02_cleaning_ex.ipynb

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      "source": [
        "<a href=\"https://colab.research.google.com/github/BlessMario/MachineIntelligence/blob/main/02_cleaning_ex.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",
      "metadata": {
        "id": "B5kDMiadf5HB"
      },
      "source": [
        "# CAS Machine Learning: Lecture 02"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YG6cg9df5HH"
      },
      "source": [
        "## 0 - Loading Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wpezm3WFf5HH"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import missingno as msno\n",
        "\n",
        "from matplotlib.colors import ListedColormap\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.impute import SimpleImputer\n",
        "from io import StringIO"
      ],
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "90FuFMrZf5HI"
      },
      "source": [
        "## 1 - Loading the Dataset into a Pandas DataFrame"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### The Airbnb Dataset\n",
        "\n",
        "The AirBnB dataset contains data from properties in the state of NY. It containts the following information:\n",
        "\n",
        "- `listing_id`: A unique identifier of the property\n",
        "- `description`: A short description/title associated with the property\n",
        "- `host_id`: The host unique identifier\n",
        "- `host_name`: The host name\n",
        "- `neighbourhood_full`: Name of the neighbourhood\n",
        "- `coordinates`: Geographical coordinates of the property _(latitude, longitude)_\n",
        "- `Listing added`: Date of registration of the property\n",
        "- `room_type`: Type of the room \n",
        "- `rating`: User rating from 0 to 5.\n",
        "- `price`: Price per night\n",
        "- `number_of_reviews`: Amount of reviews received \n",
        "- `last_review`: Date of last review\n",
        "- `reviews_per_month`: Number of reviews per month\n",
        "- `availability_365`: Number of days available per year\n",
        "- `Number of stays`: Total number of stays so far"
      ],
      "metadata": {
        "id": "ECdVeuxMp-aX"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMRFCDxTf5HJ"
      },
      "source": [
        "We first load the CSV Iris Dataset into a Pandas `DataFrame` object:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NaFnu8_JXzl9",
        "outputId": "0c65baa3-f568-4ba5-ecd0-a000338b88e2"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
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        "id": "e4jmHLwqYSTH",
        "outputId": "8d33faf8-ba2b-4df7-996c-c88d01c545f4"
      },
      "source": [
        "%ls -la sample_data/airbnb.csv"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "-rw-r--r-- 1 root root 2022986 Sep  6 12:57 sample_data/airbnb.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "StRk3HwFgiu9"
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      "source": [
        "df_airbnb = pd.read_csv(\"./sample_data/airbnb.csv\")"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Dataframes:**\n",
        "\n",
        "To better understand the data/dataframe, we use a set of functions available from pandas, which are summarised as follows:\n",
        "\n",
        "- `.head()` prints the header of a given dataframe.\n",
        "- `.dtypes` describes the datatypes of all columns in the dataframe.\n",
        "- `.info()` provides an overview of the different column data types and missing values in the given dataframe\n",
        "- `.describe()` returns a distribution of numeric columns in the dataframe\n",
        "- `.isna().sum()` provides the number of missing values per column in our DataFrame\n",
        "- `.unique()` retrieves the unique values in a given dataframe column\n",
        "\n",
        "<br>\n",
        "\n",
        "**Visualisation:**\n",
        "\n",
        "- `sns.displot()` plots the distribution of one column in your dataframe."
      ],
      "metadata": {
        "id": "6Qjv3gecq9EQ"
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    },
    {
      "cell_type": "code",
      "source": [
        "# show the header of the dataframe\n",
        "df_airbnb.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "fLvtCfhRrp6Z",
        "outputId": "5a2ced92-a8ec-4e2e-f6ab-82e62148435f"
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      "execution_count": 29,
      "outputs": [
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              "   Unnamed: 0  listing_id                                               name  \\\n",
              "0           0    13740704  Cozy,budget friendly, cable inc, private entra...   \n",
              "1           1    22005115              Two floor apartment near Central Park   \n",
              "2           2    21667615                  Beautiful 1BR in Brooklyn Heights   \n",
              "3           3     6425850                          Spacious, charming studio   \n",
              "4           4    22986519              Bedroom on the lively Lower East Side   \n",
              "\n",
              "     host_id host_name          neighbourhood_full            coordinates  \\\n",
              "0   20583125    Michel         Brooklyn, Flatlands  (40.63222, -73.93398)   \n",
              "1   82746113   Cecilia  Manhattan, Upper West Side  (40.78761, -73.96862)   \n",
              "2      78251    Leslie  Brooklyn, Brooklyn Heights   (40.7007, -73.99517)   \n",
              "3   32715865    Yelena  Manhattan, Upper West Side  (40.79169, -73.97498)   \n",
              "4  154262349    Brooke  Manhattan, Lower East Side  (40.71884, -73.98354)   \n",
              "\n",
              "         room_type price  number_of_reviews last_review  reviews_per_month  \\\n",
              "0     Private room   45$                 10  2018-12-12               0.70   \n",
              "1  Entire home/apt  135$                  1  2019-06-30               1.00   \n",
              "2  Entire home/apt  150$                  0         NaN                NaN   \n",
              "3  Entire home/apt   86$                  5  2017-09-23               0.13   \n",
              "4     Private room  160$                 23  2019-06-12               2.29   \n",
              "\n",
              "   availability_365    rating  number_of_stays   5_stars listing_added  \n",
              "0                85  4.100954             12.0  0.609432    2018-06-08  \n",
              "1               145  3.367600              1.2  0.746135    2018-12-25  \n",
              "2                65       NaN              NaN       NaN    2018-08-15  \n",
              "3                 0  4.763203              6.0  0.769947    2017-03-20  \n",
              "4               102  3.822591             27.6  0.649383    2020-10-23  "
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              "      <th>listing_id</th>\n",
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              "      <td>Private room</td>\n",
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              "      <td>82746113</td>\n",
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              "      <td>Manhattan, Upper West Side</td>\n",
              "      <td>(40.78761, -73.96862)</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>135$</td>\n",
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              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>21667615</td>\n",
              "      <td>Beautiful 1BR in Brooklyn Heights</td>\n",
              "      <td>78251</td>\n",
              "      <td>Leslie</td>\n",
              "      <td>Brooklyn, Brooklyn Heights</td>\n",
              "      <td>(40.7007, -73.99517)</td>\n",
              "      <td>Entire home/apt</td>\n",
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              "      <td>65</td>\n",
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              "      <td>2018-08-15</td>\n",
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              "      <td>6425850</td>\n",
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              "      <td>32715865</td>\n",
              "      <td>Yelena</td>\n",
              "      <td>Manhattan, Upper West Side</td>\n",
              "      <td>(40.79169, -73.97498)</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>86$</td>\n",
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              "      <td>2017-09-23</td>\n",
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              "      <td>154262349</td>\n",
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              "      <td>(40.71884, -73.98354)</td>\n",
              "      <td>Private room</td>\n",
              "      <td>160$</td>\n",
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              "      <td>2019-06-12</td>\n",
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              "      <td>27.6</td>\n",
              "      <td>0.649383</td>\n",
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      "source": [
        "# describes the datatype of all columns:\n",
        "df_airbnb.describes()\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        },
        "id": "YIo5o2mvry_y",
        "outputId": "30d2f1b4-5792-4875-fc24-d913f8d962a4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-33-5a90ffe31d5d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# describes the datatype of all columns:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdescribes\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      3\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__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'describes'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YaSKcN3I4P9T",
        "outputId": "d7a45f8e-a587-4f0a-949e-be0979f6c1ae"
      },
      "source": [
        "df_airbnb.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10019 entries, 0 to 10018\n",
            "Data columns (total 17 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Unnamed: 0          10019 non-null  int64  \n",
            " 1   listing_id          10019 non-null  int64  \n",
            " 2   name                10014 non-null  object \n",
            " 3   host_id             10019 non-null  int64  \n",
            " 4   host_name           10017 non-null  object \n",
            " 5   neighbourhood_full  10019 non-null  object \n",
            " 6   coordinates         10019 non-null  object \n",
            " 7   room_type           10019 non-null  object \n",
            " 8   price               9781 non-null   object \n",
            " 9   number_of_reviews   10019 non-null  int64  \n",
            " 10  last_review         7944 non-null   object \n",
            " 11  reviews_per_month   7944 non-null   float64\n",
            " 12  availability_365    10019 non-null  int64  \n",
            " 13  rating              7944 non-null   float64\n",
            " 14  number_of_stays     7944 non-null   float64\n",
            " 15  5_stars             7944 non-null   float64\n",
            " 16  listing_added       10019 non-null  object \n",
            "dtypes: float64(4), int64(5), object(8)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We observe from the previous results, that some columns are not associated to their proper data type, e.g. the column `coordinates` shall be mapped to `float` numbers and also the `price`column. In addition, the dates such as `last_review` shall me converted into `datetime` columns.\n"
      ],
      "metadata": {
        "id": "vcv9QveXtRZM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# provide an overview of the different column data types and missing values:\n",
        "df_airbnb.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gDUu_2LtsN_j",
        "outputId": "e2f8fb33-1062-4e9d-a4fd-d2d9f1d32017"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10019 entries, 0 to 10018\n",
            "Data columns (total 17 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   Unnamed: 0          10019 non-null  int64  \n",
            " 1   listing_id          10019 non-null  int64  \n",
            " 2   name                10014 non-null  object \n",
            " 3   host_id             10019 non-null  int64  \n",
            " 4   host_name           10017 non-null  object \n",
            " 5   neighbourhood_full  10019 non-null  object \n",
            " 6   coordinates         10019 non-null  object \n",
            " 7   room_type           10019 non-null  object \n",
            " 8   price               9781 non-null   object \n",
            " 9   number_of_reviews   10019 non-null  int64  \n",
            " 10  last_review         7944 non-null   object \n",
            " 11  reviews_per_month   7944 non-null   float64\n",
            " 12  availability_365    10019 non-null  int64  \n",
            " 13  rating              7944 non-null   float64\n",
            " 14  number_of_stays     7944 non-null   float64\n",
            " 15  5_stars             7944 non-null   float64\n",
            " 16  listing_added       10019 non-null  object \n",
            "dtypes: float64(4), int64(5), object(8)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can observe that there is some missing data especially in the columns `price`, `last_review`, `reviews_per_month`, `rating`, `number_of_stays`, and `5_stars`. Because the number of missing values is the same across the aforementioned columns, we could think that they are somehow related to each other."
      ],
      "metadata": {
        "id": "S00Kd1l3t51j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# display number of missing values per column:\n",
        "df_airbnb.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnOHSIvdscHa",
        "outputId": "e171db37-dc0d-40e0-9b1f-7a12d2da94b4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0               0\n",
              "listing_id               0\n",
              "name                     5\n",
              "host_id                  0\n",
              "host_name                2\n",
              "neighbourhood_full       0\n",
              "coordinates              0\n",
              "room_type                0\n",
              "price                  238\n",
              "number_of_reviews        0\n",
              "last_review           2075\n",
              "reviews_per_month     2075\n",
              "availability_365         0\n",
              "rating                2075\n",
              "number_of_stays       2075\n",
              "5_stars               2075\n",
              "listing_added            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From the previous results, we can see exactly what is the number of missing values. Also we can see that there is some variability in the number of missing columns and some dependency among them."
      ],
      "metadata": {
        "id": "KMzJrM2LufJx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2 - Data Cleaning\n"
      ],
      "metadata": {
        "id": "N97lT-yZu1Lx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **2.1 Data type problems:**\n",
        "It is common to see that in data analytics tasks, some of the columns are not mapped correctly to their data type. In this section, we will deal with some of these problems."
      ],
      "metadata": {
        "id": "XXWSoJsS8zVb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_airbnb.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "1HsScm17wOfc",
        "outputId": "5bdcaacb-0019-44df-8b5a-a18f97d4e44d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  listing_id                                               name  \\\n",
              "0           0    13740704  Cozy,budget friendly, cable inc, private entra...   \n",
              "1           1    22005115              Two floor apartment near Central Park   \n",
              "2           2    21667615                  Beautiful 1BR in Brooklyn Heights   \n",
              "3           3     6425850                          Spacious, charming studio   \n",
              "4           4    22986519              Bedroom on the lively Lower East Side   \n",
              "\n",
              "     host_id host_name          neighbourhood_full            coordinates  \\\n",
              "0   20583125    Michel         Brooklyn, Flatlands  (40.63222, -73.93398)   \n",
              "1   82746113   Cecilia  Manhattan, Upper West Side  (40.78761, -73.96862)   \n",
              "2      78251    Leslie  Brooklyn, Brooklyn Heights   (40.7007, -73.99517)   \n",
              "3   32715865    Yelena  Manhattan, Upper West Side  (40.79169, -73.97498)   \n",
              "4  154262349    Brooke  Manhattan, Lower East Side  (40.71884, -73.98354)   \n",
              "\n",
              "         room_type price  number_of_reviews last_review  reviews_per_month  \\\n",
              "0     Private room   45$                 10  2018-12-12               0.70   \n",
              "1  Entire home/apt  135$                  1  2019-06-30               1.00   \n",
              "2  Entire home/apt  150$                  0         NaN                NaN   \n",
              "3  Entire home/apt   86$                  5  2017-09-23               0.13   \n",
              "4     Private room  160$                 23  2019-06-12               2.29   \n",
              "\n",
              "   availability_365    rating  number_of_stays   5_stars listing_added  \n",
              "0                85  4.100954             12.0  0.609432    2018-06-08  \n",
              "1               145  3.367600              1.2  0.746135    2018-12-25  \n",
              "2                65       NaN              NaN       NaN    2018-08-15  \n",
              "3                 0  4.763203              6.0  0.769947    2017-03-20  \n",
              "4               102  3.822591             27.6  0.649383    2020-10-23  "
            ],
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              "\n",
              "  <div id=\"df-a1efa024-6a7b-452f-b2d1-09376ae3e340\">\n",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>listing_id</th>\n",
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              "  </thead>\n",
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              "      <td>0</td>\n",
              "      <td>13740704</td>\n",
              "      <td>Cozy,budget friendly, cable inc, private entra...</td>\n",
              "      <td>20583125</td>\n",
              "      <td>Michel</td>\n",
              "      <td>Brooklyn, Flatlands</td>\n",
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              "      <th>1</th>\n",
              "      <td>1</td>\n",
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              "      <td>82746113</td>\n",
              "      <td>Cecilia</td>\n",
              "      <td>Manhattan, Upper West Side</td>\n",
              "      <td>(40.78761, -73.96862)</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>135$</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-06-30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>145</td>\n",
              "      <td>3.367600</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.746135</td>\n",
              "      <td>2018-12-25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>21667615</td>\n",
              "      <td>Beautiful 1BR in Brooklyn Heights</td>\n",
              "      <td>78251</td>\n",
              "      <td>Leslie</td>\n",
              "      <td>Brooklyn, Brooklyn Heights</td>\n",
              "      <td>(40.7007, -73.99517)</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>150$</td>\n",
              "      <td>0</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>65</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>2018-08-15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>6425850</td>\n",
              "      <td>Spacious, charming studio</td>\n",
              "      <td>32715865</td>\n",
              "      <td>Yelena</td>\n",
              "      <td>Manhattan, Upper West Side</td>\n",
              "      <td>(40.79169, -73.97498)</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>86$</td>\n",
              "      <td>5</td>\n",
              "      <td>2017-09-23</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0</td>\n",
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              "      <td>4</td>\n",
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              "      <td>154262349</td>\n",
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              "      <td>Manhattan, Lower East Side</td>\n",
              "      <td>(40.71884, -73.98354)</td>\n",
              "      <td>Private room</td>\n",
              "      <td>160$</td>\n",
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              "      <td>2019-06-12</td>\n",
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              "      <td>0.649383</td>\n",
              "      <td>2020-10-23</td>\n",
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              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Task 2.1.1**: We can see that the `coordinates` column is probably stored as a string (`str`), however, the geographical coordinates are usually `float`nuumbers. In this task, you should split the `coordinates` columns into latitude and longitude and map those values as `float` numbers. Finally, remove/drop the original `coordinates` column.\n",
        "\n",
        "To achieve this, you can use the following methods:\n",
        "\n",
        "- `.str.replace(\"\",\"\")` replaces one string with another for each row in a column.\n",
        "- `.str.split(\"\")` splits a column based on a string parameter.\n",
        "- `.astype()` casts one column from one type to another.\n",
        "- `.drop()` helps to remove a specific column."
      ],
      "metadata": {
        "id": "yMKWhnNFu5aQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# remove any unnecessary characters, such as \"(\" and \")\" from the coordinates column.\n",
        "df_airbnb['coordinates'] = df_airbnb['coordinates'].str.replace('(','')\n",
        "df_airbnb['coordinates'] = df_airbnb['coordinates'].str.replace(')', '')\n",
        "df_airbnb['coordinates'].head\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kHBaloFawrsQ",
        "outputId": "7c828022-068a-41cb-ac49-df4f19ca8941"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:2: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  \n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: FutureWarning: The default value of regex will change from True to False in a future version. In addition, single character regular expressions will *not* be treated as literal strings when regex=True.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of 0                 40.63222, -73.93398\n",
              "1                 40.78761, -73.96862\n",
              "2                  40.7007, -73.99517\n",
              "3                 40.79169, -73.97498\n",
              "4                 40.71884, -73.98354\n",
              "                     ...             \n",
              "10014             40.80379, -73.95257\n",
              "10015              40.79531, -73.9333\n",
              "10016    40.68266, -73.96743000000002\n",
              "10017             40.68832, -73.96366\n",
              "10018             40.76174, -73.96625\n",
              "Name: coordinates, Length: 10019, dtype: object>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# spit the coordinates column into two different columns and display the two new obtained columns.\n",
        "coordinates = df_airbnb['coordinates'].str.split(\",\", expand = True)\n",
        "coordinates.head()\n",
        "df_airbnb['latitude'] = coordinates[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "22oTVuicx63e",
        "outputId": "e8ded045-cf50-49ab-8359-59360bec94e7"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ellipsis"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# assign each new column (latitute and longitude) as an additional column into the dataframe.\n",
        "df_airbnb['latitude'] = df_airbnb['latitude'].astype(float)\n",
        "df_airbnb['longitude'] = df_airbnb['longitude'].astype(float)\n",
        "...\n",
        "..."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xh0FAD77x--N",
        "outputId": "1e1350ee-3b55-4f06-945e-41332ee97555"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ellipsis"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display the different column types in the dataframe\n",
        "df_airbnb.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9JJQvtIpyYrO",
        "outputId": "c9375dfb-5bab-44d2-bb9f-995a2c930985"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unnamed: 0              int64\n",
              "listing_id              int64\n",
              "name                   object\n",
              "host_id                 int64\n",
              "host_name              object\n",
              "neighbourhood_full     object\n",
              "coordinates            object\n",
              "room_type              object\n",
              "price                  object\n",
              "number_of_reviews       int64\n",
              "last_review            object\n",
              "reviews_per_month     float64\n",
              "availability_365        int64\n",
              "rating                float64\n",
              "number_of_stays       float64\n",
              "5_stars               float64\n",
              "listing_added          object\n",
              "latitude              float64\n",
              "longitude             float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# correct the two new column data types and display the casted data types.\n",
        "df_airbnb['coordinates'] = df_airbnb['coordinates'].str.replace('(','')\n",
        "df_airbnb['coordinates'] = df_airbnb['coordinates'].str.replace(')', '')\n",
        "..."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 364
        },
        "id": "QjM0NT1Lycys",
        "outputId": "6fcebd8d-25bc-49cb-b056-ea983c960e5d"
      },
      "execution_count": 46,
      "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-46-13acc61a50ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# correct the two new column data types and display the casted data types.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'coordinates'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'float'\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      3\u001b[0m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'coordinates'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'coordinates'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\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[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\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/generic.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   5813\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   5814\u001b[0m             \u001b[0;31m# else, only a single dtype is given\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5815\u001b[0;31m             \u001b[0mnew_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   5816\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_constructor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_data\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__finalize__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5817\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;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    416\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    417\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mbool\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"raise\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mT\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 418\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"astype\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    419\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    420\u001b[0m     def convert(\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/internals/managers.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, f, align_keys, ignore_failures, **kwargs)\u001b[0m\n\u001b[1;32m    325\u001b[0m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\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[1;32m    326\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[0;32m--> 327\u001b[0;31m                     \u001b[0mapplied\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m)\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    328\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mTypeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mNotImplementedError\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    329\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mignore_failures\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/blocks.py\u001b[0m in \u001b[0;36mastype\u001b[0;34m(self, dtype, copy, errors)\u001b[0m\n\u001b[1;32m    589\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    590\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 591\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array_safe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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    592\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    593\u001b[0m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmaybe_coerce_values\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_values\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/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array_safe\u001b[0;34m(values, dtype, copy, errors)\u001b[0m\n\u001b[1;32m   1307\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1308\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1309\u001b[0;31m         \u001b[0mnew_values\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m   1310\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mValueError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\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   1311\u001b[0m         \u001b[0;31m# e.g. astype_nansafe can fail on object-dtype of strings\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/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_array\u001b[0;34m(values, dtype, copy)\u001b[0m\n\u001b[1;32m   1255\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1256\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[0;32m-> 1257\u001b[0;31m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mastype_nansafe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\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[0m\u001b[1;32m   1258\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1259\u001b[0m     \u001b[0;31m# in pandas we don't store numpy str dtypes, so convert to object\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/dtypes/cast.py\u001b[0m in \u001b[0;36mastype_nansafe\u001b[0;34m(arr, dtype, copy, skipna)\u001b[0m\n\u001b[1;32m   1199\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcopy\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mis_object_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\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   1200\u001b[0m         \u001b[0;31m# Explicit copy, or required since NumPy can't view from / to object.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1201\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\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[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1202\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1203\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\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;31mValueError\u001b[0m: could not convert string to float: '40.63222, -73.93398'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove/drop the coordinates column\n",
        "df_airbnb.drop('coordinates', axis=1, inplace = True)\n",
        "df_airbnb.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "2bfD5D6Uy562",
        "outputId": "b1698816-ebd4-4123-d014-2841e685f9f7"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Unnamed: 0  listing_id                                               name  \\\n",
              "0           0    13740704  Cozy,budget friendly, cable inc, private entra...   \n",
              "1           1    22005115              Two floor apartment near Central Park   \n",
              "2           2    21667615                  Beautiful 1BR in Brooklyn Heights   \n",
              "3           3     6425850                          Spacious, charming studio   \n",
              "4           4    22986519              Bedroom on the lively Lower East Side   \n",
              "\n",
              "     host_id host_name          neighbourhood_full        room_type price  \\\n",
              "0   20583125    Michel         Brooklyn, Flatlands     Private room   45$   \n",
              "1   82746113   Cecilia  Manhattan, Upper West Side  Entire home/apt  135$   \n",
              "2      78251    Leslie  Brooklyn, Brooklyn Heights  Entire home/apt  150$   \n",
              "3   32715865    Yelena  Manhattan, Upper West Side  Entire home/apt   86$   \n",
              "4  154262349    Brooke  Manhattan, Lower East Side     Private room  160$   \n",
              "\n",
              "   number_of_reviews last_review  reviews_per_month  availability_365  \\\n",
              "0                 10  2018-12-12               0.70                85   \n",
              "1                  1  2019-06-30               1.00               145   \n",
              "2                  0         NaN                NaN                65   \n",
              "3                  5  2017-09-23               0.13                 0   \n",
              "4                 23  2019-06-12               2.29               102   \n",
              "\n",
              "     rating  number_of_stays   5_stars listing_added  latitude  longitude  \n",
              "0  4.100954             12.0  0.609432    2018-06-08  40.63222  -73.93398  \n",
              "1  3.367600              1.2  0.746135    2018-12-25  40.78761  -73.96862  \n",
              "2       NaN              NaN       NaN    2018-08-15  40.70070  -73.99517  \n",
              "3  4.763203              6.0  0.769947    2017-03-20  40.79169  -73.97498  \n",
              "4  3.822591             27.6  0.649383    2020-10-23  40.71884  -73.98354  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a20489d-8369-4262-9a34-65111cb76cec\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>listing_id</th>\n",
              "      <th>name</th>\n",
              "      <th>host_id</th>\n",
              "      <th>host_name</th>\n",
              "      <th>neighbourhood_full</th>\n",
              "      <th>room_type</th>\n",
              "      <th>price</th>\n",
              "      <th>number_of_reviews</th>\n",
              "      <th>last_review</th>\n",
              "      <th>reviews_per_month</th>\n",
              "      <th>availability_365</th>\n",
              "      <th>rating</th>\n",
              "      <th>number_of_stays</th>\n",
              "      <th>5_stars</th>\n",
              "      <th>listing_added</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>13740704</td>\n",
              "      <td>Cozy,budget friendly, cable inc, private entra...</td>\n",
              "      <td>20583125</td>\n",
              "      <td>Michel</td>\n",
              "      <td>Brooklyn, Flatlands</td>\n",
              "      <td>Private room</td>\n",
              "      <td>45$</td>\n",
              "      <td>10</td>\n",
              "      <td>2018-12-12</td>\n",
              "      <td>0.70</td>\n",
              "      <td>85</td>\n",
              "      <td>4.100954</td>\n",
              "      <td>12.0</td>\n",
              "      <td>0.609432</td>\n",
              "      <td>2018-06-08</td>\n",
              "      <td>40.63222</td>\n",
              "      <td>-73.93398</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>22005115</td>\n",
              "      <td>Two floor apartment near Central Park</td>\n",
              "      <td>82746113</td>\n",
              "      <td>Cecilia</td>\n",
              "      <td>Manhattan, Upper West Side</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>135$</td>\n",
              "      <td>1</td>\n",
              "      <td>2019-06-30</td>\n",
              "      <td>1.00</td>\n",
              "      <td>145</td>\n",
              "      <td>3.367600</td>\n",
              "      <td>1.2</td>\n",
              "      <td>0.746135</td>\n",
              "      <td>2018-12-25</td>\n",
              "      <td>40.78761</td>\n",
              "      <td>-73.96862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>21667615</td>\n",
              "      <td>Beautiful 1BR in Brooklyn Heights</td>\n",
              "      <td>78251</td>\n",
              "      <td>Leslie</td>\n",
              "      <td>Brooklyn, Brooklyn Heights</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>150$</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2018-08-15</td>\n",
              "      <td>40.70070</td>\n",
              "      <td>-73.99517</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>6425850</td>\n",
              "      <td>Spacious, charming studio</td>\n",
              "      <td>32715865</td>\n",
              "      <td>Yelena</td>\n",
              "      <td>Manhattan, Upper West Side</td>\n",
              "      <td>Entire home/apt</td>\n",
              "      <td>86$</td>\n",
              "      <td>5</td>\n",
              "      <td>2017-09-23</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0</td>\n",
              "      <td>4.763203</td>\n",
              "      <td>6.0</td>\n",
              "      <td>0.769947</td>\n",
              "      <td>2017-03-20</td>\n",
              "      <td>40.79169</td>\n",
              "      <td>-73.97498</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>22986519</td>\n",
              "      <td>Bedroom on the lively Lower East Side</td>\n",
              "      <td>154262349</td>\n",
              "      <td>Brooke</td>\n",
              "      <td>Manhattan, Lower East Side</td>\n",
              "      <td>Private room</td>\n",
              "      <td>160$</td>\n",
              "      <td>23</td>\n",
              "      <td>2019-06-12</td>\n",
              "      <td>2.29</td>\n",
              "      <td>102</td>\n",
              "      <td>3.822591</td>\n",
              "      <td>27.6</td>\n",
              "      <td>0.649383</td>\n",
              "      <td>2020-10-23</td>\n",
              "      <td>40.71884</td>\n",
              "      <td>-73.98354</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a20489d-8369-4262-9a34-65111cb76cec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\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",
              "      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",
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              "    }\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-5a20489d-8369-4262-9a34-65111cb76cec 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-5a20489d-8369-4262-9a34-65111cb76cec');\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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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Task 2.1.2**: Similar to the `coordinates` column, the `price` column has some unwanted characters, such as `$`, which are attached to each `price` sample. We first need to remove this character and then convert the column to its correct datatype, i.e. `float`. Finally, visualise the price distributions.\n",
        "\n",
        "To solve this task, you can refer to the following methods:\n",
        "\n",
        "- `.str.strip()` which removes a specified string from each row in a column\n",
        "- `.astype()` casts one column from one type to another.\n",
        "- `sns.distplot()` to visualise distributions."
