ch_13_project3.ipynb

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      "source": [
        "# US Baby Names 1880-2010"
      ],
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        "id": "sT6QagkzWqIA"
      }
    },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
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      "source": [
        "for year in range(1880, 2011):\n",
        "    path: str = f'datasets/babynames/yob{year}.txt'\n",
        "    frame: pd.DataFrame = pd.read_csv(path, names= ['name', 'sex', 'birth'])\n",
        "    frame['year'] = year\n",
        "    pieces.append(frame)"
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        "data: pd.DataFrame = pd.concat(pieces, ignore_index= True)"
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              "        name sex  birth  year\n",
              "0       Mary   F   7065  1880\n",
              "1       Anna   F   2604  1880\n",
              "2       Emma   F   2003  1880\n",
              "3  Elizabeth   F   1939  1880\n",
              "4     Minnie   F   1746  1880"
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1690784 entries, 0 to 1690783\n",
            "Data columns (total 4 columns):\n",
            " #   Column  Non-Null Count    Dtype \n",
            "---  ------  --------------    ----- \n",
            " 0   name    1690784 non-null  object\n",
            " 1   sex     1690784 non-null  object\n",
            " 2   birth   1690784 non-null  int64 \n",
            " 3   year    1690784 non-null  int64 \n",
            "dtypes: int64(2), object(2)\n",
            "memory usage: 51.6+ MB\n"
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        "total_births: pd.DataFrame = data.pivot_table(\n",
        "    values= 'birth',\n",
        "    index= 'year',\n",
        "    columns= 'sex',\n",
        "    aggfunc= 'sum'\n",
        ")"
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              "sex        F       M\n",
              "year                \n",
              "1880   90993  110493\n",
              "1881   91955  100748\n",
              "1882  107851  113687\n",
              "1883  112322  104632\n",
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              "        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-2cca2a1d-0464-4d99-b09e-d48e4cd52f45\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-2cca2a1d-0464-4d99-b09e-d48e4cd52f45')\"\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-2cca2a1d-0464-4d99-b09e-d48e4cd52f45 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": "total_births",
              "summary": "{\n  \"name\": \"total_births\",\n  \"rows\": 131,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37,\n        \"min\": 1880,\n        \"max\": 2010,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          1935,\n          1920,\n          1899\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 646001,\n        \"min\": 90993,\n        \"max\": 2043983,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          1048264,\n          1198214,\n          233022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 740107,\n        \"min\": 100748,\n        \"max\": 2155427,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          1040649,\n          1064468,\n          106218\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_births.plot(title= 'Total births by sex and year')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "WSauhlHvb1p8",
        "outputId": "26567c1c-92e7-48f9-e62d-fe26e3f5eb33"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Total births by sex and year'}, xlabel='year'>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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Tpkzh2WefBSomBLSwsOCbb75hzJgxdOrUiWnTphEQEEBsbCxbtmzBxcWFVatWAdTbZ16IazLZPJ7rBCjLly83/Lx69WoFUBwdHcs9rKyslMmTJyuKoigPP/ywAihnz541HHfw4EEFUM6cOdPQlyCM7OqpvYqiKFlZWcrTTz+t+Pv7K9bW1kqbNm2U9957r9z0RkVRlDNnziiDBg1S7O3tFcAwpTYtLU2ZNm2a4unpqTg5OSmjRo1Szpw5U2HabW2n9p46dUq5/fbbFWdnZ6VFixbKrFmzlLy8vHJlq5raW9X01jfeeEMJCAhQLCwsyk0JBSqdslv2/AUFBcpzzz2nhIeHK87Ozoqjo6MSHh6ufPbZZ9e8ntpek967776rAMrbb79d7fkVRZ2qevfddyvBwcGKra2t4u3trYwbN045cOBAhbJfffWV0rNnT8Xe3l5xdnZWunTpojz//PPKlStXlOLiYqV3795KYGCgkp6eXu64jz76SAGUX3/99Zp1WblypdK1a1fFzs5OCQ0NVd555x3lu+++qzANNyQkRBk7dmyF4wcPHqwMHjy43LZjx44pgwcPVuzs7JSAgADljTfeUL799tsaTe3Vi4uLUywtLZW2bdtWWebw4cPKpEmTFA8PD8XW1lYJCQlRJk+erGzatMlQpqaf+eo+j0LUhkZRjDiSqgFpNBqWL19uWEvk119/5d577+XkyZMV+p+dnJzw9fXl1Vdf5e233y43NTAvLw8HBwfWr1/PiBEjGvIShGi2PvroI55++mkuXrxY6YwTUXvJycn4+fkxd+5c/vvf/5q6OkLUitl003Tv3h2tVktiYmK5ef1lDRw4kOLiYi5cuGBooj937hxQfjCjEKL+KIrCt99+y+DBgyUQMaLFixej1WqrnSYtRGPUpIKR7Oxszp8/b/g5KiqKI0eO4O7uTtu2bbn33nsNo8q7d+9OUlISmzZtomvXrowdO5bhw4fTo0cPpk+fzocffmhI3zxixAjatm1rwisTwvzl5OSwcuVKtmzZwvHjx/nrr79MXSWzsHnzZk6dOsVbb73FxIkT6zwjSwiTMnE3Ua3o++Wvfuj7MQsLC5W5c+cqoaGhirW1teLn56fceuutyrFjxwzniI2NVSZNmqQ4OTkpPj4+ytSpU5WUlBQTXZEQzUdUVJQCKG5ubspLL71k6uqYjcGDByvW1tbKkCFDlMuXL5u6OkLUSZMdMyKEEEII82BWeUaEEEII0fRIMCKEEEIIk2oSA1h1Oh1XrlzB2dnZaGmvhRBCCFG/FEUhKysLf39/LCyqbv9oEsHIlStXCAoKMnU1hBBCCFEHly5dIjAwsMr9TSIY0S9ydunSJVxcXExcGyGEEELURGZmJkFBQeUWK61MkwhG9F0zLi4uEowIIYQQTUx1QyxkAKsQQgghTEqCESGEEEKYlAQjQgghhDCpJjFmpCZ0Oh2FhYWmrkaDsLa2rrAysRBCCNFUmUUwUlhYSFRUFDqdztRVaTBubm74+vpK3hUhhBBNXpMPRhRFIS4uDktLS4KCgq6ZVMUcKIpCbm4uiYmJAPj5+Zm4RkIIIcT1afLBSHFxMbm5ufj7++Pg4GDq6jQIe3t7ABITE/H29pYuGyGEEE1ak29G0Gq1ANjY2Ji4Jg1LH3gVFRWZuCZCCCHE9WnywYhecxs70dyuVwghhPkym2BECCGEEE2TBCNCCCGEMCkJRoQQQghhUhKMCCFEdRQFCnNMXQshzJYEI1X4/fff6dKlC/b29nh4eDB8+HByctQvo2+++YYOHTpgZ2dH+/bt+eyzzwzHTZ8+na5du1JQUACoCdm6d+/OlClTTHIdQggjWPcy/F8IRGwwdU2EMEsSjFQiLi6Ou+++m+nTp3P69Gm2bt3KpEmTUBSFn376iblz5/LWW29x+vRp3n77bf773//y/fffA/Dxxx+Tk5PDCy+8AMDLL79Meno6n376qSkvSQhRV6mRsPcL0BXB6jlQmGvqGglhdpp80rP6EBcXR3FxMZMmTSIkJASALl26APDqq68yf/58Jk2aBEDLli05deoUX375JQ888ABOTk4sWbKEwYMH4+zszIcffsiWLVtwcXEx2fUIIa7D9vdBUfMZkREDOxbAsJdNWychzIwEI5UIDw/npptuokuXLowaNYqRI0dy++23Y2Njw4ULF3jwwQd5+OGHDeWLi4txdXU1/Ny/f3+effZZ3njjDf7zn/9www03mOIyhBDXK+UCHF2qvh74FOz8CHZ+COF3gUdrdfuVI3B+I1hYgqUtWNlCy0Hg2cZUtRaiyZFgpBKWlpZs2LCBXbt2sX79ej755BNefvllVq1aBcDXX39N3759Kxyjp9Pp2LlzJ5aWlpw/f75B6y6EMCJ9q0jYCBj+OsSfgAub4J/n4bZvYfMbsP9bQCl/nMYCukyGIf8B91YmqboQTYmMGamCRqNh4MCBvP766xw+fBgbGxt27tyJv78/kZGRhIWFlXu0bNnScOx7773HmTNn2LZtG2vXrmXRokUmvBIhRJ2kXIBjJa0iQ14EjQZufg8sbdSWkI+6wv5vAAXajoZu90Ln2yH0RlB06rGf9II1z4K22KSXIkRjJy0jldi7dy+bNm1i5MiReHt7s3fvXpKSkujQoQOvv/46Tz75JK6urowePZqCggIOHDhAWloac+bM4fDhw8ydO5fff/+dgQMH8sEHH/DUU08xePBgWrWSv5CEaDK2vasGFW1GQmBPdZtHaxjwJPz7PuRngEcbGDsfWg0uf2zsIdjyNpzfAPu/hoAe0O2ehr8GIZoICUYq4eLiwvbt2/nwww/JzMwkJCSE+fPnM2bMGEBdpO69997jueeew9HRkS5dujB79mzy8/O57777mDp1KuPHjwfgkUceYc2aNdx///1s375dVtgVoilIi4bjv6mvh7xQft+gZ6EwG1z8oe9j6hiRqwX0gPt+h63/B1vnwcHFEowIcQ0aRVGU6ouZVmZmJq6urmRkZFSYlZKfn09UVBQtW7bEzs7ORDVseM31uoVoEFvehm3vqANRH1hV9/NkJcCCjqArhsd3g09H49VRiCbgWr+/y5IxI0IIUZZOC4d/Ul/3eOD6zuXsA+1uVl8flLFjQlRFghEhhCgrahtkXgY7V2g/9vrP12ua+nz0V0mYJkQVJBgRQoiyDi9Rn7vcAdb213++lkOgRSgUZMDJ5dd/PiHMkAQjQgihl5cGp1err7vfb5xzWliUdvccXGyccwphZiQYEUIIveO/g7YAfLqAX7jxztvtXrCwgsv7IOGk8c4rxLVoi9UVp5sACUaEEEJP30XT/T41yZmxOPuUjj/Z/43xzitEVU6ugPdawfLHTF2TGpFgRAghAOKPQ9wRNcNq18nGP3+fR9TnQz9A0jnjn18Ivb1fwrKpamK+Y0vh3HpT16haEowIIURxAWz6n/q63c3g4G789wi9AdqOUXOOrJdVf00m8Qzkppq6FvVDp4MNr6prJ6GAe8lijuteguJCk1atOhKMCCGat6I8WHoPRKxXV90d8GT9vdeot8DCWn2viA319z6icrEH4fP+8O1I9d/d3Gx5U11VGmDYf+GRreDoBSkR6rIEjZgEI0KI5qsgG366Q134ztoB7v2tdB2aMr7afoH+8zZx5FL69b2fR2voV9KHv+4l0BZd3/lE7ez9Sl1vKCVCTdVvTgpz1esDGLdAXbbAzgVumqtu2/oOZCeZrn7VkGDERKZOnYpGo6nwOH/+vKmrJkTzoNOqgcjFf8HGGe77E1oNqVAsMimb99adJS4jn2eXHaWgWHt97zvoOXDwhORzMpi1IeWmls/zsusTuHLEZNUxujOroTBLzWnTY2rp9m73qjPDCjLUlpNGSoIRExo9ejRxcXHlHi1btjR1tYRoHmJ2Q8wusHGCB/6CkP6VFntzzWmKtOr0yPOJ2Xy25cL1va+dK9z0X/X11nmQk3J95xM1c+Rnddq2b1fodCsoWlj5hDr91RwcKVnCIPweNbeNnoUljH5HfX3we3XMTCMkwYgJ2dra4uvrW+4hq/oK0UDOrFGfO06AgIpdMwBbziay+UwiVhYanhnRFoDPtp7nXEKWocyuC8m8s/YMH2w4x+dbL7B4ZxQ7zyeTV3iNFpTu94NvF3W2w/b3jHZJogo6HRz4Tn3dazqMeRfs3CD+GOz+xKRVM4r0SxC5TX0dflfF/SH9S9ZIUuDIkgatWk3VKhiZN28evXv3xtnZGW9vbyZOnMjZs2erPW7ZsmW0b98eOzs7unTpwt9//13nCldHURRyC4tN8mgCCyALIUBNBHWmJNNqFevPFBbreGPVKQCmDQxl1rAwhnfwpkir8MIfx4hMyubhHw5wz9d7+XzrBT7eFME7a8/w2qpT3PvNXrq+vo47vtjFop1RFb8bLCxhxBvq6/3fQGpkfV2pALi4HVIvqN1xXe4AJ28YPU/dt2WeOq27KTu2FFAg9EZoEVJ5mW73qs/Hf1e7KBsZq9oU3rZtGzNnzqR3794UFxfz0ksvMXLkSE6dOoWjo2Olx+zatYu7776befPmMW7cOH7++WcmTpzIoUOH6Ny5s1Euoqy8Ii0d564z+nlr4tT/RuFgU/Nbunr1apycnAw/jxkzhmXLltVH1YQQZSWcgPQYsLKHVkMrLfL9rotEJufg6WTLkze1QaPR8L8Jndl9YRuHYtK56YNtKApYWmi4JdwfJ1sr8oq0ZOUXcfRSBvGZ+ey/mMb+i2m4O9owoVtA+TdoPRTChquDZze+DpO/b4ALb6b0rSLhd4JtyXdu+N1w4g/1/i+5DaavA/cm2E2uKGoXFEC3e6ou12aE2hqUFQdR29XPXyNSq2Bk7dq15X5evHgx3t7eHDx4kEGDBlV6zEcffcTo0aN57rnnAHjjjTfYsGEDn376KV988UUdq20ehg4dyueff274uaqATghhZPoumrCbwMahwu60nEI+2hQBwPOj2+FsZw2Av5s9z49uz6srT6IocGMbT+aO60gbH+dyxyuKQkxqLp9vvcDS/ZdYvOtixWAEYMT/4MJmOLUCLu2HoN5GvUwBZMWX/nv3ml66XaOB276FxWPV4PTHW+HB9WqrSVNyaa/asmbtCB1uqbqclS10nqQGZsd+a9rByNUyMjIAcHevOkHQ7t27mTNnTrlto0aNYsWKFVUeU1BQQEFBgeHnzMzMGtfJ3tqSU/8bVePyxmRvXbvxHo6OjoSFhdVTbYQQVaqmi+b3g5fJLiimva8zt/cILLfv/n4h2NtY4utix41tPNFUkjZeo9EQ4uHInJFt+ePQZQ7HpHPscjpdA93KF/TppP41e3gJrH8Fpq81bhp6AYd+VBPNBfVT73dZ9m5w3x9q3pG0KFgyCaauUQcZNxX6VpFOE0tbfarS9U41GDm9EsbOrzQQN5U6D2DV6XTMnj2bgQMHXrO7JT4+Hh8fn3LbfHx8iI+Pr/KYefPm4erqangEBQXVuF4ajQYHGyuTPCr7UhJCNDJp0eoYAY0FtB1dYbeiKPyyLwaAKf1DsbAo///awkLD5F5BDGrrVe3/eW9nO27u4gfAD7ujKy809GW1u+jSntIgSRjPmVXqc48ple939oX7l4Ojt/q5+PORJrO4HElnS6crX6uLRi+oL7iFQGE2nK2/sZt1UedgZObMmZw4cYKlS5casz4AvPjii2RkZBgely5dMvp7CCGaKf2XcPCAStO+745MITI5B0cbS27p5n/db/fAgFAAVh69QmpOJSm5Xfyh3+Pqa/1fucI48jNKB6dWkkPGwKM13Pe7mh333Fo4+0+DVK/OshJg1VPwWT8oyASPNurnuToajdo6AnDU+L+7r0edgpFZs2axevVqtmzZQmBg4DXL+vr6kpCQUG5bQkICvr6+VR5ja2uLi4tLuYcQQhiFfvxAFV00P+9VW0UmdA/Ayfa6erIB6B7kRpcAVwqLdfy6v4o/rPT990mNMwdEk3Vpn5pxtUUouFYyZqcsv3AYMEt9ve5FKMqv9+rVWtpFtTvv4+5wcLF6be3HlQRSNfx1rg9GLmyG7MT6qmmt1SoYURSFWbNmsXz5cjZv3lyjBF39+/dn06ZN5bZt2LCB/v0rTzDUXCxevPia42aEEPUgNxWid6qv299cYXdydgHrTqpdyPf0CTbKW2o0Gqb0V6dbLtkTTbFWV7GQZzv1Oe1i4/wl2FTp/61DBtas/I3PgrO/+u+wqxHlH4nZC7/cAx91U+tVlAMBvWDaWrjrJzXYqinPMDWvjqJVZxM1ErUKRmbOnMmSJUv4+eefcXZ2Jj4+nvj4ePLyShccmjJlCi+++KLh56eeeoq1a9cyf/58zpw5w2uvvcaBAweYNWuW8a5CCCFq4swa9a9Jny6VfoH/fvAyRVqF8EBXOgcYbxDj+HB/WjhYE5uex6Yzlfw16uStTrtUdJAiS0IYTfQu9TmkBl0YoA4AHVmS/+Xf+er0b1O7tB8WjYazawAFWg+Du3+FhzZWmTW4Wl1LEqOd+NNo1bxetQpGPv/8czIyMhgyZAh+fn6Gx6+//mooExMTQ1xcnOHnAQMG8PPPP/PVV18RHh7O77//zooVK+olx4gQQlQp5QJsKEnD3nFChd06XenA1Xv6GqdVRM/O2pI7e6vn/Gp7ZMUkaBoNeJW0jkhXjXEU5kLsIfV1TYMRgM63QcgNUJyndonUl8wrsPnNawc8xYVqynpFp+akmblfHWzbbvT1zbrStwrGHlBbCxuBWnfTVPaYOnWqoczWrVtZvHhxuePuuOMOzp49S0FBASdOnODmmys2jwohRL3JS4Of71SfA3qWjg0oY+eFZKJTcnG2tWJ8+PUPXL3atIGh2FlbcDA6jQ2nEioW0AcjyeeM/t7NUuwB0BWBsx+0qEUyM40GxryjzrY69VfpGCNjWz1HXQrguzFVZ+Dd+REknVYXVpz0NXi1Nc57uwaCVwc1yLmw2TjnvE6yNo0Qwrxpi+C3B9Rl410C4a5fwNresLtYq+ObfyN5fIn6V/TE7gG1yqRcUz4udkwfqP5SfGftmYpjRzylZcSoynbR1LYVwbcz9C8JWFfMUNd+Maa4o3CuZMZO5mVYPE5tuSsrOQK2v6u+Hv1/lc78ui5thqvP5zca97x1JMGIEMK8/fMfiNqmrs57z1JwLs17dCgmjXGf7ODNNafJLiime7AbT97Upt6q8tiQ1rRwsOZCUg6/H7xcfqdXe/U5SVpGjMIweLUWXTRlDfsv+PeA/HT440E1qDWWbSVBRptRahCaGasGJHHH1AHMOp06dVdbqHbPdLndeO+tFzZCfT6/SX0/E5NgRAhhvtKi4cC3gAZu+0ZdKbdEYmY+9369lzPxWbg5WPN/k7rwx2MD8HK2rbfquNhZM2uYGuws2Hiu/Mq++ib4lPPms6y9qRQXqgM/oeYzaa5mZQO3fwe2LmrK9S1vG6duCSdLkttp1OUApq5WA5KsK/DljfCWD7ztpwZT1g4w9oP6ycob3F8N0HMS1dWLTUyCESGE+Tpdkn0z9AZoN6bcrtXH4sgr0tLe15nNzwzhrj7BFbKt1of7+gUT2MKehMwCvtsZVbrDJVD95aMrUlOTi7qLO6IOQLV3L+3+qgv3lnDLx+rrHQvUVoTrtf099bnjBPBur86kmrpaHTSrKVlSpLhkevfw16pehfd6WdlAy8Hq6/Mb6uc9akGCESGE+Tq9Un2uZAGxlUevAHBn7yDcHW0arEq2VpY8N0r9BfnF1gskZZWsw2VhAZ4lrSMybuT6lO2iqWkysKp0urVkgT0Flj+qZj+tq6SzcHKF+nrQc6Xbnbxh2hqYmwIvxMCTR+CJQ9D30euoeA3ox41EmH7ciAQjQgjzlBmnNq8DdBhXbldMSi5HLqVjoYGxXf0avGrju/rTJcCVrIJiXlt1snSHYXrv2Qavk1mpbX6R6ox6G3w6Q04S/Pkw6LTVH3O1gmzY9D9AUbOm+laS3kKjURfpc2+ppqi/TucTs8ktvEaXX1hJMHJ5nzrTzIQkGBFCmCf9onOBfdT1X8pYdUxtFenf2gNvZ7uGrhkWFhrmTeqCpYWGNcfiDFlfJRgxAp0WYvaor40VjFjbw+2L1G60qG2w44OaHxt/HFY/DfPbl34my7aK1ANFUfhkUwTDP9jGbZ/vprC4igGqbsFqN5aig8it9Vqn6kgwYiJTp05Fo9Hw2GOPVdg3c+ZMNBpNufwtQoha0nfRdKzYRbOqpIvmlnrIJ1JTnQNceXRQKwBeWXGCjNyi0vENyRKM1Fn0TnXxOBtnNdOusXi1hbHz1ddb3i5tfbmW3Z/BFzfAge+gMAvcW8OtX4J/N+PV6yqKovDuurPM36DOyjodl8nnWy9UfUCbklk1Ju6qkWDEhIKCgli6dGm5dPr5+fn8/PPPBAcbNwOkEM1KTgpcLBk30GF8uV3nErI4E5+FtaWG0Z0avoumrCdvakMrL0eSsgp46+9TpdN7kyMaxXTLJkm/pkzXyWBZMV9MQbEWnU6psL1Gut2jplJXdLBsKhxYpGZ6rUzERlj/svq6w3iYshKeOAjhd9XtvWtAp1N4fdUpQ/AxupO6IO2nWyKISMiq/KCwMvlGrs4M3IAkGDGhHj16EBQUxJ9/lq4P8OeffxIcHEz37t1NWDMhmriza9SFwHy7VliDZuURtVVkcFsvXB2sTVC5UnbWlrx7W1c0GvjtwGV2pDiBpQ0U5UKGkRNtNQeJpyFiPaCB/jMr7L6UmkuvNzZy19d7rj2W4lrGzleDxuwEWD0bFnSEja9BRmxpmeQI+H26GrR0vx8m/witBtfPFN0yFmw8x+JdFwF4Y2JnPr+vB8Pae1OkVXjhz+OVB2EhA8DaEbLj1S4lEzG/YERRoDDHNI86RJXTp09n0aJFhp+/++47pk2bZsw7IkTzo5/Se1UXjaIohvEi9ZHyvS56hbpzX191+ub3ey+DR5i6Q9LC196uT9XnDuMqHQC6ZG80WQXF7ItK5bElh6oeS3Ettk7w4AZ1UKtbiDrwc8cC+LCL2lpyYQv8cjcUZEBQXzV4qecgBCC7oJjvdqhTwudN6sL9/ULQaDS8MbEzjjaWHIxO46e90RUPtLKFsGEQemPplGITMH7OY1MryoW3TfQl89IVsHGs1SH33XcfL774ItHR6odk586dLF26lK1bt9ZDBYVoBvIz1F8IUGFK77HLGUSn5GJvbcmIjj6VHGwaYzr78uOeaLUpPaQtJJ5Sp/fq+/NF9TLj4FjJoq0Dnqqwu7BYxx8lWW81Gth+Lolnlx3lwzu71T6/jJ2L2vLS9zE4+w/s/QIu/gsnl6sPAJcAuHOJ+su+Afx1JJacQi2tvBy5q3eQYXuAmz3Pj27PqytP8n//nGFUJ1+8Xa4atD35xwYJmK7F/FpGmhgvLy/Gjh3L4sWLWbRoEWPHjsXT09PU1RKi6Tq3Xk0c5tmudHYKan/6J5sjABje0ade1p+pqzY+zgDEpOZS7KHPNSKDWGtl35fqv3twfwjqXWH3ptMJJGcX4ulkyzdTemFloWHl0Su8vupkxVWUa8rCUm2FmboaHtsB3e4DS1u12+Oun9T8IQ1AURSW7ClZcbpPMJqrAov7+oUQHuhKTqGWX/dX0v1n4kAEzLFlxNpBbaEw1XvXwfTp05k1S12UaeHChcaskRDNj34V0vblVwd/f/1ZNp5OxMbSgkdubGWCilXN08kGNwdr0nOLiLcJIRAkGKmNgizY/536esATlRZZWvJL+I5egdzUwYf5k8OZ/esRvt8dTXs/F+7uc52TBny7wMSFMOpNdXqxY8P9UXnkUjqn4zKxsbLg9p6BFfZbWmiY0j+UZ5YdZdnBy8wcGtYg2YZrw/xaRjQatavEFI86RpejR4+msLCQoqIiRo0aZeQbIkQzE71DfQ690bBpxeFYPiuZYfB/t3WhS6CrKWpWJY1GQxtvJwDO6QLUjclnTTq7oUnZ97U6RsOjDbQdU2H35bRctkckARi6MCZ0C+D5UerspTdXn+JSahWzYmrLvkWDBiIAP+1VW0XGdfXDzaHybMJjuvjiZGtFTGou+y6mNmT1asT8gpEmyNLSktOnT3Pq1CksLS1NXR0hmq70S5Aeo67xEdQHgMMxaTz/h7oQ2GODWzOpR8W/HBuDMG+1q+ZYnidoLNSxL5nX0cobvRu+vwWSzxupho1U1L+w5S319cCnKk3/vuzAZRQFBrT2IMSjdFzfI4Na0SfUnZxCLc8uO1r3Kb8mlJFbxOqSQdn39q26dcfBxopxJdmGfzvQ+GZqSTDSSLi4uODi4mLqagjRtMXsVp/9wsHWmfwiLY8tOUhhsY7hHXx4ftR1LJpWz/QtI6eTCkpXF764o+4n3PKWmi1035dGqF0jlRoJv90PumLofBt0v69CEa1OYVnJL987ywzsBLX74r07uuJgY8neqFQWlUyLbUr+PHyZ/CId7X2d6RHc4ppl7+ilBuL/HI8nK7+oIapXYxKMmMjixYtZsWJFlftXrFjB4sWLG6w+QpgF/S/vUHXZ+A2nEkjILMDXxY4P76rDrIkG1MZHDUYiErOh9TB1o378S23lppZmCNUHaE3d5QOw5DbY/CZcOaK2HP18lzq11r8HTFhYaVf59nNJXMnIx83BmlElScDKCvFw5KWbOwDw7toznE/Mru8rMZpirc7QRXNv34oDV6/WI7gFrbwcySvSsuZYXENUscYkGBFCmA/DAmlqMKJvjr6jVyBOto17vH6bkm6a6JRcCkOHqhsvbK5bJtaIDWrSN4D4E+ov7qZMp4NVT6lZQre/B18NVtd6ST4Lzv5w18/q+jGV0H8GJnUPxM668m7we/sGc2MbTwqKdTz/e+PvrtHqFFYcjmXEgu2cT8zG3tqSCd0Dqj1Oo9FwR0+1dWhZyTTnxkKCESGEechOhJQIQAPB/YhNz2PH+WSASmcYNDY+LrY421mh1SlctO+kTg/NSYTEk9UffLWza8r8oMCl/Uarp0mc+wcSTqjrzXQYr85cLMoFK3u4+2dwqTytf05BMZvPJAIwqUfVv6w1Gg3v3NYVRxtLDsWks+xg4xtTAZBXqOW3A5cY/eF2Zv96hKjkHFo4WPPu7V1xsatZNuHbegRgaaHhYHQaF5IaTyuQBCNCiMapIAv+mgUxe2tWXt8q4tMJ7Fvw50F10GK/Vu7lBi02VuVm1KQUQugN6o7zm2p3ouKC0mO8O6nPTbmrRlFg2zvq6z4Pq4nEnrsA9/4OD28C/6qXzth8JpGCYh2hHg508r/2mDx/N3ueHqHmeJn3zxlScwqNdgnX60JSNq+tPEmftzfy/O/HiEjMxtXemudGtePf/wyrVTZhbxc7Brf1AtSBvY2FBCNCiMbp+O9w+Ef4p4bLrUeXLIwXMgCdTjE0Q+ubpZsCfVdNREI2hN2kbqztuJGof6EwG5z9oO8j6ramHIxEbIC4o2pLUX81HxM2Dmp2Wp9O1zxUPy7i5i5+1Y6nAHhgQCjtfZ1Jzy3i3bVnrrvqxnD8cgZjPvqXxbsukpVfTLC7A/8Z3Z5//zOUmUPD6tT9qG8pXH3sSt0TvhmZ2QQjjeWGNpTmdr2iGcosWXgs7ihkJVRfvsx4kb1RqcSk5uJka8WYLhUHLTZW+kGs58sOYo3ZXfXKsJU5+7f63Ha0YewMsQfVFpPGKCMWUqpY4r5sq0jvB8HRo8anzSkoZstZtYtmbNearc5sbWnBGxM7A2qStEMxaTV+v9pKzy3k2x1RxKbnVVkmr1DLU78eprBYR8+QFvwwvQ9bnx3C40Na17hbpjJD2nlha2XB5bQ8TsdVsZpvA2vywYg+L0dhYeNpUmsIubnql5O1tWlXHRWi3mSWGe1/fuO1y+amQkLJ2IqQAYY+//Hhfo0q7Xt1wrz1M2qy1AXzXINAW1ja6lMdRVHXSgFoP1Y9h4OHugBa3NF6qvV1uHwAPu2tPk6trLg/cgvEHlDHhlSRWbUqZbtoOvrVPG1C71B3Q8vBy8tPUKytwwDiGnj+92O8sfoUYz7cztoTlc9seXPNKSKTcvBxUVPYD2rrZZQZYQ42VtzYRu2q2XCqBoF+A2jywYiVlRUODg4kJSWRm5tLfn6+WT/y8vJISUkhMTERNzc3SZImzFdWmYRf5zdcu2zMHkABjzZkWbXg7+Pql/sdvZpOFw2UrlETlZxDkU6p/RTfuCPqfbNxgpaD1Kmuwf3VffqWo8Yi8TT8dDsU5agzf36fDmfXlu7PToJN/1Nf95pW63VeattFU9aLY9rjam/N6bhMXvzzuNFboo9fzmB9SRCQmV/MY0sO8d8VJ8gv0hrKbDyVYJi2O/+ObrRwrDyzal2NLFkocsPpeKOet66azp8MVdBoNPj5+REVFWVY+bY5cHNzw9e36TQ/C1FrWWW+JM9vBm0xWFbxlVVmvMg/x+PJL9LR2suR7kFu9V5NY/J3tcPRxpKcQi3RKTmEtR4Gh76veTBypqSLpvWw0tVig/vDmdUlAVsjkRYNP96q5ggJ6AVuwXDyTzWB2R3fq1N2t8+HwqySVpEna3X6unTRlOXhZMv8O8J55McDLDt4GQ8nW14Y077W56nKhxvPAWr69sAWDnyx7QI/7olm9bErdPJ3pYOfM38cUrspH76xJTe0MX56+Zs6eGOhgROxmcSm5xHgVvnU6IbS5IMRABsbG9q0adNsumqsra2lRUSYP30qdI2Fuu7I5X0QMqBiOUUpTXYWMpBtJ9Q1SG4JD6j1X8SmptFoCPNx5uildCISsgkLG6xef9IZyLgMrtVMUdaPF2k/tnSbvmXk0h41X0cl6dIbjLYYLv4La56BrDjwag/3LgNbZ3XF3dOrYOndpeX9usHY+VVO3a1KXbtoyhre0Yf/m9SV5/84xhfbLuDpZMNDRlhg8XBMGpvOJGJpoeGZke1o6enIgNYezPntKMnZBew4n2yYkt7Bz4Vn6ylrsIeTLT1DWrD/YhobTyXwwIDQenmfmjKLYATAwsICOzs7U1dDCGEMRXmQn66+DhsBEesgYn3FYESng3Uvqd0TGguU0IHsWXkagIFhNR/s2Ji08XZSg5HEbMZ08YOAnnB5P1zYAj3ur/rAxDNqLg6NJbQZWbrdr6vaupCXprY4eHeo/4u4WsoF2PUJnF4JuSnqNtdguH85OLirP9/2Hfx6n/pv7ewHN70KXe+sU/B0PV00ZU3uHURyTgHvrj3Lm2tO4+Vsy4Ru1ScXu5YFGyMAuLV7AC091Snng9p6seM/Qzkbn8WpuExOx2WSnF3Ac6PaY2tVf394juzoy/6Laaw/FS/BiBBCVJBVMqDP2kFdcyRinTrFc/hrpWW0xbDyCTj6s/rz6Hc4n+9KSk4hdtYWdA10a+haG0Ub7zJp4QFa36QGI4e+V9deqeqX6/6v1ef2N5f+ggewtIbAXmqLRMzuhg9GtEWweFzpGCB7d+h4C9z4DLiUyY9hZQN3/QSX9qq5Q2zqlhsm+zq7aK72+ODWJGcV8t3OKJ7//RitvZzoHFC3VZ8PXExl+7kkrCw0PDmsTbl9dtaWhAe5Ed6AXYsjOvrw1t+n2RuZSkZuEa4OppsQ0eQHsAohzFDJTJpEjTsHrXsAGvWv/oyS6b5FefDbFDUQ0VjCrV9C30fYE6n+1d0zpAU2Vk3z682wRk1CyZTLXtPUoOzyfnXsR2XyM+HoUvV174cr7te3KJli3EjkNjUQcfBQW0KejYDxH6njRK5maa0me6tjIALw/a6LFBSrY4bq2kVTlkaj4ZWxHRjazouCYh2P/3SQ9NzaDQnIL9Ky/mQ8c/9SZ3zd0SuQYA+H667b9Qr1dKStjxPFOsUQwJlK0/zfKoQwbyUtI5H5zszdGK92VYA6xTctGr4bpaY8t7RVM3KG3wXAnqhUAPq1bJpdNFCa+CwyOUedVursC/1nqjs3vq62CF3t6FI10ZlnO3UWzdWC+6nPpkh+duJ39bnTJHVgbVWDkI0gM7+Ir7ZHAjBrWJjRxgxZWGj48M7uBLs7cCk1j6eWHkFbg/VrYtPzmPnTIbr/bwOP/HiQU3GZ2FtbMuuqVhFTGqGfVWPiKb4SjAghGp+SYCReacHJK5lkh5RMcd33lbpIWtzRkr+0/1S7JVATAe4taRnp17rpBiMBbvY42FhSWKzjXEJJV82AJ9XrTYlQs9KWpSilXTR9Hq68G8evm/qcHgMFDbgeSVE+nC5pzelye72/3bf/RpGRV0SYtxO3hF/f2I6ruTpY88V9PbGztmDbuSTeXXem3FTcq2UXFDNt