejatWqiI6ORqFChbB//36tLiIA9bGe2orFTj6LiYlB3759sWPHDmnFW5VKBX9/f4SEhGQbq6DNlLJ7e2ZmJn7//XfMmjUL9+/fB/B64O/AgQMxZMiQbLtXayPutE2krlChQti7dy8qVKggfSA7fvw4qlSpAuD1h8waNWrIumpvXqSnp2PNmjWIiopCUlISKleurDODrN+cjaWtWOwUkKdPn+LatWsQQsDd3R22trZyR8ozpeze/qas6fTaPjBZqTIyMhAaGoo9e/bkeGt07969MiUjXfP2L9g3Z/cBr3vaXVxctHrWaJa///4bHTt2zPHYsGHDtHpcIQAsXboUHTp0gLGxsdxRcsUxOwXE1tY22/4gukYpu7e/iUWOvAYOHIjQ0FAEBASgXLlysg9aJN329vePrn4/9e3bFzY2NmozLQFg0KBBWL16tdYXO4GBgXJHeC8WO6RGibu3x8fHY+jQoVJvwtudmbrwyU8pVq9ejbVr1+rMoEvSbt26dZN6E169eoXvvvsO5ubmAKBTK4uvXLkSHTt2xJYtW1C7dm0Ar8ccbdiwQaeXANEmvI1FapS4e3uzZs0QGxuL4OBgFC5cONunvy+//FKmZJ8fFxcX7Nu3Dx4eHnJHIR335sKh76LNi4a+adWqVQgODsauXbuwePFibN68GeHh4fxZyScsdkjxLC0tceDAAfj4+Mgd5bM3bdo03LhxA3PnztXZWw5EBWXevHkYPHgwHBwcEB4enuuabZR3vI1FilesWLFst65IHgcPHkR4eDi2bduGsmXLZrs1qs37lBHlp8GDB+fY7uDggMqVK2PevHlSmzYvj5GT1NTUHCcguLq6ypSIxQ7lYO/evQgODsaRI0eyDeh99uwZatasifnz5+vM6qQzZ87EiBEjsHDhQpQoUULuOJ81GxsbtXWCiD5Xp0+fzrG9dOnSSExMlI7rUg9odHQ0evTogcOHD6u1a8N+iryNRdkoYff2N9na2uLFixdIT0+HmZlZtt4EXVkckYhIm9WqVUta6DGn8ZFZu9LLgcUOZaOE3dvftHTp0nce14Vpk0SkXM+ePUNGRoa0CXOWJ0+ewMDAQGeWzDA3N8fJkyfh6ekpd5RseBuLslHC7u1vYjGjPbgMAFF2HTp0QMuWLdGvXz+19rVr1+Lff//F1q1bZUqWN97e3nj06JHcMXLEYoeyUcLu7bl59eoVUlNT1dp05VOTEnTr1g2xsbEYNWpUjt3cRJ+jo0eP5jgI2c/PDz/99JMMiTSXtSo9APz222/44YcfMHHixBzXZpPz/1oWO5SN0nZvT05OxvDhw7F27Vo8fvw423H2Jnw6Bw8e5DIARG9JSUlBenp6tva0tDS8fPlShkSas7GxUfvQIoRAw4YN1c7RhgHKLHYom59//hkbNmyAh4dHrru3a/unjTf98MMPCA8Px/z58/HNN98gJCQEd+/excKFCzF58mS5431WuAwAUXbVqlXDokWLMGfOHLX2BQsWSBubaitdWeGZA5QpR0ravd3V1RXLli2Dn58frKyscOrUKZQuXRrLly/H33//rTP3w5Vg586dmDZtGpcBIHrDoUOH0KhRI3zxxRdSr8iePXtw/Phx7Ny5E3Xq1JE5oe5jsUPvpITd2y0sLHDx4kW4urqiaNGi2LBhA6pVq4abN2+ifPnySEpKkjviZ4PLABDl7MyZM5g6dSrOnDkDU1NTVKhQASNHjoS7u7vc0TS2fft2WFhYSPt7hYSE4I8//oC3tzdCQkJk/f3B21j0TkrYvb1kyZK4efMmXF1d4enpibVr16JatWoICwuDjY2N3PE+KzNnzpQ7ApFW8vHxwcqVK+WO8VGGDRuG3377DQBw7tw5DB48GEOGDEF4eDgGDx4s6z5l7NkhxZsxYwb09fUxYMAA7N69Gy1btoQQAmlpaZg+fToGDhwod0Qi+swkJiZKs5PenNGUE12ZMWphYYHz58+jRIkSGDt2LM6fP4/169fj1KlTaN68OeLi4mTLxp4dUrw3V4Ju1KgRLl++jJMnT6J06dKoUKGCjMk+b1wGgD5ntra2uH//PhwdHbPNaMqiDbOY8sLIyAgvXrwAAOzevRtdu3YFANjZ2b23oCtoLHbos1O8eHEUL15c7hifJS4DQPTa3r17pRWTdWVG0/vUrl0bgwcPRq1atXDs2DGsWbMGAHD16lUULVpU1mwsdojok+EyAESv1atXL8c/67K5c+eiX79+WL9+PebPn48iRYoAALZt24amTZvKmo1jdojok+EyAEQ5S0hIwLFjx/DgwQNkZmaqHcu6HUQfjj07RPTJPHnyBCVLlgTwenxO1lTz2rVro2/fvnJGI5JNWFgYOnfujKSkJFhZWamN31GpVDpV7Fy/fh1LlizB9evXMWvWLDg6OmLbtm1wdXVF2bJlZculJ9s7E9FnJ2sZAADSMgAAuAwAfdaGDBmCHj16ICkpCQkJCXj69Kn00KW1pyIiIlC+fHkcPXoUGzZskNYwi4qKwpgxY2TNxmKHFO/UqVM4d+6c9Hzz5s1o3bo1fvzxx2yzgahgde/eHVFRUQCAESNGICQkBCYmJhg0aBCGDRsmczoiedy9excDBgyAmZmZ3FE+yogRIzBhwgTs2rULRkZGUnuDBg1w5MgRGZNxzA59Br744guMGDEC7dq1w40bN1C2bFm0adMGx48fR0BAABe6k1FMTAyXAaDPXtu2bdGhQwe0b99e7igfxcLCAufOnYObmxssLS0RFRWFkiVL4tatW/D09MSrV69ky8YxO6R4V69elXbZXrduHerWrYtVq1bh0KFD6NChA4sdGXEZAPpc/fvvv9KfAwICMGzYMFy8eBHly5fPto1Kq1atPnW8D2JjY4P79+9n2zvx9OnT0swsubDYIcUTQkizG3bv3o0WLVoAeL0D96NHj+SM9tnYu3cvgoODceTIkWwLBz579gw1a9bEggULuOEhfTZat26drW38+PHZ2nRpUcEOHTpg+PDhWLduHVQqFTIzM3Ho0CEMHTpU9kHWHLNDile1alVMmDABy5cvR0REBAICAgAAN2/ehJOTk8zpPg8zZ85Er169clwh2draGn369MH06dNlSEYkj8zMTI0eulLoAMDEiRPh6emJYsWKISkpCd7e3qhbty5q1qyJn3/+WdZsHLNDinf27Fl07twZsbGxGDx4sDQroH///nj8+DFWrVolc0LlK168OLZv3w4vL68cj1++fBlNmjRBbGzsJ05GRPlBCIHbt2/DwcEBjx49wrlz55CUlIRKlSppxc7tLHbos/Xq1Svo6+tnuz9O+c/ExATnz59H6dKlczx+7do1lC9fHi9fvvzEyYjko8nt3fnz56Nu3boyJdRcZmYmTExMcOHCBa0obt7G21j02Thx4gSWL1+O5cuX48SJEzAxMWGh84kUKVIE58+fz/X42bNnUbhw4U+YiEh+mtzenTFjhgzJ8k5PTw/u7u457nmnDVjskOLduXMHderUQbVq1TBw4EAMHDgQ1apVQ+3atXHnzh25430WmjdvjlGjRuU49fTly5cYM2aMNHCc6HMRFRX1zj2jmjRpgpMnT37CRB9n8uTJGDZs2Ds/2MiFt7FI8Zo2bYqEhAQsXboUZcqUAQBcuXIF3bt3h5WVFbZv3y5zQuWLj49H5cqVoa+vj+DgYOnf4fLlywgJCUFGRgZOnTrFAeP0WVHa7V1bW1u8ePEC6enpMDIygqmpqdpxOVeD5tRzUryIiAgcPnxY+gULAGXKlMGcOXM41fkTcXJywuHDh9G3b1+MHDkSWZ+xVCoV/P39ERISwkKHPjtZt3dzK3Z07fauNq9ZxmKHFK9YsWJIS0vL1p6RkQEXFxcZEn2eihcvjq1bt+Lp06e4du0ahBBwd3eHra2t3NGIZJF1e7dp06YwMTFRO6ZLt3dHjx6NESNGIDAwEADw9OlTrfu55m0sUrzNmzdj4sSJCAkJQdWqVQG8Hqzcv39/DB8+PMfFvYiICppSbu/q6+vj/v37cHR0BABYWVnhzJkzKFmypMzJ/g+LHVK8N+8jGxi87szM+rO5ubnaubq0wzAR6b6YmBj07dsXO3bsyPH27ttbL2gjPT09xMXFScXOm/tiaQvexiLF0+b7yET0eePt3U+DPTtERET0wfT19XH16lU4ODhACIFixYrh4MGDKFGihNp5Oa0n9Kmw2CFFSkxMlH6wEhMT33munD+ARES6Tk9PDyqVSnouhMjxuZz7fPE2FimSra2tNGDOxsZG7Qcvizb8ABIR6brw8HC5I7wXix1SpL1798LOzg6AbvwgEhHpqnr16skd4b14G4uIiIgUjT079FlISEjAsWPH8ODBA2RmZqod69q1q0ypiIjoU2DPDileWFgYOnfujKSkJFhZWamN31GpVFxbh4hI4VjskOJ5eHigefPmmDhxIszMzOSOQ0REnxiLHVI8c3NznDt3TqtW8yQiok+HY3ZI8fz9/XHixAkWO0RE+axt27Yan7thw4YCTPJuLHZIkf7991/pzwEBARg2bBguXryI8uXLw9DQUO3cVq1afep4RESKYG1tLf1ZCIGNGzfC2tpa2nT55MmTSEhIyFNRVBB4G4sUSU9PT6PzuKggEVH+GD58OJ48eYIFCxZAX18fAJCRkYF+/frBysoKU6dOlS0bix0iIiL6aA4ODjh48CDKlCmj1n7