Chapter7_data_science.ipynb

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        "<a href=\"https://colab.research.google.com/github/noahgift/DataScience-Demo-2022/blob/main/Chapter7_data_science.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
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      },
      "source": [
        "# Chapter 7:  Data Science"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hc7QdhV05VUF"
      },
      "source": [
        "**This is a chapter in the book Minimal Python**\n",
        "\n",
        "* [Buy a copy on LeanPub](https://leanpub.com/minimalpython)\n",
        "* [Buy a copy from Amazon](https://www.amazon.com/Minimal-Python-efficient-programmer-onemillion2021-ebook/dp/B0855NSRR7)\n",
        "\n",
        "[*Licensed Under Attribution-NonCommercial-ShareAlike 4.0*](https://creativecommons.org/licenses/by-nc-sa/4.0/)\n",
        "\n",
        "![Minimal Python Book](https://d2sofvawe08yqg.cloudfront.net/minimalpython/hero?1582640276)"
      ]
    },
    {
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      "metadata": {
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      "source": [
        "## Ingest"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJntteRBwI46"
      },
      "source": [
        "*COVID-19 Data from [New York Times Github](https://github.com/nytimes/covid-19-data)*"
      ]
    },
    {
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      "source": [
        "import pandas as pd\n",
        "df = pd.read_csv(\"https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\")\n",
        "df.head()"
      ],
      "execution_count": 1,
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              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2020-01-21</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
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              "      <td>0</td>\n",
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              "      <th>1</th>\n",
              "      <td>2020-01-22</td>\n",
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              "      <th>2</th>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>Illinois</td>\n",
              "      <td>17</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-01-24</td>\n",
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              "      <td>53</td>\n",
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              "            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",
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            "text/plain": [
              "         date       state  fips  cases  deaths\n",
              "0  2020-01-21  Washington    53      1       0\n",
              "1  2020-01-22  Washington    53      1       0\n",
              "2  2020-01-23  Washington    53      1       0\n",
              "3  2020-01-24    Illinois    17      1       0\n",
              "4  2020-01-24  Washington    53      1       0"
            ]
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    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1iMW3XxnkUXF"
      },
      "source": [
        "Last five rows"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "opOpFijSkXR6",
        "outputId": "092a1605-d56e-489e-abb8-029cea5fddbf",
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          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>deaths</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>38025</th>\n",
              "      <td>2022-01-20</td>\n",
              "      <td>Virginia</td>\n",
              "      <td>51</td>\n",
              "      <td>1434686</td>\n",
              "      <td>15853</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38026</th>\n",
              "      <td>2022-01-20</td>\n",
              "      <td>Washington</td>\n",
              "      <td>53</td>\n",
              "      <td>1141184</td>\n",
              "      <td>10425</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38027</th>\n",
              "      <td>2022-01-20</td>\n",
              "      <td>West Virginia</td>\n",
              "      <td>54</td>\n",
              "      <td>401796</td>\n",
              "      <td>5590</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38028</th>\n",
              "      <td>2022-01-20</td>\n",
              "      <td>Wisconsin</td>\n",
              "      <td>55</td>\n",
              "      <td>1433460</td>\n",
              "      <td>11867</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38029</th>\n",
              "      <td>2022-01-20</td>\n",
              "      <td>Wyoming</td>\n",
              "      <td>56</td>\n",
              "      <td>133495</td>\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-3bfbccf8-00a2-4a9c-9c55-39e5bc9c56f3 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-3bfbccf8-00a2-4a9c-9c55-39e5bc9c56f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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            "text/plain": [
              "             date          state  fips    cases  deaths\n",
              "38025  2022-01-20       Virginia    51  1434686   15853\n",
              "38026  2022-01-20     Washington    53  1141184   10425\n",
              "38027  2022-01-20  West Virginia    54   401796    5590\n",
              "38028  2022-01-20      Wisconsin    55  1433460   11867\n",
              "38029  2022-01-20        Wyoming    56   133495    1601"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zr7wdjHswKfq"
      },
      "source": [
        "*What are the columns?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DwAMxSNSwNWj",
        "outputId": "a0b55275-37b1-4d78-ad42-5d43222fadf3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['date', 'state', 'fips', 'cases', 'deaths'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q3njEtIJwSCi"
      },
      "source": [
        "*What is the shape:  i.e. rows,columns?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kyCznu1cwUv_",
        "outputId": "a4f28880-44e5-477d-b182-740c9ceb3e1d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(38030, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PY_nFEZKwI7R"
      },
      "source": [
        "## EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nNw9of3LiQjU"
      },
      "source": [
        "*What are general characteristics of the data?  A good way to find out is `df.describe`*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHuBZouXCYAZ",
        "outputId": "40cbc62f-6c6f-4d1a-c336-09e8ffe1067d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>38030.000000</td>\n",
              "      <td>3.803000e+04</td>\n",
              "      <td>38030.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>32.067026</td>\n",
              "      <td>4.258494e+05</td>\n",
              "      <td>7552.449487</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>18.712544</td>\n",
              "      <td>7.135998e+05</td>\n",
              "      <td>12100.594492</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>2.559375e+04</td>\n",
              "      <td>507.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>31.000000</td>\n",
              "      <td>1.537335e+05</td>\n",
              "      <td>2667.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>46.000000</td>\n",
              "      <td>5.311112e+05</td>\n",
              "      <td>9077.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>78.000000</td>\n",
              "      <td>7.487212e+06</td>\n",
              "      <td>78529.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6b2b71f4-0694-4646-825c-6d1d4332d9ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6b2b71f4-0694-4646-825c-6d1d4332d9ff 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-6b2b71f4-0694-4646-825c-6d1d4332d9ff');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "               fips         cases        deaths\n",
              "count  38030.000000  3.803000e+04  38030.000000\n",
              "mean      32.067026  4.258494e+05   7552.449487\n",
              "std       18.712544  7.135998e+05  12100.594492\n",
              "min        1.000000  1.000000e+00      0.000000\n",
              "25%       17.000000  2.559375e+04    507.000000\n",
              "50%       31.000000  1.537335e+05   2667.500000\n",
              "75%       46.000000  5.311112e+05   9077.750000\n",
              "max       78.000000  7.487212e+06  78529.000000"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3YtAIL7HFjOw"
      },
      "source": [
        "*Cases and Deaths in the USA due to Covid-19*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9vXU4_fCj-8",
        "outputId": "399658e4-b66b-45c6-c25a-86a4841f094e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        }
      },
      "source": [
        "import seaborn as sns\n",
        "sns.scatterplot(x=\"cases\", y=\"deaths\", \n",
        "                hue=\"deaths\",size=\"deaths\", data=df)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f51f24a1090>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Wtba2SjufiO3DQErJi89sxV/iY2R4jFxWZ2xkgicefYZNH7mFhqY6ZjXX89h3nqS9reuc/VetXcZA7xDJRIo161ey47U93HTnerZv2cFtm27g+Z9sYfNDm3j1hddpXbeCvp5BWtct58Vnt3L35tt45Wc/J1weYsnyBWx7+U1yOZ1N999KLpfjyIE21qxfSbg8RCqZIpfVqaqpZNtLr3P3A7dz8ngny1YvpLdzgJr6Kt7Yuot1N6zm8Ud/gs/nRVFVBnqHUBTB7/7R5/n2175PIFjCxlvW8vrWXYyPTrB242pq66rY/uoOIpNRlq1axMOPPITDWVgOJB6J4w/4iMdTeD1uFi6dx6Kl83A4Hefci+GhUdLJNGUVYUpDQQBM02RifApVVSmvCM/4v+flJITYJ6VsPe82O4jYbNemfbsOkUykSSWSxGNJMukMecPku9/6AbffcxOBoB/NoRIIlvDdb/3bOfsLIfjdL32eb/3VP3Hj7etpP9FFRWUZQoFoJM6iZfN5fetO1t94HZlMFiktwmUhamqriMeTlJWHSKVSWKakpr6KdHE+RzqVpbqmnEQ8SX/vMM888QIPfOoeBnqH8JX4OHH0FK3rVnD8yCnW3bCavJ5HIknG03j9bnIZndGRcaprK9l481rWbFjF+Ngk+3cfYmoiQkNzPR6Pm9JQgLnzm8nldNKpDKGy0rOWErH98uwgcgY7iNg+DPbuPEQsFkcVCvF4klwuRyKWonluA1/579/AyBt87NP3YuQNmlsaaTt8imeffGm6v0MIwb0fu4Pf+cPfputULy8+s5VMKkMwHEBKSVlFGMu0qGuswefzUNdQQ2k4QNuhdqLxBMN9o1TWhFFVjbxhoCqCaCSOtMAw8qSSGRoaa7AkpJIphgdGKa8sw+nUCJQGEELQPLeBiqpy/CV+AkH/9HldaDSWbebYQeQMdhCxXes62joYHZkik86SiCdRVRXLMkmnMqTTGSqrK/jWX/0TZRVhbrlrA6qqTq8E29PVjxCC+YvmcvOdG3A4Ct2mUkpSiRQOpwOX23XOe8YicQ7tPcrJ410EQyW4nE4SiRTh8lJGhscIlgZwaBpZXcc0TTRVxTBNgsEA8xbMZva8RhTFHix6pXqvIGJ3rNts1xApJYrDSTqZxpKS/t4hWuY3k07r+Eq8eLxuErE4f/KV3+fk8Q6SiTSNsxsoqyyjtq6Sez92x3k/zIUQ+C8wLLS7o5fx0UkikzG8fg+KUhglFY3Gcbkd1NbXEI/FURQFp9NJid9LWUWIQKmf2S3NM31LbDPMDiI22zXk8P42PB4n6XShD6CxuY7xsSnKK0PkMjqKplBVV4WUsPK6JdTNqv1Ay18cfPsImXSWVDKDURx6+sa23cxqqqVpdgPxaJxkPIGqKJRXhNAcTjw+NytWL76IV227nOwgYrNdQxLRBGPDOSSF9a78JT4QgnQiRUVNBXouj0PTCJSWsPK6959PwjRN9rx1kMhEBEVVkUAoXMrwwAgLl8ylp2sAoSg4XA7KiiOX/CU+quoqqa2rvjgXa7si2I2QNts14mRbJ7mcjsfjJBQKkkqlSaUyhMKlhMpL0XN5vF4PoXDwAwWQyHiEvTsOk05lGB+dAiHI53QmxgpJlRAwf9EcMuk0AkE8mkRRFUrLSu0Acg2yn0RstmtEbCKG0+WY7puorq0kGokRmYjgcDlwe904XA6WfcCmpAN7jpFOpZGAkc+jZ3U0TcPhcBKZihMuLyURS1IaDhbmVEioqCqneY6dVfRaZAcRm+0a0NPVTzabY3R4jGBpKdFIgrLyUjweDzV1lXi9HqKROGVloQ/0Pgd2H2FkcAxVVfB43dTUVxGLJfD5fbhcTqSEqfEp3F43ZcFSkODxus9ab8p2bbGDiM12DRgfGi8sey4F46MTVNZWEI8mME2L7vZ+SgJe3B4XTfPe/9PArtf2kYynsAyTiuoyIpNRNIdGLpPDoRWG/gaCPpwuR2EtLYeD8soy5i+ZcxGv1HalsftEbLar3NR4hFwuTzKRprwiRD6vc2DXYTLpHIGgDyFgsHcEf7Dkfb/HgZ2HkJZE1Qorx0YjSRKxJHk9T1VtJSDJpNJEpmKoiorm1JizoNEOIB8CMxZEhBDzhRAHz/iJCyH+sxAiLITYIoRoL/4OFesLIcQ3hRAdQojDQohVZxzr4WL9diHEw2eUrxZCHCnu801hT2W1fQidauvEMAopajOZHLMa61m0bB7xWIK+rqHC5MFlLSxd+f7yaR/ZdxxV00jEEoDEsizMvE7drBpikTjZdBZN06iur6RpzizqG6u5+c71VNdenKRHtivbjAURKeVJKeUKKeUKYDWQBn4CfAnYKqVsAbYW/wa4G2gp/jwCfBtACBGmkGJ3LYW0ul8+HXiKdT5/xn6bZup6bLYrVSaZJTIRwVfiJZvOEZ2Ko6oKi5bMZc7CZrxeDyUl7+8p5O039jPQNczURJSJ8SnGR6ZoWTSbga4hosVOdH/Ah6qqSAvCFaUsXb3IXprkQ+RSNWfdBnRKKXuBzcDp7PWPAg8UX28GHpMFu4BSIUQNcBewRUo5JaWMAFuATcVtASnlLllYu+WxM45ls30odBzvQigKuUyW6GSUklI/qqaQSeWYGIugZ3S8JT4WLP3VmpVM02T/rsPk0jlSyTSJaBJFKHg8bkYGx1m5fhmWZZGIpUgnM/hL/YTKg8yZ1zQzF2q7Yl2qIPIp4EfF11VSyuHi6xHg9DNvHdB/xj4DxbL3Kh84T/k5hBCPCCH2CiH2jo9fOC2mzXa1Ge4fJR1P4i/xE5mMMzY0QV43CJWXEgwFC+lbveeudfWLvPbSTgzdIB5LEgiXIBCEyoIM9g0TLC1huG8Up9NBfVMtpaEgTqeDpasunOrVdu2a8SAihHACHwGeeve24hPEjK8AKaX8jpSyVUrZWlFRMdNvZ7NdErGpBMlEimQyDUhqaivI5/OkUxlGB0aZHJ8iGCphaesvPy9kuHeEA7uOYOgGei5PNpPDyJvE40kyqSyzmuvJ5w08Pjf+Eh+maeL1e1l/83Uzd6G2K9qlGOJ7N7BfSjla/HtUCFEjpRwuNkmdTqs2CDScsV99sWwQuPld5duL5fXnqW+zfSgcO3Ci2JSVJ5vMgSKora9CEYXlRhLx5DvpCX8JXSd7GOkfI53MkE6mURSFdCqDZVqUV5aSiKUxNBNkIWOiZVl4fV5aNyyfwau0XekuRXPWr/FOUxbAc8DpEVYPA8+eUf4bxVFa64BYsdnrZeBOIUSo2KF+J/BycVtcCLGuOCrrN844ls12TRvuHyU2FUcRAs2pkU5nMAyT7pP9dJ7oYbh/FCEUWq//5T7g9791iP7OQVKpDIqmkogkmBqbIlRWilAF42MRhCrwl/goDQdobplFVV0la25YOcNXarvSzeiTiBDCB9wB/D9nFH8VeFII8TmgF3ioWP4CcA/QQWEk128BSCmnhBB/Duwp1vufUsqp4uv/BHwf8AAvFn9stmte96k+opNRTNOivLoMTVWJTMYQAlweN16/l7LyUtze987kZ1kWr7+wAwnk9Tx6TkdzakyMT9HUMgs9l8fjdiGkwDRM4pEETpcD07RYu3HFpblY2xVtRoOIlDIFlL2rbJLCaK1315XA71zgON8Dvnee8r3AkotysjbbVUJKiWGYpJNZ3D4X3Sd6qaytoGF2HYoQOF0O0qkMzfMb3/M4+3ccIpvWiUUShYmEDhWA6GSCxasW0nboFA2z60hEk1TXV6JoCg6nk9JQgIUr7GVMbAX2sic221Vm/1uHScWT+Eq8jA6OE64oZWRwDG/UXcg86HJSXV9JVe25g0gmxyJ0n+hBz+XJ501y2Ry5TK7QWe514fQ40Zwq40MTLFoxj4nRKYKlfjLpHCVBP/XNdTQ0vf/8I7Zrjx1EbLarSGwqNt0fUlFbRiqRZmoiguZwkEnlcLldlJSWsHLd2Uu9d7b1MNQ9TCqRRnOoSCHIpbMAGHkDRVWIRRKEHSG8HjeqojA5HkHVVBRNw+t1M2dhIzUN9lLutrPZQcRmu4oc33+KsYEJVIdCV1sv9c01lJthspksHo+bktISnA4NX9BHx7FuJkYm0XWdVDyDntOJjkcpqw6DEGTS2UJCKQmaqmCZkshElHBFCH+Jj0BpAEVRCJUHWLHu/ecfsV3b7CBis11F0sk0uq7jQENRFA7uOEK4MoQ/6CcRSRKPJFmwfC4v/NsWHC4HQkAykQJZ6EuZmogSKAvgcjuRxeMFwwEi41H8AS96Tme4b4SyqjCBoJ9gRcgOILb3ZAcRm+0K132yl1QsRS6rIy2J1+umt2OA6llV1DXWEpmMkctGqJ1VTe2sag7vPE5lXTm6niebzpKMJdEcGi63C0VRSCXSGHkDh9OBaZikEhnCFaVIwFvipbK2Ao/XTcvS2Xbzle0XsoOIzXYFMfIGve39aC4H8ckoqXgGS0pcLiejg+PEI0kCoRKC4QCZZIaxoQnKq8oIlQfxeN0IYZFOpDFNC8swyWZymKZFSakHI58nncxQktVxuV2oqoLb6yKXzpFOZkERBEv9BEMBVm1YhtPtnNFrtSwLRbGzUVzt7CBis10hdry0i52v7GH9prV4Szyk4hkcThWpG4wPTzLUPYLDrSEUaJxbz/jwJItXL8DtdePyOCmrKOXE/lNIKbFMEwmkYin8pT6ymRyKqtDQXFtYKiWWRAg//qCPQKgEQzdwe9w0zWugsaXhF57rr2qgc5DOY93ouTzSkkyOTHFsTxtur5v1d69l1Q3LKS0PXvT3tc08O4jYbFeAXVv28Ohf/4j/+Of/ge4TPcxe2ISezaFnBYFQCbGuIfR8Hl3XmRqJUFYdJlQeRHNoOJwOpGkxNR5FUVXGhieorCtH0zQC4RIS0RS+Eg9utxPhdeF0OdDzBtlMIQ+I0+lk7uLZzFnY9J7nmNfz5HUDRRWYhoWvxEsmlWWoZ4jOoz0oqsLE8CQ1jVX0nRqgu62XYFmAha3zefWp7TQvbCSX0envGGBieHL6uO2HOzmy8xi//cefpaTUP8N32nax2UHEZrvMOk90c2L/KUzDZLBriFlz68ll83j8blSng/hUHJfHSX1zDWbeJBaNo2iFda2qaiswDYvGhfWkp9L4/V7mL2+hq63woe7R3OjZPKXlQRRFwe124a4rNGEFy4KFJxIEyakEL/3wVVRV4PS4GO4Zwcgb1DbVMH/VPNoPddB+uBNVU+k43EXdnBrCFWGEpvDqkz9nze2rObLzGB995H5+/O1nSUSTAKy+eSVP/cMzXHfrKna+/DZrb289K4Cctv/1Q1y/aS1rbl11zjbblc0OIjbbZTQ8OEJiMknLsrk0ttRj5E0yqSyjfWP0dw7StGAWmkMjOhxF0RSMvEH38R6WrlvM6OA42596nd/6b7/OKz/YytFdbUgpqW6s5LaP34yFhWlBaTjAK49vJVwZwul20rbvFBvvvZ4nvvk0lmlxw/3r2f7MG1y/aQ2N82bRe6KfTDqLqiicOtTB8b0nGB+cwFvi5eSBduYunc344ARm3uTEgXZcHifpRJpgeZAT+05NBxAAh1Mjr+cxdIOaWVUMdF54jdTek312ELkK2UHEZrsMpJQcfPMw8UiSVKwwNDefy1NRV84rj29joGOQtXe00t8+wNjAOBV1FfS3DzA5MsWKG5ax65U9RCdiPPzHv85z33vhrG/3I71j/OgbP+Yz/+UhHA7B41//MR6/B4fTwcE3j7D2jlZee/ZNjLxB6y0rOfTWER76wkcZH5zgh1978qzzrJ9TR1l1mIaWena8uBuAyroKdry0m+rGatLJDKtuXM6R3cf59O8/yI++/uOz9rdMC1VVMfIGhmHi81w4t4mmqRfxDtsuFXtohM12GRx+6yipWJqpkSkmRiKc2HuS3o4+YpMxBjoGcXlcGHkDj8/DcO8obo+LyZHCuqNen4foRAyAyMjUeZuHLNPiyM5jZBIZ9FyeBavmcXjXMQCEEBh5g4raMpoWNHLnJ2+lv32QbU+/fs5xBjoHcXmcWJY1XWYYBgDSKqQCMk0TTVOxTOucQKA5CgHE7XMz2j9GTeOF867PXTb7V7mFtiuE/SRis11ifSf6SSVSTI5MkdfzZJMZapqri8Z2NfsAACAASURBVE1NJwFonD+Lwe5hKmrKqKyvYKinkAxUCEFez08f68ymo3cb6hmhtKIUAEVT2XDPOsIVIZwuJ3d9KoDb4wIJ3cd7kcjpoPBupw52cMP9G6b/Pp0/XVEEqqrQtu8Uy65fzM++/yKrb17BGz/bOV335MEOVmxcSmQ8Sm1TDWNDE7Qsm0P74c6z3uO+hzcxb0XLr3IbbVcIO4jYbJdYNpsjl9axTBM9qyOlBAGWZSKUwge0oedRNRXN6SCfy+MP+IBCM5h6xrd9j99z3vcoqw6z5rZVlJaV8sDn7iOf1ZkYnaLzSDfJWJJgWYDK+gqMqQTJWBJf0HfB8zVNE0WAqiqYxeYpIQRHdh1j7R2t7HjpbaQlcbqcNMytp7qhgpH+QhrqyZEp/AEfy9YvIT4VR8/pJONpbt68EaEIfAEvC1bNY96KFlyumZ2XYpsZdhCx2S6hkb4RLMtCCHC4nASdDvK6wcmD7QRCJSxcvYDeE/10n+hl3Z3XIYRgcmSK+Stb6DjSBYCiKNMf6LVN1YSrQjTNn4XL46IkVILL5SCf1ek63I3q0IiORWla1Mi+bQdYt2kNJ/efoqGlnnQyg5k38AV8KOqFW7ZXbFzGaz/dwU2bN/Las29yeOdRbvzIel7/6Q46j3Zz4/3rGR0YZ87iZmJTce57+B7GBseZHJ3C7XHROL+BdbdfB4pAz+VQVBUkeC8QAG1XF1FI4/Hh0draKvfu3Xu5T8P2IdV+uJN4JE5eNzENk/5T/TicDgY6hwiUlVDVUMm+nx+gu62XJesWUdtUTSadxelwoGka8ak4ocpCsqnR/jFqmmqYGJwgk8qgqgqa08GRt47SsrKFQ28cZs2drby9ZS+rbl7B8T0naFwwi7Y9J1l9y0pEsTkKITi+5wQtK+awe8vZ/zfKqsN89r9+irLqMJ3HurEsSTwSx+VyUl5TRmQ8imVaNC9qom5OLV6fHRiuRUKIfVLK1vNtm+nMhqXAdykkjpLAbwMngSeAJqAHeEhKGSmmuP0GheyGaeA3pZT7i8d5GPjvxcP+hZTy0WL5at7JbPgC8HvywxYVbVeNWCRGYiqB1+8ho+Qw84IFq1rIZXQq68pBCKJjUVZsWMJN92+gv32AnkPdLN24hO3//gapeJqF182nfbCT/lMDLN+4lDeeeQtviQe3z01ZdZi9W/ezfONSDr15BADTMBFCYJoWRt7E4dCKTWIKiqIwNRohm86w8qbl9JzoZcM960jGUmTTWeavbGHdXWuobqgEoG527eW8fbYr1Ew3Z30DeElK+QkhhBPwAn8MbJVSflUI8SXgS8AfAXcDLcWftcC3gbVCiDDwZaCVQiDaJ4R4TkoZKdb5PLCbQhDZhJ0i13aZGHmD2EQch1PD4XIw1DWMRBIsCzDYMUQqkSKfMxjqHkJVVWITMYy8ycHtB1m6YQldR3uYHJ5k7V3X8fq/v0kmmaH19tW8/INXMfImiqrgL/XTtqfQ+e7yuohNxJi3ai5vv7KXcGVpIWgohVS2UGj6skwLl9tJLpPDW+KdLu861kNlQwUOl4NsMkNlTTmqpjJ3STOLrltA86Kmy3UrbVeRGQsiQoggcCPwmwBSSh3QhRCbgZuL1R4FtlMIIpuBx4pPEruEEKVCiJpi3S2n86oLIbYAm4QQ24GAlHJXsfwx4AHsIGKbIYZhMNI9QiaZIZvKYVomk0OTdB7uYtaCWXQd7ebkvlPc8MAGTuw5yWjfGOvvXcex3W2Eq8MIAcPdwyxdv4SjO48xb1ULB7YforK+gomhSSaHJ/GWeEkl0mSSGaDwrcnIFwLCnCXNtB/sAMDpdk7XsYoBQxQXM4xNximrCTM5PAUCNIfG+OA4TQsb6T3Zz/INSzj01hGuu72V7mM91M6uIZPMUFoRpH5OHYvXLKC8tvwS313b1Womn0SagXHgX4QQy4F9wO8BVVLK4WKdEeD0wPE6oP+M/QeKZe9VPnCe8nMIIR4BHgGYNWvW+78i2zUrPhUnMh4hNhYnHU+DgKHuYQY7hqibU0NFfQWdR7rQszrpeBpVVcmmswy0D3LdXa288m+vks/lWX3rSna+sBs9ozN3+RxO7m8nNhFj/up5vP3yHlbdsoJ92/az/MblHNh+EIBZC2ax99V9hdfzG+g+2j19Xpb5zvwM1aGS1wtzNPJ6Hldx4p5SHK2ViCYprQjScaiTGx/YyOvPvMnB1w6z8SPrefvlPdTOqaWmsQo9p7PhvutRNZUbN68nrxv4S/00zmug+j3mcdhs5zOTQUQDVgFflFLuFkJ8g0LT1TQppRRCzHgfhpTyO8B3oNCxPtPvZ7uySSnpONhBdDxGOp7CNC2y6SzZZBZLSvxBHzuf301sIkYgXEJpRZD44S7aD3Qwa0ED3Ud7uO6uVva8vBen20k2lSWfK8zdEKqCntEBpj/QSyuCTBUnCkLxyULK6QAhz5jIl8vkcPs8pOJpoPAUcVrP8V7mr57HoTeOIC2J5tQQQtDb1kfLirmFp6DNG9i3bT8Hth9k4/3XM9g5xJ4te1m76TrKa8sJhEpoWthIRZ39pGG7OGYyiAwAA1LK3cW/f0whiIwKIWqklMPF5qqx4vZB4Mw1qOuLZYO80/x1unx7sbz+PPVttvNqP9RBcipBbDyGUBX6TvbjC3jJZXUsw2R8cBJDN6ibW0usOCO8ZVULhm5wbOdxlmxYzMHXDuF0OwtPK0CoKsTYwMT0e5z55GBNT94T0xP5Tjc5CUUghEBKSSqeJlgWIDYZp6etl+vuaGWyOAs9Nh6lurGKkd5RsukcDpeDQLiE+FSCQ28c4YYHNvD2y3sIV4dZtnEpO57fxeK1C/EFfATCJay65V4q68sJhAI4XI6ZvsW2D6EZCyJSyhEhRL8QYr6U8iRwG3C8+PMw8NXi72eLuzwHfEEI8TiFjvVYMdC8DHxFCBEq1rsT+G9SyikhRFwIsY5Cx/pvAN+aqeuxXZ1O7j9FNpnFNCyyqSyJSAw9kyeZSOF0OohNxgHQNI1wVQjNqdJ+oGN6f8u0EMo7s8SlJQvLfBSfEJLRJHUtdQx3F1poVfWdiYACUDWV6HiUeSvn0nmki9hkjHB1mPYDHSxet5CjO49zcv8pbvjIBt549q1Cro3hSVpWzqX9QAenDrSzdMNS6ubWcnz3CY68dZR7fnMTei5Pb1svQsBvf/lh9JyO0+Vg02dvRyAIVYUIhEou0V22fZjN9OisLwL/VhyZ1QX8FoX1up4UQnwO6AUeKtZ9gcLw3g4KQ3x/C6AYLP4c2FOs9z9Pd7ID/4l3hvi+iN2pbgM6j3SSTebIpNJIC/SMjqIq5NJZpAWGYTLaM0rjwkbGhyZQFAVf0IcQgkwqd1ZGP0VRSCfSeANe0on09FOA01Ook4qlCBWXFgHIpDIEy4PEJmIcf7uNVbesZM+WvUyOTNG0qJGOg51suP96Dr15BD2XZ9nGpRzdeYx92/az/r51JOMpxvrGWLx2EevvXUcmkcHtdzNn2Rwe+r2PIy2LQDhwye+pzXYh9mRD2zWhp62HZCwJBlhYpGNpEIXkSXomRzaTQ9NU9KxOKl5YtypUFSIZSeLyuFAUwWj/GLl0joVrF7L9qdcAmLd6HuMD48xb1cLul95m/X3X89ZzOyivLWfWggb2bzuAv9TH0huWsX/bATLJDNfd0UommeHY7uOU1ZSx8qbl9J3sw+V1U9VQSSKapKw6PJ3kqb6ljmwqy9jAGOHKMhatnU91o53b3HbleK/JhnYQsV3Veo73kI6lMQwDQzewLImqKMSm4jicDgw9Ty6bR1UFliVJRpJkM1kcbie5VBbVoZFNZ0lOJZFIqpuqsSyL6FiUtrdPALD+vnUM94xS1VjJwKkB6lvqGe0dJZvOsHTjUhRVJRAqoXZODZlUFqfbRd3sGvpPDTA+ME7VrCqy6QwDnUOU15bhcDqIjkVxuByUVpYy2jNKfCpO85JmlqxfPL3Aoc12pbhsM9ZttpkyNjhGdCSKaRSWD9EzemGSnW5gCYFlWkgpp2dsO70ukpEkbp8bBLh9brLJQlORy+vCF/ShKIJcRscb8NKyai7zV88jOhHD6/dw3aZWUtE0i9YsxFfqo7KhopBa1uM8qx8EQM/qnNx7Ej2t03Osh2BFgEwiS0mpn+O72oiMRgiUBUjFUjQubKRlxRzC1SEWrV1oBxDbVccOIrarynDvMPHxQme4aZrk03kM08DIG6iaipQSCYUhtIYJioKmQj6XxxfwYZomTrcT07Son1ePZVooqorL48TpdhKuClFRX4H/feb6btvVxsTAOIlIgnBtGaqqMtE3QV7PE6wI0rhwFnNXzEFP5/CHAyAt3F431Y3VZ63Oa7NdLewgYrsqDJwcIJVMYeUtNIeGZVmYuolpFmZrS0ti5k2EWljh1uVxkc1k8ZZ40bMGigpCUXH73dOBwx/0UjOn9qyO8fd9fh0DTPRPMDE4gZ7OEZ2IYSEpqwpTVlfG/q37SSXSRMYiLLp+Eb6Aj66DndQ0V5MKpGhc1PiBz8FmuxzsIGK7onUf6SKfzWNOz7+Q05n1DNNAURUsy8LhcmAaJooikELi9rkRqoJlSvwhP0Y+j+bQ8Po8lDdUXJTJdompBO37TmEaJompBB6/l3xGJzYRR9NUIiMRNn50I2Pdo1TWV7Lv1X2suXsNY71jON1OktEELm8jRtr4wOdis10udhCxXXGMfJ7uIz0YOR0UpfCUYRgIihn1NAVDN1CVQkpWy7JQHSpCCizFQlM0hEPgd/uRUuJyOymtrKG66eKMeBo40Ud8Mk5sIsZ4/wSBcAkTg5NUz3YQn0wwMThB9exqssks6ViKkd4RKpurWL95Paf2nCI6HqVhQQNLNiwhn85j6uZFOS+b7XKwg4jtihCbiDI5OImRNzANC0nxycOUSCmxDImUFooqsHIWQlUwLROhgMPpQJoSd4m7kPBJEfiCPkKVoen0sB/EYPsg0eEpLGmRjKaAwnIkw53D+Er9JCJJpGVhGRYgqZ1TU1jbyu0km84RLA/yyj+/zIK1C1h281ICoSD9J/rZ+thW7vjNO8imsh/4HG22y8UOIrbLpvdoD3kjj5UvjKSypERQmBFuGSaaphXyYJgGmIUFCE3DRCt2QKuKCgKEKhBS4Pa5cfvd1M057zqcv7TxwXEm+sawLEkmniaTzOL0OElGkuSzOk6fC0xJNpnB6XWTz+WJTcRweV1UzKqk+3AXJaESlm5cirTA4XGy4pYV7P7Zbg5uLSy6qDk07v783ex45i3ufeTeD3wvbbbLxQ4itktupG+Y5EQKpIVhWEhZ+Bavagqaw4GeMQp9INIsPFlIEJoojMBSVSQSIQWKpiA0gdPlxO11EaoJ4/NfOFf4L9J5qBMjq5OMpgr5PsaiWNIiNhqlZl4dRjZPIpIg5NLQNA2H20k2kUEIqGqsQs/qZJMZ5q9ZQGQkgtvvpvNAJxWNFfjDfu7/nfuZGomgairVTdVobo3Vd6ymruWDBT2b7XK6cGJlm22GGBkTRQXTsFCEQBqSfK7QeW4YBqZV6COwrEJ/BwpYpkRzqAghULXCdx+Xx0VpuJS6uXXUtzR8oADSdbCddCxNYipJKppECkgn0limJJvOYuYNcpkcSEhMJXF6XQTKAmRThdwi6XgazaGRS+eYGBineXkzelZnamSSnsPdOFwOpFXIK17RUEHPkS6y8QwujwuX131R7qvNdjnYTyK2SyoeiSEUgWVKcpnC7G5LmoWJgaaJkTfAKnSeSylRVYFAIByFJxCHS8PlK0wQ/KDNVqf1Hu1Bzxg4nBrZhEVqKok34ENP59CcGvmcgRACq9jM5g6XMN43RmVjFaqmkogk0RwOLNPE6XVSVltOsCxI5/5T1LXUsf2HP6djfztNS5uRluSNJ1+jrK6c+gUNeIN2TnLb1c0OIrZLZqBjgFBVCIkEWVjh1jRMpCVxupzFZdQlDqeKYVqoqgJCQagCLHB7nHhKvNTPa/iF7/XLGu0ZJpfJoWdyaC4Hpm7gcDlQNIGiKuSzefxBHxJJSVkAVUuRiafxlHhITMYJVgYJVYXI6wbhmjCzFjfi9rrpPNCBy+NGz+msuW8t+17ay4mdbQBUNFRw40M3YeQNKhvsJFC2q5sdRGyXRN+JHpwuF5l4BmlKhACn20U2lUVRFZAghERRNaS0cHmdSMNCOFS8Pi/eoIeK+sqLek4DHX243R5MozDbPZfKIkWhryWfyVNaHSKfzSMUwVjnKHULGnB5XeRSOYQicLgcqJpKaXWYpqVN08ufmHmD1FSCZCRBNpVDz+rc8//eS3wygdvrwh/yk0okMXWLcE34ol6TzXap2UHENqPMvEHv8V4cLgeGbiJNC9M0kQKktNAchaVKUEBxOFA1BWmB0+PC7XVRM6d2Rs4rMRnH4XSRTqRxuJxITZLLZPCX+hBSkklkCFYFyaVyuHwuKA+QTWXxBr1UNVfj8Xuonl2N5jg30VPXoS6mRqYwcgamYVA7t5aJvnGEIigp9WPlLca7x6iZUzedpMpmu1rZQcQ2Y4baB0knUjg0DUM3ME0TxaGgOjU04UDxOMASYEry+TxuvxtP0Ee4aua/nRuGiZHNI63CPBRD1/GU+MllspSUB3Dn8limxOV1UVIewOV2UjO7Bs3l/IXHjgxPks/qHPn5IRasX8hAWz/ZZAZfyE/fZA+eEi/egA9vwDvj12mzzTQ7iNguOjNv0nmgHcswEYoACZZZWNfKzAFWHqfbjcvrxB3wYOQM0pE0dfMuzVDXkc5BVKeDxGQCh9sBSDRX4bfDU4KQAl/QjzfoofJXzOsx0jmEqZt4Az5Mw+Tw1oOoDg2X10XfsR6kJbnpM7eguZxUz7FzhtiufjP6LC2E6BFCHBFCHBRC7C2WhYUQW4QQ7cXfoWK5EEJ8UwjRIYQ4LIRYdcZxHi7WbxdCPHxG+eri8TuK+9rraF9mvUe76dh7CmmY5HN58noeI6Nj5E0s3UQaJpYJpp7HyBlkplJkY5lLFkD6T/QghSA+HkNza0gshFBwup04XE58QR/+kB+Xz/ErBxCA2FiUxFQcIQTLb1sBFJr00rEU0pLUtNTiLvHiKfEQrim72Jdns11yl6JB9hYp5YozEpp8CdgqpWwBthb/BrgbaCn+PAJ8GwpBB/gyhbzra4Avn5Fv/dvA58/Yb9PMX47tfFKxJO27T5COJskmM4XlS/IG0pLkzTxYEsM0saREmhaGbpBL58hldWrmzky/x7v1tfVi5SWpSApVEyBEoZMfMA0Tp9eNAHRDp2ZO/ft6Dz2jU1oVYt8LuwnXlXHjp29h3roFNC5rZv0nbmD9gzdi5gycznP7Umy2q9HlaM7aDNxcfP0osB34o2L5Y7KQanGXEKJUCFFTrLvldF51IcQWYJMQYjsQkFLuKpY/BjyAnWf9kjLzJl3727EMCz2nA5DX88XhuoUJhaqiIGVhpJVVXGlX0TQUVaFhwaxLcp4DJ/ohb2EYJiDR0xaoFprTgWVYOFxOsCwMy6Jh/vs7J8u0cLod9Lf1svSWFbz22Ku4fW4qm6uRhklZXRlde0/h9DipW/D+gpTNdqWZ6SAigVeEEBL4Rynld4AqKeVwcfsIcHqgfB3Qf8a+A8Wy9yofOE/5OYQQj1B4umHWrEvzofVhMN47ylj3KFJagCCf0wsTA2Uhs6DqVLAME9WpIBWBNAwcbtf0LO3qOTWX5DxHOoex8oWOfStvIC0L05KoQkWaViEZlGlhIghWBd/XeyQn4vQc7mK0axhN0xjtGubOz99DbCKGqqmUN1TQe7ybfFZHEYJswl500XZtmOkgslFKOSiEqAS2CCFOnLlRSimLAWZGFYPXd6CQY32m3+/DoO9IF7msTmIihtvvBiEKy7OrCqqjMBrLMgSqQ0OaFkJTcPu8uEtc1M+/tAmY9KxOXjfI53KF5itNQSgCIcDIGbh9GqY0cTo0AuH3F0Q69pzENEzGOoeZ7B+nubWFgbY+kKCogkMv7mH1/dfTf6CHxGScFXetvshXabNdHjMaRKSUg8XfY0KIn1Do0xgVQtRIKYeLzVVjxeqDwJlTkeuLZYO80/x1unx7sbz+PPVtM6xrXzuGnsfQDTKJDA63AyNvFtLTFvs+HC4NS4JAomoaTreTUE2YsouQDOpXMdI1hJErzEiXElRVJZ/L4/a5ME2rmNRK4vK6qZ37/pqYhk72M9E3TmlNKapDQ8/onHzj2Fl1NKfGaOcQ0ZEIKza1UjX70vQD2WwzbcY61oUQPiFEyenXwJ3AUeA54PQIq4eBZ4uvnwN+ozhKax0QKzZ7vQzcKf4ve28eHNd53uk+31l7X9DYAYIACXAXKVGkNkvyWFk8cRzZzpUiZTJJFDl2YjuZlBPfm7gS11Xm2pXYqWTulO1ynFi5Ueba4SSu8ZZry1kU2fIiyVqohaRIgABB7Gh0N3o/+3f/OE2IkiVxhUhR/VShiHP69DnfOUX029/3vu/vJ0S2lVD/aeDbrdcqQogbWlVZv3LaudqsA57nMfnkBJ7t4jseQlFwmzYykK2uc4lQBAqCQEr0iE40FSPdl2Hsum2vewBxbRe36dCsNAGBooYyK3pEJwhAURSMiEkkFaF/y/nnKFaml5EyoF6s0zH4yve47dar8FyfPT+1l/6tg+HzatPmCmA9ZyI9wFdaVbca8CUp5YNCiB8B/yCEeC8wDfxC6/hvAu8AJoAG8GsAUsqiEOL/An7UOu6/nkqyAx8E/haIEibU20n1dcJ1XE48dRzXdgg8P7Si1TU6h3s4/qOjbN6/FYIwiKiGhqbrRNMx+sYGiMQvjUrt4vgs9XIDgQjzNl5rfKoKQmBEDIQm6Nt0YeXFC