0T7WHI8jr0hLgJs90waGsmLmQJPPXClrZEd1VubWs4kUFFd9PfVNghEhROOTqQ9G1LEPeyx6qNsTTpRMB+0Jj24vXb8Fdf2O5OxCbK0s6BpYtz79xsDCQkO/VmowtfVcSdO5nQsMel59vXUeFOaUHhC1DZLPqYvIlbQQVeDgro7VAEiNrKeaVyJivTo91yUQAvvU61ul5xby3Y4oAGYPb4OlEZKDXa2jvwvzJnUB4MttkXR9fT33fL2HhVvOE5+Rbyin0yk8/esRziVk4+1sy18zB7LjP0N5dXwn2vk6G71e16NLgCs+LrbkFGrZdSHFZPWQYEQI0fiUDHZMVFoAsCLBS/2FBtBrOkz7p8I0192RahdNz5AW9ToDoSEMba8m+Np6Jql0Y6/p4BYC2Qmwe2Hp9n0lrSLhd6lTZEscu5zOoz8e4HJaSSp5j9bqc2oVqdfrw4k/1OfOk+p9SvFX2yPJKiimva8zN3e+/oGrVbm1eyAv3dweb2dbCot17LqQwnvrzjL0/a18ujmC/CItCzaeY8OpBGysLPjy/p6EB7k12mnmFhYaHh/cmrnjOtLJCGNs6kpm0wghGp+ShGfxJcHI9ogUimf+hVVOAoQOrPQQ/eBVfatCUza0nZqq+2BMWuksBysbuGku/PEgbHkLjv2mtgzpc4v0KT9w9a01p9kblYqfqz2v3dIJ3Furg2BTzjfMRRRkwbmSjKqdb6vXt0rJLmDxrosAzBnR1igp06/lkUGtefjGVlxIymHXhWSWH47lcEw6768/x497oknIVNcBmndrF7oHt6jXuhjD1IEtTV0FaRkRQjRCJQnPEpQWWGjUlNlHctyrDETKjRcxg2AksIUDbX2c0OoUtkeUaR3pNAnC71YToaVEwMFFoOjUQatepcmx4jLy2HdRbSnSB2l4hKnPKQ3UTXPmb3VNHI8w8Auvt7dRFIW31pwmt1BL10BXw4DM+qbRaAjzdmJK/1D+fHwAH93VDV8XO0Mg8vCNLbmtZzVJ6oSBtIwIIRoXRUHJikcDJODO0HbebDqTyNazSfQKda/0kAtJOYbxIuFBTXe8SFlD23tzLiGbLWcSGR9eko/DwgJu/QJGz1OzzkZuUxOZjXij3LGrj8ahX07lTHwWqTmFuHuUZA9tqG4aQxfN7VXnRjGC99ef5c/DsVho4IUx7U3SHaLRaJjQLYARHX1YtFOdWvzUTY1nxkxTIC0jQojGJS8NjVb96zLHxpObu6j9/4bBnJXQ//XfI7jpjxfRG9quZNzIuaSK00jtW0CH8TD2fXhgFfh3K7d75dEr5X7eF5WidtNAw3TT5KbChU3qayN10SiKQkJmProy92LRzigWblGDq7du7cKA1sZfw6U2HGysmDk0jDkj2tbLAFpzJi0jQojGpaSLJkVxxt3FiUFt1fETJ2IzSczKx9u54rIP5jReRK9nSAuc7axIzSnk2OX0Go89iEzK5nhsBlYWGsZ08WPV0SvsvpDC6DYlwUhuCuSlg71bvdWds3+Drhh8uoBXW6Oc8pPN5/lgwzlc7a3p29KdEA8HvimZPfPMiLbc3aeSJGqiyZCWESFE41KSYyRBccfX1Q4vZ1u6lKTf3n5OXUDsUmoub/99mvu/3cuAeZtYXbIWSb9WlXfjNEXWlhYMaqMGYlvO1Dw7pr5V5IY2ntzcWc0hsScyVZ1p41QynqK+u2r0qwyX5IC5XjEpuXy6WW3RycgrYv2pBL7+NwpFgQf6hzBrWJhR3keYjrSMCCEalzIJz3xc1FaQIe28OB6bwd/H44hIyGLRzosUanXlDgsPdG0SMxdqY2h7b9Ycj2PL2STmjKx+9VZFUQzByC3h/vQtaSk6m5BFSnYBHh5h6tTglMjSrLbGptOpY1kAWg0xyinf/vs0hVodA8M8eHZkO3ZHprAvKpXWXk68dHOHRjttVtScBCNCiMYlU98y0gJfQzDizSebz7P5TCKbS1oJbgjzZFxXP8K8nWjt5UQLRxuTVbm+DC7pojoem0FiZj7eLtdemfzklUwik3KwtbJgZCdfnGytaO/rzJn4LPZGpXKzeyuI3lm/40YSTkBuMtg4QWDv6z7d7gsprD0Zj4UG5o5Tk4Z1D27BjCHXX1XReEg3jRCicdF309ACX1f1l2+3IDe8nG0BCPN2YtHU3vz4YB/u6hNMr1B3swxEALycbQkvySa79VxSNaVLu2iGd/DByVb9W1M/jmZPZErDJD6L3Ko+hwxUF767BkVRWLwzig83nlPX4bmKVqfwv9WnALi3b0ijy14qjEdaRoQQjUtWaSr4DiUtAZYWGpY82JeLKTnc1N4bK8vm83fUsPY+HL2cwc97Y7ijZ2CVXRI6ncKqkmDEMBUYdRzN4l0X1WCkrT7XSH0GI1vU59ZDqy367Y4o3lxzGoDolFzm3xFeLmHZr/svcTouExc7K54eYZyBsKJxaj7/o4UQTUOZhGe+Zbol2vk6M6qTb7MKRADu6RuMvbUlRy6ls+l01QNZN5xOIC4jHxc7K4aUZHAF6FuygvG5hGzS7ILUjSkXMCQiMaaifIguWRm4mvEiG08l8NbfaiCi0cDyw7HMXXkCRVHQ6hR+2hvNvJL9s4e3xd1MW7+Eqnn9rxZCNHpKSSr4BKUFfq7XHiPRHHg52zJ1YCigJvjSVbF0/Vfb1cyq9/ULwc66NNdKC0cb2pd0b+xJL0kIV5ChTvE1tkt7oTgPnHzBq32VxU5dyeTJpYdRFLi7TzAf3tkNjQaW7InhpeXHmfTZTl5efoKsgmJ6h7bg/v4hxq+raFQkGBFCNB7aIshRx0YkaTzwcLI1cYUah0cHtcLZ1ooz8VmsOR5XYf/B6FQORqdhY2nB1AGhFfb3b622juyKzgHXMq0jxqYfL9JqSKVZV4u1OradS+Kh7/eTW6hlYJgH/5vQiQndAnhroroa7i/7LnH0cgbOtla8Nr4jvzzcD+tm1hrWHMm/sBCi8ciKR4NCoWKJtZOHZLEs4eZgw0M3quncF2yoONjzy21qq8it3QMqnXGjH8S6OzIF3EvSwtfHjBr9eJGrumgupeby2sqT9Ju3mQe+28eVjHxaeTny2T09DYHGPX2DmTuuI3bWFkzs5s+mZwczdWDLZtct11zJAFYhRONR0kWTSAu8XR1MXJnGZfoNoSzeFUVkcg5/Ho5lci+1hSMyKZsNpxMAeHhQ5auv9ijJv3IhKZvisFZYRW0z/oya3FS4ckR9XSYYKSzWMfnL3cRl5APg7mjD2C5+zBwapq5GXMb0G1oydUBova+6KxofCUaEEI1HVuWDVwU421nz+JDWvP33Gd5dexY7a0vGdvHjmx1qJtKb2nsT5l351FdPJxtc7KzIzC8m1TYYbzB+N03UdkBRx4q4+Bk2byoZWOvpZMO7t3flxjZe1+x2kUCkeZJgRAjReGSWZl/1lcGrFUzpH8ov+y4RlZzDk78c5uNNEcSk5gLwyKBWVR6n0Who6eXE0UvpXNL4GicYKcyFDf9Vnx3cIfagur1V+Sm9vx64BMAdvYIY1t7n+t5TmC0JRoQQjUeZdWl8pGWkAjtrS/6aNZDFOy/y7Y4ozidmAxAe5Eafltdel6e1pyNHL6VzusiHngCpker03rqmUj/6M+z/puL2Ml00V9Lz2FaSrE3frSREZSQYEUI0HlmlqeDbu8pMmsq42Fnz5E1tmDYwlO93XWTr2SReHlv9+iwtPR0BOJbjBhpLKMpRx+iU6VKplYgN6nO7m9XMrrlp4OQFbUYYiiw7cBlFUROv6d9fiMpIMCKEaDxKEp7FKy0YLC0j1+RsZ82sYW2YNaxNjcq39FKDgfPJ+eAWDGlR6oyaugQjRfmli+ENfRl8O1cootMp/FbSRXNnb2kVEdcmc6aEEI1Hmdk0MoDVuFp5OgEQlZwDHiVp4RNP1+1k0TvU5GbO/uDTqdIiOy8kE5ueh7OdFWM617H1RTQbEowIIRoNXUlW0GTFVQawGlmopzpVOi23iFz/furGM6vrdjJ9F02bEVWOOVm6X20VubV7QLmMsEJURoIRIUTjoNOiyUsDoMjWDQcb6UU2JgcbK/xLArwL3iXjOi7+C9nVrwZcQcR69bnNyEp3p+YUsuGkmvtEumhETUgwIoRoHPLS0aCuu2Ln7Gniypgn/biRM/nu4N8dFB2cXlm7k6RcUGfiWFhXuRjemuNxFGp1dA5woZO/63XWWjQHEowIIRqHvFQAMhUHvNycTFwZ86Sf0RKVnAMdJ6obT62o3UnOrVOfQweCbeX/TlvOqKsL39xFxoqImpFgRAjROJSMF0lVnGXwaj0pN4i100R148UdkJ1Y85NU00WTX6Rl14VkAIa1965rVUUzI8GIEKJxKAlG0nCWwav1RN9NE5mUAy1Cwb9H7bpqCrIheqf6uopgZHdkCvlFOvxc7WjnU3l6eiGuJsGIEKJxKNMyItlX60drfctISg46nVLaOnJyRWmhonxIOqdmZ71a1HbQFqqBjH568FW2lnTRDGnnXW0iNiH0JBgRQjQOueqYkXScpJumngS0sMfG0oLCYh2x6Xml40aid0JWAkRuhYV9YGFvWDwOYvaUHpwaBQe+VV+3GVnplF5FUdhyVp2dI100ojZk7pwQonEo0zLSxkVSwdcHSwsNIR4ORCRmE5WcQ1DbEAjoqS5y99NtEH+8tHD0DvhuFLQepi6Gd6lMYNJuTKXnv5CUQ0xqLjaWFgxo7VHPVyPMibSMCCEaBUU/ZkRxxt3RxsS1MV/6GTWRSeoie4bWEX0g0vthmLEXejygrmFzYbMaiGgs1BV5b/u2wsq8elvPql00fVu542grf+uKmpNPixCiUdBmp2AFpOKMh6O0jNQX/SDWqOQcdUPXybB7Idi5wvgPIWSAuv2Wj2HgU3B4CTi4Q+fbq13HZktJMDK0nXTRiNqRYEQI0Shoc9RgJMfCBXsbSR9eX/SDWCP1wYizL8w5BRaV3HOP1jD81RqdN7ugmH1R6rifoTJeRNSSdNMIIRoFJUftptHau5u4Juat3PRevcoCkVraEZFMkVahpaejoStIiJqSlhEhRKNgma/+VY29DHysT61KAoUrGXnkF2nrvIhdZn4RLy8/QW5BMS721pxLyAJgSDsvo9VVNB8SjAghTE+nxaowAwALJwlG6pO7ow0udlZk5hdzMSWH9r4udTrPb/svserolQrbb2rvc71VFM1Qrbtptm/fzvjx4/H390ej0bBixYprlt+6dSsajabCIz4+vq51FkKYmzKL5Nk6SzBSnzQaDS29SsaNlO2qqaX1Javy3t4zkJdv7sATw8J4Y0InBobJv5+ovVq3jOTk5BAeHs706dOZNGlSjY87e/YsLi6lEbi3twxwEkKUKJnWm6E44ObkYOLKmL+Ofs4cvZTO7gspdVrMLjm7gP3Rarfa0yPaEuBmb+wqimam1sHImDFjGDOm8oQ31+Lt7Y2bm1utjxNCNANlEp5JjpH6N6qTL7/su8Q/J+J4dXxHrCxr10i+6XQCigKdA1wkEBFG0WCzabp164afnx8jRoxg586dDfW2QoimIE+fCl6CkYYwMMwTNwdrkrMLDdNxa2NdSRfNqI6+xq6aaKbqPRjx8/Pjiy++4I8//uCPP/4gKCiIIUOGcOjQoSqPKSgoIDMzs9xDCGHGpGWkQVlbWjC6kxpIrD4eV6tjswuK2XE+GYBRnSUYEcZR78FIu3btePTRR+nZsycDBgzgu+++Y8CAASxYsKDKY+bNm4erq6vhERQUVN/VFEKYkj4VPM54SDDSIMZ19Qdg7Yl4irW6Gh+37WwShcU6Qj0caOPtVF/VE82MSZKe9enTh/Pnz1e5/8UXXyQjI8PwuHTpUgPWTgjR4Mq0jLSQYKRB9GvljrujDak5heyOTKnxcetPqTMhR3XyRVPJyr1C1IVJgpEjR47g51f1CG5bW1tcXFzKPYQQ5ktbkn01XXGSlpEGYmVpweiSbpY1x2rWVVNYrGPzGXX9mZGdJJ+IMJ5aByPZ2dkcOXKEI0eOABAVFcWRI0eIiYkB1FaNKVOmGMp/+OGH/PXXX5w/f54TJ04we/ZsNm/ezMyZM41zBUKIJq8oSx2DkK5xxsXO2sS1aT7GdVX/KFx7Mp6ikq4aRVGq7LbZHZlCVn4xnk62dA9q0WD1FOav1lN7Dxw4wNChpctHz5kzB4AHHniAxYsXExcXZwhMAAoLC3nmmWeIjY3FwcGBrl27snHjxnLnEEI0b7qSlpFCmxZYWEjTf0Pp29IDTydbkrML2HAqgeTsAhbtvMjFlBzGdPbl0UGtCQ9yA+BMfCbf/BsJwIiOPvLvJIxKoyiKYupKVCczMxNXV1cyMjKky0YIM5Q7vxsOWVE84/g285+TVtOGNPevE/ywO7rK/X1aupORW8TZkrVnAJY+0o9+rSTTqqheTX9/y9o0QgiTs9Ivkucgv+Aa2i3h/oZgpKWnI1MHhBIe5MYPuy6y8ugVQx4SG0sLhrTz4u6+wRKICKOTYEQIYVraYqyL1FxCVk6y4mtD6xXqztdTemFtqWFQGy9D90u3O7vxzKh2/HUkFncHG8Z09sPVQcbziPohwYgQwrTyyyyS5+Ju4so0TyM6Vj4zJsDNnhlDwhq4NqI5MsnUXiGEMMhVuwHURfIcTVwZIYQpSDAihKhfigKnV0NyFYkOyyQ8kxwjQjRPEowIIerX6ZXw673wy11qYHK1MqngJfuqEM2TBCNCiPqjKPDvB+rrlAiIP1axjLSMCNHsSTAihKg/Udsg7kjpzydXVCyTp44ZSUdW7BWiuZJgRAhRf3Z+pD67t1KfT62o0FWj5EjLiBDNnQQjQoj6EXcULmwGjSURQ79AsbKD1EiIP16uWGFWEgBpijNuDhKMCNEcSTAihKgfJa0iSSE3M+KnZPZY9lC3n1xerlhxttoykmvtio2VfCUJ0RzJ/3whhPGlRhmCjnezRwPwc1ZJMHJVV41+kTytrawCK0RzJcGIEML49n4Bio4M/0Esu6wGGZt0PSi2sK3QVWORpwYjOnvJvipEcyXBiBDC+GJ2A/BD4WAAPBxtyMWOg9Y91f2nVhiKWhWkAWDpKIuvCdFcSTAihDAuRVG7aYC/Yp2xtNDw6T1qF81P2WXGjSgKaIuxKVKXprdy9jRJdYUQpifBiBDCuHJToUBdhfeS4s0t4f70b+1B5wAXNmrLdNUc+qHcInl2LhKMCNFcSTAihDCu1EgArijuFGDDjCGtARjZ0Zdc7NjuOEott+pJWP4oAOmKIy2c7E1SXSGE6UkwIoQwrpJgJFrny+hOvrTxcQZgVCdfAGak3UXhjS+CxgLOb1QPUZxxd7Q1TX2FECYnwYgQwrjS1PEi0Yo3k3sHGja39XEixMOB/GLY5P0A3L8CHL0ASMZVsq8K0YxJMCKEMCpdygUAohVfwrycDds1Go2hdWTdyXhoNRge28FSy3HML5osK/YK0YxJMCKEMKqiJDUYuazxJaBF+XEgIzv6ALDpTCJFWh2Kkw+vFtzHXqWDtIwI0YxJMCKEMCpNSTdNkWsIlhaacvu6B7fA08mWrPxi3l9/ltxCLQXFOgBZsVeIZkyCESGE8eRnYFOQCoC1V+sKuy0tNMwe3gaAL7dFMvvXIwDYWFngYGPZYNUUQjQuEowIIYynJNlZkuKCv493pUXu6xfCvEld0Ghgw6kEQM3QqtFoKi0vhDB/EowIIYzHMJPGl1aejlUWu7tPMJ/e3QNrSzUAkS4aIZo3K1NXQAhhRvQ5RhRvWno6XbPo2K5+ONlZ8cIfx7i5i19D1E4I0UhJMCKEMJri5AtYoSY8G3SNlhG9wW292PXCMOmiEaKZk24aIYTRFCaeByDB2h9Pp5p1vUggIoSQYEQIYTQW6RcB0LmGSJAhhKgxCUaEEMZRmItdnjo7xto7zMSVEUI0JRKMCCGMI+0iABmKAz7e/qatixCiSZFgRAhhHCXTei8qvrT0vvZMGiGEKEuCESGEcZRM641RvK+ZY0QIIa4mwYgQ9URRFH7bf4nt55JMXZUGUVAyk+ai4ktLCUaEELUgwYgQ9WTL2USe/+MYD36/n9NxmaauTr0rSFRX602zDcDRVlIYCSFqToIRIeqBoih8uDECgCKtwrPLjlKk1Zm4VvXLMl0dM6Jza2nimgghmhoJRoSopTXH4ug/bxMbSxZ5q8zmM4kcu5yBvbUlrvbWnLySyedbLzRgLRtYcSH2uVcAsPWRab1CiNqRYESIWkjJLuCl5ceJy8jnud+PkpRVUKFM2VaRKQNCeP2WTgB8sjnCfLtr0qOxQEeuYouXb7CpayOEaGIkGBGiFt7++wwZeUUApOUW8d8VJ1AUpVyZzWcSOR6rtoo8cmMrJnTzZ0RHH/PurrmwBYATSigtvWRarxCidiQYEaKG9kSm8Mehy2g08ObEzlhZaFh7Mp41x+MMZa5uFfFwskWj0fDWrZ1xc1C7az7bYn7dNcqZNQBs0PaUmTRCiFqTYESIGigs1vHKihMA3N0nmPv6hTBzqDo2Yu5fJ0nKKuDUlUzmrz9XrlVEz9vZrlx3zakrZtRdk5cO0TsA2Kz0IsjdwbT1EUI0ORKMCFED3+yI5HxiNh6ONvxnVHsAZg4No72vM6k5hQz4v03c/PG/fLpFzbXxwIBQPJxsy53jlnB/RnXyoVindtcUFptJd835jWh0xUToArD2boO1pXytCCFqR741hKhGdkExn2xSg4yXx3bA1cEaABsrC96/IxxrSw1FWgUHG0sGt/Vi7riOPDuybYXzaDQa3pzYhRYO1pyKy2RhSeACEJWcw76o1Ia5IGPTd9HoejKqk6+JKyOEaIokM5EQ1dh2Nom8Ii2hHg7c2j2g3L7OAa78/eSNZOYX0zXQtdpWAS9nW16f0JknfznMwi3nKSjWse1ckmGWzYI7w7m1e2C9XYvRFRegRKxHgzpe5J2ufqaukRCiCZKWESGqsf5UPACjOvmi0Wgq7G/j40zPkBY17p4Y39WP0Z18KdYpfLHtQrnpvu+vO0d+kdY4FW8IF/9FU5hNouJGtmc4bX2cTV0jIUQTJC0jQlxDYbGOzWcSARjZycco59RoNLx5a2fScguxsbJgXFc/BrX1YtJnu4hNz+PH3dE8PKhV9SdqDM78DcBGbXfGdPE3cWWEEE2VBCNCXMPeqBSy8ovxdLKlW1ALo53X08mWXx/tX27b0yPa8vzvx/h0y3km9woyjE1ptBQF3dm/sQDW63rxgnTRCCHqSLpphLiG9SfVlO8jOnpjaVGxi8aYbusRSFsfJzLyivh8WxPIRXLlMBZZceQotsS16EM76aIRQtSRBCNClPhxTzTvrj1DcUmGVJ1OYUPJ+jMjO9b/LBFLCw3/Ga1OG160M4or6Xn1/p7X5azaRbNNF86IriGVjqcRQoiakGBECCAtp5BX/zrBZ1sv8MGGcwAci80gPjMfRxtL+rf2aJB6DGvvTZ+W7hQU6wz1aJR0WnRHfgFgrbY3Y7rIlF4hRN1JMCIEsON8MrqSJWY+23qB9SfjWX9SnUUzpL03dtaWDVIPjUbDi2PU1pE/Dl3m2OX0BnnfWovYgEXmZdIUJ864Daajn4upaySEaMIkGBEC2H4uCQB3RxsAnvntKH8duQLAyI7GmUVTU92DW3Br9wAUBV5bebLCQnyNwoHvAFimHcxN0kUjhLhOEoyIZk9RFLZHqMHI/DvC6RXSgqyCYmLT87C21DC0vXfdTqzTwo4FcOhHqGVA8cKY9jjYWHIoJp0VR2Lr9v71JS0aJWI9AEt1w7ijZxNK0iaEaJQkGBHN3tmELBIyC7CztqB/aw8W3tsDTye1haRfKw9c7Oo4xfbwj7DxNVg5C5ZNhYLsqste2Ayb31IXnQN8XOwMC/HN+/sM2QXFACRm5vPLvhgupebWrU7GcOh7NCjs0HYirH03Wnk5ma4uQgizIHlGRLOn76Lp18oDO2tL7Kwt+fL+Xry/7ixPDGtTt5PmpcGm/5X+fGoFJJ2BO38Cz7DS7cWFsOl12P2p+vOZ1XDv7+AawIM3tOS3A5eITsnl1b9OotXpWH0sjmKdQs+QFvzx+IC61e16FBeiPfgDlsAS7QgeaSrJ2YQQjZq0jIhmb1tJMDK4rZdhW8+QFvzySD/6tHSv20m3/h/kpoBXe5i6Bpx81WDkq8FqK8n+byB6FywaXRqI2LpC4in4diQknsHO2pJXxnYE1MGsK45cobhklO3B6DRiUkzQOnJmNZa5SSQqbqQEDKNXaB3vjxBClCHBiGjWcguL2R+VBsCgMsHIdUk8Dfu+Vl+PngehN8Cj2yCoHxRmw8nlsOYZWDQGYg+CnavaYvL4DvBoA5mX4btRcGkfwzt4M7arH9aWGiZ1D2DVrBsYGKZOM1517Ipx6nstsYfg1/tgxQzY+Dq67fMBWKodwoODK65MLIQQdSHdNKJZ2xuZSqFWR2ALe1p5OtbsoOxEdTxIhwnlu1xAHaj6z39A0UL7cdB6mLrd2Rem/Q0xe+DiDrj4r/qLPrAnTFgIbsFquQfXw8+T4fJ+WHovmpl7+eSu7ihgyAB7S7g/O8+nsPLIFcO4knqh05G9bAZO6acNmywAraJhh/NYZjZAIjghRPMgwYho1vRdNIPaetVseqqiwO/T1WBi6//BwKfgxmfA2h4yr6gtIlHbwNIWRr1V/lgLSwgdqD74j3quq9/TwR2mrISvhkDyWVj7AhaTvirdn3KBSWdfYodVe1Yl9OFsfBbtfOsnDXvhyVU4pZ8mS7Hni+LxeGnS8daks0vXifGD+9Z7enwhRPMhwYho1rZXMl7kmk6vUgMRAG0hbH8Pjv0G7q3UIERRU8lzw2xoEXrtc1UV/Ng4wMTP4NsRcOxX6DQJ2o2GtIvw/XisM2N53fYAa4p7sfJoLM/5tq9Z3WtDpyNz3Zt4Ar9bjSWpyyx+PpVAWm4R3s62vNxDpvMKIYxHghHRbF1KzSUyOQcrCw0DapLuvSgf1r+svr7xWfALh7UvQHq0+gAI7g/d7lUf1yOwF/SfCbs+gdWzwfV3+OVuyFRzjrhrk+lncYpVR514dmQ7oycdyzn2F57Z58hS7Glx09O8O6AzxVodRy9n4Odqh71Nw2SkFUI0DxKMiGZLPwC0R0gLnGuSS2T3p5AeA87+cOMcsHFUx4QcXATFBdB5ktpCYixDX4az/0DKefhykDoOxb01+HSC0yuZbLWD2amdOXIpne7BLYz3vjod2evfxBFYaXcLd/XrBICVpQU9Q4z4PkIIUUJm04hmKTm7gM+3XABgcq+g6g/IjIN/P1Bfj3hdDUQAbJ1gwBMw6FnjBiKgjkOZsBDQqIFIi1B4YJX6fsAYy304kM/Ko8adVZN66E98cs+TqdgTdPMzMjZECFHvpGVENEvz158lq6CYroGuTOoeUP0Bm16HohwI7ANd7qj/CuoF94Ox78P5TTDmHXANABd/cG+FbWokoyz2s/ywMzEpuVxKyyU1p5BnRrbj7j7BdXs/bTEFG9SBt+udb+W2rjJ9VwhR/6RlRDQ7J2IzWLr/EgCvju+IRXV/+eekwNGl6uvR/1f1wNP60vshuPuX0um/Gg2E3w3AnTY7SM8tYtOZRM4lZJOcXcgnmyLQ6eq2uN7ZZf/FryCSTMWBthOelwXwhBANQoIR0awoisL/Vp1CUdR8HT1DapBBNHoHoIBXBzUvSGPQdTIAfTnBCwOcefvWLiye1htnOyuuZOSz72JqrU95bMcawk5/DsCGVv+ha5uWRq2yEEJURbppRLPy9/F49l1Mxc7aghfG1HBKbFTJVN6WN9ZfxWqrRSiEDEQTvZPH3A9C30GgKNzR3o5FRzJZcTiWfq3KzxA6eSWD/VGpHIvN4ERsBnbWlkzsFsDE7gEkxF/Be8MsLDUKu11Gc+v9T5nmuoQQzZIEI6LZUBSFd9edAeCxwa3xd7Ov2YH6vCKhjSgYAeh6J0TvhH3fwKX9cGkvc3OT8bYaz8Lj9/PaLZ2ws1an4P64+yL//eskAC01cUy23EoLsjjzTzCz1rbkIcs1dNCkEmsVSI/Hvqq+60oIIYxIghHRbByMTiM6JRdHG8uarzabnagucAfqGjONSaeJ8Pdz6lo2mZcNm++32sAn+RPZejaR0Z39SMzK5521ZxhlsY9ZTlvpUnik0tMVYYXrfT9g6+DaMPUXQogSEoyIZuOvI+oU2FGdfXGwqeFHX98q4tNZTdXemNi5wrgFcH4D+HdXF+L7ayaOKRFMsNzF8sOhjO7sx3trzzK5eDVzbX6EQgANtBkJvl0g8RSFsUexyo6jcMT/4RTaSMbECCGaFQlGRLNQpNWx5ngcABO61WAqr15UI+2i0et+r/rQ6zUN1r3EPZabmHRmONvOJbH24Dn+tf2zZP+Daqp6t9KpvzYAOh2OFjKeXQhhGrX+9tm+fTvjx4/H398fjUbDihUrqj1m69at9OjRA1tbW8LCwli8eHEdqipE3e04n0xqTiGeTjYMrCr1e1a8uv5LWRd3qM+NafDqtYTfDZa2dLa4SHvdeR798QDTLf/BTZMDnm3h5vfKBSIGEogIIUyo1t9AOTk5hIeHs3DhwhqVj4qKYuzYsQwdOpQjR44we/ZsHnroIdatW1frygpRV38dVtd0GdfVHyvLSj72Oi18MwIW9oWEU+q2zDhIiQA0EDKg4Sp7PRzc1bEkwL2Wm7AryuBhq7/VfUNfUlcOFkKIRqbW3TRjxoxhzJgxNS7/xRdf0LJlS+bPnw9Ahw4d2LFjBwsWLGDUqFG1fXshai23sJj1pxIAuKWbf+WFEk5CRoz6etWTMH1daauIX1ewb0JrsvScBsd+ZbzlboqwxEmTBz5doMMEU9dMCCEqVe9ts7t372b48OHlto0aNYrdu3dXeUxBQQGZmZnlHkLU1cbTieQWaglyt6d7kFvlhWLKfB4v74f938LF7erPjXW8SFWC+4FXexw0BdxntUndNuxl6YoRQjRa9f7tFB8fj4+PT7ltPj4+ZGZmkpeXV+kx8+bNw9XV1fAICqrBQmZCVEHfRTMhPKDq9ObRO9Vnrw7q86bX4dx69XVTC0Y0GrV1RC+gJ7Qdbbr6CCFENRrln0ovvvgiGRkZhselS5dMXSXRRKXlFLLtXBIAE7tX0UWjKBC9S309dj4E9YXCbMiOB40FhPRvoNoaUfidYO2gvh72SsOvpyOEELVQ71N7fX19SUhIKLctISEBFxcX7O0rz4Bpa2uLra1tfVdNNAPbI5Io1il08HMhzNu58kIp5yEnCSxtIbAXjP8YvrgBdEXg103N59HU2LeAe5epSdtaDzN1bYQQ4prqvWWkf//+bNq0qdy2DRs20L9/E/xrUzQ5selqV2BHP5eqC+m7aAJ7g5UteLeHIf9Rt3W8pZ5rWI9Cb4DOk0xdCyGEqFatW0ays7M5f/684eeoqCiOHDmCu7s7wcHBvPjii8TGxvLDDz8A8Nhjj/Hpp5/y/PPPM336dDZv3sxvv/3GmjVrjHcVQlQhMbMAAC/na7S06btoyk7fHfQcdJqkLkgnhBCiXtW6ZeTAgQN0796d7t27AzBnzhy6d+/O3LlzAYiLiyMmJsZQvmXLlqxZs4YNGzYQHh7O/Pnz+eabb2Rar2gQSdlqMOJ9zWCkZCbN1WNDPFpLXg4hhGgAtW4ZGTJkCIqiVLm/suyqQ4YM4fDhw7V9KyGuW1J1LSPpMWp+EY0lBPZpwJoJIYTQa5SzaYQwlmpbRvStIv7dwNapYSolhBCiHAlGhFlLzMwHrtEyoh+82lTSvQshhBmSYESYrZyCYnIKtQB4u9hVXsgweHVgA9VKCCHE1SQYEWYrKUvtorG3tsTRppKBqNlJJQvhoSY6E0IIYRISjAizZRgv4mJbeRr4qG3qs3cndbVbIYQQJiHBiDBbhhwjTpWMFykuhG3vqq/bjGjAWgkhhLiaBCPCbCVlqYNXvV0qCUb2fAbJZ8HBE254uoFrJoQQoiwJRoTZSsyqomUk43Jpq8jIN8DerWErJoQQohwJRoTZ0g9grTCTZt3LUJQDQf2g610mqJkQQoiyJBgRZsvQMlI2x8j5TXBqBWgsYOz7YCH/BYQQwtTkm1iYraSrgxFtMax9QX3d5xHw7WKimgkhhChLghFhtvQtI4ZU8Md/g+RzYN8ChrxowpoJIYQoS4IRYZa0OoXUnDItI9oi2Pp/6s6Bs2XQqhBCNCISjAizlJJdgE4BCw14ONrC4SWQHg2OXtDnYVNXTwghRBkSjAizpO+i8XCyxVJbANvfU3fc+AzYOJqwZkIIIa4mwYgwS0llx4sc+h4yY8HZH3pOM3HNhBBCXE2CEWGW9MFIgKMC299XNw56FqyrWL1XCCGEyUgwIsxSYkkq+CHKfshJBNdg6H6/iWslhBCiMhKMCLOkHzMSpElQN7QaBFY2JqyREEKIqkgwIsySYcyIkqJucAkwYW2EEEJciwQjwizpW0ZaFCepG1z8TVgbIYQQ1yLBiDBL+pYRp4JEdYO0jAghRKMlwYgwO4qiGAaw2ubFqxulZUQIIRotCUaE2ckuKCa/SIcthVjmp6kbpWVECCEaLQlGhNnRjxcJs81QN1g7gp2rCWskhBDiWiQYEWZHP16knX2musHFHzQaE9ZICCHEtUgwIsyOvmWklb5lRMaLCCFEoybBiDA7+paRYOt0dYOMFxFCiEZNghFhdvQzafw0qeoGaRkRQohGTYIRYXb0LSOeOn32VQlGhBCiMZNgRJgdfTDips++6hpowtoIIYSojgQjwuwkZqrBiGNBySJ50jIihBCNmgQjwqzodArRqTnYUIRNviySJ4QQTYEEI8KsXMnII79IR6BlSeZVKzuwb2HaSgkhhLgmCUaEWbmQlANAN9dcdYMkPBNCiEZPghFhVi4kZgPQyUkNSqSLRgghGj8JRoRZiUxWg5EwO8m+KoQQTYUEI8KsXEhUW0QC9GNGJBgRQohGT4IRYVYuJKktI566ZHWDdNMIIUSjJ8GIMBuZ+UWGRfKcCxPVjRKMCCFEoyfBiDAbkSUzabydbbHMuqJulG4aIYRo9CQYEWZDP5OmractZEvLiBBCNBUSjAizoZ9J061FPqCApQ04eJi2UkIIIaolwYgwG/qZNO0dstQNzn5gIR9xIYRo7OSbWpgN/UyaVraZ6gbpohFCiCZBghFhFoq1Oi6mqC0j/hap6kZXCUaEEKIpkGBEmIVLaXkUaRXsrC1wNUzrlZk0QgjRFEgwIsyCfiZNK08nNIZpvdIyIoQQTYEEI8Is6GfStPJyhIxYdaO0jAghRJMgwYgwC/qZNK29nCA9Rt3oGmjCGgkhhKgpCUaEWdDPpGnXQoGckjEj7q1NWCMhhBA1JcGIMAuGYMQqQd3g5At2LiaskRBCiJqSYEQ0eak5haTlFgEQpCsZL+IRZsIaCSGEqA0JRkSTp28VCXCzxyY9Ut3oIV00QgjRVEgwIpq8XedTAOjg5wIp59WNnm1MWCMhhBC1IcGIaPLWnowHYGQnH0iJUDdKN40QQjQZEoyIJi06JYfTcZlYWmgY3t4bUi6oOyQYEUKIJkOCEdGkrStpFenb0h13XQoU5YLGElqEmrZiQgghakyCEdGkrT2hBiOjO/tCckkXTYtQsLQ2XaWEEELUigQjoslKyMznUEw6AKM6+crgVSGEaKIkGBFN1vqSLpoewW74uNjJeBEhhGiiJBgRTdY/ZbtooMxMGskxIoQQTYkEI6JJSs0pZG9UKgCjO/mpG/XdNB7STSOEEE2JBCOiSdp4OgGtTqGjnwvBHg5QXAhp0epO6aYRQogmRYIR0SStP6kuiGfookm7CIoWbJzA2dd0FRNCCFFrEoyIJunklQwABrT2UDcYumhag0ZjoloJIYSoCwlGRJOTW1hMXEY+AK29nNSNhmBEumiEEKKpkWBENDmRSTkAtHCwpoWjjbpR1qQRQogmS4IR0eREJqvBSCt9qwiUyTEiM2mEEKKpkWBENDmRSdkAtPJ0LN1YdsyIEEKIJqVOwcjChQsJDQ3Fzs6Ovn37sm/fvirLLl68GI1GU+5hZ2dX5woLoe+mMbSM5GdCtjq7RrpphBCi6al1MPLrr78yZ84cXn31VQ4dOkR4eDijRo0iMTGxymNcXFyIi4szPKKjo6+r0qJ5i0wuaRnxKmkZ0beKOPmAnYuJaiWEEKKuah2MfPDBBzz88MNMmzaNjh078sUXX+Dg4MB3331X5TEajQZfX1/Dw8fH57oqLZovRVGIKmkZaa0PRuKPqc+ebU1UKyGEENejVsFIYWEhBw8eZPjw4aUnsLBg+PDh7N69u8rjsrOzCQkJISgoiAkTJnDy5Mlrvk9BQQGZmZnlHkIAJGQWkFOoxdJCQ7B7STAStV19DhlouooJIYSos1oFI8nJyWi12gotGz4+PsTHx1d6TLt27fjuu+/466+/WLJkCTqdjgEDBnD58uUq32fevHm4uroaHkFBQbWppjBj+sGrwe4O2FhZgKKUBiMtB5mwZkIIIeqq3mfT9O/fnylTptCtWzcGDx7Mn3/+iZeXF19++WWVx7z44otkZGQYHpcuXarvaoom4oJ+Wq9+Jk3SGchJAit7COxlwpoJIYSoK6vaFPb09MTS0pKEhIRy2xMSEvD1rdl6INbW1nTv3p3z589XWcbW1hZbW9vaVE00E4ZpvV5XddEE9wMr+cwIIURTVKuWERsbG3r27MmmTZsM23Q6HZs2baJ///41OodWq+X48eP4+fnVrqZCUMm0XumiEUKIJq9WLSMAc+bM4YEHHqBXr1706dOHDz/8kJycHKZNmwbAlClTCAgIYN68eQD873//o1+/foSFhZGens57771HdHQ0Dz30kHGvRDQLhmm9no6g08LFf9UdrQabsFZCCCGuR62DkTvvvJOkpCTmzp1LfHw83bp1Y+3atYZBrTExMVhYlDa4pKWl8fDDDxMfH0+LFi3o2bMnu3btomPHjsa7CtEs5BdpuZyWB5S0jMQfg/wMsHUF33AT104IIURdaRRFUUxdiepkZmbi6upKRkYGLi6S1Kq5OhufxagPt+NsZ8WxV0ei2fUxbJgL7W6Gu38xdfWEEEJcpaa/v2VtGtFklA5edUKj0ch4ESGEMBMSjIgmQ79ab2tPRyguhOiSRHsSjAghRJMmwYhoMi6UndZ75RAU5YCDJ3h1MHHNhBBCXA8JRkSTUW5ar6GL5kawkI+xEEI0ZfItLpoERVHKJzy7uEPdEXqjCWslhBDCGCQYEU1CSk4hmfnFaDQQ6uEIaRfVHT6dTFovIYQQ10+CEdEkLN55EYDAFvbYWVlAVsnCjM6SyVcIIZo6CUZEo/fJpgg+3aKuZfT44DDISwNtgbrTuWZrIgkhhGi8JBgRjdrnWy8wf8M5AF4c0557+gZD5hV1p4OHLI4nhBBmQIIR0Wj9si+Gd9aeAeC5Ue14dHBrdUdWnPrs7G+imgkhhDAmCUZEo5RfpOX9dWcBeGJYGDOHhpXu1AcjLjJeRAghzIEEI6JRWnX0Cik5hfi52vHkTW3K78zUt4zIeBEhhDAHEoyIRkdRFBbvugjA/f1DsLa86mOaVTJmRLpphBDCLEgwIhqd/RfTOHklEztrC+7uHVyxQKZ00wghhDmRYEQ0Oot2RgFwa/cAWjjaVCwgLSNCCGFWJBgRjcrltFzWnVQTmj0wILTyQoaEZzJmRAghzIEEI6JR+XF3NDoFBrT2oL2vS8UCxYWQk6S+dpGWESGEMAcSjIhG40RsBr/siwFg2sCWlRfKLmkVsbRRk54JIYRo8qxMXQEhMvOL+GD9OX7YfRGdAmHeTgxr711F4TLTejWahqukEEKIeiPBiDCptSfieWXFCZKz1bVmxnX1Y+64jlhaVBFoGLKvykwaIYQwFxKMCJPIL9Ly9t+n+WF3NACtvBx5Y0JnBoZ5XvtACUaEEMLsSDAiGtyFpGxm/XyY03GZADw6qBVzRrbF1sqy+oP1i+TJ4FUhhDAbEoyIBnXscjr3fL2X7IJiPBxtmD85nCHtqhgfUhlpGRFCCLMjwYhoMJFJ2UxdtJ/sgmJ6hbRg4b098HGxq91JMiUYEUIIcyPBiKigsFiHtaUGjRFnq8Rn5HP/t/tIzSmkc4ALi6b1xtnOuvYnkhV7hRDC7EieEVHOvqhUOsxdy0ebIoxyvsz8Ig5cTGXKd3uJTc+jpacji6f1qVsgoijSTSOEEGZIWkZEOT/vjUarU1fNnTEkDBur2serGXlFzPv7NNvPJXElI9+w3cfFlh+m98HTybZulcvPgKJc9bUEI0IIYTYkGBEGhcU6Np1JBCA9t4h/I5K4qYNPrc5xIjaDGT8dIiY117DNz9WOTv6uvDCmHUHuDnWvoL5VxM4VbK7jPEIIIRoVCUaEwe7IFLLyiw0//3XkSo2DEUVRWLr/Eq+uPElhsY4AN3vevLUzPYJb4Gpfhy6Zyhi6aGRarxBCmBMJRoTB2hPqui/hga4cvZzBhlMJ5BQU42h77Y9JXEYec/86yYZTCQAMa+/NB5PDcXOwMW4FM2XwqhBCmCMZwCoA0OoUNpxSg5FnRrYjxMOBvCItG08nGMrodAr7olI5eimduIw8Coq1LN4ZxfD529hwKgErCw3Pj27HN1N6GT8QAcgqSXgmLSNCCGFWpGWkGdLqFLadS6RLgBtezupg0kMxaSRnF+JiZ0W/Vh5M6BbAx5si+OvIFSZ0C0BRFOauPMGSPTGVnrNHsBvzJnWlna9z/VW87CJ5QgghzIa0jDQziqLw2sqTTF98gIkLdxoWqNN30dzUwQcbKwtuCVdbH7afSyI1p5Bv/o1iyZ4YNBp1QKpVyUJ2zrZWvDmxM78/NqB+AxGQHCNCCGGmpGWkmfl2RxQ/7lEXp4tNz+PxJQdZ8lBf1p1Ug5FRndRWhzBvJzoHuHAiNpMX/jjGhpLumpdv7sBDN7ZCp1NIzS3EydYKO+sarCljDDKAVQghzJK0jJgxnU5BURTDz/8cj+Otv08DMH1gS5ztrNh/MY2p3+3ncloedtYWDG7rZSg/ITwAgPWnElAUuL9fCA/e0BIACwsNnk62DReIgAxgFUIIMyUtI2YkMTOfjacTOR6bwfHYdM7GZ+Foa0U7H2daezvxx8HLKApM6R/Cf8d1YFBbT6Yv3s/uyBQABrf1wt6mNLgYH+7P2/+cRlFgaDsvXh3f0agp4mtFWww5ag4USXgmhBDmRYIRM3A5LZcvt0Xy64FLFBbryu1Lzy1ib1Qqe6NSAbipvTdzx6lBxZB23rw8tiNvrD4FwOjO5QeG+rraMWd4WyISs3l7UhesLE3YkJadAIoONJbg6FV9eSGEEE2GBCNN2MXkHD7bep4/D8VSrFO7Y8IDXRkQ5knXAFc6+buSmV/E2fgsziZkodMpPD2ibbmgYvrAUDLyijh1JZPRnSq2ODxxU5sGu55rSjmvPjv7gkUDdg0JIYSodxKMNEHnE7NYuOUCfx2JpSQGYUBrD54Y1oZ+rdwrdKV0DnCt8lwajYY5I9rWZ3WvX3EhrH9ZfR16g2nrIoQQwugkGGnkMvKKeG7ZUY5dzqBQq6OwWEd2QWnK9qHtvJg1LIyeIe4mrGU9+3c+xB8He3cY+aapayOEEMLIJBhpxHQ6hTm/HjEsXlfWqE4+zBrahi6BVbd6mIUrR+Df99XXY98HJ2+TVkcIIYTxSTDSiH28OYJNZxKxsbLg83t7EOTugLWlBa721rg71kO69camuABWzABdMXScAJ0mmbpGQggh6oEEI41EdEoOWfnFtPd1xsrSgk2nE/hwYwQAb9/apcar55qVbe9A4klw8ISxH4CpphULIYSoVxKMmFhcRh7vrT3Ln4djAbC3tqRbkBsnYjMANSfI7T0DTVlF04jeDTsWqK/HzgdHT9PWRwghRL2RYKQBnYjN4FRcJpYaDZYWGiISs/h2RxT5RWpuEGdbK7IKig1JyHqFtOCVsR1NWWXTyM+E5Y+oeUXC74ZOE01dIyGEEPVIgpEGcj4xi0mf76qQlAygT6g7r4zrQGd/VyISszkYnUZsei7TB7bExqoZZuz/53lIjwG3YBjzrqlrI4QQop5JMNIAtDqF538/RmGxjpaejgS5O6DTKVhbapjcK4jRnX0NuUHa+TrX/+q3jdmJP+HoL6CxgElfg52LqWskhBCinkkw0gC+33WRQzHpONla8dNDffF3szd1lRqfnBQ48C3s/Fj9+cZnILifaeskhBCiQUgwUs9iUnJ5b91ZAF68ub0EImUpCiScgAOL4MjPUJynbg/uD4P/Y9q6CSGEaDASjNQjrU7hxeXHyCvS0q+VO3f3DjZ1laqXFg1r5kDvh6Hd6Pp5j/QYOPYbHF8GSWdKt/t1gwFPQMeJYCkfTSGEaC7kG9+IIpOy+WhTBIdi0kjPLSIrX03bbmdtwTu3dcXCognkydj0OpzfCPEnoNVRsLYz3rmLC+DfD9T07roidZulLbQdBX0fhZCBkktECCGaIQlGjCAxM5+PNkWwdP8ltPqV60rYWlnwxoTOhHg4mqh2tZByAU4uV19nx8ORJdD7IeOc+9I+WPlEaUtIyA3Q7R7oMA7szDylvRBCiGuSYKQO4jPy2XwmkVNxGZy8ksnJK5mGKbs3tffmwRta4uNqh6u9Na721lhbNpHpuTs+UHN72LpCQQbs+Ah6PACW1tUfqyiQcBLijkDcUfWRFQ9FeeqjMEst5+ilTtftdKu0ggghhAAkGKm1dSfjeea3o+VWzgXoHuzGC6Pb07eVh/HfNC9N7c6wcTDO+XQ6NWjwbAO2JdOI0y/B0aXq6zt/hD8ehIySsR3d7y09LuE42DhBi1CwsISCLPW4/d9C0ulrv2/4PTDqLXAw4xWGhRBC1JoEIzWk1SnMX3+Wz7ZeAKCDnwuD2nrS0c+FTv6utPZyNOQKqVRmHPw2Rf3lPWAWdL2zZi0OF7bA0nvVvBu9pkLfx8E1QN2nKJAZq547JxGyEyEnSX1kJ0JuihpwdBgPoTeqxxz/HXZ+pAYObsEw+Qfw7w67PlYXpAu9EVoNVgeSbpirju8Iv0sNiP58GC5sVs9jaQseYepgVH2rh7UDBPQEv3B1MGqLUDWAsrYHOzcJQoQQQlRKoyiKUn0x08rMzMTV1ZWMjAxcXIyYBCt6l5p6vM1IsCjTlZKfAdveRUk4SUKrW9llcyPLjiSyOzKZIRZHmOuxlZY2GWjChkH7sRA8QD0u4xKkRoKtCwT2Ku2GSLkAP05Uf3HruQZBvxng4gfaYtAWgmfb8sdFbISl94C2oPQ4CytoNRRykyE5Agqza3atdq5g7QhZV8pvt7SFYS/DlrehOB+m/AWthqhB04dd1CBkwJNqEJN1BSxt1MCoOL/0HJ5t1bEl4XfJ+A8hhBAGNf393byDkW9HwqW94NVebQnocgec+gtl3StochIMxZIUV1ZqB3Cj5Qnaai5VPI+Nk/rLWVem68anC/R7XG2Z+OVuNXhwb6UO2tz3NWQnVDwPQGAfGPiU+gt/2QNqkNJuLPS4H3Z9CtE7ype3sAJnf3DyAkfvkueS13aucGkPnFmjtpaAur3f49Dldvj7eTj3T+m5AnrBQxtLg6Ft78KWt0r3e7SByd+r9ys9Rg2GbJ3V5GQy/kMIIcRVJBipjrZIncZ6YHH5boaiXAAu6PxYp+vNbZb/4qNJKz3Oxhl6PgCBvSFiA5z9G/JS1X2WtmrXRMYlw3kM/MLh3j/UYKEoHw7/CKf+UgeM6rtroneXbwUB6DgBbvu2tEzsQbWcW7AaFLi3rL67R6dVg67cVAgbXjpdV6eDnQtg85tqPe7+tXxukbx0+Kir2lLU+TYY/1HpGBMhhBCiGhKM1EBaTiHZGanYHP2BFse/wSY3gXzFmk+Kb+VPu1t5646eDGrlhtW5NWrQ4dMJek4De7fSk2iL1fEX9i3UFgoLC/WX/qHv1RaQzFh1HMZdP1e/zkpWAuz7EvZ/UxoA3PpV/ScAu3JYnfnSbkzFfYln1Gm+LQdL64cQQohakWCkBsZ/soPjsRkAWFPMjRbHOKMLJqRVOz66qxveLteZ8EtbpKY79+lSu4CiIAsSTqmtLxZNZFqwEEIIcZWa/v5u1rNpHGwsSx5WONg4EGc7hHu7+vHY4NZYGiNbqqW1OlOltmydIbjv9b+/EEII0QQ062Bk6SP9rj0dVwghhBD1rln3AUggIoQQQphesw5GhBBCCGF6EowIIYQQwqQkGBFCCCGESUkwIoQQQgiTkmBECCGEECYlwYgQQgghTEqCESGEEEKYVJ2CkYULFxIaGoqdnR19+/Zl37591yy/bNky2rdvj52dHV26dOHvv/+uU2WFEEIIYX5qHYz8+uuvzJkzh1dffZVDhw4RHh7OqFGjSExMrLT8rl27uPvuu3nwwQc5fPgwEydOZOLEiZw4ceK6Ky+EEEKIpq/WC+X17duX3r178+mnnwKg0+kICgriiSee4IUXXqhQ/s477yQnJ4fVq1cbtvXr149u3brxxRdf1Og962uhPCGEEELUn5r+/q5Vy0hhYSEHDx5k+PDhpSewsGD48OHs3r270mN2795drjzAqFGjqiwvhBBCiOalVgvlJScno9Vq8fHxKbfdx8eHM2fOVHpMfHx8peXj4+OrfJ+CggIKCgoMP2dmZtammkIIIYRoQhrlbJp58+bh6upqeAQFBZm6SkIIIYSoJ7VqGfH09MTS0pKEhIRy2xMSEvD19a30GF9f31qVB3jxxReZM2eO4eeMjAyCg4OlhUQIIYRoQvS/t6sbnlqrYMTGxoaePXuyadMmJk6cCKgDWDdt2sSsWbMqPaZ///5s2rSJ2bNnG7Zt2LCB/v37V/k+tra22NraGn7WX4y0kAghhBBNT1ZWFq6urlXur1UwAjBnzhweeOABevXqRZ8+ffjwww/Jyclh2rRpAEyZMoWAgADmzZsHwFNPPcXgwYOZP38+Y8eOZenSpRw4cICvvvqqxu/p7+/PpUuXcHZ2RqPR1LbKVcrMzCQoKIhLly7JLJ0y5L5UJPekIrknlZP7UpHck4qayz1RFIWsrCz8/f2vWa7Wwcidd95JUlISc+fOJT4+nm7durF27VrDINWYmBgsLEqHogwYMICff/6ZV155hZdeeok2bdqwYsUKOnfuXOP3tLCwIDAwsLZVrTEXFxez/jDUldyXiuSeVCT3pHJyXyqSe1JRc7gn12oR0at1nhFzIvlLKif3pSK5JxXJPamc3JeK5J5UJPekvEY5m0YIIYQQzUezDkZsbW159dVXyw2WFXJfKiP3pCK5J5WT+1KR3JOK5J6U16y7aYQQQghhes26ZUQIIYQQpifBiBBCCCFMSoIRIYQQQpiUBCNCCCGEMKkmH4xs376d8ePH4+/vj0ajYcWKFeX2Z2dnM2vWLAIDA7G3t6djx4588cUX5crEx8dz//334+vri6OjIz169OCPP/4oVyY1NZV7770XFxcX3NzcePDBB8nOzq7vy6uz6u5LQkICU6dOxd/fHwcHB0aPHk1ERES5Mvn5+cycORMPDw+cnJy47bbbKqwzFBMTw9ixY3FwcMDb25vnnnuO4uLi+r68Ornee5KamsoTTzxBu3btsLe3Jzg4mCeffJKMjIxy52lO96QsRVEYM2ZMpedpSvcEjHdfdu/ezbBhw3B0dMTFxYVBgwaRl5dn2N+UvleMcU/M7bt23rx59O7dG2dnZ7y9vZk4cSJnz54tV8ZY36Nbt26lR48e2NraEhYWxuLFi+v78hpUkw9GcnJyCA8PZ+HChZXunzNnDmvXrmXJkiWcPn2a2bNnM2vWLFauXGkoM2XKFM6ePcvKlSs5fvw4kyZNYvLkyRw+fNhQ5t577+XkyZNs2LCB1atXs337dh555JF6v766utZ9URSFiRMnEhkZyV9//cXhw4cJCQlh+PDh5OTkGMo9/fTTrFq1imXLlrFt2zauXLnCpEmTDPu1Wi1jx46lsLCQXbt28f3337N48WLmzp3bINdYW9d7T65cucKVK1d4//33OXHiBIsXL2bt2rU8+OCDhvM0t3tS1ocffljpcg1N7Z6Ace7L7t27GT16NCNHjmTfvn3s37+fWbNmlctQ3ZS+V4xxT8ztu3bbtm3MnDmTPXv2sGHDBoqKihg5cqTRv0ejoqIYO3YsQ4cO5ciRI8yePZuHHnqIdevWNej11ivFjADK8uXLy23r1KmT8r///a/cth49eigvv/yy4WdHR0flhx9+KFfG3d1d+frrrxVFUZRTp04pgLJ//37D/n/++UfRaDRKbGyska/C+K6+L2fPnlUA5cSJE4ZtWq1W8fLyMlxzenq6Ym1trSxbtsxQ5vTp0wqg7N69W1EURfn7778VCwsLJT4+3lDm888/V1xcXJSCgoJ6vqrrU5d7UpnffvtNsbGxUYqKihRFab735PDhw0pAQIASFxdX4TxN+Z4oSt3vS9++fZVXXnmlyvM25e+Vut4Tc/+uTUxMVABl27ZtiqIY73v0+eefVzp16lTuve68805l1KhR9X1JDabJt4xUZ8CAAaxcuZLY2FgURWHLli2cO3eOkSNHlivz66+/kpqaik6nY+nSpeTn5zNkyBBA/QvHzc2NXr16GY4ZPnw4FhYW7N27t6Ev6boVFBQAYGdnZ9hmYWGBra0tO3bsAODgwYMUFRUxfPhwQ5n27dsTHBzM7t27AfW+dOnSxbAuEcCoUaPIzMzk5MmTDXEpRlOTe1IZfSpnKyt1mafmeE9yc3O55557WLhwIb6+vhXOY073BGp2XxITE9m7dy/e3t4MGDAAHx8fBg8eXO6+mdP3Sk0/K+b+XavvsnV3dweM9z26e/fucufQl9GfwxyYfTDyySef0LFjRwIDA7GxsWH06NEsXLiQQYMGGcr89ttvFBUV4eHhga2tLY8++ijLly8nLCwMUPs5vb29y53XysoKd3d34uPjG/R6jEH/n+HFF18kLS2NwsJC3nnnHS5fvkxcXBygXrONjQ1ubm7ljvXx8TFcc3x8fLn/QPr9+n1NSU3uydWSk5N54403yjUhN8d78vTTTzNgwAAmTJhQ6XnM6Z5Aze5LZGQkAK+99hoPP/wwa9eupUePHtx0002GcRTm9L1S08+KOX/X6nQ6Zs+ezcCBAw0LwRrre7SqMpmZmeXGIDVlzSIY2bNnDytXruTgwYPMnz+fmTNnsnHjRkOZ//73v6Snp7Nx40YOHDjAnDlzmDx5MsePHzdhzeuPtbU1f/75J+fOncPd3R0HBwe2bNnCmDFjyvVnNye1vSeZmZmMHTuWjh078tprrzV8hRtATe7JypUr2bx5Mx9++KFpK9uAanJfdDodAI8++ijTpk2je/fuLFiwgHbt2vHdd9+Zsvr1oqb/f8z5u3bmzJmcOHGCpUuXmroqTZKVqStQn/Ly8njppZdYvnw5Y8eOBaBr164cOXKE999/n+HDh3PhwgU+/fRTTpw4QadOnQAIDw/n33//ZeHChXzxxRf4+vqSmJhY7tzFxcWkpqZW2izdFPTs2ZMjR46QkZFBYWEhXl5e9O3b19A86uvrS2FhIenp6eWi+oSEBMM1+/r6sm/fvnLn1Y8Sb4r3pbp7opeVlcXo0aNxdnZm+fLlWFtbG/Y1t3uyefNmLly4UOEvv9tuu40bb7yRrVu3mt09gervi5+fHwAdO3Ysd1yHDh2IiYkBMLvvleruiTl/186aNcsw2DYwMNCw3Vjfo76+vhVm4CQkJODi4oK9vX19XFKDM+s/g4uKiigqKqrwl62lpaXhL5fc3FyAa5bp378/6enpHDx40LB/8+bN6HQ6+vbtW5+XUO9cXV3x8vIiIiKCAwcOGJrae/bsibW1NZs2bTKUPXv2LDExMfTv3x9Q78vx48fLfXls2LABFxeXCl/CTUlV9wTUFpGRI0diY2PDypUry/WRQ/O7Jy+88ALHjh3jyJEjhgfAggULWLRoEWC+9wSqvi+hoaH4+/tXmOZ57tw5QkJCAPP9Xqnqnpjjd62iKMyaNYvly5ezefNmWrZsWW6/sb5H+/fvX+4c+jL6c5gFU4+gvV5ZWVnK4cOHlcOHDyuA8sEHHyiHDx9WoqOjFUVRlMGDByudOnVStmzZokRGRiqLFi1S7OzslM8++0xRFEUpLCxUwsLClBtvvFHZu3evcv78eeX9999XNBqNsmbNGsP7jB49Wunevbuyd+9eZceOHUqbNm2Uu+++2yTXXBPV3ZfffvtN2bJli3LhwgVlxYoVSkhIiDJp0qRy53jssceU4OBgZfPmzcqBAweU/v37K/379zfsLy4uVjp37qyMHDlSOXLkiLJ27VrFy8tLefHFFxv0Wmvqeu9JRkaG0rdvX6VLly7K+fPnlbi4OMOjuLhYUZTmd08qw1UzLZraPVEU49yXBQsWKC4uLsqyZcuUiIgI5ZVXXlHs7OyU8+fPG8o0pe+V670n5vhd+/jjjyuurq7K1q1by30f5ObmGsoY43s0MjJScXBwUJ577jnl9OnTysKFCxVLS0tl7dq1DXq99anJByNbtmxRgAqPBx54QFEURYmLi1OmTp2q+Pv7K3Z2dkq7du2U+fPnKzqdznCOc+fOKZMmTVK8vb0VBwcHpWvXrhWmn6WkpCh333234uTkpLi4uCjTpk1TsrKyGvJSa6W6+/LRRx8pgYGBirW1tRIcHKy88sorFaZZ5uXlKTNmzFBatGihODg4KLfeeqsSFxdXrszFixeVMWPGKPb29oqnp6fyzDPPGKa5NjbXe0+qOh5QoqKiDOWa0z2pzNXBiKI0rXuiKMa7L/PmzVMCAwMVBwcHpX///sq///5bbn9T+l4xxj0xt+/aqr4PFi1aZChjrO/RLVu2KN26dVNsbGyUVq1alXsPc6BRFEUxdmuLEEIIIURNmfWYESGEEEI0fhKMCCGEEMKkJBgRQgghhElJMCKEEEIIk5JgRAghhBAmJcGIEEIIIUxKghEhhBBCmJQEI0IIIYQwKQlGhBBCCGFSEowIIZosrVZrWGRNCNF0STAihDCKH374AQ8PDwoKCsptnzhxIvfffz8Af/31Fz169MDOzo5WrVrx+uuvU1xcbCj7wQcf0KVLFxwdHQkKCmLGjBlkZ2cb9i9evBg3NzdWrlxJx44dsbW1JSYmpmEuUAhRbyQYEUIYxR133IFWq2XlypWGbYmJiaxZs4bp06fz77//MmXKFJ566ilOnTrFl19+yeLFi3nrrbcM5S0sLPj44485efIk33//PZs3b+b5558v9z65ubm88847fPPNN5w8eRJvb+8Gu0YhRP2QhfKEEEYzY8YMLl68yN9//w2oLR0LFy7k/PnzjBgxgptuuokXX3zRUH7JkiU8//zzXLlypdLz/f777zz22GMkJycDasvItGnTOHLkCOHh4fV/QUKIBiHBiBDCaA4fPkzv3r2Jjo4mICCArl27cscdd/Df//4XLy8vsrOzsbS0NJTXarXk5+eTk5ODg4MDGzduZN68eZw5c4bMzEyKi4vL7V+8eDGPPvoo+fn5aDQaE16pEMKYrExdASGE+ejevTvh4eH88MMPjBw5kpMnT7JmzRoAsrOzef3115k0aVKF4+zs7Lh48SLjxo3j8ccf56233sLd3Z0dO3bw4IMPUlhYiIODAwD29vYSiAhhZiQYEUIY1UMPPcSHH35IbGwsw4cPJygoCIAePXpw9uxZwsLCKj3u4MGD6HQ65s+fj4WFOpztt99+a7B6CyFMR4IRIYRR3XPPPTz77LN8/fXX/PDDD4btc+fOZdy4cQQHB3P77bdjYWHB0aNHOXHiBG+++SZhYWEUFRXxySefMH78eHbu3MkXX3xhwisRQjQUmU0jhDAqV1dXbrvtNpycnJg4caJh+6hRo1i9ejXr16+nd+/e9OvXjwULFhASEgJAeHg4H3zwAe+88w6dO3fmp59+Yt68eSa6CiFEQ5IBrEIIo7vpppvo1KkTH3/8samrIoRoAiQYEUIYTVpa2v+3awc1AMMwEATNJu9wCamwCorQMIVS6KPSqdIMAj9X1tU5p9Zade+tMUb6JOAHbEaAz8w5q7tr7y1EgNd8RgCAKANWACBKjAAAUWIEAIgSIwBAlBgBAKLECAAQJUYAgCgxAgBEiREAIOoB8VSaq/62YD0AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def add_prop(group):\n",
        "    group['prop'] = group['birth'] / group['birth'].sum()\n",
        "    return group"
      ],
      "metadata": {
        "id": "_1QSgCv7cLYO"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "names = data.groupby(['year', 'sex'], group_keys= False).apply(add_prop)"
      ],
      "metadata": {
        "id": "mU2Gn6bDMg4H"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vuFnCBR6Ms2T",
        "outputId": "0dcb990f-1d5b-46f1-ff3e-78d7a1eb3f74"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              name sex  birth  year      prop\n",
              "0             Mary   F   7065  1880  0.077643\n",
              "1             Anna   F   2604  1880  0.028618\n",
              "2             Emma   F   2003  1880  0.022013\n",
              "3        Elizabeth   F   1939  1880  0.021309\n",
              "4           Minnie   F   1746  1880  0.019188\n",
              "...            ...  ..    ...   ...       ...\n",
              "1690779    Zymaire   M      5  2010  0.000003\n",
              "1690780     Zyonne   M      5  2010  0.000003\n",
              "1690781  Zyquarius   M      5  2010  0.000003\n",
              "1690782      Zyran   M      5  2010  0.000003\n",
              "1690783      Zzyzx   M      5  2010  0.000003\n",
              "\n",
              "[1690784 rows x 5 columns]"
            ],
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              "      <td>Anna</td>\n",
              "      <td>F</td>\n",
              "      <td>2604</td>\n",
              "      <td>1880</td>\n",
              "      <td>0.028618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Emma</td>\n",
              "      <td>F</td>\n",
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              "      <th>3</th>\n",
              "      <td>Elizabeth</td>\n",
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              "      <th>4</th>\n",
              "      <td>Minnie</td>\n",
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              "      <td>Zymaire</td>\n",
              "      <td>M</td>\n",
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              "      <td>0.000003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1690780</th>\n",
              "      <td>Zyonne</td>\n",
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              "    </tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1690782</th>\n",
              "      <td>Zyran</td>\n",
              "      <td>M</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.000003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1690783</th>\n",
              "      <td>Zzyzx</td>\n",
              "      <td>M</td>\n",
              "      <td>5</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.000003</td>\n",
              "    </tr>\n",
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              "<p>1690784 rows × 5 columns</p>\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-1ebaf0bf-6743-4042-a4e5-ea1c0514c87e 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-1ebaf0bf-6743-4042-a4e5-ea1c0514c87e');\n",
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              "                                                    [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",
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              "\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    border-radius: 50%;\n",
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              "    display: none;\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "\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",
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              "  }\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",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    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",
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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",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-56f949cd-2e89-4c50-9f51-d7497dc88ebf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7d612951-881c-46a9-b56f-ee9b9ac2d3f4\">\n",
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              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "        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",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('names')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('names');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "names"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "names.groupby(['year', 'sex'])['prop'].sum().all()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Lxfe4ybMzjb",
        "outputId": "822046ad-568b-4650-ad0f-8356b0292e07"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_top1000(group):\n",
        "    return group.sort_values('birth', ascending= False)[:1000]"
      ],
      "metadata": {
        "id": "9G_2ol4xM952"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top1000 = (\n",
        "    names.groupby(['year', 'sex'])\n",
        "    .apply(get_top1000)\n",
        "    .reset_index(drop= True)\n",
        ")"
      ],
      "metadata": {
        "id": "Ofjf4kF7N_G8"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top1000"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "eTzM15cCOmrT",
        "outputId": "0f5a02a8-a2c9-46d6-97ab-10eda5548136"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             name sex  birth  year      prop\n",
              "0            Mary   F   7065  1880  0.077643\n",
              "1            Anna   F   2604  1880  0.028618\n",
              "2            Emma   F   2003  1880  0.022013\n",
              "3       Elizabeth   F   1939  1880  0.021309\n",
              "4          Minnie   F   1746  1880  0.019188\n",
              "...           ...  ..    ...   ...       ...\n",
              "261872     Camilo   M    194  2010  0.000102\n",
              "261873     Destin   M    194  2010  0.000102\n",
              "261874     Jaquan   M    194  2010  0.000102\n",
              "261875     Jaydan   M    194  2010  0.000102\n",
              "261876     Maxton   M    193  2010  0.000102\n",
              "\n",
              "[261877 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f42e2513-6517-4d22-b555-b55256fbf805\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>name</th>\n",
              "      <th>sex</th>\n",
              "      <th>birth</th>\n",
              "      <th>year</th>\n",
              "      <th>prop</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Mary</td>\n",
              "      <td>F</td>\n",
              "      <td>7065</td>\n",
              "      <td>1880</td>\n",
              "      <td>0.077643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Anna</td>\n",
              "      <td>F</td>\n",
              "      <td>2604</td>\n",
              "      <td>1880</td>\n",
              "      <td>0.028618</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Emma</td>\n",
              "      <td>F</td>\n",
              "      <td>2003</td>\n",
              "      <td>1880</td>\n",
              "      <td>0.022013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Elizabeth</td>\n",
              "      <td>F</td>\n",
              "      <td>1939</td>\n",
              "      <td>1880</td>\n",
              "      <td>0.021309</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Minnie</td>\n",
              "      <td>F</td>\n",
              "      <td>1746</td>\n",
              "      <td>1880</td>\n",
              "      <td>0.019188</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>261872</th>\n",
              "      <td>Camilo</td>\n",
              "      <td>M</td>\n",