lyhXUrFkTjx8/likZoNnHXyIiIqJ3SE9Px+XLl7O1X758Odv6Zp8aix1SrL1798Lb2zvHjUCfPXuGsmXLYv/+/TIkIyJSnu7du6Nnz56YPn06Dh48iIMHD2LatGn49ttv0b17d1mz8TYWKVarVq1Qv359DBo0KMfjs2fPRnh4ODZu3PiJkxERKU9mZiZ+//13zJo1C/fv3wcAFC5cGAMHDsSQIUOkcTxyYLFDilW8eHFs374dXl5eOR6/fPkymjRpgtjY2E+cjIhI2bJ61K2srGRO8hqnnpNixcfHZ5tm/iYDAwM8fPjwEyYiIvo8aEuRk4VjdkixihQpgvPnz+d6/OzZsyhcuPAnTEREpFzx8fH45ptv4OLiAgMDA+jr66s95MSeHVKs5s2bY9SoUWjatClMTEzUjr18+RJjxoxBixYtZEpHRKQs3bp1Q2xsLEaNGoXChQurbbosN47ZIcWKj49H5cqVoa+vj+DgYGnth8uXLyMkJAQZGRk4deoUnJycZE5KRKT7LC0tceDAAfj4+MgdJRv27JBiOTk54fDhw+jbty9GjhyJrLpepVLB398fISEhLHSIiPJJsWLFoK39J+zZoc/C06dPce3aNQgh4O7uDltbW7kjEREpys6dOzFt2jQsXLgQJUqUkDuOGhY7RERE9NFsbW3x4sULpKenw8zMLNts2CdPnsiUjLexiIiIKB/MnDlT7gi5Ys8OERERKRp7doiIiChfvXr1CqmpqWptci40yEUFiYiI6KMlJycjODgYjo6OMDc3h62trdpDTix2iIiI6KP98MMP2Lt3L+bPnw9jY2P8+eefGDduHFxcXLBs2TJZs3HMDhEREX00V1dXLFu2DH5+frCyssKpU6dQunRpLF++HH///Te2bt0qWzb27BAREdFHe/LkCUqWLAng9ficrKnmtWvXxv79++WMxmKHiIiIPl7JkiVx8+ZNAICnpyfWrl0LAAgLC4ONjY2MyXgbi4iIiPLBjBkzoK+vjwEDBmD37t1o2bIlhBBIS0vD9OnTMXDgQNmysdghIiKifBcTE4OTJ0+idOnSqFChgqxZWOwQERGRonHMDhERESkaix0iIiJSNBY7REREpGgsdoiIiEjRWOwQERHRRzt16hTOnTsnPd+8eTNat26NH3/8MdumoJ8aix0iIiL6aH3+X3t3FxPFuYBx/JldaItsEVrrRiyCLbZVAxGiMUVpiRdAQUq/U1pAjB+EFJQL1AurJkjamJKCabww3miaGBtrE/XCmloNrWhqVxRrpKXF72SDEorNikTcnXPhYXWzhdpdYE72/H8Jyew77+48cEGevDOzU1mpzs5OSdLFixf1/vvva8KECdq7d6/Wrl1raTbKDgAACFtnZ6fmzJkjSdq7d69eeeUV7d69Wzt37tS+ffsszUbZAQAAYTNNUz6fT5J05MgRFRQUSJKSkpLU09NjZTTKDgAACN/cuXPV0NCgL7/8Ui0tLSosLJQkXbp0SU6n09JslB0AABC25uZmtbW1qbq6WuvXr1dqaqok6euvv1ZWVpal2XhcBAAAGDMDAwOy2+2Kjo62LEOUZUcGAAARx+VyqaOjQ5I0c+ZMzZ071+JElB0AADAKrl+/rpKSErW2tio+Pl6S1NfXp6ysLO3Zs0fPPvusZdm4ZgcAAIRt+fLlGhwcVEdHh3p7e9Xb26uOjg75fD4tX77c0mxcswMAAMIWExOjEydOKCMjI2D89OnTys7OVn9/v0XJWNkBAACjICkpSYODg0HjXq9XiYmJFiR6gLIDAADC9tlnn6mmpkYul8s/5nK5tHr1ajU2NlqYjNNYAABgFCQkJKi/v1/37t1TVNT9+5+GtmNjYwPm9vb2jms27sYCAABha25utjrCsFjZAQAAEY2VHQAAEJK//vpLcXFx/u2RDM2zAis7AAAgJHa7XW63W5MnT5bNZpNhGEFzTNOUYRjyer0WJLyPlR0AABCSo0eP6qmnnpIkHTt2zOI0w2NlBwAARDRWdgAAwKjo6+vTqVOndOPGDfl8voB95eXlFqViZQcAAIyCgwcP6sMPP5TH41FcXFzA9TuGYYz7d+s8jLIDAADC9sILL6igoECffPKJJkyYYHWcAJQdAAAQttjYWP3yyy967rnnrI4ShGdjAQCAsOXl5QU8F+t/CRcoAwCAkBw4cMC/XVhYqDVr1ujChQtKS0tTdHR0wNzXX399vOP5cRoLAACExGZ7tBNEVn+pIGUHAABENK7ZAQAAEY2yAwAAQnb06FHNmjXrbx8EeuvWLc2ePVs//PCDBckeoOwAAICQNTc3a8WKFX/7VPOJEyeqsrJSTU1NFiR7gLIDAABC1t7ervz8/GH35+bm6vTp0+OYKBhlBwAAhKy7uzvoNvOHRUVF6ebNm+OYKBhlBwAAhGzq1Kk6f/78sPvPnTunKVOmjGOiYJQdAAAQsoKCAm3YsEEDAwNB++7cuaNNmzZp8eLFFiR7gO/ZAQAAIevu7lZmZqbsdruqq6v14osvSpJ+/fVXbdu2TV6vV21tbXI6nZZlpOwAAICwXLlyRVVVVTp8+LCGaoVhGMrLy9O2bds0ffp0S/NRdgAAwKj4888/9ccff8g0Tc2YMUMJCQlWR5JE2QEAABGOC5QBAEBEo+wAAICIRtkBAAARjbIDAAAiGmUHAABENMoOgFFTUVGhN954w/86JydHtbW1luXJycmRYRjas2dPwHhzc7NSUlKsCQVg3FF2AES0J554Qh9//LEGBwetjgLAIpQdAGOioqJCLS0t2rp1qwzDkGEYunz5siTp/Pnzeu211+RwOOR0OlVWVqaenh7/e3NyclRTU6Pa2lolJCTI6XRqx44dun37tpYuXaonn3xSqampOnTo0D/mKCkpUV9fn3bs2DHsnK6uLhUXF8vpdMrhcGjevHk6cuRIwJyUlBQ1NDSovLxcDodDycnJOnDggG7evKni4mI5HA6lp6fL5XIFvO/48ePKzs5WTEyMkpKStGrVKt2+fftf/CUBhIuyA2BMbN26VS+//LJWrFght9stt9utpKQk9fX1adGiRcrIyJDL5dK3336r7u5uvffeewHv37VrlyZNmqRTp06ppqZGVVVVevfdd5WVlaW2tjbl5uaqrKxM/f39I+aIi4vT+vXrVV9fP2zJ8Hg8Kigo0Pfff68zZ84oPz9fRUVFunr1asC8pqYmLViwQGfOnFFhYaHKyspUXl6u0tJStbW16fnnn1d5ebn/6/K7urqUn5+vt99+W+fOndNXX32l48ePq7q6Ooy/LIB/zQSAUbJkyRKzuLjY//rVV181V69eHTBn8+bNZm5ubsDYtWvXTEnmb7/95n/fwoUL/fvv3btnxsbGmmVlZf4xt9ttSjJPnjw5bJ6h4w8MDJjJyclmfX29aZqm2dTUZCYnJ4/4u8yePdv84osv/K+Tk5PN0tLSoONv2LDBP3by5ElTkul2u03TNM1ly5aZK1euDPjcH3/80bTZbOadO3dGPD6A0cPKDoBx1d7ermPHjsnhcPh/XnrpJUn3V0KGpKen+7ftdruefvpppaWl+ceGnqB848aNfzzm448/rvr6ejU2NgacLhvi8XhUV1enmTNnKj4+Xg6HQx0dHUErOw9nGjr+SJna29u1c+fOgN81Ly9PPp9Ply5d+sfcAEZHlNUBAPx/8Xg8Kioq0pYtW4L2TZkyxb8dHR0dsM8wjIAxwzAkST6f75GOW1paqsbGRjU0NATdiVVXV6fvvvtOjY2NSk1NVUxMjN555x3dvXs3YN7fHX+kTB6PR5WVlVq1alVQnmnTpj1SbgDho+wAGDOPPfaYvF5vwFhmZqb27dunlJQURUWN378gm82mTz/9VG+99ZaqqqoC9rW2tqqiokJvvvmmpPslZehi6nBkZmbqwoULSk1NDfuzAISO01gAxkxKSop++uknXb58WT09PfL5fProo4/U29urkpIS/fzzz+rq6tLhw4e1dOnSoGI02goLCzV//nxt3749YHzGjBn65ptvdPbsWbW3t+uDDz545BWjkaxbt04nTpxQdXW1zp49q99//1379+/nAmVgnFF2AIyZuro62e12zZo1S88884yuXr2qxMREtba2yuv1Kjc3V2lpaaqtrVV8fLxstrH/l7RlyxYNDAwEjH3++edKSEhQVlaWioqKlJeXp8zMzLCPlZ6erpaWFnV2dio7O1sZGRnauHGjEhMTw/5sAI/OMM3/3iMJAAAQgVjZAQAAEY2yAwAAIhplBwAARDTKDgAAiGiUHQAAENEoOwAAIKJRdgAAQESj7AAAgIhG2QEAABGNsgMAACIaZQcAAES0/wDno0rODP6tCgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Line plot for item_price trend:\n",
        "data_copy['item_price'].plot(kind='line', figsize=(10, 5))\n",
        "plt.xlabel('Order Index')\n",
        "plt.ylabel('Item Price')\n",
        "plt.title('Trend of Item Price Across Orders')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "jzQL7UcdK25k",
