FinalProject_group109.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gkMvgbZmAps-"
   },
   "source": [
    "# COGS 108 - Final Project: Mental Health & Gun Violence\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fTXvFha8Aps-"
   },
   "source": [
    "# Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3LvfJRiFAps_"
   },
   "source": [
    "There have been 22,680 incidents of gun violence, and counting, in 2019 alone (Gun Violence Archive), and gun control is heavily debated in the United States, especially with the number of mass shootings rising. Even President Trump chimed into this issue in one of his famous tweets after the November 2018 Thousand Oaks shooting: “It’s a mental health problem.” Trump routinely engages in a popular narrative that is held by many Americans today — that individuals with mental health issues are more prone to starting violent events. \n",
    "\n",
    "This project will explore the relationship between mental health and gun violence to evaluate the truth behind these statements. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MjDzJQBlAps_"
   },
   "source": [
    "# Names (Group 109)\n",
    "\n",
    "- Xiangyu Wei\n",
    "\n",
    "- Tara Patwardhan\n",
    "\n",
    "- Amy Le\n",
    "\n",
    "- Annika Olives\n",
    "\n",
    "- Tejas Bhakta\n",
    "\n",
    "- Yuqing Yang"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "70b-l0NFAptA"
   },
   "source": [
    "# Research Questions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "b8LcQeIKAptB"
   },
   "source": [
    "\n",
    "\n",
    "1.   Is there a relationship between mental health occurences (within a state) and gun violence instances? \n",
    "2.   Is there a relationship between the proximity of mental health facilities and elevated occurrences of gun violence incidents in the United States?\n",
    "3. Does a larger number of mental health treatment facilities correlate with a decrease in gun violence rates?\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JhsFQ89XAptB"
   },
   "source": [
    "# Background and Prior Work"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "IH5yfc0rAptC"
   },
   "source": [
    "According to Grant Duwe, who has studied mass public shootings for years as the director of research at the Minnesota Department of Corrections, “about 60 percent of mass shooters have a history of serious mental disorders and two-thirds had never been seen by a mental health professional.” Following the Sandy Hook shooting in 2012, 46% of American survey respondents agreed that people with serious mental illness were more dangerous than the general population, and a majority also supported increasing government spending on mental health screening and treatment as a way to reduce gun violence (Barry, McGinty, Vernick, & Webster, 2013). However, the link between mental illness and violence could simply be perpetuated by heavy media coverage of mass shootings and violent events (Swanson, McGinty, Fazel, & Mays, 2015), bringing up the question of whether the link really exists. \n",
    "\n",
    "For policymakers to effectively prevent more gun-related tragedies in the future, analyzing possible causes of these events is vital to the safety and security of our communities. The National Alliance on Mental Illness recognizes that “acts of violence by people with mental illness are usually the result of lack of needed mental health services.” Therefore, mental health itself may not be a predictor of gun violence, but the lack of effective resources available could influence the possibility of violence. Thus, this project explores the relationship between the locality and services provided by mental health resources and instances of gun violence in an attempt to determine if mental health and gun violence are related, and if the accessibility to mental health services impacts the number of gun violence incidents. \n",
    "\n",
    "References \n",
    "1. https://khn.org/news/gun-control-vs-mental-health-care-debate-after-mass-shootings-obscures-murky-reality/\n",
    "2. \n",
    "https://www.ncfr.org/ncfr-report/winter-2018/intersection-mental-illness-and-gun-violence\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jO7VejqQAptC"
   },
   "source": [
    "# Hypothesis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4IFmQMYKAptD"
   },
   "source": [
    "We hypothesize that states with more reported mental health occurences will have increased gun violence rate. This is due to the speculative idea that people with mental health illnesses are more likely to participate in violent acts. We hypothesize that there is a relationship between the two variables.\n",
    "\n",
    "Additionally, we also hypothesize that there will be lower gun violence incidents in locations that are in closer proximity to mental health facilities.\n",
    "\n",
    "Lastly, we hypothesize that states with more mental health facilities will have decreased gun violence rates. This is due to similar reasoning as before, where if people with mental health illnesses who do not have access to needed resources they are more likely  to participate in violent acts, so in states with more mental health facilities the opposite should hold as well. \n",
    "\n",
    "We believe that these hypotheses hold true based on our background research of the promise that mental health facilities hold with regards to preventing violent gun incidents. \n",
    "\n",
    "References\n",
    " 1.  https://www.hsph.harvard.edu/means-matter/gun-shop-project/ \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PwKLfU9lAptD"
   },
   "source": [
    "# Dataset(s)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "vKvW6CDGAptE"
   },
   "source": [
    "1. Dataset Name: Gun Violence Data\n",
    "-- Link to the dataset: https://www.kaggle.com/jameslko/gun-violence-data\n",
    "-- Number of observations: 239,677\n",
    "\n",
    "This dataset, provided by the Gun Violence Archive, contains several observations about gun-related violence in the United States, including the state, city, and exact location of the incident, as well as the number of people killed, the number of people injured, and the participant's ages. \n",
    "\n",
    "\n",
    "2. Dataset Name: Facility Locations\n",
    "-- Link to the dataset: https://findtreatment.samhsa.gov/locator\n",
    "-- Number of observations: 9685\n",
    "\n",
    "This dataset provides us with the name, address, website, and exact location of every mental health facility in the US. \n",
    "\n",
    "3. Dataset Name: State Population Data\n",
    "-- Link to the dataset: https://www.census.gov/data/tables/time-series/demo/popest/2010s-state-total.html?fbclid=IwAR1DkUqKesAlkjUVkGBQzVudeYnJTQgaxgxrp2KClqQt4esA17yiGbdpr6c \n",
    "-- Number of observations: 528\n",
    "\n",
    "This data set provides us with the population of all 50 states as well as Washington, D.C. and Puerto Rico from 2010 to 2018. It also provides us with population by geographic area (northeast, midwest, south, and west)\n",
    "\n",
    "4. Dataset Name: Drug Use, Perceptions of Great Risk, Past Year Substance Use Disorder and Treatment, and Past Year Mental Health Measures\n",
    "-- Link to the dataset: https://datafiles.samhsa.gov/study-dataset/national-mental-health-services-survey-2017-n-mhss-2017-ds0001-nid18462\n",
    "-- Number of observations: 10,230\n",
    "\n",
    "This data set provides us with mental health data by population, as well as mental mealth data by percentage of the population. it contains data for substance abuse and treatment options as well.\n",
    "\n",
    "Since we are using multiple data sets, we are going to merge the data from gun violence incidents and mental health facilities in order to calculate the distance between incidents and facilities. We are also using the state populations from 2010-2018 in order to measure the rate if incidents per capita in the United States."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "3ILgpRK3AptE"
   },
   "source": [
    "# Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "colab_type": "code",
    "id": "YQG96qgTg9Wa",
    "outputId": "095b3f17-7165-4248-b639-e3758e202343"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mThe directory '/home/jovyan/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
      "\u001b[33mThe directory '/home/jovyan/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
      "Collecting scipy==1.2\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/67/e6/6d4edaceee6a110ecf6f318482f5229792f143e468b34a631f5a0899f56d/scipy-1.2.0-cp36-cp36m-manylinux1_x86_64.whl (26.6MB)\n",
      "\u001b[K    100% |████████████████████████████████| 26.6MB 1.5MB/s eta 0:00:01  6% |██                              | 1.7MB 18.9MB/s eta 0:00:02    16% |█████▍                          | 4.5MB 17.4MB/s eta 0:00:02a 0:00:01    79% |█████████████████████████▋      | 21.3MB 18.7MB/s eta 0:00:01    90% |█████████████████████████████▏  | 24.2MB 18.3MB/s eta 0:00:01    98% |███████████████████████████████▌| 26.2MB 18.3MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: numpy>=1.8.2 in /opt/conda/lib/python3.6/site-packages (from scipy==1.2) (1.12.1)\n",
      "Installing collected packages: scipy\n",
      "  Found existing installation: scipy 0.19.0\n",
      "    Uninstalling scipy-0.19.0:\n",
      "\u001b[31mCould not install packages due to an EnvironmentError: [Errno 13] Permission denied: 'BENTO_BUILD.txt'\n",
      "Consider using the `--user` option or check the permissions.\n",
      "\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install scipy==1.2 --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mThe directory '/home/jovyan/.cache/pip/http' or its parent directory is not owned by the current user and the cache has been disabled. Please check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
      "\u001b[33mThe directory '/home/jovyan/.cache/pip' or its parent directory is not owned by the current user and caching wheels has been disabled. check the permissions and owner of that directory. If executing pip with sudo, you may want sudo's -H flag.\u001b[0m\n",
      "Collecting plotly\n",
      "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ff/75/3982bac5076d0ce6d23103c03840fcaec90c533409f9d82c19f54512a38a/plotly-3.10.0-py2.py3-none-any.whl (41.5MB)\n",
      "\u001b[K    100% |████████████████████████████████| 41.5MB 1.2MB/s eta 0:00:01  9% |███▏                            | 4.0MB 18.1MB/s eta 0:00:03    21% |██████▉                         | 8.9MB 18.7MB/s eta 0:00:02    39% |████████████▊                   | 16.5MB 18.9MB/s eta 0:00:02    41% |█████████████▍                  | 17.4MB 17.6MB/s eta 0:00:02    46% |██████████████▉                 | 19.3MB 16.8MB/s eta 0:00:02MB/s eta 0:00:01    71% |███████████████████████         | 29.8MB 18.1MB/s eta 0:00:01    83% |██████████████████████████▊     | 34.6MB 18.7MB/s eta 0:00:01\n",
      "\u001b[?25hRequirement already satisfied, skipping upgrade: six in /opt/conda/lib/python3.6/site-packages (from plotly) (1.12.0)\n",
      "Requirement already satisfied, skipping upgrade: decorator>=4.0.6 in /opt/conda/lib/python3.6/site-packages (from plotly) (4.3.0)\n",
      "Requirement already satisfied, skipping upgrade: nbformat>=4.2 in /opt/conda/lib/python3.6/site-packages (from plotly) (4.4.0)\n",
      "Requirement already satisfied, skipping upgrade: requests in /opt/conda/lib/python3.6/site-packages (from plotly) (2.12.4)\n",
      "Requirement already satisfied, skipping upgrade: pytz in /opt/conda/lib/python3.6/site-packages (from plotly) (2018.7)\n",
      "Collecting retrying>=1.3.3 (from plotly)\n",
      "  Downloading https://files.pythonhosted.org/packages/44/ef/beae4b4ef80902f22e3af073397f079c96969c69b2c7d52a57ea9ae61c9d/retrying-1.3.3.tar.gz\n",
      "Requirement already satisfied, skipping upgrade: ipython_genutils in /opt/conda/lib/python3.6/site-packages (from nbformat>=4.2->plotly) (0.2.0)\n",
      "Requirement already satisfied, skipping upgrade: traitlets>=4.1 in /opt/conda/lib/python3.6/site-packages (from nbformat>=4.2->plotly) (4.3.2)\n",
      "Requirement already satisfied, skipping upgrade: jsonschema!=2.5.0,>=2.4 in /opt/conda/lib/python3.6/site-packages (from nbformat>=4.2->plotly) (3.0.0a3)\n",
      "Requirement already satisfied, skipping upgrade: jupyter_core in /opt/conda/lib/python3.6/site-packages (from nbformat>=4.2->plotly) (4.4.0)\n",
      "Requirement already satisfied, skipping upgrade: attrs>=17.4.0 in /opt/conda/lib/python3.6/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2->plotly) (18.2.0)\n",
      "Requirement already satisfied, skipping upgrade: pyrsistent>=0.14.0 in /opt/conda/lib/python3.6/site-packages (from jsonschema!=2.5.0,>=2.4->nbformat>=4.2->plotly) (0.14.8)\n",
      "Installing collected packages: retrying, plotly\n",
      "  Running setup.py install for retrying ... \u001b[?25lerror\n",
      "    Complete output from command /opt/conda/bin/python -u -c \"import setuptools, tokenize;__file__='/tmp/pip-install-y0ab9sr5/retrying/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\\r\\n', '\\n');f.close();exec(compile(code, __file__, 'exec'))\" install --record /tmp/pip-record-m2lrdvda/install-record.txt --single-version-externally-managed --compile:\n",
      "    running install\n",
      "    running build\n",
      "    running build_py\n",
      "    creating build\n",
      "    creating build/lib\n",
      "    copying retrying.py -> build/lib\n",
      "    running install_lib\n",
      "    copying build/lib/retrying.py -> /opt/conda/lib/python3.6/site-packages\n",
      "    error: could not create '/opt/conda/lib/python3.6/site-packages/retrying.py': Permission denied\n",
      "    \n",
      "    ----------------------------------------\n",
      "\u001b[31mCommand \"/opt/conda/bin/python -u -c \"import setuptools, tokenize;__file__='/tmp/pip-install-y0ab9sr5/retrying/setup.py';f=getattr(tokenize, 'open', open)(__file__);code=f.read().replace('\\r\\n', '\\n');f.close();exec(compile(code, __file__, 'exec'))\" install --record /tmp/pip-record-m2lrdvda/install-record.txt --single-version-externally-managed --compile\" failed with error code 1 in /tmp/pip-install-y0ab9sr5/retrying/\u001b[0m\n",
      "\u001b[?25h"
     ]
    }
   ],
   "source": [
    "!pip install plotly --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "U10mmfs-AptF"
   },
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'plotly'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-22-9a042fb65ed4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mbs4\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mbs4\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mBeautifulSoup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotly\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplotly\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'plotly'"
     ]
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import requests\n",
    "import bs4\n",
    "from bs4 import BeautifulSoup\n",
    "import plotly\n",
    "import plotly.plotly as py\n",
    "import os\n",
    "import re\n",
    "import locale\n",
    "from locale import atof\n",
    "import matplotlib.pyplot as plt\n",
    "import patsy\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats\n",
    "from scipy.stats import ttest_ind, chisquare, normaltest\n",
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    },
    "colab_type": "code",
    "id": "DaWNCRyHuL37",
    "outputId": "6727cd2e-58ba-4759-b425-849022ffe99a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "The following command must be run outside of the IPython shell:\n",
      "\n",
