dataworld_shootings.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mass shootings occurring in the United States from 1982-2015\n",
    "## Taken from https://data.world/awram/us-mass-shootings\n",
    "### Only change was to break location into two fields: City and State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter Notebook Created By: Bill Glennon\n",
    "## Date: December 28, 2016"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "import pandas as pd\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# import the csv file into a panda data frame\n",
    "shootings = pd.read_csv(\"mass_shootings_main.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Look at the data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.frame.DataFrame"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show that it is a data frame\n",
    "type(shootings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71 entries, 0 to 70\n",
      "Data columns (total 23 columns):\n",
      "Case                              71 non-null object\n",
      "Shooting type                     71 non-null object\n",
      "Shooter name                      71 non-null object\n",
      "Gender                            71 non-null object\n",
      "Race                              71 non-null object\n",
      "City                              71 non-null object\n",
      "State                             71 non-null object\n",
      "Year                              71 non-null int64\n",
      "Summary                           71 non-null object\n",
      "Fatalities                        71 non-null int64\n",
      "Wounded                           71 non-null int64\n",
      "Total victims                     71 non-null int64\n",
      "Location type                     71 non-null object\n",
      "Prior signs of mental illness?    71 non-null object\n",
      "Mental health notes               67 non-null object\n",
      "Weapons obtained legally          71 non-null object\n",
      "Where weapon obtained             71 non-null object\n",
      "Type of weapons                   71 non-null object\n",
      "Weapon details                    71 non-null object\n",
      "Sources                           71 non-null object\n",
      "Mental Health Sources             67 non-null object\n",
      "Latitude                          71 non-null float64\n",
      "Longitude                         71 non-null float64\n",
      "dtypes: float64(2), int64(4), object(17)\n",
      "memory usage: 12.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information on the data set\n",
    "shootings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "71"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows\n",
    "len(shootings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of columns\n",
    "len(shootings.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Case</th>\n",
       "      <th>Shooting type</th>\n",
       "      <th>Shooter name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>...</th>\n",
       "      <th>Prior signs of mental illness?</th>\n",
       "      <th>Mental health notes</th>\n",
       "      <th>Weapons obtained legally</th>\n",
       "      <th>Where weapon obtained</th>\n",
       "      <th>Type of weapons</th>\n",
       "      <th>Weapon details</th>\n",
       "      <th>Sources</th>\n",
       "      <th>Mental Health Sources</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chattanooga military recruitment center</td>\n",
       "      <td>Mass</td>\n",
       "      <td>Mohammod Youssuf Abdulazeez</td>\n",
       "      <td>Male</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>Chattanooga</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>2015</td>\n",
       "      <td>Kuwaiti-born Mohammod Youssuf Abdulazeez, 24, ...</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Abdulazeez \"had suffered for years from depres...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>On the internet, via Armslist.com</td>\n",
       "      <td>2 assault rifles; semiautomatic handgun\\n</td>\n",
       "      <td>AK-47, AR-15, and 30-round magazines; 9mm handgun</td>\n",
       "      <td>http://www.reuters.com/article/2015/07/16/us-u...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.047157</td>\n",
       "      <td>-85.311819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charleston Church Shooting</td>\n",
       "      <td>Mass</td>\n",
       "      <td>Dylann Storm Roof</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>2015</td>\n",
       "      <td>Dylann Storm Roof, 21, shot and killed 9 peopl...</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Shooter's Choice gun store in West Columbia, S...</td>\n",
       "      <td>Handgun</td>\n",
       "      <td>.45-caliber Glock (model 41, with 13-round cap...</td>\n",
       "      <td>http://www.motherjones.com/politics/2015/06/dy...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.788387</td>\n",
       "      <td>-79.933143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Case Shooting type  \\\n",
       "0  Chattanooga military recruitment center          Mass   \n",
       "1               Charleston Church Shooting          Mass   \n",
       "\n",
       "                  Shooter name Gender            Race         City  \\\n",
       "0  Mohammod Youssuf Abdulazeez   Male  Middle Eastern  Chattanooga   \n",
       "1            Dylann Storm Roof   Male           White   Charleston   \n",
       "\n",
       "             State  Year                                            Summary  \\\n",
       "0        Tennessee  2015  Kuwaiti-born Mohammod Youssuf Abdulazeez, 24, ...   \n",
       "1   South Carolina  2015  Dylann Storm Roof, 21, shot and killed 9 peopl...   \n",
       "\n",
       "   Fatalities    ...      Prior signs of mental illness?  \\\n",
       "0           5    ...                                 Yes   \n",
       "1           9    ...                                  No   \n",
       "\n",
       "                                 Mental health notes Weapons obtained legally  \\\n",
       "0  Abdulazeez \"had suffered for years from depres...                      Yes   \n",
       "1                                                NaN                      Yes   \n",
       "\n",
       "                               Where weapon obtained  \\\n",
       "0                  On the internet, via Armslist.com   \n",
       "1  Shooter's Choice gun store in West Columbia, S...   \n",
       "\n",
       "                             Type of weapons  \\\n",
       "0  2 assault rifles; semiautomatic handgun\\n   \n",
       "1                                    Handgun   \n",
       "\n",
       "                                      Weapon details  \\\n",
       "0  AK-47, AR-15, and 30-round magazines; 9mm handgun   \n",
       "1  .45-caliber Glock (model 41, with 13-round cap...   \n",
       "\n",
       "                                             Sources Mental Health Sources  \\\n",
       "0  http://www.reuters.com/article/2015/07/16/us-u...                   NaN   \n",
       "1  http://www.motherjones.com/politics/2015/06/dy...                   NaN   \n",