      ],
      "metadata": {
        "id": "tI1TDJ0ewAbP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# compute the price mean in the dataframe:\n",
        "#df_airbnb['price'] = df_airbnb['price'].str.replace(\"$\",\"\")\n",
        "df_airbnb['price'] = df_airbnb['price'].str.strip(\"$\")\n",
        "df_airbnb['price'] = df_airbnb['price'].astype(float)\n",
        "df_airbnb['price'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "XqKMTOX6z_OX",
        "outputId": "4eaed596-ff35-4969-af24-3fe6449b7a11"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-61-81ebf868e60b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# compute the price mean in the dataframe:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#df_airbnb['price'] = df_airbnb['price'].str.replace(\"$\",\"\")\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"$\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdf_airbnb\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'price'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\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/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5485\u001b[0m         ):\n\u001b[1;32m   5486\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\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   5488\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5489\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\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[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/pandas/core/accessor.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, cls)\u001b[0m\n\u001b[1;32m    179\u001b[0m             \u001b[0;31m# we're accessing the attribute of the class, i.e., Dataset.geo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 181\u001b[0;31m         \u001b[0maccessor_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_accessor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\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    182\u001b[0m         \u001b[0;31m# Replace the property with the accessor object. Inspired by:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    183\u001b[0m         \u001b[0;31m# https://www.pydanny.com/cached-property.html\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/strings/accessor.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data)\u001b[0m\n\u001b[1;32m    166\u001b[0m         \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mStringDtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inferred_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\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    169\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_categorical\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mis_categorical_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_is_string\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStringDtype\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/strings/accessor.py\u001b[0m in \u001b[0;36m_validate\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m    223\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    224\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minferred_dtype\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mallowed_types\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 225\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Can only use .str accessor with string values!\"\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    226\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0minferred_dtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    227\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: Can only use .str accessor with string values!"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# remove the '$' character from the price column and display the dataframe.\n",
        "df_airbnb['price'].head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Z7uaF3RezUNy",
        "outputId": "481fc218-f099-4f9a-ca60-ecc59b08b922"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     45.0\n",
              "1    135.0\n",
              "2    150.0\n",
              "3     86.0\n",
              "4    160.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cast the price column from object type to float type\n",
        "..."
      ],
      "metadata": {
        "id": "z59jjv4S0UxE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# compute the price mean in the dataframe:\n",
        "..."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W7IznPrc0cNz",
        "outputId": "8c970187-bbea-47e9-a187-ec7e505a56b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "150.90512217564665"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize distribution of prices\n",
        "df_airbnb['price'].hist(grid=False,bins=100)\n",
        "fig= plt.gcf()\n",
        "fig.set_size_inches(4,4)\n",
        "plt.show()\n",
        "\n",
        "sns.distplot(df_airbnb['price'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "_pdjKdnA0hZV",
        "outputId": "983356fa-51ec-44f7-885c-41c517e95670"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 288x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1812cebb90>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Task 2.1.3**: We can notice that the columns (`last_review` and `listing_added`) are not correctly mapped to date columns. In this task, you should map those columns as `datetime` types.\n",
        "To solve this task, you can rely on the following functions:\n",
        "\n",
        "- `pd.to_datetime(column, format = \"\")`\n",
        "  - `format` defines the desired date format, such as: `\"%Y-%m-%d\"`"
      ],
      "metadata": {
        "id": "JIVsxRRQwECi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# map the last_review and listing_added columns from object type to datetime type:\n",
        "df_airbnb['last_review'] = pd.to_datetime(df_airbnb['last_review'],errors='ignore', format=\"%Y-%m-%d\")\n",
        "df_airbnb['listing_added'] = pd.to_datetime(df_airbnb['listing_added'],errors='ignore', format=\"%Y-%m-%d\")\n"
      ],
      "metadata": {
        "id": "JXltqLd6zUvA"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# display the header of both mapped columns\n",
        "\n",
        "df_airbnb[['last_review', 'listing_added', 'longitude']].head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50dt_JPs5CYh",
        "outputId": "2af7708c-214f-4cb1-ded6-bbafc836523d"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of       last_review listing_added  longitude\n",
              "0      2018-12-12    2018-06-08  -73.93398\n",
              "1      2019-06-30    2018-12-25  -73.96862\n",
              "2             NaT    2018-08-15  -73.99517\n",
              "3      2017-09-23    2017-03-20  -73.97498\n",
              "4      2019-06-12    2020-10-23  -73.98354\n",
              "...           ...           ...        ...\n",
              "10014  2018-05-28    2017-11-22  -73.95257\n",
              "10015  2018-05-06    2017-10-31  -73.93330\n",
              "10016  2016-11-27    2016-05-24  -73.96743\n",
              "10017  2019-05-31    2018-11-25  -73.96366\n",
              "10018  2019-03-12    2018-09-06  -73.96625\n",
              "\n",
              "[10019 rows x 3 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display the datatypes of both mapped columns\n",
        "df_airbnb[['last_review', 'listing_added', 'longitude']].dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRQkfWaM5Pm4",
        "outputId": "53e86394-9fec-4135-fcf4-ac08976c912f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "last_review      datetime64[ns]\n",
              "listing_added    datetime64[ns]\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **2.2 Text data issues:** \n",
        "In a dataframe, it is possible that the string values of some columns are stored on the wrong format, which include capital letters, undesired characters/strings, trailing spaces, etc. In this section, we will use string methods to clean specific text-based columns.\n",
        "\n",
        "**Task 2.2.1**: Given the `room_type` colum, we should clean its content by mapping its values onto lower case and remove white spaces. Finally, we will map some specific values onto new ones, e.g. `private` to `Private Room`, `entire home/apt` to `Entire place`, `home` to `Entire place`.\n",
        "\n",
        "\n",
        "To solve this task, you can refer to the following methods:\n",
        "\n",
        "- `.unique()` retrieves unique values in a specific column.\n",
        "- `.str.lower()` to lowercase all rows in a string column.\n",
        "- `.str.strip()` to remove all white spaces of each row in a string column.\n",
        "- `.replace({'':''})` to replace values in a column with another."
      ],
      "metadata": {
        "id": "lRm4_xWl560r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# display all unique values of `room_type`\n",
        "df_airbnb['room_type'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6giG5oxT_5fk",
        "outputId": "824083d5-beca-429f-aa2f-e847a86a4c06"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Private Room', 'Entire Place', 'Shared Room'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# map capitalized values to lowercase types.\n",
        "df_airbnb['room_type'] = df_airbnb['room_type'].str.lower()\n"
      ],
      "metadata": {
        "id": "WKPH5GdGAhiH"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# remove trailing spaces\n",
        "df_airbnb['room_type'] = df_airbnb['room_type'].str.strip()"
      ],
      "metadata": {
        "id": "sc6E6MraArD-"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace values to 'Private Room', 'Hotel Room', 'Shared Room', 'Entire Place', 'Entire Place'.\n",
        "mappings = {'private room': 'Private Room', \n",
        "            'private': 'Private Room',\n",
        "            'hotel room': 'Hotel Room', \n",
        "            'entire home/apt': 'Entire Place',\n",
        "            'shared room': 'Shared Room',\n",
        "            'home': 'Entire Place'}\n",
        "\n",
        "# map values given a dictionary as parameter\n",
        "df_airbnb['room_type'] = df_airbnb['room_type'].map(mappings)"
      ],
      "metadata": {
        "id": "zB9fizzeAxOf"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **2.3 Data range issues:** \n",
        "In a dataframe, it is possible that the range of values of specific columns is not valid. For instance, some values might be lower than the minimum lower value or larger than the upper valid limit.\n",
        "\n",
        "**Task 2.3.1**: In this task, you will analyse the `rating` column and remove all undesired values. To solve this task, we can use the following methods:\n",
        "\n",
        "- `sns.distplot()` displays the distribution of the values of a specific column.\n",
        "- `dataframe['column'] > threshold]` masks/retrieves all values at the given column which fullfill the given condition.\n",
        "- `.drop()` helps to remove a specific column by considering the given condition.\n",
        "\n"
      ],
      "metadata": {
        "id": "lzdBlLj48su6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# display the rating column using distplot()\n",
        "sns.distplot(df_airbnb['rating'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "bdQfJ7Px_6UA",