      "    $ pip install patsy==0.5.1 --upgrade\n",
      "\n",
      "The Python package manager (pip) can only be used from outside of IPython.\n",
      "Please reissue the `pip` command in a separate terminal or command prompt.\n",
      "\n",
      "See the Python documentation for more informations on how to install packages:\n",
      "\n",
      "    https://docs.python.org/3/installing/\n"
     ]
    }
   ],
   "source": [
    "pip install patsy==0.5.1 --upgrade"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KfEE7IpwAptH"
   },
   "source": [
    "# Data Cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4oFGPuHDAptH"
   },
   "source": [
    "First, we will import the data files that we will be using into dataframes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fko6n8igAptI",
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-24-da79f4a3a15b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mgun_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'GunViolenceData2013_2018.csv'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 10\u001b[0;31m \u001b[0mfacilities_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'FacilityLocations.xlsx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     11\u001b[0m \u001b[0mpop_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_excel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'StatePopulationData2010-2018.xlsx'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    176\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    177\u001b[0m                     \u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mnew_arg_name\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_arg_value\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 178\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    179\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    180\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0m_deprecate_kwarg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/excel.py\u001b[0m in \u001b[0;36mread_excel\u001b[0;34m(io, sheet_name, header, names, index_col, usecols, squeeze, dtype, engine, converters, true_values, false_values, skiprows, nrows, na_values, parse_dates, date_parser, thousands, comment, skipfooter, convert_float, **kwds)\u001b[0m\n\u001b[1;32m    305\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    306\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 307\u001b[0;31m         \u001b[0mio\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mExcelFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mengine\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    308\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    309\u001b[0m     return io.parse(\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/io/excel.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, io, **kwds)\u001b[0m\n\u001b[1;32m    392\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_contents\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    393\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstring_types\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 394\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbook\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxlrd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen_workbook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_io\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    395\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    396\u001b[0m             raise ValueError('Must explicitly set engine if not passing in'\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xlrd/__init__.py\u001b[0m in \u001b[0;36mopen_workbook\u001b[0;34m(filename, logfile, verbosity, use_mmap, file_contents, encoding_override, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[1;32m    136\u001b[0m                 \u001b[0mformatting_info\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mformatting_info\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    137\u001b[0m                 \u001b[0mon_demand\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mon_demand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 138\u001b[0;31m                 \u001b[0mragged_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mragged_rows\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    139\u001b[0m             )\n\u001b[1;32m    140\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mbk\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mopen_workbook_2007_xml\u001b[0;34m(zf, component_names, logfile, verbosity, use_mmap, formatting_info, on_demand, ragged_rows)\u001b[0m\n\u001b[1;32m    839\u001b[0m         \u001b[0mx12sheet\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX12Sheet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msheet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlogfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mverbosity\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    840\u001b[0m         \u001b[0mheading\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"Sheet %r (sheetx=%d) from %r\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0msheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msheetx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 841\u001b[0;31m         \u001b[0mx12sheet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprocess_stream\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mzflo\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mheading\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    842\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0mzflo\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    843\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mown_process_stream\u001b[0;34m(self, stream, heading)\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0melem\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mET\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0miterparse\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstream\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    549\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtag\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mrow_tag\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m                 \u001b[0mself_do_row\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0melem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m                 \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# destroy all child elements (cells)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    552\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0melem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtag\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mU_SSML12\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"dimension\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/xlrd/xlsx.py\u001b[0m in \u001b[0;36mdo_row\u001b[0;34m(self, row_elem)\u001b[0m\n\u001b[1;32m    759\u001b[0m                 \u001b[0;31m# without having to build a shared string table\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    760\u001b[0m                 \u001b[0;32mfor\u001b[0m \u001b[0mchild\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcell_elem\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 761\u001b[0;31m                     \u001b[0mchild_tag\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mchild\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtag\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    762\u001b[0m                     \u001b[0;32mif\u001b[0m \u001b[0mchild_tag\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mIS_TAG\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    763\u001b[0m                         \u001b[0mtvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_text_from_si_or_is\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mchild\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "## GunViolenceData2013_2018.csv is the Kaggle data containing gun violence data from 2013 - 2018 saved into gun_df\n",
    "## FacilityLocations.xlsx is taken from the nmhss website with the location data for mental health services in the US saved into facilities_df\n",
    "## StatePopulationData2010-2018 is taken from the US Census and is saved into pop_df\n",
    "\n",
    "## NOTE: please download dataset #1 separately or use this google drive link \n",
    "## https://drive.google.com/a/ucsd.edu/file/d/1AYcLFQbKSOTXW7AH7GrRxQ6ouyFk633Q/view?usp=sharing\n",
    "## this file was too large to upload to github sadly\n",
    "\n",
    "gun_df = pd.read_csv('GunViolenceData2013_2018.csv')\n",
    "facilities_df = pd.read_excel('FacilityLocations.xlsx')\n",
    "pop_df = pd.read_excel('StatePopulationData2010-2018.xlsx')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jHCXu2SqiBjv"
   },
   "source": [
    "Before we start the data cleaning, let's write a helper function that will help convert state names into their two-letter abbreviations. This will come in handy later for our datasets. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "zKbW1UPNiBjx"
   },
   "outputs": [],
   "source": [
    "# convert state to abbreviation\n",
    "# state-abbreviation corresponding dictionary\n",
    "state_abbrev_dic = {\n",
    "    'Alabama': 'AL',\n",
    "    'Alaska': 'AK',\n",
    "    'Arizona': 'AZ',\n",
    "    'Arkansas': 'AR',\n",
    "    'California': 'CA',\n",
    "    'Colorado': 'CO',\n",
    "    'Connecticut': 'CT',\n",
    "    'Delaware': 'DE',\n",
    "    'District of Columbia': 'DC',\n",
    "    'Florida': 'FL',\n",
    "    'Georgia': 'GA',\n",
    "    'Hawaii': 'HI',\n",
    "    'Idaho': 'ID',\n",
    "    'Illinois': 'IL',\n",
    "    'Indiana': 'IN',\n",
    "    'Iowa': 'IA',\n",
    "    'Kansas': 'KS',\n",
    "    'Kentucky': 'KY',\n",
    "    'Louisiana': 'LA',\n",
    "    'Maine': 'ME',\n",
    "    'Maryland': 'MD',\n",
    "    'Massachusetts': 'MA',\n",
    "    'Michigan': 'MI',\n",
    "    'Minnesota': 'MN',\n",
    "    'Mississippi': 'MS',\n",
    "    'Missouri': 'MO',\n",
    "    'Montana': 'MT',\n",
    "    'Nebraska': 'NE',\n",
    "    'Nevada': 'NV',\n",
    "    'New Hampshire': 'NH',\n",
    "    'New Jersey': 'NJ',\n",
    "    'New Mexico': 'NM',\n",
    "    'New York': 'NY',\n",
    "    'North Carolina': 'NC',\n",
    "    'North Dakota': 'ND',\n",
    "    'Ohio': 'OH',\n",
    "    'Oklahoma': 'OK',\n",
    "    'Oregon': 'OR',\n",
    "    'Pennsylvania': 'PA',\n",
    "    'Rhode Island': 'RI',\n",
    "    'South Carolina': 'SC',\n",
    "    'South Dakota': 'SD',\n",
    "    'Tennessee': 'TN',\n",
    "    'Texas': 'TX',\n",
    "    'Utah': 'UT',\n",
    "    'Vermont': 'VT',\n",
    "    'Virginia': 'VA',\n",
    "    'Washington': 'WA',\n",
    "    'West Virginia': 'WV',\n",
    "    'Wisconsin': 'WI',\n",
    "    'Wyoming': 'WY',\n",
    "}\n",
    "\n",
    "# convert state data\n",
    "# change state into two-letter abbreviation\n",
    "def state_to_abbrev(state):\n",
    "    if state in state_abbrev_dic.keys():\n",
    "        return state_abbrev_dic[state]\n",
    "    else:\n",
    "        return np.nan\n",
    "    \n",
    "# convert string int with comma to int\n",
    "def string_to_int(s):\n",
    "    return int(s.replace(',',\"\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YO5qkl1niBj2"
   },
   "source": [
    "One of our data sources needs to be scraped. By scraping the data off the SAMHSA website,  we can grab the data on mental health occurences by state. SAMHSA gives us two types of data, one that is mental health data by population (given to us by thousands) and another that is mental health data by percentage of the population. Thus, by scraping the website and returning both types of data, we will create separate dataframes to be used for their respective analyses.\n",
    "\n",
    "Note: \"Serious mental illness\" is defined as \"adults with any mental disorder that resulted in serious functional impairment.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lBG9rvFiiBj3"
   },
   "outputs": [],
   "source": [
    "link = \"https://www.samhsa.gov/data/sites/default/files/NSDUHsaeSpecificStates2016A/NSDUHsaeSpecificStates2016.htm?fbclid=IwAR1G3975SvsvwH19LsdqrYp4PUkMg-LyciPQW0OKgFuR6YPJTXJDgsPXyDY\"\n",
    "page = requests.get(link)\n",
    "soup = BeautifulSoup(page.text)\n",
    "tables = soup.findAll(\"table\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kyNVZKdYiBj9"
   },
   "outputs": [],
   "source": [
    "# below is a function that goes through each state and finds the table data that corresponds with that state\n",
    "# this function will return two dataframes in a tuple, one with data by the thousands and the other using percentages\n",
    "def findStateTables(tables):\n",
    "    # i is our counter, if i is even then we are dealing with a population table, if i is odd then its percentages\n",
    "    i = 0 \n",
    "    stateMHPop_df = pd.DataFrame()\n",
    "    stateMHPercent_df = pd.DataFrame()\n",
    "    states = []\n",
    "    \n",
    "    # arrays for the population by numbers data\n",
    "    serious1217, serious18 = [], []\n",
    "    anymh1217, anymh18 = [], []\n",
    "    received1217, received18 = [], [] \n",
    "    suicide1217, suicide18 = [], []\n",
    "    depression1217, depression18 = [], []\n",
    "    \n",
    "    # arrays for the percentage data\n",
    "    serious1217p, serious18p = [], []\n",
    "    anymh1217p, anymh18p = [], []\n",
    "    received1217p, received18p = [], [] \n",
    "    suicide1217p, suicide18p = [], []\n",
    "    depression1217p, depression18p = [], []\n",
    "    \n",
    "    for t in tables:\n",
    "        # for scraping number (by thousands) data\n",
    "        if i % 2 == 0:\n",
    "            # we dont want duplicate states so only add states for even i counts\n",
    "            states.append(t.find('span').text)\n",
    "            rows = t.findAll('tr')\n",
    "            # the data we need for mental health appears for the 38th 'th' (i counted) so we want to scrape from there\n",
    "            \n",
    "            # serious mental health issues\n",
    "            serious = rows[33].findAll('td')\n",
    "            # any mental health issues \n",
    "            anymh = rows[34].findAll('td')\n",
    "            # received mental health services \n",
    "            received = rows[35].findAll('td')\n",
    "            # had serious thoughts of suicide\n",
    "            suicide = rows[36].findAll('td')\n",
    "            # major depressive episode\n",
    "            depression = rows[37].findAll('td')\n",
    "            \n",
    "            # the stats we care about are for ages 12-17 and 18+ and those will be indexes 1 and 4 \n",
    "            serious1217.append(serious[1].text)\n",
    "            serious18.append(serious[4].text)\n",
    "            anymh1217.append(anymh[1].text)\n",
    "            anymh18.append(anymh[4].text)\n",
    "            received1217.append(received[1].text)\n",
    "            received18.append(received[4].text)\n",
    "            suicide1217.append(suicide[1].text)\n",
    "            suicide18.append(suicide[4].text)\n",
    "            depression1217.append(depression[1].text)\n",
    "            depression18.append(depression[4].text)\n",
    "            \n",
    "            i = i + 1\n",
    "            \n",
    "        ## for scraping percentage data\n",
    "        else:\n",
    "            rows = t.findAll('tr')\n",
    "            # the data we need for mental health appears for the 38th 'th' (i counted) so we want to scrape from there\n",
    "            \n",
