Task_4.ipynb

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    "colab": {
      "name": "Task-4.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPEPK6sXRkK5KdqWWy5KIaR",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Aashish1106/GRIP_Sparks/blob/main/Task_4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ikBfupV9DN8u"
      },
      "source": [
        "***Name - Tarigoppula Aashish Kumar***\r\n",
        "\r\n",
        "**GRIPS@ THE SPARKS FOUNDATION**\r\n",
        "\r\n",
        "***TSF TASK 4 - EXPLORATORY DATA ANALYSIS - Terrorism***\r\n",
        "\r\n",
        "\r\n",
        "\r\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oVOFD65PDs4i"
      },
      "source": [
        "importing libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fpNM1f_JDreL"
      },
      "source": [
        "#importing necessary libraries\r\n",
        "\r\n",
        "import pandas as pd\r\n",
        "import numpy as np\r\n",
        "import matplotlib.pyplot as plt\r\n",
        "import seaborn as sns"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YpVJZ7fuEDBd",
        "outputId": "24a845ad-87c9-442b-d23b-1e6f3faa6b84"
      },
      "source": [
        "terror_data = pd.read_csv('globalterrorismdb_0718dist.csv', encoding='latin-1')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (4,6,31,33,61,62,63,76,79,90,92,94,96,114,115,121) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "Ru_G4fczEXhG",
        "outputId": "04427546-48f6-44bb-deb1-1410d47a2e1e"
      },
      "source": [
        "#checking top five rows of each column\n",
        "\n",
        "terror_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>2</th>\n",
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              "      <td>160</td>\n",
              "      <td>Philippines</td>\n",
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              "      <td>Southeast Asia</td>\n",
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              "      <td>120.599741</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>10</td>\n",
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              "      <td>Voice of America</td>\n",
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              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>PGIS</td>\n",
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              "      <td>78</td>\n",
              "      <td>Greece</td>\n",
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              "      <td>Attica</td>\n",
              "      <td>Athens</td>\n",
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              "      <td>23.762728</td>\n",
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              "      <td>3</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>46.0</td>\n",
              "      <td>Embassy/Consulate</td>\n",
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              "      <td>U.S. Embassy</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Explosive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PGIS</td>\n",
              "      <td>-9</td>\n",
              "      <td>-9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>197001000003</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>101</td>\n",
              "      <td>Japan</td>\n",
              "      <td>4</td>\n",
              "      <td>East Asia</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>33.580412</td>\n",
              "      <td>130.396361</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>Facility/Infrastructure Attack</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>46.0</td>\n",
              "      <td>Embassy/Consulate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>U.S. Consulate</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Incendiary</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PGIS</td>\n",
              "      <td>-9</td>\n",
              "      <td>-9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 135 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        eventid  iyear  imonth  iday  ... INT_IDEO  INT_MISC INT_ANY  related\n",
              "0  197000000001   1970       7     2  ...        0         0       0      NaN\n",
              "1  197000000002   1970       0     0  ...        1         1       1      NaN\n",
              "2  197001000001   1970       1     0  ...       -9         1       1      NaN\n",
              "3  197001000002   1970       1     0  ...       -9         1       1      NaN\n",
              "4  197001000003   1970       1     0  ...       -9         1       1      NaN\n",
              "\n",
              "[5 rows x 135 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p6DbFgX7eu_F",
        "outputId": "3803d1cc-994d-4c2f-8545-be25d5b8d62c"
      },
      "source": [
        "#checking the shape of Data\r\n",
        "\r\n",
        "terror_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(181691, 135)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bg8kr0Ape1LJ",
        "outputId": "02f12a57-9377-4742-d385-86aa33a1c4c2"
      },
      "source": [
        "#checking data types\r\n",
        "\r\n",
        "terror_data.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "eventid        int64\n",
              "iyear          int64\n",
              "imonth         int64\n",
              "iday           int64\n",
              "approxdate    object\n",
              "               ...  \n",
              "INT_LOG        int64\n",
              "INT_IDEO       int64\n",
              "INT_MISC       int64\n",
              "INT_ANY        int64\n",
              "related       object\n",
              "Length: 135, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q-zwb1eze-md"
      },
      "source": [
        "renaming the columns which are important for the analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-T6y-H4Fe9Wj"
      },
      "source": [
        "terror_data.rename(columns={'iyear':'Year', 'imonth':'Month', 'iday':'Day', 'country_txt':'Country', 'region_txt':'Region', 'provstate':'State', 'city':'City',\r\n",
        "                   'attacktype1_txt':'Attack_Type', 'targtype1_txt':'Target_Type', 'motive':'Motive', 'weaptype1_txt':'Weapon_Type',\r\n",
        "                   'weapdetail':'Weapon_Details', 'nkill':'Kill', 'nwound':'Injured', 'summary':'Summary'}, inplace=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "WK4CpY8wfQrJ",
        "outputId": "eea6a4ce-4527-4524-fd34-f1605c6735a0"
      },
      "source": [
        "terror_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>eventid</th>\n",
              "      <th>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>approxdate</th>\n",
              "      <th>extended</th>\n",
              "      <th>resolution</th>\n",
              "      <th>country</th>\n",
              "      <th>Country</th>\n",
              "      <th>region</th>\n",
              "      <th>Region</th>\n",
              "      <th>State</th>\n",
              "      <th>City</th>\n",
              "      <th>latitude</th>\n",
              "      <th>longitude</th>\n",
              "      <th>specificity</th>\n",
              "      <th>vicinity</th>\n",
              "      <th>location</th>\n",
              "      <th>Summary</th>\n",
              "      <th>crit1</th>\n",
              "      <th>crit2</th>\n",
              "      <th>crit3</th>\n",
              "      <th>doubtterr</th>\n",
              "      <th>alternative</th>\n",
              "      <th>alternative_txt</th>\n",
              "      <th>multiple</th>\n",
              "      <th>success</th>\n",
              "      <th>suicide</th>\n",
              "      <th>attacktype1</th>\n",
              "      <th>Attack_Type</th>\n",
              "      <th>attacktype2</th>\n",
              "      <th>attacktype2_txt</th>\n",
              "      <th>attacktype3</th>\n",
              "      <th>attacktype3_txt</th>\n",
              "      <th>targtype1</th>\n",
              "      <th>Target_Type</th>\n",
              "      <th>targsubtype1</th>\n",
              "      <th>targsubtype1_txt</th>\n",
              "      <th>corp1</th>\n",
              "      <th>target1</th>\n",
              "      <th>...</th>\n",
              "      <th>weapsubtype4</th>\n",
              "      <th>weapsubtype4_txt</th>\n",
              "      <th>Weapon_Details</th>\n",
              "      <th>Kill</th>\n",
              "      <th>nkillus</th>\n",
              "      <th>nkillter</th>\n",
              "      <th>Injured</th>\n",
              "      <th>nwoundus</th>\n",
              "      <th>nwoundte</th>\n",
              "      <th>property</th>\n",
              "      <th>propextent</th>\n",
              "      <th>propextent_txt</th>\n",
              "      <th>propvalue</th>\n",
              "      <th>propcomment</th>\n",
              "      <th>ishostkid</th>\n",
              "      <th>nhostkid</th>\n",
              "      <th>nhostkidus</th>\n",
              "      <th>nhours</th>\n",
              "      <th>ndays</th>\n",
              "      <th>divert</th>\n",
              "      <th>kidhijcountry</th>\n",
              "      <th>ransom</th>\n",
              "      <th>ransomamt</th>\n",
              "      <th>ransomamtus</th>\n",
              "      <th>ransompaid</th>\n",
              "      <th>ransompaidus</th>\n",
              "      <th>ransomnote</th>\n",
              "      <th>hostkidoutcome</th>\n",
              "      <th>hostkidoutcome_txt</th>\n",
              "      <th>nreleased</th>\n",
              "      <th>addnotes</th>\n",
              "      <th>scite1</th>\n",
              "      <th>scite2</th>\n",
              "      <th>scite3</th>\n",
              "      <th>dbsource</th>\n",
              "      <th>INT_LOG</th>\n",
              "      <th>INT_IDEO</th>\n",
              "      <th>INT_MISC</th>\n",
              "      <th>INT_ANY</th>\n",
              "      <th>related</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>197000000001</td>\n",
              "      <td>1970</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>58</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>2</td>\n",
              "      <td>Central America &amp; Caribbean</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Santo Domingo</td>\n",
              "      <td>18.456792</td>\n",
              "      <td>-69.951164</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14</td>\n",
              "      <td>Private Citizens &amp; Property</td>\n",
              "      <td>68.0</td>\n",
              "      <td>Named Civilian</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Julio Guzman</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PGIS</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>197000000002</td>\n",
              "      <td>1970</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>130</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1</td>\n",
              "      <td>North America</td>\n",
              "      <td>Federal</td>\n",
              "      <td>Mexico city</td>\n",
              "      <td>19.371887</td>\n",
              "      <td>-99.086624</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>Hostage Taking (Kidnapping)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>45.0</td>\n",
              "      <td>Diplomatic Personnel (outside of embassy, cons...</td>\n",