        "outputId": "6ad3ac03-b910-4b4a-dba4-983f6338e6a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Pie chart for top 5 most ordered items:\n",
        "top_5_items = data_copy['item_name'].value_counts().head(5)\n",
        "top_5_items.plot(kind='pie', autopct='%1.2f%%', figsize=(7, 7))\n",
        "plt.title('Proportion of Top 5 Most Ordered Items')\n",
        "plt.ylabel('')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "jcsq-kzQLDAz",
        "outputId": "f9513466-4779-4594-a084-711bff6b7b16"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "# Convert item_price to float (if not already done)\n",
        "#data_copy['item_price'] = data_copy['item_price'].str.replace('$', '').astype(float)\n",
        "\n",
        "# Calculate total revenue for each item\n",
        "data_copy['total_revenue'] = data_copy['quantity'] * data_copy['item_price']\n",
        "revenue_by_item = data_copy.groupby('item_name')['total_revenue'].sum().reset_index()\n",
        "\n",
        "# Sort items by revenue to find the top 5\n",
        "top_5_items = revenue_by_item.sort_values(by='total_revenue', ascending=False).head(5)\n",
        "\n",
        "# Plot the histogram for the top 5 items\n",
        "plt.figure(figsize=(10, 6))\n",
        "bars = plt.bar(top_5_items['item_name'], top_5_items['total_revenue'], color='skyblue', edgecolor='black')\n",
        "\n",
        "# Add revenue values on top of each bar\n",
        "for bar in bars:\n",
        "    height = bar.get_height()\n",
        "    plt.text(bar.get_x() + bar.get_width() / 2, height, f'${height:.2f}', ha='center', va='bottom')\n",
        "\n",
        "plt.xlabel('Item Name')\n",
        "plt.ylabel('Total Revenue ($)')\n",
        "plt.title('Top 5 Items by Revenue')\n",
        "plt.xticks(rotation=45, ha='right')  # Rotate x-axis labels for better readability\n",
        "plt.tight_layout()  # Adjust layout to prevent overlapping\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "faQYy453avFm",
        "outputId": "6e112e23-6b06-4dcd-c933-1bdeab1b64dd"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert item_price to float after removing dollar sign\n",
        "#data_copy['item_price'] = data_copy['item_price'].str.replace('$', '', regex=False).astype(float)\n",
        "\n",
        "# Get top 5 most expensive items\n",
        "top5_items = data_copy.nlargest(5, 'item_price')\n",
        "\n",
        "# Plot histogram for top 5 item names and respective prices\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(top5_items['item_name'], top5_items['item_price'], color='blue', edgecolor='black')\n",
        "plt.xlabel('Item Name')\n",
        "plt.ylabel('Item Price')\n",
        "plt.title('Top 5 Most Expensive Items')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 757
        },
        "id": "S3Vxw9HseDML",
        "outputId": "8930c775-5798-4666-db49-bb404aeaa2df"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get bottom 10 least expensive items\n",
        "bottom10_items = data_copy.nsmallest(10, 'item_price')\n",
        "\n",
        "# Plot bar chart for bottom 10 item names and respective prices\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(bottom10_items['item_name'], bottom10_items['item_price'], color='red', edgecolor='black')\n",
        "plt.xlabel('Item Name')\n",
        "plt.ylabel('Item Price')\n",
        "plt.title('Bottom 10 Least Expensive Items')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "qWxYJWgDe8kN",
        "outputId": "ba9250c7-354b-43cc-cf36-fa3dcb4035ea"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "unique_items_count = data_copy['item_name'].nunique()\n",
        "print(\"Total number of unique items:\", unique_items_count)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvyKeM3KfbeT",
        "outputId": "e646b23c-0a5b-4097-815f-f475d79ea7a6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of unique items: 50\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get unique item prices and select the 10 lowest-priced unique items\n",
        "unique_prices = data_copy.groupby('item_name')['item_price'].min().reset_index()\n",
        "bottom10_unique_items = unique_prices.nsmallest(10, 'item_price')\n",
        "\n",
        "# Plot bar chart for bottom 10 unique item names and respective prices\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(bottom10_unique_items['item_name'], bottom10_unique_items['item_price'], color='red', edgecolor='black')\n",
        "plt.xlabel('Item Name')\n",
        "plt.ylabel('Item Price')\n",
        "plt.title('Bottom 10 Least Expensive Unique Items')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "z1fWzW4gfoGr",
