TASK 5.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "txHCnZg3lv1h"
   },
   "source": [
    "# LETSGROW MORE INTERNSHIP\n",
    "\n",
    "## NAME :- HARSHAWARDHAN CHITNIS\n",
    "\n",
    "## POSITION :- Data Science Intern\n",
    "\n",
    "## TASK LEVEL : INTERMEDIATE LEVEL TASK - 1\n",
    "\n",
    "## PROJECT NAME : EXPLORATORY DATA ANALYSIS - TERRORISM\n",
    "\n",
    "# STARTING WITH THE PROJECT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mHPwKQNgnHhS"
   },
   "source": [
    "### Importing the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "O9VXDhPll4ck"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aQ2yRJPCjuVF"
   },
   "source": [
    "### Loading the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "qmfdQ2rArfHd"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv('globalterrorismdb_0718dist.csv', encoding='latin1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ATPufCn-j3Ex"
   },
   "source": [
    "### Understanding the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 370
    },
    "id": "ze6n1DN6VdZ_",
    "outputId": "6a0919c4-36dc-40e9-dcef-a1c4a998bf89"
   },
   "outputs": [
    {
     "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>iyear</th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>approxdate</th>\n",
       "      <th>extended</th>\n",
       "      <th>resolution</th>\n",
       "      <th>country</th>\n",
       "      <th>country_txt</th>\n",
       "      <th>region</th>\n",
       "      <th>region_txt</th>\n",
       "      <th>provstate</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>attacktype1_txt</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>targtype1_txt</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>weapdetail</th>\n",
       "      <th>nkill</th>\n",
       "      <th>nkillus</th>\n",
       "      <th>nkillter</th>\n",
       "      <th>nwound</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",
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       "      <td>0.0</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>0.0</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",
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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>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",
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       "      <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  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]"
      ]
     },
     "execution_count": 3,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 655
    },
    "id": "39SFJqWjV975",
    "outputId": "2b9cf554-4414-4e2d-cd27-cec506a51c4a"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventid</th>\n",
       "      <th>iyear</th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>approxdate</th>\n",
       "      <th>extended</th>\n",
       "      <th>resolution</th>\n",
       "      <th>country</th>\n",
       "      <th>country_txt</th>\n",
       "      <th>region</th>\n",
       "      <th>region_txt</th>\n",
       "      <th>provstate</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>attacktype1_txt</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>targtype1_txt</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>weapdetail</th>\n",
       "      <th>nkill</th>\n",
       "      <th>nkillus</th>\n",
       "      <th>nkillter</th>\n",
       "      <th>nwound</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>181686</th>\n",
       "      <td>201712310022</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>182</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>11</td>\n",
       "      <td>Sub-Saharan Africa</td>\n",
       "      <td>Middle Shebelle</td>\n",
       "      <td>Ceelka Geelow</td>\n",
       "      <td>2.359673</td>\n",
       "      <td>45.385034</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>The incident occurred near the town of Balcad.</td>\n",
       "      <td>12/31/2017: Assailants opened fire on a Somali...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Insurgency/Guerilla Action</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Armed Assault</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>Military</td>\n",
       "      <td>36.0</td>\n",
       "      <td>Military Checkpoint</td>\n",
       "      <td>Somali National Army (SNA)</td>\n",
       "      <td>Checkpoint</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Somalia: Al-Shabaab Militants Attack Army Che...</td>\n",
       "      <td>\"Highlights: Somalia Daily Media Highlights 2 ...</td>\n",
       "      <td>\"Highlights: Somalia Daily Media Highlights 1 ...</td>\n",
       "      <td>START Primary Collection</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>181687</th>\n",
       "      <td>201712310029</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>200</td>\n",
       "      <td>Syria</td>\n",
       "      <td>10</td>\n",
       "      <td>Middle East &amp; North Africa</td>\n",
       "      <td>Lattakia</td>\n",
       "      <td>Jableh</td>\n",
       "      <td>35.407278</td>\n",
       "      <td>35.942679</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>The incident occurred at the Humaymim Airport.</td>\n",
       "      <td>12/31/2017: Assailants launched mortars at the...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Insurgency/Guerilla Action</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>4</td>\n",
       "      <td>Military</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Military Barracks/Base/Headquarters/Checkpost</td>\n",
       "      <td>Russian Air Force</td>\n",
       "      <td>Hmeymim Air Base</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Mortars were used in the attack.</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Seven military planes were damaged in this att...</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Putin's 'victory' in Syria has turned into a ...</td>\n",
       "      <td>\"Two Russian soldiers killed at Hmeymim base i...</td>\n",
       "      <td>\"Two Russian servicemen killed in Syria mortar...</td>\n",
       "      <td>START Primary Collection</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>181688</th>\n",
       "      <td>201712310030</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</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>Maguindanao</td>\n",
       "      <td>Kubentog</td>\n",
       "      <td>6.900742</td>\n",
       "      <td>124.437908</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>The incident occurred in the Datu Hoffer distr...</td>\n",
       "      <td>12/31/2017: Assailants set fire to houses in K...</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>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>14</td>\n",
       "      <td>Private Citizens &amp; Property</td>\n",
       "      <td>76.0</td>\n",
       "      <td>House/Apartment/Residence</td>\n",
       "      <td>Not Applicable</td>\n",
       "      <td>Houses</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-99.0</td>\n",
       "      <td>Houses were damaged in this attack.</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Maguindanao clashes trap tribe members,\" Phil...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>START Primary Collection</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>181689</th>\n",
       "      <td>201712310031</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>92</td>\n",
       "      <td>India</td>\n",
       "      <td>6</td>\n",
       "      <td>South Asia</td>\n",
       "      <td>Manipur</td>\n",
       "      <td>Imphal</td>\n",
       "      <td>24.798346</td>\n",
       "      <td>93.940430</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>The incident occurred in the Mantripukhri neig...</td>\n",
       "      <td>12/31/2017: Assailants threw a grenade at a Fo...</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>0</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>2</td>\n",
       "      <td>Government (General)</td>\n",
       "      <td>21.0</td>\n",
       "      <td>Government Building/Facility/Office</td>\n",
       "      <td>Forest Department Manipur</td>\n",
       "      <td>Office</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A thrown grenade was used in the attack.</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-9</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>\"Trader escapes grenade attack in Imphal,\" Bus...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181690</th>\n",
       "      <td>201712310032</td>\n",
       "      <td>2017</td>\n",
       "      <td>12</td>\n",
       "      <td>31</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>Maguindanao</td>\n",
       "      <td>Cotabato City</td>\n",
       "      <td>7.209594</td>\n",
       "      <td>124.241966</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12/31/2017: An explosive device was discovered...</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>0</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>20</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>An explosive device containing a detonating co...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</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>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>\"Security tightened in Cotabato following IED ...</td>\n",
       "      <td>\"Security tightened in Cotabato City,\" Manila ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>START Primary Collection</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>0</td>\n",
       "      <td>-9</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",
       "181686  201712310022   2017      12    31  ...        0         0       0      NaN\n",
       "181687  201712310029   2017      12    31  ...       -9         1       1      NaN\n",
       "181688  201712310030   2017      12    31  ...        0         0       0      NaN\n",
       "181689  201712310031   2017      12    31  ...       -9         0      -9      NaN\n",
       "181690  201712310032   2017      12    31  ...       -9         0      -9      NaN\n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "LfAgqR4WV1ED",
    "outputId": "0c715cd5-f2d6-4121-fb04-3f8a5ee05723"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181691, 135)"
      ]
     },
     "execution_count": 5,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aR8snlVJVgJd",
    "outputId": "8dd61254-36b1-4016-89a6-de8065f40de4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 181691 entries, 0 to 181690\n",
      "Columns: 135 entries, eventid to related\n",
      "dtypes: float64(55), int64(22), object(58)\n",
      "memory usage: 187.1+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315
    },
    "id": "_1EhnCk2Vhcm",
    "outputId": "c5ce9522-5cf9-42a8-eb57-8f2260c9a0b3"
   },
   "outputs": [
    {
     "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>iyear</th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>extended</th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>specificity</th>\n",
       "      <th>vicinity</th>\n",
       "      <th>crit1</th>\n",
       "      <th>crit2</th>\n",
       "      <th>crit3</th>\n",
       "      <th>doubtterr</th>\n",
       "      <th>alternative</th>\n",
       "      <th>multiple</th>\n",
       "      <th>success</th>\n",
       "      <th>suicide</th>\n",
       "      <th>attacktype1</th>\n",
       "      <th>attacktype2</th>\n",
       "      <th>attacktype3</th>\n",
       "      <th>targtype1</th>\n",
       "      <th>targsubtype1</th>\n",
       "      <th>natlty1</th>\n",
       "      <th>targtype2</th>\n",
       "      <th>targsubtype2</th>\n",
       "      <th>natlty2</th>\n",
       "      <th>targtype3</th>\n",
       "      <th>targsubtype3</th>\n",
       "      <th>natlty3</th>\n",
       "      <th>guncertain1</th>\n",
       "      <th>guncertain2</th>\n",
       "      <th>guncertain3</th>\n",
       "      <th>individual</th>\n",
       "      <th>nperps</th>\n",
       "      <th>nperpcap</th>\n",
       "      <th>claimed</th>\n",
       "      <th>claimmode</th>\n",
       "      <th>claim2</th>\n",
       "      <th>claimmode2</th>\n",
       "      <th>claim3</th>\n",
       "      <th>claimmode3</th>\n",
       "      <th>compclaim</th>\n",
       "      <th>weaptype1</th>\n",
       "      <th>weapsubtype1</th>\n",
       "      <th>weaptype2</th>\n",
       "      <th>weapsubtype2</th>\n",
       "      <th>weaptype3</th>\n",
       "      <th>weapsubtype3</th>\n",
       "      <th>weaptype4</th>\n",
       "      <th>weapsubtype4</th>\n",
       "      <th>nkill</th>\n",
       "      <th>nkillus</th>\n",
       "      <th>nkillter</th>\n",
       "      <th>nwound</th>\n",
       "      <th>nwoundus</th>\n",
       "      <th>nwoundte</th>\n",
       "      <th>property</th>\n",
       "      <th>propextent</th>\n",
       "      <th>propvalue</th>\n",
       "      <th>ishostkid</th>\n",
       "      <th>nhostkid</th>\n",
       "      <th>nhostkidus</th>\n",
       "      <th>nhours</th>\n",
       "      <th>ndays</th>\n",
       "      <th>ransom</th>\n",
       "      <th>ransomamt</th>\n",
       "      <th>ransomamtus</th>\n",
       "      <th>ransompaid</th>\n",
       "      <th>ransompaidus</th>\n",
       "      <th>hostkidoutcome</th>\n",
       "      <th>nreleased</th>\n",
       "      <th>INT_LOG</th>\n",
       "      <th>INT_IDEO</th>\n",
       "      <th>INT_MISC</th>\n",
       "      <th>INT_ANY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.816910e+05</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>177135.000000</td>\n",
       "      <td>1.771340e+05</td>\n",
       "      <td>181685.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181690.000000</td>\n",
       "      <td>29011.000000</td>\n",
       "      <td>181690.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>6314.000000</td>\n",
       "      <td>428.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>171318.000000</td>\n",
       "      <td>180132.000000</td>\n",
       "      <td>11144.000000</td>\n",
       "      <td>10685.000000</td>\n",
       "      <td>10828.000000</td>\n",
       "      <td>1176.000000</td>\n",
       "      <td>1097.000000</td>\n",
       "      <td>1147.000000</td>\n",
       "      <td>181311.000000</td>\n",
       "      <td>1955.000000</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>110576.000000</td>\n",
       "      <td>112202.000000</td>\n",
       "      <td>115571.000000</td>\n",
       "      <td>19083.000000</td>\n",
       "      <td>1890.000000</td>\n",
       "      <td>616.000000</td>\n",
       "      <td>318.000000</td>\n",
       "      <td>133.000000</td>\n",
       "      <td>4839.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>160923.000000</td>\n",
       "      <td>13127.000000</td>\n",
       "      <td>11542.000000</td>\n",
       "      <td>1863.000000</td>\n",
       "      <td>1693.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>171378.000000</td>\n",
       "      <td>117245.000000</td>\n",
       "      <td>114733.000000</td>\n",
       "      <td>165380.000000</td>\n",
       "      <td>116989.000000</td>\n",
       "      <td>112548.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>64065.000000</td>\n",
       "      <td>3.898900e+04</td>\n",
       "      <td>181513.000000</td>\n",
       "      <td>13572.000000</td>\n",
       "      <td>13517.000000</td>\n",
       "      <td>4063.000000</td>\n",
       "      <td>8124.000000</td>\n",
       "      <td>77381.000000</td>\n",
       "      <td>1.350000e+03</td>\n",
       "      <td>5.630000e+02</td>\n",
       "      <td>7.740000e+02</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>10991.000000</td>\n",
       "      <td>10400.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "      <td>181691.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.002705e+11</td>\n",
       "      <td>2002.638997</td>\n",
       "      <td>6.467277</td>\n",
       "      <td>15.505644</td>\n",
       "      <td>0.045346</td>\n",
       "      <td>131.968501</td>\n",
       "      <td>7.160938</td>\n",