              "      <td>194</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.000102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261873</th>\n",
              "      <td>Destin</td>\n",
              "      <td>M</td>\n",
              "      <td>194</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.000102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261874</th>\n",
              "      <td>Jaquan</td>\n",
              "      <td>M</td>\n",
              "      <td>194</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.000102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261875</th>\n",
              "      <td>Jaydan</td>\n",
              "      <td>M</td>\n",
              "      <td>194</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.000102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261876</th>\n",
              "      <td>Maxton</td>\n",
              "      <td>M</td>\n",
              "      <td>193</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.000102</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>261877 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-f42e2513-6517-4d22-b555-b55256fbf805')\"\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",
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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",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
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              "\n",
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              "      fill: #D2E3FC;\n",
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              "\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",
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              "\n",
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              "        document.querySelector('#df-f42e2513-6517-4d22-b555-b55256fbf805 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-f42e2513-6517-4d22-b555-b55256fbf805');\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",
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              "  </div>\n",
              "\n",
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              "      --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",
              "    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",
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              "  @keyframes spin {\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",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "      try {\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-3531b76c-953f-445f-a424-012f43dc4142 button');\n",
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              "\n",
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              "\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('top1000')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('top1000');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top1000"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Analyzing Naming Trends"
      ],
      "metadata": {
        "id": "PwBNxj1lOny2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "boys = top1000.loc[top1000.sex == 'M']"
      ],
      "metadata": {
        "id": "1WpwklWdhSKd"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "girls = top1000.loc[top1000.sex == 'F']"
      ],
      "metadata": {
        "id": "RKZefJughk4f"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_births = top1000.pivot_table(\n",
        "    values= 'birth',\n",
        "    columns= 'name',\n",
        "    index= 'year',\n",
        "    aggfunc= 'sum'\n",
        ")"
      ],
      "metadata": {
        "id": "mKXimf7_hnb7"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "total_births.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "Pygfta6riKxq",
        "outputId": "031e3af6-210c-45fe-922b-9c06a09a994f"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "name  Aaden  Aaliyah  Aarav  Aaron  Aarush  Ab  Abagail  Abb  Abbey  Abbie  \\\n",
              "year                                                                         \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-19301196-894a-48e8-85f6-d66e6b9a8462\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-19301196-894a-48e8-85f6-d66e6b9a8462')\"\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-19301196-894a-48e8-85f6-d66e6b9a8462 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": "total_births"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "total_births.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NBmvYNnFiL0m",
        "outputId": "8f8f8090-ef54-4cf3-901c-7428cbfab88f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 131 entries, 1880 to 2010\n",
            "Columns: 6868 entries, Aaden to Zuri\n",
            "dtypes: float64(6868)\n",
            "memory usage: 6.9 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subset = total_births[[\"John\", \"Harry\", \"Mary\", \"Marilyn\"]]"
      ],
      "metadata": {
        "id": "RwG-051wiXYs"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subset.plot(\n",
        "    subplots= True,\n",
        "    title= 'Number of births with given name (per year)',\n",
        "    figsize= (12, 10)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 985
        },
        "id": "JlMATRKgilI6",
        "outputId": "0585cdf5-47b9-440a-a22e-45db232407de"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([<Axes: xlabel='year'>, <Axes: xlabel='year'>,\n",
              "       <Axes: xlabel='year'>, <Axes: xlabel='year'>], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x1000 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Measuring the increase in naming diversity"
      ],
      "metadata": {
        "id": "nMIQGRZji3cg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "table = top1000.pivot_table(\n",
        "    values= 'prop',\n",
        "    index= 'year',\n",
        "    columns= 'sex',\n",
        "    aggfunc= 'sum'\n",
        ")"
      ],
      "metadata": {
        "id": "dGgIf1Erj168"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table.plot(\n",
        "    title= 'Sum of top1000.prop by year and sex',\n",
        "    yticks= np.linspace(0.5, 1.2, 13)\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "zRTj_86Kk3Te",
        "outputId": "f002a553-25ee-4576-ee12-6c63ec92ad7e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Sum of top1000.prop by year and sex'}, xlabel='year'>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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0iczMTKvRWjt37rQct7XM6/n7fSqlOHHiBM2bN7/hubf7mR4/fpwuXbpY9rOyskhOTi42r1NMTAyxsbEkJyezePFievToYfl+v5GYmBh69erF7t27WbRoES1btqRp06aW4/Xq1QPMIySjoqJKLefq1ats3LiRyZMnM3HiRKt7+KeWL19Oly5dmDt3rlV6enp6sdbXsij6fzp+/Ljl1RNAQUEBp0+fJiws7IZlaLVaunbtSteuXXn33Xd54403ePnll9m0aRNRUVHUq1eP/fv307Vr1xv+sZOdnc2QIUNo0qQJ7dq146233uKRRx6hTZs2Nt+buD3kVZeoUJs2bSrxL7mifhBFQ3o9PDzw8fFh69atVvlmzZp16ysJlg+rv082WPQXYY8ePcpVrpubW4nN/Q8++CApKSksWbLEklZYWMiHH36Iu7u7pQ9HkZUrV1q1vOzatYudO3fywAMP2FwnT09PoqKiWLhwoVXfky+++IKsrCyr+X8ee+wxjEYjc+bMsaQZDAbmzZtHRESE5S9jW8rMycnh6NGjXLp0qVjdPv/8c6vzly9fTnJycpnu83Y/0zlz5lgNVf/oo48oLCwsdv6AAQPQaDQ899xznDp1yqrF5kYeeOABfHx8ePPNN9myZUuxc6Ojo/Hw8OCNN94ocdh80XD4olaWv/8s3ozJNXU6XbFyly1bVmJLYVm0bt0aX19fZs+eTX5+viV9/vz5xf4wKsmVK1eKpRUF6EWvYvv27UtSUhKffPJJsby5ublkZ2db9seNG0diYiILFizg3XffJTQ0lEGDBpX4WlfcGaTFR1SoZ599lpycHB555BEaNWpEfn4+27dvZ8mSJYSGhlp1UB02bBj/+9//GDZsGK1bt2br1q0cO3bsttQzLCyMQYMGMWfOHNLT0+ncuTO7du1iwYIF9O7d2+ove1uEh4ezZMkSYmNjadOmDe7u7jz88MOMGDGCjz/+mMGDBxMfH09oaCjLly/nl19+Yfr06VYdfAHq169Phw4dGDlyJAaDgenTp1OjRg3Gjh1rle+///0vgGUOly+++MIyw/Irr7xiyff666/Trl07OnfuzIgRIzh//jzvvPMO3bp1o3v37pZ8ERERPP7448TFxZGWlkb9+vVZsGABZ86cKfYXflnL3LVrF126dGHSpElW8zMBVK9enQ4dOjBkyBBSU1OZPn069evXZ/jw4Td81rfqmZYmPz+frl270rdvXxISEpg1axYdOnSgZ8+eVvl8fX3p3r07y5Ytw8vLy6Yg2tHRkf79+zNjxgx0Ol2xVyweHh589NFHDBw4kFatWtG/f398fX1JTExkzZo1tG/fnhkzZuDh4UGnTp146623KCgooGbNmqxfv57Tp0+XuS6leeihh5gyZQpDhgyhXbt2HDx4kEWLFpWpP05p9/zf//6Xf/3rX9x3333069eP06dPM2/evDKVOWXKFLZu3UqPHj0ICQkhLS2NWbNmUatWLct8RAMHDmTp0qU888wzbNq0ifbt22M0Gjl69ChLly7lhx9+oHXr1vz000/MmjWLSZMm0apVKwDmzZvHvffey4QJE3jrrbfKdY/iFqvAEWVCqO+//149/fTTqlGjRsrd3V05OTmp+vXrq2effValpqZa5c3JyVFDhw5Vnp6eqlq1aqpv374qLS2t1OHsfx+uWtow886dO6umTZvesK4FBQVq8uTJqk6dOsrR0VEFBweruLg4lZeXV6brlCQrK0s98cQTysvLSwFWQ9tTU1PVkCFDlI+Pj3JyclLNmjUrNpy/aOj1tGnT1DvvvKOCg4OVXq9XHTt2VPv37y92PaDU7e9+/vln1a5dO+Xs7Kx8fX3VqFGjrIaSF8nNzVUvvPCCCggIUHq9XrVp00atW7euxPstS5lFw5r/+n9alPbll1+quLg45efnp1xcXFSPHj3U2bNnrc6/3v/nrXimf1c0nH3Lli1qxIgRytvbW7m7u6snn3xSXb58ucRziobl/3UKhrLatWuXAlS3bt1KzbNp0yYVHR2tPD09lbOzs6pXr54aPHiw+u233yx5zp8/rx555BHl5eWlPD091eOPP64uXLhQ5p+v0uTl5an//Oc/KjAwULm4uKj27durHTt2qM6dO1sNPS/6P162bJnV+aVNZTFr1ixVp04dpdfrVevWrdXWrVuLlVmSjRs3ql69eqmgoCDl5OSkgoKC1IABA9SxY8es8uXn56s333xTNW3aVOn1euXt7a3Cw8PV5MmTVUZGhsrMzFQhISGqVatWqqCgwOrc559/Xmm1WrVjx44yPSNxe2mUukk9xoQQt92ZM2eoU6eO1ZIRVdHmzZvp0qULy5Yt47HHHrul16qIZ/rtt9/Su3dvtm7dahm+XVb79++nRYsWfP7552UaDSaEvZM+PkIIUcE++eQT6tate92lH653rru7O3369LkFNROi6pE+PkIIUUG++uorDhw4wJo1a3j//fdtmi7hu+++4/fff2fOnDmMHj1aFgkVoowk8BFCiAoyYMAA3N3dGTp0KP/+979tOvfZZ58lNTWVBx98kMmTJ9+iGgpR9dj8qmvr1q08/PDDBAUFodFoWLly5XXzr1ixgvvvvx9fX188PDyIjIzkhx9+KJZv5syZhIaG4uzsTEREBLt27bI6npeXx6hRo6hRowbu7u48+uijpKamWuVJTEykR48euLq64ufnx4svvkhhYaGttyhEpREaGopSqkr37wHziutKqVvevwdu7zNVSnHt2jU+/fRTq8kEy+LMmTPk5uaycuXKYiPShBClsznwyc7OJiwsjJkzZ5Yp/9atW7n//vtZu3Yt8fHxdOnShYcffpi9e/da8hQN5500aRJ79uwhLCyM6Ohoq2nBn3/+eb777juWLVvGli1buHDhgtU7baPRSI8ePSzDoRcsWMD8+fOtJuMSQgghhJ37J0PCAPXNN9/YfF6TJk3U5MmTLftt27ZVo0aNsuwbjUYVFBSkpk6dqpRSKj09XTk6OloNczxy5IgCLMMF165dq7RarUpJSbHk+eijj5SHh4fVCr5CCCGEsF+3vY+PyWTi2rVrVK9eHTBP8hUfH09cXJwlj1arJSoqih07dgAQHx9PQUGB1ZTrjRo1onbt2uzYsYN77rmHHTt20KxZM/z9/S15oqOjGTlyJIcPH6Zly5bF6mIwGKxm1zSZTFy5coUaNWrYxZpMQgghRFWg/nhtHBQUVGwh4r+77YHP22+/TVZWlmVtnkuXLmE0Gq0CFgB/f3/LGjopKSk4OTnh5eVVLE9KSoolT0llFB0rydSpU6VToBBCCFFFnDt3jlq1al03z20NfBYvXszkyZP59ttv8fPzu52XLlFcXByxsbGW/YyMDGrXrs25c+esFlwUQgghxJ0rMzOT4ODgMnX0v22Bz1dffcWwYcNYtmyZ1SsrHx8fdDpdsRFaqampBAQEABAQEEB+fj7p6elWrT5/z/P3kWBFZRbl+Tu9Xo9ery+W7uHhIYGPEEIIUcmUpZvKbZm5+csvv2TIkCF8+eWXxRbgc3JyIjw8nI0bN1rSTCYTGzduJDIyEjAv5Ojo6GiVJyEhgcTEREueyMhIDh48aDUSbMOGDXh4eNCkSZNbeXtCCCGEqCRsbvHJysrixIkTlv3Tp0+zb98+qlevTu3atYmLiyMpKYnPP/8cML/eGjRoEO+//z4RERGW/jYuLi54enoCEBsby6BBg2jdujVt27Zl+vTpZGdnW1bm9vT0ZOjQocTGxlK9enU8PDx49tlniYyM5J577gGgW7duNGnShIEDB/LWW2+RkpLCK6+8wqhRo0ps1RFCCCGEHbJ1GFjRCrp/3wYNGqSUMq9M/dfVcTt37nzd/EU+/PBDVbt2beXk5KTatm2rfv31V6vjubm56t///rfy9vZWrq6u6pFHHlHJyclWec6cOaMeeOAB5eLionx8fNR//vOfYqvmXk9GRoYCVEZGhk3PRAghhBAVx5bPb1md/S8yMzPx9PQkIyND+vgIIYS44xmNRgoKCiq6GreFk5NTqUPVbfn8lrW6hBBCiEpGKUVKSgrp6ekVXZXbRqvVUqdOHZycnP5RORL4CCGEEJVMUdDj5+eHq6trlZ9012QyceHCBZKTk6ldu/Y/ul8JfIQQQohKxGg0WoKeGjVqVHR1bhtfX18uXLhAYWEhjo6O5S7ntgxnF0IIIcTNUdSnx9XVtYJrcnsVveIyGo3/qBwJfIQQQohKqKq/3vq7m3W/EvgIIYQQwm5I4COEEEIIuyGBjxBCCCHshgQ+QgghhLAbEvgIIYQQguXLl9OsWTNcXFyoUaMGUVFRZGdnA/Dpp5/SuHFjnJ2dadSoEbNmzbKc9/TTT9O8eXMMBgMA+fn5tGzZkpiYmAq5jxuRwEcIIYSwc8nJyQwYMICnn36aI0eOsHnzZvr06YNSikWLFjFx4kRef/11jhw5whtvvMGECRNYsGABAB988AHZ2dmMHz8egJdffpn09HRmzJhRkbdUKpnAUAghhLBzycnJFBYW0qdPH0JCQgBo1qwZAJMmTeKdd96hT58+ANSpU4fff/+djz/+mEGDBuHu7s7ChQvp3Lkz1apVY/r06WzatOmOXfNSAh8hhBDCzoWFhdG1a1eaNWtGdHQ03bp147HHHsPJyYmTJ08ydOhQhg8fbslfWFiIp6enZT8yMpIXXniB1157jXHjxtGhQ4eKuI0ykcBHCCGEsHM6nY4NGzawfft21q9fz4cffsjLL7/Md999B8Ann3xCREREsXOKmEwmfvnlF3Q6HSdOnLitdbeV9PERQgghBBqNhvbt2zN58mT27t2Lk5MTv/zyC0FBQZw6dYr69etbbXXq1LGcO23aNI4ePcqWLVtYt24d8+bNq8A7uT5p8RFCCCHs3M6dO9m4cSPdunXDz8+PnTt3cvHiRRo3bszkyZP5v//7Pzw9PenevTsGg4HffvuNq1evEhsby969e5k4cSLLly+nffv2vPvuuzz33HN07tyZunXrVvStFSOBjxBCCGHnPDw82Lp1K9OnTyczM5OQkBDeeecdHnjgAcC8IOq0adN48cUXcXNzo1mzZowZM4a8vDyeeuopBg8ezMMPPwzAiBEjWLNmDQMHDmTr1q1Wr8TuBBqllKroStwpMjMz8fT0JCMj447tjS6EEMK+5eXlcfr0aerUqYOzs3NFV+e2ud592/L5LX18hBBCCGE3JPARQgghhN2wOfDZunUrDz/8MEFBQWg0GlauXHnd/MnJyTzxxBM0aNAArVbLmDFjrpv/q6++QqPR0Lt3b6v0FStW0K1bN2rUqIFGo2Hfvn1Wx8+cOYNGoylxW7Zsma23KYQQQogqyObAJzs7m7CwMGbOnFmm/AaDAV9fX1555RXCwsKum/fMmTO88MILdOzYscTrdujQgTfffLPEc4ODg0lOTrbaJk+ejLu7u6VzlhBCCCHsm82juh544AGbAonQ0FDef/99AD777LNS8xmNRp588kkmT57Mzz//THp6utXxgQMHAubgqCQ6nY6AgACrtG+++Ya+ffvi7u5e5voKIYQQouq6Y/r4TJkyBT8/P4YOHXpTyouPj2ffvn3XLc9gMJCZmWm1CSGEEKLquiMCn23btjF37lw++eSTm1bm3Llzady4Me3atSs1z9SpU/H09LRswcHBN+36QgghhLjzVHjgc+3aNQYOHMgnn3yCj4/PTSkzNzeXxYsX37D1KC4ujoyMDMt27ty5m3J9IYQQQtyZKnzm5pMnT3LmzBnLjI9gXuwMwMHBgYSEBOrVq2dTmcuXLycnJ4eYmJjr5tPr9ej1etsrLYQQQohKqcIDn0aNGnHw4EGrtFdeeYVr167x/vvvl+v109y5c+nZsye+vr43q5pCCCGEqAJsDnyysrKslpw/ffo0+/bto3r16tSuXZu4uDiSkpL4/PPPLXmK5tzJysri4sWL7Nu3DycnJ5o0aYKzszN333231TW8vLwArNKvXLlCYmIiFy5cACAhIQGAgIAAq9FcJ06cYOvWraxdu9bWWxNCCCHELTR48GAWLFhQLP348ePUr1//ttTB5sDnt99+o0uXLpb92NhYAAYNGsT8+fNJTk4mMTHR6pyWLVtavo6Pj2fx4sWEhISUOjS9JKtWrWLIkCGW/f79+wMwadIkXn31VUv6Z599Rq1atejWrZsttyWEEEKI26B79+7MmzfPKu12vqGRRUr/QhYpFUIIcaerzIuUDh48mPT09Buu+lCSm7VIaYX38RFCCCHEP6OUIrfAWCHXdnHUodFoKuTa5SGBjxBCCFHJ5RYYaTLxhwq59u9TonF1Kns4sXr1aqsVFR544IHbuqamBD5CCCGEuG26dOnCRx99ZNl3c3O7rdeXwEcIIYSo5Fwcdfw+JbrCrm0LNze32zaCqyQS+AghhBCVnEajsel1kz2r8CUrhBBCCCFuFwl8hBBCCGE3pF1MCCGEELfF/PnzK7oK0uIjhBBCCPshgY8QQggh7IYEPkIIIYSwGxL4CCGEEMJuSOAjhBBCCLshgY8QQggh7IYEPkIIIYSwGxL4CCGEEMJuSOAjhBBCCLshgY8QQggh7IYEPkIIIYS4LQYPHoxGo+GZZ54pdmzUqFFoNBoGDx58S+tgc+CzdetWHn74YYKCgtBoNKxcufKG52zevJlWrVqh1+upX79+sbU6jEYjEyZMoE6dOri4uFCvXj1ee+01lFKWPK+++iqNGjXCzc0Nb29voqKi2Llzp1U5PXv2pHbt2jg7OxMYGMjAgQO5cOGCrbcohBBCiFskODiYr776itzcXEtaXl4eixcvpnbt2rf8+jYHPtnZ2YSFhTFz5swy5T99+jQ9evSgS5cu7Nu3jzFjxjBs2DB++OEHS54333yTjz76iBkzZnDkyBHefPNN3nrrLT788ENLngYNGjBjxgwOHjzItm3bCA0NpVu3bly8eNGSp0uXLixdupSEhAS+/vprTp48yWOPPWbrLQohhBDiFmnVqhXBwcGsWLHCkrZixQpq165Ny5Ytb/n1NeqvzSq2nqzR8M0339C7d+9S84wbN441a9Zw6NAhS1r//v1JT09n3bp1ADz00EP4+/szd+5cS55HH30UFxcXFi5cWGK5mZmZeHp68uOPP9K1a9cS86xatYrevXtjMBhwdHS84f0UlZmRkYGHh8cN8wshhBC3W15eHqdPn6ZOnTo4OzubE5WCgpyKqZCjK2g0Zco6ePBg0tPT6dy5M2vWrOHHH38EICoqioceeojNmzfj5eVV4iruJd73H2z5/HYo212V344dO4iKirJKi46OZsyYMZb9du3aMWfOHI4dO0aDBg3Yv38/27Zt49133y2xzPz8fObMmYOnpydhYWEl5rly5QqLFi2iXbt2ZQp6hBBCiEqrIAfeCKqYa790AZzcbDrlqaeeIi4ujrNnzwLwyy+/8NVXX7F58+ZbUEFrtzzwSUlJwd/f3yrN39+fzMxMcnNzcXFxYfz48WRmZtKoUSN0Oh1Go5HXX3+dJ5980uq81atX079/f3JycggMDGTDhg34+PhY5Rk3bhwzZswgJyeHe+65h9WrV5daN4PBgMFgsOxnZmbehDsWQgghxPX4+vrSo0cP5s+fj1KKHj16FPs8v1VueeBTFkuXLmXRokUsXryYpk2bWvoCBQUFMWjQIEu+on5Cly5d4pNPPqFv377s3LkTPz8/S54XX3yRoUOHcvbsWSZPnkxMTAyrV69GU0Iz3NSpU5k8efJtuUchhBDilnF0Nbe8VNS1y+Hpp59m9OjRAGXuN3wz3PLAJyAggNTUVKu01NRUPDw8cHFxAczByvjx4+nfvz8AzZo14+zZs0ydOtUq8HFzc6N+/frUr1+fe+65h7vuuou5c+cSFxdnyePj44OPjw8NGjSgcePGBAcH8+uvvxIZGVmsbnFxccTGxlr2MzMzCQ4Ovqn3L4QQQtxyGo3Nr5sqWvfu3cnPz0ej0RAdHX3brnvLA5/IyEjWrl1rlbZhwwarQCQnJwet1nqAmU6nw2QyXbdsk8lk9aqqpONAqXn0ej16vf661xBCCCHEzafT6Thy5Ijl69vF5sAnKyuLEydOWPZPnz7Nvn37qF69OrVr1yYuLo6kpCQ+//xzAJ555hlmzJjB2LFjefrpp/npp59YunQpa9assZTx8MMP8/rrr1O7dm2aNm3K3r17effdd3n66acB8xD6119/nZ49exIYGMilS5eYOXMmSUlJPP744wDs3LmT3bt306FDB7y9vTl58iQTJkygXr16Jbb2CCGEEKJiVcgIamWjTZs2KaDYNmjQIKWUUoMGDVKdO3cudk6LFi2Uk5OTqlu3rpo3b57V8czMTPXcc8+p2rVrK2dnZ1W3bl318ssvK4PBoJRSKjc3Vz3yyCMqKChIOTk5qcDAQNWzZ0+1a9cuSxkHDhxQXbp0UdWrV1d6vV6FhoaqZ555Rp0/f77M95aRkaEAlZGRYetjEUIIIW6L3Nxc9fvvv6vc3NyKrsptdb37tuXz+x/N41PVyDw+Qggh7nTXm8+mKrtZ8/jIWl1CCCGEsBsS+AghhBDCbkjgI4QQQgi7IYGPEEIIUQnZWxfdm3W/EvgIIYQQlUjR+pM5ORW0KGkFyc/PB/75nD93xJIVQgghhCgbnU6Hl5cXaWlpALi6upa4LFNVYjKZuHjxIq6urjg4/LPQRQIfIYQQopIJCAgAsAQ/9kCr1VK7du1/HORJ4COEEEJUMhqNhsDAQPz8/CgoKKjo6twWTk5OxZa3Kg8JfIQQQohKSqfT3dZ1rqoC6dwshBBCCLshgY8QQggh7IYEPkIIIYSwGxL4CCGEEMJuSOAjhBBCCLshgY8QQggh7IYEPkIIIYSwGxL4CCGEEMJuSOAjhBBCCLshgY8QQggh7IYEPkIIIYSwGzYHPlu3buXhhx8mKCgIjUbDypUrb3jO5s2badWqFXq9nvr16zN//nyr41OnTqVNmzZUq1YNPz8/evfuTUJCglWee++9F41GY7U988wzVnkSExPp0aMHrq6u+Pn58eKLL1JYWGjrLQohhBCiirI58MnOziYsLIyZM2eWKf/p06fp0aMHXbp0Yd++fYwZM4Zhw4bxww8/WPJs2bKFUaNG8euvv7JhwwYKCgro1q0b2dnZVmUNHz6c5ORky/bWW29ZjhmNRnr06EF+fj7bt29nwYIFzJ8/n4kTJ9p6i0IIIYSoojRKKVXukzUavvnmG3r37l1qnnHjxrFmzRoOHTpkSevfvz/p6emsW7euxHMuXryIn58fW7ZsoVOnToC5xadFixZMnz69xHO+//57HnroIS5cuIC/vz8As2fPZty4cVy8eBEnJ6cb3k9mZiaenp5kZGTg4eFxw/xCCCGEqHi2fH7f8j4+O3bsICoqyiotOjqaHTt2lHpORkYGANWrV7dKX7RoET4+Ptx9993ExcWRk5NjdZ1mzZpZgp6i62RmZnL48OGbcStCCCGEqOQcbvUFUlJSrIIRAH9/fzIzM8nNzcXFxcXqmMlkYsyYMbRv3567777bkv7EE08QEhJCUFAQBw4cYNy4cSQkJLBixYrrXqfoWEkMBgMGg8Gyn5mZWf4bFUIIIcQd75YHPrYaNWoUhw4dYtu2bVbpI0aMsHzdrFkzAgMD6dq1KydPnqRevXrlutbUqVOZPHnyP6qvEEIIISqPW/6qKyAggNTUVKu01NRUPDw8irX2jB49mtWrV7Np0yZq1ap13XIjIiIAOHHixHWvU3SsJHFxcWRkZFi2c+fOlf3GhBBCCFHp3PLAJzIyko0bN1qlbdiwgcjISMu+UorRo0fzzTff8NNPP1GnTp0blrtv3z4AAgMDLdc5ePAgaWlpVtfx8PCgSZMmJZah1+vx8PCw2oQQQghRddkc+GRlZbFv3z5L4HH69Gn27dtHYmIiYG5FiYmJseR/5plnOHXqFGPHjuXo0aPMmjWLpUuX8vzzz1vyjBo1ioULF7J48WKqVatGSkoKKSkp5ObmAnDy5Elee+014uPjOXPmDKtWrSImJoZOnTrRvHlzALp160aTJk0YOHAg+/fv54cffuCVV15h1KhR6PX6cj8gIYQQQlQhykabNm1SQLFt0KBBSimlBg0apDp37lzsnBYtWignJydVt25dNW/ePKvjJZUHWPIlJiaqTp06qerVqyu9Xq/q16+vXnzxRZWRkWFVzpkzZ9QDDzygXFxclI+Pj/rPf/6jCgoKynxvGRkZCihWrhBCCCHuXLZ8fv+jeXyqGpnHRwghhKh87qh5fIQQQggh7hQS+AghhBDCbkjgI4QQQgi7IYGPEEIIIeyGBD5CCCGEsBt33JIVVdHFawbiz16x7DvnXMCz4BKBTTvh7+mMRqOpwNoJIYQQ9kMCn9vgSHImoxbu5j7tXp7QbaST9gBajWLDunCecvgXvoEhNA70oIm/MxG5WwnUpuPQdhjo3Su66kIIIUSVIoHPbVArbTO7XMdRw3TZkmZEy/26eNqaxvDG2SdxPptBR4f1+GvSAcj55SNcer6FpnFPuF6LkLEALuyDs7+Aize0eAJ0jrf2hoQQQohKSiYw/ItbNoHhhb0w515w9YGWT0KrQVBowPTNSLQp+6yypuGNweRAsPYiAIY6XdH71IUrJ+HyCTBkmQMc1+qg00PyPsjP+rMA/7uh1wwIamneT/0dDiwBFDTuCTXDrx9ICSGEEJWMLZ/fEvj8xS2dufn4BqjTGRyc/kwzFsL292HLW+DTACJHU9i4F59uPYFxy9sM167CSWO8cdku3lC7HSTugNwroNFCy6cg9TAkxVvn9aoN9e8357t8Aq6cNgdLvWdBjXo3956FEEKI20ACn3KqsCUrjIWgs37reDQlk3e/XEPk5W/Iw4nTKoDTpkAycKN9TR0PN3AhzFeHLvBu8GsKWi1kX4Lvx8Khr/8sSOsADR8wtw4lfA8F2SXXwakaPDwdmj0GeRmw53PYtxicvSB8EDTpBY4u//xeCw2gc5JWJyGEEDeNBD7ldKet1VVgNPHD4RSOpWZx9nI2Zy5lczApA9Mf/2OBns60q+dDkyAPGgdWo3ktL9z1DnB0LcTPh9AOENYf3P3MJ+TnwIkNcG4XVAuEGvXB3Rd+eAUSt5vz1O0C53dbvz4Dc6tSk17g9JcO1+7+5pYq3wag94Dk/eZXb2lHoHo9c36/xua8iTtgx0w4ugbaDocHp1mXn3YU1o2DOp3gnlHg6HyzH6cQQogqSgKfcrrTAp+SnL+aw6KdiSzZfY4r2flWx6rpHXghuiFP3ROCTmtDi4qxEDZPhZ/fAf74dvBtBBH/gpzLEL8AMs6Vr8I16oOTmzko+quYVVC3s/nrwnz4pAukHjLve9eB7lOhQXdpGRJCCHFDEviUU2UIfIrkFRjZdvwShy5kcCQ5k4PnM7iQkQdAs5qevP7I3TSv5WVboac