        "outputId": "4c2e6ea5-b8bb-4ac8-e48e-b6af4c58bed1"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f181306f890>"
            ]
          },
          "metadata": {},
          "execution_count": 87
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# select all rows whose rating > 5.0\n",
        "print(df_airbnb['rating'][df_airbnb['rating'] > 5.0])\n",
        "unvalid_indeces = df_airbnb['rating'] > 5.0"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1X8k7nSuGAIv",
        "outputId": "5bf29565-6100-443a-c13d-5ae752c4e869"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Series([], Name: rating, dtype: float64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# drop all the undesired rows by their indexes\n",
        "df_airbnb = df_airbnb.drop(df_airbnb['rating'][df_airbnb['rating'] > 5.0].index)"
      ],
      "metadata": {
        "id": "u_nO04zgGIRI"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# display the rating column using distplot()\n",
        "sns.distplot(df_airbnb['rating'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "Jfg6to1LG1Q-",
        "outputId": "cfd0689a-6e83-4173-8224-dd14e9c6b906"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f181315b610>"
            ]
          },
          "metadata": {},
          "execution_count": 96
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# select all rows whose rating > 5.0\n",
        "print(df_airbnb['rating'][df_airbnb['rating'] > 5.0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kkxfJumG7RR",
        "outputId": "a9d3ba11-137b-45dd-876b-421effa56612"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Series([], Name: rating, dtype: float64)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### **2.4 Missing data issues:** \n",
        "In a dataframe, there might be some missing values. There are different reasons including: data acquisition problems, storage issues, typying errors, etc.\n",
        "\n",
        "**Task 2.4.1**: In this task, you should visualise the missing values in the dataframe to provide more insight from the data. To do so, you can use the following methods:\n",
        "\n",
        "- `msno.matrix()` visualizes a missingness matrix\n",
        "- `msno.bar()` visualizes a missngness barplot\n",
        "- `plt.show()` to show the plot\n"
      ],
      "metadata": {
        "id": "LvctR5wH9eOQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# visualise missing data in the dataframe\n",
        "msno.matrix(df_airbnb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 711
        },
        "id": "72OClCY1_7Aa",
        "outputId": "ca67ae91-7b90-4026-f301-01b724c99e7e"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1812cce9d0>"
            ]
          },
          "metadata": {},
          "execution_count": 99
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display missing data as barplot\n",
        "msno.bar(df_airbnb)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 747
        },
        "id": "9ctXA-67KKc_",
        "outputId": "5c66a4cf-6dd2-4c1d-db0e-64625c919a7d"
      },
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f180ea59850>"
            ]
          },
          "metadata": {},
          "execution_count": 104
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ],
            "image/png": 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qav8MRd53k1xl3P4VSa6V5PkZjrx9YVX9xFj6bRnm2nQym87+OY+5No/cppPZPHKbTmYrsOpD9C3/7yPjn0jy8SQfSHJWkm8neVGSy43b3C3JB5N8I8nP5+KPEV4+yWuTvCLJAat+LjJb70VuMpPb+i4yk1unvA7NcGG+tyW5apLHZ7gA3913c7+rZzg38AtX/RzMtX1nSXJIhjd/PpfkPzN85PkLSR6/abtnZTh90quTnJ3kZ8b1xyb5UJIjVv1czLX1XmQ2KzP7p7kmtzVeZCY3mV26l5UPYKsvGd5VemGGj3gcMa47Isljx4n+liQ/PK6/fZLXjy+WXp/hAmyvyfCxwR9b9XOR2XovcpOZ3NZ3kZncOuV1SJJ/G3O56rjuJmNWf7qR6Q7ut238+rIkX8xwqoJa9fMx19Z7SbJ/ho85vzXJjZJsT3K7JO8fs3lOkv3HbR87rvvcmN/2hce5/Kqfi7m23ovMZmVm/zTX5LbGi8zkJrNL/7LyAVhakvxTkudvWnfZDFcs/lKSUzJecC3DxwUfkOQNSd6Z4R2rI1f9HGS2byxyk5nc1neRmdwu4Zz2S/Kn43O/2qbbTshwlPyNdvMYt0pynVU/lxVmaK5Ny+saST6d5Jez8EZPkpsn+XKGP+aev7D+yUnunIvfANqyFwMz12TWIS/75/zszDW5yWyNF7nJbF9ZVj6Arbxk+OP40CQfTvJH47oDFm7fluGjIF9L8tJN9z1o8/ZbYZGZ3GS23ovcZCa39V2SXC/JYQs/b3zU9Ogxp+cnOXDV41y3xVybndstMpR6txt/3n/htmdkuDbBRUmOX8x61eNecWbmmsx65Wb/nJ6ZuSY3ma3xIjeZ7WuLi7quUGvtotba1zO8s/SQqrpea+07VbVtvP27Sd6U5Lgk96qq+y7c/fzx6wU9x7xqMptHbtPJbB65TSezeeS25xYyOaO1ds7G+ja+im6tvSPDaQuOyXBam1SV14gjc23PVdXBVfWb448fT3JGkkdX1VVaaxdU1UHjbftlONrqpUnuWFU/nHz/hYW3GnNtOpntOfvn3jHX5pHbdDKbR27TyWy1/LG1QlVV47evyHCBtRdX1Q+14ar2GzvAuUn+JsM5+268cd+FP6Jb31Gvlszmkdt0MptHbtPJbB657V5Vba+qyyb5oU3rt+3g+2ckuWKSxyWKl0Xm2p6pqkOS/HOS46vq+uMfeH+f5GZJnlJVV2ytnVdVRyV5WIYLRD43w3UMrrGqca8Tc206me0Z++feM9fmkdt0MptHbtPJbLUU8h1V1eWq6knjH8eLE/ijSf4kydWT/PHCDnDAePvnM5zj76iFHWZLkNk8cptOZvPIbTqZzSO3acby5VVJPpDk9Kp6U1UdmwxHu1TV9o3vx7t8Isn7MhwN+UPjY2yZvBaZa9NV1aFJPpLhExYHJrlPkrTWfjvDPLxrks9X1YeT/EuS17bW/qK19sEM56vekoWfuTadzKazf85jrs0jt+lkNo/cppPZelHId1JVl8lw1eInJTmpxo8ELrzr9JwkL8nwjtPLq+o6rbXvjNtcIcnlkpy+ld59ktk8cptOZvPIbTqZzSO3aarq4CTvTXL5DLn8zwznj39+Vb0iSVprF26U8lVVrbWzk/zvJDfIUM5sySNezLXpxrLv1CRnJrlLho82P6SqbpIkrbVjM1w48oQk70ry8Nba/cb73jbJeRn+yNtSzLXpZDad/XMec20euU0ns3nkNp3M1s/2VQ9gKxj/4H1Kho+C/1WGKxVfpqru34aPBm5rrX23tfbEqvpakocnObWqnp7hYjrXTfJjGT4+uCXIbB65TSezeeQ2nczmkdssD83wGu8RrbXTk6Sq/j7J32Y4/+OhrbU7jaX8tnbxUfLvy3BO4QdX1YuSnL+VXnSba9ONZd9Hk3wqyQNaa2dV1ckZrkfwExmOyk1r7Y1J3lhV21trF473vUKShyT5aoaycMsw16aT2XT2z3nMtXnkNp3M5pHbdDJbU20Nrix7aV+SXCvDR8Ffm+QqGSbxt5O8MhdfmXjbwva3SPK8DC9+Ts9wJMONVv08ZLb+i9xkJrf1XWQmt46ZnZjkYws/Hzh+vc6Y3UVJXr2T+z40yQ1W/RxWlJu5Ni2v/TIcaXVKkqtuuu2UJP+a5NDx59p0+y9mOF/pl5PceNXPxVxb/0Vmk/Oyf5prclvzRWZyk9nWXlY+gK2wJNk/w7n6Dh9/PjzJsTvYAbZvut/VklwmyeVW/Rxktm8scpOZ3NZ3kZncOmb2hCRfSXLkwroDMpwX8owMR8qfmeReC7dX73Gu22KuzcrsiCRXWvi5xq+PTHJBkgctrl/I+QEZSsGjVv0cVpSbuSazHpnZP+flZq7JTWZrvMhNZpeWZeN/ylzCqqpaa23jo4BV9QNJ7pfk6RmucP/Lbbh6cWq4yv2XVznedSCzeeQ2nczmkdt0MptHbtNU1Y9mOE3BS5I8vbV2xrj+x5P8dYYX4M9M8qHW2i+vbKBryFxbjhrOU/rRJJ9rrd1+XFdt4Q+PqjqktfaNVY1x1cy16WS2HPbP3TPX5pHbdDKbR27TyWz9uKhrJxsvcNp4Xr7W2teS/GWSRye5c5KXVNWBVXWtJM+qqt9f0VDXhszmkdt0MptHbtPJbB65TdNa+2SSn0vywCR/VlW/U1WPSvK2JO9qrb05ybOSHFVVh1RVrW6068Vc23vjeUjPTfKcJLetqvsn33+R4K1c9iXm2hwy23v2zz1jrs0jt+lkNo/cppPZ+nFR1xVqrX2tqv4qSUvyjCSvGr+/fZKbr3Js60pm88htOpnNI7fpZDaP3Hattfa2qvrpJCckeVySc5O8cPw+SW6c5KKtXrrsCXNtmnbxRYLfluTsDH/kvWzzEbh8P3NtOplNY/+cz1ybR27TyWweuU0ns9VyypoVWvjIyKEZzuX3exmuZH+71tpHVju69SSzeeQ2nczmkdt0MptHbnumhlMTXDHJZVtrp4/rrpbkpCSfS/JbGYp5Lwh3wlybr6oen+T4JLdsrb1n1eNZd+badDKbz/45jbk2j9ymk9k8cptOZqvllDUrtPDH72FJbpbkG0lubeLvnMzmkdt0MptHbtPJbB657ZnW2rmttS8slPE/nuS4JLdM8kette8q43fNXNsrr0tyUZKHVtW2VQ9m3Zlr08lsr9g/JzDX5pHbdDKbR27TyWy1HCG/YlV1QIaPkP9Skpu01j664iGtPZnNI7fpZDaP3KaT2Txym6aqbpjkCUl+IsnPt9ZOXfGQ9hnm2nxV9bwkJ7XWTlv1WPYF5tp0MpvP/jmNuTaP3KaT2Txym05mq6OQXwNVdWSSba21f1n1WPYVMptHbtPJbB65TSezeeS256rqwAxHxn+mtfa5VY9nX2OuTeOc1POZa9PJbBr753zm2jxym05m88htOpmthkIeAAAAAAA6cA55AAAAAADoQCEPAAAAAAAdLK2Qr6ofqqrnVdW7q+rbVdWq6lp7eN/9qupxVfXZqjqvqk6tqnssa2wAAAAAAKy/Pe2Zq+qgqjqhqs6qqnPH7W+zg+32qHuuql+pqr+pqs+Nv/PFuxjjtqr6rar62PiYX6mqU6rqqrt7fss8Qv66Se6V5Jwk/zTxvr+f5Lgkz0/yc0nek+SVVXWnJY4PAAAAAID1tqc9858neViSJya5S5Kzkrypqm6yabs97Z7vn+Q6Sd6S5Ou7GeNLkzwhyYuS3CHJg5KcmuSg3dxveRd1rar9WmsXjd8/NMmfJTmitfbZ3dzvykm+kOQPWmtPWlj/1iRXaq3daCkDBAAAAABgre1Jz1xVN07ykSQPbq29aFy3PclpST7ZWjtmXLfH3fOm3/tvSU5prT1wB+O7T5KXJbl5a+2DU5/f0o6Q3xjsDHdIckCGJ7HoZUluWFVH7NXAAAAAAADYJ+xhz3xMkguSnLxwvwuTvCLJHarqwHH1HnfPE/rtY5O8c04Zn6zHRV2PSnJ+kk9tWn/a+PXIvsMBAAAAAGCNHZXkzNbatzetPy1DAX/dhe2W1j1X1f5Jbp7ktKp6WlV9uaouqKr3VtXt9uQxtk/5hZeQw5N8tX3/uXPOXrh9d5Zz3p0kxx577LIeaulOPPHEVQ9hh2Q2j9ymk9k8cptOZvPIjV7MNXox16aTGb2Ya/PIbTqZzSO36bZYZrWExzg8wznmN9vcKS+je150hQyF/wOTfCbDOezPT/KYJG+sqlu21j6wqwdYh0IeAAAAAADW3cYZZ/ZPcqfW2n8kSVX9Y4aC/jFJ7r0nD7BK5yS5fFVtfmdk492JswMAAAAAAINzkhy2g/WbO+Vld8/nZDhby8c3yvgkaa19M8m7k9x0dw+wDoX8aUkOTHKdTes3zt/z8b7DAQAAAABgjZ2W5IiqOnjT+iOTfCcXnzN+qd1za+3cDEfC78xuLwy7DoX8GzNcEfd+m9bfP8nHWmtn9h8SAAAAAABr6nUZThtzz40VVbU9w+li3txaO39cfUl0z69JclRVXX3hdx+S5JZJ3r+7Oy/1HPJV9Yvjt/9t/PpzVfWlJF9qrb1z3ObCJC9prT0kSVprX6yqZyZ5XFV9I8mHMgR3uyTHLHN8AAAAAACst931zK21D1fVyUmeXVX7Jzkzya8lOSIL5fuU7rmqjszFR85fJsk1F8bxztbal8bvn57kAUneUFXHZzgi/9FJDk7yB7t7bsu+qOsrN/28cQnedyY5evx+27gs+t0k30zyyCRXSfLJJPdqrb1+yeMDAAAAAGC97UnP/KAkT0ny5CSXT3Jqkju21j606b572j3fK8mTFn4+euF3/UySdyRJa+2/quo2SZ6R5EUZzkLz7iS3ba2dtrsnttRCvrW2+eT4e7RNa+27GYJ78jLHAwAAAADAvmUPe+ZzkzxqXHa13R51z62145Ict4fjOyPJXfdk283W4RzyAAAAAABwqaeQBwAAAACADhTyAAAAAADQgUIeAAAAAAA6UMgDAAAAAEAH21c9AACArerYY49d9RB26sQTT1z1EAAAAC51HCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKCDpRXyVfXDVfWqqvpaVX29ql5dVdfYw/teo6peUlWfr6pzq+qMqnpyVV12WeMDAP2hW9oAACAASURBVAAAAGD9VdWtqurNVfXFqvpGVX2oqh68aZuDquqEqjpr7JTfXVW32cFjPaqqXjdu16rquB1sc2hVPbGq/rmqvlJVXx2///llP7elFPJVdXCStyW5fpJfSfKAJD+S5O27K9XH209JcpskT0hypyQvSPLbSV64jPEBAAAAALD+qupGGfri/ZM8LMkvJHl/kj+vql9b2PTPx9ufmOQuSc5K8qaqusmmh3xYkisn+dtd/NprJDk2yTuT3D/JvZOckeQ1VfXre/ucFm1f0uM8LMm1k/xoa+1TSVJVH03yr0l+Nckzd3HfW2Uo7+/QWnvzuO7tVXV4kkdX1cGttW8vaZwAAAAAAKyv+yTZluSurbVvjuveMhb1v5zkj6vqxkl+KcmDW2svSpKqemeS05Icn+SYhcc7qrV2UVVtT/KInfzOM5Nce1MP/aaq+uEkv5Pkj5b03JZ2yppjkrxno4xPktbamUneleRuu7nvAePXr29a/9VxfLWkMQIAAAAAsN4OSHJBknM3rf9aLu6zjxm3OXnjxtbahUlekeQOVXXgwvqLdvcLW2vf2slB4R9IcrVJo9+NZRXyRyX52A7Wn5bkyN3c95QMR9L/YVUdWVWXq6rbJXlkkpNaa99a0hgBAAAAAFhvLx6/PreqrlZVl6+qhyX570meNd52VJIzd1Cin5ah0L/uksZymySnL+mxkizvlDWHJzlnB+vPTnLYru7YWjuvqm6d5G8yBLbhBUl+Y0njAwAAAABgzbXWPlZVRyd5TYbzuifD0fCPaK29Yvx5V330xu17paoenuQWGc4pvzTLKuRnq6qDMny04MoZLgb7+SQ/meFk/Bcm+bWd3xsAAAAAgEuLqvqRXHzw9iMynLrmbklOqqrzWmt/2WEMRyd5bpK/WPbvW1Yhf052fCT8zt6pWPSQJEcnuW5r7dPjun+sqq8l+dOqOqm1duqSxgkAAAAAwPr63xmOiL9La+2Ccd1bq+oKSZ5TVX+VoXO+5g7uu3Fk/Nk7uG2PVNVPJPm7JG9L8tC5j7MzyzqH/GkZztuz2ZFJPr6b+94wyTkLZfyG941fb7CXYwMAAAAAYN9wwySnLpTxG96X5AoZzrRyWpIjqurgTdscmeQ7ST415xdX1Q2TvCnJR5LcYwdj2GvLOkL+75I8vaqu3Vr7TJJU1bWS3CrJY3dz3/9MclhVXbe1thjUzcev/76kMQIAwJZ07LHH7n6jFTnxxBNXPQRYOfsoAHyP/0xyk6o6oLX2nYX1N09yXoaj31+X5PeS3DPJS5KkqrYnuXeSN7fWzp/6S8dT5bwlyWcyHJ1/7l49i51YViH/ZxkuwPraqnp8kpbk95N8IcmfbGxUVddM8ukkx7fWjh9XvzjJo5L8Q1U9JcM55G+W5AlJPpjkXUsaIwAAAAAA6+35SV6Z5HVVdWKGc8gfk+S+SZ41lvQfrqqTkzy7qvZPcmaGa5EekeR+iw9WVTdLcq1cfLaYI6vqF8fv/6G19u2qunKGMv6AJE8at1l8mA/PKfl3ZCmFfGvtW1V1uyTPSvLSJJXkrUl+q7X2zYVNK8m2LJwqp7X22aq6RZLjkjw5yRUzFPl/muQprbWLljFGAAAAAADWW2vtVVV1pyS/k+QFSQ7KcJD3r2fh4O8kD0rylAyd8uWTnJrkjq21D216yN9I8isLP99zXJKhwP9shlPdbJyT/vU7GNbGdnttWUfIp7X2+ST32M02n81Qym9e//Ek91rWWAAAAAAA2De11t6Q5A272ebcDGdeedRutntgkgfuZpt3ZAe99SVhWRd1BQAAAAAAdkEhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpQyAMAAAAAQAcKeQAAAAAA6EAhDwAAAAAAHSjkAQAAAACgA4U8AAAAAAB0oJAHAAAAAIAOFPIAAAAAANCBQh4AAAAAADpYWiFfVT9cVa+qqq9V1der6tVVdY0J979BVb2yqr5cVedW1Ser6pHLGh8AAAAAAPuGqrpTVf1jVX1z7Js/UFW3W7j9sKp6wdgnf6uqTqmqG+7gcQ6qqhOq6qyxd353Vd1mB9tdsapeWFVfGrd7b1XdYdnPaymFfFUdnORtSa6f5FeSPCDJjyR5e1Vddg/uf7Mk701yYJKHJrlTkmck2baM8QEAAAAAsG+oql9N8tokH0xy9yT3TPLKJAePt1eS1yW5Y5L/meQeSfbP0Ef/0KaH+/MkD0vyxCR3SXJWkjdV1U0Wft+BGfrtOyb5/5L8QpIvJHl9VR29zOe2fUmP87Ak107yo621TyVJVX00yb8m+dUkz9zZHatqvyR/keStrbW7L9z09iWNDQAAAACAfUBVXSvJs5M8prX27IWb3rTw/TFJbpXkdq21t4/3e3eSMzMU6r85rrtxkl9K8uDW2ovGde9MclqS48fHSYbC/4ZJfqa19o5xuzcmOTXJ05L85LKe37JOWXNMkvdslPFJ0lo7M8m7ktxtN/c9OskNsovSHgAAAACALeHBSS5KctIutjkmyX9slPFJ0lr7Woaj5u+2absLkpy8sN2FSV6R5A7jkfFJcosk526U8eN2Lcmbk/xEVV19b57QomUV8kcl+dgO1p+W5Mjd3PfW49eDquo9VXVBVX2xqp5bVZdZ0vgAAAAAAFh/t05yepL7VNWnq+rCqvpUVf36wja76qOvUVWXW9juzNbat3ew3QFJrjv+/N0Mxf1m549ff2zG89ihZRXyhyc5Zwfrz05y2G7ue7Xx68kZ3nH42QwfA3hokpcvaXwAAAAAAKy/q2W4PukJSf4gyf9I8pYkz6+qR47b7KqPTi7upHe33eHj108mObSqbrBpu5/atN1eW9Y55PfGxpsCL2utPXH8/h1VtS3JH1TVDVprn1jR2AAAAAAA6Ge/JIckeWBr7dXjureN55Z/XFU99xL4nS9P8ntJXlJVD8lw4deHJ7nNePtFy/pFyzpC/pzs+Ej4nb0Dsegr49e3bFr/5vHrTfdiXAAAAAAA7Dt21Rf/YJKrZtd9dHJxJ7277c5OktbaV5P8QpIrJvloki9lOJf9ceN2Z015AruyrEL+tAzn49nsyCQf34P77srS3n0AAAAAAGCt7UlfvKs++vOttW8uPNYRVXXwDrb7TpJPbaxorf1TkuskuV6SG4xfL0hybpIPTnwOO7WsQv7vktyiqq69sWL8CMGtxtt25Q0ZTo5/h03r7zh+/cByhggAAAAAwJp7zfh1R33xv7XW/jND53z1qrrtxo1VdWiSu+Z7++jXJdk/yT0Xttue5N5J3txaO39h27TBv7bWTk9ycJKHJXlpa+1bS3lmWd455P8syW8keW1VPT5JS/L7Sb6Q5E82Nqqqayb5dJLjW2vHJ0lr7StV9dQkT6iqryd5W5KbJXlikpe01j4VAAAAAAC2gn9I8vYkf1JVV0zymQyF+v9I8qBxm79L8u4kL6uqx2Q4Nc3jklSSp208UGvtw1V1cpJnV9X+Sc5M8mtJjkhyv8VfOnbUH0zy5STXTfKYDEfIP26ZT24phXxr7VtVdbskz0ry0gxP/K1Jfmvh4wEZ12/L9x+Zf3ySbyQ5NsmjM5yT54QMpT4AAAAAAFtAa61V1c8neWqGC60eluT0JPdrrb183OaiqrpLkqcnOTHJQRkK+p9prX1h00M+KMlTkjw5yeWTnJrkjq21D23a7geTPDvJlZN8McOR+k9qrZ29zOe3rCPk01r7fJJ77Gabz2Yo5Tevb0meOS4AAAAAAGxRrbWvJ/n1cdnZNmdnuPDqg3fzWOcmedS47Gq7XT7OsizrHPIAAAAAAMAuKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAAAAdKOQBAAAAAKADhTwAAAAAAHSgkAcAAAAAgA4U8gAAAAAA0IFCHgAAAAAAOlDIAwAAAABABwp5AAAAAADoQCEPAAAAwP9l787jb6vnPY6/PmdqTgM3JM1SXUKGlCtlKJoM4aqUInQNGUOFNAiReZapIhehCBWVKXOKzN2kKEoTdWg4n/vH57s76+zzO79zqp+1f+fs1/PxWI/f2Wuvvfvub2vvtdb7OyxJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknowZYF8RKwTEZ+LiOsi4vqIODki7n0H3ufVEZER8Z2pKpskSZIkSZIkaekTEV9refGRQ+tXj4iPRMRVEXFDRJwZEfeb4PXLR8QxEXF5RMyNiHMj4pGL+G+tHREfjYgrIuJfEXFxRBw9lZ9nSgL5iFgR+CZwX2Af4JnAxsBZEbHS7XifDYBDgb9ORbkkSZIkSZIkSUuniHgGsMUE6wM4FdgReBHwFGA2lUffa2jz44D9gdcBOwOXA1+PiAcMved6wA+B+wAvBh4HHAbcMlWfB2DWFL3P/sAGwCaZ+XuAiLgA+B3wPODYJXyf9wMnAptMYdkkSZIkSZIkSUuRiFgdeDvwUuBTQ0/vCmwDbJ+ZZ7XtzwUuBg6iAnUiYgtgD2C/zPxYW3cOcCFweHufgQ8AfwK2y8yb27pzpvpzTdWUNbsC3x+E8QCZeTHwXWC3JXmDiNgDeBDwmikqkyRJkiRJkiRp6fRm4BeZ+ekJntsV+PMgjAfIzOuoXvO7DW13M/CZzna3ACcBO0TEcgARsSGwA/DuThj/bzFVgfzmwC8mWH8hsNniXtxp7TgoM6+eojJJkiRJkiRJkpYyEfEIYG/gBYvYZLI8+t4RsXJnu4sz88YJtpsDbNQeb9P+zo2IM9r88ddExCcjYs07/EEmMFWB/BrANROsvxpYfQlefwzwW+DjU1QeSZIkSZIkSdJSJiLmAB8E3pqZv1nEZpPl0TA/k17cdmu0v/dsfz9K5dSPB14F7ETNNz9VOfro52mPiP+iWjselJk56vJIkiRJkiRJkkbmIGAF4Kge/5uDwP3szBz0yv9mRFxHm94G+OpU/ofurGuYuCf8ologuj5I3en2sohYLSJWoxoKZrbHy01RGSVJkiRJkiRJ01RE3Bs4BHgtsFwnL6bzeCaT59EwP5Ne3HaDnvJ/a3/PGNru9Pb3gUv+KSY3VYH8hdR8PMM2A365mNduCjyfqpzBsg2wVfv3AVNURkmSJEmSJEnS9LUBsDxwAgvmxQCvaP++H5Pn0X/MzH+0xxcC60fEihNsdxPw+852k5l3Oz7DpKYqkD8F2CoiNhisiIj1qGD9lMW8drsJlvOpSfm3Az43RWWUJEmSJEmSJE1fP2PivBgqpN+OCtFPAdaOiG0HL4yIVYFdWDCPPhWYDTy1s90s4OnA6Zn5r7b6+8AV1NQ0XTu2vz+6sx9sYKrmkP8w8ELgSxFxKJDAEcCl1JQ0AETEusBFwOGZeThAZp49/GYRcS0wa6LnJEmSJEmSJEnLnsy8Fjh7eH1EAFwyyIsj4hTgXOCEiHgl1XP+NUAAb+m833kR8RngHRExG7iYmpFlfWDPzna3RMSrgY9HxAeAk4GNqHnszwa+OVWfcUp6yGfmDcD21B1ojwdOpD7c9p3hAVAVMnOq/ruSJEmSJEmSpPGSmfOAnak5398HfAG4FdguMy8d2nxf4GPAkcBXgHWAHTPzp0Pv+Qlgb+ARVM/611K98nfJzJyqsk9VD3ky84/AUxazzR+oUH5x7/WoqSmVJEmSJEmSJGlplpkLZcqZeTWwX1sme+1c4GVtWdx/53iqw/m/jT3VJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSDwzkJUmSJEmSJEnqgYG8JEmSJEmSJEk9MJCXJEmSJEmSJKkHBvKSJEmSJEmSJPXAQF6SJEmSJEmSpB4YyEuSJEmSJEmS1AMDeUmSJEmSJEmSemAgL0mSJEmSJElSD6YskI+IdSLicxFxXURcHxEnR8S9l+B1D46ID0XEryPixoj4Y0ScGBHrT1XZJEmSJEmSJEnTX0TsHhGfj4hLImJuRPwmIo6OiFWGtls9Ij4SEVdFxA0RcWZE3G+C91s+Io6JiMvb+50bEY8c2maViPjfiPh9e69rI+KHEbHXVH++KQnkI2JF4JvAfYF9gGcCGwNnRcRKi3n5fwObA+8CHg+8GngQ8OOIWGcqyidJkiRJkiRJWiq8ArgVOBjYEXg/cABwRkTMAIiIAE5tz78IeAowm8qj7zX0fscB+wOvA3YGLge+HhEP6GwzB7gFOBrYFdgD+BVwfES8dCo/3Kwpep/9gQ2ATTLz9wARcQHwO+B5wLGTvPbNmXlld0VEfBe4mPkVJUmSJEmSJEla9u0ylBefExFXA58AHkV1DN8V2AbYPjPPAoiIc6lM+SDgxW3dFlS4vl9mfqytOwe4EDi8vQ+Z+be2XddpEXEfYD/g7VP14aZqyppdge8PwniAzLwY+C6w22QvHA7j27pLgCuBtaeofJIkSZIkSZKkaW6ivBj4Ufs7yIt3Bf48COPb666jes138+hdgZuBz3S2uwU4CdghIpZbTHH+RvWcnzJTFchvDvxigvUXApvd3jeLiE2B/6CGBUiSJEmSJEmSxte27e8gL54sj753RKzc2e7izLxxgu3mABt1V0aZFRFrRsRzgR2Ywt7xMHWB/BrANROsvxpY/fa8UUTMAj5A9ZA/7s4XTZIkSZIkSZK0NIqItanpZc7MzB+31ZPl0TA/k17cdmsMrX8B1aP+KuA9wIGZ+ck7WPQJTdUc8lPpPcDWwE6ZOVFlSZIkSZIkSZKWca2n+5eoaWP27eE/+Rng+8Bdqelu3h0Rt2bmB6fqPzBVgfw1TNwTflEtEBOKiDcBzwX2yczTp6hskiRJkiRJkqSlSESsQM0JvwGwbWZe1nl6sjx68Pzg77qTbHd1d2Wbv34wh/3XImJF4K0R8dHMvPn2f4qFTdWUNRdS8/EM2wz45ZK8QUQcArwKeHFmHj9F5ZIkSZIkSZIkLUUiYjbwOeDBwBMy8+dDm0yWR/8xM//R2W79FqwPb3cT8PvFFOXHwMrAWrej+JOaqkD+FGCriNhgsCIi1gO2ac9NKiJeDBwJHJKZ75miMkmSJEmSJEmSliIRMQM4EdgeeGJmfn+CzU4B1o6IbTuvWxXYhQXz6FOB2cBTO9vNAp4OnJ6Z/1pMcbYF/gH89Q58lAlN1ZQ1HwZeCHwpIg4FEjgCuBS4bX6diFgXuAg4PDMPb+v+G3gH8DXgmxGxVed9r8/MJephL0mSJEmSJEla6r2XCtCPAm4Yyosva1PXnAKcC5wQEa+kpqZ5DRDAWwYbZ+Z5EfEZ4B2t1/3FwAHA+sCeg+0i4nnAVsCZwGXAmsDTgN2BV2fmTVP14aYkkM/MGyJie+DtwPHUB/8G8JLO8ADa+pks2DN/x7Z+x7Z0nQM8airKKEmSJEmSJEma9h7f/h7Slq43AIdl5ryI2Bl4K/A+YHkqoN8uMy8des2+VLh/JLAacD6wY2b+tLPNz4Hd2vutAVwF/ArYOTO/MlUfDKauhzyZ+UfgKYvZ5g9U+N5d9yzgWVNVDkmSJEmSJEnS0ikz11vC7a4G9mvLZNvNBV7WlkVt8z3gCUteyjtuquaQlyRJkiRJkiRJkzCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJk1RYmQAAIABJREFUkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg8M5CVJkiRJkiRJ6oGBvCRJkiRJkiRJPTCQlyRJkiRJkiSpBwbykiRJkiRJkiT1wEBekiRJkiRJkqQeGMhLkiRJkiRJktQDA3lJkiRJkiRJknpgIC9JkiRJkiRJUg+mLJCPiHUi4nMRcV1EXB8RJ0fEvZfwtctHxDERcXlEzI2IcyPikVNVNkmSJEmSJEnS0uHOZM3T3ZQE8hGxIvBN4L7APsAzgY2BsyJipSV4i+OA/YHXATsDlwNfj4gHTEX5JEmSJEmSJEnT3xRkzdParCl6n/2BDYBNMvP3ABFxAfA74HnAsYt6YURsAewB7JeZH2vrzgEuBA4Hdp2iMkqSJEmSJEmSprc7nDUvDaZqyppdge8PKgggMy8GvgvstgSvvRn4TOe1twAnATtExHJTVEZJkiRJkiRJ0vR2Z7LmaW+qAvnNgV9MsP5CYLMleO3FmXnjBK+dA2x054snSZIkSZIkSVoK3JmsedqbqkB+DeCaCdZfDax+J147eF6SJEmSJEmStOy7M1nztBeZeeffJOIm4NjMfPXQ+iOBV2fmIueqj4jTgVUzc6uh9Y8BzgAemZnfvtOFlCRJkiRJkiRNa3cma14aTFUP+WuYuHViUa0ZS/pamN9TXpIkSZIkSZK0bLszWfO0N1WB/IXU3D7DNgN+uQSvXT8iVpzgtTcBv1/4JZIkSZIkSZKkZdCdyZqnvakK5E8BtoqIDQYrImI9YJv23GROBWYDT+28dhbwdOD0zPzXFJVRkiRJkiRJkjS93ZmsedqbqjnkVwLOB+YChwIJHAGsAtw/M//RtlsXuAg4PDMP77z+JGAH4JXAxcABwM7A1pn50ztdQEmSJEmSJEnStLekWfPSakp6yGfmDcD2wG+B44ETqWB9+6EKCmDmBP/dfYGPAUcCXwHWAXY0jJckSZIkSZKk8XE7sual0pT0kJckSZIkSZIkSZObqjnkJUmSJEmSJEnSJAzkJS0zIiJGXQZJkiRJkiRpUQzk7yCDv9vH+tK/W0TMzMyMCH/X9G8VESuPugySJEmSpg8zD0m3h8HVEoiIORGxYURsGRF3A2jBnz+4k4iIwyLiOLC+9O8VEYcC50TEcpk5z1Be/y4R8WbgqIi4+6jLIknS0iQi3hYRTxl1OSRpKkXEplCZx6jLImnpYWi1GBGxCvBN4KvAj6jQ7/0RMcMf3EWLiLsA9wWeEhFvA0P5O8o6m1xEzAZWAzYGPmMof/t09y/3tSWyIvAi4H8M5W+faAb/HnV5lgYT1ZN1t2jW1x3nsUD/bhHxEeDZwOWjLsvSKCJmjroMkhYWEQcDp0TErqMui6Sli4HVJCJiOeAsYC7wCuDRwLeBPYFvR8TmIyzetJaZ1wEHAZ8B9o6Id7X1hvK3kw0/k8vMm4HXA+8DtgROjog5hvKL1v0Odvcv97XFy8wXAW8EDgVeaCg/ueF9bbCPDf56PFi0iJg1mIYrIlZuDd1+TxchIqKzXz06Ig6KiNnW15Lp1N1OwOPbv/1+LsIiGn8851iEFsY/uS3ndtZbZ0soM2+NiJUi4rERseKoyzPdDTUyrt468GgSQ3W2RkSsMMryLEXOpzrsvDoidhl1YZYWi/r999xjctbbssWToMltBawOHJyZp2TmWVTIvCdwd+BTEfGf4Begq4UHMzLzj8A7gFOBfSLiaDCUXxIR8fKI+GBEfC0inhURG4+6TNNR63A7MzNvAI4DPgc8HDjRUH5iQ6HV4yLi2Ig4ISLeFBFrWl8Li4jXRcT+g8eZeSjwJuBgDOUXaWhfe0JEfCAizmz73MMGI808Hiys/a7dEnW/gk8CZ1MdAU6OiHuPtnTTU2df2wc4HtgaeMRIC7WUab//7wFeA7edr3lMGDJoLGv/vkdErN3WzRt12aajiPgg8ETgKcC32n41q4V96420cEuBoZ7xxwBfAnYyLF20ofOPJwHHAk+NiFmjLdn0NVRnTwY+BOxm48/k2vnaV4C9gXsBr4mIJ464WNNeq7d57d9rRcSmUdNEe0+4SQzONVpd3ScitomIe4LnbEsr/4dNbhXgP4Bb4LYfjuuArwDPBFYGPhI1RYahQpOZ89oPxZ7AS6kL4jnAqyLimLaN9bUIEfF54CXAhtQ++DZqP3vkSAs2DbUOt7dGxLOoxp8nALdSF31OXzOBzsn2s4AvAPcHNgL2Ab4PPD0ilh9ZAaeZiFgP2A04uv2mAZCZB2MoP6mhgPRzwPrUiLP/Bj4FHDw48R5dKaefdlF8a0SsBPyY+n5+nRqxtzHwg4jY3mPowiLiqcB7qePmC1tHCi3C8D7ULo6fA9w/Ig7qrBMQEZsBZObgumAP4FvABcAFEbFz+96qiYjtgf1pjYqtoXEV4ATgPOA3EXFGROwdTsmykBa+DHrGHwD8C1geOALYxfO1iQ2d636UOve4ZPDd1cKG6uw44Erg0sy8sa33nGNIq5PBOezZ1HXBRsArIuIJoyrXdNfO/W9t//4QdX57IfAz4JiIuEu7fnef6+h01hlMqX0aNXvH2RHxAahzNutt6WJINbnLgeWAB7bH2S6U51HB1YuATYAPgsPIuyJid+BjwP9R0/08FjgHeF5EvB0M5ScSEYcADwWeBjw5M7eheqv9F/Awg+WFTwhbL4QPAt+jLvoeAJwIPBL4bNhTfiERsRVwNHAY8LTM3IoaEbQhsBP1uycgM/9ANSz+GHh7ROzVec5QfjEiYktqXzsc+O/M3IUKldenjq1rjrB401Ln2Hg0cDWwZ2YekpkHAp8G1gLWctqfBUXEatR52UeBd2bmZW39EyPieRHxUHuULqizD23Y6Tn6c+Bkqjfpg0ZWuGkmIl5PjVJ5Ynu8LdWD9FTg3cAV1PfzmdGmlxIAFwFHAbtSPUdXoYL4jYHTqenfNgLeCjzX37P52jXnYKTUT6hrg7nU/jYTeCeG8osUETtSdXUY8LrM/G7nuVntr9cGHRHxOKqD0+HUDAHf7TzttcGQ1jFsXkTsC/yS+p27nhqh98aoKeA0pBPGnwA8DvgAsDPVqLE38OWIWN5sbUGtcXYFKoT/FzVzx9ZUp6fnRsTJbTvrbWmSmS6TLNSw578BDxlctwDR/r0KNXTwD8AGoy7rdFmonhunUSMJVumsX7/V503AGzvrY9Rlng4LdXL9eeAjwJxOnV3Z6m3Ftm6NUZd1uiytzj4JfKdbL+27+WbqYPW/nfqcOeoyT4cF+B/qxHGjzrpTgYuBBw5tO7bfz+5npxp4zgD+Cuw1tN0bgXnAkVRQOvKyT5cF2Bf4NXCfzrrPApcCD2iP/U2buO7OpsLl2e3x06kRQK9sj1f1+7nA47WBy9o+N4O6sfwZVKPGXOAGYOdRl3u6LVSA8E/qnj9rtXUPA64DjmqPZ4y6nKNe2v70N+BXwC7Aq6gQeVZ7fhXgJODGdoxdddRlni4LNc3nke04eS1wSve6CVit1ev5wN1GXd7ptLTfsg9RDWXdc7YNqY4ol1NB/QqjLut0WVqdzQLeBXxx6LnXAR8Hvgis09aN/XG08/dtwBlD2xxBXZ+eCjx61GWebgvwGOp685XUdftKwFOBa6jOPE8YdRmny8KC11XbUxnaLoPfL+A/gZuBDwPLT/S6cVyG6m0/4DdUB8RBvrF/q7fXLOp1LtN3sVV48U4A/gy8KyK2yNq7ow0Z+TsVlN6bOjEaOxHx6jYctWs21fPlisz8e5SZmXkxdUJ+NXXTk3eCrXgdc6h6m5GZN0XERlSPmG8Cz8/MGyPiedS8kWM3rDcijuz2Tm5mUN+9f2Xm1W272e27eRR1cbc78MU2fc2tvRZ6mun0PLs/QGb+vq0/DdgC2C0zz4uIHSPirW2bsfx+tp5p3Rvefov6/TofOHYRPeUPAl4aEffou7zT2DrU9G6Dfe0r1CignTLzZ62X6YERsfoIyzhy3V6h7Zi5MrABcE1m3hwRz6B63x6SmcdE3ZzuTVTv0rE0+H62ET9k5p+A31LhwVeArwJrUHNXb0JdwBxgj8j5Wl3cg2rc3hn4SUS8kKrHA4GDIuJhOeZDoNt5xa+p3667UceCnYHfZfVgjnbesTc1v/dbgT3Gtad8RLwgIv472tzTmXkFNdrzCCp4f09m/l/bdvnMvBZ4MXA/qo7VZI3KXh+4vHPOFpl5EbW/3Uj1Zt7ZnvIla+rUW6j7wK0fEXePiB0i4sfAC6j6fAh1bbDKuJ7nwgLn+Jt1RpDdLSK2jIjHR8R5VNi3EnWN+qaIWHsUZZ1uOsfE7agZAT6ZmRdn5g2Z+VlqesYHAUdGxM6jKueoRcTyEbEJLDQ7wkbUjXC/l5lzI2JTqiPKycCBmfnPiHhMO/6O3Xc0IjZuo4yH6+1+bdXPWl60BzVTwKGZeXRE3CUinjJ43WhKr9vDi5LFyMyvA++j5pJ/f0Q8uB3oB8He8sAlVE+isdECgwcAz2bos7eLkh8B20TEuu3HIKNu4Pcb4MvUgeuAFsaMvfYjeyvwF2C9iHgo8EPgTGD/zLwhIjYEnkTdMGZsLozbvnYPakjb77vPZebN1FRI9+uEMjdHzbl5PVV/FwHbADv0W/LpY3AQ7xyYfwhsFDWFw+eog/uumXlB1Py3DwU2jXaTmHHUCfu2iYgt2rpzmDyUfwcVyq/Xe4GnkaHg7g/APYEtI+KLVMPPYF9bnuoh82DqYm8sxfwbWM2OiLtRPeL/QQXKO0VNlXECcAg18gdqup9NgX+MpNDTRNQ9Hr4XEUe2VXtRc5H+GXh/Zm7ZGtOuoY6vv8oxnhN9OFRvdXEOdax8CzUiY0+qJ+Rdqbp8Y0SsNa4Xdq3R4haAFoJuTU0btQ0VKAwulmdk5k3U/VhOpu5lsF9rPBsbEfFeqqHwEVRjLHBbKP8R4LVU6DIIlv/ZNlmF6uF3VZ/lnc4iYkZEzKHmqJ4TdSPc2zrktID+DOA+1Ei9B7XXjc01wrCIeFJEvKE9PJ3KOv5AnZ9dTZ2DbEfNkb4m1YlsrEXEc4BvUNcC51P727eoG+FeTo1m3JEaWXAv2u/huOscE2dTjT9/hzqna8eDr1PnbZtQ03XtPpqSjk47fp4JfLrlRt16u4XqjHhLRKwPfLdt++zWCXF36nxkrBqAWu5xF+reNO+KiAfDAvV2Le3Y2uroBKqzzpvb8WEP4AkRsVb/pdcdYSA/iUEvqsx8PzWEazVqTqtnRMQ6UT2Yn00duP44upL2L8vPgIdn5k8i4lER8fjOJt+hehEdEBH3yPk3el2ZqsePA9u3gGvstfq8iQpbtqLuUXA6NTXG9RHxH8BrqKDvpByzmxJl5uXAozLz+62Xy3M6T59LfQdfEJ0brrWw727AJ6h97ZTeCz5CQxdkwxdn51HDn79B3Z9gq9ZbeQ41zPIA4LOZ+edeCjtNtQaJbwNviYj/hMWG8gcB22TmuaMo76hMcPHfffxpqgHoe9Q0GNtm5vmtJ9bTgecDn8823/e4ifk37FuZmsrnSGDz9vRp1LQ0rwc+mJlHAzNbL6J3UNM/vGsExZ5OBiMVt4+IB2bmnzPzmZn57Mx8C0DUnNU7U40YPxhhWUeu09D4X52eV7+mpiN4BXWz5b2okP4o6nxkS+DxMWYjCyLigRGxQTt/zYjYLyKelpm/perlcmDviHgS3HYjtUEovx/VoPav1nFgLLTGw92pntuHZeZfu89n5qXA2a1X38zO/rgcFQb+lmo8G0vD37G2791E/cY9krqfyK0seIy9nrpGvZYamTGWvSJbiLU88BLmd8D5X+DlVK/4V2Tm4zLzitYQOY8K6mPcGjC6n7d1AngaNdf+eZn5SepYsA/wksx8QmZeETXnflCdnMZulPZEOvV4MTUab5eo3tzd0dg3UFPC3Z8xrLf2XXsTlV+8JSIe2Hn6D1RD7OFUR84zgf0y8x8RcVeqE+JdGLNjQsuErqOOow+hRlh0R45dBNw1Ik6irrFe3a4PoBp/9qTOjRc4/moay2kwb850XujMm0nNcfV56iB+JdVb9zLaPLjjtNCZc44a7nwNNb3KDp1tPk7N2/pRYDNq+P1zqF5qW09Ux+O0AG+g5j//BDU36Zy2vJIadfCFts89u+13VwNbjLrco9znqAP3RdSolGd1nnsV1bPqtFZn96Nu7nft0D45dvtaq48TgPcP1dnz22/YRdQcwrtQN776B5056Abf9XFdqAaKv1Pzjd6vs35bqmfan4F9JnjduO5rH6d6P76BapyAGt1ybvs+PoMKq45u9Xpw5/Vjta91jqMrU9M4fKP93q/Q2eZ5bR/7VavTj1AXLj9l/tzyY3dvDBacW3Tbdsw8sj3unrdtR/VQu7a7r43zQvV8n9v2qbcN9h9qztYfAXdtjx/WvsvzqCBw5GXvsY5Wbr9Rl1AjfPZu9fA/ne/dhtS1wK+p6d4Gr53R/TsOC3V+tgY1xeKBwHJt/XJUA+PWwD0728/u/HtN6tpgLjVNwcg/z4jqcHAvgjnU9dJmnedWpxrL5gHPatsENUrq+63+9qXCv4eM+rOMuB53a/U04f1CqNEte1MNGc8fdXlHXFePoRpez6Oumyb8zaLuAfGsVmcvGHW5R1RXw/esmT30+IfUtHj/1fn9mwMcTE3HdY9Rf4YR1dvgd+2x7TzsNOChneff3r6v5wHrtXUbUyNY/gJsOurPMKJ6G8wNv0urny/Srqna+uPb