Chicago_Crime_Analysis.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Lavan1999/Capstone-Projects/blob/main/Chicago_Crime_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yFojmG-TTSGT"
      },
      "source": [
        "# **Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "SbGF-nPCY1z7"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "# For managing warning messages.\n",
        "import warnings\n",
        "\n",
        "# Data cleaning\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import ast           # To convert string to dictionary\n",
        "import datetime\n",
        "# NLP\n",
        "import spacy\n",
        "\n",
        "# Establishing database and file system connections.\n",
        "from google.colab import drive\n",
        "\n",
        "# Statistical analysis and hypothesis testing.\n",
        "from scipy import stats\n",
        "\n",
        "# Visualization / EDA\n",
        "import plotly.graph_objects as go\n",
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.tree import plot_tree\n",
        "\n",
        "# Encoding categorical variables and scaling numerical features.\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, OrdinalEncoder, MinMaxScaler, StandardScaler\n",
        "\n",
        "# Saving trained models for future deployment.\n",
        "import pickle\n",
        "\n",
        "# Balancing the data to address class imbalance.\n",
        "from imblearn.under_sampling import NearMiss\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from imblearn.combine import SMOTETomek\n",
        "from sklearn.utils import resample\n",
        "\n",
        "# For dimensionality reduction and feature extraction.\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# Split data for training and testing & Optimizing model parameters\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "\n",
        "# Machine Learning : Training\n",
        "from xgboost import plot_importance\n",
        "# Regression\n",
        "import statsmodels.api as sm\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "from xgboost import XGBRegressor\n",
        "# Classification\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier\n",
        "# Cluster\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Machine Learning : Evaluation\n",
        "from sklearn.metrics import mean_squared_error, r2_score # Regression\n",
        "from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix,\\\n",
        "ConfusionMatrixDisplay, classification_report # Classification"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "aaHgDtsXOjHe"
      },
      "outputs": [],
      "source": [
        "# Ignore all warnings (not recommended unless you're sure)\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# Display all warnings once\n",
        "warnings.filterwarnings(\"once\")\n",
        "\n",
        "# Reset the warning filter\n",
        "warnings.resetwarnings()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ipGfChXTvGY5"
      },
      "source": [
        "# Collection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_CbeK1nvQqXw"
      },
      "source": [
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rb9nPZsRqDqU"
      },
      "source": [
        "# **Data collection**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oTOA10BDbTrS"
      },
      "source": [
        "**Loading data using path**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbLgq_jBNxnX",
        "outputId": "cfb436bd-1252-4e43-ad5b-9198c0eced2a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "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"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V6t2bvz3NmgV"
      },
      "outputs": [],
      "source": [
        "pip install --upgrade PyDrive2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FiLceUaOrBkF"
      },
      "outputs": [],
      "source": [
        "#Google drive\n",
        "#file_path = '/content/drive/My Drive/Crimes_-_2001_to_Present'\n",
        "df = pd.read_csv('/content/drive/My Drive/Crimes_-_2001_to_Present.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\", category=ImportWarning)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u0mu7GnEzcIk",
        "outputId": "993ee523-6165-45ae-8de6-9282d9cee0d7"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "luCarRIAM-C2",
        "outputId": "aa50e37a-3aeb-44ca-ca15-4b91eda39b0c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         ID Case Number                    Date                      Block  \\\n",
              "0   5741943    HN549294  08/25/2007 09:22:18 AM         074XX N ROGERS AVE   \n",
              "1     25953    JE240540  05/24/2021 03:06:00 PM        020XX N LARAMIE AVE   \n",
              "2     26038    JE279849  06/26/2021 09:24:00 AM      062XX N MC CORMICK RD   \n",
              "3  13279676    JG507211  11/09/2023 07:30:00 AM           019XX W BYRON ST   \n",
              "4  13274752    JG501049  11/12/2023 07:59:00 AM  086XX S COTTAGE GROVE AVE   \n",
              "\n",
              "   IUCR Primary Type                                        Description  \\\n",
              "0  0560      ASSAULT                                             SIMPLE   \n",
              "1  0110     HOMICIDE                                FIRST DEGREE MURDER   \n",
              "2  0110     HOMICIDE                                FIRST DEGREE MURDER   \n",
              "3  0620     BURGLARY                                     UNLAWFUL ENTRY   \n",
              "4  0454      BATTERY  AGGRAVATED P.O. - HANDS, FISTS, FEET, NO / MIN...   \n",
              "\n",
              "  Location Description  Arrest  Domestic  ...  Ward  Community Area  FBI Code  \\\n",
              "0                OTHER   False     False  ...  49.0             1.0       08A   \n",
              "1               STREET    True     False  ...  36.0            19.0       01A   \n",
              "2          PARKING LOT    True     False  ...  50.0            13.0       01A   \n",
              "3            APARTMENT   False     False  ...  47.0             5.0        05   \n",
              "4   SMALL RETAIL STORE    True     False  ...   6.0            44.0       08B   \n",
              "\n",
              "   X Coordinate Y Coordinate  Year              Updated On   Latitude  \\\n",
              "0           NaN          NaN  2007  08/17/2015 03:03:40 PM        NaN   \n",
              "1     1141387.0    1913179.0  2021  11/18/2023 03:39:49 PM  41.917838   \n",
              "2     1152781.0    1941458.0  2021  11/18/2023 03:39:49 PM  41.995219   \n",
              "3     1162518.0    1925906.0  2023  11/18/2023 03:39:49 PM  41.952345   \n",
              "4     1183071.0    1847869.0  2023  12/09/2023 03:41:24 PM  41.737751   \n",
              "\n",
              "   Longitude                       Location  \n",
              "0        NaN                            NaN  \n",
              "1 -87.755969  (41.917838056, -87.755968972)  \n",
              "2 -87.713355  (41.995219444, -87.713354912)  \n",
              "3 -87.677975  (41.952345086, -87.677975059)  \n",
              "4 -87.604856  (41.737750767, -87.604855911)  \n",
              "\n",
              "[5 rows x 22 columns]"
            ],
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              "  <div id=\"df-997ab37f-aeb8-48ed-abed-ebd3891fbac9\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "      <th></th>\n",
              "      <th>ID</th>\n",
              "      <th>Case Number</th>\n",
              "      <th>Date</th>\n",
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              "      <th>IUCR</th>\n",
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              "      <th>Arrest</th>\n",
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              "      <th>...</th>\n",
              "      <th>Ward</th>\n",
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              "      <th>FBI Code</th>\n",
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              "      <th>Y Coordinate</th>\n",
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              "      <th>Updated On</th>\n",
              "      <th>Latitude</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5741943</td>\n",
              "      <td>HN549294</td>\n",
              "      <td>08/25/2007 09:22:18 AM</td>\n",
              "      <td>074XX N ROGERS AVE</td>\n",
              "      <td>0560</td>\n",
              "      <td>ASSAULT</td>\n",
              "      <td>SIMPLE</td>\n",
              "      <td>OTHER</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>49.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>08A</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2007</td>\n",
              "      <td>08/17/2015 03:03:40 PM</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>25953</td>\n",
              "      <td>JE240540</td>\n",
              "      <td>05/24/2021 03:06:00 PM</td>\n",
              "      <td>020XX N LARAMIE AVE</td>\n",
              "      <td>0110</td>\n",
              "      <td>HOMICIDE</td>\n",
              "      <td>FIRST DEGREE MURDER</td>\n",
              "      <td>STREET</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>36.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>01A</td>\n",
              "      <td>1141387.0</td>\n",
              "      <td>1913179.0</td>\n",
              "      <td>2021</td>\n",
              "      <td>11/18/2023 03:39:49 PM</td>\n",
              "      <td>41.917838</td>\n",
              "      <td>-87.755969</td>\n",
              "      <td>(41.917838056, -87.755968972)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>26038</td>\n",
              "      <td>JE279849</td>\n",
              "      <td>06/26/2021 09:24:00 AM</td>\n",
              "      <td>062XX N MC CORMICK RD</td>\n",
              "      <td>0110</td>\n",
              "      <td>HOMICIDE</td>\n",
              "      <td>FIRST DEGREE MURDER</td>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>50.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>01A</td>\n",
              "      <td>1152781.0</td>\n",
              "      <td>1941458.0</td>\n",
              "      <td>2021</td>\n",
              "      <td>11/18/2023 03:39:49 PM</td>\n",
              "      <td>41.995219</td>\n",
              "      <td>-87.713355</td>\n",
              "      <td>(41.995219444, -87.713354912)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13279676</td>\n",
              "      <td>JG507211</td>\n",
              "      <td>11/09/2023 07:30:00 AM</td>\n",
              "      <td>019XX W BYRON ST</td>\n",
              "      <td>0620</td>\n",
              "      <td>BURGLARY</td>\n",
              "      <td>UNLAWFUL ENTRY</td>\n",
              "      <td>APARTMENT</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>05</td>\n",
              "      <td>1162518.0</td>\n",
              "      <td>1925906.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>11/18/2023 03:39:49 PM</td>\n",
              "      <td>41.952345</td>\n",
              "      <td>-87.677975</td>\n",
              "      <td>(41.952345086, -87.677975059)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13274752</td>\n",
              "      <td>JG501049</td>\n",
              "      <td>11/12/2023 07:59:00 AM</td>\n",
              "      <td>086XX S COTTAGE GROVE AVE</td>\n",
              "      <td>0454</td>\n",
              "      <td>BATTERY</td>\n",
              "      <td>AGGRAVATED P.O. - HANDS, FISTS, FEET, NO / MIN...</td>\n",
              "      <td>SMALL RETAIL STORE</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>...</td>\n",
              "      <td>6.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>08B</td>\n",
              "      <td>1183071.0</td>\n",
              "      <td>1847869.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>12/09/2023 03:41:24 PM</td>\n",
              "      <td>41.737751</td>\n",
              "      <td>-87.604856</td>\n",
              "      <td>(41.737750767, -87.604855911)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 22 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-997ab37f-aeb8-48ed-abed-ebd3891fbac9')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "        element.innerHTML = '';\n",
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              "    </g>\n",
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              "\n",
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              "  .colab-df-quickchart {\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b420cf1c-a8e0-4127-957f-654c2be8bc12 button');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bktMNE3GAQqT",
        "outputId": "ca48fda9-d53a-4d1d-a90f-e8a2dc87c825"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8077151, 22)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5hoQ21LlwJvV"
      },
      "source": [
        "# **EDA** (Exploratory Data Analysis)\n",
        "\n",
        "Null values, Outliers and Distributions"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9z7Imk49z7KM",
        "outputId": "2b13d1db-43e9-45e7-9210-4fbb8d731efd"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                           0\n",
              "Case Number                  0\n",
              "Date                         0\n",
              "Block                        0\n",
              "IUCR                         0\n",
              "Primary Type                 0\n",
              "Description                  0\n",
              "Location Description     13087\n",
              "Arrest                       0\n",
              "Domestic                     0\n",
              "Beat                         0\n",
              "District                    47\n",
              "Ward                    614846\n",
              "Community Area          613470\n",
              "FBI Code                     0\n",
              "X Coordinate             88958\n",
              "Y Coordinate             88958\n",
              "Year                         0\n",
              "Updated On                   0\n",
              "Latitude                 88958\n",
              "Longitude                88958\n",
              "Location                 88958\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "NuxsZUcGz9lE"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVvMhC25z9ik",
        "outputId": "b2d27ef6-6a26-4932-cb96-f7579205c6b6"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ID                      0\n",
              "Case Number             0\n",
              "Date                    0\n",
              "Block                   0\n",
              "IUCR                    0\n",
              "Primary Type            0\n",
              "Description             0\n",
              "Location Description    0\n",
              "Arrest                  0\n",
              "Domestic                0\n",
              "Beat                    0\n",
              "District                0\n",
              "Ward                    0\n",
              "Community Area          0\n",
              "FBI Code                0\n",
              "X Coordinate            0\n",
              "Y Coordinate            0\n",
              "Year                    0\n",
              "Updated On              0\n",
              "Latitude                0\n",
              "Longitude               0\n",
              "Location                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sjvup7z5TcIL"
      },
      "source": [
        "## **Data observation**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NXkYy0ZigRy-"
      },
      "source": [
        "Supervised [Regression / Classification [Balanced / Imbalanced] ] (or) Unsupervised [Clustering / Association]\n",
        "\n",
        "Dependent column\n",
        "\n",
        "Independent column\n",
        "\n",
        "Continuous column\n",
        "\n",
        "Category column"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5j5psLZDdfYy",
        "outputId": "4f7fb4f1-43bd-4f56-b383-e26b9faeded5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type',\n",
              "       'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat',\n",
              "       'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate',\n",
