{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An Exploratory Data Analysis of Mass Shootings in The United States *created by Chase Kregor*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Purpose:** \n", "\n", " \n", "To have a better understanding of mass shootings in the US. I decided to answer all the questions in the Inspiration Section on Kaggle. \n", "\n", "** Dataset:**\n", "\n", "The dataset contains detailed information of 398 mass shootings in the United States from 1966 to 2017.\n", "\n", "** Source: **\n", "[https://www.kaggle.com/zusmani/us-mass-shootings-last-50-years](https://www.kaggle.com/zusmani/us-mass-shootings-last-50-years)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Topics & Bookmarks: **\n", "1. The Top of The Notebook\n", "2. How many people were killed and injured per year? \n", "3. Visualize mass shootings on the U.S map\n", "4. Is there any correlation between shooter and his/her race, gender? \n", "5. Any correlation with calendar dates? Do we have more deadly days, weeks or months on average?\n", "6. What cities and states are more prone to such attacks? \n", "7. Can you find and combine any other external datasets to enrich the analysis, for example, gun ownership by state? \n", "8. Any other pattern you see that can help in prediction, crowd safety or in-depth analysis of the event? \n", "9. How many shooters have some kind of mental health problem? Can we compare that shooter with general population with same condition? \n", "10. Final Results & Closing Thoughts" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.style as style\n", "style.use('fivethirtyeight')" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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S#TitleLocationDateIncident AreaOpen/Close LocationTargetCauseSummaryFatalities...Total victimsPoliceman KilledAgeEmployeed (Y/N)Employed atMental Health IssuesRaceGenderLatitudeLongitude
01Texas church mass shootingSutherland Springs, TX11/5/17ChurchCloserandomunknownDevin Patrick Kelley, 26, an ex-air force offi...26...460.026NaNNaNNoWhiteMNaNNaN
12Walmart shooting in suburban DenverThornton, CO11/1/17Wal-MartOpenrandomunknownScott Allen Ostrem, 47, walked into a Walmart ...3...30.047NaNNaNNoWhiteMNaNNaN
23Edgewood businees park shootingEdgewood, MD10/18/17Remodeling StoreClosecoworkersunknownRadee Labeeb Prince, 37, fatally shot three pe...3...60.037NaNAdvance Granite StoreNoBlackMNaNNaN
34Las Vegas Strip mass shootingLas Vegas, NV10/1/17Las Vegas Strip Concert outside Mandala BayOpenrandomunknownStephen Craig Paddock, opened fire from the 32...59...5851.064NaNNaNUnclearWhiteM36.181271-115.134132
45San Francisco UPS shootingSan Francisco, CA6/14/17UPS facilityClosecoworkersNaNJimmy Lam, 38, fatally shot three coworkers an...3...50.0381.0NaNYesAsianMNaNNaN
\n", "

5 rows × 21 columns

\n", "
" ], "text/plain": [ " S# Title Location Date \\\n", "0 1 Texas church mass shooting Sutherland Springs, TX 11/5/17 \n", "1 2 Walmart shooting in suburban Denver Thornton, CO 11/1/17 \n", "2 3 Edgewood businees park shooting Edgewood, MD 10/18/17 \n", "3 4 Las Vegas Strip mass shooting Las Vegas, NV 10/1/17 \n", "4 5 San Francisco UPS shooting San Francisco, CA 6/14/17 \n", "\n", " Incident Area Open/Close Location Target \\\n", "0 Church Close random \n", "1 Wal-Mart Open random \n", "2 Remodeling Store Close coworkers \n", "3 Las Vegas Strip Concert outside Mandala Bay Open random \n", "4 UPS facility Close coworkers \n", "\n", " Cause Summary Fatalities \\\n", "0 unknown Devin Patrick Kelley, 26, an ex-air force offi... 26 \n", "1 unknown Scott Allen Ostrem, 47, walked into a Walmart ... 3 \n", "2 unknown Radee Labeeb Prince, 37, fatally shot three pe... 3 \n", "3 unknown Stephen Craig Paddock, opened fire from the 32... 59 \n", "4 NaN Jimmy Lam, 38, fatally shot three coworkers an... 3 \n", "\n", " ... Total victims Policeman Killed Age Employeed (Y/N) \\\n", "0 ... 46 0.0 26 NaN \n", "1 ... 3 0.0 47 NaN \n", "2 ... 6 0.0 37 NaN \n", "3 ... 585 1.0 64 NaN \n", "4 ... 