        "outputId": "8e548dcf-4d31-4822-eaeb-8f626074e576"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculate total revenue per unique item\n",
        "data_copy['total_revenue'] = data_copy['quantity'] * data_copy['item_price']\n",
        "revenue_per_item = data_copy.groupby('item_name')['total_revenue'].sum().reset_index()\n",
        "\n",
        "# Get top 5 unique products clocking highest revenue\n",
        "top5_revenue_items = revenue_per_item.nlargest(5, 'total_revenue')\n",
        "\n",
        "# Plot bar chart for top 5 revenue-generating unique items\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.bar(top5_revenue_items['item_name'], top5_revenue_items['total_revenue'], color='green', edgecolor='black')\n",
        "plt.xlabel('Item Name')\n",
        "plt.ylabel('Total Revenue')\n",
        "plt.title('Top 5 Revenue-Generating Unique Items')\n",
        "plt.xticks(rotation=90)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 702
        },
        "id": "jbGVK7qwheVv",
        "outputId": "7da724c8-8f3d-47ac-e2d0-57134b07c459"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total revenue generated:\n",
        "#data_copy['item_price'] = data_copy['item_price'].str.replace('$', '').astype(float)\n",
        "total_revenue = (data_copy['quantity'] * data_copy['item_price']).sum()\n",
        "print(\"Total Revenue:\", total_revenue)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3LpkaUELP0Z",
        "outputId": "cc0e9716-586b-46b8-9899-bb7ed7689b53"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Revenue: 39237.02\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total number of items ordered for each item_name:  (use of GroupBy)\n",
        "item_quantity = data_copy.groupby('item_name')['quantity'].sum()\n",
        "print(\"Total Quantity Ordered by Item:\")\n",
        "print(item_quantity)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bjfMa81aLx6M",
        "outputId": "6ed09dd4-36c4-44a1-f00d-5f6be4ab2699"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Quantity Ordered by Item:\n",
            "item_name\n",
            "6 Pack Soft Drink                         55\n",
            "Barbacoa Bowl                             66\n",
            "Barbacoa Burrito                          91\n",
            "Barbacoa Crispy Tacos                     12\n",
            "Barbacoa Salad Bowl                       10\n",
            "Barbacoa Soft Tacos                       25\n",
            "Bottled Water                            211\n",
            "Bowl                                       4\n",
            "Burrito                                    6\n",
            "Canned Soda                              126\n",
            "Canned Soft Drink                        351\n",
            "Carnitas Bowl                             71\n",
            "Carnitas Burrito                          60\n",
            "Carnitas Crispy Tacos                      8\n",
            "Carnitas Salad                             1\n",
            "Carnitas Salad Bowl                        6\n",
            "Carnitas Soft Tacos                       40\n",
            "Chicken Bowl                             761\n",
            "Chicken Burrito                          591\n",
            "Chicken Crispy Tacos                      50\n",
            "Chicken Salad                              9\n",
            "Chicken Salad Bowl                       123\n",
            "Chicken Soft Tacos                       120\n",
            "Chips                                    230\n",
            "Chips and Fresh Tomato Salsa             130\n",
            "Chips and Guacamole                      506\n",
            "Chips and Mild Fresh Tomato Salsa          1\n",
            "Chips and Roasted Chili Corn Salsa        23\n",
            "Chips and Roasted Chili-Corn Salsa        18\n",
            "Chips and Tomatillo Green Chili Salsa     45\n",
            "Chips and Tomatillo Red Chili Salsa       50\n",
            "Chips and Tomatillo-Green Chili Salsa     33\n",
            "Chips and Tomatillo-Red Chili Salsa       25\n",
            "Crispy Tacos                               2\n",
            "Izze                                      20\n",
            "Nantucket Nectar                          29\n",
            "Salad                                      2\n",
            "Side of Chips                            110\n",
            "Steak Bowl                               221\n",
            "Steak Burrito                            386\n",
            "Steak Crispy Tacos                        36\n",