              "      <td>Belgian Ambassador Daughter</td>\n",
              "      <td>Nadine Chaval, daughter</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1.0</td>\n",
              "      <td>800000.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PGIS</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>197001000001</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>160</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>5</td>\n",
              "      <td>Southeast Asia</td>\n",
              "      <td>Tarlac</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>15.478598</td>\n",
              "      <td>120.599741</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10</td>\n",
              "      <td>Journalists &amp; Media</td>\n",
              "      <td>54.0</td>\n",
              "      <td>Radio Journalist/Staff/Facility</td>\n",
              "      <td>Voice of America</td>\n",
              "      <td>Employee</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PGIS</td>\n",
              "      <td>-9</td>\n",
              "      <td>-9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>197001000002</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>78</td>\n",
              "      <td>Greece</td>\n",
              "      <td>8</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>Attica</td>\n",
              "      <td>Athens</td>\n",
              "      <td>37.997490</td>\n",
              "      <td>23.762728</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Bombing/Explosion</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>46.0</td>\n",
              "      <td>Embassy/Consulate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>U.S. Embassy</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Explosive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PGIS</td>\n",
              "      <td>-9</td>\n",
              "      <td>-9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>197001000003</td>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>101</td>\n",
              "      <td>Japan</td>\n",
              "      <td>4</td>\n",
              "      <td>East Asia</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>33.580412</td>\n",
              "      <td>130.396361</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>-9.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>7</td>\n",
              "      <td>Facility/Infrastructure Attack</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>7</td>\n",
              "      <td>Government (Diplomatic)</td>\n",
              "      <td>46.0</td>\n",
              "      <td>Embassy/Consulate</td>\n",
              "      <td>NaN</td>\n",
              "      <td>U.S. Consulate</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Incendiary</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>PGIS</td>\n",
              "      <td>-9</td>\n",
              "      <td>-9</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 135 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        eventid  Year  Month  Day  ... INT_IDEO  INT_MISC INT_ANY  related\n",
              "0  197000000001  1970      7    2  ...        0         0       0      NaN\n",
              "1  197000000002  1970      0    0  ...        1         1       1      NaN\n",
              "2  197001000001  1970      1    0  ...       -9         1       1      NaN\n",
              "3  197001000002  1970      1    0  ...       -9         1       1      NaN\n",
              "4  197001000003  1970      1    0  ...       -9         1       1      NaN\n",
              "\n",
              "[5 rows x 135 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dDhxcanRfV8r"
      },
      "source": [
        "loading the important columns for analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B5cR53Prfahr"
      },
      "source": [
        "terror_data = terror_data[['Year','Month','Day','Country','Region','State','City','Attack_Type','Motive','Weapon_Type','Weapon_Details','Kill','Injured','Summary']]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "yj5IpWnLgMQx",
        "outputId": "66a37c99-e6fa-41a2-9132-a4044559507f"
      },
      "source": [
        "terror_data.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Year</th>\n",
              "      <th>Month</th>\n",
              "      <th>Day</th>\n",
              "      <th>Country</th>\n",
              "      <th>Region</th>\n",
              "      <th>State</th>\n",
              "      <th>City</th>\n",
              "      <th>Attack_Type</th>\n",
              "      <th>Motive</th>\n",
              "      <th>Weapon_Type</th>\n",
              "      <th>Weapon_Details</th>\n",
              "      <th>Kill</th>\n",
              "      <th>Injured</th>\n",
              "      <th>Summary</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1970</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>Dominican Republic</td>\n",
              "      <td>Central America &amp; Caribbean</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Santo Domingo</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1970</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>North America</td>\n",
              "      <td>Federal</td>\n",
              "      <td>Mexico city</td>\n",
              "      <td>Hostage Taking (Kidnapping)</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Philippines</td>\n",
              "      <td>Southeast Asia</td>\n",
              "      <td>Tarlac</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>Assassination</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Unknown</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Greece</td>\n",
              "      <td>Western Europe</td>\n",
              "      <td>Attica</td>\n",
              "      <td>Athens</td>\n",
              "      <td>Bombing/Explosion</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Explosives</td>\n",
              "      <td>Explosive</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1970</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>Japan</td>\n",
              "      <td>East Asia</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>Fukouka</td>\n",
              "      <td>Facility/Infrastructure Attack</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Incendiary</td>\n",
              "      <td>Incendiary</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Month  Day             Country  ... Weapon_Details Kill Injured Summary\n",
              "0  1970      7    2  Dominican Republic  ...            NaN  1.0     0.0     NaN\n",
              "1  1970      0    0              Mexico  ...            NaN  0.0     0.0     NaN\n",
              "2  1970      1    0         Philippines  ...            NaN  1.0     0.0     NaN\n",
              "3  1970      1    0              Greece  ...      Explosive  NaN     NaN     NaN\n",
              "4  1970      1    0               Japan  ...     Incendiary  NaN     NaN     NaN\n",
              "\n",
              "[5 rows x 14 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBX04Tp3gU5y",
        "outputId": "8b475a8e-8058-4254-8c69-ad53db719995"
      },
      "source": [
        "#after the renaming and redicting the data checking the shape of data\r\n",
        "\r\n",
        "terror_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(181691, 14)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5VDYoiPBgkEh",
        "outputId": "892ee0fb-1190-4cc0-e462-3b73e7de5211"
      },
      "source": [
        "#checing the null values\r\n",
        "\r\n",
        "terror_data.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year                   0\n",
              "Month                  0\n",
              "Day                    0\n",
              "Country                0\n",
              "Region                 0\n",
              "State                421\n",
              "City                 434\n",
              "Attack_Type            0\n",
              "Motive            131130\n",
              "Weapon_Type            0\n",
              "Weapon_Details     67670\n",
              "Kill               10313\n",
              "Injured            16311\n",
              "Summary            66129\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "faEzRZeag0bF"
      },
      "source": [
        "checing HOT ZONE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxmr35j5nJrz",
        "outputId": "89a4f86c-7cf8-46f0-caad-6c771b617c25"
      },
      "source": [
        "print(\"Hot Zone for terrorism \")\r\n",
        "terror_data['Country'].value_counts().head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Hot Zone for terrorism \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Iraq           24636\n",
              "Pakistan       14368\n",
              "Afghanistan    12731\n",
              "India          11960\n",
              "Colombia        8306\n",
              "Name: Country, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gob4KNDgnnIc"
      },
      "source": [
        "we can see IRAQ has been 24636 times under Terrorist Attacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "d44R8V9Yn1Bu",
        "outputId": "133074de-fb19-4916-e4c7-d893bb2fb365"
      },
      "source": [
        "#plotting the countplot Country vs Count of Terrorist Attacks on County\r\n",
        "\r\n",
        "plt.figure(figsize=(30,12))\r\n",
        "sns.countplot('Country',data=terror_data)\r\n",
        "plt.title('Hot Zone for Terrorism', fontsize=69)\r\n",
        "plt.xticks(rotation=90)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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VXHs2Z9aaVNWdM/S6WMmpSY7q7jestdzu/ucMw8v9YIXN50vyh2stcyeqqotn6H0x5jnd/Vvd/d3VltndJ2bolfbFkV0ukOTPVt/KNXlcdz+hu8/ZpPJ3rKo6f4YAdMyDuvt39hV+7G32AMKtkoxdVx8zm1dp0Zb5Wf99kgd29/fWW8Au/D2x4fdkkarqsAy9tFdyTHd/YqN1dPebuvvO3f2OjZa1ly9k+M796Bra8pEkD5+zyyszfJf9yAO7e9XHQnf/IMm9Mwy7uZJFD7MLALue0AwAGPPEqurNXJIcvcD23i/DnBMr+a8MPZbmPo2/ku4+PckdMwxxs5IbVdVt1loueVySu8zZ/nfd/Q+LrrSqLpohMBpz7+7+4HrK7u7HZphDaiX7ZRh+aZG+meHG7aqfxv+R2c3rh83ZZV4Pi58wC0HH5k35TpK7dPfJaylzT93970keP7L5V6tqrHfbbvKIjM//864kv7GeQrv7lAzn4ligcY+qmhdur8fru3uRvS93m4fkJ3sK/ciTu3teT9G5uvusJL+W4fqxtwvN6l6kZX7WH8nwsMXcnmyrsJt+TyzqPVmkK2ble1Gf7O43Lbsxa3S/2TVzrZ6fYdjIlew5T9rzuvvZay18dmyNBb0/U1U/tdYyAWDKhGYAwG7xW3O2Pby7V7pBuCqzXjx/vM662UtV3SHJsXN2eWfmD0u2EffN0HtmJa/t7tdtsPzfyTDXyUruseBeHI+fDc20Lt399iQfH9l8nTUWd+ckR45sO7a7x4aEW4unZLgBvLfzZLEB/LZTVQckecDI5nMy3BRf9zBksx6dY0HMftnYkJ17+27mB9eTNvusHzmy+SNZQPg+G3pvrIfm/Wch+CIs+7P+jVmvm43aTb8nFvWeLNIlR9avuvfWFnnhenpLJ//dw/sl+9jtG9nYb58Xz9m21u90AJg0oRkAsONV1ZEZ5mNZyX9296sWUM1TM8y1sZI7z+bJYh+q6gpJ/jHjv0O/nOTu63mKf5XG5vboJL+70cJnvan+amTzgUl+daN1zHw9ybMWUM4rRtZfd43lPHBk/akZfz/WZNY77i9GNi/qfd2ubp9hLruVvGhBoeTvZ3zuuF+vqkX9t+Or1tlTYyrunOQSI9t+f4FDHD4zK/c2OyLJDRZUxzI/63f0MMfkhuyy3xMLeU82wUEj6xc1/9hmGfv+Wa3j97H9uH3NkTbP7PfH2IMwax5yGQCmTGgGAOwGd5yz7TmLqGAW4rxwZPOBGSZ6Z47ZPD2vSnLhkV3OTnKP7j51k+q/eMbDoP/s7k8tqKp5QyvNO1bX4h830tthDx8YWX+5qhrrkfdjqupCGeYcW8kzu3ssiFmPf0yyUqB61aoaG85uN1jGNe5rGR9e9JJZ3E3XhbR3F7vHyPrPdPfY57Nms5vzrx7ZfNsFVbPMz3pRde2m3xPb9Vw7fWT99RfYy3HRPtTdY/OArtaJGR7QGbPmYRlXsFJv7CTZjDlVAWDXEpoBALvBrUbWfzfJyxdYz/PnbBOa7duzMv4Ef5I8cjZ31Wa5ZZKxG3IvWFQl3f3xJGM3126xoB47/7aAMpL5w2GttrfDzyU5YGTbK9fWnPlmAdz7RjbfZJF1bTNj17hTk7xlgfVs9jXuu1lse3eV2bVhLLBaRA+nvb1tZP0izqVlf9b76sWzWrvp98Si3pNFO2Vk/U9lnXMzLsGGf5t097eSfGFk85lJPrTROpJ8YmT92MNKAMAK9t/qBgAA29ZLMn9+hEX47YzfoFqLsbka3ruRoW721t0fraovZeX5OMwXMUdVPTzJr83Z5YXd/bRNbsa