       "      <td>23.498343</td>\n",
       "      <td>-4.586957e+02</td>\n",
       "      <td>1.451452</td>\n",
       "      <td>0.068297</td>\n",
       "      <td>0.988530</td>\n",
       "      <td>0.993093</td>\n",
       "      <td>0.875668</td>\n",
       "      <td>-0.523171</td>\n",
       "      <td>1.292923</td>\n",
       "      <td>0.137773</td>\n",
       "      <td>0.889598</td>\n",
       "      <td>0.036507</td>\n",
       "      <td>3.247547</td>\n",
       "      <td>3.719512</td>\n",
       "      <td>5.245327</td>\n",
       "      <td>8.439719</td>\n",
       "      <td>46.971474</td>\n",
       "      <td>127.686441</td>\n",
       "      <td>10.247218</td>\n",
       "      <td>55.311652</td>\n",
       "      <td>131.179442</td>\n",
       "      <td>10.021259</td>\n",
       "      <td>55.548769</td>\n",
       "      <td>144.564952</td>\n",
       "      <td>0.081440</td>\n",
       "      <td>0.265473</td>\n",
       "      <td>0.193750</td>\n",
       "      <td>0.002950</td>\n",
       "      <td>-65.361154</td>\n",
       "      <td>-1.517727</td>\n",
       "      <td>0.049666</td>\n",
       "      <td>7.022848</td>\n",
       "      <td>0.247619</td>\n",
       "      <td>7.176948</td>\n",
       "      <td>0.411950</td>\n",
       "      <td>6.729323</td>\n",
       "      <td>-6.296342</td>\n",
       "      <td>6.447325</td>\n",
       "      <td>11.117162</td>\n",
       "      <td>6.812524</td>\n",
       "      <td>10.754029</td>\n",
       "      <td>6.911433</td>\n",
       "      <td>11.643237</td>\n",
       "      <td>6.246575</td>\n",
       "      <td>10.842857</td>\n",
       "      <td>2.403272</td>\n",
       "      <td>0.045981</td>\n",
       "      <td>0.508058</td>\n",
       "      <td>3.167668</td>\n",
       "      <td>0.038944</td>\n",
       "      <td>0.107163</td>\n",
       "      <td>-0.544556</td>\n",
       "      <td>3.295403</td>\n",
       "      <td>2.088119e+05</td>\n",
       "      <td>0.059054</td>\n",
       "      <td>4.533230</td>\n",
       "      <td>-0.353999</td>\n",
       "      <td>-46.793933</td>\n",
       "      <td>-32.516371</td>\n",
       "      <td>-0.145811</td>\n",
       "      <td>3.172530e+06</td>\n",
       "      <td>5.784865e+05</td>\n",
       "      <td>7.179437e+05</td>\n",
       "      <td>240.378623</td>\n",
       "      <td>4.629242</td>\n",
       "      <td>-29.018269</td>\n",
       "      <td>-4.543731</td>\n",
       "      <td>-4.464398</td>\n",
       "      <td>0.090010</td>\n",
       "      <td>-3.945952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.325957e+09</td>\n",
       "      <td>13.259430</td>\n",
       "      <td>3.388303</td>\n",
       "      <td>8.814045</td>\n",
       "      <td>0.208063</td>\n",
       "      <td>112.414535</td>\n",
       "      <td>2.933408</td>\n",
       "      <td>18.569242</td>\n",
       "      <td>2.047790e+05</td>\n",
       "      <td>0.995430</td>\n",
       "      <td>0.284553</td>\n",
       "      <td>0.106483</td>\n",
       "      <td>0.082823</td>\n",
       "      <td>0.329961</td>\n",
       "      <td>2.455819</td>\n",
       "      <td>0.703729</td>\n",
       "      <td>0.344663</td>\n",
       "      <td>0.313391</td>\n",
       "      <td>0.187549</td>\n",
       "      <td>1.915772</td>\n",
       "      <td>2.272023</td>\n",
       "      <td>2.246642</td>\n",
       "      <td>6.653838</td>\n",
       "      <td>30.953357</td>\n",
       "      <td>89.299120</td>\n",
       "      <td>5.709076</td>\n",
       "      <td>25.640310</td>\n",
       "      <td>125.951485</td>\n",
       "      <td>5.723447</td>\n",
       "      <td>26.288955</td>\n",
       "      <td>163.299295</td>\n",
       "      <td>0.273511</td>\n",
       "      <td>0.441698</td>\n",
       "      <td>0.395854</td>\n",
       "      <td>0.054234</td>\n",
       "      <td>216.536633</td>\n",
       "      <td>12.830346</td>\n",
       "      <td>1.093195</td>\n",
       "      <td>2.476851</td>\n",
       "      <td>0.974018</td>\n",
       "      <td>2.783725</td>\n",
       "      <td>0.492962</td>\n",
       "      <td>2.908003</td>\n",
       "      <td>4.234620</td>\n",
       "      <td>2.173435</td>\n",
       "      <td>6.495612</td>\n",
       "      <td>2.277081</td>\n",
       "      <td>7.594574</td>\n",
       "      <td>2.177956</td>\n",
       "      <td>8.493166</td>\n",
       "      <td>1.507212</td>\n",
       "      <td>8.192672</td>\n",
       "      <td>11.545741</td>\n",
       "      <td>5.681854</td>\n",
       "      <td>4.199937</td>\n",
       "      <td>35.949392</td>\n",
       "      <td>3.057361</td>\n",
       "      <td>1.488881</td>\n",
       "      <td>3.122889</td>\n",
       "      <td>0.486912</td>\n",
       "      <td>1.552463e+07</td>\n",
       "      <td>0.461244</td>\n",
       "      <td>202.316386</td>\n",
       "      <td>6.835645</td>\n",
       "      <td>82.800405</td>\n",
       "      <td>121.209205</td>\n",
       "      <td>1.207861</td>\n",
       "      <td>3.021157e+07</td>\n",
       "      <td>7.077924e+06</td>\n",
       "      <td>1.014392e+07</td>\n",
       "      <td>2940.967293</td>\n",
       "      <td>2.035360</td>\n",
       "      <td>65.720119</td>\n",
       "      <td>4.543547</td>\n",
       "      <td>4.637152</td>\n",
       "      <td>0.568457</td>\n",
       "      <td>4.691325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.970000e+11</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-53.154613</td>\n",
       "      <td>-8.618590e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.991021e+11</td>\n",
       "      <td>1991.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>11.510046</td>\n",
       "      <td>4.545640e+00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>33.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.009022e+11</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>31.467463</td>\n",
       "      <td>4.324651e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>101.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>110.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>-9.900000e+01</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>-99.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.500000e+04</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.014081e+11</td>\n",
       "      <td>2014.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>160.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>34.685087</td>\n",
       "      <td>6.871033e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>173.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000e+05</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>1.273412e+03</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.017123e+11</td>\n",
       "      <td>2017.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>74.633553</td>\n",
       "      <td>1.793667e+02</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>113.000000</td>\n",
       "      <td>1004.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>25000.000000</td>\n",
       "      <td>406.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>1570.000000</td>\n",
       "      <td>1360.000000</td>\n",
       "      <td>500.000000</td>\n",
       "      <td>8191.000000</td>\n",
       "      <td>751.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>2.700000e+09</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>17000.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>2454.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000e+09</td>\n",
       "      <td>1.320000e+08</td>\n",
       "      <td>2.750000e+08</td>\n",
       "      <td>48000.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>2769.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            eventid          iyear  ...       INT_MISC        INT_ANY\n",
       "count  1.816910e+05  181691.000000  ...  181691.000000  181691.000000\n",
       "mean   2.002705e+11    2002.638997  ...       0.090010      -3.945952\n",
       "std    1.325957e+09      13.259430  ...       0.568457       4.691325\n",
       "min    1.970000e+11    1970.000000  ...      -9.000000      -9.000000\n",
       "25%    1.991021e+11    1991.000000  ...       0.000000      -9.000000\n",
       "50%    2.009022e+11    2009.000000  ...       0.000000       0.000000\n",
       "75%    2.014081e+11    2014.000000  ...       0.000000       0.000000\n",
       "max    2.017123e+11    2017.000000  ...       1.000000       1.000000\n",
       "\n",
       "[8 rows x 77 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 437
    },
    "id": "07ZsB8uvWyrU",
    "outputId": "eceeac02-5741-4801-9d1b-4eaed2ee41c4"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventid</th>\n",
       "      <th>iyear</th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>extended</th>\n",
       "      <th>country</th>\n",
       "      <th>region</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>specificity</th>\n",
       "      <th>vicinity</th>\n",
       "      <th>crit1</th>\n",
       "      <th>crit2</th>\n",
       "      <th>crit3</th>\n",
       "      <th>doubtterr</th>\n",
       "      <th>alternative</th>\n",
       "      <th>multiple</th>\n",
       "      <th>success</th>\n",
       "      <th>suicide</th>\n",
       "      <th>attacktype1</th>\n",
       "      <th>attacktype2</th>\n",
       "      <th>attacktype3</th>\n",
       "      <th>targtype1</th>\n",
       "      <th>targsubtype1</th>\n",
       "      <th>natlty1</th>\n",
       "      <th>targtype2</th>\n",
       "      <th>targsubtype2</th>\n",
       "      <th>natlty2</th>\n",
       "      <th>targtype3</th>\n",
       "      <th>targsubtype3</th>\n",
       "      <th>natlty3</th>\n",
       "      <th>guncertain1</th>\n",
       "      <th>guncertain2</th>\n",
       "      <th>guncertain3</th>\n",
       "      <th>individual</th>\n",
       "      <th>nperps</th>\n",
       "      <th>nperpcap</th>\n",
       "      <th>claimed</th>\n",
       "      <th>claimmode</th>\n",
       "      <th>claim2</th>\n",
       "      <th>claimmode2</th>\n",
       "      <th>claim3</th>\n",
       "      <th>claimmode3</th>\n",
       "      <th>compclaim</th>\n",
       "      <th>weaptype1</th>\n",
       "      <th>weapsubtype1</th>\n",
       "      <th>weaptype2</th>\n",
       "      <th>weapsubtype2</th>\n",
       "      <th>weaptype3</th>\n",
       "      <th>weapsubtype3</th>\n",
       "      <th>weaptype4</th>\n",
       "      <th>weapsubtype4</th>\n",
       "      <th>nkill</th>\n",
       "      <th>nkillus</th>\n",
       "      <th>nkillter</th>\n",
       "      <th>nwound</th>\n",
       "      <th>nwoundus</th>\n",
       "      <th>nwoundte</th>\n",
       "      <th>property</th>\n",
       "      <th>propextent</th>\n",
       "      <th>propvalue</th>\n",
       "      <th>ishostkid</th>\n",
       "      <th>nhostkid</th>\n",
       "      <th>nhostkidus</th>\n",
       "      <th>nhours</th>\n",
       "      <th>ndays</th>\n",
       "      <th>ransom</th>\n",
       "      <th>ransomamt</th>\n",
       "      <th>ransomamtus</th>\n",
       "      <th>ransompaid</th>\n",
       "      <th>ransompaidus</th>\n",
       "      <th>hostkidoutcome</th>\n",
       "      <th>nreleased</th>\n",
       "      <th>INT_LOG</th>\n",
       "      <th>INT_IDEO</th>\n",
       "      <th>INT_MISC</th>\n",
       "      <th>INT_ANY</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>eventid</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.999996</td>\n",
       "      <td>0.002706</td>\n",
       "      <td>0.018336</td>\n",
       "      <td>0.091761</td>\n",
       "      <td>-0.135039</td>\n",
       "      <td>0.401371</td>\n",
       "      <td>0.166886</td>\n",
       "      <td>0.003907</td>\n",
       "      <td>0.030641</td>\n",
       "      <td>0.070646</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>0.026215</td>\n",
       "      <td>-0.016789</td>\n",
       "      <td>0.298888</td>\n",
       "      <td>0.041344</td>\n",
       "      <td>0.093720</td>\n",
       "      <td>-0.082972</td>\n",
       "      <td>0.137743</td>\n",
       "      <td>0.074173</td>\n",
       "      <td>0.071982</td>\n",
       "      <td>0.189245</td>\n",
       "      <td>0.079080</td>\n",
       "      <td>0.048798</td>\n",
       "      <td>-0.019084</td>\n",
       "      <td>0.097623</td>\n",
       "      <td>0.111425</td>\n",
       "      <td>-0.082822</td>\n",
       "      <td>0.156170</td>\n",
       "      <td>0.090551</td>\n",
       "      <td>0.039081</td>\n",
       "      <td>0.040647</td>\n",
       "      <td>-0.066449</td>\n",
       "      <td>-0.177867</td>\n",
       "      <td>0.017939</td>\n",
       "      <td>-0.137408</td>\n",
       "      <td>0.428397</td>\n",
       "      <td>0.141600</td>\n",
       "      <td>0.110578</td>\n",
       "      <td>0.033917</td>\n",
       "      <td>0.038996</td>\n",
       "      <td>-0.318032</td>\n",
       "      <td>-0.146094</td>\n",
       "      <td>0.398728</td>\n",
       "      <td>-0.011716</td>\n",
       "      <td>0.101733</td>\n",
       "      <td>0.101045</td>\n",
       "      <td>0.041027</td>\n",
       "      <td>0.081357</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>-0.138596</td>\n",
       "      <td>0.027110</td>\n",
       "      <td>0.015351</td>\n",
       "      <td>-0.013808</td>\n",
       "      <td>0.027579</td>\n",
       "      <td>0.015282</td>\n",
       "      <td>-0.029348</td>\n",
       "      <td>0.027904</td>\n",
       "      <td>-0.278927</td>\n",
       "      <td>-0.150677</td>\n",
       "      <td>-0.013498</td>\n",
       "      <td>-0.010146</td>\n",
       "      <td>0.006369</td>\n",
       "      <td>-0.010082</td>\n",
       "      <td>-0.493414</td>\n",
       "      <td>-0.169859</td>\n",
       "      <td>-0.241986</td>\n",
       "      <td>-0.009990</td>\n",
       "      <td>-0.018001</td>\n",
       "      <td>-0.014094</td>\n",
       "      <td>-0.165422</td>\n",
       "      <td>0.256113</td>\n",
       "      <td>-0.181612</td>\n",
       "      <td>-0.143600</td>\n",
       "      <td>-0.133252</td>\n",
       "      <td>-0.077852</td>\n",
       "      <td>-0.175605</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iyear</th>\n",
       "      <td>0.999996</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>0.018254</td>\n",
       "      <td>0.091754</td>\n",
       "      <td>-0.135023</td>\n",
       "      <td>0.401384</td>\n",
       "      <td>0.166933</td>\n",
       "      <td>0.003917</td>\n",
       "      <td>0.030626</td>\n",
       "      <td>0.070659</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.026211</td>\n",
       "      <td>-0.016784</td>\n",
       "      <td>0.298920</td>\n",
       "      <td>0.041367</td>\n",
       "      <td>0.093736</td>\n",
       "      <td>-0.082963</td>\n",
       "      <td>0.137736</td>\n",
       "      <td>0.074153</td>\n",
       "      <td>0.072177</td>\n",
       "      <td>0.189454</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.048794</td>\n",
       "      <td>-0.019068</td>\n",
       "      <td>0.097552</td>\n",
       "      <td>0.111364</td>\n",
       "      <td>-0.082784</td>\n",
       "      <td>0.155939</td>\n",
       "      <td>0.090345</td>\n",
       "      <td>0.038941</td>\n",
       "      <td>0.040656</td>\n",
       "      <td>-0.066417</td>\n",
       "      <td>-0.177591</td>\n",
       "      <td>0.017944</td>\n",
       "      <td>-0.137459</td>\n",
       "      <td>0.428278</td>\n",
       "      <td>0.141486</td>\n",
       "      <td>0.110542</td>\n",
       "      <td>0.034143</td>\n",
       "      <td>0.039172</td>\n",
       "      <td>-0.318815</td>\n",
       "      <td>-0.145529</td>\n",
       "      <td>0.398227</td>\n",
       "      <td>-0.011737</td>\n",
       "      <td>0.101727</td>\n",
       "      <td>0.101036</td>\n",
       "      <td>0.040990</td>\n",
       "      <td>0.081342</td>\n",
       "      <td>0.090267</td>\n",
       "      <td>-0.138298</td>\n",
       "      <td>0.026855</td>\n",
       "      <td>0.015341</td>\n",
