DuckDB_in_Jupyter_Notebooks.ipynb

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        "<a href=\"https://colab.research.google.com/github/mbyanfei/Google-Colab/blob/master/DuckDB_in_Jupyter_Notebooks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# DuckDB in Jupyter Notebooks\n",
        "A streamlined workflow for SQL analysis with DuckDB and Jupyter"
      ],
      "metadata": {
        "id": "vQivFMys2vtz"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "## Library Import and Configuration"
      ],
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m78.9/78.9 kB\u001b[0m \u001b[31m662.5 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m43.1/43.1 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
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      "source": [
        "!pip install --quiet duckdb\n",
        "!pip install --quiet jupysql\n",
        "!pip install --quiet duckdb-engine\n",
        "!pip install --quiet pandas\n",
        "!pip install --quiet matplotlib"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import duckdb\n",
        "import pandas as pd\n",
        "# No need to import sqlalchemy or duckdb_engine\n",
        "#  JupySQL will use SQLAlchemy to auto-detect the driver needed based on your connection string!\n",
        "\n",
        "# Import jupysql Jupyter extension to create SQL cells\n",
        "%load_ext sql"
      ],
      "metadata": {
        "id": "MJHaFyq_3I_5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We configure jupysql to return data as a Pandas dataframe and have less verbose output"
      ],
      "metadata": {
        "id": "javkpysP6I0W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%config SqlMagic.autopandas = True\n",
        "%config SqlMagic.feedback = False\n",
        "%config SqlMagic.displaycon = False"
      ],
      "metadata": {
        "id": "CvY8OgfV3ckB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Connecting to DuckDB\n",
        "Connect jupysql to DuckDB using a SQLAlchemy-style connection string. You may either connect to an in memory DuckDB, or a file backed db."
      ],
      "metadata": {
        "id": "9Xq5eXmM5bUA"
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    {
      "cell_type": "code",
      "source": [
        "%sql duckdb:///:memory:\n",
        "# %sql duckdb:///path/to/file.db"
      ],
      "metadata": {
        "id": "8NW45gk13eoY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Querying DuckDB\n",
        "Single line SQL queries can be run using `%sql` at the start of a line. Query results will be displayed as a Pandas DF. Note the SQL syntax highlighting!"
      ],
      "metadata": {
        "id": "xUPJhKPH5N6D"
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    {
      "cell_type": "code",
      "source": [
        "%sql SELECT 'Off and flying!' as a_duckdb_column"
      ],
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        },
        "id": "JboVd92U43VV",
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              "   a_duckdb_column\n",
              "0  Off and flying!"
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      "cell_type": "markdown",
      "source": [
        "An entire Jupyter cell can be used as a SQL cell by placing `%%sql` at the start of the cell. Query results will be displayed as a Pandas DF."
      ],
      "metadata": {
        "id": "UbWLn9rD579W"
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        "%%sql\n",
        "SELECT\n",
        "    schema_name,\n",
        "    function_name\n",
        "FROM duckdb_functions()\n",
        "ORDER BY ALL DESC\n",
        "LIMIT 5"
      ],
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              "  schema_name      function_name\n",
              "0  pg_catalog  shobj_description\n",
              "1  pg_catalog  shobj_description\n",
              "2  pg_catalog  shobj_description\n",
              "3  pg_catalog          pg_typeof\n",
              "4  pg_catalog          pg_typeof"
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              "    domScope.querySelector('#df-2865a722-1cd0-4f20-9688-d4c750ef62b0 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-732b22d8-1a89-487a-aa1a-8734ed034b66 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-732b22d8-1a89-487a-aa1a-8734ed034b66');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To return query results into a Pandas dataframe for future usage, use `<<` as an assignment operator. This can be used with both the `%sql` and `%%sql` Jupyter magics."
      ],
      "metadata": {
        "id": "8HtKdEcs6mvC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%sql my_df << SELECT 'Off and flying!' as a_duckdb_column\n",
        "my_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "GQpzinPH5GvF",
        "outputId": "cce0221a-9e4c-457e-d67a-800e0c8ed502"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   a_duckdb_column\n",
              "0  Off and flying!"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-fd0a87f5-7d09-4a15-b662-2e478ad7cad9\">\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>a_duckdb_column</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Off and flying!</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fd0a87f5-7d09-4a15-b662-2e478ad7cad9')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-c3ff0c21-65ba-4dd3-89ed-a530bec0bf64\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c3ff0c21-65ba-4dd3-89ed-a530bec0bf64')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c3ff0c21-65ba-4dd3-89ed-a530bec0bf64 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-fd0a87f5-7d09-4a15-b662-2e478ad7cad9 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-fd0a87f5-7d09-4a15-b662-2e478ad7cad9');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Querying Pandas Dataframes\n",
        "DuckDB is able to find and query any dataframe stored as a variable in the Jupyter notebook."
      ],
      "metadata": {
        "id": "ZHe_uG2666Zv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "input_df = pd.DataFrame.from_dict({\"i\":[1, 2, 3],\n",
        "                                  \"j\":[\"one\", \"two\", \"three\"]})"
      ],
      "metadata": {
        "id": "4qgw6C644LaB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataframe being queried can be specified just like any other table in the `FROM` clause."
      ],
      "metadata": {
        "id": "siVry2OI7HwF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%sql output_df << SELECT sum(i) as total_i FROM input_df\n",
        "output_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "uNxSRUVu4YvY",
        "outputId": "c2f02a19-3be6-4766-e20c-efe6d59eaa27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   total_i\n",
              "0      6.0"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-aa84dee9-1a86-4539-9b80-b6aaf5c5972d\">\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>total_i</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-aa84dee9-1a86-4539-9b80-b6aaf5c5972d')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-c6144b9b-3df5-4c8c-bce6-c5b882218f52\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c6144b9b-3df5-4c8c-bce6-c5b882218f52')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-c6144b9b-3df5-4c8c-bce6-c5b882218f52 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-aa84dee9-1a86-4539-9b80-b6aaf5c5972d 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-aa84dee9-1a86-4539-9b80-b6aaf5c5972d');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Visualizing DuckDB Data\n",
        "The most common way to plot datasets in Python is to load them using pandas and then use matplotlib or seaborn for plotting.\n",
        "This approach requires loading all data into memory which is highly inefficient.\n",
        "The plotting module in JupySQL runs computations in the SQL engine.\n",
        "This delegates memory management to the engine and ensures that intermediate computations do not keep eating up memory, efficiently plotting massive datasets."
      ],
      "metadata": {
        "id": "3_yCxgNIedj5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install and Load DuckDB httpfs extension\n",
        "DuckDB's [httpfs extension](https://duckdb.org/docs/extensions/httpfs) allows parquet and csv files to be queried remotely over http.\n",
        "These examples query a parquet file that contains historical taxi data from NYC.\n",
        "Using the parquet format allows DuckDB to only pull the rows and columns into memory that are needed rather than download the entire file.\n",
        "DuckDB can be used to process [local parquet files as well](https://duckdb.org/docs/data/parquet), which may be desirable if querying the entire parquet file, or running multiple queries that require large subsets of the file.\n"
      ],
      "metadata": {
        "id": "Pr_tB-phf3U3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%sql\n",
        "INSTALL httpfs;\n",
        "LOAD httpfs;"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        },
        "id": "nfjBv8ADZOwO",
        "outputId": "f863315c-b765-4b03-fb0e-e221e7c5022b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Success]\n",
              "Index: []"
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-c52366e7-4602-48bf-9f1a-c6550274a908\">\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>Success</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c52366e7-4602-48bf-9f1a-c6550274a908')\"\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 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-e37dba01-aad3-4b62-82c8-05f4aafb1772\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e37dba01-aad3-4b62-82c8-05f4aafb1772')\"\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",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-e37dba01-aad3-4b62-82c8-05f4aafb1772 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\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",
              "    [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-c52366e7-4602-48bf-9f1a-c6550274a908 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-c52366e7-4602-48bf-9f1a-c6550274a908');\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",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Boxplot & Histogram\n",
        "To create a boxplot, call `%sqlplot boxplot`, passing the name of the table and the column to plot.\n",
        "In this case, the name of the table is the URL of the remotely stored parquet file."