2m1+TNekF9br+GXSYjHDiR/PIMWUCNICCjCS4dBwuH4fCPHMrT1AL8G0MF/aYzzH+EZw5OJtbn/Kz4eAyc96R280tO5umwpb/mVuVdHrISjGfU72e+XoFueZ/mz4CnV40t1IJIYQQf5DAp5wqU+Dzd0aTYvGuRN5ad5RreYUAOOo0OOm0ODlo8fdwJjzEm/AQb1oEe+FTTY+7kwNaW1qGSmMymQMfJ1fr9LxMOL4ecq+agxY3H3P/oZkRcC3ZHMQ07mlu7TEVwmPz4K77Yevb5tdipoLi13Jyh3bPwj0jwdnzn9ddCCFEpSeBTzlV5sCnSNq1PN5Yc4SV+y7cMK9GA+56BxoHePBIq5o82CwQT5fbMAfQ76tg6UBzx2vvUPPossYPQ98v/mxlykiCi0fNr8kcXeFaCmx+wzw1QBH3APAOAY+a5vNMhebWqZqtIPJZ6xF0QgghqiwJfMqpKgQ+RTJyC8g2FJJfaMJQaOL0pSziz17lt7NXOXwhk/xCU7FznBy0dLrLB0edltwCI3kFRvw9nGkR7EXL2t40CfTAyeEmLO+mFHz1JCSsMe+7eMOoXX92wr7eeb+vhJ9eN79eu56a4fDop1C97j+vrxBCiDuaBD7lVJUCn+tRSmEoNJGZV0B6TgGbjqbx9Z7zHEvNuu55rk46+rYOZninutT0+odD2zOSYGZb8+ixR+eah9GX/QYg5wqkn4GrZ82vzTRacwtSfjZse9f8Ss2pGvR4G+7qZh6Wr5U1eYUQoiqSwKec7CXwKYlSikNJmew6cwUnnQZnRx1ODlrOXs5h37l09iZe5WqOuc+Ng1ZDrxY1aR3qTX6hifw/gqizl3M4ezmblMw82tapwdjohgRXd7W6xtnLOdTydsFBp4WkPZBx3vya62aO3ko/ByuGm4fQF9HozH2MarWByNFQ+x4ZMSbMQbSp8M/NWGB+XfrXNJPR3Em/MM/8b0Guud9a7hXIuWoO3o0F5mOmQvM8VQ5O5g79xgIwZJr7u+Vng1ZnDtB1juZ/LV//sa9zMH+tc/wjr6O5HBcvcKn+56ztLt7mPm5aXUU/QSHuCBL4lJM9Bz43opRi24lLzNp0kh2nLt/4BMyvzoZ3rMNDzYP44XAKK/cmceZyDu3q1WDekDboHW7hL21jIfz8NuyaYx6S/3e12kC7/4NGD0lLkD0wGeHqGfO0Cknx5i35QOkTelYKGnPwUxQIuXibWz6LAjitznzc2cscOJX2r0t1cz75Q0BUYrc88Jk5cybTpk0jJSWFsLAwPvzwQ9q2bVti3oKCAqZOncqCBQtISkqiYcOGvPnmm3Tv3t0qX1JSEuPGjeP7778nJyeH+vXrM2/ePFq3bm3Jc+TIEcaNG8eWLVsoLCykSZMmfP3119SuXZszZ85Qp06dEuuwdOlSHn/88RvelwQ+ZbM38SoLf00kI7cAvYN51JibXkewtyshNVxx1zsya/MJtp8sPUDqGRbE9H4tLKPKfr+QyaKdZ/H3cKZTA1+a1fS0bS6i6ynMNwc/Gedh7xew/yswGszHAltAt/9CnY4351qi4igFWWnmAMeynTZPqHkxAQpzy16W5q8tM39peXHQ/6UF5o9gQ1/N3MqjczKfYyowtw4V5JnP13uAs4e5o77J+MdW8Efr0h8tTCV9XdT6VFjUwnTV3MKUexXyr93cZ+fsZZ6M1Ocuc784z2DwrAVeweavJSgSd7hbGvgsWbKEmJgYZs+eTUREBNOnT2fZsmUkJCTg51e8c+q4ceNYuHAhn3zyCY0aNeKHH34gNjaW7du307KleSHNq1ev0rJlS7p06cLIkSPx9fXl+PHj1KtXj3r1zOtHnTx5krZt2zJ06FAGDBiAh4cHhw8f5p577sHPzw+j0cjFixetrj1nzhymTZtGcnIy7u7uxer2dxL43DxKKTb8nsoba4+QeCWHDnf58kjLINycHPj3oj0UmhT/6lSXsd0b8fHWk7y34RgFxj+/Fb1dHWkTWp26vu7U9XWjro8bjQI9zDNT/1NZabDzY9g5+88Zqhs8AJ1ekEVcK5Mrp+HkT+b5py4dNwc61wtuHJzNM4nXDDdvQa3Mrz+LApuiV09a3Z3/PVCYD3np5r5uRa/dcq+ajxXdhzJBbro5n+Xfq8XTCnKufy3PYKjf1bzGX93O5kBPiDvMLQ18IiIiaNOmDTNmzADAZDIRHBzMs88+y/jx44vlDwoK4uWXX2bUqFGWtEcffRQXFxcWLlwIwPjx4/nll1/4+eefS71u//79cXR05IsvvihzXVu2bEmrVq2YO3dumfJL4HPzKaXIN5qsXmut2HOe2KXmmZzr+rhx6pL5dUOXhr7oHXT8cuIS1wyFxcrSaKC+rzvNanlyd5AnDfyr0cDfHd9qekwKMnMLuJqTj081PR7OZRiWn5UGm/9nXt5D/bEYbLUg89pmdz8Koe3/8f2Lm0Qp87QH53aatzPb4Mqp4vk0WnNLhXeoefMKMbdk+Dc170ufmOLyc8zP8tIx83b1rHmm9swkc3+5v86n5eBs/vlo9rg5EJIpI8Qd4pYFPvn5+bi6urJ8+XJ69+5tSR80aBDp6el8++23xc6pUaMGb731FkOHDrWkPfXUU2zbto0zZ84A0KRJE6Kjozl//jxbtmyhZs2a/Pvf/2b48OGAObjy9PRk7NixbNu2jb1791KnTh3i4uKs6vFX8fHxtG7dml9++YV27dqVmMdgMGAwGCz7mZmZBAcHS+BzG8zcdIJpPyQA5rmEJj3chMfCa6HRaCgwmtibmM6hpAxOX8rm9KVsTqRlkZKZV2JZLo468gqNFH0nOzlo6d40gP5tg4msWwPNjf56v3gMtrxZfBHXXjPNq9yL28NkNH/gXjn952sqy9dnzJ2E/0rrAMERUK+LufWmeh1z64TuNsxFZS/yc8wzth/fYJ6M9OrpP485e0GbYRA5ytzPSIgKdMsCnwsXLlCzZk22b99OZGSkJX3s2LFs2bKFnTt3FjvniSeeYP/+/axcuZJ69eqxceNGevXqhdFotAQdzs7mBSljY2N5/PHH2b17N8899xyzZ89m0KBBpKSkEBgYiKurK//973/p0qUL69at46WXXmLTpk107ty52HX//e9/s3nzZn7//fdS7+fVV19l8uTJxdIl8Ln1lFLM2nySY6nXeKGb9eiv0qRdy+Pg+QwOnM/gaEomx1OzOHM527JoK5iDoNwCo2W/ppcLtau74uniiKeLI/X93Glf34dGAdWKz1pdkAent8K+Reb5ghzdYOQ2mQvoZjMW/vmaJvO8edHcxB1wbvf1Oxs7OJsDnOC25lF5Ie3NfWfE7aGUuXP4wWXmpW2uJZvTndyhzVDzpKGynIyoIHdU4HPx4kWGDx/Od999h0ajoV69ekRFRfHZZ5+Rm2t+H+/k5ETr1q3Zvn275bz/+7//Y/fu3ezYscNy3QEDBrB48WJLnp49e+Lm5saXX35pdc3c3FwCAwOZMGEC//nPf0q9H2nxqfzyCowkZ+Thptfh5eKEo07DwaQMvtx1jlX7ksjON5Z4no+7Ex3q+/BERAhtQr2tW4VMRljQE85ug1ptYcj35mHGonxyrsDpLea+OKc2m1tvSqPTm2fj9g41L1Zbvc5fvq4rr1buFCYjJKyFLW9BygFzmqMbtH8O2o02d+QW4jayJfCx6be5j48POp2O1NRUq/TU1FQCAgJKPMfX15eVK1eSl5fH5cuXCQoKYvz48dSt++df0YGBgTRp0sTqvMaNG/P1119bruvg4FBinm3bthW75vLly8nJySEmJua696PX69Hr9dfNI+5szo466vhY/5JtXsuL5rW8eKVHY347e5X0nHwycgu4kp3P/nPp7Dx9hUtZ+azcd4GV+y4QVsuTYR3r0iLYi5x8I1mGQjw7TKN+SjSc32WeELHz2Aq6w0rAZIJrF/42mursn19np5V8nrMnuPlCUEtzC07tSPBtJP1wKgOtzjz/VqOHzK/ANv/PvDDx5jfgt7nQ5SVo8ZT8wSDuSDZ9Vzo5OREeHs7GjRstfWtMJhMbN25k9OjR1z3X2dmZmjVrUlBQwNdff03fvn0tx9q3b09CQoJV/mPHjhESEmK5bps2ba6b56/mzp1Lz5498fWVZld75qZ3oHOD4t8D+YUm9iZeZeW+JL7ek8T+8xk8++XeYvkm1h7F02lTzb/UQztCSGSxPHZBKXPn16R485ppOZfN27Vkc2CTnmievO96/JpA3XuhbhcIamGeO0Y+FCs/jQYaRJtnRz/8DWycbP6e+O45+PUjiJpsPn6nj5ITdqVcw9kHDRrExx9/TNu2bZk+fTpLly7l6NGj+Pv7ExMTQ82aNZk6dSoAO3fuJCkpiRYtWpCUlMSrr77K6dOn2bNnD15eXgDs3r2bdu3aMXnyZPr27cuuXbsYPnw4c+bM4cknnwTgm2++oV+/fsycOdPSx2fMmDFs3ryZDh06WOp34sQJGjRowNq1a4vNFXQjMqrL/lzKMrDw17Ms3pnItbxC3PQ6XJ0cuJCeS6HJxEfOM3mAP17BhrSHFk9Ck16gv/H0CJWOscA8303GeXNQk5VqngMncYf56+vROoBX7T9HU/118woxz3sjqr5CA/z2mXmwQNHw+tCO0HWiuW+WELfILZ/AcMaMGZYJDFu0aMEHH3xAREQEAPfeey+hoaHMnz8fgC1btjBy5EhOnTqFu7s7Dz74IP/73/8ICgqyKnP16tXExcVx/Phx6tSpQ2xsrGVUV5HPPvuMqVOncv78eRo2bMjkyZPp1auXVZ6XXnqJhQsXcubMGbQ2zsgrgY8ociQ5k3FfH+DM+STedvyYKN0etJh/VJSTO5p7RppHs7h4V3BNbVC0PENhnrnlpuhV1MUE86r3KQf/nNjx77SO5ldS1euAaw3zKB53/z+DG4+a8opK/Ck33fyK+NfZf35P1WwN94w0/+EgI+/ETSZLVpSTBD7irwqNJj775TTvbjiGV8FF+uh+5nHdFupoza0fhY7uaCJGoosYBtVK7uN2yxXk/bkW1LVk86ib5P3mDqc5V/6cAdiYf+PXUQB6T3NwUy3AvHnVhuB7oGYrcPyHC9OWg1KKzLxCLl7LIzXTQGqm+d+rOfloAI1Gg1Zjfn2ZU2AkN9+I4Y+pDZQChULvoMPVydyS5+igIb/QhKHQREGhCVcnHV6uTlR3c6KGuxOBni7U9HLBt5r+5s0cbs/Sz8GW/8GBpX9+/7n7m1+N1esCde4FtxoVWUNRRUjgU04S+IiSZOYV8MvxS2xOuMiWhBTCsn9hjMPXNNaes+Qx+LVA37QHhHb4Y+0jD/MMt8pkDjwKDeYZcnPTzSvHF2Sb+73UqF+8/4Phmnkm4ssnzBPKFeT+ueyBRgeXEiD1MKT9bi6rPJzcza+gvEPMo6WCWv7RolO31P4YhUYTOq3mhvMiGU2Ky1kGUjMNpGTmkXYtj6vZ+VzNKeBqdj4ajQYfd3OgUc3ZkUvXDCRn5pGSkce1vAIMfyx8m5NvJO1aHnkFpvLd4z+g02pwddShd9Sid9DhrncgwNOZAA9nfKo5kZFbQFqmgbRrBhy0Gu7yd+cuv2rc5e9OA/9q+FXT33j+KHuSlQa/zYPdn/6ts7sG6kdB1wkQGFZh1ROVnwQ+5SSBj7gRpRR7z6Xz3d7zZB9YyYCClbTUnih/gW5+ENLO/Pro8nG4dMI8QsomGnNg5OoN/neb1x8LDAOPoD/WkHL8cy0p3R8rfzu6XrfDqcmkuJKTz9Hka/xy8hLbT1ziYFIGNdz1hNXyIqyWJwGezqRdM7fCpGTkkXrNQGpGHhezDBhNN/fXioezA/4ezvh7OOPnoae6qxMaDZgUmJTCSafFxcncsqN30KE1NwehAQyFJnLzC8nON1JQaMLJwRzMODpoyDYUcjWngPScfC5eM3AhPY+UzLx/XH8PZwfu8q9GHR83gr1dCa7uQnB1VxoGVCvbrOJVVaEBTv8MpzaZlxtJ+8s8a3c/Zh4NVqNexdVPVFoS+JSTBD7CFkaT4ufjF1mxNR6XMxuJ0u6hoSaRappcPMhGp/nzR6sQBwp1zhQ6eaJ18USvd0aXdrj0PjVufn8sGlnf3HJkuGbeCg3mDwa/puZlGLyCwalauVeYz8wr4MiFTI4kZ3Ik+RpHU6+RkpHLpaz8f/Thr9WAbzW9OVCp5kwNNye83BzxdnUyrz6RZeBSloHMvEJ83J0I8HQhyNMZTxdHnP5Y+NbZUYdfNT1+1Zxxcbp9/YeKWqxy8o0YCk0YCo1czSkgNcMcFF3KMuDp4ohfNT2+1ZwxFBo5kZbFsdRrHE/L4uzlnOs+u+DqLjQJ9KB5LS/CQ7wJq+V1W+/vjnL5JGx6Aw4t/zMtoPkfIwA7g38z85QH5fz+FvZDAp9yksBHlNfx1GvM336GnaevcCE9l5z8QlwwYEJLPg4oiv/iDnLTcK/7Oe5xSMDboYBM91ByqtUlz7Mujm7ef7RgOFDdzZGaXq74VdOj1WowFBo5fzWXxCs5KKXwdHHC08URd70DuQVGsg2FZBkKSc/J51JWPpez8snMM6+3VNRt5czlHI4kZ3L+aumLemo0EOTpQkTd6rSv50PbOtVJu5bHvnMZ7DuXTnpO/h+tMHoCPJzx8zC/CgrwNAc6Djr7/LAyFBo5fSmbY6lZJF7O5tyVXM6n53D6YjYXMoovu+Kg1dAwoBrV3ZzwcHHEw9mB0BputA71pmmQJ86OdhAUJR+AjVPgxIbix3R68KwJ7gHmfmZFm1dt8+ti30bmDvaOrn8GSEqZ/1DISzd36HdyN28OzuZXbZlJkJFk7gPn4m3e3HzNnfTlFWWlJIFPOUngI24GpRSZuYVcyMjlSnY+V3PyuZKdz7krORxNuUZCyjXSrpXS0nMdjjoNXq5OXMoycDN/aoM8nWkc6GHZald3xbeanhruTjjaafByq6Tn5PN7cia/X8hkb2I6v529Qmpm6d8LTjotzWp5Eh7ibdl83KvwpKvXUs3LxpzaDGe2mjtHY8M3u04PDnrIz/5z4WFb+DSEZo+ZFymWV26VigQ+5SSBj7hdMnIKOHc1h/NXczl/NYcr2fnk5JtHJZlHJxWSbTB/femaoVi/ExdHHSE1XHFy0JL+Rx+V7Hwjro46XPU63PQOeLk4UsNdj4+7uSVBgwalFAoI8CgKdqrh5SrLQFQUpRTnr+ZyJDmTzLxCruUVkJFbwO8XMok/e5XL2cVH4oWHeDOsQx26NQ2o+iPPCvPNfd4yzps7SBfmmTdDFlw5CWlH4eKR0jv56/TmOab+ugacRmtuPfIIMgdJuenmOYey08ytQ0VqtoZWMeYgqCrO21XFSOBTThL4iDtVodFE6jUDl7MMBHg64+suo4aqOqUUZy/nEH/2Kr+dvcqes1c5lnbN0tpXu7orQ9qH0rtFTbzd7Dh4VcrcwlOYZx4BWWgwrxXm4vXnFAwmkzn4Kcg1v9YqaR6hvAw4strc3+jUlj9bjJzc4e4+0Lwf1G4n/Y3uUBL4lJMEPkKIO1laZh5f/HqWL349S3qOud+Wo07DvQ396NOyJp0a+OKml6VA/rGsNNj/JcQvMLcsFakWCE0fMc/gHnB3xdVPFCOBTzlJ4COEqAxy840s33Oer3YlcvhCpiVdq4G7/KoRFuxJq9re3NvQjwBP5wqsaSWnFJzdDvsWw5HvwPCXV2qNH4Z748yjK0WFk8CnnCTwEUJUNsdSr7FiTxKrD1wocZTe3TU96NrInz6tahJSw60CalhFFBrMcw/t/xJ+X4Wl03XTPvDAW+Aui2JXJAl8ykkCHyFEZZaWmcf+8xnsP5fOLycvse9cuqVPkFYDPZoH8UznujQN8qzYilZ2aUdg8//g95Xm/ep14akV5uVeRIWQwKecJPARQlQll7IM/HQ0je/2X+Dn45cs6S2Cvajr40aQlwu1vF24r7EfftXklZjNkvfDkqcgPdE86ehTy2XpjQoigU85SeAjhKiqfr+QyewtJ1l94AJ/n1jayUFLv9bBjOhUl+DqrhVTwcrqWgosfAxSD5pnUX/gTWjQXRZfvc0k8CknCXyEEFXd+avmIfIX0vO4kJ7L/vPpHDhv7rSr02ro07ImY7s3wrdaFZ4o8WbLy4CvnoQzP/+ZFtAM6nWFtsPBs1bF1c1OSOBTThL4CCHsjVKKX09dYdbmE5bXYdWcHRgb3ZAnIkKq/iSJN0uhAbZOg6NrIe3wn+laR2g1EDrEmtfWE7eEBD7lJIGPEMKe7Um8yqRvD3MwydwC1LyWJ2891pxGAfL70CZZaeZJEPcs+LMVSOsIrYeYh8C7Vq/Y+lVBEviUkwQ+Qgh7ZzQpFv56lrd/SOCaoRAnBy0vP9iYmMgQmS28PM5sM48AKwqAXLyhy8sQPgR0MtnkzSKBTzlJ4COEEGZp1/IY//VBfjqaBkDXRn689VhzalTlRVJvpVObYV0cpP1u3vdrCr0+hJrhFVqtqkICn3KSwEcIIf6klGL+9jNMXXuUfKMJfw89s58Kp2Vt74quWuVkLIT4ebDpdfPCqBottH/O/PrLQQLKf8KWz29ZbU0IIUSJNBoNQ9rXYeWo9tTzdSM100C/j39l6W/nKrpqlZPOwTzK69k90OxxUCbY9h583AnO7aro2tmNcgU+M2fOJDQ0FGdnZyIiIti1q/T/sIKCAqZMmUK9evVwdnYmLCyMdevW2VxmSkoKAwcOJCAgADc3N1q1asXXX39tladnz57Url0bZ2dnAgMDGThwIBcuXCjPLQohhPhDkyAPVo5qT7cm/uQbTYxdfoBJ3x6iwGiq6KpVTq7V4dFPod8i88SHF4/C3Pvhm5HmeYHELWVz4LNkyRJiY2OZNGkSe/bsISwsjOjoaNLS0krM/8orr/Dxxx/z4Ycf8vvvv/PMM8/wyCOPsHfvXpvKjImJISEhgVWrVnHw4EH69OlD3759rcrp0qULS5cuJSEhga+//pqTJ0/y2GOP2XqLQggh/qaasyOznwrn+agGACzYcZahC34jy1BYwTWrxBo/BKN2QounzPv7F8OH4eZh8VdOVWzdqjCb+/hERETQpk0bZsyYAYDJZCI4OJhnn32W8ePHF8sfFBTEyy+/zKhRoyxpjz76KC4uLixcuLDMZbq7u/PRRx8xcOBASzk1atTgzTffZNiwYSXWddWqVfTu3RuDwYCjo+MN7036+AghxI2tP5zCc1/tI7fAyN01PfhscBtZ8uKfOh8P378ISfF/ptW4CxpEQ+unoUa9iqtbJXDL+vjk5+cTHx9PVFTUnwVotURFRbFjx44SzzEYDDg7W/9AuLi4sG3bNpvKbNeuHUuWLOHKlSuYTCa++uor8vLyuPfee0u87pUrV1i0aBHt2rUrNegxGAxkZmZabUIIIa6vW9MAvhpxDzXcnDiUlMmjH23n1MWsiq5W5VYrHIb+CL1nQ2hH0DrA5eOwYwbMioStb0NhfkXXskqwKfC5dOkSRqMRf39/q3R/f39SUkp+LxkdHc27777L8ePHMZlMbNiwgRUrVpCcnGxTmUuXLqWgoIAaNWqg1+v517/+xTfffEP9+vWtzhs3bhxubm7UqFGDxMREvv3221LvZ+rUqXh6elq24GCZVVMIIcoiLNiLr0e2I6SGK+eu5PL47B2cvpRd0dWq3LRaaDEABq+Gsafg8QVQtwsYDfDTazCnM5zbXdG1rPRu+aiu999/n7vuuotGjRrh5OTE6NGjGTJkCFqtbZeeMGEC6enp/Pjjj/z222/ExsbSt29fDh48aJXvxRdfZO/evaxfvx6dTkdMTAylvc2Li4sjIyPDsp07JyMVhBCirEJ93Ph6ZDuaBHpwOTufpz7dSUpGXkVXq2pw9oSmvWHgN9DnE3CtYZ4DaO79sPZFyJM3FOVlU/Th4+ODTqcjNTXVKj01NZWAgIASz/H19WXlypVkZ2dz9uxZjh49iru7O3Xr1i1zmSdPnmTGjBl89tlndO3albCwMCZNmkTr1q2ZOXNmsTo2aNCA+++/n6+++oq1a9fy66+/llg3vV6Ph4eH1SaEEKLsfNz1LHi6LaE1XElKzyXms52k58grmZtGo4HmfWH0b9DiSUDBrjkwM8K8LpiwmU2Bj5OTE+Hh4WzcuNGSZjKZ2LhxI5GRkdc919nZmZo1a1JYWMjXX39Nr169ylxmTk6OubJ/ayXS6XSYTKUPpyw6ZjAYbLhLIYQQtvCtpueLoRH4e+g5lprFkPm7yZbRXjeXa3XoPQtivgXvOnDtAnw1AJYPhdz0iq5dpWLzq67Y2Fg++eQTFixYwJEjRxg5ciTZ2dkMGTIEMA87j4uLs+TfuXMnK1as4NSpU/z88890794dk8nE2LFjy1xmo0aNqF+/Pv/617/YtWsXJ0+e5J133mHDhg307t3bcp0ZM2awb98+zp49y08//cSAAQOoV6/eDYMyIYQQ/0xwdVc+fzoCTxdH9iam8/jsHVxIz63oalU9de+Ff+8wr/au0cGh5TC7g3lNMFE2qhw+/PBDVbt2beXk5KTatm2rfv31V8uxzp07q0GDBln2N2/erBo3bqz0er2qUaOGGjhwoEpKSrKpTKWUOnbsmOrTp4/y8/NTrq6uqnnz5urzzz+3HD9w4IDq0qWLql69utLr9So0NFQ988wz6vz582W+r4yMDAWojIwMG56GEEKIInsTr6pWU9arkHGrVev/blB7E69WdJWqrnO7lZoeptQkD6UmeSr1wytK5WVWdK0qhC2f37JW11/IPD5CCPHPnb+aw9D5v5GQeg29g5Z3+7agR/PAiq5W1WS4BuvGw17zvHi4+UGXl6DlQLta/V3W6hJCCFFhanm78vW/23FfIz8MhSae/XIP3+5LquhqVU36atBrJgz4CqrXhew0WD0GPu4ISXsqunZ3JAl8hBBC3HTuegc+iWnNgLbBmBQ8v2Qfq/bL2om3TMMH4N87ofv/wMXbPPR93oNwdE1F1+yOI4GPEEKIW0Kn1fB672b0a/1n8LP6gAQ/t4yDE9wzEv5vL9SPgsJc+OpJ2PlxRdfsjiKBjxBCiFtGq9UwtU8zHguvhdGkeO6rfczZehKTSbqX3jIu3uZXX60GAQq+H2ue9LBARtmBBD5CCCFuMa1Ww5uPNrcEP2+sPUrMZ7tIzZRZnm8ZnSM8/D5EvWre3zUHPmoHp7dWaLXuBBL4CCGEuOV0Wg3THmvOG480w9lRy7YTl+g+fSs/HU298cmifDQa6PA8PLEUqgXBlVOw4GH4djRcK3l9TXsgw9n/QoazCyHErXciLYsxS/ZyKCkTrQbefLQ5j7eWRaJvqbxM2DgZdn9q3tc5QVh/aPd/4HNXxdbtJrDl81sCn7+QwEcIIW6P/EITL39zkGXx5wGY+FATnu5Qp4JrZQfO7oANE+H8rj8SNNCoB7R/DoLbVmjV/gkJfMpJAh8hhLh9lFK8vuYIn247DcDoLvUZ1aU+Lk66Cq6ZHUj8FX55HxL+stBp7UhoPwYaRJtfk1UiEviUkwQ+QghxeymlmPHTCd7ZcAwAZ0ctne7y5f4m/nS/O4Bqzo4VXMMq7mICbP8A9i8BU4E5LWIkdJ9aqYIfCXzKSQIfIYSoGEt2J/LBxhMk/WVh0wAPZ958rDmdG/hWYM3sRGYy7Jhh3gAiR0O3/1aa4EcCn3KSwEcIISqOUorfkzNZfziVFXvPc+6KOQh6IqI2Lz3YGHf9zVl7qsBoYu3BZC5eM9C8lhfNanrK67Uiv80zL3kB5n4/UZMrRfAjgU85SeAjhBB3htx8I2+uO8r87WcACPR05pGWNXmwWSBNgzzQlOPD2GhSfLf/AtN/PMaZyzmWdJ1WQ+PAaozoVI+HmweWq+wqZfensOY/5q/bDIeuE8DZs2LrdAMS+JSTBD5CCHFn2X7yEi8uO2D1Cqx2dVcebBbIg80CaFbT0ypQMZkUWfmFZOQUkJlXQGpmHqcuZnP6UjY7T1/hRFoWADXcnGhZ24sD5zNIu2awnN+loS+v9b6bWt6ut+8m70Q758D3L5q/dqkOnV6ENkPBQV+x9SqFBD7lJIGPEELceXLzjWw4ksr3B5PZlJBGXoHJcqyWtwthtbxIu5bHhfQ8UjLzMF5nOQwPZwf+1bkeg9uF4vbHq7OUjDy+2p3IrE0nyTeacHHU8VzUXQxuF4qzox2/AktYBxsmwCVzx3O8QqDv5xDUokKrVRIJfMpJAh8hhLizZRsK2ZSQxvcHU/jpaBq5BcYS8+kdtHi4OOLjrqeujxt1fNyo6+tG18b+eLqUPFLsRFoWL31zkF2nrwDg76Hn/7reRd/WwTjq7HShA2Mh7FsIm6ZCVgroPeCJJRDSrqJrZkUCn3KSwEcIISqPnPxCtiRcJCk9lwBPZwI9XQjycsbb1ancLTUmk2LF3iTe23DM8nqtdnVXXnqwEdFNA+y3/09eBnw5AM7+Ag7O0G8h3HV/RdfKQgKfcpLARwghBICh0MiXOxOZsekkl7LMfYA63uXDpIebUt/PvYJrV0EKcmHpIDj+A2gd4JGPodljFV0rQAKfcpPARwghxF/l5Bcye/NJZm89RX6hCQethlFd6jMm6i77bP0xFsA3/4JDX5v32/0fdJ0Eupsz1UB52fL5bacvLYUQQogbc3VyILZbQzY834moxn4UmhTvbzzOuK8PUGg03biAqkbnCH0+MQc8YJ71eeEjkH2pYutlg3IFPjNnziQ0NBRnZ2ciIiLYtWvXdfNPnz6dhg0b4uLiQnBwMM8//zx5eXmW46+++ioajcZqa9SokVUZeXl5jBo1iho1auDu7s6jjz5KamqqVZ7ExER69OiBq6srfn5+vPjiixQWFpbnFoUQQgiLkBpufDqoDW892hytBpb+dp5Ri/eQV0rn6ipNq4Nur8Hj88HRDU5vhY87Q9Keiq5Zmdgc+CxZsoTY2FgmTZrEnj17CAsLIzo6mrS0tBLzL168mPHjxzNp0iSOHDnC3LlzWbJkCS+99JJVvqZNm5KcnGzZtm3bZnX8+eef57vvvmPZsmVs2bKFCxcu0KdPH8txo9FIjx49yM/PZ/v27SxYsID58+czceJEW29RCCGEKFHfNsF89FQ4TjotPxxO5en5u8ky2Okf2E0fgeE/QY36kHkePusOe76o6FrdmLJR27Zt1ahRoyz7RqNRBQUFqalTp5aYf9SoUeq+++6zSouNjVXt27e37E+aNEmFhYWVes309HTl6Oioli1bZkk7cuSIAtSOHTuUUkqtXbtWabValZKSYsnz0UcfKQ8PD2UwGMp0bxkZGQpQGRkZZcovhBDCPv1y/KJqMuF7FTJuter54c/qclbZPmeqpNx0pRYPUGqSh3lb9ZxSBXm3tQq2fH7b1OKTn59PfHw8UVFRljStVktUVBQ7duwo8Zx27doRHx9veR126tQp1q5dy4MPPmiV7/jx4wQFBVG3bl2efPJJEhMTLcfi4+MpKCiwum6jRo2oXbu25bo7duygWbNm+Pv7W/JER0eTmZnJ4cOHS6ybwWAgMzPTahNCCCFupF19HxYPvwdvV0f2n8+g78c7SM7IvfGJVZGzp3l4+32vABqInwdzu8HlkxVdsxLZFPhcunQJo9FoFVwA+Pv7k5KSUuI5TzzxBFOmTKFDhw44OjpSr1497r33XqtXXREREcyfP59169bx0Ucfcfr0aTp27Mi1a9cASElJwcnJCS8vr1Kvm5KSUmK9io6VZOrUqXh6elq24ODgsj8MIYQQdi0s2Itlz0QS4OHMibQsHvtoB6cuZlV0tSqGVmte1uKp5eDiDcn7YHZH2LcY7rDB47d8VNfmzZt54403mDVrFnv27GHFihWsWbOG1157zZLngQce4PHHH6d58+ZER0ezdu1a0tPTWbp06S2tW1xcHBkZGZbt3Llzt/R6Qgghqpb6ftVYPjKSOj5uJKXn8vjsHRw4n17R1ao49aPgmV8gtCMUZMPKkbA0Bk5tMc8CfQewKfDx8fFBp9MVG02VmppKQEBAiedMmDCBgQMHMmzYMJo1a8YjjzzCG2+8wdSpUzGZSh4K6OXlRYMGDThx4gQAAQEB5Ofnk56eXup1AwICSqxX0bGS6PV6PDw8rDYhhBDCFrW8XVn2TCR31/TgcnY+/ef8ys/HL1Z0tSqOZ02I+RbumwAaHRxZBZ/3hLfvgpWj4NgPFVo9mwIfJycnwsPD2bhxoyXNZDKxceNGIiMjSzwnJycHrdb6MjqdeSpxVUrzV1ZWFidPniQwMBCA8PBwHB0dra6bkJBAYmKi5bqRkZEcPHjQanTZhg0b8PDwoEmTJrbcphBCCGETH3c9X42IpH39GuTkG3l6/m6+3ZdU0dWqOFoddHoBhm+ElgPNK7znXjGv+7V1WsXWzdae01999ZXS6/Vq/vz56vfff1cjRoxQXl5eltFUAwcOVOPHj7fknzRpkqpWrZr68ssv1alTp9T69etVvXr1VN++fS15/vOf/6jNmzer06dPq19++UVFRUUpHx8flZaWZsnzzDPPqNq1a6uffvpJ/fbbbyoyMlJFRkZajhcWFqq7775bdevWTe3bt0+tW7dO+fr6qri4uDLfm4zqEkII8U/kFRSqUYviVci41Spk3Go146fjymQyVXS1Kl5hgVKntiq15kWlfpt/04u35fPb5jmm+/Xrx8WLF5k4cSIpKSm0aNGCdevWWToSJyYmWrXwvPLKK2g0Gl555RWSkpLw9fXl4Ycf5vXXX7fkOX/+PAMGDODy5cv4+vrSoUMHfv31V3x9fS153nvvPbRaLY8++igGg4Ho6GhmzZplOa7T6Vi9ejUjR44kMjISNzc3Bg0axJQpU8oRDgohhBC20zvo+KB/S/yqOfPZL6eZ9kMCx1Ov8b9Hm5d74dQqQecAdTqatwoma3X9hazVJYQQ4mZZ+OtZJq06jNGkaFnbi48HhuNXzbmiq1UlyVpdQgghRAV76p4Qvni6LZ4ujuxNTKf3jF84fCGjoqtl9yTwEUIIIW6RdvV9WDmqPXV93biQkcdjH+1g3aGS55YTt4cEPkIIIcQtVMfHjW/+3Z6Od/mQW2DkmYXxzPjpOEaT9DSpCBL4CCGEELeYp4sj8wa3YXC7UADeXn+MB9//mU0JaaVO7SJuDQl8hBBCiNvAQafl1Z5NeevR5ni6OJKQeo0h83bz1NydxJ+9IgHQbSKjuv5CRnUJIYS4HTJyCpix6TgLtp8l32hexaBZTU8GtwvlobBA9A52PPS9HGz5/JbA5y8k8BFCCHE7nbuSwwcbj/Pt/gvkF5oDoGrODrSq7U14iHlrW6c6jjp5QXM9EviUkwQ+QgghKsLlLANf7T7Hwl/PkpyRZ3WsSaAH0/u3oIF/tQqq3Z1PAp9yksBHCCFERSo0mjiSfI34s1eIT0xnS0IamXmFODloGde9EUPahaLVaiq6mnccCXzKSQIfIYQQd5K0zDzGfn2AzQnm1d473uXDhwNa4uXqVME1u7PIzM1CCCFEFeDn4cy8wW14rffdODtq+fn4JR6bvYPzV3MqumqVlgQ+QgghxB1Mo9Ew8J4QVo5qT4CHMyfSsnhk1nYOJcnyF+UhgY8QQghRCTQK8OCbUe1oFFCNi9cM9P14B5sT0iq6WpWOBD5CCCFEJRHo6cLSZyJpX78GOflGhi74jSW7Eyu6WpWKBD5CCCFEJeLh7Mi8wW3p07ImRpNi3NcHeXfDMZn5uYwk8BFCCCEqGScHLe/0DWN0l/oAfLDxOC8uP0DBH7NAi9JJ4COEEEJUQhqNhheiG/LGI83QaTUsjz/P0/N3cy2voKKrdkeTwEcIIYSoxJ6IqM2nMa1xcdTx8/FL9P34V1Iz8258op2SwEcIIYSo5Lo08mPJv+7Bx92JI8mZPDLzF05ezKroat2RyhX4zJw5k9DQUJydnYmIiGDXrl2l5r333nvRaDTFth49eljypKamMnjwYIKCgnB1daV79+4cP37cqpw5c+Zw77334uHhgUajIT093er45s2bS7yORqNh9+7d5blNIYQQotJoXsuLb/7dnrq+blzIyGPYgt/IlNdexdgc+CxZsoTY2FgmTZrEnj17CAsLIzo6mrS0kucSWLFiBcnJyZbt0KFD6HQ6Hn/8cQCUUvTu3ZtTp07x7bffsnfvXkJCQoiKiiI7O9tSTk5ODt27d+ell14q8Trt2rWzuk5ycjLDhg2jTp06tG7d2tbbFEIIISqd4OquLP1XJEGezpy+lM1/lu7HZJLRXlaUjdq2batGjRpl2TcajSooKEhNnTq1TOe/9957qlq1aiorK0sppVRCQoIC1KFDh6zK9PX1VZ988kmx8zdt2qQAdfXq1eteJz8/X/n6+qopU6aUqV5KKZWRkaEAlZGRUeZzhBBCiDvN/nNX1V0vr1Uh41arDzceq+jq3HK2fH7b1OKTn59PfHw8UVFRljStVktUVBQ7duwoUxlz586lf//+uLm5AWAwGABwdna2KlOv17Nt2zZbqmdl1apVXL58mSFDhpS7DCGEEKIyal7Li9d6NQXgnQ3H2HLsYgXX6M5hU+Bz6dIljEYj/v7+Vun+/v6kpKTc8Pxdu3Zx6NAhhg0bZklr1KgRtWvXJi4ujqtXr5Kfn8+bb77J+fPnSU5OtqV6VubOnUt0dDS1atUqNY/BYCAzM9NqE0IIIaqCfm1qM6BtbZSCZxfv4XjqtYqu0h3hto7qmjt3Ls2aNaNt27aWNEdHR1asWMGxY8eoXr06rq6ubNq0iQceeACttnzVO3/+PD/88ANDhw69br6pU6fi6elp2YKDg8t1PSGEEOJO9GrPJoSHeJOZV8igz3aRkiHD3G2KLHx8fNDpdKSmplqlp6amEhAQcN1zs7Oz+eqrr0oMRsLDw9m3bx/p6ekkJyezbt06Ll++TN26dW2pnsW8efOoUaMGPXv2vG6+uLg4MjIyLNu5c+fKdT0hhBDiTqR30PFpTGvLSK9Bn+0iI9e+R3rZFPg4OTkRHh7Oxo0bLWkmk4mNGzcSGRl53XOXLVuGwWDgqaeeKjWPp6cnvr6+HD9+nN9++41evXrZUj3APEps3rx5xMTE4OjoeN28er0eDw8Pq00IIYSoSrzdnPj86bb4VdOTkHqNEZ//Rl6BsaKrVWFsfpcUGxvLJ598woIFCzhy5AgjR44kOzvb0ok4JiaGuLi4YufNnTuX3r17U6NGjWLHli1bxubNmy1D2u+//3569+5Nt27dLHlSUlLYt28fJ06cAODgwYPs27ePK1euWJX1008/cfr0aat+REIIIYQ9q+Xtyvwhbammd2Dn6St0fWcL3+2/YJcLmzrYekK/fv24ePEiEydOJCUlhRYtWrBu3TpLh+fExMRifXMSEhLYtm0b69evL7HM5ORkYmNjSU1NJTAwkJiYGCZMmGCVZ/bs2UyePNmy36lTJ8D8Wmvw4MGW9Llz59KuXTsaNWpk660JIYQQVVaTIA/mDm7Dc1/tJSk9l2e/3Mv87Wd49eGmNKvlWdHVu200yh7DvVJkZmbi6elJRkaGvPYSQghRJeXmG5mz9RSzt5wkt8CIo07D//o059Hw0kdB3+ls+fyWtbqEEEIIO+LipOO5qLvY/OK9RDf1p8Co+M+y/by17qhdzPIsgY8QQghhh/w9nPnoyXCeva8+ALM2n+Tfi/ZU+Y7PEvgIIYQQdkqr1fCfbg15t28YTjot6w6n8NxXezFW4ZYfCXyEEEIIO9enVS3mP90GJ52WHw6n8vqaIxVdpVtGAh8hhBBC0K6eD+/0DQPgs19OM++X0xVco1tDAh8hhBBCAPBwWBDjupung5my+nfWHbrxOpyVjQQ+QgghhLB4pnNdy+Km/14Uz/Qfj1WpPj8S+AghhBDCQqPR8FqvpvRtXQuTguk/HuepT3eSmlk1FjiVwEcIIYQQVhx0Wt56LIz3+oXh6qRjx6nLPPj+zxxLvVbRVfvHJPARQgghRIkeaVmL1c92oHGgB5ez83l28d5KP8+PBD5CCCGEKFVdX3e+GNoWH3cnElKv8da6hIqu0j8igY8QQgghrsvHXc+0x/4c6r712MUKrlH5SeAjhBBCiBvq0