       "      <td>-0.013828</td>\n",
       "      <td>0.027563</td>\n",
       "      <td>0.015273</td>\n",
       "      <td>-0.029357</td>\n",
       "      <td>0.027879</td>\n",
       "      <td>-0.278916</td>\n",
       "      <td>-0.150690</td>\n",
       "      <td>-0.013490</td>\n",
       "      <td>-0.010159</td>\n",
       "      <td>0.006342</td>\n",
       "      <td>-0.010093</td>\n",
       "      <td>-0.493378</td>\n",
       "      <td>-0.169931</td>\n",
       "      <td>-0.241764</td>\n",
       "      <td>-0.009984</td>\n",
       "      <td>-0.018216</td>\n",
       "      <td>-0.014238</td>\n",
       "      <td>-0.165375</td>\n",
       "      <td>0.256092</td>\n",
       "      <td>-0.181556</td>\n",
       "      <td>-0.143601</td>\n",
       "      <td>-0.133253</td>\n",
       "      <td>-0.077847</td>\n",
       "      <td>-0.175596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>imonth</th>\n",
       "      <td>0.002706</td>\n",
       "      <td>0.000139</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005497</td>\n",
       "      <td>-0.000468</td>\n",
       "      <td>-0.006305</td>\n",
       "      <td>-0.002999</td>\n",
       "      <td>-0.015978</td>\n",
       "      <td>-0.003880</td>\n",
       "      <td>0.003621</td>\n",
       "      <td>-0.004501</td>\n",
       "      <td>0.001568</td>\n",
       "      <td>0.001734</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.012477</td>\n",
       "      <td>-0.011762</td>\n",
       "      <td>-0.004422</td>\n",
       "      <td>-0.002845</td>\n",
       "      <td>0.003071</td>\n",
       "      <td>0.006705</td>\n",
       "      <td>-0.029027</td>\n",
       "      <td>-0.039298</td>\n",
       "      <td>-0.000948</td>\n",
       "      <td>0.001508</td>\n",
       "      <td>-0.004838</td>\n",
       "      <td>0.019584</td>\n",
       "      <td>0.016365</td>\n",
       "      <td>-0.006315</td>\n",
       "      <td>0.059489</td>\n",
       "      <td>0.052698</td>\n",
       "      <td>0.036580</td>\n",
       "      <td>-0.004460</td>\n",
       "      <td>-0.008286</td>\n",
       "      <td>-0.054490</td>\n",
       "      <td>-0.001661</td>\n",
       "      <td>0.014354</td>\n",
       "      <td>0.019890</td>\n",
       "      <td>0.023083</td>\n",
       "      <td>0.013361</td>\n",
       "      <td>-0.040736</td>\n",
       "      <td>-0.040042</td>\n",
       "      <td>0.147710</td>\n",
       "      <td>-0.156620</td>\n",
       "      <td>0.144738</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>0.002142</td>\n",
       "      <td>0.000581</td>\n",
       "      <td>0.009567</td>\n",
       "      <td>-0.020514</td>\n",
       "      <td>-0.086445</td>\n",
       "      <td>-0.062079</td>\n",
       "      <td>0.058090</td>\n",
       "      <td>0.003463</td>\n",
       "      <td>0.003891</td>\n",
       "      <td>0.003462</td>\n",
       "      <td>0.002938</td>\n",
       "      <td>0.001467</td>\n",
       "      <td>0.005091</td>\n",
       "      <td>-0.005816</td>\n",
       "      <td>0.001027</td>\n",
       "      <td>-0.003279</td>\n",
       "      <td>0.003397</td>\n",
       "      <td>0.011226</td>\n",
       "      <td>0.003746</td>\n",
       "      <td>-0.015730</td>\n",
       "      <td>0.002131</td>\n",
       "      <td>-0.070199</td>\n",
       "      <td>-0.000710</td>\n",
       "      <td>0.046989</td>\n",
       "      <td>0.058878</td>\n",
       "      <td>-0.016597</td>\n",
       "      <td>0.011295</td>\n",
       "      <td>-0.011535</td>\n",
       "      <td>-0.002302</td>\n",
       "      <td>-0.002034</td>\n",
       "      <td>-0.002554</td>\n",
       "      <td>-0.006336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iday</th>\n",
       "      <td>0.018336</td>\n",
       "      <td>0.018254</td>\n",
       "      <td>0.005497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.004700</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>0.009710</td>\n",
       "      <td>0.003423</td>\n",
       "      <td>-0.002285</td>\n",
       "      <td>-0.006991</td>\n",
       "      <td>-0.005367</td>\n",
       "      <td>0.011434</td>\n",
       "      <td>-0.002702</td>\n",
       "      <td>-0.004889</td>\n",
       "      <td>0.002395</td>\n",
       "      <td>-0.007817</td>\n",
       "      <td>-0.000395</td>\n",
       "      <td>-0.011802</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>-0.005333</td>\n",
       "      <td>0.014498</td>\n",
       "      <td>-0.080422</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>-0.003184</td>\n",
       "      <td>0.003845</td>\n",
       "      <td>0.008964</td>\n",
       "      <td>0.011289</td>\n",
       "      <td>-0.017420</td>\n",
       "      <td>-0.056251</td>\n",
       "      <td>-0.044442</td>\n",
       "      <td>-0.018528</td>\n",
       "      <td>-0.002186</td>\n",
       "      <td>0.041395</td>\n",
       "      <td>-0.039794</td>\n",
       "      <td>-0.003788</td>\n",
       "      <td>-0.013527</td>\n",
       "      <td>0.003300</td>\n",
       "      <td>0.004474</td>\n",
       "      <td>0.003635</td>\n",
       "      <td>-0.031937</td>\n",
       "      <td>0.029723</td>\n",
       "      <td>0.149860</td>\n",
       "      <td>0.106259</td>\n",
       "      <td>0.029735</td>\n",
       "      <td>-0.003019</td>\n",
       "      <td>0.006182</td>\n",
       "      <td>0.017279</td>\n",
       "      <td>-0.005912</td>\n",
       "      <td>0.043226</td>\n",
       "      <td>-0.000417</td>\n",
       "      <td>-0.005110</td>\n",
       "      <td>0.073744</td>\n",
       "      <td>-0.003693</td>\n",
       "      <td>-0.001987</td>\n",
       "      <td>-0.002012</td>\n",
       "      <td>-0.001268</td>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.001812</td>\n",
       "      <td>-0.003903</td>\n",
       "      <td>-0.012729</td>\n",
       "      <td>-0.001093</td>\n",
       "      <td>0.004077</td>\n",
       "      <td>-0.020800</td>\n",
       "      <td>-0.002318</td>\n",
       "      <td>-0.036193</td>\n",
       "      <td>0.019856</td>\n",
       "      <td>0.001206</td>\n",
       "      <td>0.012755</td>\n",
       "      <td>-0.010502</td>\n",
       "      <td>0.003148</td>\n",
       "      <td>-0.006581</td>\n",
       "      <td>-0.006706</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>-0.001540</td>\n",
       "      <td>-0.001621</td>\n",
       "      <td>-0.002027</td>\n",
       "      <td>-0.001199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>extended</th>\n",
       "      <td>0.091761</td>\n",
       "      <td>0.091754</td>\n",
       "      <td>-0.000468</td>\n",
       "      <td>-0.004700</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.020466</td>\n",
       "      <td>0.038389</td>\n",
       "      <td>-0.024749</td>\n",
       "      <td>0.000523</td>\n",
       "      <td>0.057897</td>\n",
       "      <td>0.017784</td>\n",
       "      <td>-0.015029</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>0.048533</td>\n",
       "      <td>0.011638</td>\n",
       "      <td>0.096563</td>\n",
       "      <td>-0.013440</td>\n",
       "      <td>0.073233</td>\n",
       "      <td>-0.033961</td>\n",
       "      <td>0.272272</td>\n",
       "      <td>-0.035143</td>\n",
       "      <td>-0.040606</td>\n",
       "      <td>0.011613</td>\n",
       "      <td>0.022996</td>\n",
       "      <td>0.013609</td>\n",
       "      <td>-0.031271</td>\n",
       "      <td>-0.014091</td>\n",
       "      <td>0.040233</td>\n",
       "      <td>0.032853</td>\n",
       "      <td>0.035114</td>\n",
       "      <td>0.014293</td>\n",
       "      <td>0.033464</td>\n",
       "      <td>-0.056545</td>\n",
       "      <td>-0.030024</td>\n",
       "      <td>-0.008929</td>\n",
       "      <td>-0.004691</td>\n",
       "      <td>0.015514</td>\n",
       "      <td>0.006779</td>\n",
       "      <td>0.042651</td>\n",
       "      <td>-0.162344</td>\n",
       "      <td>0.050917</td>\n",
       "      <td>-0.054570</td>\n",
       "      <td>0.034552</td>\n",
       "      <td>0.051013</td>\n",
       "      <td>0.207802</td>\n",
       "      <td>-0.121002</td>\n",
       "      <td>0.142656</td>\n",
       "      <td>0.138329</td>\n",
       "      <td>0.087612</td>\n",
       "      <td>0.113004</td>\n",
       "      <td>0.117305</td>\n",
       "      <td>0.206678</td>\n",
       "      <td>0.027737</td>\n",
       "      <td>-0.001488</td>\n",
       "      <td>0.005409</td>\n",
       "      <td>-0.006627</td>\n",
       "      <td>-0.002017</td>\n",
       "      <td>0.001592</td>\n",
       "      <td>-0.002096</td>\n",
       "      <td>0.025623</td>\n",
       "      <td>-0.001687</td>\n",
       "      <td>0.326351</td>\n",
       "      <td>0.020132</td>\n",
       "      <td>0.000628</td>\n",
       "      <td>0.228553</td>\n",
       "      <td>0.042814</td>\n",
       "      <td>-0.166868</td>\n",
       "      <td>-0.008114</td>\n",
       "      <td>0.028177</td>\n",
       "      <td>0.001966</td>\n",
       "      <td>0.009367</td>\n",
       "      <td>0.233293</td>\n",
       "      <td>-0.192155</td>\n",
       "      <td>0.071768</td>\n",
       "      <td>0.075147</td>\n",
       "      <td>0.027335</td>\n",
       "      <td>0.080767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nreleased</th>\n",
       "      <td>-0.181612</td>\n",
       "      <td>-0.181556</td>\n",
       "      <td>-0.011535</td>\n",
       "      <td>0.001765</td>\n",
       "      <td>-0.192155</td>\n",
       "      <td>-0.044331</td>\n",
       "      <td>-0.149511</td>\n",
       "      <td>0.002790</td>\n",
       "      <td>-0.017745</td>\n",
       "      <td>-0.030631</td>\n",
       "      <td>-0.014487</td>\n",
       "      <td>-0.008478</td>\n",
       "      <td>0.015422</td>\n",
       "      <td>0.029720</td>\n",
       "      <td>-0.127209</td>\n",
       "      <td>0.062150</td>\n",
       "      <td>-0.085664</td>\n",
       "      <td>-0.019924</td>\n",
       "      <td>0.009071</td>\n",
       "      <td>-0.049677</td>\n",
       "      <td>-0.095443</td>\n",
       "      <td>-0.048964</td>\n",
       "      <td>-0.035435</td>\n",
       "      <td>-0.017000</td>\n",
       "      <td>0.006923</td>\n",
       "      <td>-0.005140</td>\n",
       "      <td>-0.017866</td>\n",
       "      <td>0.149650</td>\n",
       "      <td>-0.092747</td>\n",
       "      <td>-0.059871</td>\n",
       "      <td>0.167344</td>\n",
       "      <td>-0.033964</td>\n",
       "      <td>-0.090308</td>\n",
       "      <td>-0.066597</td>\n",
       "      <td>0.034021</td>\n",
       "      <td>0.134428</td>\n",
       "      <td>0.000147</td>\n",
       "      <td>0.077724</td>\n",
       "      <td>-0.018704</td>\n",
       "      <td>0.035911</td>\n",
       "      <td>-0.152904</td>\n",
       "      <td>-0.358166</td>\n",
       "      <td>0.680160</td>\n",
       "      <td>0.078874</td>\n",
       "      <td>-0.060065</td>\n",
       "      <td>0.043967</td>\n",
       "      <td>-0.074036</td>\n",
       "      <td>-0.030041</td>\n",
       "      <td>-0.112769</td>\n",
       "      <td>-0.058868</td>\n",
       "      <td>0.259311</td>\n",
       "      <td>0.798374</td>\n",
       "      <td>0.176957</td>\n",
       "      <td>0.007876</td>\n",
       "      <td>0.012705</td>\n",
       "      <td>0.020070</td>\n",
       "      <td>-0.011308</td>\n",
       "      <td>0.005367</td>\n",
       "      <td>0.018364</td>\n",
       "      <td>0.047641</td>\n",
       "      <td>0.005231</td>\n",
       "      <td>-0.029934</td>\n",
       "      <td>0.498116</td>\n",
       "      <td>-0.014366</td>\n",
       "      <td>0.156331</td>\n",
       "      <td>0.336551</td>\n",
       "      <td>0.092106</td>\n",
       "      <td>0.054571</td>\n",
       "      <td>0.034843</td>\n",
       "      <td>0.049322</td>\n",
       "      <td>0.016832</td>\n",
       "      <td>-0.555478</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.039388</td>\n",
       "      <td>0.040947</td>\n",
       "      <td>0.085055</td>\n",
       "      <td>0.064759</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INT_LOG</th>\n",
       "      <td>-0.143600</td>\n",
       "      <td>-0.143601</td>\n",
       "      <td>-0.002302</td>\n",
       "      <td>-0.001540</td>\n",
       "      <td>0.071768</td>\n",
       "      <td>0.069904</td>\n",
       "      <td>-0.082584</td>\n",
       "      <td>-0.099827</td>\n",
       "      <td>0.002272</td>\n",
       "      <td>0.073022</td>\n",
       "      <td>0.014487</td>\n",
       "      <td>0.089973</td>\n",
       "      <td>-0.024153</td>\n",
       "      <td>-0.134557</td>\n",
       "      <td>0.230356</td>\n",
       "      <td>-0.176514</td>\n",
       "      <td>0.124327</td>\n",
       "      <td>0.059875</td>\n",
       "      <td>0.058735</td>\n",
       "      <td>0.075002</td>\n",
       "      <td>-0.034918</td>\n",
       "      <td>-0.084217</td>\n",
       "      <td>-0.056550</td>\n",
       "      <td>-0.028861</td>\n",
       "      <td>0.039458</td>\n",
       "      <td>-0.017830</td>\n",
       "      <td>-0.006957</td>\n",
       "      <td>0.019964</td>\n",
       "      <td>-0.018215</td>\n",
       "      <td>-0.024553</td>\n",
       "      <td>0.008949</td>\n",
       "      <td>0.241616</td>\n",
       "      <td>-0.017091</td>\n",
       "      <td>0.164101</td>\n",
       "      <td>-0.048526</td>\n",
       "      <td>0.019626</td>\n",
       "      <td>-0.000354</td>\n",
       "      <td>0.169967</td>\n",
       "      <td>0.034096</td>\n",
       "      <td>0.035953</td>\n",
       "      <td>-0.049742</td>\n",
       "      <td>0.050505</td>\n",
       "      <td>-0.097582</td>\n",
       "      <td>0.275431</td>\n",
       "      <td>0.072941</td>\n",
       "      <td>-0.111675</td>\n",
       "      <td>-0.026263</td>\n",
       "      <td>-0.116493</td>\n",
       "      <td>-0.096817</td>\n",
       "      <td>-0.150678</td>\n",
       "      <td>-0.181856</td>\n",
       "      <td>-0.290198</td>\n",
       "      <td>0.080787</td>\n",
       "      <td>0.005259</td>\n",
       "      <td>0.103751</td>\n",
       "      <td>0.014692</td>\n",
       "      <td>0.002343</td>\n",
       "      <td>0.066046</td>\n",
       "      <td>0.052445</td>\n",
       "      <td>0.069338</td>\n",
       "      <td>0.007440</td>\n",
       "      <td>0.035463</td>\n",
       "      <td>0.017931</td>\n",
       "      <td>0.011549</td>\n",
       "      <td>-0.002041</td>\n",
       "      <td>0.065077</td>\n",
       "      <td>0.007484</td>\n",
       "      <td>0.035821</td>\n",
       "      <td>0.031079</td>\n",
       "      <td>0.007029</td>\n",
       "      <td>-0.045504</td>\n",
       "      <td>-0.015442</td>\n",
       "      <td>0.039388</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.996211</td>\n",
       "      <td>0.052537</td>\n",
       "      <td>0.891051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INT_IDEO</th>\n",
       "      <td>-0.133252</td>\n",
       "      <td>-0.133253</td>\n",
       "      <td>-0.002034</td>\n",
       "      <td>-0.001621</td>\n",
       "      <td>0.075147</td>\n",
       "      <td>0.067564</td>\n",
       "      <td>-0.071917</td>\n",
       "      <td>-0.094470</td>\n",
       "      <td>0.002268</td>\n",
       "      <td>0.071333</td>\n",
       "      <td>0.015119</td>\n",
       "      <td>0.089888</td>\n",
       "      <td>-0.023829</td>\n",
       "      <td>-0.138251</td>\n",
       "      <td>0.231789</td>\n",
       "      <td>-0.177094</td>\n",
       "      <td>0.126013</td>\n",
       "      <td>0.059183</td>\n",
       "      <td>0.065882</td>\n",
       "      <td>0.077225</td>\n",
       "      <td>-0.042121</td>\n",
       "      <td>-0.100514</td>\n",
       "      <td>-0.058010</td>\n",
       "      <td>-0.030089</td>\n",
       "      <td>0.050055</td>\n",
       "      <td>-0.018750</td>\n",
       "      <td>-0.006018</td>\n",
       "      <td>0.029699</td>\n",
       "      <td>-0.010574</td>\n",
       "      <td>-0.015041</td>\n",
       "      <td>0.021273</td>\n",