8z+hNC67rX5MctcL6g5NcMclGe7W9Y4Ov/5Gvzdl2cIZQZl/GbrB/vrvHnnrfiHcnudEK66+V5KWbUN+WqqqDklxlZPNbFzhcXzKEHOdk5YcrF3GN+2B3n72AcnarqyUZm/fwXzahvnePrJ/3cMNqLfOz/vICeyjvlt8Ti3xPFqq7P1FVX0xyqRU2P2PWy/lpG5mncRMsapjLryc5fIX1757Ne7aI8ldyyALKBoDJEJoBAGM+3t1jQzctRFXddQFlXCTjk8q/f6Plj5S50vBNR25CXbtCVd0iyZPn7PKBLOfp8muMrD8ryacXXNe8Y+/IbCw066wuzFqNs+ZsW+1NtrEbvCeusS2r9ZWR9VfcpPq22tUyPkLIQq9x3f3tqvrErM69LeIatxnX5N1kXliyGefT2Ll0eFWdt7u/t4Gyl/lZL6SuXfZ7Yrufay9O8vAV1u+fYc63h1bVM5O8ZJvMgTg2B91ajQ2rvKjv9LHyD15Q+QAwCYZnBAB2usvO2TY2X9NGjJV58GxuJ/Ywm2fqpRl/WOv0JHdb8LxXY8aOlQ8u6AnvPc079jY699aZi+pdtI/3/byrLObKI+s/ucbmrNbYfDiX3qT6ttp2ucYtYs64Ly+gjN1s7Fw6q7u/uAn1jZ1LycbPp2V+1ouqa7uca4v4PbHdz7WnJPnWnO1XSPKkJCdX1Qer6qlVdZequthymvcTzlhQOd/dovJX+30OAERPMwBg51tpeJ8f+dIm1DfvxuWlMr/nzqRU1YEZ5oC5+Mgu5yS5V3efvKQmjR0rW3GcbMQ3Nvj61Rqb/+3cHYY5mMb+nodW1UMX26S5duucLdvlGneRBfQ+Wtaxu1ONBZMXqqpFB/v7cuEkG7k2L/OzXlRd2+VcSzb+e2Jbn2vd/cWqun+GocD35Vqz5eFJUlUnZZiP781J3tLdi+oFNs9m/7bb7PL3+X0OAJxLTzMAYKeb9zT2Ztw0mjeniZ5mP+7pSW44Z/vju/v1y2pMxj+fzThOvpkhFFxLO1Zr2TfP5zk4yQFb3YiZ8211AzbJdrrGXXCDZW/rG/nbwEW3ugF72Oj5tBNDs+10rm30e2Lbn2vd/dIkD0iy1iD+SkmOSfK8JJ+b9UR7VFXN6ym4UZv9vbudvtcBYPKEZgDATjdvyJll3+TarTft16yqHpj585S9OsmfLqk5PzJ2rCz8OJkN9zhW7m46Tg7a6gbsYbuEd4u2m65xZ2/w9bvdbjqflvlZL6ou59qSdfezk9w0yX9uoJhrJfmzDEM5HldVV1hI4wCAyRKaAQCwUFV1gwy9zMZ8PMl9N2EeMZZvOw33bvgpdjrnE5PT3e/t7psl+YUkr03yg3UWdZ4kRyf5eFU9YlHtAwCmR2gGAOx0Y5OeJ8PQcYt2yJxt89oyCVV1WJJXZPyJ/bOS/FJ3b8Xcb2Ofz8KPk6qqOeXupuNkN/0t25Vr3HR4f7eWc20Ldffx3X2XJJdOcp8kz07ymXUUdUCSP6+q5y6yfQDAdGynJ9kAANZjXviy7JtcWxEEbRtVtX+Slya5zMguneTo7v748lr1Y85KcoEV1m/GcXLBjD+gtpuOk2/O2faKJC9YVkOSfHuJdS2Ta9x0jJ1PZyS5/zIbkuQjS65vO3CubQPd/dUM3x0vSJKqOjzJz+6xHJnV9YS8b1Wd1N1/tFltBQB2J6EZALDTfWnOtktuQn3zyvziJtS3kzw5yS3nbH9Sd79qSW1ZyZey8ufnOFmn7v5OVX09yaErbP5Kd7962W3ahfZ1jfvogusbO3ZP7+7vL7guftwXRtafz7m0FH5PbEPdfWqSF8+WVNUlktwxyd2T3CHDsIxjHldVz+3uz256QwGAXcPwjADATjfvRsi1N6G+sTLP3KIhB7eFqrpnkofP2eX1SR63pOaMGTtWrjUbTnGR5h17n1twXVvt5JH1F11qK3av7XKN223H7XY0di4dVFUHLbUl07RdzrVJ/57Yl+7+cncf1913SnJEkr/J0JN9JQcmMb8ZALAmQjMAYEfr7jMy/kT2Ztzkus7I+ikOZZUkqaprJnnWnF1OTnKv7j5nSU0aM9Yj5+AkV1hwXWPHSbL7jpUPjKy/8lJbsXt9PMnYubPQa9wsmLnKyObddtxuR2PnUuJ82nR+T+w83f357n5wkqPn7HbHZbUHANgdhGYAwG7w/pH1R1XVwuYhqaqrJrnUGtuwkrEnohfd22nTVdWhSV6VlecKS5LvJLlbd5++vFaNmvcZ3XrBdY2V940kn15wXVvtXSPrr11VepttUHd/J0NwtpJbLriX5C0yPtTZWq5xrM97k5w9su02y2zIhO203xMk6e7nJ3n5yOaf9l0EAKyF0AwA2A3eMrL+fEl+eYH13GfOtjevoZyxm6LnW0MZW252s/4FSa44Z7cHdfe83hPL9NaMB5b3XlQlVXXlJDcca0N3j7Vhp/rnrPy+VpLbLbktu9XYNe4ymT+P4Fot6hrHOnT3tzJcp1ZyhyU2Zcp22u8JzvWCOdsusbRWAAA7ntAMANgNjp+z7ZhFVFBVB2Q8WPle1naT61sj6y+4pkZtvWOT/MKc7U/r7nk3sZaqu09LcsLI5p+tqkUN0Xi/OdvmHas7Und/Psk7Rzb/7jLbsost4xp34SR3Hdn8xcwfOpDFednI+ttW1WYMEciP22m/JzjXp+Zsu/DSWgEA7HhCMwBgx+vuEzN+Q/fmVXWnBVTz20kuN7Ltn7r7zDWUddrI+suvrUlbp6runOTxc3b5jySPXFJz1uKFI+sryZM3WnhVXTbJw0Y2fz/JSzdaxzb1NyPrr1tVd19qS3anN2T8uvHrVfUzC6jjD5Ocf2TbC3dhD8nt6h+TrPR9Ukn+eMltmZwd+HuCc513zrbvLK0VAMCOJzQDAHaLsZv2SfIXVXXQeguuqksnecI6617JKSPrr1hV27632Wz4wednfA62LyT5le4eG4ZyKz0/4z39frmqNjqc4P/NePDwku4+Y4Plb1cvSXLyyLZnzubvYZ1m59KzRjafJ8nTq2rd/20368H0WyObz0nyzPWWzdp09zeTPH1k8y9U1aOX2Z6J2km/JzjX9eZsO2VZjQAAdj6hGQCwWzw3yakj266Y5KVVtf9aC62qQzIM13TwyC7v6O61DqX04ZH1Bya52xrLWqpZqPeqJIeM7PL9JHfv7i8vr1W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\n",
            "text/plain": [
              "<Figure size 2160x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eI4-KoYPpQ7I"
      },
      "source": [
        "plotting the barplot to find Top 10 Countries having Hot Zones"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "t8f-YP0QpPh6",
        "outputId": "8a4b0c4d-33a4-4a34-8b90-4f9ced2434fa"
      },
      "source": [
        "plt.figure(figsize=(15,6))\r\n",
        "sns.barplot(terror_data['Country'].value_counts()[:10].index, terror_data['Country'].value_counts()[:10].values)\r\n",
        "plt.xlabel('Top 10 Countries under Terrorism', fontsize=15)\r\n",
        "plt.ylabel('Terrorism attack Count', fontsize=15)\r\n",
        "plt.title('Top 10 Countries under Terrorism attack ', fontsize=39)\r\n",
        "plt.xticks(rotation=90)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jI_FVMGdqGER"
      },
      "source": [
        "from above barplot we can see that Iraq is the Hot Zone of Terrorist Attacks\r\n",
        "\r\n",
        "After Iraq, Pakistan and Afganistan, India is The Hot Zone of Terrorists."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g0yX43ijqf2r",
        "outputId": "8d5b1467-8346-4d83-c20c-bb0bd9591900"
      },
      "source": [
        "print(\"Country with less terrorism \")\r\n",
        "terror_data['Country'].value_counts().tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Country with less terrorism \n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Antigua and Barbuda    1\n",
              "South Vietnam          1\n",
              "St. Lucia              1\n",
              "Andorra                1\n",
              "North Korea            1\n",
              "Name: Country, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Anhxg-7Hqlsd"
      },
      "source": [
        "form above results we can see Countries having least terrorists attacks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 278
        },
        "id": "KpO8yIdlq5vf",
        "outputId": "c9d697f8-fd48-4d9e-844b-e13bd60988d3"
      },
      "source": [
        "#Number of People Killed and Injured according to the Country\r\n",
        "\r\n",
        "country1 = terror_data.groupby(['Country'])['Country', 'Kill', 'Injured'].sum()\r\n",
        "country1.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Kill</th>\n",
              "      <th>Injured</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>39384.0</td>\n",
              "      <td>44277.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>42.0</td>\n",
              "      <td>126.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>11066.0</td>\n",
              "      <td>9150.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>3043.0</td>\n",
              "      <td>2455.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                Kill  Injured\n",
              "Country                      \n",
              "Afghanistan  39384.0  44277.0\n",
              "Albania         42.0    126.0\n",
              "Algeria      11066.0   9150.0\n",
              "Andorra          0.0      0.0\n",
              "Angola        3043.0   2455.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j8qSvpkArIAT"
      },
      "source": [
        "from above results we can see in Afgainstan more people are killed and being injured."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 384
        },
        "id": "CS0FDgLRrS57",
        "outputId": "f054b3fd-b2ca-45f2-f2f7-ca1b6ad2e1fa"
      },
      "source": [
        "print('Amount of pepople Killed and Injured per year')\r\n",
        "year1 = terror_data.groupby(['Year'])['Kill', 'Injured'].sum()\r\n",
        "year1.head(8)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Amount of pepople Killed and Injured per year\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Kill</th>\n",
              "      <th>Injured</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1970</th>\n",
              "      <td>174.0</td>\n",
              "      <td>212.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1971</th>\n",
              "      <td>173.0</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1972</th>\n",
              "      <td>566.0</td>\n",
              "      <td>409.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1973</th>\n",
              "      <td>370.0</td>\n",
              "      <td>495.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1974</th>\n",
              "      <td>539.0</td>\n",
              "      <td>865.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1975</th>\n",
              "      <td>617.0</td>\n",
              "      <td>617.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1976</th>\n",
              "      <td>674.0</td>\n",
              "      <td>756.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1977</th>\n",
              "      <td>456.0</td>\n",
              "      <td>518.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Kill  Injured\n",
              "Year                \n",
              "1970  174.0    212.0\n",
              "1971  173.0     82.0\n",
              "1972  566.0    409.0\n",
              "1973  370.0    495.0\n",
              "1974  539.0    865.0\n",
              "1975  617.0    617.0\n",
              "1976  674.0    756.0\n",
              "1977  456.0    518.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "qBbuGIlTrbcm",
        "outputId": "cdee7843-729a-49cf-c14d-74d5b147b773"
      },
      "source": [
        "print('Amount of pepople Killed and Injured per year')\r\n",
        "year2 = terror_data.groupby(['Year'])['Kill', 'Injured'].sum()\r\n",
        "year2.tail(10)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Amount of pepople Killed and Injured per year\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Kill</th>\n",
              "      <th>Injured</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2008</th>\n",
              "      <td>9157.0</td>\n",
              "      <td>18998.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2009</th>\n",
              "      <td>9273.0</td>\n",
              "      <td>19138.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010</th>\n",
              "      <td>7827.0</td>\n",
              "      <td>15947.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>8246.0</td>\n",
              "      <td>14659.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>15497.0</td>\n",
              "      <td>25445.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>22273.0</td>\n",
              "      <td>37688.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>44490.0</td>\n",
              "      <td>41128.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>38853.0</td>\n",
              "      <td>44043.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>34871.0</td>\n",
              "      <td>40001.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>26445.0</td>\n",
              "      <td>24927.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Kill  Injured\n",
              "Year                  \n",
              "2008   9157.0  18998.0\n",
              "2009   9273.0  19138.0\n",
              "2010   7827.0  15947.0\n",
              "2011   8246.0  14659.0\n",
              "2012  15497.0  25445.0\n",
              "2013  22273.0  37688.0\n",
              "2014  44490.0  41128.0\n",
              "2015  38853.0  44043.0\n",
              "2016  34871.0  40001.0\n",
              "2017  26445.0  24927.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwqqJniire4C"
      },
      "source": [
        "by observing the above, we can see no. of people are being killed and injured per year and also we can see that this rate is increasing by year."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jN-D-iKxr1-j",
        "outputId": "77428710-5a9c-4949-d96f-b3fdae15cb4b"
      },
      "source": [
        "#Terrorist activity per Year\r\n",
        "\r\n",
        "year3 = terror_data['Year'].value_counts()\r\n",
        "year3.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2014    16903\n",
              "2015    14965\n",
              "2016    13587\n",
              "2013    12036\n",
              "2017    10900\n",
              "Name: Year, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p1uAxb04sIYZ"