    "            # serious mental health issues\n",
    "            serious = rows[33].findAll('td')\n",
    "            # any mental health issues \n",
    "            anymh = rows[34].findAll('td')\n",
    "            # received mental health services \n",
    "            received = rows[35].findAll('td')\n",
    "            # had serious thoughts of suicide\n",
    "            suicide = rows[36].findAll('td')\n",
    "            # major depressive episode\n",
    "            depression = rows[37].findAll('td')\n",
    "            \n",
    "            # the stats we care about are for ages 12-17 and 18+ and those will be indexes 1 and 4 \n",
    "            serious1217p.append(serious[1].text)\n",
    "            serious18p.append(serious[4].text)\n",
    "            anymh1217p.append(anymh[1].text)\n",
    "            anymh18p.append(anymh[4].text)\n",
    "            received1217p.append(received[1].text)\n",
    "            received18p.append(received[4].text)\n",
    "            suicide1217p.append(suicide[1].text)\n",
    "            suicide18p.append(suicide[4].text)\n",
    "            depression1217p.append(depression[1].text)\n",
    "            depression18p.append(depression[4].text)\n",
    "            i = i + 1\n",
    "    \n",
    "    # setting up columns for the population dataframe\n",
    "    stateMHPop_df['state'] = states\n",
    "    stateMHPop_df['state code'] = stateMHPop_df['state'].apply(state_to_abbrev)\n",
    "    stateMHPop_df['serious 12-17'] = serious1217\n",
    "    stateMHPop_df['serious 18+'] = serious18\n",
    "    stateMHPop_df['any 12-17'] = anymh1217\n",
    "    stateMHPop_df['any 18+'] = anymh18\n",
    "    stateMHPop_df['received 12-17'] = received1217\n",
    "    stateMHPop_df['received 18+'] = received18\n",
    "    stateMHPop_df['suicide 12-17'] = suicide1217\n",
    "    stateMHPop_df['suicide 18+'] = suicide18\n",
    "    stateMHPop_df['depression 12-17'] = depression1217\n",
    "    stateMHPop_df['depression 18+'] = depression18\n",
    "    stateMHPop_df.set_index('state', inplace=True)\n",
    "    \n",
    "    # setting up columns for the percentage dataframe\n",
    "    stateMHPercent_df['state'] = states\n",
    "    stateMHPercent_df['state code'] = stateMHPercent_df['state'].apply(state_to_abbrev)\n",
    "    stateMHPercent_df['serious 12-17'] = serious1217p\n",
    "    stateMHPercent_df['%serious 18+'] = serious18p\n",
    "    stateMHPercent_df['any 12-17'] = anymh1217p\n",
    "    stateMHPercent_df['%any 18+'] = anymh18p\n",
    "    stateMHPercent_df['received 12-17'] = received1217p\n",
    "    stateMHPercent_df['%received 18+'] = received18p\n",
    "    stateMHPercent_df['suicide 12-17'] = suicide1217p\n",
    "    stateMHPercent_df['%suicide 18+'] = suicide18p\n",
    "    stateMHPercent_df['depression 12-17'] = depression1217p\n",
    "    stateMHPercent_df['%depression 18+'] = depression18p\n",
    "    stateMHPercent_df.set_index('state', inplace=True)\n",
    "    \n",
    "    return (stateMHPop_df, stateMHPercent_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gKTaNuQniBkD"
   },
   "outputs": [],
   "source": [
    "# stateMHPop_df is the dataframe with information of mental health occurences in a state by the thousands\n",
    "# stateMHPercent_df is the dataframe with information of mental health occurences in a state by percent\n",
    "stateMHPop_df, stateMHPercent_df = findStateTables(tables)\n",
    "\n",
    "# for entries with no numbers reported they currently show up as -- so we want to go through both of these\n",
    "# dataframes and convert then to NaN\n",
    "stateMHPop_df = stateMHPop_df.replace('--', np.nan)\n",
    "stateMHPercent_df = stateMHPercent_df.replace('--', np.nan)\n",
    "\n",
    "# we also want to convert the columns from their string representations to ints\n",
    "# we did not include children/teen aged from 12-17 because most of the values are Nan\n",
    "stateMHPop_df['serious 18+'] = stateMHPop_df['serious 18+'].apply(string_to_int)\n",
    "stateMHPop_df['any 18+'] = stateMHPop_df['any 18+'].apply(string_to_int)\n",
    "stateMHPop_df['received 18+'] = stateMHPop_df['received 18+'].apply(string_to_int)\n",
    "stateMHPop_df['suicide 18+'] = stateMHPop_df['suicide 18+'].apply(string_to_int)\n",
    "stateMHPop_df['depression 18+'] = stateMHPop_df['depression 18+'].apply(string_to_int)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7rfAj8gliBkI"
   },
   "source": [
    "Now that we have mental health occurences by state (for the year 2016 at least) in their respective dataframes, we will clean the state population data. Therefore, we want the population data for just 2016 for the mental health occurences analyses. We will also consolidate all of the state names into just the state codes.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "hfR3cGjtiBkI",
    "outputId": "9d0e1083-d9ba-4185-92bd-062ee39254ee"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>population 2016</th>\n",
       "      <th>state code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>4864745</td>\n",
       "      <td>AL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>741504</td>\n",
       "      <td>AK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>6945452</td>\n",
       "      <td>AZ</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2990410</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>California</td>\n",
       "      <td>39209127</td>\n",
       "      <td>CA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        state  population 2016 state code\n",
       "0     Alabama          4864745         AL\n",
       "1      Alaska           741504         AK\n",
       "2     Arizona          6945452         AZ\n",
       "3    Arkansas          2990410         AR\n",
       "4  California         39209127         CA"
      ]
     },
     "execution_count": 39,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# extract and clean 2016 state population\n",
    "pop_years_df = pop_df.iloc[8:59, [0, 9]]\n",
    "pop_years_df = pop_years_df.reset_index(drop=True)\n",
    "pop_years_df.columns = [\"state\", \"population 2016\"]\n",
    "\n",
    "# clean state\n",
    "pop_years_df[\"state\"] = pop_years_df[\"state\"].apply(lambda state: state[1:])\n",
    "pop_years_df[\"population 2016\"] = pop_years_df[\"population 2016\"].apply(int)\n",
    "\n",
    "pop_years_df[\"state code\"] = pop_years_df[\"state\"].apply(state_to_abbrev)\n",
    "\n",
    "# this dataframe will contain just the population of 2016\n",
    "pop_2016_df = pop_years_df\n",
    "\n",
    "pop_years_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "K9fGlWyliBkQ"
   },
   "source": [
    "Next, we will clean the mental health facilities data. We only need the state the facility is located in, as well as, the latitude and longitude of each facility's location so those are the columns we will be keeping in our dataframe. We will also add in a state code column as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "W52MyQCkiBkT",
    "outputId": "4a8b2bdd-8341-4015-de73-9c46451c9cad"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state code</th>\n",
       "      <th>MHFacilities</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OK</td>\n",
       "      <td>Jane Phillips Medical Center</td>\n",
       "      <td>36.752716</td>\n",
       "      <td>-95.938683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OK</td>\n",
       "      <td>Grand Lake Mental Health Center Inc</td>\n",
       "      <td>36.701457</td>\n",
       "      <td>-95.638160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OK</td>\n",
       "      <td>Grand Lake Mental Health Center Inc</td>\n",
       "      <td>36.746978</td>\n",
       "      <td>-95.990274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KS</td>\n",
       "      <td>Labette Center for</td>\n",
       "      <td>37.338636</td>\n",
       "      <td>-95.261333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>KS</td>\n",
       "      <td>Fredonia Regional Hospital</td>\n",
       "      <td>37.533772</td>\n",
       "      <td>-95.814758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state code                         MHFacilities   latitude  longitude\n",
       "0         OK         Jane Phillips Medical Center  36.752716 -95.938683\n",
       "1         OK  Grand Lake Mental Health Center Inc  36.701457 -95.638160\n",
       "2         OK  Grand Lake Mental Health Center Inc  36.746978 -95.990274\n",
       "3         KS                   Labette Center for  37.338636 -95.261333\n",
       "4         KS           Fredonia Regional Hospital  37.533772 -95.814758"
      ]
     },
     "execution_count": 40,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facilities_df = facilities_df[[\"state\", \"    name1\", \"latitude\", \"longitude\"]]\n",
    "facilities_df = facilities_df.rename(columns={\"state\": \"state code\", \"    name1\": \"MHFacilities\"})\n",
    "facilities_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "o9RSyXD0iBkZ"
   },
   "source": [
    "We also want a separate dataframe for the number of mental health facilities in each state so we will use the data cleaned in facilities_df to create a seperate dataframe called facilities_count_df that contains each state and the number of facilities in that state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "4I-rf0zhiBkb",
    "outputId": "97eb9bab-62d4-4723-defd-e71936b182e0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state code</th>\n",
       "      <th>MHFacilities</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>634</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state code  MHFacilities\n",
       "0         AK            82\n",
       "1         AL           133\n",
       "2         AR           190\n",
       "3         AZ           299\n",
       "4         CA           634"
      ]
     },
     "execution_count": 41,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# group by state\n",
    "facilities_count_df = facilities_df.groupby(\"state code\").count()\n",
    "# drop AS (American Samoa), GU (Guam), VI (Virgin Islands) because not included in the population data\n",
    "facilities_count_df = facilities_count_df.drop([\"AS\", \"GU\", \"VI\"]).reset_index()\n",
    "facilities_count_df = facilities_count_df[[\"state code\", \"MHFacilities\"]]\n",
    "facilities_count_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "g9LlAWeyiBkj"
   },
   "source": [
    "We will merge the facility count data and the population data into one dataframe. Also, we will calculate the mental health facilities per capita in each state, then place those values into a separate column. This new combined dataframe will be called mental_state_df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1630
    },
    "colab_type": "code",
    "id": "Xpm0yQ3eiBkj",
    "outputId": "b0d2a2f4-25c5-45e8-d2f0-6507a388a5d1"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state code</th>\n",
       "      <th>MHFacilities</th>\n",
       "      <th>state</th>\n",
       "      <th>population 2016</th>\n",
       "      <th>MH per capita</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>82</td>\n",
       "      <td>Alaska</td>\n",
       "      <td>741504</td>\n",
       "      <td>0.000111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>133</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>4864745</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>190</td>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2990410</td>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>299</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>6945452</td>\n",
       "      <td>0.000043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>634</td>\n",
       "      <td>California</td>\n",
       "      <td>39209127</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CO</td>\n",
       "      <td>150</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>5540921</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CT</td>\n",
       "      <td>197</td>\n",
       "      <td>Connecticut</td>\n",
       "      <td>3578674</td>\n",
       "      <td>0.000055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DC</td>\n",
       "      <td>29</td>\n",
       "      <td>District of Columbia</td>\n",
       "      <td>686575</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>DE</td>\n",
       "      <td>30</td>\n",
       "      <td>Delaware</td>\n",
       "      <td>949216</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>FL</td>\n",
       "      <td>383</td>\n",
       "      <td>Florida</td>\n",
       "      <td>20629982</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>GA</td>\n",
       "      <td>185</td>\n",
       "      <td>Georgia</td>\n",
       "      <td>10304763</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>HI</td>\n",
       "      <td>29</td>\n",
       "      <td>Hawaii</td>\n",
       "      <td>1428105</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IA</td>\n",
       "      <td>123</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>3131785</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>ID</td>\n",
       "      <td>121</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>1682930</td>\n",
       "      <td>0.000072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IL</td>\n",
       "      <td>313</td>\n",
       "      <td>Illinois</td>\n",
       "      <td>12826895</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>IN</td>\n",
       "      <td>250</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>6633344</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KS</td>\n",
       "      <td>103</td>\n",
       "      <td>Kansas</td>\n",
       "      <td>2911263</td>\n",
       "      <td>0.000035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>KY</td>\n",
       "      <td>195</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>4438229</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>LA</td>\n",
       "      <td>156</td>\n",
       "      <td>Louisiana</td>\n",
       "      <td>4678215</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MA</td>\n",
       "      <td>229</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>6826022</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MD</td>\n",
       "      <td>204</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>6004692</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ME</td>\n",
       "      <td>139</td>\n",
       "      <td>Maine</td>\n",
       "      <td>1331370</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MI</td>\n",
       "      <td>295</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>9951890</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MN</td>\n",
       "      <td>204</td>\n",
       "      <td>Minnesota</td>\n",
       "      <td>5523409</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MO</td>\n",
       "      <td>187</td>\n",
       "      <td>Missouri</td>\n",
       "      <td>6087203</td>\n",
       "      <td>0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MS</td>\n",
       "      <td>136</td>\n",
       "      <td>Mississippi</td>\n",
       "      <td>2988298</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>MT</td>\n",
       "      <td>68</td>\n",
       "      <td>Montana</td>\n",
       "      <td>1040863</td>\n",
       "      <td>0.000065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NC</td>\n",
       "      <td>231</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>10156679</td>\n",
       "      <td>0.000023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>ND</td>\n",
