TASK 4 Exploratory Data Analysis - Terrorism.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Sparks Foundation: Data Science and Business Analytics Intern"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TASK 4 Exploratory Data Analysis - Terrorism"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BY : Harshita Nayak"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question: Perform ‘Exploratory Data Analysis’ on dataset ‘Global Terrorism’ and try to find out the hot zone of terrorism."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing libaries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(action=\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing dataset\n",
    "df = pd.read_csv(\"C:\\\\Users\\\\Harshita\\\\Desktop\\\\globalterrorismdb_0718dist.csv\", low_memory=False ,encoding='latin1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "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>...</th>\n",
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       "      <th>INT_LOG</th>\n",
       "      <th>INT_IDEO</th>\n",
       "      <th>INT_MISC</th>\n",
       "      <th>INT_ANY</th>\n",
       "      <th>related</th>\n",
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       "      <th>0</th>\n",
       "      <td>197000000001</td>\n",
       "      <td>1970</td>\n",
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       "      <td>Dominican Republic</td>\n",
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       "      <td>130</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>160</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>5</td>\n",
       "      <td>...</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",
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       "      <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>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
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       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        eventid  iyear  imonth  iday approxdate  extended resolution  country  \\\n",
       "0  197000000001   1970       7     2        NaN         0        NaN       58   \n",
       "1  197000000002   1970       0     0        NaN         0        NaN      130   \n",
       "2  197001000001   1970       1     0        NaN         0        NaN      160   \n",
       "3  197001000002   1970       1     0        NaN         0        NaN       78   \n",
       "4  197001000003   1970       1     0        NaN         0        NaN      101   \n",
       "\n",
       "          country_txt  region  ... addnotes scite1 scite2  scite3  dbsource  \\\n",
       "0  Dominican Republic       2  ...      NaN    NaN    NaN     NaN      PGIS   \n",
       "1              Mexico       1  ...      NaN    NaN    NaN     NaN      PGIS   \n",
       "2         Philippines       5  ...      NaN    NaN    NaN     NaN      PGIS   \n",
       "3              Greece       8  ...      NaN    NaN    NaN     NaN      PGIS   \n",
       "4               Japan       4  ...      NaN    NaN    NaN     NaN      PGIS   \n",
       "\n",
       "   INT_LOG  INT_IDEO INT_MISC INT_ANY  related  \n",
       "0        0         0        0       0      NaN  \n",
       "1        0         1        1       1      NaN  \n",
       "2       -9        -9        1       1      NaN  \n",
       "3       -9        -9        1       1      NaN  \n",
       "4       -9        -9        1       1      NaN  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets check our dataset\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181691, 135)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# shape of our dataset\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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: 146.9+ MB\n"
     ]
    }
   ],
   "source": [
    "# info of our dataset\n",
    "df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>eventid</th>\n",
       "      <th>iyear</th>\n",
       "      <th>imonth</th>\n",
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       "      <td>1.816910e+05</td>\n",
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       "      <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",
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       "      <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",
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       "      <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>...</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",