      },
      "source": [
        "**Count of Terrorism per Year**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "fnyjfi34r__v",
        "outputId": "90eea400-932d-4d38-e4f7-f43df4031b1b"
      },
      "source": [
        "plt.figure(figsize=(15,6))\r\n",
        "sns.countplot('Year', data=terror_data)\r\n",
        "plt.xlabel('Year of Terrorism attack',fontsize=15)\r\n",
        "plt.ylabel('Terrorism attack Count',fontsize=15)\r\n",
        "plt.title('Count of Terrorism attack per Year ', fontsize=25)\r\n",
        "plt.xticks(rotation=90)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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9PQHtVG8G1WmvHWOhmuik8ccg4Cflb8/HYpmEZmeq5RueClwQEVuP3Kor23Sx7zEe/7xb1+H9Q6cfwIZNHz4TlnYsBtVyN5KGiAmjpNH4KNV09ysDZy/t1LuIWD0izqL6NZ8ymcfpZfe/RMQTfkmOiPWo1qCDaibDut9RrRMGsFtD2+WAg7t7Kj25r/xdrdcOMvNqqmU1oDotblp7nYjYiSVf4Ntfg4n2tfJ374gY8VTkiBiPSYLqTit/d4+ILRoefxWWnO52fma2X381JksZEYYlx2bT7LuD1Hodntth/79TLd/QSTfH/X21253qtdYXbYwlIpYHjhjhMU4ofzePiHePUG9Emfkg1VkS36V63t+PiFf02l/xsoiY015YEsH3l7sXZOY9td0nUp01sSbV+psdRcT0cnwPg7F8JiztWHwX8HdjiE3SODBhlNS1zPwd1SyLD1OdevfLiDgoIp7RqhMR0yLi+RFxONUi67u2dfNJquUJ1gD+NyJeWmu7NdWEGKtRjUQe2fb4j7BklPPDEbFHmXGRiHgm8C36vORAm9ZSDk+NiD1GrDmy1sQbLwfOjIiNoTplLyLezJIk8ScsmVJ/UD4H/Jpq1OfiiNgvIv62xEpErBYRO0bEKcD/G+dYjqUapV4B+F553OVKHM8BLqC6vvAhqh83+u2giPheRLwlIv526mtErFiOhw+Uou+Ow2OPRWuZgndGxD61fzPrRMRRVEn23R1bLznud4+IxhlaSyLUGnl6W0n+2uv8mSWjap+PiFfV3r8tgPOpZgVunEk2My9myY8GX46IT7W9D2tGxDsi4oSm9m19PUT12XQe1SQr50fESKOES3MvcFZE7N567hHxLKpj4VlUp1Mf0hbD76nWtwX4YEScUv8hJCKWj4jnRcQhVEuqtC8zMihj+UxoHYs7RsS/t5anKW0+THU690jHoqRB6GaxRjc3N7f6RjUd/A0sWUA5qb6k3031xahV9hjVjJIrtLV/JVXS2Kr3Zx6/cPki4OUdHnsm1RfTVt2Hqb6sJdUIxitr++a0tZ3T2reU59fYvuz739r++6gW7p4PHDDK1/DA8vrUn/NDtftXA+t1aDufpSykPspY5izlOa9HtU5g/X1dVHvdW9sNo3kta3Vm1erNWkqsW1DNhNqq/9e2OB4Edh/t+9pW77BS75IO5a3tgXLM19/H64B1uumvrc5Jpc5JI9S5pNQ5bJTv72rAb2oxtmalbMX91ZEeH3hFrW5rQff5wPy2eh9tex/+UOqdVqvzQh7/b/1Blow8PkL1g1TH45tqxs2z2t6He3n858kv29rsVcrnN/S3Qq2/B4BtR/natmI9EPht7TnV43kMeGeH9kE1EVH9GHqAatbYxW3Pc+vRHjM9fh7Mqj3mE96DsXwmlNf70rZ2reuTk2rJlI/T4d8LPf4bcHNzG9vmCKOkUcvMH1P9ar4n8N9Uv34/SHV610Lg/6hOLXt2Zv5Tti0zkJk/olq8/nNUX2SXo/ri9BvgP0q7xtGqrBZcfzFwPEtGNP4MnAK8oPQ9nnanmsXvd1RffjYq26hOU83Mo6hGU/4LuIXqi/BfgZ9Rffl8UWb2snRJ35U4Xkb1fp8H3EYV73SqL8zfBg6gSizGO5ZrqEa3D6NaK24xsCLwe6rEZ/PMPLNjB2NzHNUi5qdSjbo9QHUd3CKqkZQDqI7BkdbUm3BZjf69lGpR9vlUX84XU3353jMz37WU9pcCr6H6seQeqjUcW8d93SeB/amu032E6sedjahNYpKZP6daX/QMqqRoOaqlRM4AXpqZ31hKLA9k5m7Aa6nOKPgj1UjXYqofWY7miQvNj9TfI8A/At+kOtX+OxGxXbftaxZRPa8jqRLlFak+C79Nleh9ralRVg6hOjPiGKrPwEepTuNfRHWWwWepXpuhWXuw18+E8npvR3UK7u+ojpOgWuP03cDrcQZUaehEZg46BkmSpEknIuZTJcVvy85LykjSpOYIoyRJkiSpkQmjJEmSJKmRCaMkSZIkqZEJoyRJkiSpkZPeSJIkSZIaPWFh3WXRmmuumbNmzRp0GJIkSZI0ED//+c//lJkz2stNGIFZs2Zx5ZVXDjoMSZIkSRqIiLi5qdxrGCVJkiRJjUwYJUmSJEmNTBglSZIkSY1MGCVJkiRJjUwYJUmSJEmNTBglSZIkSY1MGCVJkiRJjUwYJUmSJEmNTBglSZIkSY1MGCVJkiRJjUwYJUmSJEmNTBglSZIkSY1MGCVJkiRJjUwYJUmSJEmNlh90AJIkSZI0Ue744k97arf2/i/pcySTgyOMkiRJkqRGJoySJEmSpEYmjJIkSZKkRiaMkiRJkqRGJoySJEmSpEYmjJIkSZKkRiaMkiRJkqRGJoySJEmSpEYmjJIkSZKkRiaMkiRJkqRGJoySJEmSpEYTmjBGxIkRcWdEXNNW/t6I+G1EXBsRn6mVHxwR8yLi+ojYvla+QymbFxEfqpVvHBGXlfLTI2L6xDwzSZIkSZp6JnqE8SRgh3pBRGwD7Aw8NzM3B/6jlG8GvAnYvLQ5JiKmRcQ04CvAjsBmwJ6lLsCngaMy8xnAImDvcX9GkiRJkjRFTWjCmJmXAgvbit8NHJmZD5U6d5bynYHTMvOhzLwJmAdsWbZ5mXljZj4MnAbsHBEBvAo4s7Q/GdhlXJ+QJEmSJE1hw3AN46bAy8uppD+KiBeV8vWBW2r1bi1lncqfBtyTmYvbyiVJkiRJPVh+0AFQxbAGsBXwIuCMiPi78X7QiNgH2Adgww03HO+HkyRJkqRJZxhGGG8Fzs7K5cBjwJrAAmCDWr2ZpaxT+d3AahGxfFt5o8w8LjNnZ+bsGTNm9O3JSJIkSdJUMQwJ4znANgARsSkwHfgTcB7wpohYMSI2BjYBLgeuADYpM6JOp5oY57zMTOBiYPfS71zg3Al9JpIkSZI0hUzoKakRcSowB1gzIm4FDgVOBE4sS208DMwtyd+1EXEGcB2wGNg3Mx8t/ewHXABMA07MzGvLQxwEnBYRnwCuAk6YsCcnSZIkSVPMhCaMmblnh13/3KH+EcARDeXnA+c3lN9INYuqJEmSJGmMhuGUVEmSJEnSEDJhlCRJkiQ1MmGUJEmSJDUyYZQkSZIkNTJhlCRJkiQ1MmGUJEmSJDUyYZQkSZIkNTJhlCRJkiQ1MmGUJEmSJDUyYZQkSZIkNTJhlCRJkiQ1MmGUJEmSJDUyYZQkSZIkNTJhlCRJkiQ1Wn7QAUiSJEnSZHPnly7uqd1a792mz5GML0cYJUmSJEmNTBglSZIkSY1MGCVJkiRJjUwYJUmSJEmNTBglSZIkSY1MGCVJkiRJjUwYJUmSJEmNTBglSZIkSY1MGCVJkiRJjUwYJUmSJEmNTBglSZIkSY1MGCVJkiRJjUwYJUmSJEmNJjRhjIgTI+LOiLimYd/7IyIjYs1yPyLi6IiYFxFXR8QLanXnRsQNZZtbK39hRPy6tDk6ImJinpkkSZIkTT0TPcJ4ErBDe2FEbABsB/yhVrwjsEnZ9gGOLXXXAA4FXgxsCRwaEauXNscC76y1e8JjSZIkSZK6M6EJY2ZeCixs2HUU8EEga2U7A6dk5WfAahGxLrA9cGFmLszMRcCFwA5l31Mz82eZmcApwC7j+XwkSZIkaSob+DWMEbEzsCAzf9W2a33gltr9W0vZSOW3NpRLkiRJknqw/CAfPCKeBHyY6nTUiX7sfahOdWXDDTec6IeXJEmSpKE36BHGpwMbA7+KiPnATOAXEbEOsADYoFZ3ZikbqXxmQ3mjzDwuM2dn5uwZM2b04alIkiRJ0tQy0IQxM3+dmWtl5qzMnEV1GukLMvN24DzgrWW21K2AezPzNuACYLuIWL1MdrMdcEHZd19EbFVmR30rcO5AnpgkSZIkTQETvazGqcBPgWdGxK0RsfcI1c8HbgTmAV8D3gOQmQuBjwNXlO3wUkapc3xp83vge+PxPCRJkiRpWTCh1zBm5p5L2T+rdjuBfTvUOxE4saH8SmCLsUUpSZIkSYLBX8MoSZIkSRpSJoySJEmSpEYmjJIkSZKkRgNdh1GSJEmSlmV3frm3eTrX2m/HPkfSrKsRxoh4RUSs0mHfKhHxiv6GJUmSJEkatG5PSb0Y2KzDvmeW/ZIkSZKkKaTbhDFG2LcK8EAfYpEkSZIkDZGO1zCW00zn1IreERE7tFVbCXgN8Ov+hyZJkiRJGqSRJr15MfDecjuBNwKL2+o8DPwW+ED/Q5MkSZIkDVLHhDEzPwt8FiAibgJ2ycxfTVRgkiRJkqTB6mpZjczceLwDkSRJkiQNl67XYYyIlYBXADOprl2sy8w8tp+BSZIkSZIGq6uEMSJeBpwFzOhQJQETRkmSJEmaQrpdVuNo4Ebg+cCKmblc2zZt/EKUJEmSJA1Ct6ekPhPY1UlvJEmSJGnZ0e0I49XAOuMZiCRJkiRpuHSbML4bODAiXjmewUiSJEmShke3p6ReCDwJ+GFEPAzc314hM9fqZ2CSJEmSpMHqNmH8CtVMqJIkSZKkZURXCWNmHjbOcUiSJEmShky31zBKkiRJkpYxXY0wRsQVLOWU1Mzcsi8RSZIkSZKGQrfXMF7LExPG1YGXAn8FLupnUJIkSZKkwev2Gsa9msojYhXgPOAnfYxJkiRJkjQExnQNY2b+Gfgc8JH+hCNJkiRJGhb9mPRmNarTUyVJkiRJU0i3k97s1FA8HXg2cCBwcT+DkiRJkiQNXreT3nyHatKbaCt/BDgX2K+fQUmSJEmSBq/bU1I3Bv6u/G1t6wIrZeYemXlnN51ExIkRcWdEXFMr+2xE/DYiro6Ib0XEarV9B0fEvIi4PiK2r5XvUMrmRcSHauUbR8Rlpfz0iJje5fOTJEmSJLXpKmHMzJsbtjsyc8S1GRucBOzQVnYhsEVm/j3wO+BggIjYDHgTsHlpc0xETIuIacBXgB2BzYA9S12ATwNHZeYzgEXA3qOMT5IkSZJUdD3pTUSsFhEHRcS3I+LH5e8H6yOCS5OZlwIL28p+kJmLy92fATPL7Z2B0zLzocy8CZgHbFm2eZl5Y2Y+DJwG7BwRAbwKOLO0PxnYpdvYJEmSJEmP1+2kN08HLgHWAn4M/AFYGzgc2C8itsnM3/chnrcDp5fb61MlkC23ljKAW9rKXww8DbinlnzW60uSJEmaxO446pc9t137wOf1MZJlS7eT3hwF3ANslZkLWoURsT5wPvB5qhHBnkXER4DFwH+PpZ9RPN4+wD4AG2644UQ8pCRJkiRNKt2ekjoHOKSeLAKU+4cD24wliIjYC3gt8ObadZELgA1q1WaWsk7ldwOrRcTybeWNMvO4zJydmbNnzJgxlvAlSZIkaUrqNmFMYNoIfYx28pu/iYgdgA8Cr8/MB2q7zgPeFBErRsTGwCbA5cAVwCZlRtTpVBPjnFcSzYuB3Uv7uVRLfkiSJEmSetBtwngx8PGI2KheWO4fDlzUTScRcSrwU+CZEXFrROwNfBl4CnBhRPwyIr4KkJnXAmcA1wHfB/bNzEfLNYr7ARcAvwHOKHUBDgL+NSLmUV3TeEKXz0+SJEmS1KbbaxgPAH4I3BARvwDuoJoA54VUE9D8azedZOaeDcUdk7rMPAI4oqH8fKprJ9vLb6SaRVWSJEmSNEbdrsM4H3gW8D7gWmAFqpG//YBnl/2SJEmSpCmk2xFGypqHXy2bJEmSJGmK6zjCGBGrRsTnIqLjDKgRsU2p85TxCU+SJEmSNCgjnZJ6ILAL8OMR6vwEeD2wfz+DkiRJkiQN3kgJ427Al8qpqI0y8yHgK8Ab+x2YJEmSJGmwRkoYnwFc1UUfv6RaI1GSJEmSNIWMlDA+DKzYRR/TgcX9CUeSJEmSNCxGShivAf6hiz5eXepKkiRJkqaQkRLGrwP7LWWW1DnAe4Dj+xyXJEmSJGnARlqH8QRge+AHEfEt4ALgD0ACG5Z9uwJnZ+aJ4x2oJEmSJGlidUwYMzMjYg9gP+AAYPe2KjdSLb3xlfELT5IkSZI0KCONMJKZCXwJ+FJEzATWL7sWZOat4x2cJEmSJGlwRkwY60qCaJIoSZIkScuIkSa9kSRJkiQtw0wYJUmSJEmNuj4lVZIkSZI0nO78yrd6arfWvm8Ycb8jjJIkSZKkRmku5gAAACAASURBVF0ljBHxLyPsi4j4Wv9CkiRJkiQNg25HGL8YEW9pL4yIacCpwG59jUqSJEmSNHDdXsO4F3BSRDyYmd8EiIjpwNnAi4BXjU94kiRJkqRB6SphzMzTImJl4JSIeBD4IfBtYFNgTmb+ZhxjlCRJkiQNQNezpGbm1yNiReAM4AZgFeDlmXnTeAUnSZIkSRqcjgljRGzWUHwpcAqwK/BmYOVWvcy8blwilCRJkiQNxEgjjNcA2VAe5e/3avcTmNbHuCRJkiRJAzZSwrjNhEUhSZIkSRo6HRPGzPzRRAYiSZIkSRouXa3DGBHbRsReHfbtFRGORkqSJEnSFNNVwggcAazdYd+awCf7E44kSZIkaVh0mzBuDlzZYd9VQNOMqk8QESdGxJ0RcU2tbI2IuDAibih/Vy/lERFHR8S8iLg6Il5QazO31L8hIubWyl8YEb8ubY6OiECSJEmS1JNuE8bFwBod9j1tFI93ErBDW9mHgIsycxPgonIfYEdgk7LtAxwLVYIJHAq8GNgSOLSVZJY676y1a38sSZIkSVKXuk0Y/w/4QERMrxeW++8H/l83nWTmpcDCtuKdgZPL7ZOBXWrlp2TlZ8BqEbEusD1wYWYuzMxFwIXADmXfUzPzZ5mZVOtF7oIkSZIkqScjLatR9xGqpHFeRJwO3AasC+wBrArsPYYY1s7M28rt21lyreT6wC21ereWspHKb20olyRJkiT1oKuEMTOvjogXAYcBb6E6DfVuqlNIP5aZv+tHMJmZEZH96GtpImIfqlNd2XDDDSfiISVJkiRpUul2hJHMvB7YcxxiuCMi1s3M28pppXeW8gXABrV6M0vZAmBOW/klpXxmQ/1GmXkccBzA7NmzJyRJlSRJkqTJpNtrGMfTeUBrptO5wLm18reW2VK3Au4tp65eAGwXEauXyW62Ay4o++6LiK3K7KhvrfUlSZIkSRqlrkcYI+IlVNcqbgqs1L4/M7fsoo9TqUYH14yIW6lmOz0SOCMi9gZuprouEuB8YCdgHvAA8LbyOAsj4uPAFaXe4ZnZmkjnPVQzsa4MfK9skiRJkqQedJUwRsSrqRK4i4CXUSViKwNbU00u86Nu+snMTqe0bttQN4F9O/RzInBiQ/mVwBbdxCJJkiRJGlm3p6QeDnwReE25/++Z+Sqq0cZHqK4hlCRJkiRNId0mjJtRjSo+BiTwZIDMvJlq5tSPjEdwkiRJkqTB6TZhfBBYrpwmehvw9Nq++3j87KSSJEmSpCmg20lvfgU8E7iQ6jrGgyNiAfAw1emqvx6f8CRJkiRJg9LtCOMXqE5FBfgw8Beq5S0uBtaiw+Q0kiRJkqTJq6sRxsw8v3Z7QUS8EHgG1Uypv83Mh8cpPkmSJEnSgHQ1whgRh0TEeq37WbkhM68GnhYRh4xbhJIkSZKkgej2lNRD6TyxzXplvyRJkiRpCuk2YQyWXMPYbiawqD/hSJIkSZKGRcdrGCNiLjC33E3g2Ii4r63aSsBzgB+MT3iSJEmSpEEZadKbB4C7y+0A7gUWttV5GPgecEz/Q5MkSZIkDVLHhDEzvwl8EyAivg4cnpk3TVRgkiRJkqTB6vYaxk7XLxIRG0XEiX2KR5IkSZI0JLpNGOcCMzrsW5Ml1zpKkiRJkqaIfsySugVwV3/CkSRJkiQNi5FmSd0f2L/cTeCciHiordpKwNrASeMSnSRJkiRpYEaaJfU64Cyq0cV/BS4Gbmur8zDwW+CMcYlOkiRJkjQwI82SeiFwIUBE3A8cn5kLJiowSZIkSdJgjTTC+DeZ+bHxDkSSJEmSNFy6ShgBIuIlwN7AplTXLj5OZm7Zx7gkSZIkSQPW1SypEfFq4FJgJvAyqllR/ww8F3gacM14BShJkiRJGoxul9U4HPgi8Jpy/98z81VUo42PAJf0PzRJkiRJ0iB1e0rqZsBHgceolth4MkBm3hwRhwEfA04ZjwAlSZIkTW63f+53PbVb5/2b9jkSjVa3I4wPAstlZlItrfH02r77qE5VlSRJkiRNId2OMP4KeCbVMhsXAQdHxAKqdRgPB349PuFJkiRJkgal2xHGL1CdigrwYeAvwAXAxcBawL79D02SJEmSNEjdrsN4fu32goh4IfAMYGXgt5n58DjFJ0mSJEkakG5HGB8nKzdk5tX9ShYj4sCIuDYiromIUyNipYjYOCIui4h5EXF6REwvdVcs9+eV/bNq/Rxcyq+PiO37EZskSZIkLYt6Shj7LSLWB94HzM7MLYBpwJuATwNHZeYzgEXA3qXJ3sCiUn5UqUdEbFbabQ7sABwTEdMm8rlIkiRJ0lQxFAljsTywckQsDzyJajbWVwFnlv0nA7uU2zuX+5T920ZElPLTMvOhzLwJmAdsOUHxS5IkSdKUMhQJY2YuAP4D+ANVongv8HPgnsxcXKrdCqxfbq8P3FLaLi71n1Yvb2gjSZIkSRqFoUgYI2J1qtHBjYH1gCdTnVI6no+5T0RcGRFX3nXXXeP5UJIkSZI0KQ1Fwgj8A3BTZt6VmY8AZwNbA6uVU1QBZgILyu0FwAYAZf+qwN318oY2j5OZx2Xm7MycPWPGjH4/H0mSJEma9LpaVgMgIvYA3kB1iudK7fszcyzXCv4B2CoingT8FdgWuJJqncfdgdOAucC5pf555f5Py/4fZmZGxHnA/0TE56lGKjcBLh9DXJIkSZK0zOoqYYyII4EPAldQTSTT13UXM/OyiDgT+AWwGLgKOA74LnBaRHyilJ1QmpwAfCMi5gELqWZGJTOvjYgzgOtKP/tm5qP9jFWSJEmSlhXdjjC+HfhIZn5qvALJzEOBQ9uKb6RhltPMfBB4Y4d+jgCO6HuAkiRJkrSM6fYaxkeoZi2VJEmSJC0juk0Yvwi8o6x1KEmSJElaBnR1SmpmfiYi/gP4bUT8CLjniVXyoL5HJ0mSJEkamG4nvXkzcADwGLAKT5z0JgETRkmSJEmaQrqd9OZI4HTgXZl5/zjGI0mSJEkaEt1ew/hU4ESTRUmSJEladnSbMJ4FbDOegUiSJEmShku3p6ReABwZEesAP+SJk96Qmef3MzBJkiRJ0mB1mzCeWv6+vWztEpjWl4gkSZIkSUOh24Rx43GNQpIkSZI0dLpdh/Hm8Q5EkiRJkjRcupr0JiKeHRFb1e6vHBGfjIhzIuK94xeeJEmSJGlQup0l9RjgdbX7nwX2B1YCPh0RH+h3YJIkSZKkweo2YdwC+ClARKwAvAU4IDN3AD5M80Q4kiRJkqRJrNuE8cnAfeX2VuX+2eX+L4CN+hyXJEmSJGnAuk0Yb6JKFAHeAFyVmXeX+2sC9/c7MEmSJEnSYHW7rMbngWMj4o3A84G31fbNAa7uc1ySJEmSpAHrdlmNEyLiBuBFwIcy86La7oXAF8YjOEmSJEnS4HQ7wkhmXgpc2lB+WD8DkiRJkiQNh44JY0RsBvw+Mx8qt0eUmdf1NTJJkiRJ0kCNNMJ4DdVEN5eX29mhXpR90/obmiRJkiRpkEZKGLcBrqvdliRJkiQtQzomjJn5I4CIWBGYCVyemTdMVGCSJEmSpMFa6jqMmfkQcDyw3viHI0mSJEkaFktNGItfA5uOZyCSJEmSpOHS7bIaBwInRcRtwPczc/E4xiRJkiRJGgLdJoznAE8CzgUyIhbRNmtqZq7V59gkSZIkSQPUbcL4FTovqyFJkiRJmoK6Shgz87BxjoOIWI1qcp0tqJLTtwPXA6cDs4D5wB6ZuSgiAvgisBPwALBXZv6i9DMX+Gjp9hOZefJ4xy5JkiRJU1G3k94AEBHTI+KFEfHq8nd6H2P5ItX1kc8Cngv8BvgQcFFmbgJcVO4D7AhsUrZ9gGNLfGsAhwIvBrYEDo2I1fsYoyRJkiQtM7pOGCPig8AdwOXABcAVwB0R8YGxBhERqwKvAE4AyMyHM/MeYGegNUJ4MrBLub0zcEpWfgasFhHrAtsDF2bmwsxcBFwI7DDW+CRJkiRpWdRVwhgRBwCfAv4H2AZ4NjCn3P9URLxvjHFsDNwFfD0iroqI4yPiycDamXlbqXM7sHa5vT5wS639raWsU7kkSZIkaZS6nfRmX+DIzPxIrex64NKIuAd4H3D0GON4AfDezLwsIr7IktNPAcjMjIi+TbwTEftQnc7Khhtu2K9uJUmSJGnK6PaU1A2AizvsuwSYOcY4bgVuzczLyv0zqRLIO8qpppS/d5b9C0pMLTNLWafyJ8jM4zJzdmbOnjFjxhjDlyRJkqSpp9uE8Q/Adh32vbrs71lm3g7cEhHPLEXbAtcB5wFzS9lcqnUgKeVvjcpWwL3l1NULgO0iYvUy2c12pUySJEmSNErdnpJ6NHB0mYX0TKrJb9YC3gjsBezfh1jeC/x3mXn1RuBtVAntGRGxN3AzsEepez7VkhrzqJbVeBtAZi6MiI9TTcgDcHhmLuxDbJIkSZK0zOl2HcYvR8RDVEtWvJ1qncQA/gi8KzOPH2sgmflLYHbDrm0b6ibVdZVN/ZwInDjWeCRJkiRpWbfUhDEiVqBa0/C7wPFU1wWuC9xGdd1h3yaikSRJkiQNj25GGB8FfgjsmJl/pFq24paRm0iSJEmSJrulTnqTmY8BNwDrjH84kiRJkqRh0e0sqR8BDomI54xnMJIkSZKk4dHtLKkfBZ4G/DIiFlDNkvq4axczc8s+xyZJkiRJGqBuE8ZryiZJkiRJWkZ0O0vq8cD8zFww/iFJkiRJkoZBN9cwtmZJfeY4xyJJkiRJGiLOkipJkiRJauQsqZIkSZKkRs