            "Steak Salad                                4\n",
            "Steak Salad Bowl                          31\n",
            "Steak Soft Tacos                          56\n",
            "Veggie Bowl                               87\n",
            "Veggie Burrito                            97\n",
            "Veggie Crispy Tacos                        1\n",
            "Veggie Salad                               6\n",
            "Veggie Salad Bowl                         18\n",
            "Veggie Soft Tacos                          8\n",
            "Name: quantity, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#order with highest total cost\n",
        "data_copy['total_cost'] = data_copy['quantity'] * data_copy['item_price']\n",
        "highest_cost_order = data_copy.loc[data_copy['total_cost'].idxmax()]\n",
        "print(\"Order with Highest Total Cost:\")\n",
        "print(highest_cost_order)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CMzzn-7jMBAr",
        "outputId": "83467a0e-bf01-42c5-8d04-624ff96c1ee0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Order with Highest Total Cost:\n",
            "order_id                              1443\n",
            "quantity                                15\n",
            "item_name     Chips and Fresh Tomato Salsa\n",
            "item_price                           44.25\n",
            "total_cost                          663.75\n",
            "Name: 3598, dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total cost for each order:\n",
        "order_total_cost = data_copy.groupby('order_id')['total_cost'].sum()\n",
        "print(\"Total Cost for Each Order:\")\n",
        "print(order_total_cost)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0cJPRDOoMOxm",
        "outputId": "0a0ae194-5c0c-4d79-ab95-40a0872e8050"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Cost for Each Order:\n",
            "order_id\n",
            "1       11.56\n",
            "2       33.96\n",
            "3       12.67\n",
            "4       21.00\n",
            "5       13.70\n",
            "        ...  \n",
            "1830    23.00\n",
            "1831    12.90\n",
            "1832    13.20\n",
            "1833    23.50\n",
            "1834    28.75\n",
            "Name: total_cost, Length: 1834, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Most expensive item:\n",
        "most_expensive_item = data_copy.loc[data_copy['item_price'].idxmax(), 'item_name']\n",
        "print(\"Most Expensive Item:\", most_expensive_item)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruXHj9hUMZ9H",
        "outputId": "39b994f6-ea1f-4d06-b86d-3e5e62360437"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Most Expensive Item: Chips and Fresh Tomato Salsa\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Average quantity per order_id:\n",
        "avg_quantity_per_order = data_copy.groupby('order_id')['quantity'].mean()\n",
        "print(\"Average Quantity per Order:\")\n",
        "print(avg_quantity_per_order)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEmOQ9fXMf7A",
        "outputId": "fba5c7de-06d3-4aeb-86d3-c157c75d4bd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average Quantity per Order:\n",
            "order_id\n",
            "1       1.0\n",
            "2       2.0\n",
            "3       1.0\n",
            "4       1.0\n",
            "5       1.0\n",
            "       ... \n",
            "1830    1.0\n",
            "1831    1.0\n",
            "1832    1.0\n",
            "1833    1.0\n",
            "1834    1.0\n",
            "Name: quantity, Length: 1834, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Number of unique customers:\n",
        "unique_customers = data_copy['order_id'].nunique()\n",
        "print(\"Number of Unique Customers:\", unique_customers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hhLVNyktMnPo",
        "outputId": "741b4eca-0b0a-497e-90a7-7d14c38726f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Unique Customers: 1834\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total revenue by item_name:\n",
        "revenue_by_item = data_copy.groupby('item_name')['total_cost'].sum()\n",
        "print(\"Total Revenue by Item:\")\n",
        "print(revenue_by_item)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gc5n3C2gMrzh",
        "outputId": "a0935ba2-5c81-4a14-820c-88edd0f20542"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Revenue by Item:\n",
            "item_name\n",
            "6 Pack Soft Drink                         369.93\n",
            "Barbacoa Bowl                             672.36\n",
