ai dataset1.ipynb

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        "<a href=\"https://colab.research.google.com/github/Pujitha-3008/Gitdevopdemo/blob/main/ai%20dataset1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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          "output_type": "stream",
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "path = \"/content/drive/MyDrive/sample_sales_data.csv\"\n",
        "df = pd.read_csv(path)\n",
        "df.drop(columns=[\"Unnamed: 0\"], inplace=True, errors='ignore')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "wbHzl3zOSIGB",
        "outputId": "0705cd9e-b5aa-476f-ae4e-cbd6c56b6c8b"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                         transaction_id            timestamp  \\\n",
              "0  a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  2022-03-02 09:51:38   \n",
              "1  931ad550-09e8-4da6-beaa-8c9d17be9c60  2022-03-06 10:33:59   \n",
              "2  ae133534-6f61-4cd6-b6b8-d1c1d8d90aea  2022-03-04 17:20:21   \n",
              "3  157cebd9-aaf0-475d-8a11-7c8e0f5b76e4  2022-03-02 17:23:58   \n",
              "4  a81a6cd3-5e0c-44a2-826c-aea43e46c514  2022-03-05 14:32:43   \n",
              "\n",
              "                             product_id category customer_type  unit_price  \\\n",
              "0  3bc6c1ea-0198-46de-9ffd-514ae3338713    fruit          gold        3.99   \n",
              "1  ad81b46c-bf38-41cf-9b54-5fe7f5eba93e    fruit      standard        3.99   \n",
              "2  7c55cbd4-f306-4c04-a030-628cbe7867c1    fruit       premium        0.19   \n",
              "3  80da8348-1707-403f-8be7-9e6deeccc883    fruit          gold        0.19   \n",
              "4  7f5e86e6-f06f-45f6-bf44-27b095c9ad1d    fruit         basic        4.49   \n",
              "\n",
              "   quantity  total payment_type  \n",
              "0         2   7.98     e-wallet  \n",
              "1         1   3.99     e-wallet  \n",
              "2         2   0.38     e-wallet  \n",
              "3         4   0.76     e-wallet  \n",
              "4         2   8.98   debit card  "
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              "      <th>payment_type</th>\n",
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              "      <th>0</th>\n",
              "      <td>a1c82654-c52c-45b3-8ce8-4c2a1efe63ed</td>\n",
              "      <td>2022-03-02 09:51:38</td>\n",
              "      <td>3bc6c1ea-0198-46de-9ffd-514ae3338713</td>\n",
              "      <td>fruit</td>\n",
              "      <td>gold</td>\n",
              "      <td>3.99</td>\n",
              "      <td>2</td>\n",
              "      <td>7.98</td>\n",
              "      <td>e-wallet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>931ad550-09e8-4da6-beaa-8c9d17be9c60</td>\n",
              "      <td>2022-03-06 10:33:59</td>\n",
              "      <td>ad81b46c-bf38-41cf-9b54-5fe7f5eba93e</td>\n",
              "      <td>fruit</td>\n",
              "      <td>standard</td>\n",
              "      <td>3.99</td>\n",
              "      <td>1</td>\n",
              "      <td>3.99</td>\n",
              "      <td>e-wallet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ae133534-6f61-4cd6-b6b8-d1c1d8d90aea</td>\n",
              "      <td>2022-03-04 17:20:21</td>\n",
              "      <td>7c55cbd4-f306-4c04-a030-628cbe7867c1</td>\n",
              "      <td>fruit</td>\n",
              "      <td>premium</td>\n",
              "      <td>0.19</td>\n",
              "      <td>2</td>\n",
              "      <td>0.38</td>\n",
              "      <td>e-wallet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>157cebd9-aaf0-475d-8a11-7c8e0f5b76e4</td>\n",
              "      <td>2022-03-02 17:23:58</td>\n",
              "      <td>80da8348-1707-403f-8be7-9e6deeccc883</td>\n",
              "      <td>fruit</td>\n",
              "      <td>gold</td>\n",
              "      <td>0.19</td>\n",
              "      <td>4</td>\n",
              "      <td>0.76</td>\n",
              "      <td>e-wallet</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a81a6cd3-5e0c-44a2-826c-aea43e46c514</td>\n",
              "      <td>2022-03-05 14:32:43</td>\n",
              "      <td>7f5e86e6-f06f-45f6-bf44-27b095c9ad1d</td>\n",
              "      <td>fruit</td>\n",
              "      <td>basic</td>\n",
              "      <td>4.49</td>\n",
              "      <td>2</td>\n",
              "      <td>8.98</td>\n",
              "      <td>debit card</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\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",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "AOA_USSPScE6",
        "outputId": "c16fb4f7-f564-4174-f5fd-95926d6c01f6"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        unit_price     quantity        total\n",
              "count  7829.000000  7829.000000  7829.000000\n",
              "mean      7.819480     2.501597    19.709905\n",
              "std       5.388088     1.122722    17.446680\n",
              "min       0.190000     1.000000     0.190000\n",
              "25%       3.990000     1.000000     6.570000\n",
              "50%       7.190000     3.000000    14.970000\n",
              "75%      11.190000     4.000000    28.470000\n",
              "max      23.990000     4.000000    95.960000"
            ],
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              "      <th></th>\n",
              "      <th>unit_price</th>\n",
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              "      <th>total</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>7829.000000</td>\n",
              "      <td>7829.000000</td>\n",
              "      <td>7829.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>7.819480</td>\n",
              "      <td>2.501597</td>\n",
              "      <td>19.709905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.388088</td>\n",
              "      <td>1.122722</td>\n",
              "      <td>17.446680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.190000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.190000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.990000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.570000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>7.190000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>14.970000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>11.190000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>28.470000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>23.990000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>95.960000</td>\n",
