03_Incident_Categorization_by_Weapon.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read in the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Report Year</th>\n",
       "      <th>Confirmed Death(s)</th>\n",
       "      <th>Weapon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1947.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Other</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1941.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Explosives</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1941.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Firearms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1941.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Firearms</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1941.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>Firearms, Explosives, Other</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Report Year  Confirmed Death(s)                       Weapon\n",
       "1       1947.0                 0.0                        Other\n",
       "4       1941.0                 0.0                   Explosives\n",
       "6       1941.0                 0.0                     Firearms\n",
       "8       1941.0                 3.0                     Firearms\n",
       "9       1941.0                14.0  Firearms, Explosives, Other"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('Terrorism Project - Raw Data.csv')\n",
    "df = df[df['Incident Country'] == 'China']\n",
    "df = df[['Report Year', 'Confirmed Death(s)', 'Weapon']]\n",
    "\n",
    "import numpy as np\n",
    "df['Confirmed Death(s)'] = np.where(df['Confirmed Death(s)'].isnull(), 0, df['Confirmed Death(s)'])\n",
    "df = df.dropna()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total_Incidents</th>\n",
       "      <th>Explosives_Used</th>\n",
       "      <th>Firearms_Used</th>\n",
       "      <th>Other_Weapons_Used</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Report Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1938.0</th>\n",
       "      <td>32</td>\n",
       "      <td>13</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1939.0</th>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1940.0</th>\n",
       "      <td>10</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1941.0</th>\n",
       "      <td>33</td>\n",
       "      <td>9</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1947.0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Total_Incidents  Explosives_Used  Firearms_Used  \\\n",
       "Report Year                                                    \n",
       "1938.0                    32               13             11   \n",
       "1939.0                    17                2             15   \n",
       "1940.0                    10                2              8   \n",
       "1941.0                    33                9             26   \n",
       "1947.0                     1                0              0   \n",
       "\n",
       "             Other_Weapons_Used  \n",
       "Report Year                      \n",
       "1938.0                        8  \n",
       "1939.0                        0  \n",
       "1940.0                        0  \n",
       "1941.0                        1  \n",
       "1947.0                        1  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "totals = df[['Report Year', 'Weapon']].groupby(['Report Year']).agg(['count'])\n",
    "totals.columns = totals.columns.droplevel(1)\n",
    "totals.columns = ['Total_Incidents']\n",
    "##################################################################################################\n",
    "working_df = df.copy()\n",
    "working_df['Explosives_Used'] = np.where(working_df['Weapon'].str.contains(\"Explosives\"), 1, 0)\n",
    "working_df['Firearms_Used'] = np.where(working_df['Weapon'].str.contains(\"Firearms\"), 1, 0)\n",
    "working_df['Other_Weapons_Used'] = np.where(working_df['Weapon'].str.contains(\"Other\"), 1, 0)\n",
    "working_df = working_df[['Report Year', 'Explosives_Used', 'Firearms_Used', 'Other_Weapons_Used']]\n",
    "working_df = working_df.groupby(['Report Year']).agg(['sum'])\n",
    "working_df.columns = working_df.columns.droplevel(1)\n",
    "##################################################################################################\n",
    "incident_count_by_weapon_year = pd.concat([totals, working_df], axis=1)\n",
    "incident_count_by_weapon_year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1938 Venn Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:1297: UserWarning: findfont: Font family [u'normal'] not found. Falling back to DejaVu Sans\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
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nVmlQhjbCJ6cGJ5IpEWeLtuSORFJjHQTmMIAdg5QYJ854ew2T2msgM2vLshwn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176c7810>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# !pip install matplotlib-venn\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib_venn import venn2, venn3, venn3_circles\n",
    "\n",
    "from pylab import rcParams          \n",
    "rcParams['figure.figsize'] = 16, 10 \n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 22}\n",
    "plt.rc('font', **font)\n",
    "\n",
    "v = venn3(subsets = (# Explosives\n",
    "                     13, \n",
    "                     # Firearms\n",
    "                     11, \n",
    "                     # Firearms + Explosives\n",
