suicide attack final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Suicide Attack worldwide"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This dataset was downloaded from the CORGIS Dataset Project website\n",
    "https://corgis-edu.github.io/corgis/csv/suicide_attacks/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Importing Modules and seeing what our dataset looks like..\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('suicide_attacks edited.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "terrorist groups            object\n",
       "attacker_age                 int64\n",
       "campaign_name               object\n",
       "campaign_sides              object\n",
       "day                          int64\n",
       "month                        int64\n",
       "year                         int64\n",
       "no_killed                    int64\n",
       "no_wounded                   int64\n",
       "target_country              object\n",
       "target_location             object\n",
       "target_name                 object\n",
       "target_type                 object\n",
       "target_weapon               object\n",
       "attackers_birth_location    object\n",
       "attackers_birth_year         int64\n",
       "attackers_education         object\n",
       "attackers_gender            object\n",
       "attackers_marital           object\n",
       "attacker_occupation         object\n",
       "attacker_religion           object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#all the columns from this particular dataset. Although i dropped the attacker age column and birth year.\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4616"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Lebanese National Resistance Front,Armed Strug...</td>\n",
       "      <td>15</td>\n",
       "      <td>Hezbollah vs. Israel</td>\n",
       "      <td>Hezbollah,Israel</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1982</td>\n",
       "      <td>115</td>\n",
       "      <td>28</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>...</td>\n",
       "      <td>Israeli Defense Forces (IDF) headquarters</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>1967</td>\n",
       "      <td>Primary</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unskilled</td>\n",
       "      <td>Secular</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Hezbollah vs. U.S. &amp; France</td>\n",
       "      <td>Hezbollah,U.S. &amp; France</td>\n",
       "      <td>18</td>\n",
       "      <td>4</td>\n",
       "      <td>1983</td>\n",
       "      <td>60</td>\n",
       "      <td>120</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>...</td>\n",
       "      <td>U.S. Embassy in Lebanon</td>\n",
       "      <td>Political</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Lebanese Islamic Jihad (Hezbollah)</td>\n",
       "      <td>24</td>\n",
       "      <td>Hezbollah vs. U.S. &amp; France</td>\n",
       "      <td>Hezbollah,U.S. &amp; France</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>1983</td>\n",
       "      <td>58</td>\n",
       "      <td>88</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>...</td>\n",
       "      <td>Barracks for French Paratroopers</td>\n",
       "      <td>Security</td>\n",
       "      <td>Truck bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1959</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Lebanese Islamic Jihad (Hezbollah)</td>\n",
       "      <td>26</td>\n",
       "      <td>Hezbollah vs. U.S. &amp; France</td>\n",
       "      <td>Hezbollah,U.S. &amp; France</td>\n",
       "      <td>23</td>\n",
       "      <td>10</td>\n",
       "      <td>1983</td>\n",
       "      <td>230</td>\n",
       "      <td>88</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>...</td>\n",
       "      <td>US Marine Barracks</td>\n",
       "      <td>Security</td>\n",
       "      <td>Truck bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1957</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Lebanese Islamic Jihad (Hezbollah)</td>\n",
       "      <td>-1</td>\n",
       "      <td>Hezbollah vs. Israel</td>\n",
       "      <td>Hezbollah,Israel</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>1983</td>\n",
       "      <td>60</td>\n",
       "      <td>10</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>...</td>\n",
       "      <td>Israeli Defense Forces (IDF) Headquarters</td>\n",
       "      <td>Security</td>\n",
       "      <td>Truck bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    terrorist groups  attacker_age  \\\n",
       "0  Lebanese National Resistance Front,Armed Strug...            15   \n",
       "1                                      Unknown Group            -1   \n",
       "2                 Lebanese Islamic Jihad (Hezbollah)            24   \n",
       "3                 Lebanese Islamic Jihad (Hezbollah)            26   \n",
       "4                 Lebanese Islamic Jihad (Hezbollah)            -1   \n",
       "\n",
       "                 campaign_name           campaign_sides  day  month  year  \\\n",
       "0         Hezbollah vs. Israel         Hezbollah,Israel   11     11  1982   \n",
       "1  Hezbollah vs. U.S. & France  Hezbollah,U.S. & France   18      4  1983   \n",
       "2  Hezbollah vs. U.S. & France  Hezbollah,U.S. & France   23     10  1983   \n",
       "3  Hezbollah vs. U.S. & France  Hezbollah,U.S. & France   23     10  1983   \n",
       "4         Hezbollah vs. Israel         Hezbollah,Israel    4     11  1983   \n",
       "\n",
       "   no_killed  no_wounded target_country  ...  \\\n",
       "0        115          28        Lebanon  ...   \n",
       "1         60         120        Lebanon  ...   \n",
       "2         58          88        Lebanon  ...   \n",
       "3        230          88        Lebanon  ...   \n",
       "4         60          10        Lebanon  ...   \n",
       "\n",
       "                                 target_name target_type target_weapon  \\\n",
       "0  Israeli Defense Forces (IDF) headquarters    Security      Car bomb   \n",
       "1                    U.S. Embassy in Lebanon   Political      Car bomb   \n",
       "2           Barracks for French Paratroopers    Security    Truck bomb   \n",
       "3                         US Marine Barracks    Security    Truck bomb   \n",
       "4  Israeli Defense Forces (IDF) Headquarters    Security    Truck bomb   \n",
       "\n",
       "  attackers_birth_location attackers_birth_year  attackers_education  \\\n",
       "0                  Lebanon                 1967              Primary   \n",
       "1                  Unknown                   -1              Unknown   \n",
       "2                  Unknown                 1959              Unknown   \n",
       "3                  Unknown                 1957              Unknown   \n",
       "4                  Unknown                   -1              Unknown   \n",
       "\n",
       "  attackers_gender attackers_marital attacker_occupation attacker_religion  \n",
       "0             Male           Unknown           Unskilled           Secular  \n",
       "1          Unknown           Unknown             Unknown           Unknown  \n",
       "2             Male           Unknown             Unknown           Unknown  \n",
       "3             Male           Unknown             Unknown           Unknown  \n",
       "4             Male           Unknown             Unknown           Unknown  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#creating a seperate table to seperately analyze the no of people killed and people wounded as well as the year of suicide attack\n",
    "df_killed=df['no_killed']\n",
    "df_wounded=df['no_wounded']\n",
    "df_year=df['year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4616.000000\n",
       "mean        9.927210\n",
       "std        43.069773\n",
       "min         0.000000\n",
       "25%         1.000000\n",
       "50%         4.000000\n",
       "75%        11.000000\n",
       "max      2739.000000\n",
       "Name: no_killed, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#giving the statiscal infromation on the number of killed\n",
    "df_killed.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4616.000000\n",
       "mean       25.560875\n",
       "std       124.148730\n",
       "min         0.000000\n",
       "25%         3.000000\n",
       "50%        10.000000\n",
       "75%        26.000000\n",
       "max      6185.000000\n",
       "Name: no_wounded, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#giving the statiscal infromation on the number of wounded\n",
    "df_wounded.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    4616.000000\n",
       "mean     2008.836005\n",
       "std         4.762003\n",
       "min      1982.000000\n",
       "25%      2006.000000\n",
       "50%      2009.000000\n",
       "75%      2013.000000\n",
       "max      2015.000000\n",
       "Name: year, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#giving the statiscal infromation on the year in which the suicide attack occured\n",
    "df_year.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that from this dataset that the dataset spans from 1982 to 2015. The maximum number of people killed during a single suicide attack is 2739 and the highest number wounded per attack is 6185. This is quite disturbing! But let's keep going..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# suicide Attacks with highest human casualty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Al-Qaeda Central</td>\n",
       "      <td>43</td>\n",
       "      <td>Al Qaeda vs. U.S. &amp; Allies</td>\n",
       "      <td>Al Qaeda,U.S. &amp; Allies</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2001</td>\n",
       "      <td>2739</td>\n",
       "      <td>6185</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>World Trade Center</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Airplane</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>1958</td>\n",
       "      <td>Post-secondary</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Muslim (NA)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     terrorist groups  attacker_age               campaign_name  \\\n",
       "199  Al-Qaeda Central            43  Al Qaeda vs. U.S. & Allies   \n",
       "\n",
       "             campaign_sides  day  month  year  no_killed  no_wounded  \\\n",
       "199  Al Qaeda,U.S. & Allies   11      9  2001       2739        6185   \n",
       "\n",
       "    target_country  ...         target_name target_type target_weapon  \\\n",
       "199  United States  ...  World Trade Center    Civilian      Airplane   \n",
       "\n",
       "    attackers_birth_location attackers_birth_year  attackers_education  \\\n",
       "199                    Egypt                 1958       Post-secondary   \n",
       "\n",
       "    attackers_gender attackers_marital attacker_occupation attacker_religion  \n",
       "199             Male         Unmarried        Professional       Muslim (NA)  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#getting more information about the row with the highest no of kills and wounded per attack\n",
    "df[df['no_killed']==df['no_killed'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>Al-Qaeda Central</td>\n",
       "      <td>43</td>\n",
       "      <td>Al Qaeda vs. U.S. &amp; Allies</td>\n",
       "      <td>Al Qaeda,U.S. &amp; Allies</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>2001</td>\n",
       "      <td>2739</td>\n",
       "      <td>6185</td>\n",
       "      <td>United States</td>\n",
       "      <td>...</td>\n",
       "      <td>World Trade Center</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Airplane</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>1958</td>\n",
       "      <td>Post-secondary</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unmarried</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Muslim (NA)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     terrorist groups  attacker_age               campaign_name  \\\n",
       "199  Al-Qaeda Central            43  Al Qaeda vs. U.S. & Allies   \n",
       "\n",
       "             campaign_sides  day  month  year  no_killed  no_wounded  \\\n",
       "199  Al Qaeda,U.S. & Allies   11      9  2001       2739        6185   \n",
       "\n",
       "    target_country  ...         target_name target_type target_weapon  \\\n",
       "199  United States  ...  World Trade Center    Civilian      Airplane   \n",
       "\n",
       "    attackers_birth_location attackers_birth_year  attackers_education  \\\n",
       "199                    Egypt                 1958       Post-secondary   \n",
       "\n",
       "    attackers_gender attackers_marital attacker_occupation attacker_religion  \n",
       "199             Male         Unmarried        Professional       Muslim (NA)  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['no_wounded']==df['no_wounded'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The row with the highest number of WOUNDED and KILLED people was in the same attack, the World Trade center disaster. This was done by a male unmarried Egyptian who had post secondary qualification. He was from the AL-Quaeda sect. The target weapon was an airplane. And the people targeted were civillians. This happened on the 11th of September 2001. \n",