5 0.0 38 1.0 \n", "\n", " Employed at Mental Health Issues Race Gender Latitude \\\n", "0 NaN No White M NaN \n", "1 NaN No White M NaN \n", "2 Advance Granite Store No Black M NaN \n", "3 NaN Unclear White M 36.181271 \n", "4 NaN Yes Asian M NaN \n", "\n", " Longitude \n", "0 NaN \n", "1 NaN \n", "2 NaN \n", "3 -115.134132 \n", "4 NaN \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv('Mass Shootings Dataset Ver 5.csv')\n", "data.head()" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "RangeIndex: 323 entries, 0 to 322\n", "Data columns (total 21 columns):\n", "S# 323 non-null int64\n", "Title 323 non-null object\n", "Location 278 non-null object\n", "Date 323 non-null object\n", "Incident Area 261 non-null object\n", "Open/Close Location 295 non-null object\n", "Target 318 non-null object\n", "Cause 246 non-null object\n", "Summary 323 non-null object\n", "Fatalities 323 non-null int64\n", "Injured 323 non-null int64\n", "Total victims 323 non-null int64\n", "Policeman Killed 317 non-null float64\n", "Age 179 non-null object\n", "Employeed (Y/N) 67 non-null float64\n", "Employed at 41 non-null object\n", "Mental Health Issues 323 non-null object\n", "Race 321 non-null object\n", "Gender 323 non-null object\n", "Latitude 303 non-null float64\n", "Longitude 303 non-null float64\n", "dtypes: float64(4), int64(4), object(13)\n", "memory usage: 53.1+ KB\n" ] } ], "source": [ "data.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2: How many people were killed and injured per year?" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fatalities = data ['Fatalities'].sum()\n", "injured = data['Injured'].sum()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "fperyr = fatalities / 51\n", "iperyr = injured / 51 " ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "From 1966 to 2017 there have been\n", "1433.000000 fatalities from mass shootings\n", "1995.000000 injuries from mass shootings\n" ] } ], "source": [ "print(\"From 1966 to 2017 there have been\")\n", "print(\"%f fatalities from mass shootings\" % (fatalities))\n", "print(\"%f injuries from mass shootings\" % (injured))" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "That means there have been\n", "28.098039 fatalities due to mass shootings per year\n", "and\n", "39.117647 people injured due to mass shootings per year\n" ] } ], "source": [ "print(\"That means there have been\")\n", "print(\"%f fatalities due to mass shootings per year\"% (fperyr))\n", "print(\"and\")\n", "print(\"%f people injured due to mass shootings per year\" % (iperyr))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3: Visualize mass shootings on the U.S map " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There arent many great python languages for US Maps. At least non that I currently understand. For the sake of time I completed this task on the popular WISIYG (What You See Is What You Get) Data Visualization Tool [Tableau](https://www.tableau.com/)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 4: Is there any correlation between shooter and his/her race, gender?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Instead of finding the r values for the various shooter attributes I came up with some visualizations to understand shooter distributions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** RACE: **" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "White American or European American 122\n", "Black American or African American 76\n", "Unknown 42\n", "Some other race 20\n", "white 12\n", "Asian American 11\n", "White 9\n", "Asian 6\n", "Black 5\n", "Latino 5\n", "Native American or Alaska Native 3\n", "black 3\n", "Two or more races 2\n", "Other 2\n", "Asian American/Some other race 1\n", "Black American or African American/Unknown 1\n", "White American or European American/Some other Race 1\n", "Name: Race, dtype: int64" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Race'].value_counts()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** Need to categorize all the different races into reasonable categories **" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "White American or European American 149\n", "Black American or African American 84\n", "Unknown 62\n", "Asian American 11\n", "Two or more races 7\n", "Latino 5\n", "Native American or Alaska Native 3\n", "Name: Race, dtype: int64" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Race'] = data['Race'].replace(['Some other race'], ['Unknown'])\n", "data['Race'] = data['Race'].replace(['white'], ['White American or European American'])\n", "data['Race'] = data['Race'].replace(['White'], ['White American or European American'])\n", "data['Race'] = data['Race'].replace(['Asian'], ['White American