              "    </tr>\n",
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              "                                                     [key], {});\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install seaborn"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "co71vzRWSrWQ",
        "outputId": "b30281ad-403a-415d-e911-0206ea9b62d6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: seaborn in /usr/local/lib/python3.7/dist-packages (0.11.2)\n",
            "Requirement already satisfied: matplotlib>=2.2 in /usr/local/lib/python3.7/dist-packages (from seaborn) (3.2.2)\n",
            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.21.6)\n",
            "Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.7.3)\n",
            "Requirement already satisfied: pandas>=0.23 in /usr/local/lib/python3.7/dist-packages (from seaborn) (1.3.5)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (0.11.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (2.8.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=2.2->seaborn) (1.4.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib>=2.2->seaborn) (4.1.1)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.23->seaborn) (2022.5)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib>=2.2->seaborn) (1.15.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "def plot_continuous_distribution(data: pd.DataFrame = None, column: str = None, height: int = 8):\n",
        "  _ = sns.displot(data, x=column, kde=True, height=height, aspect=height/5).set(title=f'Distribution of {column}');\n",
        "\n",
        "def get_unique_values(data, column):\n",
        "  num_unique_values = len(data[column].unique())\n",
        "  value_counts = data[column].value_counts()\n",
        "  print(f\"Column: {column} has {num_unique_values} unique values\\n\")\n",
        "  print(value_counts)\n",
        "\n",
        "def plot_categorical_distribution(data: pd.DataFrame = None, column: str = None, height: int = 8, aspect: int = 2):\n",
        "  _ = sns.catplot(data=data, x=column, kind='count', height=height, aspect=aspect).set(title=f'Distribution of {column}');\n",
        "\n",
        "def correlation_plot(data: pd.DataFrame = None):\n",
        "  corr = df.corr()\n",
        "  corr.style.background_gradient(cmap='coolwarm')\n",
        "\n"
      ],
      "metadata": {
        "id": "1wfZrSX-SoC-"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7mlSfNB4TDsl",
        "outputId": "3c9b4a2a-a474-46bb-83b3-dbf426b9cce1"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7829 entries, 0 to 7828\n",
            "Data columns (total 9 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   transaction_id  7829 non-null   object \n",
            " 1   timestamp       7829 non-null   object \n",
            " 2   product_id      7829 non-null   object \n",
            " 3   category        7829 non-null   object \n",
            " 4   customer_type   7829 non-null   object \n",
            " 5   unit_price      7829 non-null   float64\n",
            " 6   quantity        7829 non-null   int64  \n",
            " 7   total           7829 non-null   float64\n",
            " 8   payment_type    7829 non-null   object \n",
            "dtypes: float64(2), int64(1), object(6)\n",
            "memory usage: 550.6+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_continuous_distribution(df, 'unit_price')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "a3juud5wTKx7",
        "outputId": "f0816953-679e-42d1-8a3f-74e268b0d951"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 921.6x576 with 1 Axes>"
            ],
            "image/png": 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kh8zsEzP7g5klSMp2zm31ttkmKdt7ny+prMv+5d4yAAAAAEAfEs4iGSlpiqR7nHOTJTXon7exSpKcc06S83NQM7vRzOaZ2byqqqoeCwsAAAAA6J5wFslySeXOuTne56fVWSwr9tyy6n2t9NZvllTYZf8Cb9m/cM7d55yb5pyblpmZGbbwAAAAAIB9C1uRdM5tk1RmZqO8RTMkLZc0W9K13rJrJT3vvZ8t6Rpv9NajJNV0uQUWAAAAANBHRIb5+F+V9LiZRUtaJ+l6dZbXp8zsBkkbJV3mbfuSpLMklUpq9LYFAAAAAPQxYS2SzrmFkqbtY9WMfWzrJN0UzjwAAAAAgE8v3PNIAgAAAAAGGIokAAAAAMAXiiQAAAAAwBeKJAAAAADAF4okAAAAAMAXiiQAAAAAwBeKJAAAAADAF4okAAAAAMAXiiQAAAAAwBeKJAAAAADAF4okAAAAAMAXiiQAAAAAwBeKJAAAAADAF4okAAAAAMAXimSYFBYVy8zC9iosKg76WwQAAAAwSEUGHWCgKi/bpLteXRW24888bVTYjg0AAAAAB8IVSQAAAACALxRJAAAAAIAvFEkAAAAAgC8USQAAAACALxRJAAAAAIAvFEkAAAAAgC8USQAAAACALxRJAAAAAIAvFEkA6OcKi4plZmF7FRYVB/0tAgCAPiYy6AAAgE+nvGyT7np1VdiOP/O0UWE7NgAA6J+4IgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwhSIJAAAAAPCFIgkAAAAA8IUiCQAAAADwJaxF0sw2mNkSM1toZvO8ZWlm9pqZrfG+DvGWm5n9ysxKzWyxmU0JZzYAAAAAwKHpjSuSJznnJjnnpnmfb5X0hnNuhKQ3vM+SdKakEd7rRkn39EI2AAAAAIBPQdzaer6kh733D0u6oMvyR1ynjySlmlluAPkAAAAAAAcQ7iLpJL1qZvPN7EZvWbZzbqv3fpukbO99vqSyLvuWe8sAAAAAAH1IZJiPf5xzbrOZZUl6zcxWdl3pnHNm5vwc0CukN0pSUVFRzyUFAAAAAHRLWK9IOuc2e18rJT0nabqkij23rHpfK73NN0sq7LJ7gbds72Pe55yb5pyblpmZGc74AAAAAIB9CFuRNLMEM0va817SaZKWSpot6Vpvs2slPe+9ny3pGm/01qMk1XS5BRYAAAAA0EeE89bWbEnPmdme8/zJOfc3M/tY0lNmdoOkjZIu87Z/SdJZkkolNUq6PozZAAAAAACHKGxF0jm3TtLEfSzfLmnGPpY7STeFKw8AAAAAoGcEMf0HAAAAAKAfo0gCAAAAAHyhSAIAAAAAfKFIAgAAAAB8oUgCAAAAAHyhSAIAAAAAfKFIAgAAAAB8oUgCAAAAAHyhSAIAAAAAfKFIAgAAAAB8oUgCAAAAAHyhSAIAAAAAfKFIAgAAAAB8oUgCAAAAAHyhSAIAAAAAfKFIAgAAAAB8oUgCAAAAAHyhSALoMYVFxTKzsLwKi4qD/vYAAADgiQw6AICBo7xsk+56dVVYjj3ztFFhOS4AAAD8o0gCvcw5px0NLdqyq0ntzinCTKGQFBkKKSJhSNDxAAAAgIOiSAK9oMM5ra2q1/rqBm3a0aiG5vZ9blfwn4/qwt+9r9PH5uiMsTkampHQy0kBAACAg6NIAmG2cXuD3iutVnV9i2IiQypKi1dRWrwK0+IVHRFSh3Nq73Bqam3XPb+4Xa2fvUk/fXmlfvrySl0ytUC3njlaGYkxQX8bAAAAwD9QJIEwqapr1nul1dq0o1HJsZE6Y2yORmQnKmS2z+2T46JU8+Es/fWDJ1W2o1GPzdmoB99br1eWbdM3Tx2pq44qVmQE42MBAAAgeBRJoIc557R4c43+vrpKUREhfWZEhiYUpCgy1P0SWJgWr9vOHKNLpxbqB7OX6QcvLNdT88p1z1VTVJzO7a4AAAAIFpc3gB7U1t6h11dU6u1VVSpKi9d1xwzVlKIhvkpkVyVZiXr0hun63ZVTtKVmty783Qeat2FHD6dGbwjn1CgAAAC9jSuSQA+pa2rVi0u2qqK2WdOHpumo4Wk98ku+mems8bkak5usz//xY33u/jm649IJOn9Sfg+kRm9hahQAADCQcEUS6AE7G1o06+My7Who0dnjc3X0Yek9fqVoWEaCnv3yMZpUlKqvP7lQv35jjZxzPXoOAAAAoDsoksCntLOxRc8sKFeHky6bVqiSrMSwnWtIQrQevWG6Lpqcr1+8tlq/frM0bOcCAAAA9odbW4FPoWuJvGhKfq9M0xETGaE7L50omXTXa6uVEhela48ZGvbzAgAAAHtQJIFDtKuxRc8u2KyOjt4rkXuEQqafXzxBtbvb9P3Zy5QSF6ULJvPMJAAAAHoHt7YCh6ChuU3PfrJZbR0dunBy75bIPSIjQvrN5ybr6OHp+uafF+n15RW9ngEAAACDE0US8MmiYjR70RbtbmnXBZPylZnUgyXSQr6mfYiLjtSsr56sxs2r9PkH3ldMTskBty8sKu65rAAAABi0uLUV8KG9wynjnP9SVV2zzpmQq+zk2J49ges4pCkiGlva9MTcMqX8x291xRGFio/e97/aTBMBAACAnsAVScCHn7y4QvEjj9bxIzM1PDN8o7P6FR8dqXMm5KqxpV0vL92mjg6mBQEAAED4UCSBbnrkww168P31qp03W5MKU4OO82+yk2M1Y3SWynfu1rul1UHHAQAAwABGkQS6Ye76HfrhC8s1Y3SWdr75h6Dj7NeY3GRNKkzVwrJdWrG1Nug4AAAAGKAoksBBVNY26aY/LVBRWrzuvmKS5DqCjnRAx5VkqCA1Tm+srNT2+uag4wAAAGAAokgCB9Da3qGvPL5A9U1tuveqqUqOjQo60kFFhExnjMtRdERILy/dprb2vl18AQAA0P9QJIEDuP2lFZq3