Global_Terrorism-Data Report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Global Terrorist Attacks\n",
    "\n",
    "A Journey towards understanding the [Global Terrorism Database](https://www.start.umd.edu/gtd/). This report walks us through a visual understanding of the dataset and the growth of terrorist activities from 1970-2017. And try to answer some interesting questions like *Why is there a sudden increase in terrorist activities? Which country has more terrorist activities? etc.*\n",
    "\n",
    "### Author: Harish Gandhi Ramachandran"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>\n",
       "code_show=true; \n",
       "function code_toggle() {\n",
       " if (code_show){\n",
       " $('div.input').hide();\n",
       " } else {\n",
       " $('div.input').show();\n",
       " }\n",
       " code_show = !code_show\n",
       "} \n",
       "$( document ).ready(code_toggle);\n",
       "</script>\n",
       "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('''<script>\n",
    "code_show=true; \n",
    "function code_toggle() {\n",
    " if (code_show){\n",
    " $('div.input').hide();\n",
    " } else {\n",
    " $('div.input').show();\n",
    " }\n",
    " code_show = !code_show\n",
    "} \n",
    "$( document ).ready(code_toggle);\n",
    "</script>\n",
    "<form action=\"javascript:code_toggle()\"><input type=\"submit\" value=\"Click here to toggle on/off the raw code.\"></form>''')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#import packages\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import os\n",
    "import time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import nltk\n",
    "\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot \n",
    "init_notebook_mode(connected=True)\n",
    "import plotly.offline as offline\n",
    "import plotly.graph_objs as go\n",
    "%matplotlib inline\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score, f1_score\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "Data is available from 1970 to 2017 except the 1993 data(which is available seperately). So we need to merge it into a single dataset. The dataset itself is huge .csv file containing 18,000 rows and 135 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel('./GTD_0718dist/globalterrorismdb_0718dist.xlsx')\n",
    "df_1993= pd.read_excel('./GTD_0718dist/gtd1993_0718dist.xlsx')\n",
    "all_year_df = pd.concat([df, df_1993],ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What does our dataself look like?\n",
    "\n",
    "Top 5 rows from the dataset. To know more about what each column represents: have a look at the [codebook](https://www.start.umd.edu/gtd/downloads/Codebook.pdf)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>eventid</th>\n",
       "      <th>iyear</th>\n",
       "      <th>imonth</th>\n",
       "      <th>iday</th>\n",
       "      <th>approxdate</th>\n",
       "      <th>extended</th>\n",
       "      <th>resolution</th>\n",
       "      <th>country</th>\n",
       "      <th>country_txt</th>\n",
       "      <th>region</th>\n",
       "      <th>...</th>\n",
       "      <th>addnotes</th>\n",
       "      <th>scite1</th>\n",
       "      <th>scite2</th>\n",
       "      <th>scite3</th>\n",
       "      <th>dbsource</th>\n",
       "      <th>INT_LOG</th>\n",
       "      <th>INT_IDEO</th>\n",
       "      <th>INT_MISC</th>\n",
       "      <th>INT_ANY</th>\n",
       "      <th>related</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>197000000001</td>\n",
       "      <td>1970</td>\n",
       "      <td>7</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PGIS</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>197000000002</td>\n",
       "      <td>1970</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>130</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PGIS</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>197001000001</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160</td>\n",
       "      <td>Philippines</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PGIS</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>197001000002</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78</td>\n",
       "      <td>Greece</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PGIS</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>197001000003</td>\n",
       "      <td>1970</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101</td>\n",
       "      <td>Japan</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PGIS</td>\n",
       "      <td>-9</td>\n",
       "      <td>-9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 135 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        eventid  iyear  imonth  iday approxdate  extended resolution  country  \\\n",
       "0  197000000001   1970       7     2        NaN         0        NaN       58   \n",
       "1  197000000002   1970       0     0        NaN         0        NaN      130   \n",
       "2  197001000001   1970       1     0        NaN         0        NaN      160   \n",
       "3  197001000002   1970       1     0        NaN         0        NaN       78   \n",
       "4  197001000003   1970       1     0        NaN         0        NaN      101   \n",
       "\n",
       "          country_txt  region   ...    addnotes scite1 scite2  scite3  \\\n",
       "0  Dominican Republic       2   ...         NaN    NaN    NaN     NaN   \n",
       "1              Mexico       1   ...         NaN    NaN    NaN     NaN   \n",
       "2         Philippines       5   ...         NaN    NaN    NaN     NaN   \n",
       "3              Greece       8   ...         NaN    NaN    NaN     NaN   \n",
       "4               Japan       4   ...         NaN    NaN    NaN     NaN   \n",
       "\n",
       "   dbsource  INT_LOG  INT_IDEO INT_MISC INT_ANY  related  \n",
       "0      PGIS        0         0        0       0      NaN  \n",
       "1      PGIS        0         1        1       1      NaN  \n",
       "2      PGIS       -9        -9        1       1      NaN  \n",
       "3      PGIS       -9        -9        1       1      NaN  \n",
       "4      PGIS       -9        -9        1       1      NaN  \n",
       "\n",
       "[5 rows x 135 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "all_year_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequency of Terrorist Activities with Trend Chart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def label_percent(feature):\n",
    "    percent = []\n",
    "    total_items = sum(all_year_df[feature].value_counts())\n",
    "    for each_value in all_year_df[feature].value_counts():\n",
    "        percent.append(round(100*each_value / total_items,2))\n",
    "    \n",
    "    return percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_usa = all_year_df[all_year_df['country'] == 217]#usa country code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "line": {
          "color": "rgb(200,0,0)",
          "width": 3
         },
         "mode": "lines",
         "name": "World",
         "opacity": 0.8,
         "type": "scatter",
         "x": [
          1970,
          1971,
          1972,
          1973,
          1974,
          1975,
          1976,
          1977,
          1978,
          1979,
          1980,
          1981,
          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,
          2016,
          2017