              "       'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude',\n",
              "       'Location'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for column in df.columns:\n",
        "  print(column,'---',len(df[column].unique()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "J_kVDZ-E0MsG",
        "outputId": "34324bbd-b46d-4791-837f-024b54e1c2d4"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ID --- 7373082\n",
            "Case Number --- 7372531\n",
            "Date --- 3074782\n",
            "Block --- 38456\n",
            "IUCR --- 402\n",
            "Primary Type --- 35\n",
            "Description --- 548\n",
            "Location Description --- 217\n",
            "Arrest --- 2\n",
            "Domestic --- 2\n",
            "Beat --- 304\n",
            "District --- 24\n",
            "Ward --- 50\n",
            "Community Area --- 78\n",
            "FBI Code --- 27\n",
            "X Coordinate --- 75662\n",
            "Y Coordinate --- 125958\n",
            "Year --- 24\n",
            "Updated On --- 4993\n",
            "Latitude --- 680707\n",
            "Longitude --- 680319\n",
            "Location --- 681421\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The columns 'id' and 'case number' are unique identifiers for each row, so I dropped them to simplify the dataset and focus on the remaining features for analysis."
      ],
      "metadata": {
        "id": "-bm9VRxl1r6f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "39Kazy5KaqTn",
        "outputId": "b878b6d9-c1ec-4dbb-9992-88f71ce427ba"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['ID', 'Case Number', 'Date', 'Block', 'IUCR', 'Primary Type',\n",
              "       'Description', 'Location Description', 'Arrest', 'Domestic', 'Beat',\n",
              "       'District', 'Ward', 'Community Area', 'FBI Code', 'X Coordinate',\n",
              "       'Y Coordinate', 'Year', 'Updated On', 'Latitude', 'Longitude',\n",
              "       'Location'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert all column names to lowercase\n",
        "df.columns = [col.lower() for col in df.columns]"
      ],
      "metadata": {
        "id": "DjuQaDMHavjh"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(columns=['id', 'case number','date'], inplace=True)"
      ],
      "metadata": {
        "id": "zHlKOZFV1YfU"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "category_col = ['primary type','arrest','domestic','district','community area','fbi code','year','ward']\n",
        "continuous_col = ['block','iucr','description','location description','beat','x coordinate',\n",
        "'y coordinate','updated on','latitude','longitude','location']\n",
        "# Select only the numerical columns\n",
        "string_columns = df.select_dtypes(exclude=['int64',\"float64\"]).columns\n",
        "numeric_columns = df.select_dtypes(include=['int64',\"float64\"]).columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_HMfswT0MpD",
        "outputId": "c892df03-2c0f-4b0a-a890-7e111efe32ea"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "numeric_columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q0VMz_Tb40SZ",
        "outputId": "39dbf63b-cbc1-4780-e8cc-fcbfb6d43daa"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['beat', 'district', 'ward', 'community area', 'x coordinate',\n",
              "       'y coordinate', 'year', 'latitude', 'longitude'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fHWiosvmbkmE",
        "outputId": "a183208b-b9f8-461a-84e1-77eca492fa0a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 7373082 entries, 1 to 8077150\n",
            "Data columns (total 19 columns):\n",
            " #   Column                Dtype  \n",
            "---  ------                -----  \n",
            " 0   block                 object \n",
            " 1   iucr                  object \n",
            " 2   primary type          object \n",
            " 3   description           object \n",
            " 4   location description  object \n",
            " 5   arrest                bool   \n",
            " 6   domestic              bool   \n",
            " 7   beat                  int64  \n",
            " 8   district              float64\n",
            " 9   ward                  float64\n",
            " 10  community area        float64\n",
            " 11  fbi code              object \n",
            " 12  x coordinate          float64\n",
            " 13  y coordinate          float64\n",
            " 14  year                  int64  \n",
            " 15  updated on            object \n",
            " 16  latitude              float64\n",
            " 17  longitude             float64\n",
            " 18  location              object \n",
            "dtypes: bool(2), float64(7), int64(2), object(8)\n",
            "memory usage: 1.0+ GB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "QpKN76VbbnH6",
        "outputId": "5f36941b-7ffe-434e-d942-eb98dcc39559"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    count          mean           std          min  \\\n",
              "beat            7373082.0  1.181037e+03    702.773939   111.000000   \n",
              "district        7373082.0  1.128699e+01      6.954011     1.000000   \n",
              "ward            7373082.0  2.275423e+01     13.850022     1.000000   \n",
              "community area  7373082.0  3.744582e+01     21.534682     0.000000   \n",
              "x coordinate    7373082.0  1.164629e+06  16850.194010     0.000000   \n",
              "y coordinate    7373082.0  1.885737e+06  32288.696776     0.000000   \n",
              "year            7373082.0  2.011169e+03      6.321166  2001.000000   \n",
              "latitude        7373082.0  4.184206e+01      0.088832    36.619446   \n",
              "longitude       7373082.0 -8.767139e+01      0.061102   -91.686566   \n",
              "\n",
              "                         25%           50%           75%           max  \n",
              "beat            6.210000e+02  1.034000e+03  1.724000e+03  2.535000e+03  \n",
              "district        6.000000e+00  1.000000e+01  1.700000e+01  3.100000e+01  \n",
              "ward            1.000000e+01  2.300000e+01  3.400000e+01  5.000000e+01  \n",
              "community area  2.300000e+01  3.200000e+01  5.700000e+01  7.700000e+01  \n",
              "x coordinate    1.152976e+06  1.166166e+06  1.176399e+06  1.205119e+06  \n",
              "y coordinate    1.858942e+06  1.890745e+06  1.909207e+06  1.951573e+06  \n",
              "year            2.006000e+03  2.010000e+03  2.016000e+03  2.024000e+03  \n",
              "latitude        4.176835e+01  4.185596e+01  4.190663e+01  4.202271e+01  \n",
              "longitude      -8.771367e+01 -8.766571e+01 -8.762807e+01 -8.752453e+01  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-aa63c2ea-2a06-49e6-b4b1-8e0570ea4258\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>beat</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>1.181037e+03</td>\n",
              "      <td>702.773939</td>\n",
              "      <td>111.000000</td>\n",
              "      <td>6.210000e+02</td>\n",
              "      <td>1.034000e+03</td>\n",
              "      <td>1.724000e+03</td>\n",
              "      <td>2.535000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>district</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>1.128699e+01</td>\n",
              "      <td>6.954011</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6.000000e+00</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>1.700000e+01</td>\n",
              "      <td>3.100000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ward</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>2.275423e+01</td>\n",
              "      <td>13.850022</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+01</td>\n",
              "      <td>2.300000e+01</td>\n",
              "      <td>3.400000e+01</td>\n",
              "      <td>5.000000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>community area</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>3.744582e+01</td>\n",
              "      <td>21.534682</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.300000e+01</td>\n",
              "      <td>3.200000e+01</td>\n",
              "      <td>5.700000e+01</td>\n",
              "      <td>7.700000e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>x coordinate</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>1.164629e+06</td>\n",
              "      <td>16850.194010</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.152976e+06</td>\n",
              "      <td>1.166166e+06</td>\n",
              "      <td>1.176399e+06</td>\n",
              "      <td>1.205119e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>y coordinate</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>1.885737e+06</td>\n",
              "      <td>32288.696776</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.858942e+06</td>\n",
              "      <td>1.890745e+06</td>\n",
              "      <td>1.909207e+06</td>\n",
              "      <td>1.951573e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>2.011169e+03</td>\n",
              "      <td>6.321166</td>\n",
              "      <td>2001.000000</td>\n",
              "      <td>2.006000e+03</td>\n",
              "      <td>2.010000e+03</td>\n",
              "      <td>2.016000e+03</td>\n",
              "      <td>2.024000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>latitude</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>4.184206e+01</td>\n",
              "      <td>0.088832</td>\n",
              "      <td>36.619446</td>\n",
              "      <td>4.176835e+01</td>\n",
              "      <td>4.185596e+01</td>\n",
              "      <td>4.190663e+01</td>\n",
              "      <td>4.202271e+01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>longitude</th>\n",
              "      <td>7373082.0</td>\n",
              "      <td>-8.767139e+01</td>\n",
              "      <td>0.061102</td>\n",
              "      <td>-91.686566</td>\n",
              "      <td>-8.771367e+01</td>\n",
              "      <td>-8.766571e+01</td>\n",
              "      <td>-8.762807e+01</td>\n",
              "      <td>-8.752453e+01</td>\n",
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              "      const buttonEl =\n",
              "        document.querySelector('#df-aa63c2ea-2a06-49e6-b4b1-8e0570ea4258 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-aa63c2ea-2a06-49e6-b4b1-8e0570ea4258');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-5f434bf6-be29-435e-99bd-fae4f20d70ac\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5f434bf6-be29-435e-99bd-fae4f20d70ac')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5f434bf6-be29-435e-99bd-fae4f20d70ac button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 7373082.0,\n        \"max\": 7373082.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          7373082.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 696089.9061699056,\n        \"min\": -87.67139141894735,\n        \"max\": 1885737.0168957838,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          41.84205724295671\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11458.73821423901,\n        \"min\": 0.061102151590051036,\n        \"max\": 32288.696776318633,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.08883184387630333\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 666.6191450183316,\n        \"min\": -91.686565684,\n        \"max\": 2001.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          111.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 686959.5980999371,\n        \"min\": -87.713672954,\n        \"max\": 1858942.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          41.768354866\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 697717.9288118823,\n        \"min\": -87.66571041,\n        \"max\": 1890745.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          41.855958702\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 704316.9832446164,\n        \"min\": -87.628070856,\n        \"max\": 1909207.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          41.906632983\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 720285.5964567557,\n        \"min\": -87.524529378,\n        \"max\": 1951573.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          42.022709624\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 23
        }
      ],
      "source": [
        "df.describe().T"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 418
        },
        "id": "irSaTO__bpFj",
        "outputId": "a75c9bfc-7bbd-4da8-8110-d02ce009437f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        count  unique                            top     freq\n",
              "block                 7373082   38456               001XX N STATE ST    14966\n",
              "iucr                  7373082     402                           0486   629202\n",
              "primary type          7373082      35                          THEFT  1563982\n",
              "description           7373082     548                         SIMPLE   818060\n",
              "location description  7373082     217                         STREET  1908322\n",
              "arrest                7373082       2                          False  5502714\n",
              "domestic              7373082       2                          False  6094081\n",
              "fbi code              7373082      27                             06  1563982\n",
              "updated on            7373082    4993         02/10/2018 03:50:01 PM  2675880\n",
              "location              7373082  681421  (41.976290414, -87.905227221)    14009"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-987051fe-595c-4208-a100-103988911c05\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>unique</th>\n",
              "      <th>top</th>\n",
              "      <th>freq</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>block</th>\n",
              "      <td>7373082</td>\n",
              "      <td>38456</td>\n",
              "      <td>001XX N STATE ST</td>\n",
              "      <td>14966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>iucr</th>\n",
              "      <td>7373082</td>\n",
              "      <td>402</td>\n",
              "      <td>0486</td>\n",
              "      <td>629202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>primary type</th>\n",
              "      <td>7373082</td>\n",
              "      <td>35</td>\n",
              "      <td>THEFT</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>description</th>\n",
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              "      <td>548</td>\n",
              "      <td>SIMPLE</td>\n",
              "      <td>818060</td>\n",
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              "      <th>location description</th>\n",
              "      <td>7373082</td>\n",
              "      <td>217</td>\n",
              "      <td>STREET</td>\n",
              "      <td>1908322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>arrest</th>\n",
              "      <td>7373082</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>5502714</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>domestic</th>\n",
              "      <td>7373082</td>\n",
              "      <td>2</td>\n",
              "      <td>False</td>\n",
              "      <td>6094081</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>fbi code</th>\n",
              "      <td>7373082</td>\n",
              "      <td>27</td>\n",
              "      <td>06</td>\n",
              "      <td>1563982</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>updated on</th>\n",
              "      <td>7373082</td>\n",
              "      <td>4993</td>\n",
              "      <td>02/10/2018 03:50:01 PM</td>\n",
              "      <td>2675880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>location</th>\n",
              "      <td>7373082</td>\n",
              "      <td>681421</td>\n",
              "      <td>(41.976290414, -87.905227221)</td>\n",