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       "      <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",
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       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
       "      <td>-9.000000</td>\n",
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       "    <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>...</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
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       "      <td>-9.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "      <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",
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       "      <td>4.324651e+01</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</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",
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       "    <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>...</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",
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       "    <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>...</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",
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       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 77 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            eventid          iyear         imonth           iday  \\\n",
       "count  1.816910e+05  181691.000000  181691.000000  181691.000000   \n",
       "mean   2.002705e+11    2002.638997       6.467277      15.505644   \n",
       "std    1.325957e+09      13.259430       3.388303       8.814045   \n",
       "min    1.970000e+11    1970.000000       0.000000       0.000000   \n",
       "25%    1.991021e+11    1991.000000       4.000000       8.000000   \n",
       "50%    2.009022e+11    2009.000000       6.000000      15.000000   \n",
       "75%    2.014081e+11    2014.000000       9.000000      23.000000   \n",
       "max    2.017123e+11    2017.000000      12.000000      31.000000   \n",
       "\n",
       "            extended        country         region       latitude  \\\n",
       "count  181691.000000  181691.000000  181691.000000  177135.000000   \n",
       "mean        0.045346     131.968501       7.160938      23.498343   \n",
       "std         0.208063     112.414535       2.933408      18.569242   \n",
       "min         0.000000       4.000000       1.000000     -53.154613   \n",
       "25%         0.000000      78.000000       5.000000      11.510046   \n",
       "50%         0.000000      98.000000       6.000000      31.467463   \n",
       "75%         0.000000     160.000000      10.000000      34.685087   \n",
       "max         1.000000    1004.000000      12.000000      74.633553   \n",
       "\n",
       "          longitude    specificity  ...     ransomamt   ransomamtus  \\\n",
       "count  1.771340e+05  181685.000000  ...  1.350000e+03  5.630000e+02   \n",
       "mean  -4.586957e+02       1.451452  ...  3.172530e+06  5.784865e+05   \n",
       "std    2.047790e+05       0.995430  ...  3.021157e+07  7.077924e+06   \n",
       "min   -8.618590e+07       1.000000  ... -9.900000e+01 -9.900000e+01   \n",
       "25%    4.545640e+00       1.000000  ...  0.000000e+00  0.000000e+00   \n",
       "50%    4.324651e+01       1.000000  ...  1.500000e+04  0.000000e+00   \n",
       "75%    6.871033e+01       1.000000  ...  4.000000e+05  0.000000e+00   \n",
       "max    1.793667e+02       5.000000  ...  1.000000e+09  1.320000e+08   \n",
       "\n",
       "         ransompaid  ransompaidus  hostkidoutcome     nreleased  \\\n",
       "count  7.740000e+02    552.000000    10991.000000  10400.000000   \n",
       "mean   7.179437e+05    240.378623        4.629242    -29.018269   \n",
       "std    1.014392e+07   2940.967293        2.035360     65.720119   \n",
       "min   -9.900000e+01    -99.000000        1.000000    -99.000000   \n",
       "25%   -9.900000e+01      0.000000        2.000000    -99.000000   \n",
       "50%    0.000000e+00      0.000000        4.000000      0.000000   \n",
       "75%    1.273412e+03      0.000000        7.000000      1.000000   \n",
       "max    2.750000e+08  48000.000000        7.000000   2769.000000   \n",
       "\n",
       "             INT_LOG       INT_IDEO       INT_MISC        INT_ANY  \n",
       "count  181691.000000  181691.000000  181691.000000  181691.000000  \n",
       "mean       -4.543731      -4.464398       0.090010      -3.945952  \n",
       "std         4.543547       4.637152       0.568457       4.691325  \n",