+OzpDozlJdXiWfjbHnLTo794FCYEQQ27Bqmd7QP3dRJd2fI9mdf+4Rt2ryBEGEx1JuHffv2ySeeeOJSD+MNRaPaYPHYLI3VGsiw2xsBQlHQDB272mT+2Ay5Dd0kO1KYCZPchm46+i9dH4SUkmM/PIzveqi6ht10EIEkkoqhGTqaqeJYDpv3brmg68wcOsHk40eJZhJUVyqU5goYMYPuTX3YNQsjZpDuzVKaLYR5oajJnp/ZHybz27R5gyCEePK0No2X0O5Yb/OaWLUG889PY1s2nu0j1LDySlEEIJGeTyQVZfstV+F7PtFkjA27hi/1sFk6Po/btFENHatuhbMhGfqWCAmKqlxQACnMLHPs+4fRDA271uTYD46w87Y95G7YhlVt0CzXiabjKIpg+uBxFKEiFEhuGWwHkDZXFO0g0uZVcR2X2cPTWPUmgR+EardCRVGUsIRXhN/4BSBUlUx3+ry6vNeD+koFRVWwGxZmJFTehdCnJEAjkrywJbZj3zvE+PcOM7J/jPHvH2bT9duoLK1y8tkpYuk48XQc33YoF2uhT7waYERMom29rDZXGO0g0uZVmX1uCqvcwHNDDSxFEQSuj2rqGIaO7wVouk4kGaFv6yCRy8Shb3lyHqthoWgqCoJmJVTqjaRjCAQo0DNy/j0q8y/McOShZ/BdHyMeQQjB5GMvoKgKic4US4slnKbNtT//FnzPx2nYRFMxhCLo397uU2pzZdEuEWnzikw9NYFVbSIDie+EEiaKqiBUhcALkIEkkowS70gwcs3YZRNAAGrFGoHvY9cstIiOGY/g+z5OrYlQJIlc8oLOb1ebWNUmruXgNG027h0Fwo71ytIqTtMm0ZFEURUqyyXcpoUQ0Dc60FbubXPF0Q4ibX6M6aePUy9UsGsWUoalsDKQoSe6oqAZKnrEIBqPMnTVyKUe7ksozi5TX6mgqhqB51NbqWJVm2Hfih/g2z69F9CjUZpfoV6u0T0azmSO/PszdG/qY9tbr0KPGAghGNk3xv47b8GpWwxsG2LjnlH6t25g6JrNF+s227S5bGgvZ7V5CTPPTlEvVkLzqFYvCKpAkWroPqgIdNMglklcdgEEoLy4iud7eM0AI2qCcEBKfMcnkozSseH8elWklEwfPE5+Yp5GpcH2t+2heDKP13T50T8+Qs9YPzfc/VZSPVnmj8xw+N8OktvQhZGI0KjUiWUTRBPtWUibK492EGmzxsILJ6nmV3GbDoquhg6EtoNu6ghNQVUVjKiJkbz8ZiCn8DwPgcCq1HENjUgiihkzkTJ0UuwcOndP89JsnsLJPEvjc7hNG7thc+Shg9xy70+zMr2M23RI5FLEO1Ic+denqa/WSHVncW0Pz6mjmzrxTGId7rZNm0vPWS1nCSHe0uo6Rwjxn4UQfyGEeH0FkNqsK6vzK5QXSziWg+eGkiZmNIIMZOg9LiWqoaNHTTbu3nSph/uKzD43RXO1hm7oaIaG27Bx6xb1Ug3Xcohnzz0XsjQxz8wzkywdncGqNvG9gJNPTZDIJlg4cpL6SgXp+1SWijQKFRRVxTANVE1FIFFUgaKKteWvNm2uNM52JvI5YI8QYg/we8AXgL8D3rpeA2vz+hEEAUvH5nAaNgQSKcGzXTRdw4joqJqKZupEkjGGL9N1fadpU14oYtdsjBhEkrFQWkQIjKiJmYgwuHP4nM5ZL1YZf+Q5zEQUq9ZE0TROtaHPHJwEQNFCeftYNkmmtwO71iTVkyE/tUTg+3SN9JIdyLVnIm2uWM42se61fD7eBXxGSvlZ4MJKXNpcFri2y+wzkzQrdXzXw/c8FCEQisDzfCSgaBqRZPyyDSAAyxMLeLaDb7s0SjXKC0U00yCajLYC4tk3+DlNh+M/PMzE9w7hNmwC12Ph0DSxTAIjZqJHXvRZDzwfgC237qI0myc7kKO8WEIGAbF0HFXXyPS/vpphbdq8npztTKQqhPgo8J+BW4UQCtC2ZnuDY9csZg6O47k+iqLQrDTRY2bYVKiqCE1BN8M+kI17L98AEgQBtXwJoSgoisBpOqAIVudWiKbimMkIA7vOnMNxGhZzz59gdb5Aeb5ANJvAbdpEhrpxbZf8xBx9N6SaTgAAIABJREFUuzay62f2Mf/8NEvjc0TTcbbdtgfDDGds+clFXMuhc6QXp2ERz8QZ2NHuDWlz5XK2QeQu4D8B75VSLgohhoA/W79htVlvnKbF9BPHaFTqCEWAlKiGglO3MOJGqIulacRSMYb3b+Vytq+ffXaSSn6VWCr85k/TBkTYHS4g1ZXGOG328Eosjc+yOrPC8rFZUBWEEDQKVZYm5slu7OGqd1zHs994lOLJZTbuG2Pzjdu5+l03EklEOPLQ09QRxLPJljqvAAGDVw2zcf8WlLbMSZsrmLMKIlLKReAvTts+SZgTafMGpF4os3RsnupyCaGpYYBQFRRVw4xruE2HSFIjkorSNTpwWQeQ8lKR6mIJVVWpr9aIJGIkO9O4totu6iR7swzv2/qa58gfn2fmyWM0yk2QoR4YwMzB44y9dTfH/v0gm27awQ2//BNU82VUXaVjsIu+HUMc+ucnqSyWyG3sobJcIt3bgREzUXWNRC5NqjPzejyGNm0uGWdbnfXzQohxIURZCFERQlSFEJX1Hlybi0/hxAJzz4ZVTFJK7HozNJiyQn90oarEOpJEUnEy/TkSHZd36qs0ncezXVwr9DipLK/iOR5G1ESPm68pQ19eLDL52BEmf3CYaDZFaXoZu26hGjq+56MZGuPfeZa+7UPY1Sb54wvU8quMvmUXfTuGsGtNVk8u0zncy+QPD6Mb+lo+ZnV+hc7hcy8nbtPmjcbZLmd9Cvg5KeWR9RxMm/Ulf3ye5WOz+I4X2tm6Hoqi4Ls+mqHjWw5qIoKqq2QGO+nadHmXpRZn8rjNsPxYUQS+E/qle46DoiqoukLf9pdWoleWShRnllk6fBLN1JES6itlVEMj3dfB7MHjbPupaxCKYOSG7Rz994OcfHJ87f3X/dJtZFoS9/OHpgE48egRNl23jUalgV1tkuzOkO3vJNndnoW0ufI52yCy1A4gb2zmD52gurxKvVjFTESxa03MaIRmtYFmagRBQCQRIZpJ0L1lkHTP5W+c1Fyt4LkeqqmjOi6+UPB9v1WJJekZ6w/zPYSzjoXnp/DdgMnvP0d6oIvA80Nr3GSUuWem2PK2Pcw9N8XEI88zctMOIqkYe3/+FhrlGpqh07tjiP5tYZJ88YUZqotFVufzDO0d4/j3D6GZBkbMRAjBphu3X8In06bN68drBhEhxM+3fn1CCPE/ga8C9qnXpZT/60wXEEKowBPAnJTynUKIEeAAkAOeBH5ZSukIIUzCPMu1QAG4S0p5onWOjwLvBXzgv0gpv93a/x+B/w6owBeklH96tjf+ZiHwfWafnqCxWqexWkPVVJrlOrppIKXEjJsoioJiqMSyCXq2DZHsTF/qYZ+R0uwyTsNGUVWCuo0Zj2LGo7i2g5mIEc+l0E2TZ7/6vbBIIGJy8MvfZec7r6e6tEp2Qw++G+Y+nIaFUGDu2UmuveutLI/PsTIxT264l6F9Y/S/rL/EtR0WDp2gUazSOdLP9I+OMnL9ttBnRVVQdZWesfO3223T5o3EmWYiP3fa7w1Cf/NTSOCMQQT4HeAIkGptfxL4b1LKA0KIvyQMDp9r/VuSUo4KIe5uHXeXEGIHcDewE+gH/lUIccpN6LPATwGzwI+EEF+XUh4+izG9KWiUa8w/M4ldtXCazdCV0HbX+hw8y8ZMhnpO0XSC/l3DxDKXdw4EQh2r5WPz2LU6sc4MekSnWW6E4pCmRrIrDULy3c9+leyG7rA6SihIKfEsFwDXcoikYkg/YPHwSbq3DhJNx8hPzJHqzdK9ZZBNN24P9bdexspEaHilqAqzBycY2jtGo1TFtVwSXWk6N/ejR1+7GqzN5YXruszOzmJZ1qUeyiUlEokwODiIrp99B8drBhEp5a9BKHsipfz+6a8JId5yppMLIQaBnwU+AfyuCMt8biMsFwZ4ALiPMIi8q/U7wJeBz7SOfxdwQEppA1NCiAngutZxE1LKyda1DrSObQcRYGVynvL8CrXlEoEfIEVYxquogsD3kYHETMVQEMS70wzv39YqT738OfnEMexaAyEUmitlPMshmoyiajp6VGf+mePEskkahSq5U74hLRvoWEsGfvHINFtvuwbP8xi+YRtTP3xxtVbRVG790O2vGEB812Px8DSKrlKaybPhmjFOPPZCKEwZNUj1ZNhwzej6P4Q2F5XZ2VmSySTDw8OXdTXieiKlpFAoMDs7y8jI2WvjnW1O5NPA3rPY93L+b+D/4MXu9hywKqX0WtuzwEDr9wFgBkBK6Qkhyq3jB4BHTzvn6e+Zedn+619pEEKI9wPvBxgaurIbv5yGzeLhE1iVBlalgVO3AIFi6iiKgmoquA0bMxVH0zWSPRmGLtBn/PXCqtTJH52FICDVneXZrz6CXW6svZ7b1EfP9o1MP3qEkZt3AaGMPYDveWgRg/nnphh9624mvvMsx79/iOHrt5Pu72TPe26iWWlgJmIMXDVC7lUqq049W6EIUj1ZqsslurcMoBkaqb4OBveOEkm21XrfaFiW9aYOIABCCHK5HPl8/pzed6acyI3ATUCXEOJ3T3spRZiHeK33vhNYllI+KYT4D+c0qouMlPKvgL8C2Ldvn7yUY1lPlo7NUppexLddhKrSXK0RBAFGNEKoWiMJ3IBoJoFmGKT6swzuvnw70U+x8MI0VqnGzFPjdGzswUxEKZ5YfEkAAShMLpDbHHqFmMkoAJXFIomuNI1ijZHrt5GfmEdRVfb+wlupFSoYEYPOkR56tp35y4XveiwfnSPwfJbHZ+nZOkS6L4cRN1E1lXR/jo7B7ov/ANq8LryZA8gpzucZnGkmYgCJ1nGnL5ZXgDvO8N63ALcLId4BRAgDz38HMkIIrTUbGQTmWsfPARuAWSGEBqQJE+yn9p/i9Pe82v43FVJKZp46RmW+iPQD7LqFomnoMRO72qRRLJPoTIcBJWYSTcfpGOkjN3R5fuA1q3W8pkt1sUBteZVmuY5daxKJRyGQNEs16iuv3Kbk22HOI96ZIrepj8LkArGOJN1bB6kultBNjUgyihYx2PWz15/TrKEwtYBTbzL/zHGG9m+jWamzcOgEyZ4sPVs30NO2vm3zJuRMOZHvAN8RQvytlHL6XE4spfwo8FGA1kzkI1LKXxJC/CNhADoA/CrwtdZbvt7a/mHr9YeklFII8XXgS0KIvyBMrI8BjwMCGGtVe80RJt9P5VreNOTHZ2mUqjRKVWrLq61uaRXfdUGCZupoEQO7bhHNJkn1dNC3ZxgzGr3UQ19DBgGF4/NUFos4dSssP45HqRfKBH6Aaho0ilWSvR34ro9VaRB9FVVcMxkllk1y7N+eZvSte0j1duA5Lm7TZmj/GIneHPFsEjNx7vdfniuw9MIMG67dwvRjRzASUTpHerGqDbIbu9HNdjK9zYvcd999JBIJPvKRj5zT+x5++GEMw+Cmm24C4J577uGd73wnd9xxpu/tl4azzYk0hBB/RlghtdYCLKW87Tyu+fvAASHEx4Gngftb++8H/kcrcV4kDApIKQ8JIf6BMGHuAR+SUvoAQojfAr5NuLT2N1LKQ+cxnjckvusxd/A4VrlG4Pn4jouiKviuhwgUFF0jcMPO7cAPSPVkiHdlGNi96bKZtru2S2FiltLJZYqTC5iJKM1SjdRgJysTcyi6hgwCdMCIRZC+j6pH0AwV1wowElGcWnPtfNmNPaCqDO4do7ZS5ti/Psn2n7ke6fskujJntWT1ahROLDLz5FE6NnZTPLHI8I3b8d0AoQg6hnsYuEw9Vtq88Xj44YdJJBJrQeRyR0h55hSBEOKfgf8JfAT4TcIZQ15K+fvrO7yLz759++QTTzxxqYdxQVQXCxSnl6jly/i2i6JrOA0L3/EIvAAtYgASMx7B931iuRQdQz0vVipdAgLPZ2V8lnqhEs4hJTSLVaxqI6weC8L/h77jougqK+NzJPtzmPEoQlEQqkJ1uURuuBcJNArhcpaiqfiuhxGPkt3QSbPSQI+aGDGTRFeG7FA3mnFhgtMLz02ydPQkldkCQlNRVIXFQyeQEnq2bWDk5l0MXjN2gU+ozaXkyJEjbN9+4Q2in/jEJ3jggQfo7u5mw4YNXHvttbznPe/hQx/6EPl8nlgsxl//9V+zbds2vvGNb/Dxj38cx3HI5XJ88YtfpNlscsMNN6CqKl1dXXz605/m/vvvJ5VK8cQTT7C4uMinPvUp7rjjDhYWFrjrrruoVCp4nsfnPvc5brnllnV5FkKIJ6WU+17p+LOdieSklPcLIX7ntCWuH13gWNucB4uHp7FWq1jlGq5loygqMghC+RJA0RQQkkgihlAVsiO99O0cuWTluzKQFGcWWTk2R2OljB41kFJgV+r4tofvh1VTgnB2JFSFykKRSCpONJ0g8Hxq+RLRbIpUTxaEIJqMoUdNAsfFrtuk+rIkejpI9XaQ7std1PHnx2eZPziOVbFIdGeY+sHz6LEoA1ePghA49SZdowNnPlGbK54nn3ySAwcOcPDgQTzPY+/evVx77bW8//3v5y//8i8ZGxvjscce44Mf/CAPPfQQN998M48++ihCCL7whS/wqU99ij//8z/nN3/zN1+yDHb//fezsLDA9773PV544QVuv/127rjjDr70pS/x9re/nT/8wz/E930ajcYZRrg+nG0QcVv/LgghfhaYBzrWZ0htXo35ZyawyvW1sl2/6SAiBkLVEIpCNB3H93z0qIkeNUL5koGuSzJWGQQsPDdJ4PlU5otYqzUUQ8NtOGhRA6vSoFEokx7oRFFV7GoD1dCoLZeJd6aIZhLkx2fJDveS7OloeZsY6HGTeFcKo5XT8C2X3nN0LDxbmqUq+aMnqSyWSHRlmXtqnJGbdlFbKVNZKJAe6GTTzbvWGjbbvLl55JFHeM973kMsFv5/uP3227Esix/84Afceeeda8fZdij6MTs7y1133cXCwgKO47xmb8a73/1uFEVhx44dLC0tAbB//37uvfdeXNfl3e9+N1dfffU63t2rc7ZfTz8uhEgTWuN+hNAe98PrNqo2L6FerDL/zAT1QgWnbqMoCk6tiR41cS0HgUAzNBRVId6RJNWbZfjGnZcsgPiuz+wTR7ErDRqFKo1CmcD30aMGnuXgVBtY5RqJzgxGLEJztYZdbRJJJ+jaOogeMWgUKnRs7EFISHalyW3sYfDaMRABz/2vR5j6zjPUFor07Nh45gGdJ7NPH6NZqBLvSBF4PpqpM/2DQ9SXSqiqgtuw6GrLm7R5DYIgIJPJcPDgwbWfI0fCxtbf/u3f5rd+67d47rnn+PznP/+a3fKm+WLj66kUxK233sp3v/tdBgYGuOeee/i7v7s07hxn6yfyT61fy8Db1m84bV7O8tFprNUGnuNiVxsIBCJqoEdNfNcjkoyFCfRElGg2QXakj1T3pRNPrJcqrJ5YxLNdPMvGrjYRigIC3KaDlJLAC+gY7kWoCkuHpunbPYLbtPEdl3hXhtRAZ+hSqCqoukYkFUeocOLRQ3SNDDB49SjZTX30bl+/5rDi1Dzlk3kUTUWRkvyxGbLDfWimQRCEnurD7VlIm9O49dZbueeee/joRz+K53l84xvf4Dd+4zcYGRnhH//xH7nzzjuRUvLss8+yZ88eyuUyAwPhUugDDzywdp5kMkmlcmanjenpaQYHB3nf+96Hbds89dRT/Mqv/Mq63d+rcVZBpKVV9TmgR0q5SwixG7hdSvnxdR3dm5jS9CLN1Spu3cZp2mGpq6rieR6+5aJG9DVDKc3UiOZS9F91aRsHrUqN8vQyVqmGMDSchoPTsEARIMPkeiyXwirXcRsWRiJGz85hPNsl0ZUl0dfRaoz08PwARYrQ28P1aBZrZPu70bIRduy9CUVdP7dAz3FZeOY4iqpQWSgQScbJbepvVYv5pPs62HjTVURS7QDS5kX27t3LXXfdxZ49e+ju7mb//v0AfPGLX+QDH/gAH//4x3Fdl7vvvps9e/Zw3333ceedd5LNZrntttuYmpoC4Od+7ue44447+NrXvsanP/3pV73eww8/zJ/92Z+h6zqJROKSzUTOtjrrO8D/DnxeSnlNa9/zUspd6zy+i87lXp2VP3YSt2njWg7Sl/i2g2e7of+H44aFTRIUTUMzdfSYQaI7S+fmS5vcLU4v0Fyp4jZtrEoNoWqoukpjpYxdszCSEfRo6PinRQwC18f3vDCBnk2i6hqe7RIEPp4boGkqsVwCENSWS+hREy2ikx3sWXe72fmD4yw+N0WzVMWzXZymTcdIH9IPiHWkGHnrbqLpV+5TafPG5GJVZ10JrFd1VkxK+fjLlg68Vzu4zbmzdGQKr+kggwDf9vBcj8ALhRKlH6DoKoqqIiAsedVUIuk4uc39xLKXVnl38flJAt/HrjUAiRAKgecRScWIdqQQqkJztQZSEu3vJLtpABn42Kt1kAGe7YS9Lq6DFAqqkPhugFWuE/igx0yEKohlU+seQNymzcrRGRRVoXRigexwH8m+HIHno+gq3TuG2gGkTZvTONsgsiKE2Ewo/44Q4g5gYd1G9SahslLGWikRuB6e7eE2LFAUAsfHcxxEqyz3VB5B03XQBJquYyai9O4aWfcP1dfCsxxWJuawq3UEAillOKNw6gBY5RqRXJpINk7a7SLRkyU10El+/CTSkyAlvusihILn++E9GgpBQOi06PpomopQVTRNPe8PbxkE4eztNcqcnbpFcXKO2mIR3/VZPjLNxpuuYuGZCUpTC5ipOKM/eS25Szzja9PmcuNsg8iHCAUMtwkh5oAp4JfWbVRXOIUT8/hNN/wW7vsEjo/bdPBdD0VTCQI/DCCBRGhK6NynKEhFoEdNEp0Zcpe4N2H52El8y8F3POxyEz0eCZeoHI9EVwanZuFZNn6tiZ5J0HfdFmrFKsWJOQgkvuUiNAXpS4QukK6Homn4joeiaghVYEbMsPdFQsfwuTdK2tU6CwcnWHx6HOlLBm/YQe/Vm9GjL/VdL4zPUpiYxV6t4fs+oCD9gJlHD5EbHUSPmcggoGfHm1vltU2bV+JMKr6nK/d+E/h3wrLgOvC/AX+xfkO78ijOLeJbPnhe+O3YD+VKAi8IEx0SAi9AUQQgCIREIFBNHSEEkXSM7KZ+IolLl9CtF8vUFgv4tk8Q+LiWA4BQBEHgAwLfCQNEZrQfVdfoHttAYXIOt2Hj2w6qYRD4Psigdc8eQhVIP0A1dZCSIJBIRUFXlbMOIPWVVTzLIdHTQf7INKWpBWa+/9za60e+8l08y2bkbaGDge+4LB+eYv7JcYxEhJVjM+S2bGDu6XEGrt2KVa5TW14l1ZejZ/cIsVzq1S7dps2bljPNRE4ttm8F9hOKJQrglwlFENucBbViBadWRwiBQOK5fmjNKgMIglaeQyDU0BscVUEAuqKgCIEaNUj250j1dl6yewh8n+LUPL7tEgQSt2EhfQkEqIaGXa4T7UjiNm3sWgM9FiGwXPp2jJA/Og0yVNiVgcSpNxFKWK2lmDrSCxCqQItoKKqKFjEoL+QRIk3XztfWpHIaFtXlFSqTy8w/8QJOvcnQzXvwXZfl5yd/7PjJh56ib+8WIukEi88cpzA+g9YavxCClaMnGbx2KzM/OoIRjxLJJlF0ha4tbYXeNm1eiTOp+P4xgBDiu8BeKWW1tX0f8P+t++iuEHzXQVEUBAK/pROlCAgCiVDCJLRiaAhVQbb2CVXBjJnEerIkuy5d3wdANV+kdGIJw9TwbA/V0Ak8P2zAi5lI30WPRXDrFqqp0bl1iGguTaIjReH4DNLzCfww/6HqGtLxUSM6knAZS4uYCAGqroGqMff4C2Q39dKz7ZUbCZvFsBfFqTWwqw2MRJTj/xx+p0kP91JbKqCoCl5rlnQ6p/TF8kdOsPj0MYxkjNWZZWKdaXJbNrDw1DFWjkwzeO1WAt8n0d3Bxluu+rElsDZt2oScbU6kBzj9L9Jp7WtzBsqLK6iGjudJPNtFAkIJNaWEEEgh0Ewj3DbDJLIeM0n0dRI9D7nyi83qyWVq88uYsQhO0wEZIAM1lJt3XHzbQ4sZBI6HFouiJ6JkN/aiGTqlE/O4jbDyitb9Bn6AZhp4ro8RN5C+RNUNtKjO9HeeQdE1kv05eq/ajF2uYVcaOI0mtYUildk86Y09ONUmnu1QmcuT2dhHaWpxbbxGLEJtuYQZi5AbG2T5+amX3E/vns3Ul4u88NVHiHWmqS4UMNNxIukEVqVB394t5A+fYPm5SQau387A/m3tANLmdeHBBx/kd37nd/B9n1//9V/nD/7gDy71kM6Ksw0ifwc8LoT4Smv73cDfrsuIrjB008BzXHwnDCBStmYfQqJoIuzDkxLV0DGiJpmNvZd6yGtUV0p4VpNGoUJc1wg8Dz1i4tab6LEoUoa2s9L30eMRzFSMztHQJ8yq1Fr3HeY7XMsOu71dH8UQ2EurNOYcYl0ZzL4oftNm8PrtOHUL37aZ/NcngDA3okcNqnMrdG0PZyZBRMe3bXKb+tHiEaxybW3MpakF0kPdRNIJmqs1OsYGKY7PAtAxNkBuywYWD45jl+vhaxNz5LYM4XseZiqKHjXZ9u5bSPZ1kOy7dMuHbS5/pJRIz0No2gUXXPi+z4c+9CH+5V/+hcHBQfbv38/tt9/Ojh07LtJo14+zlT35hBDiW8ApneFfk1I+vX7DujKwKg1c20H6AVL6yKCV+5AqSkxHBkFoGhU1SfVcXnqWVqWBVajg1pqhtHzQKsl1wuUnz7KIZJPhkpaukxrqxoy/OHOqL6/iNWxUQ0FPxFAAr2GvLT05lToyCADo3j1KaWKW1EAX6ZE+Gvky0g9AhKq+mq7RtX2YZqmCbzksPn1s7TqKprLp7TdQOBJ6pnlNG0XTqK2sEutIIYRC5if3Ee1IELgBJx56ci1QF8dnGbh+B4WjJ/Esh4Ebd9J/7TYSXZnX70G3eUPi1mvUpsaRMkAIhcTIGHr8/PuHHn/8cUZHR9m0KcwB3n333Xzta1+7coIIgJTyKeCpdRzLFYdrOwSuSxCAVFQIfDTDACFRIxGEKkjmLr8PLN/1qC8X8ZoWQlMxElHKJ/OkN3aHvSwCzFQCTddRs0kMQ6exXGJleRI1qqPqRlihBaiqytLjR8gfnqTv+p0olRrxzhSZoW6khOXnjpN/fpKunSPkn5+kuVpl49v2cvLhp9FMAz0RRSZjawHk5cnywPOpzCwx8tP7mP3+87gNG+n7bLxlT7gMJSTRbJKlZ8apzORxqg3q+RKpDd1UZpaZ/9ER0ht7iWaT9F2zpR1A2pwRKWUYQAK/te1Tmxons/Pq856RzM3NsWHDi27fg4ODPPbYYxdlvOvNWQeRc0UIEQG+C5it63xZSvl/tuxsDwA54Engl6WUjhDCJFw2u5bQW/0uKeWJ1rk+CrwX8IH/IqX8dmv/fyT0bVeBL0gp/3S97ud8cMo1QBIoAi2io5g6euvbuggk0cyl7TR/JQI/oDy9iNe0CTwf1VDDBPNAB07TIjGQQ5WSZrGKZ1l4c8uY6Tgnv3sQBAz9h700i2WiHSkWnzpKoi9H/tAkIz99PcVjJymfeLFHVYtF6NoxQv7wi3mL5kqZxvIqjfwq3btH18YEksD3CdwfF0qwKzWiPRl2/eJPIoQgNdCDFtGQfsDq9ALHvvoIiq6xemKB7OgAxWMzZEb66Nu7BbvaIJZLMXDdDpKX2WywzeWJ9DykDF66Twbh0pZ+YQZob0TWLYgANnCblLImhNCB77WWxH4X+G9SygNCiL8kDA6fa/1bklKOCiHuBj4J3CWE2EFolbuT0GP9X1uCkACfBX4KmAV+JIT4upTy8Dre0zkhVIXSyUUyG3rAA8VUsatNrFqd7k2Xn4S43bCozizjuW5YNWUaa9VPgeMR70zj1h1q+SJCVXAqDaJdGYrHZxi8+Wo0UyP//CSdO0awV6ukhgeozy2RGOwi8LyXBBAAr2GhGhpmKo5bf1EG+8Uvc2HvjFNrhA2Xhk68O0t9ufSS83RuHWbolj1r29IPKBw7SWVmCWu1Rmlihq7do+H1dI3kQBerU+FYunaMMPSW3cTaM5A2Z0mYA1FouXSH+4SC0M7/43RgYICZmZm17dnZ2TWF38uddbO7kyGnMp5660cCtwFfbu1/gDBJD/Cu1jat139ChHPDdwEHpJS2lHIKmACua/1MSCknpZQO4ezmXet1P+eDUBTMaITn/99vs/zsOAtPHuX5/+efkLXww+xyonh8jsr0IoHnIR0PLRZBKhDtTCO0UCVY+h6aqRHLpdHjERL9ObymRTSbxms0aRbKpDf20iisEvgBVqmEHo+SHux5iRf6y+ncPszqiXkAUhu6cRv2Wsmw77hopoFVqqJHTDq2DhE5TSusZ88oPXtG17bry0XmHj/E4pMvMP/YoTC3AgSOi5lOsHJ4isDz6dk9ytg7b2L7nW9rB5A254QQgsTIGEJRQQiEoobbF5Bc379/P+Pj40xNTeE4DgcOHOD222+/iKNeP9b1k0wIoRIuWY0SzhqOA6tSylNrErPAqXA7AMwASCk9IUSZcMlrAHj0tNOe/p6Zl+2//lXG8X7g/QBDQ69P05jnOMjAJ5KJs+X2m6kvFhGKYMu7bkVNmGc+wetAZWEFr9bAqTUJ/AChqkg/lJpvLBcIAomiKEgZliWrukbg+tSWiii6ivR8zFQcr+mgJaJI38dIxLDLNYxUnMDySI/1Up5ewkhE0CLGj/VupIZ6yT9/nEg6SXZ0EC2iM/uDZ+m9Ziu1pSLxVBbF1EkNdFGZyxPv6WDDrVejKArx7gzJ/q41WfjK7DIT//QIiqpiZpKh/3xLL6vwwjQdW4bIDPchZUDH2Aa6d46gXqD/eps3J3o8QWbn1RetOkvTND7zmc/w9re/Hd/3uffee9m5c+dFGu36sq5BRIbzvauFEBngK8C29bzea4zjrwi1v9i3b9+Zte8vAs1ShcBxkRK0RITO7RsJTgkpXiLRxMrKKl4A7bQVAAAgAElEQVS5SuBLAs8lcHzscj2UIokYqLpGbS5PdbFAekOY+G6UqiiGSiSdpLZYxGtYxHs6QEryR0/SddXmUATSD5Cuh12ph1VcloMQgurcCpmhHmrLRXqu2ULhSOgRr5o6G27eg2Jq9Ozdgl2pI90AGfhEc2kWnzpKtDODmYrTtXsziqrRuWszAohkkmjmix/+geezcnSawgsnQjFHy0FpWBjJGNX5FXJbhygcPUnx2EkQsPntN9C7ZzQ0y2rT5jwRQlzUHMg73vEO3vGOd1y0871evC5rKlLKVSHEvwM3AhkhhNaajQwCc63D5oANwKwQQgPShAn2U/tPcfp7Xm3/JcerWfh+gAJIJyDgVIIY0oOv71pnJV/ArTQh8MPeDssl8H28WhO7XAu1uRRBI78KIrTarcwsk+jLYbaMl3zbwYxHqEzNE+/KEKrftFBaelmKwK03EULgNiwU00CVYNebGLEonuvSfc0YiqIQySZZePIFMhv7QuVi12PlyAk6Ng8wcP2u0DM+lyK1oRv1Nf5QnVqD/POTzD/6PHoySm2pQPeuzSw/N0Hf/p0sPX0M6ft07xlDj5rktm0kM9zXDiBt2lwk1rM6qwtwWwEkSpgA/yShiOMdhDmMXyXU4wL4emv7h63XH5JSSiHE14EvCSH+gjCxPkao2yWAsVa11xxh8v0/rdf9nCue5yOkDHtDVCUUFJSC0G/j9VOCLc0sgQz1uQKflmaVh/QDnHoTRQ3lWJCsVT4Fnh/OLoQIxRD9AFXXaK6sEs2lEapKs1Am1pVB0VRqCyvoiShmMoEeAS1mEnh+KN+igPR89GSUSDSNUBWqs0tYpQqpgS6Wnx0nPdJPsr+TkZ/aj6JpJHpzZ2U7a9fqTP/bEzQLFexqg9RQD9WZZZqlCp07NrH09FGyY4NEcxmSA91kNvWjtZev2rS5qKznTKQPeKCVF1GAf5BS/pMQ4jBwQAjxceBp4P7W8fcD/0MIMQEUCYMCUspDQoh/AA4TGmF9qLVMhhDit4BvE5b4/o2U8tA63s85IX2/ldQV4ae3J5GIdbV1fTmlmcUwge+B44WGV2FgABTQYxGsUhWJRMPAzCSoLxYI/IDMhjAZbq1Wifd0oJo6RipOYqCL1RMLxLuyKKqCXW2QHOhGItGjURRDQ/o+iq6j6gp6PEZldon6YoFoLo1qGqi6HibkO9KMvP0GNMMg3tuBET97mZdGoczywWNUZ5ZQIyZIiVNrkh7uo3xigUZkle49Y3Tt2kR6Y19bwr1Nm3Vi3YKIlPJZ4JpX2D9JWFn18v0WcOernOsTwCdeYf83CSXqLytkECCDgCAIEFJAIENZd0Wgxtc3qV4plPDrFqLVm4IX4LmtmnbRkprXVQIPtEgEIxMgHR/fD8IP8b5OotkUnuNipuJEc+lwtqJpmOkEvuPSs2csdFs0NKKuT7QjiRaNErge9XwRu9xEi0kCT8FaraEoKpWZZYpHT6JFDIxsnG3vvu28q6LK0wssHTyK9CVWqUr3nj5q8yuUTywQ6UjRtXuUZH8X3btH0WNt3as2bdaTy6vO9AqhUawgfRla28qgJTIIQtWIrlOHeml2IZRUV1QUBIqmIV0f3w2Q0gNNIO1wDVBoKkJRkDpETY3A9xEIFEMnkktBEAYIVBWvYRE4LnatSX2xhLVaIbtpkOrsMultG8gMdlNbWMGpNXFqzdBD3dCxixXSI/2IRBSJYONt+/AtGyMVJzPSf06zDt/xaCwXqC2u4FsutcUClZMLZDeHKbHyyUV6rtlC+cQC0g+Id3fQvWe0LZzYps3rQDuIrANevYHvOJxKPp8ymZJ+sC6GUpXlFVQUfBVEa6nKd9zQVtcPrWFFAKqhccoDHVVBi5qgSKTtE82laJQqSNcncFwQCtZyEYQg8CUikMS6M8R7sjjlOh3bNxLtSFE5uQRI6osFMpsGqM7nMdNJ9HiUxlIR1dTJbR8mNdB9zvdlFSusTs3RXFmlOrOInoihRUzs1SpmMs7q1Bw912wl/9wEywePkR7pZ+Cm3WQ3vTGatNq0uRJol6isA27DDp3+XBe31sCtN/FtG6Gtz+NWVRUUBVUICIJQcl4GBJ6PJEBRFFTTwEjGiHSkUJLRcJ+ukurqJAgCagt5vJpF4HoIBIHt4NQsrFINt9FAjegoQuCUaxjpONFsEqtcw0hEkX5ArCvL4lNHSPR3o5kGmqkzcOMuRn7yuvMKIKXxk8w/9jzT//Iovu0Q68pSOTEPAhrLRZIbevCaNoUjk3Rs2UD3VZvZcOvV7QDS5g3LvffeS3d3N7t27Vrbd9999zEwMMDVV1/N1VdfzTe/+eLq/Z/8yZ8wOjrK1q1b+fa3v722/8EHH2Tr1q2Mjo7yp3/6ohLU1NQU119/PaOjo9x11104zo/77ZwP7SCyDkjfJ3A8hBAY8Sh6PIqia6EF7kWmVqmuWe16noeUEoKAwAnC/LmiITQFxVBBgGoaKLqC1ATxjgzVYglVhCaLimGgCrBKoXpvJJsk0plGj8cw4hEiHUnivVlUIVl89FmWH3uOZr6ImUkiNJWea7bh1ptoUYPuPWNEO9LndU+VmUUmv/m91mwuJAgChKYSuGE3/cqh43Tt2kx2bAg9HqX3+p2kh87dh71Nm/PBtx1Kh15g4Ts/pHToBXz7wj+Q77nnHh588MEf2//hD3+YgwcPcvDgwbU+ksOHD3PgwAEOHTrEgw8+yAc/+EF831+TlP/Wt77F4cOH+fu//3sOHw6VoH7/93+fD3/4w0xMTJDNZrn//vt/7FrnQzuIrAPSD9Z6JpxqHbfewLcd9NjFNZmqLhfRFIUgCHtQZCAJWhVYQoRe7YqioOkGsuVh7lbryIZDtj/0FFMCJTSKimhY+SLNUg0zl2o5ELqhH7ztYJfrNOZXsEtV5n/wLLXZZZxyjeUnj1CbXUSNGOiJGJ07R+jaufm8OsGllJQmTpJ/dnytWRFC+ZjK9DwdWzeyOjFDdtMA6Y39NPIlIh1J+m+4imRf10V9tm3avBq+7TDzrX+j9PxRGvOLlJ4/ysy3/u2CA8mtt95KR8fZiYB+7Wtf4+6778Y0TUZGRhgdHeXxxx9/iaS8YRhrkvJSSh566CHuuOMOAH71V3+Vr371qxc03lO0g8h6IATSddEiBoqiougqmqmTGLg4Jkf1Qoni5CzS8XBqTfxWA8ipoKGaOkrExEhE0RIGTq2BrNhIx8O3PdIDYQDxfR8pfDzLJrB99IiOoik4hTLWagWnWg/zIlLiVuq4jSZew1rzATlF8dAksVyazHAf0UzqvO7JKtfIPztO8cgUgeOi6BpCUVB0jeLRE+S2byJwfbp2jwEQ780xevutDN16LZHLUA25zZVLZWKSwHbW/g5kEBDYDpWJqTO88/z4zGc+w+7du7n33nsplULx0VeSjp+bm3vV/YVCgUwmg9YSiTy1/2LQTqyvA3a5ikAgBOFSkqIgEWgXKJFg1+s0lopIIVqaVh6BKwgCiWbqKKqOHoshkVh1i2a+GPqh+xI9FUH6AZnBMICU55Zwyg20mI4ArMIqnu2g6jqB7eLWG+CH1WVCVfEtG16ly1toKpFs6jU7y08hpaRZLONW6qAI6vN53FZgclZrqBGDytQcnTtHyT83Qce24fCP1PXo3L2ZRG8XxjoUJ7Rpc7ZYK6Uf+yIlgwBrpXjRr/WBD3yAj33sYwgh+NjHPsbv/d7v8Td/8zcX/ToXQjuIXGQCz8cp17BXq2FOQFPxizZuvUnv3vOXDitNz7XcBQk74aVP4AJK6JZIIJECPCtUvvUdF2HoyAA00wAE6cEeSjOL4PoEjoMW1XHLdaQMcOtNVMPArTdQtFYwUARChpIkVqVOoieHUMKqLq9pr42t/8bdmMn4Ge+hsVygMr1I4dAEZiZJfalAaqgPGQTU5vMY8RiKoSNUlcKRKTq2bkQGAbHuDnr2bmuLJba5LIh0ZmkuLr8kkAhFIdJ58f1oenp61n5/3/vexzvf+U7gtaXjX2l/LpdjdXUVz/PQNO2iSs23l7MuMm6jQTSXprlUpHDoOIVnjrF6bJr0yMB5d02vTs8jPB/fdkNDHN9HIpG+F+px+ZLAdlAI8B0P33URTRtN1dETMbLD/UgRUF5aBstGeh5aIkZg2QROaN8rELiWFc5EXBc9FgnzOpaNZztkNw9ilasohk7P/p107h6jY/smNv70DXRfs/U1x2+Xq6w8d4zCkSlWJ6aximUUIzToqi+ugBC41TqRXJrV8ZNkxoZIDfVgr1aIdqTovOr8cixt2qwHqdFNKKaxpr8mFAXFNEiNjVz0ay0svOjB85WvfGWtcuv222/nwIED2LbN1NQU4+PjXHfdda8qKS+E4G1vextf/nLowvHAAw/wrnddHOeM9kzkItNYXkWogq6923CrDZASPR1HT53fEkxlZgEhwfODUN9KSoQqkK6HIhRkIEGRKIoGASADlECgdaRJdOcAKM8vEbgB+C4oCtL3wffDhkgUhBCoEQO/boWvCxCqSqQjjZFKIH0foWvkdmzCtxz0RAyzM0t2pP9VZVwCz6e5UqSRL1GZmqOxUiK3bRP1hRUg7KDxm2HzoVUsE+3qoDq7SOdVo9Tm86iGTve+HXSMDoWVbW3aXCaopsGGn/kJKhNTWCtFIp0dpMZGUA3jgs77i7/4izz88MOsrKwwODjIH//xH/Pwww9z8OBBhBAMDw/z+c9/HoCdO3fyC7/wC+zYsQNN0/jsZz8blvrDq0rKf/KTn+Tuu+/mj/7oj7jmmmt473vfe2EPooWQ61B2ejmzb98++cQTT6zb+VcOHWf1+AzRznRYYaSqKEaoO9W1c/TMJziN6uwCMpB4thfOQASE2iUBQgvzLKqmoqgaiqERuB6KYZDsza2do7KwgN8MxSB9z8O3bCKdWfymjed44Lm4lhNWY/kBvuejRXRkEApFmpkUTq2OW2sQ7czSWCoSySbo2LbpJQHEdxzqC3ma+RKe7eA1bZxqHTwfLRaldOwE3ft2sjoxg12qkBzqw6nUSG7opXDoOJnRDfiOS31hhcyWjXTuHCXR3664avP6cOTIEbZv336ph3FZ8ErPQgjxpJRy3ysd3/6Kd5GRvkesM0Px6Ik1BdzM5kHiWzae03nK84tINyAIfKQXdpALBWQgUQw9nHEoaig+6EsUTSNQINkTBpBKoURQqSOkBD8gkCB0FTOewbdspOejKIJAU0N9KQmoAZHOLFIGeE2HwLKpLiwT78wiFIFQVRL9XWROs/Z1aw1qC3nKx2dQdI3GQh6hKkSyafADrFIFww8wM0nsUoXMpkHyzxyjenKB9OYNCE2j78Y9+I6LkYzRf/M1RHOZ11Wosk2bNudPO4hcZHwrFKjKbh1e06nSYxFi56CZVVsuoATguC4gQVVDOXZFA3y0iIli6jjlOl6jiaJqBM2AzFAfjXINZ7WMaHl8nJq56Jk4eD6BG6oLSyT/f3tnHqZ3Wd77z/3b3nX2mUwmmYSEJCxhCyCL2lNRRMHatFcPjbEuqKhHao9rWz2t1tr2Ktie49FL21pa9EBrjdTWwlURpFC1ohAgLJEtAZLAZJ19ebffdp8/nl+GDNlnkswEn891TWbe57fM/b6Zmft97uV7J3GCn8+RNCK8QgCeQ1oPScIGUaVhOtMLAeJ7eJ6L67sUOtuo7OpHU2Vi9wAT23bgFwvU9gzhBB75jlZGNm0l19ZMvX+I0sJuRp/vo+Oc0xjd0kcSJ8y/xMR1Cx2tNC3usfkOi+UkxjqRY4iqEtcboJDUQ9IkJWgpkzQah784I2mEpPWQuN5ANEXFgSTBz+XQ1Eis4zg0BkdxPAdUwBH81mZG+rZD6ryUK0lSUtSUxCYpaZSQJomxNUnxfJ9wdNx0gicukBp5FtfDK4EjQlSt4QVmLG5UrzOw4Qkm+naDI7SfuYyk1sDL50lj00mehCFuPkdSD3Gy6YPlhfMY3LiJ8uIeWk7tpWlxz7S72S0Wy9zCOpFjSFxvAMLE9l14+TxuLqCyYzdtpy05ouvDWo36wAhR5kAQ0DjGDTxEwMnnUBGisQlIE6JKA6+QRxxIxscQdc3QqcSIKEopT+CIcSCJkQ7RNIVUEcchrjfwyyWcnI94QlINiSeqBG1NpJU623+yYVKqpfOClUTjVeNAAFJl6IlnaTt9KUlsGhablyxk8Knn6Fy5nIGNm2hdYUp03WKORZdfQrG7g+K8jkO8AhaL5WTDOpFjSDg2AWpyFl42x8LN53ALh58hElYq1PYMk8ax+SPvuiRRBJ6HiNG9Uk2Jx2rE9QaO5+EGPn5XG07D7HrSJAaUtBGR5jwCz0ETEMcFVVwgAdCExtAwXlOZNAqJRsfwinl2/mQDxYXzcAKfoY2bp2h9jT77gsm/vBzXYWzTNtpXLqO6a4D205eiDix83UWgSr6jleL8jiNqRLRYLCcfx3M87iLgFqAbk7a9UVW/LCLtwLeBJcBWYI2qDotpovgy8BagCrxHVTdk97oG+Ex26z9T1Zuz9QuB/wcUMMOpPqqzWG7WGBpD05R8a/NkI5JfzJNrKh/yusqefqKxmnEgjhmhmzQiJOfieh6O65A6DtHQKGmS4BXyOL5R7vUch1TAdCGCk8uhcUI6XiP2HKJaHeKUJIpBlaClzO6fPkLQUqZ8ykKSap18TxcjT29B05TCvE4c39tPByiuVCktmEdt98CUda9YoGlxD5Xtu2k/aznlhd0UOloPOsN8b7PTXuojY0RjE5lwY47i/C68Azkri8UyJzmezYYx8ElVXQlcCnxYRFYCnwbuUdUVwD3ZY4CrMPPTVwAfBP4GIHM6nwMuwUxE/JyItGXX/A3wgX2uu/I4Pp/DEtdqxONVEJPbQBUvH1DobDvoNePbdxJXQ1STlzpgNcUJXBx1zIhdcajvGsz0pIQ0DI2+lOuQkJLGEUmtAWFEY2CYys49iOsw9ORzpPWIWv8QjZEx4nqdiW07jKPrbCeeqKJpilfIEVWrAPiFAI0SCl1Tu2/dICDf3kJ+n+fSsuIU3FxA56ozWPIrl1FaMI9ookJ9dIzawDAjz25l4Oeb2bPxGQaf2MyW793Lttv+gz0PbaQ2PMLAz59h6InNbP/Renb8aD0v3PlfvPiDnxBOVI/9f47FcoxJk5RwvGoafo8B9Xqdiy++mPPOO4+zzjqLz33uc8DBJdwbjQZve9vbWL58OZdccglbt26dvNfRysTPhOM5HncnsDP7elxEngIWAr8GXJaddjPwQ+BT2fot2U7ifhFpFZGe7Ny7VXUIQETuBq4UkR8Czap6f7Z+C/DrwPeP13M6FHEjJBwdxyvmiKt1orFxnDbTrPfyZrkkSWgMjRBX6yRRjKiSRrFJhkeC+J5pBnTAdT3qw8OktToxKW4hj9fdjlMNSdOUuH/EJMtFSKp1ovEKaRgRjo6Ta2kirlRxAx9FESfTwMoQR3DzORpj4zQv6WX0uRdMX0q9RtDahON7VHb1U+hoo7x4Pmma0nbGqZAqbj6AzKHV9gwQjU8w8MiTJLU6HRecRVypU9szQL69lcL8Tnb95OFJJ1kfHAHPpTEwYooRKrVJmyo79lDp20Vwxqkn5j/OYjlKNFW2/XAD23+2EU1TxHFY+OpzOOWyC4wE0TTJ5XLce++9lMtloijil37pl7jqqqv44he/yMc//nHWrl3Lhz70IW666Sauu+46brrpJtra2nj22WdZt24dn/rUp/j2t789RSZ+x44dvPGNb2TTpk0AfPjDH+buu++mt7eXiy66iNWrV7Ny5coZvR4nRPZERJZg5q0/AHRnDgZgFybcBcbBvLjPZX3Z2qHW+w6wPivEtTppFNEYGqE+MIxfKhLX6vjFqZ3qY9u2s+vHDzLRt4s0TkgbISkm6e14rtHECkPE93BzPlG1gSOOKZ9d1I3XVEImaqRRSBqGWZNhZPpE4pg0ikxkK+tMF88lnKgSjldIanXKpywAoLqrHxUHr1wCAa+Up/W0pZlKr1Lb1U84NkHzkl7SOMYvFym0t+IW84xteYFK306i0XFTJRb4DDzyhAl59c4nGpswtvk+E307CUfG9hOsS2oNkkbDTFE8wGtpscxVtv1wA30/fZwkjEjjhCSM6Pvp42z70YYZ3VdEKJdN6DuKIqIoQkQOKuF+2223cc011wBw9dVXc88996CqRy0TP1OOuxMRkTLwL8DHVHVs32PZruO45zBE5IMi8pCIPNTf339cvkc4No4bBIjnUV40H79cIGgu4be+lA+Z2L2Hys49jD63Db9YMH/00wQNI1zPzwZZOQStZbxCYIZNRRHegi6Ki3uQOMUPfFOSu1dDS0HjlKhaQ8TBKxWNjEkhT2PMaF3l25opdLThlQrgOHS96mz8piL5jhZQJZmo43geo1tewCsVEEdoXraY0sJuM9723NOpD45SHxgkqVRpDA6Ta28xDidJQJncTYjnkcZmTZMEcT2zs3o5juDm85MFCPtSsBVcljlKmqRs/9lG0iieuh7FbP/pxhmHtpIkYdWqVcybN48rrriCZcuWHVTCfV/Zd8/zaGlpYXBw8Khl4mfKcXUiIuJjHMg3VfVfs+XdWZiK7POebH07sGify3uztUOt9x5gfT9U9UZVfZWqvqqr6/hIaWiSUh8eJdfWYra44uDlc+SajROZ2N1PWm0Qjhg/msZmtofreiRRjGqK11TELxZwggDKRbSQI9fTgRMn+LkASVNSAY0iE54Sh6RaR1wHUfDKBTMcqrWJNAzpOPcM3HyeXFsLha428l1t5Nta8EoFmpcuYuQZMxdBsoqurnPPJKnVybU24zeXaV7aS9PSXqLxCuKIcQrZ803jFFyHve8BvGzgVjQ2gZfPoSiN4THKvfPx8vkpuRQn8Cl0tpNrawIR2s44Fb+pRK69ld43vobSgqMfp2uxnAgONE9nL5qmxNWZ7aJd1+XRRx+lr6+P9evX8/TTT8/ofieC41mdJcBNwFOq+sV9Dt0OXAPckH2+bZ/13xGRdZgk+qiq7hSRu4A/3yeZ/ibgf6nqkIiMicilmDDZu4GvHK/nczjiSpXivA7Gt20nqtTItTbTfOoiXN9nYscuUoW4EeFnszBMv0ZCmnp4hZx5R58qfqfRqpIkxTM97zjikMQxaaJorQFJiiMOuEIag1vMvzSOt7kpG4ObQwUK89oZ39JH0NbEzp88RNuZK0hqdfzmErm2Zqp7Bigt6DYOwXMpLerByQekFVMtpqniNxUZ3byVYs88JOeT72ynvmeAQnenqRobm6Dz/JUMPPIk9YEhCt2dBC1NtJ1ZJKrViSpVOs47g3BsAk0SCl0dNJ+y0MxgGBklqYV0rDqDoFzC8WzVuWXu4hXzWeVhst8xcZwD7qynQ2trK69//ev52c9+dlAJ971y8L29vcRxzOjoKB0dHUctEz9Tjudv7GuBdwEbReTRbO0PMM7jVhG5FtgGrMmO3YEp730WU+L7XoDMWfwp8GB23p/sTbIDv81LJb7fZ5aS6gbTZOg3lSh0dxKOTeD4HuN9O8ybdQXShHxnB4XuMcaef5GmU3ohScFJTdluS5mIFC9wTZLd9BsSx7H5ws3EF30PYpNM94ouAvj5HE4+IBydwA0CkqSBk0KSKknd7IDaTjuVaLyC31wirtTId3fi53KI41Dp7ydoKuEGPnESZwO1MrmVcpFS73wjAlmPaVu5jHDEhMr8chHNcjDzf/li0jAk39ZK0FImrpswmZc7cMmuOA6F9oNXrlkscw3HNUn0vp8+PiWk5fgeC19zDo47/eBOf38/vu/T2tpKrVbj7rvv5lOf+tSkhPvatWunSLivXr2am2++mVe/+tV85zvf4Q1veAMiwurVq/mt3/otPvGJT7Bjx45JmXhVnZSJX7hwIevWreOf/umfZvyaHM/qrJ8ABytVuPwA5yvw4YPc6+vAfuO8VPUh4OwZmHnMSBsNcq3NphIqVdovONNMMoxTNElMnkCENAppW7nM6FXlcrilPEGpSH18AkkScsUCUZSARqRJgqRuJqKoJCiO5yKaNTHm8yRRSDxeIU1MYl4cMaGxXIGk3iCuTJgGQs8DEfyxCaJKlaCpiFssUNszyMgTpnLDz+Vxs1BUmiSICo6AouTnt5sBVUlKvqON1tNOPex8lKB0+EFVFsvJximXXQAC23+6T3XWa87hlNddMKP77ty5k2uuuYYkSUjTlDVr1vDWt76VlStXHlDC/dprr+Vd73oXy5cvp729nXXr1gHTk4mfCVYK/hix64ENRONVWs49A603EDXVFknUABwjQxL4SJwQhxFu4OGVyzQt7GZ8Vz+OiGnycwWthWZSoRppdjfTyBLPBdfDdQRNYtNEGEdmk1Ork9QbeKUiGsc0hkep7txNrrWZ8uJe0jAkqlQZfnLzpM1NSxdT3bmHuGqS4vNfexFOuYCG2c5HBNfzjMBvPk/R6l1ZXqFMRwo+TUwOxCvmZ7QDmWtYKfhZIK7VKS1aiKYJEkaZ84hQVRzHJQ5Ds0OJYlSEoKmIVypQ7Opkon/QNKmLmfeh6pCq2SaLgpd1povrkqYxUq0SpYkJLaUpjuOQhDEDDz9O18XnE46MktTqjG8xsc94okpjcITWc86gunPPFLvHt7xA05JFTPTtpG3lCtxSgTRKTK276+KImLnqeZ9iq3UgFsu+OK5D0DS9YXOvJKwTmSFj/QO4qZl7bhIYKUqKagIIaWISYnEY4eZ93CAgaGkil9WDO0ASx6Yk1gVNBHV8fNcBBL+Ypx5HaJwgdVOTTmrUdnEEr6WJ4aeexSsVSEPTSNgYGpliY1yrg6rpfn8ZpUULyHW04pdLJLUq8UQNHCHX3oYEPmkS09R65DL2FovlFwvrRGbA+PCIqd9OUjQyZb6aAo4gCrgOGqeogF8q4OQDip0dOJmuVG14mCQ0OwpFkFgRx8N1zT0836c6NoaOjFPo6aIRNsz8dEyJsBP4ZE9PpeIAACAASURBVMqMJlzm+wT5HE6wiImtfZPlxKbxMKUwv4twbHzS/tKiBYw89QxNpywirlQZ3/IC4ai5xvF95l16IU2LZ61/02KxnARYJzIDgnzOdJwnidl57BVCTBMcz0NVEd/BK5aQwKXYNrUSKQ1jc50a0UURB/Ed3JxPODZBFMWIIwSdbcTVKuIFeI6HOKlRBk6UtBHSfu6ZZhciDuPPbSMcGSXX3kru1MVMbNtOx6qVqAj5eR3k21uIJqq4hTxpklB5oYoC0dj4pAMBSKMoe2ydiMViOTjWicwAIzNius5TBUfJEtI+iOK6HlLIUTpAGWt9cMhIt6eYc3GQwPx3xJUafi6H01Q2Crcj42iaGB2sfA7xPBrDI3i5HH5LE+NbX6Q4r5PKi32EI6OACWkVgoCOC89h5PEnaV15Gsn4BCNPvZRYbzl9Oe7e8tsDFlj8YhVdWCyWo+eVU1IwC8RhRJwkpDhGYTcX4OZyeKUCTnOZ8sL5B3Qg1ZER4jBC0wTEqPTiOQiKAl4hT6ypmRESRTj5gNFNz5ndQxRT39WPXyySNMysdNfzUN0/F1LvH6Sxp9+U7Sr7JdbHnt2CE/iII/hNJfzmpsljjudRWjD/uLxuFovllYN1IjMgjWNTBht4uKUCQWszYc5j4vlt+AdpkYmqVbTeQOMIM7xQENRIvKeK63mktTpSLJiGPxHEMSXCZmOgmasx/zq+RzRRIU1i8l1TNacKPfNoDI2YHhHV/XcbqpR6F5A0GrjFAi2nnUrbyhW0nXUGC97438h3TJWDt1gshjiMefa+J9nw3Z/y7H1PEofx4S86ApIk4fzzz+etb30rAO95z3tYunQpq1atYtWqVTz6qOnbVlU+8pGPsHz5cs4991w2bHhJ/PHmm29mxYoVrFixgptvvnly/eGHH+acc85h+fLlfOQjH+FYtXfYcNYMcOIEcRwcx8NJoTE8hgdUJyr45f0HUdUnKiQTFdI4mexIx3dMKW2WGknDiMQRAsclIUGBuF6jtLDHlBC7LoXuLuoDQ/jlEmkjpDh/HigUeroR1zWhrO4u8vM6qPbtNOq9i3vJd3dNyXs0n3Yqbj5PIRdQmNeFeyChRIvFMoX+53fyvRtuRZOEqBHh53zuu/lu3vLpNXSd2jOje3/5y1/mzDPPZGzspd/Tv/zLv5xU8d3L97//fTZv3szmzZt54IEHuO6663jggQcYGhri85//PA899BAiwoUXXsjq1atpa2vjuuuu4+/+7u+45JJLeMtb3sKdd97JVVddNSN7we5EZoTXVDZ/+KsN4koVNKXy/FbKvQtwPHfKuZWBIeKxMaNHhdkUiIDGZhhVSoqkqZGXDnySRt3kXIIAEZdczzw0Vdx8HlyXwrxOgrYWM1+kqx2vqYybz9G0Yildr76AoKMN8TyaT1+G39xEWJkgaG+h/byzaD59GR3nn02uox0351Fe2GMdiMVyBMRhzPeu/zZhpU5UN9GBqB7RqNS544ZbZ7Qj6evr43vf+x7vf//7D3vubbfdxrvf/W5EhEsvvZSRkRF27tzJXXfdxRVXXEF7ezttbW1cccUV3HnnnezcuZOxsTEuvfRSRIR3v/vdk5LyM8U6kRmQjE8wtvFJ6gMDVLZuI5moUO/vR7ypL2tl5y7Ses0MnkpSyOYEgNGnkiynoWmKUyqQz8qGkzQhyBfwWpoodHWSX9SDW8zjtjQhAiMbn2Bg/cM0XtiOV8jjFvNoo0E4PIpfzBu13Pnz6HrtRRR7F+C4Hl57C0FLM0FnO02LF1LosLLrFsuRsvXBTQdV8U2ShK0PbZr2vT/2sY/xF3/xF5MtAHv5wz/8Q84991w+/vGP02iYXrCjlXvfvn07vb29+60fC6wTmQH13XuIxsaZePZ5art2M/rUM3RefAF+00sJ6okdO81uI4qz/o7IVPSiiBiF3iiOIQ5xy0Ukk5NOoghXBE0THN8jrFXRMII4hkqVkac2kWTDm6o7dtIYHCIcGGTo0Y2MPb2JwYceIalWSRsNGnv2kFRrOL6L5/u0LFtCsdM6D4vlaBnbM0LU2H+QGhiV7rE9Iwc8djj+/d//nXnz5nHhhRdOWb/++ut5+umnefDBBxkaGuILX/jCtO5/PLFOZAa8fCqfxrFR5U1TkiShsms3aT3MBkgZUUPH9SA1tcCO7xGGDajX0SQlDROIIpQU3/dxUBwX4moVJ01N7sT1SMOQQncnuX0qv5KqGRa1L+PPbkF8D7/J6GeVeuZTOEiyPA5DagODjG97kcqOHcSNxgHPs1h+kWme14qf8w94zMv5NM+bnrrDfffdx+23386SJUtYu3Yt9957L+985zvp6elBRMjlcrz3ve9l/fr1AAeVez/Uel9f337rxwLrRKZJ1GgQtE39gcl3zzNluL5LvX+ANIpQTc0uxCWrkEqRwMct5AhrNfxSAa+phDevEydwTblvGGXqvBMklSpaqTH6zLPEExXGNz/LyBNPUd2+E0UnHYnf0rxf9ZU4QrG7G7e5TH1okMru3Yy/8CLj215g6OdPsusnP2XoiacY37qNyrYXGXt6E0OPPMbA+g30P/AQca2GxWJ5iSUXnYbjugc85rouS1512rTue/3119PX18fWrVtZt24db3jDG/jHf/xHdu40k8RVlX/7t3/j7LONaPnq1au55ZZbUFXuv/9+Wlpa6Onp4c1vfjM/+MEPGB4eZnh4mB/84Ae8+c1vpqenh+bmZu6//35UlVtuuWVSUn6m2GzqNIlrdbzmEm3nrCQaN7NDgvY2kiRGQkU1MXpXIqiTzVB3PRzHwfEDkrBBrq3FzFcfHkWihDRNcD0fcT2SahVxXUQchp98gtaVpxONjhIOv7RdjkbHKJ+6hML8bnA9cp3tNIZHJp1J84plVF58Acnl0HqDev8ATi6H39TE+HPPA+AViiSuS1ypUh8YnLx3ODRMY3AIr9d2rFsse/ECj7d8eg133HArSZIQNyK8nI/rurzl02vwgmP7J/Ud73gH/f39qCqrVq3ia1/7GgBvectbuOOOO1i+fDnFYpFvfOMbALS3t/PZz36Wiy66CIA/+qM/or3dRB/++q//mve85z3UajWuuuqqY1KZBVYKftpUBwZJwxDxPBxHSOLEiDCmqdHQQkniFCeTM1EUN58jCXwkjnGSFPI50moVFcEtFJA0ReOYtFojTRLcfIE0DBne+AStZ51BXKkysfWFKXa0nns24nuEg8P4pSKIkIQRQVOJNAzRVEmjkPFnn5+8xm9qAtchHB6huGABflMT8cQElb6pibaOC1dRXtSLxfJK52il4OMwZutDmxjbM0LzvFaWvOq0Y+5AZgsrBX+C0DQBhVjAS3VKuS4iaKp4rpDEKRKYhHYa+LhhhEYxeD5areI4jtHeqtSIo5BoZITaDrOFdfN5ms88E69cMuW9hSJ+SzNR1usRtLTgFQuI55O2xkixiBNGiAC+j6DEo2NoklJavIjq9h2mtn18nMKCHsLhEcRxSBp1nFxA0NpKOGJ2Ol5TmVy7bTa0WA6EF3gsf83K2TZjTnA8Z6x/HXgrsEdVz87W2oFvA0uArcAaVR3O5rF/GTMetwq8R1U3ZNdcA3wmu+2fqerN2fqFvDQa9w7go3oit1Wpgu/iOw7aaKAiaJKgIuaYCOo6uK6H4/skgYfvutQDCFyPpF416rv1BrgO0fgYXqEw6UAAknqdpDJOy2nLqQ8NEzQ3U168iKQR4vgeTj5Pvb8fv7kJJ1UYH2f46U3kOtrJo1R37pp0OIhQXNBD5cU+nFzOOB/XJY5Cip09pI0GhYU9FLq7EN+ntGA+XtHOSrBYLIfmeO5E/h/wVeCWfdY+DdyjqjeIyKezx58CrgJWZB+XAH8DXJI5nc8Br8KIfjwsIrer6nB2zgeABzBO5EpO4Ix1DXycVHGAWLOdCeCIkIriiCCuB56LhiFBrkRUrRHkArRRJZ6o4hVypuPQcV6qPReZmiAXB6dcJp83PSBJrWZEGB0Y3biR8qlLSatVgrZ2hjf+HAC/qZmk3njJgcDkPf3WFpqWLMEp5in09OCXirhBcCJeMovF8grkuFVnqeqPgaGXLf8asFfM5Wbg1/dZv0UN9wOtItIDvBm4W1WHMsdxN3BldqxZVe/Pdh+37HOvE4LvumiakCQJ2SByxDM+2XE9vHyRJAxJqzWSUsnMASnkkShCfA+vkMfxAyMXj+A3lXFyOYoLXpJN8EolNI6Jh4dAldqLfTieS1qvTY60dTyPpN4w6sGZozAOSXD8qaWIfnMTna+6gPLiXoqdneTbWq0DsVgsM+JEl/h2q+reeM0uoDv7eiHw4j7n9WVrh1rvO8D6ARGRD4rIQyLyUH9//8yeAdCoVEiy7nONzQRDx/cR18UrFlDfI5oYx/VcQEzvR8P0g8S1GunERJY7gcQR3HwOr1AgbdTBdSn2LqS4cAFuIU9SrzP+3PMmz2HGjoDjTEq4J1GMEwTUBwcpL1kCQDQ+hrgOxd6FJmHvODQtW0p58SJ8G6KyWCzHkFlLrKuqisgJyWGo6o3AjWCqs2Z8vzgm3TtqVkBcB9d1UdclGh4Bz0M8lzCfxxfBU9OdHtfrpnoqSfAKBfA8/DAkrdUY27wZjWPE8ygvWUo0bmaIaJqaj3qDwoIFRKNjeMUCOA5NK5YRDo+Q6+gwTimXo/Wcs0nrDfyWFsSBoL0Nr1git4/Mu8VisRwrTvROZHcWiiL7vHfAxXZg0T7n9WZrh1rvPcD6CSHdG8JyHZx8wYxWr1ZJKlUkyEGxQFosEojg7w0pZeW/ru/jlctmRnoUkSYJjYEB0+2OcVDhyDCNoUG8Yom4VqN0ymIIfMRzyXV14OQC3Hwer9xE04oV+K2tBPO6yLe10rR4ES2nLafY3UWhq4vS/PnWgVgsxxBV5ZEfPsb//u0v8Qf//XP879/+Eo/88LFjIq0+MjLC1VdfzRlnnMGZZ57Jz372M4aGhrjiiitYsWIFV1xxBcPDw5N2zAU5+BPtRG4Hrsm+vga4bZ/1d4vhUmA0C3vdBbxJRNpEpA14E3BXdmxMRC7NKrvevc+9jitho0GahaK8XA5NYlTENPSlCRRyOEAul8PzfVSVVJU4jpEggEy+RABNst6Sg1Dfs5vSol6Cjk4cP8Brbkb9ACefh+xeaRyRb22h0NJCrsk6C4vleKKqfP1P/oGb//ybPP/zrYz0j/L8z7dy859/k6//yT/M+I/yRz/6Ua688kqefvppHnvsMc4880xuuOEGLr/8cjZv3szll1/ODTfcAEyVg7/xxhu57rrrACbl4B944AHWr1/P5z//+UnHs1cOfu91d95558xeEI6jExGRbwE/A04XkT4RuRa4AbhCRDYDb8weg6mueh54Fvg74LcBVHUI+FPgwezjT7I1snP+PrvmOU5QZVaaJOD7+LkcSRjiaGocSaOBUy7juS6x66DZuVEUGUcCOLkcuK7pUk8S1POIazWC9vbJpLx4HkFrK4XuboK2NtO17ns4vkdar+MHPuq6OLmA0sIFFLu6kIPIMFgslmPLoz96nI33/ZywHk5ZD+shG+/7OY/+6PFp33t0dJQf//jHXHvttQAEQUBrayu33XYb11xj3ntfc801kxLuc0UO/rjlRFT17Qc5dPkBzlXgwwe5z9eBrx9g/SHg7JnYOB2ieh0/CEijKCuIUiRV4iCgkMuhUUTO9dAwJFVFogj1PIJymaRezzraTU5Ek4R8VxdJFNF82mmmEXBsjMq2rQDk53XjlsskcYykqUne53KUmptP9NO2WCzAf9z6n/s5kL2E9ZB7bv0h51923rTuvWXLFrq6unjve9/LY489xoUXXsiXv/xldu/eTU+PqdqcP38+u3fvBuaOHLwVYDxKEscB1cnht6pK6Hnk8nnSKIJczhwXgTgm8X2cOKIRRUYiJZ/Pxt4CYnIprucyvnkTSb1OY+Cl6rFofAxU8TwPr1Qi39pKkFVlWSyWE8/w7uFDHh/a/fKuhiMnjmM2bNjAddddxyOPPEKpVJoMXe1FRCZnEc0VrBM5CsaGhyn4Zl55qimqKbHrUggCU3XVaIAqcRQZh5Km+Joiqni1GrXnn2Vs42PEo6NIPo+XyxM0NYHjUJjfM1m2uxe/pRUJAvKtrfj5/Cw9a4vFspf27rbDHJ++VFBvby+9vb1ccsklAFx99dVs2LCB7u7uSTXfnTt3Mm/ePGDuyMFbJ3IU5IIAFJI4RhVi1yOXaV/F9TpOoUBYr5OIELkuuE7W12EaBN183kib7NxBMjZK0qgzvuU5xp5+knB8BAXKS5eSnz+f0uJTKC1eTL5Umu2nbbFYMi5f83qC/IEbdIN8wOVrLpv2vefPn8+iRYt45plnALjnnntYuXIlq1evnqywuvnmmycl3OeKHLwVYDwKVNXkNVBSxyXveWgUoUlsphQCkiQ4juCCcSJJSjw2QnWHiT36Tc34zc2kSUpaHSWpVQFIGw00CvG7ukhbWmm24ocWy5xj1evOZcOPHtsvuR7kA8597dmset25M7r/V77yFd7xjncQhiGnnnoq3/jGN0jTlDVr1nDTTTdxyimncOuttwJzRw7eSsEfIY2xMROmUsV1HFwREhTCEFLFLZeI4sQ4DzUKv5qkkCaMbX5mih5Wfl43fksr0cgw9f49k+uF+T348+ZTbJ3edDSLxTI9jkYKXlV59EePc8+tP2Ro9xDt3e1cvuYyVr3u3DmXr5gOVgr+eKGKOuCJC3GCOgJJAiI4hTxRnOD7PiqKhimiKbGA7/tItmPZS9DWRmNoCL+pCXd8jKRex/F9/PYO2+thscxxRITzLztv2lVYrzSsEzlCUtfBxwNHSKKINFYQcIIcKoqfy2fNg4KKYwZNRTXGX9hKoaub+sAeNI4pLlqMIgSdXWgUUj5l6aRkiXoerusSNRqkUQPSFFI1c0ocQcMQNwgIWtsQx6azLBbL7GOdyBHiej6ahqRJbMaFKLh+gOTzSBKTxCGOa5oHvSAgjRtU+15Ak4T67p0Uehbit7SSOgJhA00SHNdF/ABxHKIkwqnXqYYhabWC4wfgOtR27qAwbx6N4WGiUTMwqth7CqXeRa+IrbPFYjm5sU7kCEniGOIYTRNczyF1XCN3okotTvCTBHV93MBH0xRxHIL2djRVxPNpDA2QxiFxZYK00cDxfQpLlhECvipuFBtV30oVx/NQV4gGB/CLRdIomXQgANUdL1Lo7sYNbM+IxWKZXWxM5AiphyExZmKheAGeOMSpUm80CNIESULiPTtJRkfQJCYcHqQxOEA4PGgaE8XEUtNGA4A0ikjGRgkCH8dx0CQ2ExFTM59ddZ+OxMnuRIPj+4hjpU4sFsvsY53IEaBZB3qQz+MWiojjUEtTcBx8EVPGW6mYUFcco2E4pfO83r8Lv6l5iiMAwHEAJUkSnCAHroNXLpNGMZIm5No7iSbGEcehuKAXN1/AK5VpOnUFjmc3kRbLbDI6Ms7W515kdGR8tk2ZVawTOQKiWpVCLgcKcdhAAXEcRBUHReOQRv9uwqFBGv27jezJPqKI4vuI65HU6wRtHYjn4be24TU1k8ZRVvklpilRISg34Xg+eB6lJcvwikWCzi5azjiL1rPPI9d66K5Zi8Vy/Hhx63Y+9r7P8quvfSfX/veP8auvfScfe99neXHrzHSonnnmGVatWjX50dzczJe+9CX++I//mIULF06u33HHHZPXXH/99SxfvpzTTz+du+66a3L9zjvv5PTTT2f58uVTpFO2bNnCJZdcwvLly3nb295GGB5YB+xosH0iR0Cj0aBer5MTIdTUCCuKQ15TNE2Ia1XqO16SE8h1zkN8n2h4GETId/fgeFn5rwoikCJIaqRTcATH9XF8j1ihaKcPWiwnlCPtE3lx63be+xsfpTpRmyL7LiKUygW+/q9fZtGSmUuJJEnCwoULeeCBB/jGN75BuVzmd3/3d6ec8+STT/L2t7+d9evXs2PHDt74xjeyadMmAE477TTuvvtuent7ueiii/jWt77FypUrWbNmDb/xG7/B2rVr+dCHPsR55503KSF/qNfiUH0ididyBDTqdfK5HGGS4AEuEEQNKtueo7L1OTSKcPMFc7IIbpCnsXsnhd5TyC1ZhlMokmahK4A4TdE0Nufmcni5AvnmZoJC0ToQi2UO83/+5Gv7ORAwIe/KRI0v/unXjsn3ueeee1i2bBmnnHLKQc+57bbbWLt2LblcjqVLl7J8+XLWr1/P+vXrWb58OaeeeipBELB27Vpuu+02VJV7772Xq6++GpgqKz8TrBM5AuIkoVqr4Qo4aYrveUQjQ2iSABAODRB0dBK0d5Dv7CaumRJd8VziJKIRR8RpTKICqriuhxfkcYKAXLmJwDoOi2XOMzoyzoYHHj/o4ClV5eH7Hz8mOZJ169bx9re/NE3jq1/9Kueeey7ve9/7JgdMHa0U/ODgIK2trXhZPtVKwZ9AHMehVCjguw7qOkgcwcsGQQmC4we4xSJOEFBYuJg4TtDUNCXiuHj5Ak6hYKYQlsvkrLiixXLSMDw4gu8fuqDF9z2GB0cOec7hCMOQ22+/nd/8zd8EzDTC5557jkcffZSenh4++clPzuj+x5qT3omIyJUi8oyIPCsinz4e30PTFK1XiWs1CENqO15EAK/chJPLU5i/gCSOCUeHUU1xmppR1zXnCOT8HLlcnlyhQK5QOB4mWiyW40xbRytRFB/ynCiKaeuYmfbd97//fS644AK6u7sB6O7uxnVdHMfhAx/4AOvXrweOXgq+o6ODkZER4jiesj5TTmonIiIu8FfAVcBK4O0isvJYfo/hoSEKGlPp20p9Vx/hUD9OLk80NkIah+Q6uwjHRoiG9uCXSkguDwhKiuN5+L4ZiRvYeSAWy0lNS2sTF1xycJFFEeHCS8+lpXVm+nff+ta3poSy9s4SAfjud7/L2Webga6rV69m3bp1NBoNtmzZwubNm7n44ou56KKL2Lx5M1u2bCEMQ9atW8fq1asREV7/+tfzne98B5gqKz8TTvZmg4uBZ1X1eQARWQf8GvDksfoGRc8hGh43OlZAUq8RtJgSW40iBKHQ3WsS5a4DXoDrugR2x2GxvOL45B99iPf9xkepHKQ66xOf/dCM7l+pVLj77rv527/928m13//93+fRRx9FRFiyZMnksbPOOos1a9awcuVKPM/jr/7qr3CzMPtXv/pV3vzmN5MkCe973/s466yzAPjCF77A2rVr+cxnPsP5558/Oc99JpzUJb4icjVwpaq+P3v8LuASVf2dl533QeCDAIsXL75w27ZtR/w9wrFRwrERwqH+vTejuGAxcbWCk8tBqQknVZxcQGBlSCyWk5KjkYJ/cet2vvinX+Ph+x/H9z2iKObCS8/lE5/90DEp751trBT8AVDVG4EbwfSJHM21QXMLaRQhnUaaxCuWSQsl/HIzOTvv3GL5hWPRkoX835v+lNGRcYYHR2jraJ1xCOtk5mR3ItuBRfs87s3Wjin5jk7CMCQIDjwW02Kx/OLR0tr0C+089nJSJ9aBB4EVIrJURAJgLXD78fhG1oFYLK9sTubQ/rFiOq/BSe1EVDUGfge4C3gKuFVVn5hdqywWy8lGPp9ncHDwF9qRqCqDg4Pkj7KS9GQPZ6GqdwB3HPZEi8ViOQi9vb309fXR399/+JNfweTzeXp7e4/qmpPeiVgsFstM8X2fpUuXzrYZJyUndTjLYrFYLLOLdSIWi8VimTbWiVgsFotl2pzUHevTQUT6gSNvWZ9KJzBwDM051sx1+8DaeCyY6/aBtfFYMJfsO0VVuw504BfOicwEEXnoYK3/c4G5bh9YG48Fc90+sDYeC+a6fXux4SyLxWKxTBvrRCwWi8UybawTOTpunG0DDsNctw+sjceCuW4fWBuPBXPdPsDmRCwWi8UyA+xOxGKxWCzTxjoRi8VisUwb60SOABG5UkSeEZFnReTTs23PyxGRr4vIHhH5+WzbcjBEZJGI/KeIPCkiT4jIR2fbpn0RkbyIrBeRxzL7Pj/bNh0MEXFF5BER+ffZtuVAiMhWEdkoIo+KyEOzbc/LEZFWEfmOiDwtIk+JyKtn26Z9EZHTs9du78eYiHxstu06GDYnchhExAU2AVcAfZgZJm9X1WM2x32miMgvAxPALap69mzbcyBEpAfoUdUNItIEPAz8+lx5HUVEgJKqToiID/wE+Kiq3j/Lpu2HiHwCeBXQrKpvnW17Xo6IbAVepapzpVFuCiJyM/Bfqvr32RyioqqOzLZdByL7+7MdM/Z7uk3SxxW7Ezk8FwPPqurzqhoC64Bfm2WbpqCqPwaGZtuOQ6GqO1V1Q/b1OGb+y5wZSK2Gieyhn33MuXdYItIL/Arw97Nty8mIiLQAvwzcBKCq4Vx1IBmXA8/NVQcC1okcCQuBF/d53Mcc+uN3MiIiS4DzgQdm15KpZGGiR4E9wN2qOqfsy/gS8PtAOtuGHAIFfiAiD4vIB2fbmJexFOgHvpGFBP9eREqzbdQhWAt8a7aNOBTWiVhOKCJSBv4F+Jiqjs22PfuiqomqrgJ6gYtFZE6FBkXkrcAeVX14tm05DL+kqhcAVwEfzsKtcwUPuAD4G1U9H6gAcy7PCZCF2lYD/zzbthwK60QOz3Zg0T6Pe7M1y1GS5Rr+Bfimqv7rbNtzMLLwxn8CV862LS/jtcDqLOewDniDiPzj7Jq0P6q6Pfu8B/guJiQ8V+gD+vbZZX4H41TmIlcBG1R192wbciisEzk8DwIrRGRp9s5gLXD7LNt00pElrm8CnlLVL862PS9HRLpEpDX7uoAppHh6dq2aiqr+L1XtVdUlmJ/De1X1nbNs1hREpJQVTpCFid4EzJmqQVXdBbwoIqdnS5cDc6K44wC8nTkeygI7HvewqGosIr8D3AW4wNdV9YlZNmsKIvIt4DKgU0T6gM+p6k2za9V+vBZ4F7AxyzsA/IGq3jGLNu1LfjN69gAAAxxJREFUD3BzVg3jALeq6pwsoZ3jdAPfNe8Z8IB/UtU7Z9ek/fifwDezN4XPA++dZXv2I3PAVwD/Y7ZtORy2xNdisVgs08aGsywWi8UybawTsVgsFsu0sU7EYrFYLNPGOhGLxWKxTBvrRCwWi+UVzNEKtIrImn2EUv/psOfb6iyLxWJ55XI0Aq0isgK4FXiDqg6LyLysafSg2J2IxWKxvII5kECriCwTkTszfbP/EpEzskMfAP5KVYezaw/pQMA6EYvluCAi7xaRx7P5JP8gIr8qIg9kon//ISLd2Xmv22duxCP7dHv/nog8mN3j89laSUS+l93z5yLyttl8jpaTmhuB/6mqFwK/C/x1tn4acJqI3Cci94vIYaV/bMe6xXKMEZGzgM8Ar1HVARFpxyjbXqqqKiLvxyjxfhLzC/xhVb0vE6esi8ibgBUYzSkBbs9CEl3ADlX9lez7tJzwJ2c56cl+zl4D/HOmLACQyz57mJ+9yzA6gT8WkXMOJZdvnYjFcux5A/DPe4cyqeqQiJwDfDsbzhUAW7Jz7wO+KCLfBP5VVfsyJ/Im4JHsnDLmF/u/gP8jIl8A/l1V/+vEPSXLKwgHGMkUq19OH/CAqkbAFhHZhPnZe/BQN7NYLMefrwBfVdVzMHpIeQBVvQF4P1AA7sti0wJcr6qrso/lqnqTqm7CKM5uBP5MRP5oVp6J5aQmG8GwRUR+E4w4qoiclx3+N8wuBBHpxIS3nj/U/awTsViOPfcCvykiHQBZOKuFl0YIXLP3RBFZpqobVfULmHd7Z2DEPt+XhR0QkYUiMk9EFgBVVf1H4C+ZuxLmljlEJtD6M+B0EekTkWuBdwDXishjwBO8NK31LmBQRJ7EjEP4PVUdPOT9bYmvxXLsEZFrgN8DEkxY6rvA/wWGMU7mIlW9TES+ArweM6nwCeA9qtoQkY9idihgyjPfCSzHOI8UiIDrVPWhE/esLJb9sU7EYrFYLNPGhrMsFovFMm2sE7FYLBbLtLFOxGKxWCzTxjoRi8VisUwb60QsFovFMm2sE7FYLBbLtLFOxGKxWCzT5v8DEcU3X6cD1hIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_pRge294dFK"
      },
      "source": [
        "### Date-based EDA"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TSVSSgwev-Pp"
      },
      "source": [