    "Throughout record (based on this dataset and between 1982 and 2015), this is recorded the suicide attack with the most impact on human lives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# suicide Attacks with no human casualty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There were a couple of suicide attacks accoding to this dataset with no live impacts. This could be due to the fact the plan for the attack was discovered before hand or people were warned of the target location or the plan for execution wasn't carried out well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Islamic Resistance</td>\n",
       "      <td>-1</td>\n",
       "      <td>Hezbollah vs. Israel</td>\n",
       "      <td>Hezbollah,Israel</td>\n",
       "      <td>19</td>\n",
       "      <td>8</td>\n",
       "      <td>1988</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>...</td>\n",
       "      <td>Israeli Defense Forces (IDF) military convoy</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Muslim (Shia)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Liberation Tigers of Tamil Eelam</td>\n",
       "      <td>23</td>\n",
       "      <td>LTTE vs. Sri Lanka &amp; India</td>\n",
       "      <td>LTTE,Sri Lanka &amp; India</td>\n",
       "      <td>19</td>\n",
       "      <td>3</td>\n",
       "      <td>1991</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>...</td>\n",
       "      <td>Sri Lankan Army camp</td>\n",
       "      <td>Security</td>\n",
       "      <td>Truck bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1968</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Hindu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>Liberation Tigers of Tamil Eelam</td>\n",
       "      <td>21</td>\n",
       "      <td>LTTE vs. Sri Lanka &amp; India</td>\n",
       "      <td>LTTE,Sri Lanka &amp; India</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>...</td>\n",
       "      <td>Sri Lankan Navy (SLN) ship</td>\n",
       "      <td>Security</td>\n",
       "      <td>Boat bomb</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>1973</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Hindu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Liberation Tigers of Tamil Eelam</td>\n",
       "      <td>23</td>\n",
       "      <td>LTTE vs. Sri Lanka &amp; India</td>\n",
       "      <td>LTTE,Sri Lanka &amp; India</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>1994</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>...</td>\n",
       "      <td>Sri Lankan Navy (SLN) ship</td>\n",
       "      <td>Security</td>\n",
       "      <td>Boat bomb</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>1971</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Hindu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>Liberation Tigers of Tamil Eelam</td>\n",
       "      <td>19</td>\n",
       "      <td>LTTE vs. Sri Lanka &amp; India</td>\n",
       "      <td>LTTE,Sri Lanka &amp; India</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "      <td>1995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>...</td>\n",
       "      <td>Sri Lankan Navy (SLN) ship</td>\n",
       "      <td>Security</td>\n",
       "      <td>Scuba bomb</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>1976</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Hindu</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4581</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Maiduguri, the capital city</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4582</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Egyptian Rebels</td>\n",
       "      <td>Egyptian Rebels</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>...</td>\n",
       "      <td>Police Guarding Karnak Temple who had surround...</td>\n",
       "      <td>Security</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4599</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Somali Rebels vs. Ethiopia &amp; Allies</td>\n",
       "      <td>Somali Rebels,Ethiopia &amp; Allies</td>\n",
       "      <td>21</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Somalia</td>\n",
       "      <td>...</td>\n",
       "      <td>A military intelligence base</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4603</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Syria &amp; Allies</td>\n",
       "      <td>Rebels,Syria &amp; Allies</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Syria</td>\n",
       "      <td>...</td>\n",
       "      <td>\"Asayish\" (Kurdish) police station</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4612</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Pakistani Rebels vs. Pakistan &amp; U.S. Allies</td>\n",
       "      <td>Pakistani Rebels,Pakistan &amp; U.S. Allies</td>\n",
       "      <td>29</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>...</td>\n",
       "      <td>Police Trying to Arrest the Terrorists</td>\n",
       "      <td>Security</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>273 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                      terrorist groups  attacker_age  \\\n",
       "37                  Islamic Resistance            -1   \n",
       "44    Liberation Tigers of Tamil Eelam            23   \n",
       "59    Liberation Tigers of Tamil Eelam            21   \n",
       "63    Liberation Tigers of Tamil Eelam            23   \n",
       "78    Liberation Tigers of Tamil Eelam            19   \n",
       "...                                ...           ...   \n",
       "4581                     Unknown Group            -1   \n",
       "4582                     Unknown Group            -1   \n",
       "4599                     Unknown Group            -1   \n",
       "4603                     Unknown Group            -1   \n",
       "4612                     Unknown Group            -1   \n",
       "\n",
       "                                    campaign_name  \\\n",
       "37                           Hezbollah vs. Israel   \n",
       "44                     LTTE vs. Sri Lanka & India   \n",
       "59                     LTTE vs. Sri Lanka & India   \n",
       "63                     LTTE vs. Sri Lanka & India   \n",
       "78                     LTTE vs. Sri Lanka & India   \n",
       "...                                           ...   \n",
       "4581                  Rebels vs. Nigeria & Allies   \n",
       "4582                              Egyptian Rebels   \n",
       "4599          Somali Rebels vs. Ethiopia & Allies   \n",
       "4603                    Rebels vs. Syria & Allies   \n",
       "4612  Pakistani Rebels vs. Pakistan & U.S. Allies   \n",
       "\n",
       "                               campaign_sides  day  month  year  no_killed  \\\n",
       "37                           Hezbollah,Israel   19      8  1988          0   \n",
       "44                     LTTE,Sri Lanka & India   19      3  1991          0   \n",
       "59                     LTTE,Sri Lanka & India   10      8  1994          0   \n",
       "63                     LTTE,Sri Lanka & India    8     11  1994          0   \n",
       "78                     LTTE,Sri Lanka & India   10      9  1995          0   \n",
       "...                                       ...  ...    ...   ...        ...   \n",
       "4581                  Rebels,Nigeria & Allies   10      6  2015          0   \n",
       "4582                          Egyptian Rebels   10      6  2015          0   \n",
       "4599          Somali Rebels,Ethiopia & Allies   21      6  2015          0   \n",
       "4603                    Rebels,Syria & Allies   23      6  2015          0   \n",
       "4612  Pakistani Rebels,Pakistan & U.S. Allies   29      6  2015          0   \n",
       "\n",
       "      no_wounded target_country  ...  \\\n",
       "37             0        Lebanon  ...   \n",
       "44             0      Sri Lanka  ...   \n",
       "59             0      Sri Lanka  ...   \n",
       "63             0      Sri Lanka  ...   \n",
       "78             0      Sri Lanka  ...   \n",
       "...          ...            ...  ...   \n",
       "4581           0        Nigeria  ...   \n",
       "4582           0          Egypt  ...   \n",
       "4599           0        Somalia  ...   \n",
       "4603           0          Syria  ...   \n",
       "4612           0       Pakistan  ...   \n",
       "\n",
       "                                            target_name target_type  \\\n",
       "37         Israeli Defense Forces (IDF) military convoy    Security   \n",
       "44                                 Sri Lankan Army camp    Security   \n",
       "59                           Sri Lankan Navy (SLN) ship    Security   \n",
       "63                           Sri Lankan Navy (SLN) ship    Security   \n",
       "78                           Sri Lankan Navy (SLN) ship    Security   \n",
       "...                                                 ...         ...   \n",
       "4581                        Maiduguri, the capital city     Unknown   \n",
       "4582  Police Guarding Karnak Temple who had surround...    Security   \n",
       "4599                       A military intelligence base    Security   \n",
       "4603                 \"Asayish\" (Kurdish) police station     Unknown   \n",
       "4612             Police Trying to Arrest the Terrorists    Security   \n",
       "\n",
       "     target_weapon attackers_birth_location attackers_birth_year  \\\n",
       "37        Car bomb                  Unknown                   -1   \n",
       "44      Truck bomb                  Unknown                 1968   \n",
       "59       Boat bomb                Sri Lanka                 1973   \n",
       "63       Boat bomb                Sri Lanka                 1971   \n",
       "78      Scuba bomb                Sri Lanka                 1976   \n",
       "...            ...                      ...                  ...   \n",
       "4581     Belt bomb                  Unknown                   -1   \n",
       "4582     Belt bomb                  Unknown                   -1   \n",
       "4599      Car bomb                  Unknown                   -1   \n",
       "4603      Car bomb                  Unknown                   -1   \n",
       "4612     Belt bomb                  Unknown                   -1   \n",
       "\n",
       "      attackers_education attackers_gender attackers_marital  \\\n",
       "37                Unknown             Male           Unknown   \n",
       "44                Unknown             Male           Unknown   \n",
       "59                Unknown           Female           Unknown   \n",
       "63                Unknown             Male           Unknown   \n",
       "78                Unknown           Female           Unknown   \n",
       "...                   ...              ...               ...   \n",
       "4581              Unknown           Female           Unknown   \n",
       "4582              Unknown             Male           Unknown   \n",
       "4599              Unknown          Unknown           Unknown   \n",
       "4603              Unknown             Male           Unknown   \n",
       "4612              Unknown             Male           Unknown   \n",
       "\n",
       "     attacker_occupation attacker_religion  \n",
       "37               Unknown     Muslim (Shia)  \n",
       "44               Unknown             Hindu  \n",
       "59               Unknown             Hindu  \n",
       "63               Unknown             Hindu  \n",
       "78               Unknown             Hindu  \n",
       "...                  ...               ...  \n",
       "4581             Unknown           Unknown  \n",
       "4582             Unknown           Unknown  \n",
       "4599             Unknown           Unknown  \n",
       "4603             Unknown           Unknown  \n",
       "4612             Unknown           Unknown  \n",
       "\n",
       "[273 rows x 21 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df['no_killed']==0) & (df['no_wounded']==0) ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A total of 273 suicide attack had no human casualties; no injured, no killed people. These were failed suicide attacks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The Trend of the number of suicide kills and injuries from 1982 to 2015, worldwide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1982     115\n",
       "1983     414\n",
       "1984      29\n",
       "1985     259\n",
       "1986      33\n",
       "1987      68\n",
       "1988       7\n",
       "1989       0\n",
       "1990      11\n",
       "1991      80\n",
       "1992       4\n",
       "1993     237\n",
       "1994     218\n",
       "1995     252\n",
       "1996     286\n",
       "1997      60\n",
       "1998     337\n",
       "1999      86\n",
       "2000     208\n",
       "2001    3191\n",
       "2002     588\n",
       "2003     802\n",
       "2004    1904\n",
       "2005    3225\n",
       "2006    2647\n",
       "2007    5679\n",
       "2008    3244\n",
       "2009    3158\n",
       "2010    2668\n",
       "2011    2394\n",
       "2012    2301\n",
       "2013    4132\n",
       "2014    4684\n",
       "2015    2503\n",
       "Name: no_killed, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of kills per year worldwide\n",
    "df.groupby('year')['no_killed'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1982       28\n",
       "1983      368\n",
       "1984       49\n",
       "1985      262\n",
       "1986       24\n",
       "1987      137\n",
       "1988       11\n",
       "1989        9\n",
       "1990        3\n",
       "1991       68\n",
       "1992        0\n",
       "1993      102\n",