       "      <td>32</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>754353</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NE</td>\n",
       "      <td>127</td>\n",
       "      <td>Nebraska</td>\n",
       "      <td>1905924</td>\n",
       "      <td>0.000067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NH</td>\n",
       "      <td>47</td>\n",
       "      <td>New Hampshire</td>\n",
       "      <td>1342373</td>\n",
       "      <td>0.000035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NJ</td>\n",
       "      <td>245</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>8874516</td>\n",
       "      <td>0.000028</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NM</td>\n",
       "      <td>54</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>2092789</td>\n",
       "      <td>0.000026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>NV</td>\n",
       "      <td>44</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>2919772</td>\n",
       "      <td>0.000015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NY</td>\n",
       "      <td>670</td>\n",
       "      <td>New York</td>\n",
       "      <td>19641589</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>OH</td>\n",
       "      <td>502</td>\n",
       "      <td>Ohio</td>\n",
       "      <td>11635003</td>\n",
       "      <td>0.000043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>OK</td>\n",
       "      <td>123</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>3926769</td>\n",
       "      <td>0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>OR</td>\n",
       "      <td>108</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>4091404</td>\n",
       "      <td>0.000026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>PA</td>\n",
       "      <td>489</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>12783538</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>RI</td>\n",
       "      <td>42</td>\n",
       "      <td>Rhode Island</td>\n",
       "      <td>1057063</td>\n",
       "      <td>0.000040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SC</td>\n",
       "      <td>84</td>\n",
       "      <td>South Carolina</td>\n",
       "      <td>4958235</td>\n",
       "      <td>0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>SD</td>\n",
       "      <td>44</td>\n",
       "      <td>South Dakota</td>\n",
       "      <td>862890</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TN</td>\n",
       "      <td>230</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>6645011</td>\n",
       "      <td>0.000035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>TX</td>\n",
       "      <td>278</td>\n",
       "      <td>Texas</td>\n",
       "      <td>27937492</td>\n",
       "      <td>0.000010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>UT</td>\n",
       "      <td>129</td>\n",
       "      <td>Utah</td>\n",
       "      <td>3042613</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>VA</td>\n",
       "      <td>201</td>\n",
       "      <td>Virginia</td>\n",
       "      <td>8410946</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>VT</td>\n",
       "      <td>52</td>\n",
       "      <td>Vermont</td>\n",
       "      <td>623644</td>\n",
       "      <td>0.000083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>WA</td>\n",
       "      <td>303</td>\n",
       "      <td>Washington</td>\n",
       "      <td>7294680</td>\n",
       "      <td>0.000042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WI</td>\n",
       "      <td>356</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>5772958</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WV</td>\n",
       "      <td>113</td>\n",
       "      <td>West Virginia</td>\n",
       "      <td>1830929</td>\n",
       "      <td>0.000062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>WY</td>\n",
       "      <td>46</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>584290</td>\n",
       "      <td>0.000079</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state code  MHFacilities  ... population 2016  MH per capita\n",
       "0          AK            82  ...          741504       0.000111\n",
       "1          AL           133  ...         4864745       0.000027\n",
       "2          AR           190  ...         2990410       0.000064\n",
       "3          AZ           299  ...         6945452       0.000043\n",
       "4          CA           634  ...        39209127       0.000016\n",
       "5          CO           150  ...         5540921       0.000027\n",
       "6          CT           197  ...         3578674       0.000055\n",
       "7          DC            29  ...          686575       0.000042\n",
       "8          DE            30  ...          949216       0.000032\n",
       "9          FL           383  ...        20629982       0.000019\n",
       "10         GA           185  ...        10304763       0.000018\n",
       "11         HI            29  ...         1428105       0.000020\n",
       "12         IA           123  ...         3131785       0.000039\n",
       "13         ID           121  ...         1682930       0.000072\n",
       "14         IL           313  ...        12826895       0.000024\n",
       "15         IN           250  ...         6633344       0.000038\n",
       "16         KS           103  ...         2911263       0.000035\n",
       "17         KY           195  ...         4438229       0.000044\n",
       "18         LA           156  ...         4678215       0.000033\n",
       "19         MA           229  ...         6826022       0.000034\n",
       "20         MD           204  ...         6004692       0.000034\n",
       "21         ME           139  ...         1331370       0.000104\n",
       "22         MI           295  ...         9951890       0.000030\n",
       "23         MN           204  ...         5523409       0.000037\n",
       "24         MO           187  ...         6087203       0.000031\n",
       "25         MS           136  ...         2988298       0.000046\n",
       "26         MT            68  ...         1040863       0.000065\n",
       "27         NC           231  ...        10156679       0.000023\n",
       "28         ND            32  ...          754353       0.000042\n",
       "29         NE           127  ...         1905924       0.000067\n",
       "30         NH            47  ...         1342373       0.000035\n",
       "31         NJ           245  ...         8874516       0.000028\n",
       "32         NM            54  ...         2092789       0.000026\n",
       "33         NV            44  ...         2919772       0.000015\n",
       "34         NY           670  ...        19641589       0.000034\n",
       "35         OH           502  ...        11635003       0.000043\n",
       "36         OK           123  ...         3926769       0.000031\n",
       "37         OR           108  ...         4091404       0.000026\n",
       "38         PA           489  ...        12783538       0.000038\n",
       "39         RI            42  ...         1057063       0.000040\n",
       "40         SC            84  ...         4958235       0.000017\n",
       "41         SD            44  ...          862890       0.000051\n",
       "42         TN           230  ...         6645011       0.000035\n",
       "43         TX           278  ...        27937492       0.000010\n",
       "44         UT           129  ...         3042613       0.000042\n",
       "45         VA           201  ...         8410946       0.000024\n",
       "46         VT            52  ...          623644       0.000083\n",
       "47         WA           303  ...         7294680       0.000042\n",
       "48         WI           356  ...         5772958       0.000062\n",
       "49         WV           113  ...         1830929       0.000062\n",
       "50         WY            46  ...          584290       0.000079\n",
       "\n",
       "[51 rows x 5 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# merge population and mental health data set\n",
    "mental_state_df = pd.merge(facilities_count_df, pop_years_df, on=\"state code\")\n",
    "mental_state_df[\"MH per capita\"] = mental_state_df[\"MHFacilities\"] / mental_state_df[\"population 2016\"]\n",
    "mental_state_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "1y2aSKLLiBkq"
   },
   "source": [
    "Now, most of the data concerning mental health and states have been cleaned. \n",
    "\n",
    "Let's move onto the gun violence data. For this dataframe, we are given an abundance of data on each incident but the columns that we will focus on include date, state, number killed (n_killed), number injured (n_injured), number of guns involved (n_guns_involved), latitude, and longitude. Therfore, these will be the columns that we will keep. Additionally, we are only curious about the data concerning adults (18+) that were involved in a gun violence incident. We will clean the data to include this in columns and add a state codes column into the gun_df dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xmlRz0CjiBkr"
   },
   "outputs": [],
   "source": [
    "# add new abbreviation for state column\n",
    "gun_df[\"state code\"] = gun_df[\"state\"].apply(state_to_abbrev)\n",
    "\n",
    "## putting together our dataframe with the cleaned data\n",
    "gun_df = gun_df[[\"date\", \"state\", \"state code\", \"n_killed\", \"n_injured\", \"latitude\", \"longitude\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZEC6snJNiBkx"
   },
   "source": [
    "Because our mental health occurences data is from 2016, we also want to perform an analyses of gun violence incidents that occurred in 2016.  Additionally, we are curious about whether the distance between gun violence and mental health facilities are related so we will also calculate the minimum distance betwen each gun violence incident and the closest mental health facility within a state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0U033HoTiBky"
   },
   "outputs": [],
   "source": [
    "# Below: the average distance of (average distance of MH to violence location) within state for 2016\n",
    "# 2016 gun violence incidence\n",
    "gun_2016_df = gun_df[gun_df[\"date\"].str.contains(\"2016\", regex=False)].reset_index(drop=True)\n",
    "\n",
    "# drop columns that does not have logitude/latitude value\n",
    "facilities_df = facilities_df.dropna(subset=[\"latitude\", \"longitude\"])\n",
    "gun_2016_df = gun_2016_df.dropna(subset=[\"latitude\", \"longitude\"])\n",
    "\n",
    "# calculate distance between two location\n",
    "def location_to_miles(mh_lat, mh_lon, gun_lat, gun_lon):\n",
    "    earth_R = 3961 # Radius of earth in miles\n",
    "    d_lat = gun_lat * math.pi / 180 - mh_lat * math.pi / 180 # distance for latitude \n",
    "    d_lon = gun_lon * math.pi / 180 - mh_lon * math.pi / 180 # distance for longtitude\n",
    "    # convert\n",
    "    a = math.pow(math.sin(d_lat/2), 2) + math.cos(mh_lat * math.pi / 180) * math.cos(gun_lat * math.pi / 180) * math.pow(math.sin(d_lon/2), 2)\n",
    "    d = 2 * math.atan2(math.sqrt(a), math.sqrt(1-a))\n",
    "    return earth_R * d\n",
    "\n",
    "# calculate the minimum distance of all mental health facilities within gun violence incident per state\n",
    "def min_d_within_state(gun_incident):\n",
    "    distance_list = []\n",
    "    gun_lat, gun_lon = gun_incident[\"latitude\"], gun_incident[\"longitude\"] # get latitude and longitude of incident\n",
    "    mh_in_state = facilities_df[facilities_df[\"state code\"] == gun_incident[\"state code\"]] # get mental health facilities of interest\n",
    "    for index, row in mh_in_state.iterrows():\n",
    "        mh_lat, mh_lon = row[\"latitude\"], row[\"longitude\"] # get latitude and longitude of mental health\n",
    "        distance_list.append(location_to_miles(mh_lat, mh_lon, gun_lat, gun_lon))\n",
    "    return min(distance_list)\n",
    "\n",
    "# calculate  the minimum distance of MH to gun violence location within each state, the next two lines took\n",
    "# a long time to calculate so we exported it into a csv for faster computation!\n",
    "\n",
    "# gun_2016_df[\"distance\"] = gun_2016_df.apply(min_d_within_state, axis=1) # apply function to create distance\n",
    "# gun_2016_df.to_csv(\"GunWithMinDistance_2016.csv\", index=False) # read it to a csv file for faster access\n",
    "\n",
    "gun_2016_df = pd.read_csv('GunWithMinDistance_2016.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1599
    },
    "colab_type": "code",
    "id": "FQFwTC1ViBk5",
    "outputId": "9a7b7b23-282d-488b-8f72-0c8eb5e46f1a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>state code</th>\n",
       "      <th>n_incidents</th>\n",
       "      <th>n_killed</th>\n",
       "      <th>n_injured</th>\n",
       "      <th>distance</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>AK</td>\n",
       "      <td>418</td>\n",
       "      <td>77</td>\n",
       "      <td>95</td>\n",
       "      <td>12.720841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>AL</td>\n",
       "      <td>1247</td>\n",
       "      <td>442</td>\n",
       "      <td>730</td>\n",
       "      <td>5.102425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>AR</td>\n",
       "      <td>664</td>\n",
       "      <td>159</td>\n",
       "      <td>316</td>\n",
       "      <td>3.157390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>AZ</td>\n",
       "      <td>524</td>\n",
       "      <td>261</td>\n",
       "      <td>270</td>\n",
       "      <td>2.399487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>CA</td>\n",
       "      <td>3523</td>\n",
       "      <td>1197</td>\n",
       "      <td>1854</td>\n",
       "      <td>2.763329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>CO</td>\n",
       "      <td>742</td>\n",
       "      <td>169</td>\n",
       "      <td>268</td>\n",
       "      <td>2.849355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>CT</td>\n",
       "      <td>772</td>\n",
       "      <td>59</td>\n",
       "      <td>341</td>\n",
       "      <td>1.076874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>DE</td>\n",
       "      <td>316</td>\n",
       "      <td>39</td>\n",
       "      <td>182</td>\n",
       "      <td>1.512354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>FL</td>\n",
       "      <td>3983</td>\n",
       "      <td>893</td>\n",
       "      <td>1716</td>\n",
       "      <td>3.606324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>GA</td>\n",
       "      <td>1585</td>\n",
       "      <td>471</td>\n",
       "      <td>801</td>\n",
       "      <td>4.250845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>HI</td>\n",
       "      <td>76</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>4.733558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>IA</td>\n",
       "      <td>621</td>\n",
       "      <td>54</td>\n",
       "      <td>183</td>\n",
       "      <td>3.005170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>ID</td>\n",
       "      <td>140</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>3.771590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>IL</td>\n",
       "      <td>4725</td>\n",
       "      <td>755</td>\n",
       "      <td>3966</td>\n",
       "      <td>1.381556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>IN</td>\n",
       "      <td>1356</td>\n",
       "      <td>340</td>\n",
       "      <td>687</td>\n",
       "      <td>2.242748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>KS</td>\n",