        "*Dealing with dates by setting index*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOzYb89evmrr",
        "outputId": "c70b0d88-67a5-4cb0-d0ff-7f53bd9779d2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "df = pd.read_csv(\"https://raw.githubusercontent.com/nytimes/covid-19-data/master/us-states.csv\")\n",
        "df[\"date\"] = pd.to_datetime(df[\"date\"])\n",
        "df = df.set_index(\"date\")\n",
        "print(f\"Columns: {df.columns}\")\n",
        "df.index"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Columns: Index(['state', 'fips', 'cases', 'deaths'], dtype='object')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2020-01-21', '2020-01-22', '2020-01-23', '2020-01-24',\n",
              "               '2020-01-24', '2020-01-25', '2020-01-25', '2020-01-25',\n",
              "               '2020-01-26', '2020-01-26',\n",
              "               ...\n",
              "               '2022-01-20', '2022-01-20', '2022-01-20', '2022-01-20',\n",
              "               '2022-01-20', '2022-01-20', '2022-01-20', '2022-01-20',\n",
              "               '2022-01-20', '2022-01-20'],\n",
              "              dtype='datetime64[ns]', name='date', length=38030, freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULM1KdpWijcS"
      },
      "source": [
        "*Filter results by date range*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O1ennZNsvlJx",
        "outputId": "e4b337cf-c300-4c1b-f45b-c3b9570f20c1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "from datetime import date, timedelta\n",
        "today = date.today()\n",
        "daybefore = today - timedelta(days = 2)\n",
        "print(f\"Today {today}\")\n",
        "print(f\"Two days ago {daybefore}\") "
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Today 2022-01-22\n",
            "Two days ago 2022-01-20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7zuHpxkm1sTA",
        "outputId": "66d7ff15-9670-417d-d947-6c987f9ed089",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "df.loc[daybefore:today].head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-20683bc4-a55f-4a4c-8851-618d17ff8f11\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>1</td>\n",
              "      <td>1104356</td>\n",
              "      <td>16792</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>2</td>\n",
              "      <td>187655</td>\n",
              "      <td>1027</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>American Samoa</td>\n",
              "      <td>60</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>4</td>\n",
              "      <td>1683915</td>\n",
              "      <td>25429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Arkansas</td>\n",
              "      <td>5</td>\n",
              "      <td>713643</td>\n",
              "      <td>9470</td>\n",
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              "  </tbody>\n",
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              "    .colab-df-convert:hover {\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-20683bc4-a55f-4a4c-8851-618d17ff8f11 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-20683bc4-a55f-4a4c-8851-618d17ff8f11');\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",
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            ],
            "text/plain": [
              "                     state  fips    cases  deaths\n",
              "date                                             \n",
              "2022-01-20         Alabama     1  1104356   16792\n",
              "2022-01-20          Alaska     2   187655    1027\n",
              "2022-01-20  American Samoa    60        1       0\n",
              "2022-01-20         Arizona     4  1683915   25429\n",
              "2022-01-20        Arkansas     5   713643    9470"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ek_hurrf5FFi"
      },
      "source": [
        "*The distribution of the data by date*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHl20wNuiuMw",
        "outputId": "1072d9e0-ff2a-4643-a775-db7e12718021",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 588
        }
      },
      "source": [
        "sns.kdeplot(df.loc[daybefore:today][\"deaths\"], shade=True)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-12-726bc3c8339e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mkdeplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdaybefore\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mtoday\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"deaths\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshade\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m             )\n\u001b[1;32m     45\u001b[0m         \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     47\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mkdeplot\u001b[0;34m(x, y, shade, vertical, kernel, bw, gridsize, cut, clip, legend, cumulative, shade_lowest, cbar, cbar_ax, cbar_kws, ax, weights, hue, palette, hue_order, hue_norm, multiple, common_norm, common_grid, levels, thresh, bw_method, bw_adjust, log_scale, color, fill, data, data2, warn_singular, **kwargs)\u001b[0m\n\u001b[1;32m   1776\u001b[0m             \u001b[0mwarn_singular\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwarn_singular\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1777\u001b[0m             \u001b[0mestimate_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mestimate_kws\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1778\u001b[0;31m             \u001b[0;34m**\u001b[0m\u001b[0mplot_kws\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1779\u001b[0m         )\n\u001b[1;32m   1780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36mplot_univariate_density\u001b[0;34m(self, multiple, common_norm, common_grid, warn_singular, fill, legend, estimate_kws, **plot_kws)\u001b[0m\n\u001b[1;32m    932\u001b[0m             \u001b[0mestimate_kws\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    933\u001b[0m             \u001b[0mlog_scale\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 934\u001b[0;31m             \u001b[0mwarn_singular\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    935\u001b[0m         )\n\u001b[1;32m    936\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py\u001b[0m in \u001b[0;36m_compute_univariate_density\u001b[0;34m(self, data_variable, common_norm, common_grid, estimate_kws, log_scale, warn_singular)\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0mdensities\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 303\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0msub_vars\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msub_data\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miter_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"hue\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfrom_comp_data\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    304\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    305\u001b[0m             \u001b[0;31m# Extract the data points from this sub set and remove nulls\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36miter_data\u001b[0;34m(self, grouping_vars, reverse, from_comp_data)\u001b[0m\n\u001b[1;32m    981\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    982\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mfrom_comp_data\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 983\u001b[0;31m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomp_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    984\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    985\u001b[0m             \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/seaborn/_core.py\u001b[0m in \u001b[0;36mcomp_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1055\u001b[0m                     \u001b[0morig\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot_data\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1056\u001b[0m                 \u001b[0mcomp_col\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1057\u001b[0;31m                 \u001b[0mcomp_col\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0morig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconvert_units\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1058\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1059\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_scale\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"log\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    669\u001b[0m         \u001b[0miloc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"iloc\"\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 670\u001b[0;31m         \u001b[0miloc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setitem_with_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    671\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    672\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_validate_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_setitem_with_indexer\u001b[0;34m(self, indexer, value)\u001b[0m\n\u001b[1;32m   1788\u001b[0m                 \u001b[0;31m# setting for extensionarrays that store dicts. Need to decide\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1789\u001b[0m                 \u001b[0;31m# if it's worth supporting that.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1790\u001b[0;31m                 \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_align_series\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1791\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1792\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCDataFrame\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_align_series\u001b[0;34m(self, indexer, ser, multiindex_indexer)\u001b[0m\n\u001b[1;32m   1907\u001b[0m             \u001b[0;31m# series, so need to broadcast (see GH5206)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1908\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0msum_aligners\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mis_sequence\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1909\u001b[0;31m                 \u001b[0mser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1910\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1911\u001b[0m                 \u001b[0;31m# single indexer\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, index, **kwargs)\u001b[0m\n\u001b[1;32m   4410\u001b[0m     )\n\u001b[1;32m   4411\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4412\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4413\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4414\u001b[0m     def drop(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mreindex\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   4462\u001b[0m         \u001b[0;31m# perform the reindex on the axes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4463\u001b[0m         return self._reindex_axes(\n\u001b[0;32m-> 4464\u001b[0;31m             \u001b[0maxes\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4465\u001b[0m         ).__finalize__(self, method=\"reindex\")\n\u001b[1;32m   4466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_axes\u001b[0;34m(self, axes, level, limit, tolerance, method, fill_value, copy)\u001b[0m\n\u001b[1;32m   4485\u001b[0m                 \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4486\u001b[0m                 \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4487\u001b[0;31m                 \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4488\u001b[0m             )\n\u001b[1;32m   4489\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m_reindex_with_indexers\u001b[0;34m(self, reindexers, fill_value, copy, allow_dups)\u001b[0m\n\u001b[1;32m   4530\u001b[0m                 \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4531\u001b[0m                 \u001b[0mallow_dups\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mallow_dups\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4532\u001b[0;31m                 \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4533\u001b[0m             )\n\u001b[1;32m   4534\u001b[0m             \u001b[0;31m# If we've made a copy once, no need to make another one\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mreindex_indexer\u001b[0;34m(self, new_axis, indexer, axis, fill_value, allow_dups, copy, consolidate)\u001b[0m\n\u001b[1;32m   1283\u001b[0m         \u001b[0;31m# some axes don't allow reindexing with dups\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1284\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mallow_dups\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1285\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_reindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1286\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1287\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;34m>=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m_can_reindex\u001b[0;34m(self, indexer)\u001b[0m\n\u001b[1;32m   3290\u001b[0m         \u001b[0;31m# trying to reindex on an axis with duplicates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3291\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_unique\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3292\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot reindex from a duplicate axis\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3293\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3294\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mreindex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlimit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: cannot reindex from a duplicate axis"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehV0NJINmR5S"