       "      <td>0.239388</td>\n",
       "      <td>0.007816</td>\n",
       "      <td>0.186334</td>\n",
       "      <td>-0.048542</td>\n",
       "      <td>0.018407</td>\n",
       "      <td>0.001140</td>\n",
       "      <td>0.171639</td>\n",
       "      <td>0.040365</td>\n",
       "      <td>-0.034581</td>\n",
       "      <td>-0.044687</td>\n",
       "      <td>-0.147728</td>\n",
       "      <td>-0.185587</td>\n",
       "      <td>0.283961</td>\n",
       "      <td>0.074702</td>\n",
       "      <td>-0.109029</td>\n",
       "      <td>-0.021746</td>\n",
       "      <td>-0.120108</td>\n",
       "      <td>-0.103234</td>\n",
       "      <td>-0.166400</td>\n",
       "      <td>-0.199290</td>\n",
       "      <td>-0.313694</td>\n",
       "      <td>0.083884</td>\n",
       "      <td>0.006123</td>\n",
       "      <td>0.106814</td>\n",
       "      <td>0.015832</td>\n",
       "      <td>0.003007</td>\n",
       "      <td>0.065494</td>\n",
       "      <td>0.047609</td>\n",
       "      <td>0.067951</td>\n",
       "      <td>0.005847</td>\n",
       "      <td>0.036875</td>\n",
       "      <td>0.019053</td>\n",
       "      <td>0.011690</td>\n",
       "      <td>-0.003997</td>\n",
       "      <td>0.074001</td>\n",
       "      <td>0.008022</td>\n",
       "      <td>0.039053</td>\n",
       "      <td>0.041983</td>\n",
       "      <td>0.013162</td>\n",
       "      <td>-0.039844</td>\n",
       "      <td>-0.016234</td>\n",
       "      <td>0.040947</td>\n",
       "      <td>0.996211</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.082014</td>\n",
       "      <td>0.893811</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INT_MISC</th>\n",
       "      <td>-0.077852</td>\n",
       "      <td>-0.077847</td>\n",
       "      <td>-0.002554</td>\n",
       "      <td>-0.002027</td>\n",
       "      <td>0.027335</td>\n",
       "      <td>0.207281</td>\n",
       "      <td>0.043139</td>\n",
       "      <td>0.097652</td>\n",
       "      <td>0.000371</td>\n",
       "      <td>-0.019197</td>\n",
       "      <td>-0.008232</td>\n",
       "      <td>0.015692</td>\n",
       "      <td>-0.012980</td>\n",
       "      <td>-0.036112</td>\n",
       "      <td>0.022374</td>\n",
       "      <td>-0.028155</td>\n",
       "      <td>-0.027084</td>\n",
       "      <td>-0.014906</td>\n",
       "      <td>-0.000879</td>\n",
       "      <td>0.010632</td>\n",
       "      <td>-0.028271</td>\n",
       "      <td>-0.074534</td>\n",
       "      <td>-0.043492</td>\n",
       "      <td>-0.036703</td>\n",
       "      <td>0.360373</td>\n",
       "      <td>-0.084607</td>\n",
       "      <td>-0.088672</td>\n",
       "      <td>0.263190</td>\n",
       "      <td>-0.015855</td>\n",
       "      <td>0.002460</td>\n",
       "      <td>0.245695</td>\n",
       "      <td>-0.004986</td>\n",
       "      <td>0.012509</td>\n",
       "      <td>0.167842</td>\n",
       "      <td>0.005490</td>\n",
       "      <td>0.004647</td>\n",
       "      <td>0.022354</td>\n",
       "      <td>0.015228</td>\n",
       "      <td>0.020319</td>\n",
       "      <td>0.023908</td>\n",
       "      <td>0.042469</td>\n",
       "      <td>0.017364</td>\n",
       "      <td>-0.073348</td>\n",
       "      <td>-0.014204</td>\n",
       "      <td>-0.011652</td>\n",
       "      <td>0.003272</td>\n",
       "      <td>-0.040726</td>\n",
       "      <td>-0.007818</td>\n",
       "      <td>-0.054102</td>\n",
       "      <td>-0.089868</td>\n",
       "      <td>-0.025805</td>\n",
       "      <td>-0.082547</td>\n",
       "      <td>-0.014522</td>\n",
       "      <td>0.005230</td>\n",
       "      <td>-0.001387</td>\n",
       "      <td>-0.003148</td>\n",
       "      <td>0.009961</td>\n",
       "      <td>-0.004359</td>\n",
       "      <td>0.022058</td>\n",
       "      <td>0.029798</td>\n",
       "      <td>0.019676</td>\n",
       "      <td>0.017453</td>\n",
       "      <td>0.000550</td>\n",
       "      <td>-0.025565</td>\n",
       "      <td>0.093611</td>\n",
       "      <td>0.174531</td>\n",
       "      <td>0.009532</td>\n",
       "      <td>0.023815</td>\n",
       "      <td>0.125162</td>\n",
       "      <td>0.037227</td>\n",
       "      <td>0.129274</td>\n",
       "      <td>-0.119776</td>\n",
       "      <td>0.085055</td>\n",
       "      <td>0.052537</td>\n",
       "      <td>0.082014</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.252193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>INT_ANY</th>\n",
       "      <td>-0.175605</td>\n",
       "      <td>-0.175596</td>\n",
       "      <td>-0.006336</td>\n",
       "      <td>-0.001199</td>\n",
       "      <td>0.080767</td>\n",
       "      <td>0.153118</td>\n",
       "      <td>-0.047900</td>\n",
       "      <td>-0.041530</td>\n",
       "      <td>0.002497</td>\n",
       "      <td>0.061389</td>\n",
       "      <td>0.010325</td>\n",
       "      <td>0.082288</td>\n",
       "      <td>-0.027752</td>\n",
       "      <td>-0.130216</td>\n",
       "      <td>0.168947</td>\n",
       "      <td>-0.156133</td>\n",
       "      <td>0.095019</td>\n",
       "      <td>0.038245</td>\n",
       "      <td>0.057241</td>\n",
       "      <td>0.088711</td>\n",
       "      <td>-0.050435</td>\n",
       "      <td>-0.087635</td>\n",
       "      <td>-0.054497</td>\n",
       "      <td>-0.032169</td>\n",
       "      <td>0.147251</td>\n",
       "      <td>-0.036417</td>\n",
       "      <td>-0.027802</td>\n",
       "      <td>0.110861</td>\n",
       "      <td>-0.024736</td>\n",
       "      <td>-0.022403</td>\n",
       "      <td>0.109622</td>\n",
       "      <td>0.211408</td>\n",
       "      <td>-0.001924</td>\n",
       "      <td>0.186036</td>\n",
       "      <td>-0.037488</td>\n",
       "      <td>0.021176</td>\n",
       "      <td>0.003725</td>\n",
       "      <td>0.153785</td>\n",
       "      <td>0.048736</td>\n",
       "      <td>-0.011847</td>\n",
       "      <td>-0.010886</td>\n",
       "      <td>-0.116204</td>\n",
       "      <td>-0.129893</td>\n",
       "      <td>0.253768</td>\n",
       "      <td>0.084257</td>\n",
       "      <td>-0.095745</td>\n",
       "      <td>-0.021654</td>\n",
       "      <td>-0.105472</td>\n",
       "      <td>-0.110426</td>\n",
       "      <td>-0.157614</td>\n",
       "      <td>-0.080172</td>\n",
       "      <td>-0.232547</td>\n",
       "      <td>0.068140</td>\n",
       "      <td>0.007858</td>\n",
       "      <td>0.098268</td>\n",
       "      <td>0.010745</td>\n",
       "      <td>0.005703</td>\n",
       "      <td>0.060831</td>\n",
       "      <td>0.065701</td>\n",
       "      <td>0.073665</td>\n",
       "      <td>0.006429</td>\n",
       "      <td>0.043566</td>\n",
       "      <td>0.016021</td>\n",
       "      <td>-0.007315</td>\n",
       "      <td>0.038062</td>\n",
       "      <td>0.097378</td>\n",
       "      <td>0.016276</td>\n",
       "      <td>0.028054</td>\n",
       "      <td>0.053484</td>\n",
       "      <td>0.007275</td>\n",
       "      <td>0.056438</td>\n",
       "      <td>-0.061946</td>\n",
       "      <td>0.064759</td>\n",
       "      <td>0.891051</td>\n",
       "      <td>0.893811</td>\n",
       "      <td>0.252193</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            eventid     iyear    imonth  ...  INT_IDEO  INT_MISC   INT_ANY\n",
       "eventid    1.000000  0.999996  0.002706  ... -0.133252 -0.077852 -0.175605\n",
       "iyear      0.999996  1.000000  0.000139  ... -0.133253 -0.077847 -0.175596\n",
       "imonth     0.002706  0.000139  1.000000  ... -0.002034 -0.002554 -0.006336\n",
       "iday       0.018336  0.018254  0.005497  ... -0.001621 -0.002027 -0.001199\n",
       "extended   0.091761  0.091754 -0.000468  ...  0.075147  0.027335  0.080767\n",
       "...             ...       ...       ...  ...       ...       ...       ...\n",
       "nreleased -0.181612 -0.181556 -0.011535  ...  0.040947  0.085055  0.064759\n",
       "INT_LOG   -0.143600 -0.143601 -0.002302  ...  0.996211  0.052537  0.891051\n",
       "INT_IDEO  -0.133252 -0.133253 -0.002034  ...  1.000000  0.082014  0.893811\n",
       "INT_MISC  -0.077852 -0.077847 -0.002554  ...  0.082014  1.000000  0.252193\n",
       "INT_ANY   -0.175605 -0.175596 -0.006336  ...  0.893811  0.252193  1.000000\n",
       "\n",
       "[77 rows x 77 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "HHAMd3D8XNSD",
    "outputId": "1359c4e3-2c8b-4e7c-cc25-54ff1bfd8ce4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.value_counts of              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",
       "181686  201712310022   2017      12    31  ...        0         0       0      NaN\n",
       "181687  201712310029   2017      12    31  ...       -9         1       1      NaN\n",
       "181688  201712310030   2017      12    31  ...        0         0       0      NaN\n",
       "181689  201712310031   2017      12    31  ...       -9         0      -9      NaN\n",
       "181690  201712310032   2017      12    31  ...       -9         0      -9      NaN\n",
       "\n",
       "[181691 rows x 135 columns]>"
      ]
     },
     "execution_count": 9,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.value_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Wq38bHuTWzzi",
    "outputId": "dd4df377-8af5-4500-cafe-7848bd6e9391"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['eventid', 'iyear', 'imonth', 'iday', 'approxdate', 'extended',\n",
       "       'resolution', 'country', 'country_txt', 'region',\n",
       "       ...\n",
       "       'addnotes', 'scite1', 'scite2', 'scite3', 'dbsource', 'INT_LOG',\n",
       "       'INT_IDEO', 'INT_MISC', 'INT_ANY', 'related'],\n",
       "      dtype='object', length=135)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "aU8X35BUXkGe",
    "outputId": "db2c22ca-a87b-4327-e6f0-b359bfc4da42"
   },
   "outputs": [
    {
     "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"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CP3nt1xIXdDY",
    "outputId": "6c7b9beb-7a65-46bb-9b71-4b4ccba6a8cc"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eventid       181691\n",
       "iyear             47\n",
       "imonth            13\n",
       "iday              32\n",
       "approxdate      2244\n",
       "               ...  \n",
       "INT_LOG            3\n",
       "INT_IDEO           3\n",
       "INT_MISC           3\n",
       "INT_ANY            3\n",
       "related        14306\n",
       "Length: 135, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4LA0edplWeaE",
    "outputId": "7d752f7f-8df7-45bd-de2e-169cc25a3f2a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "eventid            0\n",
       "iyear              0\n",
       "imonth             0\n",
       "iday               0\n",
       "approxdate    172452\n",
       "               ...  \n",
       "INT_LOG            0\n",
       "INT_IDEO           0\n",
       "INT_MISC           0\n",
       "INT_ANY            0\n",
       "related       156653\n",
       "Length: 135, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "mcxVYrtDWm1g",
    "outputId": "3b535f68-419a-46cd-d046-d4286f3d2d65"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 14,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "k8HVX_lDj95l"
   },
   "source": [
    "### Renaming and selecting the relevant columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "IQ7buEVYeB3M"
   },
   "outputs": [],
   "source": [
    "df.rename(columns={'iyear':'Year','imonth':'Month','iday':'Day','country_txt':'Country','provstate':'State','region_txt':'Region','city': 'City', 'latitude':'Latitude', 'longitude':'Longitude', 'attacktype1_txt':'Attack_Type','target1':'Target','nkill':'Killed','nwound':'Wounded','summary':'Summary','gname':'Group','targtype1_txt':'Target_Type','weaptype1_txt':'Weapon_Type','motive':'Motive'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "-wcXUUpqef7A"
   },
   "outputs": [],
   "source": [
    "df=df[['Year','Month','Day','Region','Country','State','City','Latitude','Longitude','Attack_Type','Killed','Wounded','Target','Summary','Group','Target_Type','Weapon_Type','Motive']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VV3avcMZkiWe"
   },
   "source": [
    "### Dealing with missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "2qwbEOcUfDwv"
   },
   "outputs": [],
   "source": [
    "df['Wounded'] = df['Wounded'].fillna(0).astype(int)\n",
    "df['Killed'] = df['Killed'].fillna(0).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 358
    },
    "id": "r6cW9MEggvd8",
    "outputId": "cb9d2d8c-2716-4e69-93ba-fcb1ef6c243f"
   },
   "outputs": [
    {
     "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>Region</th>\n",
       "      <th>Country</th>\n",
       "      <th>State</th>\n",
       "      <th>City</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Attack_Type</th>\n",
       "      <th>Killed</th>\n",
       "      <th>Wounded</th>\n",
       "      <th>Target</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Group</th>\n",
       "      <th>Target_Type</th>\n",
       "      <th>Weapon_Type</th>\n",
       "      <th>Motive</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>Central America &amp; Caribbean</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Santo Domingo</td>\n",
       "      <td>18.456792</td>\n",
       "      <td>-69.951164</td>\n",
       "      <td>Assassination</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Julio Guzman</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MANO-D</td>\n",
       "      <td>Private Citizens &amp; Property</td>\n",
       "      <td>Unknown</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>North America</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>Federal</td>\n",
       "      <td>Mexico city</td>\n",
       "      <td>19.371887</td>\n",
       "      <td>-99.086624</td>\n",
       "      <td>Hostage Taking (Kidnapping)</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nadine Chaval, daughter</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23rd of September Communist League</td>\n",
       "      <td>Government (Diplomatic)</td>\n",
       "      <td>Unknown</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>Southeast Asia</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>Tarlac</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>15.478598</td>\n",
       "      <td>120.599741</td>\n",
       "      <td>Assassination</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>Employee</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Journalists &amp; Media</td>\n",
       "      <td>Unknown</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>Western Europe</td>\n",
       "      <td>Greece</td>\n",
       "      <td>Attica</td>\n",
       "      <td>Athens</td>\n",
       "      <td>37.997490</td>\n",
       "      <td>23.762728</td>\n",
       "      <td>Bombing/Explosion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>U.S. Embassy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Government (Diplomatic)</td>\n",
       "      <td>Explosives</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>East Asia</td>\n",
       "      <td>Japan</td>\n",
       "      <td>Fukouka</td>\n",
       "      <td>Fukouka</td>\n",
       "      <td>33.580412</td>\n",
       "      <td>130.396361</td>\n",
       "      <td>Facility/Infrastructure Attack</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>U.S. Consulate</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Government (Diplomatic)</td>\n",
       "      <td>Incendiary</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Month  Day  ...                  Target_Type Weapon_Type Motive\n",
       "0  1970      7    2  ...  Private Citizens & Property     Unknown    NaN\n",
       "1  1970      0    0  ...      Government (Diplomatic)     Unknown    NaN\n",
       "2  1970      1    0  ...          Journalists & Media     Unknown    NaN\n",
       "3  1970      1    0  ...      Government (Diplomatic)  Explosives    NaN\n",
       "4  1970      1    0  ...      Government (Diplomatic)  Incendiary    NaN\n",