       "\n",
       "    Latitude  Longitude  \n",
       "0  35.047157 -85.311819  \n",
       "1  32.788387 -79.933143  \n",
       "\n",
       "[2 rows x 23 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show first 2 records of the data set\n",
    "shootings.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Case</th>\n",
       "      <th>Shooting type</th>\n",
       "      <th>Shooter name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>...</th>\n",
       "      <th>Prior signs of mental illness?</th>\n",
       "      <th>Mental health notes</th>\n",
       "      <th>Weapons obtained legally</th>\n",
       "      <th>Where weapon obtained</th>\n",
       "      <th>Type of weapons</th>\n",
       "      <th>Weapon details</th>\n",
       "      <th>Sources</th>\n",
       "      <th>Mental Health Sources</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>Dallas nightclub shooting</td>\n",
       "      <td>Mass</td>\n",
       "      <td>Abdelkrim Belachheb</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Dallas</td>\n",
       "      <td>Texas</td>\n",
       "      <td>1984</td>\n",
       "      <td>Abdelkrim Belachheb, 39, opened fire at an ups...</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>During his last meal with his wife, he confess...</td>\n",
       "      <td>No</td>\n",
       "      <td>Hines Boulevard Pawn Shop in Dallas, Texas</td>\n",
       "      <td>One semiautomatic handgun</td>\n",
       "      <td>9mm Smith &amp; Wesson 459 semiautomatic handgun</td>\n",
       "      <td>http://books.google.com/books?id=Hr3OBwP-lbUC&amp;...</td>\n",
       "      <td>http://books.google.com/books?id=Hr3OBwP-lbUC&amp;...</td>\n",
       "      <td>32.780105</td>\n",
       "      <td>-96.800008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>Welding shop shooting</td>\n",
       "      <td>Mass</td>\n",
       "      <td>Carl Robert Brown</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Miami</td>\n",
       "      <td>Florida</td>\n",
       "      <td>1982</td>\n",
       "      <td>Junior high school teacher Carl Robert Brown, ...</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>His second wife left him because he refused to...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Garcia Gun Center in Hialeah, Fla.</td>\n",
       "      <td>One shotgun</td>\n",
       "      <td>Mossberg 500 Persuader pump-action shotgun wit...</td>\n",
       "      <td>http://www.nytimes.com/1982/08/21/us/gunman-in...</td>\n",
       "      <td>http://news.google.com/newspapers?id=uuYLAAAAI...</td>\n",
       "      <td>25.796491</td>\n",
       "      <td>-80.226683</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Case Shooting type         Shooter name Gender  \\\n",
       "69  Dallas nightclub shooting          Mass  Abdelkrim Belachheb   Male   \n",
       "70      Welding shop shooting          Mass    Carl Robert Brown   Male   \n",
       "\n",
       "     Race    City     State  Year  \\\n",
       "69  White  Dallas     Texas  1984   \n",
       "70  White   Miami   Florida  1982   \n",
       "\n",
       "                                              Summary  Fatalities    ...      \\\n",
       "69  Abdelkrim Belachheb, 39, opened fire at an ups...           6    ...       \n",
       "70  Junior high school teacher Carl Robert Brown, ...           8    ...       \n",
       "\n",
       "    Prior signs of mental illness?  \\\n",
       "69                             Yes   \n",
       "70                             Yes   \n",
       "\n",
       "                                  Mental health notes  \\\n",
       "69  During his last meal with his wife, he confess...   \n",
       "70  His second wife left him because he refused to...   \n",
       "\n",
       "   Weapons obtained legally                       Where weapon obtained  \\\n",
       "69                       No  Hines Boulevard Pawn Shop in Dallas, Texas   \n",
       "70                      Yes          Garcia Gun Center in Hialeah, Fla.   \n",
       "\n",
       "              Type of weapons  \\\n",
       "69  One semiautomatic handgun   \n",
       "70                One shotgun   \n",
       "\n",
       "                                       Weapon details  \\\n",
       "69       9mm Smith & Wesson 459 semiautomatic handgun   \n",
       "70  Mossberg 500 Persuader pump-action shotgun wit...   \n",
       "\n",
       "                                              Sources  \\\n",
       "69  http://books.google.com/books?id=Hr3OBwP-lbUC&...   \n",
       "70  http://www.nytimes.com/1982/08/21/us/gunman-in...   \n",
       "\n",
       "                                Mental Health Sources   Latitude  Longitude  \n",
       "69  http://books.google.com/books?id=Hr3OBwP-lbUC&...  32.780105 -96.800008  \n",
       "70  http://news.google.com/newspapers?id=uuYLAAAAI...  25.796491 -80.226683  \n",
       "\n",
       "[2 rows x 23 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show last 2 records of the data set\n",
    "shootings.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Case', 'Shooting type', 'Shooter name', 'Gender', 'Race', 'City',\n",
       "       'State', 'Year', 'Summary', 'Fatalities', 'Wounded', 'Total victims',\n",
       "       'Location type', 'Prior signs of mental illness?',\n",
       "       'Mental health notes', 'Weapons obtained legally',\n",
       "       'Where weapon obtained', 'Type of weapons', 'Weapon details', 'Sources',\n",
       "       'Mental Health Sources', 'Latitude', 'Longitude'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the columns of the data set\n",
    "shootings.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Adding underscore to colmn names\n",
    "shootings.columns = ['Case', 'Shooting_type', 'Shooter_name', 'Gender', 'Race',\n",
    "       'City', 'State', 'Year', 'Summary', 'Fatalities',\n",
    "       'Wounded', 'Total_victims', 'Location_type',\n",
    "       'Prior_signs_of_mental_illness', 'Mental_health_notes',\n",
    "       'Weapons_obtained_legally', 'Where_weapon_obtained', 'Type_of_weapons',\n",
    "       'Weapon_details', 'Sources', 'Mental_Health_Sources', 'Latitude',\n",
    "       'Longitude']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71 entries, 0 to 70\n",
      "Data columns (total 23 columns):\n",
      "Case                             71 non-null object\n",
      "Shooting_type                    71 non-null object\n",
      "Shooter_name                     71 non-null object\n",
      "Gender                           71 non-null object\n",
      "Race                             71 non-null object\n",
      "City                             71 non-null object\n",
      "State                            71 non-null object\n",
      "Year                             71 non-null int64\n",
      "Summary                          71 non-null object\n",
      "Fatalities                       71 non-null int64\n",
      "Wounded                          71 non-null int64\n",
      "Total_victims                    71 non-null int64\n",
      "Location_type                    71 non-null object\n",
      "Prior_signs_of_mental_illness    71 non-null object\n",