      },
      "source": [
        "*Sort DataFrame in place by states with highest deaths and cases and show first 10 results*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QcPkd6CnH6q",
        "outputId": "1f06418c-6607-425c-8768-58eb2d78997c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        }
      },
      "source": [
        "current_df = df.loc[daybefore:today].sort_values(by=[\"deaths\", \"cases\"], ascending=False)\n",
        "current_df.head(10)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-84a8c502-a0df-4abb-b475-b3f6928e3b65\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>7487212</td>\n",
              "      <td>78529</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Texas</td>\n",
              "      <td>48</td>\n",
              "      <td>5746859</td>\n",
              "      <td>77986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Florida</td>\n",
              "      <td>12</td>\n",
              "      <td>5242386</td>\n",
              "      <td>63569</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>New York</td>\n",
              "      <td>36</td>\n",
              "      <td>4610691</td>\n",
              "      <td>62319</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>42</td>\n",
              "      <td>2523956</td>\n",
              "      <td>39093</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Illinois</td>\n",
              "      <td>17</td>\n",
              "      <td>2736601</td>\n",
              "      <td>33278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Ohio</td>\n",
              "      <td>39</td>\n",
              "      <td>2460869</td>\n",
              "      <td>31245</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Michigan</td>\n",
              "      <td>26</td>\n",
              "      <td>2077405</td>\n",
              "      <td>31239</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>Georgia</td>\n",
              "      <td>13</td>\n",
              "      <td>2152991</td>\n",
              "      <td>30982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-01-20</th>\n",
              "      <td>New Jersey</td>\n",
              "      <td>34</td>\n",
              "      <td>2037690</td>\n",
              "      <td>30476</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-84a8c502-a0df-4abb-b475-b3f6928e3b65')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-84a8c502-a0df-4abb-b475-b3f6928e3b65 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-84a8c502-a0df-4abb-b475-b3f6928e3b65');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                   state  fips    cases  deaths\n",
              "date                                           \n",
              "2022-01-20    California     6  7487212   78529\n",
              "2022-01-20         Texas    48  5746859   77986\n",
              "2022-01-20       Florida    12  5242386   63569\n",
              "2022-01-20      New York    36  4610691   62319\n",
              "2022-01-20  Pennsylvania    42  2523956   39093\n",
              "2022-01-20      Illinois    17  2736601   33278\n",
              "2022-01-20          Ohio    39  2460869   31245\n",
              "2022-01-20      Michigan    26  2077405   31239\n",
              "2022-01-20       Georgia    13  2152991   30982\n",
              "2022-01-20    New Jersey    34  2037690   30476"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OSNzoid28GEX"
      },
      "source": [
        "*There should be 50 states and District of Columbia*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ktah9SqV8I3i",
        "outputId": "d708b247-5860-4e6a-b6fb-131711da8c21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "current_df.shape"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(56, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i_M6NGQk8_2U"
      },
      "source": [
        "### State Based Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xzGQ9z_oA6V7"
      },
      "source": [
        "*Get 10 states and subset*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PeicLkAlA9y4",
        "outputId": "8998f431-4b6b-435a-dcd8-604a4215f0ab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "top_ten_states = list(current_df[\"state\"].head(10).values)\n",
        "top_ten_states"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['California',\n",
              " 'Texas',\n",
              " 'Florida',\n",
              " 'New York',\n",
              " 'Pennsylvania',\n",
              " 'Illinois',\n",
              " 'Ohio',\n",
              " 'Michigan',\n",
              " 'Georgia',\n",
              " 'New Jersey']"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uzv8KJtx9bFO"
      },
      "source": [
        "top_states_df = df[df['state'].isin(top_ten_states)].drop(columns=\"fips\")"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dkLverBTICn3"
      },
      "source": [
        "*Verify the unique states left is the same 10*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l3bJzaxyHnD6",
        "outputId": "dd7fdb7e-6e9c-40e9-b96b-94e69fed55ed",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "set(top_states_df.state.values)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'California',\n",
              " 'Florida',\n",
              " 'Georgia',\n",
              " 'Illinois',\n",
              " 'Michigan',\n",
              " 'New Jersey',\n",
              " 'New York',\n",
              " 'Ohio',\n",
              " 'Pennsylvania',\n",
              " 'Texas'}"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9ba-QT229CYE",
        "outputId": "7529388b-0aca-4774-97d7-e9cf6bd7b796",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        }
      },
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(15,5))\n",
        "ax = sns.lmplot(x=\"cases\", y=\"deaths\",\n",
        "                    height=3.5,\n",
        "                    col=\"state\",\n",
        "                    col_wrap=5,\n",
        "                    hue=\"state\", \n",
        "                    palette=\"Set2\",\n",
        "                    data=top_states_df)\n",
        "ax.fig.subplots_adjust(wspace=.2)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x360 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1260x504 with 10 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "in-JRg0lOCrp"
      },
      "source": [
        "*Interactive plot of top states*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf7j4jUBaKMS",
        "outputId": "5490d703-3fca-4958-ccc7-780be5e43e5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "top_states_march_current_df = top_states_df.loc[\"2020-03-08\":today].sort_values(by=[\"deaths\", \"cases\"], ascending=True)\n",
        "top_states_march_current_df.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c8096912-e51c-4d3d-99e8-7ffdbe2994d8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "  "
            ],
            "text/plain": [
              "               state  cases  deaths\n",
              "date                               \n",
              "2020-03-10  Michigan      2       0\n",
              "2020-03-11  Michigan      2       0\n",
              "2020-03-09      Ohio      3       0\n",
              "2020-03-10      Ohio      3       0\n",
              "2020-03-11      Ohio      4       0"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D7yg3JizOHIv",
        "outputId": "696db838-47fe-409e-923e-a7f55d97a4dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "import plotly.io as pio\n",
        "pio.renderers.default = 'colab'\n",
        "import plotly.express as px\n",
        "fig = px.scatter(top_states_march_current_df, x=top_states_march_current_df.index, y=\"cases\", size=\"deaths\", color=\"state\", facet_col=\"state\")\n",
        "fig.show()"
      ],
      "execution_count": 20,
      "outputs": [
        {
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          "data": {
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      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rN0jv8_3mD-z"
      },
      "source": [
        "### Search for Features:  Political, Health and Finance"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VLRtEYvBtoR"
      },
      "source": [
        "*Sugar Intake By State*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnLcHUUUBwzD",
        "outputId": "19bdf9e4-4d25-42c4-b378-625d98035197",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        }
      },
      "source": [
        "cdc_2013 = pd.read_csv(\"https://raw.githubusercontent.com/noahgift/sugar/master/data/education_sugar_cdc_2003.csv\")\n",
        "cdc_2013.set_index(\"State\", inplace=True)\n",
        "for column in cdc_2013.columns:\n",
        "  cdc_2013[column]=cdc_2013[column].str.replace(r\"\\(.*\\)\",\"\")\n",
        "  cdc_2013[column]=pd.to_numeric(cdc_2013[column])\n",
        "  \n",
        "cdc_2013.reset_index(inplace=True)\n",
        "cdc_2013.rename(columns={\"State\": \"state\", \"Employed\": \"employed-sugar-intake-daily\"},inplace=True)\n",
        "cdc_2013.head()"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <th>Not employed</th>\n",
              "      <th>Retired</th>\n",
              "      <th>&lt;High school</th>\n",
              "      <th>High school</th>\n",
              "      <th>Some college</th>\n",
              "      <th>College graduate</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>26.2</td>\n",
              "      <td>32.1</td>\n",
              "      <td>16.0</td>\n",
              "      <td>47.1</td>\n",
              "      <td>34.9</td>\n",
              "      <td>24.2</td>\n",
              "      <td>12.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>33.0</td>\n",
              "      <td>28.7</td>\n",
              "      <td>13.8</td>\n",
              "      <td>40.4</td>\n",
              "      <td>36.5</td>\n",
              "      <td>24.4</td>\n",
              "      <td>14.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>California</td>\n",
              "      <td>22.9</td>\n",
              "      <td>30.2</td>\n",
              "      <td>15.0</td>\n",
              "      <td>38.5</td>\n",