       "min        -9.000000      -9.000000      -9.000000      -9.000000  \n",
       "25%        -9.000000      -9.000000       0.000000      -9.000000  \n",
       "50%        -9.000000      -9.000000       0.000000       0.000000  \n",
       "75%         0.000000       0.000000       0.000000       0.000000  \n",
       "max         1.000000       1.000000       1.000000       1.000000  \n",
       "\n",
       "[8 rows x 77 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# statistical info of our dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['eventid', 'iyear', 'imonth', 'iday', 'approxdate', 'extended',\n",
       "       'resolution', 'country', 'country_txt', 'region', 'region_txt',\n",
       "       'provstate', 'city', 'latitude', 'longitude', 'specificity',\n",
       "       'vicinity', 'location', 'summary', 'crit1', 'crit2', 'crit3',\n",
       "       'doubtterr', 'alternative', 'alternative_txt', 'multiple',\n",
       "       'success', 'suicide', 'attacktype1', 'attacktype1_txt',\n",
       "       'attacktype2', 'attacktype2_txt', 'attacktype3', 'attacktype3_txt',\n",
       "       'targtype1', 'targtype1_txt', 'targsubtype1', 'targsubtype1_txt',\n",
       "       'corp1', 'target1', 'natlty1', 'natlty1_txt', 'targtype2',\n",
       "       'targtype2_txt', 'targsubtype2', 'targsubtype2_txt', 'corp2',\n",
       "       'target2', 'natlty2', 'natlty2_txt', 'targtype3', 'targtype3_txt',\n",
       "       'targsubtype3', 'targsubtype3_txt', 'corp3', 'target3', 'natlty3',\n",
       "       'natlty3_txt', 'gname', 'gsubname', 'gname2', 'gsubname2',\n",
       "       'gname3', 'gsubname3', 'motive', 'guncertain1', 'guncertain2',\n",
       "       'guncertain3', 'individual', 'nperps', 'nperpcap', 'claimed',\n",
       "       'claimmode', 'claimmode_txt', 'claim2', 'claimmode2',\n",
       "       'claimmode2_txt', 'claim3', 'claimmode3', 'claimmode3_txt',\n",
       "       'compclaim', 'weaptype1', 'weaptype1_txt', 'weapsubtype1',\n",
       "       'weapsubtype1_txt', 'weaptype2', 'weaptype2_txt', 'weapsubtype2',\n",
       "       'weapsubtype2_txt', 'weaptype3', 'weaptype3_txt', 'weapsubtype3',\n",
       "       'weapsubtype3_txt', 'weaptype4', 'weaptype4_txt', 'weapsubtype4',\n",
       "       'weapsubtype4_txt', 'weapdetail', 'nkill', 'nkillus', 'nkillter',\n",
       "       'nwound', 'nwoundus', 'nwoundte', 'property', 'propextent',\n",
       "       'propextent_txt', 'propvalue', 'propcomment', 'ishostkid',\n",
       "       'nhostkid', 'nhostkidus', 'nhours', 'ndays', 'divert',\n",
       "       'kidhijcountry', 'ransom', 'ransomamt', 'ransomamtus',\n",
       "       'ransompaid', 'ransompaidus', 'ransomnote', 'hostkidoutcome',\n",
       "       'hostkidoutcome_txt', 'nreleased', 'addnotes', 'scite1', 'scite2',\n",
       "       'scite3', 'dbsource', 'INT_LOG', 'INT_IDEO', 'INT_MISC', 'INT_ANY',\n",
       "       'related'], dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "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": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(181691, 29)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "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>vicinity</th>\n",
       "      <th>crit1</th>\n",
       "      <th>crit2</th>\n",
       "      <th>...</th>\n",
       "      <th>suicide</th>\n",
       "      <th>attacktype1</th>\n",
       "      <th>targtype1</th>\n",
       "      <th>individual</th>\n",
       "      <th>weaptype1</th>\n",
       "      <th>property</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>0.070646</td>\n",
       "      <td>0.002019</td>\n",
       "      <td>0.026215</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137743</td>\n",
       "      <td>0.074173</td>\n",
       "      <td>0.079080</td>\n",
       "      <td>0.017939</td>\n",
       "      <td>-0.011716</td>\n",
       "      <td>-0.278927</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>0.070659</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.026211</td>\n",
       "      <td>...</td>\n",
       "      <td>0.137736</td>\n",
       "      <td>0.074153</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>0.017944</td>\n",
       "      <td>-0.011737</td>\n",
       "      <td>-0.278916</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>-0.004501</td>\n",
       "      <td>0.001568</td>\n",
       "      <td>0.001734</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003071</td>\n",
       "      <td>0.006705</td>\n",
       "      <td>-0.000948</td>\n",