      "Mental_health_notes              67 non-null object\n",
      "Weapons_obtained_legally         71 non-null object\n",
      "Where_weapon_obtained            71 non-null object\n",
      "Type_of_weapons                  71 non-null object\n",
      "Weapon_details                   71 non-null object\n",
      "Sources                          71 non-null object\n",
      "Mental_Health_Sources            67 non-null object\n",
      "Latitude                         71 non-null float64\n",
      "Longitude                        71 non-null float64\n",
      "dtypes: float64(2), int64(4), object(17)\n",
      "memory usage: 12.8+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information on the data set - Notice new column names\n",
    "shootings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>Case</th>\n",
       "      <th>Shooting_type</th>\n",
       "      <th>Shooter_name</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Race</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Year</th>\n",
       "      <th>Summary</th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>...</th>\n",
       "      <th>Prior_signs_of_mental_illness</th>\n",
       "      <th>Mental_health_notes</th>\n",
       "      <th>Weapons_obtained_legally</th>\n",
       "      <th>Where_weapon_obtained</th>\n",
       "      <th>Type_of_weapons</th>\n",
       "      <th>Weapon_details</th>\n",
       "      <th>Sources</th>\n",
       "      <th>Mental_Health_Sources</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Chattanooga military recruitment center</td>\n",
       "      <td>Mass</td>\n",
       "      <td>Mohammod Youssuf Abdulazeez</td>\n",
       "      <td>Male</td>\n",
       "      <td>Middle Eastern</td>\n",
       "      <td>Chattanooga</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>2015</td>\n",
       "      <td>Kuwaiti-born Mohammod Youssuf Abdulazeez, 24, ...</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Abdulazeez \"had suffered for years from depres...</td>\n",
       "      <td>Yes</td>\n",
       "      <td>On the internet, via Armslist.com</td>\n",
       "      <td>2 assault rifles; semiautomatic handgun\\n</td>\n",
       "      <td>AK-47, AR-15, and 30-round magazines; 9mm handgun</td>\n",
       "      <td>http://www.reuters.com/article/2015/07/16/us-u...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.047157</td>\n",
       "      <td>-85.311819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Charleston Church Shooting</td>\n",
       "      <td>Mass</td>\n",
       "      <td>Dylann Storm Roof</td>\n",
       "      <td>Male</td>\n",
       "      <td>White</td>\n",
       "      <td>Charleston</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>2015</td>\n",
       "      <td>Dylann Storm Roof, 21, shot and killed 9 peopl...</td>\n",
       "      <td>9</td>\n",
       "      <td>...</td>\n",
       "      <td>No</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Shooter's Choice gun store in West Columbia, S...</td>\n",
       "      <td>Handgun</td>\n",
       "      <td>.45-caliber Glock (model 41, with 13-round cap...</td>\n",
       "      <td>http://www.motherjones.com/politics/2015/06/dy...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.788387</td>\n",
       "      <td>-79.933143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      Case Shooting_type  \\\n",
       "0  Chattanooga military recruitment center          Mass   \n",
       "1               Charleston Church Shooting          Mass   \n",
       "\n",
       "                  Shooter_name Gender            Race         City  \\\n",
       "0  Mohammod Youssuf Abdulazeez   Male  Middle Eastern  Chattanooga   \n",
       "1            Dylann Storm Roof   Male           White   Charleston   \n",
       "\n",
       "             State  Year                                            Summary  \\\n",
       "0        Tennessee  2015  Kuwaiti-born Mohammod Youssuf Abdulazeez, 24, ...   \n",
       "1   South Carolina  2015  Dylann Storm Roof, 21, shot and killed 9 peopl...   \n",
       "\n",
       "   Fatalities    ...      Prior_signs_of_mental_illness  \\\n",
       "0           5    ...                                Yes   \n",
       "1           9    ...                                 No   \n",
       "\n",
       "                                 Mental_health_notes Weapons_obtained_legally  \\\n",
       "0  Abdulazeez \"had suffered for years from depres...                      Yes   \n",
       "1                                                NaN                      Yes   \n",
       "\n",
       "                               Where_weapon_obtained  \\\n",
       "0                  On the internet, via Armslist.com   \n",
       "1  Shooter's Choice gun store in West Columbia, S...   \n",
       "\n",
       "                             Type_of_weapons  \\\n",
       "0  2 assault rifles; semiautomatic handgun\\n   \n",
       "1                                    Handgun   \n",
       "\n",
       "                                      Weapon_details  \\\n",
       "0  AK-47, AR-15, and 30-round magazines; 9mm handgun   \n",
       "1  .45-caliber Glock (model 41, with 13-round cap...   \n",
       "\n",
       "                                             Sources Mental_Health_Sources  \\\n",
       "0  http://www.reuters.com/article/2015/07/16/us-u...                   NaN   \n",
       "1  http://www.motherjones.com/politics/2015/06/dy...                   NaN   \n",
       "\n",
       "    Latitude  Longitude  \n",
       "0  35.047157 -85.311819  \n",
       "1  32.788387 -79.933143  \n",
       "\n",
       "[2 rows x 23 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show first 2 records of the data set\n",
    "shootings.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    71.000000\n",
       "mean      8.042254\n",
       "min       4.000000\n",
       "max      33.000000\n",
       "Name: Fatalities, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show summary statistics on Fatalities\n",
    "shootings['Fatalities'].describe()[['count', 'mean', 'min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    71.000000\n",
       "mean      7.225352\n",
       "min       0.000000\n",
       "max      58.000000\n",
       "Name: Wounded, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show summary statistics on Wounded\n",
    "shootings['Wounded'].describe()[['count', 'mean', 'min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Make the following fields category types\n",
    "shootings.Gender = shootings.Gender.astype('category')\n",
    "shootings.Race = shootings.Race.astype('category')\n",
    "shootings.Shooting_type = shootings.Shooting_type.astype('category')\n",
    "shootings.Year = shootings.Year.astype('category')\n",
    "shootings.Longitude = shootings.Longitude.astype('category')\n",
    "shootings.Latitude = shootings.Latitude.astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 71 entries, 0 to 70\n",
      "Data columns (total 23 columns):\n",
      "Case                             71 non-null object\n",
      "Shooting_type                    71 non-null category\n",
      "Shooter_name                     71 non-null object\n",