         ],
         "y": [
          651,
          471,
          568,
          473,
          581,
          740,
          923,
          1319,
          1526,
          2662,
          2662,
          2586,
          2544,
          2870,
          3495,
          2915,
          2860,
          3183,
          3721,
          4324,
          3887,
          4683,
          5071,
          747,
          3456,
          3081,
          3058,
          3197,
          934,
          1395,
          1814,
          1906,
          1333,
          1278,
          1166,
          2017,
          2758,
          3242,
          4805,
          4721,
          4826,
          5076,
          8522,
          12036,
          16903,
          14965,
          13587,
          10900
         ]
        },
        {
         "line": {
          "color": "rgb(0,200,0)",
          "width": 3
         },
         "mode": "lines",
         "name": "USA",
         "opacity": 0.8,
         "type": "scatter",
         "x": [
          1970,
          1971,
          1972,
          1973,
          1974,
          1975,
          1976,
          1977,
          1978,
          1979,
          1980,
          1981,
          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,
          2016,
          2017
         ],
         "y": [
          468,
          247,
          68,
          58,
          94,
          149,
          105,
          130,
          87,
          69,
          67,
          74,
          78,
          44,
          63,
          40,
          49,
          34,
          27,
          42,
          32,
          30,
          32,
          36,
          55,
          60,
          35,
          40,
          31,
          53,
          32,
          41,
          33,
          33,
          9,
          21,
          6,
          8,
          18,
          11,
          17,
          10,
          20,
          20,
          29,
          38,
          64,
          65
         ]
        }
       ],
       "layout": {
        "title": "Terrorist Attacks by Year in the world vs USA",
        "titlefont": {
         "size": 25
        },
        "xaxis": {
         "rangeslider": {
          "thickness": 0.05
         },
         "showgrid": false,
         "showline": true,
         "title": "Year"
        },
        "yaxis": {
         "showgrid": false,
         "showline": true,
         "title": "Number of Occurences"
        }
       }
      },
      "text/html": [
       "<div id=\"eab13ed9-7294-4abf-afc1-e999930f06ce\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"eab13ed9-7294-4abf-afc1-e999930f06ce\", [{\"line\": {\"width\": 3, \"color\": \"rgb(200,0,0)\"}, \"mode\": \"lines\", \"x\": [1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 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, 2016, 2017], \"y\": [651, 471, 568, 473, 581, 740, 923, 1319, 1526, 2662, 2662, 2586, 2544, 2870, 3495, 2915, 2860, 3183, 3721, 4324, 3887, 4683, 5071, 747, 3456, 3081, 3058, 3197, 934, 1395, 1814, 1906, 1333, 1278, 1166, 2017, 2758, 3242, 4805, 4721, 4826, 5076, 8522, 12036, 16903, 14965, 13587, 10900], \"name\": \"World\", \"opacity\": 0.8, \"type\": \"scatter\"}, {\"line\": {\"width\": 3, \"color\": \"rgb(0,200,0)\"}, \"mode\": \"lines\", \"x\": [1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 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, 2016, 2017], \"y\": [468, 247, 68, 58, 94, 149, 105, 130, 87, 69, 67, 74, 78, 44, 63, 40, 49, 34, 27, 42, 32, 30, 32, 36, 55, 60, 35, 40, 31, 53, 32, 41, 33, 33, 9, 21, 6, 8, 18, 11, 17, 10, 20, 20, 29, 38, 64, 65], \"name\": \"USA\", \"opacity\": 0.8, \"type\": \"scatter\"}], {\"xaxis\": {\"rangeslider\": {\"thickness\": 0.05}, \"showline\": true, \"showgrid\": false, \"title\": \"Year\"}, \"titlefont\": {\"size\": 25}, \"yaxis\": {\"showline\": true, \"showgrid\": false, \"title\": \"Number of Occurences\"}, \"title\": \"Terrorist Attacks by Year in the world vs USA\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"eab13ed9-7294-4abf-afc1-e999930f06ce\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"eab13ed9-7294-4abf-afc1-e999930f06ce\", [{\"line\": {\"width\": 3, \"color\": \"rgb(200,0,0)\"}, \"mode\": \"lines\", \"x\": [1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 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, 2016, 2017], \"y\": [651, 471, 568, 473, 581, 740, 923, 1319, 1526, 2662, 2662, 2586, 2544, 2870, 3495, 2915, 2860, 3183, 3721, 4324, 3887, 4683, 5071, 747, 3456, 3081, 3058, 3197, 934, 1395, 1814, 1906, 1333, 1278, 1166, 2017, 2758, 3242, 4805, 4721, 4826, 5076, 8522, 12036, 16903, 14965, 13587, 10900], \"name\": \"World\", \"opacity\": 0.8, \"type\": \"scatter\"}, {\"line\": {\"width\": 3, \"color\": \"rgb(0,200,0)\"}, \"mode\": \"lines\", \"x\": [1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 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, 2016, 2017], \"y\": [468, 247, 68, 58, 94, 149, 105, 130, 87, 69, 67, 74, 78, 44, 63, 40, 49, 34, 27, 42, 32, 30, 32, 36, 55, 60, 35, 40, 31, 53, 32, 41, 33, 33, 9, 21, 6, 8, 18, 11, 17, 10, 20, 20, 29, 38, 64, 65], \"name\": \"USA\", \"opacity\": 0.8, \"type\": \"scatter\"}], {\"xaxis\": {\"rangeslider\": {\"thickness\": 0.05}, \"showline\": true, \"showgrid\": false, \"title\": \"Year\"}, \"titlefont\": {\"size\": 25}, \"yaxis\": {\"showline\": true, \"showgrid\": false, \"title\": \"Number of Occurences\"}, \"title\": \"Terrorist Attacks by Year in the world vs USA\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# terrorist attacks by year\n",
    "worldterror_peryear = np.asarray(all_year_df.groupby('iyear').iyear.count())\n",
    "usaterror_peryear = np.asarray(df_usa.groupby('iyear').iyear.count())\n",
    "\n",
    "terror_years = np.arange(1970, 2018)\n",
    "\n",
    "trace1 = go.Scatter(\n",
    "         x = terror_years,\n",
    "         y = worldterror_peryear,\n",
    "        name = 'World',\n",
    "         mode = 'lines',\n",
    "         line = dict(\n",
    "             color = 'rgb(200,0,0)',\n",
    "             width = 3),\n",
    "                opacity = 0.8\n",
    "         )\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "         x = terror_years,\n",
    "         y = usaterror_peryear,\n",
    "    name='USA',\n",
    "         mode = 'lines',\n",
    "         line = dict(\n",
    "             color = 'rgb(0,200,0)',\n",
    "             width = 3),\n",
    "                opacity = 0.8\n",
    "         )\n",
    "\n",
    "layout = go.Layout(\n",
    "         title = 'Terrorist Attacks by Year in the world vs USA',\n",
    "        titlefont = dict(size = 25),\n",
    "         xaxis = dict(title='Year',\n",
    "             rangeslider = dict(thickness = 0.05),\n",
    "             showline = True,\n",
    "             showgrid = False\n",
    "         ),\n",
    "         yaxis = dict(title='Number of Occurences',\n",
    "             showline = True,\n",
    "             showgrid = False)\n",
    "         )\n",
    "\n",
    "data = [trace1,trace2]\n",
    "figure = dict(data = data, layout = layout)\n",
    "iplot(figure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Use your cursor to slide over the trend line to zoom on the year range."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "**Inference:** The Terrorist activities has seen an increase in the 21th century.But **why is there a plunge after 2004?** \n",