            "Barbacoa Burrito                          894.75\n",
            "Barbacoa Crispy Tacos                     138.71\n",
            "Barbacoa Salad Bowl                       106.40\n",
            "Barbacoa Soft Tacos                       250.46\n",
            "Bottled Water                             649.18\n",
            "Bowl                                       74.00\n",
            "Burrito                                    44.40\n",
            "Canned Soda                               191.84\n",
            "Canned Soft Drink                         603.75\n",
            "Carnitas Bowl                             830.71\n",
            "Carnitas Burrito                          616.33\n",
            "Carnitas Crispy Tacos                      95.94\n",
            "Carnitas Salad                              8.99\n",
            "Carnitas Salad Bowl                        66.34\n",
            "Carnitas Soft Tacos                       375.94\n",
            "Chicken Bowl                             8044.63\n",
            "Chicken Burrito                          6387.06\n",
            "Chicken Crispy Tacos                      524.11\n",
            "Chicken Salad                              81.09\n",
            "Chicken Salad Bowl                       1506.25\n",
            "Chicken Soft Tacos                       1199.01\n",
            "Chips                                     580.34\n",
            "Chips and Fresh Tomato Salsa             1033.96\n",
            "Chips and Guacamole                      2475.62\n",
            "Chips and Mild Fresh Tomato Salsa           3.00\n",
            "Chips and Roasted Chili Corn Salsa         73.75\n",
            "Chips and Roasted Chili-Corn Salsa         43.02\n",
            "Chips and Tomatillo Green Chili Salsa     144.55\n",
            "Chips and Tomatillo Red Chili Salsa       159.30\n",
            "Chips and Tomatillo-Green Chili Salsa      88.43\n",
            "Chips and Tomatillo-Red Chili Salsa        93.21\n",
            "Crispy Tacos                               14.80\n",
            "Izze                                       67.80\n",
            "Nantucket Nectar                          111.87\n",
            "Salad                                      14.80\n",
            "Side of Chips                             290.68\n",
            "Steak Bowl                               2479.81\n",
            "Steak Burrito                            4236.13\n",
            "Steak Crispy Tacos                        375.32\n",
            "Steak Salad                                35.66\n",
            "Steak Salad Bowl                          391.15\n",
            "Steak Soft Tacos                          554.55\n",
            "Veggie Bowl                               901.95\n",
            "Veggie Burrito                           1002.27\n",
            "Veggie Crispy Tacos                         8.49\n",
            "Veggie Salad                               50.94\n",
            "Veggie Salad Bowl                         182.50\n",
            "Veggie Soft Tacos                          90.94\n",
            "Name: total_cost, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Percentage of orders including a specific item (e.g., \"Chicken Bowl\"):\n",
        "total_orders = data_copy['order_id'].nunique()\n",
        "chicken_bowl_orders = data_copy[data_copy['item_name'] == 'Chicken Bowl']['order_id'].nunique()\n",
        "percentage = (chicken_bowl_orders / total_orders) * 100\n",
        "print(\"Percentage of Orders Including Chicken Bowl:\", percentage)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nHZTvS2qMxK2",
        "outputId": "1d0484b7-20b9-40da-f142-dcb217d8c5f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Percentage of Orders Including Chicken Bowl: 33.533260632497274\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Total revenue for each day (assuming order_id represents sequential days):\n",
        "data_copy['day'] = data_copy['order_id'].astype(int)  # Convert order_id to day\n",
        "revenue_by_day = data_copy.groupby('day')['total_cost'].sum()\n",
        "print(\"Total Revenue by Day:\")\n",
        "print(revenue_by_day)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3NzmuuFFM-ae",
        "outputId": "ce50a667-62fd-43c9-9a08-00739b78f155"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total Revenue by Day:\n",
            "day\n",
            "1       11.56\n",
            "2       33.96\n",
            "3       12.67\n",
            "4       21.00\n",
            "5       13.70\n",
            "        ...  \n",
            "1830    23.00\n",