       "      <td>529</td>\n",
       "      <td>103</td>\n",
       "      <td>236</td>\n",
       "      <td>2.984782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>KY</td>\n",
       "      <td>900</td>\n",
       "      <td>252</td>\n",
       "      <td>430</td>\n",
       "      <td>2.662688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>LA</td>\n",
       "      <td>1865</td>\n",
       "      <td>431</td>\n",
       "      <td>998</td>\n",
       "      <td>2.691681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>MA</td>\n",
       "      <td>1438</td>\n",
       "      <td>85</td>\n",
       "      <td>425</td>\n",
       "      <td>1.189467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>MD</td>\n",
       "      <td>1236</td>\n",
       "      <td>385</td>\n",
       "      <td>722</td>\n",
       "      <td>1.028769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>ME</td>\n",
       "      <td>231</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>5.097356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>MI</td>\n",
       "      <td>1185</td>\n",
       "      <td>328</td>\n",
       "      <td>629</td>\n",
       "      <td>2.418565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>MN</td>\n",
       "      <td>414</td>\n",
       "      <td>79</td>\n",
       "      <td>196</td>\n",
       "      <td>4.487808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>MO</td>\n",
       "      <td>1462</td>\n",
       "      <td>470</td>\n",
       "      <td>791</td>\n",
       "      <td>2.760953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>MS</td>\n",
       "      <td>813</td>\n",
       "      <td>267</td>\n",
       "      <td>472</td>\n",
       "      <td>3.924985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>MT</td>\n",
       "      <td>154</td>\n",
       "      <td>38</td>\n",
       "      <td>21</td>\n",
       "      <td>6.794517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>NC</td>\n",
       "      <td>1931</td>\n",
       "      <td>490</td>\n",
       "      <td>1114</td>\n",
       "      <td>3.885921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>ND</td>\n",
       "      <td>137</td>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>14.137990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>29</td>\n",
       "      <td>NE</td>\n",
       "      <td>361</td>\n",
       "      <td>51</td>\n",
       "      <td>153</td>\n",
       "      <td>1.884907</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>30</td>\n",
       "      <td>NH</td>\n",
       "      <td>252</td>\n",
       "      <td>17</td>\n",
       "      <td>36</td>\n",
       "      <td>3.072934</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>NJ</td>\n",
       "      <td>1151</td>\n",
       "      <td>262</td>\n",
       "      <td>619</td>\n",
       "      <td>1.296190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>NM</td>\n",
       "      <td>373</td>\n",
       "      <td>110</td>\n",
       "      <td>122</td>\n",
       "      <td>4.581570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>NV</td>\n",
       "      <td>488</td>\n",
       "      <td>173</td>\n",
       "      <td>264</td>\n",
       "      <td>2.690622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>NY</td>\n",
       "      <td>2418</td>\n",
       "      <td>383</td>\n",
       "      <td>1208</td>\n",
       "      <td>1.602063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>OH</td>\n",
       "      <td>2224</td>\n",
       "      <td>513</td>\n",
       "      <td>1320</td>\n",
       "      <td>1.632064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>OK</td>\n",
       "      <td>827</td>\n",
       "      <td>198</td>\n",
       "      <td>383</td>\n",
       "      <td>3.787499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>37</td>\n",
       "      <td>OR</td>\n",
       "      <td>517</td>\n",
       "      <td>76</td>\n",
       "      <td>156</td>\n",
       "      <td>3.772921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>38</td>\n",
       "      <td>PA</td>\n",
       "      <td>2000</td>\n",
       "      <td>478</td>\n",
       "      <td>1115</td>\n",
       "      <td>1.691517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>39</td>\n",
       "      <td>RI</td>\n",
       "      <td>222</td>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>1.153872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>40</td>\n",
       "      <td>SC</td>\n",
       "      <td>1400</td>\n",
       "      <td>328</td>\n",
       "      <td>681</td>\n",
       "      <td>5.729312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>41</td>\n",
       "      <td>SD</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>9.488099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>42</td>\n",
       "      <td>TN</td>\n",
       "      <td>1819</td>\n",
       "      <td>384</td>\n",
       "      <td>1043</td>\n",
       "      <td>2.645806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>43</td>\n",
       "      <td>TX</td>\n",
       "      <td>3250</td>\n",
       "      <td>1100</td>\n",
       "      <td>1401</td>\n",
       "      <td>6.100191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>44</td>\n",
       "      <td>UT</td>\n",
       "      <td>222</td>\n",
       "      <td>56</td>\n",
       "      <td>69</td>\n",
       "      <td>2.592018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>45</td>\n",
       "      <td>VA</td>\n",
       "      <td>1362</td>\n",
       "      <td>311</td>\n",
       "      <td>892</td>\n",
       "      <td>2.612481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>VT</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>4.781668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>47</td>\n",
       "      <td>WA</td>\n",
       "      <td>701</td>\n",
       "      <td>146</td>\n",
       "      <td>274</td>\n",
       "      <td>2.451734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>48</td>\n",
       "      <td>WI</td>\n",
       "      <td>807</td>\n",
       "      <td>194</td>\n",
       "      <td>423</td>\n",
       "      <td>1.544004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>49</td>\n",
       "      <td>WV</td>\n",
       "      <td>348</td>\n",
       "      <td>82</td>\n",
       "      <td>148</td>\n",
       "      <td>3.517225</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>50</td>\n",
       "      <td>WY</td>\n",
       "      <td>124</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>5.323317</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    index state code  n_incidents  n_killed  n_injured   distance\n",
       "0       0         AK          418        77         95  12.720841\n",
       "1       1         AL         1247       442        730   5.102425\n",
       "2       2         AR          664       159        316   3.157390\n",
       "3       3         AZ          524       261        270   2.399487\n",
       "4       4         CA         3523      1197       1854   2.763329\n",
       "5       5         CO          742       169        268   2.849355\n",
       "6       6         CT          772        59        341   1.076874\n",
       "7       8         DE          316        39        182   1.512354\n",
       "8       9         FL         3983       893       1716   3.606324\n",
       "9      10         GA         1585       471        801   4.250845\n",
       "10     11         HI           76        30         17   4.733558\n",
       "11     12         IA          621        54        183   3.005170\n",
       "12     13         ID          140        30         35   3.771590\n",
       "13     14         IL         4725       755       3966   1.381556\n",
       "14     15         IN         1356       340        687   2.242748\n",
       "15     16         KS          529       103        236   2.984782\n",
       "16     17         KY          900       252        430   2.662688\n",
       "17     18         LA         1865       431        998   2.691681\n",
       "18     19         MA         1438        85        425   1.189467\n",
       "19     20         MD         1236       385        722   1.028769\n",
       "20     21         ME          231        18         37   5.097356\n",
       "21     22         MI         1185       328        629   2.418565\n",
       "22     23         MN          414        79        196   4.487808\n",
       "23     24         MO         1462       470        791   2.760953\n",
       "24     25         MS          813       267        472   3.924985\n",
       "25     26         MT          154        38         21   6.794517\n",
       "26     27         NC         1931       490       1114   3.885921\n",
       "27     28         ND          137        14         30  14.137990\n",
       "28     29         NE          361        51        153   1.884907\n",
       "29     30         NH          252        17         36   3.072934\n",
       "30     31         NJ         1151       262        619   1.296190\n",
       "31     32         NM          373       110        122   4.581570\n",
       "32     33         NV          488       173        264   2.690622\n",
       "33     34         NY         2418       383       1208   1.602063\n",
       "34     35         OH         2224       513       1320   1.632064\n",
       "35     36         OK          827       198        383   3.787499\n",
       "36     37         OR          517        76        156   3.772921\n",
       "37     38         PA         2000       478       1115   1.691517\n",
       "38     39         RI          222         9         78   1.153872\n",
       "39     40         SC         1400       328        681   5.729312\n",
       "40     41         SD          130        18         28   9.488099\n",
       "41     42         TN         1819       384       1043   2.645806\n",
       "42     43         TX         3250      1100       1401   6.100191\n",
       "43     44         UT          222        56         69   2.592018\n",
       "44     45         VA         1362       311        892   2.612481\n",
       "45     46         VT           85         9          8   4.781668\n",
       "46     47         WA          701       146        274   2.451734\n",
       "47     48         WI          807       194        423   1.544004\n",
       "48     49         WV          348        82        148   3.517225\n",
       "49     50         WY          124        17         12   5.323317"
      ]
     },
     "execution_count": 45,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gun_state_2016_df = gun_2016_df.groupby(\"state code\").sum().reset_index() # group gun incidents by state in 2016\n",
    "gun_state_2016_df[\"n_incidents\"] = gun_2016_df.groupby(\"state code\").count().reset_index()[\"date\"] # count number of incidents\n",
    "# calculate the minimum distance\n",
    "gun_state_2016_df[\"distance\"] = gun_2016_df.groupby(\"state code\").mean().reset_index()[\"min distance (mi) of MH per state\"]\n",
    "gun_state_2016_df = gun_state_2016_df[[\"state code\", \"n_incidents\", \"n_killed\", \"n_injured\", \"distance\"]] # select column\n",
    "gun_state_2016_df = gun_state_2016_df[gun_state_2016_df[\"state code\"] != \"DC\"].reset_index()\n",
    "gun_state_2016_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "v8wqSetIiBk9"
   },
   "source": [
    "Most of our analysis will be state-based, so we went through the gun data and calculated the number of gun violence incidents per state, along with the number of people killed and the number of people injured for each state as well."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 204
    },
    "colab_type": "code",
    "id": "IZtmwb2hiBk-",
    "outputId": "00c72582-8d5b-4bda-8b00-7af132d899e2"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state code</th>\n",
       "      <th>n_incidents</th>\n",
       "      <th>n_killed</th>\n",
       "      <th>n_injured</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>1349</td>\n",
       "      <td>267</td>\n",
       "      <td>325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>5471</td>\n",
       "      <td>1880</td>\n",
       "      <td>2998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AR</td>\n",
       "      <td>2842</td>\n",
       "      <td>773</td>\n",
       "      <td>1347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>2328</td>\n",
       "      <td>1094</td>\n",
       "      <td>1096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>16306</td>\n",
       "      <td>5562</td>\n",
       "      <td>7644</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state code  n_incidents  n_killed  n_injured\n",
       "0         AK         1349       267        325\n",
       "1         AL         5471      1880       2998\n",
       "2         AR         2842       773       1347\n",
       "3         AZ         2328      1094       1096\n",
       "4         CA        16306      5562       7644"
      ]
     },
     "execution_count": 46,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gun_state_df = gun_df.groupby(\"state code\").sum().reset_index() \n",
    "gun_state_df[\"n_incidents\"] = gun_df.groupby(\"state code\").count().reset_index()[\"date\"] # count number of incidents\n",
    "gun_state_df = gun_state_df[[\"state code\", \"n_incidents\", \"n_killed\", \"n_injured\"]] # select column\n",
    "gun_state_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RFDW3MAlwbtA"
   },
   "source": [
    "Since we are curious about the relationship between these incidents and the number of mental health facilities, we will merge the gun_state_df with the mental_state_df that we created earlier.\n",
    "\n",
    "We will also drop adolescents (children under 17 years old) from the dataframe as we are only interested in adult data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 221
    },
    "colab_type": "code",
    "id": "ydt9DRLgj_Tu",
    "outputId": "b280629b-f336-48c5-ad05-90a2c80a2421"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state code</th>\n",
       "      <th>%serious 18+</th>\n",
       "      <th>%any 18+</th>\n",
       "      <th>%received 18+</th>\n",
       "      <th>MHFacilities</th>\n",
       "      <th>population 2016</th>\n",
       "      <th>MH per capita</th>\n",
       "      <th>n_incidents</th>\n",
       "      <th>n_killed</th>\n",
       "      <th>n_injured</th>\n",
       "      <th>n_incident_capita_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>4.23</td>\n",
       "      <td>18.47</td>\n",
       "      <td>12.98</td>\n",
       "      <td>133</td>\n",
       "      <td>4864745</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>5471</td>\n",
       "      <td>1880</td>\n",
       "      <td>2998</td>\n",
       "      <td>0.001125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>4.53</td>\n",
       "      <td>20.01</td>\n",
       "      <td>13.54</td>\n",
       "      <td>82</td>\n",
       "      <td>741504</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>1349</td>\n",
       "      <td>267</td>\n",
       "      <td>325</td>\n",
       "      <td>0.001819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>4.04</td>\n",
       "      <td>17.52</td>\n",
       "      <td>11.87</td>\n",
       "      <td>299</td>\n",
       "      <td>6945452</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>2328</td>\n",
       "      <td>1094</td>\n",
       "      <td>1096</td>\n",
       "      <td>0.000335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>5.45</td>\n",