    "\n",
    "There might be two possible reasons:\n",
    "1. Hypothesis 1: The data collected in the 21th century is high - *Highly Unlikely explanation! Because the data collection procedure mentioned in the [codebook](https://www.start.umd.edu/gtd/using-gtd/) indicates the all the data collected is from the publicly available information*\n",
    "2. Hypothesis 2: Some kind war/terrorist activity started to evolve - *May be! Let's dive in to explore more.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let us explore the motive behind all these attacks:\n",
    "\n",
    "We will aggregate all the rows of the *motive* column in our database to form a generic understanding of the slang. Them *motive* column itself is as text category, containing a textual representation of the attacks motive.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# words_string = nltk.word_tokenize(all_year_df.summary)\n",
    "tuples = [tuple(str(x)) for x in all_year_df.summary.value_counts().reset_index().values]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "motive=all_year_df.motive.str.lower().str.replace(r'\\|', ' ').str.cat(sep=' ')\n",
    "words=nltk.tokenize.word_tokenize(motive)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9251a75128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "word_dist = nltk.FreqDist(words)\n",
    "stopwords = nltk.corpus.stopwords.words('english')\n",
    "words_except_stop_dist = nltk.FreqDist(w for w in words if w not in stopwords) \n",
    "wordcloud = WordCloud(stopwords=STOPWORDS,background_color='black').generate(\" \".join(words_except_stop_dist))\n",
    "plt.imshow(wordcloud)\n",
    "fig=plt.gcf()\n",
    "fig.set_size_inches(10,6)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: The motive column of the dataset is created only after 1997, thus all the attack motives are after 1997.\n",
    "\n",
    "**Inference**: The wordcloud above, shows the frequency of words used in the motive section of the dataset. The bigger the word, the more frequent it occurs. It can be seen that **al(for al-kaeda), pro, anti** are highly frequent words. It suggests that terrorist activities have some motive related to the **government actions**."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Terrorist activities across regions and countries"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's check which regions contribute to higher terrorist attack counts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "text": [
          27.78,
          24.68,
          10.45,
          9.65,
          9.24,
          6.87,
          5.68,
          2.83,
          1.91,
          0.44,
          0.31,
          0.16
         ],
         "type": "bar",
         "x": [
          "Middle East & North Africa",
          "South Asia",
          "South America",
          "Sub-Saharan Africa",
          "Western Europe",
          "Southeast Asia",
          "Central America & Caribbean",
          "Eastern Europe",
          "North America",
          "East Asia",
          "Central Asia",
          "Australasia & Oceania"
         ],
         "y": [
          50683,
          45034,
          19063,
          17605,
          16861,
          12530,
          10354,
          5156,
          3492,
          810,
          566,
          284
         ]
        }
       ],
       "layout": {
        "font": {
         "size": 12
        },
        "title": "Terrorist Activities across Regions",
        "titlefont": {
         "size": 25
        },
        "xaxis": {
         "dtick": 1,
         "title": "Region"
        },
        "yaxis": {
         "title": "Number of Occurences"
        }
       }
      },
      "text/html": [
       "<div id=\"2db47fe2-dec5-45fa-a6df-b706fd53e302\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"2db47fe2-dec5-45fa-a6df-b706fd53e302\", [{\"text\": [27.78, 24.68, 10.45, 9.65, 9.24, 6.87, 5.68, 2.83, 1.91, 0.44, 0.31, 0.16], \"y\": [50683, 45034, 19063, 17605, 16861, 12530, 10354, 5156, 3492, 810, 566, 284], \"x\": [\"Middle East & North Africa\", \"South Asia\", \"South America\", \"Sub-Saharan Africa\", \"Western Europe\", \"Southeast Asia\", \"Central America & Caribbean\", \"Eastern Europe\", \"North America\", \"East Asia\", \"Central Asia\", \"Australasia & Oceania\"], \"type\": \"bar\"}], {\"xaxis\": {\"dtick\": 1, \"title\": \"Region\"}, \"titlefont\": {\"size\": 25}, \"yaxis\": {\"title\": \"Number of Occurences\"}, \"font\": {\"size\": 12}, \"title\": \"Terrorist Activities across Regions\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"2db47fe2-dec5-45fa-a6df-b706fd53e302\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"2db47fe2-dec5-45fa-a6df-b706fd53e302\", [{\"text\": [27.78, 24.68, 10.45, 9.65, 9.24, 6.87, 5.68, 2.83, 1.91, 0.44, 0.31, 0.16], \"y\": [50683, 45034, 19063, 17605, 16861, 12530, 10354, 5156, 3492, 810, 566, 284], \"x\": [\"Middle East & North Africa\", \"South Asia\", \"South America\", \"Sub-Saharan Africa\", \"Western Europe\", \"Southeast Asia\", \"Central America & Caribbean\", \"Eastern Europe\", \"North America\", \"East Asia\", \"Central Asia\", \"Australasia & Oceania\"], \"type\": \"bar\"}], {\"xaxis\": {\"dtick\": 1, \"title\": \"Region\"}, \"titlefont\": {\"size\": 25}, \"yaxis\": {\"title\": \"Number of Occurences\"}, \"font\": {\"size\": 12}, \"title\": \"Terrorist Activities across Regions\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "trace = go.Bar(\n",
    "            x = [i for i in all_year_df.region_txt.value_counts().index],\n",
    "            y = all_year_df.region_txt.value_counts(), text= label_percent(feature='region_txt'))\n",
    "layout = go.Layout(title = \"Terrorist Activities across Regions\",\n",
    "                   titlefont = dict(size = 25),\n",
    "                   xaxis = dict(title='Region',dtick=1),\n",
    "                   yaxis = dict(title='Number of Occurences'),\n",
    "                   \n",
    "                  font=dict(size=12))\n",
    "data = [trace]\n",
    "fig = dict(data=data, layout = layout)\n",
    "iplot(fig, filename=\"terrorregion_bar\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference**: Almost 50% of the attacks are from Middle East&North Africa and the South Asia regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Countries with country code, used for plotting in the map\n",
    "countries={'Afghanistan':'AFG',\n",
    "'Aland Islands':'ALA','Albania':'ALB','Algeria':'DZA','American Samoa':'ASM','Andorra':'AND','Angola':'AGO','Anguilla':'AIA','Antigua and Barbuda':'ATG',\n",
    "'Antarctica':'ATA','Argentina':'ARG','Armenia':'ARM','Aruba':'ABW','Australia':'AUS','Austria':'AUT','Azerbaijan':'AZE','Azerbaidjan':'AZE',\n",
    "'Bahrain':'BHR','Bahamas':'BHS','Bangladesh':'BGD','Barbados':'BRB','Belarus':'BLR',\n",
    "'Belgium':'BEL','Belize':'BLZ','Benin':'BEN','Bermuda':'BMU','Bhutan':'BTN','Bolivia':'BOL','Bosnia and Herzegovina':'BIH','Bosnia-Herzegovina':'BIH',\n",
    "'Botswana':'BWA','Bouvet Island':'BVT','Brazil':'BRA','British Virgin Islands':'VGB','British Indian Ocean Territory':'IOT',\n",
    "'Brunei':'BRN','Brunei Darussalam':'BRN','Bulgaria':'BGR','Burkina Faso':'BFA','Burma':'MMR',\n",
    "'Burundi':'BDI','Cabo Verde':'CPV','Cape Verde':'CPV','Cambodia':'KHM','Cameroon':'CMR',\n",
    "'Canada':'CAN','Cayman Islands':'CYM','Central African Republic':'CAF','Chad':'TCD','Chile':'CHL',\n",