    "                     0, \n",
    "                     # Other\n",
    "                     8, \n",
    "                     # Explosives + Other\n",
    "                     0, \n",
    "                     # Firearms + Other\n",
    "                     0,\n",
    "                     # Explosives + Firearms + Other\n",
    "                     0), \n",
    "                     set_labels = ('Explosives', 'Firearms', 'Blunt Objects'))\n",
    "v.get_patch_by_id('100').set_color('yellow')\n",
    "v.get_patch_by_id('010').set_color('blue')\n",
    "v.get_patch_by_id('001').set_color('grey')\n",
    "\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# savepath = os.path.join('./Latex_Compilation', 'work_home_venn.png')\n",
    "# plt.savefig(savepath, bbox_inches='tight')\n",
    "\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1941 Venn Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/matplotlib_venn/_venn3.py:117: UserWarning: Bad circle positioning\n",
      "  warnings.warn(\"Bad circle positioning\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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RYcYi4EP4biD70Si/SC4bHf0fxI/+bw0cj8SAEn8RkcASHXsuB24C2vH1/CISD2X43v8v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\nivGdi8rw5WflQC1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UNlaGDkdERBgAzg0dRJQo8c9ZdjpwMWrdKTOoZ5CCFw9Qf1BlPuOr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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11aa43850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# !pip install matplotlib-venn\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib_venn import venn2, venn3, venn3_circles\n",
    "\n",
    "from pylab import rcParams          \n",
    "rcParams['figure.figsize'] = 16, 10 \n",
    "\n",
    "font = {'family' : 'normal',\n",
    "        'weight' : 'bold',\n",
    "        'size'   : 22}\n",
    "plt.rc('font', **font)\n",
    "\n",
    "v = venn3(subsets = (# Explosives\n",
    "                     7, \n",
    "                     # Firearms\n",
    "                     24, \n",
    "                     # Firearms + Explosives\n",
    "                     1, \n",
    "                     # Other\n",
    "                     0, \n",
    "                     # Explosives + Other\n",
    "                     0, \n",
    "                     # Firearms + Other\n",
    "                     0,\n",
    "                     # Explosives + Firearms + Other\n",
    "                     1), \n",
    "                     set_labels = ('Explosives', 'Firearms', 'Blunt Objects'))\n",
    "v.get_patch_by_id('100').set_color('yellow')\n",
    "v.get_patch_by_id('110').set_color('green')\n",
    "v.get_patch_by_id('111').set_color('grey')\n",
    "v.get_patch_by_id('010').set_color('blue')\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# savepath = os.path.join('./Latex_Compilation', 'work_home_venn.png')\n",
    "# plt.savefig(savepath, bbox_inches='tight')\n",
    "\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import MaxNLocator\n",
    "\n",
    "year    = [1,2,3,4]\n",
    "labels  = ['a', '1938', '1939', '1940', '1941']\n",
    "lengend_labels = ['Firearms', 'Explosive Devices', 'Blunt Instruments']\n",
    "\n",
    "other      = incident_count_by_weapon_year['Other_Weapons_Used'].tolist()[0:4]\n",
    "firearms   = incident_count_by_weapon_year['Firearms_Used'].tolist()[0:4]\n",
    "explosives = incident_count_by_weapon_year['Explosives_Used'].tolist()[0:4]\n",
    "\n",
    "fig = plt.figure(figsize=(20,10))\n",
    "ax1 = fig.add_subplot(111)\n",
    "\n",
    "ax1.plot(year, firearms, linewidth=4, color ='#668cff')\n",
    "ax1.plot(year, explosives, linewidth=4, color ='#ff1a1a')\n",
    "ax1.plot(year, other, linewidth=4, color ='#999999')\n",
    "\n",
    "# plt.xlabel('Year', fontsize=30)\n",
    "plt.ylabel('# of Incidents', fontsize=32)\n",
    "\n",
    "plt.title('Number of Terrorist Incidents by Weapon Type', fontsize=38, y=1.0)\n",
    "\n",
    "ax1.xaxis.set_major_locator(MaxNLocator(4))\n",
    "plt.xticks(fontsize=30, rotation=25)\n",
    "ax1.set_xticklabels(labels)\n",
    "ax1.grid(True)\n",
    "ax1.legend(lengend_labels, loc=2)\n",
    "\n",
    "# plt.show()\n",
    "plt.savefig('./images/incidents_by_weapon_typoe_line_plot.png')\n",
    "plt.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# percentage_df = concident_count_by_weapon_year.copy()\n",
    "# percentage_df['Percent with Explosives'] = ((percentage_df['Explosives_Used']/\n",
    "#                                             percentage_df['Total_Incidents'])*100).\\\n",
    "#                                             apply(lambda x: round(x,2))\n",
    "# percentage_df['Percent with Firearms'] = ((percentage_df['Firearms_Used']/percentage_df['Total_Incidents'])*100).\\\n",
    "#                                             apply(lambda x: round(x,2))\n",
    "# percentage_df['Percent with Other Weapons'] = ((percentage_df['Other_Weapons_Used']/\n",
    "#                                                percentage_df['Total_Incidents'])*100).\\\n",
    "#                                                apply(lambda x: round(x,2))\n",
    "# percentage_df = percentage_df[['Percent with Explosives', 'Percent with Firearms', 'Percent with Other Weapons']]\n",
    "# percentage_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# percentage_df.to_csv('percentage_df.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}