       "      <td>21.02</td>\n",
       "      <td>15.54</td>\n",
       "      <td>190</td>\n",
       "      <td>2990410</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>2842</td>\n",
       "      <td>773</td>\n",
       "      <td>1347</td>\n",
       "      <td>0.000950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>3.61</td>\n",
       "      <td>17.15</td>\n",
       "      <td>11.59</td>\n",
       "      <td>634</td>\n",
       "      <td>39209127</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>16306</td>\n",
       "      <td>5562</td>\n",
       "      <td>7644</td>\n",
       "      <td>0.000416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state code  %serious 18+  ...  n_injured  n_incident_capita_2016\n",
       "0         AL          4.23  ...       2998                0.001125\n",
       "1         AK          4.53  ...        325                0.001819\n",
       "2         AZ          4.04  ...       1096                0.000335\n",
       "3         AR          5.45  ...       1347                0.000950\n",
       "4         CA          3.61  ...       7644                0.000416\n",
       "\n",
       "[5 rows x 11 columns]"
      ]
     },
     "execution_count": 60,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# a dataframe that has aggregated data for mental health facilities/ gun violence incidents for 2013-2018\n",
    "aggregate_df = pd.merge(mental_state_df, gun_state_df, on=\"state code\")\n",
    "\n",
    "# calculate number of mental health facilities per capita\n",
    "aggregate_df[\"n_incident_capita_2016\"] = aggregate_df[\"n_incidents\"] / aggregate_df[\"population 2016\"]\n",
    "\n",
    "aggregate_df = pd.merge(stateMHPercent_df, aggregate_df, on=\"state code\")\n",
    "\n",
    "# drop any adolescents from mental health data\n",
    "aggregate_df = aggregate_df.drop(['serious 12-17', 'any 12-17', 'received 12-17', 'suicide 12-17', 'depression 12-17'], axis=1)\n",
    "\n",
    "# drop redundant columns from dataframe\n",
    "# suicide and depression is a subset of mental health illness\n",
    "aggregate_df = aggregate_df.drop(['state', '%suicide 18+', '%depression 18+'], axis=1)\n",
    "\n",
    "\n",
    "aggregate_df = aggregate_df.drop(aggregate_df.index[7]) # dropping Washington DC\n",
    "aggregate_df.index = range(len(aggregate_df)) #reindex\n",
    "aggregate_df = aggregate_df.apply(pd.to_numeric, errors='ignore')\n",
    "aggregate_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1636
    },
    "colab_type": "code",
    "id": "SOWM-GqbiBlC",
    "outputId": "132d8875-2bf6-42af-b126-e21bfacfebb9"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state code</th>\n",
       "      <th>%serious 18+</th>\n",
       "      <th>%any 18+</th>\n",
       "      <th>%received 18+</th>\n",
       "      <th>population 2016</th>\n",
       "      <th>MH per capita</th>\n",
       "      <th>index</th>\n",
       "      <th>n_incidents</th>\n",
       "      <th>n_killed</th>\n",
       "      <th>n_injured</th>\n",
       "      <th>n_incident_capita_2016</th>\n",
       "      <th>n_killed_capita_2016</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AL</td>\n",
       "      <td>4.23</td>\n",
       "      <td>18.47</td>\n",
       "      <td>12.98</td>\n",
       "      <td>4864745</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>1</td>\n",
       "      <td>1247</td>\n",
       "      <td>442</td>\n",
       "      <td>730</td>\n",
       "      <td>0.000256</td>\n",
       "      <td>0.000091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>4.53</td>\n",
       "      <td>20.01</td>\n",
       "      <td>13.54</td>\n",
       "      <td>741504</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>0</td>\n",
       "      <td>418</td>\n",
       "      <td>77</td>\n",
       "      <td>95</td>\n",
       "      <td>0.000564</td>\n",
       "      <td>0.000104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AZ</td>\n",
       "      <td>4.04</td>\n",
       "      <td>17.52</td>\n",
       "      <td>11.87</td>\n",
       "      <td>6945452</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>3</td>\n",
       "      <td>524</td>\n",
       "      <td>261</td>\n",
       "      <td>270</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AR</td>\n",
       "      <td>5.45</td>\n",
       "      <td>21.02</td>\n",
       "      <td>15.54</td>\n",
       "      <td>2990410</td>\n",
       "      <td>0.000064</td>\n",
       "      <td>2</td>\n",
       "      <td>664</td>\n",
       "      <td>159</td>\n",
       "      <td>316</td>\n",
       "      <td>0.000222</td>\n",
       "      <td>0.000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CA</td>\n",
       "      <td>3.61</td>\n",
       "      <td>17.15</td>\n",
       "      <td>11.59</td>\n",
       "      <td>39209127</td>\n",
       "      <td>0.000016</td>\n",
       "      <td>4</td>\n",
       "      <td>3523</td>\n",
       "      <td>1197</td>\n",
       "      <td>1854</td>\n",
       "      <td>0.000090</td>\n",
       "      <td>0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>CO</td>\n",
       "      <td>4.61</td>\n",
       "      <td>20.05</td>\n",
       "      <td>15.86</td>\n",
       "      <td>5540921</td>\n",
       "      <td>0.000027</td>\n",
       "      <td>5</td>\n",
       "      <td>742</td>\n",
       "      <td>169</td>\n",
       "      <td>268</td>\n",
       "      <td>0.000134</td>\n",
       "      <td>0.000031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>CT</td>\n",
       "      <td>3.59</td>\n",
       "      <td>18.00</td>\n",
       "      <td>17.05</td>\n",
       "      <td>3578674</td>\n",
       "      <td>0.000055</td>\n",
       "      <td>6</td>\n",
       "      <td>772</td>\n",
       "      <td>59</td>\n",
       "      <td>341</td>\n",
       "      <td>0.000216</td>\n",
       "      <td>0.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DE</td>\n",
       "      <td>4.23</td>\n",
       "      <td>17.71</td>\n",
       "      <td>15.06</td>\n",
       "      <td>949216</td>\n",
       "      <td>0.000032</td>\n",
       "      <td>8</td>\n",
       "      <td>316</td>\n",
       "      <td>39</td>\n",
       "      <td>182</td>\n",
       "      <td>0.000333</td>\n",
       "      <td>0.000041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>FL</td>\n",
       "      <td>3.56</td>\n",
       "      <td>17.30</td>\n",
       "      <td>12.75</td>\n",
       "      <td>20629982</td>\n",
       "      <td>0.000019</td>\n",
       "      <td>9</td>\n",
       "      <td>3983</td>\n",
       "      <td>893</td>\n",
       "      <td>1716</td>\n",
       "      <td>0.000193</td>\n",
       "      <td>0.000043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GA</td>\n",
       "      <td>4.17</td>\n",
       "      <td>17.74</td>\n",
       "      <td>12.03</td>\n",
       "      <td>10304763</td>\n",
       "      <td>0.000018</td>\n",
       "      <td>10</td>\n",
       "      <td>1585</td>\n",
       "      <td>471</td>\n",
       "      <td>801</td>\n",
       "      <td>0.000154</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HI</td>\n",
       "      <td>3.18</td>\n",
       "      <td>15.55</td>\n",
       "      <td>9.03</td>\n",
       "      <td>1428105</td>\n",
       "      <td>0.000020</td>\n",
       "      <td>11</td>\n",
       "      <td>76</td>\n",
       "      <td>30</td>\n",
       "      <td>17</td>\n",
       "      <td>0.000053</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>ID</td>\n",
       "      <td>4.98</td>\n",
       "      <td>21.62</td>\n",
       "      <td>16.64</td>\n",
       "      <td>1682930</td>\n",
       "      <td>0.000072</td>\n",
       "      <td>13</td>\n",
       "      <td>140</td>\n",
       "      <td>30</td>\n",
       "      <td>35</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>IL</td>\n",
       "      <td>3.74</td>\n",
       "      <td>15.73</td>\n",
       "      <td>13.64</td>\n",
       "      <td>12826895</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>14</td>\n",
       "      <td>4725</td>\n",
       "      <td>755</td>\n",
       "      <td>3966</td>\n",
       "      <td>0.000368</td>\n",
       "      <td>0.000059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>IN</td>\n",
       "      <td>4.93</td>\n",
       "      <td>19.95</td>\n",
       "      <td>15.36</td>\n",
       "      <td>6633344</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>15</td>\n",
       "      <td>1356</td>\n",
       "      <td>340</td>\n",
       "      <td>687</td>\n",
       "      <td>0.000204</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>IA</td>\n",
       "      <td>3.93</td>\n",
       "      <td>17.72</td>\n",
       "      <td>16.75</td>\n",
       "      <td>3131785</td>\n",
       "      <td>0.000039</td>\n",
       "      <td>12</td>\n",
       "      <td>621</td>\n",
       "      <td>54</td>\n",
       "      <td>183</td>\n",
       "      <td>0.000198</td>\n",
       "      <td>0.000017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>KS</td>\n",
       "      <td>4.37</td>\n",
       "      <td>18.17</td>\n",
       "      <td>14.20</td>\n",
       "      <td>2911263</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>16</td>\n",
       "      <td>529</td>\n",
       "      <td>103</td>\n",
       "      <td>236</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>KY</td>\n",
       "      <td>5.25</td>\n",
       "      <td>22.08</td>\n",
       "      <td>17.94</td>\n",
       "      <td>4438229</td>\n",
       "      <td>0.000044</td>\n",
       "      <td>17</td>\n",
       "      <td>900</td>\n",
       "      <td>252</td>\n",
       "      <td>430</td>\n",
       "      <td>0.000203</td>\n",
       "      <td>0.000057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>LA</td>\n",
       "      <td>4.53</td>\n",
       "      <td>17.31</td>\n",
       "      <td>13.35</td>\n",
       "      <td>4678215</td>\n",
       "      <td>0.000033</td>\n",
       "      <td>18</td>\n",
       "      <td>1865</td>\n",
       "      <td>431</td>\n",
       "      <td>998</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>0.000092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>ME</td>\n",
       "      <td>4.35</td>\n",
       "      <td>17.62</td>\n",
       "      <td>19.37</td>\n",
       "      <td>1331370</td>\n",
       "      <td>0.000104</td>\n",
       "      <td>21</td>\n",
       "      <td>231</td>\n",
       "      <td>18</td>\n",
       "      <td>37</td>\n",
       "      <td>0.000174</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>MD</td>\n",
       "      <td>3.41</td>\n",
       "      <td>16.59</td>\n",
       "      <td>13.66</td>\n",
       "      <td>6004692</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>20</td>\n",
       "      <td>1236</td>\n",
       "      <td>385</td>\n",
       "      <td>722</td>\n",
       "      <td>0.000206</td>\n",
       "      <td>0.000064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>MA</td>\n",
       "      <td>4.32</td>\n",
       "      <td>19.34</td>\n",
       "      <td>18.86</td>\n",
       "      <td>6826022</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>19</td>\n",
       "      <td>1438</td>\n",
       "      <td>85</td>\n",
       "      <td>425</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>MI</td>\n",
       "      <td>4.44</td>\n",
       "      <td>17.33</td>\n",
       "      <td>16.12</td>\n",
       "      <td>9951890</td>\n",
       "      <td>0.000030</td>\n",
       "      <td>22</td>\n",
       "      <td>1185</td>\n",
       "      <td>328</td>\n",
       "      <td>629</td>\n",
       "      <td>0.000119</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>MN</td>\n",
       "      <td>4.25</td>\n",
       "      <td>18.24</td>\n",
       "      <td>19.08</td>\n",
       "      <td>5523409</td>\n",
       "      <td>0.000037</td>\n",
       "      <td>23</td>\n",
       "      <td>414</td>\n",
       "      <td>79</td>\n",
       "      <td>196</td>\n",
       "      <td>0.000075</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>MS</td>\n",
       "      <td>3.92</td>\n",
       "      <td>17.49</td>\n",
       "      <td>11.98</td>\n",
       "      <td>2988298</td>\n",
       "      <td>0.000046</td>\n",
       "      <td>25</td>\n",
       "      <td>813</td>\n",
       "      <td>267</td>\n",
       "      <td>472</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.000089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>MO</td>\n",
       "      <td>4.17</td>\n",
       "      <td>18.03</td>\n",
       "      <td>15.70</td>\n",
       "      <td>6087203</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>24</td>\n",
       "      <td>1462</td>\n",
       "      <td>470</td>\n",
       "      <td>791</td>\n",
       "      <td>0.000240</td>\n",
       "      <td>0.000077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>MT</td>\n",
       "      <td>5.34</td>\n",
       "      <td>20.57</td>\n",
       "      <td>16.48</td>\n",
       "      <td>1040863</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>26</td>\n",
       "      <td>154</td>\n",
       "      <td>38</td>\n",
       "      <td>21</td>\n",
       "      <td>0.000148</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>NE</td>\n",
       "      <td>4.17</td>\n",
       "      <td>18.39</td>\n",
       "      <td>15.93</td>\n",
       "      <td>1905924</td>\n",
       "      <td>0.000067</td>\n",
       "      <td>29</td>\n",
       "      <td>361</td>\n",
       "      <td>51</td>\n",
       "      <td>153</td>\n",
       "      <td>0.000189</td>\n",
       "      <td>0.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>NV</td>\n",
       "      <td>4.21</td>\n",
       "      <td>18.33</td>\n",
       "      <td>11.63</td>\n",
       "      <td>2919772</td>\n",
       "      <td>0.000015</td>\n",
       "      <td>33</td>\n",
       "      <td>488</td>\n",
       "      <td>173</td>\n",
       "      <td>264</td>\n",
       "      <td>0.000167</td>\n",
       "      <td>0.000059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>NH</td>\n",
       "      <td>5.07</td>\n",
       "      <td>19.36</td>\n",
       "      <td>20.72</td>\n",
       "      <td>1342373</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>30</td>\n",
       "      <td>252</td>\n",
       "      <td>17</td>\n",
       "      <td>36</td>\n",
       "      <td>0.000188</td>\n",
       "      <td>0.000013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>NJ</td>\n",
       "      <td>3.57</td>\n",
       "      <td>15.50</td>\n",
       "      <td>12.67</td>\n",
       "      <td>8874516</td>\n",
       "      <td>0.000028</td>\n",
       "      <td>31</td>\n",
       "      <td>1151</td>\n",
       "      <td>262</td>\n",
       "      <td>619</td>\n",
       "      <td>0.000130</td>\n",
       "      <td>0.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>NM</td>\n",
       "      <td>4.43</td>\n",
       "      <td>19.19</td>\n",
       "      <td>12.95</td>\n",
       "      <td>2092789</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>32</td>\n",
       "      <td>373</td>\n",
       "      <td>110</td>\n",
       "      <td>122</td>\n",
       "      <td>0.000178</td>\n",
       "      <td>0.000053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>NY</td>\n",
       "      <td>4.08</td>\n",
       "      <td>17.54</td>\n",
       "      <td>14.47</td>\n",
       "      <td>19641589</td>\n",
       "      <td>0.000034</td>\n",
       "      <td>34</td>\n",
       "      <td>2418</td>\n",
       "      <td>383</td>\n",