              "      <td>14009</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-987051fe-595c-4208-a100-103988911c05')\"\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",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-987051fe-595c-4208-a100-103988911c05 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-987051fe-595c-4208-a100-103988911c05');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
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              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "\n",
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          "metadata": {},
          "execution_count": 24
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      "source": [
        "df[string_columns].describe().T"
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    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "O8SEYA4AqgUC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c0f7a801-a9cd-47bb-9f5a-53e226529085"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "549002"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop_duplicates()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 860
        },
        "id": "Rocuj8uV5M5Z",
        "outputId": "2f306d43-0d93-4491-e692-386a24f1977e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             block  iucr         primary type  \\\n",
              "1              020XX N LARAMIE AVE  0110             HOMICIDE   \n",
              "2            062XX N MC CORMICK RD  0110             HOMICIDE   \n",
              "3                 019XX W BYRON ST  0620             BURGLARY   \n",
              "4        086XX S COTTAGE GROVE AVE  0454              BATTERY   \n",
              "6                 002XX N Wells st  1320      CRIMINAL DAMAGE   \n",
              "...                            ...   ...                  ...   \n",
              "8077146       003XX N JEFFERSON ST  1154   DECEPTIVE PRACTICE   \n",
              "8077147            0000X W 95TH ST  031A              ROBBERY   \n",
              "8077148          031XX S AVERS AVE  0910  MOTOR VEHICLE THEFT   \n",
              "8077149     078XX S SOUTH SHORE DR  1320      CRIMINAL DAMAGE   \n",
              "8077150           005XX S WELLS ST  1320      CRIMINAL DAMAGE   \n",
              "\n",
              "                                               description  \\\n",
              "1                                      FIRST DEGREE MURDER   \n",
              "2                                      FIRST DEGREE MURDER   \n",
              "3                                           UNLAWFUL ENTRY   \n",
              "4        AGGRAVATED P.O. - HANDS, FISTS, FEET, NO / MIN...   \n",
              "6                                               TO VEHICLE   \n",
              "...                                                    ...   \n",
              "8077146            FINANCIAL IDENTITY THEFT $300 AND UNDER   \n",
              "8077147                                    ARMED - HANDGUN   \n",
              "8077148                                         AUTOMOBILE   \n",
              "8077149                                         TO VEHICLE   \n",
              "8077150                                         TO VEHICLE   \n",
              "\n",
              "                           location description  arrest  domestic  beat  \\\n",
              "1                                        STREET    True     False  2515   \n",
              "2                                   PARKING LOT    True     False  1711   \n",
              "3                                     APARTMENT   False     False  1922   \n",
              "4                            SMALL RETAIL STORE    True     False   632   \n",
              "6        PARKING LOT / GARAGE (NON RESIDENTIAL)   False     False   122   \n",
              "...                                         ...     ...       ...   ...   \n",
              "8077146                               APARTMENT   False     False  1214   \n",
              "8077147                                SIDEWALK   False     False   634   \n",
              "8077148                                  STREET   False     False  1031   \n",
              "8077149                                  STREET   False     False   421   \n",
              "8077150                                  STREET   False      True   123   \n",
              "\n",
              "         district  ward  community area fbi code  x coordinate  y coordinate  \\\n",
              "1            25.0  36.0            19.0      01A     1141387.0     1913179.0   \n",
              "2            17.0  50.0            13.0      01A     1152781.0     1941458.0   \n",
              "3            19.0  47.0             5.0       05     1162518.0     1925906.0   \n",
              "4             6.0   6.0            44.0      08B     1183071.0     1847869.0   \n",
              "6             1.0  42.0            32.0       14     1174694.0     1901831.0   \n",
              "...           ...   ...             ...      ...           ...           ...   \n",
              "8077146      12.0  42.0            28.0       11     1172269.0     1902384.0   \n",
              "8077147       6.0   9.0            49.0       03     1177527.0     1841973.0   \n",
              "8077148      10.0  22.0            30.0       07     1151205.0     1883542.0   \n",
              "8077149       4.0   7.0            46.0       14     1198338.0     1853426.0   \n",
              "8077150       1.0  34.0            32.0       14     1174805.0     1897873.0   \n",
              "\n",
              "         year              updated on   latitude  longitude  \\\n",
              "1        2021  11/18/2023 03:39:49 PM  41.917838 -87.755969   \n",
              "2        2021  11/18/2023 03:39:49 PM  41.995219 -87.713355   \n",
              "3        2023  11/18/2023 03:39:49 PM  41.952345 -87.677975   \n",
              "4        2023  12/09/2023 03:41:24 PM  41.737751 -87.604856   \n",
              "6        2023  11/04/2023 03:40:18 PM  41.886018 -87.633938   \n",
              "...       ...                     ...        ...        ...   \n",
              "8077146  2023  08/19/2023 03:40:26 PM  41.887589 -87.642827   \n",
              "8077147  2023  08/19/2023 03:40:26 PM  41.721698 -87.625345   \n",
              "8077148  2023  08/19/2023 03:40:26 PM  41.836324 -87.720674   \n",
              "8077149  2023  09/02/2023 03:41:45 PM  41.752631 -87.548737   \n",
              "8077150  2023  08/19/2023 03:40:26 PM  41.875155 -87.633649   \n",
              "\n",
              "                              location  \n",
              "1        (41.917838056, -87.755968972)  \n",
              "2        (41.995219444, -87.713354912)  \n",
              "3        (41.952345086, -87.677975059)  \n",
              "4        (41.737750767, -87.604855911)  \n",
              "6        (41.886018055, -87.633937881)  \n",
              "...                                ...  \n",
              "8077146  (41.887589411, -87.642826595)  \n",
              "8077147   (41.72169837, -87.625345212)  \n",
              "8077148  (41.836323827, -87.720674149)  \n",
              "8077149  (41.752631346, -87.548737069)  \n",
              "8077150   (41.87515458, -87.633648765)  \n",
              "\n",
              "[6824080 rows x 19 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>block</th>\n",
              "      <th>iucr</th>\n",
              "      <th>primary type</th>\n",
              "      <th>description</th>\n",
              "      <th>location description</th>\n",
              "      <th>arrest</th>\n",
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              "      <th>beat</th>\n",
              "      <th>district</th>\n",
              "      <th>ward</th>\n",
              "      <th>community area</th>\n",
              "      <th>fbi code</th>\n",
              "      <th>x coordinate</th>\n",
              "      <th>y coordinate</th>\n",
              "      <th>year</th>\n",
              "      <th>updated on</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>location</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>020XX N LARAMIE AVE</td>\n",
              "      <td>0110</td>\n",
              "      <td>HOMICIDE</td>\n",
              "      <td>FIRST DEGREE MURDER</td>\n",
              "      <td>STREET</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>2515</td>\n",
              "      <td>25.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>01A</td>\n",
              "      <td>1141387.0</td>\n",
              "      <td>1913179.0</td>\n",
              "      <td>2021</td>\n",
              "      <td>11/18/2023 03:39:49 PM</td>\n",
              "      <td>41.917838</td>\n",
              "      <td>-87.755969</td>\n",
              "      <td>(41.917838056, -87.755968972)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>062XX N MC CORMICK RD</td>\n",
              "      <td>0110</td>\n",
              "      <td>HOMICIDE</td>\n",
              "      <td>FIRST DEGREE MURDER</td>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>1711</td>\n",
              "      <td>17.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>01A</td>\n",
              "      <td>1152781.0</td>\n",
              "      <td>1941458.0</td>\n",
              "      <td>2021</td>\n",
              "      <td>11/18/2023 03:39:49 PM</td>\n",
              "      <td>41.995219</td>\n",
              "      <td>-87.713355</td>\n",
              "      <td>(41.995219444, -87.713354912)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>019XX W BYRON ST</td>\n",
              "      <td>0620</td>\n",
              "      <td>BURGLARY</td>\n",
              "      <td>UNLAWFUL ENTRY</td>\n",
              "      <td>APARTMENT</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1922</td>\n",
              "      <td>19.0</td>\n",
              "      <td>47.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>05</td>\n",
              "      <td>1162518.0</td>\n",
              "      <td>1925906.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>11/18/2023 03:39:49 PM</td>\n",
              "      <td>41.952345</td>\n",
              "      <td>-87.677975</td>\n",
              "      <td>(41.952345086, -87.677975059)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>086XX S COTTAGE GROVE AVE</td>\n",
              "      <td>0454</td>\n",
              "      <td>BATTERY</td>\n",
              "      <td>AGGRAVATED P.O. - HANDS, FISTS, FEET, NO / MIN...</td>\n",
              "      <td>SMALL RETAIL STORE</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>632</td>\n",
              "      <td>6.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>08B</td>\n",
              "      <td>1183071.0</td>\n",
              "      <td>1847869.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>12/09/2023 03:41:24 PM</td>\n",
              "      <td>41.737751</td>\n",
              "      <td>-87.604856</td>\n",
              "      <td>(41.737750767, -87.604855911)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>002XX N Wells st</td>\n",
              "      <td>1320</td>\n",
              "      <td>CRIMINAL DAMAGE</td>\n",
              "      <td>TO VEHICLE</td>\n",
              "      <td>PARKING LOT / GARAGE (NON RESIDENTIAL)</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>122</td>\n",
              "      <td>1.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>14</td>\n",
              "      <td>1174694.0</td>\n",
              "      <td>1901831.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>11/04/2023 03:40:18 PM</td>\n",
              "      <td>41.886018</td>\n",
              "      <td>-87.633938</td>\n",
              "      <td>(41.886018055, -87.633937881)</td>\n",
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              "      <th>8077146</th>\n",
              "      <td>003XX N JEFFERSON ST</td>\n",
              "      <td>1154</td>\n",
              "      <td>DECEPTIVE PRACTICE</td>\n",
              "      <td>FINANCIAL IDENTITY THEFT $300 AND UNDER</td>\n",
              "      <td>APARTMENT</td>\n",
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              "      <td>12.0</td>\n",
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              "      <td>1902384.0</td>\n",
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              "      <td>08/19/2023 03:40:26 PM</td>\n",
              "      <td>41.887589</td>\n",
              "      <td>-87.642827</td>\n",
              "      <td>(41.887589411, -87.642826595)</td>\n",
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              "      <th>8077147</th>\n",
              "      <td>0000X W 95TH ST</td>\n",
              "      <td>031A</td>\n",
              "      <td>ROBBERY</td>\n",
              "      <td>ARMED - HANDGUN</td>\n",
              "      <td>SIDEWALK</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>634</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>49.0</td>\n",
              "      <td>03</td>\n",
              "      <td>1177527.0</td>\n",
              "      <td>1841973.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>08/19/2023 03:40:26 PM</td>\n",
              "      <td>41.721698</td>\n",
              "      <td>-87.625345</td>\n",
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              "      <th>8077148</th>\n",
              "      <td>031XX S AVERS AVE</td>\n",
              "      <td>0910</td>\n",
              "      <td>MOTOR VEHICLE THEFT</td>\n",
              "      <td>AUTOMOBILE</td>\n",
              "      <td>STREET</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>1031</td>\n",
              "      <td>10.0</td>\n",
              "      <td>22.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>07</td>\n",
              "      <td>1151205.0</td>\n",
              "      <td>1883542.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>08/19/2023 03:40:26 PM</td>\n",
              "      <td>41.836324</td>\n",
              "      <td>-87.720674</td>\n",
              "      <td>(41.836323827, -87.720674149)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8077149</th>\n",
              "      <td>078XX S SOUTH SHORE DR</td>\n",
              "      <td>1320</td>\n",
              "      <td>CRIMINAL DAMAGE</td>\n",
              "      <td>TO VEHICLE</td>\n",
              "      <td>STREET</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "      <td>421</td>\n",
              "      <td>4.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>46.0</td>\n",
              "      <td>14</td>\n",
              "      <td>1198338.0</td>\n",
              "      <td>1853426.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>09/02/2023 03:41:45 PM</td>\n",
              "      <td>41.752631</td>\n",
              "      <td>-87.548737</td>\n",
              "      <td>(41.752631346, -87.548737069)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8077150</th>\n",
              "      <td>005XX S WELLS ST</td>\n",
              "      <td>1320</td>\n",
              "      <td>CRIMINAL DAMAGE</td>\n",
              "      <td>TO VEHICLE</td>\n",
              "      <td>STREET</td>\n",
              "      <td>False</td>\n",
              "      <td>True</td>\n",
              "      <td>123</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>32.0</td>\n",
              "      <td>14</td>\n",
              "      <td>1174805.0</td>\n",
              "      <td>1897873.0</td>\n",
              "      <td>2023</td>\n",
              "      <td>08/19/2023 03:40:26 PM</td>\n",
              "      <td>41.875155</td>\n",
              "      <td>-87.633649</td>\n",
              "      <td>(41.87515458, -87.633648765)</td>\n",
              "    </tr>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VaM2FrWXS-8K"
      },
      "source": [
        "## **Correlation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "gsijyAVc9vJt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "73ad6eff-f225-4ea6-adf0-de05867e74fc"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
          ]
        }
      ],
      "source": [
        "# Calculating correlation between column.\n",
        "# Remove the column if it correlated from 0.7 to 1 because it highly correlated."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "SizOT1ln5bS6",