       "      <td>-0.001661</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>-0.005816</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>-0.005367</td>\n",
       "      <td>0.011434</td>\n",
       "      <td>-0.002702</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>-0.005333</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>-0.003788</td>\n",
       "      <td>-0.003019</td>\n",
       "      <td>-0.003903</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>0.017784</td>\n",
       "      <td>-0.015029</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.033961</td>\n",
       "      <td>0.272272</td>\n",
       "      <td>0.011613</td>\n",
       "      <td>-0.008929</td>\n",
       "      <td>0.207802</td>\n",
       "      <td>-0.002096</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>country</th>\n",
       "      <td>-0.135039</td>\n",
       "      <td>-0.135023</td>\n",
       "      <td>-0.006305</td>\n",
       "      <td>0.003468</td>\n",
       "      <td>-0.020466</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.148597</td>\n",
       "      <td>-0.012291</td>\n",
       "      <td>-0.006646</td>\n",
       "      <td>-0.038480</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.050360</td>\n",
       "      <td>-0.021384</td>\n",
       "      <td>-0.019703</td>\n",
       "      <td>0.027918</td>\n",
       "      <td>-0.023708</td>\n",
       "      <td>0.032051</td>\n",
       "      <td>0.069904</td>\n",
       "      <td>0.067564</td>\n",
       "      <td>0.207281</td>\n",
       "      <td>0.153118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>region</th>\n",
       "      <td>0.401371</td>\n",
       "      <td>0.401384</td>\n",
       "      <td>-0.002999</td>\n",
       "      <td>0.009710</td>\n",
       "      <td>0.038389</td>\n",
       "      <td>0.148597</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.073678</td>\n",
       "      <td>0.025521</td>\n",
       "      <td>-0.013163</td>\n",
       "      <td>...</td>\n",
       "      <td>0.112672</td>\n",
       "      <td>0.007642</td>\n",
       "      <td>0.041396</td>\n",
       "      <td>-0.059963</td>\n",
       "      <td>0.013257</td>\n",
       "      <td>-0.183279</td>\n",
       "      <td>-0.082584</td>\n",
       "      <td>-0.071917</td>\n",
       "      <td>0.043139</td>\n",
       "      <td>-0.047900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vicinity</th>\n",
       "      <td>0.070646</td>\n",
       "      <td>0.070659</td>\n",
       "      <td>-0.004501</td>\n",
       "      <td>-0.005367</td>\n",
       "      <td>0.017784</td>\n",
       "      <td>-0.012291</td>\n",
       "      <td>0.073678</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.003330</td>\n",
       "      <td>-0.000534</td>\n",
       "      <td>...</td>\n",
       "      <td>0.008352</td>\n",
       "      <td>0.006504</td>\n",
       "      <td>0.024644</td>\n",
       "      <td>-0.011986</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>-0.019811</td>\n",
       "      <td>0.014487</td>\n",
       "      <td>0.015119</td>\n",
       "      <td>-0.008232</td>\n",
       "      <td>0.010325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crit1</th>\n",
       "      <td>0.002019</td>\n",
       "      <td>0.001995</td>\n",
       "      <td>0.001568</td>\n",
       "      <td>0.011434</td>\n",
       "      <td>-0.015029</td>\n",
       "      <td>-0.006646</td>\n",
       "      <td>0.025521</td>\n",
       "      <td>0.003330</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.008984</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019039</td>\n",
       "      <td>0.027544</td>\n",
       "      <td>-0.040026</td>\n",
       "      <td>-0.025591</td>\n",
       "      <td>0.029257</td>\n",
       "      <td>-0.007595</td>\n",
       "      <td>0.089973</td>\n",
       "      <td>0.089888</td>\n",
       "      <td>0.015692</td>\n",
       "      <td>0.082288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crit2</th>\n",
       "      <td>0.026215</td>\n",
       "      <td>0.026211</td>\n",
       "      <td>0.001734</td>\n",
       "      <td>-0.002702</td>\n",
       "      <td>0.001249</td>\n",
       "      <td>-0.038480</td>\n",
       "      <td>-0.013163</td>\n",
       "      <td>-0.000534</td>\n",
       "      <td>-0.008984</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.005157</td>\n",
       "      <td>-0.101522</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>-0.008549</td>\n",