      "Gender                           71 non-null category\n",
      "Race                             71 non-null category\n",
      "City                             71 non-null object\n",
      "State                            71 non-null object\n",
      "Year                             71 non-null category\n",
      "Summary                          71 non-null object\n",
      "Fatalities                       71 non-null int64\n",
      "Wounded                          71 non-null int64\n",
      "Total_victims                    71 non-null int64\n",
      "Location_type                    71 non-null object\n",
      "Prior_signs_of_mental_illness    71 non-null object\n",
      "Mental_health_notes              67 non-null object\n",
      "Weapons_obtained_legally         71 non-null object\n",
      "Where_weapon_obtained            71 non-null object\n",
      "Type_of_weapons                  71 non-null object\n",
      "Weapon_details                   71 non-null object\n",
      "Sources                          71 non-null object\n",
      "Mental_Health_Sources            67 non-null object\n",
      "Latitude                         71 non-null category\n",
      "Longitude                        71 non-null category\n",
      "dtypes: category(6), int64(3), object(14)\n",
      "memory usage: 11.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# Information on the data set - Notice new column types\n",
    "shootings.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Female', 'Male'], dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show unique values for Gender\n",
    "shootings.Gender.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Asian', 'Black', 'Latino', 'Middle Eastern', 'Native American',\n",
       "       'Other', 'White'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show unique values for Race\n",
    "shootings.Race.cat.categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Mass', 'Spree'], dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show unique values for Shooting_type\n",
    "shootings.Shooting_type.cat.categories"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Data Analysis (Numbers and Visualization)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Imports\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Fatalities</th>\n",
       "      <th>Wounded</th>\n",
       "      <th>Total_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shooting_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Mass</th>\n",
       "      <td>517</td>\n",
       "      <td>450</td>\n",
       "      <td>967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>54</td>\n",
       "      <td>63</td>\n",
       "      <td>117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Fatalities  Wounded  Total_victims\n",
       "Shooting_type                                    \n",
       "Mass                  517      450            967\n",
       "Spree                  54       63            117"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Counts by Shooting type\n",
    "shootings.groupby('Shooting_type').sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By Race"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Wounded</th>\n",
       "      <th>Total_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Race</th>\n",
       "      <th>Shooting_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Asian</th>\n",
       "      <th>Mass</th>\n",
       "      <td>72.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>103.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Black</th>\n",
       "      <th>Mass</th>\n",
       "      <td>71.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>116.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Latino</th>\n",
       "      <th>Mass</th>\n",
       "      <td>22.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>34.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Middle Eastern</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Native American</th>\n",
       "      <th>Mass</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Other</th>\n",
       "      <th>Mass</th>\n",
       "      <td>13.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">White</th>\n",
       "      <th>Mass</th>\n",
       "      <td>325.0</td>\n",
       "      <td>327.0</td>\n",
       "      <td>652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>44.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>102.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Fatalities  Wounded  Total_victims\n",
       "Race            Shooting_type                                    \n",
       "Asian           Mass                 72.0     31.0          103.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       "Black           Mass                 71.0     45.0          116.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       "Latino          Mass                 22.0     12.0           34.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       "Middle Eastern  Mass                  5.0      2.0            7.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       "Native American Mass                  9.0      3.0           12.0\n",
       "                Spree                10.0      5.0           15.0\n",
       "Other           Mass                 13.0     30.0           43.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       "White           Mass                325.0    327.0          652.0\n",
       "                Spree                44.0     58.0          102.0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by Race, Shooting type, and  impact to victimes wounded or fatal\n",
    "shootings.groupby(['Race', 'Shooting_type']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Race             Shooting_type\n",
       "Asian            Mass              6\n",
       "Black            Mass             11\n",
       "Latino           Mass              4\n",
       "Middle Eastern   Mass              1\n",
       "Native American  Mass              2\n",
       "                 Spree             1\n",
       "Other            Mass              1\n",
       "White            Mass             38\n",
       "                 Spree             7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by Race and shooting type\n",
    "shootings[['Race', 'Shooting_type']].groupby(['Race', 'Shooting_type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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EpaRE8zy9TIcOHbBnz55X2kdCQgI2b96M4cOHIzMzE6ampkXWm5mZISMj45Xa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\nhBCiFiR5EkIIIYSoBUmehBBCCCFq4f8BA+J/AoDPopIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d77563e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the shooting data by Race\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns_race = (sns.factorplot(x=\"Race\", hue='Shooting_type', data=shootings, aspect=1.5, kind=\"count\", legend_out=False, \\\n",
    "                        palette=\"muted\").add_legend(title=\"Shooting Type\"))\n",
    "sns_race.set_xticklabels(rotation=45)\n",
    "sns_race.set(xlabel='Race', ylabel='Count')\n",