            "1831    12.90\n",
            "1832    13.20\n",
            "1833    23.50\n",
            "1834    28.75\n",
            "Name: total_cost, Length: 1834, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values in the choice_description column\n",
        "print(\"Missing values in choice_description:\", data['choice_description'].isnull().sum())\n",
        "\n",
        "# Drop rows where choice_description is NaN (if necessary)\n",
        "data_cleaned = data.dropna(subset=['choice_description'])\n",
        "\n",
        "# Analyze the most common customizations\n",
        "customization_counts = data_cleaned['choice_description'].value_counts().head(10)\n",
        "\n",
        "print(\"Top 10 Most Common Customizations:\")\n",
        "print(customization_counts)\n",
        "\n",
        "# Visualize the results\n",
        "plt.figure(figsize=(10, 6))\n",
        "customization_counts.plot(kind='bar', color='skyblue')\n",
        "plt.title('Top 10 Most Common Customizations')\n",
        "plt.xlabel('Customization')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 850
        },
        "id": "eI_PGDzxYnEg",
        "outputId": "3d1e904d-b596-4d84-a887-6b333b36961b"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Missing values in choice_description: 1246\n",
            "Top 10 Most Common Customizations:\n",
            "choice_description\n",
            "[Diet Coke]                                                                          134\n",
            "[Coke]                                                                               123\n",
            "[Sprite]                                                                              77\n",
            "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Lettuce]]                42\n",
            "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream, Guacamole, Lettuce]]     40\n",
            "[Fresh Tomato Salsa (Mild), [Pinto Beans, Rice, Cheese, Sour Cream]]                  36\n",
            "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese, Sour Cream]]                         33\n",
            "[Lemonade]                                                                            33\n",
            "[Fresh Tomato Salsa, [Rice, Cheese, Sour Cream, Lettuce]]                             29\n",
            "[Fresh Tomato Salsa, [Rice, Black Beans, Cheese]]                                     28\n",
            "Name: count, dtype: int64\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['price_range'] = pd.cut(data['item_price'], bins=[0, 5, 10, 15, 20], labels=['Low', 'Medium', 'High', 'Premium'])\n",
        "popular_items_by_price = data.groupby(['price_range', 'item_name'])['quantity'].sum().reset_index().sort_values(by='quantity', ascending=False)\n",
        "print(popular_items_by_price)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GUQFEEKPaAsW",
        "outputId": "9703b209-8802-4aea-f2a1-637d28a302a5"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    price_range                              item_name  quantity\n",
            "25          Low                    Chips and Guacamole       455\n",
            "67       Medium                           Chicken Bowl       422\n",
            "10          Low                      Canned Soft Drink       351\n",
            "68       Medium                        Chicken Burrito       343\n",
            "117        High                           Chicken Bowl       271\n",
            "..          ...                                    ...       ...\n",
            "131        High  Chips and Tomatillo-Green Chili Salsa         0\n",
            "130        High    Chips and Tomatillo Red Chili Salsa         0\n",
            "129        High  Chips and Tomatillo Green Chili Salsa         0\n",
            "128        High     Chips and Roasted Chili-Corn Salsa         0\n",
            "0           Low                      6 Pack Soft Drink         0\n",
            "\n",
            "[200 rows x 3 columns]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-33-c8ae8a8e59fc>:2: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
            "  popular_items_by_price = data.groupby(['price_range', 'item_name'])['quantity'].sum().reset_index().sort_values(by='quantity', ascending=False)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert item_price to float (remove '$' and convert)\n",
        "#data['item_price'] = data['item_price'].str.replace('$', '', regex=False).astype(float)\n",
        "\n",
        "# Group by item_name and get the minimum price (assuming prices are consistent for each item)\n",