       "      <td>-0.002711</td>\n",
       "      <td>-0.024153</td>\n",
       "      <td>-0.023829</td>\n",
       "      <td>-0.012980</td>\n",
       "      <td>-0.027752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crit3</th>\n",
       "      <td>-0.016789</td>\n",
       "      <td>-0.016784</td>\n",
       "      <td>0.000176</td>\n",
       "      <td>-0.004889</td>\n",
       "      <td>0.048533</td>\n",
       "      <td>-0.040288</td>\n",
       "      <td>-0.010175</td>\n",
       "      <td>-0.054995</td>\n",
       "      <td>-0.040589</td>\n",
       "      <td>-0.031425</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.026620</td>\n",
       "      <td>0.028124</td>\n",
       "      <td>0.251296</td>\n",
       "      <td>0.016806</td>\n",
       "      <td>0.018320</td>\n",
       "      <td>0.013345</td>\n",
       "      <td>-0.134557</td>\n",
       "      <td>-0.138251</td>\n",
       "      <td>-0.036112</td>\n",
       "      <td>-0.130216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>success</th>\n",
       "      <td>-0.082972</td>\n",
       "      <td>-0.082963</td>\n",
       "      <td>-0.002845</td>\n",
       "      <td>-0.011802</td>\n",
       "      <td>0.073233</td>\n",
       "      <td>-0.037827</td>\n",
       "      <td>-0.030909</td>\n",
       "      <td>-0.002223</td>\n",
       "      <td>-0.010733</td>\n",
       "      <td>-0.013476</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.031155</td>\n",
       "      <td>0.048408</td>\n",
       "      <td>-0.059637</td>\n",
       "      <td>-0.013868</td>\n",
       "      <td>-0.008178</td>\n",
       "      <td>-0.036876</td>\n",
       "      <td>0.059875</td>\n",
       "      <td>0.059183</td>\n",
       "      <td>-0.014906</td>\n",
       "      <td>0.038245</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>suicide</th>\n",
       "      <td>0.137743</td>\n",
       "      <td>0.137736</td>\n",
       "      <td>0.003071</td>\n",
       "      <td>0.003593</td>\n",
       "      <td>-0.033961</td>\n",
       "      <td>-0.050360</td>\n",
       "      <td>0.112672</td>\n",
       "      <td>0.008352</td>\n",
       "      <td>0.019039</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.029962</td>\n",
       "      <td>-0.023440</td>\n",
       "      <td>0.000775</td>\n",
       "      <td>-0.039928</td>\n",
       "      <td>-0.073382</td>\n",
       "      <td>0.058735</td>\n",
       "      <td>0.065882</td>\n",
       "      <td>-0.000879</td>\n",
       "      <td>0.057241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>attacktype1</th>\n",
       "      <td>0.074173</td>\n",
       "      <td>0.074153</td>\n",
       "      <td>0.006705</td>\n",
       "      <td>-0.005333</td>\n",
       "      <td>0.272272</td>\n",
       "      <td>-0.021384</td>\n",
       "      <td>0.007642</td>\n",
       "      <td>0.006504</td>\n",
       "      <td>0.027544</td>\n",
       "      <td>0.005157</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.029962</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.014513</td>\n",
       "      <td>0.016438</td>\n",
       "      <td>0.658954</td>\n",
       "      <td>0.031976</td>\n",
       "      <td>0.075002</td>\n",
       "      <td>0.077225</td>\n",
       "      <td>0.010632</td>\n",
       "      <td>0.088711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>targtype1</th>\n",
       "      <td>0.079080</td>\n",
       "      <td>0.079082</td>\n",
       "      <td>-0.000948</td>\n",
       "      <td>-0.000052</td>\n",
       "      <td>0.011613</td>\n",
       "      <td>-0.019703</td>\n",
       "      <td>0.041396</td>\n",
       "      <td>0.024644</td>\n",
       "      <td>-0.040026</td>\n",
       "      <td>-0.101522</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.023440</td>\n",
       "      <td>0.014513</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005267</td>\n",
       "      <td>0.019846</td>\n",
       "      <td>0.004897</td>\n",
       "      <td>-0.056550</td>\n",
       "      <td>-0.058010</td>\n",
       "      <td>-0.043492</td>\n",
       "      <td>-0.054497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>individual</th>\n",
       "      <td>0.017939</td>\n",
       "      <td>0.017944</td>\n",
       "      <td>-0.001661</td>\n",
       "      <td>-0.003788</td>\n",
       "      <td>-0.008929</td>\n",
       "      <td>0.027918</td>\n",
       "      <td>-0.059963</td>\n",
       "      <td>-0.011986</td>\n",
       "      <td>-0.025591</td>\n",