       "1994      438\n",
       "1995      869\n",
       "1996     1751\n",
       "1997      514\n",
       "1998     5488\n",
       "1999      300\n",
       "2000      502\n",
       "2001     7421\n",
       "2002     2457\n",
       "2003     3913\n",
       "2004     5956\n",
       "2005     8317\n",
       "2006     5857\n",
       "2007    13413\n",
       "2008     7946\n",
       "2009     9054\n",
       "2010     7194\n",
       "2011     6102\n",
       "2012     6216\n",
       "2013     9666\n",
       "2014     8634\n",
       "2015     4920\n",
       "Name: no_wounded, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of injuries per year worldwide\n",
    "df.groupby('year')['no_wounded'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xb5023d0>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1224x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the graph of wounded people against killed people for each year worldwide\n",
    "plt.figure(figsize=(17, 9))\n",
    "sns.set_theme(style='darkgrid')\n",
    "sns.lineplot(data=df.groupby('year')['no_killed'].sum(), marker='o')\n",
    "sns.lineplot(data=df.groupby('year')['no_wounded'].sum(), marker='o')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of attacks')\n",
    "xaxis=[1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015]\n",
    "plt.xticks(xaxis, rotation=55)\n",
    "plt.legend(labels=['number of killed', 'number of wounded'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the year 2007 had the highest number of casualties. The total number of people killed that year was 5679 and the number of injured people are 13413\n",
    "The year with the least number of deaths is 1989 with 0 casualty and the year with the least number of injured people is 1992 with 0 casualty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Observing those years to see the attacks better..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Hezbollah</td>\n",
       "      <td>-1</td>\n",
       "      <td>Hezbollah vs. Israel</td>\n",
       "      <td>Hezbollah,Israel</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>1989</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>...</td>\n",
       "      <td>Israeli Defense Forces (IDF) military convoy</td>\n",
       "      <td>Security</td>\n",
       "      <td>Truck bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Popular Front for the Liberation of Palestine</td>\n",
       "      <td>-1</td>\n",
       "      <td>Palestinian Resistance vs. Israel</td>\n",
       "      <td>Palestinian Resistance,Israel</td>\n",
       "      <td>30</td>\n",
       "      <td>10</td>\n",
       "      <td>1989</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Lebanon</td>\n",
       "      <td>...</td>\n",
       "      <td>Israeli Naval Patrol Ship</td>\n",
       "      <td>Security</td>\n",
       "      <td>Boat bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 terrorist groups  attacker_age  \\\n",
       "39                                      Hezbollah            -1   \n",
       "40  Popular Front for the Liberation of Palestine            -1   \n",
       "\n",
       "                        campaign_name                 campaign_sides  day  \\\n",
       "39               Hezbollah vs. Israel               Hezbollah,Israel    9   \n",
       "40  Palestinian Resistance vs. Israel  Palestinian Resistance,Israel   30   \n",
       "\n",
       "    month  year  no_killed  no_wounded target_country  ...  \\\n",
       "39      8  1989          0           6        Lebanon  ...   \n",
       "40     10  1989          0           3        Lebanon  ...   \n",
       "\n",
       "                                     target_name target_type target_weapon  \\\n",
       "39  Israeli Defense Forces (IDF) military convoy    Security    Truck bomb   \n",
       "40                     Israeli Naval Patrol Ship    Security     Boat bomb   \n",
       "\n",
       "   attackers_birth_location attackers_birth_year  attackers_education  \\\n",
       "39                  Unknown                   -1              Unknown   \n",
       "40                  Unknown                   -1              Unknown   \n",
       "\n",
       "   attackers_gender attackers_marital attacker_occupation attacker_religion  \n",
       "39             Male           Unknown             Unknown           Unknown  \n",
       "40          Unknown           Unknown             Unknown           Unknown  \n",
       "\n",
       "[2 rows x 21 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the year 1989 in which there was the least number of death casualties\n",
    "df[df['year']==1989]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of attacks recorded were simply two of which there were only wounded casualties. These attacks were in Lebanon and the attacks were carried out by Hezbollah and Popular Front for the Liberation of Palestine. Attacks were targeted on security. A truck and a boat bomb were used for these attacks respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>LTTE vs. Sri Lanka &amp; India</td>\n",
       "      <td>LTTE,Sri Lanka &amp; India</td>\n",
       "      <td>16</td>\n",
       "      <td>11</td>\n",
       "      <td>1992</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>...</td>\n",
       "      <td>Navy Commander Clancy Fernando traveling in a car</td>\n",
       "      <td>Security</td>\n",
       "      <td>Motorcycle bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Hindu</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   terrorist groups  attacker_age               campaign_name  \\\n",
       "48    Unknown Group            -1  LTTE vs. Sri Lanka & India   \n",
       "\n",
       "            campaign_sides  day  month  year  no_killed  no_wounded  \\\n",
       "48  LTTE,Sri Lanka & India   16     11  1992          4           0   \n",
       "\n",
       "   target_country  ...                                        target_name  \\\n",
       "48      Sri Lanka  ...  Navy Commander Clancy Fernando traveling in a car   \n",
       "\n",
       "   target_type    target_weapon attackers_birth_location attackers_birth_year  \\\n",
       "48    Security  Motorcycle bomb                  Unknown                   -1   \n",
       "\n",
       "    attackers_education attackers_gender attackers_marital  \\\n",
       "48              Unknown             Male           Unknown   \n",
       "\n",
       "   attacker_occupation attacker_religion  \n",
       "48             Unknown             Hindu  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the year 1992 in which ther was the least number of wounded casualties\n",
    "df[df['year']==1992]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The year 1992 had only one suicide attack recorded in this dataset. There were 4 death casualties so this isn't quite a failed suicide attack. The religion of the attacker is Hindu and his target was on security personnel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1247</th>\n",
       "      <td>Taliban (IEA)</td>\n",
       "      <td>-1</td>\n",
       "      <td>Afghan Rebels vs. U.S. &amp; Allies</td>\n",
       "      <td>Afghan Rebels,U.S. &amp; Allies</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>...</td>\n",
       "      <td>US Ambassador Ronald Neumann</td>\n",
       "      <td>Political</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1248</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Afghan Rebels vs. U.S. &amp; Allies</td>\n",
       "      <td>Afghan Rebels,U.S. &amp; Allies</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>...</td>\n",
       "      <td>NATO-ISAF checkpoint</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1249</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>LTTE vs. Sri Lanka &amp; India</td>\n",
       "      <td>LTTE,Sri Lanka &amp; India</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>15</td>\n",
       "      <td>40</td>\n",
       "      <td>Sri Lanka</td>\n",
       "      <td>...</td>\n",
       "      <td>Truck transporting soldiers</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1250</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Afghan Rebels vs. U.S. &amp; Allies</td>\n",
       "      <td>Afghan Rebels,U.S. &amp; Allies</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>...</td>\n",
       "      <td>Afghan National Army (ANA) military convoy</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1251</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Iraqi Rebels vs. U.S. &amp; Allies</td>\n",
       "      <td>Iraqi Rebels,U.S. &amp; Allies</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>2007</td>\n",
       "      <td>4</td>\n",
       "      <td>12</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>...</td>\n",
       "      <td>Iraqi Police station</td>\n",
       "      <td>Security</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1767</th>\n",
       "      <td>Al-Qaeda Central</td>\n",
       "      <td>-1</td>\n",
       "      <td>Al Qaeda vs. U.S. &amp; Allies</td>\n",
       "      <td>Al Qaeda,U.S. &amp; Allies</td>\n",
       "      <td>27</td>\n",
       "      <td>12</td>\n",
       "      <td>2007</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>...</td>\n",
       "      <td>Benazir Bhutto, Former Pakistani Prime Ministe...</td>\n",
       "      <td>Political</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1768</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Pakistani Rebels vs. Pakistan &amp; U.S. Allies</td>\n",
       "      <td>Pakistani Rebels,Pakistan &amp; U.S. Allies</td>\n",
       "      <td>30</td>\n",
       "      <td>12</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>...</td>\n",
       "      <td>Ijazul Haq, Federal Minister of Religion and s...</td>\n",
       "      <td>Political</td>\n",
       "      <td>Motorcycle bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1769</th>\n",
       "      <td>Islamic State of Iraq</td>\n",
       "      <td>-1</td>\n",
       "      <td>Iraqi Rebels vs. U.S. &amp; Allies</td>\n",
       "      <td>Iraqi Rebels,U.S. &amp; Allies</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>...</td>\n",
       "      <td>Sahwa/Awakening Council checkpoint</td>\n",
       "      <td>Security</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1770</th>\n",
       "      <td>Islamic State of Iraq</td>\n",
       "      <td>-1</td>\n",
       "      <td>Iraqi Rebels vs. U.S. &amp; Allies</td>\n",
       "      <td>Iraqi Rebels,U.S. &amp; Allies</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2007</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>...</td>\n",
       "      <td>Iraqi Police checkpoint</td>\n",
       "      <td>Security</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1771</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Iraqi Rebels vs. U.S. &amp; Allies</td>\n",
       "      <td>Iraqi Rebels,U.S. &amp; Allies</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>2007</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>...</td>\n",
       "      <td>Sahwa/Awakening Council checkpoint</td>\n",
       "      <td>Security</td>\n",
       "      <td>Truck bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>525 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           terrorist groups  attacker_age  \\\n",
       "1247          Taliban (IEA)            -1   \n",
       "1248          Unknown Group            -1   \n",
       "1249          Unknown Group            -1   \n",
       "1250          Unknown Group            -1   \n",
       "1251          Unknown Group            -1   \n",
       "...                     ...           ...   \n",
       "1767       Al-Qaeda Central            -1   \n",
       "1768          Unknown Group            -1   \n",
       "1769  Islamic State of Iraq            -1   \n",
       "1770  Islamic State of Iraq            -1   \n",
       "1771          Unknown Group            -1   \n",
       "\n",
       "                                    campaign_name  \\\n",
       "1247              Afghan Rebels vs. U.S. & Allies   \n",
       "1248              Afghan Rebels vs. U.S. & Allies   \n",
       "1249                   LTTE vs. Sri Lanka & India   \n",
       "1250              Afghan Rebels vs. U.S. & Allies   \n",
       "1251               Iraqi Rebels vs. U.S. & Allies   \n",
       "...                                           ...   \n",
       "1767                   Al Qaeda vs. U.S. & Allies   \n",
       "1768  Pakistani Rebels vs. Pakistan & U.S. Allies   \n",
       "1769               Iraqi Rebels vs. U.S. & Allies   \n",
       "1770               Iraqi Rebels vs. U.S. & Allies   \n",
       "1771               Iraqi Rebels vs. U.S. & Allies   \n",
       "\n",
       "                               campaign_sides  day  month  year  no_killed  \\\n",
       "1247              Afghan Rebels,U.S. & Allies    5      1  2007         10   \n",
       "1248              Afghan Rebels,U.S. & Allies    5      1  2007          0   \n",
       "1249                   LTTE,Sri Lanka & India    6      1  2007         15   \n",
       "1250              Afghan Rebels,U.S. & Allies    8      1  2007          0   \n",
       "1251               Iraqi Rebels,U.S. & Allies   10      1  2007          4   \n",
       "...                                       ...  ...    ...   ...        ...   \n",
       "1767                   Al Qaeda,U.S. & Allies   27     12  2007         30   \n",
       "1768  Pakistani Rebels,Pakistan & U.S. Allies   30     12  2007          0   \n",