    "'Christmas Island':'CHR','China':'CHN','Colombia':'COL','Comoros':'COM','Congo, Democratic Republic of the':'COD',\n",
    "'Congo, Republic of the':'COG','Cook Islands':'COK','Costa Rica':'CRI','Cote d\\'Ivoire':'CIV',\n",
    "\"Ivory Coast (Cote D'Ivoire)\":'CIV','Croatia':'HRV','Cuba':'CUB','Curacao':'CUW','Cyprus':'CYP',\n",
    "'Czech Republic':'CZE','Denmark':'DNK','Djibouti':'DJI','Dominica':'DMA','Dominican Republic':'DOM',\n",
    "'Ecuador':'ECU','Egypt':'EGY','El Salvador':'SLV','Equatorial Guinea':'GNQ','Eritrea':'ERI','Estonia':'EST',\n",
    "'Ethiopia':'ETH','Falkland Islands (Islas Malvinas)':'FLK','Falkland Islands':'FLK','Faroe Islands':'FRO',\n",
    "'Fiji':'FJI','Finland':'FIN','France':'FRA','French Polynesia':'PYF','Gabon':'GAB',\n",
    "'Gambia, The':'GMB','Georgia':'GEO','Germany':'DEU','Ghana':'GHA','Gibraltar':'GIB',\n",
    "'Greece':'GRC','Greenland':'GRL','Grenada':'GRD','Guam':'GUM','Guatemala':'GTM',\n",
    "'Guernsey':'GGY','Guinea-Bissau':'GNB','Guinea':'GIN','Guyana':'GUY','French Guyana':'GUY','Haiti':'HTI',\n",
    "'Honduras':'HND','Heard and McDonald Islands':'HMD','Hong Kong':'HKG','Hungary':'HUN','Iceland':'ISL',\n",
    "'India':'IND','Indonesia':'IDN','Iran':'IRN','Iraq':'IRQ','Ireland':'IRL','Isle of Man':'IMN',\n",
    "'Israel':'ISR','Italy':'ITA','Jamaica':'JAM','Japan':'JPN','Jersey':'JEY','Jordan':'JOR',\n",
    "'Kazakhstan':'KAZ','Kenya':'KEN','Kiribati':'KIR','Korea, North':'KOR','Korea, South':'PRK',\n",
    "'South Korea':'PRK','North Korea':'KOR','Kosovo':'KSV','Kuwait':'KWT','Kyrgyzstan':'KGZ',\n",
    "'Laos':'LAO','Latvia':'LVA','Lebanon':'LBN','Lesotho':'LSO','Liberia':'LBR','Libya':'LBY','Liechtenstein':'LIE',\n",
    "'Lithuania':'LTU','Luxembourg':'LUX','Macau':'MAC','Macedonia':'MKD','Madagascar':'MDG',\n",
    "'Malawi':'MWI','Malaysia':'MYS','Maldives':'MDV','Mali':'MLI','Malta':'MLT','Marshall Islands':'MHL',\n",
    "'Martinique (French)':'MTQ','Mauritania':'MRT','Mauritius':'MUS','Mexico':'MEX','Micronesia, Federated States of':'FSM',\n",
    "'Moldova':'MDA','Moldavia':'MDA','Monaco':'MCO','Mongolia':'MNG','Montenegro':'MNE','Montserrat':'MSR',\n",
    "'Morocco':'MAR','Mozambique':'MOZ','Myanmar':'MMR','Namibia':'NAM','Nepal':'NPL','Netherlands':'NLD',\n",
    "'Netherlands Antilles':'ANT','New Caledonia':'NCL','New Caledonia (French)':'NCL','New Zealand':'NZL','Nicaragua':'NIC',\n",
    "'Nigeria':'NGA','Niger':'NER','Niue':'NIU','Northern Mariana Islands':'MNP','Norway':'NOR','Oman':'OMN',\n",
    "'Pakistan':'PAK','Palau':'PLW','Panama':'PAN','Papua New Guinea':'PNG','Paraguay':'PRY','Peru':'PER',\n",
    "'Philippines':'PHL','Pitcairn Island':'PCN','Poland':'POL','Polynesia (French)':'PYF','Portugal':'PRT',\n",
    "'Puerto Rico':'PRI','Qatar':'QAT','Reunion (French)':'REU','Romania':'ROU','Russia':'RUS','Russian Federation':'RUS',\n",
    "'Rwanda':'RWA','Saint Kitts and Nevis':'KNA','Saint Lucia':'LCA','Saint Martin':'MAF','Saint Pierre and Miquelon':'SPM',\n",
    "'Saint Vincent and the Grenadines':'VCT','Saint Vincent & Grenadines':'VCT','S. Georgia & S. Sandwich Isls.':'SGS','Samoa':'WSM',\n",
    "'San Marino':'SMR','Saint Helena':'SHN','Sao Tome and Principe':'STP','Saudi Arabia':'SAU','Senegal':'SEN',\n",
    "'Serbia':'SRB','Seychelles':'SYC','Sierra Leone':'SLE','Singapore':'SGP','Sint Maarten':'SXM',\n",
    "'Slovakia':'SVK','Slovak Republic':'SVK','Slovenia':'SVN','Solomon Islands':'SLB','Somalia':'SOM','South Africa':'ZAF',\n",
    "'South Sudan':'SSD','Spain':'ESP','Sri Lanka':'LKA','Sudan':'SDN','Suriname':'SUR',\n",
    "'Swaziland':'SWZ','Sweden':'SWE','Switzerland':'CHE','Syria':'SYR','Taiwan':'TWN','Tajikistan':'TJK',\n",
    "'Tadjikistan':'TJK','Tanzania':'TZA','Thailand':'THA','Timor-Leste':'TLS','Togo':'TGO','Tonga':'TON',\n",
    "'Trinidad and Tobago':'TTO','Tunisia':'TUN','Turkey':'TUR','Turkmenistan':'TKM','Tuvalu':'TUV',\n",
    "'Uganda':'UGA','Ukraine':'UKR','United Arab Emirates':'ARE','United Kingdom':'GBR','United States':'USA',\n",
    "'U.S. Minor Outlying Islands':'UMI','Uruguay':'URY','Uzbekistan':'UZB','Vanuatu':'VUT',\n",
    "'Vatican City State':'VAT','Venezuela':'VEN','Vietnam':'VNM','Virgin Islands':'VGB',\n",
    "'Virgin Islands (USA)':'VIR','Virgin Islands (British)':'VGB','West Bank':'WBG','Yemen':'YEM',\n",
    "'Zaire':'ZAR','Zambia':'ZMB','Zimbabwe':'ZWE',\n",
    " 'Yugoslavia':'CZE', 'Czechoslovakia':'CZE','Democratic Republic of the Congo':'COD',\n",
    "'East Germany (GDR)': 'DEU','West Germany (FRG)': 'DEU',\n",
    " 'East Timor':'TLS',\n",
    " 'French Guiana':'GUF',\n",
    " 'Gambia':'GMB',\n",
    " 'Guadeloupe':'GLP',\n",
    " 'Ivory Coast':'CIV',\n",
    " 'Martinique':'MTQ',\n",
    " 'New Hebrides':'IRL',\n",
    " 'North Yemen':'YEM',\n",
    " \"People's Republic of the Congo\":'COG',\n",
    " 'Republic of the Congo':'COD',\n",
    " 'Rhodesia':'ZWE',\n",
    " 'Serbia-Montenegro':'SRB',\n",
    " 'South Vietnam':'VNM',\n",
    " 'South Yemen':'YEM',\n",
    " 'Soviet Union':'RUS',\n",
    " 'St. Kitts and Nevis':'KNA',\n",
    " 'St. Lucia':'LCA',\n",
    " 'Vatican City':'ITA',\n",
    " 'Wallis and Futuna':'WLF',\n",
    " 'West Bank and Gaza Strip':'GZ',\n",
    " 'Western Sahara':'ESH'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "codes = [countries[country] if country in countries else 0 for country in all_year_df.country_txt]\n",
    "all_year_df['codes'] =codes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "autocolorscale": false,
         "colorbar": {
          "autotick": false,
          "title": "Scale of Attacks"
         },
         "colorscale": [
          [
           0,
           "rgb(165,0,38)"
          ],
          [
           0.1111111111111111,
           "rgb(215,48,39)"
          ],
          [
           0.2222222222222222,
           "rgb(244,109,67)"
          ],
          [
           0.3333333333333333,
           "rgb(253,174,97)"
          ],
          [
           0.4444444444444444,
           "rgb(254,224,144)"
          ],
          [
           0.5555555555555556,
           "rgb(224,243,248)"
          ],
          [
           0.6666666666666666,
           "rgb(171,217,233)"
          ],
          [
           0.7777777777777778,
           "rgb(116,173,209)"
          ],
          [
           0.8888888888888888,
           "rgb(69,117,180)"
          ],
          [
           1,
           "rgb(49,54,149)"
          ]
         ],
         "locations": [
          "IRQ",
          "PAK",
          "AFG",
          "IND",
          "COL",
          "PHL",
          "PER",
          "GBR",
          "SLV",
          "TUR",
          "SOM",
          "NGA",
          "THA",
          "YEM",
          "ESP",
          "LKA",
          "USA",
          "DZA",
          "FRA",
          "EGY",
          "LBN",
          "CHL",
          "RUS",
          "GZ",
          "LBY",
          "ISR",
          "SYR",
          "GTM",
          "ZAF",
          "NIC",
          "UKR",
          "BGD",
          "ITA",
          "DEU",
          "GRC",
          "NPL",
          "SDN",
          "ARG",
          "COD",
          "IDN",
          "IRN",
          "KEN",
          "BDI",
          "MLI",
          "MMR",
          "MEX",
          "AGO",
          "JPN",
          "UGA",
          "SAU",
          "MOZ",
          "CMR",
          "HND",
          "BOL",
          "IRL",