       "      <td>1208</td>\n",
       "      <td>0.000123</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>NC</td>\n",
       "      <td>4.72</td>\n",
       "      <td>18.98</td>\n",
       "      <td>16.01</td>\n",
       "      <td>10156679</td>\n",
       "      <td>0.000023</td>\n",
       "      <td>27</td>\n",
       "      <td>1931</td>\n",
       "      <td>490</td>\n",
       "      <td>1114</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.000048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>ND</td>\n",
       "      <td>4.03</td>\n",
       "      <td>17.06</td>\n",
       "      <td>14.48</td>\n",
       "      <td>754353</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>28</td>\n",
       "      <td>137</td>\n",
       "      <td>14</td>\n",
       "      <td>30</td>\n",
       "      <td>0.000182</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>OH</td>\n",
       "      <td>5.13</td>\n",
       "      <td>19.72</td>\n",
       "      <td>16.50</td>\n",
       "      <td>11635003</td>\n",
       "      <td>0.000043</td>\n",
       "      <td>35</td>\n",
       "      <td>2224</td>\n",
       "      <td>513</td>\n",
       "      <td>1320</td>\n",
       "      <td>0.000191</td>\n",
       "      <td>0.000044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>OK</td>\n",
       "      <td>4.60</td>\n",
       "      <td>18.93</td>\n",
       "      <td>15.13</td>\n",
       "      <td>3926769</td>\n",
       "      <td>0.000031</td>\n",
       "      <td>36</td>\n",
       "      <td>827</td>\n",
       "      <td>198</td>\n",
       "      <td>383</td>\n",
       "      <td>0.000211</td>\n",
       "      <td>0.000050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>OR</td>\n",
       "      <td>5.19</td>\n",
       "      <td>22.61</td>\n",
       "      <td>17.52</td>\n",
       "      <td>4091404</td>\n",
       "      <td>0.000026</td>\n",
       "      <td>37</td>\n",
       "      <td>517</td>\n",
       "      <td>76</td>\n",
       "      <td>156</td>\n",
       "      <td>0.000126</td>\n",
       "      <td>0.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>PA</td>\n",
       "      <td>4.20</td>\n",
       "      <td>18.76</td>\n",
       "      <td>16.35</td>\n",
       "      <td>12783538</td>\n",
       "      <td>0.000038</td>\n",
       "      <td>38</td>\n",
       "      <td>2000</td>\n",
       "      <td>478</td>\n",
       "      <td>1115</td>\n",
       "      <td>0.000156</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>RI</td>\n",
       "      <td>4.24</td>\n",
       "      <td>19.23</td>\n",
       "      <td>18.96</td>\n",
       "      <td>1057063</td>\n",
       "      <td>0.000040</td>\n",
       "      <td>39</td>\n",
       "      <td>222</td>\n",
       "      <td>9</td>\n",
       "      <td>78</td>\n",
       "      <td>0.000210</td>\n",
       "      <td>0.000009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>SC</td>\n",
       "      <td>3.98</td>\n",
       "      <td>18.12</td>\n",
       "      <td>14.42</td>\n",
       "      <td>4958235</td>\n",
       "      <td>0.000017</td>\n",
       "      <td>40</td>\n",
       "      <td>1400</td>\n",
       "      <td>328</td>\n",
       "      <td>681</td>\n",
       "      <td>0.000282</td>\n",
       "      <td>0.000066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>SD</td>\n",
       "      <td>4.12</td>\n",
       "      <td>17.83</td>\n",
       "      <td>14.95</td>\n",
       "      <td>862890</td>\n",
       "      <td>0.000051</td>\n",
       "      <td>41</td>\n",
       "      <td>130</td>\n",
       "      <td>18</td>\n",
       "      <td>28</td>\n",
       "      <td>0.000151</td>\n",
       "      <td>0.000021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>TN</td>\n",
       "      <td>4.98</td>\n",
       "      <td>19.63</td>\n",
       "      <td>15.10</td>\n",
       "      <td>6645011</td>\n",
       "      <td>0.000035</td>\n",
       "      <td>42</td>\n",
       "      <td>1819</td>\n",
       "      <td>384</td>\n",
       "      <td>1043</td>\n",
       "      <td>0.000274</td>\n",
       "      <td>0.000058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>TX</td>\n",
       "      <td>3.26</td>\n",
       "      <td>16.04</td>\n",
       "      <td>10.97</td>\n",
       "      <td>27937492</td>\n",
       "      <td>0.000010</td>\n",
       "      <td>43</td>\n",
       "      <td>3250</td>\n",
       "      <td>1100</td>\n",
       "      <td>1401</td>\n",
       "      <td>0.000116</td>\n",
       "      <td>0.000039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>UT</td>\n",
       "      <td>5.20</td>\n",
       "      <td>22.27</td>\n",
       "      <td>15.72</td>\n",
       "      <td>3042613</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>44</td>\n",
       "      <td>222</td>\n",
       "      <td>56</td>\n",
       "      <td>69</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>VT</td>\n",
       "      <td>5.32</td>\n",
       "      <td>20.85</td>\n",
       "      <td>20.39</td>\n",
       "      <td>623644</td>\n",
       "      <td>0.000083</td>\n",
       "      <td>46</td>\n",
       "      <td>85</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>0.000136</td>\n",
       "      <td>0.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>VA</td>\n",
       "      <td>4.60</td>\n",
       "      <td>19.96</td>\n",
       "      <td>16.54</td>\n",
       "      <td>8410946</td>\n",
       "      <td>0.000024</td>\n",
       "      <td>45</td>\n",
       "      <td>1362</td>\n",
       "      <td>311</td>\n",
       "      <td>892</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.000037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>WA</td>\n",
       "      <td>4.80</td>\n",
       "      <td>20.72</td>\n",
       "      <td>16.37</td>\n",
       "      <td>7294680</td>\n",
       "      <td>0.000042</td>\n",
       "      <td>47</td>\n",
       "      <td>701</td>\n",
       "      <td>146</td>\n",
       "      <td>274</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>0.000020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>WV</td>\n",
       "      <td>5.18</td>\n",
       "      <td>20.90</td>\n",
       "      <td>17.01</td>\n",
       "      <td>1830929</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>49</td>\n",
       "      <td>348</td>\n",
       "      <td>82</td>\n",
       "      <td>148</td>\n",
       "      <td>0.000190</td>\n",
       "      <td>0.000045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>WI</td>\n",
       "      <td>4.24</td>\n",
       "      <td>18.51</td>\n",
       "      <td>16.07</td>\n",
       "      <td>5772958</td>\n",
       "      <td>0.000062</td>\n",
       "      <td>48</td>\n",
       "      <td>807</td>\n",
       "      <td>194</td>\n",
       "      <td>423</td>\n",
       "      <td>0.000140</td>\n",
       "      <td>0.000034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>WY</td>\n",
       "      <td>4.62</td>\n",
       "      <td>19.46</td>\n",
       "      <td>13.98</td>\n",
       "      <td>584290</td>\n",
       "      <td>0.000079</td>\n",
       "      <td>50</td>\n",
       "      <td>124</td>\n",
       "      <td>17</td>\n",
       "      <td>12</td>\n",
       "      <td>0.000212</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state code  %serious 18+  ...  n_incident_capita_2016  n_killed_capita_2016\n",
       "0          AL          4.23  ...                0.000256              0.000091\n",
       "1          AK          4.53  ...                0.000564              0.000104\n",
       "2          AZ          4.04  ...                0.000075              0.000038\n",
       "3          AR          5.45  ...                0.000222              0.000053\n",
       "4          CA          3.61  ...                0.000090              0.000031\n",
       "5          CO          4.61  ...                0.000134              0.000031\n",
       "6          CT          3.59  ...                0.000216              0.000016\n",
       "7          DE          4.23  ...                0.000333              0.000041\n",
       "8          FL          3.56  ...                0.000193              0.000043\n",
       "9          GA          4.17  ...                0.000154              0.000046\n",
       "10         HI          3.18  ...                0.000053              0.000021\n",
       "11         ID          4.98  ...                0.000083              0.000018\n",
       "12         IL          3.74  ...                0.000368              0.000059\n",
       "13         IN          4.93  ...                0.000204              0.000051\n",
       "14         IA          3.93  ...                0.000198              0.000017\n",
       "15         KS          4.37  ...                0.000182              0.000035\n",
       "16         KY          5.25  ...                0.000203              0.000057\n",
       "17         LA          4.53  ...                0.000399              0.000092\n",
       "18         ME          4.35  ...                0.000174              0.000014\n",
       "19         MD          3.41  ...                0.000206              0.000064\n",
       "20         MA          4.32  ...                0.000211              0.000012\n",
       "21         MI          4.44  ...                0.000119              0.000033\n",
       "22         MN          4.25  ...                0.000075              0.000014\n",
       "23         MS          3.92  ...                0.000272              0.000089\n",
       "24         MO          4.17  ...                0.000240              0.000077\n",
       "25         MT          5.34  ...                0.000148              0.000037\n",
       "26         NE          4.17  ...                0.000189              0.000027\n",
       "27         NV          4.21  ...                0.000167              0.000059\n",
       "28         NH          5.07  ...                0.000188              0.000013\n",
       "29         NJ          3.57  ...                0.000130              0.000030\n",
       "30         NM          4.43  ...                0.000178              0.000053\n",
       "31         NY          4.08  ...                0.000123              0.000019\n",
       "32         NC          4.72  ...                0.000190              0.000048\n",
       "33         ND          4.03  ...                0.000182              0.000019\n",
       "34         OH          5.13  ...                0.000191              0.000044\n",
       "35         OK          4.60  ...                0.000211              0.000050\n",
       "36         OR          5.19  ...                0.000126              0.000019\n",
       "37         PA          4.20  ...                0.000156              0.000037\n",
       "38         RI          4.24  ...                0.000210              0.000009\n",
       "39         SC          3.98  ...                0.000282              0.000066\n",
       "40         SD          4.12  ...                0.000151              0.000021\n",
       "41         TN          4.98  ...                0.000274              0.000058\n",
       "42         TX          3.26  ...                0.000116              0.000039\n",
       "43         UT          5.20  ...                0.000073              0.000018\n",
       "44         VT          5.32  ...                0.000136              0.000014\n",
       "45         VA          4.60  ...                0.000162              0.000037\n",
       "46         WA          4.80  ...                0.000096              0.000020\n",
       "47         WV          5.18  ...                0.000190              0.000045\n",
       "48         WI          4.24  ...                0.000140              0.000034\n",
       "49         WY          4.62  ...                0.000212              0.000029\n",
       "\n",
       "[50 rows x 12 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# a dataframe that has aggregated data for mental health occurences (in thousands)\n",
    "# and gun violence incidents but only for 2016\n",
    "aggregate_2016_df = pd.merge(stateMHPercent_df, pop_2016_df, on=\"state code\")\n",
    "aggregate_2016_df = pd.merge(aggregate_2016_df, mental_state_df[['state code', \"MH per capita\"]], on=\"state code\")\n",
    "aggregate_2016_df.set_index('state', inplace=True)\n",
    "\n",
    "# drop any adolescents from mental health data\n",
    "# only interested in adults \n",
    "aggregate_2016_df = aggregate_2016_df.drop(['serious 12-17', 'any 12-17', 'received 12-17', 'suicide 12-17', 'depression 12-17'], axis=1)\n",
    "\n",
    "# drop redundant columns from dataframe \n",
    "# suicide and depression is a subset of mental health illness\n",
    "aggregate_2016_df = aggregate_2016_df.drop(['%suicide 18+', '%depression 18+'], axis=1)\n",
    "\n",
    "aggregate_2016_df = pd.merge(aggregate_2016_df, gun_state_2016_df, on=\"state code\")\n",
    "aggregate_2016_df = aggregate_2016_df.drop('distance', axis=1)\n",
    "aggregate_2016_df.index = range(len(aggregate_2016_df)) #reindex\n",
    "\n",
    "\n",
    "# calculate number of gun violence incidents per capita in 2016\n",
    "aggregate_2016_df[\"n_incident_capita_2016\"] = aggregate_2016_df[\"n_incidents\"] / aggregate_2016_df[\"population 2016\"]\n",
    "\n",
    "# calculate number of individual killed from a gun violence incident per capita in 2016\n",
    "aggregate_2016_df[\"n_killed_capita_2016\"] = aggregate_2016_df[\"n_killed\"] / aggregate_2016_df[\"population 2016\"]\n",
    "\n",
    "aggregate_2016_df=aggregate_2016_df.apply(pd.to_numeric, errors='ignore')\n",
    "aggregate_2016_df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XSsqOCbQAptJ"
   },
   "source": [
    "# Data Visualizations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uxE1jdgqdGAL"
   },
   "source": [
    "Here, we define a function called heatPlot that will setup the preferences for our heatmap, and plot using plotly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "JCT-V3MMdDcP"
   },
   "outputs": [],
   "source": [
    "def heatPlot(df, item):\n",
    "    '''\n",
    "    Generate a heatmap over the American map by using module plotly. The function plotly.plotly.iplot in plotly \n",
    "    can identify the abbreviation (called 'code' in the data) of each state in America, so it is used to assign \n",
    "    values of item to corresponding states.\n",
    "    \n",
    "    :@ df: pandas.core.frame.DataFrame, input data.\n",
    "    :@ item: string, the column of the data that need to be plot.\n",
    "    :@ returns  res: plotly.tools.PlotlyDisplay, the heatmap.\n",
    "    '''\n",
    "    import pandas\n",
    "    \n",
    "    assert isinstance(df, pandas.core.frame.DataFrame), 'The input data is not a DataFrame.'\n",
    "    assert isinstance(item, str), 'The input item is not a string.'\n",
    "    \n",
    "    \n",
    "    for col in df.columns:\n",
    "        df[col] = df[col].astype(str)\n",
    "    \n",
    "    ### color scale\n",
    "    scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\\\n",
    "                [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]\n",
    "\n",
    "    try:\n",
    "        data = [ dict(\n",
    "                type='choropleth',\n",
    "                colorscale = scl, ### setting color scale\n",
    "                autocolorscale = False,\n",
    "                locations = df['state code'], ### locate the rows to corresponding states\n",
    "                z = df[item].astype(float), ### determine the item we wanna plot\n",