cad+evF4jcpJCjpOtyXEROr0sdna3tCiv6/heUkAAAD0LIoksB/PL9ysh97foOuPHarzJ+UHHce34vQETSlK1ZLNNSqtrA86DgAAAAYQiiSwD6u21enWZ5ZoWvEQfeesMUHHOWTHHJahrKQYvb6iQrVNrUHHAQAAwADRrSJpZsd2ZxkwENQ2teo/HpuvxNhI/e7KKYqK6L9/3xIRMp05LkcdzumVZdsk67/fCwAAAPqO7v5W+etuLgMOqrCoWGYWtldhUfEhZ+vocPrmU4u0aUejfvu5KcpKju3B7zwYqfHROmlUlrbsalLy9AuDjgMAAIABIPJAK83saEnHSMo0s5ldViVLighnMAxc5WWbdNerq8J2/JmnjTrkfe/9+1q9trxC/33O4Zo+LK0HUwVrdE6S1lU1aM1nrtLyLbU6PC856EgAAADoxw52RTJaUqI6C2dSl1etpEvCGw3oXe+tqdadr6zSORNy9fljhwYdp0eZmU4enaX23XW6edZCNbW2Bx0JAAAA/dgBr0g6596R9I6Z/dE5t7GXMgG9bvOu3frak5+oJCtRP7t4gsws6Eg9Li46Qttf/qVWXfpD/eLVVfru2YcHHQkAAAD91AGLZBcxZnafpKFd93HOnRyOUOgGC4W17BQUFqls0+D4u4PmtnZ95bH5amnr0L1XTVVCTHf/teh/mtbN15VHFukP763XyaOzdfRh6UFHAgAAQD/U3d+Y/yzpXkl/kMQ9cX2B6+izzxn2Nz98YbkWldfo3qumanhmYtBxwu67Z4/R+6XV+q8/L9LL3/iMkmOjgo4EAACAfqa7o7a2Oefucc7Ndc7N3/MKazKgFzw1r0x/mrNJXz7xMJ0xLifoOL0iPjpSd18+Sdtqm/SD2cuCjgMAAIB+qLtF8gUz+4qZ5ZpZ2p5XWJMBYbZ0c42+95elOrYkXd88dWTQcXrV5KIhuunEw/Tsgs16ecnWoOMAAACgn+nura3Xel+/1WWZkzS8Z+MAvaOqrlk3PjJP6QnR+tUVkxUZ0d2/Uxk4vjpjhN5aVaXvPLdEU4uHDIg5MwEAANA7uvXbs3Nu2D5elEj0S81t7fqPx+ZrR2OL7r9mmtITY4KOFIioiJDuvnySGlvadcszi+WcCzoSAAAA+oluXZE0s2v2tdw590jPxgHCyzmn7z23VPM37tRvPjdZ4/JTgo4UqJKsRH3nrDH6/uxlenzOJl11VHHQkQAAANAPdPfW1iO6vI+VNEPSAkkUSfQrD76/QX+eX66vnVyicybkBR2nT7j6qGK9vqJCP3lxhY4tydCwjISgIwEAAKCP6+6trV/t8vqipCmSBv48CRhQ3lpVqZ+8uFynj83WN04ZXIPrHEgoZLrjkomKjgzp5lkL1dbeEXQkAAAA9HGHOsJIg6RhPRkECKeFZbt00+MLNDonWXddNkmhkAUdqU/JSYnVjy8Yp4Vlu/S7t9cGHQcAAAB9XLeKpJm9YGazvdeLklZJeq6b+0aY2Sdm9lfv8zAzm2NmpWY2y8yiveUx3udSb/3QQ/uWgH+1rqpen//jx0pPjNYfP3+EEmK6e0f34HLuxDydNzFPv3pjjRaX7wo6DgAAAPqw7l6RvFPSL7zX7ZKOd87d2s19vy5pRZfPP5N0t3OuRNJOSTd4y2+QtNNbfre3HfCpVNY26ZoH58okPfL5I5WVxBQXB/Kj88cpIzFGN89aqN0t7UHHAQAAQB/V3Wck35G0UlKSpCGSWrqzn5kVSDpb0h+8zybpZElPe5s8LOkC7/353md562d42wOHpLapVdc+9LF2NLTooeuPYBCZbkiJj9Kdl07U2qoG3f7SioPvAAAAgEGpu7e2XiZprqRLJV0maY6ZXdKNXf9P0i2S9ozekS5pl3OuzftcLinfe58vqUySvPU13vZ7Z7nRzOaZ2byqqqruxMcgVNPYqqv/MEdrKup071VTNaEgNehI/cZxIzL0heOG6dGPNupvS7cGHQcAAAB9UHdvbf2upCOcc9c6566RNF3Sfx9oBzM7R1Klc27+p8z4L5xz9znnpjnnpmVmZvbkoTFAhOKS9dn7P9KKrZ0l8viR/HPi1y1njNbEghR96+nFKtvRGHQcAAAA9DHdLZIh51xll8/bu7HvsZLOM7MNkp5U5y2tv5SUamZ7RjspkLTZe79ZUqEkeetTvPMA3dbQ3Kbsz96utVX1uu+aqTrl8OygI/VL0ZEh/eZzUyRJ//nEJ2ppY0oQAAAA/FN3i+TfzOwVM7vOzK6T9KKklw60g3PuNudcgXNuqKQrJL3pnLtS0luS9twWe62k5733s73P8ta/6Zxz3f5OMOjVNbXqmQXlikzJ0UPXHaETR2UFHalfK0yL188vnqBFZbt0xysrg44DAACAPuSARdLMSszsWOfctyT9XtIE7/WhpPsO8ZzfljTTzErV+QzkA97yBySle8tnSuruqLCAttbs1pMfl6mhuV2Vf/6+jinJCDrSgHDm+Fxdc3Sx7n93vV5fXhF0HAAAAPQRB5tQ7/8k3SZJzrlnJT0rSWY23lt3bndO4px7W9Lb3vt16nzGcu9tmtQ5mA/gy/IttXpzZaUSYyN18ZQ8/fj2ZUFHGlC+c9YYLdi0UzfPWqjnbjpWJVmJQUfq85xz2rC9UfM27NCyLbXasmu3cq65S/e/u07NrR0KhaQIM4VCppjIkNITYpSeGK30xGjlJMcqKTYq6G8BAADggA5WJLOdc0v2XuicW2JmQ8OSCOim9g6n90ur9UnZLhUOidNZ43MVGxURdKwBJzYqQr+/eprO+/V7uvGReXrupmOVEkfR2VtNY6teXb5Nr6+o0PyNO1Vd3zlLUkJ0hPKHxKljd52GZSQoNipCzjm1d3S+dre2q6q+WaVV9f84VnZyjEZkJWlEVqKS+VkDAIA+6GBF8kBzJsT1ZBDAj+r6Zr22vEKVdc2aVJCqz4zIUCjEtKPhkp8ap+wIBI0AACAASURBVHuumqrP3f+Rvv7kJ3rg2iMUwc9bjS1temnJNr24eIveK61Wa7tTXkqsjh+ZqWnFaZo2dIhKMhMVCpls5ok65YtX7PdYre0d2tHQorKdjVpTUa/3Sqv1Xmm18lJjNX1omorS4sXUugAAoK84WJGcZ2ZfdM7d33WhmX1BUo9O6wF0R0eH07yNOzVn/XbFREborPE5GpGVFHSsQWH6sDT98Pyx+u5zS3XHK6t065mjg44UmHVV9Xr0o416en656pralJ8ap+uPHaazxudqYkHKIRW+qIiQspNjlZ0cq2nFaarZ3ao1lXVaVFajvyzcopzkWB05PE3FFEoAANAHHKxIfkPSc2Z2pf5ZHKdJipZ0YTiDAXvbvHO3/r6mSpV1zRqRlagTR2UqPvpg/wijJ115ZLGWb6nVve+s1YisRF08tSDoSL3GOaf3S7fr939fq3fXVCsqwnTmuFxdfXSxphUP6fFylxIXpWnFaZpUmKoVW+r08cYden7hFuWnxmnGmCwNiY/u0fMBAAD4ccDfwp1zFZKOMbOTJI3zFr/onHsz7MkAT2Vdkz5Yu10btzcqITpCZ43L0YhsrkIG5fvnjtX66gbd8sxiDUmI0smjB/ZcnR0dTq+vqNBv316rRWW7lJUUo2+eOlKXTy9UVlJs2M8fGQppfEGKDs9L1rItNfpg7XY9PmeTjhmerklFqQpxdRIAAASgW5dznHNvqXP+R6BXOOe0ZVeTFpbvUmllvWIjQzquJEMTClIUFdHd6U8RDtGRId13zTR97v6P9OXHFujRG47U9GFpQcfqcc45vba8Qne9tlort9WpMC1OP7lwnC6ZWqCYyN4f1CkiZJpQkKrhmYl6a2Wl3i2t1prKep16+MAu8gAAoG/ivkD0Kc2t7VqxrU5LNtdoR0OLoiNCmj40TVOKUwP55R37lhgTqYeuO0KX/v5D3fDHj/Xkl47S2LyUoGP1mA/WVuuOV1bpk027NDwjQXdfPlHnTshTZB/4S4zEmEidMyFXqyvq9fbqSj358SbFlfzbjEoAAABhRZFE4Bqa27SuqkFrq+tVvmO32p1TdnKMThmTpZHZSVyB7KPSE2P02A1H6pJ7PtC1D87VrC8dHXSkT21x+S7d8coqvbumWrkpsfrpReN1ydSCPlEguzIzjcpJUl5qrP66eKtaLvqe5q7foSOG9vyzmgAAAPtCkUSvi0hM05qKOm2padKWXbtVWdcsqXNwkQkFKRqdk6Ss5PA/e4ZPLy81To/ccKQu//2HuvTeDxWdUxJ0pENSWlmvu15bpZeWbNOQ+Ch97+wxuuqo4j4/L2lSbJQunVqgn//2AX1oJ2p7fbNOOTybv3wBAABhR5FEWLV3OK3aVqf5G3do/sadmrdxpwpuekQvLd2myJApOzlWRw9P1/DMBKUnRHM1pR8qyUrU018+Rlc/MEfZV9yuTTsaVZQWH3Ssbinf2ahfvbFGT88vV1xUhL42Y4S++JlhSoqNCjpat0VGhFT91zt12vkX6/3S7aptatMFk/IU08dLMAAA6N8okuhRDc1t+mTTLs3ziuMnm3apvrlNkpSVFKNpQ4do8dO/1Je//SNlJsUwqf0AMSwjQc98+RhNvvkBzV6YoNPHZvfpkXUrapv027dK9cTcTTKZrj1mqP7zpBKlJ8YEHe2QTStOU2pctF5eulXPfrJZF0zOVxxlEgAAhAlFEp9KU2u75m/cqQ/XbtcHa6u1uLxGbR1OZtKo7CRdMDlP04rTNLV4iAqGxMnMdM9Vs5WTckfQ0dHDspNjVfH4tzX9+8/rpaXbdFRji44YmtanpqfYXt+se99Zq0c+3Kj2DqfLjijUf55UorzUuKCj9YiSrESdMyFPLy7ZqmcWlOuiyfnMtQoAAMKC3zDgW83uVr21slKvLt+mt1dVqbGl3ZuaIEU3Hj9cRw5P1+SiVCX3o9sD0TM6mht04eR8vbGiUh+t26HyHbt1+tgcJcYG+5+amsZW3f/uOj34/no1tbbrwskF+vqMESpK7x+34PoxLCNB503M0wuLtujp+eW6aEqBEmP4Tz0AAOhZ/HaBbmlr79Dbq6r05Meb9PaqKrV1OGUlxejCyfmaMSZL04el88sqJElRESGdPjZbRenxentVpR6fu1GnjsnW8MzEXs+yvb5Zj360UQ+8t151TW06Z0KuvnHKSJVk9X6W3lSUFq8LJuXr+UWb9ZdPNuuSqQV9fuAgAADQv/CbPw6oorZJj3+0UU/NK9e22iZlJMbo88cN0xnjcjSpIFUhnnHEPpiZDs9NVm5yrF5etk0vLN6qwzITdOxhGRqSEB3285dW1umB9zbo2QXlam7r0KmHZ+vmU0bq8LzksJ+7r8gfEqdzJ+Tp+YVbNHvRFl04OZ/RXAEAQI+hSGKfIpIy9P+eX6onPy5Ta3uHThiZqR+cN1YzxmTxyyi6bUhCtC6bVqD5G3dq/sadWle9UePyUnTksDQl9PAV7MaWNr26rELPLCjXu2uqFRMZ0kVTCnTDcUNVktV3B/4Jp8K0eJ0+NlsvLd2ml5du0znjc/nLHwAA0CMokvgX9U1tmrNhu/K/dL+emLtJl0wt0JdPKBmQz5Khd0SGQjpyWLrG56do7vodWrK5Riu21mp4ZoJG5ySrKC3+kEfvrW9u09z12/XSkm16eclWNbS0q2BInGaeOlJXHlnUr0dh7SkjspN0Umu73lpVpTdWVuqUMVlMswMAAD41iiQkdc73uKhslz5av10dHVL9ole1aNadKhhCgUTPiI+O1ImjsjSpMFXzN+1UaUW9VlfUKzYqpJLMRGUnxyo9MVrpCTGKjvz3q94WGa3VFXXaUN2gpVtq9X5ptRaV7VJbh1NSTKTOnZinCyfnd44Uy1W3fzGhIFWNLe2as36HkmIjddTw9KAjAQCAfo4iCW3euVtvrarU9oYWDU2P14mjsvTDn96jgiG/CzoaBqDU+GjNGJ2tE0dmaeP2Bq2qqNPqinot3VL7j20SoiMUCplMnc9btnV0qOibz+q0u/8uSQqZND6/c5TgY0syNLV4CIPJHMSRw9JU19SmOet3KD0huk/P8wkAAPo+iuQg1tbeofdKq7WovEZJsZE6Z0KuhmckcNsbekVEyDQ8M1HDMxPlnFNtU5u21zerur5FtU2t6nBOzknOdW77wazf6uHf/ULF6QkanpnA9DI+mZlOGp2pnY0tenV5hVLio5SVFBt0LAAA0E9RJAepnQ0temnpVlXXt2hSYaqOOSydQXQQGDNTSlyUUuKiNDxz39u8/OEsnT/pyd4NNsBEhkI6e3yunvy4TC8s2qorjijs8UGPAADA4EBzGGScc1q+tVZPfLxJ9c1tOndirk4YmUmJBAaJhJhInTshV02t7XpxyVa1d7igIwEAgH6I9jCIdDind1ZX6bXlFcpKitWV04s1PGNgT8wO4N9lJcfq1MOztbWmSX9fXRV0HAAA0A9xT9Mg0dreoVeWbdPaqgZNLkzVcSMyFOJZSGDQGpmdpIraJi3YtEt5qXEalcPgOwAAoPsokoNAY0ubXli0Vdtqm3TCyExNKkwNOhKAPuCYwzK0taZJb6ysUGZSjNISooOOBAAA+glubR3gane36ql55aqqb9bZ43MpkQD+ISJkOnNcjiJDIb20ZKta2zuCjgQAAPoJiuQAVt/Upmc/2aym1nZdPCVfJVk8DwngXyXFRun0sdna3tCiN1dWyjkG3wEAAAdHkRygGprb9Own5drd0q4LJuUrNyUu6EgA+qji9AQdOSxNK7fVafnW2qDjAACAfoAiOQDtbmnXc59sVl1Tm86blKecFCYdB3Bg04elKT81Tu+srtKuxpag4wAAgD6OIjnANLe167mFm7Vrd6vOm5in/FSuRAI4uJCZTh+brZCZXllWwfySAADggCiSA0iHc/rb0m2qrm/WOeNzVZgWH3QkoOdYSGYWtldhUXHQ32HgkmKjdPLoLG2rbdLc9TuCjgMAAPowpv8YQD5Yu10btjfqxFGZGpqREHQcoGe5Dt316qqwHX7maaPCduz+ZGR2kjZsb9DHG3aoKD2euxoAAMA+cUVygFi5tVbzN+7U+PwUTSxgig8Ah+6EkZlKio3Uq8u2qbmtPeg4AACgD6JIDgDbapr0+spKFaTG6YSRmUHHAdDPxURG6IxxOaprbtPbq6qCjgMAAPogimQ/19Dcpr8u3qKE6AidNT5XESELOhKAASA3JU7Th3ZOCRI/5oSg4wAAgD6GItmPOef0+ooKNbV16JwJeYqLjgg6EoABZPrQNOWmxCr99K+ofGdj0HEAAEAfQpHsxxZvrtGG7Y06riRDmUkxQccBMMCEQqbTx+ZIMs2ctYgpQQAAwD9QJPupqPRCvbumWsXp8ZpYkBJ0HAADVEpclHa8do/mbtihe99ZG3QcAADQR1Ak+6G2jg5lnPtfio4I6dQx2TLjuUgA4dOw7C2dOzFPd7+2WovLdwUdBwAA9AEUyX7oo7U7FJ19mE4Zk6WEGKYCBRB+Pz5/nDISY3TzrIVqamVKEAAABjuKZD+zraZJ8zftVN0nL2t4ZmLQcQAMEinxUbrz0olaW9Wgn768Mug4AAAgYBTJfqSjw+nNlZVKjInUzrcfDDoOgEHmuBEZuu6YofrjBxv03prqoOMAAIAAUST7kUXlu1RV36zjR2TItewOOg6AQejbZ4zW8MwEfevpRarZ3Rp0HAAAEBCKZD9R39SmD9dtV3F6vEqyuKUVQDDioiN092WTVFnXrO8/vzToOAAAICAUyX7i72uq1OGkE0dmMkorgEBNLEzVV08u0V8WbtGLi7cGHQcAAASAItkPbNjeoDWV9Zo+NE2p8dFBxwEA3XRSiSYWpOi7f1miytqmoOMAAIBeRpHs49o7nN5eVaUh8VGaUpwadBwAkCRFRYR01+WT1NTarlueWSznXNCRAABAL6JI9nFLNteoZnerjh+ZqcgQf1wA+o7DMhN125lj9PaqKj0+Z1PQcQAAQC+imfRhLW0dmrt+hwqGxKk4LT7oOADwb64+qlifGZGhn7y4QuurG4KOAwAAeglFsg9bsGmndre269iSDAbYAdAnhUKmOy6ZqKgI08ynFqqtvSPoSAAAoBdQJPuohuY2Ldi0UyOyEpWTHBt0HADYr5yUWP34wvH6ZNMu3fvO2qDjAACAXkCR7KM+3rBDbR1ORx+WHnQUADio8ybm6dyJefq/19do6eaaoOMAAIAwo0j2QbsaW7Rkc43G5aVoCNN9AOgnfnT+WKUnRuvmWQvV1NoedBwAABBGFMk+6MN12xUy05HD0oKOAgDdlhofrTsumag1lfW645VVQccBAABhRJHsY3Y0tGh1Rb0mFaYqISYy6DgA4MvxIzN1zdHFeuC99fpgbXXQcQAAQJiErUiaWayZzTWzRWa2zMx+6C0fZmZzzKzUzGaZWbS3PMb7XOqtHxqubH3Z/I07FRkyTS5KDToKAByS284co+EZCfqvpxapprE16DgAACAMwnlFslnSyc65iZImSTrDzI6S9DNJdzvnSiTtlHSDt/0NknZ6y+/2thtUaptatXJbrcblpSg+mquRAPqnuOgI3X35JFXWNes7f1ki51zQkQAAQA8LW5F0neq9j1Hey0k6WdLT3vKHJV3gvT/f+yxv/QwbZJMnLti4U5I0uZirkQD6t4mFqZp52ki9uHir/jy/POg4AACgh4X1GUkzizCzhZIqJb0maa2kXc65Nm+Tckn53vt8SWWS5K2vkfRvc1+Y2Y1mNs/M5lVVVYUzfq9qbGnTsi21Gp2TrOTYqKDjAMCn9qXjD9NRw9P0g9nLtL66Ieg4AACgB4W1SDrn2p1zkyQVSJouaXQPHPM+59w059y0zMzMT52xr1hYtkttHU7TiocEHQUAekREyHT35ZMUFRHS15/8RC1tHUFHAgAAPaRXRm11zu2S9JakoyWlmtmeBwALJG323m+WVChJ3voUSdt7I1/QmtvataisRiVZiRqSwLyRAAaO3JQ4/ezi8VpcXqNfvMaUIAAADBThHLU108xSvfdxkk6VtEKdhfISb7NrJT3vvZ/tfZa3/k03SEZoWFxeo5b2Dh3B1UgAA9AZ43L12emF+v076/TO6oHzSAIAAINZOK9I5kp6y8wWS/pY0mvOub9K+rakmWZWqs5nIB/wtn9AUrq3fKakW8OYrc9o73BaWLZLRWnxykqODToOAITF/ztnrEZlJ2nmrIWqqG0KOg4AAPiUwjbHhHNusaTJ+1i+Tp3PS+69vEnSpeHK01etqaxTY0u7JhcyUiuAgSsuOkK/vXKyzv31+/raE5/oT188ShGhQTUwNwAAA0qvPCOJ/VtcXqPUuCgVp8cHHQUAwqokK0k/umCc5qzfoV++sSboOAAA4FOgSAaoorZJW2uaNKEgRYNsykwAg9QlUwt08ZQC/frNNXq/tDroOAAA4BBRJAO0qHyXoiJMh+clBx0FAHrNjy4Yq8MyE/X1Jz/RthqelwQAoD+iSAaksaVNqyvqNTonWTGREUHH+XcWkpmF5QVgcIuPjtS9V03R7pZ2feXx+cwvCQBAPxS2wXZwYMu21Kq9w2liQUrQUfbNdeiuV8Mz59vM00aF5bgA+o+SrCT9/JKJuulPC/TjF5frf84fF3QkAADgA1ckA9DR4bS4vEaFQ+KUnhgTdBwACMTZE3J14/HD9ciHG/XsgvKg4wAAAB8okgFYW12v+uY2TWTKDwCD3C2nj9JRw9P0neeWaPmW2qDjAACAbqJIBmBxeY2SYiM1LCMh6CgA9gjjc8E8G7x/kREh/fqzU5QaF60bH52nHQ0tQUcCAADdwDOSvaxmd6vKd+7W0cPTFeKXS6DvCONzwRLPBh9IZlKM7r16qi7//Yf68mPz9egNRyo6kr/nBACgL+P/1L1sz61bY3KTAk4CAH3HpMJU/fySCZqzfoe+P3upnHNBRwIAAAfAFcle1OGclm+tVXF6vJJio4KOAwB9yvmT8rW6ok6/fWutRmUn6bpjhwUdCQAA7AdXJHvRph2Nqm9u09jc5KCjAECf9M1TR+nUw7P1oxdX6N01VUHHAQAA+0GR7EXLttQqLipCwzMTg44CAH1SKGS6+/JJGpGVqK88tkArtzGSKwAAfRFFspfsbmnXuqp6jcpJUkSIQXYAYH8SYyL14HVHKD4mQtc/9LG21uwOOhIAANgLRbKXrNxWqw4njc3jtlYAOJi81Dg9dN101TW16fqHPlZdU2vQkQAAQBcUyV7gnNOyrbXKTo5RRmJM0HEAoF84PC9Zv7tyikor6/Xlxxaotb0j6EgAAMBDkewFFXXN2l7forG5KUFHAYB+5fiRmfrfi8brvdJq3fL0YnV0MC0IAAB9AdN/9ILlW2oVGTKNzGGQHQDw69JphaqobdKdr65WcmykfnDeWJnxrDkAAEGiSIZZe4fTmoo6Dc9MUExkRNBxAKBfuumkEu1qbNUf3luvlLgozTxtVNCRAAAY1CiSYbZxe4Oa2jo0OodBdgDgUJmZvnv2GNU2tepXb5YqOS5KX/jM8KBjAQAwaFEkw2zltjrFRUWoKC0+6CgA0K+Zmf73ogmqa2rTj19cocSYSF0xvSjoWAAADEoMthNGzW3tWlfdoBFZicwdCQA9ICJk+r8rJumEkZm67bkleurjsqAjAQAwKFEkw2hdVYPaO5xG5SQFHQUABoyYyAj9/uqpOq4kQ99+drGemkeZBACgt1Ekw2jltjolx0YqNyU26CgAMKDERkXo/mumdZbJZxbr6fnlQUcCAGBQoUiGSSghVWU7GjUqJ4lh6gEgDLqWyW89vYjbXAEA6EUUyTBJGH28nMRorQAQRnvK5GdGZOqWZxbrwffWBx0JAIBBgSIZJgmHn6jMxBilJUQHHQUABrTOMjlVZ4zN0f/8dbl+9cYaOeeCjgUAwIBGkQyD9dUNiskbySA7ANBLYiIj9JvPTdZFU/J112urdftLKyiTAACEEfNIhsHzCzfLuQ6NyqZIAhgALBTWZ70LCotUtmnjpz5OZERId14yUUkxkbr/3fXa2fj/27vz+Kire//j788smex7CNnZg4jKJqDirtS1qHXXurTVWpdqvbe19/Z3b+2+WVqX1qXWqrdWW7dKbatSpQqiICKoIMiehSWEJJB9mTm/PzJaqogEMvlOktfz8Rhm5sx3vvkM+eYL75zzPadTPzrnEAX9/M4UAIDeRpCMgaHpiWpa/oJST7rB61IA4MC5iGa/sDpmu795Znmv7cvnM9362YOVlZKgX/5jjWoa23X3JZOUEuKfOwAAehO/po2BC6eWqu75u7wuAwAGJTPTTSeN0U8+d4heXVurC+57TTWNbV6XBQDAgEKQBAAMSBccXqrfXDZZ62qa9bm7F2ptTZPXJQEAMGAQJAEAA9YJY/P12NXT1doR1tm/flXz12z3uiQAAAYEgiQAYEA7rCRTf77uKBVlJumK372h/3tto9clAQDQ7xEkAQADXnFWsp74ypE6dkye/ueZFfr2M++qKxzxuiwAAPotgiQAYFBIDQX0m8um6Eszhuuh1zbpkvsXaXtju9dlAQDQLxEkAQCDht9n+n9njNPs8w/T8qoGnXHnfL25qc7rsgAA6HcIkgCAQeecScV66itHKRTw64J7X9dDCzfKOed1WQAA9BsESQDAoDSuMF1/uX6Gjh2Tp2/PWaGv/XGZWjq6vC4LAIB+gSAJABi0MpKD+s1lU/QfJ4/RM8s365xfL9TG2mavywIAIO4RJAEAg5rPZ7rhxNF68Mqp2rqrTWfetUBzV27zuiwAAOIaQRIAAEnHjsnTX66fobKcZF318BJ99y8r1d4V9rosAADiEkESAOAt88nMYnIrKS3rUSkl2cl64pojdfkRZXrg1Q06+1cLtW57U4w+OAAA/VfA6wIAAIOci2j2C6tjsuubZ5b3+D2JQb++M2u8ZozO0zeeWK4z7lig73z2YJ03pVhmFoMqAQDof+iRBABgD04el6+/33iMJpRk6htPvq0bHn1LO1s7vS4LAIC4QJAEAOATDM1I1O+/NE1f/0y5/v7uVp12+3y9uanO67IAAPAcQRIAgL3w+0zXHT9Kj19zhMyk8+99XXe+uEZd4YjXpQEA4BmCJAAA+2BSaZb+duPROv2QAv187vs6797XtIE1JwEAgxRBEgNPDGeAZKINYHBLTwzqjosm6o6LJmr99madevsrevi1jYpEnNelAQDQp5i1FQNPDGeAlPZvFkgAA8tnDyvU1GHZ+saTb+t/n1mhuSu36afnHqqCjCSvSwMAoE/QIwkAwH4YmpGoh648XD84e7yWbKzXzF+8oqffqpJz9E4CAAY+giQAAPvJzHTJtDL9/cajNSY/TV/743Jd+8hS7Whq97o0AABiiiAJAMABGpaboj99+Qh989SxevG9Gn3ml69o7sptXpcFAEDMECQBAOgFfp/pmmNH6pnrj1JuakhXPbxE33hiuRrbOr0uDQCAXkeQBACgFx1UkK4518/QdceP1BNvVumUX87Xa+t2eF0WAAC9KmZB0sxKzGyema00sxVmdmO0PdvM5prZmuh9VrTdzOwOM1trZm+b2aRY1QYAQCwlBHz6+mfG6vFrjlRCwKeLfvO6bp2zQi0dXV6XBgBAr4hlj2SXpP9wzo2TNF3SdWY2TtI3Jb3onBst6cXoc0k6VdLo6O1qSXfHsDYAAGJuclmW/vrVGbriyGF6cOFGnXr7fL2xsc7rsgAAOGAxC5LOuS3OuaXRx42S3pNUJGmWpIeimz0k6azo41mSHnbdXpeUaWYFsaoPAIC+kJwQ0K2fPViPXT1dEed0/r2v6XvPrlRbZ9jr0gAA2G99co2kmQ2TNFHSIkn5zrkt0Ze2SsqPPi6SVLnb26qibR/d19VmtsTMlmzfvj1mNQMABgDzycxidispLdvnUqaPyNFzNx6jS6eV6bcLNui02+draUX9Xt9TUloWF7Wjd8Xy+8r3FkBfCcT6C5hZqqQnJd3knNtlZh++5pxzZtajlZudc/dJuk+SpkyZwqrPAIBP5iKa/cLqmO3+5pnlPdo+JRTQ984ar88cPFS3PPm2zr17oa46ZoS+dtIYJQb9H9u+qrIiZvX3tHb0nlh+XyW+twD6Rkx7JM0sqO4Q+Yhz7qlo87YPhqxG72ui7dWSSnZ7e3G0DQCAAWXG6Fw9d9PRuuDwEt378nqdeecCLa9s8LosAAD2WSxnbTVJv5X0nnNu9m4vzZF0efTx5ZKe2a39sujsrdMl7dxtCCwAAANKWmJQPzrnUD30halqbOvSOXcv1G3Pr1Z7F9dOAgDiXyx7JI+S9HlJJ5jZsujtNEk/lnSyma2RdFL0uST9TdJ6SWsl/UbStTGsDQCAuHDsmDw9/7VjdPbEIt01b61m3fWq3q3e6XVZAADsVcyukXTOLZBkn/DyiXvY3km6Llb1AAAQrzKSgrrtvMN06vih+uZT7+isX72q608