          "VEN",
          "KHM",
          "CAF",
          "BRA",
          "CHN",
          "CZE",
          "SSD",
          "ECU",
          "GEO",
          "HTI",
          "BHR",
          "KSV",
          "ETH",
          "TJK",
          "ZWE",
          "RWA",
          "BIH",
          "NER",
          "BEL",
          "NAM",
          "PRT",
          "CYP",
          "SWE",
          "NLD",
          "PAN",
          "AUT",
          "SEN",
          "MKD",
          "PRY",
          "CHE",
          "AUS",
          "JOR",
          "TUN",
          "SLE",
          "MYS",
          "CAN",
          "PNG",
          "TCD",
          "DOM",
          "URY",
          "ALB",
          "KWT",
          "CIV",
          "CRI",
          "SUR",
          "ZMB",
          "TZA",
          "HRV",
          "GLP",
          "BGR",
          "BFA",
          "TGO",
          "AZE",
          "TWN",
          "ZAR",
          "HUN",
          "DNK",
          "POL",
          "PRK",
          "JAM",
          "MAR",
          "KGZ",
          "LBR",
          "MAC",
          "NCL",
          "CUB",
          "LSO",
          "KAZ",
          "LAO",
          "MDG",
          "HKG",
          "GUY",
          "GIN",
          "ARM",
          "SRB",
          "MLT",
          "TTO",
          "DJI",
          "MDV",
          "ARE",
          "MDA",
          "UZB",
          "FIN",
          "NZL",
          "NOR",
          "GHA",
          "MRT",
          "LVA",
          "SVK",
          "FJI",
          "EST",
          "SWZ",
          "LUX",
          "BLR",
          "VNM",
          "MTQ",
          "TLS",
          "ERI",
          "BWA",
          "GNB",
          "BEN",
          "BLZ",
          "GAB",
          "LTU",
          "QAT",
          "GUF",
          "SGP",
          "ROU",
          "BTN",
          "BRN",
          "SVN",
          "BHS",
          "COM",
          "MWI",
          "ESH",
          "MNE",
          "GRD",
          "COG",
          "ISL",
          "SLB",
          "BRB",
          "GMB",
          "DMA",
          "PYF",
          "VUT",
          "GNQ",
          "KNA",
          "MUS",
          "SYC",
          "TKM",
          "ATG",
          "AND",
          "FLK",
          "LCA",
          "KOR",
          "WLF",
          0
         ],
         "marker": {
          "line": {
           "color": "rgb(180,180,180)",
           "width": 0.5
          }
         },
         "reversescale": true,
         "text": [
          "IRQ",
          "PAK",
          "AFG",
          "IND",
          "COL",
          "PHL",
          "PER",
          "GBR",
          "SLV",
          "TUR",
          "SOM",
          "NGA",
          "THA",
          "YEM",
          "ESP",
          "LKA",
          "USA",
          "DZA",
          "FRA",
          "EGY",
          "LBN",
          "CHL",
          "RUS",
          "GZ",
          "LBY",
          "ISR",
          "SYR",
          "GTM",
          "ZAF",
          "NIC",
          "UKR",
          "BGD",
          "ITA",
          "DEU",
          "GRC",
          "NPL",
          "SDN",
          "ARG",
          "COD",
          "IDN",
          "IRN",
          "KEN",
          "BDI",
          "MLI",
          "MMR",
          "MEX",
          "AGO",
          "JPN",
          "UGA",
          "SAU",
          "MOZ",
          "CMR",
          "HND",
          "BOL",
          "IRL",
          "VEN",
          "KHM",
          "CAF",
          "BRA",
          "CHN",
          "CZE",
          "SSD",
          "ECU",
          "GEO",
          "HTI",
          "BHR",
          "KSV",
          "ETH",
          "TJK",
          "ZWE",
          "RWA",
          "BIH",
          "NER",
          "BEL",
          "NAM",
          "PRT",
          "CYP",
          "SWE",
          "NLD",
          "PAN",
          "AUT",
          "SEN",
          "MKD",
          "PRY",
          "CHE",
          "AUS",
          "JOR",
          "TUN",
          "SLE",
          "MYS",
          "CAN",
          "PNG",
          "TCD",
          "DOM",
          "URY",
          "ALB",
          "KWT",
          "CIV",
          "CRI",
          "SUR",
          "ZMB",
          "TZA",
          "HRV",
          "GLP",
          "BGR",
          "BFA",
          "TGO",
          "AZE",
          "TWN",
          "ZAR",
          "HUN",
          "DNK",
          "POL",
          "PRK",
          "JAM",
          "MAR",
          "KGZ",
          "LBR",
          "MAC",
          "NCL",
          "CUB",
          "LSO",
          "KAZ",
          "LAO",
          "MDG",
          "HKG",
          "GUY",
          "GIN",
          "ARM",
          "SRB",
          "MLT",
          "TTO",
          "DJI",
          "MDV",
          "ARE",
          "MDA",
          "UZB",
          "FIN",
          "NZL",
          "NOR",
          "GHA",
          "MRT",
          "LVA",
          "SVK",
          "FJI",
          "EST",
          "SWZ",
          "LUX",
          "BLR",
          "VNM",
          "MTQ",
          "TLS",
          "ERI",
          "BWA",
          "GNB",
          "BEN",
          "BLZ",
          "GAB",
          "LTU",
          "QAT",
          "GUF",
          "SGP",
          "ROU",
          "BTN",
          "BRN",
          "SVN",
          "BHS",
          "COM",
          "MWI",
          "ESH",
          "MNE",
          "GRD",
          "COG",
          "ISL",
          "SLB",
          "BRB",
          "GMB",
          "DMA",
          "PYF",
          "VUT",
          "GNQ",
          "KNA",
          "MUS",
          "SYC",
          "TKM",
          "ATG",
          "AND",
          "FLK",
          "LCA",
          "KOR",
          "WLF",
          0
         ],
         "type": "choropleth",
         "z": [
          24639,
          14376,
          12734,
          12002,
          8327,
          6916,
          6138,
          5364,
          5321,
          4321,
          4165,
          3907,
          3851,
          3361,
          3263,
          3029,
          2872,
          2766,
          2705,
          2528,
          2514,
          2378,
          2276,
          2263,
          2249,
          2204,
          2201,
          2051,
          2034,
          1973,
          1709,
          1648,
          1576,
          1351,
          1277,
          1215,
          967,
          816,
          811,
          761,
          688,
          683,
          613,
          566,
          548,
          524,
          503,
          409,
          395,
          371,
          363,
          332,
          323,
          316,
          308,
          297,
          292,
          283,
          273,
          253,
          247,
          225,
          221,
          218,
          214,
          207,
          196,
          191,
          189,
          184,
          160,
          159,
          156,
          154,
          151,
          140,
          132,
          132,
          131,
          129,
          126,
          118,
          118,
          115,
          114,
          114,
          113,
          110,
          102,
          99,
          96,
          91,
          91,
          90,
          82,
          80,
          77,
          74,
          69,
          66,
          62,
          60,
          59,
          56,
          52,
          52,
          51,