    "plt.gcf().subplots_adjust(bottom=0.35)\n",
    "plt.gcf().subplots_adjust(left=0.10)\n",
    "plt.savefig('/tmp/ByRace.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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></th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Wounded</th>\n",
       "      <th>Total_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>Shooting_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Arizona</th>\n",
       "      <th>Mass</th>\n",
       "      <td>6.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Arkansas</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">California</th>\n",
       "      <th>Mass</th>\n",
       "      <td>72.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>148.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Colorado</th>\n",
       "      <th>Mass</th>\n",
       "      <td>31.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Connecticut</th>\n",
       "      <th>Mass</th>\n",
       "      <td>42.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">D.C.</th>\n",
       "      <th>Mass</th>\n",
       "      <td>12.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Florida</th>\n",
       "      <th>Mass</th>\n",
       "      <td>36.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>47.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Georgia</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>9.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Hawaii</th>\n",
       "      <th>Mass</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Illinois</th>\n",
       "      <th>Mass</th>\n",
       "      <td>11.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Iowa</th>\n",
       "      <th>Mass</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Kentucky</th>\n",
       "      <th>Mass</th>\n",
       "      <td>15.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Massachusetts</th>\n",
       "      <th>Mass</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Michigan</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Minnesota</th>\n",
       "      <th>Mass</th>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Nebraska</th>\n",
       "      <th>Mass</th>\n",
       "      <td>9.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>13.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Nevada</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">New York</th>\n",
       "      <th>Mass</th>\n",
       "      <td>25.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">North Carolina</th>\n",
       "      <th>Mass</th>\n",
       "      <td>12.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Ohio</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oklahoma</th>\n",
       "      <th>Mass</th>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Oregon</th>\n",
       "      <th>Mass</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Pennsylvania</th>\n",
       "      <th>Mass</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">South Carolina</th>\n",
       "      <th>Mass</th>\n",
       "      <td>13.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Tennessee</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Texas</th>\n",
       "      <th>Mass</th>\n",
       "      <td>57.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>115.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Utah</th>\n",
       "      <th>Mass</th>\n",
       "      <td>6.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Virginia</th>\n",
       "      <th>Mass</th>\n",
       "      <td>33.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Washington</th>\n",
       "      <th>Mass</th>\n",
       "      <td>26.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Wisconsin</th>\n",
       "      <th>Mass</th>\n",
       "      <td>20.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>64 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Fatalities  Wounded  Total_victims\n",
       "State           Shooting_type                                    \n",
       " Arizona        Mass                  6.0     13.0           19.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Arkansas       Mass                  5.0     10.0           15.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " California     Mass                 72.0     76.0          148.0\n",
       "                Spree                14.0      3.0           17.0\n",
       " Colorado       Mass                 31.0     83.0          114.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Connecticut    Mass                 42.0      5.0           47.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " D.C.           Mass                 12.0      8.0           20.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Florida        Mass                 36.0     11.0           47.0\n",
       "                Spree                 6.0     14.0           20.0\n",
       " Georgia        Mass                  5.0      0.0            5.0\n",
       "                Spree                 9.0     13.0           22.0\n",
       " Hawaii         Mass                  7.0      0.0            7.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Illinois       Mass                 11.0     25.0           36.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Iowa           Mass                  6.0      1.0            7.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Kentucky       Mass                 15.0     13.0           28.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Massachusetts  Mass                  7.0      0.0            7.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Michigan       Mass                  5.0      5.0           10.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Minnesota      Mass                  7.0      1.0            8.0\n",
       "                Spree                10.0      5.0           15.0\n",
       "...                                   ...      ...            ...\n",
       " Nebraska       Mass                  9.0      4.0           13.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Nevada         Mass                  5.0      7.0           12.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " New York       Mass                 25.0     23.0           48.0\n",
       "                Spree                 5.0      2.0            7.0\n",
       " North Carolina Mass                 12.0     11.0           23.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Ohio           Mass                  5.0      7.0           12.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Oklahoma       Mass                 15.0      6.0           21.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Oregon         Mass                  NaN      NaN            NaN\n",
       "                Spree                 4.0     25.0           29.0\n",