or European American'])\n", "data['Race'] = data['Race'].replace(['Black'], ['Black American or African American'])\n", "data['Race'] = data['Race'].replace(['black'], ['Black American or African American'])\n", "data['Race'] = data['Race'].replace(['White American or European American/Some other Race'], ['Two or more races'])\n", "data['Race'] = data['Race'].replace(['Asian American/Some other race'], ['Two or more races'])\n", "data['Race'] = data['Race'].replace(['Black American or African American/Unknown'], ['Two or more races'])\n", "data['Race'] = data['Race'].replace(['Other'], ['Two or more races'])\n", "\n", "data['Race'].value_counts()" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A = data['Race'].value_counts()\n", "B = data['Race'].value_counts()\n", "\n", "\n", "B.plot(kind='bar')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** GENDER: **" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Male 272\n", "Unknown 21\n", "M 20\n", "Female 5\n", "Male/Female 4\n", "M/F 1\n", "Name: Gender, dtype: int64" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Gender'].value_counts()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Male 292\n", "Unknown 26\n", "Female 5\n", "Name: Gender, dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Gender'] = data['Gender'].replace(['M'], ['Male'])\n", "data['Gender'] = data['Gender'].replace(['Male/Female'], ['Unknown'])\n", "data['Gender'] = data['Gender'].replace(['M/F'], ['Unknown'])\n", "data['Gender'] = data['Gender'].replace(['Unkown'], ['Unknown'])\n", "\n", "data['Gender'].value_counts()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "A = data['Gender'].value_counts()\n", "B = data['Gender'].value_counts()\n", "\n", "\n", "B.plot(kind='bar')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 5: Any correlation with calendar dates? Do we have more deadly days, weeks or months on average? " ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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S#TitleLocationDateIncident AreaOpen/Close LocationTargetCauseSummaryFatalities...Employeed (Y/N)Employed atMental Health IssuesRaceGenderLatitudeLongitudeYearMonthDay
01Texas church mass shootingSutherland Springs, TX2017-11-05ChurchCloserandomunknownDevin Patrick Kelley, 26, an ex-air force offi...26...NaNNaNNoWhite American or European AmericanMaleNaNNaN2017115
12Walmart shooting in suburban DenverThornton, CO2017-11-01Wal-MartOpenrandomunknownScott Allen Ostrem, 47, walked into a Walmart ...3...NaNNaNNoWhite American or European AmericanMaleNaNNaN2017111
23Edgewood businees park shootingEdgewood, MD2017-10-18Remodeling StoreClosecoworkersunknownRadee Labeeb Prince, 37, fatally shot three pe...3...NaNAdvance Granite StoreNoBlack American or African AmericanMaleNaNNaN20171018
34Las Vegas Strip mass shootingLas Vegas, NV2017-10-01Las Vegas Strip Concert outside Mandala BayOpenrandomunknownStephen Craig Paddock, opened fire from the 32...59...NaNNaNUnclearWhite American or European AmericanMale36.181271-115.1341322017101
45San Francisco UPS shootingSan Francisco, CA2017-06-14UPS facilityClosecoworkersNaNJimmy Lam, 38, fatally shot three coworkers an...3...1.0NaNYesWhite American or European AmericanMaleNaNNaN2017614
\n", "

5 rows × 24 columns

\n", "
" ], "text/plain": [ " S# Title Location Date \\\n", "0 1 Texas church mass shooting Sutherland Springs, TX 2017-11-05 \n", "1 2 Walmart shooting in suburban Denver Thornton, CO 2017-11-01 \n", "2 3 Edgewood businees park shooting Edgewood, MD 2017-10-18 \n", "3 4 Las Vegas Strip mass shooting Las Vegas, NV 2017-10-01 \n", "4 5 San Francisco UPS shooting San Francisco, CA 2017-06-14 \n", "\n", " Incident Area Open/Close Location Target \\\n", "0 Church Close random \n", "1 Wal-Mart Open random \n", "2 Remodeling Store Close coworkers \n", "3 Las Vegas Strip Concert outside Mandala Bay Open random \n", "4 UPS facility Close coworkers \n", "\n", " Cause Summary Fatalities \\\n", "0 unknown Devin Patrick Kelley, 26, an ex-air force offi... 26 \n", "1 unknown Scott Allen Ostrem, 47, walked into a Walmart ... 3 \n", "2 unknown Radee Labeeb Prince, 37, fatally shot three pe... 3 \n", "3 unknown Stephen Craig Paddock, opened fire from the 32... 59 \n", "4 NaN Jimmy Lam, 38, fatally shot three coworkers an... 3 \n", "\n", " ... Employeed (Y/N) Employed at Mental Health Issues \\\n", "0 ... NaN NaN No \n", "1 ... NaN NaN No \n", "2 ... NaN Advance Granite Store No \n", "3 ... NaN NaN Unclear \n", "4 ... 