          50,
          50,
          50,
          46,
          43,
          39,
          38,
          36,
          36,
          35,
          34,
          33,
          31,
          30,
          29,
          27,
          27,
          27,
          26,
          26,
          25,
          24,
          23,
          23,
          22,
          22,
          22,
          22,
          21,
          21,
          20,
          20,
          20,
          19,
          18,
          18,
          18,
          17,
          17,
          16,
          16,
          14,
          13,
          12,
          10,
          10,
          10,
          9,
          8,
          8,
          8,
          8,
          7,
          7,
          7,
          7,
          6,
          6,
          6,
          5,
          5,
          5,
          5,
          5,
          5,
          4,
          4,
          4,
          3,
          3,
          3,
          3,
          2,
          2,
          2,
          2,
          2,
          2,
          1,
          1,
          1,
          1,
          1,
          1,
          1
         ]
        }
       ],
       "layout": {
        "geo": {
         "projection": {
          "type": "Mercator"
         },
         "showcoastlines": false,
         "showframe": false
        },
        "title": "1970-2017 Global Terrorism map"
       }
      },
      "text/html": [
       "<div id=\"4d8ed787-30c6-44de-bb09-d47b934af012\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"4d8ed787-30c6-44de-bb09-d47b934af012\", [{\"marker\": {\"line\": {\"color\": \"rgb(180,180,180)\", \"width\": 0.5}}, \"z\": [24639, 14376, 12734, 12002, 8327, 6916, 6138, 5364, 5321, 4321, 4165, 3907, 3851, 3361, 3263, 3029, 2872, 2766, 2705, 2528, 2514, 2378, 2276, 2263, 2249, 2204, 2201, 2051, 2034, 1973, 1709, 1648, 1576, 1351, 1277, 1215, 967, 816, 811, 761, 688, 683, 613, 566, 548, 524, 503, 409, 395, 371, 363, 332, 323, 316, 308, 297, 292, 283, 273, 253, 247, 225, 221, 218, 214, 207, 196, 191, 189, 184, 160, 159, 156, 154, 151, 140, 132, 132, 131, 129, 126, 118, 118, 115, 114, 114, 113, 110, 102, 99, 96, 91, 91, 90, 82, 80, 77, 74, 69, 66, 62, 60, 59, 56, 52, 52, 51, 50, 50, 50, 46, 43, 39, 38, 36, 36, 35, 34, 33, 31, 30, 29, 27, 27, 27, 26, 26, 25, 24, 23, 23, 22, 22, 22, 22, 21, 21, 20, 20, 20, 19, 18, 18, 18, 17, 17, 16, 16, 14, 13, 12, 10, 10, 10, 9, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 5, 5, 5, 5, 5, 5, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1], \"reversescale\": true, \"autocolorscale\": false, \"text\": [\"IRQ\", \"PAK\", \"AFG\", \"IND\", \"COL\", \"PHL\", \"PER\", \"GBR\", \"SLV\", \"TUR\", \"SOM\", \"NGA\", \"THA\", \"YEM\", \"ESP\", \"LKA\", \"USA\", \"DZA\", \"FRA\", \"EGY\", \"LBN\", \"CHL\", \"RUS\", \"GZ\", \"LBY\", \"ISR\", \"SYR\", \"GTM\", \"ZAF\", \"NIC\", \"UKR\", \"BGD\", \"ITA\", \"DEU\", \"GRC\", \"NPL\", \"SDN\", \"ARG\", \"COD\", \"IDN\", \"IRN\", \"KEN\", \"BDI\", \"MLI\", \"MMR\", \"MEX\", \"AGO\", \"JPN\", \"UGA\", \"SAU\", \"MOZ\", \"CMR\", \"HND\", \"BOL\", \"IRL\", \"VEN\", \"KHM\", \"CAF\", \"BRA\", \"CHN\", \"CZE\", \"SSD\", \"ECU\", \"GEO\", \"HTI\", \"BHR\", \"KSV\", \"ETH\", \"TJK\", \"ZWE\", \"RWA\", \"BIH\", \"NER\", \"BEL\", \"NAM\", \"PRT\", \"CYP\", \"SWE\", \"NLD\", \"PAN\", \"AUT\", \"SEN\", \"MKD\", \"PRY\", \"CHE\", \"AUS\", \"JOR\", \"TUN\", \"SLE\", \"MYS\", \"CAN\", \"PNG\", \"TCD\", \"DOM\", \"URY\", \"ALB\", \"KWT\", \"CIV\", \"CRI\", \"SUR\", \"ZMB\", \"TZA\", \"HRV\", \"GLP\", \"BGR\", \"BFA\", \"TGO\", \"AZE\", \"TWN\", \"ZAR\", \"HUN\", \"DNK\", \"POL\", \"PRK\", \"JAM\", \"MAR\", \"KGZ\", \"LBR\", \"MAC\", \"NCL\", \"CUB\", \"LSO\", \"KAZ\", \"LAO\", \"MDG\", \"HKG\", \"GUY\", \"GIN\", \"ARM\", \"SRB\", \"MLT\", \"TTO\", \"DJI\", \"MDV\", \"ARE\", \"MDA\", \"UZB\", \"FIN\", \"NZL\", \"NOR\", \"GHA\", \"MRT\", \"LVA\", \"SVK\", \"FJI\", \"EST\", \"SWZ\", \"LUX\", \"BLR\", \"VNM\", \"MTQ\", \"TLS\", \"ERI\", \"BWA\", \"GNB\", \"BEN\", \"BLZ\", \"GAB\", \"LTU\", \"QAT\", \"GUF\", \"SGP\", \"ROU\", \"BTN\", \"BRN\", \"SVN\", \"BHS\", \"COM\", \"MWI\", \"ESH\", \"MNE\", \"GRD\", \"COG\", \"ISL\", \"SLB\", \"BRB\", \"GMB\", \"DMA\", \"PYF\", \"VUT\", \"GNQ\", \"KNA\", \"MUS\", \"SYC\", \"TKM\", \"ATG\", \"AND\", \"FLK\", \"LCA\", \"KOR\", \"WLF\", 0], \"locations\": [\"IRQ\", \"PAK\", \"AFG\", \"IND\", \"COL\", \"PHL\", \"PER\", \"GBR\", \"SLV\", \"TUR\", \"SOM\", \"NGA\", \"THA\", \"YEM\", \"ESP\", \"LKA\", \"USA\", \"DZA\", \"FRA\", \"EGY\", \"LBN\", \"CHL\", \"RUS\", \"GZ\", \"LBY\", \"ISR\", \"SYR\", \"GTM\", \"ZAF\", \"NIC\", \"UKR\", \"BGD\", \"ITA\", \"DEU\", \"GRC\", \"NPL\", \"SDN\", \"ARG\", \"COD\", \"IDN\", \"IRN\", \"KEN\", \"BDI\", \"MLI\", \"MMR\", \"MEX\", \"AGO\", \"JPN\", \"UGA\", \"SAU\", \"MOZ\", \"CMR\", \"HND\", \"BOL\", \"IRL\", \"VEN\", \"KHM\", \"CAF\", \"BRA\", \"CHN\", \"CZE\", \"SSD\", \"ECU\", \"GEO\", \"HTI\", \"BHR\", \"KSV\", \"ETH\", \"TJK\", \"ZWE\", \"RWA\", \"BIH\", \"NER\", \"BEL\", \"NAM\", \"PRT\", \"CYP\", \"SWE\", \"NLD\", \"PAN\", \"AUT\", \"SEN\", \"MKD\", \"PRY\", \"CHE\", \"AUS\", \"JOR\", \"TUN\", \"SLE\", \"MYS\", \"CAN\", \"PNG\", \"TCD\", \"DOM\", \"URY\", \"ALB\", \"KWT\", \"CIV\", \"CRI\", \"SUR\", \"ZMB\", \"TZA\", \"HRV\", \"GLP\", \"BGR\", \"BFA\", \"TGO\", \"AZE\", \"TWN\", \"ZAR\", \"HUN\", \"DNK\", \"POL\", \"PRK\", \"JAM\", \"MAR\", \"KGZ\", \"LBR\", \"MAC\", \"NCL\", \"CUB\", \"LSO\", \"KAZ\", \"LAO\", \"MDG\", \"HKG\", \"GUY\", \"GIN\", \"ARM\", \"SRB\", \"MLT\", \"TTO\", \"DJI\", \"MDV\", \"ARE\", \"MDA\", \"UZB\", \"FIN\", \"NZL\", \"NOR\", \"GHA\", \"MRT\", \"LVA\", \"SVK\", \"FJI\", \"EST\", \"SWZ\", \"LUX\", \"BLR\", \"VNM\", \"MTQ\", \"TLS\", \"ERI\", \"BWA\", \"GNB\", \"BEN\", \"BLZ\", \"GAB\", \"LTU\", \"QAT\", \"GUF\", \"SGP\", \"ROU\", \"BTN\", \"BRN\", \"SVN\", \"BHS\", \"COM\", \"MWI\", \"ESH\", \"MNE\", \"GRD\", \"COG\", \"ISL\", \"SLB\", \"BRB\", \"GMB\", \"DMA\", \"PYF\", \"VUT\", \"GNQ\", \"KNA\", \"MUS\", \"SYC\", \"TKM\", \"ATG\", \"AND\", \"FLK\", \"LCA\", \"KOR\", \"WLF\", 0], \"colorscale\": [[0.0, \"rgb(165,0,38)\"], [0.1111111111111111, \"rgb(215,48,39)\"], [0.2222222222222222, \"rgb(244,109,67)\"], [0.3333333333333333, \"rgb(253,174,97)\"], [0.4444444444444444, \"rgb(254,224,144)\"], [0.5555555555555556, \"rgb(224,243,248)\"], [0.6666666666666666, \"rgb(171,217,233)\"], [0.7777777777777778, \"rgb(116,173,209)\"], [0.8888888888888888, \"rgb(69,117,180)\"], [1.0, \"rgb(49,54,149)\"]], \"colorbar\": {\"autotick\": false, \"title\": \"Scale of Attacks\"}, \"type\": \"choropleth\"}], {\"geo\": {\"projection\": {\"type\": \"Mercator\"}, \"showframe\": false, \"showcoastlines\": false}, \"title\": \"1970-2017 Global Terrorism map\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"4d8ed787-30c6-44de-bb09-d47b934af012\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"4d8ed787-30c6-44de-bb09-d47b934af012\", [{\"marker\": {\"line\": {\"color\": \"rgb(180,180,180)\", \"width\": 0.5}}, \"z\": [24639, 14376, 12734, 12002, 8327, 6916, 6138, 5364, 5321, 4321, 4165, 3907, 3851, 3361, 3263, 3029, 2872, 2766, 2705, 2528, 2514, 2378, 2276, 2263, 2249, 2204, 2201, 2051, 2034, 1973, 1709, 1648, 1576, 1351, 1277, 1215, 967, 816, 811, 761, 688, 683, 613, 566, 548, 524, 503, 409, 395, 371, 363, 332, 323, 316, 308, 297, 292, 283, 273, 253, 247, 225, 221, 218, 214, 207, 196, 191, 189, 184, 160, 159, 156, 154, 151, 140, 132, 132, 131, 129, 126, 118, 