       "      <td>0.003311</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000775</td>\n",
       "      <td>0.016438</td>\n",
       "      <td>0.005267</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005754</td>\n",
       "      <td>0.006625</td>\n",
       "      <td>-0.048526</td>\n",
       "      <td>-0.048542</td>\n",
       "      <td>0.005490</td>\n",
       "      <td>-0.037488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weaptype1</th>\n",
       "      <td>-0.011716</td>\n",
       "      <td>-0.011737</td>\n",
       "      <td>0.007863</td>\n",
       "      <td>-0.003019</td>\n",
       "      <td>0.207802</td>\n",
       "      <td>-0.023708</td>\n",
       "      <td>0.013257</td>\n",
       "      <td>0.001496</td>\n",
       "      <td>0.029257</td>\n",
       "      <td>-0.008549</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.039928</td>\n",
       "      <td>0.658954</td>\n",
       "      <td>0.019846</td>\n",
       "      <td>0.005754</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.018999</td>\n",
       "      <td>0.072941</td>\n",
       "      <td>0.074702</td>\n",
       "      <td>-0.011652</td>\n",
       "      <td>0.084257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>property</th>\n",
       "      <td>-0.278927</td>\n",
       "      <td>-0.278916</td>\n",
       "      <td>-0.005816</td>\n",
       "      <td>-0.003903</td>\n",
       "      <td>-0.002096</td>\n",
       "      <td>0.032051</td>\n",
       "      <td>-0.183279</td>\n",
       "      <td>-0.019811</td>\n",
       "      <td>-0.007595</td>\n",
       "      <td>-0.002711</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.073382</td>\n",
       "      <td>0.031976</td>\n",
       "      <td>0.004897</td>\n",
       "      <td>0.006625</td>\n",
       "      <td>0.018999</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052445</td>\n",
       "      <td>0.047609</td>\n",
       "      <td>0.022058</td>\n",
       "      <td>0.065701</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>0.014487</td>\n",
       "      <td>0.089973</td>\n",
       "      <td>-0.024153</td>\n",
       "      <td>...</td>\n",
       "      <td>0.058735</td>\n",
       "      <td>0.075002</td>\n",
       "      <td>-0.056550</td>\n",
       "      <td>-0.048526</td>\n",
       "      <td>0.072941</td>\n",
       "      <td>0.052445</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>0.015119</td>\n",
       "      <td>0.089888</td>\n",
       "      <td>-0.023829</td>\n",
       "      <td>...</td>\n",
       "      <td>0.065882</td>\n",
       "      <td>0.077225</td>\n",
       "      <td>-0.058010</td>\n",
       "      <td>-0.048542</td>\n",
       "      <td>0.074702</td>\n",
       "      <td>0.047609</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>-0.008232</td>\n",
       "      <td>0.015692</td>\n",
       "      <td>-0.012980</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.000879</td>\n",
       "      <td>0.010632</td>\n",
       "      <td>-0.043492</td>\n",
       "      <td>0.005490</td>\n",
       "      <td>-0.011652</td>\n",
       "      <td>0.022058</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>0.010325</td>\n",
       "      <td>0.082288</td>\n",
       "      <td>-0.027752</td>\n",
       "      <td>...</td>\n",
       "      <td>0.057241</td>\n",
       "      <td>0.088711</td>\n",
       "      <td>-0.054497</td>\n",
       "      <td>-0.037488</td>\n",
       "      <td>0.084257</td>\n",
       "      <td>0.065701</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>22 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              eventid     iyear    imonth      iday  extended   country  \\\n",
       "eventid      1.000000  0.999996  0.002706  0.018336  0.091761 -0.135039   \n",
       "iyear        0.999996  1.000000  0.000139  0.018254  0.091754 -0.135023   \n",
       "imonth       0.002706  0.000139  1.000000  0.005497 -0.000468 -0.006305   \n",
       "iday         0.018336  0.018254  0.005497  1.000000 -0.004700  0.003468   \n",
       "extended     0.091761  0.091754 -0.000468 -0.004700  1.000000 -0.020466   \n",
       "country     -0.135039 -0.135023 -0.006305  0.003468 -0.020466  1.000000   \n",
       "region       0.401371  0.401384 -0.002999  0.009710  0.038389  0.148597   \n",
       "vicinity     0.070646  0.070659 -0.004501 -0.005367  0.017784 -0.012291   \n",
       "crit1        0.002019  0.001995  0.001568  0.011434 -0.015029 -0.006646   \n",
       "crit2        0.026215  0.026211  0.001734 -0.002702  0.001249 -0.038480   \n",
       "crit3       -0.016789 -0.016784  0.000176 -0.004889  0.048533 -0.040288   \n",
       "success     -0.082972 -0.082963 -0.002845 -0.011802  0.073233 -0.037827   \n",
       "suicide      0.137743  0.137736  0.003071  0.003593 -0.033961 -0.050360   \n",
       "attacktype1  0.074173  0.074153  0.006705 -0.005333  0.272272 -0.021384   \n",
       "targtype1    0.079080  0.079082 -0.000948 -0.000052  0.011613 -0.019703   \n",
       "individual   0.017939  0.017944 -0.001661 -0.003788 -0.008929  0.027918   \n",
       "weaptype1   -0.011716 -0.011737  0.007863 -0.003019  0.207802 -0.023708   \n",
       "property    -0.278927 -0.278916 -0.005816 -0.003903 -0.002096  0.032051   \n",
       "INT_LOG     -0.143600 -0.143601 -0.002302 -0.001540  0.071768  0.069904   \n",
       "INT_IDEO    -0.133252 -0.133253 -0.002034 -0.001621  0.075147  0.067564   \n",
       "INT_MISC    -0.077852 -0.077847 -0.002554 -0.002027  0.027335  0.207281   \n",
       "INT_ANY     -0.175605 -0.175596 -0.006336 -0.001199  0.080767  0.153118   \n",
       "\n",
       "               region  vicinity     crit1     crit2  ...   suicide  \\\n",
       "eventid      0.401371  0.070646  0.002019  0.026215  ...  0.137743   \n",
       "iyear        0.401384  0.070659  0.001995  0.026211  ...  0.137736   \n",
       "imonth      -0.002999 -0.004501  0.001568  0.001734  ...  0.003071   \n",
       "iday         0.009710 -0.005367  0.011434 -0.002702  ...  0.003593   \n",
       "extended     0.038389  0.017784 -0.015029  0.001249  ... -0.033961   \n",
       "country      0.148597 -0.012291 -0.006646 -0.038480  ... -0.050360   \n",
       "region       1.000000  0.073678  0.025521 -0.013163  ...  0.112672   \n",
       "vicinity     0.073678  1.000000  0.003330 -0.000534  ...  0.008352   \n",
       "crit1        0.025521  0.003330  1.000000 -0.008984  ...  0.019039   \n",
       "crit2       -0.013163 -0.000534 -0.008984  1.000000  ...  0.002770   \n",
       "crit3       -0.010175 -0.054995 -0.040589 -0.031425  ... -0.026620   \n",
       "success     -0.030909 -0.002223 -0.010733 -0.013476  ... -0.031155   \n",
       "suicide      0.112672  0.008352  0.019039  0.002770  ...  1.000000   \n",
       "attacktype1  0.007642  0.006504  0.027544  0.005157  ... -0.029962   \n",
       "targtype1    0.041396  0.024644 -0.040026 -0.101522  ... -0.023440   \n",
       "individual  -0.059963 -0.011986 -0.025591  0.003311  ...  0.000775   \n",
       "weaptype1    0.013257  0.001496  0.029257 -0.008549  ... -0.039928   \n",
       "property    -0.183279 -0.019811 -0.007595 -0.002711  ... -0.073382   \n",
       "INT_LOG     -0.082584  0.014487  0.089973 -0.024153  ...  0.058735   \n",
       "INT_IDEO    -0.071917  0.015119  0.089888 -0.023829  ...  0.065882   \n",
       "INT_MISC     0.043139 -0.008232  0.015692 -0.012980  ... -0.000879   \n",
       "INT_ANY     -0.047900  0.010325  0.082288 -0.027752  ...  0.057241   \n",
       "\n",
       "             attacktype1  targtype1  individual  weaptype1  property  \\\n",
       "eventid         0.074173   0.079080    0.017939  -0.011716 -0.278927   \n",
       "iyear           0.074153   0.079082    0.017944  -0.011737 -0.278916   \n",
       "imonth          0.006705  -0.000948   -0.001661   0.007863 -0.005816   \n",
       "iday           -0.005333  -0.000052   -0.003788  -0.003019 -0.003903   \n",
       "extended        0.272272   0.011613   -0.008929   0.207802 -0.002096   \n",
       "country        -0.021384  -0.019703    0.027918  -0.023708  0.032051   \n",
       "region          0.007642   0.041396   -0.059963   0.013257 -0.183279   \n",
       "vicinity        0.006504   0.024644   -0.011986   0.001496 -0.019811   \n",
       "crit1           0.027544  -0.040026   -0.025591   0.029257 -0.007595   \n",
       "crit2           0.005157  -0.101522    0.003311  -0.008549 -0.002711   \n",
       "crit3           0.028124   0.251296    0.016806   0.018320  0.013345   \n",
       "success         0.048408  -0.059637   -0.013868  -0.008178 -0.036876   \n",
       "suicide        -0.029962  -0.023440    0.000775  -0.039928 -0.073382   \n",
       "attacktype1     1.000000   0.014513    0.016438   0.658954  0.031976   \n",
       "targtype1       0.014513   1.000000    0.005267   0.019846  0.004897   \n",
       "individual      0.016438   0.005267    1.000000   0.005754  0.006625   \n",
       "weaptype1       0.658954   0.019846    0.005754   1.000000  0.018999   \n",
       "property        0.031976   0.004897    0.006625   0.018999  1.000000   \n",
       "INT_LOG         0.075002  -0.056550   -0.048526   0.072941  0.052445   \n",
       "INT_IDEO        0.077225  -0.058010   -0.048542   0.074702  0.047609   \n",
       "INT_MISC        0.010632  -0.043492    0.005490  -0.011652  0.022058   \n",
       "INT_ANY         0.088711  -0.054497   -0.037488   0.084257  0.065701   \n",
       "\n",
       "              INT_LOG  INT_IDEO  INT_MISC   INT_ANY  \n",
       "eventid     -0.143600 -0.133252 -0.077852 -0.175605  \n",
       "iyear       -0.143601 -0.133253 -0.077847 -0.175596  \n",
       "imonth      -0.002302 -0.002034 -0.002554 -0.006336  \n",
       "iday        -0.001540 -0.001621 -0.002027 -0.001199  \n",
       "extended     0.071768  0.075147  0.027335  0.080767  \n",
       "country      0.069904  0.067564  0.207281  0.153118  \n",
       "region      -0.082584 -0.071917  0.043139 -0.047900  \n",
       "vicinity     0.014487  0.015119 -0.008232  0.010325  \n",
       "crit1        0.089973  0.089888  0.015692  0.082288  \n",
       "crit2       -0.024153 -0.023829 -0.012980 -0.027752  \n",
       "crit3       -0.134557 -0.138251 -0.036112 -0.130216  \n",
       "success      0.059875  0.059183 -0.014906  0.038245  \n",
       "suicide      0.058735  0.065882 -0.000879  0.057241  \n",
       "attacktype1  0.075002  0.077225  0.010632  0.088711  \n",
       "targtype1   -0.056550 -0.058010 -0.043492 -0.054497  \n",
       "individual  -0.048526 -0.048542  0.005490 -0.037488  \n",
       "weaptype1    0.072941  0.074702 -0.011652  0.084257  \n",
       "property     0.052445  0.047609  0.022058  0.065701  \n",
       "INT_LOG      1.000000  0.996211  0.052537  0.891051  \n",
       "INT_IDEO     0.996211  1.000000  0.082014  0.893811  \n",
       "INT_MISC     0.052537  0.082014  1.000000  0.252193  \n",
       "INT_ANY      0.891051  0.893811  0.252193  1.000000  \n",
       "\n",
       "[22 rows x 22 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " # Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'No of Attacks in year (1970-2017)')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Countplot of attacks in recent years\n",
    "plt.figure(figsize=(18,6))\n",
    "sns.countplot(df['iyear'])\n",
    "plt.xlabel(\"Attacks frequency\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Number of Attacks\")\n",
    "plt.title(\"No of Attacks in year (1970-2017)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency of Attacks')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,8))\n",
    "sns.countplot(x=df[\"iyear\"], hue=\"success\", data = df)\n",
    "plt.xlabel(\"Years\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Frequency of Attacks\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Countries facing Most Terrorism')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot to show countries with most no. of terrorism\n",
    "plt.figure(figsize=(18,8))\n",
    "sns.barplot(x=df['country_txt'].value_counts()[:20].index, y=df['country'].value_counts()[:20])\n",
    "plt.xlabel(\"Countries\")\n",
    "plt.ylabel(\"Count\")\n",
    "plt.xticks(rotation=90)\n",
    "plt.title(\"Countries facing Most Terrorism\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Regions facing Most Terrorism')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot to show Regions with most terrorism\n",
    "plt.figure(figsize=(18,8))\n",
    "sns.countplot(x= df[\"region_txt\"], hue=\"success\", data=df)\n",
    "plt.xlabel(\"Regions\", fontsize=20)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Terrorism Count\", fontsize=20)\n",
    "plt.title(\"Regions facing Most Terrorism\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Types of Weapons')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# barplot of waepons used\n",
    "plt.figure(figsize=(14,7))\n",
    "sns.barplot(x= df[\"weaptype1_txt\"].value_counts()[:50].index,y = df[\"weaptype1_txt\"].value_counts()[:50].values)\n",
    "plt.xlabel(\"Weapons used\", fontsize=20)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Frequency of a Particluar weapon used\", fontsize=12)\n",
    "plt.title(\"Types of Weapons\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Types of Attacks')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,8))\n",
    "sns.barplot(x=df['attacktype1_txt'].value_counts()[:30].index,y=df['attacktype1_txt'].value_counts()[:30].values)\n",
    "plt.xlabel(\"Attacks\", fontsize=20)\n",
    "plt.xticks(rotation=90)\n",
    "plt.ylabel(\"Count of a particular Attack\", fontsize=20)\n",
    "plt.title(\"Types of Attacks\", fontsize=20)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}