       "1769               Iraqi Rebels,U.S. & Allies   31     12  2007          0   \n",
       "1770               Iraqi Rebels,U.S. & Allies   31     12  2007          0   \n",
       "1771               Iraqi Rebels,U.S. & Allies   31     12  2007         12   \n",
       "\n",
       "      no_wounded target_country  ...  \\\n",
       "1247          50    Afghanistan  ...   \n",
       "1248           4    Afghanistan  ...   \n",
       "1249          40      Sri Lanka  ...   \n",
       "1250           3    Afghanistan  ...   \n",
       "1251          12           Iraq  ...   \n",
       "...          ...            ...  ...   \n",
       "1767           0       Pakistan  ...   \n",
       "1768           0       Pakistan  ...   \n",
       "1769           7           Iraq  ...   \n",
       "1770           7           Iraq  ...   \n",
       "1771           5           Iraq  ...   \n",
       "\n",
       "                                            target_name target_type  \\\n",
       "1247                       US Ambassador Ronald Neumann   Political   \n",
       "1248                               NATO-ISAF checkpoint    Security   \n",
       "1249                        Truck transporting soldiers    Security   \n",
       "1250         Afghan National Army (ANA) military convoy    Security   \n",
       "1251                               Iraqi Police station    Security   \n",
       "...                                                 ...         ...   \n",
       "1767  Benazir Bhutto, Former Pakistani Prime Ministe...   Political   \n",
       "1768  Ijazul Haq, Federal Minister of Religion and s...   Political   \n",
       "1769                 Sahwa/Awakening Council checkpoint    Security   \n",
       "1770                            Iraqi Police checkpoint    Security   \n",
       "1771                 Sahwa/Awakening Council checkpoint    Security   \n",
       "\n",
       "        target_weapon attackers_birth_location attackers_birth_year  \\\n",
       "1247        Belt bomb                  Unknown                   -1   \n",
       "1248         Car bomb                  Unknown                   -1   \n",
       "1249         Car bomb                  Unknown                   -1   \n",
       "1250         Car bomb                  Unknown                   -1   \n",
       "1251        Belt bomb                  Unknown                   -1   \n",
       "...               ...                      ...                  ...   \n",
       "1767        Belt bomb                  Unknown                   -1   \n",
       "1768  Motorcycle bomb                  Unknown                   -1   \n",
       "1769        Belt bomb                  Unknown                   -1   \n",
       "1770        Belt bomb                  Unknown                   -1   \n",
       "1771       Truck bomb                  Unknown                   -1   \n",
       "\n",
       "      attackers_education attackers_gender attackers_marital  \\\n",
       "1247              Unknown          Unknown           Unknown   \n",
       "1248              Unknown             Male           Unknown   \n",
       "1249              Unknown           Female           Unknown   \n",
       "1250              Unknown             Male           Unknown   \n",
       "1251              Unknown          Unknown           Unknown   \n",
       "...                   ...              ...               ...   \n",
       "1767              Unknown             Male           Unknown   \n",
       "1768              Unknown          Unknown           Unknown   \n",
       "1769              Unknown           Female           Unknown   \n",
       "1770              Unknown           Female           Unknown   \n",
       "1771              Unknown          Unknown           Unknown   \n",
       "\n",
       "     attacker_occupation attacker_religion  \n",
       "1247             Unknown           Unknown  \n",
       "1248             Unknown           Unknown  \n",
       "1249             Unknown           Unknown  \n",
       "1250             Unknown           Unknown  \n",
       "1251             Unknown           Unknown  \n",
       "...                  ...               ...  \n",
       "1767             Unknown           Unknown  \n",
       "1768             Unknown           Unknown  \n",
       "1769             Unknown           Unknown  \n",
       "1770             Unknown           Unknown  \n",
       "1771             Unknown           Unknown  \n",
       "\n",
       "[525 rows x 21 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#For the year 2007 in which there was the highest number of wounded and killed casualties\n",
    "df[df['year']==2007]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is observed that the year 2007 had the highest number of casualties under 525 different attacks. Although, it is not recorded as the year that had the highest number of attacks."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of Attacks per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2014    545\n",
       "2007    525\n",
       "2013    446\n",
       "2008    414\n",
       "2005    355\n",
       "2006    339\n",
       "2009    307\n",
       "2015    305\n",
       "2012    287\n",
       "2010    283\n",
       "2011    257\n",
       "2004    175\n",
       "2003     92\n",
       "2002     66\n",
       "2001     46\n",
       "2000     28\n",
       "1985     22\n",
       "1999     20\n",
       "1995     19\n",
       "1996     17\n",
       "1998     15\n",
       "1994     11\n",
       "1997      9\n",
       "1993      6\n",
       "1983      5\n",
       "1991      4\n",
       "1984      3\n",
       "1987      3\n",
       "1990      3\n",
       "1986      3\n",
       "1988      2\n",
       "1989      2\n",
       "1982      1\n",
       "1992      1\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0xb63fee0>,\n",
       "  <matplotlib.axis.XTick at 0xb63feb0>,\n",
       "  <matplotlib.axis.XTick at 0xb663be0>,\n",
       "  <matplotlib.axis.XTick at 0xb66e670>,\n",
       "  <matplotlib.axis.XTick at 0xb66eb80>,\n",
       "  <matplotlib.axis.XTick at 0xb6740d0>,\n",
       "  <matplotlib.axis.XTick at 0xb6745e0>,\n",
       "  <matplotlib.axis.XTick at 0xb674af0>,\n",
       "  <matplotlib.axis.XTick at 0xb67a040>,\n",
       "  <matplotlib.axis.XTick at 0xb637a30>,\n",
       "  <matplotlib.axis.XTick at 0xb663520>,\n",
       "  <matplotlib.axis.XTick at 0xb872af0>,\n",
       "  <matplotlib.axis.XTick at 0xb879040>,\n",
       "  <matplotlib.axis.XTick at 0xb879550>,\n",
       "  <matplotlib.axis.XTick at 0xb879a60>,\n",
       "  <matplotlib.axis.XTick at 0xb879f70>,\n",
       "  <matplotlib.axis.XTick at 0xb8795b0>,\n",
       "  <matplotlib.axis.XTick at 0xb872730>,\n",
       "  <matplotlib.axis.XTick at 0xb663a00>,\n",
       "  <matplotlib.axis.XTick at 0xb87e310>,\n",
       "  <matplotlib.axis.XTick at 0xb87e850>,\n",
       "  <matplotlib.axis.XTick at 0xb87ed90>,\n",
       "  <matplotlib.axis.XTick at 0xb8852e0>,\n",
       "  <matplotlib.axis.XTick at 0xb8857f0>,\n",
       "  <matplotlib.axis.XTick at 0xb885d00>,\n",
       "  <matplotlib.axis.XTick at 0xb88b250>,\n",
       "  <matplotlib.axis.XTick at 0xb885370>,\n",
       "  <matplotlib.axis.XTick at 0xb87e3a0>,\n",
       "  <matplotlib.axis.XTick at 0xb879250>,\n",
       "  <matplotlib.axis.XTick at 0xb88b760>,\n",
       "  <matplotlib.axis.XTick at 0xb88bc70>,\n",
       "  <matplotlib.axis.XTick at 0xb8931c0>,\n",
       "  <matplotlib.axis.XTick at 0xb8936d0>,\n",
       "  <matplotlib.axis.XTick at 0xb893c10>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the graph for the number of attacks per year worldwide.\n",
    "plt.figure(figsize=(15, 6))\n",
    "sns.set_theme(style='darkgrid')\n",
    "sns.lineplot(data=df['year'].value_counts(), marker='o')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of attacks')\n",
    "xaxis=[1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015]\n",
    "plt.xticks(xaxis, rotation=55)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The year with the highest number of attacks is 2014 (545) although as mentioned above the year 2007 had the highest number of casualties. The year 1982 and 1992 had the least number of attacks. 1 attack per year according to this dataset.\n",
    "It is also seen that from the year 1998, there was a continuous increase in the number of attacks all around the world. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Number of Attacks, deaths, and injured per Country from 1982 to 2015"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "47"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding the number of Countries represented in this dataset\n",
    "df['target_country'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Iraq                               1938\n",
       "Afghanistan                        1058\n",
       "Pakistan                            487\n",
       "Syria                               172\n",
       "Sri Lanka                           115\n",
       "Israel                              113\n",
       "Nigeria                             103\n",
       "Somalia                              91\n",
       "Yemen                                87\n",
       "Russia                               86\n",
       "Lebanon                              66\n",
       "Palestinian Territory, Occupied      59\n",
       "Libya                                29\n",
       "Turkey                               29\n",
       "Algeria                              24\n",
       "Egypt                                21\n",
       "Mali                                 19\n",
       "India                                15\n",
       "Saudi Arabia                         12\n",
       "China                                12\n",
       "Morocco                              11\n",
       "Indonesia                            10\n",
       "Iran                                  8\n",
       "Uzbekistan                            6\n",
       "United Kingdom                        4\n",
       "United States                         4\n",
       "Chad                                  4\n",
       "Bangladesh                            3\n",
       "Ukraine                               3\n",
       "Jordan                                3\n",
       "Kuwait                                3\n",
       "Niger                                 2\n",
       "Kenya                                 2\n",
       "Tunisia                               2\n",
       "Tajikistan                            2\n",
       "Finland                               2\n",
       "Spain                                 1\n",
       "Tanzania                              1\n",
       "Kazakhstan                            1\n",
       "Bolivia                               1\n",
       "Argentina                             1\n",
       "Mauritania                            1\n",
       "Sweden                                1\n",
       "Djibouti                              1\n",
       "Uganda                                1\n",
       "Bulgaria                              1\n",
       "Qatar                                 1\n",
       "Name: target_country, dtype: int64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the countries alongside the number of attacks from 1982 to 2015\n",
    "df['target_country'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([0,\n",
       "  1,\n",
       "  2,\n",
       "  3,\n",
       "  4,\n",
       "  5,\n",
       "  6,\n",
       "  7,\n",
       "  8,\n",
       "  9,\n",
       "  10,\n",
       "  11,\n",
       "  12,\n",
       "  13,\n",
       "  14,\n",
       "  15,\n",
       "  16,\n",
       "  17,\n",
       "  18,\n",
       "  19,\n",
       "  20,\n",
       "  21,\n",
       "  22,\n",
       "  23,\n",
       "  24,\n",
       "  25,\n",
       "  26,\n",
       "  27,\n",
       "  28,\n",
       "  29,\n",
       "  30,\n",
       "  31,\n",
       "  32,\n",
       "  33,\n",
       "  34,\n",
       "  35,\n",
       "  36,\n",
       "  37,\n",
       "  38,\n",
       "  39,\n",
       "  40,\n",
       "  41,\n",
       "  42,\n",
       "  43,\n",
       "  44,\n",
       "  45,\n",
       "  46],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the graph of country against number of attacks\n",
    "plt.figure(figsize=(16, 8))\n",
    "sns.lineplot(data=df['target_country'].value_counts(), marker='o')\n",
    "plt.xticks(rotation=90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From here, we can see that Iraq has received the highest number of attacks throughout this period followed by Afghanistan\n",
    "Nigerian ranks the 7th most attack country according to this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target_country\n",
       "Afghanistan                         4745\n",
       "Algeria                              281\n",
       "Argentina                             85\n",
       "Bangladesh                            13\n",
       "Bolivia                                2\n",
       "Bulgaria                               6\n",
       "Chad                                  39\n",
       "China                                 62\n",
       "Djibouti                               1\n",
       "Egypt                                246\n",
       "Finland                                6\n",
       "India                                123\n",
       "Indonesia                            252\n",
       "Iran                                 160\n",
       "Iraq                               20084\n",