1.0 NaN Yes \n", "\n", " Race Gender Latitude Longitude Year \\\n", "0 White American or European American Male NaN NaN 2017 \n", "1 White American or European American Male NaN NaN 2017 \n", "2 Black American or African American Male NaN NaN 2017 \n", "3 White American or European American Male 36.181271 -115.134132 2017 \n", "4 White American or European American Male NaN NaN 2017 \n", "\n", " Month Day \n", "0 11 5 \n", "1 11 1 \n", "2 10 18 \n", "3 10 1 \n", "4 6 14 \n", "\n", "[5 rows x 24 columns]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import time\n", "import datetime\n", "data['Date'] = pd.to_datetime(data['Date'])\n", "data['Year'] = data['Date'].dt.year\n", "data['Month'] = data['Date'].dt.month\n", "data['Day'] = data['Date'].dt.day\n", "\n", "data.head()\n" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Day\n", "1 127\n", "2 63\n", "3 40\n", "4 9\n", "5 72\n", "6 44\n", "7 70\n", "8 34\n", "9 39\n", "10 30\n", "11 13\n", "12 96\n", "13 11\n", "14 60\n", "15 38\n", "16 90\n", "17 39\n", "18 47\n", "19 33\n", "20 87\n", "21 45\n", "22 21\n", "23 34\n", "24 52\n", "25 33\n", "26 42\n", "27 29\n", "28 17\n", "29 61\n", "30 35\n", "31 22\n", "Name: Fatalities, dtype: int64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "years = data.groupby([\"Year\"])[\"Fatalities\"].sum()\n", "last10years = years[30:41] \n", "last10years\n", "months = data.groupby([\"Month\"])[\"Fatalities\"].sum()\n", "months\n", "days = data.groupby([\"Day\"])[\"Fatalities\"].sum()\n", "days" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "last10years.plot(kind='bar')\n", "plt.show()\n", "months.plot(kind='bar')\n", "plt.show()\n", "days.plot(kind='bar')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** FINDINGS: What we can see here is that in terms of years 2015 and 2016 were significantly worse than any other year. 2017 was still pretty bad but was better than the previous two year. In terms of months I am not sure there are any real trends. I would argue that there is no real trend in the day, it is basically random which make sense, expect for the first of the month seems to be an outlier which is super interesting. **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 6: What cities and states are more prone to such attacks? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** I decided to focus on states. **" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CityState
0Sutherland SpringsTX
1ThorntonCO
2EdgewoodMD
3Las VegasNV
4San FranciscoCA
\n", "
" ], "text/plain": [ " City State\n", "0 Sutherland Springs TX\n", "1 Thornton CO\n", "2 Edgewood MD\n", "3 Las Vegas NV\n", "4 San Francisco CA" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['Location']= data['Location'].astype(str)\n", "locations = pd.DataFrame(data['Location'].str.split(',').tolist(), columns = ['City','State','blah','',','])\n", "locations = locations[['City','State']]\n", "locations.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "need to make the cities and states uniform" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([' TX', ' CO', ' MD', ' NV', ' CA', ' PA', ' Florida', ' Ohio',\n", " ' California', ' WA', ' LA', ' Texas', ' Missouri', None,\n", " ' Virginia', ' North Carolina', ' Tennessee', ' Texas ',\n", " ' Kentucky', ' Alabama', ' Pennsylvania', ' Kansas',\n", " ' Massachusetts', ' Virginia', ' Washington', ' Arizona',\n", " ' Michigan', ' Mississippi', ' Nebraska', ' Colorado',\n", " ' Minnesota', ' Georgia', ' Maine', ' Oregon', ' South Dakota',\n", " ' New York', ' Louisiana', ' Illinois', ' South Carolina',\n", " ' Wisconsin', ' Montana', ' New Jersey', ' Indiana', ' Oklahoma',\n", " ' New Mexico', ' Idaho', ' Souderton', ' West Virginia', ' Nevada',\n", " ' Albuquerque', ' Connecticut', ' Arkansas', ' Utah', ' Lancaster',\n", " ' Vermont', ' San Diego', ' Hawaii', ' Alaska', ' Wyoming',\n", " ' Iowa'], dtype=object)" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "locations.State.unique()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": true }, "outputs": [], "source": [ "locations['State'] = locations['State'].replace([' TX'], [' Texas '])\n", "locations['State'] = locations['State'].replace([' Texas'], [' Texas '])\n", "locations['State'] = locations['State'].replace([' CO'], [' Colorado'])\n", "locations['State'] = locations['State'].replace([' MD'], [' Maryland'])\n", "locations['State'] = locations['State'].replace([' NV'], [' Nevada'])\n", "locations['State'] = locations['State'].replace([' CA'], [' California'])\n", "locations['State'] = locations['State'].replace([' WA'], [' Washington'])\n", "locations['State'] = locations['State'].replace([' LA'], [' Louisiana'])\n", "locations['State'] = locations['State'].replace([' PA'], [' Pennsylvania'])" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([' Texas ', ' Colorado', ' Maryland', ' Nevada', ' California',\n", " ' Pennsylvania', ' Florida', ' Ohio', ' Washington', ' Louisiana',\n", " ' Missouri', None, ' Virginia', ' North Carolina', ' Tennessee',\n", " ' Kentucky', ' Alabama', ' Kansas', ' Massachusetts', ' Virginia',\n", " ' Arizona', ' Michigan', ' Mississippi', ' Nebraska', ' Minnesota',\n", " ' Georgia', ' Maine', ' Oregon', ' South Dakota', ' New York',\n", " ' Illinois', ' South Carolina', ' Wisconsin', ' Montana',\n", " ' New Jersey', ' Indiana', ' Oklahoma', ' New Mexico', ' Idaho',\n", " ' Souderton', ' West Virginia', ' Albuquerque', ' Connecticut',\n", " ' Arkansas', ' Utah', ' Lancaster', ' Vermont', ' San Diego',\n", " ' Hawaii', ' Alaska', ' Wyoming', ' Iowa'], dtype=object)" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "locations.State.unique()" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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State
California30
Florida20
Texas18
Washington15
Georgia13
North Carolina11
Arizona11
Ohio10
New York10
Illinois9
\n", "
" ], "text/plain": [ " State\n", " California 30\n", " Florida 20\n", " Texas 18\n", " Washington 15\n", " Georgia 13\n", " North Carolina 11\n", " Arizona 11\n", " Ohio 10\n", " New York 10\n", " Illinois 9" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "statetotals = pd.DataFrame(locations['State'].value_counts())\n", "statetotals.head(n=10)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "states = pd.DataFrame(locations['State'].value_counts())\n", "top10states = states[0:10]\n", "\n", "top10states.plot(kind='bar')\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": true }, "outputs": [], "source": [ "statetotals = pd.DataFrame(locations['State'].value_counts())\n", "top20 = statetotals[0:20]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": true }, "outputs": [], "source": [ "top20.to_csv(\"ratio.csv\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I used the cencus data for population to come up with which state has the highest ratio of mass shootings to population. \n", "\n", "https://www.census.gov/data/datasets/2016/demo/popest/state-total.html" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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StateNumberofShootingsPopulationRatio
12Indiana16,633,0531.510000e-07
18Maryland16,016,4471.660000e-07
13Iowa13,134,6933.190000e-07
27New Jersey38,944,4693.350000e-07
35South Carolina512,784,2273.910000e-07
32Oklahoma511,614,3734.310000e-07
23Missouri36,093,0004.920000e-07
29New York1019,745,2895.060000e-07
43West Virginia11,831,1025.460000e-07
19Massachusetts46,811,7795.870000e-07
\n", "
" ], "text/plain": [ " State NumberofShootings Population Ratio\n", "12 Indiana 1 6,633,053 1.510000e-07\n", "18 Maryland 1 6,016,447 1.660000e-07\n", "13 Iowa 1 3,134,693 3.190000e-07\n", "27 New Jersey 3 8,944,469 3.350000e-07\n", "35 South Carolina 5 12,784,227 3.910000e-07\n", "32 Oklahoma 5 11,614,373 4.310000e-07\n", "23 Missouri 3 6,093,000 4.920000e-07\n", "29 New York 10 19,745,289 5.060000e-07\n", "43 West Virginia 1 1,831,102 5.460000e-07\n", "19 Massachusetts 4 6,811,779 5.870000e-07" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "dataframe = pd.read_csv(\"stateratios.csv\")\n", "\n", "dataframe = dataframe.sort_values('Ratio')\n", "\n", "dataframe.head(n=10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** FINDINGS: Indiana, Maryland, Iowa, New Jersey, South Carolina, Oklahoma, Missouri, New York, West Virginia, Massachusetts are the top 10 states to live in terms of number of mass shootings per person. This is based on the most recent population statistics from the US Census estimates.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 7: Can you find and combine any other external datasets to enrich the analysis, for example, gun ownership by state? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Did this in question 6." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 8: Any other pattern you see that can help in prediction, crowd safety or in-depth analysis of the event? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"pattern\" I wanted to explore is what the causes for the mass shootings were. " ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Cause