118, 115, 114, 114, 113, 110, 102, 99, 96, 91, 91, 90, 82, 80, 77, 74, 69, 66, 62, 60, 59, 56, 52, 52, 51, 50, 50, 50, 46, 43, 39, 38, 36, 36, 35, 34, 33, 31, 30, 29, 27, 27, 27, 26, 26, 25, 24, 23, 23, 22, 22, 22, 22, 21, 21, 20, 20, 20, 19, 18, 18, 18, 17, 17, 16, 16, 14, 13, 12, 10, 10, 10, 9, 8, 8, 8, 8, 7, 7, 7, 7, 6, 6, 6, 5, 5, 5, 5, 5, 5, 4, 4, 4, 3, 3, 3, 3, 2, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 1, 1], \"reversescale\": true, \"autocolorscale\": false, \"text\": [\"IRQ\", \"PAK\", \"AFG\", \"IND\", \"COL\", \"PHL\", \"PER\", \"GBR\", \"SLV\", \"TUR\", \"SOM\", \"NGA\", \"THA\", \"YEM\", \"ESP\", \"LKA\", \"USA\", \"DZA\", \"FRA\", \"EGY\", \"LBN\", \"CHL\", \"RUS\", \"GZ\", \"LBY\", \"ISR\", \"SYR\", \"GTM\", \"ZAF\", \"NIC\", \"UKR\", \"BGD\", \"ITA\", \"DEU\", \"GRC\", \"NPL\", \"SDN\", \"ARG\", \"COD\", \"IDN\", \"IRN\", \"KEN\", \"BDI\", \"MLI\", \"MMR\", \"MEX\", \"AGO\", \"JPN\", \"UGA\", \"SAU\", \"MOZ\", \"CMR\", \"HND\", \"BOL\", \"IRL\", \"VEN\", \"KHM\", \"CAF\", \"BRA\", \"CHN\", \"CZE\", \"SSD\", \"ECU\", \"GEO\", \"HTI\", \"BHR\", \"KSV\", \"ETH\", \"TJK\", \"ZWE\", \"RWA\", \"BIH\", \"NER\", \"BEL\", \"NAM\", \"PRT\", \"CYP\", \"SWE\", \"NLD\", \"PAN\", \"AUT\", \"SEN\", \"MKD\", \"PRY\", \"CHE\", \"AUS\", \"JOR\", \"TUN\", \"SLE\", \"MYS\", \"CAN\", \"PNG\", \"TCD\", \"DOM\", \"URY\", \"ALB\", \"KWT\", \"CIV\", \"CRI\", \"SUR\", \"ZMB\", \"TZA\", \"HRV\", \"GLP\", \"BGR\", \"BFA\", \"TGO\", \"AZE\", \"TWN\", \"ZAR\", \"HUN\", \"DNK\", \"POL\", \"PRK\", \"JAM\", \"MAR\", \"KGZ\", \"LBR\", \"MAC\", \"NCL\", \"CUB\", \"LSO\", \"KAZ\", \"LAO\", \"MDG\", \"HKG\", \"GUY\", \"GIN\", \"ARM\", \"SRB\", \"MLT\", \"TTO\", \"DJI\", \"MDV\", \"ARE\", \"MDA\", \"UZB\", \"FIN\", \"NZL\", \"NOR\", \"GHA\", \"MRT\", \"LVA\", \"SVK\", \"FJI\", \"EST\", \"SWZ\", \"LUX\", \"BLR\", \"VNM\", \"MTQ\", \"TLS\", \"ERI\", \"BWA\", \"GNB\", \"BEN\", \"BLZ\", \"GAB\", \"LTU\", \"QAT\", \"GUF\", \"SGP\", \"ROU\", \"BTN\", \"BRN\", \"SVN\", \"BHS\", \"COM\", \"MWI\", \"ESH\", \"MNE\", \"GRD\", \"COG\", \"ISL\", \"SLB\", \"BRB\", \"GMB\", \"DMA\", \"PYF\", \"VUT\", \"GNQ\", \"KNA\", \"MUS\", \"SYC\", \"TKM\", \"ATG\", \"AND\", \"FLK\", \"LCA\", \"KOR\", \"WLF\", 0], \"locations\": [\"IRQ\", \"PAK\", \"AFG\", \"IND\", \"COL\", \"PHL\", \"PER\", \"GBR\", \"SLV\", \"TUR\", \"SOM\", \"NGA\", \"THA\", \"YEM\", \"ESP\", \"LKA\", \"USA\", \"DZA\", \"FRA\", \"EGY\", \"LBN\", \"CHL\", \"RUS\", \"GZ\", \"LBY\", \"ISR\", \"SYR\", \"GTM\", \"ZAF\", \"NIC\", \"UKR\", \"BGD\", \"ITA\", \"DEU\", \"GRC\", \"NPL\", \"SDN\", \"ARG\", \"COD\", \"IDN\", \"IRN\", \"KEN\", \"BDI\", \"MLI\", \"MMR\", \"MEX\", \"AGO\", \"JPN\", \"UGA\", \"SAU\", \"MOZ\", \"CMR\", \"HND\", \"BOL\", \"IRL\", \"VEN\", \"KHM\", \"CAF\", \"BRA\", \"CHN\", \"CZE\", \"SSD\", \"ECU\", \"GEO\", \"HTI\", \"BHR\", \"KSV\", \"ETH\", \"TJK\", \"ZWE\", \"RWA\", \"BIH\", \"NER\", \"BEL\", \"NAM\", \"PRT\", \"CYP\", \"SWE\", \"NLD\", \"PAN\", \"AUT\", \"SEN\", \"MKD\", \"PRY\", \"CHE\", \"AUS\", \"JOR\", \"TUN\", \"SLE\", \"MYS\", \"CAN\", \"PNG\", \"TCD\", \"DOM\", \"URY\", \"ALB\", \"KWT\", \"CIV\", \"CRI\", \"SUR\", \"ZMB\", \"TZA\", \"HRV\", \"GLP\", \"BGR\", \"BFA\", \"TGO\", \"AZE\", \"TWN\", \"ZAR\", \"HUN\", \"DNK\", \"POL\", \"PRK\", \"JAM\", \"MAR\", \"KGZ\", \"LBR\", \"MAC\", \"NCL\", \"CUB\", \"LSO\", \"KAZ\", \"LAO\", \"MDG\", \"HKG\", \"GUY\", \"GIN\", \"ARM\", \"SRB\", \"MLT\", \"TTO\", \"DJI\", \"MDV\", \"ARE\", \"MDA\", \"UZB\", \"FIN\", \"NZL\", \"NOR\", \"GHA\", \"MRT\", \"LVA\", \"SVK\", \"FJI\", \"EST\", \"SWZ\", \"LUX\", \"BLR\", \"VNM\", \"MTQ\", \"TLS\", \"ERI\", \"BWA\", \"GNB\", \"BEN\", \"BLZ\", \"GAB\", \"LTU\", \"QAT\", \"GUF\", \"SGP\", \"ROU\", \"BTN\", \"BRN\", \"SVN\", \"BHS\", \"COM\", \"MWI\", \"ESH\", \"MNE\", \"GRD\", \"COG\", \"ISL\", \"SLB\", \"BRB\", \"GMB\", \"DMA\", \"PYF\", \"VUT\", \"GNQ\", \"KNA\", \"MUS\", \"SYC\", \"TKM\", \"ATG\", \"AND\", \"FLK\", \"LCA\", \"KOR\", \"WLF\", 0], \"colorscale\": [[0.0, \"rgb(165,0,38)\"], [0.1111111111111111, \"rgb(215,48,39)\"], [0.2222222222222222, \"rgb(244,109,67)\"], [0.3333333333333333, \"rgb(253,174,97)\"], [0.4444444444444444, \"rgb(254,224,144)\"], [0.5555555555555556, \"rgb(224,243,248)\"], [0.6666666666666666, \"rgb(171,217,233)\"], [0.7777777777777778, \"rgb(116,173,209)\"], [0.8888888888888888, \"rgb(69,117,180)\"], [1.0, \"rgb(49,54,149)\"]], \"colorbar\": {\"autotick\": false, \"title\": \"Scale of Attacks\"}, \"type\": \"choropleth\"}], {\"geo\": {\"projection\": {\"type\": \"Mercator\"}, \"showframe\": false, \"showcoastlines\": false}, \"title\": \"1970-2017 Global Terrorism map\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = [ dict(\n",
    "        type = 'choropleth',\n",
    "        locations = all_year_df.codes.value_counts().index,\n",
    "        z = all_year_df.codes.value_counts(),\n",
    "        text = all_year_df.codes.value_counts().index,\n",
    "        colorscale = [[0.0, 'rgb(165,0,38)'], [0.1111111111111111, 'rgb(215,48,39)'], [0.2222222222222222, 'rgb(244,109,67)'], [0.3333333333333333, 'rgb(253,174,97)'], [0.4444444444444444, 'rgb(254,224,144)'], [0.5555555555555556, 'rgb(224,243,248)'], [0.6666666666666666, 'rgb(171,217,233)'], [0.7777777777777778, 'rgb(116,173,209)'], [0.8888888888888888, 'rgb(69,117,180)'], [1.0, 'rgb(49,54,149)']],\n",
    "    autocolorscale = False,\n",
    "        reversescale = True,\n",
    "        marker = dict(\n",
    "            line = dict (\n",
    "                color = 'rgb(180,180,180)',\n",
    "                width = 0.5\n",
    "            ) ),\n",
    "        colorbar = dict(\n",
    "            autotick = False,\n",
    "            title = 'Scale of Attacks'),\n",
    "      ) ]\n",
    "\n",
    "layout = dict(\n",
    "    title = '1970-2017 Global Terrorism map',\n",
    "    geo = dict(\n",
    "        showframe = False,\n",
    "        showcoastlines = False,\n",
    "        projection = dict(\n",
    "            type = 'Mercator'\n",
    "        )\n",
    "    )\n",
    ")\n",
    "\n",
    "fig = dict( data=data, layout=layout )\n",
    "iplot( fig, validate=False, filename='d3-world-map' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference**: Iraq, Pakistan, Afghanistan and India are some countries which contribute more to the terrorist activities.\n",
    "\n",
    "Now let's concentrate on Iraq! **Why does Iraq has major terrorist activities? Was Iraq a birth place for terrorism?** **When did it all start? And why(hard to answer objectively)**?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Is Iraq a birth place of terrorism?\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_iraq = all_year_df[all_year_df['country'] == 95]#Iraq country code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "type": "bar",
         "x": [
          2014,
          2016,
          2013,
          2015,
          2017,
          2012,
          2011,
          2010,
          2009,
          2008,
          2007,
          2006,
          2005,
          2004,
          2003,
          1992,
          1997,
          1994,
          1995,
          1996,
          1999,
          2000,
          1998,
          1980,
          2002,
          1982,
          1989,
          1988,
          1976,
          1983,
          1991,
          1993,
          1981,
          1987,
          2001,
          1984,
          1979,
          1975
         ],
         "y": [