+lNp912hOgqc244Jg3ofq+uqpXUZeQGWhoUFL+5mU70UnksFNeuMunzTYaFaMS+hepPu1Fl/LPAn5t/IaS41rGbkZR5xfX2qHWjOpVowL6N6i86mQuc9W31eRvUY+gKw+ajLPR0W6oYvP6NuSvrszvr/AS5s+9q/2onja0ZVzumwUL1e5lLDAC+l5hp9T+f5vaj5bm+ggoXB/QoGz49ToDBngnWDwPRJVA+X4VD+kVRgdTPVK2FsT3zavnRzOxE8mwrbf0m7eKOmQfps+027sp1EPrfz+mV+X5to/6B6TJ3Q6m3DzvpZnX8/kZo673ft+3pE52R71r+zzNNxacfKL1E9cQc3AjuYmvbt4Z3t1gY+1o4XB3TWL/P72iLqrXtTsI2pOUf/2I6bO1BB1ueoxuxBoLAW8OBRl30EdTWj/b7/pJ2jzaMaxxb43lFT1gxC+V0XV+/L8kLdL+Qq2g0MqdGw57bf/HnUueybhl7zRKqR8UrGuCMA8xtxVmnHzyuozhEnML+B7P7t8TxqFPJZ7Zjw0/b8vtR1xQaj+AwjqLOJjqfRfrN+SAVWy9NpsKZCwfe3+h3n/S2o0XcXUddKv+w8N2to213b+cflVE/csauzoWPnrm0f+h7VoP3Utv6+VCP3n6jR7ntR52o3UFPPDl4/NucfLHgeu3mrj3nUtcCD2/rVgXe29We037XvUNesY9fhtVtvVMeAjagp8G6hsqBBR6eZ7TduLjW1zQXAL6gcbmw76yytyyBsGGttDsMc/J1sm77LNt116yUidqEu8C6iTrq/0tY/iJrfcHngksw8q62fkWM4l2ubJ+04akjgRdRFyyeok+0Dgc9l3dzkLlTPoeuBGzJz7oiKPG0M9pk2Nc1nqZPKYzPzI+35DamT8TWBv2TmD7uvG1W5+9aGPS9PhVHnUT0Q1qaClgOB4zJz/7btilQPwLnU8PqrBu8xDnXW5hp9f9b8trSpaa7qPB4cH54InET17nhDZp7fnn80sGpmfmE0n2D02tQ+H6dOpj+cmddGxObURcu6wKsy86S27QZUw9DcrCGZ47SvzcqaTuu2zxt1M9vvUlNEvX6i7TuPV8zMGzuPZ+aY3ai6HRe/TjXwnEmF8Pu2pz9OXaTsk5l/bnN3r0P1pv9le/1Y7GsDQ+doc7KmClkxa37WValpfA6jGrq/To1mvIQaNn7N0HuNVd3BbceH11KjQHfMzB91nht8nzeiLoLnUr91nx1NaUcrItahpsLbKzO/HBG/oIL2D1NB/UuoHnzHZ+aB7TXPom58+OnM/ERbN1b7WeccYw41VeWgN+R/UFOt/Jz6Pv6+fWd3pUaaBRUAvor6HTyFNm1GZl7d/ycZjaibAv9zaN2R1Pnu5pl5WWcqoNdTI1u+lpkntG3Han/raudpn6SOA6/NzKPa+u45ykupaTO+nJmfGn5+nETEPsAHqNGMSZ3frge8OTOPatP/fJi6L9LdqNGN78vMY0ZT4tEZOvf4NDWa51Kqvu5LdQB7Vdb0x4O6fSA168K3gK9mu/H3OOkcD1ahGrSvBP5GTd3zaOo87bDM/EHbfi/qPHclqvPJF7KmwVzg+kHT3KhbBPpeqIu1hVqMqBvLPZfOMGiXJa7TbsvxLtQB6Nu0XjKLeM3YtBAP1xUVvP+U1uulrV+dOjhdDTwTWGXUZZ2uC/N7Em1G9exboKf8orZf1pfu97A9nknNCflfnXVrU3P5zQM+NPza4b/L+tLq5xbgYe3xFq1u3gKsNUH9PLtt/zHgQRO831jsa0OfeVfqRrbfA7bs1gN14furdjxYdeh147avPZRqgF15aP1mVAPFAe3xnKHnV6Fu1Dd7XOpqEftJd6Tiru17+mbgxHbcfB51E82fU9MRTNh7ctSfZxR11/69G9Vg8V2qMfsxQ9seQoWB17S6fdeoyz/iuptJTTdwYDtm/owKEx44vF37u3E7Nuw96rL3XE+3fT9bfZ1GzZ2/J3W/hk0726zTvq9XDI65bX33XHiZP4YO1dng37OAh1Oh+gM7z+9A9Uz+LrBx5z3mdP69OdWp5zo6I/jGYaHuDXJp+/16QGf9mm39u4e2n0mb3mFQx6P+DKNeqA5zPwf+jwVHHXdHFqw57nUGPIAa7fOyQX203/2/U6N//mOoTu8P3Md6443UyJ3tgRWphoo9Wr2dQbtucFmgzmZRveF/AmzS1s1ux9V5wNfoTF8zwevtGb+ULeN2g6blqGDgWe3mJIP1q1CBwgeoH1DdDpk1uqD9+1TqwnhD4KBY8Eav3deMY8v6EVSvl0OBy3N+T+RZWT3Rnkxd9B0DPL3d9FBDcv6N035JTRuVwEsi4tmL2r7XAo5ItqNwRDwpIj5JhSuPpi6SB9v8iRoa+GZgv4j4SPe1w3+XZRGxLTX1xR6Z+YOIeEBWr/ePAS8FXhgL36H+y9SJ9z7UzYnW7D45DvvaBDc/eyEVWG1GDbEfbDcrMy+iLl62oRo7bjNO+1qzDnBiZv5jaP3gnhi7AWT1YJ4Nt9X1M4D9qQaNcamr23Q+8waddadQ52vbUb0g30QdCzaiwqn9qQuaRb3XWOgcE/YBPkPN530l1fP2tIh4dUSs1rY9irpx/Bvby//Yf4lHq/vblpm3Zt1M811UD/kDqV7ep7ZRn7dtFxHrZubvqJDmk32Xe8RmwAI3Hz2TmkN5X6rH3u/aNcLsrBu6Hkrtf4MbV9M5F45xOIZSozgHdZbt9/4H1I26V8zM8wbPU4HV3tTv33ERsUl7j5sBIuIh1H2nHkp1vvh5r59khNr39Trqvhf7AD+IiA9E3Xj5b9T1/pZtVNVtI8oy88rB68dhf+v+rkXE2hGxUUTclnVk3aT66bQRPhHxnLb+1sG5SKvPcaqzVSdYvS7V6Hr6oD6oKWuuAJ6WmX+NiHtD1WlmXtDqdmzqbVhEzKQaGr8DnJWZN2bmlVkjLZ5JHSsObb9jmm9V6prqnMz8TVs3LzNPpI4HjwNeHhHbTPTiHLORs8uCsQrkqeEec2lzfg1C+cz8OzVkd/9sU1zo9pkglH8udYOYNw0OUOMsIo7fV52+AAAgAElEQVSngoOVgB2Bx7eh0GQNeZ6VmddSofyfqAvj2aMq73Q3FMrvTk3PckREbDrioo1UROxB3XF9M2rUxX2pBsjbvoOZeTkVMrydCuW3G0VZp4HVqOHdf2oXIF+PiAdm5rOpnqSHAC+KiHt0grw51IiMlwMnd07Kx0Yn5Fu/rdqNunH3qsBzI2LNFjQMhkreRF3ELBSQjpPM/Hxmvi4iVoyIr0TEPdpT84APAY+NiLe3bW9uz92HOvlejeoFPpYi4sHAbyPi4+3fUHX2T2D3zHwLcCQ1jPwmqgFoLH/XhhvMImIL6vz2UGoUxhOp84x/USMaZ7WLZjLzJ1lD6++VmW/tteAjNjS8/iERsW9EPIG6ydzNVA/ll1INGl+KiC3ad3l34BdR0+j9vb1+mb+2iog9I+JY4MyIeMagA0lmHkvNd7s9df4xCP4Gx4Nk/vzoCxiHBrM2Rcj5EbH1YF3bv75GTQty//adHTw3j/pdeyawPvDFiFinU1e/pub3fkxmXtDTxxiJ4d+2LN/OzCdTv2kvpm4e/P6IOIU6PmxF3QNooZBqTPa37u/aM6jRUT+g9qMTW0dFOh2cbgJeFhHPbetv7r7fmNTZMVSHm7WHntqQujHrL9p2p1G95nfPzJ9FxJbAIRGx7vB7jkO9wUKNP4NRUytSI4FycK7RtjuDmn5lN+DN3UaicTNBR6cZ1LXBip1181r9fRY4B9iJ2k8366eU+rfKadBNv6+F2sHvSd206kaq59kKE2zntDV3vI67Q6SfwiRTiYzLQrWqnwVs23l8BjWcsntTocFNPO4CrDvqco+gnm73cD7mT41xP2re0pF/jhHW30rU6IqXUuHomsAbqIP626iQpbv92sDWoy73COtrRRa84dxLWfAGRB9i/vQ161ANZPtSN9/sDk0dq2kw2mfekWqY2KA9Xhn433ZcPZC6aAFYgZoD92rG8MaQnfqa3fn31lTvxoto0yJRvR8/TM0DfHqrwzcC51OjpgbHhrHb19rnXh94PjV1w4XAUe187uhWR4Mbu86g5iB95qjLPKJ6WnmCdTtT0wg+uLPutLb/DabGWH1Qf+3vQtMEjctC9bT9W1v+SU3FtW17bhbwKODH7bnTqGD56FGXu+c6ei/wByrY+3k7TnZv0L0cdZ+kee0YO7h530qtfq9kkuH2y/JC9WQfTE82c+i5l7U6ey+wztBzM9p3+ZTh143DwoLXlmtT11EL3byWusbfth1HL2j1eSY19dtYHj9bvezRzs+OaOcgR7a6+RKwWme7zahRoH8BNht1uUdUVx9l/pR4a3fW7wJcS00b8lU6U5hR57qHte/nvUf9GUZUbxOeL1Cdv/4O3Lc97p4Pf5C6ifUljGHu0a23dn7RrZvPUh00NxnafiY1nc3JVEeBsTtPWxaXkRegtw+64E6+CdW6dAXwNGC5znPdg/6jmSCwH9dlSU8CJzrpGdcToXZR8tF2kF61s34TqmX4MuDg21vHy9JC9Trozoe5E3DP2/H6GUOPx25faxdqX2sXII8beu717eTyWIZC+UXV4bK+MD9wOrzVzV/pzLXf2e49VOBycTtpvAE4aNTlH/VCDZecB+zWWbcC8HkqVP4ONc3Dh6kw/pBRl3mEdTU42b4rcGT79xOp+VovAe7e1q1PjaL6Rbt4uYAaqTEI42f1XfYR1dcif7+pIOaj7bj5I6ph6BrqpmmLrPtxWIC3trpZoPGGms7i753tvkpNR7NFe7wVNRXX3Uf9GUZUb91z/k2YP0JxU2r6xZ+13/9Ht21mUTfA/QBwKvCczuuX+f2NaqC+op2nrUE1/n+Saihbbmjb91INF1dRN0T/cjueHjyKsk+nhWq0OBt46dD6Q9qx9R0sHMp399WxuVYY+txPp+7BdUU713g7nbnjh163JfPPf8e5Q8BDqA4Ur2qPN6Sm+vlm+/tVWqNse/7+jGEHp6H97B3M75Czblu3AvMbIP/E/HsmrUiNZLwSOyHSzkNe2Xm8fqu339JprADuTjUIPZlORjeOSzse/IAa5TPoYLI+dZ3wA2DTzrb/SV1jjf39CZalZeQF6OVDLngzsPe0g8+g5fxvVE/52UOveU17fix7Wg3Vxe7AI4br8o7U/zgt1DyZX2j70U+pQCaYH9DchwpRLwaOGHV5R1RHa1EXc59oj/dr9fXkO/h+Y3WjyM7nfk87GbyKzg1gOs+/nuqV+07GtPfG8H7STgbPpOafPp/qMbr18O9V2yePo25Gt1f3PUb9OUZYd7OArwDfAtboPLcCcHz7Dv+SGqGxU+f5sToWdH6PlqN6il5IhcozqOHhF1Ph6CCUn0WNxNiIGil12w3/Rv1Z+qyv9u9tqdEV76IaHDds61dtz32jnb9dRIXyu42izNNloUYLDM7Tug3cW1Ghy0uojgF/pN2QmgoSDqJG7G006s8w4vrbhhpp90EWvIHhbsAPW70N3wh3lc6/l/nfNqoh8UJq+pTuaLLDqHPdu1IhwvKd555GhaY/pW48us841dlQ/XU7f23Y9qsrqOlSu9sNQvm3MxTKj/MC7EV1ijiCajA7gppi5SQ6N1tmwXPf5ds+++F27rLMn7cNf6+om6C/px0770N1kvgQdQ782ravfZpOKL+o91qWl7Z/dH/XjmvnFm8C1m/r1m/701/b797e7RzlOjodT8ZhP1tEHd6V6tk9j85IICpH+m2n3l5L9fD+y7ife7Q6WoNqnPgH8BzaORx17nsRNTr0o8C72/73Y8aoUXYclpEXoNcPWzvzpVQL+32pIVynU73R9mDhYeUn0WmBGpeFBS+K16KChHnMH5q12AP08HuM+jONsC7vS/USuhV4SWf9IJTfuNXvL+hcBI7LQk11cWDbv75DzTP6gttzoBna18bqwE6nAYIKZOZRvV7u1tbP7Gw76A2+UE/wcV2o4c0zqWkufsb8UH6iUT4zJvr3srwM18PQd+3l7SJks/Z4Zvu7EnACcD01PcFy3efHZen8xs8AHkmFnv85OM9o63enQvlLFnWcnGhfXNYXamqo69tx8XKqofE7wMOHtns5FWotMFpjnJYJvqM7USNVBseAe7V9by51MTxosF2B+dOH7N9nmafbQjWA3dz2oy+2dd3f+13bfvZ/wGMX9/9gWV2oRsT3MjSCkWqE/TPVQHZDe/yQoW3mDD0el2Po6lRDz4rt8V2oXpABPJgaNXAV8Lyh1x1MnQ9/nM4UeeO6UFP9XAi8oj2+HzV9yLfa9/YLtB7L3f2LauT+BnDSqD9DT/tat9Fn87a/3YNqmJ1BhX7/C9y1bXPPdgyYR/XEnTOKsk+npR0Xv0iNYr+pHTvfQuvMRHW2O+X/2TvrML2q449/JkaUQHB3d7cCpUBxDe4QggUJxbU4RQuUEty1LVa0P7y4u7sGh0CIQLLz++M7N+/Zm3c3mzTZu9l753nOk33PFc4djn5n5jvozPBD3Nsveb4Uc1sz+psLGbYbgH2jrhM6Z12PjJBfxpq6eNHtbSsl+tWVKKqsP7Xz/RzIYPYq2hPfQC0SslTnqvZcCm9Aq32oNtyfkYQHxuI0Nwo7/QF5cnRLrk/R2u0sutAYcNkAWYafjol1KLB8prsWvmNfxME5XdHfVqBOF4hJdjSNeTazDeM8lNxrGbgp+thTQI/xeC7ta/uhBFdzFf09k1BPzdE5GOKQHxz6zDbcKSi/fNHf0BZKPT2i8OYmQfnmdN/eS+ijF40Pex1CVzfUub9bHFJ+RtzfY3lelaEgz/inkOH/rqQ+20xnoPwHocuZim5z0QUlY/0BGWqzA/Cu1ECXbrk5bQlgtaLbXaC+LPfv30N/1xBAXsxtryHDz1nIq+98Kq++7Lt7IePO21GytTONNNgIeBEZimYtsa7yuWgujXn+MEQ9sF+M1StRQr+yg1P9UHTANsB0sT/7V7bPRUDzXdQH5U+lpBzBjM2vvyHyvO2NvLy/BS6NawdFn7sGWCH33PyIXuTsor9pEutrkZj7t4jf/RHouWRyTy/kpXx8Urco8DyiNtu16O8ousQc9ivy4P49isa7LvrXGSjJN8iRpwcCUdN1ojRjNT9Gc9fmQlEpY0D55NrcobepJmX72mqh5rzUId9fkBPsVcDIGMPpmWtqShg5W5ZSeANa7UO1WP1K8D3S2EttLWSR+hhZRisLsZKWjIwFqB9KAPMZshSvmOmuznN5gLQB2K3o72lFva0ZuluHxslg5o9JNg/Kl/VQl/aTHrHhuS3G4dW0wBjWRF9rt/x9ue9dHAEE/cglGEWAyxfo8DKWp3z8Ls2mcTx1vDQC5d8GflfW8Rm6yAyGiyDPx2+AKxAfade4dhrwHjWv29SrtCvy1G0A/lD097Sy7rIN8zTUjI1vkiStorEHfV+UcO3motteRB/L1R2OQKhZk7o7kLF1yfjdvaXvK0sBFs10gPjkP0OeVFni4CUQYPNWrA+3kQAwZdFdU3M6Nc/lIcAjSX0KtmwBbF30N7QFvcVeY0oE5m2e258cGnu5+Ypud9EldPQWik75Cnlr9yHnKEENlN8j93ypEiyTiwag5gTWFVgs/r495q9sbpsPgc9ZAtdpk+d3Bq7J67O9FQTWPRX97AIU8XMAjSlYZkN0NechZ4GuyNh9X25v0i51NA79dUAUbnegqJVeuesXUOOUn7OJd5ROb/HdpxGJz3P1c1FLkJvmWimlnnK66Y4Ms+vm53YEyl+Pos12BnrWeb4U60GZSuENaLUPFTXGJ8DVSV260X4OeRV9QZJ8s4wFeXG8AQyiMRfkurH5HkYtmUkKwOQB0tG0Y4C0jt6uQEadT1Go6e3Ausn1DJQfAexfdHsL1FM+iVoXxJs8bWwgRyBPl66555pKvlyqvob4W9+OfvY+OuT9kVpIdAbKf0JyaClrGd/NHwqrfAOB0KXyWEZeKwsmvzcA9kAeLWeiaKlRyNizTYzf34D9mnhf9zLNddTxGAKmQt7IDQio6p5cS0H5Neo9314L8v6sR//xT+DF5PfdMddlSUhXAf5EE6B8GQsC2xuAvvG7Q6wBGSifecp3jWu9KaFXX27fMDOwMALz+kRd79hPDAUeTu4dy0mnLDobly6b0M2eKJJgzqLbWbCOMk/ImWJ8/hJzV+YQloKlyyMg8CvgT/V03d5LjMdzgb3i964IQF45uWcaFFF2ZO65uxDNz8Bm3t/uxyza949E/Oc9kvpsvGbRBA9Hf/uFoAEqU0ERAb+rU/849aMZuyB6pK/QXri0Ue00pneeGRmB3iWcNXP3LhvXf6NEZ4Em9JbO93PF2HsJ7f0bYWmIYurb0N2+VN7w7b4U3oCJ/kHNh9AcEgvRYbn6hZGFeBVKDl6FPmZFIaj7xO908t2GGn3NclHXITeZZADp7q3V5qILCs/9EFgnfl8Rm6JHGRuUvyUm2tKFa9H4QLw1Cp8cRO2AMhVKPpd5yneO/rU1on3oWea+hmi1fkFepH1QwrkGFAbdl5rnsqGDzTBg/aLbXYCedifhsGX8Qfnlge2K/o5W1llXFKnyYMxTGU3IVsk9UyO6i3tjfns51orXGccBhXZ+GKZ2cOuO8mAcgYwZvWKsXogOJQNoTI2Xj15p96A8AlWeIQFasj6CovLeAeakBsZnnvFTIu++vwO9i/6OtlLQHvY9lPCrW9QZNU/5G6lFS+UpbkoB9uX0tT2i8Pk51tNXCNqjGK/7xbUHkmfa9fw1EXXbHXmR3hPrRen6V04fHYC1EXXn2whg2ZVabpUUpFkWGb3vKaPeELXsfwnnudh/7ENj4+FMMV5vQOeDTsgA9GTuvkYJXMugT3R2H4GiBb5AZ4Ksn2XG/87A3ggIvJcwfpRFR/GdM6Mz03skNJ4IdL8Z7Wdnq7NWXk4t18hYYH57LoiiZ/Zc3dbx7+KIlus9cnl+4vrdyIj2HSWjr0R7/3mS31MSkYkox+D7sRf5A42jpnoh49BbwCNlGZtlLoU3YKJ+TOPOvDvKwD4IUQ/0QB64N8aEeh6wJLJMXR4DovRgfKZHdAi+PKlLNzoPocPKN+QSciAw9TdKApDGN++AIgd+F7+PQPRIZyBA9DlgveT+ecklxSpbQWFYw4CjgT/mrvWJfjQcJfI7P+49I3ffQOStWxbP+HljgT42fi8U4/A6ZKz4DtiMxp7ypdo0xnfPRy10OeXPnKANTZk2QigKqgEdSEZRM8rmQeOpqEX8PI2MYmvGtdICV7GJfgOBoEMQ2PcOsD4CmC+OtWEfElC+jIUaULAe4gfOjLIrhY6+AT6iRlPQBRmDPgW2Lbr9ba2g/e5QctQDCJT/CIVHV8khZdQeCZyNAKujEDf8CGDTuKc3Mpz9CrxQdJvbcqEx6Nk99nbflmVfNi6d5Oq7ocR8eVC+A3I2sVgnOjT3nvZckOfo27EPuTGpTx1xzkCeys8jAHUY5fTyTsdeFuUzS5SnEXf+luSijeO+jjR2tivNvi3G2dLIoeQ9YAVqDhWroLPn32jMENANOQKsSQlzcSE+/WeBzeP3XbGOZtF3SyAjz3s0jmiZFyXAXQeYpujvaGWddYzx9ybCGqdAxrJHCRoaBMp/gDDItZJnV0YR7vNSYueJMpXCGzDRPqTxwvQvdGh7NSaHX1ByiVmA2REf+o8IOP4RHZ6XLKrtbUFnufouyGDxCY0zh3dE3FaPAcch0GZQspBtgg41e0zqtrelgkCsS+Lv/ihUd7v4vTvaWP4fEU5e9oK8jz8DDqQxfcMcJMnUgO2ij71ILtQNUbT8WKa+Fgv3yejAMic6kFyKDnKrInD+AwQydMs9W5rNdq5/3AcsldRXG5qmdZZt+i5CAPvbJMkyEWiQ9xjqhLyyHo5SxkToqYfjNchgvQwwIzq8PYLA5XWRIeNKBCAcWlJ9jelD6JD7IfJWW5dahM/esZe4GzlUrICA01+AI4r+hqJ1l6vrEv/OjsLGL0NekKmez0Fg/bqt1da2WGL8PRFzXBqlsgzySh5KzQDUCzgY2Lvodk8OBRkdj0HUn0ck9aVac6mdhzqh89LcuTWiBwJfvkYGxm6IJvSOtK+VcM9mUeZAoNX7yJCYenCnjmHHIqeL+4H+6XuK/pbW0lfy9+aIInUTahFSM6IotM9R7ovM4L0xosAYi86mDIXGhp3FECj/HKIIySimDkH40HVoD7c4cqL4kSTiuExjNHRwN8IznonxuVyuHy6JPOU/RVErmyLs7VVK6gyQ6O0XtM+9hyS/YNyzIMIqPwKOR7nhnkRGjzG0lkV/S1UmcV8pugET/YPES/oJsnJmlrurYhLZJX5PEYv+jmgTOWtR7S1QT+kkuigCsJaiZrVbLCbWNwjPg1jgM5B0ZuSZ8EqyAVidkh740Ia6G/JKOCXR40LogDIShQf2KLKdbaEgi/HbwFzxu1cs2i8ioP5oal7e3Wmc3C9bnBagThKZ9lwQyDJ//H0O8B8SjnME/P0ac928Rbe3IB2NCVOmZqT4J7Bses+43pH8XZpNUDK2LqSWgPphYJWmdJfo+vjYTJYyygwZxTZCVA3b5q5NH2PzbQTG9AJuDd2W5iBcR2czxr9zIc+q95G3fMcoO6Oon8HIyP00iWG2TGOzju7WRHvXFFzohKjy3iNCy6mB9aWMlqqjtxkQqHJQprPk2tpx7VRqwEx6vbRjtQV67Rh97xUaA6ilGqNJv+mJknm/FvPaI7m9WncEUv2AvCCfR84UpeMJzu23pol/Z0GRUo8ig21qqOiSe75n8nep+lt8864xb10CrJG7NgPyav4cGTAGovNB6aIJQh9pX1s/9muPh06eB1aIa1MCu8T4/DnKjyR5C8pQYr+aUu4uHHuyBuDwpD7dh8yFHBBHhc7eI/L/lKWg6IsNk98rhs5+BXZu4pnpY777Hhkk/0PNiFa6ea2MpfAGTNSP0UH3QQSIpl5DXyE+uh5RVyUDq+lsJwSCDkUeGzdlG0fkyfwf5M33BQq9HwkcHddvQNa+0oXeo2SHfUmSmACLICtoGlXwe0STtAkwX9HtbgsF2Cv60VbIS/5VlAz3uOh/o8hRIcVzpTgQt+Q7Y1zek/yeAXmDbw38oehvKFpvsdneExkUG9Chd5ye8rl3HIrokkrR7+ro4o/UQPnf5a7NmtuEb49oChYput0F6MlQCH0D8qrKaC9SCr0/xjq6S/zuQ7lpCTZEHkAZcDwHNfBq3eQgMgvyulqKhOaNEh9Qou98E+Ptv8BqhNEa7Xe/J6F3IwfylVx3PdBh99KkLqVteBl5KpduTMb35w2uLe4rCJSff0KebQ+FmnG6J3Ja+m+sizvG2vBauj4iUP5WBNb/g5pnfbvPIZLXWfy9OXLy2jPRxXIoIjsPym8KnJr2szKOWWRE/AElCp4qqe9ADQOZBgF9wxEWclgRbW1LBdHMZrQ0A+LfTxHGsUJy3+wob972JA5gZZjbog89