psycho68
terrorism65
anger44
frustration18
domestic dispute12
revenge10
unemployement10
racism6
unknown4
failing exams3
religious radicalism1
drunk1
suspension1
domestic disputer1
breakup1
robbery1
\n", "
" ], "text/plain": [ " Cause\n", "psycho 68\n", "terrorism 65\n", "anger 44\n", "frustration 18\n", "domestic dispute 12\n", "revenge 10\n", "unemployement 10\n", "racism 6\n", "unknown 4\n", "failing exams 3\n", "religious radicalism 1\n", "drunk 1\n", "suspension 1\n", "domestic disputer 1\n", "breakup 1\n", "robbery 1" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "causes = pd.DataFrame(data['Cause'].value_counts())\n", "causes" ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "causes = data['Cause'].value_counts()\n", "\n", "causes = pd.DataFrame(data['Cause'].value_counts())\n", "\n", "causes.plot(kind='bar')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** FINDINGS: As you can see mental issues, terrorism (another form of mental issue), and anger have been the top 3 \"Causes\" for mass shootings in the US in the last 50 years. **" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 9: How many shooters have some kind of mental health problem? Can we compare that shooter with general population with same condition? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I decided to define mental health problems as \"psycho\" out of all the causes " ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The percentage of shooters that have a mental health problems is 27.642276 percent\n" ] } ], "source": [ "totalshooterswithcauses = causes.sum()\n", "\n", "result = 68 / totalshooterswithcauses * 100\n", "print(\"The percentage of shooters that have a mental health problems is %f percent\" % (result))\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 10: Final Results & Closing Thoughts: \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "** RESULTS: **\n", "\n", "** How many people were killed and injured per year?** \n", "28.098039 people were killed due to mass shootings per year. \n", "\n", "39.117647 people were injured due to mass shootings per year\n", "\n", "** Is there any correlation between shooter and his/her race, gender?**\n", "The answer is overwhelmingly white men. \n", "\n", "** Any correlation with calendar dates? Do we have more deadly days, weeks or months on average?**\n", "2015 and 2016 were the worst years for the number of fatalities due to mass shootings. The most deadly day of the month is the first. \n", "\n", "** What cities and states are more prone to such attacks?**\n", "California, Florida, Texas, Washington, and Georgia are the top five states with the highest number of mass shootings over the past 50 years. That being said, Indiana, Maryland, Iowa, New Jersey, and South Carolina have the highest number of mass shootings per population. \n", "\n", "** Any other pattern you see that can help in prediction, crowd safety or in-depth analysis of the event?**\n", "Mental issues, terrorism (another form of mental issue), and anger have been the top 3 \"Causes\" for mass shootings in the US in the past 50 years.\n", "\n", "** How many shooters have some kind of mental health problem? Can we compare that shooter with general population with same condition? **\n", "The percentage of shooters that have a mental health problems is 27.642276 percent.\n", "\n", " \n", "** CLOSING THOUGHTS: **\n", "- This notebook was created in 6 hours. I'm sure there are some errors in it. Feedback is highly encouraged!\n", "\n", "- Also, while I didn't answer every question precisely, I answered them to the best of my data science capabilities. \n", "\n", "- In doing so, I hope to have proven a significant amount of depth and variety in my data science skills. \n", "\n", "- If you have any questions, comments, or need to get in contact with me, check out my [about page](https://chasekregor.github.io/about/). " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }