003 Lego Analysis.ipynb

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      "source": [
        "# Introduction\n",
        "\n",
        "Today we'll dive deep into a dataset all about LEGO. From the dataset we can ask whole bunch of interesting questions about the history of the LEGO company, their product offering, and which LEGO set ultimately rules them all:\n",
        "\n",
        "<ul type=\"square\">\n",
        "<li>What is the most enormous LEGO set ever created and how many parts did it have?</li>\n",
        "\n",
        "<li>How did the LEGO company start out? In which year were the first LEGO sets released and how many sets did the company sell when it first launched?</li>\n",
        "\n",
        "<li>Which LEGO theme has the most sets? Is it one of LEGO's own themes like Ninjago or a theme they licensed liked Harry Potter or Marvel Superheroes?</li>\n",
        "\n",
        "<li>When did the LEGO company really expand its product offering? Can we spot a change in the company strategy based on how many themes and sets did it released year-on-year?</li>\n",
        "\n",
        "<li>Did LEGO sets grow in size and complexity over time? Do older LEGO\n",
        "sets tend to have more or fewer parts than newer sets?</li>\n",
        "</ul>\n",
        "\n",
        "**Data Source**\n",
        "\n",
        "[Rebrickable](https://rebrickable.com/downloads/) has compiled data on all the LEGO pieces in existence. I recommend you use download the .csv files provided in this lesson."
      ]
    },
    {
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        "<img src=\"https://i.imgur.com/49FNOHj.jpg\">"
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      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
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        "id": "DQJpOoKbXpUi",
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      "execution_count": null,
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        {
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          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
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      ]
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    {
      "cell_type": "markdown",
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      "source": [
        "# Import Statements"
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    {
      "cell_type": "code",
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        "id": "z5Wk7rs-IquQ"
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      "source": [
        "import pandas as pd"
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      "outputs": []
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    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "# Data Exploration"
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      "source": [
        "**Challenge**: How many different colours does the LEGO company produce? Read the colors.csv file in the data folder and find the total number of unique colours. Try using the [.nunique() method](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.nunique.html?highlight=nunique#pandas.DataFrame.nunique) to accomplish this."
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      "cell_type": "code",
      "metadata": {
        "id": "yd4G9pK7IquU"
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      "source": [
        "colors = pd.read_csv('/content/data/colors.csv')"
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      "execution_count": 15,
      "outputs": []
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        "colors['name'].nunique()"
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      "execution_count": 16,
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        "**Challenge**: Find the number of transparent colours where <code>is_trans == 't'</code> versus the number of opaque colours where <code>is_trans == 'f'</code>. See if you can accomplish this in two different ways."
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        "colors['is_trans'].value_counts()"
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              "is_trans\n",
              "f    107\n",
              "t     28\n",
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      "source": [
        "### Understanding LEGO Themes vs. LEGO Sets"
      ]
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      "source": [
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      "source": [
        "The <code>sets.csv</code> data contains a list of sets over the years and the number of parts that each of these sets contained.\n",
        "\n",
        "**Challenge**: Read the sets.csv data and take a look at the first and last couple of rows."
      ]
    },
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        "sets = pd.read_csv('/content/data/sets.csv')"
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      "execution_count": 18,
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              "  set_num                        name  year  theme_id  num_parts\n",
              "0   001-1                       Gears  1965         1         43\n",
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              "2  0011-3  Castle 2 for 1 Bonus Offer  1987       199          0\n",
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              "summary": "{\n  \"name\": \"sets\",\n  \"rows\": 15710,\n  \"fields\": [\n    {\n      \"column\": \"set_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 15710,\n        \"samples\": [\n          \"5026-1\",\n          \"7691-1\",\n          \"21041-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 13437,\n        \"samples\": [\n          \"Wii U Starter Pack\",\n          \"Lego Mosaic Johnny Thunder\",\n          \"Ferrari F138\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 1949,\n        \"max\": 2021,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          2003,\n          1965,\n          1956\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"theme_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 201,\n        \"min\": 1,\n        \"max\": 709,\n        \"num_unique_values\": 571,\n        \"samples\": [\n          499,\n          507,\n          458\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_parts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 369,\n        \"min\": 0,\n        \"max\": 9987,\n        \"num_unique_values\": 1247,\n        \"samples\": [\n          112,\n          1044,\n          658\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
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        "id": "XprDBmzwIquk",
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        "outputId": "bc217a43-09f9-4e5d-895c-a15fe0a966fe"
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      "source": [
        "sets.tail()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           set_num                                 name  year  theme_id  \\\n",
              "15705      wwgp1-1  Wild West Limited Edition Gift Pack  1996       476   \n",
              "15706   XMASTREE-1                       Christmas Tree  2019       410   \n",
              "15707      XWING-1                  Mini X-Wing Fighter  2019       158   \n",
              "15708      XWING-2                    X-Wing Trench Run  2019       158   \n",
              "15709  YODACHRON-1      Yoda Chronicles Promotional Set  2013       158   \n",
              "\n",
              "       num_parts  \n",
              "15705          0  \n",
              "15706         26  \n",
              "15707         60  \n",
              "15708         52  \n",
              "15709        413  "
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"sets\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"set_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"XMASTREE-1\",\n          \"YODACHRON-1\",\n          \"XWING-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Christmas Tree\",\n          \"Yoda Chronicles Promotional Set\",\n          \"Mini X-Wing Fighter\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9,\n        \"min\": 1996,\n        \"max\": 2019,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1996,\n          2019,\n          2013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"theme_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 157,\n        \"min\": 158,\n        \"max\": 476,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          476,\n          410,\n          158\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_parts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 170,\n        \"min\": 0,\n        \"max\": 413,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          26,\n          413,\n          60\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ez-UXSMUIqum"
      },
      "source": [
        "**Challenge**: In which year were the first LEGO sets released and what were these sets called?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s2aL6qrGIqum",
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          "height": 206
        },
        "outputId": "712c2c69-b08f-49ab-c84d-6e5a43796797"
      },
      "source": [
        "sets.sort_values('year').head()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      set_num                               name  year  theme_id  num_parts\n",
              "9521  700.1-1         Extra-Large Gift Set (ABB)  1949       365        142\n",
              "9534  700.2-1               Large Gift Set (ABB)  1949       365        178\n",
              "9539  700.3-1              Medium Gift Set (ABB)  1949       365        142\n",
              "9544  700.A-1              Small Brick Set (ABB)  1949       371         24\n",
              "9545  700.B-1  Small Doors and Windows Set (ABB)  1949       371         12"
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              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a98a9f2e-1096-4173-b97a-a8e55330671e 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-a98a9f2e-1096-4173-b97a-a8e55330671e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4d0bc2d3-1176-45f6-a634-bd75155d7535\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4d0bc2d3-1176-45f6-a634-bd75155d7535')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4d0bc2d3-1176-45f6-a634-bd75155d7535 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"sets\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"set_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"700.2-1\",\n          \"700.B-1\",\n          \"700.3-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Large Gift Set (ABB)\",\n          \"Small Doors and Windows Set (ABB)\",\n          \"Medium Gift Set (ABB)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1949,\n        \"max\": 1949,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"theme_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 365,\n        \"max\": 371,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          371\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_parts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76,\n        \"min\": 12,\n        \"max\": 178,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JJoK3M8TBAVU"
      },
      "source": [
        "**Challenge**: How many different sets did LEGO sell in their first year? How many types of LEGO products were on offer in the year the company started?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-Tf1w7IBBg9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "4a8cc39c-5544-4cad-c67a-22af91583b61"
      },
      "source": [
        "sets[sets['year'] == 1949]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      set_num                               name  year  theme_id  num_parts\n",
              "9521  700.1-1         Extra-Large Gift Set (ABB)  1949       365        142\n",
              "9534  700.2-1               Large Gift Set (ABB)  1949       365        178\n",
              "9539  700.3-1              Medium Gift Set (ABB)  1949       365        142\n",
              "9544  700.A-1              Small Brick Set (ABB)  1949       371         24\n",
              "9545  700.B-1  Small Doors and Windows Set (ABB)  1949       371         12"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b8320f08-5855-4905-ad00-c7ede58c4216\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
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              "      <td>700.1-1</td>\n",
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              "      <td>1949</td>\n",
              "      <td>365</td>\n",
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              "    </tr>\n",
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              "      <th>9539</th>\n",
              "      <td>700.3-1</td>\n",
              "      <td>Medium Gift Set (ABB)</td>\n",
              "      <td>1949</td>\n",
              "      <td>365</td>\n",
              "      <td>142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9544</th>\n",
              "      <td>700.A-1</td>\n",
              "      <td>Small Brick Set (ABB)</td>\n",
              "      <td>1949</td>\n",
              "      <td>371</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9545</th>\n",
              "      <td>700.B-1</td>\n",
              "      <td>Small Doors and Windows Set (ABB)</td>\n",
              "      <td>1949</td>\n",
              "      <td>371</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b8320f08-5855-4905-ad00-c7ede58c4216')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-b8320f08-5855-4905-ad00-c7ede58c4216 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-b8320f08-5855-4905-ad00-c7ede58c4216');\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",
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              "\n",
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              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
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              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "summary": "{\n  \"name\": \"sets[sets['year'] == 1949]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"set_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"700.2-1\",\n          \"700.B-1\",\n          \"700.3-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Large Gift Set (ABB)\",\n          \"Small Doors and Windows Set (ABB)\",\n          \"Medium Gift Set (ABB)\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1949,\n        \"max\": 1949,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1949\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"theme_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 365,\n        \"max\": 371,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          371\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_parts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 76,\n        \"min\": 12,\n        \"max\": 178,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          178\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RJMMYQYqIquo"
      },
      "source": [
        "**Challenge**: Find the top 5 LEGO sets with the most number of parts."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toJvjRuQIqup",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "32321e69-af57-4583-f5ab-3b1108b4f58e"
      },
      "source": [
        "sets.sort_values('num_parts', ascending=False).head()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        set_num                           name  year  theme_id  num_parts\n",
              "15004  BIGBOX-1  The Ultimate Battle for Chima  2015       571       9987\n",
              "11183   75192-1          UCS Millennium Falcon  2017       171       7541\n",
              "10551   71043-1                Hogwarts Castle  2018       246       6020\n",
              "295     10256-1                      Taj Mahal  2017       673       5923\n",
              "221     10189-1                      Taj Mahal  2008       673       5922"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"sets\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"set_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"75192-1\",\n          \"10189-1\",\n          \"71043-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"UCS Millennium Falcon\",\n          \"Taj Mahal\",\n          \"The Ultimate Battle for Chima\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 2008,\n        \"max\": 2018,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2017,\n          2008,\n          2015\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"theme_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 240,\n        \"min\": 171,\n        \"max\": 673,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          171,\n          673,\n          571\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_parts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1765,\n        \"min\": 5922,\n        \"max\": 9987,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          7541,\n          5922,\n          6020\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uSyhOzAHIqur"
      },
      "source": [
        "**Challenge**: Use <code>.groupby()</code> and <code>.count()</code> to show the number of LEGO sets released year-on-year. How do the number of sets released in 1955 compare to the number of sets released in 2019?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "qjdrktZAIqus",
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          "height": 1000
        },
        "outputId": "fa724e92-2c6f-49bc-f6ea-4f891385b0bb"
      },
      "source": [
        "sets.groupby('year').count()['set_num']"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year\n",
              "1949      5\n",
              "1950      6\n",
              "1953      4\n",
              "1954     14\n",
              "1955     28\n",
              "       ... \n",
              "2017    786\n",
              "2018    816\n",
              "2019    840\n",
              "2020    674\n",
              "2021      3\n",
              "Name: set_num, Length: 71, dtype: int64"
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              "      <th>1949</th>\n",
              "      <td>5</td>\n",
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              "      <td>6</td>\n",
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              "      <td>18</td>\n",
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              "      <td>13</td>\n",
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              "      <th>1966</th>\n",
              "      <td>111</td>\n",
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              "      <th>1967</th>\n",
              "      <td>28</td>\n",
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              "      <th>1968</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1969</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
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              "      <th>1970</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1971</th>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
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              "      <th>1972</th>\n",
              "      <td>39</td>\n",
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              "      <th>1973</th>\n",
              "      <td>70</td>\n",
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              "      <th>1974</th>\n",
              "      <td>39</td>\n",
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              "      <th>1976</th>\n",
              "      <td>76</td>\n",
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              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1978</th>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1979</th>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1980</th>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1981</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1982</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1984</th>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1985</th>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1986</th>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1987</th>\n",
              "      <td>233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1988</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1989</th>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xJrmIOULIquv"
      },
      "source": [
        "**Challenge**: Show the number of LEGO releases on a line chart using Matplotlib. <br>\n",
        "<br>\n",
        "Note that the .csv file is from late 2020, so to plot the full calendar years, you will have to exclude some data from your chart. Can you use the slicing techniques covered in Day 21 to avoid plotting the last two years? The same syntax will work on Pandas DataFrames."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nckj4lSGIquw"
      },
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_ZqAN-5MIquz",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 571
        },
        "outputId": "051cb249-bcbf-4492-ff16-865124a5c806"
      },
      "source": [
        "sets_by_year = sets.groupby('year').count()\n",
        "plt.figure(figsize=(16,10))\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "# plt.plot(sets.groupby('year').count().index, sets.groupby('year').count()['set_num'])\n",
        "plt.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2])"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e6430837f70>]"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xrDeNYYXIqu1"
      },
      "source": [
        "### Aggregate Data with the Python .agg() Function\n",
        "\n",
        "Let's work out the number of different themes shipped by year. This means we have to count the number of unique theme_ids per calendar year."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "dc": {
          "key": "266a3f390c"
        },
        "tags": [
          "sample_code"
        ],
        "id": "qx8pTau4Iqu2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "c9ba3634-a5f5-4d01-ca6f-e6c33b299071"
      },
      "source": [
        "sets.groupby('year').count()['theme_id']"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "year\n",
              "1949      5\n",
              "1950      6\n",
              "1953      4\n",
              "1954     14\n",
              "1955     28\n",
              "       ... \n",
              "2017    786\n",
              "2018    816\n",
              "2019    840\n",
              "2020    674\n",
              "2021      3\n",
              "Name: theme_id, Length: 71, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>theme_id</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1949</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1950</th>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1953</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1954</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1955</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1956</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1957</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1958</th>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1959</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1960</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1961</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1962</th>\n",
              "      <td>41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1963</th>\n",
              "      <td>20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1964</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1965</th>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1966</th>\n",
              "      <td>111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1967</th>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1968</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1969</th>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1970</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1971</th>\n",
              "      <td>51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1972</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973</th>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975</th>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1976</th>\n",
              "      <td>76</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1978</th>\n",
              "      <td>78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1979</th>\n",
              "      <td>92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1980</th>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1981</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1982</th>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1983</th>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1984</th>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1985</th>\n",
              "      <td>168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1986</th>\n",
              "      <td>159</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1987</th>\n",
              "      <td>233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1988</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1989</th>\n",
              "      <td>134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1990</th>\n",
              "      <td>108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1991</th>\n",
              "      <td>148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1992</th>\n",
              "      <td>135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1993</th>\n",
              "      <td>137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1994</th>\n",
              "      <td>164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1995</th>\n",
              "      <td>175</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996</th>\n",
              "      <td>198</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1997</th>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1998</th>\n",
              "      <td>381</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1999</th>\n",
              "      <td>340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2000</th>\n",
              "      <td>367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2001</th>\n",
              "      <td>383</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2002</th>\n",
              "      <td>504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2003</th>\n",
              "      <td>441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2004</th>\n",
              "      <td>395</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2005</th>\n",
              "      <td>351</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2006</th>\n",
              "      <td>313</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2007</th>\n",
              "      <td>315</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>341</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>421</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>435</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>503</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>607</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>605</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>690</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>774</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>757</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>786</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>816</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>840</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>674</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BqF1o997Iqu3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bb267e4b-3645-4421-aa81-fd7f756c3137"
      },
      "source": [
        "sets.groupby('year').count()['theme_id'].nunique()"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "62"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "immCqqw1Iqu5"
      },
      "source": [
        "**Challenge**: Plot the number of themes released by year on a line chart. Only include the full calendar years (i.e., exclude 2020 and 2021)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2pamQEkIqu5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 577
        },
        "outputId": "ce3e79f6-eebd-42db-aafb-56a06872269b"
      },
      "source": [
        "# plt.figure(figsize=(16,10))\n",
        "# plt.xticks(fontsize=14)\n",
        "# plt.yticks(fontsize=14)\n",
        "# plt.plot(sets.groupby('year').count().index[:-2], sets.groupby('year').count().theme_id[:-2])\n",
        "themes_by_year = sets.groupby('year').agg({'theme_id': pd.Series.nunique})\n",
        "themes_by_year.rename(columns= {'theme_id':'nr_themes'}, inplace=True)\n",
        "plt.figure(figsize=(16,10))\n",
        "plt.xticks(fontsize=14)\n",
        "plt.yticks(fontsize=14)\n",
        "plt.xlabel('Year', fontsize=14)\n",
        "plt.ylabel('Number of Themes', fontsize=14)\n",
        "plt.plot(themes_by_year.index[:-2], themes_by_year.nr_themes[:-2])"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7e64306b1d50>]"
            ]
          },
          "metadata": {},
          "execution_count": 65
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uBbt9-lJIqu7"
      },
      "source": [
        "### Line Charts with Two Seperate Axes"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j7lQ_amFIqu7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "a12e4c1a-9ec9-40b3-9547-78d67a8d7861"
      },
      "source": [
        "axis_1 = plt.gca() #get the axis\n",
        "axis_2 = axis_1.twinx() #create another axis that shares the same x-axis\n",
        "axis_1.plot(sets_by_year.index[:-2], sets_by_year.set_num[:-2], color='g')\n",
        "axis_2.plot(themes_by_year.index[:-2], themes_by_year.nr_themes[:-2], color='b')\n",
        "axis_1.set_xlabel('Year')\n",
        "axis_1.set_ylabel('Number of Sets', color='green')\n",
        "axis_2.set_ylabel('Number of Themes', color='blue')"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Number of Themes')"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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AqhFwhw5q/9b//S/z60qvOduQZE4IIYTIB86dUxvdOztDkybZn/t4MgfQtUpX5naeC8CJOycA+KLlF7g7uQNqHd6//0JoKFSrlvl1ZWTONiSZE0IIIfIB3ahc06aq0jQr92LucSPiBgA1/dJv3Nq7Zm9mdpgJqESvb82+6d53cEhtRZIZSeZsw8HWAQghhBDCfMaul6tQuAJezl4Z3n+n/ju0KNsCfw9/7O3sjYpBl8zJNGvOkmROCCGEyOOSkmDLFnVs7Hq5zAQVCzIpjrRr5lJSwE7m/3KE/DILIYQQedzBgxAerrbXqp11jgYYlsyZqmRJ1bYkIUH1vBM5Q5I5IYQQIo9bvVo9t2ypkqnsHAk5Aqi2JJbm4KASOpB1czlJkjkhhBAiD7t+Hb79Vh1365b9udEJ0Zy9dxaAWn61rBKPrJvLeZLMCSGEEHnYu+9CdLRqR9KzZ/bnHgs9hhYtfh5++Hn4WSUe3bo5GZnLOZLMCSGEEHnUihWwfLma3pw9+8kFB9ZcL6cj7UlyniRzQgghRB4UFQVDhqjj99/PupFvWvptvCSZy1ckmRNCCCHyoM8+U+vlypaFsWMN+4x+ZM4KxQ86smYu50kyJ4QQwmxareorJnLG0aPw3XfqeOZMtV/qkyQmJ3L8znHAuiNzaXvNabVWu41IQ5I5IYQQZgkLgxIlnrz4XlhGSgq89RYkJ0P37tChg2GfO33vNAnJCXg5exFYKNBq8ZUsqdbuxcWpfVyF9UkyJ4QQwiyHDsHt27BkCZw9a+to8r+ffoI9e8DTE77/3vDP6dbL1fKrhZ3Gen/9Ozmp5B5k3VxOkWROCCGEWe7eTT2eP992cRQEoaEwerQ6/uKL1KTJEDlRyaoj6+ZyliRzQgghzHLvXurxb7+p6T9hHe+/r6a169SBwYON+2xOJnPSay5nSTInhBDCLGlH5m7cSN3wXVhWdDQsXqyOZ89+8rZdaWm1Wv02Xtba+SEtaU+SsySZE0IIYRZdMqdLLmSq1Tpu3FDVoZ6eUL++cZ+9HHaZiPgInOydCCoWZJ0A05BkLmdJMieEEMIsumnWHj3U8z//QESE7eLJr27cUM+6jeyNoSt+qFa8Go72jhaMKnOyZi5nSTInhBDCLLqRuY4doVIliI2Fv/+2bUz5kVnJXA6ul4P0a+ak15z1STInhBDCLLpkrlgx6NtXHctUq+XlpWSuVCnQaFRin3ZNpbAOSeaEEEKYJW0y17u3+kt8+3a4fNm2ceU3lphmteY2Xmk5O0NAgDqWdXPWJ8mcEEIIk2m1qWvmihZViUarVurnBQtsF1d+ZGoyFxoVyu2o22jQUMO3huUDy4Ksm8s5kswJIYQwWVhYal+5YsXU8+uvq+f582W/VksyNZnTTbE+VeQpPJw8LBxV1qTXXM6RZE4IIYTJdFOsnp5qag2ga1f18+XLsHOn7WLLb0xO5nJ4ilVH2pPkHEnmhBBCmCztFKuOmxu89JI6lkIIy4iLS/21NnVkLqeKH3Qkmcs5kswJIYQwWdrih7R0Va1LlkBMTM7GlB/dvKmeXV2hUCHjPpuTOz+kJWvmco4kc0IIIUyWVTLXtCkEBkJkJCxblvNx5Tdpp1g1GsM/FxkfyfkH54GcH5nLbb3mkpOT+eSTTwgMDMTV1ZXy5cvz+eefo00TnFarZezYsfj7++Pq6krr1q05f/68DaM2jCRzQgghTJbZNCuAnV3q6Ny8eTkaUr5k6nq5o6FHASjhWYJi7sWecLZllS6tnqOj4f79HL11piZNmsSsWbOYMWMGp0+fZtKkSUyePJnp06frz5k8eTLTpk1j9uzZ7N27F3d3d9q2bUtcXJwNI38ySeaEEEKYLKuROYA+fdTzpk1w/bp145g0CUaNyr/Vs6Ymc7op1pwufgBwcQF/f3Vs7anWyMhIIiIi9I/4+PgM5+zevZvOnTvTsWNHypYty4svvkibNm3Yt28foEblpk6dypgxY+jcuTM1atRgwYIF3Lp1i+XLl1v3C5hJkjkhhBAmyy6ZCwyEhg3VFNuOHdaL4c4dGD0aJk+GNWusdx9bMjeZq+Vby6LxGCqniiCCgoLw9vbWPyZMmJDhnKeffppNmzZx7tw5AI4ePcrOnTtp3749AJcvXyYkJITWrVvrP+Pt7U3Dhg0JDg627hcwk4OtAxBCCJF3ZZfMAVSuDHv3Wvcv8127Uo8nTYJOnax3L1sxt8dcThc/6JQpA8HB1k/mTp06RYkSJfQ/O+v65KQxevRoIiIiqFy5Mvb29iQnJ/Pll1/Sq1cvAEJCQgDw9fVN9zlfX1/9e7mVJHNCCCFMltWaOZ2cqGhMm8zt3Am7d8PTT1vvfsY6eFAlte7upl/DlGQuMTmRE3dOALaZZoWcG5nz9PTEy8sr23P++usvFi1axOLFi6latSpHjhxh2LBhBAQE0Fe3wDOPkmlWIYQQJnvSyFxO/GWua0ysW581aZL17mWsv/+GevWgQwfz1vOZksyduXeGhOQEvJy9KOtT1vSbmyE3tSf58MMPGT16ND179qR69er07t2b4cOH66dk/fz8AAgNDU33udDQUP17uZUkc0IIIUxm62QuJgYOHVLHc+eqth0rVsCpU9a5nzG0WvjiC3W8fTv8+qtp10lIAF1+YUwyp1svV9O3JnYa2/x1n5saB8fExGBnl/7Xwd7enpRHWXZgYCB+fn5s2rRJ/35ERAR79+6lcePGORqrsSSZE0IIYZKYmNSGwFlNs+p6jV29ap1K0/37ITERAgKgTRvo0kW9/vXXlr+Xsdavh6NHU38eOVIVaxjr9m2VGDo5Zf3rnBlb7fyQVm7qNff888/z5Zdfsnr1aq5cucKyZcv49ttv6dq1KwAajYZhw4bxxRdfsGLFCo4fP06fPn0ICAigi+43Vi4lyZwQQgiT6NbLOTpCVsuVSpZUPefi401LZJ5Et16uSRM1KjdqlPp50aLUqUlb0U33DhkCtWrBw4fwwQfGX0f3PUqUUL+WhrLVzg9p6ZK5yEj1/W1p+vTpvPjii7zzzjtUqVKFDz74gDfffJPPP/9cf87IkSMZOnQogwYNon79+kRFRbFu3TpcXFxsGPmTSTInhBDCJGmnWLPalcDRMXVq0BpTbbr1ck2bqueGDaF5czVa9913lr+fofbtg61bwcFBjcjNmaN+jX77DbZsMe5apqyX02q1uSKZc3UFXXGordfNeXp6MnXqVK5evUpsbCwXL17kiy++wMnJSX+ORqNh/PjxhISEEBcXx8aNG3nqqadsGLVhJJkTQghhEt3IXFbr5XSstW4qJUVVroIamdPRjc79+KPtRoN0o3KvvgqlSkGDBvD22+q1t95SI5WGMiWZuxZ+jYdxD3G0c6Rq8aqGf9AKFi+GAwdURa+wDknmhBBCmEQ3MvekdVxp101Z0smTEB6uWn7UrJn6ert2UKMGREXBDz9Y9p6GOHs2dT/akSNTX//yS/Dzg3PnjKu4NSWZ043KBRULwsneKfuTraxlS6hbV43SCeuQZE4IIYRJnlTJqmOt9hS6KdZGjdR0po5Gk5pEff89xMZa9r5PMmWKWuzfqRNUTTMo5uOTOvX71Vdg6P7t5iRztpxiFTlHkjkhhBAmMTaZs/TInK74QbdeLq0ePdSI4N27MG+eZe+bndu3YcECdTx6dOZxtWmjplnfecewCk9TkrncUMkqco4kc0IIIUzypN0fdKw1zaobmUu7Xk7HwQHef18dT5kCSUmWvXdWpk5VfeGaNMk8Lo1GTf06O8PGjfD770++pozMiSeRZE4IIYRJTJlmtVSvsRs31PXs7NQ0a2b694ciReDSJfjnH8vcNzvh4TB7tjrWFWFkpnx5GDNGHQ8fDtHRWZ+blKRG+8DwZO5h7EOuhqs57Zp+NZ9wtsgPJJkTQghhEkOTuVKl1IhUbGzqZ8ylm2KtWRM8PTM/x90dhg5Vx5MmWb9p7ezZEBEBQUHQsWP25374oUrO7txJv7fs40JDITkZ7O1TW3w8iW5ULtAnEB8XH8M+JPI0SeaEEEKYxNDWJE5OquEtWG6qNbv1cmkNGQJubnD4sJrWtJa4ODXFCqr44knNfZ2doVkzdbx/f9bn6aZYAwJUQmcImWIteCSZE0IIYRJDW5OA5dfNZbdeLq0iReCNN9Sxoe1A7txRO0gYs87ut98gJESNtr3yimGfqV9fPRuSzBm1Xi70CCDJXEEiyZwQQgijJSXBgwfq+Ekjc2DZitbIyNQ9T5+UzAGMGKFGtTZtgoMHsz83OVlNkb72miqcMERycupesCNGqJFIQ+iSuQMHsj7HnOIHqWQtOCSZE0IIYbT799WzRgOFCz/5fEv2mtuzR+3+UKaMYUlOmTKpo2VPGp374YfU5GrqVDV9+iTLl6uecYUKwcCBTz5fp3ZtNR1782ZqkcPjjE3m4pPiOXX3FCAjcwWJJHNCCCGMplsvV6hQ+oa9WbHkyJyh6+XS0jUR/ucfuHAh83Nu3YKPP1bHjo6q+GD+/Oyvq9WmJoiDB4OHh+ExuburYgnIeqrV2GTu5N2TJKUkUdi1MCW9jBjOE3maJHNCCCGMZmglq44l18wZul4urerVoUMHNaKX1fTpsGFqCrdhw9QEbcoUNY2ala1bVSLm4pJaOWuMJ62bMzaZSzvFqtFojA9I5EmSzAkhhDCasclc2pE5c1qEJCWpaVYwbmQOUnu/zZunihXSWrsWlixRa+vmzIFBg9T08YULsHRp1tfUJX39+0Px4sbFA5ZP5g7fVjs/yBRrwSLJnBBCCKMZ2pZEp3Rp9RwTk7rezhRHj6omu97e6fc9NUSzZqrBcHw8TJuW+npMjJoiBXjvPdW7zt1dtTWBrHvUHTkC69erdW+63SaMlTaZe/weKSlqPR2oXn2GkErWgkmSOSGEEEYzpi0JqL5qAQHq2JypVt16uaeffnIvt8dpNKmjcz/8oBr8AnzxBVy+rBKmzz5LPX/oUHB1VRWwmzdnvN7kyer55ZehXDnjYtGpUUNVvz54oGJI6+5dSExU39PP78nXStGmcDRElflKJWvBIsmcEEIIoxk7zQqWWTdnynq5tF54ASpXVltvzZkDJ0+mthWZPj19AUPRojBggDp+vAr28mX48091rCuuMIWTkxoJhIxTrbopVj8/VZDxJJceXiIyIRJne2cqFa1kelAiz5FkTgghhNGMnWYF8ytatVrTKlnTsrNTW2kBfPcdvPWWWof3wgvQuXPG899/X62j27ABDh1Kff2bb9Q0aJs2qsWIObJaN2dq8UN13+o42BlQYizyDUnmhBBCGM3YaVYwv9fclSuqfYiDQ2oCZIpevdSU7+3baqTP3V2NymWmbFno0UMd66ZV796FX39Vx7ppW3NYOpmTKdaCR5I5IYQQRsvpadYbN1ITpzp11H6rpnJ2huHDU3/+7LPUAo3M6KZRlyyBixdV4hcbC/XqwbPPmh6HTr166vngwfRtUExN5qT4oeCRZE4IIYTRTEnmTJlmffhQJXEVK6pkClTbEHO9+abqPdeyJbz7bvbn1qwJ7dqpadXPPoMZM9Tro0apogpzVamiRgejo+Hs2dTXjW5LEiJtSQoqSeaEEEIYRatNXTNnyjSrIb3mYmPVtGa5cuo5Lk61Ftm9O7UowRyennDsmNqv1ZDiAt2o4G+/qQSzYkXo2tX8OECtyatTRx2nnWo1Jpm7E32HW5G30KChhm8NywQm8gxZISmEEMIoERGqZQYYNzKnm8qMilIJUVZ7uq5bB2+8kdpjrVo1mDhR7eBgq00NmjeHBg1g3z718wcfqCTMUurXhx07VDLXt696LbNk7p9T/7D/1n7KeJehrE9ZyvqUpYxPGX1LkopFKuLhZMSeYiJfsPnI3M2bN3nttdcoUqQIrq6uVK9enQO6XY4BrVbL2LFj8ff3x9XVldatW3P+/Pl013jw4AG9evXCy8sLHx8fBgwYQFRUVE5/FSGEKBB0U6zu7qoPm6FcXcHXVx1nNdWamAivvqoSuVKl1G4NR45Ax462S+RA3Xv0aHXs5wd9+lj2+o8XQWi1GZO5qIQoev7Tk0m7JvHOmnfosLgDQT8E4f6VO53/UKW4MsVaMNk0mXv48CFNmjTB0dGRtWvXcurUKb755hsKFSqkP2fy5MlMmzaN2bNns3fvXtzd3Wnbti1xcXH6c3r16sXJkyfZsGEDq1atYvv27QyyxKIKIYQQGZjSlkTnSevmtm9Xo3bFisG5c2qUypIjYObo0gX++ku1KXFxsey1dcnckSOQkKCaCOv+mtM1Wz577yxJKUl4OHnQuVJnavrWxMvZC4DYpFgAni1rgYoMkefYdJp10qRJlCpVirlz5+pfCwwM1B9rtVqmTp3KmDFj6PyoAdCCBQvw9fVl+fLl9OzZk9OnT7Nu3Tr2799PvUclQdOnT6dDhw5MmTKFAN3/BWnEx8cTHx+v/zkyMtJaX1EIIfIdU9qS6JQtC3v3Zp3M/fuven7+ecsnTObSaOCll6xz7XLl1LTzgwdw/LhqvwJqv1dnZ3V85t4ZAOr412F5z+X6z4bFhXEl7ArRCdE0KtnIOgGKXM2mI3MrVqygXr16vPTSSxQvXpzatWvz008/6d+/fPkyISEhtG7dWv+at7c3DRs2JDg4GIDg4GB8fHz0iRxA69atsbOzY+/evZned8KECXh7e+sfQUFBVvqGQgiR/5hSyaqTXa85rTY1mevSxZTI8i6NJrVFyf79ma+XO3tflbpWLlI53Wd9XHyo5VeLJqWbYG+XS4YxRY6yaTJ36dIlZs2aRcWKFVm/fj1vv/027777LvPnzwcgJCQEAF/dIotHfH199e+FhIRQvHjxdO87ODhQuHBh/TmP++ijjwgPD9c/Tp06ZemvJoQQ+ZY506zZ9Zo7cgSuXVM95NL8G77AeFIypxuZk626xONsOs2akpJCvXr1+OqrrwCoXbs2J06cYPbs2fTVlfNYgbOzM866cWsgQrfbshBCiCcyd5oVMk/mdKNybdoYV1iRX6QtgtCtEMp0ZK5oZYRIy6Yjc/7+/hmmOKtUqcK1a9cA8PPzAyA0NDTdOaGhofr3/Pz8uHPnTrr3k5KSePDggf4cIYQQlmOtadaCOsWqo0vmTp5MbR6sS+ZStCmcu38OgEpFZGROpGfTZK5JkyacTdvuGjh37hxlHo3DBwYG4ufnx6ZNm/TvR0REsHfvXho3bgxA48aNCQsL4+DBg/pzNm/eTEpKCg0bNsyBbyGEEAWLOcmcbpo1PBzCwlJfv3JFTbPa2ak2JAVRiRLg7692mli/Xr2mS+auhV8jLikOJ3snyvqUtVmMIneyaTI3fPhw9uzZw1dffcWFCxdYvHgxP/74I4MHDwZAo9EwbNgwvvjiC1asWMHx48fp06cPAQEBdHn0T7cqVarQrl07Bg4cyL59+9i1axdDhgyhZ8+emVayCiGEMI85a+bc3FI/l3aqdcUK9dy0qWnTt/mFbnROt/pHl8zp1stVLFxRihxEBjZN5urXr8+yZcv4/fffqVatGp9//jlTp06lV69e+nNGjhzJ0KFDGTRoEPXr1ycqKop169bhkqZmfdGiRVSuXJlWrVrRoUMHmjZtyo8//miLrySEEPmeOWvmIPN1cwV9ilVHl8zp6JK5s/dkvZzIms238+rUqROdOnXK8n2NRsP48eMZP358lucULlyYxYsXWyM8IYQQjzFnmhVUMrd/f+q6uQcPYNs2dfyopWiB9XgyV6KEetZXssp6uXwlOVn1FSxTBtLsl2A0m2/nJYQQIu+Ii1N7q4Lpydzj7UnWrFF/qVWrpprnFmRpWqZSuLCalgapZM0vhg2DX35Rx8nJas/fOnXU1nVbt5p+XUnmhBBCGEy3Xs7BAby9TbvG49OsMsWaqkgR0G2EJD3m8p+//4aaNdXxypVw+TKcOQPDh8PHH5t+XUnmhBBCGCztejlTN75Pm8zFxcHaterngj7FqqObatUlcxHxEdyOug3INGted+8e6LqmrVmjtod76ino319Nt5pKkjkhhBAGM3e9HKTvNbd5M0RHq7VhdeuaHV6+0LKleq5WTT3rih/8PPzwdjFxOFTkCr6+cOqUmmJdtw6ee069HhMD9mYUKdu8AEIIIUTeYU5bEh3dmrmHD2HBAnXcubPpI335zYABarRG1ypV1svlH/36wcsvq36CGk3qtnV790JlM/7zSjInhBDCYOa2JQHw8FBrw+7fV2uIQKZY03JwgGefTf1ZKlnzj08/VSOu16+rKVbdzqL29jB6tOnXlWROCCGEwSwxzQpqqvX+fTXd5OUFLVqYG1n+JSNz+cuLL6rnuLjU18zdjl7WzAkhhDCYJaZZIXXdHECHDuDkZN718jMZmcs/kpPh88/VGlEPD7h0Sb3+ySepLUtMIcmcEEIIg1limhVS182BTLFmJzklmfP3zwMyMpcffPklzJsHkyen/wdMtWrw88+mX1eSOSGEEAaz5DQrgKMjtG9v3rXys6vhV4lPjsfZ3pnS3qVtHY4w04IF8OOP0KtX+urVmjVVvzlTSTInhBDCYJaaZm3cWD1362Z68+GCQNeW5KkiT2FvZ0bvCpEr3LwJFSpkfD0lBRITTb+uFEAIIYQwmKWmWevVgwsXVIsGkTXZ+SF/CQqCHTvSLzMAVdVdu7bp1zU7mUtOSeb4neOU8S5DIVczdokVQgiRqyUnqwpUMH9kDqB8efOvkd/pkrnKRWS9XH4wdqyqXL15U43GLV0KZ8+q6ddVq0y/rtHTrMPWDeOXQ6rkIjklmebzmlNnTh1KfVeKrVe2mh6JEEKIXO3BA9Bq1XGRIraNpaDQtSWRkbn8oXNntSfrxo3g7q6Su9On1Wu63SBMYfTI3N+n/ua1Gq8BsPLcSi6HXebMkDP8dvQ3Pt78Mbv67zI9GiGEELmWbr1coUKqcEFYn35kTipZ841mzWDDBste0+iRuXsx9/DzULvErjm/hpeCXuKpIk/Rv3Z/joeasUusEEKIXM1S6+WEYcLiwgiNDgVUAYTIX6KiICIi/cNURidzvh6+nLp7iuSUZNZdWMdz5dS4YExijFTaCCFEPmaptiTCMLpK1gDPALycvWwcjbCEy5ehY0c1xertrUa5CxUCHx/1bCqjp1n71erHy0text/TH41GQ+tyapfYvTf3yjCwEELkY5ZqSyIMo18vJzs/5BuvvabWnf76K/j6gkZjmesancx92uJTqhWvxvXw67xU9SWcHdQusfYae0Y3MWOXWCGEELmaTLPmLFkvl/8cPQoHD0IlC+fnRidzC44uoEfVHvokTueV6q/wx4k/LBaYEEKI3EWmWXOWjMzlP/Xrw/XruSCZ6/dvP9pVaEdxh+LpXo+Mj6Tfv/3oU7OPxYITQgiRO9y9C2vWqGM/P9vGUlDIyFz+8/PP8NZbqs9ctWoZq8Jr1DDtukYnc1qtFg0ZJ3lvRNzA21n2ZBFCiPwmIkLtn3rhApQsCT172jqi/C8pJYkLDy4A0mMuP7l7Fy5ehH79Ul/TaNQ6Oo1GNeY2hcHJXO05tdGgQaPR0GpBKxzsUj+arE3m8sPLtKvQzrQohBBC5EqxsfDCC2qdT9Giqj+Wr6+to8r/roRdISE5ARcHF0p7l7Z1OMJC+vdX23b9/ruNCiC6VOoCwJGQI7Qt3xYPJw/9e072TpT1KUv3oO6WiUoIIYTNJSZCjx6wbRt4esK6dVBZZvxyhK4tyVNFnsJOY3QXMZFLXb0KK1ZAhQqWva7Bydy4FuMAKOtTlh7VeuDi4GLZSIQQQuQaKSkwYIDaZsjZWT3XrWvrqAoOWS9nHTdv3mTUqFGsXbuWmJgYKlSowNy5c6lXrx6glpKNGzeOn376ibCwMJo0acKsWbOoWLGiRe7fsqWqaLVZMqfTt1ZfwuLCWHhsIRcfXOTDJh9S2LUwh24fwtfdlxJeJSwboRBCiByl1cKwYfDbb2BvD0uWQPPmto6qYJFKVst7+PAhTZo04dlnn2Xt2rUUK1aM8+fPUyhNt97Jkyczbdo05s+fT2BgIJ988glt27bl1KlTuLiYP4j1/PMwfDgcPw7Vq2csgHjhBdOua3Qydyz0GK0XtMbbxZsrYVcYWHcghV0Ls/T0Uq6FX2NB1wWmRSKEECJXGD8epk9Xx/Pmqb+ARM7SjcxJMmc5kyZNolSpUsydO1f/WmBgoP5Yq9UydepUxowZQ+fOnQFYsGABvr6+LF++nJ4WqPx56y31PH58xvfMKYAweiJ++PrhvF7rdc4PPZ9uqrVDxQ5sv7rdtCiEEELkCufPw6efquNp01THepHzdCNzMs1qmMjISCIiIvSP+Pj4DOesWLGCevXq8dJLL1G8eHFq167NTz/9pH//8uXLhISE0Lp1a/1r3t7eNGzYkODgYIvEmZKS9cPURA5MSOYO3DrAm3XfzPB6Cc8ShESFmB6JEEIIm5s/Xz23awdDh9o2loLqYexD7kTfAVQBhHiyoKAgvL299Y8JEyZkOOfSpUv69W/r16/n7bff5t1332X+o9/0ISEqh/F9rFzb19dX/54lxcVZ7lpGT7M62zsTER+R4fVz989RzF3aggshRF6VkqLWyUH6Pli5yZWwK5TyKoW9nb2tQ7Ea3ahcCc8SeDp72jiavOHUqVOUKJG6Zt/Z2TnDOSkpKdSrV4+vvvoKgNq1a3PixAlmz55N3759cyTO5GT46iuYPRtCQ+HcOShXDj75BMqWVUVHpjB6ZO6FSi8wfvt4EpMTAdCg4Vr4NUZtHEX3KtKaRAgh8qqtW+HaNfDxMX0htjWtPreawO8D+WzbZ7YOxar06+WkWbDBPD098fLy0j8yS+b8/f0JCgpK91qVKlW4du0aAH6PtjYJDQ1Nd05oaKj+PXN9+aVahzp5Mjg5pb5erZraHcJURidz37T5hqiEKIpPKU5sYizN5zWnwrQKeDp78mXLL02PRAghhE3pplh79AALFO5Z3JYrWwDy/T7gh28fBqBqsao2jiR/adKkCWfPnk332rlz5yhTpgygiiH8/PzYtGmT/v2IiAj27t1L48aNLRLDggXw44/Qq5eqFNepWRPOnDH9ukZPs3q7eLOh9wZ2XdvF0dCjRCVEUce/Dq3LtX7yh4UQQuRKkZHw99/qOIdmnIx26eElAM4/OM/NiJv5thXWjms7AGhSqomNI8lfhg8fztNPP81XX33Fyy+/zL59+/jxxx/58ccfAdBoNAwbNowvvviCihUr6luTBAQE0KVLF4vEcPNm5j3mUlJUk25TGZ3M6TQp3YQmpeU3mhBC5Af//AMxMfDUU9Coka2jyZwumQM1SvdajfxXahsRH8HR0KMANC3d1MbR5C/169dn2bJlfPTRR4wfP57AwECmTp1Kr1699OeMHDmS6OhoBg0aRFhYGE2bNmXdunUW6TEHEBQEO3bAo8FAvb//Vtt8mcrgZC74ejD3Y+/T6alO+tcWHF3AuK3jiE6IpkvlLkxvPx1nh4zz1EIIIXI33RRr376W2y/SkrRabbpkbvPlzfkymQu+HkyKNoVAn8B8O/JoS506daJTp05Zvq/RaBg/fjzjM2sEZwFjx6r/x27eVKNxS5fC2bNq+nXVKtOva/CaufHbx3Pyzkn9z8dDjzNgxQBaB7ZmdNPRrDy3kgk7M5YCCyGEyN2uXFHFDxoN9O5t62gydz/2PpEJkfqfdevn8pud13YC0KxMMxtHIqyhc2e1Nd7GjeDurpK706fVa889Z/p1DR6ZOxJyhM+f/Vz/8x8n/qBhiYb89IJquFfKqxTjto7j0xafmh6NEEKIHLfg0cY9LVtCqVK2jSUrulG5wq6FiYiP4ErYFa6EXaGsT1nbBmZhuvVyTUvJFGt+1awZbNhg2WsaPDL3MPYhvu6pjfS2Xd1G+wrt9T/XL1Gf6xHXLRudEEIIq9JqU5O53Fr4AHD54WUAgooFUT+gPgBbLuev0bmE5AT23twLyHq5/C4hAW7cUK2A0j5MZXAy5+vhy+Uw9T9TQnICh24folHJ1FWykfGRONo5ZvVxIYQQudCuXXDxInh4QLduto4ma7qRuXKFyvFs2WeB/DfVeuj2IeKS4ijiWkS28cqnzp9XI3OurqoIIjBQPcqWVc+mMniatUOFDozeOJpJrSex/Mxy3Bzd0s3pHws9RvnC5U2PRAghRI7TFT689JJaw5Nb6ZM5n3I0Kd2Er3Z+xebLm9FqtWhyY8WGCXZcfTTFWrppvvlOIr3XXwcHB1Xs4O9vuWIjg5O5z1t+Trc/u9F8XnM8nDyY32U+Tvap7Yt/PfIrbcq1sUxUQgghrC4mBv76Sx3n5ilWgEthqSNzT5d6Gkc7R25G3uTCgwtULFLRxtFZxs7rj4ofSkvxQ3515AgcPAiVLTzwanAyV9StKNv7bSc8LhwPJ48M++IteWkJHk4elo1OCCGE1SxfDhERaoqnWS7PH9JOs7o5utG4VGO2X93Olitb8kUyl6JN0Veyynq5/CsoCO7ds/x1jd7Oy9vFO9MNjgu7Fk43UieEECJ3S9tbzs7ovw1yTmJyItfC1erwcoXKAeS7dXNn7p3hQewDXB1cqe1vRvdYketERKQ+Jk2CkSNVK6D799O/FxFh+j1M3gFCCCFE3nXzpup1BdCnj21jeZJr4ddI0abg4uCCn4fa8PzZss/y2bbP2HJ5S75YN6cblWtUspEMjOQzPj7p18ZptdCqVfpztFp1TnKyafeQZE4IIQqghQtVB/pmzaBcOVtHk720U6y6pK1RyUa4OLgQGh3K6XunCSoWZMsQzabvLydTrPnOlhwYPJZkTgghChitNv0Ua26XNpnTcXZwpkmpJmy6vIktl7fk+WROv/ODFD/kO82bw/jx8MEH4OZmnXsYtEqizpw6PIx9CMD4beOJSYyxTjRCCCGsbv9+tYWQq6tqSZLbpW1LklZ+WTd3I+IGV8KuYKexS9e/VeQfn30GUVHWu75Bydzpe6eJToxWAW37jKgEK0YkhBDCqnSjct26gZeXbWMxRNq2JGk9G6iSua1XtpKiTcnxuCxFNypXy68Wns6eNo5GWINWa93rGzTNWsuvFv3+7UfTUk3RarVM2T0lyzYkY5uPtWiAQgghLCc+Hn7/XR3nhSlWyHyaFaB+QH3cHd25H3ufE3dOUMO3hi3CM5uuWbBMseZv1qzRMSiZm9d5HuO2jmPV+VVoNBrWXliLg13Gj2rQSDInhBC52KpV8PAhlCgBLVvaOhrDZJXMOdo70rR0U9ZfXM/my5vzbDKnaxYsxQ/521NPPTmhe/DAtGsblMxVKlqJP178AwC7z+zY1GcTxd2Lm3ZHIYQQNqObYu3dG+wztgzNdR7GPiQsLgyAwEIZN69sGdiS9RfXs+XKFoY1GpazwVlAWFwYx0OPA5LM5XeffQbe3ta5ttHVrCnj8u66BCGEKMhCQ2HNGnWc16ZY/Tz8cHPMWAqoK4LYdmUbySnJmTa1z812X9+NFi0VClfQ99AT+VPPnlDcSuNgJrUmufjgIlP3TOX0vdMABBUL4r2G71G+cHmLBieEEMJyFi9WTUkbNrT83pDWktUUq05t/9p4OXsRHh/OkZAj1A2om5PhmU228CoYrN3T2ugNXNZfWE/QD0Hsu7WPGr41qOFbg70391L1h6psuLjBGjEKIYSwgLzUW05Hl8wF+mScYgVwsHPgmTLPAHmzRYmuWbAUP+RvuaKaNa3Rm0YzvNFwJraemP71jaMZtXEUz5V/zmLBCSGEsIyjR9XDyQl69LB1NIZ70sgcQMuyLVl1bhWbL2/mg6c/yKnQzBaXFMe+m/sAGZnL71KsvELN6JG503dPM6D2gAyv96/dn1N3T1kkKCGEEJalG5V74QUoXNi2sRjjcthlIPtkTtdvbse1HSQmJ+ZIXJZw8NZBEpITKO5enIqFK9o6HJGHGZ3MFXMvxpGQIxlePxJyRCpchRAiF0pMVHuxArz+uk1DMZohI3M1fGtQ3L04UQlRbLy0MadCM1va/Vg11l5UJfI1o6dZB9YZyKBVg7j08BJPl3oagF3XdzFp1yRGNBph8QCFEEKYZ906uHsXfH2hbVtbR2O4pJQkroZfBbJP5uw0drwc9DIz9s/g9xO/075i+5wK0Sz6ZK6UTLEK8xg9MvfJM58w9pmxTN83nebzmtN8XnNm7JvBp80/ZcwzY6wRoxBCCDPoplh79QIHk3oY2MaNiBskpSThZO9EgGdAtue+Uv0VAJadWZbr9w/XarVM3jWZNedVn5jmZZvbOCJhTXXqqEbdAOPHQ4wVfnsa/b+1RqNheOPhDG88nMj4SADZS04IIXKpBw9g5Up1nJeqWCF9JaudJvuxh8YlG1PWpyxXwq6w+txqXqr6Uk6EaLSklCTeXfsusw7MAmB4o+HU8a9j46iENZ0+DdHRUKiQahz81lvglrFlolnM+jeaJHFCCJG7/fEHJCRArVpQI4/tdmXIejkdjUZDz6o9mbhrIr+f+D1XJnPRCdH0/Kcnq86tQoOGb9t+myd3rRDGqVUL+vWDpk1Vi5IpU8Aj8+3tGWvijqh5aMBdCCGEsebNU895rfABjEvmQE21Ttw1kdXnVxMWF4aPi48VozNOSFQInRZ34uDtg7g4uLCo2yK6Velm67BEDpg3D8aNU/siazSwdm3myx00GknmhBBCPOb0adi/X/3F8corto7GeMYmc9WLV6dqsaqcvHuSZaeX0a92P2uGZ7BTd0/RYVEHroZfpahbUVa+spJGJRvZOiyRQypVUiPkAHZ2sGmT5bf1MroAQgghRN6gK3zo0MF6e0Jak7HJnEaj4ZVqKmv9/cTvVovLGFfDrtLk1yZcDb9KxcIVCR4QLIlcAZaSYp3/F41K5hKTE2m1oBXn75+3fCRCCCEsJjkZfvtNHee1wgcdY5M5gJ7VegKw6fImQqJCrBKXMf4+9TdhcWFUL16d3QN2U6FwBVuHJGzs4kUYOhRat1aPd99Vr5nDqGTO0d6RY6HHzLujEEIIq9u4EW7dgiJFoFMnW0djvPC4cO7H3gey3pc1M+ULl6dhiYakaFNYcnKJtcIz2MHbBwHoUbUHRd2K2jgaYWvr10NQEOzbpwqSatSAvXuhalXYYMb29kZPs75W/TV+OfyL6XcUQghhdbop1ldeUfux5jW6bbyKuRUzunNCbppq1SVz9QLq2TgSkRuMHg3Dh6sE7ttv1WPvXhg2DEaNMv26RhdAJKUk8euBX9l4aSN1/evi7uSe7v1v235rejRCCCHMFh4Oy5ap44I0xarzctWXGfHfCIJvBHP54WUCCxk+smdJ4XHhnLt/DoC6AXVtEoPIXU6fhr/+yvh6//4wdarp1zV6ZO7E3RPU8a+Dp7Mn5x6c43DIYf0jsz1bhRBC5KwlSyAuTk3n1M2jOYQ5yZy/pz/Pln0WgD9O/GHRuIxxOOQwAGW8y8gUqwCgWDE4ciTj60eOmFcYYXQyt6Xvliwfm/tuNjmQiRMnotFoGDZsmP61uLg4Bg8eTJEiRfDw8KB79+6Ehoam+9y1a9fo2LEjbm5uFC9enA8//JCkpCST4xBCiLxO11uub1/VuyovMieZg9wx1Xrg1gFARuVEqoEDYdAgmDQJduxQj4kT4c031XumMrk1yYUHF1h/YT2xibGA2mvOVPv372fOnDnUeKw9+fDhw1m5ciVLlixh27Zt3Lp1i27dUpssJicn07FjRxISEti9ezfz589n3rx5jDW1654QQuRxFy7Arl2qn9Vrr9k6GtOl3crLFN2qdMPRzpHjd45z4s4JS4ZmMN16ubr+kswJ5ZNPVGPg6dOheXP1mDEDPv0Uxpixvb3Rydz9mPu0WtCKp6Y/RYfFHbgddRuAASsG8P76940OICoqil69evHTTz9RqFAh/evh4eH88ssvfPvtt7Rs2ZK6desyd+5cdu/ezZ49ewD477//OHXqFAsXLqRWrVq0b9+ezz//nJkzZ5KQkGB0LEIIkdctWKCe27SBgOz3ps/VzB2ZK+RaiA4VOwDw+3HbjM4dvCXFDyI9jUYVQNy4oda2hoer4/feM28U3ehkbvj64TjaOXJt+DXcHFN3iu1RtQfrLq4zOoDBgwfTsWNHWrdune71gwcPkpiYmO71ypUrU7p0aYKDgwEIDg6mevXq+Pr66s9p27YtERERnDx5Mst7xsfHExERoX9ERkYaHbcQQuQ2KSmpyVxeLXwASE5J5krYFcD0ZA5Sp1r/OPmHWbNHpgiPC+f8A9WTVUbmRGY8PdXDEoyuZv3v4n+sf209Jb1Kpnu9YpGKXA27atS1/vjjDw4dOsT+/fszvBcSEoKTkxM+Pj7pXvf19SUkJER/TtpETve+7r2sTJgwgc8++8yoWIUQIrfbtg2uXgVvb+jc2dbRmO5m5E0SUxJxsHPI8HeNMZ6v9Dzuju5ceniJfTf30bBkQwtGmb1Dtw8BqvihiFuRHLuvKJiMHpmLToxONyKn8yD2Ac4OzgZf5/r167z33nssWrQIFxcXY8Mwy0cffUR4eLj+cerUqRy9vxBCWIOut1yPHuDqattYzHH5oeoxV9anLPZ29iZfx83RjS6VuwCw6PgiS4RmMOkvJ3KS0clcs9LNWHB0gf5nDRpStClM3jVZXwpuiIMHD3Lnzh3q1KmDg4MDDg4ObNu2jWnTpuHg4ICvry8JCQmEhYWl+1xoaCh+fn4A+Pn5Zahu1f2sOyczzs7OeHl56R+elhrnFEIIM924AVWqQLt2cPCg4Z+LioK//1bHeXmKFcxfL5dWr+q9AFXVmpicaPb1DKWvZJUpVpEDjE7mJj83mR8P/Uj7Re1JSE5g5MaRVPuhGtuvbmdS60kGX6dVq1YcP36cI0eO6B/16tWjV69e+mNHR0c2bdqk/8zZs2e5du0ajRs3BqBx48YcP36cO3fu6M/ZsGEDXl5eBAUFGfvVhBDC5n7/Hc6cUdv+1KsHPXuqCtUnWboUoqOhYkV49EdknqVP5nzMT+aeK/8cvu6+3Iu5x7oLxq/rNpW+klXakohHEhOhVSs4b4Xt7Y1O5qoVr8a5IedoWqopnSt1Jjohmm5VunH4zcOUL1ze4Ot4enpSrVq1dA93d3eKFClCtWrV8Pb2ZsCAAYwYMYItW7Zw8OBB+vXrR+PGjWnUqBEAbdq0ISgoiN69e3P06FHWr1/PmDFjGDx4MM7Ohk/5CiFEbrFxo3oOClLVbX/+qUbqBg+GxyYi0tH1luvTJ+/2ltO5FGa5kTkHOwderf4qAL8d+83s6xkiLC6MCw9UBi4jc0LH0RGOWWl7e6MLIAC8Xbz5+JmPLR1LBt999x12dnZ0796d+Ph42rZtyw8//KB/397enlWrVvH222/TuHFj3N3d6du3L+PHj7d6bEKIvG3TJrh0CXr3hhxetpuluDjVRBTUlj9JSfDRR7B2Lfzwg1oT16cPeHml/1xSEmzZopK4Pn1yPm5L0yVClkjmAHrX6M13e75jxdkVhMWF4ePiY5HrZkVX/FDWp6wUP4h0XnsNfvlFNQq2JJOSuYexD/nl8C+cvnsagKBiQfSr3Y/CroXNCmbr1q3pfnZxcWHmzJnMnDkzy8+UKVOGNWvWmHVfIUTBotXCSy/Bw4cwfrx69OkD9qavtbeI4GCIjQU/v9SRuTVrYOtWtQn3vn0wa1bWn3/2WShdOsfC1UtITuCVf16hYYmGjGwy0qxrfRv8Lftu7gOgavGqlgiPWn61qFa8GifunGDJySUMrGtGq30D6PrLyaiceFxSEvz6qxqBr1sX3NNvb8+3Jm5vb3Qyt/3qdp7//Xm8nb31VTrT9k1j/PbxrHxlJc+Ueca0SIQQIofcv68SOVAFB/37w5QpMGECPP+87aYpdVOsrVunj6FFC9izB1asUCN3mbVMc3SEN97IkTAz2HF1B0tPL+XfM//Su0Zv/D39TbrON7u/4YMNHwAw9pmxVC5a2SLxaTQaetfozaiNo/jt2G9WT+YO3FbFD1LJKh534gTUqaOOz51L/545f+4YncwNXjOYHlV7MKvjLH3JeHJKMu+sfofBawZz/O3jpkcjhBA54MoV9ezrCx9+CF99BadOqd5sTZqofRObNMn5uNImc4/TaFR8ubF/nK7Bb7I2mflH5zO66WijrzFl9xQ+3PAhAOOaj+PTFp9aMEJ4tfqrjN44mh3XdnD54WUCC5m2TZghZGROZGXLFutc1+gCiAsPLvB+4/fT9f6xt7NnROMR+nUOQgiRm+mSuXLl4P334eJFtTbN1VXta9q0KSxenLMxPXwIB9SADq1a5ey9zXU57LL++OdDPxu928LXu762aiIHUNKrJK3KqV/YhccWWvz6Og9jH3Lx4UUA6vjXsdp9RN524YKqWI9V29tnOtpuDKOTuTr+dTh973SG10/fO01N35rmRSOEEDng6qPNasqWVc8+Pmp07sIF6NZNvaarDs0pW7eq7bgqV4aSpm96YBO6kTmAiw8vsu3qNoM/O3nXZEZuVOvsrJXI6fSu0RtQVa3W2t5LV/wQ6BMoxQ8ig/v31T/WnnoKOnSA22p7ewYMUP+wNJVBydyx0GP6x7sN3uW9de8xZfcUdl7byc5rO5myewrD1w9neKPhpkcihBA5RDcyp0vmdAICYNw4dRwcrBYr55TsplhzO93IXIBnAKBG5wwxeddkRm0cBcCnzT+1aiIH0K1KN9wc3Tj/4Ly+yMLSpL+cyM7w4Wp967Vr4JZmM60ePWCdGW0QDVozV2t2LTQaTbp/yYzckLFi6dWlr9KjWg/ToxFCiBygS+bKlMn4XtWqqvVHRAQcPw61a+dMTHk6mXu0/daYZmN4Z807/H3qb6a1n5Zth4PlZ5anS+TGtRhn9Tg9nDzoVqUbC48tZMHRBVbZq1WfzMl6OZGJ//5T06uPj75XrJg6Y2AKg5K5y+9dfvJJQgiRR2Q1MgeqPcnTT6t/Je/cmTPJ3LVrqrLNzk5VruYlcUlx3I5Sc0UvVX2JOQfncDT0KIuOLWJow6GZfiYmMYb31r0HwPBGw3MkkdPpXaM3C48t5I+Tf/Bdu+9wsney6PV123hJJavITHR0+hE5nQcPwJy9DgyaZi3jU8bghxBC5GZabcY1c4/TVbLu2pUjIaHbtbBBA/D2zpl7WsrVMPWL6eHkQRHXIrxRR/VH+enQT1muS5u4cyLXwq9R2rs0X7T8IsdiBWgV2Ap/D38exD5g7fm1Fr32w9iH+q3IpPhBZKZZM1iQur09Go1aKzt5suoTaSqTmgbfirzFzms7uRN9hxRtSrr33m34runRCCGElT18CJGR6jirBrtNm6rnnTtV8mftvnN5eYpVV/xQ1qcsGo2GXtV78cF/H3D8znEO3DpA/RL1051/8cFFJu+aDMC3bb7FzTGTYQorsrezp1f1XkwJnsKCYwvoXNlyvV7SFj+Y20Rf5E+TJ6sCiAMHICEBRo6EkyfVyJw5/3g0Opmbd2Qeb656Eyd7J4q4FkGT5k85DRpJ5oQQuVraHnOurpmf06ABODjAzZtqCjSztXWWotXm7WROV/wQ6KP6thVyLcSLQS+y6Pgifj70c4Zkbvj64cQnx9O6XGu6VemW4/EC9K7ZmynBU1h1bhUPYx9SyLWQRa4rU6ziSapVU0sqZswAT0+IilIV9IMHg79pvbYBE5K5T7Z8wthnxvJRs4+w0xjd2UQIIWwqu/VyOm5uqkv7vn1qdM6aydyJE3Dnjrpno0bWu4+1pB2Z03mjzhssOr6IxScW803bb/Bw8gBg9bnVrDy3Egc7B6a1m5ZuMCAn1fCtQU3fmhwNPcpfJ//izXpvWuS6UvwgDOHtDR9beHt7o7OxmMQYelbrKYmcECJPetJ6OZ2cWjenG5V75hnzFkDbyuMjcwDNyzSnQuEKRCVE8dfJvwBVKKErehjWcBhVilXJ+WDT0PWcW3BswRPONJxuZE7akojsPHyotg8cMEA9vvlGTbOaw+iMbEDtASw5tcS8uwohhI0YMjIH6dfNWVNenmKF1LYkabfH0mg0vFFbFULoes59s/sbLj68iL+HP2Obj835QB/zavVXsdfYs/v6bo6Hmr8N5YPYB/rEVkbmRFa2b1d/9kybppK6hw/VcWCges9URidzE1pNYNvVbbSY14Kha4YyYv2IdA8hhMjNsusxl5ZuZO7ECQgLs04sCQmw7dFmCXk1mctsmhWgb62+2GvsCb4RzNrza/lyx5cATGkzBU9nzxyOMiN/T3+6VukKwMz9M82+nq74oVyhchZbgyesa+LEiWg0GoYNG6Z/LS4ujsGDB1OkSBE8PDzo3r07oaGhFrvn4MGqQfDly7B0qXpcugQ9e6r3TGV8MrdzAusvrCc0OpTjd45zOOSw/nEk5IjpkQghRA4wdGTO1xcqVFAFCsHB1oll717Vd6pYMahe3Tr3sKaohCjuxtwFMiZzfh5+PF/peQC6/dWN2KRYmpVuxivVXsnpMLM0pP4QQG3vFRYXZta19FOsMiqXJ+zfv585c+ZQo0aNdK8PHz6clStXsmTJErZt28atW7fo1s1yhToXLqhtu+xTt7fH3h5GjFDvmcroAohvgr/h186/8nqt102/qxBC2Iiha+ZAjc5duKDWzbVvb/lYdFOsrVqphsF5ja7HnI+LDz4uPhnef6P2Gyw/s5y4pDjsNHbM6DDDZkUPmXmmzDNUK16NE3dOMO/IPIY1GmbytaSSNe+IioqiV69e/PTTT3zxRWqfw/DwcH755RcWL15My5YtAZg7dy5VqlRhz549NLJAhVKdOnD6NFSqlP7106ehphnb2xv9x4ezvTNNSjUx/Y5CCGEjYWEQHq6ODalQ1U21WmvdXJ5fL5dJ8UNabSu0pYRnCQAG1x9MDd8amZ5nKxqNRj86N3P/zAx9Uw118s5J/j37LwBNSze1WHzCcJGRkUREROgf8fHxWZ47ePBgOnbsSOvH/sc7ePAgiYmJ6V6vXLkypUuXJtiM4fljx1If774L772nCiB27lSPKVPUnq3Dzdje3uiRufcavsf0fdOZ1n6a6XcVQggb0E2xFiuW+ZY6j9MVQezbp9a3OVlw56eICDXNCnk4mcuk+CEtBzsHFnRdwKpzq/isxWc5GZrBetXoxaiNo7jw4AL/XfyPdhXaGfV5rVbL26vfJiklieefep7GJRtbKVKRnaCgoHQ/jxs3jk8//TTDeX/88QeHDh1i//79Gd4LCQnByckJHx+fdK/7+voSEhJicmy1aqnG42k3RBmZcXt7Xn1VraczhdHJ3L5b+9h8eTOrzq2iavGqONo5pnt/aY+lpkUihBBWZuh6OZ1KlaBwYdU24PBhaGjBfdm3bYPkZLUuz5p97KxJX/zgXTbLc1oGtqRlYMucCcgEHk4e9KvVj6l7pzJj3wyjk7n5R+ez49oO3BzdmN5+eq6aRi5ITp06RYkSJfQ/O2fS5+f69eu89957bNiwARcXlxyL7XIObG9vdDLn4+Jjs67dQghhDmPWy4Fax9akCaxcqdbNWTKZW/toW9C8OioHaaZZsxiZyysGNxjM1L1TWXN+DRcfXKR84fIGfe5+zH0++O8DAMY1Hyf7k9uQp6cnXl5e2Z5z8OBB7ty5Q506qfvmJicns337dmbMmMH69etJSEggLCws3ehcaGgofn5+JseWE/9YMzqZm9t5rjXiEEIIqzO0LUlaumRu505VcWYJf/8Nc+ao4w4dLHNNW8iqLUleU6FwBdpXaM/aC2uZdWAWU9pMMehzozaO4n7sfaoWq8rwRmYseBI5olWrVhw/nr6nYL9+/ahcuTKjRo2iVKlSODo6smnTJrp37w7A2bNnuXbtGo0bW276/NYt9efJnTuQ8tgyzXdN3BHV6GROCCHyKmOnWSF13dyuXWrNi7mzaP/9p9bGpKTAwIHQqZN517OlJxVA5CVDGgxh7YW1/HL4F8Y/Ox43x+wXVe66totfDv8CwOxOs3G0d8z2fGF7np6eVKtWLd1r7u7uFClSRP/6gAEDGDFiBIULF8bLy4uhQ4fSuHFji1SyAsybB2++qdbfFimS/s8TjSYHk7nA7wPRkPWfZpfeu2RaJEIIYWWmJHN166o/eO/cUW1KKlY0/f7BwdC1KyQmwksvwaxZ5ieHthIWF6bvzZbXR+YA2lVoR7lC5bj08BKLji1iYN2BWZ6bmJzIW6vfAtSuSFLBmn9899132NnZ0b17d+Lj42nbti0//PCDxa7/yScwdix89JFl2xEZncwNazgs3c+JKYkcDjnMugvr+PDpDy0VlxBCWJwpyZyLC9Svr0bmdu0yPZk7dkxNqcbEQJs2sHBh+saheY1uirWYWzHcndxtG4wF2GnsGFx/MO//9z4z9s/gjTpvZFnMMHXPVE7cOUER1yJMaj0phyMVlrR169Z0P7u4uDBz5kxmzjR/V5DMxMSo3R4s3VfS+NYkjd7L9PWZ+2Zy4PYBswMSQghrCA9P3ZbL2AXJTZqoRG7nTnj9dePvffEitG2r7t+4sdrCx5JtTmzhSW1J8qJ+tfoxZvMYjoUeY+e1nTQr0yzDOVfDrvLptk8BtTVZEbciORylyMsGDIAlS2D0aMte12K5YfuK7fnn1D+WupwQQliUrpK1SBHw8DDus2nXzRnr1i147jkICVFbdq1eDe55fyAr3xQ/pFXItRCv1XgNgBn7Z+hf12q1hEaFsvfGXt5c9SYxiTE8U+YZ+tbsa6tQRR41YYJqS9SiBQwdqoqq0j5MZbECiL9P/U1h18KWupwQQliUKVOsOk8/rZ7PnIF796Bo0dT3YmJg2jT1r+3ExIyfDQmBu3ehfHlYvx4K5ZM92PNT8UNag+sP5qdDP/HPqX9ov6g9V8KucDXsKrFJsfpzHOwcmNVxlvSUE0abMEH9OaDbzuvxAghTGZ3M1Z5TO10BhBYtIVEh3I2+yw8dLbdIUAghLMmcZK5IEahSRe2fuHs3vPACJCXB3Lnw6adq9C07AQGwYQP4+xt/79xKNzKX35K5mn41eabMM2y/up11F9bpX9egoaRXScr4lGFYw2EEFQvK5ipCZO6bb+DXX01brpEdo5O5LpW6pPvZTmNHMfditCjbgspFK1sqLiGEsCjdNKupDTybNFHJ3I4dKpH73//g7Fn1XtmyMGZM5omiRgMNGhg/tZvb6Ubm8tM0q878LvP5/fjv+Hr4UtanLGV9ylLSqyRO9nl8oaOwOWfn1D2fLcnoZG5ci3GWj0IIIazMnJE5UOvmfv4Zvv02tdFn0aKq1cCbb6o/pAsKrVabLwsgdMr6lOWjZh/ZOgyRD733HkyfrpZmWJI0DRZCFAiWSOZAJXJubvD++/DBB/CEHYTypfux94lOjAagtHdpG0cjRN6xbx9s3gyrVkHVquD4WK/ppSZub29wMmf3md0TF3tq0JA0Nsm0SIQQworMTebKl4cpU1Qxw7BhYMZWjXmeblQuwDMAF4ec27BciLzOxwe6WWF7e4OTuWU9lmX5XvCNYKbtnUaKNiXLc4QQwlYiI+HBA3VszqbX779vmXjyuvxa/CCEtc210vb2BidznSt3zvDa2XtnGb1pNCvPrqRXjV6MbzHeosEJIYQl6IofChUqmNOilpafix+EyItMWjN3K/IW47aMY/7R+bSt0JYjbx2hWvFqT/6gEELYgLlTrCI9ffGDjMwJYZTAwOz7yV0ycXt7o5K58LhwvtrxFdP3TaeWXy029dmU6XYnQgiRm0gyZ1lXwq8AMjInhLGGDUv/c2IiHD4M69bBh2Zsb29wMjd512Qm7ZqEn4cfv3f/PdNpVyGEyI3M7TEn0svPbUmEsKb3Mt/enpkz4YAZ29sbnMyN3jgaV0dXKhSuwPyj85l/dH6m5y3tYWJdrRBCWImMzFmOVqvlarjKjmWaVQjLaN8ePvrI9AIJg5O5PjX7yD50Qog8SZI5ywmJCiEuKQ47jR0lvUraOhwh8oW//4bCZmxvb3AyN6/LPNPvIoQQNqRL5mSa1Xy6StZSXqVwtHd8wtlCiLRq105fAKHVQkiI6l/5gxnb28sOEEKIfC06Gu7dU8cyMmc+XY85KX4QwnhduqT/2c4OihWDFi2gshnb20syJ4TI13TFD97eqvu6MI8UPwhhunFW2t7ezjqXFUKI3EHWy1mW7P4gRO4jyZwQwiZWr4aDB61/H1kvZ1my+4MQxrOzA3v77B8OZsyVyjSrECLHXbwIzz+vpj1v3wZnZ+vdSzfNKiNzliEjc0IYb1nW29sTHAzTpkGKGdvbSzInhMhxR4+qKq6HD2HrVmjb1nr3kmlWy0lOSeZa+DVARuaEMEbnTPZZOHsWRo+GlSuhVy8Yb8b29jLNKoTIcWfOpB7/+6917yXJnOXcjLxJYkoijnaOBHgG2DocIfKkW7dg4ECoXh2SkuDIEZg/37ylIJLMCSFy3NmzqccrVpg3vfAksmbOcnRTrGV8ymBvZ2/bYITIY8LDYdQoqFABTp6ETZvUqFy1auZfW5I5IUSOSzsyd/Om9QohYmPhzh11LCNz5tO1JZEpViGMM3kylCsHq1bB77/D7t3QrJnlri9r5oQQOUqrTU3mqlSB06fVVGv9+pa/l674wdMTChWy/PULGil+EMI0o0eDq6salZs/Xz0ys9TE7e1lZE4IkaNCQyEiQpXqv/++es1a6+ZOn1bPZcum30JHmOb8g/OAjMwJYaw+feDll9X+q97eWT9MJSNzQogcpRuVCwyErl3hzTfhxAnVrqR8ecvc4+5d+PJLmDVL/VyhgmWuW5BptVo2X94MQP0AKwyjCpGPzZtn3evLyJwQIkfpkrlKldS/Ups3Vz9bYnQuOhq++EIlhd9/DwkJ0KoVTJpk/rULuuN3jnM76jZujm48U+YZW4cjhEhDkjkhRI7SVbLqNpXW9V8yJ5lLTFSjcOXLwyefQGQk1K4N//0HGzdCxYrmxSxg3YV1ALQMbImzgxW7PAshjCbJnBAiR+lG5h5P5nbuhHv3TLvma6/BO++o9XjlyqlqsQMH4LnnzI9XKLpkrl35djaORAjxOEnmhBA5Ku00K6j+b7VqqV5zq1YZf72rV+Gvv9TxtGmq6KFnT1VgISwjMj6Sndd2AtCugiRzQuQ28sedECLHxMamtgvRjcyBeVOtCxao55YtYehQcHIyL0aR0ZYrW0hMSaRC4QqUL2yhKhUhhMVIMieEyDHnz6s+c4UKQbFiqa/rkrn16yEmxvDrabWpyVzfvpaLU6QnU6xC5G6SzAkhckzaKda0fd9q1YLSpdXI3caNhl9v9264cAHc3aFbN4uGmqkdV3ew+/pu698oF9Fqtay9sBaQKVYhcitJ5oQQOebxSlYdjca0qVZdF/UXXwQPD/Pjy86D2Ae0/q01bRe2JTE50bo3y0XOPzjPlbArONk70aJsC1uHI4TIhCRzQogc83gla1q6ZG7lSkhOfvK1YmPhzz/V8euvWyS8bO29sZeE5ASiEqK4HXXb+jfMJdaeV6Nyz5R5BncndxtHI4TIjCRzQogc83gla1rPPAM+Pmr3huDgJ19r+XK1LViZMuqz1rbnxh798c2Im9a/YS6x7qKslxMit5NkTgiRI7TarKdZARwdoWNHdWzIVKtuirVPn5xpQ7L35l798c3IgpHMxSbGsvXKVkDWywmRm0kyJ4TIETdvqu22HByy3oNVN9W6fLlK/rJy6xZs2KCO+/SxaJiZStGmpE/mCsjI3Par24lLiqOkV0mCigXZOhwhRBYkmRNCpLNwIXz8MSQlWfa6uinWcuXUKFxm2rVTfeIuXIDjx7OPMSUFmjSBChUsG2dmzt0/R1hcmP7ngjIyl7YliSZt+bEQIleRZE4IoZeSAm+9BV99lbqrgqVkN8Wq4+kJbduq4x49Mt/eS6tNnWLNicIHUMUPaRWYZE63Xk6mWIXI1SSZE0LoXb6spkIBJk/OfqrTWNlVsqY1YwaULKnOb99eFTmkdeAAnDoFLi7w0kuWiy87uuKHEp4lALgVeStnbmxDV8KucObeGew19rQq18rW4QghsiHJnBBC7+TJ1OOjR9WODIYIDVWP7BiazJUurdbDFS2qErfOnSEuLvV93ahc167g7W1YfObSrZfrVkV1Ji4Ia+bWX1D/8RuXaoyPi49tgxFCZEuSOSGE3okT6llXHTpp0pM/c/MmBAVB9eoQGZn1ebpp1szakjyucmVYt05Nu27dqqZck5IgPh5+/12dk1Pbd8UkxnAs9BgA3at0B9Q0q9aSw5a5kLQkESLvsGkyN2HCBOrXr4+npyfFixenS5cunNX9if9IXFwcgwcPpkiRInh4eNC9e3dCHxsCuHbtGh07dsTNzY3ixYvz4YcfkmTp1dtCFAC6kbm33lJVp1u3wr592X9m2DB48ED1h/v778zPiYqC69fVsSHJHEDdurBiBTg7q+cBA2DVKnWvgABo3dqw65jr4K2DJGuTKeFZggYlGgAqwQuPD8+ZAGwgITmBTZc2AdC+YnsbRyOEeBKbJnPbtm1j8ODB7Nmzhw0bNpCYmEibNm2I1i3aAYYPH87KlStZsmQJ27Zt49atW3RLswljcnIyHTt2JCEhgd27dzN//nzmzZvH2LFjbfGVhMjTdMlcu3bQq5c6zm50bs2a9Amcbgr0cefOqedixaBIEcPjadECliwBe3tYsAD691ev9+6tXssJuvVyDUs2xNXRlUIuhYD8PdW6+/puIhMiKe5enFp+tWwdjhDiSbS5yJ07d7SAdtu2bVqtVqsNCwvTOjo6apcsWaI/5/Tp01pAGxwcrNVqtdo1a9Zo7ezstCEhIfpzZs2apfXy8tLGx8cbdN/r169rAe3169ct+G2EyFsSE7VaJyetFrTaixe12pMn1bFGo9WeOZPx/OhorbZsWXXOK6+o80CrvXQp47mLF6v3mjY1LbbfflOf1z1OnTLtOqbo/md3LZ+inbRzklar1Wqrzqyq5VO06y+sz7kgctioDaO0fIq299Letg5FiGzJ399KrlozFx6upi0KFy4MwMGDB0lMTKR1mvmUypUrU7p0aYIf7fcTHBxM9erV8fX11Z/Ttm1bIiIiOJl2NXca8fHxRERE6B+R2S30EaKAuHgREhLAzQ3KllXr4J5/XqVPU6ZkPP/zz+HKFShVCn78EVo9KnhcsCDjuYYWP2Tltddg+nR13KwZVKli2nVMoRuZa1SyEQAlvFRFa34emdP3l5OWJELkCbkmmUtJSWHYsGE0adKEatWqARASEoKTkxM+Pj7pzvX19SUkJER/TtpETve+7r3MTJgwAW9vb/0jKEg6mwuh+7dPlSqpBRCjRqnnBQvgdpq95U+cSE3wpk8HD4/UgoQFCzK2NDE3mQMYMkQ1El6xwvRrGOtGxA1uRt7EXmNPXf+6QP5vT7Lz2k6Ohh5Fg4bnyj1n63CEEAbINcnc4MGDOXHiBH/88YfV7/XRRx8RHh6uf5w6dcrq9xQit9Mlc4/+LQWoHRaaNFEjdlOnqtdSUuDtt1V1aefOqVtwde2qkrpLl2DnzvTXNqaSNTvVqsFj/7azKl2z4Oq+1XF3cgdSk7n82Dg4Ij6C3st6A/B6rdcp5l7MxhEJIQyRK5K5IUOGsGrVKrZs2ULJkiX1r/v5+ZGQkEBYWFi680NDQ/Hz89Of83h1q+5n3TmPc3Z2xsvLS//w9PS04LcRIm/StSWpWjX967rRudmzITwc5s5VyZq7O0yblnqeuzu8/LI6TlsIkZJi2O4PuZGuv1zDEg31r+mnWfNhMjds3TCuhF2hrE9ZprabautwhBAGsmkyp9VqGTJkCMuWLWPz5s0EBgame79u3bo4OjqyadMm/Wtnz57l2rVrNG7cGIDGjRtz/Phx7ty5oz9nw4YNeHl5yfSpEEbQjcw9nsx17KjWz0VEwBdfwMiR6vXPPlMNftPSTbX+9RfExKjja9dU018nJ7UWLy95fL0cpBmZy2dr5padXsbcI3PRoGFBlwV4OXvZOiQhhIFsmswNHjyYhQsXsnjxYjw9PQkJCSEkJITY2FgAvL29GTBgACNGjGDLli0cPHiQfv360bhxYxo1Un+4tmnThqCgIHr37s3Ro0dZv349Y8aMYfDgwTg7O9vy6wmRZyQmprYPSTvNCmr9nC6BmzJF9XmrWRPeey/jdZo2hcBA1Tx42TL1mm5UrkIF1bsur0hKSeLArQNA/h+ZC4kKYdCqQQCMajKKZmWa2TgiIYQxbJrMzZo1i/DwcFq0aIG/v7/+8eeff+rP+e677+jUqRPdu3fnmWeewc/Pj6VLl+rft7e3Z9WqVdjb29O4cWNee+01+vTpw/jx423xlYTIk86fVwmdp6eqTn3cK6+o/VIBNBqYMyfzxMzODvr0Uce6qVZLFD/YwvHQ48QmxeLt7E2loqmL/QI8AwAIjQolMTnRVuFZjFarpf+//bkXc49afrX47NnPbB2SEMJINv13staA7XBcXFyYOXMmM2fOzPKcMmXKsGbNGkuGJkSBolsvFxSkkrXHOTnBmDFqZ4h334WGDTOeo9Onj5qC3bgRbtzIu8mcbr1cgxINsNOk/ru3uHtxHOwcSEpJIiQqhFLemWS/ecicg3NYe2EtzvbOLOy6ECd7J1uHJIQwUq4ogBBC2FZW6+XSevNNNYL33XfZX6tcOXjmGdWeZOHCvFv8kNl6OQA7jR3+Hv5A3m9Pcu7+Od7/730AJraeSNXi2fwGEELkWpLMCSEybUuSmQoVMh+5e5yuEGLevNSROXPbkuQ0/TZeJTIOQ+aHdXOJyYm8tvQ1YhJjaBXYincbvmvrkIQQJpJkTgiRZVsSU734Iri6qlE5XbPhvJTMPYx9yNn7akixYclMkrk8XtEaEhVCt7+6sf/WfnxcfJjXZV66qWQh8qMJEyZQv359PD09KV68OF26dOGsburgkbi4OAYPHkyRIkXw8PCge/fuGdqf5Ubyf68QBVx8PFy4oI4tlcx5eUG3bqk/+/uDt7dlrp0T9t3cB0D5QuUp6lY0w/t5uXHw36f+ptoP1Vh1bhVO9k78+sKvlPQq+eQPCpHHbdu2jcGDB7Nnzx42bNhAYmIibdq0ITo6Wn/O8OHDWblyJUuWLGHbtm3cunWLbmn/MMul8lCjACGENZw9C8nJameFgADLXbdvX1i0SB3nxlG5a+HXuB9zn9r+tTO8pyt+eHy9nE5enGZ9GPuQoWuHsui4+o9Sy68Wv3X9jWrFnzC3LkQ+sW7dunQ/z5s3j+LFi3Pw4EGeeeYZwsPD+eWXX1i8eDEtW7YEYO7cuVSpUoU9e/boW6LlRjIyJ0QBl7b4wZD1cIZq2TK1nUluK35I0abQYl4L6vxYh7YL23I89Hi697NbLwep7UnyyjTrfxf/o/qs6iw6vgg7jR1jmo1h7xt7JZET+UZkZCQRERH6R3x8/BM/Ex4eDkDhwoUBOHjwIImJibRu3Vp/TuXKlSldujTBwcHWCdxCJJkTooCz9Ho5HXt7+OADddyhg2Wvba7z989zOewyoBKdWnNqMWjlIEKiQtBqtU8emctD06yjN46m7cK23Iy8yVNFnmJ3/9183vJzaUEi8pWgoCC8vb31jwkTJmR7fkpKCsOGDaNJkyZUe1T5FRISgpOTEz6PbQDt6+tLSEiItUK3CJlmFaKAM6Qtianeew8GDAAPD8tf2xy6kbcavjV4qshT/H3qb3469BO/n/id/rX68yD2Ac72ztT0q5np53XTrLm9Ncl/F/9j0q5JAAxtMJSJrSfi5uhm46iEsLxTp05RokQJ/c9P2gFq8ODBnDhxgp07d1o7tBwhI3NCFHCGtiUxVW5L5ACCb6gpk7bl27LkpSXs6LeD+gH1iUqIYtq+aQDU8a+T5eiVbmQuKiGKiPiInAnaSAnJCby7VrUbea/he0xrP00SOZFveXp64uXlpX9kl8wNGTKEVatWsWXLFkqWTC3+8fPzIyEhgbCwsHTnh4aG4ufnZ63QLUKSOSEKsNhYuHhRHVtjZC63erwhcNPSTdnzxh4WdVtEae/SALSr0C7Lz7s7uePtrMpzc+u6ual7pnL2/lmKuxfnsxayRZcQWq2WIUOGsGzZMjZv3kxgYGC69+vWrYujoyObNm3Sv3b27FmuXbtG48aNczpco8g0qxAF2OnTaqeGIkWgeHFbR5MzohKiOH5HFTykXRNnp7Hj1eqv0rVyVw7dPkSDEg2yvU4JrxKE3w3nZuRNqhSrYtWYjXUz4ibjt6n9qSe3noy3Sx7qCyOElQwePJjFixfz77//4unpqV8H5+3tjaurK97e3gwYMIARI0ZQuHBhvLy8GDp0KI0bN87VlawgyZwQVpeSkvnrGo1lq0dNYa1K1tzswK0DpGhTKO1dWl+VmparoytNSjd54nVKeJbg1N1TuXJk7sMNHxKdGE3jko3pXbO3rcMRIleYNWsWAC1atEj3+ty5c3n99dcB+O6777Czs6N79+7Ex8fTtm1bfvjhhxyO1HgyzSqElTx4AM2bq6rOzB5ubrBihW1jtPZ6udwo+LpaL5dVpaqh9O1JrFDRGnw9mPsx90367LYr2/j9xO9o0DCjwwzZ2UGIR7RabaYPXSIH4OLiwsyZM3nw4AHR0dEsXbo016+XA0nmhLCKqCjVjmP79qzPiYuD//0v65G7nGCttiS52Z6bj9bLlTAvmbPWll7/XfyPp399mqo/VGX39d1GfTYpJYkha4cA8GbdN6njX8eisQkhcidJ5oSwsPh46NIF9u6FwoVh3z64ezf948oV8PRUI2Nr1tguVmu2JcmNtFpthuIHU+nbk0RZtj3JjH0zAAiNDqXFvBb8dPAngz/7w/4fOHHnBIVdC/NFyy8sGpcQIveSZE4IC0pKgldfhU2bwN0d1q6F+vWhaNH0jzJl4K231GcmTjTvnikp8OOPKmk0RlSUSiqh4CRzV8KucCf6Dk72TmaPWlljZO56+HVWn18NQJvybUhMSWTQqkEMXj2YhOSEbD8bGhXKJ1s+AeCrll9RxK2IxeISQuRukswJYSFaLQwaBEuXgpMT/PsvNMimIHLYMHXerl3qYarff4c334S2bSE01PDPnT6tnn19VYJZEOj6y9X2q42zQ/ZNRZ/EGvuz/nL4F7XVWNkWrOu1ji+e/QINGn448APP/fYcd6LvZPnZjzZ9RER8BHX86/BGnTcsFpMQIveTalYhLECrVVtXzZ0Ldnbwxx/QqlX2nwkIgN694ZdfYNIk04ohtFr1WYCwMHj/fVi40LDPFsj1chaaYoXUkbmQqBCSUpJwsDPvj9OklCR+OfwLAIPqDEKj0fDxMx9Tw7cGvZb2YvvV7dT7sR4/v/AzySnJXA2/ypWwK/qHbguyGe1nYG9nb96XE0LkKZLMCWEBEybAt9+q459/hq5dDfvchx/Cr7/CypVq/ZqxidXatXD8OLi6qoKKRYugX78nJ5JQ8NbLgWWTueLuxbHX2JOsTSY0KlQ/UpdWijaFjzd9TKWilXi91uvZXm/t+bXciLhBUbeidKvSTf/685WeZ+8be+n8R2fOPzhP24Vts7zG0AZDaVwqdzc3FUJYniRzQphp+XL4+GN1/O23KpkyVKVKKvFbuhS+/hrmzTPu3rpRuXfeUYUXM2bA22/DsWPg4pL9ZwtaW5LYxFgOhxwGoHFJ8xMeezt7/Dz8uBl5k5uRNzNN5jZc3MDEXROx09hRvXh16gbUzfJ6cw7OAeD1mq9nmAKuUqwK+wbu481Vb7Lp0iZKeJWgrE9ZynqXVc8+ZSlfuDw1fGuY/b2EEHmPJHNCmEGrhU8/VcfDhsHw4cZfY9QolcwtWgSffw6lShn2uT17VOsTR0d1Xw8P+OcfOH9eFVXo4spKQRuZO3T7EEkpSfh5+Om37DJXCa8SKpmLuAkZczlWnVsFqBG6t1a/xZ4BezKdAr0Wfo21F9YCMLDuwEzv5ePiw58v/mmRuIUQ+YsUQAhhhv/+g6NHVeXqJ5+Ydo0GDaBFC1UJ+913hn9ONyr32mtQogR4e8PUqeq1CRPg3LmsPxseDtevq+OCksylnWLVWGi7C926uVuRGduTaLVaVp1XyZwGDQduHWD2gdmZXueXQ6rw4dmyz/JUkacsEpsQouCQZE4IM+gSqoEDVU85U40apZ5//FHtHPEkZ86oallQ6+50XnoJ2rWDhAQ13arVZv75vWqtPAEB4ONjcth5iqWaBaelb0+SSUXrqbunuBJ2BRcHFyY/NxmA/23+H7cjb6c7LykliZ8P/wyoRr9CCGEsSeaEMNH+/bBlCzg4wIgR5l2rbVuoWROio8GQbQC//lolap07Q5U0e7xrNDBzplovt3mzmrpNKyREra/r2FH9nF3rlPxGt42XJQsEsmtPoptibRnYkuGNhlM/oD4R8RGM+C/9b5Y159dwK/IWRd2K0qVyF4vFJoQoOCSZE8JEulG5V181fJ1bVjQaGDlSHU+bBrGxWZ978yb89ps61o3opVWuXOqU74gR8PAhRESo18qXh1mz1JRux44wfbp5cecVNyJucDPyJvYae+r6Z12EYKzsGgfrplg7VeyEvZ09szvNxk5jxx8n/uC/i//pz9MVPvSr1c/s3ndCiIJJkjkhTHDunCpagNQkzFwvvwxly6rtvubOzfq8qVMhMRGaNYPGWQwyffABBAWpa73wgkrivvgCYmKgUSPYtg1WrYKSJS0Te26nWy9Xw7cG7k7uFrtuViNz92Pu6/dV7fiUGgat41+HoQ2GAvDO6neITYzlathV1p5/VPhQJ/PCByGEeBJJ5oQwwZQpapqzUyfLFRA4OKimvwD/+58qhoiPT39OWBjMUQM5mY7K6Tg5qRE4gJ074d491QZl6VLYvRueecYyMecVluwvl1aAZwCQcWRu3YV1pGhTqOFbI13l7PhnxxPgGcDFhxeZuHMivxz+BS1aWga2pGKRihaNTQhRcEgyJ0QaWq2Wv0/9zfXw61mec/s2zJ+vjrNLqEzRvz/Uq6eqTUeMUAnYb79BcrJ6f9YsiIxUveE6dMj+Ws88A2PHQvXq8NNPaseHrl3VlG5Bo9vGy9LJnG6aNTIhksj4SP3raadY0/Jy9uL7dt8DMHHXRGYdUBm3FD4IIcwhyZwQaaw6t4qXlrzEoFWDsjzn++9VtejTT0PTppa9v5sbBAer5CsgAK5ehT59oHZt1Zz4e5UHMHKkYUnZZ5+pBsJvvKFG/gqihOQEDt46CFimWXBans6eeDp5AqntSRKTE1l3YR0AnZ7qlOEz3at0p32F9iQkJ3Av5h7F3IpJ4YMQwiySzAmRxq6LR2D9FLb+U5H4hJQM74eHp05fjh5tnRgcHFTypWv+6+2ttuzq2hVCQ6F0aejZ0zr3zo+OhhwlPjmewq6FqVC4gsWv//i6uV3XdxEWF0ZRt6I0KJGxXFij0TCjwwxcHNQWHf1q9cPJ3snicQkhCg5J5oRIY/WiQAh+n7hl06gSlMQ//6Tv1TZnjqoMDQpKbe9hLW5uahr30iVV0OD8qNDxww/Vrg/CMNZoFpzW4xWtupYkHSp2yHLD+3KFyvHLC7/QsWJHRjQ2s6+NEKLAk2ROiEe0Wji3+dE0nH08ly868eKLqvpz61ZVjKDbYWHkSLDLof97ChdWfeUuXIC1a2Hw4Jy5rzUdvHWQdgvb8e7ad0nRZhwBtST9ejkLNgtO6/GROV0y9/h6uce9Wv1VVr26Cl8PX6vEJYQoOCSZE+KR4D1JJISUB4dYeLc8TXpvwt0d9u2DZ59V69Zu31btPF55JefjK1lS7e5gqwKG6+HXGbx6MMdDj5t8jcj4SIatG0aDnxuw/uJ6pu+bzvB1w9FmtVWFBehG5izZLDittCNz5++f5+z9szjYOdCmfBur3E8IIR4nyZwQj8z4KUIdVF4G3jfxaPM1Fy6oHRMcHOD0afX2iBGq9UdBkpSSxEtLXuKHAz/QYXEH7sXcM+rzWq2WZaeXUWVmFb7f+z0p2hRal2sNwLR905iye4o1wiY0KpTLYZfRoKF+QH2r3EPfniTyJqvPrwageZnmeLt4W+V+QgjxOEnmhEBNoa78xwMA53p/AHDo9iF8fbXMnKkSuT59oFs3GJR1oWu+NWHHBPbeVBu63oi4wWtLXzN4evRa+DW6/NmFbn9142bkTcoXKs9/r/3Hht4b+KbNNwCM3DiSRccWPeFKxttwaQMAQcWCrJZcpd2fVT/FmkkVqxBCWIskc0KgdkOIinACzxu80M4de409d2Pu6ttNVKigesv98w+4W24DgTxh/839fLbtMwDGNBuDq4Mr6y+u58vtXz7xs78d/Y2gmUGsOLsCRztHPm72McffPs5z5Z8DYETjEYxopAoA+v3bj42XNlos7tuRtxmxXl27c6XOFrvu43Rr5i48uMC2q9sASeaEEDlLkjkhSG0CTI2F1CtZm8pFKwNwOOSw7YLKBWISY3ht2Wska5PpUbUH458dz+xOswEYt3VclsmXVqtl/Lbx9Fneh+jEaJqWbsqRt47wRcsvcHV0TXfu122+pkfVHiSmJNL1z64cvm3+r3mKNoXey3pzN+YuNX1r8knzT8y+ZlZ0I3MPYh+QlJJEpSKVrNICRQghsiLJnCjwQkNhzZpHP9SaT1CxIOr41wHUVGtB9uF/H3Lu/jlKeJbgh44/oNFo6FOzD2/UfgMtWl7959UMW1klJifSf0V/xm0dB8DoJqPZ9vo2gooFZXoPO40d87vM59myzxKVEEWHxR24/PCyWXFP3jWZTZc34eboxh8v/qHv6WYNvh6+2GlS/yiVUTkhRE4roD3hhUi1eLHaLktTch/aYmcIKhZEbb/a/HbstwI9Mrf2/Fp+OPADAHM7z6Wwa2H9e9PaT+PA7QMcCTlCj797sKXvFhztHQmPC+fFJS+y8dJG7DR2/NDhB96s9+StqpwdnFnWYxnN5jbj+J3jtFzQkqalM26vYaexo2PFjrwU9FKWPeP23NjDmM1jAJjefrp+lNVaHOwc8PPw00/JSzInhMhpksyJAk83xaqtMQ9XB1fKeJex2MjcoduHSEpJynQngNzsXsw9+q/oD8C7Dd7Vr3HTcXV05e+X/qbOj3XYdX0X/9v0P95r9B4dFnXg+J3juDu689dLf9Gh4hM2kE3D28Wbtb3W0viXxlwJu8KVsCuZnrfg6AL+qvIXszrOoph7sXTvhcWF8co/r+inhfvV6mfcFzdRCc8S3Iq8hbezN01KNcmRewohhI4kc6JAO3pUPRwck0mq9geVi1bG3s6eWn61AFWJeT/mPkXcihh97fsx92k2txlJKUlcfPciJb1KWjh669Bqtby56k1CokKoUrQKE1tPzPS88oXLM6/zPLr91Y0pwVOYd3Qe92Lu4efhx+pXV+sTYmOU8CrB3jf28vepv0lITsjw/s3Im0zfN51/Tv/Djms7+On5n3ih0gvp4r4SdoVAn0DmdJpjlR0fMqNrT9KuQjsc7WV7DiFEzpJkThRoulG5Sk+f56TbQ4KKqZEkbxdvyhcqz8WHFzkccljfE80Yi44vIiYxBoDFxxczsslIi8VtTQuOLmDp6aU42DmwsNvCDAULaXWt0pURjUbw7Z5vuRdzj6BiQax5dQ1lfMqYfH9/T3+GNhya5fuv1XiN3st6c+ruKTr/0Zl+tfoxtd1U/jr5F3+d/AsHOwd+7/57jvZ561alG9uubmNw/XywPYcQIs+RAghRYCUmwsKF6rh4E1UBkXaRvm5kyZTqSq1Wyy+Hf9H/vODoAqvucmApMYkxjPhPtfMY32K8QaNrE1tPpH+t/vSu0Ztd/XeZlcgZoo5/HQ4OOsgHjT9Ag4a5R+ZSfVZ13l37LgCfP/s5DUs2tGoMj+tTsw8PRz2kWZlmOXpfIYQASeZEPrZxIzz3HLz5Jty4kfH9devg7l0oXhzCS/4JpE/mavvVBuBQiPHr5g6HHOZY6DGc7J1wtnfm5N2THAk5YtL3yEm/Hf2NB7EPCPQJNHgk0dHekV86/8KCrgvwcfGxboCPuDi48HWbr9n2+jYCfQK5Fn6N2KRYWpdrnWdGQIUQwlIkmRP5zqFD0KaNSuQ2boQff4SKFWHUKHj4MPU83RTrq69qOfPwBGC5kbm5h+cC0KVyF/2art+O/WbK13miq2FXKfd9OQatNG9rCq1Wy7R90wAY2mAo9nb2lgjPqpqVacbRt47yboN3aVu+LQu6LEjXJkQIIQoC+VNP5BsXL8Irr0DdurBhAzg6wttvQ9OmEBcHkydDuXLw9ddw8yasXKk+91y3W8QkxuBk70S5QuX016vtr0bmzt0/R1RClMFxxCXFsei42pqqf63+9KnZB1Dr5pJSkiz0bVON2zqOy2GX+enQT/pN5U2x8dJGTt09hYeTB/1r97dghNbl6ezJ9+2/Z91r6/D39Ld1OEIIkeMkmRN5XlgYDB0KlSvDH2pbVXr1grNn4YcfYPt2lbhVrarOHTlSjdQlJECtWoDfUQCeKvIUDnapNUHF3YtTwrMEWrQcDTlqcDz/nvmXh3EPKelVktblWtO2fFuKuRUjNDqUDRc3WOx7A5y+ezrdiN8nW0zf6eD7vd8D8HrN12WTeCGEyEMkmRN5Xv/+MGMGJCVB27ZqmnXhQggMVO9rNNCpk2pBMm8elCoFsbHqvb594dTdUwCZ7lCgG50zpt/cr0d+BVRSZG9nj6O9Iz2r9QQsP9X66bZPSdGm8HSpp3G0c2TjpY1svbLV6Oucv3+e1edXA2RbSSqEECL3kWRO5GmnTsGyZSphW7NGFTXUrp35ufb2Knk7dw6++w7efRcGDUqTzBXNmMzV8Xu0bs7AnSCuh1/Xj769Xut1/eu6qdZlZ5YRER9h6NfL1pGQI/x18i80aJjVcRYD6wwEYMzmMUZXzk7fNx2AjhU78lSRpywSnxBCiJwhyZzI077+Wj137gzt2xv2GRcXGDYMvv8e3NwsOzI3/+h8tGhpXqY55QuX179e178ulYtWJi4pjn9O/WNYoE8wdstYAHpU60EN3xp8/MzHuDi4sOv6LtZdWGfwdcLjwpl7RBVsvNfwPYvEJoQQIudIMifyrBs3YJGqM2DUKNOuodVqs03mdBWtJ++eJD4pPttrpWhT9EnR4wUEGo2G3jV6A5aZat1zYw8rz63ETmPHp80/BdQuBLqmtWO2GD46N/fIXKISoggqFmRSc2QhhBC2JcmcyLO++041/m3eHBo1Mu0aNyNvEpkQib3GnopFKmZ4v5RXKQq7FiYpJYkTd05ke63tV7dz6eElPJ086V6le4b3X6vxGgBbrmzhWvi1LK+z+/pufYKZFV2hQ9+afalUtJL+9VFNRuHh5MGh24dYdmZZttcASE5J1k+xvtvg3Rzb/koIIYTlSDIn8qSHD1X/ODB9VA5UNShAxSIVcbJ3yvC+RqNJ7Tf3hHVzulG5ntV64u7knuH90t6laVG2BQCLji3K8L5Wq2XclnE0+bUJNWbV4OtdX5OiTclw3tYrW9l4aSOOdo6MbT423XvF3IsxrOEwQE3DJqckZxvz6vOrufTwEoVcCtG7Zu9szxVCCJE7STInzHYt/Bq7r++2yLXi4mDpUoh6Qlu3H35Q59SoAe3amX6/7KZYdfQ7QWSzbi4iPoIlJ5cAGadY00o71Zp2GjQhOYHX/32d8dvHA5CsTWbkxpE8//vz3Iu5pz9Pq9UyZvMYAAbWGUhZn7IZ7vH+0+/j4+LDybsn+ePEH1nGAqntSAbWGYibo1u25wohhMidJJkTZgmPC6fxL41p8msT9t3cZ/b1+vSB7t2hdeusE7rYWFW8AKpnnDkzg9lVsuoYMjL354k/iU2KpXLRyjQskfW+oC8GvYiLgwun753m4O2DgPo1bL+oPQuOLsBeY8+cTnP4sdOPuDi4sOb8GmrPqc3OazsBWHdhHbuu78LFwYWPn/k403v4uPjw4dMfAqp1SWJyYqbnHQ89zubLm7HX2DO4gWwQL4QQeZUkc8IsYzaP4VbkLQB+OviTWddaswaWqMEt9u6Frl0hPk3NwYk7J7gSdoV589SeqmXKQI8eZt2SU/dUMlelWJUsz9GNzB0NOZrltKWut1z/Wv2zXXfm5exFl8pdALUP6vXw6zSd25TNlzfj7ujOyldWMqjuIAbWHcjeN/ZSqUglbkTcoMW8FkzcOZExW9So3OD6gwnwDMjyPu82fJdibsW48OAC84/Oz/ScaXvV1l1dq3SltHfpLK8lhBAid9NojW1IlQ/duHGDUqVKcf36dUqWLGnrcPKMA7cO0OCnBmhRv4U8nDwIeT8k0/ViTxITo3ZouHIFunRR23FFR0O3bvDnn7Du0ipe+P0FXOw8KPLLPW5cdWLaNLXzg6m0Wi1FJhfhYdxDDr95mFp+tTI9L0WbgvdEb6ISojj5zskMU7JHQ45Sa04t7DX23BhxAz8Pv2zvu+b8Gjou7khh18K4OLhwK/IW/h7+rH51tb4Vik5UQhRvr36bhccW6l/zcPLg8nuXKepWNNv7fBf8HSP+G4G/hz8vBr2Y4bv/fPhn4pLi2NFvB01LN832WkIIkRvJ39+Kw5NPESKj5JRk3lr1Flq0vFLtFfbf2s+FBxf4+9Tf9K3V1+jrff65SuRKlYLffoM9e6BjR7V+7qU+9/mvak+0aIk91p4bV50oXERL//7mVV7eib7Dw7iHaNBQqUilLM+z09hR07cmu67v4tDtQ/pkTqvVsvDYQoauVRllx6c6PjGRA2hTvg3F3YtzJ/oOAFWLVWVNrzWZjo55OHmwoMsCni37LIPXDCYuKY7hjYY/MZEDeLv+23wT/A03I2/qK1YfV9e/Lk1KNXnitYQQQuRekswJk8w6MIuDtw/i7ezNd22/45fDv/Dx5o+Ze2Su0cncyZMwZYo6nj4dPDzUmrnff4eXXtKy/Pci0PhTnhm4iuCfxpAIBLT6Bze37oDpCZ1uvVy5QuVwdXTN9tw6/nXYdX0Xh28f5rUar3E3+i5vrnpT3/6jUclGzGg/w6D7Otg50L9WfybumkjLwJb88/I/+Lj4ZHm+RqOhf+3+NCnVhF3Xd+mLKJ7ExcGF1a+uZunppZlWxTrYOfBq9VelHYkQQuRxkswJo92KvMX/Nv0PgImtJ+Lr4Uufmn34ZMsnbLu6jQsPLlChcAWDrpWSAm+9pfZV7dxZPXTaPx9L6d5fc2X+WAj+AG/fd0m86QSO0Zwo+yazD9zl7fpvm/w9DKlk1dFXtIYc4t8z/zJw5UDuxtzF0c6RT1t8ysgmI3GwM/x/p09bfErHpzrSsERDHO0dDfpMpaKV0vWUM0RNv5rU9Ktp1GeEEELkLVIAUQBptTDnl1hWr0nG0BWTDx/CtGmwcyeMWD+CyIRIGpZoyKC6gwAo6VWStuXbAjDvyDyDY5k7V13T3V1dXydFm0Lf5X25EjgOt06qanPlctUHrmmXs+D2gGHrh7H/5n6D7/U4Y5I5XUXr9qvb6fJnF+7G3KVa8WrsG7iP/zX7n1GJHICzgzNNSzc1OJETQgghsiLJXAGj1cIL/c7y1huudOpoT4On49i1K+vzY2PV/qfly8N770GzZvDn/15Dc6cGszvNxk6T+luoX61+gErmntSsFlRF6siR6vizz6B0miVj47aMY8mpJTjaObJmehvGqCJO7O3ht8m16VK5CwnJCby05CUexD4w+tcBUitZDUnmgooF4WTvRIo2BQ0aRjUZxYGBB7IsmhBCCCFyiiRzBYhWq6XjWztYNf/RVJ1DLAf2uNC0qZrePJVmB6nkZDVq9tRTKuF6+BDKBqaAXRKc74R21mGmjq7F1aupn3mh0gsUdi3MzcibbLi04YnxfPghPHgANWuqRFFn4bGFfLHjCwDmdJpD87LNGT8efvpJFUSULathbue5lCtUjqvhV+m7vG+ma8KeRLf7gyHJnKO9I0MbDKWuf12299vOxNYTcXZwNvqeQgghhKVJMldAJKck8+yQP1n7YzMAGg34k3Lj2kKdH8EuiRUroHp1GDBAFR7UqAH9+6vN7EuVUoldrx8/g3eCcK25ErR2zJ+vkr3334dbtyAxzpmXK/SHeHd+DF5EVBRZPjZsgPnzVcPfOXPA4dEs5a5ruxiwYgCg9hntV1uN9mk08MYb8MIL6jwfFx/+fulvnO2dWXVuFZN3TTbq1+N+zH1Co0MBqFy0skGfmdJmCgcGHZA2HkIIIXIV6TNH/u9TE50QzTPvz+LQjBGAHc/128P6XxpyL+Ye7Ra149CxaBy2Tibp5AvpPle4MPzvf9C9bwhzj89iws4JJKYk8s/L/1AqqhujRsGWLebF9vbbamsugEsPL9HwZxVXl8pd+Oflf9JN42bm50M/M3DlQOw0dlQrXo2yPmUp611WPT96VClWBRcHl3Sf23ltJ83mNqOMdxmuDLti3pcQQghhE/n9729DSTVrPhcSFUKz/03gwg9TADvavXKRNb80QqNRm7Jv7rOZ539/nh3FOuPc9BkqHVnKpZNFGDoU2vQ+wq9nvuWjWX+QmKK2hOpepTtdK3dFo4FNm+C//1TCdyjrbUuzVLEifPWVOg6PC9fvQ1rHvw4Luy58YiIHMKD2AA7eOsjsg7M5FnqMY6HHMpzj7uhO+4rt6VKpCx0qdqCQayF98UN2Oz8IIYQQeYGMzJF/M/ujIUdp99WXhMyaD0mutOlyl7X/FMPusRwpJjGGl5a8xJrza3C0c+R/Tcew+cpGdlzboT/n6VJPM6zhMLpW6Zpp5WZsbOrxrP2zeP+/EdTwrcmeN/ZkGZ+zM9jZQVJKEp0Wd2L9xfUEeAaw7419lPAqYdR3PXvvLJceXuJK2BX1CFfPFx9c5H7sff15DnYONC/TnLikOHZd38WIRiP4pu03Rt1LCCFE7pBf//42liRzWO83wzffwPXr8PHHUKyYxS4LqL5s58/DsWNw/LgqUNBJTE7kwK39HAk5ivbYqxDvzbNtoli/ygPHLDphJCQn0Hd5X/448Yf+NQc7B3pU7cF7Dd+jfon6Bsf2IPYB/t/4k5CcwKFBhzJsUfW4oWuGMmP/DNwc3djRb4e+DYglaLVaDt0+xPIzy1l+djkn7pxI9/7Pz//MgDoDLHY/IYQQOUeSOUWSOazzmyEiQrXaCA8HT09VuTl8uNrdwFh37qikTZe4HTumdk1Iuwl9dho3TWTTf464Zr/JAckpyXy44UOWnVlGr+q9eKf+O9lu5p6dnn/35M+TfzKk/hCmd8h8KymAGftm6LfDWvryUrpW6WrS/Qx14cEF/j3zL8vPLicqIYp1vdbh6+Fr1XsKIYSwDknmFEnmsN5vhg0bYPTo1PVkRYsnMWDYLVp1v0R0ShiOdo64Obrh7uSOu6M79snu3LzsxaUz7pw56cTx4xqOHYPQ0Myv7+6uKlBr1AD3QpGsu7iW04/Wgnm5eNOhQgeerlSJ119XCWVO+u/if7Rd2JZCLoW49f6tDAUIAOsurKPj4o6kaFOY2Goio5qOytkghRBC5GmSzCn5pgBi5syZfP3114SEhFCzZk2mT59OgwYNbBrTJ1cbca7baSjXDjZ9yb07FZj0v9JMmhIPLX4El3AIrZH6uFcStJn8J9Gk4FT0Gm4lL+Fd+gqFy96keLkQipeMxsPZDTuNHT8fXUBktUjsNfYMbzSccS0+wMPJhGFAC2kV2IqSXiW5EXGDYeuGUcuvFj4uPvpHTGIMLy95mRRtCv1q9WNkk5E2i1UIIYTIy/JFMvfnn38yYsQIZs+eTcOGDZk6dSpt27bl7NmzFC9e3GZxRSdGE5UUAdX+gsrLcDw6mOQt/yPlQUVYujjzD7k8AN9j6R/FT5LgFEMCEAZcBXj46JFGwxINmdNpTq7Yi9Pezp7Xa77OFzu+YM7BOVme90yZZ5jdabZs9i6EEEKYKF9MszZs2JD69eszY8YMAFJSUihVqhRDhw5l9OjRGc6Pj48nPs2Cs5s3bxIUFGTxYdoz987gYOeAj4sP3s7eONo7EhUF334LP/4IPj5qijTtwz8ghdikGGISY4hOiCY6MTrT45jEGKITo/Wv1/CtQa/qvbC3s7dY/OaKjI9k8q7J3Iy8SVhcWLrHw7iH1PGvw18v/kURtyK2DlUIIUQeZMo0a26cyTNXnk/mEhIScHNz4++//6ZLly761/v27UtYWBj//vtvhs98+umnfPbZZxleL+hz7kIIIUReYmwy9+eff9KnT590M3lLliyx+UyeufL8dl737t0jOTkZX9/0FYm+vr6EhIRk+pmPPvqI8PBw/eNU2k1JhRBCCJGnREZGEhERoX/EZ9Hu4dtvv2XgwIH069ePoKAgZs+ejZubG7/++msOR2xZeT6ZM4WzszNeXl76h2dOl3oKIYQQwmKCgoLw9vbWPyZMmJDhnISEBA4ePEjr1q31r9nZ2dG6dWuCg4NzMlyLy/MFEEWLFsXe3p7Qx/p3hIaG4ufnZ6OohBBCCJFTTp06RYkSqTsHOTs7Zzgnu5m8M2fOWD1Ga8rzI3NOTk7UrVuXTZs26V9LSUlh06ZNNG7c2IaRCSGEECIneHp6pptxyyyZy8/y/MgcwIgRI+jbty/16tWjQYMGTJ06lejoaPr162fr0IQQQgiRC+Tnmbw8PzIH0KNHD6ZMmcLYsWOpVasWR44cYd26dRmGUoUQQghRMOXnmbx8MTIHMGTIEIYMGWLrMIQQQgiRS+XXmbx8k8wJIYQQQmSnR48e3L17l7FjxxISEkKtWrXyxUyeJHNCCCGEKDDy40xevlgzJ4QQQghRUEkyJ4QQQgiRh0kyJ4QQQgiRh0kyJ4QQQgiRh0kyJ4QQQgiRh0k1K6ppIMDt27dtHIkQQgghDKX7e1v393hBJckc6Lf2aNCggY0jEUIIIYSxQkNDKV26tK3DsBmNVqvV2joIW0tKSuLw4cP4+vpiZ5czM8+RkZEEBQVx6tQpPD09c+SeuYl8/4L9/UF+DQr69wf5NSjo3x/M/zVISUkhNDSU2rVr4+BQcMenJJmzkYiICLy9vQkPD8fLy8vW4eQ4+f4F+/uD/BoU9O8P8mtQ0L8/yK+BpUgBhBBCCCFEHibJnBBCCCFEHibJnI04Ozszbtw4nJ2dbR2KTcj3L9jfH+TXoKB/f5Bfg4L+/UF+DSxF1swJIYQQQuRhMjInhBBCCJGHSTInhBBCCJGHSTInhBBCCJGHSTInhBBCCJGHSTJnhu3bt/P8888TEBCARqNh+fLl6d4PDQ3l9ddfJyAgADc3N9q1a8f58+fTndOiRQs0Gk26x1tvvZXunGvXrtGxY0fc3NwoXrw4H374IUlJSdb+ek9kie8PEBwcTMuWLXF3d8fLy4tnnnmG2NhY/fsPHjygV69eeHl54ePjw4ABA4iKirL213sic7//lStXMvy31z2WLFmiPy+3/vcHy/weCAkJoXfv3vj5+eHu7k6dOnX4559/0p2TX38PAFy8eJGuXbtSrFgxvLy8ePnll/VbDOrk1u8/YcIE6tevj6enJ8WLF6dLly6cPXs23TlxcXEMHjyYIkWK4OHhQffu3TN8P0N+j2/dupU6derg7OxMhQoVmDdvnrW/nkEs9Wvw7rvvUrduXZydnalVq1am9zp27BjNmjXDxcWFUqVKMXnyZGt9LYNZ4vsfPXqUV155hVKlSuHq6kqVKlX4/vvvM9wrt/4eyA0kmTNDdHQ0NWvWZObMmRne02q1dOnShUuXLvHvv/9y+PBhypQpQ+vWrYmOjk537sCBA7l9+7b+kfZ/0OTkZDp27EhCQgK7d+9m/vz5zJs3j7Fjx1r9+z2JJb5/cHAw7dq1o02bNuzbt4/9+/czZMiQdNuq9erVi5MnT7JhwwZWrVrF9u3bGTRoUI58x+yY+/1LlSqV7r/77du3+eyzz/Dw8KB9+/ZA7v7vD5b5PdCnTx/Onj3LihUrOH78ON26dePll1/m8OHD/2/vXmOiuro+gP9n5OZAcLgMIOUmRZECKmhChyqUSIaSFrV8qKH1EtRKKyYiVQkpLaZN0xprranaKlETLw1IKW1MK4R2KAU7pULAABpgEEpoQYIIqFCu6/1gOG+P4POMMDBzeNYvmQ+zz2KfvfZZTJYzc1CImas18PDhQ2g0GshkMmi1Wly7dg1DQ0OIi4sT/cfh5pp/SUkJkpOT8fvvv6OoqAjDw8PQaDSi67t3715cuXIFubm5KCkpwd9//434+HjhuCE13tzcjJdffhlRUVGorq5GSkoKduzYgcLCwlnNdzLG2INx27Ztw8aNGyc9T19fHzQaDby9vVFZWYnDhw/j4MGDOH369IzlZghj5F9ZWQkXFxdcvHgRdXV1ePfdd5Geno7jx48LMeZcA2aBmFEAoPz8fOF5fX09AaDa2lphbHR0lFQqFWVlZQljkZGRtGfPnifO++OPP5JcLqeOjg5h7MsvvyR7e3saHBw0ag7TMdX8w8LCKCMj44nz3rx5kwDQ9evXhbGrV6+STCajv/76y7hJTMNU83/cihUraNu2bcJzqVx/oqnvga2tLZ0/f140l6OjoxAzl2ugsLCQ5HI59fb2CjE9PT0kk8moqKiIiKSTPxFRZ2cnAaCSkhIiepSLpaUl5ebmCjG3bt0iAKTT6YjIsBo/cOAABQYGis61ceNGiomJmemUntpU9uDfMjMzafny5RPGT548SQ4ODqLf+7S0NPL39zd+EtMw3fzH7dq1i6KiooTnUqoBU+B35mbI4OAgAMDGxkYYk8vlsLa2RllZmSj20qVLcHZ2RlBQENLT09Hf3y8c0+l0CA4OhqurqzAWExODvr4+1NXVzXAWU2dI/p2dnSgvL4eLiwvCw8Ph6uqKyMhI0f7odDoolUqsWrVKGIuOjoZcLkd5efksZfP0nub6j6usrER1dTW2b98ujEn1+gOG70F4eDhycnLQ3d2NsbExZGdn459//sGLL74IYG7XwODgIGQymegPptrY2EAulwsxUsq/t7cXAODo6AjgUU0PDw8jOjpaiFm6dCm8vLyg0+kAGFbjOp1ONMd4zPgc5mQqe2AInU6HiIgIWFlZCWMxMTGor6/HvXv3jLT66TNW/r29vcIcgLRqwBS4mZsh48Wanp6Oe/fuYWhoCIcOHUJbWxva29uFuNdffx0XL15EcXEx0tPTceHCBWzatEk43tHRIXqRAyA87+jomJ1kpsCQ/G/fvg0AOHjwIN58800UFBQgNDQUa9euFb5X1NHRARcXF9HcFhYWcHR0lHz+jztz5gwCAgIQHh4ujEn1+gOG78Hly5cxPDwMJycnWFtbIykpCfn5+fDz8wMwt2vg+eefh62tLdLS0tDf34+HDx9i3759GB0dFWKkkv/Y2BhSUlLwwgsvICgoCMCjtVtZWUGpVIpiXV1dhbUbUuNPiunr6xN9v9bUproHhpDCa4Gx8v/tt9+Qk5Mj+iqBVGrAVLiZmyGWlpb49ttv0dDQAEdHRygUChQXFyM2Nlb0fbCdO3ciJiYGwcHBeOONN3D+/Hnk5+ejqanJhKufPkPyH/9OUFJSEhITExESEoKjR4/C398fZ8+eNeXyp83Q6z9uYGAAX3/9tehdOakzdA/ee+899PT04KeffkJFRQVSU1Px2muvoaamxoSrnz5D8lepVMjNzcWVK1dgZ2eHBQsWoKenB6GhoZPWiTlLTk5GbW0tsrOzTb0Uk/lf3wNj5F9bW4v169cjMzMTGo3GiKub2yxMvYC5bOXKlaiurkZvby+GhoagUqkQFhYm+rjkcWFhYQAAvV6PZ599Fm5ubvjjjz9EMeN3Abm5uc3c4o3gv+W/cOFCAMBzzz0n+rmAgAC0trYCeJRjZ2en6PjIyAi6u7sln/+/ffPNN+jv78eWLVtE41K+/sB/34OmpiYcP34ctbW1CAwMBAAsX74cpaWlOHHiBL766qs5XwMajQZNTU3o6uqChYUFlEol3Nzc4OvrC0AavwO7d+8Wbszw8PAQxt3c3DA0NISenh7ROzN37twR1m5Ijbu5uU24+/POnTuwt7fH/PnzZyKlpzadPTDEk/Zg/JipGSP/mzdvYu3atdi5cycyMjJEx6RQA6YkrX/6SdSCBQugUqnQ2NiIiooKrF+//omx1dXVAP6/0VGr1aipqRG9mBcVFcHe3n5CE2SunpS/j48P3N3dJ9zG3tDQAG9vbwCP8u/p6UFlZaVwXKvVYmxsTGh8zZ0h1//MmTNYt24dVCqVaHwuXH/gyXsw/v3Qx9+FmjdvnvDO7f9KDTg7O0OpVEKr1aKzsxPr1q0DYN75ExF2796N/Px8aLVaLFq0SHR85cqVsLS0xM8//yyM1dfXo7W1FWq1GoBhNa5Wq0VzjMeMz2FKxtgDQ6jVavz6668YHh4WxoqKiuDv7w8HB4fpJzJFxsq/rq4OUVFR2Lp1Kz766KMJ5zHnGjALJr4BQ9Lu379PVVVVVFVVRQDos88+o6qqKvrzzz+JiOjy5ctUXFxMTU1N9N1335G3tzfFx8cLP6/X6+mDDz6giooKam5upu+//558fX0pIiJCiBkZGaGgoCDSaDRUXV1NBQUFpFKpKD09fdbzfdx08yciOnr0KNnb21Nubi41NjZSRkYG2djYkF6vF2JeeuklCgkJofLyciorK6PFixdTQkLCrOY6GWPkT0TU2NhIMpmMrl69OuGYOV9/ounvwdDQEPn5+dGaNWuovLyc9Ho9ffrppySTyeiHH34Q4uZyDZw9e5Z0Oh3p9Xq6cOECOTo6UmpqqijGXPN/++23acGCBfTLL79Qe3u78Ojv7xdi3nrrLfLy8iKtVksVFRWkVqtJrVYLxw2p8du3b5NCoaD9+/fTrVu36MSJEzRv3jwqKCiY1XwnY4w9IHr0OlBVVUVJSUm0ZMkSoa7G717t6ekhV1dX2rx5M9XW1lJ2djYpFAo6derUrOb7OGPkX1NTQyqVijZt2iSao7OzU4gx5xowB9zMTUNxcTEBmPDYunUrEREdO3aMPDw8yNLSkry8vCgjI0N0W3lraytFRESQo6MjWVtbk5+fH+3fv1/0ZwqIiFpaWig2Npbmz59Pzs7O9M4779Dw8PBspjqp6eY/7uOPPyYPDw9SKBSkVquptLRUdPzu3buUkJBAdnZ2ZG9vT4mJiXT//v3ZSPE/Mlb+6enp5OnpSaOjo5Oex1yvP5Fx9qChoYHi4+PJxcWFFAoFLVu2bMKfKpnLNZCWlkaurq5kaWlJixcvpiNHjtDY2Jgoxlzznyx3AHTu3DkhZmBggHbt2kUODg6kUCjo1Vdfpfb2dtE8htR4cXExrVixgqysrMjX11d0DlMy1h5ERkZOOk9zc7MQc+PGDVq9ejVZW1vTM888Q5988sksZflkxsg/MzNz0jm8vb1F5zLXGjAHMiIiY73LxxhjjDHGZhd/Z44xxhhjTMK4mWOMMcYYkzBu5hhjjDHGJIybOcYYY4wxCeNmjjHGGGNMwriZY4wxxhiTMG7mGGOMMcYkjJs5xhhjjDEJ42aOMcYYY0zCuJljjEkCESE6OhoxMTETjp08eRJKpRJtbW0mWBljjJkWN3OMMUmQyWQ4d+4cysvLcerUKWG8ubkZBw4cwBdffAEPDw+jnnN4eNio8zHG2EzgZo4xJhmenp44duwY9u3bh+bmZhARtm/fDo1Gg5CQEMTGxsLOzg6urq7YvHkzurq6hJ8tKCjA6tWroVQq4eTkhFdeeQVNTU3C8ZaWFshkMuTk5CAyMhI2Nja4dOmSKdJkjLGnIiMiMvUiGGPsaWzYsAG9vb2Ij4/Hhx9+iLq6OgQGBmLHjh3YsmULBgYGkJaWhpGREWi1WgBAXl4eZDIZli1bhgcPHuD9999HS0sLqqurIZfL0dLSgkWLFsHHxwdHjhxBSEgIbGxssHDhQhNnyxhj/xk3c4wxyens7ERgYCC6u7uRl5eH2tpalJaWorCwUIhpa2uDp6cn6uvrsWTJkglzdHV1QaVSoaamBkFBQUIz9/nnn2PPnj2zmQ5jjE0Lf8zKGJMcFxcXJCUlISAgABs2bMCNGzdQXFwMOzs74bF06VIAED5KbWxsREJCAnx9fWFvbw8fHx8AQGtrq2juVatWzWoujDE2XRamXgBjjE2FhYUFLCwevYQ9ePAAcXFxOHTo0IS48Y9J4+Li4O3tjaysLLi7u2NsbAxBQUEYGhoSxdva2s784hljzIi4mWOMSV5oaCjy8vLg4+MjNHj/dvfuXdTX1yMrKwtr1qwBAJSVlc32MhljbEbwx6yMMclLTk5Gd3c3EhIScP36dTQ1NaGwsBCJiYkYHR2Fg4MDnJyccPr0aej1emi1WqSmppp62YwxZhTczDHGJM/d3R3Xrl3D6OgoNBoNgoODkZKSAqVSCblcDrlcjuzsbFRWViIoKAh79+7F4cOHTb1sxhgzCr6blTHGGGNMwvidOcYYY4wxCeNmjjHGGGNMwriZY4wxxhiTMG7mGGOMMcYkjJs5xhhjjDEJ42aOMcYYY0zCuJljjDHGGJMwbuYYY4wxxiSMmznGGGOMMQnjZo4xxhhjTMK4mWOMMcYYk7D/AzR1ZnPQE4IqAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BHYaUf-Iqu9"
      },
      "source": [
        "**Challenge**: Use the <code>.groupby()</code> and <code>.agg()</code> function together to figure out the average number of parts per set. How many parts did the average LEGO set released in 1954 compared to say, 2017?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W7BcH9vuIqu9"
      },
      "source": [
        "parts_per_set = sets.groupby('year').agg({'num_parts':pd.Series.mean}) #To get the average"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bAeTe2XqIqvB"
      },
      "source": [
        "### Scatter Plots in Matplotlib"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SAViZ_TYIqvB"
      },
      "source": [
        "**Challenge**: Has the size and complexity of LEGO sets increased over time based on the number of parts? Plot the average number of parts over time using a Matplotlib scatter plot. See if you can use the [scatter plot documentation](https://matplotlib.org/3.1.0/api/_as_gen/matplotlib.pyplot.scatter.html) before I show you the solution. Do you spot a trend in the chart?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EQNZ0D7JIqvB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "c001d346-901b-4158-ae12-32924ca4a449"
      },
      "source": [
        "plt.scatter(parts_per_set.index[:-2], parts_per_set.num_parts[:-2])"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7e64301597e0>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xK226Ip-IqvE"
      },
      "source": [
        "### Number of Sets per LEGO Theme"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VKHa1FePIqvE"
      },
      "source": [
        "LEGO has licensed many hit franchises from Harry Potter to Marvel Super Heros to many others. But which theme has the largest number of individual sets?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hOBcNrC9IqvE",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        },
        "outputId": "f0e60920-5d9f-4e12-890b-faa2c3bc962f"
      },
      "source": [
        "set_theme_count = sets[\"theme_id\"].value_counts()\n",
        "set_theme_count.head()"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "theme_id\n",
              "158    753\n",
              "501    656\n",
              "494    398\n",
              "435    356\n",
              "503    329\n",
              "Name: count, dtype: int64"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>theme_id</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>158</th>\n",
              "      <td>753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>501</th>\n",
              "      <td>656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>494</th>\n",
              "      <td>398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>435</th>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>329</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J-i6JULGIqvG"
      },
      "source": [
        "<img src=\"https://i.imgur.com/Sg4lcjx.png\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J_0iuerKIqvG"
      },
      "source": [
        "### Database Schemas, Foreign Keys and Merging DataFrames\n",
        "\n",
        "The themes.csv file has the actual theme names. The sets .csv has <code>theme_ids</code> which link to the <code>id</code> column in the themes.csv."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cp1tMW6oIqvH"
      },
      "source": [
        "**Challenge**: Explore the themes.csv. How is it structured? Search for the name 'Star Wars'. How many <code>id</code>s correspond to this name in the themes.csv? Now use these <code>id</code>s and find the corresponding the sets in the sets.csv (Hint: you'll need to look for matches in the <code>theme_id</code> column)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3uN3wN5sIqvH"
      },
      "source": [
        "themes = pd.read_csv('/content/data/themes.csv')"
      ],
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAO2XlQGIqvJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c9ecbf64-24e1-4e67-c807-022654f44942"
      },
      "source": [
        "for theme_id in themes[themes['name'] == 'Star Wars'].id.values:\n",
        "  print(sets[sets['theme_id'] == theme_id])\n",
        "# themes[themes['name'] == 'Star Wars']"
      ],
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       set_num                                name  year  theme_id  num_parts\n",
            "8786   65081-1  R2-D2 / C-3PO Droid Collectors Set  2002        18          1\n",
            "12051   8000-1                           Pit Droid  2000        18        223\n",
            "12058   8001-1                        Battle Droid  2000        18        336\n",
            "12066   8002-1                     Destroyer Droid  2000        18        567\n",
            "12071   8007-1                               C-3PO  2001        18        339\n",
            "12072   8008-1                        Stormtrooper  2001        18        360\n",
            "12073   8009-1                               R2-D2  2002        18        239\n",
            "12074   8010-1                         Darth Vader  2002        18        388\n",
            "12081   8011-1                          Jango Fett  2002        18        425\n",
            "12083   8012-1                  Super Battle Droid  2002        18        378\n",
            "15273  K8008-1      Darth Vader / Stormtrooper Kit  2002        18          0\n",
            "           set_num                                               name  year  \\\n",
            "850        11912-1                Star Wars: Build Your Own Adventure  2016   \n",
            "855        11920-1  Parts for Star Wars Build Your Own Adventure: ...  2019   \n",
            "1717       20006-1                            Clone Turbo Tank - Mini  2008   \n",
            "1728       20007-1                     Republic Attack Cruiser - Mini  2009   \n",
            "1738       20009-1                                AT-TE Walker - Mini  2009   \n",
            "...            ...                                                ...   ...   \n",
            "15686         VP-4            Star Wars Co-Pack of 7101 7111 and 7171  2000   \n",
            "15689         VP-8                 Star Wars Co-Pack of 7130 and 7150  2000   \n",
            "15707      XWING-1                                Mini X-Wing Fighter  2019   \n",
            "15708      XWING-2                                  X-Wing Trench Run  2019   \n",
            "15709  YODACHRON-1                    Yoda Chronicles Promotional Set  2013   \n",
            "\n",
            "       theme_id  num_parts  \n",
            "850         158         73  \n",
            "855         158         70  \n",
            "1717        158         64  \n",
            "1728        158         84  \n",
            "1738        158         94  \n",
            "...         ...        ...  \n",
            "15686       158          0  \n",
            "15689       158          0  \n",
            "15707       158         60  \n",
            "15708       158         52  \n",
            "15709       158        413  \n",
            "\n",
            "[753 rows x 5 columns]\n",
            "       set_num                            name  year  theme_id  num_parts\n",
            "11013  75023-1  Star Wars Advent Calendar 2013  2013       209        254\n",
            "11046  75056-1  Star Wars Advent Calendar 2014  2014       209        273\n",
            "11080  75097-1  Star Wars Advent Calendar 2015  2015       209        291\n",
            "11131  75146-1  Star Wars Advent Calendar 2016  2016       209        282\n",
            "11173  75184-1  Star Wars Advent Calendar 2017  2017       209        309\n",
            "11206  75213-1  Star Wars Advent Calendar 2018  2018       209        307\n",
            "11245  75245-1  Star Wars Advent Calendar 2019  2019       209        280\n",
            "11281  75279-1  Star Wars Advent Calendar 2020  2020       209        312\n",
            "12019   7958-1  Star Wars Advent Calendar 2011  2011       209        267\n",
            "14352   9509-1  Star Wars Advent Calendar 2012  2012       209        235\n",
            "      set_num                      name  year  theme_id  num_parts\n",
            "14630  9748-1       Droid Developer Kit  1999       261        658\n",
            "14636  9754-1  Dark Side Developers Kit  2000       261        578\n"
          ]
        }
      ]
    },
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      "source": [
        "sets[sets['theme_id'] == 261]"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      set_num                      name  year  theme_id  num_parts\n",
              "14630  9748-1       Droid Developer Kit  1999       261        658\n",
              "14636  9754-1  Dark Side Developers Kit  2000       261        578"
            ],
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"sets[sets['theme_id'] == 261]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"set_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"9754-1\",\n          \"9748-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Dark Side Developers Kit\",\n          \"Droid Developer Kit\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1999,\n        \"max\": 2000,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2000,\n          1999\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"theme_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 261,\n        \"max\": 261,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_parts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56,\n        \"min\": 578,\n        \"max\": 658,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          578\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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          "metadata": {},
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      "source": [
        "sets[sets['theme_id'] == 158]"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           set_num                                               name  year  \\\n",
              "850        11912-1                Star Wars: Build Your Own Adventure  2016   \n",
              "855        11920-1  Parts for Star Wars Build Your Own Adventure: ...  2019   \n",
              "1717       20006-1                            Clone Turbo Tank - Mini  2008   \n",
              "1728       20007-1                     Republic Attack Cruiser - Mini  2009   \n",
              "1738       20009-1                                AT-TE Walker - Mini  2009   \n",
              "...            ...                                                ...   ...   \n",
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              "      <th>num_parts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>850</th>\n",
              "      <td>11912-1</td>\n",
              "      <td>Star Wars: Build Your Own Adventure</td>\n",
              "      <td>2016</td>\n",
              "      <td>158</td>\n",
              "      <td>73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>855</th>\n",
              "      <td>11920-1</td>\n",
              "      <td>Parts for Star Wars Build Your Own Adventure: ...</td>\n",
              "      <td>2019</td>\n",
              "      <td>158</td>\n",
              "      <td>70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1717</th>\n",
              "      <td>20006-1</td>\n",
              "      <td>Clone Turbo Tank - Mini</td>\n",
              "      <td>2008</td>\n",
              "      <td>158</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1728</th>\n",
              "      <td>20007-1</td>\n",
              "      <td>Republic Attack Cruiser - Mini</td>\n",
              "      <td>2009</td>\n",
              "      <td>158</td>\n",
              "      <td>84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1738</th>\n",
              "      <td>20009-1</td>\n",
              "      <td>AT-TE Walker - Mini</td>\n",
              "      <td>2009</td>\n",
              "      <td>158</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15686</th>\n",
              "      <td>VP-4</td>\n",
              "      <td>Star Wars Co-Pack of 7101 7111 and 7171</td>\n",
              "      <td>2000</td>\n",
              "      <td>158</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15689</th>\n",
              "      <td>VP-8</td>\n",
              "      <td>Star Wars Co-Pack of 7130 and 7150</td>\n",
              "      <td>2000</td>\n",
              "      <td>158</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15707</th>\n",
              "      <td>XWING-1</td>\n",
              "      <td>Mini X-Wing Fighter</td>\n",
              "      <td>2019</td>\n",
              "      <td>158</td>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15708</th>\n",
              "      <td>XWING-2</td>\n",
              "      <td>X-Wing Trench Run</td>\n",
              "      <td>2019</td>\n",
              "      <td>158</td>\n",
              "      <td>52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15709</th>\n",
              "      <td>YODACHRON-1</td>\n",
              "      <td>Yoda Chronicles Promotional Set</td>\n",
              "      <td>2013</td>\n",
              "      <td>158</td>\n",
              "      <td>413</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-25d1a18b-23f2-4419-ba66-ceb86d291e7f')\"\n",
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              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
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              "\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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "      fill: #D2E3FC;\n",
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              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f9634c75-1b88-4c0a-8f55-70cb5695159b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9634c75-1b88-4c0a-8f55-70cb5695159b')\"\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",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"sets[sets['theme_id'] == 158]\",\n  \"rows\": 753,\n  \"fields\": [\n    {\n      \"column\": \"set_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"K4487-1\",\n          \"911946-1\",\n          \"75145-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 594,\n        \"samples\": [\n          \"Imperial Shuttle\",\n          \"Resistance Trooper Battle Pack\",\n          \"ARC-170 Starfighter - Mini - Korean Duracell promo package with 8 AA batteries\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 1999,\n        \"max\": 2020,\n        \"num_unique_values\": 22,\n        \"samples\": [\n          2016,\n          2002,\n          2014\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"theme_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 158,\n        \"max\": 158,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          158\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_parts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 372,\n        \"min\": 0,\n        \"max\": 5462,\n        \"num_unique_values\": 384,\n        \"samples\": [\n          811\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "aqt07DSTIqvP",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "d20b31b1-6e2b-442d-bc5b-6657a231eaa9"
      },
      "source": [
        "sets[sets['theme_id'] == 209]"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       set_num                            name  year  theme_id  num_parts\n",
              "11013  75023-1  Star Wars Advent Calendar 2013  2013       209        254\n",
              "11046  75056-1  Star Wars Advent Calendar 2014  2014       209        273\n",
              "11080  75097-1  Star Wars Advent Calendar 2015  2015       209        291\n",
              "11131  75146-1  Star Wars Advent Calendar 2016  2016       209        282\n",
              "11173  75184-1  Star Wars Advent Calendar 2017  2017       209        309\n",
              "11206  75213-1  Star Wars Advent Calendar 2018  2018       209        307\n",
              "11245  75245-1  Star Wars Advent Calendar 2019  2019       209        280\n",
              "11281  75279-1  Star Wars Advent Calendar 2020  2020       209        312\n",
              "12019   7958-1  Star Wars Advent Calendar 2011  2011       209        267\n",
              "14352   9509-1  Star Wars Advent Calendar 2012  2012       209        235"
            ],
            "text/html": [
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              "      <td>209</td>\n",
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              "\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-b5e7cdde-5393-49e4-9d61-e0c9b67e0d49 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-b5e7cdde-5393-49e4-9d61-e0c9b67e0d49');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-91fd8b79-2f1a-4ef9-a5d1-2a1810f72327\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-91fd8b79-2f1a-4ef9-a5d1-2a1810f72327')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-91fd8b79-2f1a-4ef9-a5d1-2a1810f72327 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"sets[sets['theme_id'] == 209]\",\n  \"rows\": 10,\n  \"fields\": [\n    {\n      \"column\": \"set_num\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"7958-1\",\n          \"75056-1\",\n          \"75213-1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Star Wars Advent Calendar 2011\",\n          \"Star Wars Advent Calendar 2014\",\n          \"Star Wars Advent Calendar 2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2011,\n        \"max\": 2020,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          2011,\n          2014,\n          2018\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"theme_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 209,\n        \"max\": 209,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          209\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"num_parts\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 235,\n        \"max\": 312,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          267\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmTCXWKKIqvQ"
      },
      "source": [
        "### Merging (i.e., Combining) DataFrames based on a Key\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "esKQULhcIqvR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "f0da88a8-8f1f-4c51-b3ee-b8fc1b39c76e"
      },
      "source": [
        "set_theme_count = pd.DataFrame({'id':set_theme_count.index, 'set_count':set_theme_count.values})\n",
        "set_theme_count.head()"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id  set_count\n",
              "0  158        753\n",
              "1  501        656\n",
              "2  494        398\n",
              "3  435        356\n",
              "4  503        329"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7b722241-ddf5-44a1-b41b-1ecda815bc99\" 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>id</th>\n",
              "      <th>set_count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>158</td>\n",
              "      <td>753</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>501</td>\n",
              "      <td>656</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>494</td>\n",
              "      <td>398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>435</td>\n",
              "      <td>356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>503</td>\n",
              "      <td>329</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7b722241-ddf5-44a1-b41b-1ecda815bc99')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-7b722241-ddf5-44a1-b41b-1ecda815bc99 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-7b722241-ddf5-44a1-b41b-1ecda815bc99');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-78b09fb7-c5bb-4309-9d46-8ab53ac7598f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-78b09fb7-c5bb-4309-9d46-8ab53ac7598f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-78b09fb7-c5bb-4309-9d46-8ab53ac7598f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "set_theme_count",
              "summary": "{\n  \"name\": \"set_theme_count\",\n  \"rows\": 571,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 207,\n        \"min\": 1,\n        \"max\": 709,\n        \"num_unique_values\": 571,\n        \"samples\": [\n          30,\n          621,\n          400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59,\n        \"min\": 1,\n        \"max\": 753,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          35,\n          64,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i0LobgIvIqvT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "3ea5187b-f786-4fc9-c17d-77c0ef146567"
      },
      "source": [
        "merged_df = pd.merge(set_theme_count, themes, on='id')\n",
        "merged_df.head()"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    id  set_count       name  parent_id\n",
              "0  158        753  Star Wars        NaN\n",
              "1  501        656       Gear        NaN\n",
              "2  494        398    Friends        NaN\n",
              "3  435        356    Ninjago        NaN\n",
              "4  503        329  Key Chain      501.0"
            ],
            "text/html": [
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              "      <td>494</td>\n",
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              "      <th>4</th>\n",
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              "      <td>Key Chain</td>\n",
              "      <td>501.0</td>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-dbb76ead-9210-4616-ae9a-09d2b4e09dad 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-dbb76ead-9210-4616-ae9a-09d2b4e09dad');\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",
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              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
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              "            title=\"Suggest charts\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ec5ddd5b-77b9-4362-9acb-f755fc554dce button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "merged_df",
              "summary": "{\n  \"name\": \"merged_df\",\n  \"rows\": 571,\n  \"fields\": [\n    {\n      \"column\": \"id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 207,\n        \"min\": 1,\n        \"max\": 709,\n        \"num_unique_values\": 571,\n        \"samples\": [\n          30,\n          621,\n          400\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"set_count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59,\n        \"min\": 1,\n        \"max\": 753,\n        \"num_unique_values\": 97,\n        \"samples\": [\n          35,\n          64,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 417,\n        \"samples\": [\n          \"Halloween\",\n          \"Lamborghini\",\n          \"Mini\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"parent_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 195.5437596718359,\n        \"min\": 1.0,\n        \"max\": 697.0,\n        \"num_unique_values\": 67,\n        \"samples\": [\n          279.0,\n          112.0,\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I7UMP7VXIqvU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 655
        },
        "outputId": "8ea228ef-1e66-4c8a-a2da-0b21d5cc0c65"
      },
      "source": [
        "plt.figure(figsize=(16,10))\n",
        "plt.xticks(fontsize=14, rotation=45)\n",
        "plt.yticks(fontsize=14)\n",
        "plt.xlabel('Theme Name', fontsize=14)\n",
        "plt.ylabel('Nr of Sets', fontsize=14)\n",
        "plt.bar(merged_df.name[:10], merged_df.set_count[:10])"
      ],
      "execution_count": 107,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<BarContainer object of 10 artists>"
            ]
          },
          "metadata": {},
          "execution_count": 107
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1600x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}