6SKkmSJElq5CypkiRJkqRGXSWMmfm28Q5EkqRh9JqzTui57Xd327uPkUjSxLvtM70tkrDuB9fvcyQalG6vYQQgKhtExEsj4snjFZQkSZIkafC6Thgj4j3AAuBm4P9RltmIiLMj4oDxCU+SJEmSNChdJYwR8QHg88DXgFcBUdt9CfCPfY9MkiRJkjRQ3U56sy9wSGZ+JiKmte27Hti0v2FJkiRJkgat24RxHeDnHfY9BqzUn3AkSZKkyeeyk+7sue2L91qrj5FUbjz69p7a/d371ulzJJrsur2GcR7wyg77XgFc159wJEmSJEnDouMIY0S8AvhFZv4Z+AJwTEQ8DJxZqqwVEXsD/wq8c9wjlSRpFF5z9jE9t/3uru/pYySS1L1f/2dvI5XP+Zf+j1JKMPIpqRcDLwEuz8zjI2J14BDgY2X/+cADwGGZ+T/jG6YkSZIkaaKNlDDWZ0IlMz8bEV+lSiLXBBYCP83Me8cxPkmSJEnSgHQ76Q0AmXk/8INxikWSJEmSNESWljDuFBHP6qajzDylD/FIkiRJkobE0hLGQ7rsJwETRkmSJEmaQpaWMG4DXDkRgUiSJEmShsvSEsa/ZuZfJiQSSdKkt9O3PtlTu/Pf8OE+RyJJkvphuUEHIEmSJEkaTkOVMEbEtIi4KiK+U+5vHBGXRcS8iDg9IqaX8hXL/Xll/6xaHweX8usjYvvBPBNJkiRJmvw6JoyZuVxmXj6RwQD7A7+p3f80cFRmPgNYBOxdyvcGFpXyo0o9ImIz4E3A5sAOwDERMW2CYpckSZKkKWVoRhgjYibwGuD4cj+AVwFnlionA7uU2zuX+5T925b6OwOnZeZDmXkTMA/YcmKegSRJkiRNLUOTMAJfAD4IPFbuPw24JzMXl/u3AuuX2+sDtwCU/feW+n8rb2gjSZIkSRqFoUgYI+K1wJ2Z+fMJfMx9IuLKiLjyrrvumqiHlSRJkqRJYygSRmBr4PURMR84jepU1C8Cq0VEa+mPmcCCcnsBsAFA2b8qcHe9vKHN42TmcZk5OzNnz5gxo7/PRpIkSZKmgKWtwzghMvNg4GCAiJgD/FtmvjkivgnsTpVEzgXOLU3OK/d/Wvb/MDMzIs4D/iciPg+sB2wCTPTEPZKkMXrN2Uf11O67ux7Y50gkSVq2DUXCOIKDgNMi4hPAVcAJpfwE4BsRMQ9YSDUzKpl5bUScAVwHLAb2zcxHJz5sSZIkSZr8hi5hzMxLgEvK7RtpmOU0Mx8E3tih/RHAEeMXoSRJkiQtG4YuYZQkTbydzjm4p3bn7/KpPkciSZKGybBMeiNJkiRJGjImjJIkSZKkRiaMkiRJkqRGXsMoaZlx+Onb99TukH+8oM+RSJIkTQ4mjJIkTYDXnnlKT+2+s/tb+xyJJEnd85RUSZIkSVIjE0ZJkiRJUiMTRkmSJElSI69hlCRJ0jLt4v++q6d227x5Rp8jkYaPCaMkTWI7nrtPT+2+t/NxfY5Ey6rXn/mdntqdt/tr+xyJJGk8eEqqJEmSJKmRI4ySJE0irz3ztJ7afWf3Nz3u/uvOPKunfr69+249tZMkTU6OMEqSJEmSGpkwSpIkSZIaeUqqJI3SgWft0FO7o3b7fp8jkSRJGl8mjJKG3udO3b6ndu/f84I+R9I/bzy3t6QT4Js7m3hKkqSJ4SmpkiRJkqRGjjBKkiRpUvrOGX/qqd1r91izz5FIU5cJozQkzjlxx57a7fL27z3u/qkn9Xb65p57De/pm5IkSRoMT0mVJEmSJDVyhFHSE3z95O16bvu2uT/oYySSJEkaJBNGSePm2P/q7fTYd/+zp8dKkiQNA09JlSRJkiQ1MmGUJEmSJDUyYZQkSZIkNTJhlCRJkiQ1MmGUJEmSJDUaioQxIjaIiIsj4rqIuDYi9i/la0TEhRFxQ/m7eimPiDg6IuZFxNUR8YJaX3NL/RsiYu6gnpMkSZIkTXZDkTACi4H3Z+ZmwFbAvhGxGfAh4KLM3AS4qNwH2BHYpGz7AMdClWAChwIvBrYEDm0lmZIkSZKk0RmKhDEzb8vMX5Tb9wO/AdYHdgZOLtVOBnYpt3cGTsnKz4DVImJdYHvgwsxcmJmLgAuBHSbwqUiSJEnSlLH8oANoFxGzgOcDlwFrZ+ZtZdftwNrl9vrALbVmt5ayTuWSJEkaEqed9aee2r1ptzX7HImkpRmKEcaWiFgFOAs4IDPvq+/LzASyj4+1T0RcGRFX3nXXXf3qVpIkSZKmjKEZYYyIFaiSxf/OzLNL8R0RsW5m3lZOOb2zlC8ANqg1n1nKFgBz2sovaXq8zDwOOA5g9uzZfUtEJUmSAN541rU9tfvmbpv3ORJJ6t1QjDBGRAAnAL/JzM/Xdp0HtGY6nQucWyt/a5ktdSvg3nLq6gXAdhGxepnsZrtSJkmSJEkapWEZYdwaeAvw64j4ZSn7MHAkcEZE7A3cDOxR9p0P7ATMAx4A3gaQmQsj4uPAFaXe4Zm5cGKegiRJmgp2O+uKpVdqcNZuL+pzJJI0eEORMGbm/wHRYfe2DfUT2LdDXycCJ/YvOkmSJElaNg3FKamSJEmSpOFjwihJkiRJamTCKEmSJElqNBTXMEqT2fdP2KmndjvsfX6fI5EkSZL6yxFGSZIkSVIjE0ZJkiRJUiNPSZUkSVPCG866tKd239rtFX2ORJKmDhNGLbN+9LXX9NTule/8bp8jkSSps73O/kNP7U7adcM+RyJpWWTCKEmSNMV97Ft/7LntoW9Yr4+RSJpsvIZRkiRJktTIEUZJkiR15diz7+ip3bt3XbvPkUiaKI4wSpIkSZIamTBKkiRJkhqZMEqSJEmSGnkNoyady//zdT233fJfvt3HSCRJ/bDLmT/oue05u2/Xx0gkSe0cYZQkSZIkNTJhlCRJkiQ1MmGUJEmSJDXyGkZNqGuOeX1P7bZ4z3l9jkSSJEnS0pgwFncd+189t53x7n/uYySSJEmSNBw8JVWSJEmS1MgRxiF1x7Gf6and2u/+YJ8jqdx89C49tdvofef0ORJJkiRJE8URRkmSJElSI0cY++yurx7Xc9sZ79qnj5FU/njMB3pqt957PtvnSCRJkiRNNo4wSpIkSZIamTBKkiRJkhqZMEqSJEmSGk3JhDEidoiI6yNiXkR8aNDxSJIkSdJkNOUSxoiYBnwF2BHYDNgzIjYbbFSSJEmSNPlMuYQR2BKYl5k3ZubDwGnAzgOOSZIkSZImnamYMK4P3FK7f2spkyRJkiSNQmTmoGPoq4jYHdghM99R7r8FeHFm7tdWbx+gtfDhM4Hrl9L1msCfxhheP/qwn8kTy1TtZ5hisZ/JE8tU7WeYYpmq/QxTLFO1n2GKxX4mTyxTtZ9himWi+9koM2c8oTQzp9QGvAS4oHb/YODgPvR75TD0YT+TJ5ap2s8wxWI/kyeWqdrPMMUyVfsZplimaj/DFIv9TJ5Ypmo/wxTLsPQzFU9JvQLYJCI2jojpwJuA8wYckyRJkiRNOssPOoB+y8zFEbEfcAEwDTgxM68dcFiSJEmSNOlMuYQRIDPPB87vc7fHDUkf9jP+fdjP+PdhPxPTzzDFMlX7GaZYpmo/wxTLVO1nmGKxn/Hvw37Gv48p1c+Um/RGkiRJktQfU/EaRkmSJElSH5gwSpIkSZIamTBKkiRJkhpNyUlv/n97Zx52R1Gm7/tJApiFIJthiRAYZUCQRRBF+UFAQRBkEdlENhUdHX8giqIwCsiMiiOLDDpsQsBRUEAERHYIDCAQSAIhQJAlYMIW1oBhzzt/vHVC5+ScL93f13ypr3nv6+rrnFPd/Zyn6tSp7lq6KgiCIAiCoA4kLQVsA6ycgmbi6z0/v+hc9Q1JKwCY2ROSlgf+HzCtr7PKS/qxmR1Wh8eBjqTNgCfNbJqkj+PrhN9rZpcuYmtBUJmY9CYIgiAIgqADkvYBjgCuxCuKAKOBrYCjzOzsGr5jKzO7quSxI4HlzezBtvB1zeyukhpfBb4HCDgG2A+4G9gU+JmZ/bqkzontQcDewNkAZnZgGZ0OuqsBGwD3mNl9Fc5bBXjKzF6RJDxeHwLuAU4zszdKaOwAXGlmr/TGe0HnBGBjvGPmCuATwGXA5sAkM/tOBa0ReIPFe4E3gfuTx7kVPa0J7Mj8DR8Xm9m9VXR60N/fzM6s6Gdl4FYze6kQvo2ZXV5SY2PAzGyCpA/g6XRfWi2h10g628z26aPGpngeuNvMrqxw3kfwhoXZkobi/9VWPv6xmb1QUudA4EIz+3t19x30osLYGUmfAnZi/j/WRWUzcQn9H5rZjyp4GQ1cY2bTC+FfNLMzSmoI2BUw4HxgS7zguA84uWrB06Z9rZltWfGc5czs6cLnL5D+WHjBXipjStoZuN7Mnk2tpMeSLjTAt81sRgmN44ALzOymKnHooLMM8A3gMeDXwGGkFkX8T/5cBa0tgF2Y/wJxupk9UNFTNheIOi4O6fi4QHTWqOXiIGlxYA/gMTO7WtLngY/h+fhUM3u9gtbqwGeZPx//zsxmV/SUTXlc8NOnMrmD5oAuR5NOVmVpHeWopGnAR9p7EyUtjZdla5TV6uE7HjWzVUoctxtwAvAUsBiwn5lNSPsmmtmHSn7fFOAjwFDgEeB9qadxaeA6M1u/pM7fgevxyrRS8M+BQwDM7KySOn8ys53S+x1THMfj5c5PzGxcSZ27gY3NbI6kY4B/Av6E3+9gZl8sofEy8A+8cncO3pP8Zpnvb9OZCqyDp/FMYOXkazG8wrhOSZ3d8PS8C9gCuBl/nOyDwF5mNqWkzqHAnsC5QOv/PBov6881s5+WjVsP31EqH6djDwT+Ff9Prw8cZGYXpX2l8rKkI4Bt8Ur5VXievg5vzLnCzP6jpJeL24PwtL4WwMx2KKlzm5ltnN4fgMfvQmBr4JKyaZzyznppXflTgTn4ffsnUvhnS+q8gOflB/G8fJ6ZzSpzbke9qDAuSGoZWgNvJSv+sfYB/mZmB9XwHWUvED/GW/0mAp8BTjCz/0r7qlwgfgW8B1gcmA0sAVwMbIcPmSgVJ0ntLZjC02oagJmtW1JnnndJ/4YPh/kdsD0ww8wOLqlzj5l9IL3/PXALcB7wSbww3aqExiz8ork88HvgHDObVOb723T+AkwBRgJrpfd/wAuv9cxsx5I6PwFWAK7Bb5Ifxm90vo7fLJ1XUiebC0QdF4d0bFwgumvUcnGQ9Fs8fYcBzwMjgD8mLzKzfUvqHIj/n28APg1MSno7A183s/EldbIpj9OxfS6Tm1iOpnOzKUtrLEfvBz7c3mgjH6Z6u5m9v6ROe5kzbxewpZkNL6ExGdjWzB5PDWdnA983swslTTKzDUp6KeabO81svcK+KjpLAkfj9xaHmNljkh4ys9XLnN/pOyXdjOe5hyUthzfKrNezwjydYj6+A//d5naKZ09e8Arm5/Br5Tp4mX6OmV1fIU53m9k6kt4FPA6sZGYvSxoMTGn5LKFzF/DRVNlcDvitmX1K0rp4Y//HSurcD6zd3uCXGginVsjH3XqxBaxhZkuU1JkCbGJmL0kag1/vfmNmvyibB5PG+vj97BPA6EKj661VylG8cex0vFNF+DV0D4Cyv3tbPp4AfNrMZkkaDtxiZh8sqXOvma3V8la8pkiaXKFBZxKwIV6O7w7sANyR4vZHM3uxjM48zCy2tg24v0u48BuUsjqzu2wvAm+U1JgCDEnv3w38BTg+fZ5UwcuU9LoY8AywePo8BLirgs7FwP8AawKrAmOAv6f3q1bQmVR4PxEYXvA3pYLOtML7O9r2Ta7iBb9h+wEwFe95PQIvAMt6mVzIJzN746X4WxV+n5vS+6XxnqvS+RhYrEP44hXz8V1dtinAqxXy8Yj0fgxwO15prJyPgcF4ZWY2MDKFD62YjyemfDwWHyI0Fr+obw5s3st8PAEfKgYwvGI+vrforbf5GG953hrvlZkFXA7sCyxZ5fcu5L0ngcGFfF0ljacUzh0GjE/vV6n4m2dTHhfi1acymQaWo0U/ZFCWUl85ui/eCPPfeE/nYcDJKWy/CjrP4Q20m7dtY/FG20pxSp9XxG8AD2wvNxaicwfp2oDfZLfC3wXcWVancN6GeMPdIcD0Xpw/sfD+tk55qqTOFXjlG+CC1n8JWLZsvNrTEW90OBD4K/D3Cl6OAf4Xvy78J3AJcDjeG3tyBZ0pvNW5M7TtP18lH9/XqWxJZc60x8EopgAAFOVJREFUCjpP4pW0Vdu2MfiolLI6U9s+j8CvV8dV+I9P6vQ+fa5Sbg0CDsYboddPYQ/1Ih/fmcqXZfHGpN7m4/OA/dP7M4GN0vs1gAkVdNrz8mJ4pfEcYFbV+MUsqZ15RdKHO4R/GKgyrv154P1mNrJtWxK/OS3DEEvj7s2HxHwGGCnpPPzGvywtjdfxDPda+vwGUHo4qnnPywXAqXhL73TgdTN7xMweqeBnqKQNJG2I31T+o+CvyvCP8ZJ+lFqUxqehVa2hSKXGeeMtSpjZ/WZ2tJmtDeyGXzyrDHMclIb0vBcYkVrNkLQs1X6ruWlIFsBKeAUJ82FY6npWB510fjsrUuE3B0bhvTmf6bA9U1JjkKVhqCnPjAW2TUPYqsTpDTN708zmAA9aGtpoZi9TLU4b4TdNhwMvmPd2vWxm11uFVmTSb55+Y1nq0Uv5eaHPyxS4W9L+6f2dkjYCkLQGUHYIqJnZXDO70sy+hP/2v8KH7D5Uwcug1Oq8JF7RWyqFL4FfcKrQmlhtCfyGADN7tKJOTuUx1FAmN7QchbzK0lrKUfNhlRvhQy9fTdt4/CZuXFkdvNd2TquMKWzjST3LJXhR0j8VvD2Ol6U7AmtX8LJzQaM43HhZ4NsVdFoad+C9ci8DN1Y9H1hP0mxJLwLrS1oR5vV+Da6g82XgB5JuwPPJZEnXAVcD3yqpMV/eMLMnzOxEM9sEH1lQCjM7FPgu8P/Nn1f8Jp53Tsd7ucvyF+BySa3K5nkwb9h2lWvnN4FrJF0m6dS0XY73wFcZpfFnvPH3kbZtOv6/KMuTkub1lKX7g+2B5fDhtmV4TdKw9H7DVmDq/a9yXzvXzI4H9gcOl3QSvZsUdCn8vuJ2YJlCPh5Btd/qy8Dmkh4EPgD8VdJDwGlpX1na8/LrZnaxme2JV/KrUbWG+U7Y8Ix3K95FfWXa7sUL/A0r6Pw7Pp6+075jSmr8mQ49Hkl7bgUvl5F6eNrCV6CtRa+k3nC8JegifOhT1fPH4y2SrW3FFL5Ay8xCdBYDjgQeTdtcvMfgd8AqJTVKt/wsRGdPvPXtSfy5mavxFquZwFcq6OyOD+u6KsVpuxS+PP78V1mdbYAH0m9/atouT2HbVND5NbBpl32l/OBDPddvCxuCD6l6s4KXW4Fh6f2gQvhSVGhhL5w3Gr8AnwQ82ovzp+OVsYfTaysfj6BaC+dSwDi81+JWvJL4EH6Tul5f83ErzUrqHJy++xG8Zf0a/EI1BTiigs5BeE/0aXjrdqvFdHnghgo6HyKT8jgdW0uZnM7pazl6HZmUowvLgxXj1eeylJrK0YLeqJQXPwSMqiOevfCwHt7o0en322tRxentShu8B3+TXpy3Fl6J3gV/bGFQhXPH1vyb9Tlt8CH9hwBbFcIGAUtU1BkEfDSlyy7p/eA641vBy2hghS77Pl5So2P8SZXOPnjbDh+2XldchwGr9eK8kek/v2Fv8g4VRnWU2eIZxh6QTzs9b5IFM3tiEXgYCvN6UNr3rWxmMxc8q5L+cHwY01O9PH89vEA/uS8+CnqD8UJgTi/OXQpv/S/b69U6b4QVJmHpC8m/zJ9FG4IP3Zhp3hJcRWcZYHXgAevD1O2SBuGTYBQnC5lgvXiIvy9IGo33Di7wH5L0cSs5SYakJczs1Q7hy+E3y6UmAOhw/nb4RaqW6eBTq+coM3u44nkjgdXwyvQMM3uywrlrmNn91Zx21VoJwPyZpHfjz0A8ama3VdRZG795u9sqzHbYRWuRl8fJR+1lchPK0XRuVmVpHeVo6gU5GW/UmYG32o/Ge6y/bmYTK+qNYv58XPo/XpdGW5yKM78+D3zNSj53uhCdRZI2dem8zWm8yNKmi3Yt/9ucdHLy0hSdqDB2QBWmp367dXLy0lSdnLzUqZO0VgFmm9nzaVjXRvjzcpXW2uqic5+Z3d2fGqEzcLzUqZO0NqIw22VvK6A56eTkpak6fdWQTzTzVTO7tS38o8ApVn5Clg3w5yB7XYGosaJXV5z6I236tQJbV0WvxrSpteLZ5TtKT/o1UHRy8tIUnagwdkDSm/iQrHPxmbHuWVQ6OXlpqk5OXmrW+R7wVfy5idZU5zfhw1B+bWbH9ZdOTl6aqpOTl5p1NseXeXgeH5pzEz6xwOvA3lZyGZGcdHLy0lSdGr38zbrMICnpATN7X0mdPlcgaqyE1BWnbNKmLp0M07guP92e4RRwuJkt02V/tjo5eWmyzryTosK4IPKpaPfGn6PYHZ+q/hx8KYLp/amTk5em6uTkpWadqXiPzjD8WbvV7a0pnm+18utA9VknJy9N1cnJS806k4Ct07mrAceZ2c6StgK+Y2ZbDzSdnLw0VadGLyfi6/mdjc9kC95juQ/wsJl9o6ROnysQNVZC6opTNmlTl06GaVyXn1fw2Vo7TcZ2sJm9e6Dp5OSlyTrzsEXwsGvuGwtORbsxPjHBDODm/tTJyUtTdXLyUrNOa3mEwfhCz8VJYqpMx91nnZy8NFUnJy9vh05BqzgF/9SBqJOTl6bq1OUlHb8tPizwkrSdjK+xVkXjROBSvBHwY2nbPYWd1F8adcYpp7RpahrXmDY302WSMKotF5KNTk5emqzT2qKHsQPqsmioJAGbWS8W8eytTk5emqqTk5eadcbhU4sPxxeCfwOfJXVLfF2+3fpLJycvTdXJyUvNOmfgyzVci68hNdPMviWfWGiima050HRy8tJUnbq81ImkbfHZO4uTkF1sZqWXHKlDI0fqilcT07imOP0z8KylpZ/a9o2ykpPo5KSTk5cm68w7JyqMCyLp82b2uxx0cvLSVJ2cvNSsMwTYFb9pOh+fXnxPfIr5X1pas60/dHLy0lSdnLzUrLMYcAC+HtWdwBlm9qZ8ttL3WMl1C3PSyclLU3Vq9LIU8H38hn0Unp+fwpdC+an1YRbrRUVdcWpi2tRFpE3QNKLCGARBEARB0AFJV+C9lGdZWspFvsTLfsCWVv5ZyD5XIGqs6NUVp2zSpi6dDNO47rTZCXhPE3Ry8tJknRaDqhz8TkHSCEk/kjRV0guSZkm6RdJ+/a2Tk5em6uTkpZ909u1vnZy8NFUnJy9vk87dNf0fFrlOTl6aqlOXF2CMmR1jhXU/zewJM/spsGoFnT8AzwFbmNkyZrYssAU+i+sf+lED6otTTmlTl05uaVx32oxt03luAOvk5KXJOkD0MHZE0kXAhcDVwG74MzjnAv+GPwdRanHvOnRy8tJUnZy8NFUnJy9N1cnJS+gMHC9N1anRy5VJ4yxLz/zIF1HfD9jKzD5ZUmeamf1z1X11a6Rj64pTNmlTl06GaZxN2uSmk5OXJuvMwyrOkvNO2IA72z5PSK+D8EWn+00nJy9N1cnJS1N1cvLSVJ2cvITOwPHSVJ0avSwNHAPch7fMPwvcm8KWqaBzJfBdYFQhbBRwKHB1f2nUHKds0qbBaZxN2uSmk5OXJuu0thiS2pl/SNoUQNIO+B8dM5sLqJ91cvLSVJ2cvDRVJycvTdXJyUvoDBwvTdWpxYuZPQecCXwDeK/50K61zOxQfMmjsuwOLAtcL+k5Sc8C44Fl8B7Q/tKoLU6ZpU1dOlmlcV1+GqqTk5cm6zhVa5jvhA1YF7gNbxW6EVgjhS8PHNifOjl5aapOTl6aqpOTl6bq5OQldAaOl6bq1OjlQGAa8CdgOrBjYd/Esjrp+DWBTwIj2sK36WeNWuKUW9o0MY1zS5vcdHLy0mQdM4sKY+UEg/1z0cnJS1N1cvLSVJ2cvDRVJycvoTNwvDRVp4oGMKV1swWMAW4HDkqfJ1XQ6XMFog6NmuOUTdo0OI2zSZvcdHLy0mSdeedUPeGdvgGP5qKTk5em6uTkpak6OXlpqk5OXkJn4Hhpqk4VDWBq2+cRwOXAccDkCjp9rkDUoVFznLJJmwancTZpk5tOTl6arNPahhAsgKS7uu3CHxjtN52cvDRVJycvTdXJyUtTdXLyEjoDx0tTderyAjwpaX0zmwxgZi9J2h44A/hgBZ1BZvZS0pguaSxwvqRVKf9MZR0aUF+cckqbunRyS+Oc0iY3nZy8NFkHICqMXRgFfAp/9qGIgJv7WScnL03VyclLU3Vy8tJUnZy8hM7A8dJUnbq87AO8UQwwszeAfSSdUkGnjgpEXZWQuuKUU9rUpZNbGueUNrnp5OSlyTpAVBi78We8G3dy+w5J4/tZJycvTdXJyUtTdXLy0lSdnLyEzsDx0lSdWryY2Ywe9t1UVod6KhC1VELqilNmaVOXTlZpXJefhurk5KXJOgDIfCxrEARBEARBEARBEMxHrMMYBEEQBEEQBEEQdCQqjEEQBEEQBEEQBEFHosIYBEEQ9IikCyQ9KOldHfZdIeleSYsvCm8LQ9IgSb+U9KQkk3Rkh2OsxDa2/913RtLY5GmdRe2lG5K2lvTNDuG7SdrvbfrO7NMlCIJgIBKT3gRBEAQL4yDgXuD7wBGtQEmfA7YGtjCz1xaRt4XxWeDrwJeAe4BOk1FsUng/FLgW+Hfg0kL4PW+XwV4wEff84KI20gNbA58DTmgL3w1YDhjX34aCIAiC3hEVxiAIgqBHzGxG6pn7D0m/MbMHJA0HjgfONrPxb9d3SxpqZi/3QWJN4DkzO6PbAWZ2S+H7RqS3DxbDqyJpMDC4vSLdLbykpoAlzGw20GtvQRAEQVCFGJIaBEEQlOEXwDTgv9LnI4BhwCEAktaRdKmkF9N2nqQVWidLGi7pJEnTJM2R9HAaKjqy+CVpSOG3JJ0gaRYwpZshScMknSjpCUmvSJogaevC/vHA0cDShaGlY3oTeUlfljRV0quSHpH03bb94yTdLmknSVOBV4CPdAtP53xD0t+S5gOSDm7TPFLS05I2lTQhnbtrp6GXkr4k6R5JL6dzrpe0dto3Jh2/h6QzJc2WNEPSF9L+70p6TNIsScdI6vHeQNJ2kq6S9FTSuqUt3Y8Evg2sWkj3cZLGAbsAmxfCjyyjWdBeV9Ilkp6X9JKk2yRt1YPXPVL6fq2nOAVBEATdiR7GIAiCYKGY2RvppvtGST8Avgn8q5nNkvQ+4CbgduAL+LXlaOASSRubr980DBgMHA7MAt6b3p+HL7Be5DvADcDe9NyweRqwA3AY8ABwAHCppC3M7EZ8KOq38KGR26RzHq8ad0nfAX4M/AwYD2wIHC1pjpmdVDh0TDrmR8ATwMPdwiUdgFe+jwOuALYAjpW0hJn9tKA5DDgrnX8/8BiwYpu/zYCTgR8CfwVG4kNWl2qLyjHAb/FK2xeBsyRtAKyaPm+ID8WdBJzbQ5KsBlwC/ByYC2wLXCZps7TG3OnA+4EtgZ3TObPS6yrAu/HfBt4aIrwwTSStieezacC/AM8AG+F5aQEk7Q+cAnzFzMb1EJ8gCIKgJ8wstthiiy222EpteCXN8Bv31lq