       " Pennsylvania   Mass                  6.0      5.0           11.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " South Carolina Mass                 13.0      4.0           17.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Tennessee      Mass                  5.0      2.0            7.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Texas          Mass                 57.0     58.0          115.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Utah           Mass                  6.0      4.0           10.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Virginia       Mass                 33.0     23.0           56.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       " Washington     Mass                 26.0     27.0           53.0\n",
       "                Spree                 6.0      1.0            7.0\n",
       " Wisconsin      Mass                 20.0      8.0           28.0\n",
       "                Spree                 NaN      NaN            NaN\n",
       "\n",
       "[64 rows x 3 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by State, Shooting type, and  impact to victimes wounded or fatal\n",
    "shootings.groupby(['State', 'Shooting_type']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State            Shooting_type\n",
       " Arizona         Mass             1\n",
       " Arkansas        Mass             1\n",
       " California      Mass             9\n",
       "                 Spree            2\n",
       " Colorado        Mass             3\n",
       " Connecticut     Mass             3\n",
       " D.C.            Mass             1\n",
       " Florida         Mass             5\n",
       "                 Spree            1\n",
       " Georgia         Mass             1\n",
       "                 Spree            1\n",
       " Hawaii          Mass             1\n",
       " Illinois        Mass             2\n",
       " Iowa            Mass             1\n",
       " Kentucky        Mass             2\n",
       " Massachusetts   Mass             1\n",
       " Michigan        Mass             1\n",
       " Minnesota       Mass             1\n",
       "                 Spree            1\n",
       " Mississippi     Mass             1\n",
       " Missouri        Mass             1\n",
       " Nebraska        Mass             1\n",
       " Nevada          Mass             1\n",
       " New York        Mass             3\n",
       "                 Spree            1\n",
       " North Carolina  Mass             2\n",
       " Ohio            Mass             1\n",
       " Oklahoma        Mass             1\n",
       " Oregon          Spree            1\n",
       " Pennsylvania    Mass             1\n",
       " South Carolina  Mass             2\n",
       " Tennessee       Mass             1\n",
       " Texas           Mass             5\n",
       " Utah            Mass             1\n",
       " Virginia        Mass             1\n",
       " Washington      Mass             5\n",
       "                 Spree            1\n",
       " Wisconsin       Mass             3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by State and shooting type\n",
    "shootings[['State', 'Shooting_type']].groupby(['State', 'Shooting_type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BUCgU+Prrr/M9PyEEZs2aheHDhxuTDhEREb0jjBqca2VlhW3btiEqKgr3799H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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d77563908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the shooting data by State\n",
    "sns.set_style(\"darkgrid\")\n",
    "order_by_state = shootings.sort(['State'])\n",
    "sns_state = sns.factorplot(x=\"State\", hue='Shooting_type', data=order_by_state, aspect=1.5, kind=\"count\", legend_out=False, \\\n",
    "                         palette=\"muted\")\n",
    "sns_state.set_xticklabels(rotation=90)\n",
    "sns_state.axes[0][0].legend(title=\"Shooting Type\")\n",
    "sns_state.set(xlabel='State', ylabel='Count')\n",
    "plt.gcf().subplots_adjust(bottom=0.35)\n",
    "plt.gcf().subplots_adjust(left=0.10)\n",
    "plt.savefig('/tmp/ByState.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# By Gender"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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></th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Wounded</th>\n",
       "      <th>Total_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gender</th>\n",
       "      <th>Shooting_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
       "      <th>Mass</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Male</th>\n",
       "      <th>Mass</th>\n",
       "      <td>513</td>\n",
       "      <td>448</td>\n",
       "      <td>961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>46</td>\n",
       "      <td>63</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      Fatalities  Wounded  Total_victims\n",
       "Gender Shooting_type                                    \n",
       "Female Mass                    4        2              6\n",
       "       Spree                   8        0              8\n",
       "Male   Mass                  513      448            961\n",
       "       Spree                  46       63            109"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by Gender, Shooting type, and  impact to victimes wounded or fatal\n",
    "shootings.groupby(['Gender', 'Shooting_type']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  Shooting_type\n",
       "Female  Mass              1\n",
       "        Spree             1\n",
       "Male    Mass             62\n",
       "        Spree             7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by State and shooting type\n",
    "shootings[['Gender', 'Shooting_type']].groupby(['Gender', 'Shooting_type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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99eEaNGiItmz5XP/977vasWO7vvhih1JSzuvtt9/U+PFTtGXLTo0d+6YWLVqgY8eOOuNS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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d74d5aa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the shooting data by Gender\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns_gender = sns.factorplot(x=\"Gender\", hue='Shooting_type', data=order_by_state, aspect=1.5, kind=\"count\", legend_out=False, \\\n",
    "                         palette=\"muted\")\n",
    "sns_gender.axes[0][0].legend(title=\"Shooting Type\")\n",
    "sns_gender.set(xlabel='Gender', ylabel='Count')\n",
    "plt.gcf().subplots_adjust(bottom=0.35)\n",
    "plt.gcf().subplots_adjust(left=0.10)\n",
    "plt.savefig('/tmp/ByGender.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By Prior Signs of Mental Illness"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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></th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Wounded</th>\n",
       "      <th>Total_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Prior_signs_of_mental_illness</th>\n",
       "      <th>Shooting_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">No</th>\n",
       "      <th>Mass</th>\n",
       "      <td>143</td>\n",
       "      <td>85</td>\n",
       "      <td>228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Yes</th>\n",
       "      <th>Mass</th>\n",
       "      <td>374</td>\n",
       "      <td>365</td>\n",
       "      <td>739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>49</td>\n",