siPmMgQAP6zbD9XsvMruEblI4GPEEIIIcrkpQcbU9/PnYvXDMQu3Ueh0VTRVbKZBD5CCCGEKBNnRx3v92+B3kHL5oSLxK04iFKVa44fCXyEEEIIUWZNgzyZ8UQrtBpYFn+eNytZZ2cJfIQQQghhk/ub+PO/Ps0BmL3lJJ/+fKqCa1R2EvgIIYQQwmZ92wQztntDAP675ghv/5BQKZa2kMBHCCGEEOUysnM9Rt5bD4AZm04wcO5OLl4zVHCtrk8CHyGEEEKUi0ajYVz3RrzfvwWuTjq2n7xMjw9+5ufjd+48P+UKfGbOnEloaCjOzs5ERESwa9euUvPee++9aDSaYluPHj0seV599VUaNWqEm5sb3t7eREVFsXPnTqtyevbsSe3atXF2diYwMJCBAwdy4cIFy/GEhAS6dOmCv78/zs7O1K1bl1deeYWCgoLy3KIQQgghyqhXi5qsGt2eu/zcSbtmYODcXQyZt4vjd+DaXhpl4zi0JUuWEBMTw+zZs4mIiGD69OksW7aMhIQE/Pz8iuW/cuUK+fl/TnJ0+fJlwsLC+PTTTxk8eDAAixcvxs/Pj7p165Kbm8t7773HsmXLOHHiBL6+vgC89957REZGEhgYSFJSEi+88AIA27dvB+DUqVNs2bKFVq1a4eXlxf79+xk+fDhDhw7ljTfeKNO9ZWZm4unpSUZGBh4eHrY8FiGEEMLu5eQX8ta6BBb+epZCk0KrgR7Ngwit4Yq3qxPV3ZwIru5CeEj1m3pdWz6/bQ58IiIiaNOmDTNmzADAZDIRHBzMs88+y/jx4294/vTp05k4cSLJycm4ubld9wZ+/PFHunbtWmKeVatW0bt3bwwGA46OjiXmiY2NZffu3fz8889lujcJfIQQQoh/7vSlbN78/ijrDqcUO9auXg0WD7/npl7Pls9vB1sKzs/PJz4+nri4OEuaVqslKiqKHTt2lKmMuXPn0r9//1KDnvz8fObMmYOnpydhYWEl5rly5QqLFi2iXbt2pQY9J06cYN26dfTp06dM9RJCCCHEzVHHx43ZA8OJP3uVrccucjUnn6s5BVzNzqdpzYptWLAp8Ll06RJGoxF/f3+rdH9/f44ePXrD83ft2sWhQ4eYO3dusWOrV6+mf//+5OTkEBgYyIYNG/Dx8bHKM27cOGbMmEFOTg733HMPq1evLlZOu3bt2LNnDwaDgREjRjBlypRS62MwGDAY/ux9npmZecN7EEIIIUTZhId4Ex7iXdHVsHJbR3XNnTuXZs2a0bZt22LHunTpwr59+9i+fTvdu3enb9++pKWlWeV58cUX2bt3L+vXr0en0xETE1NsquwlS5awZ88eFi9ezJo1a3j77bdLrc/UqVPx9PS0bMHBwTfnRoUQQghxR7Kpj09+fj6urq4sX76c3r17W9IHDRpEeno63377bannZmdnExQUxJQpU3juuedueK277rqLp59+2uq12l+dP3+e4OBgtm/fTmRkZIl5Fi5cyIgRI7h27Ro6na7Y8ZJafIKDg6WPjxBCCFGJ2NLHx6YWHycnJ8LDw9m4caMlzWQysXHjxlKDjyLLli3DYDDw1FNPlelaJpPJKigp6ThwwzwFBQWWvH+n1+vx8PCw2oQQQghRddnUxwfMI6UGDRpE69atadu2LdOnTyc7O5shQ4YAEBMTQ82aNZk6darVeXPnzqV3797UqFHDKj07O5vXX3+dnj17EhgYyKVLl5g5cyZJSUk8/vjjAOzcuZPdu3fToUMHvL29OXnyJBMmTKBevXqWgGvRokU4OjrSrFkz9Ho9v/32G3FxcfTr16/UDtBCCCGEsC82Bz79+vXj4sWLTJw4kZSUFFq0aMG6dessHZ4TExPRaq0bkhISEti2bRvr168vVp5Op+Po0aMsWLCAS5cuUaNGDdq0acPPP/9M06ZNAXB1dWXFihVMmjSJ7OxsAgMD6d69O6+88gp6vd58Iw4OvPnmmxw7dgylFCEhIYwePZrnn3/e5ocihBBCiKrJ5nl8qjKZx0cIIYSofG5ZHx8hhBBCiMpMAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdKFfgM3PmTEJDQ3F2diYiIoJdu3ZdN396ejqjRo0iMDAQvV5PgwYNWLt2reV4aGgoGo2m2DZq1ChLnnvvvbfY8WeeecbqOomJifTo0QNXV1f8/Px48cUXKSwsLM8tCiGEEKIKcrD1hCVLlhAbG8vs2bOJiIhg+vTpREdHk5CQgJ+fX7H8+fn53H///fj5+bF8+XJq1qzJ2bNn8fLysuTZvXs3RqPRsn/o0CHuv/9+Hn/8cauyhg8fzpQpUyz7rq6ulq+NRiM9evQgICCA7du3k5ycTExMDI6Ojrzxxhu23qYQQgghqiCNUkrZckJERARt2rRhxowZAJhMJoKDg3n22WcZP358sfyzZ89m2rRpHD16FEdHxzJdY8yYMaxevZrjx4+j0WgAc4tPixYtmD59eonnfP/99zz00ENcuHABf39/y7XHjRvHxYsXcXJyuuF1MzMz8fT0JCMjAw8PjzLVVQghhBAVy5bPb5tedeXn5xMfH09UVNSfBWi1REVFsWPHjhLPWbVqFZGRkYwaNQp/f3/uvvtu3njjDasWnr9fY+HChTz99NOWoKfIokWL8PHx4e677yYuLo6cnBzLsR07dtCsWTNL0AMQHR1NZmYmhw8fLvFaBoOBzMxMq00IIYQQVZdNr7ouXbqE0Wi0Ci4A/P39OXr0aInnnDp1ip9++oknn3yStWvXcuLECf79739TUFDApEmTiuVfuXIl6enpDB482Cr9iSeeICQkhKCgIA4cOMC4ceNISEhgxYoVAKSkpJRYr6JjJZk6dSqTJ08u070LIYQQovKzuY+PrUwmE35+fsyZMwedTkd4eDhJSUlMmzatxMBn7ty5PPDAAwQFBVmljxgxwvJ1s2bNCAwMpGvXrpw8eZJ69eqVq25xcXHExsZa9jMzMwkODi5XWUIIIYS489kU+Pj4+KDT6UhNTbVKT01NJSAgoMRzAgMDcXR0RKfTWdIaN25MSkoK+fn5Vn1vzp49y48//mhpxbmeiIgIAE6cOEG9evUICAgoNrqsqJ6l1U2v16PX6294LSGEEEJUDTb18XFyciI8PJyNGzda0kwmExs3biQyMrLEc9q3b8+JEycwmUyWtGPHjhEYGFisw/G8efPw8/OjR48eN6zLvn37AHNgBRAZGcnBgwdJS0uz5NmwYQMeHh40adKkzPcohBBCiKrL5nl8YmNj+eSTT1iwYAFHjhxh5MiRZGdnM2TIEABiYmKIi4uz5B85ciRXrlzhueee49ixY6xZs4Y33njDao4eMAdQ8+bNY9CgQTg4WDdEnTx5ktdee434+HjOnDnDqlWriImJoVOnTjRv3hyAbt260aRJEwYOHMj+/fv54YcfeOWVVxg1apS06gghhBACKEcfn379+nHx4kUmTpxISkoKLVq0YN26dZaOxImJiWi1f8ZTwcHB/PDDDzz//PM0b96cmjVr8txzzzFu3Dircn/88UcSExN5+umni13TycmJH3/8kenTp5OdnU1wcDCPPvoor7zyiiWPTqdj9erVjBw5ksjISNzc3Bg0aJDVvD9CCCGEsG82z+NTlck8PkIIIUTlc8vm8RFCCCGEqMwk8BFCCCGE3ZDARwghhBB2QwIfIYQQQtgNCXyEEEIIYTck8BFCCCGE3ZDARwghhBB2QwIfIYQQQtgNCXyEEEIIYTck8BFCCCGE3ZDARwghhBB2QwIfIYQQQtgNCXyEEEIIYTck8BFCCCGE3ZDARwghhBB2QwIfIYQQQtgNCXyEEEIIYTck8BFCCCGE3ZDARwghhBB2QwIfIYQQQtiNcgU+M2fOJDQ0FGdnZyIiIti1a9d186enpzNq1CgCAwPR6/U0aNCAtWvXWo5v3bqVhx9+mKCgIDQaDStXriyxnCNHjtCzZ088PT1xc3OjTZs2JCYmAnDmzBk0Gk2J27Jly8pzm0IIIYSoYmwOfJYsWUJsbCyTJk1iz549hIWFER0dTVpaWon58/Pzuf/++zlz5gzLly8nISGBTz75hJo1a1ryZGdnExYWxsyZM0u97smTJ+nQoQONGjVi8+bNHDhwgAkTJuDs7AxAcHAwycnJVtvkyZNxd3fngQcesPU2hRBCCFEFaZRSypYTIiIiaNOmDTNmzADAZDIRHBzMs88+y/jx44vlnz17NtOmTePo0aM4OjreuEIaDd988w29e/e2Su/fvz+Ojo588cUXZa5ry5YtadWqFXPnzi1T/szMTDw9PcnIyMDDw6PM1xFCCCFExbHl89umFp/8/Hzi4+OJior6swCtlqioKHbs2FHiOatWrSIyMpJRo0bh7+/P3XffzRtvvIHRaCzzdU0mE2vWrKFBgwZER0fj5+dHREREqa/EAOLj49m3bx9Dhw4tNY/BYCAzM9NqE0IIIUTVZVPgc+nSJYxGI/7+/lbp/v7+pKSklHjOqVOnWL58OUajkbVr1zJhwgTeeecd/vvf/5b5umlpaWRlZfG///2P7t27s379eh555BH69OnDli1bSjxn7ty5NG7cmHbt2pVa7tSpU/H09LRswcHBZa6TEEIIISofh1t9AZPJhJ+fH3PmzEGn0xEeHk5SUhLTpk1j0qRJZS4DoFevXjz//PMAtGjRgu3btzN79mw6d+5slT83N5fFixczYcKE65YbFxdHbGysZT8zM1OCHyGEEKIKsynw8fHxQafTkZqaapWemppKQEBAiecEBgbi6OiITqezpDVu3JiUlBTy8/NxcnIq03UdHBxo0qSJVXrjxo3Ztm1bsfzLly8nJyeHmJiY65ar1+vR6/U3vL4QQgghqgabXnU5OTkRHh7Oxo0bLWkmk4mNGzcSGRlZ4jnt27fnxIkTllYbgGPHjhEYGFimoKfoum3atCEhIcEq/dixY4SEhBTLP3fuXHr27Imvr2+ZyhdCCCGEfbD5VVdsbCyDBg2idevWtG3blunTp5Odnc2QIUMAiImJoWbNmkydOhWAkSNHMmPGDJ577jmeffZZjh8/zhtvvMH//d//WcrMysrixIkTlv3Tp0+zb98+qlevTu3atQF48cUX6devH506daJLly6sW7eO7777js2bN1vV78SJE2zdutVqniAhhBBCCABUOXz44Yeqdu3aysnJSbVt21b9+uuvlmOdO3dWgwYNssq/fft2FRERofR6vapbt656/fXXVWFhoeX4pk2bFFBs+3s5c+fOVfXr11fOzs4qLCxMrVy5sljd4uLiVHBwsDIajTbfV0ZGhgJURkaGzecKIYQQomLY8vlt8zw+VZnM4yOEEEJUPrdsHh8hhBBCiMpMAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8hhBBC2A0JfIQQQghhNyTwEUIIIYTdKFfgM3PmTEJDQ3F2diYiIoJdu3aVmnf+/PloNBqrzdnZ2SrP4MGDi+Xp3r27VZ5jx47Rq1cvfHx88PDwoEOHDmzatMly/PLly3Tv3p2goCD0ej3BwcGMHj2azMzM8tyiEEIIIaogmwOfJUuWEBsby6RJk9izZw9hYWFER0eTlpZW6jkeHh4kJydbtrNnzxbL0717d6s8X375pdXxhx56iMLCQn766Sfi4+MJCwvjoYceIiUlxXwjWi29evVi1apVHDt2jPnz5/Pjjz/yzDPP2HqLQgghhKiiNEopZcsJERERtGnThhkzZgBgMpkIDg7m2WefZfz48cXyz58/nzFjxpCenl5qmYMHDyY9PZ2VK1eWePzSpUv4+vqydetWOnbsCMC1a9fw8PBgw4YNREVFlXjeBx98wLRp0zh37lyZ7i0zMxNPT08yMjLw8PAo0zlCCCGEqFi2fH7b1OKTn59PfHy8VaCh1WqJiopix44dpZ6XlZVFSEgIwcHB9OrVi8OHDxfLs3nzZvz8/GjYsCEjR47k8uXLlmM1atSgYcOGfP7552RnZ1NYWMjHH3+Mn58f4eHhJV7zwoULrFixgs6dO5daL4PBQGZmptUmhBBCiKrLpsDn0qVLGI1G/P39rdL9/f0tr5z+rmHDhnz22Wd8++23LFy4EJPJRLt27Th//rwlT/fu3fn888/ZuHEjb775Jlu2bOGBBx7AaDQCoNFo+PHHH9m7dy/VqlXD2dmZd999l3Xr1uHt7W11vQEDBuDq6krNmjXx8PDg008/LfV+pk6diqenp2ULDg625XEIIYQQopKx6VXXhQsXqFmzJtu3bycyMtKSPnbsWLZs2cLOnTtvWEZBQQGNGzdmwIABvPbaayXmOXXqFPXq1ePHH3+ka9euKKXo3bs3BQUFvPzyy7i4uPDpp5+yatUqdu/eTWBgoOXclJQU0tPTOXbsGHFxcXTu3JlZs2aVeB2DwYDBYLDsZ2ZmEhwcLK+6hBBCiErEllddDrYU7OPjg06nIzU11So9NTWVgICAMpXh6OhIy5YtOXHiRKl56tati4+PDydOnKBr16789NNPrF69mqtXr1puaNasWWzYsIEFCxZY9S0KCAggICCARo0aUb16dTp27MiECROsgqMier0evV5fpnoLIYQQovKz6VWXk5MT4eHhbNy40ZJmMpnYuHGjVQvQ9RiNRg4ePFhiIFLk/PnzXL582ZInJyfHXFmtdXW1Wi0mk6nUcoqO/bVVRwghhBD2y6YWH4DY2FgGDRpE69atadu2LdOnTyc7O5shQ4YAEBMTQ82aNZk6dSoAU6ZM4Z577qF+/fqkp6czbdo0zp49y7BhwwBzx+fJkyfz6KOPEhAQwMmTJxk7diz169cnOjoagMjISLy9vRk0aBATJ07ExcWFTz75hNOnT9OjRw8A1q5dS2pqKm3atMHd3Z3Dhw/z4osv0r59e0JDQ2/GsxJCCCFEJWdz4NOvXz8uXrzIxIkTSUlJoUWLFqxbt87S4TkxMdGqZebq1asMHz6clJQUvL29CQ8PZ/v27TRp0gQAnU7HgQMHWLBgAenp6QQFBdGtWzdee+01y2soHx8f1q1bx8svv8x9991HQUEBTZs25dtvvyUsLAzAEgw9//zzGAwGgoOD6dOnT4lD7IUQQghhn2yex6cqk3l8hBBCiMrnls3jI4QQQghRmUngI4QQQgi7IYGPEEIIIeyGBD5CCCGEsBsS+AghhBDCbkjgI4QQQgi7IYGPEEIIIeyGBD5CCCGEsBsS+AghhBDCbkjgI4QQQgi7IYGPEEIIIeyGBD5CCCGEsBsS+AghhBDCbkjgI4QQQgi7IYGPEEIIIeyGBD5CCCGEsBsS+AghhBDCbkjgI4QQQgi7IYGPEEIIIexGuQKfmTNnEhoairOzMxEREezatavUvPPnz0ej0Vhtzs7OVnmUUkycOJHAwEBcXFyIiori+PHjVnmuXLnCk08+iYeHB15eXgwdOpSsrCyrPAcOHKBjx444OzsTHBzMW2+9VZ7bE0IIIUQVZXPgs2TJEmJjY5k0aRJ79uwhLCyM6Oho0tLSSj3Hw8OD5ORky3b27Fmr42+99RYffPABs2fPZufOnbi5uREdHU1eXp4lz5NPPsnhw4fZsGEDq1evZuvWrYwYMcJyPDMzk27duhESEkJ8fDzTpk3j1VdfZc6cObbeohBCCCGqKmWjtm3bqlGjRln2jUajCgoKUlOnTi0x/7x585Snp2ep5ZlMJhUQEKCmTZtmSUtPT1d6vV59+eWXSimlfv/9dwWo3bt3W/J8//33SqPRqKSkJKWUUrNmzVLe3t7KYDBY8owbN041bNiwzPeWkZGhAJWRkVHmc4QQQghRsWz5/LapxSc/P5/4+HiioqIsaVqtlqioKHbs2FHqeVlZWYSEhBAcHEyvXr04fPiw5djp06dJSUmxKtPT05OIiAhLmTt27MDLy4vWrVtb8kRFRaHVatm5c6clT6dOnXBycrLkiY6OJiEhgatXr9pym0IIIYSoohxsyXzp0iWMRiP+/v5W6f7+/hw9erTEcxo2bMhnn31G8+bNycjI4O2336Zdu3YcPnyYWrVqkZKSYinj72UWHUtJScHPz8+64g4OVK9e3SpPnTp1ipVRdMzb27tY3QwGAwaDwbKfkZEBmF+bCSGEEKJyKPrcVkrdMK9NgU95REZGEhkZadlv164djRs35uOPP+a111671Ze/rqlTpzJ58uRi6cHBwRVQGyGEEEL8E9euXcPT0/O6eWwKfHx8fNDpdKSmplqlp6amEhAQUKYyHB0dadmyJSdOnACwnJeamkpgYKBVmS1atLDk+Xvn6cLCQq5cuWI5PyAgoMR6/fUafxcXF0dsbKxl32QyceXKFWrUqIFGoynT/ZRVZmYmwcHBnDt3Dg8Pj5tadmUlz6Rk8lyKk2dSnDyTkslzKc4enolSimvXrhEUFHTDvDYFPk5OToSHh7Nx40Z69+4NmIOFjRs3Mnr06DKVYTQaOXjwIA8++CAAderUISAggI0bN1oCnczMTHbu3MnIkSMBc6tReno68fHxhIeHA/DTTz9hMpmIiIiw5Hn55ZcpKCjA0dERgA0bNtCwYcMSX3MB6PV69Hq9VZqXl1eZn0d5eHh4VNlvvPKSZ1IyeS7FyTMpTp5JyeS5FFfVn8mNWnqK2DycPTY2lk8++YQFCxZw5MgRRo4cSXZ2NkOGDAEgJiaGuLg4S/4pU6awfv16Tp06xZ49e3jqqac4e/Ysw4YNA0Cj0TBmzBj++9//smrVKg4ePEhMTAxBQUGW4Kpx48Z0796d4cOHs2vXLn755RdGjx5N//79LdHdE088gZOTE0OHDuXw4cMsWbKE999/36pFRwghhBD2zeY+Pv369ePixYtMnDiRlJQUWrRowbp16ywdiRMTE9Fq/4ynrl69yvDhwy0djMPDw9m+fTtNmjSx5Bk7dizZ2dmMGDGC9PR0OnTowLp166wmOly0aBGjR4+ma9euaLVaHn30UT744APLcU9PT9avX8+oUaMIDw/Hx8eHiRMnWs31I4QQQgg7d8sH1wullFJ5eXlq0qRJKi8vr6KrcseQZ1IyeS7FyTMpTp5JyeS5FCfPxJpGqTKM/RJCCCGEqAJkkVIhhBBC2A0JfIQQQghhNyTwEUIIIYTdkMBHCCGEEHZDAh8bbN26lYcffpigoCA0Gg0rV660Op6VlcXo0aOpVasWLi4uNGnShNmzZ1vlSUlJYeDAgQQEBODm5karVq34+uuvrfJcuXKFJ598Eg8PD7y8vBg6dChZWVm3+vbK5UbPJDU1lcGDBxMUFISrqyvdu3fn+PHjVnny8vIYNWoUNWrUwN3dnUcffbTYLNyJiYn06NEDV1dX/Pz8ePHFFyksLLzVt1cu//SZXLlyhWeffZaGDRvi4uJC7dq1+b//+z/LWnJFKtMzgZvzvVJEKcUDDzxQYjmV6bncrGeyY8cO7rvvPtzc3PDw8KBTp07k5uZajtvb75Sq9nt26tSptGnThmrVquHn50fv3r1JSEiwynOzfo9u3ryZVq1aodfrqV+/PvPnz7/Vt3fbSeBjg+zsbMLCwpg5c2aJx2NjY1m3bh0LFy7kyJEjjBkzhtGjR7Nq1SpLnpiYGBISEiyTNfbp04e+ffuyd+9eS54nn3ySw4cPs2HDBlavXs3WrVvv2PmIrvdMlFL07t2bU6dO8e2337J3715CQkKIiooiOzvbku/555/nu+++Y9myZWzZsoULFy7Qp08fy3Gj0UiPHj3Iz89n+/btLFiwgPnz5zNx4sTbco+2+qfP5MKFC1y4cIG3336bQ4cOMX/+fNatW8fQoUMt5VS2ZwI353ulyPTp00tcVqayPZeb8Ux27NhB9+7d6datG7t27WL37t2MHj3aaj41e/udUtV+z27ZsoVRo0bx66+/smHDBgoKCujWrdtN/z16+vRpevToQZcuXdi3bx9jxoxh2LBh/PDDD7f1fm+5ihtJX7kB6ptvvrFKa9q0qZoyZYpVWqtWrdTLL79s2Xdzc1Off/65VZ7q1aurTz75RCml1O+//64AtXv3bsvx77//Xmk0GpWUlHST7+Lm+vszSUhIUIA6dOiQJc1oNCpfX1/L/aanpytHR0e1bNkyS54jR44oQO3YsUMppdTatWuVVqtVKSkpljwfffSR8vDwUAaD4Rbf1T9TnmdSkqVLlyonJydVUFCglKrcz0Spf/Zc9u7dq2rWrKmSk5OLlVOZn0t5n0lERIR65ZVXSi3X3n6nKFW1f88qpVRaWpoC1JYtW5RSN+/36NixY1XTpk2trtWvXz8VHR19q2/ptpIWn5uoXbt2rFq1iqSkJJRSbNq0iWPHjtGtWzerPEuWLOHKlSuYTCa++uor8v6/vfuPibr+4wD+5BA4LjsMA8kQytEofgbm6GxmG4Q4bRlqm279oB9D0a3fLpo2XTnGJmDSOZsukNnWqIXeorGiOzZoRoNxJVgiELIKZRY/Q7w77vX9g/HZTvArzuOuu8/zsd0/n8/78773+7W7116fz70/95mYwBNPPAFg6uxt0aJFeOSRR5RjsrKyoNFo0Nzc7Okp3ZZr164BgMs/cGs0GoSEhKCpqQkA0NraCrvdjqysLKXNgw8+iJiYGJw5cwbAVEySk5OVfwcHgLVr12JkZAQdHR2emIrbzCUmsxkeHoZer8eCBVN/tu5PMQHmHpfx8XFs27YNRqNx1ocP+1Nc5hKTgYEBNDc3IzIyEqtWrcKSJUuwZs0al5ipLacA/p9np3/2Dg8PB+C+PHrmzBmXPqbbTPfhL1j4uFF5eTkSEhIQHR2N4OBg5OTkwGg04vHHH1faVFdXw263Y/HixQgJCUF+fj5qamoQFxcHYOq36cjISJd+FyxYgPDwcFy6dMmj87ld01+8wsJCDA4Owmazobi4GH/88Qf6+/sBTM03ODh4xsNhlyxZosz30qVLLl/W6f3T+3zJXGJyvStXruCDDz5wuQzvTzEB5h6XN954A6tWrcLTTz89az/+FJe5xKSnpwcAsG/fPrz66quoq6tDeno6MjMzlXUvasspgH/nWafTiddffx2PPfYYkpKSALgvj96ozcjIiMuaMV/HwseNysvL8eOPP8JkMqG1tRUlJSXYuXMn6uvrlTZ79+7F0NAQ6uvr0dLSgjfffBPPPvsszp4968WRz4+goCB89dVX6OzsRHh4OHQ6HSwWC9atW+ey/kBNbjUmIyMjWL9+PRISErBv3z7PD9hD5hIXk8kEs9mMQ4cOeXewHjKXmDidTgBAfn4+8vLykJaWhrKyMsTHx+PTTz/15vDnxVy/P/6cZ3fu3In29nZ8/vnn3h6Kz7rlh5TS7K5evYr33nsPNTU1WL9+PQAgJSUFVqsVBw8eRFZWFrq7u/Hxxx+jvb0diYmJAIDU1FQ0NjbCaDTi6NGjiIqKwsDAgEvfDocD//zzz6yX9v/rVqxYAavViuHhYdhsNkRERCAjI0O5xBwVFQWbzYahoSGXs5XLly8r842KisJPP/3k0u/03Qr+GJNpo6OjyMnJwZ133omamhoEBQUp+/wtJsDN42I2m9Hd3T3jrHbTpk1YvXo1Ghoa/C4uN4vJPffcAwAuD30GgIceegh9fX0AoLqc4s95dteuXcpC7OjoaGW7u/JoVFTUjDvBLl++DL1ej9DQ0PmYkleo87R7Htjtdtjt9hln7YGBgcpZ2fj4OAD83zYGgwFDQ0NobW1V9pvNZjidTmRkZMznFOZVWFgYIiIicOHCBbS0tCg/VaxYsQJBQUH4/vvvlbbnz59HX18fDAYDgKmYnD171iVRfffdd9Dr9TMSvi+5UUyAqSs92dnZCA4OhslkclnTAPhvTIAbx+Xdd9/FL7/8AqvVqrwAoKysDBUVFQD8Ny43isl9992HpUuXzri1ubOzE7GxsQDUl1P8Mc+KCHbt2oWamhqYzWbcf//9LvvdlUcNBoNLH9NtpvvwG95eXe1LRkdHpa2tTdra2gSAlJaWSltbm1y8eFFERNasWSOJiYlisVikp6dHKioqRKvVypEjR0RExGazSVxcnKxevVqam5ulq6tLDh48KAEBAVJbW6u8T05OjqSlpUlzc7M0NTXJAw88IFu3bvXKnG/mZjGprq4Wi8Ui3d3dcurUKYmNjZXc3FyXPrZv3y4xMTFiNpulpaVFDAaDGAwGZb/D4ZCkpCTJzs4Wq9UqdXV1EhERIYWFhR6d61zdbkyGh4clIyNDkpOTpaurS/r7+5WXw+EQEd+LiYh7PivXw3V3/fhaXNwRk7KyMtHr9fLFF1/IhQsXZM+ePaLVaqWrq0tpo6ac4o95dseOHRIWFiYNDQ0u+WB8fFxp44482tPTIzqdTt555x359ddfxWg0SmBgoNTV1Xl0vvONhc8tsFgsAmDG64UXXhARkf7+fnnxxRdl6dKlotVqJT4+XkpKSsTpdCp9dHZ2Sm5urkRGRopOp5OUlJQZt13+/fffsnXrVlm4cKHo9XrJy8uT0dFRT051zm4Wk48++kiio6MlKChIYmJiZM+ePTNuK7569aoUFBTIXXfdJTqdTp555hnp7+93adPb2yvr1q2T0NBQufvuu+Wtt95Sbu3+r7ndmNzoeADy+++/K+18KSYi7vmsXO/6wkfEt+LirpgUFRVJdHS06HQ6MRgM0tjY6LJfbTnF3/LsjfJBRUWF0sZdedRiscjDDz8swcHBsnz5cpf38BcBIiLuvopERERE9F/ENT5ERESkGix8iIiISDVY+BAREZFqsPAhIiIi1WDhQ0RERKrBwoeIiIhUg4UPERERqQYLHyIiIlINFj5ERESkGix8iIhuYnJyUnnAJRH5NhY+RORTqqqqsHjxYly7ds1l+8aNG/Hcc88BAE6fPo309HRotVosX74c+/fvh8PhUNqWlpYiOTkZd9xxB5YtW4aCggKMjY0p+ysrK7Fo0SKYTCYkJCQgJCQEfX19npkgEc0rFj5E5FO2bNmCyclJmEwmZdvAwABqa2vx0ksvobGxEc8//zxee+01nDt3Dp988gkqKytx4MABpb1Go8Hhw4fR0dGBEydOwGw2Y/fu3S7vMz4+juLiYhw/fhwdHR2IjIz02ByJaP7wIaVE5HMKCgrQ29uLb775BsDUFRyj0Yiuri48+eSTyMzMRGFhodL+5MmT2L17N/76669Z+/vyyy+xfft2XLlyBcDUFZ+8vDxYrVakpqbO/4SIyGNY+BCRz2lra8PKlStx8eJF3HvvvUhJScGWLVuwd+9eREREYGxsDIGBgUr7yclJTExM4N9//4VOp0N9fT2Kiorw22+/YWRkBA6Hw2V/ZWUl8vPzMTExgYCAAC/OlIjcbYG3B0BEdKvS0tKQmpqKqqoqZGdno6OjA7W1tQCAsbEx7N+/H7m5uTOO02q16O3txYYNG7Bjxw4cOHAA4eHhaGpqwssvvwybzQadTgcACA0NZdFD5IdY+BCRT3rllVdw6NAh/Pnnn8jKysKyZcsAAOnp6Th//jzi4uJmPa61tRVOpxMlJSXQaKaWOVZXV3ts3ETkXSx8iMgnbdu2DW+//TaOHTuGqqoqZfv777+PDRs2ICYmBps3b4ZGo8HPP/+M9vZ2fPjhh4iLi4Pdbkd5eTmeeuop/PDDDzh69KgXZ0JEnsS7uojIJ4WFhWHTpk1YuHAhNm7cqGxfu3Ytvv76a3z77bdYuXIlHn30UZSVlSE2NhYAkJqaitLSUhQXFyMpKQmfffYZioqKvDQLIvI0Lm4mIp+VmZmJxMREHD582NtDISIfwcKHiHzO4OAgGhoasHnzZpw7dw7x8fHeHhIR+Qiu8SEin5OWlobBwUEUFxez6CGiW8IrPkRERKQaXNxMREREqsHCh4iIiFSDhQ8RERGpBgsfIiIiUg0WPkRERKQaLHyIiIhINVj4EBERkWqw8CEiIiLVYOFDREREqvE/B6Jpt57ar5sAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = boys.loc[boys.year == 2010]"
      ],
      "metadata": {
        "id": "uqDvp00RlJBN"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "XmM-7jsxnYnq",
        "outputId": "a2bac61b-616e-4231-9f3a-e10e5b5388c8"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           name sex  birth  year      prop\n",
              "260877    Jacob   M  21875  2010  0.011523\n",
              "260878    Ethan   M  17866  2010  0.009411\n",
              "260879  Michael   M  17133  2010  0.009025\n",
              "260880   Jayden   M  17030  2010  0.008971\n",
              "260881  William   M  16870  2010  0.008887"
            ],
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              "      <td>Jacob</td>\n",
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              "      <td>0.011523</td>\n",
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              "      <th>260878</th>\n",
              "      <td>Ethan</td>\n",
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              "      <td>2010</td>\n",
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              "      <td>Michael</td>\n",
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              "      <td>Jayden</td>\n",
              "      <td>M</td>\n",
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              "      <td>2010</td>\n",
              "      <td>0.008971</td>\n",
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              "      <th>260881</th>\n",
              "      <td>William</td>\n",
              "      <td>M</td>\n",
              "      <td>16870</td>\n",
              "      <td>2010</td>\n",
              "      <td>0.008887</td>\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",
              "      fill: #174EA6;\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "\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",
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              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-68d90409-c34c-4944-86b8-e23977ceaaca 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-68d90409-c34c-4944-86b8-e23977ceaaca');\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",
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              "\n",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
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              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "  }\n",
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              "  .colab-df-quickchart:hover {\n",
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              "    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",
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              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
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              "    border-bottom-color: var(--fill-color);\n",
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              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
              "    }\n",
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              "      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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              "      quickchartButtonEl.style.display =\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"Prince\",\n          \"Emery\",\n          \"Trevon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"M\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2855,\n        \"min\": 193,\n        \"max\": 21875,\n        \"num_unique_values\": 699,\n        \"samples\": [\n          2367\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2010,\n        \"max\": 2010,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          2010\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0015040950330155135,\n        \"min\": 0.00010166552358798176,\n        \"max\": 0.011522970613922804,\n        \"num_unique_values\": 699,\n        \"samples\": [\n          0.0012468512659728126\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g30Bd59enZKQ",