        "items_and_prices = data.groupby('item_name')['item_price'].min().reset_index()\n",
        "\n",
        "# Print items and their prices\n",
        "for index, row in items_and_prices.iterrows():\n",
        "    print(f\"Item: {row['item_name']}, Price: ${row['item_price']:.2f}\")"
      ],
      "metadata": {
        "id": "F56AMrLvmjgV",
        "outputId": "c785e0cd-ebe1-4e34-d9de-e2ed7a71ca5b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Item: 6 Pack Soft Drink, Price: $6.49\n",
            "Item: Barbacoa Bowl, Price: $8.69\n",
            "Item: Barbacoa Burrito, Price: $8.69\n",
            "Item: Barbacoa Crispy Tacos, Price: $8.99\n",
            "Item: Barbacoa Salad Bowl, Price: $9.39\n",
            "Item: Barbacoa Soft Tacos, Price: $8.99\n",
            "Item: Bottled Water, Price: $1.09\n",
            "Item: Bowl, Price: $7.40\n",
            "Item: Burrito, Price: $7.40\n",
            "Item: Canned Soda, Price: $1.09\n",
            "Item: Canned Soft Drink, Price: $1.25\n",
            "Item: Carnitas Bowl, Price: $8.99\n",
            "Item: Carnitas Burrito, Price: $8.69\n",
            "Item: Carnitas Crispy Tacos, Price: $8.99\n",
            "Item: Carnitas Salad, Price: $8.99\n",
            "Item: Carnitas Salad Bowl, Price: $9.39\n",
            "Item: Carnitas Soft Tacos, Price: $8.99\n",
            "Item: Chicken Bowl, Price: $8.19\n",
            "Item: Chicken Burrito, Price: $8.19\n",
            "Item: Chicken Crispy Tacos, Price: $8.49\n",
            "Item: Chicken Salad, Price: $8.19\n",
            "Item: Chicken Salad Bowl, Price: $8.75\n",
            "Item: Chicken Soft Tacos, Price: $8.49\n",
            "Item: Chips, Price: $1.99\n",
            "Item: Chips and Fresh Tomato Salsa, Price: $2.29\n",
            "Item: Chips and Guacamole, Price: $3.89\n",
            "Item: Chips and Mild Fresh Tomato Salsa, Price: $3.00\n",
            "Item: Chips and Roasted Chili Corn Salsa, Price: $2.95\n",
            "Item: Chips and Roasted Chili-Corn Salsa, Price: $2.39\n",
            "Item: Chips and Tomatillo Green Chili Salsa, Price: $2.95\n",
            "Item: Chips and Tomatillo Red Chili Salsa, Price: $2.95\n",
            "Item: Chips and Tomatillo-Green Chili Salsa, Price: $2.39\n",
            "Item: Chips and Tomatillo-Red Chili Salsa, Price: $2.39\n",
            "Item: Crispy Tacos, Price: $7.40\n",
            "Item: Izze, Price: $3.39\n",
            "Item: Nantucket Nectar, Price: $3.39\n",
            "Item: Salad, Price: $7.40\n",
            "Item: Side of Chips, Price: $1.69\n",
            "Item: Steak Bowl, Price: $8.69\n",
            "Item: Steak Burrito, Price: $8.69\n",
            "Item: Steak Crispy Tacos, Price: $8.69\n",
            "Item: Steak Salad, Price: $8.69\n",
            "Item: Steak Salad Bowl, Price: $9.39\n",
            "Item: Steak Soft Tacos, Price: $8.99\n",
            "Item: Veggie Bowl, Price: $8.49\n",
            "Item: Veggie Burrito, Price: $8.49\n",
            "Item: Veggie Crispy Tacos, Price: $8.49\n",
            "Item: Veggie Salad, Price: $8.49\n",
            "Item: Veggie Salad Bowl, Price: $8.75\n",
            "Item: Veggie Soft Tacos, Price: $8.49\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get unique item names and their corresponding prices\n",
        "unique_items = data_copy[['item_name', 'item_price']].drop_duplicates()\n",
        "\n",
        "# Display result\n",
        "print(unique_items)"
      ],
      "metadata": {
        "id": "ipbIauWTmsHd",
        "outputId": "5e203919-37bc-41f8-e758-1958815897c9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                  item_name  item_price\n",
            "0              Chips and Fresh Tomato Salsa        2.39\n",
            "1                                      Izze        3.39\n",
            "2                          Nantucket Nectar        3.39\n",
            "3     Chips and Tomatillo-Green Chili Salsa        2.39\n",
            "4                              Chicken Bowl       16.98\n",
            "...                                     ...         ...\n",
            "4237                    Chips and Guacamole        8.50\n",
            "4354                       Steak Soft Tacos       18.50\n",
            "4489                    Chips and Guacamole       17.80\n",
            "4509                                  Chips        1.99\n",
            "4510                          Barbacoa Bowl       11.49\n",
            "\n",
            "[209 rows x 2 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "5nz8Qa4wnKcZ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}