      ],
      "metadata": {
        "id": "RNceigBAgF5V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%sqlplot boxplot --table https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet --column trip_distance"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "DBXg6u2hYZAt",
        "outputId": "7efb04db-6eb7-4a71-a676-3ca77a0723a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': \"'trip_distance' from 'https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet'\"}, ylabel='trip_distance'>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JEyeiS5cucHJywsWLFzF37lwMGzZMZR8pbv369Rg6dCiaNm2KGTNmwNraGmfPnsX+/fsxcOBAte8RBAHvvPMODh8+jGHDhsHLywsHDhzAlClT8N9//2Hp0qXl3p4XL14Uj7Fz5sxBQUEBZs+eLak3bG1tsXHjRnz55Zd4/PgxFixYAOD5iaaye+u6devw9OlTDB8+HCYmJrCxscHBgwfRvXt31K9fH3PmzEFubi6+/fZbtG3bFmfOnFE5Dpa1zTdu3IgPPvgArVu3Fq9ku7m5lfkZv/rqKxgYGGDy5MnIzMzEwoULMWjQIJw6dUosc+jQIXTv3h3e3t6YPXs2DAwMxBPHuLg4tG7dGiEhIfjnn3+wZcsWLF26FLVq1RK3j1LRY0H79u0xduxYrFixAp988onYtahoFyN1x+/SjB49GtbW1pgzZw6uXbuG1atX4/bt2+LJTWk0rYf//PNPnDx5Ev3790fdunWRlJSE1atXo2PHjrh8+TKqV69e5mdbv349LCwsMHHiRFhYWODQoUOYNWsWsrKysGjRIsn6inb3lslkeO+997Bw4UI8fPgQNjY2Yrk9e/YgKysL7733HoDnF4/eeecdHD9+HMOHD0eTJk1w8eJFLF26FP/880+5u7CuX78eQ4YMQatWrbBgwQLcv38fy5cvx4kTJ3D27FlYW1ujXbt2YkKsrH/i4uJgYGCA48ePi8tKS0vD1atXMXr0aI3WXVLdV5S631hJF/c0qQ8FQcC7776L48eP46OPPkKTJk2wc+dOhIWFqSxPk3Opsn4fq1evRtOmTfHOO++gWrVq2LNnD0aOHAmFQoFRo0ZptJ1KIggC7t+/j6ZNm4rT/vvvP6SmpqJly5Yq5Vu3bq3RxbKUlBTxc7wqZ8+eBQCVuLy9vWFgYICzZ8+K+/iroMl5/K1bt7Br1y706dMHrq6uuH//Pr7//nt06NABly9fhqOjo2SZ8+bNg7GxMSZPnoy8vDwYGxvj999/R+/eveHh4YEFCxYgPT0dQ4YMERt8XsSlS5fQtm1b1KlTRzyX2rZtG4KDg7Fjxw706tVLo+NsuQkvqWnTpkKHDh1Upq9bt04AILRr104oKChQOy8xMVGc5uzsLAAQduzYIU7LzMwUHBwchDfffFPjeM6dOycAEEaOHCmZPnDgQAGAMHv27BLj2LlzpwBA+PPPP0tcflpamspylJ48eaIybcuWLQIA4dixY+K02bNnCwCEoUOHSsr26tVLqFmzpvj/9evXBQMDA6FXr15CYWGhpKxCoRAEQRCys7MFa2tr4cMPP5TMT0lJEeRyucp0TSjjS0tLk0xXfkf79++XTB8/frwAQIiLixOnZWdnC66uroKLi4sY++HDhwUAQrNmzYT8/Hyx7IABAwSZTCZ0795dslxfX1/B2dm53PEnJiYKAISaNWsKDx8+FKfv3r1bACDs2bNHnDZq1ChB3c+gtO85LCxMACCMGTNGnKZQKISgoCDB2NhY3G7jxo0TrKysVPb/4pydndV+zpkzZ6qdXq9ePUlcyu1/6tQpcVpqaqogl8tVfmdKJiYmAgBxO61YsaLUGNPT0wVjY2Ohb9++kukFBQWCq6ur4OzsLDx69EgyT7mPCsL/7VNKkZGR4robNGggrFu3Tli3bp3QoEEDwdjYWDh//rxYdsCAAYKdnZ1kOyYnJwsGBgbC559/rjZehUIh9OjRQ7CwsBAuXbokTi/pO3V2dhbCwsLE/0s7fs2YMUMwMTERMjIyxGmpqalCtWrVJMtu3769YGlpKdy+fbvM7VLWsUD5mW1sbIQuXboIeXl5wptvvinUq1dPyMzMVLsNlDIyMgRLS0vBx8dHyM3NLTGWsLAwyf62a9cuAYDwxRdfSN4TGhoqyGQy4caNG4Ig/N/vbd26dSrrLr69g4ODBVNTU8k2uXz5smBoaKjyO+zQoYPQtGlTyTTluqysrITU1FTJPC8vL8HOzk5IT08Xp50/f14wMDAQ3n//fXFaeba5ubm5ZL8ojfL41qRJEyEvL0+cvnz5cgGAcPHiRUEQnm/zBg0aCIGBgZLt/+TJE8HV1VXo0qWLOG3RokUl/oYFQfVYsH37dgGAcPjwYZWyJR2/lfPU7f/e3t6SY/XChQsFAMLu3btL3RblqYfV1Zvx8fECAOGXX37R6LOpW8aIESOE6tWrC0+fPpVMHzx4sOSc5dq1awIAYfXq1ZJy77zzjuDi4iJ+Rxs3bhQMDAwk9ZwgCMKaNWsEAMKJEyfEacW3p3LfUMaen58v2NnZCc2aNZP8JqOiogQAwqxZs8RpTZs2lRx3W7RoIfTp00cAIFy5ckUQhP87nhY9bpalpLqvtN+Yut+6pvWh8niycOFCsVxBQYHg7++vskxNz6VK+32oW0ZgYKBQv3591Y1RThs3bhQACD/99JM47c8//1TZZ5WmTJkiAFDZF4s6duyYIJPJhJkzZ5ZYpqzjgTqjRo0SDA0N1c6ztbUV+vfv/8rWpel5/NOnT1XOaRMTEwUTExNJna783dSvX1/l+/Ty8hIcHBwk9fDvv/8uAJDUY8V/e0XXV3y/69Spk+Dp6Sn5nhQKheDn5yc0aNBAnFbasehFvPZOlh9++KHG92E5OjpKrjIru0KcPXsWKSkpGi1DedVh7Nixkunjx48v873K+xGioqLKPRodAEkLxNOnT/HgwQO0adMGAHDmzBmV8h999JHkf39/f6Snp0uaXRUKBWbNmqXSH1Z5NTImJgYZGRkYMGAAHjx4IL4MDQ3h4+ODw4cPl/tzlMbV1RWBgYGSaXv37kXr1q3Rrl07cZqFhQWGDx+OpKQksduQ0vvvvy8ZZMLHxweCIIhdQItO//fff1FQUPBCsfbr10/s+ww8377A86sxr0LRq48ymQyjR49Gfn6+eLOttbU1cnJyEBMTU+pykpKS1N70uXfvXpUryAkJCbhz545k+t69e9GmTRvJfVa2trZquxAq7du3D3v37sWSJUtQr169MlurIyIikJ+fr7LMs2fPIjExEePHj1e5n6e0K+aPHz8GAGRnZyM2Nhbh4eEIDw/HwYMHIQgCFi5cKJbt168fUlNTJU3/ERERUCgUJXbFmTdvHqKiorB+/Xp4eHiU+tlKo+749f777yMvL0/sbw88b50rKCgQryympaXh2LFjGDp0qKSbCqB+u5R1LAAAe3t7fPfdd4iJiYG/vz/OnTuHn3/+GVZWVqV+hpiYGGRnZ2P69Okq93eU9h3t3bsXhoaGKsfSSZMmQRAE7Nu3r9T1FldYWIgDBw4gODhYsk2aNGmickwpS+/evSUtQ8nJyTh37hzCw8MlrRrNmzdHly5d1F6N1mSbv4ghQ4ZIujIVP+6cO3cO169fx8CBA5Geni4es3NyctCpUyccO3ZMo27F6o4FZVF3/C7N8OHDJcfqjz/+GNWqVSvz6n556uGi9eazZ8+Qnp4Od3d3WFtbq6031Sm6jOzsbDx48AD+/v548uQJrl69Ks5T1927YcOG8PHxkYwE+/DhQ+zbtw+DBg0SfyPbt29HkyZN0LhxY0ld+9ZbbwFAuerav/76C6mpqRg5cqTkNxkUFITGjRtLuov5+/sjLi5O/Gznz5/H8OHDUatWLXF6XFwcrK2t0axZM41jKEvx31hZyqoP9+7di2rVquHjjz8WyxkaGmLMmDEqyyrvuZQ6RZeRmZmJBw8eoEOHDrh169ZLDQZ19epVjBo1Cr6+vpLWPGVrvbr7LZXfcfEBq5RSU1MxcOBAcSCtVyk3N7fE+2dNTU1LjOlFaXIeb2JiIp7TFhYWIj09HRYWFmjUqJHa7zcsLEzyfSqP92FhYZDL5eL0Ll26vHB9//DhQxw6dAh9+/YVjyEPHjxAeno6AgMDcf36dUm34VfptSdirq6uGpd1d3dXOTFo2LAhANVuWyW5ffs2DAwMVLqQlNUFAwA6dOiA3r17Y+7cuahVqxbeffddrFu3TuP+3w8fPsS4ceNQu3ZtmJmZwdbWVvz86n74xU/QlEmDsg/+zZs3YWBgUOqOdf36dQDAW2+9BVtbW8nr999/FwfeeFXUfZ+3b99Wu32VTbW3b9+WTC/+uZU/JCcnJ5XpCoXihQ+aZW3fl2FgYKAymEXxfXXkyJFo2LAhunfvjrp162Lo0KHYv3+/RstPSUnBmTNnVE6yoqOjUbt2bUk3g9u3b6NBgwYqyyhtnw8ICED37t0xceJEbN++HXPnzsXKlStLLL9p0ybY2NhI7ksDIN7bVt4TAOVBtW3btpLvvV69emjXrh1OnjwpTlPeR7N161Zx2tatW+Hl5SVu86L279+PuXPnYsaMGejdu3e54ipO3f7euHFjtGrVSnLitmnTJrRp0wbu7u4A/u+kW9Ptoum+2r9/fwQFBeH06dP48MMPNRq98kW/o9u3b8PR0VHlnoGSftdlSUtLQ25ubrn3VXWKfy/KWEo6DikTnaJe1/GhrOUqj9lhYWEqx+wff/wReXl5Gh3z1B0LylKe+hiAyndlYWEBBwcH8RiXmZmJlJQU8fXw4UMA5auHc3NzMWvWLPE+xFq1asHW1hYZGRkaH/svXbqEXr16QS6Xw8rKCra2tuJFkaLLUHb3Ln5cff/993HixAlxP9q+fTuePXsmGR3w+vXruHTpksp3pjwGlaeuLW1/bdy4seS35e/vj+TkZNy4cQMnT56ETCaDr6+vJEGLi4tD27ZtX+kABuXZVzSpD2/fvg0HBweVe6PUbYPynkupc+LECXTu3Fm8X9TW1haffPJJuZZRXEpKCoKCgiCXyxERESG5SKes09SdLz59+lRSpqicnBz06NED2dnZ2L17t8r20VRaWprkt6i82GlmZiYZsbF4XK96sDVNzuMVCgWWLl2KBg0aSH7zFy5cUPvdlHS8fxV1idKNGzcgCAJmzpyp8htXjpvwqs+nlV77qIn6NKKeTCZDREQE/vjjD+zZswcHDhzA0KFDsWTJEvzxxx9l/kD69u2LkydPYsqUKfDy8oKFhQUUCgW6deum9gpnSS2FQrEbXEujXO7GjRvVjmRXfKS6l/Uqvs+SPver2B6vc3nlZWdnh3PnzuHAgQPYt28f9u3bh3Xr1uH999/Hhg0bSn3vvn37YGpqKt4rpbR3715069btlY725ebmhjfffBObNm1Se4/BnTt3EBcXp3J1/GUo+4AXv6cUeL7dlP3agedXz4KDg7Fz506sWrUK9+/fx4kTJzB//nyV9yYmJmLQoEHo0qULvvjiC43jUXdTP1Dy/v7+++9j3LhxuHv3LvLy8vDHH3+UmsiWRdN9NT09XXzWzOXLl6FQKHQ+elRJ+2JJ2/RVeJ3HoZc9PpS1XOUxe9GiRfDy8lJbVpOTsRc5Frzq+njcuHGSY1mHDh3KfdP6mDFjsG7dOowfPx6+vr6Qy+WQyWTo37+/Ri2DGRkZ6NChA6ysrPD555/Dzc0NpqamOHPmDKZNmyZZxt69e+Hi4qJycbN///6YMGECNm3ahE8++QS//vorWrZsKTmpUygU8PT0xDfffKM2juIXEl8VZU+TY8eO4datW2jRogXMzc3h7++PFStW4PHjxzh79qzKfacvS5fnbuU9lyru5s2b6NSpExo3boxvvvkGTk5OMDY2xt69e7F06VKNB7IqKjMzE927d0dGRgbi4uJU7mNycHAA8Ly1prjk5GTY2NiotJbl5+cjJCQEFy5cwIEDB16qRbNVq1aSBH727NmYM2cOHBwcUFhYiNTUVMn90vn5+UhPT1f5HNowf/58zJw5E0OHDsW8efNgY2MDAwMDjB8/Xu138zL7oqb1k3K9kydPLrHXgPJC66v20mfpr/KEUJmRFl2m8qF5mg444ezsDIVCgZs3b0oOoteuXdM4jjZt2qBNmzb48ssvsXnzZgwaNAi//fYbPvjggxI/76NHjxAbG4u5c+dKBpdQXv18EW5ublAoFLh8+XKJFbbyiqOdnZ1kyFNtcnZ2Vrt9lV1CdP28uNKU9H2WtV8rFArcunVL0iKjbl81NjZGz5490bNnTygUCowcORLff/89Zs6cWeqPOjo6GgEBAZIDkHK0r+LJkrOzs9r9rDz7fG5uboktv1u2bIEgCGq7Oir3v4SEhHLtf56enjAyMlLb1H/v3j2VLjH9+vXDhg0bEBsbiytXrkAQBJVuibm5uQgJCYG1tTW2bNmiNkGpUaOGyihd+fn5aivP0vTv3x8TJ07Eli1bkJubCyMjI0k8yqvDxZ8z87JGjRqF7OxsLFiwADNmzMCyZcswceLEUt9T9DsqT0Xi7OyMgwcPIjs7W9IqVvx3rWzxKb5di7eYKUeOfNl9taRYS1rO1atXUatWrRd69MDrGN5c+X1YWVmV+Zspaf0lHQtedbzXr1+XXAx6/PgxkpOTxcFhpk6dKrnRX7kvlKcejoiIQFhYGJYsWSJOe/r0qcr+VNJnO3LkCNLT0xEZGYn27duL0xMTE1XKRkdHqx3YxsbGBkFBQdi0aRMGDRqEEydOYNmyZZIybm5uOH/+PDp16vTS27no/qrs2qh07do1SZ1Zr1491KtXD3Fxcbh165bY1bV9+/Zij4bCwkLJZ9fEq9xXNKkPnZ2dERsbi8ePH0suNBTfJ8pzLlXSZ9izZw/y8vLwv//9T9JC/aK3ajx9+hQ9e/bEP//8g4MHD6rtpVSnTh3Y2tqqfSjz6dOnVc7hFAoF3n//fcTGxqodjKq8Nm3aJOlmqKyDlOv966+/JPv+X3/9BYVCUeK55YvS5Dw+IiICAQEB+OmnnyTvzcjI0GiwEuXvQ5O6RNP6Sbm9jIyMXvi4/KJe+lKqubn5K3uY3r179yTDQmdlZeGXX36Bl5eXxs8tUnadWrFihWR68YOqOo8ePVK5GqrcSZUnqdWrVweg+qUqr4IWf78m6y1JcHAwDAwM8Pnnn6tcJVCuJzAwEFZWVpg/f77a+9qKP9H9dXj77bdx+vRpybDdOTk5WLt2rdqrjxWJ8uSs+PdZ0vdcVNEWEEEQsHLlShgZGYndxYo/tsDAwEAc+apo0lN8+Ppnz54hJiZGpfuM8pEFxR/u/Pbbb+OPP/7A6dOnxWlpaWmSrnPA82fBqOt2dfr0aVy8eLHELk6bN28WuwwW16JFC7i6umLZsmUq26q0lgVLS0u8/fbbOHnypOQejitXruDkyZMqo6R27twZNjY22Lp1K7Zu3YrWrVurdFf46KOP8M8//2Dnzp2SewOLcnNzw7FjxyTT1q5dW+7Wm1q1aqF79+749ddfsWnTJnTr1k1Sgdja2qJ9+/b4+eefcefOHcl7X7TFJSIiAlu3bsVXX32F6dOno3///vjss8/ESk6p+P7UtWtXWFpaYsGCBWIXGU1iefvtt1FYWKjS0rd06VLIZDLxWGtlZYVatWqpbNfiz6wzNDREYGAgdu3aJdkmV65cwYEDBzTYAiVzcHCAl5cXNmzYINkPExIS8Pvvv5c5qmRJSqrflPcePXjwoNzL9Pb2hpubGxYvXix2Hyqq6DG7pONTSceCksq/qLVr10rqldWrV6OgoED87j08PNC5c2fx5e3tDaB89bChoaHKfvjtt9+q/CZL+mzq6t78/HyV/e/+/ftqu3srDR48GJcvX8aUKVNgaGiI/v37S+b37dsX//33n9oHX+fm5pZ5n21RLVu2hJ2dHdasWSOpC/bt24crV66oxOjv749Dhw7h9OnTYiLm5eUFS0tLfPXVVzAzMxO3vaZe9b5SVn349ttvo6CgQDKEfGFhIb799lvJcspzLlWefSIzMxPr1q0r56d6HmO/fv0QHx+P7du3i6PiqtO7d29ERUXh33//FafFxsbin3/+EYc8VxozZgy2bt2KVatWqYxE/CLatm0r+S0qE4u33noLNjY2KkP3r169GtWrVy/XPaZFlfTYHU3O49X95rdv367xPVhFj/dFuzLGxMSojEng7OwMQ0PDMusnOzs7dOzYEd9//73aC7OaHJdf1Eu3iHl7e2P16tX44osv4O7uDjs7O5UrPJpq2LAhhg0bhj///BO1a9fGzz//jPv375frx+Pl5YUBAwZg1apVyMzMhJ+fH2JjY9U+x6W4DRs2YNWqVejVqxfc3NyQnZ2NH374AVZWVmJFbmZmBg8PD2zduhUNGzaEjY0NmjVrhmbNmqF9+/ZYuHAhnj17hjp16uD3339Xe1VOU+7u7vj0008xb948+Pv7IyQkBCYmJvjzzz/h6OiIBQsWwMrKCqtXr8bgwYPRokUL9O/fH7a2trhz5w6io6PRtm3bl+oypYnp06djy5Yt6N69O8aOHQsbGxts2LABiYmJ2LFjh867TpVGWXmNHTsWgYGBYgVc2vcMPL/Jdf/+/QgLC4OPjw/27duH6OhofPLJJ2JrzgcffICHDx/irbfeQt26dXH79m18++238PLykgx1Wnz4+uPHjyMrK0vt/WHt2rWT3JwKPL8qvXHjRnTr1g3jxo0Th693dnaWPH/m8ePHcHJyQr9+/dC0aVOYm5vj4sWLWLduHeRyOWbOnKmyfRISEnDhwgVMnz5d7VUgAwMDrF69Gj179oSXlxeGDBkCBwcHXL16FZcuXSr1BHv+/PmIjY3FW2+9Jd7Uv2LFCtjY2Ih9+ZWMjIwQEhKC3377DTk5OVi8eLHKtvnll1/Qu3dvXLhwQfK5LSwsEBwcDOD5d/LRRx+hd+/e6NKlC86fP48DBw680JDB77//PkJDQwE8HxykuBUrVqBdu3Zo0aIFhg8fDldXVyQlJSE6OrrcD8pMTU3Fxx9/jICAALEVZOXKlTh8+DDCw8Nx/Phx8XdWfH+ysrLC0qVL8cEHH6BVq1YYOHAgatSogfPnz+PJkycldpPt2bMnAgIC8OmnnyIpKQlvvPEGfv/9d+zevRvjx4+X3P/zwQcf4KuvvsIHH3yAli1b4tixYyoJIvD8cRz79++Hv78/Ro4ciYKCAnz77bdo2rSp5Dt7EYsWLUL37t3h6+uLYcOGicPXy+Vytc+O04S3tzcOHjyIb775Bo6OjnB1dYWPjw9Onz6NgIAAsftPeRgYGODHH39E9+7d0bRpUwwZMgR16tTBf//9h8OHD8PKygp79uwR1w8An376Kfr37w8jIyP07NmzxGOBl5cXDA0N8fXXXyMzMxMmJibis5ReRH5+Pjp16oS+ffvi2rVrWLVqFdq1a4d33nmn1PeVpx7u0aMHNm7cCLlcDg8PD8THx+PgwYOoWbOmRp/Nz88PNWrUQFhYGMaOHQuZTIaNGzeqnOjt3btXbXdvpaCgINSsWRPbt29H9+7dVbbZ4MGDsW3bNnz00Uc4fPgw2rZti8LCQly9ehXbtm0Tn8+mCSMjI3z99dcYMmQIOnTogAEDBojD17u4uGDChAmS8v7+/ti0aRNkMpl4QczQ0BB+fn44cOAAOnbsWO4HmpdU970ITerDnj17om3btpg+fTqSkpLE50sVvyfIyspK43Opkn4fXbt2FXujjBgxAo8fP8YPP/wAOzu7cvd+mDRpEv73v/+hZ8+eePjwoeQBzgAkLcKffPIJtm/fjoCAAIwbNw6PHz/GokWL4OnpiSFDhojlli1bhlWrVsHX1xfVq1dXWWavXr3Ek/3MzEwxWVU+Z3DlypWwtraGtbV1mY8sMDMzw7x58zBq1Cj06dMHgYGBiIuLw6+//oovv/xSMrhRedZV0mN3NDmP79GjBz7//HMMGTIEfn5+uHjxIjZt2qRyn2FpFixYgKCgILRr1w5Dhw7Fw4cPxbqk6AUuuVyOPn364Ntvv4VMJoObmxuioqLU3u/13XffoV27dvD09MSHH36I+vXr4/79+4iPj8fdu3fF55y96uPsSw9fn5KSIgQFBQmWlpYCAHFYWOXwt+qGgi9p+PqgoCDhwIEDQvPmzQUTExOhcePGwvbt28sdU25urjB27FihZs2agrm5udCzZ0/h33//LXP4+jNnzggDBgwQ6tWrJ5iYmAh2dnZCjx49hL/++kuy/JMnTwre3t6CsbGxZJl3794VevXqJVhbWwtyuVzo06ePcO/ePZX1ljQ8vLrtIgiC8PPPPwtvvvmmYGJiItSoUUPo0KGDEBMTIylz+PBhITAwUJDL5YKpqang5uYmhIeHq8SuidKGrw8KClL7nps3bwqhoaGCtbW1YGpqKrRu3VqIiopSiRGAynda0r5SUhxlUQ5LumjRIpV5xb+LgoICYcyYMYKtra0gk8kkw/mW9D2HhYUJ5ubmws2bN4WuXbsK1atXF2rXri3Mnj1bMiRrRESE0LVrV8HOzk4wNjYW6tWrJ4wYMUJITk6WxFR8+PrJkycLHh4ekjIKhUKws7OTDP1b1IULF4QOHToIpqamQp06dYR58+YJP/30k2R/ysvLE8aNGyc0b95csLKyEoyMjARnZ2dh2LBhJQ5RO336dAGAcOHCBbXzlY4fPy506dJFsLS0FMzNzYXmzZsL3377rTi/+PD1Sn///bfQuXNnwdzcXLC0tBTeffdd4Z9//lG7jpiYGAGAIJPJhH///VcyT7kPqXsV3baFhYXCtGnThFq1agnVq1cXAgMDhRs3bpQ4fHdpj7LIy8sTatSoIcjlcpVh4ZUSEhLEY4KpqanQqFEjyfDEmh4LQkJCBEtLSyEpKUlSTvlIhq+//lqcVtLjEP73v/8Jfn5+gpmZmWBlZSW0bt1a2LJlizi/+PD1gvD8MRQTJkwQHB0dBSMjI6FBgwbCokWLJMOuC8LzoaKHDRsmyOVywdLSUujbt6+Qmpqq9nEBR48eFX9X9evXF9asWaN2/yht+Hp1v21BEISDBw8Kbdu2FT9jz549hcuXL0vKlOf4e/XqVaF9+/aCmZmZAEDcR5THsqKfraTjW0nD+589e1YICQkRatasKZiYmAjOzs5C3759hdjYWEm5efPmCXXq1BEMDAwEAMKtW7dKPRb88MMPQv369cVHAiiHWC7t+F3S/n/06FFh+PDhQo0aNQQLCwth0KBBkscDlEbTevjRo0fCkCFDhFq1agkWFhZCYGCgcPXqVZWYSvtsJ06cENq0aSOYmZkJjo6OwtSpU4UDBw5IyoSGhgpvv/12qTGPHDlSACBs3rxZ7fz8/Hzh66+/Fpo2bSrWx97e3sLcuXMlj5Eoa/h6pa1bt4p1u42NjTBo0CDh7t27Kuu9dOmSgP//aISivvjiCwFAqUOel6Skuq+031hJw9drUh8KwvPHoAwePFiwsrIS5HK5MHjwYOHs2bMqy9T0XEoQVH8fyt/v//73P6F58+aCqamp4OLiInz99dfCzz//XO4h2Tt06FBi3aKuTktISBC3g7W1tTBo0CAhJSVFUkY55H9Jr6LxKbd5WXVbWdauXSs0atRIMDY2Ftzc3ISlS5eqHMfLsy519Yym5/FPnz4VJk2aJDg4OAhmZmZC27Zthfj4eKFDhw6SR0uUdExV2rFjh9CkSRPBxMRE8PDwECIjI9XWY2lpaULv3r2F6tWrCzVq1BBGjBghJCQkqD0u37x5U3j//fcFe3t7wcjISKhTp47Qo0cPISIiQlKupGPRi5AJgpZGLiiDi4sLmjVrhqioKF2HQlSq8PBwREREqO1W9Cp4eHigR48ekiHcT58+DR8fH1y6dKlCd/WsSgoKCuDo6IiePXuq9HUnel20cSxQPmz4zz//LNeojBVVQUEBatasiQULFmDkyJEllpswYQJ++uknpKSkiN3TqXSvuz4k/VERzuPDw8Nx5MgRjUdarwhe+6iJRKS5/Px89OvXD3379lWZN3/+fCZhFciuXbuQlpaG999/X9ehUBXDY0H5PHz4EBMmTJA836i4p0+f4tdff0Xv3r2ZhBGR1uhVIlbWQ53NzMxU+swT6RNjY2PxmRVFtW7dWvLAZtKdU6dO4cKFC5g3bx7efPPNlx7tiqg8eCwoPzs7uxLv5UtNTcXBgwcRERGB9PR0jBs3TrvBvWKZmZllPqRX08HPKrPHjx+X2Ypna2tb4uMoiF4VvUrElM9pKElYWBjWr1+vnWCIqEpavXo1fv31V3h5efF4Q6TnLl++jEGDBsHOzg4rVqx45cN5a1vxZ7upU0HuSNGpxYsXY+7cuaWWSUxM1PjRSUQvqsLcI6aJgwcPljrf0dGR3TWIiIioSrp8+TLu3btXahldPXO0Irl16xZu3bpVapl27drB1NRUSxFRVaVXiRgREREREVFlUHEf8ERERERERFRJ6dU9YkTlpVAocO/ePVhaWqp9IDERERFVPIIgIDs7G46OjuID64kqGyZiVKndu3cPTk5Oug6DiIiIXsC///6LunXr6joMoteCiRhVapaWlgCeH8itrKx0HA0RERFpIisrC05OTmI9TlQZMRGjSk3ZHdHKyoqJGBERkZ7hbQVUmbHTLRERERERkZYxESMiIiIiItIyJmJERERERERaxkSMiIiIiIhIy5iIERERERERaRkTMSIiIiIiIi1jIkZERERERKRlTMSIiIiIiIi0jA90JiIivVNYWIi4uDgkJyfDwcEB/v7+MDQ01HVYREREGmOLGBER6ZXIyEi4u7sjICAAAwcOREBAANzd3REZGanr0IiIiDTGRIyIiPRGZGQkQkND4enpifj4eGRnZyM+Ph6enp4IDQ1lMkZERHpDJgiCoOsgiF6XrKwsyOVyZGZmwsrKStfhENFLKCwshLu7Ozw9PbFr1y4YGPzftUSFQoHg4GAkJCTg+vXr7KZIpOdYf1NVwBYxIiLSC3FxcUhKSsInn3wiScIAwMDAADNmzEBiYiLi4uJ0FCEREZHmmIgREZFeSE5OBgA0a9ZM7XzldGU5IiKiioyJGBER6QUHBwcAQEJCgtr5yunKckRERBUZEzEiItIL/v7+cHFxwfz586FQKCTzFAoFFixYAFdXV/j7++soQiIiIs0xESMiIr1gaGiIJUuWICoqCsHBwZJRE4ODgxEVFYXFixdzoA4iItILfKAzERHpjZCQEERERGDSpEnw8/MTp7u6uiIiIgIhISE6jI6IiEhzHL6eKjUOf0tUORUWFiIuLg7JyclwcHCAv78/W8KIKhHW31QVsEWMiIj0jqGhITp27KjrMIiIiF4Y7xEjIiIiIiLSMiZiREREREREWsZEjIiIiIiISMuYiBEREREREWkZEzEiIiIiIiItYyJGRERERESkZUzEqpgFCxagVatWsLS0hJ2dHYKDg3Ht2jVJmY4dO0Imk0leH330kaTMnTt3EBQUhOrVq8POzg5TpkxBQUGBpMyRI0fQokULmJiYwN3dHevXr1eJ57vvvoOLiwtMTU3h4+OD06dPS+Y/ffoUo0aNQs2aNWFhYYHevXvj/v37r2ZjEBERERHpCBOxKubo0aMYNWoU/vjjD8TExODZs2fo2rUrcnJyJOU+/PBDJCcni6+FCxeK8woLCxEUFIT8/HycPHkSGzZswPr16zFr1iyxTGJiIoKCghAQEIBz585h/Pjx+OCDD3DgwAGxzNatWzFx4kTMnj0bZ86cwRtvvIHAwECkpqaKZSZMmIA9e/Zg+/btOHr0KO7du4eQkJDXuIWIiIiIiF4/mSAIgq6DIN1JS0uDnZ0djh49ivbt2wN43iLm5eWFZcuWqX3Pvn370KNHD9y7dw+1a9cGAKxZswbTpk1DWloajI2NMW3aNERHRyMhIUF8X//+/ZGRkYH9+/cDAHx8fNCqVSusXLkSAKBQKODk5IQxY8Zg+vTpyMzMhK2tLTZv3ozQ0FAAwNWrV9GkSRPEx8ejTZs2ZX6+rKwsyOVyZGZmwsrK6oW3ExEREWkP62+qCtgiVsVlZmYCAGxsbCTTN23ahFq1aqFZs2aYMWMGnjx5Is6Lj4+Hp6enmIQBQGBgILKysnDp0iWxTOfOnSXLDAwMRHx8PAAgPz8ff//9t6SMgYEBOnfuLJb5+++/8ezZM0mZxo0bo169emKZ4vLy8pCVlSV5ERERERFVNNV0HQDpjkKhwPjx49G2bVs0a9ZMnD5w4EA4OzvD0dERFy5cwLRp03Dt2jVERkYCAFJSUiRJGADx/5SUlFLLZGVlITc3F48ePUJhYaHaMlevXhWXYWxsDGtra5UyyvUUt2DBAsydO7ecW4KIiIiISLuYiFVho0aNQkJCAo4fPy6ZPnz4cPFvT09PODg4oFOnTrh58ybc3Ny0HWa5zJgxAxMnThT/z8rKgpOTkw4jIiIiIiJSxa6JVdTo0aMRFRWFw4cPo27duqWW9fHxAQDcuHEDAGBvb68ycqHyf3t7+1LLWFlZwczMDLVq1YKhoaHaMkWXkZ+fj4yMjBLLFGdiYgIrKyvJi4iIiIioomEiVsUIgoDRo0dj586dOHToEFxdXct8z7lz5wAADg4OAABfX19cvHhRMrphTEwMrKys4OHhIZaJjY2VLCcmJga+vr4AAGNjY3h7e0vKKBQKxMbGimW8vb1hZGQkKXPt2jXcuXNHLENEREREpI/YNbGKGTVqFDZv3ozdu3fD0tJSvNdKLpfDzMwMN2/exObNm/H222+jZs2auHDhAiZMmID27dujefPmAICuXbvCw8MDgwcPxsKFC5GSkoLPPvsMo0aNgomJCQDgo48+wsqVKzF16lQMHToUhw4dwrZt2xAdHS3GMnHiRISFhaFly5Zo3bo1li1bhpycHAwZMkSMadiwYZg4cSJsbGxgZWWFMWPGwNfXV6MRE4mIiIiIKioOX1/FyGQytdPXrVuH8PBw/Pvvv3jvvfeQkJCAnJwcODk5oVevXvjss88k3fxu376Njz/+GEeOHIG5uTnCwsLw1VdfoVq1/8vtjxw5ggkTJuDy5cuoW7cuZs6cifDwcMl6V65ciUWLFiElJQVeXl5YsWKF2BUSeP5A50mTJmHLli3Iy8tDYGAgVq1aVWLXxOI4/C0REZH+Yf1NVQETMarUeCAnIiLSP6y/qSrgPWJERERERERaxkSMiIiIiIhIy5iIERERERERaRkTMSIiIiIiIi1jIkZERERERKRlTMSIiIiIiIi0jIkYERERERGRljERIyIiIiIi0jImYkRERERERFrGRIyIiIiIiEjLmIgRERERERFpGRMxIiIiIiIiLWMiRkREREREpGVMxIiIiIiIiLSMiRgREREREZGWMREjIiIiIiLSMiZiREREREREWsZEjIiIiIiISMuYiBEREREREWkZEzEiIiIiIiItYyJGRERERESkZUzEiIiIiIiItIyJGBERERERkZYxESMiIiIiItIyJmJERERERERaxkSMiIiIiIhIy5iIERERERERaRkTMSIiIiIiIi1jIkZERERERKRlTMSIiIiIiIi0jIkYERERERGRljERIyIiIiIi0jImYkRERERERFrGRIyIiIiIiEjLmIgRERERERFpGRMxIiIiIiIiLWMiRkREREREpGVMxIiIiIiIiLSMiRgREREREZGWMREjIiIiIiLSMiZiREREREREWsZEjIiIiIiISMuYiBEREREREWkZEzEiIiIiIiItYyJGRERERESkZUzEiIiIiIiItIyJGBERERERkZYxESMiIiIiItIyJmJERERERERaxkSMiIiIiIhIy5iIERERERERaRkTMSIiIiIiIi1jIkZERERERKRlTMSqmAULFqBVq1awtLSEnZ0dgoODce3aNUmZp0+fYtSoUahZsyYsLCzQu3dv3L9/X1Lmzp07CAoKQvXq1WFnZ4cpU6agoKBAUubIkSNo0aIFTExM4O7ujvXr16vE891338HFxQWmpqbw8fHB6dOnyx0LEREREZG+YSJWxRw9ehSjRo3CH3/8gZiYGDx79gxdu3ZFTk6OWGbChAnYs2cPtm/fjqNHj+LevXsICQkR5xcWFiIoKAj5+fk4efIkNmzYgPXr12PWrFlimcTERAQFBSEgIADnzp3D+PHj8cEHH+DAgQNima1bt2LixImYPXs2zpw5gzfeeAOBgYFITU3VOBYiIiIiIn0kEwRB0HUQpDtpaWmws7PD0aNH0b59e2RmZsLW1habN29GaGgoAODq1ato0qQJ4uPj0aZNG+zbtw89evTAvXv3ULt2bQDAmjVrMG3aNKSlpcHY2BjTpk1DdHQ0EhISxHX1798fGRkZ2L9/PwDAx8cHrVq1wsqVKwEACoUCTk5OGDNmDKZPn65RLGXJysqCXC5HZmYmrKysXum2IyIioteD9TdVBWwRq+IyMzMBADY2NgCAv//+G8+ePUPnzp3FMo0bN0a9evUQHx8PAIiPj4enp6eYhAFAYGAgsrKycOnSJbFM0WUoyyiXkZ+fj7///ltSxsDAAJ07dxbLaBJLcXl5ecjKypK8iIiIiIgqGiZiVZhCocD48ePRtm1bNGvWDACQkpICY2NjWFtbS8rWrl0bKSkpYpmiSZhyvnJeaWWysrKQm5uLBw8eoLCwUG2ZossoK5biFixYALlcLr6cnJw03BpERERERNrDRKwKGzVqFBISEvDbb7/pOpRXZsaMGcjMzBRf//77r65DIiIiIiJSUU3XAZBujB49GlFRUTh27Bjq1q0rTre3t0d+fj4yMjIkLVH379+Hvb29WKb46IbKkQyLlik+uuH9+/dhZWUFMzMzGBoawtDQUG2ZossoK5biTExMYGJiUo4tQURERESkfWwRq2IEQcDo0aOxc+dOHDp0CK6urpL53t7eMDIyQmxsrDjt2rVruHPnDnx9fQEAvr6+uHjxomR0w5iYGFhZWcHDw0MsU3QZyjLKZRgbG8Pb21tSRqFQIDY2ViyjSSxERERERPqILWJVzKhRo7B582bs3r0blpaW4r1WcrkcZmZmkMvlGDZsGCZOnAgbGxtYWVlhzJgx8PX1FUcp7Nq1Kzw8PDB48GAsXLgQKSkp+OyzzzBq1CixNeqjjz7CypUrMXXqVAwdOhSHDh3Ctm3bEB0dLcYyceJEhIWFoWXLlmjdujWWLVuGnJwcDBkyRIyprFiIiIiIiPQRh6+vYmQymdrp69atQ3h4OIDnD1GeNGkStmzZgry8PAQGBmLVqlWS7oC3b9/Gxx9/jCNHjsDc3BxhYWH46quvUK3a/+X2R44cwYQJE3D58mXUrVsXM2fOFNehtHLlSixatAgpKSnw8vLCihUr4OPjI87XJJbScPhbIiIi/cP6m6oCJmJUqfFATkREpH9Yf1NVwHvEiIiIiIiItIyJGBERERERkZYxESMiIiIiItIyJmJERERERERaxkSMiIiIiIhIy5iIERERERERaRkTMSIiIiIiIi1jIkZERERERKRlTMSIiIiIiIi0jIkYERERERGRljERIyIiIiIi0jImYkRERERERFrGRIyIiIiIiEjLmIgRERERERFpGRMxIiIiIiIiLWMiRkREREREpGVMxIiIiIiIiLSMiRgREREREZGWMREjIiIiIiLSMiZiREREREREWsZEjIiIiIiISMuYiBEREREREWkZEzEiIiIiIiItYyJGRERERESkZUzEiIiIiIiItIyJGBERERERkZYxESMiIiIiItIyJmJERERERERaxkSMiIiIiIhIy5iIERERERERaRkTMSIiIiIiIi1jIkZERERERKRlTMSIiIiIiIi0jIkYERERERGRljERIyIiIiIi0jImYkRERERERFrGRIyIiIiIiEjLmIgRERERERFpGRMxPZOfn49r166hoKBA16EQEREREdELYiKmJ548eYJhw4ahevXqaNq0Ke7cuQMAGDNmDL766isdR0dEREREROXBRExPzJgxA+fPn8eRI0dgamoqTu/cuTO2bt2qw8iIiIiIiKi8quk6ANLMrl27sHXrVrRp0wYymUyc3rRpU9y8eVOHkRERERERUXmxRUxPpKWlwc7OTmV6Tk6OJDEjIiIiIqKKj4mYnmjZsiWio6PF/5XJ148//ghfX19dhUVERERERC+AXRP1xPz589G9e3dcvnwZBQUFWL58OS5fvoyTJ0/i6NGjug6PiIiIiIjKgS1ieqJdu3Y4d+4cCgoK4Onpid9//x12dnaIj4+Ht7e3rsMjIiIiIqJykAmCIOg6CKLXJSsrC3K5HJmZmbCystJ1OERERKQB1t9UFbBFTE/s3bsXBw4cUJl+4MAB7Nu3TwcRERERERHRi2IipiemT5+OwsJClemCIGD69Ok6iIiIiIiIiF4UEzE9cf36dXh4eKhMb9y4MW7cuKGDiIiIiIiI6EUxEdMTcrkct27dUpl+48YNmJub6yAiIiIiIiJ6UUzE9MS7776L8ePH4+bNm+K0GzduYNKkSXjnnXd0GBkREREREZUXEzE9sXDhQpibm6Nx48ZwdXWFq6srmjRpgpo1a2Lx4sW6Do+IiIiIiMqBiZiekMvlOHnyJKKjozFy5EhMmjQJsbGxOHToEKytrTVezrFjx9CzZ084OjpCJpNh165dkvnh4eGQyWSSV7du3SRlHj58iEGDBsHKygrW1tYYNmwYHj9+LClz4cIF+Pv7w9TUFE5OTli4cKFKLNu3b0fjxo1hamoKT09P7N27VzJfEATMmjULDg4OMDMzQ+fOnXH9+nWNPysRERERUUXFREyPyGQydO3aFVOmTMHo0aPRvn37ci8jJycHb7zxBr777rsSy3Tr1g3Jycnia8uWLZL5gwYNwqVLlxATE4OoqCgcO3YMw4cPF+dnZWWha9eucHZ2xt9//41FixZhzpw5WLt2rVjm5MmTGDBgAIYNG4azZ88iODgYwcHBSEhIEMssXLgQK1aswJo1a3Dq1CmYm5sjMDAQT58+LffnJiIiIiKqSPhAZz0SGxuL2NhYpKamQqFQSOb9/PPP5V6eTCbDzp07ERwcLE4LDw9HRkaGSkuZ0pUrV+Dh4YE///wTLVu2BADs378fb7/9Nu7evQtHR0esXr0an376KVJSUmBsbAzg+fD7u3btwtWrVwEA/fr1Q05ODqKiosRlt2nTBl5eXlizZg0EQYCjoyMmTZqEyZMnAwAyMzNRu3ZtrF+/Hv3799foM/KBkERERPqH9TdVBWwR0xNz585F165dERsbiwcPHuDRo0eS16t05MgR2NnZoVGjRvj444+Rnp4uzouPj4e1tbWYhAFA586dYWBggFOnToll2rdvLyZhABAYGIhr166JscbHx6Nz586S9QYGBiI+Ph4AkJiYiJSUFEkZuVwOHx8fsYw6eXl5yMrKkryIiIiIiCqaaroOgDSzZs0arF+/HoMHD36t6+nWrRtCQkLg6uqKmzdv4pNPPkH37t0RHx8PQ0NDpKSkwM7OTvKeatWqwcbGBikpKQCAlJQUuLq6SsrUrl1bnFejRg2kpKSI04qWKbqMou9TV0adBQsWYO7cuS/wyYmIiIiItIeJmJ7Iz8+Hn5/fa19P0S5/np6eaN68Odzc3HDkyBF06tTpta//Zc2YMQMTJ04U/8/KyoKTk5MOIyIiIiIiUsWuiXrigw8+wObNm7W+3vr166NWrVq4ceMGAMDe3h6pqamSMgUFBXj48CHs7e3FMvfv35eUUf5fVpmi84u+T10ZdUxMTGBlZSV5ERERERFVNGwR0xNPnz7F2rVrcfDgQTRv3hxGRkaS+d98881rWe/du3eRnp4OBwcHAICvry8yMjLw999/w9vbGwBw6NAhKBQK+Pj4iGU+/fRTPHv2TIwzJiYGjRo1Qo0aNcQysbGxGD9+vLiumJgY+Pr6AgBcXV1hb2+P2NhYeHl5AXjeunXq1Cl8/PHHr+WzEhERERFpCxMxPXHhwgUxISk6xDvwfPRDTT1+/Fhs3QKeD4px7tw52NjYwMbGBnPnzkXv3r1hb2+PmzdvYurUqXB3d0dgYCAAoEmTJujWrRs+/PBDrFmzBs+ePcPo0aPRv39/ODo6AgAGDhyIuXPnYtiwYZg2bRoSEhKwfPlyLF26VFzvuHHj0KFDByxZsgRBQUH47bff8Ndff4lD3MtkMowfPx5ffPEFGjRoAFdXV8ycOROOjo6SUR6JiIiIiPSSQFXK4cOHBQAqr7CwMOHJkydC165dBVtbW8HIyEhwdnYWPvzwQyElJUWyjPT0dGHAgAGChYWFYGVlJQwZMkTIzs6WlDl//rzQrl07wcTERKhTp47w1VdfqcSybds2oWHDhoKxsbHQtGlTITo6WjJfoVAIM2fOFGrXri2YmJgInTp1Eq5du1auz5uZmSkAEDIzM8v1PiIiItId1t9UFfA5YlSp8TkkRERE+of1N1UF7JqoR/766y9s27YNd+7cQX5+vmReZGSkjqIiItK+wsJCxMXFITk5GQ4ODvD394ehoaGuwyIiItIYR03UE7/99hv8/Pxw5coV7Ny5E8+ePcOlS5dw6NAhyOVyXYdHRKQ1kZGRcHd3R0BAAAYOHIiAgAC4u7vzghQREekVJmJ6Yv78+Vi6dCn27NkDY2NjLF++HFevXkXfvn1Rr149XYdHRKQVkZGRCA0NhaenJ+Lj45GdnY34+Hh4enoiNDSUyRgREekN3iOmJ8zNzXHp0iW4uLigZs2aOHLkCDw9PXHlyhW89dZbSE5O1nWIFRL7mBNVHoWFhXB3d4enpyd27doFA4P/u5aoUCgQHByMhIQEXL9+nd0UifQc62+qCtgipidq1KiB7OxsAECdOnXEIewzMjLw5MkTXYZGRKQVcXFxSEpKwieffCJJwgDAwMAAM2bMQGJiIuLi4nQUIRERkeY4WIeeaN++PWJiYuDp6Yk+ffpg3LhxOHToEGJiYtCpUyddh0dE9NopW/6bNWumdr5yOnsIEBGRPmAipidWrlyJp0+fAgA+/fRTGBkZ4eTJk+jduzc+++wzHUdHRPT6OTg4AHj+UPs2bdqozFf2FFCWIyIiqsh4jxhVauxjTlR58B4xoqqD9TdVBbxHTE8YGhoiNTVVZXp6ejpPOIioSjA0NMSSJUsQFRWF4OBgyaiJwcHBiIqKwuLFi3lMJCIivcCuiXqipIbLvLw8GBsbazkaIiLdCAkJQUREBCZNmgQ/Pz9xuqurKyIiIhASEqLD6IiIiDTHRKyCW7FiBQBAJpPhxx9/hIWFhTivsLAQx44dQ+PGjXUVHhGR1oWEhODdd99FXFwckpOT4eDgAH9/f7aEERGRXmEiVsEtXboUwPMWsTVr1khONIyNjeHi4oI1a9boKjwiIp0wNDREx44ddR0GERHRC2MiVsElJiYCAAICAhAZGYkaNWroOCIiIiIiInpZHKxDTxw+fFiShBUWFuLcuXN49OiRDqMiIiIiIqIXwURMT4wfPx4//fQTgOdJWPv27dGiRQs4OTnhyJEjug2OiIiIiIjKhYmYnti+fTveeOMNAMCePXuQlJSEq1evYsKECfj00091HB0REREREZUHEzE9kZ6eDnt7ewDA3r170adPHzRs2BBDhw7FxYsXdRwdERERERGVBxMxPVG7dm1cvnwZhYWF2L9/P7p06QIAePLkCYdsJiIiIiLSMxw1UU8MGTIEffv2hYODA2QyGTp37gwAOHXqFJ8jRkRERESkZ5iI6Yk5c+agWbNm+Pfff9GnTx+YmJgAeP4snenTp+s4OiIiIiIiKg+ZIAiCroMgel2ysrIgl8uRmZkJKysrXYdDREREGmD9TVUBW8QqsBUrVmD48OEwNTXFihUrSi07duxYLUVFREREREQviy1iFZirqyv++usv1KxZE66uriWWk8lkuHXrlhYj0x+8okZERKR/WH9TVcAWsQosMTFR7d9ERERERKTfOHw9ERERERGRlrFFrAKbOHGixmW/+eab1xgJERERERG9SkzEKrCzZ89K/j9z5gwKCgrQqFEjAMA///wDQ0NDeHt76yI8IiIiIiJ6QUzEKrDDhw+Lf3/zzTewtLTEhg0bUKNGDQDAo0ePMGTIEPj7++sqRCIiIiIiegEcNVFP1KlTB7///juaNm0qmZ6QkICuXbvi3r17OoqsYuOoS0RERPqH9TdVBRysQ09kZWUhLS1NZXpaWhqys7N1EBEREREREb0oJmJ6olevXhgyZAgiIyNx9+5d3L17Fzt27MCwYcMQEhKi6/CIiIiIiKgceI+YnlizZg0mT56MgQMH4tmzZwCAatWqYdiwYVi0aJGOoyMiIiIiovLgPWJ6JicnBzdv3gQAuLm5wdzcXDL/7t27cHR0hIEBGzsB9jEnIiLSR6y/qSpgi5ieMTc3R/PmzUuc7+HhgXPnzqF+/fpajIqISLsKCwsRFxeH5ORkODg4wN/fH4aGhroOi4iISGNsNqlk2MBJRJVdZGQk3N3dERAQgIEDByIgIADu7u6IjIzUdWhEREQaYyJGRER6IzIyEqGhofD09ER8fDyys7MRHx8PT09PhIaGMhkjIiK9wUSMiIj0QmFhISZNmoQePXpgx44dePr0Kfbs2YOnT59ix44d6NGjByZPnozCwkJdh0pERFQmJmJERKQX4uLikJSUBD8/PzRs2FDSNbFhw4bw9fVFYmIi4uLidB0qERFRmZiIVTIymUzXIRARvRbJyckAgE8++URt18RPP/1UUo6IiKgi46iJlQwH6yCiysrOzg4A0LZtW+zatUt8TEebNm2wa9cudOjQAcePHxfLERERVWRMxPTQv//+CwBwcnJSmXf58mU4OjpqOyQiIp3jhSgiItIn7JqoJwoKCjBz5kzI5XK4uLjAxcUFcrkcn332GZ49eyaWc3Jy4rN0iKhSSk1NBQAcP34cwcHBkq6JwcHBOHHihKQcERFRRcZETE+MGTMGa9euxcKFC3H27FmcPXsWCxcuxE8//YSxY8fqOjwiotfOwcEBALBgwQJcvHgRfn5+sLKygp+fHxISEjB//nxJOSIioopMJrAvh16Qy+X47bff0L17d8n0vXv3YsCAAcjMzNRRZBVbVlYW5HI5MjMzYWVlpetwiOglFBYWwt3dHZ6entixYwdOnDiB5ORkODg4oG3btujduzcSEhJw/fp19gwg0nOsv6kqYIuYnjAxMYGLi4vKdFdXVxgbG2s/ICIiLTM0NMSSJUsQFRWF3r17w8TEBD169ICJiQl69+6NqKgoLF68mEkYERHpBSZiemL06NGYN28e8vLyxGl5eXn48ssvMXr0aB1GRkSkPSEhIYiIiFDbNTEiIgIhISG6DpGIiEgj7JqoJ3r16oXY2FiYmJjgjTfeAACcP38e+fn56NSpk6RsZGSkLkKskNi1gahyKiwsRFxcnNg10d/fny1hRJUI62+qCjh8vZ6wtrZG7969JdPUDV9PRFQVGBoaomPHjroOg4iI6IUxEdMT69at03UIRERERET0ivAeMSIiIiIiIi1ji1gF1qJFC8TGxqJGjRp48803IZPJSix75swZLUZGREREREQvg4lYBfbuu+/CxMQEABAcHKzbYIiIiIiI6JXhqIl6oLCwECdOnEDz5s1hbW2t63D0CkddIiIi0j+sv6kq4D1iesDQ0BBdu3bFo0ePXnpZx44dQ8+ePeHo6AiZTIZdu3ZJ5guCgFmzZsHBwQFmZmbo3Lkzrl+/Linz8OFDDBo0CFZWVrC2tsawYcPw+PFjSZkLFy7A398fpqamcHJywsKFC1Vi2b59Oxo3bgxTU1N4enpi79695Y6FiIiIiEgfMRHTE82aNcOtW7deejk5OTl444038N1336mdv3DhQqxYsQJr1qzBqVOnYG5ujsDAQDx9+lQsM2jQIFy6dAkxMTGIiorCsWPHMHz4cHF+VlYWunbtCmdnZ/z9999YtGgR5syZg7Vr14plTp48iQEDBmDYsGE4e/YsgoODERwcjISEhHLFQkRERESklwTSC/v27RO8vLyEPXv2CPfu3RMyMzMlrxcBQNi5c6f4v0KhEOzt7YVFixaJ0zIyMgQTExNhy5YtgiAIwuXLlwUAwp9//imJTSaTCf/9958gCIKwatUqoUaNGkJeXp5YZtq0aUKjRo3E//v27SsEBQVJ4vHx8RFGjBihcSyayMzMFAC88DYiIiIi7WP9TVUBW8T0xNtvv43z58/jnXfeQd26dVGjRg3UqFED1tbWqFGjxitZR2JiIlJSUtC5c2dxmlwuh4+PD+Lj4wEA8fHxsLa2RsuWLcUynTt3hoGBAU6dOiWWad++PYyNjcUygYGBuHbtmti9Mj4+XrIeZRnlejSJRZ28vDxkZWVJXkREREREFQ1HTdQT69atg5OTEwwNDSXTFQoF7ty580rWkZKSAgCoXbu2ZHrt2rXFeSkpKbCzs5PMr1atGmxsbCRlXF1dVZahnFejRg2kpKSUuZ6yYlFnwYIFmDt3btkfloiIiIhIh5iI6YmhQ4ciOTlZJQlKT09H586dERYWpqPIKpYZM2Zg4sSJ4v9ZWVlwcnLSYURERERERKrYNVFPCIKg9oHOjx8/hqmp6StZh729PQDg/v37kun3798X59nb2yM1NVUyv6CgAA8fPpSUUbeMousoqUzR+WXFoo6JiQmsrKwkLyIiIiKiioYtYhWcsnVHJpNh5syZqF69ujivsLAQp06dgpeX1ytZl6urK+zt7REbGysuMysrC6dOncLHH38MAPD19UVGRgb+/vtveHt7AwAOHToEhUIBHx8fscynn36KZ8+ewcjICAAQExODRo0aifez+fr6IjY2FuPHjxfXHxMTA19fX41jISIiIiLSV0zEKrizZ88CeN4idvHiRckAGMbGxnjjjTcwefJkjZf3+PFj3LhxQ/w/MTER586dg42NDerVq4fx48fjiy++QIMGDeDq6oqZM2fC0dERwcHBAIAmTZqgW7du+PDDD7FmzRo8e/YMo0ePRv/+/eHo6AgAGDhwIObOnYthw4Zh2rRpSEhIwPLly7F06VJxvePGjUOHDh2wZMkSBAUF4bfffsNff/0lDnEvk8nKjIWIiIiISG/pethG0kx4ePgrGcL18OHDAgCVV1hYmCAIz4eNnzlzplC7dm3BxMRE6NSpk3Dt2jXJMtLT04UBAwYIFhYWgpWVlTBkyBAhOztbUub8+fNCu3btBBMTE6FOnTrCV199pRLLtm3bhIYNGwrGxsZC06ZNhejoaMl8TWIpC4e/JSIi0j+sv6kqkAmCIOgwDyR6rbKysiCXy5GZmcn7xYiIiPQE62+qCjhYBxERERERkZYxESMiIiIiItIyJmJERERERERaxkSMiIiIiIhIyzh8PRER6Z3CwkLExcUhOTkZDg4O8Pf3h6Ghoa7DIiIi0hhbxIiISK9ERkbC3d0dAQEBGDhwIAICAuDu7o7IyEhdh0ZERKQxtogREZHeiIyMRGhoKIKCgjBlyhSYmZkhNzcX+/btQ2hoKCIiIhASEqLrMImIiMrE54hRpcbnkBBVHoWFhXB3d0etWrWQlpaG27dvi/OcnZ1ha2uL9PR0XL9+nd0UifQc62+qCtgiRkREeiEuLg5JSUlISkqCmZmZZF5qaqqYmMXFxaFjx446iJCIiEhzvEeMiIj0wn///Sf+3alTJ8THxyM7Oxvx8fHo1KmT2nJEREQVFVvEiIhIL6SkpAAAmjdvjt27d8PA4Pm1xDZt2mD37t3w8vLCxYsXxXJEREQVGVvEiIhILzx8+BAAYG5urna+crqyHBERUUXGRIyIiPSCsgXsjz/+QHBwsKRrYnBwME6dOiUpR0REVJGxtiIiIr2gHICjUaNGuHjxIvz8/GBlZQU/Pz8kJCSgUaNGknJEREQVGYevp0qNw98SVR6FhYVwcHBAWloagoKC0L17d8lzxKKjo2FnZ4d79+5x+HoiPcf6m6oCDtZBRER6wdDQEGvWrEHv3r1x6NAhREdHi/OqV68OAFi9ejWTMCIi0gvsmkhERHojJCQEO3bsgJ2dnWS6nZ0dduzYgZCQEB1FRkREVD7smkiVGrs2EFVOhYWFiIuLQ3JyMhwcHODv78+WMKJKhPU3VQXsmkhERHrH0NCQg3IQEZFeYyJGRER6hy1iRESk73iPGBER6ZXIyEi4u7sjICAAAwcOREBAANzd3REZGanr0IiIiDTGRIyIiPRGZGQkQkND4enpKXmgs6enJ0JDQ5mMERGR3uBgHVSp8WZfosqjsLAQ7u7u8PT0xK5du2Bg8H/XEhUKBYKDg5GQkIDr16+zmyKRnmP9TVUBW8SIiEgvxMXFISkpCZ988okkCQMAAwMDzJgxA4mJiYiLi9NRhERERJpjIkZERHohOTkZANCsWTO185XTleWIiIgqMiZiRESkFxwcHAAACQkJaucrpyvLERERVWRMxIiISC/4+/vDxcUF8+fPh0KhkMxTKBRYsGABXF1d4e/vr6MIiYiINMdEjIiI9IKhoSGWLFmCqKgoBAcHS0ZNDA4ORlRUFBYvXsyBOoiISC/wgc5ERKQ3QkJCEBERgUmTJsHPz0+c7urqioiICISEhOgwOiIiIs1x+Hqq1Dj8LVHllJ+fj1WrVuHmzZtwc3PDyJEjYWxsrOuwiOgVYf1NVQFbxIiISK9ERkZi0qRJSEpKEqctX74cS5YsYYsYERHpDSZiRESkNyIjIxEaGoqgoCBMmTIFZmZmyM3Nxb59+xAaGsruiUREpDfYNZEqNXZtIKo8CgsL4e7ujlq1aiEtLQ23b98W5zk7O8PW1hbp6em4fv06B+wg0nOsv6kqYIsYERHphbi4OCQlJSEpKQlmZmaSeampqWJiFhcXh44dO+ogQiIiIs1x+HoiItIL//33n/h3p06dJMPXd+rUSW05IiKiiootYkREpBdSUlIAAM2bN8fu3bthYPD8WmKbNm2we/dueHl54eLFi2I5IiKiiowtYkREpBcePnwIADA3N1c7XzldWY6IiKgiYyJGRER6QdkC9scffyA4OFjSNTE4OBinTp2SlCMiIqrIWFsREZFeUA7A0ahRI1y4cAF+fn6wsrKCn58fLl68iEaNGknKERERVWQcvp4qNQ5/S1R5FBYWwsHBAWlpaZDJZChafSn/t7Ozw7179zh8PZGeY/1NVQFbxIiISC8YGhrCz88PAFD8GqLyf19fXyZhRESkF5iIERGRXsjPz0d0dDTkcjmcnJwk8+rVqwe5XI7o6Gjk5+frKEIiIiLNMREjIiK9sGrVKhQUFGDx4sVITEzE4cOHsXnzZhw+fBi3bt3CwoULUVBQgFWrVuk6VCIiojLxOWJERKQXbt68CQDo0aMHDA0NVQbl6NGjh6QcERFRRcYWMSIi0gtubm4AgKioKLXzldOV5YiIiCoyjppIlRpHXSKqPPLz82Fubo6aNWvi7t27qFbt/zp1FBQUoG7dukhPT0dOTg6MjY11GCkRvSzW31QVsEWMiIj0grGxMSZMmID79++jbt26WLt2Le7du4e1a9eibt26uH//PiZMmMAkjIiI9ALvESMiIr2xcOFCAMDSpUsxYsQIcXq1atUwZcoUcT4REVFFx66JVKmxawNR5ZSfn49Vq1bh5s2bcHNzw8iRI9kSRlSJsP6mqoAtYkREpHcyMzOxfPlypKWlwdbWFoMGDYKtra2uwyIiItIYEzEiItIr1tbWyMzMFP/PycmBnZ0d5HI5MjIydBcYERFROXCwDiIi0hvFk7CiMjMzYW1trd2AiIiIXhATMSIi0gtpaWklJmFKmZmZSEtL01JEREREL46JGKmYM2cOZDKZ5NW4cWNx/tOnTzFq1CjUrFkTFhYW6N27N+7fvy9Zxp07dxAUFITq1avDzs4OU6ZMQUFBgaTMkSNH0KJFC5iYmMDd3R3r169XieW7776Di4sLTE1N4ePjg9OnT7+Wz0xEFV/Lli0l/w8ePBjnz5/H4MGDSy1HRERUETERI7WaNm2K5ORk8XX8+HFx3oQJE7Bnzx5s374dR48exb179xASEiLOLywsRFBQEPLz83Hy5Els2LAB69evx6xZs8QyiYmJCAoKQkBAAM6dO4fx48fjgw8+wIEDB8QyW7duxcSJEzF79mycOXMGb7zxBgIDA5GamqqdjUBEFcp///0n/p2bm4tffvkFzZs3xy+//ILc3Fy15YiIiCoqDl9PKubMmYNdu3bh3LlzKvMyMzNha2uLzZs3IzQ0FABw9epVNGnSBPHx8WjTpg327duHHj164N69e6hduzYAYM2aNZg2bRrS0tJgbGyMadOmITo6GgkJCeKy+/fvj4yMDOzfvx8A4OPjg1atWmHlypUAAIVCAScnJ4wZMwbTp09XG3teXh7y8vLE/7OysuDk5MThb4kqAZlMBgCwsLBAdna2ynxzc3M8efIEAMCqjUi/cfh6qgrYIkZqXb9+HY6Ojqhfvz4GDRqEO3fuAAD+/vtvPHv2DJ07dxbLNm7cGPXq1UN8fDwAID4+Hp6enmISBgCBgYHIysrCpUuXxDJFl6Eso1xGfn4+/v77b0kZAwMDdO7cWSyjzoIFCyCXy8WXk5PTS24JIqoojIyMAACPHz9GVlaWZF5WVpaYhCnLERERVWRMxEiFj48P1q9fj/3792P16tVITEyEv78/srOzkZKSAmNjY5WRyWrXro2UlBQAQEpKiiQJU85XziutTFZWFnJzc/HgwQMUFhaqLaNchjozZsxAZmam+Pr3339faBsQUcUTGBgo/i2Xy+Hh4YGdO3fCw8MDcrlcbTkiIqKKis8RIxXdu3cX/27evDl8fHzg7OyMbdu2wczMTIeRlc3ExAQmJia6DoOIXoMtW7bA0tJS/P/KlSuS+1OLliMiIqro2CJGZbK2tkbDhg1x48YN2NvbIz8/X+Whqffv34e9vT0AwN7eXmUUReX/ZZWxsrKCmZkZatWqBUNDQ7VllMsgoqrFwsICrVq1KrVMq1atYGFhoaWIiIiIXhwTMSrT48ePcfPmTTg4OMDb2xtGRkaIjY0V51+7dg137tyBr68vAMDX1xcXL16UjG4YExMDKysreHh4iGWKLkNZRrkMY2NjeHt7S8ooFArExsaKZYio6jl9+nSJyVirVq34iAsiItIb7JpIKiZPnoyePXvC2dkZ9+7dw+zZs2FoaIgBAwZALpdj2LBhmDhxImxsbGBlZYUxY8bA19cXbdq0AQB07doVHh4eGDx4MBYuXIiUlBR89tlnGDVqlNht8KOPPsLKlSsxdepUDB06FIcOHcK2bdsQHR0txjFx4kSEhYWhZcuWaN26NZYtW4acnBwMGTJEJ9uFiCqG06dP4/Hjxxg8eDBu3rwJNzc3bNy4kS1hRESkV5iIkYq7d+9iwIABSE9Ph62tLdq1a4c//vgDtra2AIClS5fCwMAAvXv3Rl5eHgIDA7Fq1Srx/YaGhoiKisLHH38MX19fmJubIywsDJ9//rlYxtXVFdHR0ZgwYQKWL1+OunXr4scff5TcZN+vXz+kpaVh1qxZSElJgZeXF/bv368ygAcRVT0WFhbYuXOnrsMgIiJ6YXyOGFVqfA4JERGR/mH9TVUB7xEjIiIiIiLSMiZiREREREREWsZ7xIiISO8UFhYiLi4OycnJcHBwgL+/PwwNDXUdFhERkcbYIkZERHolMjIS7u7uCAgIwMCBAxEQEAB3d3dERkbqOjQiIiKNMREjIiK9ERkZidDQUHh6eiI+Ph7Z2dmIj4+Hp6cnQkNDmYwREZHe4KiJVKlx1CWiyqOwsBDu7u7w9PTErl27YGDwf9cSFQoFgoODkZCQgOvXr7ObIpGeY/1NVQFbxIiISC/ExcUhKSkJn3zyiSQJAwADAwPMmDEDiYmJiIuL01GEREREmmMiRkREeiE5ORkA0KxZM7XzldOV5YiIiCoyJmJERKQXHBwcAAAJCQlq5yunK8sRERFVZEzEiIhIL/j7+8PFxQXz58+HQqGQzFMoFFiwYAFcXV3h7++vowiJiIg0x0SMiIj0gqGhIZYsWYKoqCgEBwdLRk0MDg5GVFQUFi9ezIE6iIhIL/CBzkREpDdCQkIQERGBSZMmwc/PT5zu6uqKiIgIhISE6DA6IiIizXH4eqrUOPwtUeVUWFiIuLg4JCcnw8HBAf7+/mwJI6pEWH9TVcAWMSIi0jv5+fmIiIjA9evX0aBBA/j4+MDMzEzXYREREWmMLWJUqfGKGlHlExwcjN27d6tMf/fdd7Fr1y7tB0RErxzrb6oKOFgHERHpjZKSMADYvXs3goODtRsQERHRC2KLGFVqvKJGVHnk5uaievXqZZZ78uQJuykS6TnW31QVsEWMiIj0wrhx48S/DQ0NMX36dNy4cQPTp0+XDNRRtBwREVFFxUSMiIj0wt69e8W/s7OzERgYiNOnTyMwMBDZ2dlqyxEREVVUHDWRiIj0QkZGBgDAxcUFHh4eSEpKEue5uLigXr16uHPnjliOiIioImOLGBER6QW5XA4ASEpKgoeHB+Lj45GdnY34+Hh4eHjgzp07knJEREQVGRMxIiLSC927dxf/PnjwIHbu3Il79+5h586dOHjwoNpyREREFRVHTaRKjaMuEVUeBw4cQLdu3cost3//fgQGBmohIiJ6XVh/U1XAFjEiItILDx8+fKXliIiIdImJGBER6QUHBwcAgL+/v9r5yunKckRERBUZEzEiItIL/v7+cHFxgbW1NR4/foxRo0aha9euGDVqFB4/fgxra2u4urqWmKgRERFVJBy+noiI9IKhoSGWLFmC0NBQDBgwADNmzECzZs2QkJCAAQMGICoqChEREZKHOxMREVVUTMSIiEhvhISEICIiApMmTYKfn5843dXVFREREQgJCdFhdERERJrjqIlUqXHUJaLKqbCwEHFxcUhOToaDgwP8/f3ZEkZUibD+pqqALWJERKR3DA0N0bFjR12HQURE9MI4WAcREREREZGWMREjIiIiIiLSMiZiREREREREWsZEjIiIiIiISMuYiBEREREREWkZEzEiIiIiIiItYyJGRERERESkZUzEiIiIiIiItIyJGBERERERkZYxESMiIiIiItIyJmJERERERERaxkSMiIiIiIhIy5iIERERERERaRkTMSIiIiIiIi1jIkZERERERKRlTMSIiIiIiIi0jIkYERERERGRljERIyIiIiIi0jImYkRERERERFrGRIyIiIiIiEjLmIgRERERERFpWTVdB0BERFReMplMZZogCDqIhIiI6MWwRYyIiPSKuiSstOlEREQVERMx0gvfffcdXFxcYGpqCh8fH5w+fVrXIRGRDpSVbDEZIyIifcFEjCq8rVu3YuLEiZg9ezbOnDmDN954A4GBgUhNTdV1aESkRcWTLEEQxFdp5YiIiCoimcBO9VTB+fj4oFWrVli5ciUAQKFQwMnJCWPGjMH06dMlZfPy8pCXlyf+n5WVBScnJ2RmZsLKykqrcRNVJg+S/0Xczp9eybKePMnBzZu3yv2+Xbt2iX8HBweXe74m3Nzqo3p18xd6r1KdOo5o3f09wLj6Sy2HqCrLysqCXC5n/U2VGhMxqtDy8/NRvXp1RERESE6swsLCkJGRgd27d0vKz5kzB3PnzlVZDg/kRC9n56o56JW6VNdh6I3Erhvg6hes6zCI9BYTMaoKOGoiVWgPHjxAYWEhateuLZleu3ZtXL16VaX8jBkzMHHiRPF/ZYsYEb0c/17DsHPnq1lWlWgRa9n1pZZBRESVHxMxqlRMTExgYmKi6zCIKp1aDk7oNXKOTmOYveb/7v06uyZScm9Y8fvCzqzeobW4iIiIXgQTMarQatWqBUNDQ9y/f18y/f79+7C3t9dRVESkC4IgSBKukgblYI97IiLSBxw1kSo0Y2NjeHt7IzY2VpymUCgQGxsLX19fHUZGRLpQVpLFJIyIiPQFW8Sowps4cSLCwsLQsmVLtG7dGsuWLUNOTg6GDBmi69CISAeKt4wVnU5ERKQvmIhRhdevXz+kpaVh1qxZSElJgZeXF/bv368ygAcRVR1MuoiISN9x+Hqq1Dj8LRERkf5h/U1VAe8RIyIiIiIi0jImYkRERERERFrGRIyIiIiIiEjLmIgRERERERFpGRMxIiIiIiIiLWMiRkREREREpGVMxIiIiIiIiLSMiRgREREREZGWVdN1AESvk/J55VlZWTqOhIiIiDSlrLeV9ThRZcREjCq17OxsAICTk5OOIyEiIqLyys7Ohlwu13UYRK+FTOClBqrEFAoF7t27B0tLS8hkMl2HQ0SvUFZWFpycnPDvv//CyspK1+EQ0SskCAKys7Ph6OgIAwPeSUOVExMxIiLSS1lZWZDL5cjMzGQiRkREeoeXGIiIiIiIiLSMiRgREREREZGWMREjIiK9ZGJigtmzZ8PExETXoRAREZUb7xEjIiIiIiLSMraIERERERERaRkTMSIiIiIiIi1jIkZERERERKRlTMSIiIiIiIi0jIkYERG9kDlz5sDLy+u1rqNjx44YP368+L+LiwuWLVv2WtdJRESkDUzEiIhIonjyU5LJkycjNjb29QdUxJ9//onhw4drVJZJGxERVWTVdB0AERHpF0EQUFhYCAsLC1hYWGh13ba2tlpdHxER0evCFjEiIhKFh4fj6NGjWL58OWQyGWQyGdavXw+ZTIZ9+/bB29sbJiYmOH78uErXxPDwcAQHB2Pu3LmwtbWFlZUVPvroI+Tn52u07pycHLz//vuwsLCAg4MDlixZolKmaCuXIAiYM2cO6tWrBxMTEzg6OmLs2LEAnrfq3b59GxMmTBA/BwCkp6djwIABqFOnDqpXrw5PT09s2bJFso6OHTti7NixmDp1KmxsbGBvb485c+ZIymRkZGDEiBGoXbs2TE1N0axZM0RFRYnzjx8/Dn9/f5iZmcHJyQljx45FTk6ORtuBiIiqBiZiREQkWr58OXx9ffHhhx8iOTkZycnJcHJyAgBMnz4dX331Fa5cuYLmzZurfX9sbCyuXLmCI0eOYMuWLYiMjMTcuXM1WveUKVNw9OhR7N69G7///juOHDmCM2fOlFh+x44dWLp0Kb7//ntcv34du3btgqenJwAgMjISdevWxeeffy5+DgB4+vQpvL29ER0djYSEBAwfPhyDBw/G6dOnJcvesGEDzM3NcerUKSxcuBCff/45YmJiAAAKhQLdu3fHiRMn8Ouvv+Ly5cv46quvYGhoCAC4efMmunXrht69e+PChQvYunUrjh8/jtGjR2u0HYiIqGpg10QiIhLJ5XIYGxujevXqsLe3BwBcvXoVAPD555+jS5cupb7f2NgYP//8M6pXr46mTZvi888/x5QpUzBv3jwYGJR87e/x48f46aef8Ouvv6JTp04AnidDdevWLfE9d+7cgb29PTp37gwjIyPUq1cPrVu3BgDY2NjA0NAQlpaW4ucAgDp16mDy5Mni/2PGjMGBAwewbds28b0A0Lx5c8yePRsA0KBBA6xcuRKxsbHo0qULDh48iNOnT+PKlSto2LAhAKB+/friexcsWIBBgwaJ99k1aNAAK1asQIcOHbB69WqYmpqWug2JiKhqYIsYERFppGXLlmWWeeONN1C9enXxf19fXzx+/Bj//vtvqe+7efMm8vPz4ePjI06zsbFBo0aNSnxPnz59kJubi/r16+PDDz/Ezp07UVBQUOp6CgsLMW/ePHh6esLGxgYWFhY4cOAA7ty5IylXvMXPwcEBqampAIBz586hbt26YhJW3Pnz57F+/XrxHjoLCwsEBgZCoVAgMTGx1PiIiKjqYIsYERFpxNzcXNchSDg5OeHatWs4ePAgYmJiMHLkSCxatAhHjx6FkZGR2vcsWrQIy5cvx7Jly+Dp6Qlzc3OMHz9e5T624u+XyWRQKBQAADMzs1Ljevz4MUaMGCHer1ZUvXr1yvMRiYioEmMiRkREEsbGxigsLHyh954/fx65ublisvLHH3/AwsJCvM+sJG5ubjAyMsKpU6fEZOXRo0f4559/0KFDhxLfZ2Zmhp49e6Jnz54YNWoUGjdujIsXL6JFixZqP8eJEyfw7rvv4r333gPw/H6vf/75Bx4eHhp/xubNm+Pu3bv4559/1LaKtWjRApcvX4a7u7vGyyQioqqHXROJiEjCxcUFp06dQlJSEh48eCC2BGkiPz8fw4YNw+XLl7F3717Mnj0bo0ePLvX+MACwsLDAsGHDMGXKFBw6dAgJCQkIDw8v9X3r16/HTz/9hISEBNy6dQu//vorzMzM4OzsLH6OY8eO4b///sODBw8APL9fKyYmBidPnsSVK1cwYsQI3L9/X+PPBwAdOnRA+/bt0bt3b8TExCAxMRH79u3D/v37AQDTpk3DyZMnMXr0aJw7dw7Xr1/H7t27OVgHERFJMBEjIiKJyZMnw9DQEB4eHrC1tVW5f6o0nTp1QoMGDdC+fXv069cP77zzjsrQ7yVZtGgR/P390bNnT3Tu3Bnt2rWDt7d3ieWtra3xww8/oG3btmjevDkOHjyIPXv2oGbNmgCeDy6SlJQENzc38fljn332GVq0aIHAwEB07NgR9vb2CA4O1vjzKe3YsQOtWrXCgAED4OHhgalTp4qtb82bN8fRo0fxzz//wN/fH2+++SZmzZoFR0fHcq+HiIgqL5kgCIKugyAiIv0XHh6OjIwM7Nq1S9ehEBERVXhsESMiIiIiItIyJmJERPTa3blzRzKce/FXebo/EhERVQbsmkhERK9dQUEBkpKSSpzv4uKCatU4kC8REVUdTMSIiIiIiIi0jF0TiYiIiIiItIyJGBERERERkZYxESMiIiIiItIyJmJERERERERaxkSMiIiIiIhIy5iIERERERERaRkTMSIiIiIiIi37f8pNk7BOJJAaAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, create a query that filters by the 90th percentile.\n",
        "Note the use of the `--save`, and `--no-execute` functions.\n",
        "This tells JupySQL to store the query, but skips execution. It will be referenced in the next plotting call."
      ],
      "metadata": {
        "id": "CzZe8VgygJot"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%sql --save short_trips --no-execute\n",
        "SELECT *\n",
        "FROM 'https://d37ci6vzurychx.cloudfront.net/trip-data/yellow_tripdata_2021-01.parquet'\n",
        "WHERE trip_distance < 6.3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "id": "j9QKSvUWYn0K",
        "outputId": "d49f7622-6b98-4604-8e43-51ad799db52e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Skipping execution..."
            ],
            "text/html": [
              "<span style=\"None\">Skipping execution...</span>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To create a histogram, call `%sqlplot histogram` and pass the name of the table, the column to plot, and the number of bins.\n",
        "This uses `--with short_trips` so JupySQL uses the query defined previously and therefore only plots a subset of the data."
      ],
      "metadata": {
        "id": "Dbw06QARgNiX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%sqlplot histogram --table short_trips --column trip_distance --bins 10 --with short_trips"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "6rgP4x-NYpcE",
        "outputId": "0a4f0223-6d47-4bd3-df2e-2d502a385d63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': \"'trip_distance' from 'short_trips'\"}, xlabel='trip_distance', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Summary\n",
        "You now have the ability to alternate between SQL and Pandas in a simple and highly performant way! You can plot massive datasets directly through the engine (avoiding both the download of the entire file and loading all of it into Pandas in memory). Dataframes can be read as tables in SQL, and SQL results can be output into Dataframes. Happy analyzing!"
      ],
      "metadata": {
        "id": "exzkl7g47jja"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "lKGmZ3hDgRmW"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}