YJfk+ft0kAADxoE9mbQUAAJ/uxIPyNfdrx+jMwwr1y3+sUcFls7W9sd3rsgAA+BiCJAAAcSQzOUG/uGCC7rl0svyp2XrsjQot3lCnSISJygEA8YMgCQBAHDpl/FBt/u11GpWXqtfW79Afl1RqRxO9kwCA+ECQBAAgTkVad+nUQwp02vihamzr0qOLK7VkU50ijt5JAIC3YjbZDgAA6B2j89NUlJWkl1bV6NW1O7Suplkzx+UrKyXB69IAAIMUPZIAAPQDyQkBnX5IgU45eKjqWzr0yOIKLa2op3cSAOAJeiQBAOgnzEzlQ9NUnJWkF1fVaP6aWq2radLJ4/KVmUzvJACg79AjCQBAP5MSCujMQws0c1y+aps79MiiCi2rbJCjdxIA0EcIkgAA9ENmpoMK0vX5aWUqykrSy+9v15NLq7WztdPr0iRJJaVlMrOY3UpKy7z+iAAwqDG0FQCAfiw1MaBZhxVq5ZZd6QUcVwAAHx5JREFUeuX9Wj2yaJNmjMrVIUUZMjPP6qqqrNDsF1bHbP83zyyP2b4BAJ+OHkkAAPo5M9PBhRm6ZHqphmYkat7q7Xp6WbV2xUnvJABg4CFIAgAwQKQnBnX2hCKdUD5EW3e26ZFFFXq3eifXTgIAeh1BEgCAAcTMdEhxhi6ZVqYhaSG9uKpGTy2tVn1Lh9elAQAGEIIkAAADUEZSUOdMKtIJY4eopqldj7xeoUXrd6grEvG6NADAAMBkOwAA7C/zeTqhzacxMx1SlKERuSl65f3ten1DnVZva9Rx5UO8Lg0A0M8RJAEA2F8u0i9mJk0JBXTqIQUaW9usl9/frqffqlburFu0uaFVhZlJvfI1AACDC0NbAQAYJIbnpujSaaU6YkSOkkYerhN//rLuemmN2jrDXpcGAOhnCJIAAAwiAb9PU4dna/P9X9GxY/J02wvv6/jb/qnHl1QqHGF2VwDAviFIAgAwCIV3bdc9n5+sP149XUPSQvr6E2/r9Dvma97qGpYLAQB8KoIkMJhEJwaJ1Q1A/zNtRI6evvYo3XnRRDV3dOnK372hs3+9kEAJANgrJtsBBpN+MjEIgL7l85nOPKxQMw/O1xNvVunX89bpyt+9ocNKMnX98aN04tgh8vn4ZREA4F8IkgAAQJIUCvh1ybQynTe5RE8urdKv5q3VVQ8vUVlOsi4/YpjOm1KstMSg12UCAOIAQRIAAPybhIBPF00t1bmTi/X8iq363asb9d1nV+rnL6zWWROLdM6kYk0qzRwQQ9ob2zq1uaFNmxtatb2xXfUtHapv6VRDS4daOsLqDEfUGXbqDEcU9JuSEgJKSfArKcGvvLSQijKTVJyVpKLMZOWnhwbE3wkA7AuCJAAA2KOg36czDi3UGYcW6u2qBj346kY9ubRKjyyq0LCcZJ01sUhnHFqokXkpcR2gmtu7tKG2Weu2N2n99n/dV9a1qLG962PbB/2mzOQEpST4FfT7ojdTZ9iptTOs5vYutXSE1fSR92YmB3VIUYYOK87UYSWZmjYiW+n04AIYoAiSAADgUx1anKnZF0zQd2YdrOfe3aqn36rW7S+u0S//sUYl2Uk6vnyIjivP07ThOUoJ9f1/L8IRp80NrVpf26z10aC4vrZJ62qatXVX24fbmUnFWUkakZuqw4dlqTAzSQWZSSrKTNSQtERlJgeVGgrsUzBubu9SdUOrqutbVVnfopWbd2lZZYPufnmdwhGngM80dXi2TjwoXycdNERlOSmx/CsAgD5FkAQAAPssLTGo86aU6LwpJdqys1Uvvlejf66u0eNLqvTwa5vkM2lMfpqyT7lB71bvVE5qgjKSgkoK+g+417IrEtGu1i7tbO1U2uQzdeucFdq0o1mb6lpUVdeqjnBktzoDGpGXqiNH5mhEXopG5qVqRF6qynKSlRj0H+hfgyQpJRTQmPw0jclP+7f21o6w3q5q0LzV2/Xie9v0vWdX6nvPrtTksixdeHiJLBDqla8PAF4iSAIAgP1SkJGkS6eX6dLpZWrrDGvxhjot2VSv5ZUNSh5zpF5cVfPhtgl+nzKSg0pJ8Csx6FdiwK9Q0Cefz2Tq7ik0WfSaxIg6whF1dEXU2hFWS/TW2hn+cH/ZJ31Zjy+pVGlOisrz03TyuHyVZadoZF6KRuSlKjc1wbPhtkkJfk0bkaNpI3L0zVPHqmJHi55bsUWPvVGprz/xtoqve0jzVtdoYkmmMpMTPKkRAA4UQRIAABywxKBfx4zJ0zFj8iRJZtP0v39+R3UtHdrZ0qmG1k7tbOlUS0dYdc0dauvqDop7EvCZgn6fEgI+JQX9ykwOqjAzSckJfmUkBZWRFNTsK49XpLleK2P0eYpLSlVZsalX9lWak6yrjxmpq44eocUb6nT6V3+oFckn6p3qnRo7NE2HD8tWFoESQD9DkAQAADGRmZyw1x63iHNyTnLOyUlyTgr4Tb596EmMNNf3u3VxzUzTRuRox19n66Zrvqg3N9XrneqdWrWlUWPy03TkyBylJzE5D4D+gSAJAAA84esez6roH4NKSiigY8bkaXJZlt6qaNDyqgat3d6kyaVZmjIsS0G/z+sSAWCvCJIAAAAeSQkFNGN0rg4rydCra3do8cY6rdyySzNG5WpMfmpcL6sCYHDj110AAAAeS0sM6pTxQ3Xe5GIlJ/j13Iqtemb5Zu1q6/S6NADYI4IkAABAnCjMTNIFh5fo2DF5qq5v1SOvV+idqp1yznldGgD8G4IkAABAHPGZaUJJpi6dXqYh6SG9tLpGT71VrUZ6JwHEEYIkAABAHMpICuqciUU6cewQbdvVpkcWVWjNtkavywIASQRJAACAuGVmGl+UoYunliozOai/vbtVc1du+8Q1OAGgrxAkAQAA4lxmcoLOm1yiw4dlaeWWXXp0cYVqGtu8LgvAIEaQBAAA6Af8PtORI3N17qRidUWc/vRGlZZXNTARDwBPECQBAAD6kaKsJF08tVQl2Un65+rt+tu7W9XeFfa6LACDDEESAACgn0lK8OuzhxVqxqhcrd/epD8sqtDWXQx1BdB3CJIAAAD9kJlpclmWzp1cLCfp8SWVequi3uuyAAwSBEkAAAYj88nMYnZD3ynI6B7qOiwnRa+sqVXe2d9SQ0uH12UBGOACXhcAAAA84CKa/cLqmO3+5pnlMds3Pi4x6NcZhxZoWWWDXg5P0el3LNCdF0/UpNIsr0sDMEDRIwkAAPBRMeyxjVnJZppYmqWtv/+GfD7p/Hte070vr1MkwqyuAHofPZIAAAAfFcMe21j31nZsXaNnbzha33zybf3o76u0aEOdbjvvMGWnJMT06wIYXOiRBAAAGGAykoL69SWT9N1ZB2vBmlqddvt8vbGxzuuyAAwgBEkAAIAByMx02RHD9NS1RyoU9OnC+17Xr+atZagrgF5BkAQAABjAxhdl6NkbZujU8UP1s+dX6/LfLVZtU7vXZQHo5wiSAAAAA1xaYlB3XjRRPzz7EC3aUKeZv3hFzyyrlnP0TgLYPwRJAACAQcDMdPG0Uj17wwyVZifrxseW6YsPLdHmhlavSwPQDxEkAQAABpEx+Wl68itH6n/OGKfX1u3QzF+8ogcWbFBnOOJ1aTFXUloWs2VdzEwlpWVef0Sgz7D8BwAAwCDj95m+OGO4Tj4oX9/68zv67rMr9fvXN+m/TjtIJx00JKbrXXqpqrIiZsu6SLFf2gWIJ/RIAgAADFKlOcl6+AtT9cAVUySTrnp4iS7+zSK9VVHvST2x7jEE0HvokQQAABjEzEwnjM3X0aPz9OjiCv1i7vs6+9cLNW14tq45bqSOG5PXZyGMHkOg/yBIAgAAQEG/T5cdMUznTCrWY4sr9NsFG3Tl797Q2KFpumRaqU4/tFDZKQlel7lX4YhTVySicMQpEpHCzslM8pvJ7zNZMCTnHL2TQC+IWZA0swcknSGpxjk3PtqWLemPkoZJ2ijpfOdcvXX/NN8u6TRJLZKucM4tjVVtAAAA2LPUUEBfOnqELjtimOYs36z756/X/zyzQt/5y0odMyZPsyYU6tgxecpM7rtQ2RWOqLG9S41tXWps64zedz9u6QirvSuits6wuiJ7X86k9OYndde8tUoM+pUU9Csx6FdaYkDpiUGlJwWUkRRUdkqCkhPoawE+TSx/Sh6UdJekh3dr+6akF51zPzazb0af3yLpVEmjo7dpku6O3gMAAMADCQGfzp1crM9NKtJ7Wxr1zLJqzVm+WS+tqpGZVJ6fpukjcjRteLZG56epJDtJoYC/x18nEnGqbW7X5oY2JZcfpaUV9R8LjK2d4Y+9LyXkV1ooqMzkoEIBv0IBn0JBn4J+34c9kD4zOTmFI923Z+77mU6+4ma1dYbV2hlWa0dY1Q2tWt3WqN0jaHKCX7mpIeWmJmhoeqIKMpOUGiJcAruL2U+Ec+4VMxv2keZZko6LPn5I0j/VHSRnSXrYda+K+7qZZZpZgXNuS6zqAwAAwKczM40rTNe4wnTdcspYLa2o18J1O7Roww499kaFHly4UZLkM6kwM0ml2clKTwwqJRRQasivxAS/usJOneGIOroiau0Mq665Q/UtHapr6lBtU4c6okuP5J31X5q/plZBvyktMai0xICGpIU+fNx9Cyo1FJDf1/Phqf+3+Ekd9f0ffqw9HHFqau9SQ0uH6pq7a6ptatfyqp1aGmmQJKUlBro/X1ayynKSlUKwxCDX1z8B+buFw62S8qOPiyRV7rZdVbTtY0HSzK6WdLUklZaWxq5SAAAA/BufzzRlWLamDMuWNFodXRGt3LJLG2qbtKG2RZt2NKuirkXbG9vV3N6lpvYutXVGFPCbEgLdvYWJQZ+ykxOUlxpSeX66ctMSVJSZpMKMJJ123BH6zoN/VSjg69PrGP0+U0ZSUBlJQZXlpHzYHo44bW9s1+adrdqys00VO1q0emujJCk3NUFlOSkalZeq/PQQ111i0PHsVynOOWdmex/Ivuf33SfpPkmaMmVKj98PAAAwoFlsQ1hxSakqKzZJ6h7+OqEkUxNKMntl353bNygx2PPhsbHi95mGZiRqaEaiJMk5p9qmDm3a0axNO1r0VkW93txUr7TEgEblpSqhsJzJfDBo9HWQ3PbBkFUzK5BUE22vllSy23bF0TYAAAD0hIuwhEaMmJny0kLKSwtpyrBstXWGtaG2WWtqmvR21U4VfP7nOnH2yzpvcok+N6lIQ9ITe+1rl5SWqaqyotf291G7/4IA2Bd9HSTnSLpc0o+j98/s1n69mT2m7kl2dnJ9JAAAAOJZYtCvgwrSdVBButq7wvr+LV/V1Gu/q588t0q3vbBax5fn6fIjh2nGqNwD7qVkjU3Em1gu//GouifWyTWzKknfVneA/JOZfVHSJknnRzf/m7qX/lir7uU/roxVXQAAAEBvCwX8an7nH/rTNXO1fnuTHn+zSo8vqdQ/frtYY/JTdeVRw3XWhCIlJcTP0F3gQMRy1taLPuGlE/ewrZN0XaxqAQAAAPrKiLxU3XLKWN100mj9ZfkWPbBgg/7rqXf0s+dX60tHD9dlRwxjORH0ez6vCwAAAAAGolDAr3MnF+uvX52hx66erkOKMvTT51Zrxk9e0p0vrtGutk6vSwT2G78KAQAAAGLIzDR9RI6mj8jRssoG3fXSGv187vu6f8EG3XDCKH3+iDKFAgx5Rf9CkAQAAAB6Qw+WXknIH6nWYy7T91s7desj89Qw//dqXvmyJFa3Q/9AkAQAAAB6w34svbJpR7NeXRtS4Mz/1LiLv6Xjy4d8uG7l7phVFfGGIAkAAAB4pCwnRaXZyVq1tVEL1tbqj0sqNb4wXUeOylVSkOGuiF8ESQAAAMBDZqaDCtI1Ii9Fi9bXaVlVg9bWNOmo0bk6uCD9gNegBGKBWVsBAACAOBAK+HXMmDxdPLVU2akJevG9Gj29rFo7W5ndFfGHIAkAAADEkdzUkM6dVKzjy/O0bWe7fv/6JqVNPlPdS68D8YEgCQAAAMQZM9OhxZm6dHqpirKSlH3Sl/XE0irtoncScYIgCQAAAMSptMSgZh1WqNpnZ6u2sUOPLKrQys276J2E5wiSAAAAQBwzMzWveEmXTCtVXlpIc9/bpr++s0WtHWGvS8MgRpAEAAAA+oH0pKDOmVSkGaNytbG2RY8s3qTq+lavy8IgRZAEAAAA+gmfmSaXZemCw0sU9Pn05NIqLd5QpwhDXdHHCJIAAABAP5OXFtJFU0s1Jj9Nr63foT8vq1Zze5fXZWEQIUgCAAAA/VBCwKfPHJyvEw8aos0NbfrD4gpV1LV4XRYGCYIkAAAA0E+ZmcYXZujCw0uUGPDr6beq9dr6HQx1RcwRJAEAALDvzCczi8kN+y83NaQLp5booII0Ld5Qp6eWVquJoa6IoYDXBQAAAKAfcRHNfmF1THZ988zymOx3sAj6fZo5bqhKspL10qoa/WFRhU4ZP1Sl2clel4YBiB5JAAAAYAA5qCBdFx5eoqRg91DXRRt2yDHUFb2MIAkAAAAMMDmpIV1weInKh6bp9fV1embZZrV0MNQVvYcgCQAAAAxACQGfPjMuXyeUD1FVfaseXVypzQ2tXpeFAYIgCQAAAAxQZqZDijN0/pRi+X2mJ5dWaWlFPUNdccAIkgAAAMAANyQ9URcdXqLhuSmav6ZWf31ni9o7w16XhX6MIAkAAAAMAqGgX6cfUqCjR+dqQ22zHn2jUjWNbV6XhX6KIAkAAAAMEmamSaVZ+tykYoUjTn9aUqV3qnd6XRb6IYIkAAAAMMgUZibpoqklKspM0kurapRz+s1qbmdWV+w7giQAAAAwCCUnBDRrQqGmj8hWysHH6Yw7F+jtqgavy0I/QZAEAAAABimfmaYNz9G2R/9bbZ1hfe7uhbr35XWKRJjVFXtHkAQAAAAGufbKd/X3G4/WiWPz9aO/r9Llv1usrTuZiAefjCAJAAAAQJnJCbr70kn6wdnjtWRjvWb+4mX9+a1q1pzEHhEkAQAAAEjqntX1kmll+tuNR2vUkFTd9Mdluu4PS1XX3OF1aYgzBEkAAAAA/2Z4booev+ZIfeOUcs1duU0zf/Gynn17M72T+BBBEgAAAMDH+H2ma48bpWeum6GhGYm6/g9v6aqHl2jLzlavS0McIEgCAAAA+ETjCtP152uP0rdOO0gL1tbq5Nmv6KGFGxVmZtdBjSAJAAAAYK8Cfp+uOmaEXrjpWE0szdS356zQmXcu0Bsb67wuDR4hSAIAAADYJ6U5yXr4C1N118UTVd/SofPueU03PfbWpy4VUlJaJjOLya2ktKyPPj12F/C6AAAAAAD9h5npjEMLdcLYIbr7n+t07yvr9fyKbfrijOG6+tgRSk8Mfuw9VZUVmv3C6pjUc/PM8pjsF3tHjyQAAACAHktOCOg/ZpbrH187ViePy9dd89bq2J/O0/3z16u9K+x1eYgxgiQAAACA/Vaak6w7LpqoZ2+YofFFGfr+X9/TcT/7px5auFFtnQTKgYogCQAAAOCAjS/K0P99cZp+/8VpKspM0rfnrNCMn8zTfa+skwUTvS4PvYxrJAEAAAD0mhmjc3XUqBwt2lCnu15aqx/+bZWKrn1QC9bU6tCSjD1eQ4n+hyAJAAAAoFeZmaaPyNH0ETlaWlGvz9zwYy1NStXSynqNzEvVhOJMFWYmysy8LhX7iSAJAAAAIGYmlWapds5Pdf2XLtPbVTv1bvVOra1pUmZyUOMLMzR2aJpSQsSS/obvGAAAAICYS08MasaoXE0bnq01NU1aUb1TC9bWauG6Wg3LSVH50DQNz01R0M80Lv0BQRIAAABAnwn6fRpXkK5xBemqa+7Qis07tXpbo9bXNivoN43IS9WYIakqzU5WgFAZtwiSAAAAADyRnZKgo0fn6ahRudrc0KrVWxu1pqZJq7c2KuAzleUka2ReqspykpWcQHSJJ3w3AAAAAHjKZ6birGQVZyXruPIhqqpv0frtzVpX26R125slSXlpIZVmJaskO0mFmUkMgfUYQRIAAABA3PD7TGU5KSrLSdFxLk/bGttVUdeiyh0tequyXm9W1MtvpoLMRJVmJyuhYIw6wxGCZR8jSAIAAACIS2amoemJGpqeqKnDstUZjqi6oVWVdS2qqGvRwnU7VHDZbB1y6/MaX5ihCSWZmlCaqQklmSrKTGJ5kRgiSAIAAADoF4J+n4blpGhYTookqbm9Sz/6+jW65af3aFllvR5+fZPuX7BBkpSbGtKEkgwdVpyp8qFpKh+appKsZPl8hMveQJAEAAAA0C+lhAJqWTVf/3vmOElSR1dEq7bu0rLKBi2raNCyygb9472aD7dPDPo0akiqxgxJ0+j8NJUPTdWovDQVZiZ+4gyxJaVlqqqsiNlnKC4pVWXFppjtP1YIkgAAAAAGhISAT4cWZ+rQ4kxddkR3W2Nbp9bUNGnNtkat3tqkNTWNenVdrZ56q/rD9/l9poKMRJVEJ/Ppvu9+vKWhWT97bpX8MerJvHlmeUz2G2sESQAAAAADVlpiUJNKszSpNOvf2ne2dGpNTaPWbW9SZV2rKutbVFnXonmrt2t7Y/uH2xVf+5DumrdWSUG/kkN+pSQElJLgV3Ko+z4pwa/EoF+JAb9CQZ8Sg36FAj75Bvj1mQRJAAAAAINORnJQU4Zla8qw7I+91tYZVlV9d7j87AWX67Sv/K9aOsJqbu9SS0dY9S0damkPK+zcJ+4/IeBTYiAaLIO+D4NmKNAdNBMCPoUCPgWyi2P5MWOGIAkAAAAAu0kM+jVqSKpGDUlV0/LnNH3E7R/bxjmn9q6IWjvDausMq60zovbOsNq6ImrrDKu9M6K2rn+91tTW3r1NV1iR3fJn8ujpffjJek9cBUkzO0XS7ZL8ku53zv3Y45IAAAAA4GPMrHtIa9Dfo/c559QVceroiqi9K6If3jk3RhXGVtwESTPzS/qVpJMlVUl6w8zmOOdWelsZAAAAAPQOM1PQbwr6fUoJSZGWnV6XtF/2PMetN6ZKWuucW++c65D0mKRZHtcEAAAAAPgIc3u5QLQvmdm5kk5xzn0p+vzzkqY5567/yHZXS7o6+rRc0uo+LfTjciXVelwD8AGOR8QTjkfEE45HxBuOScSTTzoea51zp+zpDXEztHVfOefuk3Sf13V8wMyWOOemeF0HIHE8Ir5wPCKecDwi3nBMIp7sz/EYT0NbqyWV7Pa8ONoGAAAAAIgj8RQk35A02syGm1mCpAslzfG4JgAAAADAR8TN0FbnXJeZXS/peXUv//GAc26Fx2Xti7gZZguI4xHxheMR8YTjEfGGYxLxpMfHY9xMtgMAAAAA6B/iaWgrAAAAAKAfIEgCAAAAAHqEILmfzOwUM1ttZmvN7Jte1wOY2UYze8fMlpnZEq/rweBiZg+YWY2ZvbtbW7aZzTWzNdH7LC9rxODxCcfjrWZWHT1HLjOz07ysEYOHmZWY2TwzW2lmK8zsxmg750j0ub0cjz0+R3KN5H4wM7+k9yWdLKlK3TPOXuScW+lpYRjUzGyjpCnOORY3Rp8zs2MkNUl62Dk3Ptr2U0l1zrkfR3/hluWcu8XLOjE4fMLxeKukJufcbV7WhsHHzAokFTjnlppZmqQ3JZ0l6QpxjkQf28vxeL56eI6kR3L/TJW01jm33jnXIekxSbM8rgkAPOOce0VS3UeaZ0l6KPr4IXX/QwXE3Cccj4AnnHNbnHNLo48bJb0nqUicI+GBvRyPPUaQ3D9Fkip3e16l/fwGAL3ISXrBzN40s6u9LgaQlO+c2xJ9vFVSvpfFAJKuN7O3o0NfGUaIPmdmwyRNlLRInCPhsY8cj1IPz5EESWDgmOGcmyTpVEnXRYd2AXHBdV9HwbUU8NLdkkZKmiBpi6Sfe1sOBhszS5X0pKSbnHO7dn+NcyT62h6Oxx6fIwmS+6daUsluz4ujbYBnnHPV0fsaSU+rewg24KVt0WsxPrgmo8bjejCIOee2OefCzrmIpN+IcyT6kJkF1f2f9kecc09FmzlHwhN7Oh735xxJkNw/b0gabWbDzSxB0oWS5nhcEwYxM0uJXjAtM0uRNFPSu3t/FxBzcyRdHn18uaRnPKwFg9wH/2GPOlucI9FHzMwk/VbSe8652bu9xDkSfe6Tjsf9OUcya+t+ik6J+0tJfkkPOOd+4HFJGMTMbIS6eyElKSDpDxyT6Etm9qik4yTlStom6duS/izpT5JKJW2SdL5zjglQEHOfcDwep+4hW07SRklf3u36NCBmzGyGpPmS3pEUiTb/t7qvS+MciT61l+PxIvXwHEmQBAAAAAD0CENbAQAAAAA9QpAEAAAAAPQIQRIAAAAA0CMESQAAAABAjxAkAQAAAAA9QpAEAAAAAPQIQRIAgP1kZlPM7I7o4+PM7Mhe2m+hmT3RG/sCACAWWEcSAIBeYGa3Smpyzt12gPsJOOe6eqcqAABigx5JAACizGyYmb272/P/NLNbzeyfZvYTM1tsZu+b2dHR148zs2fNbJikayR9zcyWffD6Hvb/oJndY2ZLovs5I9p+hZnNMbOXJL24ex1m5jez28zsXTN728xuiLZPNrOXzexNM3vezApi+pcDAMBuAl4XAABAPxFwzk01s9MkfVvSSR+84JzbaGb3aN96JIdJmipppKR5ZjYq2j5J0qHOubpoMP3A1dH3THDOdZlZtpkFJd0paZZzbruZXSDpB5K+cKAfEgCAfUGQBABg3zwVvX9T3cFuf/3JOReRtMbM1ksaG22f65yr28P2J0m654PhrtGgOV7SeElzzUyS/JK2HEBNAAD0CEESAIB/6dK/X/aRuNvj9uh9WAf27+dHJyf44HlzD/ZhklY45444gDoAANhvXCMJAMC/bJM0xMxyzCwk6YwevLdRUto+bHeemfnMbKSkEZJWf8r2cyV92cwCkmRm2dH35JnZEdG2oJkd3INaAQA4IARJAACinHOdkr4rabG6A9yqHrz9L5LO3ttkO1EV0f3/XdI1zrm2T9nv/dH3vG1myyVd7JzrkHSupJ9E25ZJ6pWlRwAA2Bcs/wEAQB8xswclPeucY41IAEC/Ro8kAAAAAKBHmGwHAIBeZmbfknTeR5ofd85d4UE5AAD0Ooa2AgAAAAB6hKGtAAAAAIAeIUgCAAAAAHqEIAkAAAAA6BGCJAAAAACgR/4/7IDpv0FpLwUAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_continuous_distribution(df, 'quantity')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "V2Z-j_sNTVMN",
        "outputId": "eef9cb68-76c6-4100-bb68-56635a3c60fb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 921.6x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_continuous_distribution(df, 'total')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "QNeJhj-RTbT7",
        "outputId": "fd59a646-2464-483a-bac4-823acca2e9a7"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 921.6x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_unique_values(df, 'timestamp')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xFDkYqT_T0T2",
        "outputId": "7caf7d89-e4df-40e5-a1f9-ae3bcb9a649f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column: timestamp has 7738 unique values\n",
            "\n",
            "2022-03-02 19:32:20    2\n",
            "2022-03-05 10:11:33    2\n",
            "2022-03-01 18:12:17    2\n",
            "2022-03-03 13:28:14    2\n",
            "2022-03-07 17:13:29    2\n",
            "                      ..\n",
            "2022-03-01 10:00:58    1\n",
            "2022-03-02 11:05:58    1\n",
            "2022-03-04 17:48:49    1\n",
            "2022-03-04 14:53:27    1\n",
            "2022-03-06 13:50:36    1\n",
            "Name: timestamp, Length: 7738, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['converted_datetime'] = pd.to_datetime(df['timestamp'], format='%Y-%m-%d %H:%M:%S')\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "id": "Gz0PCcPgT6Do",
        "outputId": "1cfff00f-e3bf-45fc-8a9e-9640a7927442"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         transaction_id            timestamp  \\\n",
              "0  a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  2022-03-02 09:51:38   \n",
              "1  931ad550-09e8-4da6-beaa-8c9d17be9c60  2022-03-06 10:33:59   \n",
              "2  ae133534-6f61-4cd6-b6b8-d1c1d8d90aea  2022-03-04 17:20:21   \n",
              "3  157cebd9-aaf0-475d-8a11-7c8e0f5b76e4  2022-03-02 17:23:58   \n",
              "4  a81a6cd3-5e0c-44a2-826c-aea43e46c514  2022-03-05 14:32:43   \n",
              "\n",
              "                             product_id category customer_type  unit_price  \\\n",