       "\n",
       "[5 rows x 18 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O2ZE5vQMklgy"
   },
   "source": [
    "### Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 608
    },
    "id": "b2ic1sz7mUS8",
    "outputId": "7ba12a2c-d11a-43c5-a331-777a79e9bfe5"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.countplot('Year', data=df, edgecolor='black')\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('No. of Terrorist Activities by Year',fontsize=20)\n",
    "plt.xlabel('Year', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EE4qounjktZm"
   },
   "source": [
    "It is observed that terrorist activities have increased in the 2010s (as compared to earlier decades) and hit their peak in 2014."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 594
    },
    "id": "M2lOxLe5qDWy",
    "outputId": "7c748cda-69c5-4209-c19c-a03ddafdb5d8"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.countplot('Month', data=df, edgecolor='black')\n",
    "plt.title('No. of Terrorist Activities by Month',fontsize=20)\n",
    "plt.xlabel('Month', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "75h6qb1nlJiu"
   },
   "source": [
    "The distribution for terrorist activities is pretty even across all months, although the month of May reports the highest no. of terrorist activities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "id": "2TaLOrpSqQId",
    "outputId": "6e7f47ca-0210-449a-bee3-4b6ab59a5387"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.countplot('Day', data=df, edgecolor='black')\n",
    "plt.title('No. of Terrorist Activities by Day', fontsize=20)\n",
    "plt.xlabel('Day', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nRyoflHkl6D7"
   },
   "source": [
    "The distribution is very even across all days except for 31st, which is probably due to the fact that not every month has 31 days. It is observed that the most terrorist activities are reported on the 15th of a month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 594
    },
    "id": "IpgfQVspwY02",
    "outputId": "b958f112-6974-4735-c16f-c0040334dd81"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(df['Region'].value_counts()[:10].index,df['Region'].value_counts()[:10].values)\n",
    "plt.title('No. of Terrorist Activities by Region', fontsize=20)\n",
    "plt.xlabel('Regions', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aIkmhfznmWmo"
   },
   "source": [
    "It is observed that terrorist activities are more frequent in Middle Eastern and North African regions, followed by South Asia ranging above the 40000 mark. These regions can be identified as the hotzones for terrorism."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 594
    },
    "id": "jJ1MVa7sqvv_",
    "outputId": "f56b850b-cf55-4200-c405-80d21269ee7d"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(df['Country'].value_counts()[:10].index,df['Country'].value_counts()[:10].values)\n",
    "plt.title('No. of Terrorist Activities by Country', fontsize=20)\n",
    "plt.xlabel('Countries',fontsize=16)\n",
    "plt.ylabel('Count',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PGUivnS3mq0J"
   },
   "source": [
    "It is observed that Iraq is the most prone to terrorist activities followed by Pakistan, Afghanistan and India, eaching having suffered from over 10000 attacks in the past few decades."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "id": "0EIiaTzUGCAE",
    "outputId": "5e5b14be-1628-4e37-9be6-c13d382b0147"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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mp509Zb7mE9bZ0+WsTJ//P1bVmzPNK/Rv3f36VfZ5TqZ7/7OZJjI/O9P8VbfJNMTxvpnC3g8uauPnqurcTIFLZep1d88k52caZrZWr0/y2qp6bZILk9w109/s9kxzRi3nr5O8INNwx88kec06zpdMc3r9QVV9OFOvwcsyzfF1pyQPnq/nqfOTIBes9pm+M9Pfxg/Nf4tvyTRs8OFJPpLpe7zUv2b623xKVd0qX5/D6oT5QQfr/UwAYMe622KxWCyW6/WSqffKJStsu0mSj811OsmWZeo8LtOPvquSfCnJBzL9OPumFc7V62zfQZl6WlycKXT4TJK/TXLPVa7nnF24H6fMxzhmmW23yfQ0r/dn+gH7uUxPvDot09Pktiyq+6z5OA9c4Tyrbl/vtWd6otiy7V7p3mQaAvk7mX7AL0zEfEmmYXAPX2b/22cKLS7PFHzt6Hw/M9d5zRru+zPnui9aVHaz+bv03vl+X5XpR/8Lk9xmyf6/kik4+/J8nIsWbTtnte9dkj9f9B2/xwp1lr2/cxtPnO/b1XOdU3b0fcwUtPxkpnBl+/z5Xprpb+mZSQ5ZVPdnMwVPH53vw/ZMw+B+Lckt1vi9vqb9SR6VKYT5fKbejH+T5Dt2sP8L5v2ftxN/U3fLNFn42Zn+Pflipn8r/iPTk/nut8J+q32m+yX540wh3MKxfifJN89lFy1zvIfN1/25RZ/3oTvzmVgsFovFspalutc11yYAAFzvVNU5meaOukN3X7DJzQGAPYI5nQAAYBVVda9ME5CfIXACgLUzpxMAACyjqv5XpiGeT8w0nPL4zW0RAOxZDK8DAIBlVNVFmeb9+miSZ3X3yze3RQCwZ9nw4XVV9ctV9YGqen9VvaKqvml+XPHbq+rCqnpVVd14rnuT+f2F8/ZDFx3nGXP5R6rqoRt9HQAA7N26+9Du3tLd3yFwAoD129DQqaoOSvKLSbZ293cnuWGmJ/78fpIXdPftk1yR5Nh5l2OTXDGXv2Cul6q647zfnTI9heOPq+qGG3ktAAAAAKxsM+Z02pLkplX135ke6XpZkgcn+fF5+6mZHqF8YpKj5vVkehTzS6qq5vJXdveXk3ysqi5Mcq9Mj4Bd1q1vfes+9NBDR18LAAAAwPXW+eef/5nu3n+5bRsaOnX3pVX1h0k+nuSLSf4pyflJruzuq+dql2SasDHz6yfmfa+uqs8mudVcfu6iQy/eZ1mHHnpozjvvvFGXAgAAAHC9V1UXr7Rto4fX7Zupl9JhSb4tyc0yDY/bXec7rqrOq6rztm3btrtOAwAAAMASGz2R+Pcm+Vh3b+vu/07ymiT3TbJPVS30ujo4yaXz+qVJDkmSefstk1y+uHyZfa7R3Sd199bu3rr//sv29AIAAABgN9jo0OnjSY6oqm+e52Z6SJIPJnljksfMdY5O8rp5/fT5febtZ3d3z+WPm59ud1iSw5O8Y4OuAQAAAIAd2Og5nd5eVacleVeSq5O8O8lJSf4+ySur6rfnspfOu7w0yV/OE4Vvz/TEunT3B6rq1ZkCq6uTPLm7v7qR1wIAAADAymrqOLT327p1a5tIHAAAAGCcqjq/u7cut22jh9cBAAAAcD0gdAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDbdnsBlwX3eNXX7bZTbheOP95T9jsJgAAAAC7iZ5OAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAw3IaGTlV1h6p6z6Llv6rqKVW1X1WdWVUXzK/7zvWrql5cVRdW1Xur6u6LjnX0XP+Cqjp6I68DAAAAgNVtaOjU3R/p7rt2912T3CPJF5K8NsnTk5zV3YcnOWt+nyQPT3L4vByX5MQkqar9khyf5N5J7pXk+IWgCgAAAIDNt5nD6x6S5D+6++IkRyU5dS4/Ncmj5/WjkrysJ+cm2aeqDkzy0CRndvf27r4iyZlJHraxzQcAAABgJZsZOj0uySvm9QO6+7J5/ZNJDpjXD0ryiUX7XDKXrVQOAAAAwHXApoROVXXjJD+Q5K+XbuvuTtKDznNcVZ1XVedt27ZtxCEBAAAAWIPN6un08CTv6u5Pze8/NQ+by/z66bn80iSHLNrv4LlspfJv0N0ndffW7t66//77D74EAAAAAFayWaHTj+XrQ+uS5PQkC0+gOzrJ6xaVP2F+it0RST47D8M7I8mRVbXvPIH4kXMZAAAAANcBWzb6hFV1syTfl+RJi4p/L8mrq+rYJBcneexc/oYkj0hyYaYn3T0xSbp7e1U9J8k753rP7u7tG9B8AAAAANZgw0On7v58klstKbs809PsltbtJE9e4TgnJzl5d7QRAAAAgF2zmU+vAwAAAGAvJXQCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABguA0Pnapqn6o6rao+XFUfqqr7VNV+VXVmVV0wv+47162qenFVXVhV762quy86ztFz/Quq6uiNvg4AAAAAVrYZPZ1elOQfu/s7k9wlyYeSPD3JWd19eJKz5vdJ8vAkh8/LcUlOTJKq2i/J8UnuneReSY5fCKoAAAAA2HwbGjpV1S2T3D/JS5Oku7/S3VcmOSrJqXO1U5M8el4/KsnLenJukn2q6sAkD01yZndv7+4rkpyZ5GEbeCkAAAAArGKjezodlmRbkr+oqndX1Z9X1c2SHNDdl811PpnkgHn9oCSfWLT/JXPZSuUAAAAAXAdsdOi0Jcndk5zY3XdL8vl8fShdkqS7O0mPOFlVHVdV51XVedu2bRtxSAAAAADWYKNDp0uSXNLdb5/fn5YphPrUPGwu8+un5+2XJjlk0f4Hz2UrlX+D7j6pu7d299b9999/6IUAAAAAsLINDZ26+5NJPlFVd5iLHpLkg0lOT7LwBLqjk7xuXj89yRPmp9gdkeSz8zC8M5IcWVX7zhOIHzmXAQAAAHAdsGUTzvkLSf6qqm6c5KNJnpgp/Hp1VR2b5OIkj53rviHJI5JcmOQLc9109/aqek6Sd871nt3d2zfuEgAAAABYzYaHTt39niRbl9n0kGXqdpInr3Cck5OcPLZ1AAAAAIyw0XM6AQAAAHA9IHQCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMNteOhUVRdV1fuq6j1Vdd5ctl9VnVlVF8yv+87lVVUvrqoLq+q9VXX3Rcc5eq5/QVUdvdHXAQAAAMDKNqun04O6+67dvXV+//QkZ3X34UnOmt8nycOTHD4vxyU5MZlCqiTHJ7l3knslOX4hqAIAAABg811XhtcdleTUef3UJI9eVP6ynpybZJ+qOjDJQ5Oc2d3bu/uKJGcmedhGNxoAAACA5W1G6NRJ/qmqzq+q4+ayA7r7snn9k0kOmNcPSvKJRfteMpetVA4AAADAdcCWTTjn/br70qq6TZIzq+rDizd2d1dVjzjRHGodlyS3ve1tRxwSAAAAgDXY8J5O3X3p/PrpJK/NNCfTp+Zhc5lfPz1XvzTJIYt2P3guW6l86blO6u6t3b11//33H30pAAAAAKxgQ0OnqrpZVd1iYT3JkUnen+T0JAtPoDs6yevm9dOTPGF+it0RST47D8M7I8mRVbXvPIH4kXMZAAAAANcBGz287oAkr62qhXO/vLv/saremeTVVXVskouTPHau/4Ykj0hyYZIvJHliknT39qp6TpJ3zvWe3d3bN+4yAAAAAFjNhoZO3f3RJHdZpvzyJA9ZpryTPHmFY52c5OTRbQQAAABg123G0+sAAAAA2MsJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMNyWzW4AjPbxZ995s5uw17vtb75vs5sAAADAdZyeTgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABguDWHTlV1/6q6+Qrbbl5V9x/XLAAAAAD2ZOvp6fTGJHdcYdsd5u0AAAAAsK7QqVbZdpMkX93FtgAAAACwl9iy2saqOjTJty8q2rrMELubJvmpJB8f2jLgeum+J9x3s5uw13vrL7x1s5sAAABcD6waOiU5OsnxSXpeTsg39njq+f3VSZ68OxoIAAAAwJ5nR6HTKUnOyRQsnZ0pWPrgkjpfTvLv3b19dOMAAAAA2DOtGjp198VJLk6SqnpQknd191Ub0TAAAAAA9lw76ul0je5+0+5sCAAAAAB7jzU/va6qblxVx1fVh6vqC1X11SXL1buzoQAAAADsOdbc0ynJ8zLN6fQPSV6TaS4nAAAAALiW9YROj0lyfHc/d3c1BgAAAIC9w5qH1yW5eZJ/3V0NAQAAAGDvsZ7Q6fVJ7r+7GgIAAADA3mM9w+tOSPKyqqK23bcAACAASURBVPpakjck2b60Qnd/dFTDAAAAANhzrSd0Whha96wkx69Q54a71BoAAAAA9grrCZ1+KknvroYAAAAAsPdYc+jU3afsxnYAAAAAsBdZz0TiAAAAALAma+7pVFUn76BKd/exu9geAAAAAPYC65nT6cG59pxO+yW5RZIr52VNquqGSc5Lcml3P6qqDkvyyiS3SnJ+kp/s7q9U1U2SvCzJPZJcnuRHu/ui+RjPSHJskq8m+cXuPmMd1wIAAADAbrTm4XXdfWh3H7ZkuWWSByb5ZJIfXsd5fynJhxa9//0kL+ju2ye5IlOYlPn1irn8BXO9VNUdkzwuyZ2SPCzJH89BFgAAAADXAbs8p1N3vzlTIHTCWupX1cFJHpnkz+f3lakX1WlzlVOTPHpeP2p+n3n7Q+b6RyV5ZXd/ubs/luTCJPfa1WsBAAAAYIxRE4l/NMnd1lj3hUl+LcnX5ve3SnJld189v78kyUHz+kFJPpEk8/bPzvWvKV9mHwAAAAA22S6HTlW1JckxmYKfHdV9VJJPd/f5u3retaiq46rqvKo6b9u2bRtxSgAAAACyvqfXnb1M8Y2TfEem3kc/u4bD3DfJD1TVI5J8U5JvSfKiJPtU1Za5N9PBSS6d61+a5JAkl8zh1i0zTSi+UL5g8T7X6O6TkpyUJFu3bl06CToAAAAAu8l6ejrdIEktWa5K8pokD+nuP9vRAbr7Gd19cHcfmmki8LO7+/FJ3pjkMXO1o5O8bl4/fX6fefvZ3d1z+eOq6ibzk+8OT/KOdVwLAAAAALvRmns6dfcDd2M7npbklVX120neneSlc/lLk/xlVV2YZHumoCrd/YGqenWSDya5OsmTu/uru7F9AAAAAKzDmkOn0br7nCTnzOsfzTJPn+vuLyX5kRX2f26S5+6+FgIAAACws9Y1kXhV3bmqTquqbVV19fz66qq68+5qIAAAAAB7nvVMJH7PJG9K8sVMcyp9Msm3Jvn+JI+sqvtv1FPpAAAAALhuW8/wut9N8v5Mk4ZftVBYVbdI8s/z9iPHNg8AAACAPdF6htcdkeR3FwdOSTK///0k9xnZMAAAAAD2XOsJnXoXtwMAAABwPbGe0OntSZ45D6e7RlXdLMnTkpw7smEAAAAA7LnWM6fTM5Ock+Tiqvq7JJdlmkj8EUluluQBw1sHAAAAwB5pzaFTd7+jqo5I8ptJHppkvyTbk7wxyXO6+327p4kAAAAA7GlWDZ2q6gZJHpnkY939/u5+b5LHLKlz5ySHJhE6AQAAAJBkx3M6/USSVyT5/Cp1rkryiqr6sWGtAgAAAGCPtpbQ6S+6+2MrVejui5K8NMnRA9sFAAAAwB5sR6HT3ZP80xqO889Jtu56cwAAAADYG+wodLpFkivWcJwr5roAAAAAsMPQ6TNJbreG49x2rgsAAAAAOwyd3pK1zdV0zFwXAAAAALJlB9tfmOQtVfWCJE/r7q8s3lhVN0ryvCQPTnK/3dNEAPYEb7r/Aza7CdcLD3jzmza7CQAAsCarhk7d/a9V9dQkz0/y+Kr6pyQXz5tvl+T7ktwqyVO7+9zd2lIAAAAA9hg76umU7n5hVb0rydOS/GCSm86bvpjknCS/193/sttaCAAAAMAeZ4ehU5J095uTvLmqbpDk1nPx5d391d3WMgAAAAD2WGsKnRZ099eSfHo3tQUAAACAvcSOnl4HAAAAAOsmdAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAw3JbNbgAAsPle8tTXb3YT9no///zv3+wmAABsKD2dAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMt2WzGwAAwM577k88ZrObcL3w6//vtM1uAgDscfR0AgAAAGA4oRMAAAAAwwmdAAAAABjOnE4AALBJPvTcsze7CXu97/r1B292EwCut/R0AgAAAGA4oRMAAAAAwxleBwAAsBOe9axnbXYT9nruMezZ9HQCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4bZsdgMAAABgI736r++12U24Xnjsj7xjs5vAJtPTCQAAAIDhNjR0qqpvqqp3VNW/VdUHquq35vLDqurtVXVhVb2qqm48l99kfn/hvP3QRcd6xlz+kap66EZeBwAAAACr2+jhdV9O8uDu/lxV3SjJW6rqH5L8SpIXdPcrq+pPkhyb5MT59Yruvn1VPS7J7yf50aq6Y5LHJblTkm9L8s9V9R3d/dUNvh4AAABgA93ltDM2uwl7vX97zJi+PRva06knn5vf3mheOsmDk5w2l5+a5NHz+lHz+8zbH1JVNZe/sru/3N0fS3JhEoNyAQAAAK4jNnxOp6q6YVW9J8mnk5yZ5D+SXNndV89VLkly0Lx+UJJPJMm8/bNJbrW4fJl9AAAAANhkGx46dfdXu/uuSQ7O1DvpO3fXuarquKo6r6rO27Zt2+46DQAAAABLbNrT67r7yiRvTHKfJPtU1cL8UgcnuXRevzTJIUkyb79lkssXly+zz+JznNTdW7t76/77779brgMAAACAa9vop9ftX1X7zOs3TfJ9ST6UKXx6zFzt6CSvm9dPn99n3n52d/dc/rj56XaHJTk8yTs25ioAAAAA2JGNfnrdgUlOraobZgq8Xt3df1dVH0zyyqr67STvTvLSuf5Lk/xlVV2YZHumJ9aluz9QVa9O8sEkVyd5sifXAQAAAFx3bGjo1N3vTXK3Zco/mmWePtfdX0ryIysc67lJnju6jQAAAADsuk2b0wkAAACAvZfQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOE2NHSqqkOq6o1V9cGq+kBV/dJcvl9VnVlVF8yv+87lVVUvrqoLq+q9VXX3Rcc6eq5/QVUdvZHXAQAAAMDqNrqn09VJntrdd0xyRJInV9Udkzw9yVndfXiSs+b3SfLwJIfPy3FJTkymkCrJ8UnuneReSY5fCKoAAAAA2HwbGjp192Xd/a55/aokH0pyUJKjkpw6Vzs1yaPn9aOSvKwn5ybZp6oOTPLQJGd29/buviLJmUketoGXAgAAAMAqNm1Op6o6NMndkrw9yQHdfdm86ZNJDpjXD0ryiUW7XTKXrVQOAAAAwHXApoROVXXzJH+T5Cnd/V+Lt3V3J+lB5zmuqs6rqvO2bds24pAAAAAArMGGh05VdaNMgdNfdfdr5uJPzcPmMr9+ei6/NMkhi3Y/eC5bqfwbdPdJ3b21u7fuv//+Yy8EAAAAgBVt9NPrKslLk3you//vok2nJ1l4At3RSV63qPwJ81Psjkjy2XkY3hlJjqyqfecJxI+cywAAAAC4Dtiywee7b5KfTPK+qnrPXPbMJL+X5NVVdWySi5M8dt72hiSPSHJhki8keWKSdPf2qnpOknfO9Z7d3ds35hIAAAAA2JENDZ26+y1JaoXND1mmfid58grHOjnJyeNaBwAAAMAom/b0OgAAAAD2XkInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAAADCc0AkAAACA4YROAAAAAAwndAIAAABgOKETAAAAAMMJnQAAAAAYTugEAAAAwHBCJwAAAACGEzoBAAAAMJzQCQAAAIDhhE4AAAAADCd0AgAAAGA4oRMAAAAAwwmdAAAAABhO6AQAAADAcEInAAAAAIYTOgEAAAAwnNAJAAAAgOGETgAAAAAMJ3QCAAAAYDihEwAAAADDCZ0AAAAAGE7oBAAAAMBwQicAAAAAhhM6AQAA/P/t3Xm4bEV57/HvD9CAQ1AUh4gRzUXQOKCAM4qKCAgRZwlGUCMxj3PUqNFcMEaj18chGCfkAqLGEac4gIgXQY0KIoM4gQPOTAIRlfm9f1Q1p9ln7969N8uzT2++n+c5T/euVWutOqtWV1e/q1YtSdLgDDpJkiRJkiRpcAadJEmSJEmSNDiDTpIkSZIkSRqcQSdJkiRJkiQNzqCTJEmSJEmSBmfQSZIkSZIkSYMz6CRJkiRJkqTBGXSSJEmSJEnS4Aw6SZIkSZIkaXAGnSRJkiRJkjQ4g06SJEmSJEkanEEnSZIkSZIkDc6gkyRJkiRJkgZn0EmSJEmSJEmDM+gkSZIkSZKkwRl0kiRJkiRJ0uAMOkmSJEmSJGlwBp0kSZIkSZI0OINOkiRJkiRJGpxBJ0mSJEmSJA3OoJMkSZIkSZIGZ9BJkiRJkiRJgzPoJEmSJEmSpMEZdJIkSZIkSdLg1mnQKcmhSc5N8u2xtM2SHJPkzP56856eJAclOSvJaUnuPbbOvj3/mUn2XZf/B0mSJEmSJC1uXY90OhzYdU7ay4Bjq2or4Nj+N8BuwFb93/7AO6AFqYADgPsC9wEOGAWqJEmSJEmStH5Yp0Gnqjoe+M2c5EcD7+nv3wPsNZZ+RDVfA26W5LbAI4Fjquo3VXUhcAxrB7IkSZIkSZK0gtaHOZ1uXVW/6u9/Ddy6v78d8LOxfD/vaQulS5IkSZIkaT2xPgSdrlFVBdRQ20uyf5KTkpx03nnnDbVZSZIkSZIkLWJ9CDqd02+bo7+e29N/Adx+LN8WPW2h9LVU1cFVtX1Vbb/55psPXnBJkiRJkiTNb30IOn0KGD2Bbl/gk2PpT+1PsbsfcHG/De9oYJckN+8TiO/S0yRJkiRJkrSe2Ghd7izJB4CdgFsm+TntKXSvAz6c5BnA2cATe/bPArsDZwG/B54GUFW/SfJq4MSe71+qau7k5JIkSZIkSVpB6zToVFV7L7Do4fPkLeDZC2znUODQAYsmSZIkSZKkAa0Pt9dJkiRJkiRplTHoJEmSJEmSpMEZdJIkSZIkSdLgDDpJkiRJkiRpcAadJEmSJEmSNDiDTpIkSZIkSRqcQSdJkiRJkiQNzqCTJEmSJEmSBmfQSZIkSZIkSYMz6CRJkiRJkqTBGXSSJEmSJEnS4Aw6SZIkSZIkaXAGnSRJkiRJkjQ4g06SJEmSJEkanEEnSZIkSZIkDc6gkyRJkiRJkgZn0EmSJEmSJEmDM+gkSZIkSZKkwRl0kiRJkiRJ0uAMOkmSJEmSJGlwBp0kSZIkSZI0OINOkiRJkiRJGpxBJ0mSJEmSJA3OoJMkSZIkSZIGZ9BJkiRJkiRJgzPoJEmSJEmSpMEZdJIkSZIkSdLgDDpJkiRJkiRpcAadJEmSJEmSNDiDTpIkSZIkSRqcQSdJkiRJkiQNzqCTJEmSJEmSBmfQSZIkSZIkSYMz6CRJkiRJkqTBGXSSJEmSJEnS4Aw6SZIkSZIkaXAGnSRJkiRJkjQ4g06SJEmSJEkanEEnSZIkSZIkDc6gkyRJkiRJkgZn0EmSJEmSJEmDM+gkSZIkSZKkwRl0kiRJkiRJ0uAMOkmSJEmSJGlwBp0kSZIkSZI0OINOkiRJkiRJGpxBJ0mSJEmSJA3OoJMkSZIkSZIGZ9BJkiRJkiRJgzPoJEmSJEmSpMEZdJIkSZIkSdLgDDpJkiRJkiRpcAadJEmSJEmSNDiDTpIkSZIkSRqcQSdJkiRJkiQNzqCTJEmSJEmSBmfQSZIkSZIkSYMz6CRJkiRJkqTBGXSSJEmSJEnS4GY66JRk1yTfT3JWkpetdHkkSZIkSZLUzGzQKcmGwNuA3YC7AnsnuevKlkqSJEmSJEkww0En4D7AWVX1o6q6HPgg8OgVLpMkSZIkSZKY7aDT7YCfjf39854mSZIkSZKkFZaqWukyLEuSxwO7VtXf9r//BrhvVT1nLM/+wP79z62B76/zgq47twTOX+lCaNmsv9ll3c0262+2WX+zy7qbbdbf7LLuZpv1N9tWc/3doao2n2/BRuu6JAP6BXD7sb+36GnXqKqDgYPXZaFWSpKTqmr7lS6Hlsf6m13W3Wyz/mab9Te7rLvZZv3NLututll/s+36Wn+zfHvdicBWSe6Y5IbAk4FPrXCZJEmSJEmSxAyPdKqqK5M8Bzga2BA4tKrOWOFiSZIkSZIkiRkOOgFU1WeBz650OdYT14vbCFcx6292WXezzfqbbdbf7LLuZpv1N7usu9lm/c2262X9zexE4pIkSZIkSVp/zfKcTpIkSZIkSVpPGXRaAUmuSnJKklOTnJzkAddhW8clWXQG/GnzjeXfKcmnl1uu9U2SSvLGsb9fnOTAJW5jp/G6SnJ4kscPWMyF9ntgkhcvcZ3ByrbUc2d9lWTLJN+ekzbx2CbZL8l//PFLp5HltI9JLlnmvuY9t5Nsn+SgCettmeSvl7NPrZHkFUnOSHJar/P7Tsi7pDZt/PO+WH1qekn26t+n24ylrdW2zlnnBUkuTbLplPtY1udZk03qByV5VpKnrqNyrKr+5fpgvrY0ySFJ7rrE7VzSX1dVHY23KUl2T/KDJHdYxvfKoH3Cvv8f9zo7Ocn9J+Sdt52dVKYk/3QdyzfvttfV75/12Vhf9YzeX31Rkg36smX3OVbz7w6DTivjD1W1bVXdE3g58G8rXaDrgcuAxya55XJWTrIRsBOw7ADhnO1l1Dhdh23M9Jxs0gJWvH2sqpOq6nkTsmwJGHS6Dnrneg/g3lV1D2Bn4Gd/jH1NUZ+a3t7Al/vrUtY5EXjsH6VEY/xenGjBflBVvbOqjliBMuk6Wqgtraq/rarvrGzp1i9JHg4cBOxWVWevwP43nCf5JVW1LfAy4F0D7/I6BZ000aiv+pfAI4DdgAPAPsdCDDqtvD8FLgRIcpMkx/Zo9+lJHj3KlOSfk3w/yZeTfGDO6IwnJPlGj9zv2PNvkuSDSb6b5OPAJmPbekeSk3p09lVj6bsm+V6Sk1kHncN17EraxG0vnLugXz34Yr9CdGySP+/phyd5Z5KvAx8GngW8sEe2d+yrPzjJV5P8aDzqn+QlSU7s23zV2H6+n+QI4NvAjr1+3t3r4vNJNmGCtJEZb0lyEvD8JNsl+VKSbyY5Oslt51nnf/eyfDvJwUkytq3XL+XcWa0WOhZz8jwqyX8nuWU/Nw6aW/c9mPiGfqxPT/Kknv62JH/V3388yaH9/dOTvKafG0s6F64npmofRxY6/n3ZS3vaqUleN7bafO3nNVd5kzykf+ZPSfKtJDcFXkf7/J6S5IW9/k7oZbtmdFbfznFJPtrb1vePPn/itsD5VXUZQFWdX1W/XKi9GjehTduu1++pwLPH8o/X54FJDu318qMkdgynlOQmwIOAZwBPnnKdvwBuArySsUBV2tXcjyU5KsmZSf7PnPXe3NvCY5Ns3tOe2ev91CRHJrlRTx//rr7WdnQtk/pB14z6ndA32LC3r6O+zd/19A8medTYtg5P8viF8nc3sV0czEJt6TUjeZNc0vsapyb5WpJb9/Q7pvVrTk/yr3O2u6rqKMmDgXcDe1TVD8cWrdWPT3JEkr3G1n3/WJ/j9v3YnpnkgLE8T+mfmVOSvCs9wNSP/Rv799KCI5mA44H/len6Onfq/ZEdetKfzW1L0/o5m/TyvD9zRkrl2iMdl9QHnnDcJvZ1+/tPpP1mOSPJ/hOOx8yoqnOB/YHnpNkpyaeTbNDrZPQdtkGSs5JsnmTPJF/v9fiF0Wdy3DR5ZolBp5UxagS+BxwCvLqnXwo8pqruDTwUeGM/eXcAHgfckxZJnXs7yEZVdR/gBfQoK/D3wO+r6i49bbux/K+oqu2BewAPSXKPJBvTGuM9e97bDPtfXi+8Ddgnaw/xfyvwnn6F6P20qyAjWwAPqKrHAu8E3twj2yf05beldcL3oP0QJckuwFbAfYBtge36lx09/e09Mn52//tt/e+LaPW8mBv2+juol/3xVbUdcCjwmnny/0dV7VBVd6MFkPYYW7bUc2c1m+9YAJDkMbSrULtX1fk9ea26pwVrt6V9VncG3pAWCDwBGH2J3w4YDXnfkdbRgOWdC6vRktrHOevOe/yT7AY8GrhvH0E1/sN0wXrvXgw8u1+J3BH4A+1cOKG3BW8GzgUe0cv2JK7dhtyrb/uuwJ2ABy79kKxKn6d13n+Q5O1JHtLTJ7VXLJLnMOC5vY4n2QZ4JK2NPiDJDa7z/+b64dHAUVX1A+CCJNN8NzwZ+CCtDdx6Tqd5W9rn5e7Ak5LcvqffGDipt4VfYs3n8mO93u8JfJcW/BoZfVf/wzL/b9cXC/WD5pqvXXwGcHFV7QDsADwzyR2BDwFPBEhyQ+DhwGcm5AfbxSEt1JaOuzHwtf7ZOR54Zk//d+AdVXV34Fdz1llNdfQnwCeAvarqe3OWzdeX+7/AfgD9s/IA2jkN7XvjcbTfUE9Iu5XqLrS27