        "outputId": "9ad91f06-93fb-4ac4-c727-a2c6428be1ea"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 1000 entries, 260877 to 261876\n",
            "Data columns (total 5 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   name    1000 non-null   object \n",
            " 1   sex     1000 non-null   object \n",
            " 2   birth   1000 non-null   int64  \n",
            " 3   year    1000 non-null   int64  \n",
            " 4   prop    1000 non-null   float64\n",
            "dtypes: float64(1), int64(2), object(2)\n",
            "memory usage: 46.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prop_cumsum = df.prop.sort_values(ascending= False).cumsum()"
      ],
      "metadata": {
        "id": "YbNp3HNSnkYw"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prop_cumsum"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UN7dEVOLoOck",
        "outputId": "e6516414-cdbb-4c54-934b-eec24ccbc499"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "260877    0.011523\n",
              "260878    0.020934\n",
              "260879    0.029959\n",
              "260880    0.038930\n",
              "260881    0.047817\n",
              "            ...   \n",
              "261872    0.842748\n",
              "261873    0.842850\n",
              "261874    0.842953\n",
              "261875    0.843055\n",
              "261876    0.843156\n",
              "Name: prop, Length: 1000, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prop_cumsum.searchsorted(0.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Q6eKJSzoPXY",
        "outputId": "e69ae45d-bf69-4035-9b69-eb39293f7891"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "116"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = boys.loc[boys.year == 1900]"
      ],
      "metadata": {
        "id": "DQ4axrPjoXdi"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "in1900 = df.prop.sort_values(ascending= False).cumsum()"
      ],
      "metadata": {
        "id": "Gr37JDeRoonc"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "in1900.searchsorted(0.5) + 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2AiTlf_cotYT",
        "outputId": "cc453b4c-5e92-42ff-e477-fce9f7343866"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_quantile_count(group, q = 0.5):\n",
        "    return group.prop.sort_values(ascending= False).cumsum().searchsorted(q) + 1"
      ],
      "metadata": {
        "id": "1bYni3roow2L"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diversity = top1000.groupby(['year', 'sex']).apply(get_quantile_count)"
      ],
      "metadata": {
        "id": "RUh5qMPSpxNP"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diversity = diversity.unstack()"
      ],
      "metadata": {
        "id": "WnrXkKphp8EG"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "diversity.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "dn1RXStbp9EM",
        "outputId": "adfa6718-6e69-4240-a626-8e4bf8ed3b51"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex    F   M\n",
              "year        \n",
              "1880  38  14\n",
              "1881  38  14\n",
              "1882  38  15\n",
              "1883  39  15\n",
              "1884  39  16"
            ],
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\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",
              "      --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-099b5ef1-2a14-45db-8d87-15c611d708de 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": "diversity",
              "summary": "{\n  \"name\": \"diversity\",\n  \"rows\": 131,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37,\n        \"min\": 1880,\n        \"max\": 2010,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          1935,\n          1920,\n          1899\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"F\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 45,\n        \"min\": 38,\n        \"max\": 246,\n        \"num_unique_values\": 55,\n        \"samples\": [\n          80,\n          43,\n          72\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"M\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21,\n        \"min\": 14,\n        \"max\": 117,\n        \"num_unique_values\": 48,\n        \"samples\": [\n          45,\n          87,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "diversity.plot(title= 'Number of popular names in top 50%')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "Set47cIcqDZB",
        "outputId": "05bb94fd-b3aa-4298-c04f-b5aef7b16911"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Number of popular names in top 50%'}, xlabel='year'>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The 'last letter' revolution"
      ],
      "metadata": {
        "id": "vmncjhqPqEwp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "last_letters = names.name.map(lambda x: x[-1])"
      ],
      "metadata": {
        "id": "_N-1XwlkroNV"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "last_letters.name = 'last_letter'"
      ],
      "metadata": {
        "id": "QZinIqqstLXm"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "last_letters"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A1KH9h2LtNoS",
        "outputId": "01b692eb-ebbb-4435-cd0c-bbf0f469a3bf"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0          y\n",
              "1          a\n",
              "2          a\n",
              "3          h\n",
              "4          e\n",
              "          ..\n",
              "1690779    e\n",
              "1690780    e\n",
              "1690781    s\n",
              "1690782    n\n",
              "1690783    x\n",
              "Name: last_letter, Length: 1690784, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "table = names.pivot_table(\n",
        "    values= 'birth',\n",
        "    index= last_letters,\n",
        "    columns= ['sex', 'year'],\n",
        "    aggfunc= 'sum'\n",
        ")"
      ],
      "metadata": {
        "id": "AqkHVkmltT4A"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subtable = table.reindex(columns= [1910, 1960, 2010], level= 'year')"
      ],
      "metadata": {
        "id": "gCSw6utJtx_u"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "subtable.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "UAqx8MEQt0Fl",
        "outputId": "4c22d2d7-3733-49e2-9030-f8199bdf33a0"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex                 F                            M                    \n",
              "year             1910      1960      2010     1910      1960      2010\n",
              "last_letter                                                           \n",
              "a            108376.0  691247.0  670605.0    977.0    5204.0   28438.0\n",
              "b                 NaN     694.0     450.0    411.0    3912.0   38859.0\n",
              "c                 5.0      49.0     946.0    482.0   15476.0   23125.0\n",
              "d              6750.0    3729.0    2607.0  22111.0  262112.0   44398.0\n",
              "e            133569.0  435013.0  313833.0  28655.0  178823.0  129012.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ac3c1260-242a-41fd-ac08-e80a0104f0d8\" 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 tr th {\n",
              "        text-align: left;\n",
              "    }\n",
              "\n",
              "    .dataframe thead tr:last-of-type th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th colspan=\"3\" halign=\"left\">F</th>\n",
              "      <th colspan=\"3\" halign=\"left\">M</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <th>1910</th>\n",
              "      <th>1960</th>\n",
              "      <th>2010</th>\n",
              "      <th>1910</th>\n",
              "      <th>1960</th>\n",
              "      <th>2010</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_letter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>108376.0</td>\n",
              "      <td>691247.0</td>\n",
              "      <td>670605.0</td>\n",
              "      <td>977.0</td>\n",
              "      <td>5204.0</td>\n",
              "      <td>28438.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>NaN</td>\n",
              "      <td>694.0</td>\n",
              "      <td>450.0</td>\n",
              "      <td>411.0</td>\n",
              "      <td>3912.0</td>\n",
              "      <td>38859.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>5.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>946.0</td>\n",
              "      <td>482.0</td>\n",
              "      <td>15476.0</td>\n",
              "      <td>23125.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>6750.0</td>\n",
              "      <td>3729.0</td>\n",
              "      <td>2607.0</td>\n",
              "      <td>22111.0</td>\n",
              "      <td>262112.0</td>\n",
              "      <td>44398.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>133569.0</td>\n",
              "      <td>435013.0</td>\n",
              "      <td>313833.0</td>\n",
              "      <td>28655.0</td>\n",
              "      <td>178823.0</td>\n",
              "      <td>129012.0</td>\n",
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              "  </tbody>\n",
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              "type": "dataframe",
              "variable_name": "subtable",
              "summary": "{\n  \"name\": \"subtable\",\n  \"rows\": 26,\n  \"fields\": [\n    {\n      \"column\": [\n        \"last_letter\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 26,\n        \"samples\": [\n          \"i\",\n          \"q\",\n          \"a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"F\",\n        1910\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36172.39681093332,\n        \"min\": 5.0,\n        \"max\": 133569.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          108376.0,\n          8.0,\n          10877.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"F\",\n        1960\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 171999.83979211867,\n        \"min\": 20.0,\n        \"max\": 691247.0,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          80812.0,\n          25810.0,\n          691247.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"F\",\n        2010\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 149279.5579600389,\n        \"min\": 35.0,\n        \"max\": 670605.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          55820.0,\n          53.0,\n          670605.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"M\",\n        1910\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9719.339443097197,\n        \"min\": 22.0,\n        \"max\": 28655.0,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          164.0,\n          25404.0,\n          977.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"M\",\n        1960\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 110663.49964906997,\n        \"min\": 78.0,\n        \"max\": 343318.0,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          1286.0,\n          219080.0,\n          5204.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"M\",\n        2010\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 134854.30945039,\n        \"min\": 342.0,\n        \"max\": 688677.0,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          42956.0,\n          342.0,\n          28438.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "subtable.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGQtkGBOuW96",
        "outputId": "a81f95d0-475c-47ac-d1ae-9aead4adaf69"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex  year\n",
              "F    1910     396416.0\n",
              "     1960    2022062.0\n",
              "     2010    1759010.0\n",
              "M    1910     194198.0\n",
              "     1960    2132588.0\n",
              "     2010    1898382.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "letter_prop = subtable / subtable.sum()"
      ],
      "metadata": {
        "id": "6xTwXTTku0s7"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "letter_prop"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 927
        },
        "id": "ddBSCM59u8LJ",
        "outputId": "59e9ae74-caf8-4b5e-9c85-8d9dd7e8b949"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sex                 F                             M                    \n",
              "year             1910      1960      2010      1910      1960      2010\n",
              "last_letter                                                            \n",
              "a            0.273390  0.341853  0.381240  0.005031  0.002440  0.014980\n",
              "b                 NaN  0.000343  0.000256  0.002116  0.001834  0.020470\n",
              "c            0.000013  0.000024  0.000538  0.002482  0.007257  0.012181\n",
              "d            0.017028  0.001844  0.001482  0.113858  0.122908  0.023387\n",
              "e            0.336941  0.215133  0.178415  0.147556  0.083853  0.067959\n",
              "f                 NaN  0.000010  0.000055  0.000783  0.004325  0.001188\n",
              "g            0.000144  0.000157  0.000374  0.002250  0.009488  0.001404\n",
              "h            0.051529  0.036224  0.075852  0.045562  0.037907  0.051670\n",
              "i            0.001526  0.039965  0.031734  0.000844  0.000603  0.022628\n",
              "j                 NaN       NaN  0.000090       NaN       NaN  0.000769\n",
              "k            0.000121  0.000156  0.000356  0.036581  0.049384  0.018541\n",
              "l            0.043189  0.033867  0.026356  0.065016  0.104904  0.070367\n",
              "m            0.001201  0.008613  0.002588  0.058044  0.033827  0.024657\n",
              "n            0.079240  0.130687  0.140210  0.143415  0.152522  0.362771\n",
              "o            0.001660  0.002439  0.001243  0.017065  0.012829  0.042681\n",
              "p            0.000018  0.000023  0.000020  0.003172  0.005675  0.001269\n",
              "q                 NaN       NaN  0.000030       NaN       NaN  0.000180\n",
              "r            0.013390  0.006764  0.018025  0.064481  0.031034  0.087477\n",
              "s            0.039042  0.012764  0.013332  0.130815  0.102730  0.065145\n",
              "t            0.027438  0.015201  0.007830  0.072879  0.065655  0.022861\n",
              "u            0.000684  0.000574  0.000417  0.000124  0.000057  0.001221\n",
              "v                 NaN  0.000060  0.000117  0.000113  0.000037  0.001434\n",
              "w            0.000020  0.000031  0.001182  0.006329  0.007711  0.016148\n",
              "x            0.000015  0.000037  0.000727  0.003965  0.001851  0.008614\n",
              "y            0.110972  0.152569  0.116828  0.077349  0.160987  0.058168\n",
              "z            0.002439  0.000659  0.000704  0.000170  0.000184  0.001831"
            ],
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th colspan=\"3\" halign=\"left\">F</th>\n",
              "      <th colspan=\"3\" halign=\"left\">M</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <th>1910</th>\n",
              "      <th>1960</th>\n",
              "      <th>2010</th>\n",
              "      <th>1910</th>\n",
              "      <th>1960</th>\n",
              "      <th>2010</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>last_letter</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>a</th>\n",
              "      <td>0.273390</td>\n",
              "      <td>0.341853</td>\n",
              "      <td>0.381240</td>\n",
              "      <td>0.005031</td>\n",
              "      <td>0.002440</td>\n",
              "      <td>0.014980</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>b</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000343</td>\n",
              "      <td>0.000256</td>\n",
              "      <td>0.002116</td>\n",
              "      <td>0.001834</td>\n",
              "      <td>0.020470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>c</th>\n",
              "      <td>0.000013</td>\n",
              "      <td>0.000024</td>\n",
              "      <td>0.000538</td>\n",
              "      <td>0.002482</td>\n",
              "      <td>0.007257</td>\n",
              "      <td>0.012181</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>d</th>\n",
              "      <td>0.017028</td>\n",
              "      <td>0.001844</td>\n",
              "      <td>0.001482</td>\n",
              "      <td>0.113858</td>\n",
              "      <td>0.122908</td>\n",
              "      <td>0.023387</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>e</th>\n",
              "      <td>0.336941</td>\n",
              "      <td>0.215133</td>\n",
              "      <td>0.178415</td>\n",
              "      <td>0.147556</td>\n",
              "      <td>0.083853</td>\n",
              "      <td>0.067959</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>f</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000010</td>\n",
              "      <td>0.000055</td>\n",
              "      <td>0.000783</td>\n",
              "      <td>0.004325</td>\n",
              "      <td>0.001188</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>g</th>\n",
              "      <td>0.000144</td>\n",
              "      <td>0.000157</td>\n",
              "      <td>0.000374</td>\n",
              "      <td>0.002250</td>\n",
              "      <td>0.009488</td>\n",
              "      <td>0.001404</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>h</th>\n",
              "      <td>0.051529</td>\n",
              "      <td>0.036224</td>\n",