jqKwM2qtNWI8ZkaMraLe8vMWsBaiXSkNPW/ooQ9yLkyjxDaNuf4hhBFtmHsum8M6IRxpsbSu6O+qSiv1n6IbMFE/Rl7KXwDHxO/50eHkJmoet/tQMs/anI7SDdCKyNp5HEoocS4CRh8nQmqQtXOjmJRPATaJ+uURgH9C0d9UgA5vQIe6BmTMGBT1XdGB7j4EWM2FwscfLOOk2twGGUULNCAw/jpqVAVzxZgtJaic09E6iBf4AhpHr3RBnHxvISqHWVDkyps09gRv95vGJvS2E/JouTD0dzXafP+HZqjJcnPjvtE/BxT9Pa2suwxUyDaD61ID5VeOur4IbFg0fs8a8+CNrd3etlJiDGYJv85K6jM9zoqM3gflnivrGN0rxlfKNZqC8uvTBJ1Pc+tKey65+Wmm0OGTiNrniZj3ZkDRUy8Q1CtlLE31ERTFeFns33ZK6jsgr8hHgb8W3f6iCwIQWkzjmdd32cZosm52QqDy/dSSKP8LnUs/Ql7wCyfPdYx+N+b5or+lIP3tjHKunE1CkxfXVkAg4EfAaYgG9FdKDCwn/eXs6Gvp2nA8cAs6p26Q1K+U7dnid1n3HjOjs9M51KL7uyBWhTeirEhiqM09Xxq9IV74XvF3tt/vhvCfO2MPt3XumdI5aNbRW0Z7141w2kTMHEtRo6/ZKPdM7zrvKU1fq0r7A+R7o7Dwi6h5Cv0jmVAWRBaqvcq2YYzvTxft+WOyPR+YMqkfGBufJ6lj2UTWv83jntvqvbs9F5Sw9V2UPHMFxAc8mACjom8Njgn3XWTwGMvbu72XXF9bFEUUbEXiaQssS+MEdN2Qt8I7lNDTNqe/nZFH7duIruFHxGs4Q1yfG3lIZiGBQylZOGUTepsXeVP9JZn3u6IIlaEoomfp5P6xkuUA+yEQereiv6dgXWa6WQcdgN9DB73hwIm5e/fOP1e2gijdbkSHlL1z11aMdaF/mXWU6KMjAj8fY+w8Iq+hA/MGNHEoLkvJzUu9EHAwbXoNeZTeGnPWncghYCSwQ9HtbwM6WxpFYyxHzSlnuehfrwMHRN0syAtyKDmAoWwFGXs+Bi7K67Oleo/fpfD0pubNPWX0rXOBtaPuJuR5u3TsQUYhp52FxqW/shQUPTYE2J9ITBr1HRLdroD4lH9E0UGlzSOS090FyIFueeQ88TwyNt4d/e4BYLY6z5UW6Iv57Ufg0PidGtM2izH6KDJglHJM5vsIovb5nEjoGnXLo2iyBmDLqOuOHKCOLrr9BemsEd0pwh+/pDF9zdLUQPn1kFf9TIiXv5R7tqpE3yi6ARPU6GY2esgDfnSUK6iF2kyHPGNeJcKky1JiAsgn3miITVDm3Z1xHxpwAAK1HqVGX5N5+u2IjBqXJO8rxeIeeuyHQONMH30Q5+h3wJVRtwZKvnkCJeXyTnS2cyxI30afG0Ec9LL+Fv/OgTxffgL+VHS7C9BTOj77IG/uAQgo6Im8XUYDf07G5Jwo+fKpJCBCyTeRC8VGZ7ekLutju4cOrweWa0L3GRi/e9Hf0hZKMs+tgTyx7gP2Sa53rnd/WQvyUP5nzHXnICPkTsjA/UJze5f2WKhv8MqS9x2KouxWj/oMfJkDGWW/JTHYlq3kdLYFOsS9jUCXPQgP3LjeA/HK34sipRqAPYv+hoL1tzMC736hRseYeS2vBjyFDI2fI+eJHygpkFBHd9cgA+yYHATjuD/tq+tRMk/v2KM9hfb9WyCD4+6hw1WT+x6JsfkLkUOpzCXWgZNj3koBwJNivD5CLW/S7LG/Sx0qyr7f2A6d1b+NOexuwpEOOfB8Q+RqqcoYnc0U+soojzpSwz+mQHkwGhC9T1lzIuWNq2tEP3sG2CKpT0H5QWjvOxJYvuhvKEhvHXK/t0H0NS8BGyf1SyED46/oPPoSip4qtQNK2UvhDRjvBjdetA9GnpB/phZ6NDtwMQL+zkTeuZsjz7UfKJm3cujgZRof3uZGnGnfAncn9WkCsP1QmOVryOqZbrhTLuZSbIgQONqAPKi2j7oMQOhNDpTP9Fh0uwvW2YbIy/tIlGl8KeQ99CtwbXLfGugw8wFwSJn1hzghj0cUBAvnrl0TffBYagkRJ5jztT2W6GM/AfvG75QfeA6U8LsBeRVNm3t2X+QdU4HxjfWSgao9SJJSl72vNaOvGWK/MQoZd06hMQ9nqUD5+OaF69R1Q5641yV1WV+bm8pbKNPJjrFmDor187LoV1cCc+f01gfxj25UVHvbQgkdvIciGldFhuvPYt6fPu5ZAOVcuRw4nMZebKWc25I97cIowvjQFjyTN2g3UALKQRon/L0c0bjNTs2IfXHU9U7uuwt5NV9QxnWgCT1eGOtAH2BjZLjOvLw/QkbIsWgwyjpG6+jhj8CWwHpJXRfgaGTQqAD5sXV2ZsxT68TvbP2cHkXR7gL0LbqdrayTDoieeMmk7qpsLwH8DoHyz9EYlF869iYfAk9TUqq8pA91oXGkz6bIyPMyjUH5BREO9yJyuMvW3lIZs6uS9KGiGzDBDZf1/FsE4ge0umUAACAASURBVI1EXkNLxrX5kXdy5hnzPgrdWrTINhekp5kJmhDkbZtNGvMg/rkGGnPepqD8YUQSuqwu9+7SAKahx6uir52W1Keg/EmhzyvLpp86+jIUunYfjQ8k0wIHhZ6OiLo5oq+li1WpNtuhryljrhoCvJBc65r8fQ0CZ04o80a7ubGFPDY+I0e5hXi870LA+565a9tFn+w3sdvaHkuZ57YW6mdGBJg20JirukvRbStAF8uGHp5AlHipUWcgcpRYp5nnS7UW5L59fsRpexSNqQU/Rt5qsyR1Y41JSgL61dmbLhz7jwx874AceD6LfjhdM+8qXX+jsaOTAVMhMPme5voQY4Pxo8u0hiLP+I2QY9i2SX0nRB/1CgKaOyE6vUdy+9xSjM9x6HAtFBHVgKik7o1zQkcUrf0VkbS6Ko30VneeQqDyLnGOKFUOpEQH6bw0VehkxqRuuZjffkARLR2iz+2MIszK6HTYB1GsfIAM2rch56YlqeFGq1IflO+GHFH6FNH2ogs1I2wHZJS4hohkj/q6oHxcmzbRbwXGl7gU3oAJarTCZJ5AlrlZkVfp2zGRplQEc6Asz3NTJ2FCey809g7dAIXmbp0M/rmAv8ZG6Mzk3rFAAyoABhpzBO+e1Geg/NTIe3n+ottadEGhf68C/4jfHZJ+N0uM34epJXNND4Sl2AA1obdFkHGxgcbRAlMkf98Q18saFphutmdFxsXFCKAPWAYBVm+jPBmdUJRPPxTxM1v6riirk/AjtrfS1Pw9PvN6Mn7HoiGpSl19zYgS+pXa0APMhpK0PhJ7kPeB/jFup0NOEyfHvaWd++P788DyKijybu2kLvMczRxQxsr1U6aSWw8yLuW/AVfl7uuInAE+Q2DMtPnny1JyOtsGRV30zd3TFwHsG7TgHWUE4w04I+b3XwkPZWrngVVQvpWHkXH2JeBZSgbCj2t8IY/SxZBTxBo5/R5DEtVSlXHqemvgEpSv5vCW/j9oTyU3L22F6D+/RvlqTkyurUeNbuUNlFNkOCXNxYXO6IuhM9L3yBC2bHo9/k1B+XZ7ZhoPvWXzfVcURXBv9KkzaWwEykD5l0ii8ZLrpRmjValfCm9AixqZ28DERudBoGf87hAb8bcQKL9C2TY9dXSW57KaDSUyeTM22hmoMifium2gsed3qQ/Gzeg15QhOQfnO8W/pJtWmvhlZ2z+hxmmYhvj+FYEzpbSoN6GvbMMzPzJmvEtywKUxKP/HottbdEFJ+F5HkVANiON8m7i2QWx+hiPjz6MouuWwottdsM7Wj/n+dJIEyy14Lj3kjJUkrCpN6m0Gaga0HYtuTyt8b3ORKz0QWHoT8kz7DNgt1omh1El0WKaSG2MrIaBqMwT29Y76e2JNXSJ+Lx1z2wJFt7/oAuwaa8FP0Z8+QtEZqV47AgcigOZVkuizMhRytFnUcvd8jvimH43fM8Z4fRxFhnYj50Wf/F3avCvIueRyRE/2l7Sfxb8bojPX8+jc0Cm9XvbS1HoR/W+nGMv7tGabii7IOLb9BD67BwKZt0vqSnmWB3aI9eAs5AV/fezDBiX3zIEMktfHvnib5FrpzvLx3feHnj4EVkz7EY1B+XeRc8VY4HJZSqKPXsjY+iCKwn6PGq/+DMn9myCKmsHAykW3vyptqxTegHE2MNm4oJDTC1Ao6qV17s1A+VcQaF+6CRV5hy6Q/O5H0M4g2pXPYrLom0wmc1LjVBvU2m2e3AqNQfndim5PQToYK8QKAcnLEIlskbfGEMSlOX1yX7cYw3cB3Yv+lrZUkjG5MPJUeIfGhp8p6t1ftoI8X0aihGA7oRDdjxBYtWfcMx2i9vl3bLhT6pAyrg3boZwObyBAqgHo34Ln8uDLiyRUGVUZp/5mQh6SY/Got6eS6yfrACcCNyNamuVy966LjLK/xDhuoAlP3LIVxBn/RehwduSpdiEKIf+YSGqIPLL2QwmDS5UbKb4/7W9zxN7/QGSMHYgcUO4glzwTRUwdXba9G6IeOBJYMH7vjkLoeyIAdF0EKHwcfW7/6FufEKBCft0MfZc9AqipyNlsj9ydJA8XJaElQGf1YyfguTWREWgwQWuZ6rM9F3RWGkoSKTAB70j5q9u9zprQwSoILD40fs9OUIHGOL14HM+X9VzVDdH27ICcnd5CzgFjomKTv9eI9aPUyamBzsiI8ThBi42iP48AfkOgfOopvw3CPyqjbFUalcIb0OKGasMzFIFUDTThcYa4wb5CSSLL5v3SAwFQgxFf4U6hp32peXA3BcrPBVwEDCz6OyaHQsk8H+t8/3Lxb7Y47xh9amj8m1EQnB0bodtQEpMl0UFwGDke76qM0W0elH8D2KPodrWVgrgOHwGuoDEf9eIIQHgPWG1c+i1LoZaf4N8IqJoKedZejTwb92/u2eTvLGFfacGX/+H/QWk238hTeViM0ZeQoewrctyZce/8KFHpzkW3uy0UBNw9i5KMTolA9/OAn5G36EJxX09khPyeknmQ1tHZSohr+t/UgOMpUKLDIYjLOw/KW72/23NBIfMfAv+HchI0hN465+5bHjgNeXa/G/edntdTjN0nq33cWE46Y9FZJr/L0tfOQAaxFkfhZfpCRp5LKJmXN0r8/j3wh6b6SXP9hwQ0Hde97blEH+qPaMs6oXPnEGTUnhe4Lsbp2ckzpdmf5XTVVB6CDsi5bgwon1zrASwTf5cKY2tCVzMiTO2IXH3PZJ09i4RTPrmnlP2uKvVL4Q1osmGNwyPnRgmsfhcHlBWQ18ZLwFZ1nl0amKfobyhAZxaHk8diYR8N7EktfDILl0xB+c1Jwm6K/obJqVASz8c6370DiXct8mj5ESW22h55RX4LXBLXjwW+jGd+poSeLxOg42xMLoS85L8qWz9rRjczIQ/vo+K3JXPcCggMPD6538rWx/Lfi7w4bqJxCOrc6ODbAOzX3DsoIUdwVca/xP7jKwT0ZQk1V40+9lwcUjLAIBuzKRVXuwdemtHdVsh54uZ0rkfefTeHXu9AYP01iFv+qOS+0s1xMYcNBT4Fbs9d74SoCjKHgLmLbnNBepoq+XsAoqYZTfAp58djcu+iCMR/DZ21euSud6SE56xm9Fz6yNnQw3wIyDskfq9DM0m76zzfGZg6+d3u1wTkDPcD8jrO9v6dUF6yhUjyuuXHae496Z5tyknR1smhIGemNZBh9kEEwveJa0shKssG4Kai21qgjlL2ieWQg+YKwDRR1wlRvr2OnMJWR5FolyDD7lRFtLstldiDzI8iPfeLunQ/Ox+KOMsbgNr9nFaV8S+daKPi7g0AZnYRAvfeBJ539xHA02a2LTqUHGVmuPs/kmdfKKLNRYu7O/Ckmd2Lktn+DLzh7qPj+igz6+TuX5hZlhj3XLRo3eDuPxfV9slR3H2wmfXL9FsieQC4FbjIzH5BxrErgJPcfaiZ3YlAgz3NrKO772Zml6Dkyz8AX7n7MwBm1iEb6+1Zxvc73b0hnnnTzPoizuA3JmETJyfpgDZA85tZN3cfbmYe+nrazJ4C1jSz49G06MU2t/Ul+2Yz2xBYC22k+yBjWHbPB2Z2Svw8K9aGv8ZzlrxjP8Svuae7X9aKn1HJ5CeLIKD4Lnf/OuoOQ0bFPWJ9mAr4MdmXjMweLsNaUE/MbGpgL+D3iM/756jv5O6fxBjcDIH2OyAv+gHufmPcV4p1NJWYnz4ws4OQR/e6Zramuz8Q10eZ2W2AI0BmajPbyN1/Kq7VrStmdhnwhZmd4u7DEbjSC0VbrGxm87v7O9F/Rscz5pLXgNfM7B0EyPdFUVXZPaMRLUQlgLt/ZWb7IvDlUjP7xt3vKLpdBciPCEhe1Mx2QRz7m7X0YXf/DZ0TSjGvxVm8P3Af8Hbs/XsA16KI4jmAF8zsH+5+uruPrqeX3J5tADCrmZ3s7kNb94uKlzgrvWFmsyD6kJvc/fu43AuBpPcjXKl0EufybL6/EuFFs6Kz/Idmtpe7f2xmL6FIvEuBh4C3gemBdd39xyLaXqTkxlj29ztm9jiwn5ld6O4jzWwKdx/p7u+a2ZtovzbQzN5w90vb+5xWyYRJh6Ib0JyY2YLIu+oIZI0bbmadYjJ5HNFk9AIONbMdi2xrkWJmlqv6DjgFLTY3m9nK2YUUlEc8a31Q8rBKJkBKCMbj7oOBfYBb0KbxGuCXAFs6uPsQ5C1/EbCJmV3r7oPd/Wp3v6NMYLyZzWNmXRID4wZmNnNLnk1A+Vfd/dp4Pj/W26009a3u/jmiJNsYWDXTb+hrKjSfvRJ1pQPjM4k18RZkCFsWraWHmNl02T3u/iHi4b8OgfJLRH226RyIaKf2dPdLW/cLKmnLko5PM5s+/lwIeTe+F/V3Iyqprd39BTNbETjGzPq0eoPbsLj7D8CfUYLbWYCN4lJD7He/cPe/u/vqwFLuvmuZwXjQdwO4+0UosmA4cHh+vwvcjnIp3VAmMD7kPeDhODt1RPQDGyNKkfmAC8xswVg7DWpzfyJDEFAzU1ZR5nW1OXH3rxAt3CkoAXOpJMbkt8ChKGfNxcBBiE6qpe9I931lOJu+jPI1rIb2Z/OgPHizIlrU3ZEn7sHhZDKW4ToHFO6L6FreKSMYn5NpgNlQNA9m1hntUV5ADmR/j/oynatSMP4q1O8OQlQ076CIllvMbO5YP19AUfCnI0e8ldz92UIaX6AEbuaxjoLYOrI573xEBXpPnEdHxrUlEUZ5MVoPdjazKcvU3yoZD/E24KbfXEFgwn+AX4mwNxRKk4V1rYw2jI9SQsoVGoeorUyN26sDCj96EtE7rJJ7bp74t0drtLMq7a+gEN1rYmxeFHUdqFEj9UZg3xDgnqLbW5B+rgauit+7Ie+pzSfwfaXhhszNa/MgXttVgPmibhpq/NQ7oE3PNMibYwiwTdHfULD+OsTYHIiAlClROP0olNBwmtz98wB/yNVtgOggqvwFVWmyIFq821By+N0RJcYawN2ISmSpuK8nAqquJ7i+qzLWXLdi6K0B2Cy7HqVD8rsKefaxqC37ITDw/4CVm9Fxu18/89+KqHv+RtBIRd3hCGi/H1ggqV8L+GPye0Fk7KjyS42//kuRwLXOd+8Tc9ho4MLxeM5y77iehAKivRaEaewX+7Oh2XqaXJ8OeSh/BMzfjM4yasHSUibV0e1FiMbyKkQxOxQ4oOh2tbIOepLL4YOStz4HrB6/D4z+dymK0nsWmD33TCk5z6nRK/ZEToj/RQD7GlHfLdbTr5ER/GDkYPEiMoiDnJ6eKdP+oyrjVwpvwJiGNHPAQNa5J4ARRJIYGoPyKwDzFv0NBegsXYh3QGFYD5FwZSKQPgPlV4wJZYvQ5RLJhr064FVlvAsC+7Lktv2T+gyUnwrRIvUvon0F66Yn4lJuQHkdRiEO1xZvanJjvIxz3E5xCBkaevwQOCyuzR/rQgPKUfAGAuOPKrLNRRcUHn5jrAUr5a79Mw5sx5AD5ZN7snV1GWCtor+nKm2r5Oak+VA+miORF9qcKEJvJAL7Fov7poixPJgqgeu4dLo8cC85UL7oNrbVQmNQfvfof3eTc0IpSwkgYN9c3UnRn04lAVmogfIPICPalgh83zuud0He9P8o+ruq0vZLsnc4GfgTMv6PAgaN64yZmwP3j/66S9Hf1Iq664SMEM8DGyb1U8S/y4ROtmhCZxkYv3trtHdyKSjPyFmx93ga2L+e/tpziTm8Idt7IaP+nsCp8bs/ogHtG7/PifufoITnziZ02A14FRkq7oi/x+QLiesbIqD+ZyKvTex9uyMM4Kr4XYp+V5XxK4U3wL2x1S0WnbVRUo40OcIfELBcF5Qvc0GJNEegsKMlcteMmqf8aOQNMxQ4peh2V6V9FBons9o9qc9A+TQhUekWIpRMswHRrLQ4IqXOZvstYK6iv6cV9bZ5zGvHIW7l9YE7Q5cnJ/cNAM5EoOD6SX3p1gYURnk5Sur9BeGJDHRP7vlnotdpi25zVSbPEnu1/WJMzkDNuL8GMoy9CuyKQqBPiUPKkcnzpVsLxqHPeqD8b8QhuSrN6i4F5XeLvvYkMGPRbWtlPcyBQKfvyYGZyAjbgKgHZkvqD0FG7+Exbo/NPTdD8nfp1tSqNF+am8djXTiSGihf9946e91RlBBYRmDdStRA+FQvayFD9+/rPDeACowfl26nBXonv0szlyHHuRtpDCB3Qgnjp4q92rFAt7g2O3K0+BF4JO4t3X6NxtjklsjQP3f8XgRFIY8mF0WMIo6njL/7oBx7PwALFv1NVWm7pfCkrjk+q4sR8N4HTRJnm9k17v6yuz9oZkchy/vdZraJuz9UXMuLkeCxGpX8ngdttE8EznMlxCG4WjsB77oSHW6PvA5mRxbiy+O+UvKPVjLxxGvJrAAuNrMGd7/cla/A3P3X5F4vqJmtJjk+xx7ocPFvYF1gkJn19ySRYQvesR+1KIMPJ23r24aY2RQIzLsLONODCzMSzH2GuDbfdfcrPXggc8+Xcl5z9xHBMzoc2Bt5Bu3g7sPMrKu7j3D3LSPZ4bFIv98W2ORKJkMxs0VQ2O5XwMsu7uRsP/eQma2NAJjjUIK/54BD3P3CuK+U47M5cXdPEmo+Y2bHAJ2Bf5rZvMCHZVg/M0nXwHGJ1/KtNLj75WbWDfjN3b+cxM1sU+JKxHcgcBRwZozHy+LaicF3+2cAMzvf3T9x9zPM7EWUrO9Hd787rnd099HJ2LZqzFaSSm6fuhQyCM0IvObuj8XZ4Kq4/YS4b590XNfZ62YJ5EuXsybOBU/CWNhIZ2QA/xhRwo2R5HzQz92vaN0WFyepfloi7j5mn1u2uczdB5vZASiK8dJYKy8FPjGzpYG5gJdcuUYMOQR8iuhZ7kkxpzKJK4Fyd+ASFEHwnrt/ENdej7NWAzrXj87WWrRXazCzdYG9UA6v37v7WwV8RiWTixRtEcgKGvgfoXB7Q5aoYcgCtXhy3xooWelgFCJSGqsd2qjskH4zsBgCqDIuq1lQIr/P0ETxCI35IXskf5fGQlyVSV+QN0xmhR9QdHsK0kE6NhdAId+dkXfGAcgz+Rqga+65KZp4RxaG2q/ob2tlPfZGG8K/xu+OmV5izvsEuKLodhaso+Y802ZHgOgvwN+T+q7J3+tPqrZVpf0XBLD8iiLulknqM77NKWNNmJOSeqbF99r47FNz8//KwCZFf0PB+tugpXNVvb5VhjNC7C3Sc9LKKHT+2/zeAQHyY3nKj0uPValKUwXl7vkm+ltDrAm3J9dnRp7ywxG4NdaYjD78G5WXd14vMyAqruHAgXWur1am8wGi3M1YEsZ7nirDetDMt6fR7LtG3cwoMmpQ/J4DuBDlP+tSVFvbSkFsHRmedmWd6/Oi3AQjgf1y15ZBCecr2p+qjLMU3gB3B3FZvUTw3QKHIeDqihgE1wJLJvevRpLwpL0XBEZ1QpQXq0VdBk4tHJuYyxAA8xbiUt4O2DT0t3+dd5Z2UarKpCvIO+YeSpgALAekbI0y1g8COkfdVCjJ5ojY7HRGyTe3RonoetI49L60nJAIxHoUhd9noX+dkus3xTzXrei2FqWf5O+VEQfkKUBfajQ1c8bGeghwQXJ/t9y7KvClKi0uNA7jPSTms1uBhaKuyaSjZdp3oLDlWZPf69DCnAz19FTGcYrAlwaUf2C86d7KoDNkoP46PSNF/So0D8r/Cpyd9tGqVGV8C6ISHBZrwWJxBjgL+Al4PLlvBhSV10A4kCXXtiWXh6oqDrAewkG+AQ5P6ss0v6V73RlQBEEDtWTx49RB/h1Ff1PB+kxB+X5Rd0ScNb8B3keUZ4sX2c6CdZT2lw7Iw/2pWGdXz+/PYq/3b3RmzV8rZSLcqox/KZyyJmQ48IC7P2lm+6BFexd3v9HMvkMg1jAzu9jdn3P3/xba2taXZV20Myu7wmA2AmY0s+vc/Q0z2xlxKL+OwosOBIhQmzfQxruRuHtpQp8raT1x9y/NrK+7Dyu6La0t2ZiK8TgIAaTPeNBIufuPZnZ13H4qSjb0EuK8/bsHLUu8YyAa03t4LQyu3UlTtATu7mb2OPIu6B9z/88RTtkT6IEy2I9o3Ra3DUn62q7AXxHoPh0y3L5jZpu5+7tm9hfAga3MrLO793f34bl3lSZ0t5KWSzNjc3RCD3KGmXVFCfyOM7Pj3P1N1OfGkrLsO8xsRjTHj4hQ8U1QbodNWvJ8Mr7H/D8o2zg1s6mR99mJwE0+Dpq35Lm0306JeHDbrbj7q2a2kLt/Z2a/A4a5+wvu/riZnYQSa55mZniNvuZ4M+uEqG1uRh6AlVQyIbIR8AKRuyb2bsehCMcTzexsd/+Ti77mQuBud38u944pUK6MW1u15W1fPkWOOwPd/TpoTPfW3teEHJ3RBsCqCCAFeNTM/uCid2uSAi/3jn2B7YLy+Jt697d38cYUs5eY2S/ufqqZvYLydg0Grnb3d4prZTGS0LRl/aVDYG4vobPo1cBFwC5m9nSyN3vfzPYGBsf8l+7bWkyrVEm5JbOyFvMfj05rZn2QF3gHlHT0OsSHPiw4mG5Hi9LlwD6ecFK3dzGzHdAk0N/dLwsOyLeA2RBH8E0u3q9pgJ8z3QR39ebAX4Cd3P2BYr6gkrLK+HDAthcxs+URZdRZwEWZYcLM5gB+cfdvzawL8vw7ChnLrnD385J3/BH4B3Cou1/c2t/QWmJmXZL5amlE8fM58IG7fxbg+6PI6+oiBHBNiWjLzgf2dfcri2h7WxAzWxPIuOBvd/cPAvzbF+iFIs4+NLPZ4p5+wJpewtwrlUx8SQ/BZnYsAuXvBk5y9zcKbVwbkDCqno08+pZHOXwuaimIkgMSZvQS8aGb2foo/8D0wPHeQm7knM4GABsDW7r7T5OqrW1Fwgj0BfAworZ4OepXQaD8csBhqYHfzFZw96cLaG4l7UBiL/tfdPZcO+o6ufJH9UaRU72AVfLn9uZA1EpqYmbdMieKsurMlAPvcuA8hH