IG9r3AVsE/Pf2Pg61V1z6r68oQy7gmcziJ9nSRbA0cC+1XViT15rba0ql7GmtE4+7C4IfrA0/R1n95/s2wPPC/JLaYo23qvqn4EbAjcaiztauB9rDkXdgZOrarzaCOG71dV96J9R/7jPJudJs/MMOi0MkaNwDbArsARvUEJ8NokpwFfoH1gb01r6D9ZVZdW1W+B/5qzvY/112/SbvsAeDDtRKeqTgNOG8v/xLTRTN8C/pLWKGwD/Liqzqz2SMP3DfkfXh9U1f8ARwBzr2zfH/jP/v69tEZ05CNVddWEzX6iqq7uQ5hHneld+r9vASfTju1WfdnZVfW1sfV/XFWn9Pfj9TfJh/rr1sDdgGOSnEK7krzFPPkf2iPlpwMPo9X5yFLPnVm20KM6R+nzHQtox+ylwKOq6sKx9Pnq/kHAB6rqqqo6h/aDaQf6F3Ha/ArfAc7pwaj7A1/t6y7nXFiNlto+jlvo+O8MHFZVvweoqt+MrbNQvY98BXhT2oiYm1XVlfPkuQHw7v4Z+whrOloA36iqn/fOxykL7ON6p6ouoQW09wfOAz6UZD8mt1cja+VJcjNa/Yw6tu+dsPvPVNVlvfN8LmufR5rf3rSOL/11mlvs9gY+2M//I4EnjC07tqourqpLae3iHXr61az5nnsfa76T75Y2ovB0Wid+/NxY7LtaTOwHzTVfu7gL8NTe3/g6cAta3+ZztM/kn9AujB5fVX+YkB9sFwczoS0ddzkwmqNpvE4fCHygv5/bZq6mOrqC1td6xjzL1urLVdWXgK36CJW9gSPHvvuPqaoL+jn+MVr79HBaHZzYz/eH0wJ10AJQR04o2xv6Ovv38k3q62wOfBLYp6pOHdvGQm3pUgzRB56mr/u8tFFfXwNuz5o2YbU6FBjNl/d02sUxaL/Xju7fZy9h/r7ONHlmhve+r7CqGg1V3BzYvb9uV1VXJPkJsPEUm7msv17FInXarzK9GNihqi5McviU+1gt3kILBB22WMbud4ssv2zsfcZe/62qrnVvdpIt59ne+PpXMd2tbKNtBDijqiZNPLgx8HZg+6r6WdpQ2vH6nvrcWQUuAG4+J20z4Mf9/ULH4oe0zsOdgZPG0uer+3lV1S/6j+JdaVd7NqNdGb6kqn7br/Qs51xY1QZqHxcz8TNQVa9L8pm+/68keeQ823ghcA5thNUGtCuVc7e/4D6ur3qQ4DjguN6p+jva1eOF2qtp2rRpWCdLlGQz2o+Puycp2hXdSvKSCevcnfaD4ph+of6GtPZ2NEnqtPUwujBwOG2kwqn9R/VOY3kW+67WGtP0g+ZrF0MbSXj03MxJjqONHnwSawKT8+ZPshN+Bgc1T1u675wsV/QLyrD28V7ogtxqqqOraX2uY5P8U1W9dmzZQn25I4Cn0EZrPm0sfe7xqr7ee6rq5fPs+9JFAuIvqaqPXlOA1rYt1Ne5GPgpLdA1Pl/XNHV1JdcebDL3e/M694Gn6OvuRLsIeP+q+n1vN1bFb9Akd6Idu3OBu4zSez/lnCQPo42SG416eivwpqr6VD8uB86z2WnyzAxHOq2wtCfAbEj7QbwpcG5vZB7Kmkj1V4A9k2ycNqfCfLcbzHU8faLbJHejdeShzZHyO+DiPsx9t57+PWDLtPkXYGmThM6MPsLhw1z7asdXWTM/xT60SP18fgvcdIrdHA08vdcVSW6X5FaLrLMc3wc2T3/aRZIbJJkbBR815uf38kzztImFzp2Z1q8G/qo3/KMfUbvShq9OcjZtKPUR8xzfuU6gDW3esF8hezDwjb7sa7Rhy8f3fC9m4XNNTN0+jlvo+B8DPC1r5oDZbAll+IuqOr2qXk+bDHkb1m4LNgV+1a8I/00vsyZIsnWS8Suc29LaNJjcXs3bplXVRcBFSUajYqa5nUDTezzw3qq6Q1VtWVW3pwWQ1pr7Y8zewIE9/5ZV9We0uUcWuwq/AWvq/q9Z00bflNaG3wDrd9kW6AdN42jg7/vxJ8mdk9y4L/sQ7Yf5jsBRU+TXQBZoS6edJPsrXLv/u2r1XWMkOgAABz5JREFUkc6Pot1eOs25fzitz0Zde0L2RyTZLG3ezb1ox/BY4PGjvn5fvpzRRjC5r3M58BjaCMJpHmZyRdbcPn4OcKskt+ijEqf5LQlL6wPD5L7upsCFPeC0DXC/KcuwXuv9zXfSbv2fL4h7CG3U7viI3E2BX/T3c4PELCHPzJjlqPUs26QPpYQWHd63qq5K8n7gv/pVipNogSCq6sQkn6Ld5nQO7Z7fixfZxzuAw5J8lzb3wTf7tk5N8q2+7Z/RGkuq6tK0Cd0+k+T3tAZimgDLLHoj8Jyxv59LO1YvoQ1Nftq8a7XbGj+aNqnfcxfaeFV9Pu3+7v/uV3cvoV0tGXTof1VdnjZ530Fp95xvRLuCecZYnouSvJs2cfmvaT+aFzPvubNKPBV4W5I39b9fVVU/zCLzY1bV95LsA3wkyZ4Tsn6cNoz4VNrVr3+sql/3ZScAu1TVWUnOpl0BMui0tiW1j3MsdPyPSrItcFKSy4HPMv1TXV7QO35X0z5bn+vvr+pDxA+njbw5Mu2R40fhqItp3AR4a78qeiVwFu32gouY0F4t0qY9DTi0j8T5/B+3+Nc7ewOvn5N25ALpI0+mjRAc9/Gefs6Eff0OuE+SV9KuGo8eCPDPtNu0zuuvq7WPsi7M7QdN4xDabTcnp31pnkf70Q3t8/Ze2lQQl0+RX8NZqC396MS1mucD/5nkpbTbtla1qvpNkl2B45Oct0jec3o/+BNzFn2D1vZtAbyvqk4C6O3V59OeTH0F7WEWy3lC3sS+TlX9LsketBGklyyyrYOB05KcXFX7JPmXXv5fzN3uJEvoA8Pkvu5RwLP6cf0+LUA1q0Z91RvQPnfvBd60QN5P0UaWjo8uPZB2PC8EvgjccZ71pskzMzJ/QE7rmyQ3qapL+pX644H9q+rklS6XJEmSJK0W/ffW6cC9q2qxC/3SgtKeIvnmqpo0MnjV8/a62XFwj6ieTJvQzoCTJEmSJA0kyc60kf5vNeCk6yLJy2gj4+ab7+t6xZFOkiRJkiRJGpwjnSRJkiRJkjQ4g06SJEmSJEkanEEnSZIkSZIkDc6gkyRJ0jIk2SvJ8UnOTfKHJGcn+UR/LPcoz05JDuyP0l7OPrbt6282XMklSZLWDYNOkiRJS5TkecDHgTOBZwCPAv61L37YWNadgANYfp9r276+QSdJkjRzNlrpAkiSJM2gFwOfqKpnjKV9EXj3ckc1SZIkrTZ2iiRJkpZuM+DX8y2oqqsBkhxIG6UEcEWSSlKjfEleleTkJP+T5PwkX0xyv7Hl+wGH9T/PHK2fZMu+fKMkL0/yvSSXJfllkjcm2XhsGxsleXWSHya5tO/ny0keNNiRkCRJWoAjnSRJkpbuG8C+SX4EfLKqfjBPnkOALWi33z0IuGrO8tsBbwZ+DtwYeApwfJLtqup04DO0W/ZeCTyh5wP4VX99H7An8Hrgq8BdgFcDWwKP63leCrwQeAVwCvCnwPZ4u54kSVoHUlWL55IkSdI1ktwZ+Chw9550AXAMcFhVfX4s34G00U43qKorJ2xvQyDAGcBRVfX8nr4fbbTTVlV11lj+HYHjgX2r6oix9H1owah7VdUpST4NXF5Vj73O/2lJkqQl8vY6SZKkJeojm+4FPAR4DW0U0WOAo5O8cpptJNk5yf9LcgFwJXAFcGdg6ylW3xW4HPhov4VuoyQbAaOA14P764nA7klek+RBSW445X9RkiTpOjPoJEmStAxVdVVVHV9Vr6yqnYE7AacDByS5+aR1k9wb+CxwCe32u/sBOwCnAhtPWHXkVsANgd/RglWjf+f25bfor6+ljbT6K+AE4IIkhyW55dT/UUmSpGVyTidJkqQBVNUvkxwC/DuwFW3ep4U8jja66bFVdcUosQerLppidxcAlwI7LrD8l71MV9DmfHp9ktsAewBvAm4EPGmK/UiSJC2bQSdJkqQlSnLbqvrVPIu26a+jJ9td1l83AX47lu9GtInFx59m9zDgz4Efj+UbX3/cUbRJwjetqmOnKXNV/Ro4JMnuwN2mWUeSJOm6MOgkSZK0dN9O8gXaLXI/pj0VbnfgWcCHq+qnPd93+uuLknwOuKqqTqIFjV4AHJ7kMNpcTv8M/GLOfkbrPzvJe2i30J1WVccl+QBtTqc30UZVXU17ct3uwEur6gdJPkm7Ze9k4ELaPFS7Au8a7lBIkiTNz6fXSZIkLVGSZ9GCO/cEbk0btfQD4APAW6rq8p5vQ+Ag4AnALWl9r/RlzwX+AbgN8G3g5cArAapqp7F9HQDs3/NtANyxqn6SZAPgucDTaZOPXwb8BDgaeE1VXZzkRX3fW9FGV/20l/E147f1SZIk/TEYdJIkSZIkSdLgfHqdJEmSJEmSBmfQSZIkSZIkSYMz6CRJkiRJkqTBGXSSJEmSJEnS4Aw6SZIkSZIkaXAGnSRJkiRJkjQ4g06SJEmSJEkanEEnSZIkSZIkDc6gkyRJkiRJkgb3/wEIkBjZ8/Cj3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(df['State'].value_counts()[:10].index,df['State'].value_counts()[:10].values)\n",
    "plt.title('No. of Terrorist Activities by State', fontsize=20)\n",
    "plt.xlabel('States',fontsize=16)\n",
    "plt.ylabel('Count',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aA9O0bQqnKU2"
   },
   "source": [
    "It is observed that Baghdad reports the highest number of terrorist activities (over 7500) among other major states and provinces of the world, distantly followed by Northern Ireland. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 597
    },
    "id": "RczB8ndGHQf0",
    "outputId": "a10efffa-34af-4efc-b4f7-25048c52c47f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(df['City'].value_counts()[1:11].index,df['City'].value_counts()[1:11].values)\n",
    "plt.title('No. of Terrorist Activities by City', fontsize=20)\n",
    "plt.xlabel('Cities',fontsize=16)\n",
    "plt.ylabel('Count',fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dKsGuX6Cnr74"
   },
   "source": [
    "It is observed that the city of Baghdad reports the highest number of terrorist activities (around 7500) among other major cities of the world, distantly followed by Karachi in Pakistan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 636
    },
    "id": "d-TglmQhIvmb",
    "outputId": "a04db917-c036-4918-82a9-dfac8405c3af"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.countplot('Attack_Type', data=df, order=df['Attack_Type'].value_counts().index)\n",
    "plt.xticks(rotation=15)\n",
    "plt.title('Terrorist Activities by Attack Type', fontsize=20)\n",
    "plt.xlabel('Attack Type', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "yZK3r2Qtn3L4"
   },
   "source": [
    "It is observed that among the primary types of terrorist attacks, Bombings/Explosions are the most common, followed by Armed Assaults and Assassinations, albeit not very closely."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 628
    },
    "id": "k_z8xWA6bwVU",
    "outputId": "5f7d75d6-fcdb-47e7-9dd6-38b49805a60a"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "sns.countplot('Target_Type', data=df, order=df['Target_Type'].value_counts()[:10].index)\n",
    "plt.xticks(rotation=15)\n",
    "plt.title('Terrorist Activities by Target Type', fontsize=20)\n",
    "plt.xlabel('Target Type', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vsOlahe7oTTK"
   },
   "source": [
    "It is observed that most frequently, the targets of terrorist attacks tend to be Private Citizens (or civilians) and Property, followed by the military and police forces, as well as government and business bodies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 506
    },
    "id": "Lr-quns6dNog",
    "outputId": "87348d20-991c-4222-fb38-abe1529c0b21"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light",
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "sns.barplot(df['Group'].value_counts()[1:16].values, df['Group'].value_counts()[1:16].index, orient='h')\n",
    "plt.title('No. of Terrorist Activities by Terrorist Group', fontsize=20)\n",
    "plt.xlabel('Groups', fontsize=16)\n",
    "plt.ylabel('Count', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0y-rGgMJor5h"
   },
   "source": [
    "It is observed that acts of terrorism have most frequently been orchestrated by the Taliban (ranging over 7000) followed by the ISIL, the SL, the FMLN, and Al-Shabaab."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "La3NGW_UpLi0"
   },
   "source": [
    "## Recording Casualties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 357
    },
    "id": "3smSvQwGfYRn",
    "outputId": "8e92df77-bbb5-4361-b266-090e77eff786"
   },
   "outputs": [
    {
     "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>Group</th>\n",
       "      <th>Country</th>\n",
       "      <th>Killed</th>\n",
       "      <th>Wounded</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Islamic State of Iraq and the Levant (ISIL)</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>31058</td>\n",
       "      <td>23697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Taliban</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>29269</td>\n",
       "      <td>27871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>16917</td>\n",
       "      <td>7670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Shining Path (SL)</td>\n",
       "      <td>Peru</td>\n",
       "      <td>11595</td>\n",
       "      <td>3030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Liberation Tigers of Tamil Eelam (LTTE)</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>10928</td>\n",
       "      <td>10991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Al-Shabaab</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>8176</td>\n",
       "      <td>6025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Farabundo Marti National Liberation Front (FMLN)</td>\n",
       "      <td>El Salvador</td>\n",
       "      <td>8019</td>\n",
       "      <td>4049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Islamic State of Iraq and the Levant (ISIL)</td>\n",
       "      <td>Syria</td>\n",
       "      <td>6883</td>\n",
       "      <td>4031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Nicaraguan Democratic Force (FDN)</td>\n",
       "      <td>Nicaragua</td>\n",
       "      <td>6630</td>\n",
       "      <td>728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Tehrik-i-Taliban Pakistan (TTP)</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>6014</td>\n",
       "      <td>9518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Group  ... Wounded\n",
       "0       Islamic State of Iraq and the Levant (ISIL)  ...   23697\n",
       "1                                           Taliban  ...   27871\n",
       "2                                        Boko Haram  ...    7670\n",
       "3                                 Shining Path (SL)  ...    3030\n",
       "4           Liberation Tigers of Tamil Eelam (LTTE)  ...   10991\n",
       "5                                        Al-Shabaab  ...    6025\n",
       "6  Farabundo Marti National Liberation Front (FMLN)  ...    4049\n",
       "7       Islamic State of Iraq and the Levant (ISIL)  ...    4031\n",
       "8                 Nicaraguan Democratic Force (FDN)  ...     728\n",
       "9                   Tehrik-i-Taliban Pakistan (TTP)  ...    9518\n",
       "\n",
       "[10 rows x 4 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = df[['Group','Country','Killed', 'Wounded']]\n",
    "data = data.groupby(['Group','Country'], axis=0).sum().sort_values(['Killed', 'Wounded'], ascending=False).drop('Unknown').reset_index().head(10)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "JCodxQTqpY7m"
   },
   "source": [
    "The ISIL has been most actively terrorising Iraq with over 31000 people killed and 23000 wounded. The Taliban has been committing its equal share of terrorism in Afghanistan with over 29000 killed and 27000 wounded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 130
    },
    "id": "LiJXE9Exg3pn",
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    {
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       "      <th>Year</th>\n",
       "      <th>1970</th>\n",
       "      <th>1971</th>\n",
       "      <th>1972</th>\n",
       "      <th>1973</th>\n",
       "      <th>1974</th>\n",
       "      <th>1975</th>\n",
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       "      <th>1988</th>\n",
       "      <th>1989</th>\n",
       "      <th>1990</th>\n",
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       "      <th>1994</th>\n",
       "      <th>1995</th>\n",
       "      <th>1996</th>\n",
       "      <th>1997</th>\n",
       "      <th>1998</th>\n",
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       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
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       "      <th>2006</th>\n",
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       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>174</td>\n",
       "      <td>173</td>\n",
       "      <td>566</td>\n",
       "      <td>370</td>\n",
       "      <td>539</td>\n",
       "      <td>617</td>\n",
       "      <td>674</td>\n",
       "      <td>456</td>\n",
       "      <td>1459</td>\n",
       "      <td>2100</td>\n",
       "      <td>4400</td>\n",
       "      <td>4851</td>\n",
       "      <td>5136</td>\n",
       "      <td>9444</td>\n",
       "      <td>10450</td>\n",
       "      <td>7094</td>\n",
       "      <td>4976</td>\n",
       "      <td>6482</td>\n",
       "      <td>7208</td>\n",
       "      <td>8152</td>\n",
       "      <td>7148</td>\n",
       "      <td>8429</td>\n",
       "      <td>9742</td>\n",
       "      <td>7690</td>\n",
       "      <td>6103</td>\n",
       "      <td>6966</td>\n",
       "      <td>10924</td>\n",
       "      <td>4688</td>\n",
       "      <td>3393</td>\n",
       "      <td>4403</td>\n",
       "      <td>7729</td>\n",
       "      <td>4805</td>\n",
       "      <td>3317</td>\n",
       "      <td>5743</td>\n",
       "      <td>6331</td>\n",
       "      <td>9380</td>\n",
       "      <td>12824</td>\n",
       "      <td>9157</td>\n",
       "      <td>9273</td>\n",
       "      <td>7827</td>\n",
       "      <td>8246</td>\n",
       "      <td>15497</td>\n",
       "      <td>22273</td>\n",
       "      <td>44490</td>\n",
       "      <td>38853</td>\n",
       "      <td>34871</td>\n",
       "      <td>26445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>212</td>\n",
       "      <td>82</td>\n",
       "      <td>409</td>\n",
       "      <td>495</td>\n",
       "      <td>865</td>\n",
       "      <td>617</td>\n",
       "      <td>756</td>\n",
       "      <td>518</td>\n",
       "      <td>1600</td>\n",
       "      <td>2506</td>\n",
       "      <td>3645</td>\n",
       "      <td>3337</td>\n",
       "      <td>3342</td>\n",
       "      <td>4047</td>\n",
       "      <td>5291</td>\n",
       "      <td>5130</td>\n",
       "      <td>5814</td>\n",
       "      <td>5775</td>\n",
       "      <td>6960</td>\n",
       "      <td>5539</td>\n",
       "      <td>6128</td>\n",
       "      <td>7591</td>\n",
       "      <td>9915</td>\n",
       "      <td>7569</td>\n",
       "      <td>14292</td>\n",
       "      <td>10795</td>\n",
       "      <td>9072</td>\n",
       "      <td>8183</td>\n",
       "      <td>5341</td>\n",
       "      <td>5869</td>\n",
       "      <td>22774</td>\n",
       "      <td>7162</td>\n",
       "      <td>7384</td>\n",
       "      <td>12022</td>\n",
       "      <td>12784</td>\n",
       "      <td>15550</td>\n",
       "      <td>22524</td>\n",
       "      <td>18998</td>\n",
       "      <td>19138</td>\n",
       "      <td>15947</td>\n",
       "      <td>14659</td>\n",
       "      <td>25444</td>\n",
       "      <td>37688</td>\n",
       "      <td>41128</td>\n",
       "      <td>44043</td>\n",
       "      <td>40001</td>\n",
       "      <td>24927</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Year     1970  1971  1972  1973  1974  ...   2013   2014   2015   2016   2017\n",
       "Killed    174   173   566   370   539  ...  22273  44490  38853  34871  26445\n",
       "Wounded   212    82   409   495   865  ...  37688  41128  44043  40001  24927\n",
       "\n",
       "[2 rows x 47 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_year = df.pivot_table(columns='Year', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "h6Mu8bbxp5JJ"
   },
   "source": [