              "0  3bc6c1ea-0198-46de-9ffd-514ae3338713    fruit          gold        3.99   \n",
              "1  ad81b46c-bf38-41cf-9b54-5fe7f5eba93e    fruit      standard        3.99   \n",
              "2  7c55cbd4-f306-4c04-a030-628cbe7867c1    fruit       premium        0.19   \n",
              "3  80da8348-1707-403f-8be7-9e6deeccc883    fruit          gold        0.19   \n",
              "4  7f5e86e6-f06f-45f6-bf44-27b095c9ad1d    fruit         basic        4.49   \n",
              "\n",
              "   quantity  total payment_type  converted_datetime  \n",
              "0         2   7.98     e-wallet 2022-03-02 09:51:38  \n",
              "1         1   3.99     e-wallet 2022-03-06 10:33:59  \n",
              "2         2   0.38     e-wallet 2022-03-04 17:20:21  \n",
              "3         4   0.76     e-wallet 2022-03-02 17:23:58  \n",
              "4         2   8.98   debit card 2022-03-05 14:32:43  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a029d1d5-494f-48a3-aca0-c53394a9c3a2\">\n",
              "    <div 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>transaction_id</th>\n",
              "      <th>timestamp</th>\n",
              "      <th>product_id</th>\n",
              "      <th>category</th>\n",
              "      <th>customer_type</th>\n",
              "      <th>unit_price</th>\n",
              "      <th>quantity</th>\n",
              "      <th>total</th>\n",
              "      <th>payment_type</th>\n",
              "      <th>converted_datetime</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>a1c82654-c52c-45b3-8ce8-4c2a1efe63ed</td>\n",
              "      <td>2022-03-02 09:51:38</td>\n",
              "      <td>3bc6c1ea-0198-46de-9ffd-514ae3338713</td>\n",
              "      <td>fruit</td>\n",
              "      <td>gold</td>\n",
              "      <td>3.99</td>\n",
              "      <td>2</td>\n",
              "      <td>7.98</td>\n",
              "      <td>e-wallet</td>\n",
              "      <td>2022-03-02 09:51:38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>931ad550-09e8-4da6-beaa-8c9d17be9c60</td>\n",
              "      <td>2022-03-06 10:33:59</td>\n",
              "      <td>ad81b46c-bf38-41cf-9b54-5fe7f5eba93e</td>\n",
              "      <td>fruit</td>\n",
              "      <td>standard</td>\n",
              "      <td>3.99</td>\n",
              "      <td>1</td>\n",
              "      <td>3.99</td>\n",
              "      <td>e-wallet</td>\n",
              "      <td>2022-03-06 10:33:59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ae133534-6f61-4cd6-b6b8-d1c1d8d90aea</td>\n",
              "      <td>2022-03-04 17:20:21</td>\n",
              "      <td>7c55cbd4-f306-4c04-a030-628cbe7867c1</td>\n",
              "      <td>fruit</td>\n",
              "      <td>premium</td>\n",
              "      <td>0.19</td>\n",
              "      <td>2</td>\n",
              "      <td>0.38</td>\n",
              "      <td>e-wallet</td>\n",
              "      <td>2022-03-04 17:20:21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>157cebd9-aaf0-475d-8a11-7c8e0f5b76e4</td>\n",
              "      <td>2022-03-02 17:23:58</td>\n",
              "      <td>80da8348-1707-403f-8be7-9e6deeccc883</td>\n",
              "      <td>fruit</td>\n",
              "      <td>gold</td>\n",
              "      <td>0.19</td>\n",
              "      <td>4</td>\n",
              "      <td>0.76</td>\n",
              "      <td>e-wallet</td>\n",
              "      <td>2022-03-02 17:23:58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>a81a6cd3-5e0c-44a2-826c-aea43e46c514</td>\n",
              "      <td>2022-03-05 14:32:43</td>\n",
              "      <td>7f5e86e6-f06f-45f6-bf44-27b095c9ad1d</td>\n",
              "      <td>fruit</td>\n",
              "      <td>basic</td>\n",
              "      <td>4.49</td>\n",
              "      <td>2</td>\n",
              "      <td>8.98</td>\n",
              "      <td>debit card</td>\n",
              "      <td>2022-03-05 14:32:43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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          "metadata": {},
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        }
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        "df.info()"
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      },
      "execution_count": 13,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7829 entries, 0 to 7828\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype         \n",
            "---  ------              --------------  -----         \n",
            " 0   transaction_id      7829 non-null   object        \n",
            " 1   timestamp           7829 non-null   object        \n",
            " 2   product_id          7829 non-null   object        \n",
            " 3   category            7829 non-null   object        \n",
            " 4   customer_type       7829 non-null   object        \n",
            " 5   unit_price          7829 non-null   float64       \n",
            " 6   quantity            7829 non-null   int64         \n",
            " 7   total               7829 non-null   float64       \n",
            " 8   payment_type        7829 non-null   object        \n",
            " 9   converted_datetime  7829 non-null   datetime64[ns]\n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), object(6)\n",
            "memory usage: 611.8+ KB\n"
          ]
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    {
      "cell_type": "code",
      "source": [
        "df['hour'] = df['converted_datetime'].dt.hour\n",
        "df['weekday'] = df['converted_datetime'].dt.dayofweek\n",
        "df.head()"
      ],
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      "execution_count": 14,
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              "                         transaction_id            timestamp  \\\n",
              "0  a1c82654-c52c-45b3-8ce8-4c2a1efe63ed  2022-03-02 09:51:38   \n",
              "1  931ad550-09e8-4da6-beaa-8c9d17be9c60  2022-03-06 10:33:59   \n",
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              "4  a81a6cd3-5e0c-44a2-826c-aea43e46c514  2022-03-05 14:32:43   \n",
              "\n",
              "                             product_id category customer_type  unit_price  \\\n",
              "0  3bc6c1ea-0198-46de-9ffd-514ae3338713    fruit          gold        3.99   \n",
              "1  ad81b46c-bf38-41cf-9b54-5fe7f5eba93e    fruit      standard        3.99   \n",
              "2  7c55cbd4-f306-4c04-a030-628cbe7867c1    fruit       premium        0.19   \n",