       "Israel                               721\n",
       "Jordan                                57\n",
       "Kazakhstan                             1\n",
       "Kenya                                213\n",
       "Kuwait                                36\n",
       "Lebanon                             1007\n",
       "Libya                                155\n",
       "Mali                                  45\n",
       "Mauritania                             0\n",
       "Morocco                               31\n",
       "Niger                                 21\n",
       "Nigeria                             1347\n",
       "Pakistan                            6279\n",
       "Palestinian Territory, Occupied       67\n",
       "Qatar                                  1\n",
       "Russia                               782\n",
       "Saudi Arabia                          85\n",
       "Somalia                              829\n",
       "Spain                                  1\n",
       "Sri Lanka                           1584\n",
       "Sweden                                 0\n",
       "Syria                               2058\n",
       "Tajikistan                             6\n",
       "Tanzania                              10\n",
       "Tunisia                               21\n",
       "Turkey                               115\n",
       "Uganda                                76\n",
       "Ukraine                                5\n",
       "United Kingdom                        52\n",
       "United States                       2974\n",
       "Uzbekistan                            12\n",
       "Yemen                               1128\n",
       "Name: no_killed, dtype: int64"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of people Killed per country throughout 1982 to 2015\n",
    "df.groupby('target_country')['no_killed'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "target_country\n",
       "Afghanistan                        11903\n",
       "Algeria                             1380\n",
       "Argentina                            200\n",
       "Bangladesh                            67\n",
       "Bolivia                               10\n",
       "Bulgaria                              32\n",
       "Chad                                 153\n",
       "China                                297\n",
       "Djibouti                              15\n",
       "Egypt                                880\n",
       "Finland                              107\n",
       "India                                243\n",
       "Indonesia                            833\n",
       "Iran                                 565\n",
       "Iraq                               48902\n",
       "Israel                              5077\n",
       "Jordan                               120\n",
       "Kazakhstan                             0\n",
       "Kenya                               5080\n",
       "Kuwait                               300\n",
       "Lebanon                             1767\n",
       "Libya                                277\n",
       "Mali                                 112\n",
       "Mauritania                             3\n",
       "Morocco                              107\n",
       "Niger                                 30\n",
       "Nigeria                             2726\n",
       "Pakistan                           14695\n",
       "Palestinian Territory, Occupied      329\n",
       "Qatar                                 12\n",
       "Russia                              2511\n",
       "Saudi Arabia                         624\n",
       "Somalia                             1051\n",
       "Spain                                  3\n",
       "Sri Lanka                           3996\n",
       "Sweden                                 2\n",
       "Syria                               3498\n",
       "Tajikistan                            53\n",
       "Tanzania                              74\n",
       "Tunisia                                0\n",
       "Turkey                              1069\n",
       "Uganda                                60\n",
       "Ukraine                                7\n",
       "United Kingdom                       784\n",
       "United States                       6304\n",
       "Uzbekistan                            24\n",
       "Yemen                               1707\n",
       "Name: no_wounded, dtype: int64"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of wounded per country throughout 1982 to 2015\n",
    "df.groupby('target_country')['no_wounded'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0xd133b80>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plotting the graph for number of killed and wounded per country from 1982 to 2015\n",
    "plt.figure(figsize=(16, 8))\n",
    "sns.set_theme(style='darkgrid')\n",
    "sns.lineplot(data=df.groupby('target_country')['no_killed'].sum(), marker='o')\n",
    "sns.lineplot(data=df.groupby('target_country')['no_wounded'].sum(), marker='o')\n",
    "plt.xticks(rotation=90)\n",
    "plt.legend(labels=['number of killed', 'number of wounded'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the graph above that Iraq has the highest number of people killed and woundedfrom 1982 to 2015\n",
    "Observe also how the number of those killed are most times smaller than those wounded."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# TERRORIST GROUPS "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Terrorist groups and the number of attacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "128"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#number of unique terrorist groups in the terrorist group column\n",
    "df['terrorist groups'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown Group                                    2538\n",
       "Taliban (IEA)                                     661\n",
       "Islamic State                                     184\n",
       "Tehrik-i-Taliban Pakistan                         137\n",
       "Islamic State of Iraq and Syria                   126\n",
       "Al-Qaeda in Iraq                                  118\n",
       "Islamic State of Iraq                             113\n",
       "Liberation Tigers of Tamil Eelam                   83\n",
       "HAMAS                                              69\n",
       "Al-Shabaab                                         64\n",
       "Jabhat an-Nusrah li-Ahl ash-Sham                   49\n",
       "Palestinian Islamic Jihad                          39\n",
       "Al-Aqsa Martyrs Brigade                            31\n",
       "Ansar al-Sunna                                     28\n",
       "Jama'at Al-Tawhid Wa'al-Jihad                      25\n",
       "Al-Qaeda in the Arabian Peninsula                  23\n",
       "Al-Qaeda in the Land of the Islamic Maghreb        22\n",
       "Boko Haram                                         20\n",
       "Lashkar-e Jhangvi Al-almi                          18\n",
       "Al-Qaeda Central                                   15\n",
       "Riyadus Salikhiin                                  11\n",
       "Kurdistan Workers Party                            10\n",
       "Ansar Beit al-Maqdis                               10\n",
       "Chechen Republic of Ichkeria                       10\n",
       "Popular Front for the Liberation of Palestine       9\n",
       "Name: terrorist groups, dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['terrorist groups'].value_counts().head(25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Turkistan Islamic Party                                                        1\n",
       "Jamaa Al-Islamiya Al-Alamiya,Islamic Unity Brigades,Abdullah Azzam Brigades    1\n",
       "Jund al-Islam                                                                  1\n",
       "Taliban (IEA),Islamic Movement of Uzbekistan                                   1\n",
       "Ansar al-Islam                                                                 1\n",
       "Aisha Umm-al Mouemeneen                                                        1\n",
       "Jihad Islami                                                                   1\n",
       "Jamaat Ansarullah                                                              1\n",
       "Lebanese Islamic Jihad (Hezbollah),Lebanese National Resistance Front          1\n",
       "Jabhat an-Nusrah li-Ahl ash-Sham,Islamic State of Iraq and Syria               1\n",
       "Mujahideen Shura Council,Al-Qaeda in Iraq                                      1\n",
       "Popular Front for the Liberation of Palestine,HAMAS                            1\n",
       "Yekineyen Parastina Gel                                                        1\n",
       "Hizbal Islam in Somalia                                                        1\n",
       "Palestinian Islamic Jihad,HAMAS                                                1\n",
       "HAMAS,Unknown Group                                                            1\n",
       "Jund al-Sham                                                                   1\n",
       "Arab Egyptian League                                                           1\n",
       "Al-Qaeda in Iraq,Arab Resistance Movement - Al-Rashid Brigades                 1\n",
       "Lebanese National Resistance Front,Lebanese Communist Party                    1\n",
       "Soldiers of the Prophet's Companions Group                                     1\n",
       "Islamic Army in Iraq                                                           1\n",
       "Babbar Khalsa International                                                    1\n",
       "Jundullah (Pakistan),Tehrik-i-Taliban Pakistan                                 1\n",
       "Hizb-i-Islami,Taliban (IEA)                                                    1\n",
       "Name: terrorist groups, dtype: int64"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['terrorist groups'].value_counts().tail(25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of wounded people caused by the terrorist groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "terrorist groups\n",
       "Arab Egyptian League                          0\n",
       "Lashkar-e Taiba,Hizbul Mujahedin              0\n",
       "Benghazi Revolutionaries Shura Council        0\n",
       "Islamic Resistance                            0\n",
       "Aden-Abyan Army                               0\n",
       "                                          ...  \n",
       "Islamic State of Iraq                      4645\n",
       "Tehrik-i-Taliban Pakistan                  4866\n",
       "Taliban (IEA)                              7262\n",
       "Al-Qaeda Central                          12339\n",
       "Unknown Group                             54888\n",
       "Name: no_wounded, Length: 128, dtype: int64"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of wounded people caused by the terrorist groups\n",
    "df.groupby('terrorist groups')['no_wounded'].sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of killed people caused by the terrorist groups"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "terrorist groups\n",
       "Shumukh al-Islam                                                          0\n",
       "Lebanese National Resistance Front,Lebanese Communist Party               0\n",
       "Lebanese National Resistance Front,Syrian Social Nationalist Party        0\n",
       "Benghazi Revolutionaries Shura Council                                    0\n",
       "Hizbal Islam in Somalia                                                   0\n",
       "                                                                      ...  \n",
       "Tehrik-i-Taliban Pakistan                                              2087\n",
       "Islamic State                                                          2192\n",
       "Taliban (IEA)                                                          2919\n",
       "Al-Qaeda Central                                                       3319\n",
       "Unknown Group                                                         22796\n",
       "Name: no_killed, Length: 128, dtype: int64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Number of killed people caused by the terrorist groups\n",
    "df.groupby('terrorist groups')['no_killed'].sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Total Number of people killed and wounded from 1982 to 2015 from all countries:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "117989"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total number of people wounded\n",
    "df['no_wounded'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45824"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total number of people killed\n",
    "df['no_killed'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NIGERIA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Nigeria had a number of attacks as well. Below focuses on suicide attacks on Nigerian soil."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>35</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Nigerian Police Headquarters</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>1976</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Married</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Muslim (Sunni)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>27</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>24</td>\n",
       "      <td>80</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>United Nations (UN) Headquarters</td>\n",