    "Most deaths due to terrorism occurred in 2014, numbering at around 44000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "8k1EckXnhIkG",
    "outputId": "cc4d2473-85b0-48c1-d450-36cf182c857a"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Month</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
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       "      <th>12</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>31320</td>\n",
       "      <td>30565</td>\n",
       "      <td>31479</td>\n",
       "      <td>34672</td>\n",
       "      <td>37310</td>\n",
       "      <td>38047</td>\n",
       "      <td>38558</td>\n",
       "      <td>38225</td>\n",
       "      <td>34485</td>\n",
       "      <td>35174</td>\n",
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       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>35097</td>\n",
       "      <td>37030</td>\n",
       "      <td>49929</td>\n",
       "      <td>40675</td>\n",
       "      <td>45224</td>\n",
       "      <td>41370</td>\n",
       "      <td>47150</td>\n",
       "      <td>50687</td>\n",
       "      <td>58054</td>\n",
       "      <td>41599</td>\n",
       "      <td>39512</td>\n",
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      "text/plain": [
       "Month       1      2      3      4      5   ...     8      9      10     11     12\n",
       "Killed   31320  30565  31479  34672  37310  ...  38225  34485  35174  31054  30976\n",
       "Wounded  35097  37030  49929  40675  45224  ...  50687  58054  41599  39512  37541\n",
       "\n",
       "[2 rows x 12 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_month = df.pivot_table(columns='Month', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_month.drop(0, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "MStyrXT7qFKk"
   },
   "source": [
    "Most deaths due to terrorism have occured in the month of July, numbering at around 38000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {
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    },
    "id": "n_9PPiTrhxU5",
    "outputId": "cfdfa185-f8d7-46d7-fadc-9156bc443d6b"
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   "outputs": [
    {
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       "      <th>Day</th>\n",
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       "      <td>15627</td>\n",
       "      <td>12851</td>\n",
       "      <td>15145</td>\n",
       "      <td>12175</td>\n",
       "      <td>13659</td>\n",
       "      <td>12601</td>\n",
       "      <td>13362</td>\n",
       "      <td>12712</td>\n",
       "      <td>12427</td>\n",
       "      <td>14270</td>\n",
       "      <td>16997</td>\n",
       "      <td>14074</td>\n",
       "      <td>14925</td>\n",
       "      <td>13670</td>\n",
       "      <td>14402</td>\n",
       "      <td>12638</td>\n",
       "      <td>15917</td>\n",
       "      <td>14012</td>\n",
       "      <td>14424</td>\n",
       "      <td>13686</td>\n",
       "      <td>13279</td>\n",
       "      <td>11210</td>\n",
       "      <td>13726</td>\n",
       "      <td>12824</td>\n",
       "      <td>12849</td>\n",
       "      <td>12044</td>\n",
       "      <td>12224</td>\n",
       "      <td>13565</td>\n",
       "      <td>11512</td>\n",
       "      <td>11304</td>\n",
       "      <td>6546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>19863</td>\n",
       "      <td>15883</td>\n",
       "      <td>15583</td>\n",
       "      <td>14657</td>\n",
       "      <td>16449</td>\n",
       "      <td>14580</td>\n",
       "      <td>21526</td>\n",
       "      <td>15255</td>\n",
       "      <td>17632</td>\n",
       "      <td>16717</td>\n",
       "      <td>35288</td>\n",
       "      <td>15775</td>\n",
       "      <td>14824</td>\n",
       "      <td>17632</td>\n",
       "      <td>18372</td>\n",
       "      <td>15400</td>\n",
       "      <td>16590</td>\n",
       "      <td>15879</td>\n",
       "      <td>18263</td>\n",
       "      <td>23150</td>\n",
       "      <td>15984</td>\n",
       "      <td>14673</td>\n",
       "      <td>15936</td>\n",
       "      <td>15708</td>\n",
       "      <td>16812</td>\n",
       "      <td>14098</td>\n",
       "      <td>16294</td>\n",
       "      <td>16267</td>\n",
       "      <td>13378</td>\n",
       "      <td>14325</td>\n",
       "      <td>10301</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Day         1      2      3      4      5   ...     27     28     29     30     31\n",
       "Killed   15627  12851  15145  12175  13659  ...  12224  13565  11512  11304   6546\n",
       "Wounded  19863  15883  15583  14657  16449  ...  16294  16267  13378  14325  10301\n",
       "\n",
       "[2 rows x 31 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_day = df.pivot_table(columns='Day', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_day.drop(0, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "PHWwxLsPqTgr"
   },
   "source": [
    "Most deaths due to terrorism have occured on the 11th of a month, numbering at around 17000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
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    },
    "id": "-nrUqyRxiQFv",
    "outputId": "404c81be-e6bf-4832-a35f-d11298e379bc"
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Region</th>\n",
       "      <th>Australasia &amp; Oceania</th>\n",
       "      <th>Central America &amp; Caribbean</th>\n",
       "      <th>Central Asia</th>\n",
       "      <th>East Asia</th>\n",
       "      <th>Eastern Europe</th>\n",
       "      <th>Middle East &amp; North Africa</th>\n",
       "      <th>North America</th>\n",
       "      <th>South America</th>\n",
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       "      <th>Southeast Asia</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>150</td>\n",
       "      <td>28708</td>\n",
       "      <td>1000</td>\n",
       "      <td>1152</td>\n",
       "      <td>7415</td>\n",
       "      <td>137642</td>\n",
       "      <td>4916</td>\n",
       "      <td>28849</td>\n",
       "      <td>101319</td>\n",
       "      <td>15637</td>\n",
       "      <td>78386</td>\n",
       "      <td>6694</td>\n",
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       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>260</td>\n",
       "      <td>8991</td>\n",
       "      <td>2009</td>\n",
       "      <td>9213</td>\n",
       "      <td>12045</td>\n",
       "      <td>214308</td>\n",
       "      <td>21531</td>\n",
       "      <td>16704</td>\n",
       "      <td>141360</td>\n",
       "      <td>26259</td>\n",
       "      <td>52856</td>\n",
       "      <td>18332</td>\n",
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       "Region   Australasia & Oceania  ...  Western Europe\n",
       "Killed                     150  ...            6694\n",
       "Wounded                    260  ...           18332\n",
       "\n",
       "[2 rows x 12 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_region = df.pivot_table(columns='Region', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_region"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ct_hHC_uqv73"
   },
   "source": [
    "Most deaths due to terrorism have occured in the Middle East and North Africa numbering at around 137000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 226
    },
    "id": "m6-x2_Bjic9r",
    "outputId": "34459bdb-a2c4-4d35-a4e3-8423dce9ce25"
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   "outputs": [
    {
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       "      <th>Country</th>\n",
       "      <th>Afghanistan</th>\n",
       "      <th>Albania</th>\n",
       "      <th>Algeria</th>\n",
       "      <th>Andorra</th>\n",
       "      <th>Angola</th>\n",
       "      <th>Antigua and Barbuda</th>\n",
       "      <th>Argentina</th>\n",
       "      <th>Armenia</th>\n",
       "      <th>Australia</th>\n",
       "      <th>Austria</th>\n",
       "      <th>Azerbaijan</th>\n",
       "      <th>Bahamas</th>\n",
       "      <th>Bahrain</th>\n",
       "      <th>Bangladesh</th>\n",
       "      <th>Barbados</th>\n",
       "      <th>Belarus</th>\n",
       "      <th>Belgium</th>\n",
       "      <th>Belize</th>\n",
       "      <th>Benin</th>\n",
       "      <th>Bhutan</th>\n",
       "      <th>Bolivia</th>\n",
       "      <th>Bosnia-Herzegovina</th>\n",
       "      <th>Botswana</th>\n",
       "      <th>Brazil</th>\n",
       "      <th>Brunei</th>\n",
       "      <th>Bulgaria</th>\n",
       "      <th>Burkina Faso</th>\n",
       "      <th>Burundi</th>\n",
       "      <th>Cambodia</th>\n",
       "      <th>Cameroon</th>\n",
       "      <th>Canada</th>\n",
       "      <th>Central African Republic</th>\n",
       "      <th>Chad</th>\n",
       "      <th>Chile</th>\n",
       "      <th>China</th>\n",
       "      <th>Colombia</th>\n",
       "      <th>Comoros</th>\n",
       "      <th>Costa Rica</th>\n",
       "      <th>Croatia</th>\n",
       "      <th>Cuba</th>\n",
       "      <th>...</th>\n",
       "      <th>Soviet Union</th>\n",
       "      <th>Spain</th>\n",
       "      <th>Sri Lanka</th>\n",
       "      <th>St. Kitts and Nevis</th>\n",
       "      <th>St. Lucia</th>\n",
       "      <th>Sudan</th>\n",
       "      <th>Suriname</th>\n",
       "      <th>Swaziland</th>\n",
       "      <th>Sweden</th>\n",
       "      <th>Switzerland</th>\n",
       "      <th>Syria</th>\n",
       "      <th>Taiwan</th>\n",
       "      <th>Tajikistan</th>\n",
       "      <th>Tanzania</th>\n",
       "      <th>Thailand</th>\n",
       "      <th>Togo</th>\n",
       "      <th>Trinidad and Tobago</th>\n",
       "      <th>Tunisia</th>\n",
       "      <th>Turkey</th>\n",
       "      <th>Turkmenistan</th>\n",
       "      <th>Uganda</th>\n",
       "      <th>Ukraine</th>\n",
       "      <th>United Arab Emirates</th>\n",
       "      <th>United Kingdom</th>\n",
       "      <th>United States</th>\n",
       "      <th>Uruguay</th>\n",
       "      <th>Uzbekistan</th>\n",
       "      <th>Vanuatu</th>\n",
       "      <th>Vatican City</th>\n",
       "      <th>Venezuela</th>\n",
       "      <th>Vietnam</th>\n",
       "      <th>Wallis and Futuna</th>\n",
       "      <th>West Bank and Gaza Strip</th>\n",
       "      <th>West Germany (FRG)</th>\n",
       "      <th>Western Sahara</th>\n",
       "      <th>Yemen</th>\n",
       "      <th>Yugoslavia</th>\n",
       "      <th>Zaire</th>\n",
       "      <th>Zambia</th>\n",
       "      <th>Zimbabwe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>39384</td>\n",
       "      <td>42</td>\n",
       "      <td>11066</td>\n",
       "      <td>0</td>\n",
       "      <td>3043</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>37</td>\n",
       "      <td>23</td>\n",
       "      <td>30</td>\n",
       "      <td>258</td>\n",
       "      <td>1</td>\n",
       "      <td>44</td>\n",
       "      <td>1244</td>\n",
       "      <td>76</td>\n",
       "      <td>14</td>\n",
       "      <td>79</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>42</td>\n",
       "      <td>79</td>\n",
       "      <td>11</td>\n",
       "      <td>203</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>134</td>\n",
       "      <td>4205</td>\n",
       "      <td>543</td>\n",
       "      <td>2347</td>\n",
       "      <td>365</td>\n",
       "      <td>1990</td>\n",
       "      <td>1119</td>\n",
       "      <td>228</td>\n",
       "      <td>1008</td>\n",
       "      <td>14698</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>248</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>96</td>\n",
       "      <td>1288</td>\n",
       "      <td>15530</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>3883</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "      <td>74</td>\n",
       "      <td>15229</td>\n",
       "      <td>60</td>\n",
       "      <td>307</td>\n",
       "      <td>73</td>\n",
       "      <td>2742</td>\n",
       "      <td>76</td>\n",
       "      <td>6</td>\n",
       "      <td>351</td>\n",
       "      <td>6888</td>\n",
       "      <td>3</td>\n",
       "      <td>3065</td>\n",
       "      <td>2261</td>\n",
       "      <td>123</td>\n",
       "      <td>3410</td>\n",
       "      <td>3771</td>\n",
       "      <td>6</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>227</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1500</td>\n",
       "      <td>97</td>\n",
       "      <td>1</td>\n",
       "      <td>8776</td>\n",
       "      <td>119</td>\n",
       "      <td>324</td>\n",
       "      <td>70</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>44277</td>\n",
       "      <td>126</td>\n",
       "      <td>9150</td>\n",
       "      <td>0</td>\n",
       "      <td>2455</td>\n",
       "      <td>0</td>\n",
       "      <td>755</td>\n",
       "      <td>71</td>\n",
       "      <td>113</td>\n",
       "      <td>126</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>8225</td>\n",
       "      <td>3</td>\n",
       "      <td>212</td>\n",
       "      <td>516</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>166</td>\n",
       "      <td>150</td>\n",
       "      <td>11</td>\n",
       "      <td>160</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "      <td>219</td>\n",
       "      <td>2454</td>\n",
       "      <td>786</td>\n",
       "      <td>1063</td>\n",
       "      <td>146</td>\n",
       "      <td>942</td>\n",
       "      <td>1681</td>\n",
       "      <td>755</td>\n",
       "      <td>1842</td>\n",
       "      <td>10328</td>\n",
       "      <td>2</td>\n",
       "      <td>38</td>\n",
       "      <td>73</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>150</td>\n",
       "      <td>4935</td>\n",
       "      <td>15561</td>\n",
       "      <td>9</td>\n",
       "      <td>12</td>\n",
       "      <td>2169</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>80</td>\n",
       "      <td>94</td>\n",
       "      <td>14109</td>\n",
       "      <td>87</td>\n",
       "      <td>1109</td>\n",
       "      <td>234</td>\n",
       "      <td>7818</td>\n",
       "      <td>34</td>\n",
       "      <td>35</td>\n",
       "      <td>433</td>\n",
       "      <td>9899</td>\n",
       "      <td>3</td>\n",
       "      <td>1145</td>\n",
       "      <td>2841</td>\n",
       "      <td>27</td>\n",
       "      <td>6106</td>\n",
       "      <td>20702</td>\n",
       "      <td>6</td>\n",
       "      <td>200</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>247</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>3014</td>\n",
       "      <td>862</td>\n",
       "      <td>4</td>\n",
       "      <td>9328</td>\n",
       "      <td>281</td>\n",
       "      <td>211</td>\n",
       "      <td>62</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 205 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Country  Afghanistan  Albania  Algeria  ...  Zaire  Zambia  Zimbabwe\n",
       "Killed         39384       42    11066  ...    324      70       154\n",
       "Wounded        44277      126     9150  ...    211      62       222\n",
       "\n",
       "[2 rows x 205 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_country = df.pivot_table(columns='Country', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_country"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RBT9EK1Wq5VL"
   },
   "source": [
    "Most deaths due to terrorism have occured in Iraq, numbering at around 78000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "id": "PHt3mSo-ivvG",
    "outputId": "bb822498-4482-4c6e-bbd8-6d0f60b70d2e"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>State</th>\n",
       "      <th>(Region) of Republican Subordination (Province)</th>\n",
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       "      <th>Agadez (Region)</th>\n",
       "      <th>Agadez Department</th>\n",
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       "      <th>Zambales</th>\n",
       "      <th>Zambezia</th>\n",
       "      <th>Zamboanga Sibugay</th>\n",
       "      <th>Zamboanga del Norte</th>\n",