              "      <td>29.9</td>\n",
              "      <td>21.4</td>\n",
              "      <td>11.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Connecticut</td>\n",
              "      <td>18.9</td>\n",
              "      <td>24.3</td>\n",
              "      <td>15.0</td>\n",
              "      <td>27.8</td>\n",
              "      <td>26.9</td>\n",
              "      <td>19.9</td>\n",
              "      <td>10.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>District of Columbia</td>\n",
              "      <td>18.5</td>\n",
              "      <td>34.6</td>\n",
              "      <td>18.5</td>\n",
              "      <td>45.6</td>\n",
              "      <td>39.0</td>\n",
              "      <td>28.9</td>\n",
              "      <td>8.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b44d937a-4c0d-4502-9691-88c6fe75e62d')\"\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b44d937a-4c0d-4502-9691-88c6fe75e62d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                  state  ...  College graduate\n",
              "0                Alaska  ...              12.9\n",
              "1               Arizona  ...              14.6\n",
              "2            California  ...              11.5\n",
              "3           Connecticut  ...              10.2\n",
              "4  District of Columbia  ...               8.4\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BT7YQzUcCRSn"
      },
      "source": [
        "*Combine Sugar Data and Covid-19 Data*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "outputId": "f496eb26-0551-47da-8e69-00ff0998b9e0",
        "id": "mxZ76ZI8EDQ2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "cdc_employed_df = cdc_2013[[\"employed-sugar-intake-daily\", \"state\"]]\n",
        "sugar_covid_df = df.merge(cdc_employed_df, how=\"inner\", on=\"state\")\n",
        "sugar_covid_df.head()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-67816e56-4742-4ff5-881f-0b9f50aed710\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-67816e56-4742-4ff5-881f-0b9f50aed710')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
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              "\n",
              "    .colab-df-convert: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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-67816e56-4742-4ff5-881f-0b9f50aed710 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-67816e56-4742-4ff5-881f-0b9f50aed710');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        state  fips  cases  deaths  employed-sugar-intake-daily\n",
              "0  California     6      1       0                         22.9\n",
              "1  California     6      2       0                         22.9\n",
              "2  California     6      2       0                         22.9\n",
              "3  California     6      2       0                         22.9\n",
              "4  California     6      2       0                         22.9"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9syyMOxhTHMo"
      },
      "source": [
        "*What about data from the 2016 Election?*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2RZa-tDmTPXP",
        "outputId": "15c9540b-5348-4bb7-d3b3-00f21aa3d70a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "election_df = pd.read_csv(\"https://raw.githubusercontent.com/paiml/minimal-python/master/data/2016-Electoral-Votes.csv\")\n",
        "election_df.rename(columns={\"State\": \"state\"},inplace=True)\n",
        "election_df.drop(columns=\"Votes\", inplace=True)\n",
        "election_df = pd.concat([election_df, pd.get_dummies(election_df[\"Winning Party\"])], axis=1);\n",
        "election_df.head()\n",
        "#election_df[\"Republican?\"] = pd.get_dummies(election_df, columns=[\"Winning Party\"])\n",
        "#election_df.head()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f01fc8ea-d54b-4bcb-a317-c217c6e0e241\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>Winning Party</th>\n",
              "      <th>Democrats</th>\n",
              "      <th>Republicans</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Alabama</td>\n",
              "      <td>Republicans</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Alaska</td>\n",
              "      <td>Republicans</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Arizona</td>\n",
              "      <td>Republicans</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Arkansas</td>\n",
              "      <td>Republicans</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>California</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f01fc8ea-d54b-4bcb-a317-c217c6e0e241 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-f01fc8ea-d54b-4bcb-a317-c217c6e0e241');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        state Winning Party  Democrats  Republicans\n",
              "0     Alabama   Republicans          0            1\n",
              "1      Alaska   Republicans          0            1\n",
              "2     Arizona   Republicans          0            1\n",
              "3    Arkansas   Republicans          0            1\n",
              "4  California     Democrats          1            0"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6CuViW_V2Hx",
        "outputId": "845c3c6d-dc35-4076-8d36-e7742d49e73b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        }
      },
      "source": [
        "sugar_covid_df = sugar_covid_df.merge(election_df, how=\"inner\", on=\"state\")\n",
        "sugar_covid_df.head()"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-f13ee837-ec15-4e7b-8401-62e0c58a4bb0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>state</th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <th>Winning Party</th>\n",
              "      <th>Democrats</th>\n",
              "      <th>Republicans</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>California</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>22.9</td>\n",
              "      <td>Democrats</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f13ee837-ec15-4e7b-8401-62e0c58a4bb0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f13ee837-ec15-4e7b-8401-62e0c58a4bb0 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-f13ee837-ec15-4e7b-8401-62e0c58a4bb0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "        state  fips  cases  ...  Winning Party  Democrats Republicans\n",
              "0  California     6      1  ...      Democrats          1           0\n",
              "1  California     6      2  ...      Democrats          1           0\n",
              "2  California     6      2  ...      Democrats          1           0\n",
              "3  California     6      2  ...      Democrats          1           0\n",
              "4  California     6      2  ...      Democrats          1           0\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RmBJqPnXE5wR"
      },
      "source": [
        "*Generate Heatmap*"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cee_tCSBGdEM",
        "outputId": "0a11f1e4-414a-444b-a8b5-be15fcdf7201",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "sugar_covid_df.corr()"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-31aa770b-c50f-498e-8bfc-7aea8574c6a0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fips</th>\n",
              "      <th>cases</th>\n",
              "      <th>deaths</th>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <th>Democrats</th>\n",
              "      <th>Republicans</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>fips</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.122509</td>\n",
              "      <td>-0.112906</td>\n",
              "      <td>0.269213</td>\n",
              "      <td>-0.188256</td>\n",
              "      <td>0.188256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cases</th>\n",
              "      <td>-0.122509</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.925070</td>\n",
              "      <td>-0.120347</td>\n",
              "      <td>0.212449</td>\n",
              "      <td>-0.212449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>deaths</th>\n",
              "      <td>-0.112906</td>\n",
              "      <td>0.925070</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.196950</td>\n",
              "      <td>0.337624</td>\n",
              "      <td>-0.337624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>employed-sugar-intake-daily</th>\n",
              "      <td>0.269213</td>\n",
              "      <td>-0.120347</td>\n",
              "      <td>-0.196950</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.767348</td>\n",
              "      <td>0.767348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Democrats</th>\n",
              "      <td>-0.188256</td>\n",
              "      <td>0.212449</td>\n",
              "      <td>0.337624</td>\n",
              "      <td>-0.767348</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Republicans</th>\n",
              "      <td>0.188256</td>\n",
              "      <td>-0.212449</td>\n",
              "      <td>-0.337624</td>\n",
              "      <td>0.767348</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31aa770b-c50f-498e-8bfc-7aea8574c6a0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-31aa770b-c50f-498e-8bfc-7aea8574c6a0 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-31aa770b-c50f-498e-8bfc-7aea8574c6a0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                 fips     cases  ...  Democrats  Republicans\n",
              "fips                         1.000000 -0.122509  ...  -0.188256     0.188256\n",
              "cases                       -0.122509  1.000000  ...   0.212449    -0.212449\n",
              "deaths                      -0.112906  0.925070  ...   0.337624    -0.337624\n",
              "employed-sugar-intake-daily  0.269213 -0.120347  ...  -0.767348     0.767348\n",
              "Democrats                   -0.188256  0.212449  ...   1.000000    -1.000000\n",
              "Republicans                  0.188256 -0.212449  ...  -1.000000     1.000000\n",
              "\n",
              "[6 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u6wVCIMIE74B",
        "outputId": "b633b152-3b85-4249-f989-4417f6146ec9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        }
      },
      "source": [
        "import plotly.graph_objects as go\n",
        "\n",
        "fig = go.Figure(data=go.Heatmap(\n",
        "                   z=sugar_covid_df.corr(),\n",
        "                   x=['fips','cases', 'deaths', 'employed-sugar-intake-daily', 'Democrats', 'Republicans'],\n",
        "                   y=['fips','cases', 'deaths', 'employed-sugar-intake-daily', 'Democrats', 'Republicans'],\n",
        "                   hoverongaps = False))\n",
        "fig.show()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "            <script src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js?config=TeX-AMS-MML_SVG\"></script><script type=\"text/javascript\">if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}</script>\n",
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZFaYmi8fBE6_"
      },
      "source": [
        "sugar_covid_df.to_csv(\"covid-eda.csv\")"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lLqU5s5pH8PB"
      },
      "source": [
        "*What about Real-Estate Data?*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M4W9aBagTHFC"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IrRjhxe30C_q"
      },
      "source": [
        "## Modeling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sdsYYSuy0Exu"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFOuQGyn0E6w"
      },
      "source": [
        "## Conclusion\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cmcf2OJ40mm4"
      },
      "source": [
        ""
      ]
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}