+v8Fv4hcvHPd+4E1guy46Q4CPJ61VCuEGTCzhYy28YrFvIWwQcDdwRSHsSODpCvEbkTzslz6PBF4Cjmg7rlX5G5w+j0vnrd923ALhyedM4My2Y38FvAC8q+DdgB3bjhubwtdJnw8B7ughTmPS8WcWwkYCrwN/a8Uhhd8G/L5Ceg1Kv+UVwBmF8J8D0zscfz4wvpea5+AVzKFdzpuXLniF8lVgj0X9n4kttthiG+hbDEkNgiAIqvCf6fVYM7P0/pPAhcBcSUMkDcF716bjPUAASNpb0iRJL+GVlRvTrjXavuMvJXx8GBDeQwmAmc1NnzetFKOe2QQYDpzXiluK37XAKGB04diZZja5g0Z7+GhgpaL3xO/xitwHC2EGXLYQj5OBDSQdL2kzdZ+x9pp5ov4c5CzgejN7s3DMA8DKPX2ZpNGSzpI0E3gD/y23ZsHfsTQlNbfEK7MLe6b1QHyynd3NrKee0iAIgqAEUWEMgiAIqvBa2yv4rJeH4jf5xW110nBBSTsDZ+NDJncFPspbwxXbl+t4soSPFYGXzGxOh3OHSVqiTGRKsFx6ncr8cbsuhReHQ3bz3R6+Ypfw1udlCmHP2UImyDGzq4H9gc3wIbNPp+dDh7cd+nzb59e6hC2wfEqL9HzjxcDH8CGwW+CV98t6Oq8nKmguS7khxbvgFd9rFnZgEARBsHDiGcYgCIKgrzyL9zCe3mHf0+l1V+BWM2s9u4akzbvoWZfwIo8DIyQNa6s0jgLmmNmrJTTK8Gx63Z7OFcJphffdfLeHtyo972kLH9X2nT1pzv8FZmfhzyQujy8lcjzwIvC9MudX4H3ABsC2ZnZ5K1DS0H7QfIa25ze7sBfw38DFkrY1s1f64C0IguAdT/QwBkEQBH3lGmBt/Dm629u26emYofgzZUX26sN3TsArU59rBUhS+nxjt5N6wV+Bl4GVOsTtdjN7sReaM/DJa3ZtC98NmE0PM8MuDDObZWanAP8LfKC3Oj3QqsTN+y0lrYo/j1qkW09lp/CymtcAu0laWE/mDOAT+HDWCyQttpDjgyAIgh6IHsYgCIKgrxyJT5ZyqaQz8F7FlYGtgHHm6zReBfxS0uHArcCn8Zv6XmFm90o6BzhJ0pL4IvYH4Osu1raEgpk9n5Z++EWqxNyAN7auAWxhZjv3dH4XzblJ8xRJz+Bps3nyfVjVHjFJR+HDWMfjab9B0qu7dxHgPrxCdmyaLXdJ4Ch8Ep/240ZJ2g+fiOjp1HhwH7CjpJ14q+JcVvMovKHgBknH4j2OGwDPWNs6m2b2UFpu43rgfyTtmZ5xDYIgCCoSPYxBEARBnzCz+/FnEucAp+LPnh2F9xg9kA47BTgWOAj4I76Uw+f7+NUH4EtO/BC4KGlub76kRm2Y2c+Ar+BLPVyEz9a5F96L11vN0/C02Bn4M7An8G2bf0mNskzAexNPxmcW/Rpeif9Fb/11Iw31/Sw+Mc35+PIpP8ErZkX+gM8Q+7Pk78gU/ivgSuCMFP6VsppmNg2f0OhpfPjzhXiP8iNdvN6DT5zzKeC01AMdBEEQVKQ1JXoQBEEQBEEQBEEQzEf0MAZBEARBEARBEAQdiQpjEARBEARBEARB0JGoMAZBEARBEARBEAQdiQpjEARBEARBEARB0JGoMAZBEARBEARBEAQdiQpjEARBEARBEARB0JGoMAZBEARBEARBEAQdiQpjEARBEARBEARB0JGoMAZBEARBEARBEAQd+T+MpkFtbjTD7wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYyFIkEssfqR"
      },
      "source": [
        "**Attack Types and Weapon Types used against Country**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 494
        },
        "id": "tjieTbMlstjc",
        "outputId": "f12679e5-3610-46f4-a73c-980da58ead7f"
      },
      "source": [
        "attack_type = terror_data.groupby(['Country'])['Attack_Type', 'Weapon_Type'].max()\r\n",
        "attack_type.head(10)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Attack_Type</th>\n",
              "      <th>Weapon_Type</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Afghanistan</th>\n",
              "      <td>Unknown</td>\n",
              "      <td>Vehicle (not to include vehicle-borne explosiv...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Albania</th>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Algeria</th>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Andorra</th>\n",
              "      <td>Armed Assault</td>\n",
              "      <td>Firearms</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Angola</th>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Antigua and Barbuda</th>\n",
              "      <td>Facility/Infrastructure Attack</td>\n",
              "      <td>Incendiary</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Argentina</th>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Armenia</th>\n",
              "      <td>Hostage Taking (Kidnapping)</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Australia</th>\n",
              "      <td>Unknown</td>\n",
              "      <td>Vehicle (not to include vehicle-borne explosiv...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Austria</th>\n",
              "      <td>Unknown</td>\n",
              "      <td>Unknown</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                        Attack_Type                                        Weapon_Type\n",
              "Country                                                                                               \n",
              "Afghanistan                                 Unknown  Vehicle (not to include vehicle-borne explosiv...\n",
              "Albania                                     Unknown                                            Unknown\n",
              "Algeria                                     Unknown                                            Unknown\n",
              "Andorra                               Armed Assault                                           Firearms\n",
              "Angola                                      Unknown                                            Unknown\n",
              "Antigua and Barbuda  Facility/Infrastructure Attack                                         Incendiary\n",
              "Argentina                                   Unknown                                            Unknown\n",
              "Armenia                 Hostage Taking (Kidnapping)                                            Unknown\n",
              "Australia                                   Unknown  Vehicle (not to include vehicle-borne explosiv...\n",
              "Austria                                     Unknown                                            Unknown"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2Y-IhpWs7WR",
        "outputId": "dcb87561-dc59-4699-b484-a0f08e1ea712"
      },
      "source": [
        "#Number of Attacking type used \r\n",
        "\r\n",
        "terror_data['Attack_Type'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Bombing/Explosion                      88255\n",
              "Armed Assault                          42669\n",
              "Assassination                          19312\n",
              "Hostage Taking (Kidnapping)            11158\n",
              "Facility/Infrastructure Attack         10356\n",
              "Unknown                                 7276\n",
              "Unarmed Assault                         1015\n",
              "Hostage Taking (Barricade Incident)      991\n",
              "Hijacking                                659\n",
              "Name: Attack_Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kR5zEpwstA3S"
      },
      "source": [
        "Attacking types used for Attack Puropse"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 478
        },
        "id": "W3fwfKwKtHgb",
        "outputId": "bb69c19b-4114-488a-b24c-2c1c1228ab25"
      },
      "source": [
        "\r\n",
        "plt.figure(figsize=(15,6))\r\n",
        "sns.barplot(terror_data['Attack_Type'].value_counts()[:10].index, terror_data['Attack_Type'].value_counts()[:10].values)\r\n",
        "plt.xlabel('Attack type used by Terrorist',fontsize=15)\r\n",
        "plt.ylabel('Attack Type Count',fontsize=15)\r\n",
        "plt.title('Top Attacking Type used by Terrorist ', fontsize=30)\r\n",
        "plt.xticks(rotation=90)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c20J_kqvtaWG"
      },
      "source": [
        "we can see Bombing and Explosion and Armed Assaults are used much than anyother types."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Se36rJvAtmUv",
        "outputId": "cae5c120-acec-447c-f979-1154f735d8b9"
      },
      "source": [
        "\r\n",
        "#Weapon types used for attacking purpose\r\n",
        "\r\n",
        "terror_data['Weapon_Type'].value_counts()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Explosives                                                                     92426\n",
              "Firearms                                                                       58524\n",
              "Unknown                                                                        15157\n",
              "Incendiary                                                                     11135\n",
              "Melee                                                                           3655\n",
              "Chemical                                                                         321\n",
              "Sabotage Equipment                                                               141\n",
              "Vehicle (not to include vehicle-borne explosives, i.e., car or truck bombs)      136\n",
              "Other                                                                            114\n",
              "Biological                                                                        35\n",
              "Fake Weapons                                                                      33\n",
              "Radiological                                                                      14\n",
              "Name: Weapon_Type, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_TTJZ-DtreH"
      },
      "source": [
        "weapon type used for Attacing purpose"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "id": "N8k8B5yRtxSw",
        "outputId": "ddc5269b-6908-4a74-891a-736de7483932"
      },
      "source": [
        "plt.figure(figsize=(15,6))\r\n",
        "sns.barplot(terror_data['Weapon_Type'].value_counts()[:15].index, terror_data['Weapon_Type'].value_counts()[:15].values)\r\n",
        "plt.xlabel('Weapons used by Terrorists', fontsize=15)\r\n",
        "plt.ylabel('Weapons Count' ,fontsize=15)\r\n",
        "plt.title('Top Weapons used by Terrorists ', fontsize=30)\r\n",
        "plt.xticks(rotation=90)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w84fRvvbuqC1"
      },
      "source": [
        "we can see Explosives and Firearms are used most."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCo9zcMUuwPU",
        "outputId": "0f2e37b0-4ca7-467a-cf7d-02000c9d9929"
      },
      "source": [
        "\r\n",
        "#Region which is used for attacking purpose\r\n",
        "\r\n",
        "region = terror_data['Region'].value_counts()\r\n",
        "region.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Middle East & North Africa    50474\n",
              "South Asia                    44974\n",
              "South America                 18978\n",
              "Sub-Saharan Africa            17550\n",
              "Western Europe                16639\n",
              "Name: Region, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oKatOVfBu250"
      },
      "source": [
        "Region in which most Attacks has been occured."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "rElf_hrEu-Jt",
        "outputId": "98e9ea04-be65-4324-f96e-663d8fb181e9"
      },
      "source": [
        "\r\n",
        "plt.figure(figsize=(15,6))\r\n",
        "sns.countplot('Region', data=terror_data)\r\n",
        "plt.xlabel('Region of Terrorism attacks', fontsize=15)\r\n",
        "plt.ylabel('Region attacks Count', fontsize=15)\r\n",
        "plt.title('Region has Maximun Terrorist Attacks ', fontsize=30)\r\n",
        "plt.xticks(rotation=90)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Edz22TYavbh9"
      },
      "source": [
        "we can see Middle East and North Africa, South Asia Regions are under most attacks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5wdudWAxvne5",
        "outputId": "9bad2764-1bce-4407-fbae-a978886637ff"
      },
      "source": [
        "#City which is Maximum times under attack\r\n",
        "\r\n",
        "city = terror_data['City'].value_counts()\r\n",
        "city.head()\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Unknown    9775\n",
              "Baghdad    7589\n",
              "Karachi    2652\n",
              "Lima       2359\n",
              "Mosul      2265\n",
              "Name: City, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oQGOMvELvsOY"
      },
      "source": [
        "cities with maximum attacks."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "C4R2OjChvyI4",
        "outputId": "26418a78-d2a1-444e-8896-73ef567f2e38"
      },
      "source": [
        "\r\n",
        "plt.figure(figsize=(15,6))\r\n",
        "sns.barplot(terror_data['City'].value_counts()[:15].index, terror_data['City'].value_counts()[:15].values)\r\n",
        "plt.xlabel('Cities with most attacks', fontsize=15)\r\n",
        "plt.ylabel('Count', fontsize=15)\r\n",
        "plt.title('City which is Hot Zone ', fontsize=36)\r\n",
        "plt.xticks(rotation=90)\r\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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sSLIdzUWbvuMMIsnurL9+4A+BJ82zTMV8Rs9rMUtNdOsu5++nOxZyy9nWapQ0mUwYJY1VOynMmk7R3gv9QrhEuq1vGzoW7VCaCW0A7plkV5rJbmYmaLiK9WdUHafTWNcl7UY0Mx/Oup5imwzOTFN/u3Yx7Q1Zf3EoX+zc37GdSEQdVXVuVb0I+LdO8S5Jdh4qpkUanTjqjxe432i9NT11RmcoXrLxp+176yiaMcbQdKH+86pazJjD62j3v6BTtKDfT9v9v3uRpe/3s1S+OLI96T1LJHWYMEpaCt0r7jcADs8ipszrSnLzjRwH2Z0QZIOuercT2RzTKXoW6y+zcfQGzIS40Oe+EuiuwfYI1q2x+OOqGu2iCOsnhXvRTJbT99hK9FnWJecAzx4qkAnwkZHtWwwSxSJV1bmsv0zDXy9w13079y8BvtdTZ3QCoCVp6Wpb0I4Bukue/E3PLMobqtu1f+8kCxmP+Fes/73ulDHFMq+q+hHrz+b6jOV6bklLz4RR0lI4hmaWxxmPAN6ZZNNZ6vdKcl+a1ogdNyKWbhfH229o4sr6k9/8Jesviv3ODTzmQnWTvGewrmXzcz11R+u/lHUtIJcDGzJr47JpW1c+0Cl6QZL7zFZ/2izy9XnTke05F2BfYQ7t3H9skjkXmU/yEODx3f1n6QL+M9afLGmXDQ9xTu9i/bUS31ZVR4zx+N3fzyrg5XNVbi+q/b9O0WlV9e0xxrMQ/9G5/+Qkj1nm55e0REwYJY1d+0Xur4FzO8UHAJ9Ocu/+vdZJslOSw2i6Od12I8PpXvHfBthvQw5SVacA32o3u1f7z66q0zYwtoXqJobdVqTZWgu/zrrkoVt/dIKcleqVwM/b+5sDn0ryiLl2SLJjkr9d8siW3lFJXpHklnNVapdxeGWn6CLg+0sa2XgdzPpLQHx4tgsDSe5Fsz7ljJ8Cb+2r2445/nqn6DkLbJ1bsCTPZ/1W0VOBF43zOdrPm+77/hVJej+7kmxLc4Fu5kJSAf8yzngW6L9p1oKF5vvlh5PsN99FkCQ3TrJPknG1zkoaM9dhlLQkqupnSXYDjgPu1RbvBqxJ8iXgU8B3WPelcXua1oBHA7sypgtaVfW9JGuA1W3Re5K8nObLdXe6+rdU1XzdNd8BvH2kbKxLacziizSLgI92rxud8AaAqvp9ks/TdEftWundUQGoqvPaL8cfpmkd3QY4KclnaF5P/wtcSbPO3F1pZoPdDTiLZibRSbY98CTgX5KcTpOMnEXzPvkNTRfI+9B0z7xNZ79XTcjFAACq6idJ9qf5e4ZmHb8vJXkfcCLNhC2rgD1oznXzmV2BZ3bWF+3zftaNoXskcHGSrwNXtPsDfKuqXrHYuJPcCXjDSPGNgBM2vPMCAPv2rOH6NJoeAdvQfB4eluQpNInZecCNacY3HsD6C96/uao+vTHBbIj2c+cJNF3ob9vGdxjw90mOpln+56c0f8utgT+i+azfva0raYUyYZS0ZKrqgiQPAv4T2J/mMyc0E7c8YAGH+FW7b99YpcV4JvBp1i0ufYf21vXRBRznfTTjM2emub8SOHIjY5tXVV3dJg/dVrbzq+q8OXb7LBOaMAJU1bFJHkczqcjM77u7eP202wR4cHubz+uqajkuXIxVVZ3QJkBH0ox13oymB8BsvQB+Czytqo6d59DvAv6MdYvHb8W6cb90yjbELbjud6fVfRUX6TpjLavqh0keCnyCZmkVgD9pb7N5G/DiMcSzQarqkiT3o7nYM/Pa/SPgn4aKSdLGs0uqpCVVVVdW1d/QzN73Dppp5+fcheaq+kuAnavqn6vq1xsZw9eBuwAH0rTYrGXxC4ZTVb8A/q9T9MGq+vls9cdsNNmbL/kbbX28ZJYJclasqjqR5nXzduAXc1T9Hc3f9TXLEdcSey1NC9mP56lXNJOa7F5VL13yqJZIVX2QZsH7Y1h/sqOua2haIu9RVf+1gGNeAzyGZqzxsTStcb9iXevixKiqbwF3o2nVvGKOqmcAj2mXXhm0pbltKd2NZm3KNcz/e/8uzfndc2kjk7ShsjTLhknS7NqF5+9K073u5jRfCC8HzgfWVNWKWJB7VJK7s/5MgPerqq8MFc/1SZLNaboZ7kLT/W4TmglOzqF5zVw+YHhLIslONBc6bkPTIhaapOF8mnOeL6mcKEm2BB5KM8nVVjTjWC+gGXu7XBdmVqz2PfBAmvfAtjTd1C8BTq+q+S7EDSbJKpq4b0nTFfUamvfuuTRdg6fqdSxNIxNGSVqgJO8A/qbd/FpVXW9m75QkSddPdkmVpAVIciuaSShm9M7SKEmSNE1MGCVpHkm2At7LuuU0zgfmHUslSZI06ZwlVZJ6JDkE2A64Gc2yIDfrPPwPVbXoSXMkSZImjWMYJalHkvNp1hIb9c6qevYyhyNJkjQIWxglaX5X0MyOekhVvX/oYCRJkpaLLYzAtttuWzvttNPQYUiSJEnSIM4444yfVNWq0XJbGIGddtqJNWvWDB2GJEmSJA0iyQ/6yp0lVZIkSZLUy4RRkiRJktTLhFGSJEmS1GtZE8YkhyW5NMm3OmXbJDkpyffbn1u35UnyliTnJPlmknt39tm3rf/9JPt2yu+T5Kx2n7ckyXKenyRJkiRNk+VuYXwvsMdI2cuAz1TVLsBn2m2ARwO7tLcDgHdAk2ACBwL3A3YFDpxJMts6z+zsN/pckiRJkqQFWtaEsapOAS4bKd4TOKK9fwTw+E75kdX4ErBVklsCjwJOqqrLqupy4CRgj/axLavqS9WsFXJk51iSJEmSpEVaCWMYt6+qi9v7Pwa2b+/fGrigU+/Ctmyu8gt7ynslOSDJmiRr1q5du3FnIEmSJElTaCUkjNdqWwZrmZ7rkKpaXVWrV626zvqUkiRJknS9txISxkva7qS0Py9tyy8CduzU26Etm6t8h55ySZIkSdIGWAkJ4/HAzEyn+wLHdcqf2s6Wen/g523X1U8Cj0yydTvZzSOBT7aPXZHk/u3sqE/tHEuSJEmStEibLeeTJflvYDdg2yQX0sx2+lrgQ0n2B34APKmtfiLwGOAc4EpgP4CquizJq4CvtvVeWVUzE+k8h2Ym1hsB/9PeJEmSJEkbIM2wweu31atX15o1a4YOQ5IkSZIGkeSMqlo9Wr4SuqRKkiRJklYgE0ZJkiRJUq9lHcM4Sda+4/1Dh7Boq579V0OHIEmSJGmK2MIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqtWISxiR/l+TsJN9K8t9Jtkiyc5IvJzknyQeT3KCte8N2+5z28Z06x3l5W/69JI8a6nwkSZIkadKtiIQxya2B5wOrq+quwKbAk4F/B95YVXcALgf2b3fZH7i8LX9jW48kd273uwuwB3Bwkk2X81wkSZIkaVqsiISxtRlwoySbATcGLgYeDhzdPn4E8Pj2/p7tNu3juydJW35UVV1VVecB5wC7LlP8kiRJkjRVVkTCWFUXAa8HfkiTKP4cOAP4WVVd01a7ELh1e//WwAXtvte09W/eLe/ZR5IkSZK0CCsiYUyyNU3r4M7ArYCb0HQpXcrnPCDJmiRr1q5du5RPJUmSJEkTaUUkjMAjgPOqam1V/RY4BnggsFXbRRVgB+Ci9v5FwI4A7eM3A37aLe/ZZz1VdUhVra6q1atWrRr3+UiSJEnSxFspCeMPgfsnuXE7FnF34NvA54C92zr7Ase1949vt2kf/2xVVVv+5HYW1Z2BXYCvLNM5SJIkSdJU2Wz+Kkuvqr6c5Gjga8A1wJnAIcDHgaOSvLote0+7y3uA9yU5B7iMZmZUqursJB+iSTavAZ5bVb9b1pORJEmSpCmxIhJGgKo6EDhwpPhcemY5rarfAE+c5TivAV4z9gAlSZIk6XpmpXRJlSRJkiStMCaMkiRJkqReJoySJEmSpF4mjJIkSZKkXiaMkiRJkqReJoySJEmSpF4mjJIkSZKkXiaMkiRJkqRemw0dgIbxo7e/aOgQFu1Wz/3PoUOQJEmSrldsYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9TJhlCRJkiT1MmGUJEmSJPUyYZQkSZIk9VoxCWOSrZIcneS7Sb6T5AFJtklyUpLvtz+3busmyVuSnJPkm0nu3TnOvm397yfZd7gzkiRJkqTJtmISRuDNwCeq6k7APYDvAC8DPlNVuwCfabcBHg3s0t4OAN4BkGQb4EDgfsCuwIEzSaYkSZIkaXFWRMKY5GbAQ4D3AFTV1VX1M2BP4Ii22hHA49v7ewJHVuNLwFZJbgk8Cjipqi6rqsuBk4A9lvFUJEmSJGlqrIiEEdgZWAscnuTMJO9OchNg+6q6uK3zY2D79v6tgQs6+1/Yls1WLkmSJElapJWSMG4G3Bt4R1XdC/gV67qfAlBVBdS4njDJAUnWJFmzdu3acR1WkiRJkqbGSkkYLwQurKovt9tH0ySQl7RdTWl/Xto+fhGwY2f/Hdqy2cqvo6oOqarVVbV61apVYzsRSZIkSZoWKyJhrKofAxckuWNbtDvwbeB4YGam032B49r7xwNPbWdLvT/w87br6ieBRybZup3s5pFtmSRJkiRpkTYbOoCO5wEfSHID4FxgP5qE9kNJ9gd+ADyprXsi8BjgHODKti5VdVmSVwFfbeu9sqouW75TkCRJkqTpsWISxqr6OrC656Hde+oW8NxZjnMYcNh4o5MkSZKk658V0SVVkiRJkrTymDBKkiRJknotOGFM8tQkN5/lsW2SPHV8YUmSJEmShraYFsbDgdvP8tjO7eOSJEmSpCmxmIQxczx2c+CKjYxFkiRJkrSCzDlLapI9gT07Rf+UZO1ItS2AB7NuKQtJkiRJ0hSYb1mN7YC7dbZvD9xipM7VwKeAV48xLkmSJEnSwOZMGKvqUOBQgCSfA55dVd9djsAkSZIkScOar4XxWlX1sKUMRJIkSZK0siw4YQRIcivgscAONGMXu6qqXjquwCRJkiRJw1pwwphkL+C/gU2BS2nGLnYVYMIoSZIkSVNiMS2M/0ozuc3TquqyJYpHkiRJkrRCLCZh3BF4nsmiJEmSJF0/bLKIul8A7rhUgUiSJEmSVpbFtDC+CPhAkl8CJwE/G61QVVeOKzBJkiRJ0rAWkzB+s/15OM0EN3023bhwJEmSJEkrxWISxqcze6IoSZIkSZoyC04Yq+q9SxiHJEmSJGmFWcykN5IkSb2lJPQAACAASURBVJKk65EFtzAmWcs8XVKraruNjkiSJEmStCIsZgzj27luwrg1sDuwJXDYuIKSJEmSJA1vMWMYD+orTxLgQ8BvxxSTJEmSJGkF2OgxjFVVwLuBv934cCRJkiRJK8W4Jr25HXCDMR1LkiRJkrQCLGbSm+f0FN8A+CNgH+DD4wpKkiRJkjS8xUx687aesquAC4GDgX8ZS0SSJEmSpBVhMZPeuGajJEmSJF2PmARKkiRJknotKmFMcrsk70hyVpKL2p8HJ7ndUgUoSZIkSRrGYia9uQ/wOeA3wMeAS4DtgScA+yR5WFV9bUmilCRJkiQtu8VMevN64Ezg0VV15UxhkhsDJ7aPP3y84UmSJEmShrKYLqm7Aq/rJosA7fbrgfuNMzBJkiRJ0rAWkzD+Grj5LI9tQ9NVVZIkSZI0JRaTMH4ceG2SB3UL2+1/A04YZ2CSJEmSpGEtZgzji4DjgM8nuRS4FNiOZuKbLwAvHn94kiRJkqShLDhhrKqfAg9KsgdwX+CWwMXAl6vqU0sUnyRJkiRpIHN2SU1yyyQfSfKombKq+kRVvaqqnlNVr2qq5SNJtlvyaCVJkiRJy2a+MYwvAW4HzNWC+ClgZ+ySKkmSJElTZb6E8bHAO6uqZqvQPvYuYM9xBiZJkiRJGtZ8CeNtgW8v4DjfAXba6GgkSZIkSSvGfAnjr4EtF3Ccm7Z1JUmSJElTYr6E8WvA4xZwnD3bupIkSZKkKTFfwngwsH+SfWerkOSpwH7A28YZmCRJkiRpWHOuw1hVH0nyZuDwJH8LfAL4IVDAbYBHAauBN1bVsUsdrCRJkiRp+cyZMAJU1YuTnAy8kGaZjRu2D10FnA7sWVUfW7IIJUmSJEmDmDdhBKiqE4ATkmwG3Lwt/mlVXbNkkUmSJEmSBrWghHFGmyBeskSxSJIkSZJWkPkmvZEkSZIkXU+ZMEqSJEmSepkwSpIkSZJ6mTBKkiRJknqZMEqSJEmSepkwSpIkSZJ6mTBKkiRJknqtqIQxyaZJzkzysXZ75yRfTnJOkg8muUFbfsN2+5z28Z06x3h5W/69JI8a5kwkSZIkafKtqIQReAHwnc72vwNvrKo7AJcD+7fl+wOXt+VvbOuR5M7Ak4G7AHsAByfZdJlilyRJkqSpsmISxiQ7AH8KvLvdDvBw4Oi2yhHA49v7e7bbtI/v3tbfEziqqq6qqvOAc4Bdl+cMJEmSJGm6rJiEEXgT8A/A79vtmwM/q6pr2u0LgVu3928NXADQPv7ztv615T37SJIkSZIWYUUkjEkeC1xaVWcs43MekGRNkjVr165drqeVJEmSpImxIhJG4IHA45KcDxxF0xX1zcBWSTZr6+wAXNTevwjYEaB9/GbAT7vlPfusp6oOqarVVbV61apV4z0bSZIkSZoCKyJhrKqXV9UOVbUTzaQ1n62qfYDPAXu31fYFjmvvH99u0z7+2aqqtvzJ7SyqOwO7AF9ZptOQJEmSpKmy2fxVBvVS4KgkrwbOBN7Tlr8HeF+Sc4DLaJJMqursJB8Cvg1cAzy3qn63/GFLkiRJ0uRbcQljVZ0MnNzeP5eeWU6r6jfAE2fZ/zXAa5YuQkmSJEm6flgRXVIlSZIkSSuPCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqZcJoyRJkiSplwmjJEmSJKmXCaMkSZIkqddmQwcgLYUvHvLYoUNYlAcc8LGhQ5AkSZKuwxZGSZIkSVIvE0ZJkiRJUi8TRkmSJElSLxNGSZIkSVIvE0ZJkiRJUi8TRkmSJElSLxNGSZIkSVIvE0ZJkiRJUq/Nhg5A0uIdffgeQ4ewKHvv94mhQ5AkSdIGsIVRkiRJktTLhFGSJEmS1MsuqZJWlLd84FFDh7Boz9/nk0OHIEmStCRsYZQkSZIk9TJhlCRJkiT1skuqJC2j/Y6drBluAQ7fy1luJUm6vrKFUZIkSZLUa0UkjEl2TPK5JN9OcnaSF7Tl2yQ5Kcn3259bt+VJ8pYk5yT5ZpJ7d461b1v/+0n2HeqcJEmSJGnSrYiEEbgGeHFV3Rm4P/DcJHcGXgZ8pqp2AT7TbgM8GtilvR0AvAOaBBM4ELgfsCtw4EySKUmSJElanBWRMFbVxVX1tfb+L4DvALcG9gSOaKsdATy+vb8ncGQ1vgRsleSWwKOAk6rqsqq6HDgJmLwBQ5IkSZK0AqyIhLEryU7AvYAvA9tX1cXtQz8Gtm/v3xq4oLPbhW3ZbOWSJEmSpEVaUQljkpsCHwFeWFVXdB+rqgJqjM91QJI1SdasXbt2XIeVJEmSpKmxYhLGJJvTJIsfqKpj2uJL2q6mtD8vbcsvAnbs7L5DWzZb+XVU1SFVtbqqVq9atWp8JyJJkiRJU2JFJIxJArwH+E5V/WfnoeOBmZlO9wWO65Q/tZ0t9f7Az9uuq58EHplk63aym0e2ZZIkSZKkRdps6ABaDwT+Gjgrydfbsn8EXgt8KMn+wA+AJ7WPnQg8BjgHuBLYD6CqLkvyKuCrbb1XVtVly3MKkiRJkjRdVkTCWFWnAZnl4d176hfw3FmOdRhw2PiikyRJkqTrpxXRJVWSJEmStPKYMEqSJEmSepkwSpIkSZJ6mTBKkiRJknqZMEqSJEmSepkwSpIkSZJ6mTBKkiRJknqtiHUYJUnT4THHvnroEBbtxL1eMXQIkiStWCaMkiQt0J9+5N1Dh7AoH3/CM4YOQZI04UwYJUkSAH929DFDh7AoJ+z950OHIElTzzGMkiRJkqReJoySJEmSpF4mjJIkSZKkXiaMkiRJkqReJoySJEmSpF4mjJIkSZKkXiaMkiRJkqReJoySJEmSpF4mjJIkSZKkXiaMkiRJkqReJoySJEmSpF4mjJIkSZKkXiaMkiRJkqReJoySJEmSpF4mjJIkSZKkXpsNHYAkSdJS2+sjpw0dwqId+4QHDR2CJJkwSpIkTbrnH3vB0CEs2lv22nHoECQtgAmjJEmSVrRjjv7J0CEs2p/vve3QIUhj4RhGSZIkSVIvWxglSZKkAZ357kuHDmFR7vWM7RZV/8evP2eJIlkat3jJHYYOYUWxhVGSJEmS1MsWRkmSJEnaAJe85eShQ1i07Z+/26Lq28IoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeplwihJkiRJ6mXCKEmSJEnqZcIoSZIkSeo1lQljkj2SfC/JOUleNnQ8kiRJkjSJpi5hTLIp8Hbg0cCdgackufOwUUmSJEnS5Jm6hBHYFTinqs6tqquBo4A9B45JkiRJkibONCaMtwYu6Gxf2JZJkiRJkhYhVTV0DGOVZG9gj6p6Rrv918D9qupvR+odABzQbt4R+N4yhbgt8JNleq4heH6TzfObXNN8buD5TTrPb3JN87mB5zfpPL/xum1VrRot3GwZA1guFwE7drZ3aMvWU1WHAIcsV1AzkqypqtXL/bzLxfObbJ7f5JrmcwPPb9J5fpNrms8NPL9J5/ktj2nskvpVYJckOye5AfBk4PiBY5IkSZKkiTN1LYxVdU2SvwU+CWwKHFZVZw8cliRJkiRNnKlLGAGq6kTgxKHjmMWyd4NdZp7fZPP8Jtc0nxt4fpPO85tc03xu4PlNOs9vGUzdpDeSJEmSpPGYxjGMkiRJkqQxMGGUJEmSJPUyYZQkSWOTZNMkrx86DknSeEzlpDeSFifJ1sAuwBYzZVV1ynARbbwk957r8ar62nLFslSSvKCq3jxf2SRLcg/gwe3mqVX1jSHj0fyq6ndJHjR0HEslyTZzPV5Vly1XLMuh/f+wY1V9c+hYtDBJXllV/9zZ3hQ4sqr2GTCssUlyw6q6ar4yjY+T3iyDJDcEngDsRCdJr6pXDhXTOCQ5AZj1BVRVj1vGcMYuyT9U1euSvJWe86yq5w8Q1tgleQbwAmAH4OvA/YEvVtXDBw1sIyX53BwP16SfH0CSr1XVvUfKzqyqew0V0zgleQHwTOCYtmgv4JCqeutwUY1Pkn/uK5/0/w0ASd4B3Br4MPCrmfKqOmbWnSZEkvNo/iek5+Gqqtstc0hjl+Rk4HE031nOAC4FTq+qFw0Z1zgluStwZ9a/UHrkcBGNT5LDgf+tqn9rv4N+CDizqg4aNrLxmOV/33XKJlmSP+a6ecNgr09bGJfHccDPaT50p+nqx0yXoz8HbgG8v91+CnDJIBGN13fan2sGjWLpvQC4L/ClqnpYkjsB/zpwTButqh42dAxLJclTgL8Edk5yfOehLYFpat3YH7hfVf0KIMm/A18EpiJhpJNI0XxpfSzrPncm3RbAT4HuhZliXfI/sapq56FjWAY3q6or2guKR1bVgUmmpoUxyYHAbjQJ44nAo4HTgKlIGIGnAx9I8nLgYcCJVfWmgWPaaEluQXMh6kZJ7sW6izZbAjceLLAxS/I+4PY0F/F/1xYXA74+TRiXxw5VtcfQQYxbVX0eIMkbqmp156ETkkx8klVVJ7Q/jxg6liX2m6r6TZKZLh3fTXLHoYMalyRP7Suf8CvJXwAuBrYF3tAp/wUwNV/qaL4M/K6z/Tv6W3UmUlV1/3a04/4+OVA4Y1VV+w0dw1JL8pC+8knvzt/aLMktgScB/2/oYJbA3sA9aFrd9kuyPesuek+skaEYbwbeBZwOnJLk3lMwFONRwNNoekT9Z6f8F8A/DhHQElkN3LlWUDdQE8bl8YUkd6uqs4YOZIncJMntqupcgCQ7AzcZOKaxSfKHwEu4bteAie/S2LowyVbAR4GTklwO/GDgmMbpvp37WwC7A19jgq8kV9UPgB8keQTw66r6ffs6vRMwTZ8zhwNfTnJsu/144D0DxrPUbkzzRWjiJdmBpiX4gW3RqcALqurC4aIau7/v3N8C2JWmJ9E0/G94Jc3Fi9Oq6qtJbgd8f+CYxmnmc/OaJFvSdLndceigxuANI9uX07SivoGmhWqiX5vtBfwjkjyhqj4ydDxL6Fs0PfcuHjqQGY5hXAZJvg3cATiPpktqaMY53H3QwMYkyR7AIcC5NOd2W+BZVTUVV8qTfAN4J80XgWtbO6rqjMGCWiJJHgrcDPhEVV09dDxLoU2Oj5qGVv8kZ9BMCLM1zVXkrwJXT8vEBnDtFfOZCVROraozh4xnnJKcxbrx0ZsCq4BXVtXbhotqPJKcBPwX8L626K+AfarqT4aLamkl2RF4U1U9YehYNLckB9O0SD0ZeDHwS+Dr14eW8WnQdinum1ti4sd/w7VzMNwT+AqdoWxDzg1iwrgMkty2r7xtJZgK7aDqO7Wb352mmaqSnFFV9xk6jqU0Mwse67egTnrXlV5JNge+VVUT3+12ZpB/kucBN2onafp6Vd1z6NjGYZbZKH9RVb9d9mCWwMj/hmuAS6rqmqHiGae+1+E0vTb7JAlwdlXdeehYNlaSVTQTTu3E+v8Xnj5UTEslyU7AltM0C+w0T6gFkOTFnc1rx39Py+uzvXh/HTNDwYZgl9Tl8QzgFOALM5M3TKFdgDvSvHHvkWTSx4h1v6yekOQ5wLGsf6VnKiYXSfIqmjEB5wK/b4snvuvKjJHZfDeh6Z7zoeEiGqskeQCwD80EMdC0VE2Lr9FcyLicpvfCVsCPk1wCPHPSW/mr6gftdPfb0/w/vlX72fnDgUMbh58m+Svgv9vtp9BMgjM1RmbQ3oSmRWBaLrQdR9ON+NOsP454KiT5TFXtDlBV54+WTYFpnlBrqsd/w7