/MhWiSpkHRFk/V002d/ATnoojjy1uz/W1RzGwG4O8IJ9rJ3a+N+rL2sWze6o4oouZBUQPXZwZEM1sGMXYY6n9jQPnkPaXUXyX/u3QY9y0TT8ysY/o7sSB9H9bKOdEgeDXxsO2BQrZ2RXzCpQHjQx5Am5pLzKxfDPTFEI/+Gcja29Xdv0PUNRm4tTNwATJsVGB8Ja0uZQPjQ+ZAvN23hUGxl5ldgoDTl8zsaES9cj06IG+UgfFhbAMlbtqkvYLxZtbPzLonYPzOaJ4bhMCEi81s7eg/qyJQfh/gO0RhcxZwSpnB+JBVUd6Vm1CfAR1YjkSJhM8OPX+K+L4rML6ScYqZXRAAe7MSnkMd4u8T0H5kU5RMcupJ3Mw2K2FIxN3PAe5E8/yLwKOJAcPG9Y4ESDgUeNbMppukDW9bsijKhzQtor4Y6/yQlzrgy9+AG8sAxoOiE1EExtLAqWa2ZNQ/jqIMngVOMrM9kmeehkZ7j0oqabHEHu55YImkv40KT9MhyMFuXkTXmH+2Aq1aIAkYX6pEpJnEuncUAuSPcffL3P1oBJq+ATxoZsuk+5F4rl6y4P4VGC9xJesegJy/rg7nz1KOywDRR5lZL+AZdN7cBFE/P2Vmfzaz6dz9eWBHdL66DJ3BGkkZ9VfJxJFW24RZ44zhO5nZsWa2uZnNndzWgDis1jSzKcOCtzEwjbtf5e6vt1Z724q4+2A0OdyMQPk9XKG7K6HQor8A25rZFBFtsDDyJh2IFq/ToNpwV1JJK8k0yLC4nJkdCDwB/BFF+TyOvP7mBXD3Ye7+GTTebLv72+7+SOs3fdKLma2FknrdZmZTmNkswGkowdxqaIM4P3CKmW3gkg2RF8fBiOuwr7ufEu8r5bwWgN6iKBnwFy4akc6hr38iT+UVgK4A7v5pBsaXVWeVjFvM7Ax0CHmwJffnQPmTkQfav939h0nXyrYrSdRnBrh3Q0bGZYHDzWwhqG+szp6pAyScCPzFSxBinxgzTkeUb6ORgWeJmOPqGjJyOtsf0Xj1b6ln/eQmTenB3e9A43d1tIZmIOkTyCj7OjDIzBZJ31GBCJX8D/IoohEcYGbZ3na0icoso2X8pcD2tQspqYMTiM5oNuSoOcLMOgO4+73ICaAr8IiZLZftRwJgzYPxe3o7pv+cEAlQ/kDEwvBCwc0pRLKzd0T73Iwcv7ZC59Dfo2jsPyNGCpBzxc6Ii3/vsV5YSSUTKK1OWWNmNwFro432NMgz8kR3/28c7K4A1kW88t+iBX11d3+1VRvaxiSME+ejpH17ufvFZjYF8nqZCTgUuM7dfw3Q61cPrv0qhKaSSiaupABAnWv3IhD+deAVlDhnhJnNhbyJtnD3FgFe7U1MVFoHA3ugg9ppKGHfwe7+Y9yzNaJYGQEc7e73NPGuUs1rKdgX/x4MnIQoGe6Iezq7+2+mEN/LgEXc/f0i213J5CFmNh+K5LnSxQ2/DoC7/6cFz45xuEjqSkVZlgOFFwC+Sua0/REocANwsieUPmY2vbt/XecdKZBwaet+TetIvo/k+5GJ9ucglGhuPxdfev6ZemB8u9YZjMmzsjCiceuGIjBGxT0bIM/HR4Aj3f2lqF8dmMrdby+k8ZW0SzGzk5GzxB3IKPsd8h49De3tBhXYvEomYzFFRn0E3Ofuu0VdSnf5EDJ4j0BRoK8kzw5EoP3e7XU9mBhSb//WnsXMlgMWdPdrkrr5kCPK8VlfibXW0J5iH2AFd38h6hcA3i2T3iqZtDLJPeUsCTM1sw2BxYEtEQfYHsDsyANm7QBX+qHO/zACr1YqOxgPYyyZ+yIL3oWJp/xyyFP+FGBHE33N/QkYX8owt0oqmVSSAwAWNbMNzGwrU0I13H1dxBm8obtvH2B8N2B9ZGT8qrDGFygBFv+CNsiD0Cb6FqC3K+Ft5vlyE3A88nw53pRHZCwpw7yW82L09F/ETT0U2N/MVo5rv4WhdglEa1YKuoZKJor8CEwNLGpmuwD3EBEW4xKvAg/8VgAAIABJREFURT9mXt4dkn7a7iW3JmwD/BMYaMrfgIuW7E/AtsBRZrZg3LsFouiauxmvvnYJJOR0toGZnYfoB06Is0JG+3MOijo738wWzUUgpAlwD0B0Zu1SZ2a2Iuh7Qwe7IEqQhxA10lNmtp6Z9XT3u5CX3+oosebS8ewjGRhvVaRUJf+jWC066ihEK7IU6pNPIoD+hAyMbyqqo5JKoNn+0RHR9q5lZv1AVElm1jEcFTujef9rYG9TsmrMbBPEIDCgPa4HE1PKBCpH/xgIXGWiS81kZkSTNyTu6xJ7C0esEz8AW2X7Fnd/yxUJ1CyVXiWVtFRazUPelGCpE/LmPjrx5NgOOAYBC0e6+33JM6Wy2rVEmvGUfxmYD1jMq4RqlVQyySUW89PQvNYHJWi9yt33jOuZJ/McKCrobOA4dz+7qDa3BTGzZVFipj8ho+xwYEl3/yXn+bIFSuRqKHHTp0W1uQjJAVZrIEqfWVHivr+5EgT3ReDfCyi67HVEZ3YMcIS7n1tI4yuZrCSAFQe2R/3IgcOAc1oKrOf6a1d3HzGp2ttWJdaEvyMQ+U7PJZszs4OQQfJp4DWUGOxkdz8ueceBlAhICGB5EKJ0+w1YGEXQXuXux8c9A5FDytdILy/m3rEFcCPyhLyk9VrfOmJmW6JcIQPcfZApoffNKGfIC4jW4VAUUXwY0t1IM1sPeS0/BWzq7t8W8gGVTNaSj0rJXUvpaBdATnYdgC+9lli4VNGMlYyf5J2cEDj6DfJAHmpmiwFXIQeBy939TFMS6z8gQ9DaKGfIfMiLeXhEA3VzUdtUUskYMbOZkOPvViiC/QpT3qPXgMfcfeu4r0sYf7oDb6MEr4cV1vBK2rV0ao3/iJlthg4ovwInuZIndHX3Ee5+fRhGjwFOiMU9m0CrBTwn7v6Vme0bPy80swZ3v9TEFdm3AuMrqWTSS3jwDUKUIXciL45dkIdGD3ffIcD4NRCoPD2i5jo7ni8VnUMmJjqaG4ANkYGiAXlS3W5mG8VGuou7/+ru/zLxkFI2MB4aeX7uig4b7yIP5hmBfmbW391vDk+g4+Ke0cAnKH/IufF8KftaJS2XBDCeEs1lDiwwgWD8PsDvzGxXVxRfKSQMjSeh6J4LIhoIYKaIDvrI3c8ys2HIELkUcFB4z2fvWBl5+5UixN7MlkHr4zGIKulbEw3Ls8B6ZnaRu3/p7ueYIqiOQqDzi7lXjUSA852t2f5WlJeBa4Bzo/+MQPRSZ3okrTWzW4B7Ed3bI8Db7n5PGG1nqcD4Sloq+T1DFpVSbz0IL9EO7t7g7m8j4Cp9VwXGV9KsJHuHnVC0/1TAMOAhMxvooirbC+VTOcHM/oScOOdA56ovzOxX4PPkne0yD1cl/7u4++CIqOsIXBa446VmdhFwnJm97+5HZs5hwIJozf2wqDZX0v6lVTzk45B3KDqEfAEsE4t46g25DeKeex1RPQyb5A2bjCU85c9FFr6BuUNdtQGqpJJJJBFaeRWK9tnC3bMQt2lRspczgKPc/dTwjt8GeNPd/x33lXJ8mtn0CDR+CbjY3b8zUfkcgvj5XkKgyoh0bUieLx2wbGarIeDlZODaMMiuDRwNLAJs5O5PmpLjTomSq33vwRtf1r5WyfhJ1k9MXMDfAd2RkecS5JXbZB/KgfEZV/pu7n7lJG94GxIz2xaBy8u7+9dm1hPpYiWgF3C/1zhwpwFw9+/id6b/GYDZ3f3ZQj6ilSX2/SegeeztqLsVUVtu7u4vm9msXkt+vqQHH3rZxMzmRmD7jsBnwN3uvndc6xSOTjMjj/k73X33Ou8o3RpayYSLKbJ9dXffqui2VNL+JLd3WBG4C50R/gtsAmyK5rqt3P1zUx6uRePa18DT7n67mS2PHKMudPdjC/iUSiZDscaME9uiqLMrUKTov+L3VMBuCLxfwSvWjkomkUx0D3mrQzPj7j+Z2V+AUSih3x1mtlmEVGbekDea2W/ASxUYP24JYGYgmiw65K5VAEwllUw66YK8G9909yEWdA/h3XcjWtzXMbO/uvvHZnZG4oFaSoA0vPSWQRx95wUY3zE84s+M2wYAN5tZ3wDlG+mqDEBCHcBkHuBn4FZXHhFQIvQvgOtRlNQy7v45iXdQ8q7S9bVKWiZpX8v6iYsLODuoNCCwFDPbp974yx2o90NRL3uUCYxPdPBbVPUzsyGIYqU7MmosCGxoZhu6+50ZEJ88n+n/K8qVY2QuYMYEjL8bAS4bBRi/PLCFmZ3v7p94LTlp6dZRd//AzE4CfgH2AuY2sz7ADx4UoOEp+iIwbz3wvQxraCUTLrn5fGXgSOBKM+udOZ6MzzsqqaQpyfW1+RF15Q3A2RH186CZfYj4vv8V54IPkZfyHfFcHzPbHO07nsjA+KoPVtIS8RrjhKG+twWwK6JU3St+fwK8ghgoRtfDOCupZGLIRAXkrTGX3IpAb5TcZYSLB+xMBB7vCdxiZpu6+JmmcPeR7n7zxGxPexd3/9LMNq8MGJVUMmmkiUPtSDN7E1jRzGaOQ3AnYFR4cTyNIle6o7kvBZVLBSIksg6wOwKtMvAp29wMi7WhARlsHzSzVcu46UkOKDujxMC9kWfGR1GfeUK+CVyLPCYXY2wKhwp8qaRJyR2Gl0Kh3zMCr7n7Y3FQuSpurwvK1wHj23US0kzya0Ly99NRDkCg+jPAPjG/LYNouobn31fGcZro8HOgo5mtg/S2GDUwvhuwMeIFzjv5lGodzfTl7u+Z2d/QmtAf2MmV+Da7bwqkq6FAJzMbVcb+VcmESTKfzwosgBIGnzEhYLyZLQJ85DXqrkoqwZRg+sWknyyJonp+RhzdPyVY0rmIPm8gcJOZbeWiG8kMshsg7+X73L1/vK90xtpKJlxirzsAgfL/ArZz95NjnV0EGAx87O6enb+KbG8l7VcmKiCfgPHXAesjMOEL4BQz+5crjPeMuH0vZPXc0kvEMzqxJQPjK4twJZVMHMnGUrr4hgdHL2CIu7+HQtnWQXxzR7v713FfN2AaRL9SuqSG0ORctA/wJaJa2d3MPnb3L3Kg/NlAD+CDsoHxuYPsmsAFwP7IgL0tyk9weYDxHUNvbwGdyUVIVVLJuCTpa7sgii1DiamHmdkD7r5JGBevjEeOQQDfHtmzyTsOAM6kZGC8KTHY1Gie/87dPzVR9vRG6nk37usGLI3mvx+KaXlxktOZAR2S+f1alIT0HnRW2NjdXzIlUeuLQOfDXRFApZE6a2gHwijh7m/FWtkFOCu85G9FnvO/B/6IDEG/UUkl4ylhPHwWAVH/dvcfo77ZM2ZunB+MnAWWI8cpX0l5JRxv1gbWQklbAX5CidC3RY4BmbNOllDzPGq5pu6LqKnhcd81ZvaaR5LvCoyvZEIkQPl9kPHnelMeuMuQQzEwpm9VYHwlk0wmCiCf84w/FHn27Y0OH3siK+c0ZnZxdPwz0ObyWJSoaOuJ0Y4ySwXGV1LJRJNl0YEkm9N2BP6MPEi/NLOb3P0oM1sB6AdMb2aHA13j2S2AA8sauZIcyjYGOrv7zQEkH4+MGvsDH5kS9n2dA+WPTtaS0hgZcyCfoxwFNwCzAo8CR5vZD+5+a+irM7AkCt8tHchXyf8uZrY+Mvz8GSWD/Ablc+hvZo+7+yqu6J/L0F7xOEST9FDyjm2Bv1I+MH5blBdpTqS3V8zsIHf/mIRyxsR5ux5wGnCCu7/Q6g0vWHLrwUbAXGb2EPCMu98X0UCXIUP2SqakrkuinFOnufsV8Xwp1oNcP1sXGf6XMbO7gAfc/Tl3f8dEA+oo0e1+wHMoj8iR2Vgsi84qmajyMeLx3g9Y3Mxmc/dPxwOM3x/lvBnoQUVVSSUhZwO3ufs3prxHX7iouM5B0bMDzewsdz8owPgMlD8fRRx/lXdCTMD4iqKxkgmWxFN+NHCJmQ1297uT61XfqmSSyv+c1DW3EK+KknC87+4XJPdchxIbHoeS+X1lSvQ6ALjZ3d/5nxpRSSWVVDIRxMx2AK5GANMl4a18M3Ah8CqwObA64vTub2bHIu/v6ZGH2lDEkX5qvK+UB2ITB/UdiDt5B08S2iID7T7A8cAgd/+myReVSEycrY8hgO8Gdx8Y9RsCJyHvofMQfc0cCBA8xt3PKqTBlUzWYmaDEEXIJigRsJtZL2RkPBG4xN3/FPdOj5KNPpd7xy4oaujWVm18K0kSLTXG887MtgMuQqDVlWgu2x94HiWf+zDu2wA5piwM/D0bp2VcE8xsJ8Sl/zLydlwAGAYc7+4Xm9m8cX0mYGbgEeAOd784ni+d52MYKs5EQPuHwE7A48Df3P3OuGcB1Mf2R0afs9z927hWOp1VMnHEzGZE9IF/Qjzyf/MmqGdyGEBpqMsqGT8xs85Z1E6sjRej/vWPWGPnQvPYAYhH/uC4t4u7/5p7V+nW0EpaR8IpagBwXOURX0lrygQB8hGCO3tq/Tazk6l5xe/s7o9ZcMPH9QyUPwa4wsUDVk2qlVRSSZuRWIzPBzYDdkAJXfoi4HOomfUGDkeRP7e5+27xzNpo7vvK3Z+Jd5X6QBzefYcCCwF7u/ttUZ+B8v0RiHCB1xKWllbiEHweonu71d13TK79HtHWbIn4g99DgOm5cb1aSytpsZhZF+C/wM/uvnbUZfkJeiMKjF7AKnUOw6WZ18xs2dQIYaJzuBK4zt3/YmZzI3qyl1CC0q+AzVz0NQui6M9nM0+rMukuEzObFrgUgezXuJKf/x4lvd0EnReuj3unQ1QsPySekKXQWQ7YXB+4Ajjd3TNami+REeNt4M/ufm/cuzCKcnnc3c/Lv6uSSupJrr9Nj6IteiYGxamQp/ueaM87KA/KV2B8JS2R/BxuZrMhB6fBiMbylgDl50R88fuj3AWH1Xu+kkpaQ6zijK+kFWW8KWsCTPkXCmXbyN1fiks3o831wijRxmOu5IdZwtbtzWwU8vT7zcSBWE2wlVRSSZuRMBRmXHLXovDd6wKM7+DuQyJUHGAPM7vW3TOv+jFSpg1k/vCfzPn3mtloZIQdZGa4+23u3mDine4U1+4ioXkoq7iSdO+PQia3N9GGXBjXHjazxxGPZjeULPgLKFdfq2TiiCsM/HlgSzNb0t1f8lp+giFmdj+ir5kK+Dr3bCn6WhYtZWa7u/vlUT0XAt/PC6/upxG11EBEHXIkSj63k4vr+9TMoFHGkHozWw8ZGBcETge+gzHz2RCUt+BgM3vM3T/JR0uVQWdmtqC7v5VFYgA9kfPSTQHGL4q4bC8EHkbnrxNjrN7l7m+YcjuMSbxZgfGVNCc5IH1bRE8zDzDSRI10hotK5Ai0F/4L0GCinR2avSd5x0EEeF+B8ZVkEgbsoZnzppn1A0a7+5VhSHwGOeVgZre6+0cm+ppRwCFmNqW7793e14BK2qZUYHwlrSnjnQwuJsazUSKry8xsqbAivYC4k19HYMJucf9IM5si/t4Zecrc6e6jq01jJZVU0tYkvLUHANcBswDTZddirhuCDigXAhuZ2T113lGaDWRyKFsr9DMyPHBx9/sQ/cV7wIXh+ZfpZwCwRhZRUIlAeQTu3QxcYGa7N77sg939A+RZVArAqpJJJo8i8G9AgMtZMrWuQOb5XZemoCTyAIoUuNTMdo2621BkyjBEJ/Ioip4ajigZP0B0LP8XkQZjDnRl2u+apBPyfuyPoi3eDtC5M4CL+/dSxBc/Vb33tHedmdmBwANmthFoXXT3n9D8f7eZTQP8E4Hwx7j7LcCpKOrscDPrG88NifdZAZ9RyWQmOTD+KkSFdDqK0NsauNbMloi+eCxKunkm4vhu5MhnZguhxOAHVmB8JZmYWQ/kpPmwmc1rNdqynib6mi9QvsGuCJTfLJxLPkL97RKqhMCVVFJJSWRCk7o+COyKErJeBvQzs1fDI2hb4B/AoeEReXnOU36PidT2SiqppJJJIq48F4eiObK/mT3n7pcgL6FO4UV6BgK0Xiu0sW1ATPyP/0bJWhcPD9xszr/PzHoiUOEyM9s785RHNAaVl3ci0ff2jZ8Xm1lDrKNjgXvtHbCqZNKJu99oZouhqIvpzexc5MG8KrAtcHCeoqBMkkRLNaB5q2MATv81s6kRKHqtu2cRBIsgJ5ergOdSj+WyScxLo8IAew2wIXCame3r7iOsxif8DtLvDAU2t0j5AlHdHR9Oy3cCuPvtMCYRbkdEo5eNxRGIumapuDZGqvWgkpaKmc2KOLxPQx7xP0X9dkBv4GcAd//ezE5CRrUhea9Rd3/TzBZw93db9QMqaesyDLgH+APyhO+NaI0vDcN/J1fS+OWpecq7md3m7h+a2cHu/nNhra+kkkoqaUVpsYd8hFICYzZ9jwM7AlMjUH6xmGBfQxb2BuAwU9IvPLjkK6mkkkomB3H3wdS8lS/KvJWD2qGTu/8IHBJAfdm90wajw10f4KnEU74rgCvp411x7/VmNk+qrwqMbywRpbEvMm5famYDCm5SJe1Isv2cux+FqFaWAu5H1BhHACe4+6C4t7TzWjIOb0bGscxTfjQyxi4MEAbHRRHAfKrXONFLq7uM/gjYHvg/YFPgKDPr6u6/ReTs7xDQ/G2BTW11MSVPxt1vQuOtE3CyKYF3KjMB8wLfxr6jK/IoPRSY193/0YrNrqR9SRfUt95IwPi7gWmA7YKyZhEAF5XUPu7+t3ovqsD4SvLikieBe1EE1FDU10bH9ewclXnKd0K5pbaO6xUYX0kllZRGxukhn3g5NsQGO5tM3cRpuyM1T/ldzewVd3/VzLZClA+nm9lv7n7dpPyQSiqppJKJLc15KwddyK/JvaXwTkv5RzMJr8erEUXD6QiUX9HdR8Qz0yPu80HAS+7+fmu3e3KT6HsDkWdR56LbU8nkJ/XGKsgAlu3n3P1UM7sFmB05aXzp7i/H86WPXMmtAZeFTi4zs9OAvwZo9SWKLDjeEx70sqwJ9SS8IDu6+89xHrgZAcmrmdnDwPTAdsiA8WKBTW1VMbOLgRFmdkicre4ws47ACQiUJ/OURxSgHwCXmNmlwBzIQLSvi96sGqOVTKjMCExJ0IKYqBcXATZy95fNbH7gcjM70t0fSPZyVcLgSpqUOv3jO+AUYC3gZjPb1N2fgMagvJmtggzaXVq/1ZVUUkklxYo1t66aeIDvB35w902ibgwoH78NWAUlQPwCWCezbJrZEsBFwPYVAFNJJZVMrmJmMyDvja2A/dz97wU3qRBJN9tmNh8wMzLsfhgeVV2RR+TpwCfARoABK6OEh5tna0EFJLRMzKy7i7O6kkqalXpgSXMASnNjsBqfjSXWgPOBvmhPe4OZ7QjsgRLf3ptGS1WglSQ7M5jZlMD1KMnrOygR+tPu/kDc1+77m4l/ezfgPXd/MKIFMqBzUwTKO3C0u98R9UcDO6C8Dt8C57r7aYV8QCWTnTQ1F5k4vh9F1DQjUO6Lzd39hTj77w70Q57xT7dmmyuZPCV3PlgZGOnuz0dE3nLAOSh58Gbu/njy3Dzu/r6Z9fAS0+RVUkkl5ZVxAfJTowSuGwJ3ufsuUV8PlP8j8oC5zN0PyDbXZtYl9SKtpJJKKpkcxcxmBK4A/uPu5xTdniLFlKDpBERZ1gPxRR7q7hfGYW5L4HhgLuBj5I11srufXFCTJ3upQL5KWipBcbS6u29VdFvakwQo/3dgc2AHd7/elKS0c2Y0KwOwPL6SgPK90DlhPuBidz81rpfmnJDN46aErBsCh7uokfKg/J/d/baoXwxFSg139+ejrupnlTQrOYB0ZuA3YAp3/yzO7QcAxyAapPXd/REz64Popf6KDEN1aWoqqSSVXF/bATgZRff0c/cPon5l4CwEym+M8m+tixw6VwBeibmxmtsqqaSSUkmzgDyAmU2HPBt3Af7t7jtHfR6U74Koa2YB1nX3oVFfgQiVVFJJu5DKWxnMbDPgBuAMxA08PYoc6IsoG0408QPPjCjNRgFvufst8Xy1JlRSyUSUOp5p/wSuBE73FiYXrcZlyyRA+fOQ0XFfd78guVbpsAlJQPnewC3AgsAFwFmZl3hZJDxG90cOTxcCx3kkB86B8se4+7/rPV8BVpU0JWY2rbt/mxh/tgcOR44RI1CkyjnAVwg43T7q70ORGEsB57n7KfG+al6rpEUSfe0ylJvmfg/6u7hmiC/+nPj3IWBF1NeOLKC5lVRSSSVtQsYJyMMY/t8jgZ1pHpS/AlgMWMWrJK6VVFJJO5UyHlBiM90FgSlDgV0Tr9AZgMOQx9VG7n53E++ogIRKKplEYmazAmsDayKw+McWPpcC+osAH1Wh401LREtdiWhqShUtlesrndx91Hg8m9LX3Iz66ab1QOf2LmbWDTk6nQdcjsD3FJQ/FuVdOSozZldSybjEzA5B4PvK7v529KUbEEj6NTAtsBfwFMpH8BqK1NgQmB94FnjM3W+P91V7tkrqSn7+N7N5gLuQc+bp7v5b1K+IqC3fdeVkmRsZJGcH7nT3y+O+qq9VUkklpZRxJnUFcPevzeyU+LmzmV3l7jvnwPjZET3BM8Doeu+ppJJKKmkPUjYwHsYk8u4ALIoozIZlG/LYZF+Iwk/3MbOHEH9kQ+4d1Wa7kkomgZjZMghMGYwcJ36M+maNhzmA9WAEBC5HJPurZGxx9y/NbPOyRUvl+sp6wBJmdqu7t6ivBBjf2d1/MrMtEZ1Buwbjmxp/7j7clAi9I6IHwcyOcfev3f22WGv/iuhEKqlknBJOE28B3wO3mdn6yNv9QkQ/80vcdytwJ6IV7BtjsIrEqKTFYmbnAM+Z2XXJ/NYd6Ak84e6/mdkswN+QN/zMwKNmtkesFwMt4Yyv+lollVRSZunQ0hvDc+MU4CpgYzP7l5l1MbOOZjYvcBzK0H7u+HjMVFJJJZVUMnmIuw9HYc4Lx+9RwaGMu7+DQLyZgdHV5rqSSlpVPkaH35mAxc1sNmjeeJgDWPdH9AX/396dx9s5X3sc/ywJYipqiqm9RamhpcZSah4a4RJTjTVEqOmGmGpKb0XM80xiqiEqZoLEUKrVakvVULNLKWomSCLxvX+s3+axe5KcNMnZZ5/9fb9eeSXnGfbrl/N6nr33s37rt9Zh7Q2wtrLK6qBo9Fg6SuVa+Sn5LLASWdu8Xcr19ln5cT5Jp5bt7X4WaSZ199fyEbFJRPwkIr4DUIJRlwIHks1ejy0rkilZ8etKurpBw7cmU661O4B9yIS7u8gkiecrgc/uku4DdgP+G9iudn5EzFB9P/N3OKtXYj7dyVIzr5REndo1MwFYANgpIs4H7iFLkx1K9l1ZC9i49lqVazJ8rZlZK5uiL8ElKH8c2dRqQ+BZMiN+WPl5Qz/ImZk1t4kFmcr2YcBqEXEcQGVZ6qzA52RQPlopUGXWaJLeBo4n61KvDuwYEbNN7Pi6YOH+ZDbuvpLO74jxdhWttloqIvoA55MJOgdKeriNY/7t2aKNyZ/7IpuVdtnAX+X/uxNwK3AOWTrkqvI7qAWlLiOD8jsDx5eSSOjLZoj+LLV2KQlx95HlaN4lVzstVLknayvY7yabbq4BXwZFW+39zKbYyuUaW0PSAxGxGdA3sr/WU2Rp4x8DSwB3SFqmTCqOBJ4C/q15t685M2t17SpZUyXprYg4gVzutgvwNbIG3bWSXprG4zMzsw5UHtxqgYQfAEsBM5N1RZ+KiCvJTJd+EfF1MvtlEWA18ov4vu4hYjZ91AU25yfv1dklvVRKqQwia08PAsZHxPn19eDbCMafAewlaUiH/mesaZSg8NeAPYFLqrXzI2IPYB7gQ+BX7bze+kl6vKPG3ygRsS1ZMmQQ2Qi9F3AjMCAiekg6SdLoyB5ctfI1VwFv1F7DASubEmXl4n1k0t0gMkj6a+CxyrUUwHhgbDnH15hNUplYvCIi9pQ0tDwrnAosCnwWEddKujoi7gI+kjSunDcbmSE/N/Bco8ZvZtZZtaupq5mZdW3ly/ZNkkaXn3cjAyefk4GCmcgVUmcBs5EPetuUfR+X486UNPjfX93MplZdYHN7YH9gcTKocjtwsqQXI5tmDiab9x0KXFS7r+tebwB5T+/nYLzVq69/XgIwfwAeAn5OliM4HViaDPBBrqD9ZS3rfRLB+KEd9h9pkIhYksyIHyHp+IhYAbgfGEWWlvoOMEhSrYb87MBikv7WqDFbc6m7v+aQ9FGtHndEzAysS95z3chSNg+RCRabkytddpZ0XYOGb00kIhYkV/lsCdSC8jOT19SiZDPhqySNqV2XEbEiWd7mRPK97sRGjd/MrLP6jwPydV8CJtk0zMzMOq+SCf974AZgJzJQcAcZfB9BBlt2AfoDQ4B9yaD8EmTG3yvAy6U2qRs0mU1HJRh/OXAmmUkbwBFkQ7+fSXqsrF4ZSAbtjwFOqPb3iYilgSdxmRqbjIhYE3iPLFN5FdlD5HMysPcu0A/4B7ly9lNJG7bxGl1+JUYbExiLAyeQkxfjgQeBuyTtERHLA78DPgAukHRs3Wv5M9TarZSS2pVcHTWCTK54qRKUPxX4BvA88BKwPDBE0vGNGbE1o4hYgAzKbwXsLemico39iZxkPBS4WtLYiFiG/J4yJ3BebVWV39vMzL7KGfJmZi0uIuYCdiBrUN9C1rTdG9hH0luV4w4rx+w0sWZz/rJtNv1ExCJkyYs7yYz4D8v2R8gA6WaV2tPzkZlpj0o6u43X+rYkLyG3NpUyNfMDrwMnSvp5uaYOIwPyL0s6t3L8pWSZjL7A+ErSTn8yINgqmfGrAn8v2coLSno9Ik4hezvsALwqaUJEjCJLws0KbCLpzw0ctjWpEoy/ErgXWJhMlLifbND9ZAmYrkdmMK9FXoNP1kpG+TubTYl2BuWvkjQuIjYAxkl6oJzra83MrI4D8mZmRilzsSNwCplx+5Ck9cu+bpImlH/fAfQE1qyvFWxm01dELAb8hZwsu6ZsGwEsC2xesuOXlfRk2ddD0pjGjdiaSVsrXiNiIDAA2FTSb6ufB2Us+w12AAAWNklEQVT/3ORKqbOBAyRdWdm3JnAdMFDSRR3yn2igiFiZDIzuLml4RHQru0YBn0jqXY6bF/gVWWrq/yTd1pABW9Oq3YcRcSbwNnCqpE8i4ghy1cqLwP4lKN8D2AC4mGzGPKy8hgOkNsUmE5SfDziKUr6mco6rKZiZtWGGyR9iZmZdXcm0vRo4iHy4Wzwili37JlQOfQb4Ov78MGuEnmRzzWfgiwmy5fgyGL8kcElErA9QeyAu2c5mk1TJap+/svl64G9A/4joWReM34Qsi3Q2cEo1GF88DWzRCsH44kmyjM9WkJ+d5ff1V2DtiPhuKSe1CZkdP7IWjC81+s0mqu59fJYy4bMg8LikTwBKH59zyP4iZ0fEMuVzYBSwWi0YX451MN6mmKQ3gf3Iz4YLIqKfpLHAKmQZrouAxerOcTDezKwN/vJnZtaC2grQSfoAGE42hJwbOK4ED2rnzAjMCLwD9HCQz2z6mMS99Vj5c3pE3EU21NyiBONnIrMguwNfaeLqh2Frr4jYBnggIv4HQNITwDDy2lq1HDNjyYjsD2wKHF5r6F0LLJeMyLcl/bEB/40OVzKWPyXLum0aEb0qu28ha/A/BjxM9mIZIunZ2gEOjtrkVCbMtiXLlt0CrAb0KNu7l+NOISfJvglcGBHfkzRW0ivlOD//21RpIyjftwTlVwB2kfRUQwdoZtYkXLLGzKzFRET3WoPHiPgvYA7gn8B7kj6PiHmAbYGTgT8AQ4E3yS/ag4FDJZ3VgKGbdXnVpd0RsRDwGTCzpFdLoP5/gKPJIEwvSfeXibMtgNOBo9qqGW82OWVS5yyy5AVkc++TJf0mIq4mMyCXrnx+zA4sLKm2YqMlSmC00cC1es8uC9wNDJN0YOWYlYG1gXmAP0m6sWxvid+ZTRsRsRkZBB1BJk4sDzwH9Jb0Zl2JwcPJpsK7SbqhUWO2rquUrzmTfGboX3028HubmdnkOSBvZtYiImLlauO4iNgJOAZYCHiJzII8VdKYEuDbFjiWDCA8DLwCPFj7wu2akGbTTkTMK+nt2n0VETuSjfh6AmPIklJnkJNjx5E9H2qlCBYDvg+cVclU9v1pkzSRmvEbAUeQGbjrkEG/p8ls3MHAiGqgeVKv1dWV4OiHwGOS3q9sP5q8d1et9XOYyPkOWFm7lVJS/YGxwCCyIfCWZPLEM8BWkt6qC8qvKOmRRo3Zur6I6AlcBtwp6YwGD8fMrKk4IG9m1gIiYg+yodeekoZGxAbATWStxyeBnYBvk0GXg0pQfl7yYW8w8BSwcaUmtQMJZtNIRBxCBvDWkPRMRGwBXEOuTvkXMC+wN7liZT/gCaB3+bMk2UztQUk3l9fz/WntFhFLAx9Keq38fBlZ43w9st75nsBG5Eqq0UA/SQ82ZrSdQ1m9cjOwEnAXcCP5GQvwLTJL/jbgEGBcq01W2LRVSkntA8wEnCbp+rJ9VmAzcmXLs0Cf+qB8Oc6fCTbdRMSstT4GZmbWfg7Im5m1gIhYGziKzKLtTzZuXQ8YKOnTiJiNrDm6ERlE6F+C8vORmbjjJZ1TXqvlMiHNppdShqY3cBowHugF7Eo2bz1K0sfluHXJe3MkmQnZZnDFgRebEhGxDDnBcxtwraSryvangPsl/az8fDDZ9LsnWSrpzgYNudMoJXs2J+/XdciJsevJEg5nAxsDK0t6r0FDtC4iIjYnkygA9pF0QWXfLOR1eBrwD7KvyBsdP0prdX4+MDObMg7Im5m1iIhYHfgFsDLwETBU0rERMaOkz0pw4UwyiHAzcHAJ1s9cmjX5y7bZdFCa8a0LnAcIeA+4QtK5tf2SxpdmfsOAHSVdU/bNQPb7831p7VZX93w3YAfys2E42Zh0LWA74HxJt5bjfgjMJmlkY0bdGJP73CslGxYjV5MtTa5quQM4GDhF0qEdMlDr0sqk7D3kBNo+1VUqEdGDXNF4ObCDpOGNGaWZmZm1l7usm5l1cSUDF0kPkXVHHwIWAOYs2z8rQfnRZMPIEWT9+IsiohswrvZaDvqZTXulSeZ9ZDmad8nmmQuVYDtArfTA3cCLwBrwRaDwc9+X1h61zwL46nu5pEuB/YEjgW2Aa4EfAHMBP6x8hvyuFoyvXJtdWt3ExQYRcUpE3BoRR5Sa3kh6Q9LvydUt2wCPAT8rL/F8QwZuTS8itouI82o/S7qPvMaWA34ZEWtU9o0hM+iXdTDezMysOXRv9ADMzGz6Kg0i55L0vqTfRoSAHsBBEfGUpEuqQfmIGEAG639TrUFqZtNPyYC/j0yWGAT8FPg12TCyFjwNsqzN2HKOA/HWLvWBZWBtYEHgVTKL+2ng6Yi4maxHvSI5MfQD4I9kjfQvtEpZpMrvbFfgJOCvZBP0XwDLRMSZkv5Ujv0EeAB4ICI2LKff3YhxW3MrGe+rA3tHxBhJBwFIujMiepMlpo6LiCNKsgWSPgWeK+e7dJmZmVkn1xLZLWZmraaaCRkRWwK/johVAMoy52PIbNuTI2L3sv2ziJhJ0kdkSYyh9a9lZlOv7v6co/w9g6RxwL1kpvJoYHhEbBgRs0fEPGSd4P8iA6Rm7VYXWL4ZWJMMtu8F/LVcZz1KY9edgBPITHmA+Tp+xJ1HCYCeDJwsaSPgDHJybHvg+IhYqXJsdwBJo2rB+FZZTWDTTsl4P4WcBOofEWdU9o0g+478kPwOt3Yb5zsYb2Zm1sm5hryZWRdTlwk5D/DfwBDgCuBMSY+WfWsCRwOrAgdKuqxs/yKzyjXjzaafiOhDNoSchSwVdZOklyJiZrKm/KnAN8iyFy8BywNDJB3fmBFbM6l//y414K8ng3xXSHo7IlYE/kwG3/sCY6oroyJiNUktOwEUEXMBpwMfSTqgNMH9A3AlMKr8fQ9wbC1T3mxq1H2HWxjoDwwAzpLUv3Jcb+AWYGtJNzRksGZmZvYfc0DezKyLioidyZrwr5KZkPOTD28DJT1WjlkTOAJYBxgg6fzGjNastZRg/JVkRvzCwBLA/cBhkp4sQfn1gMPJBps7AE9Keryc75IENkUi4gCgH7CFpOfLthHAksC2kh6pHNu99Dao/dyS11vJeN+KnBB7kezB8jvgALJ01IXALsDtwKBWnrywqVM+E8ZIGjGJoPxJkg6vnPNNSS83ZsRmZmY2NbyE0sysC4qIXsBQ4Drg58CPyAe6jYFBEbECfFG+5niyLm7LBVvMOlpplAxZw/t4MhD6/fLv5YCzI2JZSWPJRq8nA28COBhv7RERJ0XEgZWfa9/3VwKoC8YvB/SR9EgpW3NGOWZ89TVb9Xorv4ebJD1Mfn5+RgZFPyz36AvkCpZNgUUbN1JrZmUlxv7AbRGxUen9U2um/BpwAfA4cGhEnFY59ZVyvp/pzczMmow/vM3MupBKbeo+wKPAhZL+LulZSWcBOwMbAUeWUgVI+i2wlaQLGzJosy6urg/DLCUovyDweGkEiaTBwDnA4mRQfplSR3gUsJqkYbUXaNXgqE1eRCxAZrw/XNtWuV5+D3wjIlaPiBuB7wKbS/pb6WXwQ2DRiOjZ0ePuzErgHWABYKHa9oiYDehJ1pT/uqThDRieNaH63jyS3ieTJkYCN0TEJiUoP0PZ/wK5CuNFsqb8OtUsen8mmJmZNR8H5M3MmlhEXFxK0wBfNu4D5gC6AZ9Ujg3gNuBX5BL8AyNiqbL7jcoxZjYNVUoPbAvcSZaOWg3oUbbXGkGeApwNfBO4MCK+J2msJGdBWrtIehPYXtLvIqJXRBxZ2f0k8E/yc+AHwJqS/hoRM5GfCXsCN0t6o8MH3hxGkM9O+5V7eWdgO+CdElD1PWqTVVeOZsmIWKWUnnkM2Jds2j28BOVr/XxmIyeDLgBWlvQb9/cxMzNrbv7SaGbWpCJifkBkgKXe08AyZKZkrR6wSsbt88BrwDbkEmkqWVZ+wDObDiJiM7Jm/NvA7MCcwICIWEDS+FopmxKUvxj4HllX/gvOgrRJqQSDx5aM90PJbNpD4IsSZWeRk7WvA8tHxPpkWbNzgXMkXVFey5OzdSQ9TTZJ35Fskn4scIqkayvH+B61SaoE43cmJ3nuA+6JiJNKJvweZOPg6yNit4j4EbA1sCHwx1qvB0/+mJmZNTc3dTUza2IR0UPSmIjYCpi3VnamlBwYSU68riPp7drxwC/JoOBo4HRgrVIf18ymgzJ51p9sAjkImBXYkqwP/wxZMuqtiOgmaUI5Z8Vqk02z9oqI+cr1tBxwIrAscFEpi0RE7A5sD6wBjAGeAq6RdF7Z7x4FkxAR3wAWA8ZKeqhs8+/MJqkuM3494Aa+rA3fh2zePVLSThGxIHAauQLjY7LHz4m1e9jMzMyanwPyZmZNrixlHgpsC+wh6dKSbduHfKAbBwwEPiKDCAOBXchyNrcD65fMSTObxiJiG2AfYCbgNEnXl+2zApuRGcvPko01vxKUL8c50GftVgJ9NwLLS/q/iPgOcCawFF8Nys8DzAWMBz6V9K+y3dfbFPLvzKZESZhYnMx6P1rS6IiYEzicLBs1StL25djeQHfgXUkPlG2+3szMzLqA7o0egJmZTR1JH0fEQDLAPqQE9IZExM3AW8BRZAmMbsB7wPGSbomIvmS5m08bNXazFjAWWLv8e77aRkmfRMQt5cfTgFsjYov6+t0OvNgUmoXMpu0VERdKejoi9icnfvpFBJIGS3oHeAe+LE9TMnh9vU0h/86svSJiDeBB8rvXZSUYP4OkDyLihHJY34gYRvaCuK3ufAfjzczMughnyJuZNZHqkuc29i0FHEnWt91b0sWVfWsDn5HN556JiFWBm4E7Je3WAUM3a1kRsS5wD/AEsE91RUopI7UlcDmwg6ThjRmldRURcS8wh6RVKtuWJIPySwCXSjquUeMza1UlO/5soBdwpaS9Si34GUovkTnJ3g/7Ag9J+nEDh2tmZmbTkQPyZmZNoq7+6OrACmRzyJcl/bps/xbwv2RQfi9JQ+peYx7gp8ABwF8kbVX/2mY2dSJiO2BtSftUtm1CNvD7DXCUpN9X9s0CLCLpuY4eqzWntt6zI2ImSePKBOwdwCGSzq0dGxHfJmtWr0SWKvtLA4Zu1tJKUP50sj78XrXkiYjoXoLyc5Hf456oJlaYmZlZ1+KAvJlZk4mIXcmHudfIEhhzkYG+gyW9EBGLA8cAPwH2q8uUXwzYHJhb0sCyzUugzaaRkvF+AjnpdYakgyr7egG3AfcDR9QaQtad7/vR2i0iVgDGSXqqsm0hsmHk22RvkTHkd/4JEbE0sKSkmxsyYDMjIhYAzgG2AvrVkicqQfmZJI0r25wwYWZm1gU5IG9m1kQiYgPgOmAwMIysCb8pcA1wEpl5Oz4ilgB+SQbll6sL1swq6ZPybwf/zKaxiFgE2I8sPXCWpP6Vfb2Am4CHgSMl3d+YUVqzK2XK/g48R15TZ5DNH8dGxPbAlcDmkm4vdeK/UiPe7/9mjVMXlO8r6ZKy3QF4MzOzFuCAvJlZE6iUHDgZWIVs9vV62TcCWArYRtIjlXO+AywqaVRDBm3WYurKSi0M9AcG8O9B+d7ALcDWkm5oyGCtadVdZ5sCGwK7kxnxDwDHAh8DQ4AZgR0lvd2g4ZrZRJSg/JnkSpb9JZ3b4CGZmZlZB+ne6AGYmVm7BCAyGP9hJRh/O7Ac0FvS30qgb2FJF0p6Gni6HOdMSLPpJCL6AGMkjagFSyW9FhFnlEMGRMQYSYcDSLotIr4l6eXGjdqaSV3W7AzAhPK+fntE3A2cSNadXgt4hAzGzwJ8C1iIDNabWSci6c2I6A/MSU6emZmZWYtwhryZWSc0sSXLEXEhsB6wMnA52Zyvt6THSiOwY8gHuwGS3u/IMZu1onLf3QisDWwiaWRdBvPiZD3v71KpKV9Z9eLJMpukuuupN7AlsAgZZB8MvCjp04joBiwM7EmWMlscmAPoI+mmhgzezCarWkrQzMzMWsMMjR6AmZl9VV3wZcmIWKnUhAe4Ffga8DiZLb9WCcZ3B/qUP3c5GG82fZRa3F8o91p/YCRwQ0RsUgu0l/0vALcDLwL9I2Kd6j3uYLxNTuXz4KfAcLKR9wRyddSDwF4R8XVJEyS9IuloYGdgL7JhpIPxZp1Ypa9PTO5YMzMz6xqcIW9m1klFxM7AQKAn8DpwnaQjIuJE4BDgDjLgsgDwI+A44H8lndigIZt1afWTZeRqlH9Jerlkwl8ErEbWhr+zHDcbcC7wBHBvtc+DWXtFxErkSoyzgYskfRARc5CNve8AdpX0TkR0kzShjfPb3G5mZmZmZh3PNeTNzDqJumDfemTg5QIyG74P0C8i5pfUNyJGA7sAzwMfAq8BR0g6o5zvMhhm01jl/qxOlr0RETdIOjQi9iBrd18fEfsBL5A1vDcEhtaC8b4/bXLaKFs2f/n7LkkflH9fA/wDOKYE42eWNLat68vBeDMzMzOzzsMZ8mZmnUxE9CRr/24NHC1pdETMCRxOZsTfJGn3iJibbOD3Gtno9blyvoN9ZtNQG5NlN/DVybK1gJGSdoqIBYHTgO2Aj4HPgRMlDW7I4K2plTI1KwNjgZ9IWqRsH0GWrNmslC1bD9idLFHjWtRmZmZmZp2YA/JmZp1IRKxB1gT+J3CZpKNqAfZKUL4fWaJg5/rGrxNrBmtmU28yk2V7AqMkbV+O7U2uRHxX0gNlmyfLbJLqJn/WB24BDgBGA1eSteG3JksjbS7p0YiYFdgP2BjoK+mlhgzezMzMzMzaxSVrzMw6lxeB64FewHy1jRHRvdQMPoHMuN0XGAH8uHqyg/Fm00cbk2WjS4C9dl8C9I2IYcD2km6rO9/BeJusSjB+QUDA5cAwYB7gXmAo8CmwgqRXI6IHsC1wMFm2zMF4MzMzM7NOzgF5M7NORNIbEbE/MB7YMyL+LOli4PNKUP5kYHaySaSZdYypnSxzMN7apTL58xZwtaSPgY8j4iyylnxPYNuIeAP4PlnK7ARJQ8r5XillZmZmZtaJOSBvZtbJlKB8f/I9+sISWxkiaXwJ/r0fEYdIGgcOvph1BE+WWQd6ERhOTv7MW9so6faI+ITMiD8MmAA8ChwoaSh4JYaZmZmZWTNwDXkzs04qIhYAzgG2IusCX1K2OwBv1iB192W/SlZy9zJpNpMny2xqlX4Fp5PNgfeRdEFlXwBzlR8/kzS6bHcw3szMzMysCThD3sysk5L0ZkTsR2ZBDomIWSSd6wCfWeNU7kuAiyLic0mXlGB81ILx5Vjfq/YfqVspdV5EjK9N/gDdJL0HGYQvf4eD8WZmZmZmzcEBeTOzTqwE//oDcwIzNno8ZubJMusYk5r8qRzzefnb156ZmZmZWZNwQN7MrJMrmZJbSfqk0WMxs+TJMusIE5v8afS4zMzMzMzsP+eAvJlZE6gF412T2qzz8GSZdQRP/piZmZmZdS1u6mpmZmY2lTxZZtNbRMzqyR8zMzMzs+bngLyZmZmZWZPw5I+ZmZmZWXNzQN7MzMzMzMzMzMzMrAPM0OgBmJmZmZmZmZmZmZm1AgfkzczMzMzMzMzMzMw6gAPyZmZmZmZmZmZmZmYdwAF5MzMzMzMzMzMzM7MO4IC8mZmZmZmZmZmZmVkHcEDezMzMzMzMzMzMzKwD/D/C92GwUfF4EQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fill missing data (reviews_per_month, number_of_stays, 5_stars) with 0's\n",
        "df_airbnb.fillna({\n",
        "    'reviews_per_month': df_airbnb['reviews_per_month'].mean(),\n",
        "    'number_of_stays': 0,\n",
        "    '5_stars': 0\n",
        "}, inplace = True)\n"
      ],
      "metadata": {
        "id": "vosdOcVdQRDD"
      },
      "execution_count": 103,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "..."
      ],
      "metadata": {
        "id": "bT2QCT0FQXSx",
        "outputId": "95ba5a49-b055-46ec-f03d-386c7ce88bc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 707
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f1f1232fd90>"
            ]
          },
          "metadata": {},
          "execution_count": 39
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x720 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# display relationship between price and room_type\n",
        "sns.boxplot(data=df_airbnb, x='room_type', y='price')\n",
        "plt.ylim(0, 400)\n",
        "plt.xlabel('Room Type')\n",
        "plt.ylabel('Price')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "id": "5YtGoDv5PUQZ",
        "outputId": "759b2325-0a37-4b65-fdb7-233fcca015b2"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute median price per room_type\n",
        "df_airbnb.groupby('room_type').median()['price']\n",
        "\n",
        "df_airbnb.groupby('room_type').max()['price']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Hdgnt-uGPchy",
        "outputId": "65c7c6d7-26b4-4a84-fdc0-60f86ce19d8c"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "room_type\n",
              "Entire Place    163.0\n",
              "Private Room     70.0\n",
              "Shared Room      50.0\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# compute median price per room_type\n",
        "..."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0c0LtUyPfl3",
        "outputId": "4c6ecaba-d7f3-4273-c4fb-e2154a3ad57a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "room_type\n",
              "Entire Place    209.662077\n",
              "Private Room     88.025922\n",
              "Shared Room      70.410959\n",
              "Name: price, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EHsc-syLQQA6"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}