       "      <td>Political</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1984</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3003</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Nigerian Police station</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3004</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>26</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Headquarters of the military anti-terrorism Jo...</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1985</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>43</td>\n",
       "      <td>57</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>St. Theresa's Catholic Church in Madalla</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4585</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>13</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Nigerian army checkpoint</td>\n",
       "      <td>Security</td>\n",
       "      <td>Motorcycle bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4592</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Civilian Vigilante Office in the Igwanda area ...</td>\n",
       "      <td>Security</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4594</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Angry mob chasing the bomber</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4604</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>12</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>23</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>30</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Weekly market</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>2003</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Female</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4611</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>27</td>\n",
       "      <td>6</td>\n",
       "      <td>2015</td>\n",
       "      <td>5</td>\n",
       "      <td>15</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Outside Molai leprosy hospital</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>103 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     terrorist groups  attacker_age                campaign_name  \\\n",
       "2902       Boko Haram            35  Rebels vs. Nigeria & Allies   \n",
       "2947       Boko Haram            27  Rebels vs. Nigeria & Allies   \n",
       "3003       Boko Haram            -1  Rebels vs. Nigeria & Allies   \n",
       "3004       Boko Haram            26  Rebels vs. Nigeria & Allies   \n",
       "3029       Boko Haram            -1  Rebels vs. Nigeria & Allies   \n",
       "...               ...           ...                          ...   \n",
       "4585    Unknown Group            -1  Rebels vs. Nigeria & Allies   \n",
       "4592    Unknown Group            -1  Rebels vs. Nigeria & Allies   \n",
       "4594    Unknown Group            -1  Rebels vs. Nigeria & Allies   \n",
       "4604    Unknown Group            12  Rebels vs. Nigeria & Allies   \n",
       "4611    Unknown Group            -1  Rebels vs. Nigeria & Allies   \n",
       "\n",
       "               campaign_sides  day  month  year  no_killed  no_wounded  \\\n",
       "2902  Rebels,Nigeria & Allies   16      6  2011          6           0   \n",
       "2947  Rebels,Nigeria & Allies   26      8  2011         24          80   \n",
       "3003  Rebels,Nigeria & Allies    4     11  2011          5           0   \n",
       "3004  Rebels,Nigeria & Allies    4     11  2011          0           7   \n",
       "3029  Rebels,Nigeria & Allies   25     12  2011         43          57   \n",
       "...                       ...  ...    ...   ...        ...         ...   \n",
       "4585  Rebels,Nigeria & Allies   13      6  2015          3           4   \n",
       "4592  Rebels,Nigeria & Allies   15      6  2015          8           8   \n",
       "4594  Rebels,Nigeria & Allies   15      6  2015          2           0   \n",
       "4604  Rebels,Nigeria & Allies   23      6  2015         10          30   \n",
       "4611  Rebels,Nigeria & Allies   27      6  2015          5          15   \n",
       "\n",
       "     target_country  ...                                        target_name  \\\n",
       "2902        Nigeria  ...                       Nigerian Police Headquarters   \n",
       "2947        Nigeria  ...                   United Nations (UN) Headquarters   \n",
       "3003        Nigeria  ...                            Nigerian Police station   \n",
       "3004        Nigeria  ...  Headquarters of the military anti-terrorism Jo...   \n",
       "3029        Nigeria  ...           St. Theresa's Catholic Church in Madalla   \n",
       "...             ...  ...                                                ...   \n",
       "4585        Nigeria  ...                           Nigerian army checkpoint   \n",
       "4592        Nigeria  ...  Civilian Vigilante Office in the Igwanda area ...   \n",
       "4594        Nigeria  ...                       Angry mob chasing the bomber   \n",
       "4604        Nigeria  ...                                      Weekly market   \n",
       "4611        Nigeria  ...                     Outside Molai leprosy hospital   \n",
       "\n",
       "     target_type    target_weapon attackers_birth_location  \\\n",
       "2902    Security         Car bomb                  Nigeria   \n",
       "2947   Political         Car bomb                  Unknown   \n",
       "3003    Security         Car bomb                  Unknown   \n",
       "3004    Security         Car bomb                  Unknown   \n",
       "3029    Civilian         Car bomb                  Unknown   \n",
       "...          ...              ...                      ...   \n",
       "4585    Security  Motorcycle bomb                  Unknown   \n",
       "4592    Security        Belt bomb                  Unknown   \n",
       "4594    Civilian        Belt bomb                  Unknown   \n",
       "4604    Civilian        Belt bomb                  Unknown   \n",
       "4611    Civilian        Belt bomb                  Unknown   \n",
       "\n",
       "     attackers_birth_year  attackers_education attackers_gender  \\\n",
       "2902                 1976              Unknown          Unknown   \n",
       "2947                 1984              Unknown             Male   \n",
       "3003                   -1              Unknown          Unknown   \n",
       "3004                 1985              Unknown          Unknown   \n",
       "3029                   -1              Unknown          Unknown   \n",
       "...                   ...                  ...              ...   \n",
       "4585                   -1              Unknown             Male   \n",
       "4592                   -1              Unknown             Male   \n",
       "4594                   -1              Unknown             Male   \n",
       "4604                 2003              Unknown           Female   \n",
       "4611                   -1              Unknown          Unknown   \n",
       "\n",
       "     attackers_marital attacker_occupation attacker_religion  \n",
       "2902           Married        Professional    Muslim (Sunni)  \n",
       "2947           Unknown             Unknown           Unknown  \n",
       "3003           Unknown             Unknown           Unknown  \n",
       "3004           Unknown             Unknown           Unknown  \n",
       "3029           Unknown             Unknown           Unknown  \n",
       "...                ...                 ...               ...  \n",
       "4585           Unknown             Unknown           Unknown  \n",
       "4592           Unknown             Unknown           Unknown  \n",
       "4594           Unknown             Unknown           Unknown  \n",
       "4604           Unknown             Unknown           Unknown  \n",
       "4611           Unknown             Unknown           Unknown  \n",
       "\n",
       "[103 rows x 21 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#displaying all rows that has Nigeria as the target country.\n",
    "nigeria=df.loc[(df['target_country']=='Nigeria')]\n",
    "nigeria"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "103 rows were found. Although there is no guarantee that these were the only attacks done in Nigeria soil. But based on this dataset, 103 suicide attacks on Nigeria were recorded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     103.000000\n",
       "mean     2013.796117\n",
       "std         1.323685\n",
       "min      2011.000000\n",
       "25%      2012.000000\n",
       "50%      2014.000000\n",
       "75%      2015.000000\n",
       "max      2015.000000\n",
       "Name: year, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking the year to which this suicide bombing started in Nigeria according to this dataset\n",
    "nigeria['year'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first attack was in 2011...\n",
    "looking at all the records in 2011 and from the record below, we can see the first attack... on 16th June 2011 at Abuja, the Federal Capital Territory with the weapon of attack being a car bomb."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2902</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>35</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>16</td>\n",
       "      <td>6</td>\n",
       "      <td>2011</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Nigerian Police Headquarters</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>1976</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Married</td>\n",
       "      <td>Professional</td>\n",
       "      <td>Muslim (Sunni)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>27</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>26</td>\n",
       "      <td>8</td>\n",
       "      <td>2011</td>\n",
       "      <td>24</td>\n",
       "      <td>80</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>United Nations (UN) Headquarters</td>\n",
       "      <td>Political</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1984</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Male</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3003</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Nigerian Police station</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3004</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>26</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>2011</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Headquarters of the military anti-terrorism Jo...</td>\n",
       "      <td>Security</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>1985</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3029</th>\n",
       "      <td>Boko Haram</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>25</td>\n",
       "      <td>12</td>\n",
       "      <td>2011</td>\n",
       "      <td>43</td>\n",
       "      <td>57</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>St. Theresa's Catholic Church in Madalla</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Car bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     terrorist groups  attacker_age                campaign_name  \\\n",
       "2902       Boko Haram            35  Rebels vs. Nigeria & Allies   \n",
       "2947       Boko Haram            27  Rebels vs. Nigeria & Allies   \n",
       "3003       Boko Haram            -1  Rebels vs. Nigeria & Allies   \n",
       "3004       Boko Haram            26  Rebels vs. Nigeria & Allies   \n",
       "3029       Boko Haram            -1  Rebels vs. Nigeria & Allies   \n",
       "\n",
       "               campaign_sides  day  month  year  no_killed  no_wounded  \\\n",
       "2902  Rebels,Nigeria & Allies   16      6  2011          6           0   \n",
       "2947  Rebels,Nigeria & Allies   26      8  2011         24          80   \n",
       "3003  Rebels,Nigeria & Allies    4     11  2011          5           0   \n",
       "3004  Rebels,Nigeria & Allies    4     11  2011          0           7   \n",
       "3029  Rebels,Nigeria & Allies   25     12  2011         43          57   \n",
       "\n",
       "     target_country  ...                                        target_name  \\\n",
       "2902        Nigeria  ...                       Nigerian Police Headquarters   \n",
       "2947        Nigeria  ...                   United Nations (UN) Headquarters   \n",
       "3003        Nigeria  ...                            Nigerian Police station   \n",
       "3004        Nigeria  ...  Headquarters of the military anti-terrorism Jo...   \n",
       "3029        Nigeria  ...           St. Theresa's Catholic Church in Madalla   \n",
       "\n",
       "     target_type target_weapon attackers_birth_location attackers_birth_year  \\\n",
       "2902    Security      Car bomb                  Nigeria                 1976   \n",
       "2947   Political      Car bomb                  Unknown                 1984   \n",
       "3003    Security      Car bomb                  Unknown                   -1   \n",
       "3004    Security      Car bomb                  Unknown                 1985   \n",
       "3029    Civilian      Car bomb                  Unknown                   -1   \n",
       "\n",
       "      attackers_education attackers_gender attackers_marital  \\\n",
       "2902              Unknown          Unknown           Married   \n",
       "2947              Unknown             Male           Unknown   \n",
       "3003              Unknown          Unknown           Unknown   \n",