CJ4WxsYVwGSfaj6Tb2AJqBuacCp1TVcYMGNiazzTZWVXsPGdfGuj5MnQ6Q5HvA3aa4C2r3St01wA+mZRxVO+nGS2imu//3dpzRC6doyZdDgaNnurcneSTNEkWHA2+uqvsNGd/GaluGD6SZVfraizXTMFyhbT19K83/vaKZqOn5U5IMA5Bk387mNcD5VXX6UPGM07S2BifZgmas8Odovrd0Z9n8RFXdaZZdJ1rbC+yTVbXb0LEshbaX1Fer6g5Dx7IxkpxWVQ9K8gvW73I7M5Rty4FCM2FcTu10wE+i+YK3dVX9wcAhjUU7DmdmtrF7zMw2Ns1jVaZJko8Az66qS4eOZSkkuQnXnRjmf6alW+M0S3JWVd1tpOybVXX3afhCm+QcmmVDpqrl7fokyQ1oPlMK+N60XHhL8mqaXlEnDh3LOKVZ2/WFwK2AH3UeugI4dBrGD/eZloRqxjSP/16p7JK6DJK8m6b17RKa1sW9mZ5uKzC9s40BkOS5wAeq6mft9tbAU6rq4GEjG5t/A85M8i1WyODqMTsFeHD7d/sUzcQwf0HTjXOiteOM/gG4C+svPj0V3YmBi5O8FDiq3f4L4JK2G+fvZ99tYlxAs0bv1Bjppnkd09L6DZDkMTTLFvwfTQvAzkmeVVX/M2xkY/EC4B+TXA1czQpo4RiHqnoz8OYkz6uqaVnP9TpmSaheNVxEY/fYzv2pGf89y7j9aw05FMqEcXncnOYN+zOaRbV/Mg0v7I417cyTh9LMJPpLmsW1p8Uzq+rtMxtVdXmSZwLTkjAeAfw7zXIM0/AlfFSq6sok+wMHz0wMM3RQY/IB4IM0/zz/BtgXWDtoROP1lzRdNj/abp/elm1K01tjIiV5UXv3XODkJB9n/Ys1/9m742SYWYP3gTQXSj/Ybj8R+PYgES2d/wQeVlXnACS5PfBxYOITxmnpATWHdyV5PjCzlubJwLumqOfJnqwbe3oNTYPFVIxvby8YfnJKuw+fwRxDoYDBhkLZJXUZJfkjmkVH/w7YtKqmYr2trimdbews4O4zC6i2H1bfrKq7DBvZeCT5alXdd/6akynJmcBzgDcC+1fV2X1dHSfRzAy+M90027Kp/ntOg3bc96yq6l+WK5alkuRLwINmLo62sxOfWlX3Hzay8Rl9r7WzpH5lGt5/7bnsA+xcVa9qlwy5ZVV9ZeDQxqLt+bU5zQVTgL8GfldVzxguqvFJclh3xtB2aMbx0zKpT5LjgOdN05jolc4WxmWQ5LE0k948hGaWv8/SdE2daO36aLM+NkXLMnwC+GCSd7Xbz2rLpsWpSf4NOJ71Wzmm5e/3QuDlwLFtsng7mgkPpsHM1fCLk/wpzZicObu0TJJp7XLblxAm2QS4aVVdMUBIS2FrmolEZrpQ3bQtm3hJ/ry9uybJiTSzLhdNK+pXBwtsvA6m6XHycJqujL8E3g5MfDLcum9V3aOz/dk0ay5PiwuTHFxVz2mHY3ycphfYtNgaODvJV+jMCDvpQ2nm+l4Nw34vs4VxGSR5G02CeGpV/Wi++pMizcKi0HyRWw18g6YZ/e7Amqp6wFCxjVP7Re5ZwMyVuZOAd1fVVEw13vk7dtWkfykfleSmAFX1y6FjGZf2YtSpNGOG30rzBf1fqur4QQMbkySfounS+BI6XW6r6qWDBjYmSf6L5rx+R5NobEkz++t/DBrYGLSzgx9Ec3EmNBdMD6qqI+babxIkOXyux2sKFn/PujVez6yqe7Vl3xhJsiZWkq8BT6yq/2u3b0czI/OcX9gnSZLX0Xym3Ad4bVV9ZOCQxiYrcJ3CcZjl+9iMQb+XmTAuk3bm0Jkrc1+ZphkpkxwDHFhVZ7Xbd6X5YjDRy2poOiS5G3AkTctbaMb4PbWqzh40sI2Q5N+r6qVJnlhVHx46nqUy7V1uZ2Z6TbIPcG/gZcAZ07CsBlw7M/jM0idfrqofDxmPFi7Jl4E/pplZ895ta/+nZpLHSZdkd5rlec6l+b9wW2C/qpro3ied1m9ozuufgK/Q9oqqqmOGiEuTzy6pyyDJE4HX0wyqDvDWJH9fVUcPGtj43HEmWQSoqm+14zWnQpJdaGYSvTPrd4ub6HUYk/xVVb2/MwHHeiZ84o2udwEvmvkikGQ3mq45fzxkUBvpMUleRtPVdmoTRqa8yy2weTu27/HA26rqt0mm6SruVcDFNJ+bf5jkD6vqlIFjGps2iXomsBOd71PdsWMT7C3AscB2SV5DM7v7K4YNaXyq6jPt//Y7tkXfq6qr5tpnQvzZyPaZNGM1/4ym2/REJ4xZwesUjlOSGwMvAm5TVQfMvFar6mNDxWTCuDxeQdNf/lK49p/Mp4FpSRi/2Q4gf3+7vQ8wNZPe0FyFPJBm0pSHAfsBmwwa0XjcpP3ZNxveNH1pvUn3qnFVndxOADDJPgFcDtw0SXfM21T90wReneRmwItZ1+X274YNaazeBZxP053/lDSL3U/FGMYkz6BZmmEH4OvA/Wlmz56mru7H0XQJ/zTrZqScClX1gSRn0AzFCPD4qvrOwGGNzUhLHMAdkvwcOGuSe4BV1X7txHzPr6o3Dh3PuFXVg9qf0z6L7+E0M6bOXNi+iObi8GAJo11Sl8HojIztmLhvTMMsjQBJtgCezbrpqU8B3lFVvxkuqvHpdIu79u84UzZ0bEslyQur6k1DxzEOSY6lWff0fW3RXwH3qaq9hotqPJIcV1V7Dh2HxifJZtOw7FI7u/R9gS+13W7vBP+/vTsPk6us8jj+/SXIDiLwsKgQcARBkEURhRCGwASBIIsgiCKiKKOOiIIyAwok4MiuOOJCAIEouyyyL0KAECUQQkCWgAs7siWsEhI0Z/5430rfVKqXdFf3zb39+zxPnu57b9XtU9WVrjr3fd9z+GFENH9Qr6zGlOKy4+gvOfFYlflHT2tRlTK3stmCjgJo25A+oK9NagD/607uWgmS7oqIzcuOoz9J2gpYJyLOlrQysFxEPFZ2XO0gaUpEbLYorSH2COPAuF7SDcAFeXtv4NoS42mrnBj+OP+ro9k5yf+zpG+QrvQsW3JM/e0QoBYJI/AlYCwdU3Em5n2VV9dkUdJhkfpltmwCHxVv/t7ddHBSf7+qeysi3pKEpCUiYrqkD3R/t0q5WtJOEVGb9/MGSQeRZtY8Txo9Fen/Yi3W15I+/64fEc/DvDoT40lrbm+n4wJjVU3KBRcvYv4qorWofq7Ummgz0pTis4HFSbPchpcZVxvNkbQU+f1PqcdrqVOmnTAOgIj4rqQ96Hghj4uIy8uMqZ0kDSdVwxvG/FciK73Gr+BgYGngm6Ty4iNJ1RrrrFXT2EqKiJdJv7vaaFq/0fhdNZr91mFKamPq25Qub1VdXU0Hr4unJa0AXAHcJOll4ImSY2q3g4EjJM0mrbety/8/SI/tAxExo+xA+skajWQxeyHvmynp7c7uVCGNke9jCvuC+kwJ3x3YlDR7iIh4VlKd/p4eTVp6soak80j5w/5lBuQpqdZnkqaT1hXdQ2EdRx3eaPKUnBMi4jtlxzKQJD0ZEWuWHUdfSOqytUTV+zUNBq2qwNa9Mmwd5RL47wSuj4g5Zcdj3cvl/UfVYXp0K5J+DqxJR9GwPYGngO8CV0fEyLJis+41ptwW2r8sA/yxLhWmASStRFr7LdLU/pdKjccJY//Li6tPAFYh/eLrdBUSSZMj4mPd37KaJN0ZER8vO452a1FlbN4hYKmIqPQMBEkvkj4AXABMpmnUtOr9mhpqvo5janNftFb7qkbS/3V1vOpTbmHeY7wwIv5Qdiz9Sakp+jrMX0G7spVgC9OkNyBN97uGwlS4ulTPliTgU8BWedck4NKo+IfiwVL9XNJ3SP/vRpGq2H8JOD8iflpqYH0kqcv3tjKnFFf6A2GFnAh8sk4VxmC+F/YESSeR1ogV31hqMVceuDePVl3C/GsBKl2eehBUGVuN9GayD/BZ0gefC6rcf7FZXddxSNoR2Al4T1NytTxQhxGPe/LX4aR2PRfl7U8DD5USUfvdA3w/r1u8nJQ81mqKcU0rwTbeF57M/xbP/6BG1bMjIiTdAcwhPa67qp4sZoOi+nlEnCxpFKmq9AeAoyLippLDaodT8tclSe/t95Eudm9EWqKxRUlxeYRxIEiaFBGV/gDXSp6y0pmIiCq/ac4j6ewWu6MmvbYGBUlLkBLHk4CxEXFaySG1haRp5HUchUpq85rcV5WkjUlrcI4Bjioceh2YkNelVp6kO4GtGtP+ck/GiXWa0SBpRWAP4DOknmLrlBxS29S5Emzdp4NL2ov0fnAr6QP5CKDy/bElrRERT3VybOcy+/j1lzyzZkZNEn4AJF0GHB25x7mkDYExEbFnWTF5hHFgTJF0EWnxf3EEruojVINijn9EfLHsGKx3cqI4mpQsrkVHM+q6mJOvlDcqqVW9vyQAEXEfcJ+k8yOiDgUoOvMu0qjpzLy9bN5XJ+8H1iMVRavVLBvqXQn2cDrW93W1r6q+Rz37Y98kaYeIeLy4U9IXST3BK50wSvo4cDzpb+axpGq2KwNDJO0XEdeXGV8bfaCRLAJExAOS1i8zICeMA2N54E1g+8K+oKPMf6V1Mlf+VeCeiJg20PG0W+4zeQBpTUdxnYpHGBdhksYDG5Ja2IyNiAdKDqk/XCzpdGAFSV8hreM4o+SY2mlzSWPoqMDcWP9dlwrMx5OmvE8gPbatSRWnK0/SiaRKhn8lTbk9NiJeKTeqtqtdJdgupoMvR6oEWxdDGsliNgMYUlYwbXQIcKOk0RHxZwBJHhQKuwAAEwtJREFUh5OWZfx7qZG1x2nAEaQiWrcAO0bEnXl0/wJSZdE6uF/SmaQlJgCfA+4vMR5PSR0IklaMiJlN+9auUWGK80lzra/Ku3YmvbDXAi6JiBNLCq0tJF0CTCf9wT2G9B/34Yg4uNTArEuS5tKx5rT4h65uRadGkS5GCbihJus4gHpXYG6QtBqp9xvA5Ih4rsx42kXSf5KKiJRa2W+gFCrBXlflUfE8HXxTUu/a4nTwYcCqEfFfpQTWZrnuwkbM3x/7/oj47/Kiag9J2wGnA7sBXwY2B0bXYSq/pGkRsUn+/uGIWL9wbF6T+6rLAxVfI11EhNQb9BeR+p6XE5MTxv4naRLpKshreXt9UiK1YbmRtYek24GdIuKNvL0sqcDIDqRRxg+WGV9fNf4INdaG1XGdkVVfTddx1LoCM9SyyuYiW+Wv3ST9OiI+392+KsrvcxuSLpR+GniMdAGgFuu/YV4F+0aV1IlRr/7YI0jLL/4A7FVmotFOxSrZzRWz61BBu0jSUqR134+UHQt4SupA+SFwlaTRpGpO40mjVHWxCoW1maRpK6tGxCylhsZV17ha/EpeePwc6TGblWIQreOodQXmmlbZPKWLY3VqHA5pmcI8Sn17P1JSLG0haV3Smu99gJdI04lVx5oFuY7EZY2LbWXH0w6FdlkClgC2A17IbUTqMLNmY0mvkdt/5e/J20t2frdqkbQLqSjT4sDakjYBjokS+0c7YRwAEXFNvlp3I2kdwO4R8WjJYbXTecBkSb/L258Ezs8FOOpQIn5cHgX4PnAlqTDFkeWGZIPcYFnH0Rhd3Kywr05Jx8F0VNkc2aiyWXJMfVLHxKJZXhN2BAt+YJ0DjCstsPaYDkwEdo6IvwBI+na5IbVP3S+21b1dVkQMLTuGAXI0aSrxrQARMU3S2mUG5Cmp/UjST5l/7dR2pAIAj0M9mjM3SPoosGXenFSHfluDsTy1VcNgWcdRd5LujoiP5vYoH4uI2ZIejIgNur1zBeQZGR9k/um248uLqL0kHRcRh5cdRztJ2o3UAmU46cLThcCZEVHqh9V2kTSFjott42i62Oa/nbYokHRnRHy8+H6ukltmeYSxfzUnTfe0vFUNRMTdkp4gfzCQtGZEPFlyWH1V6/LUVmlzC9/PajpWq6uAeSp/c4XiY8qLqK1qV2WzQdLRwDakhPFaYEfgDtKSjLq4WtIyEfEPSfsCHwZ+EhGV/R1GxBXAFXmG0K7At4BVJP0CuDwibiw1wL5brPEYJB0TEXcC5JYo5UZm1uFBSZ8FhkpaB/gmaT1qaTzCaH2W51qfArwbeAFYE5he9avkknYCTiVVF2suT71jRDxdZnw2eEn6F6kCrIClSG17yNtLRsQ7yoqtnST9ElgaGAmcCewJ3BURB5QaWD8oVNm8PiLmlB1PXyk1td8YuDciNpa0KvCbiBhVcmhtI+l+0mPcCDiH9BrdKyLq0L5gnrwk49PA3hGxXdnx9MVgKppi1SVpaVKv0HkV0EmtiVwltc4kDSf11qplLzFJ95HWFP0+VxMdCexbhw91dS5PbbaoK1QmbnxdltS2YETZsbWDpBVb7H69ym0ZGiTdFRGbS7qHlPC/TmpHtF7JobVNI8GQdBTwTESc5aRj0TZYLraZtZunpA6Ms2jRS6xG3o6IGZKGSBoSERMknVp2UO0QETfnKai3kqYDbFuX8tRmFdCYbvumpHeTClWsXmI87TYVWAN4mfSBdQXgOUnPA1+JiCovY5iSp9ueQXrve4NUAbZOXs+zTvYFtpY0BHDCsQgbREVTrIIkXdnVcVdJrb9XI+K6soPoR6/kK/+3A+dJeoGOhumVNQjKU5st6q7OSceJdKwBP7PEeNrtJuC3EXEDgKTtgT2As4Gf01EltnIi4uv5219Kuh5YPiLuLzOmfrA3aYnCARHxnKQ1SaXwzcx6YwvgKVK188mkz5+LBE9JHQCSjgeGUt9eYsuQRgKGkPpLvhM4LyJq0dfIzAZWrrr8VEQ8l7f3I43iTAfGRMTMMuNrF0l/iogPNe1rTL+dVwm3SiQNA16JiFfz9kjSlP4ngNPqsD7TzKw/5F6uo0h9UDcCriFV732w1MBwwjggJE3I3zae7MYIVV16ic3TaIAbfmGZWS9Jmgr8R0TMlLQ1qbT/QcAmwPoRsWepAbaJpBuBm0mPD9KI1ShgB+DuKq6FkzSZ1Gv42dxs+vfAcaQPP29HxJdLDbANCrNPFjiEZ5+YWRtIWoKUOJ4EjI2I00qNx5/r+4+kQxrf5q8BvAjcERGPlRNV+3TVABeofANcMyuHpPsiYuP8/c+AFyNiTN6u5MhbK/kC29HAVnnXJGAs8CqwZqNxepUUe4VJOhmYGxGH5fV908rsI2ZmtqjLieJoUrK4FnAl8KuIeKbMuLyGsX8t12LfMOB7ksZExIUtjlfJaXQ0wL2Fpga4pKa/ZmYLa6ikxSLin6S1wwcWjtXmfSsiXgIOkrRc2ow3CocrlyxmxTU32wKHA0TEXPe5MzPrnKTxwIak3rVjI+KBkkOaxyOMJcil1H9fxelGRcUr/ZIejoj1C8fujYhNy4vOzKpK0veAnYCXSH1dPxwRIen9wLkRMbzUANtE0odIjewb7TVeAr6wKH1IWFiSfkKqZPt3YBdg3Yh4W9LqwFURsVmpAZqZLaIkzaWjaGQxQSt9unttrtRWSV6XU4dLrXML389qOuYrEWbWKxHxv5JuJiUeNxbWRA8hrWWsi9OBQyJiAoCkbYBxwJZlBtVH3yKtxVwd2KrQU3I1UiNqMzNrISKGlB1DZzzCWIJcNe7Iqhe9cQNcM7PeK67V7GqfmZlZmTzC2I8k/YkFR9pWBJ4F9hv4iNrLDXDNzPrkb5KOJBUMg9Q65G8lxmNmZrYAjzD2o9yPqihILScq39TezMz6RtK7SFVRG1VSJ5L6TL5cXlRmZmbzc8JoZmZmZmZmLXlKqpmZ2QCSdGVXxyNil4GKpb9IGg6MIbWSWoyOKn/vKzMuMzNbeE4YzczMBtYWwFOkfrWTmb93YV2cBXwbuAf4V8mxmJlZH3hKqpmZ2QCSNBQYBewDbARcA1wQEQ+WGlgbSZocER8rOw4zM+s7J4xmZmYlkbQEKXE8CRgbEaeVHFJbSDoeGApcBsxu7I+IqaUFZWZmveKE0czMbIDlRHE0KVlcC7gS+FVEPFNmXO0iaUKL3VH1/sNmZoORE0YzM7MBJGk8sCFwLXBhRDxQckhmZmadcsJoZmY2gCTNBRr9eItvwo1KossPfFTtJ2k0sAGwZGNfRBxTXkRmZtYbrpJqZmY2gCJiSNkx9DdJvwSWBkYCZwJ7AneVGpSZmfWKRxjNzMysrSTdHxEbFb4uC1wXESPKjs3MzBZO7a9ympmZ2YCblb++KendwNvA6iXGY2ZmveQpqWZmZtZuV0tagdQuZCppreYZ5YZkZma94SmpZmZm1m9yC5ElI+LVsmMxM7OF5ympZmZm1haSPipptcL2fsDFwLGSViwvMjMz6y0njGZmZtYupwNzACRtDRwPjAdeBcaVGJeZmfWS1zCamZlZuwyNiJn5+72BcRFxKXCppGklxmVmZr3kEUYzMzNrl6GSGhejtwNuKRzzRWozswryH28zMzNrlwuA2yS9RGqtMRFA0vtJ01LNzKxiXCXVzMzM2kbSx0k9F2+MiH/kfesCy0bE1FKDMzOzheaE0czMzMzMzFryGkYzMzMzMzNryQmjmZmZmZmZteSE0czMekTSHpJukfSKpNmSHpX0I0nvzsfXkhSSdi7c5zBJ27Q4V0j6xgCG3yOSzpE0pbC9uaQxLW43Jhd2qZTOHk8fzre9pG+12L+XpP3b9XOazr1Nfv1s2B/nNzOz+TlhNDOzbkk6BbgY+BvweWB74Mek1gk/yzf7O7AFcEfhrocB27Q45RbAJf0Ubl8cC+xf2N4cOLqcUPpFux/P9sACCSOwF/M/j2ZmVlFuq2FmZl2S9EngEOCAiPhV4dBtksaRkgYiYjZwZ0/OGRE9ut1Ai4i/lh2DmZnZosQjjGZm1p1vA1ObkkUAIuJfEXEdLDglVdLjwErA0Xl/NKantpqSKmlXSVMkvSXpOUknSnpH4fh7JV0s6QVJsyT9VdKxnQUtaaykRwvby0h6W9LUwr6VJc2VNCpvz5uSmqdU/rQQb0i6telnbCrpTklvSrpX0oiunsjCc/QZSWdLek3S05L2zccPk/SspBclnSBpSNP9t5U0OT9Hz0v6uaRlC8ffIelkSU/macPPSrpc0uI9eTxNP2u0pJvy8/1afpzbF46PAQ4FhhXOd46kc4A9gH8v7B/Tk3MWzr2RpKvy9Oc3JN3V+B11Eutn8uP9Wt5eqNeKmZl1ziOMZmbWqZywbQmc0ou77w5MAH4LnJn3PdTJz9mL1PT9dOAI4N+A40gXNr+TbzYeWAo4EHgFeB+wXhc/fyJwlKRVI+L5/Dj+CWwsafmIeA0YAcwF/tji/teQHvehpCm0AK8Vji8NnEuamvscaarnZZKGRcSbXcQFcAJwHimx+hJwrqRNgWF5+yPAD4B7gQsBJG0AXA/clO+3BnB8fh52yOc9HPgc8D/AY8BqwE7A0B48nmZrA1cBJ5Oeox2B6yRtHRGTSL/TdYBtSb9rgBfz1zWBFYCv5+2ne3hOJK0HTAIeAb4KzAA2y493AZK+SHrdHBgR5+TdC/taMTOzTjhhNDOzrqwELAE8ubB3jIh7Jf0TeLqrKaiSBJwEjI+Irxf2zwZ+Jum4iJhBWn+3T0RclW9yazch/JGUII4gJa0jgGtJydKWpORrBHBvRLzRIv4X8yhpZ1NolwK+FRG35Hj/Tkrwts7n7sotEXFEvt9kYE9gF2C9iPgXcL2kXUmJ2IX5PkcCTwC75NsgaSZwkaQtIuKPpOfo/Ig4t/CzLs5fZ3XzeJof/2mN7/NI5wRgA+AAYFJEPJ0f8+zm8+W4hjTv7+6c+dDRwKvAiIiYlffd1CpGSV8FfgLsFxEXFg4t7GvFzMw64SmpZmbWE9GP516XNCJ1saTFGv+AW4AlgUY1zGnAcZL2l7RmdyeNiH8AU0lJIaRE7nbSyGNx38Rexj2H+RORxujpe3tw35sLcb5GGpm7rZEIZn8B3lPY3hy4vOk2l5KS4q3y9jRg/zy1daOcjPdKntZ5rqRn8s94m7Redd1+Pue2wEWFZLEz3wROBfZuShZhIV8rZmbWOSeMZmbWlRnAbFJC119Wzl+vJSUQjX+P5f2NqYh7A1NIU0CfkDRN0nbdnHsiMELS4sDH8nZj33LAJvQ+YXw9IuY2NiJiTv52yR7c95Wm7Tmd7Cuea3Xg+eINcvI4A1gx7/oBqWrt14H7gKckHdyDeOaTR/+uJI3EHgWMBD4KXEfPHl9fzrkSqeJud/YgJdU3tzjWm9eKmZm14ITRzMw6FRFvk6YKfqIff8zM/PVAUgLR/O+6HMszEbE/KaHYgrRu8EpJK3Vx7onAxqT2H3NII08TSaN1I0lr++7o9N6Llr8DqxR3SBpKej5mAkTEWxFxVESsRRq1uwg4VdIOLJz3A5sCB0XEWRFxW0RMIU3D7a2ennMGKTnuzueAZUivgfmS2F6+VszMrAUnjGZm1p1Tgc0kfaH5gKQh3SQjzaNkrTwCPAOsFRFTWvybUbxxRMzNa+PGkgrPDOvi3BMBkYrATMojgn8CZpGKv0yPiBe7uP8cgOaEpCSTgd1zktjwKVI9ggWS3oj4M6lg0Gzgg3l3Tx9PI4mb3dghaRgwvOl2nf1+W+3v6TlvBvbqQYxPky4ErAtcqkJF3YaFfK2YmVkLLnpjZmZdioirJP0IOEvScOB3wBukqpNfBR6n8yIv04HRkq7P93kkIl5vOv9cSYcCv5a0PGlEcQ6psuVupIIw7wBuIFW/fJRUiOdQ0sjRw13EPlPSQ6S1iocXft4kYDRwRjcPf3r+erCkW4DXIuKRbu7TXxpVU6+Q9AvSWskTgBtywRskXQ7ck283i/TcLUZauwk9fzzTSQnZKZKOBJYjJV3PtLjdqrllxwPASxHxeN6/q6Td8nmeXYhzjgXuBm6XdAppxHFTYEZza5eI+Ftut3Eb8BtJ++TzLvRrxczMWvMIo5mZdSsiDiWtC1sHOJ9UtfJQ0mjQ17q463eBf5BaOtxNahfR6vwXAbuS1hReAlxGWoc3lZQ8vkUaGTyYtA7uXOBNYPseFEdprFG8vcW+7qajTiRVcD2YNMJ3eje37zcR8SCpDcUqpOfnB6RWJHsWbvYHUpJ9Pimx/wiwR576CT18PBExmzR6+U9ShdljSW1Obmu66cXAOcCJpN/vmLz/58CNwK/y/gN7es6cwG4FvERq3XF5foxPdBLrQ6TCOZ8gXQDoy2vFzMyaKKI/C9+ZmZmZmZlZVXmE0czMzMzMzFpywmhmZmZmZmYtOWE0MzMzMzOzlpwwmpmZmZmZWUtOGM3MzMzMzKwlJ4xmZmZmZmbWkhNGMzMzMzMza8kJo5mZmZmZmbXkhNHMzMzMzMxa+n/wa08jO0v/rQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhnHDKJcwHCl"
      },
      "source": [
        "We can see Baghdad and Karachi, Lima is under most Attacks."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ok1yOARxwSbb"
      },
      "source": [
        "***Thank You.***"
      ]
    }
  ]
}