          3933,
          3360,
          2852,
          2751,
          2466,
          1437,
          1308,
          1179,
          1137,
          1106,
          1047,
          838,
          617,
          323,
          102,
          35,
          21,
          18,
          17,
          12,
          12,
          10,
          7,
          6,
          6,
          5,
          4,
          4,
          3,
          3,
          3,
          3,
          3,
          3,
          3,
          2,
          2,
          1
         ]
        }
       ],
       "layout": {
        "font": {
         "size": 12
        },
        "title": "Number of Terrorist Activities in Iraq",
        "titlefont": {
         "size": 25
        },
        "xaxis": {
         "dtick": 1,
         "title": "Year"
        },
        "yaxis": {
         "title": "Number of Occurences"
        }
       }
      },
      "text/html": [
       "<div id=\"c49c5710-eadf-4da9-81fd-32ab22918dfe\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"c49c5710-eadf-4da9-81fd-32ab22918dfe\", [{\"y\": [3933, 3360, 2852, 2751, 2466, 1437, 1308, 1179, 1137, 1106, 1047, 838, 617, 323, 102, 35, 21, 18, 17, 12, 12, 10, 7, 6, 6, 5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 1], \"x\": [2014, 2016, 2013, 2015, 2017, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 1992, 1997, 1994, 1995, 1996, 1999, 2000, 1998, 1980, 2002, 1982, 1989, 1988, 1976, 1983, 1991, 1993, 1981, 1987, 2001, 1984, 1979, 1975], \"type\": \"bar\"}], {\"xaxis\": {\"dtick\": 1, \"title\": \"Year\"}, \"titlefont\": {\"size\": 25}, \"yaxis\": {\"title\": \"Number of Occurences\"}, \"font\": {\"size\": 12}, \"title\": \"Number of Terrorist Activities in Iraq\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"c49c5710-eadf-4da9-81fd-32ab22918dfe\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"c49c5710-eadf-4da9-81fd-32ab22918dfe\", [{\"y\": [3933, 3360, 2852, 2751, 2466, 1437, 1308, 1179, 1137, 1106, 1047, 838, 617, 323, 102, 35, 21, 18, 17, 12, 12, 10, 7, 6, 6, 5, 4, 4, 3, 3, 3, 3, 3, 3, 3, 2, 2, 1], \"x\": [2014, 2016, 2013, 2015, 2017, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004, 2003, 1992, 1997, 1994, 1995, 1996, 1999, 2000, 1998, 1980, 2002, 1982, 1989, 1988, 1976, 1983, 1991, 1993, 1981, 1987, 2001, 1984, 1979, 1975], \"type\": \"bar\"}], {\"xaxis\": {\"dtick\": 1, \"title\": \"Year\"}, \"titlefont\": {\"size\": 25}, \"yaxis\": {\"title\": \"Number of Occurences\"}, \"font\": {\"size\": 12}, \"title\": \"Number of Terrorist Activities in Iraq\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "trace = go.Bar(\n",
    "            x = [i for i in df_iraq.iyear.value_counts().index],\n",
    "            y = df_iraq.iyear.value_counts())\n",
    "layout = go.Layout(title = \"Number of Terrorist Activities in Iraq\",\n",
    "                   titlefont = dict(size = 25),\n",
    "                   xaxis = dict(title='Year',dtick=1),\n",
    "                   yaxis = dict(title='Number of Occurences'),\n",
    "                   \n",
    "                  font=dict(size=12))\n",
    "data = [trace]\n",
    "fig = dict(data=data, layout = layout)\n",
    "iplot(fig, filename=\"terrorIraq_barchart\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference**: NO! Although Iraq is the major contributor to global terrorist activities, it only started from the year 2004. This is mainly because of the [Iraq war](https://en.wikipedia.org/wiki/Iraq_War)! \n",
    "\n",
    "Now this gets us back to our previous question? \n",
    "\n",
    "**Why is there a plunge after 2004?** - Due to Iraq War(ie Hypothesis2: there is a increase in terrorist activities in the 21 century is due to a WAR!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ## Let's Concentrate on USA\n",
    "\n",
    "# The terrorist activities in USA is 2872, which is comparatively less than the activities across eastern regions of the world. However, we will explore the states and identify the causalities!\n",
    "\n",
    "# all_year_df['iday'][all_year_df.iday == 0] = 1\n",
    "# all_year_df['imonth'][all_year_df.imonth == 0] = 1\n",
    "# k= all_year_df[['iday', 'imonth', 'iyear']]\n",
    "# k.rename(columns={'iyear':'YEAR','imonth':'MONTH', 'iday':'DAY'}, inplace=True)\n",
    "# all_year_df['date'] = pd.to_datetime(k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "\n",
    "# all_year_df['text'] = all_year_df['date'].dt.strftime('%B %-d, %Y') + '<br>' +\\\n",
    "#                      all_year_df['nkill'].astype(str) + ' Killed, ' +\\\n",
    "#                      all_year_df['nwound'].astype(str) + ' Injured'\n",
    "\n",
    "# fatality = dict(\n",
    "#            type = 'scattergeo',\n",
    "#            locationmode = 'USA-states',\n",
    "#            lon = all_year_df[all_year_df.nkill > 0]['longitude'],\n",
    "#            lat = all_year_df[all_year_df.nkill > 0]['latitude'],\n",
    "#            text = all_year_df[all_year_df.nkill > 0]['text'],\n",
    "#            mode = 'markers',\n",
    "#            name = 'Fatalities',\n",
    "#            hoverinfo = 'text+name',\n",
    "#            marker = dict(\n",
    "#                size = all_year_df[all_year_df.nkill > 0]['nkill'] ** 0.255 * 8,\n",
    "#                opacity = 0.95,\n",
    "#                color = 'rgb(240, 140, 45)')\n",
    "#            )\n",
    "        \n",
    "# injury = dict(\n",
    "#          type = 'scattergeo',\n",
    "#          locationmode = 'USA-states',\n",
    "#          lon = all_year_df[all_year_df.nkill == 0]['longitude'],\n",
    "#          lat = all_year_df[all_year_df.nkill == 0]['latitude'],\n",
    "#          text = all_year_df[all_year_df.nkill == 0]['text'],\n",
    "#          mode = 'markers',\n",
    "#          name = 'Injuries',\n",
    "#          hoverinfo = 'text+name',\n",
    "#          marker = dict(\n",
    "#              size = (all_year_df[all_year_df.nkill == 0]['nwound'] + 1) ** 0.245 * 8,\n",
    "#              opacity = 0.85,\n",
    "#              color = 'rgb(20, 150, 187)')\n",
    "#          )\n",
    "\n",
    "# layout = dict(\n",
    "#          title = 'Terrorist Attacks by Latitude/Longitude in United States (1970-2015)',\n",
    "#          showlegend = True,\n",
    "#          legend = dict(\n",
    "#              x = 0.85, y = 0.4\n",
    "#          ),\n",
    "#          geo = dict(\n",
    "#              scope = 'usa',\n",
    "#              projection = dict(type = 'albers usa'),\n",
    "#              showland = True,\n",
    "#              landcolor = 'rgb(250, 250, 250)',\n",
    "#              subunitwidth = 1,\n",
    "#              subunitcolor = 'rgb(217, 217, 217)',\n",
    "#              countrywidth = 1,\n",
    "#              countrycolor = 'rgb(217, 217, 217)',\n",
    "#              showlakes = True,\n",
    "#              lakecolor = 'rgb(255, 255, 255)')\n",
    "#          )\n",
    "\n",
    "# data = [fatality, injury]\n",
    "# figure = dict(data = data, layout = layout)\n",
    "# iplot(figure)\n",
    "\n",
    "# # Here the chart shows the location of the attack. It is divided into 2 categories:\n",
    "# #   1. **Fatalities**: Number of people got killed\n",
    "# #   2. **Injuries**: Number of people who got wounded\n",
    "\n",
    "# # The bigger the circle, the greater the causalities."
   ]
  }
 ],
 "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}