       "      <td>61</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Fatalities  Wounded  \\\n",
       "Prior_signs_of_mental_illness Shooting_type                        \n",
       "No                            Mass                  143       85   \n",
       "                              Spree                   5        2   \n",
       "Yes                           Mass                  374      365   \n",
       "                              Spree                  49       61   \n",
       "\n",
       "                                             Total_victims  \n",
       "Prior_signs_of_mental_illness Shooting_type                 \n",
       "No                            Mass                     228  \n",
       "                              Spree                      7  \n",
       "Yes                           Mass                     739  \n",
       "                              Spree                    110  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by Mental Illness, Shooting type, and  impact to victimes wounded or fatal\n",
    "shootings.groupby(['Prior_signs_of_mental_illness', 'Shooting_type']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Prior_signs_of_mental_illness  Shooting_type\n",
       "No                             Mass             21\n",
       "                               Spree             1\n",
       "Yes                            Mass             42\n",
       "                               Spree             7\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by mental illness and shooting type\n",
    "shootings[['Prior_signs_of_mental_illness', 'Shooting_type']].groupby(['Prior_signs_of_mental_illness', \\\n",
    "                                                                       'Shooting_type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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z4+Xlbu8yREQkj8tWeJozZw6TJ0++Z5/BYMhWeNq8eTPvvfceo0aNIiQk5L7j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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d74efe4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the shooting data by Mental Illness\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns_mental_illness = sns.factorplot(x=\"Prior_signs_of_mental_illness\", hue='Shooting_type', data=shootings, aspect=1.5, kind=\"count\", legend_out=False, \\\n",
    "                         palette=\"muted\")\n",
    "sns_mental_illness.axes[0][0].legend(title=\"Shooting Type\")\n",
    "sns_mental_illness.set(xlabel='Mental Illness Known', ylabel='Count')\n",
    "plt.gcf().subplots_adjust(bottom=0.35)\n",
    "plt.gcf().subplots_adjust(left=0.10)\n",
    "plt.savefig('/tmp/ByMentalIllness.jpg')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# By Year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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></th>\n",
       "      <th>Fatalities</th>\n",
       "      <th>Wounded</th>\n",
       "      <th>Total_victims</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th>Shooting_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1982</th>\n",
       "      <th>Mass</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1984</th>\n",
       "      <th>Mass</th>\n",
       "      <td>28.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>48.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1986</th>\n",
       "      <th>Mass</th>\n",
       "      <td>15.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1987</th>\n",
       "      <th>Mass</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>6.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1988</th>\n",
       "      <th>Mass</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1989</th>\n",
       "      <th>Mass</th>\n",
       "      <td>15.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>56.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1990</th>\n",
       "      <th>Mass</th>\n",
       "      <td>10.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1991</th>\n",
       "      <th>Mass</th>\n",
       "      <td>35.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>61.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1992</th>\n",
       "      <th>Mass</th>\n",
       "      <td>9.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1993</th>\n",
       "      <th>Mass</th>\n",
       "      <td>23.0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>57.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1994</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1995</th>\n",
       "      <th>Mass</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1996</th>\n",
       "      <th>Mass</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1997</th>\n",
       "      <th>Mass</th>\n",
       "      <td>9.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1998</th>\n",
       "      <th>Mass</th>\n",
       "      <td>10.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>4.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>29.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2000</th>\n",
       "      <th>Mass</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2001</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2003</th>\n",
       "      <th>Mass</th>\n",
       "      <td>7.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2004</th>\n",
       "      <th>Mass</th>\n",
       "      <td>5.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2005</th>\n",
       "      <th>Mass</th>\n",
       "      <td>7.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>10.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2006</th>\n",
       "      <th>Mass</th>\n",
       "      <td>13.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>20.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2007</th>\n",
       "      <th>Mass</th>\n",
       "      <td>54.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2008</th>\n",
       "      <th>Mass</th>\n",
       "      <td>18.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>42.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2009</th>\n",
       "      <th>Mass</th>\n",
       "      <td>39.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>77.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2010</th>\n",
       "      <th>Mass</th>\n",
       "      <td>9.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>11.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2011</th>\n",
       "      <th>Mass</th>\n",
       "      <td>19.0</td>\n",
       "      <td>21.0</td>\n",
       "      <td>40.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2012</th>\n",
       "      <th>Mass</th>\n",
       "      <td>66.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>133.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2013</th>\n",
       "      <th>Mass</th>\n",
       "      <td>24.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>32.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>11.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2014</th>\n",
       "      <th>Mass</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2015</th>\n",
       "      <th>Mass</th>\n",