              "3  80da8348-1707-403f-8be7-9e6deeccc883    fruit          gold        0.19   \n",
              "4  7f5e86e6-f06f-45f6-bf44-27b095c9ad1d    fruit         basic        4.49   \n",
              "\n",
              "   quantity  total payment_type  converted_datetime  hour  weekday  \n",
              "0         2   7.98     e-wallet 2022-03-02 09:51:38     9        2  \n",
              "1         1   3.99     e-wallet 2022-03-06 10:33:59    10        6  \n",
              "2         2   0.38     e-wallet 2022-03-04 17:20:21    17        4  \n",
              "3         4   0.76     e-wallet 2022-03-02 17:23:58    17        2  \n",
              "4         2   8.98   debit card 2022-03-05 14:32:43    14        5  "
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              "      <td>fruit</td>\n",
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              "      <td>4</td>\n",
              "      <td>0.76</td>\n",
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              "      <td>17</td>\n",
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              "      <th>4</th>\n",
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              "      <td>2022-03-05 14:32:43</td>\n",
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              "      <td>fruit</td>\n",
              "      <td>basic</td>\n",
              "      <td>4.49</td>\n",
              "      <td>2</td>\n",
              "      <td>8.98</td>\n",
              "      <td>debit card</td>\n",
              "      <td>2022-03-05 14:32:43</td>\n",
              "      <td>14</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
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        "get_unique_values(df, 'hour')"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "SM8GrlivUbBy",
        "outputId": "86f9028b-ce8e-4c80-c07e-ac3854b162f2"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column: hour has 11 unique values\n",
            "\n",
            "11    738\n",
            "16    730\n",
            "18    727\n",
            "10    723\n",
            "12    721\n",
            "9     718\n",
            "19    709\n",
            "14    702\n",
            "17    699\n",
            "13    693\n",
            "15    669\n",
            "Name: hour, dtype: int64\n"
          ]
        }
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        "outputId": "4a6e3488-f4f3-4ec1-ab53-467074d300a7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column: weekday has 7 unique values\n",
            "\n",
            "2    1168\n",
            "1    1165\n",
            "6    1132\n",
            "4    1108\n",
            "0    1104\n",
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            "Name: weekday, dtype: int64\n"
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      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column: product_id has 300 unique values\n",
            "\n",
            "ecac012c-1dec-41d4-9ebd-56fb7166f6d9    114\n",
            "80da8348-1707-403f-8be7-9e6deeccc883    109\n",
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            "7c55cbd4-f306-4c04-a030-628cbe7867c1    104\n",
            "3bc6c1ea-0198-46de-9ffd-514ae3338713    101\n",
            "                                       ... \n",
            "49f7d4a9-713a-4824-b378-aebb33ff8b2f      5\n",
            "a8fab83a-16d4-4db0-a83a-f824ecd8604a      5\n",
            "c8de27d0-2c44-4b5a-b178-59c45d054ccb      5\n",
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    {
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        "id": "nLi5SyD2UqYO",
        "outputId": "1ec92703-8898-4655-e80e-69e1210cb712"
      },
      "execution_count": 18,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Column: category has 22 unique values\n",
            "\n",
            "fruit                    998\n",
            "vegetables               846\n",
            "packaged foods           507\n",
            "baked goods              443\n",
            "canned foods             431\n",
            "refrigerated items       425\n",
            "kitchen                  382\n",
            "meat                     382\n",
            "dairy                    375\n",
            "beverages                301\n",
            "cheese                   293\n",
            "cleaning products        292\n",
            "baking                   264\n",
            "snacks                   263\n",
            "frozen                   263\n",
            "seafood                  253\n",
            "medicine                 243\n",
            "baby products            224\n",
            "condiments and sauces    181\n",
            "personal care            177\n",
            "pets                     161\n",
            "spices and herbs         125\n",
            "Name: category, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_categorical_distribution(df, 'category')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "-dJ1-UxcUvDb",
        "outputId": "a5edf662-9096-4032-e065-1d4242fe6676"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_unique_values(df, 'customer_type')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOZeSgfXU1cB",
        "outputId": "2f703463-7da6-4afb-c141-d9a7125cfd1b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column: customer_type has 5 unique values\n",
            "\n",
            "non-member    1601\n",
            "standard      1595\n",
            "premium       1590\n",
            "basic         1526\n",
            "gold          1517\n",
            "Name: customer_type, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_categorical_distribution(df, 'customer_type')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "HEO-ziWNU7ws",
        "outputId": "9159dcda-5a27-415f-cf8d-fbbe03b8801d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_unique_values(df, 'payment_type')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xnoeQ9wiVCEE",
        "outputId": "1799d406-ceca-4b65-998d-82ee42155d18"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column: payment_type has 4 unique values\n",
            "\n",
            "cash           2027\n",
            "credit card    1949\n",
            "e-wallet       1935\n",
            "debit card     1918\n",
            "Name: payment_type, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plot_categorical_distribution(df, 'payment_type')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "fTwb_G6EVI4r",
        "outputId": "b57e8c3b-8ff5-4620-ff4c-f4755250f0d3"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}