        "outputId": "b09bd8f9-54b3-4e66-966a-65526403865e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    beat  district      ward  community area  x coordinate  \\\n",
              "beat            1.000000  0.953262  0.643211       -0.499842     -0.496809   \n",
              "district        0.953262  1.000000  0.684717       -0.494722     -0.539630   \n",
              "ward            0.643211  0.684717  1.000000       -0.530480     -0.448335   \n",
              "community area -0.499842 -0.494722 -0.530480        1.000000      0.250956   \n",
              "x coordinate   -0.496809 -0.539630 -0.448335        0.250956      1.000000   \n",
              "y coordinate    0.620985  0.627772  0.640790       -0.756321     -0.462292   \n",
              "year           -0.036771 -0.004180  0.020590       -0.015917      0.011851   \n",
              "latitude        0.621387  0.628328  0.640939       -0.755389     -0.464224   \n",
              "longitude      -0.493415 -0.536661 -0.444054        0.242633      0.999831   \n",
              "\n",
              "                y coordinate      year  latitude  longitude  \n",
              "beat                0.620985 -0.036771  0.621387  -0.493415  \n",
              "district            0.627772 -0.004180  0.628328  -0.536661  \n",
              "ward                0.640790  0.020590  0.640939  -0.444054  \n",
              "community area     -0.756321 -0.015917 -0.755389   0.242633  \n",
              "x coordinate       -0.462292  0.011851 -0.464224   0.999831  \n",
              "y coordinate        1.000000  0.009310  0.999994  -0.457203  \n",
              "year                0.009310  1.000000  0.009237   0.012130  \n",
              "latitude            0.999994  0.009237  1.000000  -0.459188  \n",
              "longitude          -0.457203  0.012130 -0.459188   1.000000  "
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              "      <td>-0.015917</td>\n",
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          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "# Select numerical columns (including both int and float types)\n",
        "numerical_df = df.select_dtypes(include=['number'])\n",
        "\n",
        "# Compute the correlation matrix for numerical columns\n",
        "numerical_df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "OMCaKJd1wQ7c",
        "outputId": "3e43e635-b698-4a73-d4ed-5e570f29f0c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "  and should_run_async(code)\n"
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              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "correlation_matrix = numerical_df.corr()\n",
        "fig = px.imshow(correlation_matrix, color_continuous_scale='Cividis',\n",
        "                title=\"Correlation Matrix\")\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K_O72m6-Br6M",
        "outputId": "1a11f86e-bb7e-44a5-8ae2-ed93a3b0bc2e"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning:\n",
            "\n",
            "`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['block', 'iucr', 'primary type', 'description', 'location description',\n",
              "       'arrest', 'domestic', 'beat', 'district', 'ward', 'community area',\n",
              "       'fbi code', 'x coordinate', 'y coordinate', 'year', 'updated on',\n",
              "       'latitude', 'longitude', 'location'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "My5zUVHqu4Y9"
      },
      "source": [
        "## **Data visualization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykf41FhRG7SM"
      },
      "source": [
        "**Univariate analysis :**\n",
        "\n",
        "1.   **Distribution of categorical data**: bar plot, pie chart\n",
        "2.   **Distribution of continuous data**: Box plots, Histograms, kernel density plots\n",
        "\n",
        "\n",
        "**Bivariate analysis :** correlation, multi correlation & influence\n",
        "\n",
        "\n",
        "1.   **Continuous vs continuous**: Scatter plots also add a trend line to show any potential patterns or correlations.\n",
        "2.   **Continuous vs categorical**: bar plot, Box plots or violin plots\n",
        "3. **Categorical vs categorical**: contingency table or a stacked bar chart"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "def plot(df, column):\n",
        "    plt.figure(figsize=(20,5))\n",
        "    plt.subplot(1,2,1)\n",
        "    sns.boxplot(data=df, x=column)\n",
        "    plt.title(f'Box Plot for {column}')\n",
        "\n",
        "    plt.subplot(1,2,2)\n",
        "    sns.histplot(data=df, x=column, kde=True, bins=50)\n",
        "    plt.title(f'Distribution Plot for {column}')"
      ],
      "metadata": {
        "id": "37gyn2ywL1xr"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ivzmFB2nbuOt",
        "outputId": "da1fe0d1-2a27-4f6e-825f-b5c0f3e9bdf5"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 7373082 entries, 1 to 8077150\n",
            "Data columns (total 19 columns):\n",
            " #   Column                Dtype  \n",
            "---  ------                -----  \n",
            " 0   block                 object \n",
            " 1   iucr                  object \n",
            " 2   primary type          object \n",
            " 3   description           object \n",
            " 4   location description  object \n",
            " 5   arrest                bool   \n",
            " 6   domestic              bool   \n",
            " 7   beat                  int64  \n",
            " 8   district              float64\n",
            " 9   ward                  float64\n",
            " 10  community area        float64\n",
            " 11  fbi code              object \n",
            " 12  x coordinate          float64\n",
            " 13  y coordinate          float64\n",
            " 14  year                  int64  \n",
            " 15  updated on            object \n",
            " 16  latitude              float64\n",
            " 17  longitude             float64\n",
            " 18  location              object \n",
            "dtypes: bool(2), float64(7), int64(2), object(8)\n",
            "memory usage: 1.0+ GB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "MCHKrqLSz1vI",
        "outputId": "be0076df-99e0-4b46-f83e-7788a44b9af4"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning:\n",
            "\n",
            "`should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n",
            "\n"
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              "                   block  iucr primary type          description  \\\n",
              "1    020XX N LARAMIE AVE  0110     HOMICIDE  FIRST DEGREE MURDER   \n",
              "2  062XX N MC CORMICK RD  0110     HOMICIDE  FIRST DEGREE MURDER   \n",
              "\n",
              "  location description  arrest  domestic  beat  district  ward  \\\n",
              "1               STREET    True     False  2515      25.0  36.0   \n",
              "2          PARKING LOT    True     False  1711      17.0  50.0   \n",
              "\n",
              "   community area fbi code  x coordinate  y coordinate  year  \\\n",
              "1            19.0      01A     1141387.0     1913179.0  2021   \n",
              "2            13.0      01A     1152781.0     1941458.0  2021   \n",
              "\n",
              "               updated on   latitude  longitude                       location  \n",
              "1  11/18/2023 03:39:49 PM  41.917838 -87.755969  (41.917838056, -87.755968972)  \n",
              "2  11/18/2023 03:39:49 PM  41.995219 -87.713355  (41.995219444, -87.713354912)  "
            ],
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              "      <th>block</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>020XX N LARAMIE AVE</td>\n",
              "      <td>0110</td>\n",
              "      <td>HOMICIDE</td>\n",
              "      <td>FIRST DEGREE MURDER</td>\n",
              "      <td>STREET</td>\n",
              "      <td>True</td>\n",
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              "      <td>41.917838</td>\n",
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              "      <td>(41.917838056, -87.755968972)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>062XX N MC CORMICK RD</td>\n",
              "      <td>0110</td>\n",
              "      <td>HOMICIDE</td>\n",
              "      <td>FIRST DEGREE MURDER</td>\n",
              "      <td>PARKING LOT</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "      <td>1711</td>\n",
              "      <td>17.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>01A</td>\n",
              "      <td>1152781.0</td>\n",
              "      <td>1941458.0</td>\n",
              "      <td>2021</td>\n",
              "      <td>11/18/2023 03:39:49 PM</td>\n",
              "      <td>41.995219</td>\n",
              "      <td>-87.713355</td>\n",
              "      <td>(41.995219444, -87.713354912)</td>\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-914ac3eb-f5a9-42ba-bdb0-900c75da640d 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-914ac3eb-f5a9-42ba-bdb0-900c75da640d');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f9a3dfb3-45ba-4a72-8cea-fd77f96ed99d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f9a3dfb3-45ba-4a72-8cea-fd77f96ed99d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f9a3dfb3-45ba-4a72-8cea-fd77f96ed99d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in ['beat','x coordinate','district','ward','year','community area',\n",
        "          'y coordinate','latitude','longitude']:\n",
        "    plot(df, i)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "QBiVu2PgQMkM",
        "outputId": "e984ba95-2dea-4426-955a-cdb000181386"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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AAAAAge2777666667tHjx4mmXj46O6qUvfan++te/ztORYXcwUdeCqNUMj2zoMzwatbSKVq8X7tDyZi2top39oawlSRNuWlqFGHg0qvCwLEt9qbi2T+Y1OpkPOfAwLa0qFR6JWMQJVybzJfV2EHhg/gwsW6mlq1bP92EAwILDv94AAAAAAnvqqadUKu14Knsul9Pzzz8/D0eE3YmpRrBLBTXqeJSqVmLkS2WVAoYQVqJD5h4aBR5ta2k1SwmLGbYdSXZqKqRAZ7w638LMtahXzprAI8SWVtXwpH+WQMNUfWSmwp2tMeq0tKr83bIsp8pjgjkeAAAsSFR4AAAAAPDtZz/7mfP/1113nfr6+py/l0ol3XTTTdpnn33m4ciwOzGb8+XcpCyra9br1AcTuWLJmdfgRyRZWSNqWYo2mhkSm7uWVvGopagllWxpJKRQoOkMjzkcWi7VHvNUSAPnjRGnpVXtNexMxJTJFjWRY44HAAALEYEHAAAAAN/e9ra3SaqcGb1hw4ZpX4vH49pnn330uc99bh6ODLuTjAk88lMNrxOJWEpEI8qXysoVyupM+F/PBB6JWESWNXvgYSo8Qmtp1STwsCxLqZg0Uaht4gdh23bTwMNUeIyEWOFh2mNVqjmmhzZm/ko+pOoVqVIxY+a+1I8pMRUtk1R4AKEZHh5WOu1tCHtfXx/zSAD4QuABAAAAwLdyubJhuGbNGt11111asmTJPB8RdkdOS6vcZNPrpeKVwCMbsFIgkuqWVBtMPhunwiO0llbVzflZAg9J6ohZmijY2j4ZfKM+XyqrWA1qms3wSLdthsf0x2AGwwd93eptn6iENXapqHikVlViKn8m8u2r8HhuZFId8agWdyfbtgawsxgeHtbatfspk/EWePT29unxxx8j9ADgGYEHAAAAgMCefPLJ+T4E7Macllb5VoFHVJlsMfDgclPh0Wh+R+VrZmZIoKUcZmh5o8AjVb04jJZWuerzE41YikV3fIzODI+pgmzbbljl4sX0wGN6pY55nnPF8Co8to1XAo/SZFqW1elcbgbAT+baU+GRmSrox/c+r454VO8/ck3DlmjAriKdTiuTSev0z2zUwLKVrm4zMrRJV378FKXTadeBx1xVkVCtAuz8CDwAAAAAhOKmm27STTfdpKGhIafyw/jWt741T0eF3cF4rrJZXm5R4WEqMnIBB2tEUibwmD18kGotrWxJVjzYmfy2bTvVDR2JBoFHzJJkh9LSygQLjQIdM8OjVLY1liuqN7Xj3A2vTODR35mQctO/5rxuIVZ4bJ2oLFKeHJW0h3N5V7WiZbJNFR7bJvKy7cr9bxqd0l6LOlvfCNgFDCxbqaWrVrflvueqioRqFWBhIPAAAAAAENhFF12kiy++WC972cu0xx57hHLGN+DWWNZlSyunNVL7KzziUUuWJdl27fp+5Ypl2dVRIKkGbbTCrPDItwg8VCooFbOULdpKTxZCDTz6OuIqzQw8qq9bLsQZHvUVHvW6qoHSRJtmeGTqBr0/MTxO4AGEYK6qSOZqHexaqAqaewQeAAAAAAK78sortXHjRr3vfe+b70PBbsjM8HDT0kqqDKwOwpnh0STwsKzKkPRcsSwrGWxT2wwsT0QjikVmX7MjVgkZt4dS4VFdr8nj60lGlS0WNTKZD7xpXyiVndewvyOubaPTv96OllbbqxUepYnpizkzPHLtqfDIZOsDjwkd9cJwWoIBaG8VyXysg4WPqqD5QeABAAAAILB8Pq9XvepV830Y2E05MzxatbSqbpwHDjxMhUeDeRr16+WK5cAVHuZ4G1V3SJLpdDUewkZ9raVV48fXm4xqeKKo0RAGl9dXPfR2xLVtxtdrgUd4IYSp8CjPqPDoTFZneOSLoc0nqZeue6zjuaKGxnJa3psKdQ3ALc4833XwWu6cqAqaHwQeAAAAAAL74Ac/qO9+97s677zz5vtQsBsa8zC0XAqhpVWqdUurytejkoqBA4+pQvOB5ZIUrw6/nsgHr4Jo2dJKlQoPSRqZzAdez4QAPcnYrEO8TbBUae0VTgix1WlpNTrtcjO0vGxX1mv2nPuRqbYcM2HYX4cnCDwwLzjzfNfBa7nzoypobhF4AAAAAAgsm83qa1/7mm688UYdeuihisen9/S/9NJL5+nIsDsw7ZBazvCIh1PhYbmY4SHVWkIFrvCohhiNBpZLtQqPMAIPU+HRrKVVb6rytfqKBb9GzfyOztlngZjn2balQslWIhY88NhmWlrNqPCIRSJKxSLKFsuayBVDDzzS1ZZWL1rVpz8+PaInhse17gWLQ10DcIMzz3cdvJbAdAQeAAAAAAL785//rMMOO0yS9MADD0z7Gv3p0W5jHmd4BB1+HUlWZng0CwSk2kZ9JKQZHh1uKjwChjlSrXWUqwqPieCBR/3A8tnEIpYilqm6KLV83t1o1NJKkjqTMWWLeU3mSwozisiXbKd65sV79uvuZ0a0bSKv0cm8+jsTIa60o6e3Tag7GdPi7mRb18HCw5nnuw5eS6CCwAMAAABAYLfccst8HwJ2Y+PVs+bLuamm10vFTEuroEPLTYVH87P/w6rwcNfSqvLfiXzwtk95FzM8TOAxOhVCS6vqHJD+BhUelmUpGYtqqlBSrlhWT+AVpW3js1d4SJW2VtsnpIl8MYSVasYLtnP/3amY9uzv0LMjU3pieEKHr25f4DE8ltNP7tukgc64Tl63T9vWAQBgZxD8tAgAAAAAAOZRraXVRNPrJaupQPAKD7czPMKp8Mi6qPAwLa3KtjSZDxbouGppZQKPEIaWt6rwkMJ77STJtm1tnajO8JgY3eHrXYnKuaGTIQyAr1ctKlFvqvI4X7C0Uin0xPB4qOvM9PT2yvfFyGTBCbMAANhVUeEBAAAAILBjjjmm6RnlN9988xweDXY3426HlpsKj0JJtm37Xs/b0HLJqrbA8ivrVHg0Xi9qSXa5JCsS1Vi2qK6k/4/7OQ9Dy0dDGFpuQpO+jsZVDuZYcgGrc6RKQGY2/stTs7W0qjy2dlV4mGBn36VduvUvw9qczmqqUGoaaAXx3Eit8ml0Mq9lDEkHAOzCCDwAAAAABGbmdxiFQkH33XefHnjgAW3YsGF+Dgq7DWeGR4uh5aZKwJZULPsLPGzbrlV4tNigToQ1wyPfusLDsiyVc5OKdvRoLFvQij7/m9p5F4GHGVo+MlcVHtXwKBdChYKZ35GKWbILuR2+7lR4BKyUmWmiGnj0dlTuvycVV1cyqolcSZmpQlsCj1LZ1qbRWuAxMlkg8AAA7NJCCzxyuZxyudovCplMJqy7DuTpp5+e70PAAsD7BEC78PMFmFt8z82fyy67bNbLL7zwQo2Pt7ddC3Zvtm07La1aVXjEIpYsVQIPvxvnUwVbVqSyMe2+pVU4Mzw6Es03xO3chNTRo0w2WGWCqaJw09LKhBVBuAk8Uk6FRwiBx0Rl76K/Y/Ytka5Eeys8euseZ0e8EnhkQxg2P5uhsawKpVq4NxJCRQ4AADuz0AKPT3/607rooovCurvQ/Nu//dt8HwIAYDfGv0MAdnfvfe979fKXv1z/+Z//Od+Hgl3URL4k053KbjG03LIsJWIR5Ypl37MMxqtn/UdUCVCaCavCI1udW9FsaLlUq3AxAZBfORdDy3tDbGmVrg4+bzS0XJIS1eqcMIKBrdUKj/7U7I+vM9mmGR6mpVWqLvCohitTIVeTGPXtrCQCDwDAri+0wOPcc8/VOeec4/w9k8lor732CuvuffvkJz+p1atXz/dhYCf39NNPsykJoC34dwiYW/ybvvO54447lErRPgXtY+Z3RCzJLu7Ynmim+sDDTwOh8XwlDIhH1XRujRROhYdt266GlktSuTq0fSwbrOrCywyP9FRB5bKtSIvwp5m5bmm1vTqwfKCjQeBRDSHCbWllaaL6svTNqPCQpMk2VXiYwGOvRR16dvtUKEPmAQDYmYUWeCSTSSWTybDuLjSrV6/WC1/4wvk+DADAbop/hwDsLt7+9rdP+7tt29q8ebP++Mc/6rzzzpuno8LuYDxX2cDtSjRvL2UkopXr5UtldfhYb6K6Ce5mOafCI+U/8MgXyzINiZJNhpZLtQqPsQAtrUplW6XqfJNmLa1M4FG2K+v1NanOaMVswvc3DTzCG1q+bbza0irVqKVV5fKpQkmlsq1ogDDHiHYvUtmWLEvqrhsobwKPdlR41M/veNHKPj27fUojk3nZtt0yrAMAYKFiaDkAAACAwPr6+qb9PRKJaP/999fFF1+s17/+9fN0VNgdmM39LpcDn80mfr7oN/AwFR6tN4xNVYIVoMLDVDREI5ZikVaBR/AKj/pAoVngEY9a6kpENZEvaWQyHyjwMBUevU1neFQrPArBKzycllYNKjxS8YgiViXMmcwX1ZPy/9iMWN9ySVJPMjatGsZpadWGCo/BTFbFsq2OeFRrlnbJsqRCydZErqTuBmEPAAALHf/CAQAAAAjsqquumu9DwG7KzKvodFvhURd4+FvPfYWH09Iq0SHbtltce3Ym8DCVKc2EUeFRv16kRRVAf2dCE/kpjQYcXO6qpVU8zKHlzQMPy7LUmYhpPFfUZL4UTuDRv0LSjqGOqfBox9By085q1UCHYpGIelNxpacKGpnME3gAAHZZ/AsHAAAAIDR33323Hn74YUnSwQcfrJe85CXzfETY1ZkZHp0t2j0Z9S2t5O4m09fLm0CgdYWHCVesSFRTBX+BR77Uep6GYTsVHv4DDxMENavuMPo743p+dCrQIOxsoeSEGM2Gls9lSyupMsdjPFfURD7YAHgj1l+p8JgZ6nS0ZV5IxbMjlQBsz4FKLdNAZy3w2GtRZ+jrAQCwMyDwAAAAABDY0NCQ3vWud+nWW29Vf3+/JGl0dFTHHHOM/vu//1tLly6d3wPELmssSIVHwvt6ztByF8vFIpYsSbakCZ9n8HsJIEyFRyZQSyv3AYsJKNIBBmGb6o5oxJo222KmMIeWbzMtrVKN26A5g8tz4QQRpqVVowqPsFtaFctlbU5nJUl7DVTCjYGuhJ7aNqkRBpcDAHZhPs5nAQAAAIDpPvKRj2hsbEwPPvigtm/fru3bt+uBBx5QJpPRRz/60fk+POzCajM83H28DVop4AwtdzEyxLIs53pm9odX5jjdBR7BKzy8rNffWUmMglR4mIHlfR3xpoO0nZZWIczw2DZRrfBo0NJKklLx8AIWqRZ49KUaBB4hV3gMpnMqlW11JqIaqAZTAyG8XgAA7Oyo8AAAAAAQ2K9//WvdeOONOvDAA53LDjroIF1xxRUMLUdbOS2t3FZ4RIPO8KhWXERat7SSKpUguVJt9odXeQ8VF+W8meHh/wx+L+v1V6sVRkOo8Gg2v6P+ePKlssq23XK+SCOlsq3tzgyPZhUl4bXQkupneExf07S0yhXLKpVtRV2+r1oZrrbt2qMv5QRJJvgI8nq1kpkqaFN6SlvSWW0dz2v/5T1aEc5DAgDAFQIPAAAAAIGVy2XF4ztuWMbjcZXL4ZwhjYWvXLb1wKa0VvSltKwnFcp9jucqm7euZ3jUbZz7Wq96Jn7cRYWHJMUjlaZWEz4rE3KeWlqFUeFhAo/WD9BUDKQDDC0frVYbtA48aseTL5adCgw/65Wr41T6mrS0CrOFVr5UVrRnsSSpd0aFR/3jyBZK6mrS1suLyerskfr7M69XZqqgYht+Lj+1bUI/vW/TtMtGp/J66xp/rxUAAH4QeAAAAAAI7Nhjj9U//MM/6Hvf+55WrlwpSXr++ed19tln67jjjpvno8PO4t5nR/Xbx7dqWU9S73753qHc53h1hkeXnxkeftYzQ8tdNogO2tIq7yGACGNouZeAxczwCNIiyW2FRzRiKRaxVCzbygUIPLaZ6o7OuGJNqimcFlohBB5D40VZVkRRqzYbxIhYljriUU0VSpoKMfAwLbI66p6nzkRUiWhE+VI50NyVRjaNTkmSelMx7b2oUw9symgiV1LZpps6gJ3P8PCw0um0p9v09fUxl24BIPAAAAAAENiXvvQlveUtb9E+++yjvfbaS5L07LPP6pBDDtHVV189z0eHncFkwdbvn9omqdJSx7btpjMb3KrN8HC3AR60pdWEU+HhvqVV5XZzUeERQkurgvuWVmYAd5AKD7eBh1QJIYq5knKFkuTi+rMxA8sXdzWfWJ8MGIzV2zJWeYzdcWvW93wqHqkEHiHO8TBD0OsDD8uy1N8Z19BYTiOTBfWFtlqFCR8PXtWnI1YP6KHNGZVtacp//gYAbTE8PKy1a/dTJuMt8Ojt7dPjjz9G6LGTI/AAAAAAENhee+2le+65RzfeeKMeeeQRSdKBBx6o448/fp6PDDuLe4dLKpQqvYTypbKyxfK0zVi/zCar6xkegVtaeavwcFpa+dzMNseZjHpraeU3UMqVKsfpKvBIxZz1/DKBh6kWaSYZi2oiVwpUdWEGli/uSja9nnmf5ArBQ4ih8cpj7GrwEDsSUY1MFpyQIgxO4DGjomSgK1ENPPLq85cZNTRRnVPTnYzJsix1JWMayxY1VbTDXQgAAkqn08pk0jr9Mxs1sGylq9uMDG3SlR8/Rel0msBjJ0fgAQAAAMC3m2++WWeeeabuvPNO9fb26nWve51e97rXSap8mDz44IN15ZVX6jWvec08HynmU3LPg/V0prJJbdoSZaYK4QQeZmi51xkexbIk7612nKHlLis8zH7zeMCWVl4qPIplW9lCeYfN7rDX66nOowhSUeKpwiMWvM2Us16LgCXMGR5j1SAg2eA9Y74PQq3wmKWllVQbXD4ymdc+IZd4mPCxu9qWq7saeLRxRrrGsgX95rGtWtvJrCgA3g0sW6mlq1bP92EgZDRSBAAAAODb5z//eZ122mnq7e3d4Wt9fX36//6//0+XXnrpPBwZdhalsq1FrztdkvSiVX1a2lM5sz4ToA1SPVNd4LrCI0BLK9u2a0PLXVd4VP7rv6WV+4oLO5+V2VIfy/l7fnMF9zNDekKo8Bid9B54ZIv+gwFzrDOHhzdaK4zAI+OEZLN/3QRTc1LhUR1cPtqGFGJm4NFT/e9kGys8/vxcWo8Pjeu+ofCeOwDAwkbgAQAAAMC3P/3pT3rDG97Q8Ouvf/3rdffdd8/hEWFnc9MTGSWWrVEiIq17wWJnozkTYJO8njO03GUC4Zy5XyrLtr1txE4VSipXb+K2eMLM+vDd0spDxYVkO8GP3xAiV3I/w6NW4RG8pZW7GR6VJ93MGfHDBG29Hc0bXtQCj+Ab6abCo1FVUGe8cixhBR5lu1LhI81W4VEJPIIMmp9NoVR23qtdycqa3an2Bx7mcQxO2ooku9q2DgBg4SDwAAAAAODb4OCg4vHGG5WxWEzDw8NzeETY2TyxrTIzYU1fRB3xqLPRHF6FR+V+vM7wsG2p5HEf1mzs2+WSXHa0cmZ9BB1a7qbiQpK6ggYe1U33pIsAyVR4jOeKKpX9bWrXZng0HyIuhVN1kam+X1pWeFSDgkLJVtnnYzNqLa1m/3qq+lyH1dIqWxeczAw8zKyUbKGsnNdvgCZM8BiPWs571VR6tLOl1faJSuBhS+p4wRHtWwgAsGAQeAAAAADwbdWqVXrggQcafv3Pf/6z9thjjzk8Iuxstk+ZGRuVhMBsNKcDzH0wbNuuDS13OQ8kXpdUeC0UMOFKOTfpeiB4ZWi5/8DDW4VHrdLF71wNZz0XQ9JN4CHVNry98lLhkapupAdpaZWpvh97W6xX//hzPgfcG2Nz3NLKBCepWESRyPT3aTwaUcppDRbKcpJqs3RMyFH//+0aWl4q2877R5I69ntlW9YBACwsBB4AAAAAfHvjG9+o8847T9lsdoevTU1N6YILLtCb3vSmeTgy7CxGJquzBKqbvWajeWwq+G5rfYupLpcVHpZlOZvZXgMP04bLzk24vk3cGVrufTPbtm0ngHDTYkqSuqqb534qPGzbrmtp1TpASsaiznM5F4FHuBUezVtaRSOWE475mfdSz2lpFZmboeWN5ncYqbhp6xZ+hUdXfeBhWloV2hN4pKcKqi++6dj38MCvFQBg4Wv+LzwAAAAANPGpT31KP/7xj/XCF75QZ555pvbff39J0iOPPKIrrrhCpVJJn/zkJ+f5KDGfTIVHKmYqPKotrbIF2bbtulJiNuas8ohVu383ErGI8qWyCh43fM165dyk+7UCtLQq2pVWPZKHCo+E/wqPQsmWGWvipqWVVKny2DaRr67X4Wk927brWlq1DjwS1WPKBaiEqM3wiEvKNb1uMhZVoVSsrOcikGm4ZouWVp2JcGd4mOBkZjsrIxWPSlMF5UKc8z1zYHn9/08WJcn/93kjpp3Vsp6kxqdymlSH7tk0qYMOCH0pAMACQuABAAAAwLfly5fr9ttv14c+9CGde+65zhBoy7K0fv16XXHFFVq+fPk8HyXm00g18OioBhI9qbgsScWyrcl8adoZ4V6N1W2yeglOErGIlPPT0soEHl4qPPy3tDL735YlxRpUB8wUZIaHOTvey3q1wMP7evWzP7y0tApW4VE5zp5UrJYmNZCMRTSeC7ae1HpouTPDo1ByfoYGMdmywqMaHIVY4TExS+DRlYjJUuVpjnT1hbaWsb06sHxRV0L90YL+MlrW754e13tDXwkAsJAQeAAAAAAIZPXq1frlL3+pkZERPf7447JtW/vtt58GBgbm+9Awz7KFkjO/wHQQikYsdSVjGs8VlckWggUezua1t7Pv/ba0cmZ45Kdc38YUSkwVyiqX7R1mKjRjji8ZjbgOdExLq4yPACJXNJUI7tczz72fipLR6jTrZCzitFlqJpSWVlN1Q8tbvIyJENbLFUvKVmdYNKrwMMGEbQcPV6TWFR7m8pA6aEmavcIjErHUmYxqIldSrGdJeItVjVQrPAa6EuqIT+kvo2Xd8fS4SmVbUQ/fZwCAXQszPAAAAACEYmBgQEcccYRe/vKXE3ZAkrR1vNIyyC4VVD9iw5zNnwk4x2O2QclumI3sQtnbGe5+KjzM47Yljee9Pd589fjctrOSgrW0MpvtSZcD4KXa4HI/FR5e5ndI9YFHgJZWWT8zQ/yvZx6jbZfVqEtYLBJxQrgw2lq1nOGRaN8Mj+4Zs1HM92a0HYGHqfDoTGhZp6VSdlyj2ZLufWYk9LUAAAsHgQcAAAAAoC2GxyqBR2liZFrFQP0cjyDGc5Xbz9xkbcVsZHvdWzYhgpeh5dGIJbtYuZ2pLnDLHJ+bAeJGGC2tvAQsPc5r6X29jNfAwwzb9lqaU5UrlpSt3rbXRVVQMoQWWulqFUs5O960asaEE2EMLnc1w0MKdYbHRPXOZlZsmcAj7AoP27Y1MlF5bgc644pYlqYe/4Mk6fqHBkNdCwCwsBB4AAAAAADawgk8xkenXd7rVHgECzzGAld4eFwv531oeeX645XbewwFCn4qPOIhVHh4Cjz8t7TyW+FRLNvO7A8v6p9/NyGZU+HhM2CRpNHqYyxPjTe9ngkn5qLCoyNmWlqFU+FRtm1N5Gf/XuxJVl7baM/iUNYyJnIl5UtlWZbU35mQJE09/ntJ0v97bGuoawEAFhYCDwAAAABAWwyP1yo86jmBh4+qgHqN2ui0Upvh4belldfAY3La7d0y++zeWlpFfa0l1c3w8FHh4Wc9L+2lpOnPg582UyZg60nGXM14SFbDo3yACo9Rp8JjrOn1nMHlYVR4FFpVeJih5YGXkiRN5kuy7cqw+84ZIYv53gy7pZUZWN7XEXdey8K25yRJg5lsqGsBABYWAg8AAAAAQFvUWlptn3a5aWmVDljhYWZ49HoNPPxWeJih5R5aWtVf32sVRK2llZ8ZHn4CDz8trfxXeJgZLj0uX7+IZTlhlZ82U+Y56XVdUWJaWvlPBkarG/OtAg+npVUYFR75FjM84uHO8DDBY1cipsiMtl1OS6vucCs8zMDyRdXqDkkqTY5KkrZP5FUoBR/+DgBYmAg8AAAAAABtUT/Do57ZcB7LFmTb/jddnQqPuWppNccVHnM9tDzvtLRyPzOkN4QKD7cBhFSruvDTZsqs5zZgqQ0tDzDDw2VLq8545ZiCVnjYtq1sywqPcGd4TJjAI7njeu0aWr69GngMdNUCj/LUmEzhjvk6AGD3Q+ABAAAAAGiL2gyP6YFHdzKmiCWV7Vpo4UfGmeHhfsNcqmtp5fEMd7Op72VouVSr8PA6pN3s6Xuq8KgGAn6eV38VHv4DD6fiwsUAcaMWQvhpaeW1wiN44GFaWpXmqMKjUK58X9Xf5w5rxc0Mj0BLOcabzNKptbRaHCjcnMm0tFpUF3jILqs/VXls5mdPW0RieipTClT5AwBoHwIPAAAAAEBb1GZ4jE67PGJZTiskswnth+8ZHs7wa3/rea3wsP3O8Kjup/qZ4eFnPoq/GR5BWlqZCg/3r1+tzZT/Cg+3AUsiQLhijE65bGllhpYHTCGy5j0TjSgWmf11NDM8bElWsivQelLzSitT9RGJJ5XJhddmaraWVpI0UH0vmZ897dB/5Lt1+6aS7npqpPWVAQBzjsADAAAAANAWjVpaSbXKAK9VD/XGs7Uh1F4EnuGR99rSym+FR+WM+GTUfYsp09IqXyx73qivtbSa26HlPX4qPPy0tPIYsAQJVwxnaHmLllap6usWtMIjV6y8ZxpVd0hSLBpRPFrp/RTt6Am0nlTf0mrH5zUWiahadKGtE8Fm9hi5YkkT1WBooGv6e2egs7LY1jZVeJRtW12HHCOJ4egAsLPy9lshAAAAAAAu2LbtBB7lWQKPvo64nhuZcjah/Zhe4eF+g9O0tDIzMtzKODM8/A4t91jhUd1n91Lh0RGvXXcsW1Sy231YEmxouY/AY8pHS6vq48v6aWnlscIjyLwQw5nh0aLCw5nhUSgpyLmppsKj0fwOIxWPqlAqKtLR63sto9UsnY6YpWzJ1vCE/2queiPV4KQrEd1h3ky7KzweGpxSrHeZJGnbOHNCsPsZHh5WOp32dJu+vj4tXbq0TUcE7IjAAwAAAAAQurFc0dlAn9nSSqptOvtpveSs0WR2QDNJHy2tcsWSUwHhfWi5v8Aj76OlVTRiqTsZ03iuqLFsUUu6k+7X8zG0PEilTm1o+Ry1tHICFm9Dy/OlsmzblmVZntesVXi4nOGRDxZ45EqtKzykSuAxli0qEkKFR6vAozMujeSkrSEFHmZ+R/3AcmMgZSo82hNG3PLX2us4VShpMl9UZ4KtNewehoeHtXbtfspkvAUevb19evzxxwg9MGdoaQUAAABgp/HpT39aRxxxhHp6erRs2TK97W1v06OPPjrtOtlsVmeccYYWL16s7u5uveMd79Dg4OC06zzzzDM68cQT1dnZqWXLluljH/uYisXpm2233nqrXvrSlyqZTGrt2rXauHHjDsdzxRVXaJ999lEqldIrXvEK/eEPf/B8LLsrU93RGY/ILu54trXZ5A6vwsM9p6WVhyKB+rDCzk95Wq/szPDw2dLKQ+Ah1beZ8rZeLkBLq/FcUWWPFTNzPrTcCVi8zfCQamGQV2aGRynbvKWVqcgolm0VPT6P9cys+lS8+Wtovh5NhdHSqvJaNAw8YpWgKKwKj+0N5ndULqscw9Y2VHgUS2XdVg08TPRFlQd2J+l0WplMWqd/ZqPOvep6V39O/8xGZTJpz1UhQBAEHgAAAAB2GrfddpvOOOMM3XnnnbrhhhtUKBT0+te/XhMTtRZCZ599tn7+85/rhz/8oW677TZt2rRJb3/7252vl0olnXjiicrn87r99tv17W9/Wxs3btT555/vXOfJJ5/UiSeeqGOOOUb33XefzjrrLH3wgx/Udddd51zn+9//vs455xxdcMEFuueee/TiF79Y69ev19DQkOtj2Z2ZwGNR5+xnmtcqPIIHHm7P2DfMRnZZkqLuNr/Hq5vzHXFLsr1tfjszPDyGO35aWkn+52qYEMHLeuZ1tG1pIu9tPfN89Hh4/UKZ4eEyYIlFIopFKlvbfud4OBUeLVpaxaOWotW1AhQ9KVut8DAtshrpqFbKRDqDtbTKFUvKlyrPzWwzPKRa4BHWDI/RZhUepqVVG2Z43PHXbRrNllSaTGuPrspj2jZB4IHdz8CylVq6arWrPwPLVs734WI3RN0dAAAAgJ3Gr3/962l/37hxo5YtW6a7775br33ta5VOp/XNb35T3/3ud3XsscdKkq666iodeOCBuvPOO/XKV75S119/vR566CHdeOONWr58uQ477DBdcskl+vjHP64LL7xQiURCV155pdasWaPPfe5zkqQDDzxQv/3tb3XZZZdp/fr1kqRLL71Up512mk499VRJ0pVXXqlrr71W3/rWt/SJT3zC1bHszpzAo0G7InOW/Vi1MiAS8dYuyLZtZ0O/KxmTl6ka8WhtQz+S7HR1G2etFrMRZmNnfc7wqBYxeK/wMHM13G8wl8u2CiXvFSXJWGUAdqFUeT3cDiAvlW2NmcDKZcWFVGnFJPlsaWUqSjy00ErEIirmS77WK5bKzmveqqWVZVnqiEc1nis6ban8yJkZHi5aWkkKPMPDVHckYpGGQZl5ecOq8Jis9nrrSu74GPs7qi2t2lDh8bP7NlXWf/R3GtjrTdo0YWtbm2aFALs7r7NCmBOCelR4AAAAANhpmQ+7ixYtkiTdfffdKhQKOv74453rHHDAAdp77711xx13SJLuuOMOvehFL9Ly5cud66xfv16ZTEYPPvigc536+zDXMfeRz+d19913T7tOJBLR8ccf71zHzbHMlMvllMlkpv3ZVZnAY6DB5nJndUPWtv0NoJ4qlFSqtv7x0hJJkiKWpXi0ErBEEm4DDzMo2fvHaD8zPKxYQmaL3W+Fh5f5KOYsfcnbDA/LsnwNLjfVOZLPCg8/La08VngEXq/u+Si3aGkl1dpa5bwv5XBmeLgYWi4Fb2nVan6HVF/hEU7gMVUNPGarYjEBa9iBR65Y0q8f3CJJmnjoNvUnqfAA2sXMCtlvP/d/1q7dT8PDw/N96NhJUOEBAAAAYKdULpd11lln6cgjj9QhhxwiSdqyZYsSiYT6+/unXXf58uXasmWLc536sMN83Xyt2XUymYympqY0MjKiUqk063UeeeQR18cy06c//WlddNFFLp+BhW14vHlLq4hlKRWPKFsoaypf8jz41wygjkYsJzzxIhGLqFAqua7wMJvX/gIP7zM8rLrjSkT9Vni432A2FQzRSK21kvv1Yto+kff0+Ez4kIpHPAUsgYaWe5zhUVuv4Gs903qpKxFx1QbNVGVkA1R4mJe8dYVH5T0VdGi5q8AjXnk/DU0UfA9/rzdVLX2a7TEOVCs8RiYLKpTK06q5grjt0WGNZYta0hnT0889pD4TeIznQ3lMAGrqZ4W4aYk1MrRJV378FKXTaao8IInAAwAAAMBO6owzztADDzyg3/72t/N9KKE599xzdc455zh/z2Qy2muvvebxiNqnVYWHVDkLPVsoOxuYXpjN9e5kzNdmYyIa0YRKslxWeJiN3S4f4Yqp8JjIl1QslRVzsQlrKk8S0Yjnx+dnaHnex8DyHddzH7CY8MFtCywjGfc/w8PXkPQA642aGSWztF6aTar63OfnoMLDhAVBW1o53xdNHmN1jriyxUobM68VWfVKZdsJn2Z7jL2pqKIRS6WyrW3jea3oS/leq94v798sSTp63x7dLVs9CUsRq1IZFfQxAZidmRUCeEVLKwAAAAA7nTPPPFO/+MUvdMstt2jPPfd0Ll+xYoXy+bxGR0enXX9wcFArVqxwrjM4OLjD183Xml2nt7dXHR0dWrJkiaLR6KzXqb+PVscyUzKZVG9v77Q/u6ra0PLGgYdpq+Mn8PAzj6GeaRPlfoZHZfO6M+6/wkOa3sqpmUiyS5L3dlaSvwDCtGzyFXgkvQ+gNxU6XgfO17eYsm33lRCFUtmZ/eDlPROkpVW6OrC8N+nuOU1Wvx/yczjDIxqwwmMi27rCIxaxVM5PSZJGAraAylZ/VliqVanUi1iWFleHmYc5uPyJ4UpoeegelZ8XUcvSQGdlnW3jtLUCgJ0JgQcAAACAnYZt2zrzzDP1v//7v7r55pu1Zs2aaV8//PDDFY/HddNNNzmXPfroo3rmmWe0bt06SdK6det0//33a2hoyLnODTfcoN7eXh100EHOdervw1zH3EcikdDhhx8+7Trlclk33XSTcx03x7I7qw0tb7zxas7Qzub9nK1vzp73d2a1aY3keWi5j5ZWKheVrM4McRtCmOPyE0D0+hhannMqPLxXsPgJWMZ8tJeSasdXtqVi2X0wUH9szTbnG63nq6XVVGUj3G2Fh3mtfXw7SJKseEomK3E7wyNoS6uJvPm+aP6cmqHtI5Pu35OzMaFVKh5tWPm0pDspKdw5HoOZbOW+u2qPc3F3NfCYYHA5AOxMaGkFAAAAYKdxxhln6Lvf/a5++tOfqqenx5mF0dfXp46ODvX19ekDH/iAzjnnHC1atEi9vb36yEc+onXr1umVr3ylJOn1r3+9DjroIL3vfe/Tf/zHf2jLli361Kc+pTPOOEPJZGUj7PTTT9eXvvQl/fM//7Pe//736+abb9YPfvADXXvttc6xnHPOOdqwYYNe9rKX6eUvf7k+//nPa2JiQqeeeqpzTK2OZXdmZng0bWmVCF7h4WXgdT0zF8NtS6sgQ8vN7XJTJddVEHNd4WHOnE/6qGDxMzMk46O9lCTFo5YsqzLsPldwP6PBzAzpTsZctRQzEk6Fh58ZHh5bWjkVHp6XkiRFO/sq/41Yikebt0HriIfT0ipbbfXVqqKkNJVRrG+ZRiaDVUM0m99hLO1JSptrP4OCKpbKTniyuK5ibXFXUtI4FR4AsJMh8AAAAACw0/jKV74iSTr66KOnXX7VVVfplFNOkSRddtllikQiesc73qFcLqf169fry1/+snPdaDSqX/ziF/rQhz6kdevWqaurSxs2bNDFF1/sXGfNmjW69tprdfbZZ+vyyy/XnnvuqW984xtav369c513vvOdGh4e1vnnn68tW7bosMMO069//etpg8xbHcvuqtI/v3VLq44ALa38VggY3lta+Z/hYW63farkOhSwqoFHkJkabttnSfUVHkECFu9Dy70GVpZlKRmrDLvPFUvqdrmt4QwsD9BCyyuvgYcJm/y2tIpUA4+OJtUPhjO0PJ7yFeYYJihLtagoKWcrFR6jQQOPahrUrILFVHiE1dJq20ReZVuKWFJ/qrZurcKDwAMAdiYEHgAAAAB2Gm568qdSKV1xxRW64oorGl5n9erV+uUvf9n0fo4++mjde++9Ta9z5pln6swzzwx0LLuj7dUNQmvGBuFMTksrX4FHwAoPE3i4rfAww5l9VEBItcoQry2t/FR4dDszNTwEHtUz9VM+Wlr1Bhha7iewSsai1cDD/Ua9mRnieUi6aTPlq8LDtLRy9xqa595vS6totVqj00Uol4hGZEmyJWVy/qekOxUXrQIP09JqIlhLKzfrLe0Jt6XVUCbn3G80UguSzKyQys87W5EWIRMAYG4wwwMAAAAAEKqhsUq/+8VdiWkbhDOlTEsrHz18nAoPjxvYhmlpFUl2uFwvwAyPutuZyoZWajM8gszUcL+5nC2G0dLK/XpjPltaSfVVFx4CDydg8VrhUZ3hUfAzw8MMLfc4wyOECo9WLMuSOaxM1l/gYdt2XYVH8/eNCTwCV3i4aGm1pDvcoeVmfsfy3tS0y/s64opFLJXKttIuv68BAO1H4AEAAAAACJXZaDStZRoJ0tKqdsZ+sAqPuZzhUX8/rcz1DA+zoT9XQ8tN8OM1gJBqoUzOw/vGWc9rhUd84c3wSLlsu2au5rfCo1CyZebGt2ppVcqGM7TcTUursCs8BqsB7rKe6T/PLMvSomqVB3M8AGDnQeABAAAAAAiVCTyW9rQv8Ahc4TEPMzzq76eVIC2t+qptotxWk0i1GRVzN7TczPDw19JK8lvh4a+lla8ZHlN+Z3h4XkqStwoPSUpWB5tnsv56aJnqjsqQ9FYVHhlJmpuh5d0m8AgnhBistrRaNqPCQ6qb4xFSuAIACI7AAwAAAAAQquHq5t+ynh03COuZNjjzMsPDaWnV5XK9YBUe3abCw+UgcWdoeYuN5NmYwCNXLLt+bk144GeGh3kNMp6GlpuWVj4qPPy0tPK5np9wxUj7nOFRlmTFm4eFszEzPJqFAfWSASs83M7vkOpbWrW/wmNJT7hDy4dMS6tZfp4t6aqsxeByANh5EHgAAAAAAELlusKjujFbKNkqlrxtKNcCD38VHkmnpZW7GR7j1fU6Aw8t9zrDw8/Q8pgzO8XtBrMJRvys56ulVaCh5dWgzEPVRdAKj3yxLNv2NlvD6wyPeNSSGXkTSXV7WkuSIh09krxXeKR9zvBwO79DkkpmaPmczPCo/NxJTxV8VebMNFT9eba8d8efZwPVllZBH9dsxvO27n8+rWe3T/oKhQFgd+XvVBgAAAAAABqYHng03qhLRCOKWFLZrmxk9nioZqi1RAo2w8NNS6tS2dZE9cxy3zM8qpvQptKglUjC/wwPy7LU1xHX9om80lMFrehrXmkj1VV4uNwsrzfnQ8vj3geJ+13PBB62pLyHUK5cN8jabUsry7KUjEU1VSgpkurxdJxSLfBIuXzPBJ3hMeUEHm4qPCotreaiwqO/Oky8WLa1bTyvlf3uQs1Gpg8tnx5sdFVf20m/fciauGuwqM0TQ87fe1MxrT94ReDHAwC7Oio8AAAAAACh2l5t77K4evZzI5Zl+Z7j4Wxg+6gQkOoCDxdDy8frKhf8z/CorOe27VOtwsPfev3V52XUxZnntm3XDS33vk1g2kSN54quqyCciotALa18DC33OCQ9GrEUtazqet4CFvNUdLtsaSXV5nj4qvCohiRuQ6vaDA+/FR7uQ7JyCBUetm07lQ6dTb4PIxHLma0RxuByM8Njtoq1zkTl/TRVKHmuAGplJFu5P/MzJ5Mt6uEtmVDX2Bnc9pdhffv2pzSZd18hBgDNEHgAAAAAAEJlzuLu62wdRqSqm3lTHs+QDlzhEXVf4WHWSsYiilc3ib3qSpqWVm5nePgfWi7Vnvu0i8HlJVsqVTdrgwwtL9tyKmGasW27LoDw39LK19ByjxUelmU5r4GXipLRqcrGfmci6rzX3DBzPPwEHtEOr4FH5b9+Kzy8tLQyFR6T+ZLvNlPZQlkmUmj1GE04ETTwKJTK2jZhWlrtWCllAlvb9h7aNmPFUzI51PteuVrHHbhMkvsKsYViZCKv+54d1ehUQU9vm5zvwwndg5vSem7M+/wfAMEQeAAAAAAAQmU22ftdbGabDcOsh83kctnWeC7g0HIzwyOWaNmqKOhaUq2lldcZHr4DD1Ph4SLwMBmFZcnT5ryRikcUqw6fcPP4JvIllas7135aWqV8DBJ3hpb7CVjitTkebpnQz833wGxreW1pZdt2XYWH25ZWQSs8PAwtz00680n8trUygUIiFnFm1DRi5ngEHVy+dTwn265U+sxWsRaNWM7zHWZbq1j/HpIqz20yHtVAR2VtNwHmQnLPsyPO/w+HUI2zMxmZzOvGh4f0/54v+gowAfhH4AEAAAAACJXZlOvzEHh4OTt6PF9rF+Rnw1yavrE/1SJsCTogXaofWt76DO1i2VYkXjmb3E+LKam20Z52sbmcr6YPyVhEluW9gsWyLE+Dy00oEo9arjfn65lQIOfhPeO3wkPy10LLBE19nc3bus3kt8JjqmDLilZeA68VHmNzMMNDsp1ZJr4DDxfzOwwTeGwdDzZMfKjazmpZT1KRBiGLaWsVZuARH6gEHuZnqPlvJltQuRxu66z5Mpkv6uHNY87fg4ZTzZRtWz/847P67h+e0WODY6G3H5vNptEpSZX5Px0veHnb1wNQQ+ABAAAAAAhNoVR2KiL6XWz2pnwEHmZTPRGN+A4EIhFLpjvVRL5V4BGsfZbkLfCobwvlp+JCqm2Qujkj3Cznd16I5G1wuam26EnFfQUsSV8VHv5mePhdz8xO8VrhYQKgaKrL0+1MW6qoJcVdvmecGR6+W1p5G3TfWw08/M7xmHIxv8MwLa2CbqKbgeXLZmlnZXTGzeDy8NpNxaqBR3+1NV1XMqpoxJJtS2O59rW1emxoTNc/uMVTNZNff3ourVLZdgKsrWO5tgURo5MFbUpnNTyW0y8f2KLv/uEZPT/e3se4OZ11/r/zhevaula99FSBeSgLzDPbJ3Xdg1tcV4CiNQIPAAAAAEBoMnUb7G4GUjsVHh7Ojq4PIPxsmBumuKB14BFCS6tELdgptGihZY4nZqnhWeWtmMoCM0uimVrg4X+LwDw3GReBTpCB5dL0GR5uNkiLpbITIvmp0vEzM8Rp6+Zijs30tUyFh7eWVqZKw0UWUFvLqfAoq9jiPTkbLy2tJKm3Osdm1G/g4aPCI2ibpMFqYLJ8loHlhglgQm1pNbBSUi24tCxLfSn3IaZfv31sqx7eMqYHNqXbtoZUCcb//NyoJOm1+y1RxJKyxVpYHjYTsqViESWiEW0dz+u254qKL9m7LetJtQoPSUqteWnLSsIwjObK+s6dT+tHdz83J1UsCMfvn9ymR7aM6Wd/2tTy9wO4Q+ABAAAAAAiN2YzrScYUc3GmeUfCzPDwXuERJICQapvD4y02Kp2AJem/pVV3ovZctKryMIGHy33kWTktrVwMOTYtrdyeqT8bLy2tggwsl6YHM63mr8w8Jj/vGV8trSZ9Bh7ODA9vLa3MHA5TteFGfTjiZxPdy9BySepNmQqPYDM8OjxUeGwNWOEx5FR4NAs82tDSqn96hYck9XW6b1Pnx0Su6ASWj2wZa3HtYB7anFG2UFZfR1wvXNGjgep8lHa1tTKBx96LO3Xqkfs481jiS/dpy3pThZLzPk9FpUg8qT8+N9GWterdO1RSqWxrZLIQ6vsR7WPbtrZVW+9tHc/r+gcHCatCQOABAAAAAAjNqMfNbD8zPMyGeZCZGlJtczjdYmizaSHTHSBgiUYs50zwVm0rnMAjwCd2Z2i5i7Ppw6nwcN/SygQQfuevxKK1odU5F2dNm4qSzkTUdbunev5aWpk5Nh5neMT9zfAwbakSHh5exLJUzo5L8hdCzHlLK08VHtUN9KAVHtXAY3lP45ZWHck2trSqe/+0u8JjS6bWgml4LKdtbRoibtu27n1mVJL0kr36FbEsLQ2pIqcR8/040JlQKh51AqxY3/K2rLc5PVVdL659eivflL99qr0hUmrNS7V5orZRvnUXGwK/q5rMl5x/W6KWpceHx3X/VsKqoAg8AAAAAACh8drKx5wd7meGh595DPVMS5+WgUdIFSVuqyDSPs7Wn8k8/+5meFSHlgdIWDxVeIQwE8VLmykzM8T3gHtnSLqPGR4eKzxSMVPh4a2llQk8kh6LdEpTlU1Yr22mSrbtVNe4bmmVCja0fLJQdL2e2UAPWuExWB1avrzZDI+E97Z8zeSKZcV6l0qqVXXU/3/bAo+6mROS9HCbqjwm8yWlpwqyLOmglb2Swpu50sjIxPTvRxMIm+HwYds8Wnku9+jr0F49le/pO5+daNtslFLZ1sDRp0qSTJfHdoVHCNf26nuzryOuYw9YJkl6YFtZqX1fNp+HteAReAAAAAAAQpN2zmx3WeFhWlr5meERoMWUVDe0uWXgEU5Fibl9pkUVRLoaGnjdvK7naWh5dQ8uFWBoea+noeVmhof/59NLmylnZojPgMyEEF7arpkqhkWd3io8ajM8vFV4jOUqL2LCY0hWrgYeXis8zLerJfeVQT2mwmMi2NByLy2tMtmip1ZkMw1VN+Cbt7QKd4bH5rHKaxGP1N57Uu392+7AY/WiTknSo1vG2tJax4Si3cmYU3HlBFTj/t4brYzUVXhItZ+Psf42BR7V53KP/pSWdFgqjm/XRL6sO/66rS3rXfeXtBLL1igRqVTNSO17LhEuE3gs7krooJW9OqQaAnYd+Nr5PKwFj8ADAAAAABAarxUe9S2t3G6uZUKquEhWb+62wsPvkG3DGezdYq5G7Wz94BUebs6md1pazVmFR/AKHS9tpoIGLOY96iXw2O5zhkfKmeHR5el2fis8ytmMJO9tpnJ1VUGW5e59GniGh4eWVr2puNP2bGTCf0BgZng0r/AId4bH8+nKa9GTsKY9t6a9VXqqEHoQUbZtDY5VHuu6FyxWIhbReK6o50amWtzSu1pgXfv+X1INqNJThUAB1WxyhZITls2s8Ij1rwh1LalSbWFaoa3s65BlWZp67PeSpOse3BL6ehO5ojbevVWSdMiSqFYNdEgKXt2EubGtGngsqs6V2asaOLar3drugsADAAAAABAaE3i4rvCobl6WbXcDqKX6M/ZDmuGRcxd4dCeDBh7uqiCcllYBljPPTSZbULncfHPUtLQKUuHhJfAwjz9QhYeHNlO1Fmg+h6Q7gYf3llZmE8vrWpFUt8oeNrXHzAwPjyFZabISeHhtaWX29r0MujczPLyuZZjn302FRyRiaaC6ub3dZ0VJvlh2NiPdtrQKI4h4PlMNBOLTX0sTuOZLZU/vRTe2jedVKNlKRCNa2pPUfssqFUbtGF5eaxFY+37siEedn69bx8KtTDABW1ci6gSl5t+naM/i0NtMDY/nVCzbSsUizntw8i+3S5Kuf3BQpRY/j7268eFBbZ8qqTC6Rfv1R5xqmZHJvIrl9rTQQnjqKzyk2r+LsX4CjyAIPAAAAAAAofE6rDkWjShe3aR12wM/rJka5mz4Vi2txmfZoPOjdw5neJgNPdtuvZ7ZO52roeWmwiWMGR5ZLy2tfK5XX4Xk1nZnZoDHoeXVx2VFopr0sKmdyfqt8PDX0spkhG7nd0jBhpbbtu2pwkOqtS/yOyTdzECIR2vhyWw6q8dTsm1Pg+0b2ZSpHG/3jLdOLBpxQoGw21ptcSpZkopYlg5cUWmr8/jQuIoug2i3Gv38duZ4hDx7wrz+A3Xfix3xqGIRybIi2jIe7nO5ebRSFbNHf4dToZN95n51xiPaOp7ToyGHSE9tnays8fSfFI1Y6k7GlIxFVLaDVTdhbph/KxZVv+FN5WO0e5Hrk0CwIwIPAAAAAEBovFZ4SLWzxN2etTzbGcJ+OBUeLQKPMIZsV25vQgG3gYf/tZKxqHPmeavN0VpLq+AVHhkPQ8uDVOj4amnlcz3TZipXdHcGf6FUdl5jrxUesWhEJucaz7nf7PJb4eHM8PBYBWFaWnmq8AgwtDxfKqtUfe7dVHhI0kBXsMDDtCVa1pNq2rYrFo0oUQ2qwmhr5VR4JHZc08tsHi/M/I4VfZVKlpX9KfWkYsqXyvrr1olQ12r089RUJoQ9uNy8/vXt5SzLUne1gmZTJuTAw8zv6KurCioXtWdf5f343MhkqOuZ+yulhyRVHtuS7vaERwjXZL7oBOkmkOuIRxW1KmHc0Hjrf08xOwIPAAAAAEBo0lM7bi614vUM+rGQAginwsNlS6ug69UqPJpvsGVCqPCQapujo1PNN3zzZdPSKowKDxeBR4hDy/MuQrJ0wPVSdW3X3GRyZkPfsrwFf4bZzx9r8b6sl3ECD29rlab8zvCo/DflYe5Lb7Jy3VEfMyhMdUcsYjmDrlsxA+P9DkkfyrQeWG7UBpcH36A0FR4zW1pJ7RtcPjPwsCxLL1haaWtlNvDDMpabPbBe0lN5vbaGvElvvh8HZoSPPdXlN4cYeNi2rU3pSoXHyr6OaV9b1lV57cJ+Ps2clWJ60LmsNgSewGNnZqo7+jrizs+1+jBucIwKHb8IPAAAAAAAofFT4eE18Ahjw1ySUtVAoVVLq1rAEmy9WhVEixkePgdQz+QEHi3OqA+zwsNNS6ugMzWkuhkeLlpamTkMMzc83YpHI84AbDcn8JsZFX0dtcHZXiSqt/ESeIxVq0E8t7Sa8tfSKu+nwqN6cKWy7aoSqJ752eC2ukOqvd7bfbb1GaoO8V7e03h+h1ELPIJVeOSKJees7u5ZKjzqB5eHJV+ytb36nl1RN6ukv/r9Oe7xtWqlUWBtNum3jec9za9pZbYKD6n2/G4OcVN5LFvURK6kiLVjULa0u02Bx2ilwqNYrfCQpMUmPGJw+U5t2/jss566TBgXcru13QmBBwAAAAAgNGZzvd9LS6u6ob9uOBvmIVV4ZIt2w7Vt29Z4LpwKD7dVECaASYVU4dF0czQaV3XvOtAMD/N6p11snIfRIsy0tMq6aGk1cyisHyaUM62c3Ky3yOP8DsPs6Y/n3bW0KpftWksrjwFLecrf0PJahYf7ACIRizjPo9f1vM7vkKRF1V3DoC2tlruq8Ki8l4MGHs9un5ItqZybVGqWh9qOCo9t2cp7uq8j7jwOqS7EzIW3VqFu4PrM7/++jrgS0YhKtq1MLpzAw7btWoXHjO/HWkur8IakD1ZDsiXdyR0qkZZW34+bqxUgYSiVbW0eraxZzMxW4ZH3XE2FueP8W7FD4EGFR1AEHgAAAACA0KR9zEvw3tIqnBkesYhklyrH22hTdDJfUrXjU/CWVh2th5ZP5UvKFisLBq3wMGc0jzbZHI2kupz/DxJ4LK5usI3liso2eR1t23aGlgep8DDtt9xUeDTaVPK0nqkocfEWNdUSXtq61Uu4bLVmjOWKznvU63um5LPCwwQ/HTFvC5rh317X81Xh0WkqPPwGHqallZcKj2DVEE9vq8zLKI5unnVuSDsqPLZOVV7LFX3TH6eXNnVumftKRCNOaGlUZk9U25CFFHiM5Yoqlm1FrB0rAk2Fx6YQN5VNNcxsgf9S09JqNLwKj8FMVsWyrVhEKo2POJcv7krIUuX7Joy5Mrursm3r6W0TyrsI1v1oFMabMG4LFR6+EXgAAAAAAEJjNtf9zPBotlFeL6wZHpZlqTRZOcO90aao2aCLRixPZ5fPpidpNhAbb2KY1jJ2saAA+YOk2uZopmngUenTn4xFmg5mbqU3FVO8WpHSbIM5VywrXyo7t/Er4QQerTeitoUSeFRe+7yLCg8Tnvldz1RpjLsMPEy1RDmf9dxCy7S0Gp30dia42UNNJby9Sfs7/Q0S91fhEdbQci8zPIJtLj+1rdKeqDCyedavm9B0PFdUsRTOJuzWqcr97NE7M/CoVa2EtZbzs7tj9u/9RdXAI5MPJ/Aw1R2ztZczM1I2jxVULoeznqkG7JrlZ5sJPDaFWOFh5ncs645Ldu01ikUjzr/BDC7379ZHh/WT+zbpjr9ua8v9N/q3yVR4bKHCwzcCDwAAAABAKLKFknMmpK8ZHi42C4ulsiaq1wtSIWCUWwxtrg9XggQC5j4q99n4jGkzYLk0lQm8Xp+p8Giy4RutCzyCsCzL2bRpFniYdlYRS+pKBGhpZVpMtZgibtu285wu7g4eeLir8DAzA4K1tDJzOVoxm7rlbMbzWub9XyjZzveVG06Fh8cQcKCr9XtyNvNR4TFcnX+w3FWFRzgtrZwKjwaBR0c8qkS1VZLXOSiNbKtWeCyfUeGRjEWcENNs5AflVOclZ//eN6HwVEhFJeZ7f7bvxc64ZJdLKpRsDYU068I8T92zPL6l3ZXHNpjJhhawPDdSCciWd89SUcLg8kAeGxzT/c+nJUnPj4YXUhmT+aLzc21m4GH+qaKllX8EHgAAAACAUJiN12jEmnXDpxFzlribllb1G29BKzyk2oZvo01R0zomjLVMi5hmQ8vNcZjjCsLN0PJIshJ4eJnF0MjirtYbbKadVXcypoiPgd6G25ZWmalKSxsprJZWLio8AlaUJKLehpabgKU8Ne55LbuYc9Yb8RAM+JnhIdU2npu9J2fjBB4e1jNDy708rnpOhYerGR7htLQyFR7F0U2zft2yrFDneERS3TKjYpbMeL9allVXlRZy4NGgHWFXtSfbVDGcQMB8bwzMUnEYsSxn0LcJmoKaqH5jzPbv35LOmCJWJVzcOhFOCGEqPFbMEngsqVYmbR0Lb0bJ7iIzVdCNj9SGwG8bz6kUUkhlmH/rK9WR07fnTYXH9qmS68pXTEfgAQAAAAAIhdmA6+uIe6pO8DLDw2yYpeKRHTYJ/CiZCo8Gm6LmLO9lPa3P8m7FhCbNzsw2G3Sm1VYQboaWR0Kq8JBqFRTbxltXeAStzjH9/wslu+nZ0tuqG4vdydgOMwO86HBaWrW+buAZHtWXYtxltYB5fc08Dq96q5vMXlo/5X0GHr5neOS9V3iYofHbfbS0KpTKzjG6+d4Pq6WV2Xhv1NJKcvd97Va0Z4mkyvs7NsvP024XVWletGpH2FUNCsKq8Gg0sNwojlae56e3T4ayntPSapbAIxqxnPdSWHM8nAqPnlkCDyo8fCmVbf3qgS3KF8ta0ZtSMhZR2a79WxKWZrOlEhGpnKu8tibUgjcEHgAAAACAUJg2NV7aWUl1MzxcbBZmnA2z4O2spNoMg+0NNmC99PFvxWzy54vlhpUJtQqPdOD13A0trwYeoVR4VAOPJhtDZp7IzAHCXiXqAppck/kCYQwsl6RUNSxx02XKqfCYo5ZWZr1y1mfgkTKBh7sNdCvZJRMxmcoXtwacCg9/La06PVV4VN5j2ULZVbu8euZ9E7FmH0A9U31LKy+zUOqVyrazuVkc3dLweu0IPLobBBC1NnzhtNapVXg0CDwSJvAIu8KjUeBReZ6f2RY88LBtu2lLK6k2GH5zSHM8ahUes1SUdNcCv2K5PUO3d0V3PzOiLZmsErGITjhkhZZW/+0fDqntmVEbWL7j7xaWZamYHpRUC7XgDYEHAAAAACAU9RUeXpizxLPFcsve5qYlUpCB1/VaVXgMORUewQOP+k2wRmdMj4TY0srL0PJUKBUeledoW9MZHtXXr8HQYreiEcuZL5BrUhkUxsByyd/Qcv8zPLy1tDKBVjlghYfbECLa0SNJikctxSJ+h5a3v8KjOxlz3iNeqzzMWfGLupKuWq+ZCo9S2Vbe54Dv7RN5lcq2LEml8e0Nrxdm4BHrWSyp8Qa9E3iENcMj17yllTmOXElSJNjPiGKp7Py8aVRtFWaFR7ZYdtoemdZcM63srwQem0Kr8KgEHrNVeFSq2iKybe8t5HZnJvx61QsWq7cj7vzbH9acF8P5t6nBbCkTeDxLhYcvBB4AAAAAgFCMBgw8JCnbYibDWNgVHtXWUY02RJ3Aw8Xg4lbqZ5s0CjzMcZTmaoZHmBUeLlpahfn6mRZVuWLrCo/FgQMPM8Oj9XXNZr7/GR6V/457Hlrut8Kj8tjczrqIdPRK8jf3xbS08lvh4WVNy7KcM/u9zvEw7+ElLgfdx6O1Ad9+21oNjVU2wftSUclu/Nqb6qjxENpMORUeDQOP8GZ42LbdsqVVKh6RyZeiXQOB1jP/HiWiESeQmqlgKjxCCDzM69ERjzYMAvfo65AkbckEDzxKZVubqsO0ZxtablmWU7kzEVJgtTsYnap87y+vth9rd4VHo0pAM1+GCg9/CDwAAAAAAKEwlQReZxdEI5YzQyJbaL7J26olilflFhUeYba0kmrPzfYGbZ9GJqqb1yHM8Ki1tGq82RvqDA/T0srF0PKgLa2k2jG7CTzCqvBwNbS8yaBkN5LVHd8xlxvno05IFnSGh7uzwCPVCg9/gYep8HAfQBRKZRWqz3ujjetGzOvuZT2p1pbNzEFwo76tlR9mQ3VxZ/OfbZ3V12si4IB0yUWFRzK8llbZklS2JUtSd2L29SzLcuZfRHsWBVovXffvUaOZUsUR09Iq+NDyCWd+R+P36B59psIj+Fn7g5msimVbsYjV8D1jnucJl9Viu7tCqew8V+bfTzN3ZXgsp7LPdnUzlcq283OiUbVjMV15bz63nQoPPwg8AAAAAAChMGeae63wkOoGl7fYLBwLaei1YSoptrcYWr48hAqP+vvZkp49FDDHEUaFh3mOsoWysg3aPkWSXZLCCjwqm8ONnkuptgkZtKWVVBd4NGtpNd68bYhbboeWl8q28xgHAlZ4TORrLXKaGQla4eG1pVXKBB7e3zNmE9EEe26YnwnRiKXELIO1mzEBS7P35Gy2jlUrgzy8b2qDy/0FEaaabKCzeajjzLnIlwJvwLqf4VH0PZvEmCiYdk+xpm3CzOOLdgcLPCZazNOQapvKI5MFZz6UX63md0i1Co/N6eAVHqad1cr+DkUbPJ8mPBoPIRzbHZif3clYxAl0+zvjikUsFcu2xrz9GGnI/IyIWLV/W2aiwiMYAg8AAAAAQCicM2p9hBFms7DVWcvODIiwKjwmK8PBG50B7lR49IZT4bHCBB4NWpqY4whjhkdPMua0h2k0x8OZ4RFiS6utTVpambY9S0OomDFtuJpXeFTPmg+pwqNQlhRp/Fylpwoy+8J+vg+kWuAhNZ+/YtRmePh7z5ih5dvdVnh0VlpadcT8V3h4aWllzoTuiEcbnqnfiFPh4TXwcN43Xio8TOARsMKjRRjYkYjKkmSrdUDcSrRFhYcJQoplu2X1XSsm8GhVnWcqJGLdi4OtVz1Tv6tJAGHnp9Rfff8HHVzuKvCozvDYHEKFh9kI33Ogo+F1zLHQ0sqd2eaQRSzL+fdqJBvO8Pf692bD6qNRZngEscsGHnvvvbe+9rWvae+9957vQwEAAMAc4XdAYH6NTvmvvuhyuTET9gyP2tDywg5nMOeLZefsedPWIihT4THYIPAwg0xL1SAmiEjEcl6LRgOOw21pZYaWN25pNZSpVsyE8Hy6aWlVG1oeLGBJ1lUzRKoVDrMxlQS9qZhiHqsRnPu3LJVzlc1MN4OpR52QbNzXeibwcBtCBJvhUQkgJvIl5Zu8bvUmC5WfCV7bWUnSQFe1hZzHoc3ODI8e90FZR0iBx0CLllYRywq8lhGrVnj0NNikj0Vq8y+CtrUyL0GrdnZmkz5whUe+dYspSdqjt3I8Qed4uKkoWVmt8Bgcy7mq3mrGVHg0CzzMYyfwcMdUqc5sy2kCj+25cFpamXCsq0FrN6k2tHz7RJ7Xz4ddNvBIpVJ64QtfqFQqnF9KAQAAsPPjd0BgftV6pns/m75W4dGqpVV1hkeTTSUvzFnx+VJ5h7WHq7Mo4lHL9zyGmVb0VTZOtszS0sS2bedM9DAqPKRalcFoq8AjxAqPbKHcsK2PCXpW9IUZeDR+z4Q1tDxi1ebMRDsaBx4mNPDbzsoo5yrhRaPXbfqaleuUsj4rPJwZHl5bWnl/z/SkalVHbgMWU8XQ4SPwWORzaPnW6vf+Ek8VHmaGh9+WVpXvjVYVHpW1gs/xmMiXnJZ2zaognLZWATddJ4puKzzCbWnVbFNZkvboqfyMfDZg4OFsYjd5fEt7kopGLJXKduAh2LUKj86G13FaWrFh7oqZd9XfMf3nd63CI5zAw828Fzs/qZ5k5d+c50OoCNrd7LKBBwAAAABgbqWrG5h+ZniYjZlWm4Um8AhrhoddyCkRrezAztwUrQ0sT3lupdPI8iYtrcZyRRWrZ/2WfQ6gnqnPaSHUoqVVCBUenYmoM9dhW4O2VuY5XR5Ci7BktaVSs1Y7YQ0tl2ob/G4qPAZ8hH71ytnKEOVWFR6lsu3MHgha4eF2rkZtaLn390wkYjmBqNsh6aaKwV+FR3WGh9eh5eP+Z3j4bTNlNsAXtajwkGqb+JMBhlFvnaj8LI1HpEST739TTWd+9vo16bqlVViBR+uWVlJtSPxQwADCTUuraMTS8urm+aZ0sE1sdxUeDC33YraWVpK0zFR4hBV4ONVHzd+by7vDCeN2RwQeAAAAAIBQ1Co8fAQeCXcbMxmnpVU4FR6S1Jea/Qx3034prPkdUm2Gx2wtrUzgkopZsovhTEfta9LSqli2FUlUNsvCqPCwLMtpa2XOkK83lS85M1iWhTAE3rSZalThYdt2XUur4IGHGS4baVrhUR1YHrAiyARerQKPTN3MkLkaWm5aWvmpuJDqBpd7rPDojHv/nvc7w2ObqfDonrsZHkNO4NH6ee1MBq/w2DpZbRUWax7m1gaXB2tpZfK0Vu0Iu6rPYzToDA+XLa3mMvCQpD36q4PLR4MNLq8FHo0rPLrNv6v54EPndwfp6s/vvhk/vxd3JRWxKjOcYn3LA6/jNoxbUQ08nmOOh2cEHgAAAACAUIw2ODvSjS6XG3hmwzysGR5S3dDmGZuipsXMshAGbBt7VHu4b0lnd9iAMuubACYMTkurWTaXx+rCpTBmeEi1M+Jnq/AwIU9HPBpKS7JWMzzq50SEEXiYgMVs+M9me1gtrbKVao10i1DAhAad8YhU9rfR3pustZNzM1cj2uG/pZVUazM18/utkcmC/5ZW/R7XkipB2VZfFR6mSs3762DbthOwLnLR0irMCo9W2Zz5Xg1a4eF2aHkYMzzKZdt5HVq1tDKBR6O5Sm4US2Wn0qxl4FFt57c5QIVHqWw7t29W4dFpBtzbwee91LNtW8+PTClX2HUqR4rlsvMe75/xO0w0YmlxNfyML9838FrOvJcW780VIbVb2x0ReAAAAAAAAiuXbWVMhYePwKPT5QbeWDsqPBrMMHAqPEIaWC7VqkVyxfIOZ++b9cMMPEz4lJmlUmA8V9mgi0cqMyrCYGZlzLbBXD+/I4wWYaalVa5BS6vt1U3rZCziqx3STKbCo9kMD/MaBm5pVZ3h0arCw4SMvS3OYm+mOxnxNFcjyNByqVY1MVsV0GymArS0cmZ4eGhpNZYrKl+qvKcWe5rhYSo8vAcDE/mSpqqbx25aWoUxw2N4wm2FR/CWVlY8pXzZ3J/LllYdPa4H289kNvctq3VQZp7vIDM1zPynaMRqGR6vrFZ4bApQ4TE0llWhZCsWsZw2ibOJRKxQ3iszPTtm60f3PKf/uvNp/XXYXys9P8ZzRZXbVKkyNlWUrcrMrtl+1pgTH5LLXxB4rXGX1UfLe6jw8IvAAwAAAAAQ2FiuqOr4CV/zNcwH/6lCqemGhjPDI8QKjz6nwmP65rKp8Ahj3oSRikeddkcz53iY9XvDrPDobDy0fKy6SZcIcWfAnAW7dWLHzcMtmXArZlItWlptqx7D4q5EKAFLykVLq5GQWmiVqi2tGs1eMUwLFjPc1o+IZTnBWKtZF7ZtK9rVL6nWesirJT2V52aryw3mIEPLB7qq7bMmCq5b+pjqpK5E1NOaZpO0ULJVKHnbqB+qfm90JaLqcDEbpTbzKEiFR+W906qgJIyWVtHeJZKkRDTiBJWNJGMRVccqaduUv016s7nfmYi2DHOdllYBKjzGs7V2Vq1+1qxw5jj538Q2G+Ar+zsUjTRfrx2Dy4cmK+/vyXxJP//zZl3/4BZl21zt8dfhcX3zt0/q9ie2teX+6ytUZ3sNlzoVHsEDj0mvLa1GqfDwisADAAAAABCYqSBIxSO+zvzuiEeds8ybnUhs1gmzwqM2tHnm0PLwKzykusHl6ekbbGb9vgBn68/U57S0alLhEQ2nukOqVXjM1tLKVMw0OyPZC1NxMdFg09cZWO6hLVEzKRctrUacGR4BKzymMpJat2IylQtBQ7IBZ9ZFi5khubKsaOU91dmiHUsjpsJjuMFg+5kmC6b1ko8Kj+rjypfKDd8nMznzOzwGc4loxNl89hpEmPkRbmfbhFrhEXc3w2MiX1Kp7O/s+ljP0mn31YxlWTJX2zbhM/Cobu63amcl1VqITeRLzu28cju/Q5JW9lde4yAVHs+NVDbAm7WzMtoxuHxbdXj3qmq1ysNbxnTtnzeHdv+zeXBT5Wfi/c+lPQeKbjQaWG6YnweJJXsHWqdYLjvVXK0CjyVdwauPdlcEHgAAAACAwMyGup/5HVJlk8tsoE4VZ99UyxfLzryGtlR4zGxpNRb+0HKp0tJJ2rFn/HanpVWI7bqaDC0fNxUe4eUrzsyDVi2twmBa7eSLZeVLO75nagPLw6ooqba0SrWu8Ag6tLw0vl2SNDjWfFN01KnwCBh4VAOaVi2ttlcHXSejanlmeSNLe9y3tLJtO1CFR0c86rQYcju43BzXYo9VOpWfYf7aWpkNzaUuQxanwiOMGR4tWlp1xKPOa+2z4MKZx9Fqg9cwx7Rt0t/jczsUWpI6E7WWd34HlzsBi4vvQzPHKcgMj+e2t57fYTjzscKq8LAiGs1Vft4ed+AyveOlqyrHNDrVliBCqvyMf7o6xyJfKuuJofDbaJmfff0NwurelJkts9h38CfVvmejlqVUi/ZnA9Uwbut4XuUAa+6OCDwAAAAAAIGlnfkd/s9sN5tOjTbV6luqdIcZCjSo8BhyWjCFW+HhtDRJT99cM+uH29KqupE9W0urXBtaWnU13sweNAFSSC2tErGIs2E0WdhxM8iELl43rhvpcNPSKqSh5SbwMFUxjZhNuiAzPKRaQDPSooXW9uo3ZypAVZBT4eFiczlXLDut8vwEHpZlOVUebud41AaWe3+f1gIPfxUebgMPs06+VPa9ybx10gQeza9nWVatymOW7zM3nMDD5WvY4QQewVpauV3PVJ35HVzupcJjj2qFx9BYzvdrtylda2nVihmMHVbgEV+yt0p25edvf0dcq/o7nOo3t6GiV09vm5gWMjy0ORP6GqMtZpB1JWKyJFnRmEam/AeNTru1ZLRl+7P+jqgsqzKk3sscIhB4AAAAAABC0KodhBvmbNzsLGfrS7X5Hd3JmO+zy2djNovrqxIKpbJTIRB2hYfT0mqHGR5zO7R8rNrSKhFmS6vuxi2tBtNmJkp4AVJP9fHN1vlme0jzNAxXMzxCamnlVHi02IA1m3RBKzz6XQ73NhUereY+NONlaLmp7kjEIopF/G1hmdeiVXsww7x3l/gKPPzN1jDzgtyGgYloRDGf7bMkKVsoKZ2tDoNv0dJKqmtr1aD6rpVo14Ak9xUe5v3lO/BwKi7crWeCJr8VHl4CjyVdScWjlmzb/3qm3eIKFz9Lw57hkVixn6TKe9WyLFmW5QTd29oUeDxeHYy+37JuSdKzI1Oz/psWRKvfYSIRy3lfDrdo/deMqT5y816JRSwtqv78Gnbx8xI1BB4AAAAAgMBGp6qb9QFa+XS1qPAwmylBQpXZOBUedZu9ZjO2fsMhLKal05YZLU3aEXg4Q8tn2cjePFYdzuxiw9Mts/E1a0ursXBbWklST7Lxmedm4zq8wKM6w6NBS6ty2XaeZzMs269iNfDIZItNhwGbgKU3FWx7Z6DJ+6Te9uqZzakWbZCaWVoXeLQaJG428/3M7zC8V3hUZ3j4mP0StKWV22qy+vZZfs7cN0FaOZ+Vixnp6klW3h8tCoAa8trSKniFh/uWVlItaPI7uNxL4BGJWE7ou3nUX1sr8/q5CY/NMbmdYdNKcsXaytp171XzPdaOwKNYLuuprZV2Vi/Zu99p4/VwiFUe5bLtBCjNfocx4eDQuP/wyMt8GakWxjHHwxsCDwAAAABAYGFUeLSa4bGpujm0ykUbDy+cGR51Z22aM2iX9iQVCbGaRKpraTWjXVFthkf4FR7pqcIOPcCfG622Qwoxz3EqPCamb2bbtl3bpAuxRZiZ5TJb4LF9wt8shkacGR4dvbNu1GeyBaf9UpDWbpJk5yaUrFbeNGtrZQKK8Co8WrS0CqPCo6eyVrZQbnnWuRlY7qedlWHai213eVb2tgDvG78trbzO8JDq5nj42MjeXK22Ko1tbdlWR1IILa2qFR6uW1pV/hu4wsPleiZo8rup7AQeLlstrqzO8diU9hewmH+f3FQfOhUe2bAqPNbusPbiLm9VVF48u31K+VJZXYmoVvSmdPAevZIqba1aBaZujeWKKtuVuUQ9TUIrM1smSIXHuId5LxKBh18EHgAAAACAwNKTzftfu2E2AFoFHiv7w52pUV/hYTZQnPkdIbZfMhr1ix9pY0ursi2N1511btu2nkmbwCO8QMec6Vso2crUbbBlporKFiottMJsEWY2Ymfbpw+7pZWZ4WHF4srO8h4dcQaIx5RoMYzWjUXV4QrNBpeboeXBZ3hUA48WG5a1wMP/e6YzEXM2orfO0vqsnmlp1Rkg8Fhk5pO4HlpebWnlY9aM35ZWwz7m2zgVHh6rSSRpS3WjvTi2zdX1ne8z3y2tKhUenV4rPGbrVeeCM8PDbYVHr/+WVrZte26h1ajKz41Ku8XKcbqp8HD+XS2UAg3brqxtK7FsjaTp79VaK8PwN+WfqLaz2ndptyzL0guWdSsRjSiTLep5nxUyM406JxvEmwaApnBvyOf7UvL+3lzqYeYRagg8AAAAAACBhTrDo8FeQi3wCLfCw2wWl8q1TfqwB2zXM5td2yfyyhVLztpmHkNfwM3reql4dNaBstsn8hrLlWXbZfWEGHik4lGnhUr95pfZtO/riDuVEmFoNlvAtFdZ7KM10WxiEUum2CeT3XFD2wQs/QHbWRmLTeDRpM2OeY6DhmS1oeWtWlpVh5YHqPCQamFCqzkeJjjoCPCecSo8XLa0Ms+pac/mhd+WVmaj3UsYaFriTOaCVXi4Yb6n/RRc2Lbto8Kj8o221ceCZdt2nhO3m8rLncDDe8XFVKHkVHa5bVPkrNekequRSis49+0WO+JR5+eW1/flTE+N5GTFEopHpv9bb0LlTLboexD7bMq2rb8OT0iS1lbnd8SjEb1weeX/H9oUTlurtIt2VlKtwmNoPPgMD6/zZQg8vCHwAAAAAAAEZs407w80w6N5S6vnRyubUWEHHolYxNmIM6HAsKnwaEPgMdAZdyoAzIZXeqog052jJ8QKD0naa6BTUu1M2cr/VzaRSukhZ/hxWEzAUN/exGzauxmy60VP05ZWpsIjnNfQsiyZLCozyyazOUs4rJkvJvBotClaLJWdwffLuoOFLCYUGG3Z0qoaQAQcdO/2rOVahYf/hMWZ4eG1wiPQDA/3IUS+WHbeq0s9DErvTPqv8DDfj6VxdxUeJvCY8tHSaqJQViRe+b53u8lbfetrslD2vEk/lS/JHKXb2S+mpdWgjwDCtCjqTEQVdfmz1Kny87GJ7bSzctlu0bIs53mf8BGO1Xtsa+V9szhlTauE6EzEnFAyzLZWW6dsTRVKSsYi01pZHrCi0tbq6e2ToaxjAo9WFapmhsdwkAoPj+3WnMCDoeWeEHgAAAAAAAIzGwa9gWZ4VDYAWlV4hD3DQ9rxLHBzxrWbliFeWZblnOFrNqzNJlFvKhZ6AHHIqj5J0oPP186GNeFHYftzoa4l1fq517cr8tJz3gtT4TFVlBSpbaZmCyVn0zmsllaSlKhu9Det8Ag58GjU0mpwLKeyLcWjlgY6Qmpp5brCI9h7dEm3ywqP6sD2QDM8Ot3PF8gXy87PssUewgfDT0sr054oFrGcY3Wjy2f7LEnaXG2lVHRZ4WE2zPNlyYp5e3+btlTxSOXsfDdiEamcrxyj1yoIEwB1JqKu5y8FGVruZWC5s16DtoZuDPpot2jeK61m5rTyl2rgsSi14/O6uA2Dy58dq1SL7Luka1qYZOYATeZLTpVkECbobVWhaor3hgNVeHh7v1Dh4Q+BBwAAAAAgMNOOKchmrzljuSwpkurZ4eub0u1paSXteBb4YBsrPKS6weXV1jJmoznMzXnj4JWVs2Ef2JR2LntiqBp4bGtD4FHdKDYbuVLt+Qw7QKo/qzrWs9i53Gxux6OWeoP2X6qTrO6izF7hUfkeCOs1XNxZ+X5otOFrAsA9+joUcTF4uhnT0mq24fZGtlDSRL6yARlkaLlU27Dc6rrCI8AMjy53YU79daIRy9c8InOc+WJZRZetfczru9TlGfvOWqbCw8cm9haPLa2SsYjzfRbtGvC0lgnJvLxnLMtSaXy7JO+hgNeWQVKtwiOTLSpb8LaBPpH1vt7yAAHLkPOz1P2/Td1OhUdYgceO28mL/v/27jxOjrrOH/+rqs/pue8jmcl9QUhCQgiBgLDEBH8oRpEF7wUUFVgPdsXF9cvhypcFviCi7KLrz40oPGRR12WBbxRBQCDgEhJIQggJuTP32T1H3/X9o+pT3ZPpnq5zjuT1fDx4aHpqpqo7NZ2uz6ve77eo7Cswl8eM/pj6XjRTq1IUAl6P/rtWqCrNCL3Cw2BLq94Ra0FLMp1GNKm+L5id4WFlvsypjIEHERERERER2RZ2YIaHV5YR1Fo9eUqqRn1tKJbUFzacHloOZO4CF3enulnhkf1zO06o8Kh0JfDQKjxac1R4uBF4FI9d+HKrpZUkSSjVFo48ZXX64/rrGfKPO4TWrPFaWonqIDtt3bLpLa3yVHhkZtrYf01FUJlWgHA09wKiuMM4nYjBZ3M1SVR4dBVYHBXtjOzM8BB/H71DhRdGxXOsKvabCh+EgFeGRzvfhg0unIv3mlqT4aq9Cg8ReBhraSVJkr5o7imtMbUvvQ2ayaogPfAwudBrtmUQAJQVefU2g2bvpLdS4ZF5/49BUcy1CRPVcmb+bRKDy+1UeMSSKRzo1X4/xq3wcG5hfjCuvja5ZmuIQNJu4KEoiv6eV6hCNeABlKR2U8SA+ecpZst4ZAkBr7E3UVZ4WMPAg4iIiIiIiGwT8wus3BWdTdz1KIbcCqIFS2nQq89tcNKMSrVqZMuudgCZRSWzi5BGNZafUOEx5Oz8h2ynaRUex/pG9L8nMcMj0Xvc8f2JGR49OWZ4mLkr2SjR1spbVqs/1jPkTsWMaGkVmYgZHsViaHnuha7j/c5VPI2aY5NnAVEEL6mhPtshktFFPCcrPPqH4wUXl/VB9xbPG0mS9PZbRoMI8RqYrSbLHpBuZtE8kUrr8wCMtrQCMovmJ4bRhfQMW2uDJgIPs1UQoqWVmYoLSZIyba1MDi631tJK3ddIIoWIyRDCSrWcPsPDxtDy99oHkUwDqZGw3topW3WxqOxzqMLD44UYlZHrc4UIaftH7O0vkQYSKfX3p9DfoSRJSIbV3xlRcWrGYFYYZ/Q9VLxXDowkHGnfdapg4EFERERERES2xJIpDGmLe2Z60OciFvE8xaMX1cTAcjfmdwDAF9bNgVeW8Py7nXhuT4d+l6rTMycEsVglZniIdlNuBCzlRT60VKktQd5pDSOaSOFYnzrsNdF71PH9iYWv7PkM7foMD+crZkQA5i3PrvBQ91dtYfD0ePQKj+jYdkX6DA+HQpaqIhF4jF/h4dTvREWBOR5iYT411Gd7X0ZmeKQVRW//4sQMj2RaKbi43DNoP+jMDiKMEAvstaXmfjfEvJC0ApiZRd0ZiUFRtDkZw+HC36DRKzxMBh69w+ZbWgGw39LK5KB78Z5semaIhcAj5PfqQa3ZQKfDQkBW4sDQ8reP9wMA4u37cy7Wi5ZWkWgS8aSxdm7j8ZbXA1DbEuYKPEXllt0Kj+GkGnYEvbKhGTOpSBeAzE0YZujVRybOlfIiH3xa0N7tYLuwkx0DDyIiIiIiIrJFLDh4ZElfxLFKr/A4oU98q4N3s+cyt7YEV583GwDw7f/cCUVRn49YvHdaQ3mmpVXfUBy/3qa2lrpseZMr+8ue43G4ZxhpBSj2y0gP9Tu+LxEyZA+J7nSppRWQqfDwZFd4DIoKD2f//gJiaHmOhUNRGeF0hUckmtQrHbK1aiGgU78Tldpt23157tAWrZfEQrQdRgIP8RJLAII2WloFfZl+//memyCOx2qFB5AdeLjb0sojSwhqvcWiKeMVHqKqrCbkA2D8+/T35qxZOUb0iBkeHnMVHkntPGu3GECIihSjRIBgNmAZtLi/7LZWZnTaqfCw0dJKtESMt+/P+fWirN+zXgeqPLwVjQDUBf9cAYtTLa3Et5cY/OyiV3j0m5+/Im4MMVt9JOZ4sK2VcQw8iIiIiIiIyBb9zvYin6W+99nEXbkntrRycl5BPn978QJUF/v1BaiaEr8+qNdpDVkVHo++fhjRRBqnN5Vh7Txzi4lGLZ2RmeMh5nc0lzvfPgvIam2ihQ7ptOLqTJQyUeGRFXj02mxNlI9fr/AYu5gt7vitcaiqpNgn67MrcrXZcToErNQrPPK0tAqLwMN+hUddVkurfO2YouLOa5/HgaHsY0O4XMQ5W11ip8LD3GwNqy2tgMz7ZdTEOrYeeBSbC6dFlYB3wio81Pkipis8LLS0ApDV0sr9GR5Apr2f2ednqaWV3/4Mj8M9og1i/rlPVQ7O8fBVZgKPXPSWVnkq0owaSRprZyUktQoP8f5rhl4NZLL6iHM8zGPgQURERERERLaIFjhODNwO5ekT7+S8gnzKgj58c+Mi/c9uDSzP/tkdAzFsfvUwAOCL5891dMB2NjHHY9fxAbzfqQYeLRUuBR4loxe9eobiSKUVyJJzYUC2XDM8el2a4ZGvwiMSTeBor3qOLqwvdWRfkiTpLdVy3QV+XG9p5cx5WllgAVEfWu5gS6tYMp13EVa8xHbmd+j7K83/Ombr1gMP6+eNaL81YrB9UKeNwEO8X4pFWyNEMFdrMvDIhNHmQlnLQ8u1geoioDHKaksr0W7PTOCRSKUR09o3Ga0QEOpLzVd4xJIpPZA0Mw9JHFssmUYyZa3dlHh/S/Z35N1GBMxOV3jkIlpaRZNpxExUOJ1Iy+MMBx6psGhpZaHCw2I1EAMP8xh4EBERERERkS19Q+oCTGXI/jBxfVEtT0srt2Z4CFec1ay3f7KyAGmUCDziqTS6B2NoLA/i0mWNru1vaZNa4XGgewg7j6vzQppdDjx6h+JIpxX9juSakgC8Bnqkm5Xd0kpUC7g1tFzcpd4WSYyqTNjbHgGgVu44EfwJYlH0xAqPcDSBiHZbf2O5UxUe6u9vvsXKzNBy+y2tivwe/a7zfH3pRYWHnfkdggiFCvXdFyFdja0KD3MzPLrCYoaH9QqPkQms8DAbeFgeWq4NVG8PRw0PZVcURX/drS4qmwk8RFjn80jwm3xvq9NbWhlfOBdVVn6vnDcIyMXvkeHVqhWtVHmk0or+b3ByoD3vdk4OLvdWNAAAKopyv5/6PLJ+TkbiNgKPhFbhYbSlVUS0tLIwtNxi9REDD/MYeBAREREREZEteoWHA7MLxCLViYHH8QkKPDyyhLsvX4alM8rwiVXNru3H75VHtVu6+rzZhgamWlVbGkBdaQCKArzwnnqHaotLLa3EeZBWgEM9Q5ZasJghFqpkXxADWqup433q+eJ0S6uKgAQllcRANKWfkwCwp03tb7+k0ZnqDiFfhUeb1j++IuQzvXiWT4MWnBzPs5DXNehcSysgU3WRbxEv6mCFR5P23AotUooZHnYqkczM8FAURX9d6yz8foh9Rc1UeIiApdhcQK2/N5dWGQ4gRuIpDCfUioKQydM0qbW0iifTedusnSiWUt93gExrMaP0llYmAojsIdRmq/NEhUaudnX5iG3rywKm9idJUtYcD/ODy9sGRpBMK/DJElKR/IGnGFze48Bwbb2l1Tg3Uog5HrYCD5MtrVJh64HHcMz8DA8AmRkeg+arSk5VDDyIiIiIiIjIFjEI2JHAQ2+bkmlplUor+l3Jbra0EpbOKMdTf3s+Llna4Op+RABQEvDiqrNbXN0XkJnjEddasMx0KfDweWSsnq0GVl/91XYc7hkGYK4FixleWdYrLzoGk3ivI4J32sLwyBJWza4c/5tN8sgS4l2HAAA7jw3oj+/RKjyWNJY5ur86UeFxwiKsPr/DoeoOAJhTEwIAHOoeyvl1fYaHAy2tgMwiXr7B5WIQd8hnP9AR7xuFBg3rMzxsDLs3M8OjfziBhPY8rYQseoWHiTXsDpsVHrIviKG4sbZIYoE+HY/Ca3YFMpVERVANWQpV5giitVeRz2N6/pJ4PzZzF73V+R3Z++s00dJKBJ+i8ssMcYxWKjxEO6u6Ei/GG3QvAubBWBKxpPlgRUilFXjL1X9/x6tkEW2t7AUe6v8anuER7gQAhKNJ00Pg9QH3JkNcVniYx8CDiIiIiIiIbBF33zo5w0MOhDCi3RncPRhDIqXAI0uutpmaaDMr1UXYq1Y364O33SRadQHqwn1TmTuBBwB8/8oVqCr2Y9fxMO5/9j0A1u5gNyqktcvpHEzg8f85CgC4eHGdHhg4Kd6+HwDw9vGswEOr8FjscOCRuQt89EKXGzNt5tSUAFDbnp14B38qrehtapwKPGoKBB5iLdGJllbidcpXvQKo1RYi8Kix8T5jpqWV+HutCPkQ8Jp/nuL90lSFh8XAw+uR4ddWEbuHjS30dughWa+l+UTiGI3O8RCtvcy2swIyFR49Q3E9FC7EXuChVW+ZqPCwUy0nXpMhg63Wsh3tU0PrhtLx/50K+jwo8qn7GRgxVpWTS/dwEpLXBxlA6TivrRhcHrFRUCJaWpUa/DdYiY8g5FN/EYwGcQCQTCuZeS9saeU6Bh5ERERERERkS6allf1Fe79HhjYXGr3aoppYpGwoC7oyA2KyfOODC/GVC+fha+sXTMj+TtfmeADArKoQfB53BqQDwMzKEH70qTPhkSV9UbDBxcCj2Kc+l+PhOH775jEAwFVnu9OSLN6+D0CmwiOdVvQZHqc53NKqPk+f/1aHB5YDwKxqtcIjEk2OmePRqw2elwCkhvod2V9NqbpYmb+llVbh4cgMj8ItrcLRJOLaQGc7rdAybabSSKXHDyJaBzLvbVZkZngYCzyyZ+qYHVoOZAaP9wwZWzTX575YbIMmjrHdYJsp8TpYafNWGfLrcy7yhXAnGopaa1EEZKq3OsIxwy3CRIBUZ6FarjSg/vs8GLVS4aEGHo0FAg8AKNPK7cJmBsucoDWsvv8U+wB5nEodvcIjYa3CQ/IXQbuvwlQIoVa6FK4YyyZedo8swW+y3EkPPAyel8TAg4iIiIiIiGzSAw8HKjwkSdLbE/VqCyZ6+x4HF3engiWNZfjWJYsN31lqV3aFx9zaEtf3d+68Gnzn0iX6n91qaQWoC2MA8NSeAfQNJ9BQFsQFC2pd2VesTQ083j7WD0VRcKR3GMPxFPxeGbOrix3dl7jrPF/g4WSFR9DnQVO5+jt28IS2ViKUKA96AMXY3e+F1Jao+8rb0srRCg8x/D2Wt9WOOI6SgBdBn/V9Fvk8EMUMI4nx2/qIWTMzK0OW9iXCFaNry91DMSTTCmQJqCqyEHhov2dGKzw6syo8rKjRfrFNV3iYnN8BqAvrZgeX26nwEKFFPJk2XA3RaaPCQ8w6stbSyliFBwC9WjEctV7h0RpWv7fEP34ob3eGh6ekGoB6o4WZEELMvzEzx2Moa1aI2Won8V7ZFTEejp3qGHgQERERERGRLU7O8AAydxH3DquLhW4s7p6KZlYW6f3Q59U5uzCfz9+cOxvXnDcHtaUBnDuvxrX9hLQKj/ZBdaHsirNmulYNlOg+Ap9HQjiaxJHeYb2d1aL6Usf3KdqAnbgAK+4sdvp3Yk6tel6cGHiIO/WrzU6eHkemwiN3PxonKzyqiv0IaAuaHQO5F7PFou4Mm6+pJEl6W59Cba2O6YGHtX2KhfZEGpC8hQNFERzUlQZNz7gAMq3jeowGHhF7g+5rtPOtzWDgYXYA9YnMDi63E3gEvB69KrLD4ByPjqyh5WbZmuGhnaeNpYX/jS8TIYStCg/1fbzUN/45Kv49S6QBOVQ+7ra5eEvVwEOEQUbpFR4Gz0sgu3WW+XNFvFdGE2lLf3+nIgYeREREREREZIuY4VFV7EylglgP6NAWr91a3D3VSJKEM1sqAACnOTxrYrx93vqR0/CXb1+M5iprd7EbUezNLIxJEvDXZ7nTzgoAkE5iXpW64PjWsYGsgeXOtrMCMgubkWhy1OK5GzM8AOgVKod6Tgw81AXZypD98EEwPMPDRrWFIEmSHmTkm+NxuEcNPERrLzsyczwKVHjorcms/T0GfB74tZDNU1a4okkEBw3l1qrlRFGI8cBDtLSyVuEhWlqdWOGUz5BYVLZQvQLkDxjzsRN4APlb1uXjxNDyiI2WVsYqPLSWVrYqPNQQtFCFh9cj6wGCr7LJ9H48pWoIb3qmhvZZp81EhYf4lbESeIT8Xv0YOcfDGAYeREREREREZItoaVXhUIVHTVC9VN2ydwDptOLa4u6p6LuXLcU/bVqKDy8zvzhkh5WBxWZkZ23r5te4Gq4AwMIadcFx57H+zMDyBudDpJKAV18819sDpRV9poHdaoQTzanJXeEhFtmqLS4k5yICj1wLeJIvADGWImShPVEuTQXmeIjAY3aN/eonccwFAw9tGLTVCg8gs4DqNRB4iIV1qzNDRPVdt8EZHuLv1npLK3MVHiLwKLPYJrDOREurtKLoA8AtV5SYDjyio77PDFHFMBRPIm2iLVI0kdJfjwlraRXRKjwKBB5AZo6Ht2qG6f14LQYedSbPSyCrwiNg7dzk4HJzGHgQEREREdGU8dJLL+EjH/kImpqaIEkSfve73436uqIouPXWW9HY2IiioiKsX78e+/btG7VNb28vPv3pT6OsrAwVFRW49tprMTg4OGqbt99+G+effz6CwSCam5txzz33jDmWJ554AosXL0YwGMQZZ5yBZ555xvSxnAoSqbR+x6hTLa3mV8hIRQdxZCCOLbvbXRnQfKpqqQ7hs+fMstTOZiorzmp9cuVqF6s7NAtr1XPx7WMDeuCxxIWqGUmSxizCdkaiSKUVeLNmDjglE3gMj3pcLLJVOdjSSjyv7sGxfek9xZXq/8oSfB5nzlUxx6NtIF/goYY8LQ6EZZkKD2MtrWY4EXiU1xXc1m6Fh+mWViKks1jhIQIPozM8tGJDS3fRA5lB4kZaWo3EU1AUQIL1tmv1JgKWkXhK/7fWSkurkF+dLaMohYO4bOIcLQl4URYovIwsWlqFR5KW5k0oipKp8CjQ0goAKorUzx12KjzMni91JRZmeGiBh9n2WUKtCIg5uNwQBh5ERERERDRlDA0NYfny5XjooYdyfv2ee+7Bgw8+iIcffhivv/46iouLsXHjRkSjmcWJT3/609i9ezeeffZZPPXUU3jppZdw3XXX6V8Ph8PYsGEDZs2ahW3btuHee+/F7bffjp/85Cf6Nq+++io++clP4tprr8X27duxadMmbNq0Cbt27TJ1LKeCfm2FSZIy/bTt8nkkRLY9CQD40fP7WeFBBflkYGjvK1jWUIQPnlbv+v5Ehcdbx/r1BUE3WloBY+8CF4OuG8qtzWEYjwg8DnUPjVqs1AMPFyo8Ysmxfel9tbPV/RX7HasOatJbWuV+jxZtvJwYPG+kpVUsmblz3k6lTql2R72RllYiOGi02dLKaIVHpqWV1Rke6nMbjCURKVAxIAdL9aqgUosVFw3l6jlpZDaDOGdDAQ9ki7+HZlpaidcy5PdYqiiRJUkf5j5ooq2VaGc1s7LI0O+iaGkVT6URS6ZNH2fPUBwjCQWKkkaJgY8UeoWHpcBDm+FhuqWVmOExYjjU0VtaWTw3WeFhDgMPIiIiIiKaMj70oQ/he9/7Hj72sY+N+ZqiKHjggQfwne98Bx/96EexbNkyPPLII2htbdUrQfbs2YMtW7bgpz/9KdasWYN169bhhz/8IX71q1+htbUVAPDoo48iHo/jZz/7GU4//XRcddVV+OpXv4r7779f39cPfvADXHLJJfjmN7+JJUuW4J/+6Z+wcuVK/OhHPzJ8LKcK0c6qvMjn6OJr5I3/RtAr4Z22sB6qMPCgfCRJQvfv7sL9H25BwOvcnIl8ZlX4EfTJiCbUBb3G8qBjLd1OVH/CXAE3A8DmqhA8soSRRGrUIGWx2OpkhUeR34NiLRg4cRHPXzcXAFBT4txrOl5Lq1RawdFe9XFnZngUbmnVpgUvQZ+MqmLrzzPT0spIhUcmLLNCtLTqHUkilR5/oTeeTOvznaxWeIT8mRkNhUIBUeES8nvg9Vhb7myuVP/uj/UNF9jS/vwOIFOpYSTw0Od3lAUth4BWBpcf1V4Lo20CvR5ZD/zCI+bbWonWcqlIt6HPFKKy1EqFh97SymTVhQg8oonMOV7IsN0KDwYepjDwICIiIiKiaeHgwYNob2/H+vXr9cfKy8uxZs0abN26FQCwdetWVFRU4KyzztK3Wb9+PWRZxuuvv65vc8EFF8Dvzywwbdy4EXv37kVfX5++TfZ+xDZiP0aOJZdYLIZwODzqv+mub0gNPKocXuxNRyO4bEmF/ufSgNdyX3Yip3lkCUubyvU/u9HOStBbWmmLoq397szvAACfR0az1l7pQHemFWCmpZWzYVKt3tYqPupxf90c9eslzrXsairPH3i0h6OIp9LweSRHgiQjLa1EZdDMypCtKhbxvmhkhkemwsPacwx6ASWdQloBegq01hGtd3yyhHQ0Yml/QKYapdC8BI8W+FhtZwVkFvWP9Y0gXSDQcSLwqC0VFR6FF7H1+R022tiJxXZTgYdW4SHCICMyczzMD0gXreWSfe2Gts9UeDSabqFltcLD75X1vwfx+oxH8gUQ14pdrJ6fhQKPrq4u7N+/39R/XV1dlo5lOnAumiciIiIiInJRe7t68VtfP7pdTX19vf619vZ21NWNvsvV6/Wiqqpq1DZz5swZ8zPE1yorK9He3l5wP4WOJZe77roLd9xxR+EnO41kBpY7H0Z84owq/G7PAOLJNKs7aMo5Y2Y53jishqSLG9xpZwWMvQu8Va/wcGemzeyaYhzqGcah7mGcO099rNOFllaA2tbqUM8wugdPrPDQAg8HZ5SI16u1X21Dkx0yHNaGtDdXhhypVDPS0up4v7pQaje4Mjq0XFEUPTRoLA8iZqHoQpYkpIb64S2tRkc4Nu7wbBHQVdoMyerLgnivY7Bg4CGef6mNYLyhPAhZUqtTugZjenVVLkNaaFDsQIWHkZkh4vd/vGMqRK/wiCYBg/mFqHxqqSoCYGz2R1nQi/awOri83ORfv6jwSPS3GdyXDxIA2V+EnuEUFhjcTyyZhiekhtZW2ky1VIXQGYnhSO8wljdXjLutp1Q9N/0e2XIF4ngzPLq6ujB//gKEwwOmfmZZWTn279+H2trCYel0w8CDiIiIiIhogtxyyy246aab9D+Hw2E0N7s/YNlNop2DnZYs+VSFvLhqdTMe2XoYzVUMPGhqWTZzYio8xALn3o5BRBOprMDDnd+JOTXFeGFvFw5qFR6DsaS+cO9kSysgM8cj+67lwVgK3ooGAA5XeGiv11A8hfBIEuVZIe0hbZHViXZWQFZLq1gKQO4FzuMODCwHMoGHp7Rm3DZTAyMJfaZCXVkAR611mUJqsAfe0mq0h6M4A+V5txNVC3ZDMlHh0VEo8NBaWpXZqPDweWQ0lhfheP8IjvYOjxsuONPSKtOuLp1Wxp0FIkJHOxUeYmE/EksYDzxGtbQyVqlTqg8uTwAl5o7xiFYxkewzFnh4ZAkhHzCUANoi8cLfoBFzaLySWrFhVnNVCG8c7tNfn/F4y6wNR882XoXHwMAAwuEBfPnuzaisM9baq6+zFQ9/628wMDDAwIOIiIiIiGiyNDSoC2AdHR1obGzUH+/o6MCKFSv0bTo7O0d9XzKZRG9vr/79DQ0N6OjoGLWN+HOhbbK/XuhYcgkEAggEnFvAmwp6h0SFhzvzC/5uwyIEvDI+umKGKz+fyKozZlTo/9/NwGP5zAp4ZAl72sK46ievoWdIXfByM/AAgIPd6kLec3vU98Km8iCKfM52Rq/T7nDPnpnwfq/6/Ip9QMDnXAutoM+D6mI/eobiON4/MirwEG10ZjkwsBzIVHhEEymkldyv2bF+0dLK3t9jccALCQA8XvSO5G8hJCokqov9tubciHkcheZOdGlzX6pthmQNWvuttgL78zhQ4QEAzVVa4NE3jLNmV+XdzpmWVur5n0wr6B2O6wFgLo5UeNhpaVUVAiLGAg8ROoWjSdOBxyHR0spghQcAlPokDCUUtIaNzwzp0gKPkA+WWsqJ9mdGWlqJ6iOr8zsAYzM8KuuaUDtjluV9nEw4w4OIiIiIiKaFOXPmoKGhAc8995z+WDgcxuuvv461a9cCANauXYv+/n5s27ZN3+b5559HOp3GmjVr9G1eeuklJBKZC+Nnn30WixYtQmVlpb5N9n7ENmI/Ro7lVNGvtbRyo8IDUIeh/+Olp2HpjPx3EhNNhrk1xVg3vwbnzK3SQwI3zK4pxuarV6O8yIcdR/v1FjNuzPAAsgMPtcLjF1sPAwCuOrvF8X2tbFHfc1/e36M/9n6PuqBXGbDfWupE+QaXizY6sx2q8CjSghoFQCxPFyAxw8Pu36MsSRCZQudg/oVsMb/D6sByIRVR/64KBR6t2v5qim0GHtoCf7vBllZ2KjyArMHlvWNnvWQbiqp/sXZaWvk8MmpK1H87C72e+gyPMhszPLRjHcp3Up5gYDihz+EwE8yViQqPqPmh5UdESyuDFR4AUOJX3yvMBB7dQ+q2RV5r7zNi1tHRAucJkGlpZaV1lpCZdxQbt5KLVAw8iIiIiIhoyhgcHMSOHTuwY8cOAOpw8B07duDIkSOQJAlf//rX8b3vfQ9PPvkkdu7cic997nNoamrCpk2bAABLlizBJZdcgi9+8Yv4y1/+gldeeQU33ngjrrrqKjQ1qWX+n/rUp+D3+3Httddi9+7dePzxx/GDH/xgVKupr33ta9iyZQvuu+8+vPvuu7j99tvxxhtv4MYbbwQAQ8dyqujVFg3cmOFBNJXJsoRffmENfnXdWkfmPozn/AW1ePLG87CwPnO7dKPNhet8ZmtVDkd6h7G7dQBvHO6DV5Zw1Wrn2+9dsLAWkgTsaQvrC9rv92izH4LOL1npczwGRi9SHnK4wkOWJT30iCZzL04e73OmwgMAin3q+dc5mH/BN3t+hx1GKzwOaXNRZpTZC8PF8RYOPMTQcrsVHtqd+wVaFTlR4QEAddrg8s4Cg8vFwrqdyq7sGR5GBnyL16CmxK+3aTNCDC2PjBjbjxCJJtCjVY2aqfAo0c7/VhMtrbqGtQoPi4FHi3aeHDFU4aG2tLJT4VFd7IcsAWkF6Mkxx4NGY0srIiIiIiKaMt544w1cdNFF+p9FCPH5z38emzdvxs0334yhoSFcd9116O/vx7p167BlyxYEg5kFnEcffRQ33ngjLr74YsiyjMsvvxwPPvig/vXy8nL84Q9/wA033IBVq1ahpqYGt956K6677jp9m3PPPRePPfYYvvOd7+Db3/42FixYgN/97ndYunSpvo2RYzkV6BUeLrW0IiLVrOpi/Pb68/B/fr8XlSG/7YXdfJoqiuD3yogn07hny14AwIbT61FXFkS4s8A3m1RV7MeymRV462g/XnyvE1eubsH+XvcrPI5nVXgoiqJXeDg1wwNQ+/WPJFLIlUEkU2m0a4HBzEr7+wz5JGBEQcc4gUe7FvLYrfBIDqoVHu0FFugPisCj3N55Ko63fZyAJZpMw1NcAcDenAQgE0CNd+d+IqUgnlLnodgNPOrLAninbfwAKZpI6SHdXBvVZKIaJaUoeSuPsol2TWbPUVFlE0+lEU8bb58mfg/Lgx4o8cKVE0KpVuHRZqrCI9PSygoRjLX2jyCZSsPryR/QekrFDA/rvwtej4y60iDaw1G0DkRRZ6O12amAgQcREREREU0ZF1544bh3A0qShO9+97v47ne/m3ebqqoqPPbYY+PuZ9myZfjzn/887jZXXHEFrrjiClvHciroHXZ3hgcRZZQEvLj9stNd3YdHljCrKoR9nYN48b0uAMBn1rjXF/7ChbV462g/XtjbhY+dOROH+9SF9AoXAo8ZekurzOJyVySGkUQKsuRM+CBUl/jRGYmhPzb237T2cBSptAK/R3ZkMHuxto46XksrUeHRYHOhVFR4dI6zQJ9OK3rVzEyHKjx6h+KIJlII5pjrIipbvDIQsDCAOpuRCg+tOAB+j2xp4HW27MHl+RzuGYaiqEGCnfaRHllCyO/BcDyF4TyVR9lGDyw3zuuR9f0MJYxXeIhqiaYyH942sb8S7fw309JK/K5YrfCoLwvC75ERT6XRNhAd9zUS7dbstLQCgMYKNfBo6x/BiuYKWz/rZMeWVkRERERERGRZ/7C6wODWDA8imnjZM0nm1hZj7bxq1/Z10WK1FdHL+7qxtz2CZBpIRwf1RXwn5ZrhcVhbZJ1RWWR78TqbGEDdH0uP+ZpoZ9VYEYTsQDs00dJq3AqPsJjhYa+Flpjh0TZOi6mOSBTRRBpeWUJDqb2/yPIinx5i5KuCEIvXxV7J0gDqbGKGR9tAFMnU2L87ABjRwgK71R0A9Dv1x6tgEfN05tSW2H5+4piHDWQDosql2ULbNdHWasjEGA9R4dFk8pwRLa3CsZThuSFihkfIZ+319MgSZohqoHHCMUVR9BkedlpaAUCT9rvbWqC9GzHwICIiIiIiIht6tX7blZzhQXTSyA48PrNmlu1F1vEsm1GOqmI/IrEkfvHaIQBAvPOgK/vMFXiIWROzqpwdPF+thcC5KjxESy0n5ncAmbvUx5vh0e7QDI9kfzskAAMjibyzBEQ7q+aqkO35NpIkFZzjIYIeJ0KyutIA/F4ZqbSSN9QR1RHFQePtmvIRYcLBrqG82xzQXk877awEEXiMmKjwaDFZ4QEAZUXavBATFR6HtaqgJpNVQT6PhNRQH4DM0PNCukRLKxsZhF4NNM4cj0gsDdmvnr+2Kzz03wPj7b5OVQw8iIiIiIiIyJJUWtHvpqxkhQfRSUMEHkGfjMtXzXR1X7Is4YIFao/7/9x+HAAQ7zzgyr7E0PKOcBQJ7e59N+Z3AJkKj0gckLyj21Yd0yo8ZtgYQJ0tU+GRv6WVCAvszvBQkjHUa3fgv9cxmHMbEXjMdug1LTTHIxN42A/JZFnCzIrx79wf0V5mJyo8FtaXAgD2deZ+LQHgQJeDgYdWZWCkpZUID8y2tAKyKzzMBB6ZllZmJfrbR/2M8cSSKfRH1SEmRRZbWgGZsGq8eS9dWiVJwINx53wYIX4PWOFRGAMPIiIiIiIismRgJAExcqWiiBUeRCeLv1pSh0X1pfjG+oUon4DfbdHWKpFS31DiHe4EHjXFAfg9MtJKpj2SmDUxu9rZCo/igBdF2rwJX03zqK8d1wMPZwIBUdkwnEjnbOkTiSYQiamr9HZneADA7Ao14N7XGcn5dVE1M6emxPa+gMwx56u40FtaORB4AMBMbYH/WJ6F7OGEcy2t5tepr1H3YAx9WsXkiUSANKfWuQqPQi2t4sm03u5troX9Wgk8MjM8zN9AkexrAwAc7s1fKSO0aTN80okYAjaKdETly5FxKjwylST2z01RodbWzwqPQhh4EBERERERkSWinVVZ0Gv7zkUimjrqSoP4/TcuwJc+MG9C9nf+glpkd7Byq8JDliU0alUeYnC5WKx0usIDAGpK1IVbX+2cUY873dLKK0tIDQ+oP7tv7GKoCHfKgl4UO7BIP6tSrVh5ryN34HGwW31N59Q4VeGhvk75WlqJVl5OdVacWWA2g2gH5cRrWRzw6pU++ao89MDDgQoP0VapUIXH4Z4hpNIKSgJeSyGZaGlldIZHLJlCq9aqyUqFR1Kr8DDS0uqgFnIm+1pttc4zMuDeyXZrhVq7UQY/kRIREREREZEl/cPa/A62syIiG6qK/Vg+swIA4JWBRPdR1/alD/7tH4GiKPpi8iyHKzyATFsrf+2sUY+LwGOGQ4EHACTDXQBGzycR2hxqZyXMrlTf8/O3tFIfn+3AAj1QeKFXX1R24C56IDO4PN9sBjEqpdTmEGphYb1a5ZErQOofjus3FzhRhWS0pdV+LXyZV1tsKRQQFR5GZ3gc7R2BogDFfg8qLMxGSfZrFR5GAg+tRViir9X0frK16DM8xmtp5XyFR0ckhlTaeOXMqYiBBxEREREREVkiFmEqQgw8iMieCxfVAgBmVQSAdP5ZFHaJRcr/84e92LKrHZFoctTjTqrOUeGRTitZLa2cCzxS4wQemfkdzuxvtlbhsa8jAkUZvfCaSiv6ArBTbcLqRUurHDM8kqm0vqjsVEur5ipR4TH2tZS8AQxog+jrSp0JkBaIOR45Ag8RyDWUBR2pKDHa0koPPOqstSUTYVAyDcjB0oLbH9FaUbVUWwtYROAxXnsp4YAWyCV7j5veTzYRjHUPxjAcz/2e1aVXH9k/N2tKAmo1V1pBZ4RVHuNh4EFERERERESW9GsrJlVO9REholPWJ89uwZo5VfjrZVWu7ue6D8zFjIoiHOsbwVcefROAuphc5LfRzD8PvcKjbrYeDHQPxhBPpSFLzlVcAJkKj2M5Ag/RKqnZoYqS5go/JAnoG06ge3D03InW/hHEU2n4vbJ+R7pdosIjZ7uuSAxpBVBSSThUcDFuhYe/fg4UqJUITszwAIAFWqiQq6WVk+2sgEzgkVIAOZD/Z+7vUo9lvsXAw+uREdJ+p7zldQW3P6S1QbM66D7Rp7a0ah0YQSyZGndb8ZombAYe5SEfyrST7liOcxPIqvBw4GOSR5Yy4R/bWo2LgQcRERERERFZ0itaWrHCg4hsqi8L4vEvrcXF88tc3c+82hL8/hsX4FNrWvTHWlyY3wEA1cV+SAA8oXL0jaiLsCKQaCwvgs/B2UeZllZjF0LfONQLAFjZUunIvoJeWa+IObEq4YBoEVYVgkd2puJiQX0JvLKE7sEYjp0wL0FUtCQj3ZBtzGPIJmYzdEZiiCZGL577GxYAAOocGP4uiAqPXC3CnBxYDqhBRNCnnnee0pq824kKj/m11gfPi7ZW3oqGgtuKygyrv4vp4X4EvRIUJX/4IOgtrWwGHkDmXMk3O6TTwZZWQCb8a8vxe04ZDDyIiIiIiIjIkj7O8CCiaagk4MX//tgZeOSas3HO3Cpcc96cwt9kgdcjo1R7ezzQGwOQqRpwsp0VAKTCnQCA4ycEAtFECruOhwEAZ812JvAAgAV1YpF+dOBxSFugd2p+BwCE/F6cPqMcAPA/WngjiKqP1ECnY/urDPn06oTjJ1TMiMCjvjTg2P5EFUX3YAx9Q6MrZkSANNfB11NUeXjKcgce6bSC921WeADq6wgAvurmgtse0gaJ22mD1liq7m+8weXD8SRateoIuy2tgKw5HjkGl6fTCrqHxNByZwIPURXWNjB+qHOqY+BBREREREREloiFmUq2tCKiaeiChbX41XVrccnSwnegW1UeUBc6D2qBxxNvHAMALG4sPNfAjHwVHjuPDyCeSqOmJODonBIxaPvENkwHXVigB4CztbDmLwf7Rj0uAolk2LnAQ5KkvG2tAo3OV3iUBLx6ADbm9exytqWV2B8AePNUeBzvH0E0kYbfI9s6Z6rEDJualgJbZkKKWTb211Sm7u+wFp7kIlpnlQZkpKNjZ6aYpVd45Gh/1j0YQzINKOkUihxqtybaxOWq5KIMBh5ERERERERkSZ82w4MVHkREuVUE1KW3A30xvLyvGy/v74bPI+GL5891dD9JrcKhIxJFPJnWH3/jkBoQnDWr0tIw6HwW6oO2Ry/Q63fqOxx4nDVbne0ypsJDDzy6HN1frsHlQ/EUvFUzAAD1Zc5VeABq2y4A2NeZWYRPpxXHZ3gAQIk2d8JTUp3z62J+x+yaELw22q5VF6uvUaEKj1Ra0Ssk7LSXaypTb744PM7gcvF6Npc787lFBB5He8dWXIhKktRQn2Pt1kRLq/YwKzzGw8CDiIiIiIiILMlUeDDwICLKpVKr8DjQE8PdW94FAHx6zSx9odQp6eEB+DzqDIP2rIHGYn6Hk+2sgMwC/XudEX0gO5BZULbTmiiX1Vrgsb9zEL1ZbZ/e1yoiUg5WeADATK3C41jW4vn+nhgkSUbIq7bZcpI+uDwrQOqIRDGSSMEjS46eL6XabA1fZVPOr4vX1E47K0CdYQMAvqqZSKWVvNu19o8gkVLg98hoLLfe6s1IS6sDWpgz06nAo1ILxnKELGK+TCrc7ci+AOivDys8xsfAg4iIiIiIiCzp49ByIqJxVWiBx/u9Mew8PoBivwc3/tV8F/akYE6l+l78p73q4n86rWDbEbXCY9UsZwOPebUlkCWgfziBrkG1XVcildYHRjtZkQAAVcV+fQFehDjtA1G94iN6ZJej+2vOMZvhvS51kbk66FyljCAGl2dXeIh2Vi1VIUcH3Iv5I/7GhTm/7sTAcgAoDXrhkQDJ68PxcDzvdqId1MyqIluD7vWWVgYqPJwKPLJneGQHfwCw7bD6uxfvPuzIvoCsoeWc4TEuBh5ERERERERkSaalFWd4EBHlUuwD0rHMAuwXL5iLmhJn2yEJGxaog70fe/0IFEXBge5B9A8nEPDKOL2p3NF9BX0efbFXVCUc7R1GKq2gyOdxvOUTkKnyECHHb7cfQ1oBzmgoQrK/zdF9Ze7czyws7+1WA4+qIueXU0WFx3tZFR5uDCwHgHpt/oivqgkD0eSYr4vAY57NCg9JkvQZNof68gcehx2Y3wFkWlod6R1GOk9FyQGHA48ZlUWQJGA4nkLPCQPnX96nVnZED213ZF8A0Fih/t11RmJIpNIFtj51MfAgIiIiIiIi09JpBf1ahUcVKzyIiHKSJAmJ7iMA1BY/X3B4dke29fPLEPTJ2NsRwZtH+vT5HSuaK+D3urBIr1UlvNehViXo7axqih2dFyKcPUcbXH6oD4qi4NfaAHgR9DhpUYP63Ha1DmBPWxgA8J4IPFys8OiKxPR/W92Y3wGoYZVWDIF3u0a3RlIURZ/hYbelFQCU+9XX6kh/LO82Ysj4LJtt0OpKfPDIEuLJNDoiY1s+KYrieEurgNeDBi1AOpzVSqsjHMXejggkANFDbzmyLwCoKQ7ores6I/lfU2OcP4+nCgYeREREREREZFo4moC4gbKCgQcRUV4jh94EAPz9xkUoCTg7+yFbScCDDy9T5zI89vpRvKG11HF6foewsH50VUJmgd7Z+SSCqPDYfXwAr+zvwYHuIRT5PPjAnFLH9zWruhiXLmuEogB3b3kXA8MJtIbVqkY3Ao+SgBczKtSqkn2dJ7yetc4GHgBQHVSXhPd0jg4Geobi6B9OQJLUtmV2marwsDGwHAC8sqS/hoe6x7a16h2KI6xVtMwoc64y9bTGMgDAi3szc2REdceCmiDS0UjO77NCliU0iLZW/dbbWh0Op1B/1Z3oGR5b4XMyYOBBREREREREpomhsSUBryt3DhMRnSwGXvkVfnHlHHzy7BbX9/WpNeo+nnq7Fa/sVxddz5pV5cq+Foq5Ex0RJFJpvKnNC3F6YLkwszKEpvIgkmkFt//3bgDAh85oQMjvzr9B39ywCF5Zwgt7u/Djl94HACT6WhHwuHNn/Hy9rdXoihmnKzwAoKZIfQ57Okcvmot2VjMrixD0eWzvJxN4jFPh0etM4AFkzsmdx/vHfE28njMqihBw8HPLR8+cAQD47fbjeiutl8Xv3gznw7/GMjXUaRuwNri8bziO19tTCM5ahv+7t9/BI5s6+KmUiIiIiIiITGvXLrTd6NNORHRSUdJoLJ2YSrgzmyuwuKEUsWRaXxBd2eJOhceCOnVx+Z22MC6+70U8s7MdQKYdlBtWz1HDG7Ew/4lVM13b1+yaYnzmnFkAgH95QQ084u37XdufqJh59p0OfP5nf9EX6OfW2K+0OFG1Fni82xUdNe9Cn9/hQHUHkGlpdWwgnnPmhKIojrW0AoBz5qrnx9b3e8Z8TZ+J4nDFzIbT6lEa8OJY3wj+51AvFEXBn7UKj5UznA+rxBwPK4PLk6k0ntnZhmQaiB7dhU8ur3b68KYEBh5ERERERERk2nGtlUKT1j6CiIgmnyRJ+PSaTCXJwvoSlIeca9+TbW5tMWRtYPOR3mFUF/vxnUuX6G213CDaWgFqFcI5c9xdsP3bv5o/qg1ZvH2fa/sSAdILe7vw4ntd8MgS/ubc2XoLIydVBCSkE1EMxdN6EABkAo/5DgUexT4gHR9BMp2Z1ZGtdSCK4XgKHlnCzEr7nyfOmaueD/9zqA/JEwKWA13uzUT5/85oBAD89s3jeLc9gu7BGIp8HpxW7/zfXWO5+jq19puv8HhpXze6B+MIeIDuJ++FRz4553gw8CAiIiIiIiLTxJ3DjS4sxBARkXUfPXMGirR2RKtcamcFqAu9lyxtQG1pALd8aDH+/K2L8IXz57q6iJodeFy+ciZklxdsq0sC+PIHMoPmY23uVXisml0JryzB55HwybOb8ae/uxC3X3a6K/uSJUmvVtmutSIDgPcdHFgOqAFcovsoAGCfNusl22taJcbSGeUIeO230FrSWIayoBeDsSR2tYZHfe1gt7p/N1qEfXyl2tbq6Z1t+OM7HQDUahO/x/ml9yaLFR572yPYeXwAAHBukxepwbFVMCcLBh5ERERERERkmrjQFncaEhHR1FAW9OEz56hVHh9a2uDqvh761Er85dsX40sfmIeQ372B7MKCuhI0VxUh6JNdbWeV7Zp1czCnphhVRR5XKzzm1ZbgD9+4AK98669w18eXocWBmRbjibXuBQDsONqvP/Z+p7OBBwAkeo4AyAy3z7b1gLrovnauM5U6HlnCGu1nndjWys2ZKKtnV2FmZREGY0n864tq+7N1C2od3w8ANJSpgUe7iRkeqbSCF7Sh6qtnV6Kx+OSOBE7uZ0dERERERESuEK0UxJ2GREQ0ddzyoSX4y7cvxgUL3Vl0FSRJgiRNXFscWZbwxJfOxTNfPR/NVe4GAkLI78XTX12Hn//1XCgJa4OijZpbW4K6son5dzWuBR7bj/QDAN4+1o9WbRHd0cCjWws8OiOjHlcURQ8lzp3nXGsyEZ68diATeKTSCg71qMPRnZpPkk2WJXxcG14+HE8BAC5YUOP4foBMK9FWE4HHsb5hRJNphPwe19vATQUMPIiIiIiIiMg0UeHBGR5ERFOPLEsTtnA+0RrKg5jrwqL1eEJ+L4p8J9cyaqxNDTzebQ+jZzCGrz++AwDw4WWNqAj5HdtPXAs89nWMDjyO9o7geP8IfB4JZ82udGx/mTkevfqg9Nb+EcSTafg9smufWz62MlNxVF8WcDQ0yiZaiXYPxhBPjh0En8v72vySuTXFrreBmwpOrt9UIiIiIiIimhBt/WKGBwMPIiKi6SYV6UFNyIu0Alz78zdwoGsI9WUBfG/TUkf3k+g+DEBtKZXIGiS+9UA3AGD5zApH26EtbihFZciH4XhKn1mxu1X931nVIddmzMypKcbKlgoAwPkLal2rfKoq9sPvlaEoQEe4cJWHoig4oM1mmedSCDPVMPAgIiIiIiIiUyLRBCKxJAC2tCIiIpqultSp/4aLOR73XbHC0eoOAEiFu1Dkk5BIKTjcM6Q/7kY7K0CtblozJzPHYySewl3/910AwHnz3WkzJXzrksVY2VKBa9fNcW0fkiShSavyONo3XHD79nAUQ/EU/B4ZzZUT0wZusjHwICIiIiIiIlPatL7R5UW+CRlSS0RERM5bXJep0rzmvDlY59LciZaKAIDM4HJFUfCqFnic43DgAQDnzK0CoM7xuOf37+JwzzAay4O4acNCx/eVbc3cavz2+vOwpLHM1f2In//2sYGC277fqYZMs2vcq26Zahh4EBERERERkSnH+9X5HaKPNBEREU0/q2eEIEtqG6ibL1nk2n5mV6hVI28e7gMAHOgeQmckBr9XxsoW5+Z3CGvnqcHN6wd6sfnVQwCAuz5+BsqCPsf3NRnO1FpnbT/SN+52iqJgv9bOav4Ez72ZTAw8iIiIiIiIyBQxv4MDy4mIiKavudVBPHvTB/Db689F0OdxbT9nzSwGAPz7q4fw+oEevZ3VqpZKV/a7sL4EVcV+xFNpKArw12fNxIWL6hzfz2Q5UwuJth/ph6IoebfrHYpjYCQBjyxhVnXxRB3epGPgQURERERERKa0DbDCg4iI6GQwr7bE9faUF84txaYVTUilFdzw2HY89XYrAGCtC+2sAHXOhWhr1VAWxD9eepor+5ksS5vK4ZEldEZi6BpK5t3u/S61nVVzZRH83lMnBmCzVSIiIiIiIjKllRUeREREZJAkSfjfHz8De9oi2NsRQfdgDIB7gQcAXLtuLjrDMdx8yWKUF50crayEIr8HSxpLset4GHs6o3m3e19rZzWv7tRpZwUw8CAaRY4WHvZDU4s80j/qf4mmCr6fEBHRyUxUeDRVsMKDiIiICgv5vXj4s6tw2Q9fRiSWRJHPg+UzK1zb36pZlfj1V8517edPtjObK7XAYyTn18MjCXRGYpAAzK05ddpZAQw8iAAA5eXl8PkDwIEXJ/tQyKKigy9N9iEQjeHzB1BeXj7Zh0FEROS4tgH1bsLGclZ4EBERkTFzaopx/5UrcP2j27Dx9PpTqs2S085sqcAvXjucN/B4tyMCAJhRWeR6y7Kp5tR6tkR51NfX45e/eAQDA7wjm4icU15ejvr6+sk+DCIiIkcpioLWfq3Cg4EHERERmfDB0+rx+rfXoyzIZWk7xODy93pigDz6tVQUBe+2hQEAixtKJ/zYJhvPLCJNfX09FyaJiIiIiAroG04glkwDAOrLA5N8NERERDTdVBX7J/sQpr3Z1SFUhHzoH07AXzd71Nc6IjH0DSfgkSXMP8XmdwAA64aIiIiIiIjIMFHdUVMSQMDrmeSjISIiIjr1SJKEFc0VAIBA0+JRXxPVHfNqi0/Jz2oMPIiIiIiIiMgwvZ0VB5YTERERTZozm9W2Vv6mRfpjqbSC9zoGAQCLG8om5bgmGwMPIiIiIiIiMkwMLOf8DiIiIqLJc2ZLBQAgkBV4HO4dwkgihSKfB7OqQpN0ZJOLgQcREREREREZ1jqgVng0ssKDiIiIaNIs11pa+SqbEE0qAIB32yIAgEUNpZBlabIObVJxaDkREREREREZ1tbPCg8iIiKiyVZe5ENLhR9H+uPY1plCl9SHA91DAIDFDaWTfHSTh4EHERERERERGdbGCg8iIiKiKWFZQxGO9MdxOJzG4XA3AKAq5EddaWCSj2zysKUVERERERERGdaqVXg0ssKDiIiIaFJdc1Ytep99GIurZCysK0FzVREuWFgDSTo121kBrPAgIiIiIiIig1JpBe1hraUVKzyIiIiIJlVZ0IPIm09h5d9+FbUzGif7cKYEVngQERERERGRIV2RGFJpBR5ZQl0pAw8iIiIimloYeBAREREREZEhrdr8jvrSADzyqdsqgYiIiIimJgYeREREREREZEhrvxhYzvkdRERERDT1MPAgIiIiIiIiQ3a3hgEA82tLJvlIiIiIiIjGYuBBREREREREhrx5uA8AsHJWxeQeCBERERFRDgw8iIiIiIiIqKBkKo23jw0AAM5sqZzkoyEiIiIiGouBBxERERERERW0tyOCkUQKpQEvW1oRERER0ZTEwIOIiIiIiIgKevNIPwBgRUsFZFma3IMhIiIiIsqBgQcREREREREVtP2IOr/jzOaKyT0QIiIiIqI8GHgQERERERFRQTu0Co8zZ3F+BxERERFNTQw8iIiIiIiIaFx9Q3Ec6B4CAKyYWTG5B0NERERElAcDDyIiIiIiIhrXjqP9AIC5NcWoLPZP7sEQEREREeXBwIOIiIiIiIjGpc/vaGE7KyIiIiKauhh4EBERERER0bjeFPM7Wiom9TiIiIiIiMbDwIOIiIiIiIjySqUVvaUVAw8iIiIimsoYeBAREREREVFe73cNYjCWRMjvwaL60sk+HCIiIiKivBh4EBERERERUV5vHlbndyybWQ6vh5eQRERERDR18dMqERERERER5aQoCp56uw0AsJIDy4mIiIhoimPgQURERERERDm9+F4XXt7fDb9HxlWrWyb7cIiIiIiIxsXAg4iIiIiIiMZIpRXc9cy7AIDPrZ2FlurQJB8REREREdH4GHgQERERERHRGL/edhR7OyIoC3px41/Nn+zDISIiIiIqiIEHERERERERjTIcT+K+P7wHAPjqxQtQEfJP8hERERERERXGwIOIiIiIiIh00UQKdz3zLjojMTRXFeGza2dN9iERERERERninewDICIiIiIiosmXTKXx2zeP4/t/fA9tA1EAwM0bFyPg9UzykRERERERGcPAg4iIiIiIyIaHHnoI9957L9rb27F8+XL88Ic/xNlnnz3Zh1XQ/s5BPPHGUexv78ORnmG0RhIYiqcBALXFXlx9Vg2WFA9j//79Y763vLwctbW1E33IRERERETjYuBBRERERERk0eOPP46bbroJDz/8MNasWYMHHngAGzduxN69e1FXVzfZhzeu7sEYfvzSgVGPpYYHMPDaEzj85tN4I5XI+71lZeXYv38fQw8iIiIimlIYeBAREREREVl0//3344tf/CKuvvpqAMDDDz+Mp59+Gj/72c/wD//wD5N8dOObV1uCj51egf//B/dg/aar0FBbhVJfDTwrrwdwfd7v6+tsxcPf+hsMDAww8CAiIiKiKcW1wENRFABAOBx2axdERERERFOK+OwrPgvTyS0ej2Pbtm245ZZb9MdkWcb69euxdevWnN8Ti8UQi8X0Pw8MDACYnOumAIDPLQ3hgW1PovKyD6EoXYRkDEgW+L54dAQA8M477yASiRTcz9GjRwEAHUfeR2xkyNCx9Xe1m9rHVN7PyfRcrO4HACRJMvzeeLI9f54z5vYzVc/LiTq2k+nvcqL2czI9F4HvmXzPnIjzPxKJTMpnULevmSTFpZ987NgxNDc3u/GjiYiIiIimtKNHj2LmzJmTfRjkstbWVsyYMQOvvvoq1q5dqz9+880348UXX8Trr78+5ntuv/123HHHHRN5mEREREREU45b10yuVXg0NTXh6NGjKC0thSRJANT0prm5GUePHkVZWZlbu6aTAM8VMornChnFc4WM4rlCZpx4viiKgkgkgqampsk+NJqibrnlFtx00036n9PpNHp7e1FdXQ1JkvgeRIbxXCGjeK6QGTxfyCieK2TURF8zuRZ4yLKcN6EpKyvjLwIZwnOFjOK5QkbxXCGjeK6QGdnnS3l5+SQfDU2UmpoaeDwedHR0jHq8o6MDDQ0NOb8nEAggEAiMeqyiomLMdnwPIqN4rpBRPFfIDJ4vZBTPFTJqoq6ZZNd+MhERERER0UnM7/dj1apVeO655/TH0uk0nnvuuVEtroiIiIiIaGK4VuFBRERERER0srvpppvw+c9/HmeddRbOPvtsPPDAAxgaGsLVV1892YdGRERERHTKmdDAIxAI4LbbbhtTwk10Ip4rZBTPFTKK5woZxXOFzOD5QldeeSW6urpw6623or29HStWrMCWLVtQX19v6efxnCKjeK6QUTxXyAyeL2QUzxUyaqLPFUlRFGVC9kREREREREREREREROQSzvAgIiIiIiIiIiIiIqJpj4EHERERERERERERERFNeww8iIiIiIiIiIiIiIho2mPgQURERERERERERERE056pwOOuu+7C6tWrUVpairq6OmzatAl79+4dtU00GsUNN9yA6upqlJSU4PLLL0dHR8eobY4cOYJLL70UoVAIdXV1+OY3v4lkMjlqmxdeeAErV65EIBDA/PnzsXnzZmvPkCbFRJ0rL7zwAiRJGvNfe3v7hDxPss+pc+WrX/0qVq1ahUAggBUrVuTc19tvv43zzz8fwWAQzc3NuOeee9x6WuSSiTpfDh06lPO95bXXXnPz6ZGDnDhX3nrrLXzyk59Ec3MzioqKsGTJEvzgBz8Ysy9+ZpneJupc4WeWUwevmcgMXjeRUbxuIqN4zURG8ZqJzJhO102mAo8XX3wRN9xwA1577TU8++yzSCQS2LBhA4aGhvRtvvGNb+C///u/8cQTT+DFF19Ea2srPv7xj+tfT6VSuPTSSxGPx/Hqq6/i5z//OTZv3oxbb71V3+bgwYO49NJLcdFFF2HHjh34+te/ji984Qv4/e9/b+ZwaRJN1Lki7N27F21tbfp/dXV1E/I8yT4nzhXhmmuuwZVXXplzP+FwGBs2bMCsWbOwbds23Hvvvbj99tvxk5/8xLXnRs6bqPNF+OMf/zjqvWXVqlWOPydyhxPnyrZt21BXV4df/vKX2L17N/7xH/8Rt9xyC370ox/p2/Azy/Q3UeeKwM8sJz9eM5EZvG4io3jdREbxmomM4jUTmTGtrpsUGzo7OxUAyosvvqgoiqL09/crPp9PeeKJJ/Rt9uzZowBQtm7dqiiKojzzzDOKLMtKe3u7vs2//uu/KmVlZUosFlMURVFuvvlm5fTTTx+1ryuvvFLZuHGjncOlSeTWufKnP/1JAaD09fVN3JMhV1k5V7LddtttyvLly8c8/i//8i9KZWWlfu4oiqJ861vfUhYtWuT8k6AJ49b5cvDgQQWAsn37drcOnSaY3XNFuP7665WLLrpI/zM/s5x83DpX+Jnl1MVrJjKD101kFK+byCheM5FRvGYiM6bydZOtGR4DAwMAgKqqKgBqSpNIJLB+/Xp9m8WLF6OlpQVbt24FAGzduhVnnHEG6uvr9W02btyIcDiM3bt369tk/wyxjfgZNP24da4IK1asQGNjIz74wQ/ilVdecfvpkIusnCtGbN26FRdccAH8fr/+2MaNG7F371709fU5dPQ00dw6X4TLLrsMdXV1WLduHZ588klnDpomhVPnysDAgP4zAH5mORm5da4I/Mxy6uE1E5nB6yYyitdNZBSvmcgoXjORGVP5usly4JFOp/H1r38d5513HpYuXQoAaG9vh9/vR0VFxaht6+vr9T5b7e3toz6Iia+Lr423TTgcxsjIiNVDpkni5rnS2NiIhx9+GL/5zW/wm9/8Bs3Nzbjwwgvx5ptvuvysyA1WzxUjjJxPNL24eb6UlJTgvvvuwxNPPIGnn34a69atw6ZNm/gBfppy6lx59dVX8fjjj+O6667TH+NnlpOLm+cKP7OcmnjNRGbwuomM4nUTGcVrJjKK10xkxlS/bvKaf0qqG264Abt27cLLL79s9UfQKcLNc2XRokVYtGiR/udzzz0X77//Pr7//e/jF7/4heP7I3fxfYXMcPN8qampwU033aT/efXq1WhtbcW9996Lyy67zPH9kbucOFd27dqFj370o7jtttuwYcMGB4+OphI3zxV+Zjk18bMNmcHrJjKK7y1kFK+ZyCheM5EZU/26yVKFx4033oinnnoKf/rTnzBz5kz98YaGBsTjcfT394/avqOjAw0NDfo22dPZxdfF18bbpqysDEVFRVYOmSaJ2+dKLmeffTb279/v0DOgiWLnXDHC6vlEU5Pb50sua9as4XvLNOTEufLOO+/g4osvxnXXXYfvfOc7o77GzywnD7fPlVz4meXkxmsmMoPXTWQUr5vIKF4zkVG8ZiIzpsN1k6nAQ1EU3HjjjfjP//xPPP/885gzZ86or69atQo+nw/PPfec/tjevXtx5MgRrF27FgCwdu1a7Ny5E52dnfo2zz77LMrKynDaaafp22T/DLGN+Bk09U3UuZLLjh070NjY6PAzIrc4ca4YsXbtWrz00ktIJBL6Y88++ywWLVqEyspK+0+EJsREnS+58L1lenHqXNm9ezcuuugifP7zn8edd945Zj/8zDL9TdS5kgvfV05OvGYiM3jdREbxuomM4jUTGcVrJjJjWl03mZlw/pWvfEUpLy9XXnjhBaWtrU3/b3h4WN/my1/+stLS0qI8//zzyhtvvKGsXbtWWbt2rf71ZDKpLF26VNmwYYOyY8cOZcuWLUptba1yyy236NscOHBACYVCyje/+U1lz549ykMPPaR4PB5ly5YtFmez00SbqHPl+9//vvK73/1O2bdvn7Jz507la1/7miLLsvLHP/5xQp8vWefEuaIoirJv3z5l+/btype+9CVl4cKFyvbt25Xt27crsVhMURRF6e/vV+rr65XPfvazyq5du5Rf/epXSigUUn784x9P6PMleybqfNm8ebPy2GOPKXv27FH27Nmj3HnnnYosy8rPfvazCX2+ZJ0T58rOnTuV2tpa5TOf+cyon9HZ2alvw88s099EnSv8zHLq4DUTmcHrJjKK101kFK+ZyCheM5EZ0+m6yVTgASDnf//+7/+ubzMyMqJcf/31SmVlpRIKhZSPfexjSltb26ifc+jQIeVDH/qQUlRUpNTU1Ch/93d/pyQSiVHb/OlPf1JWrFih+P1+Ze7cuaP2QVPfRJ0rd999tzJv3jwlGAwqVVVVyoUXXqg8//zzE/U0yQFOnSsf+MAHcv6cgwcP6tu89dZbyrp165RAIKDMmDFD+ed//ucJepbklIk6XzZv3qwsWbJECYVCSllZmXL22WcrTzzxxAQ+U7LLiXPltttuy/kzZs2aNWpf/MwyvU3UucLPLKcOXjORGbxuIqN43URG8ZqJjOI1E5kxna6bJO2AiYiIiIiIiIiIiIiIpi1LQ8uJiIiIiIiIiIiIiIimEgYeREREREREREREREQ07THwICIiIiIiIiIiIiKiaY+BBxERERERERERERERTXsMPIiIiIiIiIiIiIiIaNpj4EFERERERERERERERNMeAw8iIiIiIiIiIiIiIpr2GHgQEREREREREREREdG0x8CDiIiIiIiIiIiIiIimPQYeREREREREREREREQ07THwICIiXSqVQjqdnuzDICIiIiIimrJ43URENHUx8CAimqIeeeQRVFdXIxaLjXp806ZN+OxnPwsA+K//+i+sXLkSwWAQc+fOxR133IFkMqlve//99+OMM85AcXExmpubcf3112NwcFD/+ubNm1FRUYEnn3wSp512GgKBAI4cOTIxT5CIiIiIiMgmXjcREVE2Bh5ERFPUFVdcgVQqhSeffFJ/rLOzE08//TSuueYa/PnPf8bnPvc5fO1rX8M777yDH//4x9i8eTPuvPNOfXtZlvHggw9i9+7d+PnPf47nn38eN99886j9DA8P4+6778ZPf/pT7N69G3V1dRP2HImIiIiIiOzgdRMREWWTFEVRJvsgiIgot+uvvx6HDh3CM888A0C98+ihhx7C/v378cEPfhAXX3wxbrnlFn37X/7yl7j55pvR2tqa8+f9+te/xpe//GV0d3cDUO9Uuvrqq7Fjxw4sX77c/SdERERERETkMF43ERGRwMCDiGgK2759O1avXo3Dhw9jxowZWLZsGa644gr8r//1v1BbW4vBwUF4PB59+1QqhWg0iqGhIYRCIfzxj3/EXXfdhXfffRfhcBjJZHLU1zdv3owvfelLiEajkCRpEp8pERERERGRNbxuIiIiwTvZB0BERPmdeeaZWL58OR555BFs2LABu3fvxtNPPw0AGBwcxB133IGPf/zjY74vGAzi0KFD+PCHP4yvfOUruPPOO1FVVYWXX34Z1157LeLxOEKhEACgqKiIH9qJiIiIiGja4nUTEREJDDyIiKa4L3zhC3jggQdw/PhxrF+/Hs3NzQCAlStXYu/evZg/f37O79u2bRvS6TTuu+8+yLI6suk//uM/Juy4iYiIiIiIJgqvm4iICGDgQUQ05X3qU5/C3//93+Pf/u3f8Mgjj+iP33rrrfjwhz+MlpYWfOITn4Asy3jrrbewa9cufO9738P8+fORSCTwwx/+EB/5yEfwyiuv4OGHH57EZ0JEREREROQOXjcREREAyJN9AERENL7y8nJcfvnlKCkpwaZNm/THN27ciKeeegp/+MMfsHr1apxzzjn4/ve/j1mzZgEAli9fjvvvvx933303li5dikcffRR33XXXJD0LIiIiIiIi9/C6iYiIAA4tJyKaFi6++GKcfvrpePDBByf7UIiIiIiIiKYkXjcREREDDyKiKayvrw8vvPACPvGJT+Cdd97BokWLJvuQiIiIiIiIphReNxERkcAZHkREU9iZZ56Jvr4+3H333fzQTkRERERElAOvm4iISGCFBxERERERERERERERTXscWk5ERERERERERERERNMeAw8iIiIiIiIiIiIiIpr2GHgQEREREREREREREdG0x8CDiIiIiIiIiIiIiIimPQYeREREREREREREREQ07THwICIiIiIiIiIiIiKiaY+BBxERERERERERERERTXsMPIiIiIiIiIiIiIiIaNr7fxlYtfIkZeBVAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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xqNbbb7/dsFgsQcc/7bTTgupcsWJFs9fD/l/r2LFjgx7797//bUgyPv7442bj91dTU9PssVdffdWQZHz66aeBx/yvkcsvvzxo7Pbt2w2LxWL8/ve/D3p8zZo1RkxMTNDjLR3roYceMkwmU7PXAgAAQE/hf4/oPw9oSUpKinHMMccEPve/N/N7/PHHm72fP1Br7ydbez9/4PtS//lMnz59DJfLFXj89ddfNyQZs2fPDjx24Pvhg+2ztdomT55sDBgwIPB5W87ZJBlxcXFBj33zzTeGJOOpp55qdqz9+c8T7rvvPsPhcBhFRUXGkiVLjGOOOcaQZPzrX/8KOs7+5wOhnsv4zyMP9Z7dr6XXitvtNrKysoyRI0catbW1gcffffddQ5Ixc+bMwGOTJ082JBl33XVXSMe76qqrDLPZ3OJr038+c9tttxmSjP/973+B5yorK428vDxj4MCBgXNJ/+tm5MiRhtvtDoy9/PLLDZPJZJxzzjlB+x83blzQz90wjMB5/P7nuX/+858NSUZOTk7Q69F/Prf/2FBfj/5ahw8fbtTX1wcenz17tiHJWLNmTeCxA1+fDofjoOeHl19+uZGbmxt0fv31118f9LW/v5bOpZYtW2ZIMv7+978HHvO/RsaPH280NjYGHq+srDRSU1ON66+/PmgfRUVFRkpKStDjoZ4jAggPlngCepA5c+Zo4cKFWrhwoV566SWdfvrpuu666/Tvf/87MOa9996TxWLRrbfeGrTt//3f/8kwDL3//vuBx2bNmqUjjzxSkydP1s0336zTTjut2Xatueaaa5SZmanc3FxNmjRJ1dXVevHFF4PWnd3f3r17tXr1al199dVKT08PPD5q1CidddZZeu+990I+dig++ugjud1u3XbbbTKbf/hzef3118tutzebsmu1WjVlypRD7tfhcOjTTz/VNddco/79+wc9578Tw+PxaMGCBTr//PM1aNCgwPO9e/fWz3/+c3322WdyuVxB21577bVBd3KMHTtWhmHo2muvDTxmsVh03HHHaevWrc3quvjii5WSkhK0vST94he/CFoGbOzYsXK73a1Ovz2UG264IajWU045RR6PRzt27GjX/q666ip98cUXgbu5JN9dOP369dNpp53W6rb7391WV1enffv26cQTT5Qkff31183G33jjjUGf//vf/5bX69Ull1yiffv2BT5ycnI0ZMgQffzxxy0eq7q6Wvv27dNJJ50kwzBanOoMAAAAn6SkJFVWVh70ef8d8m+99Va7G0qH+n7e76qrrlJycnLg84suuki9e/cO+3nJgdpyziZJEydODMw0kHznT3a7vcVzgpbce++9yszMVE5OjiZMmKAtW7bo4YcfDtwdf6D2nMscjq+++kolJSW6+eabg2ZOT5o0ScOGDWt23iZJN9100yH36/V69d///lc//vGPWzxH9Z/PvPfeezrhhBOCln9NSkrSDTfcoO3bt2v9+vVB21111VVBM/H9523XXHNN0LixY8dq165damxsDHr8zDPPDFryy3/eduGFFwa9Hv2Ph/pzbsmUKVOCerD4Z/e0d59XXXWVCgsLg86RXn75ZcXHx+vCCy9sddv9z6UaGhpUWlqq/Px8paamtnjedv311wet7LBw4UJVVFTo8ssvDzpvs1gsGjt27EHP20I5RwRweAgogB7khBNO0MSJEzVx4kRdccUVmj9/vkaMGKFp06YF1s/csWOHcnNzg97YSD9Ms93/AnJcXJyef/55bdu2TZWVlYE1JUM1c+ZMLVy4UIsXL9a3336rwsJCXXnllQcd7z/20KFDmz03fPhw7du3LzB1MxwOdry4uDgNGjSo2cX0Pn36hNRAz/9mbuTIkQcd43A4VFNTc9Cv1ev1Nlvf9sCwwx829OvXr9nj5eXlzfbblu0ltbiPUB14rLS0tMPa56WXXiqr1aqXX35Zkm85s3fffVdXXHHFIV+TZWVl+uUvf6ns7GzFx8crMzNTeXl5gf0cyP+c36ZNm2QYhoYMGaLMzMygj++++y7QyE+Sdu7cGQjYkpKSlJmZGQhQWjoWAAAAfKqqqpqdo+zv0ksv1cknn6zrrrtO2dnZuuyyy/T666+3KawI9f2835AhQ4I+N5lMys/PP2jvu3Bpyzmb1Py9t+R7/x3qe+8bbrhBCxcu1KJFi7Ry5UqVlJTojjvuOOj49pzLHI7WzhOHDRvW7PsRExMT1JPgYBwOh1wuV6vnbf7jH+xr3b8+v7acd3m93mbnCV35vO2ss85S7969A+dtXq9Xr776qn7605+2+vst+Xphzpw5M9B3JSMjQ5mZmaqoqAj5vE3y9VA58LxtwYIFQedtbT1HBHB46EEB9GBms1mnn366Zs+erU2bNunII49s8z4+/PBDSb67CjZt2tTsTUBrjjrqKE2cOLHNx+ys2rLObEc4WN+Plh43Dmie19btD7aPAx2sWfrh7LMlaWlpOu+88/Tyyy9r5syZevPNN1VfXx/UX+VgLrnkEi1dulS//vWvNXr0aCUlJcnr9epHP/pRiye0B/6cvV6vTCaT3n///Ra/rqSkJEm+78VZZ52lsrIy3XnnnRo2bJgSExO1Z88eXX311e2+0w8AAKC72717t5xOp/Lz8w86Jj4+Xp9++qk+/vhjzZ8/Xx988IFee+01nXHGGVqwYEFIPfI64v38wW6W8Xg87erb1x6H+957yJAh3eq8zWq1Bs2Qj7TDPe86nO3b+noM93mbxWLRz3/+c/3lL3/RM888o88//1yFhYUhnbfdcssteuGFF3Tbbbdp3LhxSklJkclk0mWXXRbyeZvk60ORk5PTbPz+qwa09RwRwOEhoAB6OP900aqqKknSgAED9NFHH6mysjLoDoYNGzYEnvf79ttvdf/992vKlClavXq1rrvuOq1ZsyZomaBw8h9748aNzZ7bsGGDMjIylJiYKOngb7zae7z9pya73W5t27at3W/S/ftau3btQcdkZmYqISHhoF+r2WxudodMNKWlpamioiLoMbfbrb1794btGIf6mV511VX66U9/qhUrVgQarx0qdCsvL9eiRYt03333BTWf899dE4rBgwfLMAzl5eXpiCOOOOi4NWvW6Pvvv9eLL74Y1KB84cKFIR8LAACgJ/rHP/4hSSooKGh1nNls1plnnqkzzzxTjz32mB588EHdfffd+vjjjzVx4sSwN7Y98D2jYRjavHmzRo0aFXispffJku+u+v3PMdpSW1vO2aKhLecy4T5vO+OMM4Ke27hxY7u/H5mZmbLb7a2et/mPf7Cvdf/6OoNQX4+HI5TztkcffVTvvPOO3n//fWVmZh7yd1uS3nzzTU2ePFmPPvpo4LG6uroWv56W+Jc5y8rKavVcPhzniADahiWegB6soaFBCxYsUFxcXGD66bnnniuPx6Onn346aOzjjz8uk8mkc845J7Dt1VdfrdzcXM2ePVvz5s1TcXGxbr/99g6rt3fv3ho9erRefPHFoDcha9eu1YIFC3TuuecGHvMHFaG+WWnJxIkTFRcXpyeffDLoDpG//e1vcjqdmjRpUrv2m5mZqVNPPVXPP/+8du7cGfSc/zgWi0Vnn3223nrrraAp4sXFxXrllVc0fvx42e32dh2/IwwePFiffvpp0GPPPffcQWdQtMehfqbnnHOOMjIy9PDDD+uTTz4J6S4c/x1BB94B9MQTT4Rc1wUXXCCLxaL77ruv2X4Mw1BpaelBj2UYhmbPnh3ysQAAAHqaxYsX64EHHlBeXp6uuOKKg44rKytr9tjo0aMlSfX19ZLCc46wv7///e9BfTHefPNN7d27N3DOJPneJy9fvjywpK4kvfvuu82WOGpLbaGes0VLW85lwvEzOe6445SVlaW5c+cGftaS9P777+u7775r93mb2WzW+eefr3feeUdfffVVs+f97+vPPfdcffnll1q2bFnguerqaj333HMaOHCgRowY0a7jd4RQX4+HIyEhQdLBf6ajRo3SqFGj9Ne//lX/+te/dNlllwXNXjgYi8XS7HzrqaeeCvmcs6CgQHa7XQ8++KAaGhqaPe9wOALHkQ7vHBFA2zCDAuhB3n///cBdHCUlJXrllVe0adMm3XXXXYE3iD/+8Y91+umn6+6779b27dt19NFHa8GCBXrrrbd02223Be46+N3vfqfVq1dr0aJFSk5O1qhRozRz5kzdc889uuiii4LCgnD64x//qHPOOUfjxo3Ttddeq9raWj311FNKSUnRrFmzAuPGjBkjSbr77rt12WWXKTY2Vj/+8Y8Db4BDkZmZqRkzZui+++7Tj370I/3kJz/Rxo0b9cwzz+j4448P6QL4wTz55JMaP368jj32WN1www3Ky8vT9u3bNX/+fK1evVqS73u8cOFCjR8/XjfffLNiYmL05z//WfX19XrkkUfafeyOcN111+nGG2/UhRdeqLPOOkvffPONPvzwQ2VkZITtGIMHD1Zqaqrmzp2r5ORkJSYmauzYsYFlxWJjY3XZZZfp6aeflsVi0eWXX37Ifdrtdp166ql65JFH1NDQoD59+mjBggXatm1bm+r63e9+pxkzZmj79u06//zzlZycrG3btuk///mPbrjhBv3qV7/SsGHDNHjwYP3qV7/Snj17ZLfb9a9//euw1oQFAADoTvznK42NjSouLtbixYu1cOFCDRgwQG+//XZQA+QD3X///fr00081adIkDRgwQCUlJXrmmWfUt2/fQPPiQ72fbKv09HSNHz9eU6ZMUXFxsZ544gnl5+fr+uuvD4y57rrr9Oabb+pHP/qRLrnkEm3ZskUvvfRSUNPqttYW6jlbNIV6LjN69GhZLBY9/PDDcjqdslqtOuOMM5SVlRXysWJjY/Xwww9rypQpOu2003T55ZeruLhYs2fP1sCBAw/rJroHH3xQCxYs0GmnnaYbbrhBw4cP1969e/XGG2/os88+U2pqqu666y69+uqrOuecc3TrrbcqPT1dL774orZt26Z//etfUV1O6kChvh4PR3x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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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5//zzzenTp63t7777rpFkHnroIWvb+PHjjSTzwAMP+PV6U6dONdHR0R7rksOHD5uIiAiP9+vNQw89ZCSZt956q9Q+99rH3/VYRX/mdrvdbN++3WOse434+eefe7yXhISEUmtnX5/5Tp06mZycHGv73LlzjSSzdetWa5u3ddvMmTONzWbzWDNOmjSp1HrRGGP+97//GUnmlVde8di+YsUKr9sBBA+lnAD4bd68eVq1apVWrVqlRYsWafDgwZo4caLeeusta8z7778vh8OhO++80+PYP/3pTzLG6IMPPrC2TZ8+XV26dNH48eP1hz/8QQMHDix1XFl+97vfqXHjxkpKStKoUaN08uRJvfjiix51ZYs7dOiQtmzZogkTJqhhw4bW9m7dumno0KF6//33/X5tf3z00UfKzc3V3Xff7dGw+dZbb1V8fHypskjR0dG6+eab/Tr3uHHjdPDgQa1du9ba9sorryg2Nla//e1vfR7ncrm0fPlyjR492ut1cqe/vv/+++rTp48uueQSa19cXJxuu+02/fzzz1YJpvfff1+JiYm6/vrrrXGRkZG68847lZ2drXXr1nmc/7e//a1H5onT6dSHH36oMWPG6Nxzz7W2d+rUScOGDfPrWkjSbbfd5pG6O2DAADmdTu3Zs8faVvw3ebKysnT06FENGDBAp06d0vfff1/uayxdulSdOnVSx44ddfToUevhTkUv/rMAAABAaImLi1NWVpbP/e7fiH/77bcr3Si6IvfzUsE9fb169aznV199tZo1axbwdUlJFVmzSdKQIUOszAKpYP0UHx+vn376ya/XmzZtmho3bqzExEQNGjRIu3bt0uOPP67f/OY3Xsc7nU6tXLlSY8aMUZs2baztzZo10//7f/9Pn3zyiTIzM/19u+X66quvdPjwYf3hD3/w6KMwatQodezY0Ws52zvuuMOvc48bN045OTl64403rG2vvfaa8vPzPSoPePPmm2+qe/fupbItJM91mz/rsYr+zAcOHKjOnTt7bHv//fd18cUXq0+fPta2xo0b+yxl5c3NN9/s0X/Cnd1T/LNUfN128uRJHT16VP369ZMxxms545KWLl2qhIQEDR061GPd1qtXL8XFxbFuA0IIgQkAfuvTp4+GDBmiIUOG6IYbbtB7772nzp07a/LkyVZvgj179igpKcnjBlsqSqct/kVxVFSUXnjhBe3evVtZWVlW7wR/PfTQQ1q1apXWrFmjb775RgcPHtRNN93kc7z7tTt06FBqX6dOnXT06FGdPHnS79cvj6/Xi4qKUps2bTyuhSQ1b97c7yZhQ4cOVbNmzfTKK69IKgg4vPrqq7rqqqtKXfvijhw5oszMTJ1//vnlzt3XdXLvd/953nnneQRevI1za926dan5nD59Wuedd16p1/L2+r4UD2pIUoMGDSTJo4bo9u3b9etf/1oJCQmKj49X48aNrcVARkZGua/x448/avv27WrcuLHHo3379pKKGukBAODNxx9/rNGjRyspKUk2m61SJQCNMXryySfVvn17RUdHq3nz5nr00UcDP1mgBsrOzi7zPvnaa69V//79NXHiRDVt2lTXXXedXn/99QoFKSpyPy+p1D2wzWZTu3btfPa2C5SKrNmk0vfaUsH9tr/1+m+77TatWrVKq1ev1saNG3X48GHdf//9PscfOXJEp06d8rkecblcpXpxnI2y1okdO3YsdT0iIiLUokULv87dsWNH9e7d21q3SQW/UHbxxRerXbt2ZR67a9cuv9Zt/qzHKvozL7luK/5aJQV63bZ3717rlwnj4uLUuHFjDRw4UJL/67aMjAw1adKk1NotOzubdRsQQugxAaDS7Ha7Bg8erLlz5+rHH39Uly5dKnyODz/8UJJ05swZ/fjjj15vgHzp2rWrhgwZUuHXDFUVqc3pcDj0//7f/9O///1v/etf/9L69et18ODBcn/rJtgq8h4rwldPElPYyO3EiRMaOHCg4uPj9fDDD6tt27aKiYnRpk2b9Oc//9mvBafL5VLXrl2tniglJScnV/4NAABqvJMnT6p79+763e9+5/O3hMtz1113aeXKlXryySfVtWtXHTt2TMeOHQvwTIGaZ//+/crIyCjzi+DY2Fh9/PHHWrt2rd577z2tWLFCr732mi677DKtXLnSrx54VXGv6+sXt5xOZ6X68lVGeffa5TnvvPNq1LotOjq6VCCgLOPGjdNdd92l/fv3KycnR5999pmeeeaZKpzh2QvWus3pdGro0KE6duyY/vznP6tjx46qW7euDhw4oAkTJvi9bmvSpIlHMKg4X70jAVQ/AhMAzkp+fr6kgt9AkqSWLVvqo48+UlZWlsdvY7hL5bRs2dLa9s033+jhhx/WzTffrC1btmjixInaunWrEhISqmSu7tfesWNHqX3ff/+9GjVqpLp160ryvQCo7OsVT0HOzc3V7t27z/rmfNy4cZo1a5b++9//6oMPPlDjxo3LLX/UuHFjxcfHa9u2beXO3dd1cu93//nNN9/I5XJ53Jx7+3n7mk9sbKx+/PHHUvu8vX5lpaam6pdfftFbb72lSy+91Nq+e/fuUmN9/ezbtm2rr7/+WpdffnlAPh8AgNplxIgRGjFihM/9OTk5evDBB/Xqq6/qxIkTOv/88/X4449r0KBBkqTvvvtOzz77rLZt22b9dmpFfqEDqM3czXHLu1e22+26/PLLdfnll+upp57SP/7xDz344INau3athgwZEvB7wJL3wMYY7dy5U926dbO2NWjQQCdOnCh17J49ezzWGBWZW0XWbMHQuHFj1alTx+d6xG63W78UFOh1m7tMq9uOHTvO+npcd911mjJlil599VWdPn1akZGRuvbaa8s9rm3btn6t2/xZjwXiZ96yZcsqX7dt3bpVP/zwg1588UWNGzfO2r5q1apSY8tat3300Ufq379/lQVYAAQGpZwAVFpeXp5WrlypqKgoKwV05MiRcjqdpX4DZPbs2bLZbNaCPC8vTxMmTFBSUpLmzp2rlJQUpaen65577qmy+TZr1kw9evTQiy++6HFzv23bNq1cuVIjR460trkDFN4WAf4aMmSIoqKi9M9//tPjt4mef/55ZWRkaNSoUZU+t1RQ27Vbt276z3/+ozfffFPXXXedIiLKjjfb7XaNGTNG//3vf/XVV1+V2u+e58iRI/XFF19ow4YN1r6TJ09qwYIFatWqlVVvdOTIkUpLS9Nrr71mjcvPz9fTTz+tuLg4K+XWF4fDoWHDhmn58uXau3evtf27776zsmkCwf2bOcV/Drm5ufrXv/5VamzdunW9pghfc801OnDggP7973+X2nf69OmAlgEDANQ+kydP1oYNG7RkyRJ98803Gjt2rIYPH259CfTf//5Xbdq00bvvvqvWrVurVatWmjhxIhkTQDnWrFmjRx55RK1bty6zFr63/5Z69OghqSBwKAVmjVDcSy+95NH34o033tChQ4c8gpht27bVZ599ZpXOlaR33323VCmjiszN3zVbsDgcDl1xxRV6++23Pcpapaena/HixbrkkksUHx8vKTA/kwsvvFBNmjTR/PnzrZ+1JH3wwQf67rvvznrd1qhRI40YMUKLFi3SK6+8ouHDh6tRo0blHvfb3/5WX3/9tZYtW1ZqX/F1mz/rsUD8zEeOHKnPPvtMX3zxhbXtyJEjPjMTKsPbus0Yo7lz55Ya6+tnf80118jpdOqRRx4pdUx+fn7A/vsFcPbImADgtw8++MD6jYrDhw9r8eLF+vHHH/XAAw9YN4ajR4/W4MGD9eCDD+rnn39W9+7dtXLlSr399tu6++67raZpf//737VlyxatXr1a9erVU7du3fTQQw/pr3/9q66++mqPIEEgPfHEExoxYoT69u2rW265RadPn9bTTz+thIQETZ8+3RrXq1cvSdKDDz6o6667TpGRkRo9erR18+OPxo0ba+rUqZoxY4aGDx+uK6+8Ujt27NC//vUv9e7dOyBll8aNG6d7771Xkvw+3z/+8Q+tXLlSAwcO1G233aZOnTrp0KFDWrp0qT755BPVr19fDzzwgF599VWNGDFCd955pxo2bKgXX3xRu3fv1ptvvmn9Ns5tt92m5557ThMmTNDGjRvVqlUrvfHGG1q/fr3mzJlTZh1ftxkzZmjFihUaMGCA/vCHP1g30l26dNE333xT+YtTTL9+/dSgQQONHz9ed955p2w2m15++WWv6ee9evXSa6+9pilTpqh3796Ki4vT6NGjddNNN+n111/X73//e61du1b9+/eX0+nU999/r9dff10ffvihz8brAACUZe/evVq4cKH27t2rpKQkSdK9996rFStWaOHChfrHP/6hn376SXv27NHSpUv10ksvyel06p577tHVV1+tNWvWBPkdAKHBvV7Jz89Xenq61qxZo1WrVqlly5Z65513PBobl/Twww/r448/1qhRo9SyZUsdPnxY//rXv9SiRQtdcsklkgqCBPXr19f8+fNVr1491a1bVxdddFGls5caNmyoSy65RDfffLPS09M1Z84ctWvXTrfeeqs1ZuLEiXrjjTc0fPhwXXPNNdq1a5cWLVrk0Yy6onPzd80WTH//+9+1atUqXXLJJfrDH/6giIgIPffcc8rJydH//d//WeN69Oghh8Ohxx9/XBkZGYqOjtZll12mJk2a+P1akZGRevzxx3XzzTdr4MCBuv7665Wenq65c+eqVatWAfnluXHjxunqq6+WJK9fmHtz33336Y033tDYsWP1u9/9Tr169dKxY8f0zjvvaP78+erevbvf67FA/Mzvv/9+vfzyyxo+fLjuuusu1a1bVwsWLLCyNgKhY8eOatu2re69914dOHBA8fHxevPNN732M3Gv2e+8804NGzZMDodD1113nQYOHKjbb79dM2fO1JYtW3TFFVcoMjJSP/74o5YuXaq5c+daPwsAQWYAoBwLFy40kjweMTExpkePHubZZ581LpfLY3xWVpa55557TFJSkomMjDTnnXeeeeK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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['description'].dtype"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2RDKaBCxs5SD",
        "outputId": "b4d56189-2685-4685-f874-4a4cc95914b6"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dtype('O')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "location_counts = df['district'].value_counts()\n",
        "plt.figure(figsize=(10, 6))\n",
        "location_counts.plot(kind='bar')\n",
        "plt.title('Count of Each districk')\n",
        "plt.xlabel('district')\n",
        "plt.ylabel('Count')\n",
        "plt.xticks(rotation=45)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 582
        },
        "id": "bfzVCJZS8TwW",
        "outputId": "e089e6de-2c65-4bf7-8527-19dfb9034fa5"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [
        "SsiAHF2Ssyzd"
      ],
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}