       "3004              Unknown          Unknown           Unknown   \n",
       "3029              Unknown          Unknown           Unknown   \n",
       "\n",
       "     attacker_occupation attacker_religion  \n",
       "2902        Professional    Muslim (Sunni)  \n",
       "2947             Unknown           Unknown  \n",
       "3003             Unknown           Unknown  \n",
       "3004             Unknown           Unknown  \n",
       "3029             Unknown           Unknown  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nigeria[nigeria['year']==nigeria['year'].min()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most attacked group in Nigeria is shown below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Civilian     68\n",
       "Security     29\n",
       "Political     5\n",
       "Unknown       1\n",
       "Name: target_type, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nigeria['target_type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target_type', ylabel='count'>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='darkgrid')\n",
    "sns.countplot(nigeria['target_type'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is clear that the most targeted group of people are civillians (68 attacks), then security (29 attacks), then politicians (5 attacks)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "finding out the number of unique places these attacks happened in Nigeia and the number of times it happened:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#finding out the number of places (cities) these attacks happened.\n",
    "nigeria['target_location'].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Nigeria, MaiduguriBorno                  25\n",
       "Nigeria, KanoKano                        14\n",
       "Nigeria, PotiskumYobe                    13\n",
       "Nigeria, GombeGombe                       7\n",
       "Nigeria, KadunaKaduna                     6\n",
       "Nigeria, JosPlateau                       6\n",
       "Nigeria, DamaturuYobe                     5\n",
       "Nigeria, Bauchi BauchiBauchi              3\n",
       "Nigeria, BiuBorno                         3\n",
       "Nigeria, AbujaAbuja Capital Territory     3\n",
       "Nigeria, KondugaBorno                     2\n",
       "Nigeria, LagosLagos                       1\n",
       "Nigeria, GwozaBorno                       1\n",
       "Nigeria, GujbaYobe                        1\n",
       "Nigeria, JalingoTaraba                    1\n",
       "Nigeria, MadallaNiger                     1\n",
       "Nigeria, GarkidaAdamawa                   1\n",
       "Nigeria, AzareBauchi                      1\n",
       "Nigeria, KontagoraNiger                   1\n",
       "Nigeria, YolaAdamawa                      1\n",
       "Nigeria, SokotoSokoto                     1\n",
       "Nigeria, Tashan AladeBorno                1\n",
       "Nigeria, Bale VillageBorno                1\n",
       "Nigeria, Beni SheikBorno                  1\n",
       "Nigeria, NgamduYobe                       1\n",
       "Nigeria, ZariaKaduna                      1\n",
       "Nigeria, DikwaBorno                       1\n",
       "Name: target_location, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cities in which these attacks happened and how many times it occured in these cities\n",
    "location=nigeria['target_location'].value_counts()\n",
    "location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.patches.Wedge at 0x6b61730>,\n",
       "  <matplotlib.patches.Wedge at 0x6b61e50>,\n",
       "  <matplotlib.patches.Wedge at 0x6b6c550>,\n",
       "  <matplotlib.patches.Wedge at 0x6b6cc10>,\n",
       "  <matplotlib.patches.Wedge at 0x6b7d310>,\n",
       "  <matplotlib.patches.Wedge at 0x6b7d9d0>,\n",
       "  <matplotlib.patches.Wedge at 0x6b8a0d0>,\n",
       "  <matplotlib.patches.Wedge at 0x6b8a790>,\n",
       "  <matplotlib.patches.Wedge at 0x6b8ae50>,\n",
       "  <matplotlib.patches.Wedge at 0x6b98550>,\n",
       "  <matplotlib.patches.Wedge at 0x6b40340>,\n",
       "  <matplotlib.patches.Wedge at 0x6ba62e0>,\n",
       "  <matplotlib.patches.Wedge at 0x6ba69a0>,\n",
       "  <matplotlib.patches.Wedge at 0x6bb40a0>,\n",
       "  <matplotlib.patches.Wedge at 0x6bb4760>,\n",
       "  <matplotlib.patches.Wedge at 0x6bb4e50>,\n",
       "  <matplotlib.patches.Wedge at 0x6bc2550>,\n",
       "  <matplotlib.patches.Wedge at 0x6bc2c10>,\n",
       "  <matplotlib.patches.Wedge at 0x6bd0310>,\n",
       "  <matplotlib.patches.Wedge at 0x6bd09d0>,\n",
       "  <matplotlib.patches.Wedge at 0x6bdd0d0>,\n",
       "  <matplotlib.patches.Wedge at 0x6bdd790>,\n",
       "  <matplotlib.patches.Wedge at 0x6bdde50>,\n",
       "  <matplotlib.patches.Wedge at 0x6bea550>,\n",
       "  <matplotlib.patches.Wedge at 0x6beac10>,\n",
       "  <matplotlib.patches.Wedge at 0x6bf8310>,\n",
       "  <matplotlib.patches.Wedge at 0x6bf89d0>],\n",
       " [Text(0.9400246923255018, 0.8979719248497392, 'Maiduguri Borno'),\n",
       "  Text(-0.4837188467119198, 1.206654912282584, 'Kano Kano'),\n",
       "  Text(-1.2138912136162132, 0.46526134752992027, 'Potiskum Yobe'),\n",
       "  Text(-1.2614909615363707, -0.3140709377864227, 'Gombe Gombe'),\n",
       "  Text(-1.0423215202056997, -0.7768950048211658, 'Kaduna Kaduna'),\n",
       "  Text(-0.695235193641235, -1.0984753185769058, 'Jos Plateau'),\n",
       "  Text(-0.29479691233630506, -1.26613379248679, 'Damaturu Yobe'),\n",
       "  Text(0.019824782179119663, -1.2998488289072505, 'Biu Borno'),\n",
       "  Text(0.25604750532429427, -1.2745350819091663, 'Abuja FCT'),\n",
       "  Text(0.48371885377286894, -1.2066549094520198, 'Bauchi Bauchi'),\n",
       "  Text(0.6614124915127749, -1.1191664380559594, 'Konduga Borno'),\n",
       "  Text(0.7609094100220744, -1.0540478498340855, 'Yola Adamawa'),\n",
       "  Text(0.823753038363655, -1.0056992253087635, 'Gwoza Borno'),\n",
       "  Text(0.8835322512411867, -0.9536093335410893, 'Zaria Kaduna'),\n",
       "  Text(0.940024666052314, -0.8979719523533215, 'Sokoto Sokoto'),\n",
       "  Text(0.9930201272995905, -0.8389940564616089, 'Bale Village Borno'),\n",
       "  Text(1.0423214883827538, -0.7768950475164331, 'Azare Bauchi'),\n",
       "  Text(1.0877453449970342, -0.71190593791405, 'Beni Sheik Borno'),\n",
       "  Text(1.1291227174092329, -0.6442684914151786, 'Garkida Adamawa'),\n",
       "  Text(1.1662996790734341, -0.5742343237679238, 'Tashan Alade Borno'),\n",
       "  Text(1.1991379292478825, -0.5020639666806418, 'Madalla Niger'),\n",
       "  Text(1.22751530748283, -0.4280258986268628, 'Gujba Yobe'),\n",
       "  Text(1.2513262480654452, -0.35239554608771073, 'Kontagora Niger'),\n",
       "  Text(1.2704821727312041, -0.27545425894728637, 'Jalingo Taraba'),\n",
       "  Text(1.2849118201808574, -0.19748826385260423, 'Lagos Lagos'),\n",
       "  Text(1.2945615111771511, -0.1187875994316382, 'Ngamdu Yobe'),\n",
       "  Text(1.299395348235116, -0.03964503733056441, 'Dikwa Borno')])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lab=('Maiduguri Borno', 'Kano Kano','Potiskum Yobe','Gombe Gombe','Kaduna Kaduna','Jos Plateau','Damaturu Yobe', 'Biu Borno','Abuja FCT', 'Bauchi Bauchi','Konduga Borno','Yola Adamawa','Gwoza Borno','Zaria Kaduna','Sokoto Sokoto','Bale Village Borno','Azare Bauchi','Beni Sheik Borno','Garkida Adamawa','Tashan Alade Borno','Madalla Niger','Gujba Yobe','Kontagora Niger','Jalingo Taraba','Lagos Lagos','Ngamdu Yobe','Dikwa Borno')\n",
    "plt.figure(figsize=(10, 10))\n",
    "exp=[0.2, 0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2,0.2]\n",
    "plt.pie(location, labels=lab,explode=exp, shadow=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So these attacks happened in 27 unique places in Nigeria, with the highest number of attacks being in Maiduguri (25), Borno (14) and the next being in Kano (13), then Potiskum in Yobe. There must be something in Maiduguri that makes their attacks easily carried out there. This could be that the sect's major place of residence (or headquarters) is Maiduguri, or the supply of weapons are easily gotten in Maiduguri, or the major place of target is simply Maiduguri for some reason, or it could be anything."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown    47\n",
       "Male       29\n",
       "Female     27\n",
       "Name: attackers_gender, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nigeria['attackers_gender'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='attackers_gender', ylabel='count'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYAAAAEJCAYAAACdePCvAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAZ0UlEQVR4nO3de3BU9f3/8deym4CCCIYEqaVxihJQULHVJCCJgkaSsAVER24C1gtgpWpR5CYXr4BKCt6K/VJ+1mIFbUCIMTDIkFKSgGIlg0ONIkkhQBISiOGWhN3P7w+GrVEIG+BsgM/zMcNMztlzPp/3cpJ97fmcPZ91GWOMAADWadLYBQAAGgcBAACWIgAAwFIEAABYigAAAEsRAABgKQIAACzlaewCGmrfvoPy+7l1AQCC0aSJS61bNz/hY+ddAPj9hgAAgLOAISAAsBQBAACWIgAAwFIEAABYigAAAEsRAABgKQIAACx13t0HEKxLWjZTs6ZhjV3GBe1Ida2qvj/S2GUAOE0XbAA0axqmIeMXNXYZF7T3Zg9VlQgA4HzFEBAAWIoAAABLEQAAYCkCAAAsRQAAgKUIAACwFAEAAJYiAADAUgQAAFiKAAAASxEAAGApAgAALEUAAIClCAAAsBQBAACWIgAAwFIEAABYigAAAEsRAABgKQIAACxFAACApQgAALAUAQAAliIAAMBSBAAAWMrxAJg1a5YmTJggScrJyZHX61VSUpLS0tKc7hoAUA9HAyA3N1dLly6VJB05ckSTJk3Sm2++qczMTG3ZskXZ2dlOdg8AqIdjAbB//36lpaVp9OjRkqT8/HxFR0erffv28ng88nq9ysrKcqp7AMApOBYAU6dO1RNPPKGWLVtKkkpLSxUZGRl4PCoqSiUlJU51DwA4BY8TjX7wwQdq166d4uPjlZ6eLkny+/1yuVyBbYwxdZaDFRHR4qzViTMXGXlJY5cA4DQ5EgCZmZkqKytTv379VFlZqUOHDqm4uFhutzuwTVlZmaKiohrcdnn5Afn95pTb8cIUGmVlVY1dAoB6NGniOukbZ0cCYOHChYGf09PTtXHjRs2YMUNJSUkqKirSz3/+c2VkZGjgwIFOdA8ACIIjAXAiTZs21cyZMzV27FhVV1crMTFRffr0CVX3AIAfcRljTj2ecg5pyBDQkPGLQlCRvd6bPZQhIOAcV98QEHcCA4ClCAAAsBQBAACWIgAAwFIEAABYigAAAEsRAABgKQIAACxFAACApQgAALAUAQAAliIAAMBSBAAAWIoAAABLEQAAYCkCAAAsRQAAgKUIAACwFAEAAJYiAADAUgQAAFiKAAAASxEAAGApAgAALEUAAIClCAAAsBQBAACWIgAAwFIEAABYigAAAEsRAABgKQIAACxFAACApQgAALAUAQAAlnI0AObOnauUlBSlpqZq4cKFkqScnBx5vV4lJSUpLS3Nye4BAPXwONXwxo0blZeXp+XLl+vo0aNKSUlRfHy8Jk2apHfffVft2rXTqFGjlJ2drcTERKfKAACchGNnADfffLP++te/yuPxqLy8XD6fT99//72io6PVvn17eTweeb1eZWVlOVUCAKAejg4BhYWFad68eUpNTVV8fLxKS0sVGRkZeDwqKkolJSVOlgAAOAnHhoCO+/3vf6+HHnpIo0ePVmFhoVwuV+AxY0yd5WBERLQ42yXiDERGXtLYJQA4TY4FwLZt21RTU6POnTvroosuUlJSkrKysuR2uwPblJWVKSoqqkHtlpcfkN9vTrkdL0yhUVZW1dglAKhHkyauk75xdmwIaOfOnZoyZYpqampUU1OjTz/9VIMGDdL27dtVVFQkn8+njIwMJSQkOFUCAKAejp0BJCYmKj8/X/3795fb7VZSUpJSU1N12WWXaezYsaqurlZiYqL69OnjVAkAgHq4jDGnHk85hzRkCGjI+EUhqMhe780e6tgQUOtLw+UJb+pI2zjmaE219lXWNHYZcFh9Q0COXwQGTocnvKk2zX6wscu4oP1q/P9JIgBsxlQQAGApAgAALEUAAIClCAAAsBQBAACWCioATjRfz7fffnvWiwEAhE69AbB//37t379fDz30kCorKwPLe/fu1aOPPhqqGgEADqj3PoBx48Zp/fr1kqTY2Nj/7eTx6M4773S2MgDnpZaXNlXT8PDGLuOCV11To+8rq8+ojXoDYMGCBZKkiRMn6qWXXjqjjgDYoWl4uEYufKyxy7jg/b/750pyMACOe+mll1RcXKzKykr9cOaIa6+99ow6BwA0nqACYN68eVqwYIEiIiIC61wulz799FPHCgMAOCuoAFi2bJlWrVqltm3bOl0PACBEgvoYaLt27XjxB4ALTFBnAPHx8Zo9e7Z69+6tZs2aBdZzDQAAzl9BBUB6erokKSsrK7COawAAcH4LKgDWrFnjdB0AgBALKgAWLlx4wvX333//WS0GABA6QQVAQUFB4Oeamhp99tlnio+Pd6woAIDzgr4R7IdKSko0efJkRwoCAITGaU0H3bZtWxUXF5/tWgAAIdTgawDGGG3ZsqXOXcEAgPNPg68BSMduDBs/frwjBQEAQqNB1wCKi4t19OhRRUdHO1oUAMB5QQVAUVGRHnnkEZWWlsrv96t169aaP3++OnTo4HR9AACHBHUR+Nlnn9WDDz6ozz77TJs2bdKYMWM0Y8YMp2sDADgoqAAoLy/XgAEDAssDBw7Uvn37HCsKAOC8oALA5/Np//79geWKigqn6gEAhEhQ1wCGDRume++9V8nJyXK5XMrMzNSIESOcrg0A4KCgzgASExMlSbW1tdq2bZtKSkp0xx13OFoYAMBZQZ0BTJgwQUOHDtXw4cNVXV2tv//975o0aZL+/Oc/O10fAMAhQZ0B7Nu3T8OHD5ckNW3aVCNHjlRZWZmjhQEAnBX0ReCSkpLA8t69e2WMcawoAIDzghoCGjlypPr376+ePXvK5XIpJyeHqSAA4DwXVADcfffd6tKli/Ly8uR2u/XAAw+oY8eOTtcGAHBQUAEgSZ06dVKnTp2crAUAEEKn9X0AwXr99deVmpqq1NRUzZ49W5KUk5Mjr9erpKQkpaWlOdk9AKAejgVATk6O/vWvf2np0qVatmyZvvrqK2VkZGjSpEl68803lZmZqS1btig7O9upEgAA9XAsACIjIzVhwgSFh4crLCxMHTp0UGFhoaKjo9W+fXt5PB55vV5lZWU5VQIAoB5BXwNoqKuvvjrwc2FhoT755BMNGzZMkZGRgfVRUVF1Pl4ajIiIFmetRpy5yMhLGrsEnAGO3/ntTI+fYwFw3DfffKNRo0Zp/PjxcrvdKiwsDDxmjJHL5WpQe+XlB+T3n/oeBH6xQ6OsrMqRdjl+oeHE8ePYhU4wx69JE9dJ3zg7ehF406ZNGjlypMaNG6cBAwbo8ssvr3MHcVlZmaKiopwsAQBwEo4FwO7du/W73/1Or7zyilJTUyVJ119/vbZv366ioiL5fD5lZGQoISHBqRIAAPVwbAhowYIFqq6u1syZMwPrBg0apJkzZ2rs2LGqrq5WYmKi+vTp41QJAIB6OBYAU6ZM0ZQpU0742PLly53qFgAQJEevAQAAzl0EAABYigAAAEsRAABgKQIAACxFAACApQgAALAUAQAAliIAAMBSBAAAWIoAAABLEQAAYCkCAAAsRQAAgKUIAACwFAEAAJYiAADAUgQAAFiKAAAASxEAAGApAgAALEUAAIClCAAAsBQBAACWIgAAwFIEAABYigAAAEsRAABgKQIAACxFAACApQgAALAUAQAAliIAAMBSBAAAWIoAAABLORoABw4cUN++fbVz505JUk5Ojrxer5KSkpSWluZk1wCAU3AsADZv3qzBgwersLBQknTkyBFNmjRJb775pjIzM7VlyxZlZ2c71T0A4BQcC4AlS5Zo2rRpioqKkiTl5+crOjpa7du3l8fjkdfrVVZWllPdAwBOweNUwy+88EKd5dLSUkVGRgaWo6KiVFJS0uB2IyJanHFtOHsiIy9p7BJwBjh+57czPX6OBcCP+f1+uVyuwLIxps5ysMrLD8jvN6fcjl/s0Cgrq3KkXY5faDhx/Dh2oRPM8WvSxHXSN84h+xTQ5ZdfrrKyssByWVlZYHgIABB6IQuA66+/Xtu3b1dRUZF8Pp8yMjKUkJAQqu4BAD8SsiGgpk2baubMmRo7dqyqq6uVmJioPn36hKp7AMCPOB4Aa9asCfwcHx+v5cuXO90lACAI3AkMAJYiAADAUgQAAFiKAAAASxEAAGApAgAALEUAAIClCAAAsBQBAACWIgAAwFIEAABYigAAAEsRAABgKQIAACxFAACApQgAALAUAQAAliIAAMBSBAAAWIoAAABLEQAAYCkCAAAsRQAAgKUIAACwFAEAAJYiAADAUgQAAFiKAAAASxEAAGApAgAALEUAAIClCAAAsBQBAACWIgAAwFIEAABYqlECYMWKFUpJSVFSUpIWLVrUGCUAgPU8oe6wpKREaWlpSk9PV3h4uAYNGqTY2FhdddVVoS4FAKwW8gDIyclRXFycWrVqJUm68847lZWVpUcffTSo/Zs0cQXdV5vWzU+nRDRAQ45HQ4W3jHCsbRzj1PFr0+IyR9pFXcEcv/q2CXkAlJaWKjIyMrAcFRWl/Pz8oPdv3YAX9XkT+zekNJyGiIgWjrXddfQsx9rGMU4dv1fumeZIu6jrTI9fyK8B+P1+uVz/SyRjTJ1lAEBohDwALr/8cpWVlQWWy8rKFBUVFeoyAMB6IQ+A7t27Kzc3VxUVFTp8+LBWrVqlhISEUJcBANYL+TWAtm3b6oknntDw4cNVW1uru+++W9ddd12oywAA67mMMaaxiwAAhB53AgOApQgAALAUAQAAliIAAMBSBMBp2Llzp3r16vWT9TExMSfdZ8OGDbrvvvucLAtn2c6dOxUTE6OpU6fWWb9161bFxMQoPT39pPv26tVLO3fudLpE6+3cuVNdunRRv3796vzbvXu3I32d6O/+fBbyj4EC55NWrVpp3bp18vl8crvdkqTMzExddhlz3ZwroqKi9NFHHzV2GeclAuAsS09P17p161RZWakdO3aoR48emj59ep1t3nnnHa1evVpvv/22Hn74YXXt2lWbNm1SRUWFpkyZosTERO3du1eTJ0/Wrl275PF49MQTTwTe6axbt06S1LNnT02cOFEpKSmaP3++3G63Dh8+rJKSEhUVFam4uFj33HOPxowZ0wj/ExeG5s2bq1OnTvrss88UFxcnSVq/fr26d+8uSfrb3/6mjz76SIcPH1ZYWJheffVV/fKXvwzs7/P5NHv2bG3cuFE+n0933XWXRo4c2RhPxSp79+7V1KlTtWfPHrlcLo0bN07du3fXa6+9pl27dqmwsFAVFRUaM2aMcnNztXnzZnXq1ElpaWny+XyaPn26vvnmG+3du1cxMTGaM2dOUO2fbwgAB/z73/9WRkaG3G63+vTpo8GDBwceS09P16pVq/T222/roosukiTV1tZq8eLFWrNmjebOnavExEQ999xziouL0/33368dO3Zo8ODBWrZsmdq1a6eCggK53W75fD5t3LhRKSkpWrdunWbMmKHMzEx9/fXXWrRokaqqqnT77bdr6NChatmyZWP9d5z3kpOTtXLlSsXFxSk/P18xMTEyxujAgQNas2aN3n33XTVr1kxz587VokWL9MwzzwT2XbJkiSRp6dKlqqmp0QMPPKAuXbro17/+dWM9nQtOaWmp+vXrF1j2er366quvNHDgQPXu3VulpaUaMmSIli1bJkkqKCjQ4sWL9cUXX2jEiBFasWKFrrzySqWkpOjrr79WVVWVwsLCtHjxYvn9fo0YMULZ2dm69tprA3288MILJ2y/RQvnJkd0AgFwGpo0+emlkx9OatetW7fAL0L79u1VWVkp6dgv3jPPPKM5c+aoefP/zWras2dPSdLVV1+t/fv3S5Ly8vL0/PPPB9q4/vrrtXnzZiUkJCg3N1cej0fDhw/Xxx9/rKqqKu3du1cdOnSQJMXGxio8PFwRERFq1aqVqqqqCIAz0KtXL/3xj3+U3+/XJ598ouTkZGVmZqpFixZ69dVX9fHHH6uwsFDr1q1T586d6+ybm5urrVu3Ki8vT5J06NAhff311wTAWXSiIaDY2Fh99913mjdvniTp6NGj2rFjhySpR48e8ng8+tnPfqbIyMjAd5G0bdtWlZWVio2NVatWrbRo0SJ99913Kiws1KFDh+q0n5OTc8L2f3z8z3UEwGlo2bKlqqqq6qwrLy/XpZdeKklq2rRpYL3L5dLxm62bN2+ul156SS+88IJ69uypiy++uM72P54l9YeMMfL5fLr11lv1+uuvKzw8XI899pg++eQTrVixQrfccktg25P1j9NzfBho06ZNysvL07hx45SZmandu3fr3nvv1bBhw5SQkKA2bdpo69atdfb1+Xx66qmnlJSUJEmqqKioE/5wht/v1zvvvBP43pHS0lJFRERo9erVCgsLC2zn8fz0JfDTTz/VvHnzNHz4cN11113at2/fT/6GTtb++YZPAZ2GFi1aKDo6WitXrgysW7x4seLj4+vd74orrlCvXr108803B945nExcXJw+/PBDSdKOHTv0xRdf6IYbbtC1116r7du3q7CwUB06dFBsbKzeeust3XbbbWf+xHBSycnJevXVV9WlS5fAi8bFF1+s6OhojRw5Ul27dtXq1avl8/nq7BcXF6clS5aotrZWBw8e1JAhQ/Tll182wjOwS1xcnN577z1J0rfffiuv16vDhw8HtW9ubq6Sk5M1cOBAtWzZUhs2bDjhcT3d9s8lnAGcppdfflnTp0/XG2+8odra2sDHBdeuXXvKfcePH6++ffvK6/WedJvJkydr6tSpgY8aPv/884Fps3/1q18Fftni4uL0wQcf6KabbjrzJ4WTuu222zR58mQ99thjgXVhYWHy+/1KSUmRMUY33XSTvvnmmzr7DRo0SEVFRRowYICOHj2qu+66S7GxsaEu3zpTpkzR1KlTA39js2fPDnp8/p577tGTTz6pjz/+WGFhYbrxxht/8pHeM2n/XMJkcABgKYaAAMBSBAAAWIoAAABLEQAAYCkCAAAsRQDgvJCfn19nVs7f/va3qqioOK22NmzYoL59+56t0s4Z3bp1YwZSNAgBgPPCt99+q5KSksDy+vXrG7Ea4MLAjWA4p/j9fr344ovavHmzDh48KGOMZsyYoXnz5qmqqkoTJ04MbDtixAi9/fbb+s9//qP58+erpqZGFRUV6t+/vx5//HFJ0ocffqiFCxeqSZMmat26tWbNmlWnv88//1xPPvmk5syZoxtvvFFr1qzRW2+9pdraWjVr1kxPP/20unXrptdee01ffvmlSktLFRMTozFjxmjy5MmqqamRMUZ33323hg4dWu9zy8/P1/Tp01VbW6tf/OIX2rVrlyZMmKDY2Nh6+y0uLlZZWZmKi4vVtm1bvfzyy4qKitLnn3+u5557Ti6XS127dpXf7w/0FezzeOWVV87ewcP5xwDnkC+++MKMHTvW+Hw+Y4wx8+fPN6NGjTL/+Mc/zMMPPxzYrmPHjqa8vNz4/X4zbNgws337dmOMMXv27DGdO3c25eXlZuvWrSY2Ntbs2rXLGGPMwoULzTPPPGPy8vJMamqqyc3NNbfffrvZunWrMcaY7du3m759+5qKigpjjDEFBQWmR48e5uDBg2bevHnmzjvvNLW1tcYYYyZOnGjmz59vjDGmtLTUPP7444GaT6S2ttYkJCSYtWvXGmOMyc3NNTExMSYvL++U/fbu3dtUVVUZY4wZNWqUmTt3rqmurjbdu3c3OTk5xhhjVqxYYTp27Gh27NjRoOcBu3EGgHNKt27ddOmll+r999/Xjh07tGHDhnonT3O5XPrTn/6ktWvXKiMjQ9u2bZMxRocPH1Zubq5uueUWtWvXTpIC8/Bv2LBBe/bs0ejRozV48GB16tRJ0rFhpdLS0jrz9btcLv33v/+VJN1www2BeYDuuOMOPf3008rPz1d8fLymTJlywllijysoKJAkJSYmSjo2hcfVV18dVL8333xzYJqBa665RpWVlSooKJDH4wnMP9W3b9/ANZKGPA/YjWsAOKesXbtWo0aNkiT17t27zncpnMihQ4c0YMAAffXVV7rmmms0fvx4eTweGWPkdrvrzLB65MgRbdu2TZLkdrv1l7/8RUuXLtXmzZslHRt+io+P10cffRT4t2TJksAL9fHZW6VjcwOtXLlSycnJ2rp1q7xer/bs2XPSOt1u909mlDz+DWOn6rdZs2aBfX44u+uP2zv+ot6Q5wG7EQA4p6xfv1633XabhgwZoi5dugRm2HS73Tp69Ghgu+PLRUVFOnDggB5//HH16tVLGzZsUE1Njfx+v2JjY5Wbm6vS0lJJ0vvvv6+XX35ZkhQZGakbb7xRTz/9tMaPH6/Dhw8rPj5e69evD4REdna2fvOb3+jIkSM/qfP4lNCpqamaNm2aWrRoEXiHfSIdOnRQeHi4/vnPf0o6dj2goKBALperQf0ed/xLabKzsyUdm8L4+PdOnE57sBPngTinDBo0SOPGjZPX69XRo0fVo0cPrVq1Stddd53eeOMNPfroo3r99dfVp08f3XfffZo7d65uvfVWJScnKzw8XB07dtRVV12loqIi9ezZU0899ZQefPBBScde9F988UUVFhYG+hswYIBWrlypmTNnasaMGXr22Wf1hz/8QcYYeTwevfXWWyccgnrkkUc0efJkLV68WG63W7fffnu9M7J6PB699tprmjZtmubMmaMrr7xSbdq0UbNmzXTVVVcF3e9xYWFheuONNzR9+nTNmTNHnTt3DsxHfzrtwU7MBgqEyKxZs/TAAw+oTZs22r17t/r166fVq1fzbW1oNJwBAGfJ8uXLtWDBghM+5vV6dcUVV2jkyJGBaxTPP/88L/5oVJwBAICluAgMAJYiAADAUgQAAFiKAAAASxEAAGApAgAALPX/AThAkauXwDtGAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='darkgrid')\n",
    "sns.countplot(nigeria['attackers_gender'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From this, it is clear that females are actively involved in these suicide attacks just as males are since 27 of the attacks were perpetuated by females and 29 by males."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown Group    83\n",
       "Boko Haram       20\n",
       "Name: terrorist groups, dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the different terrorist groups responsible for the attacks\n",
    "nigeria['terrorist groups'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Some attacks are not claimed by any terrorist groups. According to this dataset, Most of these suicide attackers' terrorist groups are unknown. Only 20 of the attacks are clearly stated to be done by Boko Haram. Some people assume that every other attack was perpetuated by them but what if they are wrong? What if some attacks were done by some other terrorist groups in Nigeria?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unknown           102\n",
       "Muslim (Sunni)      1\n",
       "Name: attacker_religion, dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Checking the religion of these attackers, it is seen that most of the religion are unknown. This is logical as no group will like to identify with these terrorist groups.\n",
    "nigeria['attacker_religion'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finding the Total number of attacks per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015    43\n",
       "2014    29\n",
       "2012    23\n",
       "2011     5\n",
       "2013     3\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nigeria['year'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x6c77ca0>,\n",
       "  <matplotlib.axis.XTick at 0x6c77c70>,\n",
       "  <matplotlib.axis.XTick at 0x6c92040>,\n",
       "  <matplotlib.axis.XTick at 0x6cab250>,\n",
       "  <matplotlib.axis.XTick at 0x6cab760>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='darkgrid')\n",
    "sns.lineplot(data=nigeria['year'].value_counts(), marker='o')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of attacks')\n",
    "xaxis=[2011, 2012, 2013, 2014, 2015]\n",
    "plt.xticks(xaxis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "searching for the attack with the highest number of casualties"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4267</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>2014</td>\n",
       "      <td>120</td>\n",
       "      <td>270</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Grand Mosque of Kano</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     terrorist groups  attacker_age                campaign_name  \\\n",
       "4267    Unknown Group            -1  Rebels vs. Nigeria & Allies   \n",
       "\n",
       "               campaign_sides  day  month  year  no_killed  no_wounded  \\\n",
       "4267  Rebels,Nigeria & Allies   28     11  2014        120         270   \n",
       "\n",
       "     target_country  ...           target_name target_type target_weapon  \\\n",
       "4267        Nigeria  ...  Grand Mosque of Kano    Civilian     Belt bomb   \n",
       "\n",
       "     attackers_birth_location attackers_birth_year  attackers_education  \\\n",
       "4267                  Unknown                   -1              Unknown   \n",
       "\n",
       "     attackers_gender attackers_marital attacker_occupation attacker_religion  \n",
       "4267          Unknown           Unknown             Unknown           Unknown  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nigeria[nigeria['no_wounded']==nigeria['no_wounded'].max()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "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>terrorist groups</th>\n",
       "      <th>attacker_age</th>\n",
       "      <th>campaign_name</th>\n",
       "      <th>campaign_sides</th>\n",
       "      <th>day</th>\n",
       "      <th>month</th>\n",
       "      <th>year</th>\n",
       "      <th>no_killed</th>\n",
       "      <th>no_wounded</th>\n",
       "      <th>target_country</th>\n",
       "      <th>...</th>\n",
       "      <th>target_name</th>\n",
       "      <th>target_type</th>\n",
       "      <th>target_weapon</th>\n",
       "      <th>attackers_birth_location</th>\n",
       "      <th>attackers_birth_year</th>\n",
       "      <th>attackers_education</th>\n",
       "      <th>attackers_gender</th>\n",
       "      <th>attackers_marital</th>\n",
       "      <th>attacker_occupation</th>\n",
       "      <th>attacker_religion</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4267</th>\n",
       "      <td>Unknown Group</td>\n",
       "      <td>-1</td>\n",
       "      <td>Rebels vs. Nigeria &amp; Allies</td>\n",
       "      <td>Rebels,Nigeria &amp; Allies</td>\n",
       "      <td>28</td>\n",
       "      <td>11</td>\n",
       "      <td>2014</td>\n",
       "      <td>120</td>\n",
       "      <td>270</td>\n",
       "      <td>Nigeria</td>\n",
       "      <td>...</td>\n",
       "      <td>Grand Mosque of Kano</td>\n",
       "      <td>Civilian</td>\n",
       "      <td>Belt bomb</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>-1</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>Unknown</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     terrorist groups  attacker_age                campaign_name  \\\n",
       "4267    Unknown Group            -1  Rebels vs. Nigeria & Allies   \n",
       "\n",
       "               campaign_sides  day  month  year  no_killed  no_wounded  \\\n",
       "4267  Rebels,Nigeria & Allies   28     11  2014        120         270   \n",
       "\n",
       "     target_country  ...           target_name target_type target_weapon  \\\n",
       "4267        Nigeria  ...  Grand Mosque of Kano    Civilian     Belt bomb   \n",
       "\n",
       "     attackers_birth_location attackers_birth_year  attackers_education  \\\n",
       "4267                  Unknown                   -1              Unknown   \n",
       "\n",
       "     attackers_gender attackers_marital attacker_occupation attacker_religion  \n",
       "4267          Unknown           Unknown             Unknown           Unknown  \n",
       "\n",
       "[1 rows x 21 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nigeria[nigeria['no_killed']==nigeria['no_killed'].max()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Nigeria, the same suicide attack yielded the highest number of wounded and killed. This was the suicide attack in a mosque in Kano in 2014 (November 28th) and the target group were civillians."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011     78\n",
       "2012    183\n",
       "2013     54\n",
       "2014    573\n",
       "2015    459\n",
       "Name: no_killed, dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sum of casualties per year (killed)\n",
    "nigeria.groupby('year')['no_killed'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "2011     144\n",
       "2012     642\n",
       "2013      85\n",
       "2014    1013\n",
       "2015     842\n",
       "Name: no_wounded, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#sum of casualties per year (wounded)\n",
    "nigeria.groupby('year')['no_wounded'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Graph of casualties per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x6c06c10>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style='darkgrid')\n",
    "sns.lineplot(data=nigeria.groupby('year')['no_killed'].sum(), marker='o')\n",
    "sns.lineplot(data=nigeria.groupby('year')['no_wounded'].sum(), marker='o')\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of attacks')\n",
    "xaxis=[2011, 2012, 2013, 2014, 2015]\n",
    "plt.xticks(xaxis)\n",
    "plt.legend(labels=['number of killed', 'number of wounded'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Target Weapon: These terrorist must have found the easiest mode to cary out their attacks. Below we search for the most frequently used weapon of attack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belt bomb          56\n",
       "Car bomb           36\n",
       "Motorcycle bomb     6\n",
       "Backpack bomb       2\n",
       "Truck bomb          2\n",
       "Cart bomb           1\n",
       "Name: target_weapon, dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nigeria['target_weapon'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is seen that most weapon of attack was the belt bomb. This shows that it was easier to go unnoticed with a belt bomb than other form of weapon. The next easiest form was car bomb. No wonder religious places and social gatherings took alot of precautions when it came to ensuring that cars be parked a distance away from the places of worship and social gatherings. But i don't think alot of precaution was taken for belt bombs because people were barely bodily searched... but this is my opinion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\DELL\\anaconda3\\lib\\site-packages\\seaborn\\_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='target_weapon', ylabel='count'>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the graph showing the number of target weapon used per attack\n",
    "plt.figure(figsize=(9, 6))\n",
    "sns.countplot(nigeria['target_weapon'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Total number of people killed and wounded from 2011 to 2015."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2726"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total Number wounded\n",
    "nigeria['no_wounded'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1347"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Total Number killed\n",
    "nigeria['no_killed'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}