       "      <th>Zamboanga del Sur</th>\n",
       "      <th>Zamfara</th>\n",
       "      <th>Zamora-Chinchipe</th>\n",
       "      <th>Zanjan</th>\n",
       "      <th>Zanzan</th>\n",
       "      <th>Zanzibar South</th>\n",
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       "      <th>Zaporizhzhya</th>\n",
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       "      <th>Zarqa</th>\n",
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       "      <th>Zebbug</th>\n",
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       "      <th>Zhejiang</th>\n",
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       "      <th>Ziguinchor</th>\n",
       "      <th>Zinder</th>\n",
       "      <th>Zinguldak</th>\n",
       "      <th>Zolsky</th>\n",
       "      <th>Zubin Potok</th>\n",
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       "      <th>Zugdidi</th>\n",
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       "      <th>khon kaen</th>\n",
       "      <th>phayao</th>\n",
       "      <th>sakon nakhon</th>\n",
       "      <th>samut prakan</th>\n",
       "      <th>saraburi</th>\n",
       "      <th>sulawesi utara</th>\n",
       "      <th>unknown</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>5</td>\n",
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       "      <td>3</td>\n",
       "      <td>256</td>\n",
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       "      <td>55</td>\n",
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       "      <td>37</td>\n",
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       "      <td>260</td>\n",
       "      <td>1427</td>\n",
       "      <td>1223</td>\n",
       "      <td>73</td>\n",
       "      <td>159</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>66</td>\n",
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       "      <td>2</td>\n",
       "      <td>226</td>\n",
       "      <td>33</td>\n",
       "      <td>5</td>\n",
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       "      <td>109</td>\n",
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       "      <td>6</td>\n",
       "      <td>7</td>\n",
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       "      <td>32</td>\n",
       "      <td>89</td>\n",
       "      <td>109</td>\n",
       "      <td>188</td>\n",
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       "      <td>558</td>\n",
       "      <td>1</td>\n",
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       "      <td>18</td>\n",
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       "      <td>3</td>\n",
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       "      <td>535</td>\n",
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       "      <td>155</td>\n",
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       "      <td>5</td>\n",
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       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>28</td>\n",
       "      <td>48</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>136</td>\n",
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       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>517</td>\n",
       "      <td>801</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>110</td>\n",
       "      <td>86</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>189</td>\n",
       "      <td>623</td>\n",
       "      <td>1427</td>\n",
       "      <td>148</td>\n",
       "      <td>288</td>\n",
       "      <td>18</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>43</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>104</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>27</td>\n",
       "      <td>75</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>11</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>597</td>\n",
       "      <td>207</td>\n",
       "      <td>1151</td>\n",
       "      <td>127</td>\n",
       "      <td>3</td>\n",
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       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>37</td>\n",
       "      <td>0</td>\n",
       "      <td>215</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>17</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 2855 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "State    (Region) of Republican Subordination (Province)  ...  unknown\n",
       "Killed                                                 0  ...        3\n",
       "Wounded                                                0  ...        4\n",
       "\n",
       "[2 rows x 2855 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_state = df.pivot_table(columns='State', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_state"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-HqYFanCrLcc"
   },
   "source": [
    "Most deaths due to terrorism have occured in the province of Baghdad numbering at around 21000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 210
    },
    "id": "BmwigNeIi23l",
    "outputId": "fe76842c-a693-4d5a-d2b6-60a5d72e39fa"
   },
   "outputs": [
    {
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       "      <th>City</th>\n",
       "      <th>62 miles South of Dushanbe</th>\n",
       "      <th>A Coruna district</th>\n",
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       "      <th>Ab Bashak</th>\n",
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       "      <th>Ab-e Khorma</th>\n",
       "      <th>Aba</th>\n",
       "      <th>Aba Khel</th>\n",
       "      <th>Abaagu</th>\n",
       "      <th>Abaarey</th>\n",
       "      <th>...</th>\n",
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       "      <th>silvan</th>\n",
       "      <th>sirajganj district</th>\n",
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       "      <th>yokohama</th>\n",
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       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "      <td>34</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
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       "      <td>8</td>\n",
       "      <td>6</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>59</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>49</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 36674 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "City     62 miles South of Dushanbe  A Coruna district  ...  yokohama  zinarag\n",
       "Killed                            3                  3  ...         0        0\n",
       "Wounded                           0                  3  ...         0        0\n",
       "\n",
       "[2 rows x 36674 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_city = df.pivot_table(columns='City', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I7U4LVlhrVnD"
   },
   "source": [
    "Most deaths due to terrorism have occured in the city of Baghdad numbering at around 21000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 144
    },
    "id": "RojJcdtni-H8",
    "outputId": "15080c4a-a121-49b6-9fb8-4bc478f240d8"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attack_Type</th>\n",
       "      <th>Armed Assault</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wounded</th>\n",
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      ],
      "text/plain": [
       "Attack_Type  Armed Assault  Assassination  ...  Unarmed Assault  Unknown\n",
       "Killed              160297          24920  ...              880    32381\n",
       "Wounded              77365          13887  ...            14027    14725\n",
       "\n",
       "[2 rows x 9 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {
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     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_attack_type = df.pivot_table(columns='Attack_Type', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_attack_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "btTjNcQoridN"
   },
   "source": [
    "Most deaths due to terrorism have occured from armed assaults numbering at around 160000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 181
    },
    "id": "M_g4UPpYjNpC",
    "outputId": "a46128bf-e3e3-4510-b0e1-3930b57b59e0"
   },
   "outputs": [
    {
     "data": {
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       "      <th>Target_Type</th>\n",
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       "      <td>4329</td>\n",
       "      <td>1874</td>\n",
       "      <td>3617</td>\n",
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       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>46</td>\n",
       "      <td>3478</td>\n",
       "      <td>54531</td>\n",
       "      <td>10227</td>\n",
       "      <td>234</td>\n",
       "      <td>10359</td>\n",
       "      <td>41184</td>\n",
       "      <td>1796</td>\n",
       "      <td>908</td>\n",
       "      <td>71038</td>\n",
       "      <td>893</td>\n",
       "      <td>419</td>\n",
       "      <td>64703</td>\n",
       "      <td>178671</td>\n",
       "      <td>24477</td>\n",
       "      <td>497</td>\n",
       "      <td>8223</td>\n",
       "      <td>1290</td>\n",
       "      <td>40679</td>\n",
       "      <td>3559</td>\n",
       "      <td>1353</td>\n",
       "      <td>5303</td>\n",
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      ],
      "text/plain": [
       "Target_Type  Abortion Related  ...  Violent Political Party\n",
       "Killed                     10  ...                     3617\n",
       "Wounded                    46  ...                     5303\n",
       "\n",
       "[2 rows x 22 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_target_type = df.pivot_table(columns='Target_Type', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_target_type"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BBfiVRBCrs5P"
   },
   "source": [
    "Most deaths due to terrorism have occured among civilians and private citizens numbering at around 140000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 344
    },
    "id": "07qqxiRJjecD",
    "outputId": "d6b2eeab-df23-45eb-9c71-a86f9f9c1ee9"
   },
   "outputs": [
    {
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       "      <th>14 March Coalition</th>\n",
       "      <th>14th of December Command</th>\n",
       "      <th>15th of September Liberation Legion</th>\n",
       "      <th>16 January Organization for the Liberation of Tripoli</th>\n",
       "      <th>1920 Revolution Brigades</th>\n",
       "      <th>19th of July Christian Resistance Brigade</th>\n",
       "      <th>1st of May Group</th>\n",
       "      <th>2 April Group</th>\n",
       "      <th>20 December Movement (M-20)</th>\n",
       "      <th>22 May 1948</th>\n",
       "      <th>23 May Democratic Alliance (Algeria)</th>\n",
       "      <th>23rd of September Communist League</th>\n",
       "      <th>28 February Armed Group</th>\n",
       "      <th>28 May Armenian Organization</th>\n",
       "      <th>28s</th>\n",
       "      <th>28th of December Group</th>\n",
       "      <th>2nd of June Movement</th>\n",
       "      <th>31 January People's Front (FP-31)</th>\n",
       "      <th>313 Brigade (Syria)</th>\n",
       "      <th>4 August National Organization</th>\n",
       "      <th>7 April Libyan Organization</th>\n",
       "      <th>9 February</th>\n",
       "      <th>9 May People's Liberation Force</th>\n",
       "      <th>A Resistance Group</th>\n",
       "      <th>A'chik Matgrik Elite Force (AMEF)</th>\n",
       "      <th>AFB</th>\n",
       "      <th>AGEL</th>\n",
       "      <th>ATALA</th>\n",
       "      <th>Aba Cheali Group</th>\n",
       "      <th>Abbala extremists</th>\n",
       "      <th>Abd al-Krim Commandos</th>\n",
       "      <th>Abdul Ghani Kikli Militia</th>\n",
       "      <th>Abdul Qader Husseini Battalions of the Free Palestine movement</th>\n",
       "      <th>Abdullah Azzam Brigades</th>\n",
       "      <th>Abida Tribe</th>\n",
       "      <th>Abkhazian Separatists</th>\n",
       "      <th>Abkhazian guerrillas</th>\n",
       "      <th>Abstentionist Brigades</th>\n",
       "      <th>...</th>\n",
       "      <th>Young Cuba</th>\n",
       "      <th>Young Liberators of Pattani</th>\n",
       "      <th>Young Officer Union of the New Generation and Reformist Armed Forces of the Philippines (YOU-RAFP)</th>\n",
       "      <th>Young Pioneers</th>\n",
       "      <th>Youth Action Group</th>\n",
       "      <th>Youth Gang</th>\n",
       "      <th>Youth Movement for the Total Liberation of Azawad</th>\n",
       "      <th>Youth for Revolution</th>\n",
       "      <th>Youth of Islamic Awakening</th>\n",
       "      <th>Youth of the Land of Egypt</th>\n",
       "      <th>Youth of the Star</th>\n",
       "      <th>Youths</th>\n",
       "      <th>Youths of Ali Movement</th>\n",
       "      <th>Zaghawa ethnic group</th>\n",
       "      <th>Zagros Eagles</th>\n",
       "      <th>Zairian Dissidents</th>\n",
       "      <th>Zapatista National Liberation Army</th>\n",
       "      <th>Zarate Willka Armed Forces of Liberation</th>\n",
       "      <th>Zawiya Martyrs Brigade</th>\n",
       "      <th>Zebra killers</th>\n",
       "      <th>Zehri Youth Force (ZYF)</th>\n",
       "      <th>Zeliangrong United Front</th>\n",
       "      <th>Zemun Clan</th>\n",
       "      <th>Zero Point</th>\n",
       "      <th>Zero Tolerance</th>\n",
       "      <th>Zetas</th>\n",
       "      <th>Zimbabwe African Nationalist Union (ZANU)</th>\n",
       "      <th>Zimbabwe African People's Union</th>\n",
       "      <th>Zimbabwe Guerrillas</th>\n",
       "      <th>Zimbabwe Patriotic Front</th>\n",
       "      <th>Zimbabwe People's Army (ZIPA)</th>\n",
       "      <th>Zintani Militia</th>\n",
       "      <th>Zionist Resistance Fighters</th>\n",
       "      <th>Zomi Revolutionary Army (ZRA)</th>\n",
       "      <th>Zulu Militants</th>\n",
       "      <th>Zulu Miners</th>\n",
       "      <th>Zuwar al-Imam Rida</th>\n",
       "      <th>Zviadists</th>\n",
       "      <th>Zwai Tribe</th>\n",
       "      <th>leftist guerrillas-Bolivarian militia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Killed</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>180</td>\n",
       "      <td>0</td>\n",
       "      <td>58</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>81</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>28</td>\n",
       "      <td>143</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wounded</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>80</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>52</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td>53</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>578</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>100</td>\n",
       "      <td>10</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 3537 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Group    1 May  14 K Triad  ...  Zwai Tribe  leftist guerrillas-Bolivarian militia\n",
       "Killed       2           0  ...           5                                      0\n",
       "Wounded      0           0  ...           0                                      0\n",
       "\n",
       "[2 rows x 3537 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casualties_by_group = df.pivot_table(columns='Group', values=('Killed','Wounded'), aggfunc='sum')\n",
    "casualties_by_group"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rOW79ITpsI3h"
   },
   "source": [
    "Most deaths due to terrorism have been caused by the terrorist activities committed by Islamic State of Iraq and the Levant (ISIL) numbering at around 39000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "j1zWTbObsT4G"
   },
   "source": [
    "# Thank You!"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "collapsed_sections": [],
   "name": "eda_terrorism.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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   "pygments_lexer": "ipython3",
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