              "      <td>0.075852</td>\n",
              "      <td>0.045562</td>\n",
              "      <td>0.037907</td>\n",
              "      <td>0.051670</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>i</th>\n",
              "      <td>0.001526</td>\n",
              "      <td>0.039965</td>\n",
              "      <td>0.031734</td>\n",
              "      <td>0.000844</td>\n",
              "      <td>0.000603</td>\n",
              "      <td>0.022628</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>j</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000090</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000769</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>k</th>\n",
              "      <td>0.000121</td>\n",
              "      <td>0.000156</td>\n",
              "      <td>0.000356</td>\n",
              "      <td>0.036581</td>\n",
              "      <td>0.049384</td>\n",
              "      <td>0.018541</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>l</th>\n",
              "      <td>0.043189</td>\n",
              "      <td>0.033867</td>\n",
              "      <td>0.026356</td>\n",
              "      <td>0.065016</td>\n",
              "      <td>0.104904</td>\n",
              "      <td>0.070367</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>m</th>\n",
              "      <td>0.001201</td>\n",
              "      <td>0.008613</td>\n",
              "      <td>0.002588</td>\n",
              "      <td>0.058044</td>\n",
              "      <td>0.033827</td>\n",
              "      <td>0.024657</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n</th>\n",
              "      <td>0.079240</td>\n",
              "      <td>0.130687</td>\n",
              "      <td>0.140210</td>\n",
              "      <td>0.143415</td>\n",
              "      <td>0.152522</td>\n",
              "      <td>0.362771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>o</th>\n",
              "      <td>0.001660</td>\n",
              "      <td>0.002439</td>\n",
              "      <td>0.001243</td>\n",
              "      <td>0.017065</td>\n",
              "      <td>0.012829</td>\n",
              "      <td>0.042681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>p</th>\n",
              "      <td>0.000018</td>\n",
              "      <td>0.000023</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.003172</td>\n",
              "      <td>0.005675</td>\n",
              "      <td>0.001269</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>q</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000030</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>r</th>\n",
              "      <td>0.013390</td>\n",
              "      <td>0.006764</td>\n",
              "      <td>0.018025</td>\n",
              "      <td>0.064481</td>\n",
              "      <td>0.031034</td>\n",
              "      <td>0.087477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>s</th>\n",
              "      <td>0.039042</td>\n",
              "      <td>0.012764</td>\n",
              "      <td>0.013332</td>\n",
              "      <td>0.130815</td>\n",
              "      <td>0.102730</td>\n",
              "      <td>0.065145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>t</th>\n",
              "      <td>0.027438</td>\n",
              "      <td>0.015201</td>\n",
              "      <td>0.007830</td>\n",
              "      <td>0.072879</td>\n",
              "      <td>0.065655</td>\n",
              "      <td>0.022861</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>u</th>\n",
              "      <td>0.000684</td>\n",
              "      <td>0.000574</td>\n",
              "      <td>0.000417</td>\n",
              "      <td>0.000124</td>\n",
              "      <td>0.000057</td>\n",
              "      <td>0.001221</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>v</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000060</td>\n",
              "      <td>0.000117</td>\n",
              "      <td>0.000113</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.001434</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>w</th>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000031</td>\n",
              "      <td>0.001182</td>\n",
              "      <td>0.006329</td>\n",
              "      <td>0.007711</td>\n",
              "      <td>0.016148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x</th>\n",
              "      <td>0.000015</td>\n",
              "      <td>0.000037</td>\n",
              "      <td>0.000727</td>\n",
              "      <td>0.003965</td>\n",
              "      <td>0.001851</td>\n",
              "      <td>0.008614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y</th>\n",
              "      <td>0.110972</td>\n",
              "      <td>0.152569</td>\n",
              "      <td>0.116828</td>\n",
              "      <td>0.077349</td>\n",
              "      <td>0.160987</td>\n",
              "      <td>0.058168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>z</th>\n",
              "      <td>0.002439</td>\n",
              "      <td>0.000659</td>\n",
              "      <td>0.000704</td>\n",
              "      <td>0.000170</td>\n",
              "      <td>0.000184</td>\n",
              "      <td>0.001831</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "  .colab-df-spinner {\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
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              "  @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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              "      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",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      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",
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              "    (() => {\n",
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              "        document.querySelector('#df-fef464a4-e2fe-4ed8-b725-34db41ea10ec button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_b171c8de-97f0-4955-819b-f14fb6faa24f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
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              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('letter_prop')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_b171c8de-97f0-4955-819b-f14fb6faa24f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('letter_prop');\n",
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              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "letter_prop",
              "summary": "{\n  \"name\": \"letter_prop\",\n  \"rows\": 26,\n  \"fields\": [\n    {\n      \"column\": [\n        \"last_letter\",\n        \"\"\n      ],\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 26,\n        \"samples\": [\n          \"i\",\n          \"q\",\n          \"a\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"F\",\n        1910\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.09124857929784197,\n        \"min\": 1.2613012592831772e-05,\n        \"max\": 0.3369414958023894,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.27338957055214724,\n          2.0180820148530835e-05,\n          0.027438347594446237\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"F\",\n        1960\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0850616053276896,\n        \"min\": 9.89089355321449e-06,\n        \"max\": 0.34185252479894285,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          0.03996514449111847,\n          0.0127641981304233,\n          0.34185252479894285\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"F\",\n        2010\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.08486566759713639,\n        \"min\": 1.9897556011620174e-05,\n        \"max\": 0.3812400156906442,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          0.03173375933053252,\n          3.013058481759626e-05,\n          0.3812400156906442\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"M\",\n        1910\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.050048607313655125,\n        \"min\": 0.0001132864396131783,\n        \"max\": 0.14755558759616474,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          0.0008444989134800564,\n          0.13081494145150827,\n          0.0050309477955488725\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"M\",\n        1960\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.051891645104009766,\n        \"min\": 3.6575278487921717e-05,\n        \"max\": 0.1609865571784142,\n        \"num_unique_values\": 24,\n        \"samples\": [\n          0.0006030231812239401,\n          0.1027296411683832,\n          0.0024402275545018543\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": [\n        \"M\",\n        2010\n      ],\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07103644548378038,\n        \"min\": 0.00018015341485538738,\n        \"max\": 0.3627705066735778,\n        \"num_unique_values\": 26,\n        \"samples\": [\n          0.02262769031733339,\n          0.00018015341485538738,\n          0.014980125180285107\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(2, 1, figsize= (10, 10))\n",
        "letter_prop['M'].plot(kind= 'bar', ax= axes[0], title= 'Male', rot= 0)\n",
        "letter_prop['F'].plot(kind= 'bar', ax= axes[1], title= 'Female', legend= False, rot= 0)"
      ],
      "metadata": {
        "id": "W8LQ8wWvu-dB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 889
        },
        "outputId": "cbd77a79-6169-403f-a6d6-fb625d4ecb7d"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Female'}, xlabel='last_letter'>"
            ]
          },
          "metadata": {},
          "execution_count": 67
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x1000 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "letter_prop = table / table.sum()"
      ],
      "metadata": {
        "id": "Ar_T14i03pvw"
      },
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dny_ts = letter_prop.loc[['d', 'n', 'y'], 'M'].T"
      ],
      "metadata": {
        "id": "JOAMtUww5Ocg"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dny_ts.head()"
      ],
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        "outputId": "d48026cc-6f88-4391-cae4-578cb76b8c50"
      },
      "execution_count": 72,
      "outputs": [
        {
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          "data": {
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              "last_letter         d         n         y\n",
              "year                                     \n",
              "1880         0.083055  0.153213  0.075760\n",
              "1881         0.083247  0.153214  0.077451\n",
              "1882         0.085340  0.149560  0.077537\n",
              "1883         0.084066  0.151646  0.079144\n",
              "1884         0.086120  0.149915  0.080405"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dny_ts",
              "summary": "{\n  \"name\": \"dny_ts\",\n  \"rows\": 131,\n  \"fields\": [\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 37,\n        \"min\": 1880,\n        \"max\": 2010,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          1935,\n          1920,\n          1899\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"d\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.04434300033545638,\n        \"min\": 0.023387284540203182,\n        \"max\": 0.17720529931645995,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          0.1756355889449757,\n          0.13832355693172552,\n          0.10501986480634168\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"n\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.06660901550335353,\n        \"min\": 0.13174082992144692,\n        \"max\": 0.3627705066735778,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          0.13634664521851267,\n          0.1514578174261697,\n          0.14060705341844132\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"y\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.03038955917717214,\n        \"min\": 0.05722324219769439,\n        \"max\": 0.1612172063153641,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          0.10053437806599536,\n          0.06905515243295242,\n          0.08480671825867556\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dny_ts.plot(title= 'Proportions of boys born with names ending in d/n/y over time')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "_xFaHbf66CPC",
        "outputId": "8813e5a8-4198-4a84-e6ac-f4a74eafdf8d"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Proportions of boys born with names ending in d/n/y over time'}, xlabel='year'>"
            ]
          },
          "metadata": {},
          "execution_count": 74
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Boy names that became girl names (and vice versa)"
      ],
      "metadata": {
        "id": "odIOMkva6G_1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "all_names = pd.Series(top1000.name.unique())"
      ],
      "metadata": {
        "id": "mwipfWQ76cYT"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "all_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sjod54Kk7qjO",
        "outputId": "85dac07c-021a-4f97-fef7-8eefc4fd3df4"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0            Mary\n",
              "1            Anna\n",
              "2            Emma\n",
              "3       Elizabeth\n",
              "4          Minnie\n",
              "          ...    \n",
              "6863        Masen\n",
              "6864        Rowen\n",
              "6865       Yousef\n",
              "6866       Joziah\n",
              "6867       Maxton\n",
              "Length: 6868, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lesley_like = all_names.loc[all_names.str.contains('Lesl')]"
      ],
      "metadata": {
        "id": "N5xSnMk77rJK"
      },
      "execution_count": 77,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lesley_like"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDsStoMx7ycK",
        "outputId": "194f6972-0ab6-4f18-beab-3ebe93a9d858"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "632     Leslie\n",
              "2294    Lesley\n",
              "4262    Leslee\n",
              "4728     Lesli\n",
              "6103     Lesly\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filtered = top1000.loc[top1000.name.isin(lesley_like)]"
      ],
      "metadata": {
        "id": "6XsHJ_2c7zTv"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filtered.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "mZ147wGf79nU",
        "outputId": "e93e6ee4-c251-4a99-845d-d2b5e3e5d89d"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        name sex  birth  year      prop\n",
              "632   Leslie   F      8  1880  0.000088\n",
              "1108  Leslie   M     79  1880  0.000715\n",
              "2462  Leslie   F     11  1881  0.000120\n",
              "3014  Leslie   M     92  1881  0.000913\n",
              "4512  Leslie   F      9  1882  0.000083"
            ],
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              "    async function quickchart(key) {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "filtered",
              "summary": "{\n  \"name\": \"filtered\",\n  \"rows\": 400,\n  \"fields\": [\n    {\n      \"column\": \"name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Lesley\",\n          \"Lesly\",\n          \"Leslee\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sex\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"birth\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1428,\n        \"min\": 5,\n        \"max\": 6105,\n        \"num_unique_values\": 335,\n        \"samples\": [\n          26,\n          1365\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34,\n        \"min\": 1880,\n        \"max\": 2010,\n        \"num_unique_values\": 131,\n        \"samples\": [\n          1935,\n          1920\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prop\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0008563232042583883,\n        \"min\": 3.35376374921624e-05,\n        \"max\": 0.0034588146712080654,\n        \"num_unique_values\": 400,\n        \"samples\": [\n          0.0001474532433735708,\n          4.015099034396958e-05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "filtered.groupby('name')['birth'].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhD6twzJ8D1s",
        "outputId": "449e5de2-1ab8-4928-b95e-abb9cdc206de"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "name\n",
              "Leslee     11\n",
              "Lesley    117\n",
              "Lesli       7\n",
              "Leslie    249\n",
              "Lesly      16\n",
              "Name: birth, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "table = filtered.pivot_table(\n",
        "    values= 'birth',\n",
        "    index= 'year',\n",
        "    columns= 'sex',\n",
        "    aggfunc= 'sum'\n",
        ")"
      ],
      "metadata": {
        "id": "tscYrlQT8M6l"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table = table.div(table.sum(axis= 'columns'), axis= 'index')"
      ],
      "metadata": {
        "id": "IDH-C8oG8aUe"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "table.plot(title= 'Proportion of male/female Lesley-like names over time')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "egbBcxTQ8b2p",
        "outputId": "71ec439e-1c2f-4322-c901-b0d95d85e943"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: title={'center': 'Proportion of male/female Lesley-like names over time'}, xlabel='year'>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "COrY_fa38ggu"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}