       "      <td>14.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Spree</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    Fatalities  Wounded  Total_victims\n",
       "Year Shooting_type                                    \n",
       "1982 Mass                  8.0      3.0           11.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1984 Mass                 28.0     20.0           48.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1986 Mass                 15.0      6.0           21.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1987 Mass                  NaN      NaN            NaN\n",
       "     Spree                 6.0     14.0           20.0\n",
       "1988 Mass                  7.0      4.0           11.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1989 Mass                 15.0     41.0           56.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1990 Mass                 10.0      4.0           14.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1991 Mass                 35.0     26.0           61.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1992 Mass                  9.0     10.0           19.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1993 Mass                 23.0     34.0           57.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1994 Mass                  5.0     23.0           28.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1995 Mass                  6.0      0.0            6.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1996 Mass                  6.0      1.0            7.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1997 Mass                  9.0      5.0           14.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "1998 Mass                 10.0     11.0           21.0\n",
       "     Spree                 4.0     25.0           29.0\n",
       "...                        ...      ...            ...\n",
       "2000 Mass                  7.0      0.0            7.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2001 Mass                  5.0      4.0            9.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2003 Mass                  7.0      8.0           15.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2004 Mass                  5.0      7.0           12.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2005 Mass                  7.0      4.0           11.0\n",
       "     Spree                10.0      5.0           15.0\n",
       "2006 Mass                 13.0      7.0           20.0\n",
       "     Spree                 8.0      0.0            8.0\n",
       "2007 Mass                 54.0     32.0           86.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2008 Mass                 18.0     24.0           42.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2009 Mass                 39.0     38.0           77.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2010 Mass                  9.0      2.0           11.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2011 Mass                 19.0     21.0           40.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2012 Mass                 66.0     67.0          133.0\n",
       "     Spree                 6.0      1.0            7.0\n",
       "2013 Mass                 24.0      8.0           32.0\n",
       "     Spree                11.0      5.0           16.0\n",
       "2014 Mass                  9.0      3.0           12.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "2015 Mass                 14.0      3.0           17.0\n",
       "     Spree                 NaN      NaN            NaN\n",
       "\n",
       "[62 rows x 3 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by Year, Shooting type, and  impact to victimes wounded or fatal\n",
    "shootings.groupby(['Year', 'Shooting_type']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Shooting_type\n",
       "1982  Mass             1\n",
       "1984  Mass             2\n",
       "1986  Mass             1\n",
       "1987  Spree            1\n",
       "1988  Mass             1\n",
       "1989  Mass             2\n",
       "1990  Mass             1\n",
       "1991  Mass             3\n",
       "1992  Mass             2\n",
       "1993  Mass             4\n",
       "1994  Mass             1\n",
       "1995  Mass             1\n",
       "1996  Mass             1\n",
       "1997  Mass             2\n",
       "1998  Mass             2\n",
       "      Spree            1\n",
       "1999  Mass             4\n",
       "      Spree            1\n",
       "2000  Mass             1\n",
       "2001  Mass             1\n",
       "2003  Mass             1\n",
       "2004  Mass             1\n",
       "2005  Mass             1\n",
       "      Spree            1\n",
       "2006  Mass             2\n",
       "      Spree            1\n",
       "2007  Mass             4\n",
       "2008  Mass             3\n",
       "2009  Mass             4\n",
       "2010  Mass             1\n",
       "2011  Mass             3\n",
       "2012  Mass             6\n",
       "      Spree            1\n",
       "2013  Mass             3\n",
       "      Spree            2\n",
       "2014  Mass             2\n",
       "2015  Mass             2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by year and shooting type\n",
    "shootings[['Year', 'Shooting_type']].groupby(['Year', 'Shooting_type']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year\n",
       "1982    1\n",
       "1984    2\n",
       "1986    1\n",
       "1987    1\n",
       "1988    1\n",
       "1989    2\n",
       "1990    1\n",
       "1991    3\n",
       "1992    2\n",
       "1993    4\n",
       "1994    1\n",
       "1995    1\n",
       "1996    1\n",
       "1997    2\n",
       "1998    3\n",
       "1999    5\n",
       "2000    1\n",
       "2001    1\n",
       "2003    1\n",
       "2004    1\n",
       "2005    2\n",
       "2006    3\n",
       "2007    4\n",
       "2008    3\n",
       "2009    4\n",
       "2010    1\n",
       "2011    3\n",
       "2012    7\n",
       "2013    5\n",
       "2014    2\n",
       "2015    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looking at shootings by year and shooting type\n",
    "shootings[['Year', 'Shooting_type']].groupby(['Year']).size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rRXJysjh58qQIDQ0V4eHh4vLly1XKce3aNTFgwAARGRkprly5Ik6fPi369u0r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8d74c880b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the shooting data by Year\n",
    "sns.set_style(\"darkgrid\")\n",
    "sns_year = sns.factorplot(x='Year', data=shootings, aspect=1.5, kind=\"count\", \\\n",
    "                         palette=\"muted\")\n",
    "sns_year.set(xlabel='Year', ylabel='Count')\n",
    "sns_year.set_xticklabels(rotation=45)\n",
    "plt.gcf().subplots_adjust(bottom=0.35)\n",
    "plt.gcf().subplots_adjust(left=0.10)\n",
    "plt.savefig('/tmp/ByYear.jpg')"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py35]",
   "language": "python",
   "name": "conda-env-py35-py"
  },
  "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": 0
}