    "                locationmode = 'USA-states', ### chose the map model\n",
    "                marker = dict(line = dict ( color = 'rgb(255,255,255)',\n",
    "                                            width = 2 ) ) ) \n",
    "               ]\n",
    "    except:\n",
    "        raise ValueError('Cannot convert the data in required column.')\n",
    "\n",
    "    layout = dict(\n",
    "            title = '2016 US {}'.format(item),\n",
    "            geo = dict(\n",
    "                scope='usa',\n",
    "                projection=dict( type='albers usa' ),\n",
    "                showlakes = True,\n",
    "                lakecolor = 'rgb(255, 255, 255)'),\n",
    "                 )\n",
    "\n",
    "    fig = dict( data=data, layout=layout )\n",
    "    res = py.iplot( fig, filename='d3-cloropleth-map' )\n",
    "    \n",
    "    assert isinstance(res,plotly.tools.PlotlyDisplay)\n",
    "    \n",
    "    return res"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gePT0a0pfAnq"
   },
   "source": [
    "The following heatmaps are interactive and will not show unless ran. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "hHGfF0v1fRtX"
   },
   "source": [
    "The data is not normalized, so these visualizations below do not take into account the population bias that may occur. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Kf-3o5JehE5F"
   },
   "source": [
    "**Percentage of Mental Illness in Each State**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Roa9qRP0Tqhe"
   },
   "outputs": [],
   "source": [
    "plotly.tools.set_credentials_file(username='tybhakta', api_key='VNUJhrn2aaoUwpMuQInl')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 583
    },
    "colab_type": "code",
    "id": "VbuMMuOVbKHB",
    "outputId": "46587bf2-4a39-426d-d00b-93ef54c37a4e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~tybhakta/0 or inside your plot.ly account where it is named 'd3-cloropleth-map'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tybhakta/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 81,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#This is an interactive heatmap of the number of any type of Mental Illness by percentage per state.\n",
    "heatmap_any18 = heatPlot(stateMHPercent_df, '%any 18+')\n",
    "heatmap_any18"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HiUrD2BWVUwX"
   },
   "source": [
    "This heatmap displays the number of individuals with mental illness in each state. Oregon has the highest percentage of mental illness with 22.61%, followed by Utah with 22.27%, then Kentucky with 22.08%. The states with the lowest rates of mental illness are New Jersey (15.5%), Hawaii (15.55%), and Illinois (15.73%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "peFNN3_p1Fd3"
   },
   "source": [
    "**Percentage of Gun Incidents per Capita in 2016**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 583
    },
    "colab_type": "code",
    "id": "WRHTNU6kX3Y5",
    "outputId": "784d3196-0c1f-4fea-e5fe-a89eca7f499d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~tybhakta/0 or inside your plot.ly account where it is named 'd3-cloropleth-map'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tybhakta/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 57,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gun_incident_percapita = heatPlot(aggregate_2016_df,\"n_incident_capita_2016\")\n",
    "gun_incident_percapita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wyshqlrSgyGn"
   },
   "source": [
    "This heatmap displays the number of gun incidents per capita in each state. Alaska, Louisiana, and Illinois have the highest rates of gun-related incidents per capita. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "GZgW_hwI1j7K"
   },
   "source": [
    "**Percent of Individuals Who Have Recieved Treatment**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 583
    },
    "colab_type": "code",
    "id": "Pe_2au9_NCMO",
    "outputId": "9e1070d4-2e84-4dcd-fea4-ee221375f362"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~tybhakta/0 or inside your plot.ly account where it is named 'd3-cloropleth-map'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tybhakta/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 82,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gun_treatment = heatPlot(aggregate_2016_df,\"%received 18+\")\n",
    "gun_treatment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "fcEaJXLd2FgS"
   },
   "source": [
    "**Individuals Who Have Been Killed from Gun Violence**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 583
    },
    "colab_type": "code",
    "id": "c4oNKvplNnf9",
    "outputId": "11f67800-e7a7-4867-86ae-983e8cd1c7b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "High five! You successfully sent some data to your account on plotly. View your plot in your browser at https://plot.ly/~tybhakta/0 or inside your plot.ly account where it is named 'd3-cloropleth-map'\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~tybhakta/0.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 83,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gun_killed = heatPlot(aggregate_2016_df,\"n_killed_capita_2016\")\n",
    "gun_killed"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X-Ly_udMQdbv"
   },
   "source": [
    "The last two heatmaps, Percent of Individuals Who Have Recieved Treatment and Individuals Who Have Been Killed from Gun Violence, appear to be inverses of each other, especially in the Northeast and Southeast regions. We will explore this relationship in the data analysis section. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "E-6zve7JMSwn"
   },
   "source": [
    "#Data Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ORDcKBDgkFpk"
   },
   "source": [
    "First, we are creating scatterplots and a correlation matrix from our 2016 dataset to get a better feel for our data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 2009
    },
    "colab_type": "code",
    "id": "Smf8L9kiiBlM",
    "outputId": "f1cf1003-0dbd-43b3-b266-6a2b6f187e86"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RGewP6onik+O/DWA1gMsB3AHgt6SUmwH8ztwXEZFvspl09T4jpoHeHLKZdEQt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\nSSkfAPAAAAwNDXHJddKKyvlttvSO1c97c23aLx9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AV65b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\nzxtjfjeyp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mPbnZsHdd+zL1f/O+ochHmc4+ZvB3GcWa+W\nDCzr0uvHT7k6dkyjQkdet12+tnphXJr7fLc+vL/hvqtk8Ir1K/Wp96/TjicP6tr7fqDrdv/A1+cT\ndB2vlwWOxbPJKcO3P/aCbrt8bd1xr5dzQo2Elds33jpV96ZUspodoxMlbT09fbr0dmaNMdVjtw1r\nevWZD8yd7/nNO59tuf9x8/APtRNBqD1GqSxxsePJg/qHu9zleGHmepd2Nszu6ESpemG88r3bH3tB\nn7tyPeuLIxDFQl67rh1cVJvdZtqtU9P16/TUgifUqcvpEfe06n8j6T9Za/cv/IYx5uMxbE/qtTJ9\nXJh3G3p5MtjL3ZKVg/6F21zpXL0eDPGETzyCnPYwiBzXy8HoRMl1hiu/v/2q9YtOAni9U71ZxgEk\nn1Pf0t9d0Cfft05rB5Z5rn9OF9y3X7VeWx+aG1Y1q1EVG9b06qZLz9N1u3/A0wYIRVAzxNTr4//i\n4xfpn/7ZjxzbQlhjODdjRvrw9rVwbDtbtvqTpw7qwKHjseUgzOOcoGfzclKvltx2+dpFFzCytHxB\nf3dB563o9rzvKhn8wu9cUO3fK7/n5/MJs45XssCxeDY1yvD5K5dpdW9xUR0I6onvMHO7c/OgRsZL\n1aWRyGq2ONWjM5cv0ereYrV//9D9Pwys/2mUV2onglR7jHLrZed7Pm9Zm7kNa3p1RrGzYa11ymj+\n9Cw/QKt6iwWtWr5EO66+QOf2L63OVCMFe2yw8Al1aS7rZWvnzcxEXU6PuJ/O/oikXzh8byjKDcmS\nyvRxZ/ct1UCP+7UMWrmrpdmTAF6eDPZSQGpPPD13+3u1d9umeSdZvD6xwxM+4WmWEb+5XSiIu7Pq\n5cDL052V36+sK1nLa2fYLOMAks+pb3lnb1HrVvWooyPnuf41uuBe+fsPbBlSX7FzUe1duD1OB8Tc\n1YqgNBsHun2itF4fP+Iws0vv6ZM0YY3h3IwZ6cPbW+3YdnXfUn3xmsFQcuC2/YR5nBP0bF5O6tUS\nPxeO0ySXM1ra5W/f5XJG1lpfn8/CXPUVOwOZ6atRFjgWz6ZGGS525n0tn+dWvZqx+4aNyufk6Qmy\nRrNA1b4fspodTvVoaVe+euwmyVNOWzlvSu1EkGqPUX7tzB7HHDtltjZzt152vu74zz/Tzs2D82vt\njRurYwQyirDlckbn9nfrgrPPUC5nQpk1ZmR8SksKOd37T39j0exgX3zqIDPIpVSsT45ba4cbfO9I\ns983xjwo6SpJR621F9T8+z+X9IeSZiU9Za39TACbm3kL7/y69bLz1VvsrHaITidw3DwJ0OzJ4IXr\npnq5u7fRWnJen9jhCZ9whL3GYe1aZOVyuW4nODk9q9fGTrp6KrNeDrw83Vn5/TfePBXInepZWC8R\naGdOfcuZy5e4roELa12xUP/Jgnf2FvXc7e9VoSOvvmKnXho5obu/O6zNG9eov7ugydKMzlq+ZN72\n9HcXMn1hAfGqZPcdSzv16NZLZK2d14d6GSN4mdnlZGk28DFcbTvs7Mhpz0cv1Jaa9VPrvR59OKTw\ncuCl/dT2RQPLunTb5Wt13opuWdmGx3puBD2bl5N6x5RWtu4ag0Gs/5oUK7q7fB+jLnwS0c3n45Qr\nPzPdLPy7k9Mzjlk464wix+IZ5TXDTk/QFgt5jYxPuc5gpWY8vu1SnZya1SvHJvS5//SiRk5MeTof\n4famVLKaHU79Wu16sV5yKqml86aN+lFqJ/yojE1HxlU3x50dOcexQGWJq1eOTWjFsoK+c/CoRsZL\n2n7VevUWO3V8cloraupzX7FTu2/cOG/NcTKKoDXLtN9ZY2rPp61avkSrerrmZf2up4d14NBxff63\n3c0gt/DcXhaOVdLMWJvetbiMMb8p6YSkPZWL48aY90r6Y0lXWmunjDErrbVHm/2toaEhu2/fvnA3\nOMHKZas33jql63b/oLpOcuUpsmYXMkfGp3TNvc8tKjpup6tYePB9xfqVuu3yd+nWh/e7en03f99L\n0Qm4SEVS3ZKe31YzUk+5bHV8sqTXj5+qTqVYuTvxS9/7ub5z8O1mv7qvqB1XX6CPfPUnrvPUag6c\nti9lUxWTX5fO/exTnn/n1TuujOR1/PCzbQkUWSNzk+HamlIs5DVTtpqeKbvul+odmHZ15BZdmKut\nLyPjU/rjvS/opkvPm9en775xo9at7NHY5HT1prTaKVel1ms0Wpao/Prl5sKdlzFCvZ+9Yv1K/Yvf\nete8ky27b9ios3qXqLcY3IGm03tZtbxL0zNlzVotuvDf5hhDRMDrGLvZ+FSS7/Fvo7FzGMcCta9b\naZsDy7r0mQ+s06e/6e44toHE1eB6n6/UfH/5+XzCOnYbPjKuN948pe1PvOj4tzlhGIjE5Vd6u/5M\nlmY1a62WdOa1orv+jEn1+tw9H71QUzNlXzfct5ppp9//q1sulpHIavASMYZoVneLhbyOvDXlKqer\nlnfpd77sP4NHx0/pg/c+v+j3H992qVb2LKF2Jksia7CTRsc4CzPrdNz1pWcWn4Ot7dcXXmBc2dOl\nd55RVEdH3BMao45U5bceLzcPNzt+qXs+rUnmG/1tqfmNUmiZpw8y7jXHW2Kt/WtjzLkL/vmfSbrD\nWjt1+meaXhhvd7Ud1c7NgyrNlD2tN+LlSYB6hWHh9ECV4lLv6SI/vD6pwRM+wQt6rQ2nkyuHxya1\n9aH9euTjF+ng6+PVjmbXtYO689vD1Z9xs9aIUw7cHnTkckbv6O5Sb7EQyFrqANKtUlP8zKRxbGJq\nXj9ZqWOPb7u0YX0pzcxq88Y1i/r0rQ/tn1cDy2XL0wYIRaM1QytjwJMl56cIF2p1Zpcw3svj2y7V\nr05Oc4CbIWk6wex1jJ3LGc2WVXeN7se3XarREyXfWQ5yNi8vFj5pF+T6r0my8PN1O56ofXK2NFPW\n77v4fMJYJ7FSQweWdWnn5sFFN+JXssCxeLYtvJDoVGMWtmtjjGRt9aZQyVv7bjXT/d2FRU897tw8\nqH//f/+/+uI1g2Q2o9zU3T0fvVDf2HqJpmfL6sjn1JEz2vLg84ty+vVbLm4pgx05o13XDs67uWnX\ntYPqqHnqnBzCD6cZC15/c3JRZjdvXFOtg9LpcwsPLz4HW9uv157LqJzvr72xAwhas9mLKxrNlDQ2\nOa2TpZn659OaZL52O+rd7O90foQaHo9UXxx38C5J/8AY80VJpyR9ylr7k5i3KdFqT/aNjJe06/cG\nPQ3anKYSWjhdhVPRWb6kY9HrfefgUX3+t63O7lsa0LtEnNxmxK1KZv/0995dN6v5nKl2gpL0iUcO\n6MCh4/N+xu9aI14vanGQAqBWo4uFTjfknJyqf0JveqbcsJ+sHAQ069PdHjwAXjU6GV3pT7dftd71\nGKFRVsPua53ey6npMge4GRLmUkBh8DPGjiPLYfczlRrw2tjJwC/qJpXX8cToiZImptzdjBT0sZv0\ndu4Oj03qrqeHq9NRru4r6qwziolsXwiW18zmckb93YVqTXY69nfTvlvNdC5ntKK70HQaVWRbvQxv\nefDH82YpfPhjF9XN6axtbZrfydKs7vz28LwM3vntYX35+g1Sd3DvEe2p3rFUvbrpdG6h9hzswjHe\nqWnncScQFjfnB5zGJY98/CJd/2c/0var1vvKfCNh3ICK1sQ6f4Ux5v80xtzj9OXzz3YNGil0AAAg\nAElEQVRIeoekiyV9WtKjxpi66TTG3GKM2WeM2TcyMuLz5dKvtmEeOHRcL49MaHVfcd7PNBq0VZ4E\nqPyO0x0zTkWnssa429drVbk8t77aa2MnNTI+pXI5nUsLpCm/bjOykNO+qmT2+OS0Y3YGerp0dt9S\nFTryGjkxVfdnvHLK8OhEyfPfClMaMp6m/AL1+M1wvcHwwLIulWZm67bZ0YmSXjlWv1/ubDINWWXK\nMjd9bOXg4ey+pRroqT/FJbIjqhpcOalSa3VfUcaYan9637Mva+fmQa3uK2rDml595cPv0cMfu6i6\nDvJCTlkNu+9zei95Iw5wIxZmftMy1qtoNMZ2ahNxZbnVfsZNG3d6b2EdV/rVSoYrn8PJ0oy2X7Ve\nG9b0Vr/ntL8quR6dKLn6fNwcu3mtubX75sCh49r60H790Td+qkJHnjFHyrQ6Bt6wple7b9yor99y\nsbZftV7lsvMFktqa3OjYvxm/5yMqymWrWSvtePKgPnT/D7X1of06cOh4IusLGvOT32Z1d2khX/3/\nTsdtSzpznvvrWpXzWlsf2l/N4MiJKfLXhoIYQ7jpu+vVzXd0F5qeg104xss7nPPPN+n603BeE96F\nfR7CS26cLlQfHZ+qnqfwk/lGkn6s0o7tLu7FHfZJ2i9piaTfkPTS6a+/J8nvHGuHJT1u5/xYUlnS\nino/aK2931o7ZK0dGhgY8Ply6dfZkZvXMO979mXtunbQ9YFD7ZMAz93+Xu3dtqnu0xVORSdv1NKB\niheVp0Guufc5bdr5fV1z73MaPjKeysaepvy6zUitRvuq0pnUnlCX6men1QPhWmm4wystGU9TfoF6\n/GZ44WB4w5pefeYD6/Sh+39Yt82WZmZ1zzMv6U9/793z6tiuawd14tRMw7adyxm984y5Ncaj6GOR\nHlHVYKc+uPYi3IFDx3XX08Pade2gdvzuBdr+xIu67K5n9cF7n3fdf0XR9zm9l2Ih2Qe4WRRmftMw\n1qvlNMaW5Ngm0phlt208yHF/mPxmuPZz+Ie7ntWOJw/qU+9fV71Q47S/Krl2c+wkNT9281Nz07Jv\n0FwrY+Ar1q/Up96/rnqReceTB3VsouSYndqa7Da/9fg5H1FRyfsXvvWi79dHcnjNr5u6e3xyuvrz\n9zzzknbfsPjYa0V3l+f+uhY1FBVBjCHc9N0L6+bXb7m4bh3efcPGhjksFvKLrjHsunZQxYLz+DIt\n5zXhXZjHcV5z43ShunJT9IFDx3Xfsy/rP17/G4HV3iTX8nZtd8ba+N+gMeaHkv6+tXbm9H93Svp/\nrLUXu/jdcyU9aa294PR/3yrpndbaf2uMeZekZySdY5u80aGhIbtv377W3khK/WpiSsNvjM9bu+Y/\nXr9BK3uWqBzAmt8VI+NTuube5xZNI1S77mTY07k22gY30wX6WIMwktvgs5jfZnmpTK82sKxLt12+\nVuet6NbSrrxWdC++YyuotSNbzU8Y2xTmNor8unbuZ5/y/Duv3nFlJK/jh59tS6DIHkPykuGFU/Z+\n5cPv0fYnXnRss5U2vevaQZ2aLmtpIa/jk9O679mXNXJiylXbTtP6uahKZH79qJe/0YnSor6qWVto\nJOC+z9V7McYob+ZuMnW7hmqbSewYolFNjCpLYWv2Pup9BpISO6W8l/0SUJ+XyBrs9Dlsv2q9djx5\nsO7+Kpet3njrlK7b/YPqU7u3Xna++rsLemdvUWcuX+L58/HbThiPRCaR+S2XrQ6PndT1f/Yj19k5\nOn5KH7z37fWbN6zp1W2Xr9X5K5ep2BlNhmrzHkT7gSuJGUM0q7u7rh3Und8eri7jt7qvqCc+canK\nZeOq1sXQvyF8iazBrY5xK78/sKxLt152vnqLnTpZmtW715yhd3Q37vtfHZ3QL0ZPamkhr5OlWf2t\n/qU6t7973k13tdm2svNqv9dtRUsSmV83vGa83nJau2/cqC997+f6zsGj1Z+7Yv1KfeF3LpAN6BpZ\no1oeZ53PynGwPGY4KWuO90laLulXp/972el/a8gY85eSLpO0whhzWNLnJT0o6UFjzIuSSpJuanZh\nvN3VW7vmC986qC9fvyHQNb8rd8csPOES1VqRUmtPg6RtDcK0a7SvvK5bGFS+GmXYizCzlLYnnoB2\ns7B+zVrbsM1W6s7E1Iz+4IEfLfp7btp2VH0sUE+9/NXrT89b0e27/4qq71u4/mll2/d89EI9vu1S\nTc+UOVmZcM3GYEGN9eLWrE049QthrgveCi9tPMt9ntPn8Otn9lRvIF54YXz4yLju/u6wdm4e1O2P\nvaADh45XL6T7vbDnt+Zmed+guVzOKJ8zrrNTLludODWjXdcOVh/kGDkxpTPPWKLVvdGtU79wGcKt\nD+2XJD13+3sTUR8RrkZ19/Ftl+rEqZnqMn6VJ2LfPDkz78Kfn7/fbv0bwtfq8VLtGHnrQ/urY+Te\nYuMxci5ndG5/t3qWdDpeDFw4Nn/4YxdxXhOeec14vWsLfcVOffJ963Tw9fFqHj/5vnWB3gznVMvj\nvu7UrtcTknJx/A5JB4wx39fc1f3flPSFZr9krf0Dh2/dENymZV/t2jUVYUyh5/WCZiN+76SpTJmx\n8C4YN+/VaQ3CFN5BkwrN9lXUBwaVzC1f0qFHt16ivJFyuZyvDIeZpVYyDiAatfXrl8cn67ZZY0z1\nZ9et6tEbb51y/Lly2XJyDqlSb0xoZV31X/XGgFH2ffX68C0P/lh7t20K9KZShKPZGCzI45Vmwnwy\nwG+bSOqJd8a3c5w+h2Kho+5+q837yHhJ269a39ITr5XMVl633fcHvPPSlkcnStry4I81sKyr+iDH\nydKsVi13v7bnQn7qLvWnvTWrux25Ke24+oLq7F53fnu46exeC2ciIl+IQqu1rJUx8sLxZWVd40ob\nuPu7w/PG5q8cm6BdwDM/Ga937BP1zcKVPmFyekZvvHlKA8u6dHhsMvLrTu063ol7zXFJkrX2K5Iu\nkrRX0uOSLrHWfi3erWofUa53UCk6Z/ctrZ6A8qqVNRBaea/tegdNXJK0DsfCzF23+wf61clp3x1k\nmFlK0ucGoLm80aJ1u3ZuHlS+prTkckZnLl+yqG3v3DyoL3zrxbZYBwjZs3BMuKK7q2n/5TQG7Ct2\nRtb3MR5MNzf7L4jjlWbCXtMta+PBrL0fv7x+DvWeeL32vh/IWu831dVm9hOPHFi0fmg77g945yXD\nlfxWsvuh+3+oj3z1J5os+etv/dZd6k97a7b/J0uz+shXf6IP3f9DbX1ovw4cOt5wXLgwh1/41ou6\nr84a5eQLQQuiloVxTv+63T/QTZeepw1reqs/c88zL2k37QIeBdVfR3EsWFHbHn7zzme1/YkX9an3\nr6u2hyjPM7TreCcRT46bucejfkvS37bW/ntjzDnGmAuttT+Oe9vaQZRPSAShladuW3mv7XoHTVyS\nlMugn/QOM0tJ+twANJfL5fS151+Zt7TJ155/RV+8ZnDBz8217Ue3XqJfHp/U6ERJdz09t77dwdfH\nmcUEqeem/2rUH0fV9zEeTLek7L+wZ6TK2ngwa+/HL6+fQ5B5r83s4bFJ3fntYe24+oJI135G+nnJ\ncND12m/dpf60t2b732tOF+awsq7to1svCWw9W6CepNSyerX49sde0Par1ldntB05MaWzepfEvq1I\nl6Rk3Itm7SHK49Q0fn5BSMTFcUn3SipL+keS/r2kcUmPSXpPnBvVTqKYQi+oqQMXPvGxYU2vbr3s\nfJ0szWhkXE3/rt/3mpU1CNMkiFz6yd3C3ymXy4E+JRZ2lpI6JSaAxfq7C/rk+9ZV68EV61fqc1eu\nV2lmViPjU/NqVi5nZK3VF5/6mW697Hx99h//mo5PTuu+Z1/mqVVkVm2fLKk6zVhFpT+Oqu9jPJhu\nSdl/UcxAEOd4sJXjPqffZXw7x8vnUC/vez56oaysXhs76WnfLMzsgUPH9ZGv/kTP3f7ehtsT5vIB\nSCe3GQ4yv5Jz3S2Xy9XpfZ3+JvWnvTXa/7U5HVjWpdsuX6vzVnTLytZd+qpeDr9z8Kg+/9vWcXke\n6iiCkoRaVmkDlXP5lQcEFj6t2lv0fu6WtoEkZNyLcrk870GZ+559WQcOHVdvsXPecWpUWU/b5xeE\npFwcv8ha+xvGmAOSZK0dM8ZwhilGQTe6yjQRC09ErVvV4/nv1t6ZuWFNrz71/nW6/bEXWv67zbTr\nHTRRCKvI+8ldvd/ZfeNGXbF+ZfWuXqm1u9bJEtB+Gp3sr9SDcrmsYxMlXf9nP3KsWcVCXp/5wDp9\n+ptv93u7rh1UscBTq0i3ev3vVz/yHk3PWN380L55eb/z23OzJkjRP/VLH55uQey/IMatSXmCPQyt\nHPcFecyYdW5yuDDvxUJeR96a0pZ7n/f8+frJLPsTtbzWziDzK9XP8BXrV+rYRElbH9pPRuHawiyv\nHVimb31ik14/fkpbH26cJa+1lDqKrCl05HXF+pW66dLz5p3L333DRv3oX/8j5XI51w81tdI2uLCO\noHnNVLlsdWyipB1PHqxmeOfmQX3t+Ve0uq+ovds2VW/gph8ITyLWHJc0bYzJS7KSZIwZ0NyT5AhZ\nuWw1Mj6l18ZOamR8SuWyDWUNPKcprEYnSp7/Vu0aCLdedn61M23177oR5boT7WJh3v547ws6PHZy\nXib98pO7er+z9aH9+tyV6wNdd4MsAe1jZqasw2Mn9YvRCb34y7f0x3tfmNevVupBLpernpyT6tes\nmbKtXhiv/Mynv/mCZlhzHClXr/899KvJ6oXxyr99+psv6LbL10ry3x/XG/96QR+ebq3sv6COk7K8\nppvf475y2eqNt04FdsyYJQtr1sxM2XUOa/M+W5bvz9dPZoM8B4D0Kpetjo6f0v/61Um9+Nqb+sQj\nB1zXzqDyK9XP8OeuXN907I325qb+vjRyQjNlW70wLjlnyWstpY4iyfwcU/V3F/S5K9cvOpe/9eH9\nyuVyrsfmrbSNMK57IJlaPe738jpeMzVac3Oe9PaU6p+7cr3OOqNYbQv0A+FKypPj90jaK2mlMeaL\nkq6V9Ll4Nyn7nO6yWrW8K/A18IKcOrD2DuKTpZnQpyREuGqL/IY1vbrp0vMaPjXphZ/cOf1OPmd4\nSgyAZ+Wy1fDR8XlPpOzcPKi7vzusL14zOK9fdVOzpmfqL/MwPcM9hUi3evlfWsjXzfv5K5fpudvf\n6/upX+68hl9BrRWe5RkI/Iy/K+1yYopju4WcZrX60vd+7jmHrRyT+8lsFMsHINnq5Xfn5kHd9fSw\n59rZap7qZZiMohEv9feRmy9ylSWvtZSMIqn8HlPlckb5nGk51620jaDG80i2KI/7/WSq0fWH2u2j\nHwhXIp4ct9b+haTPSPrfJb0u6Xettd+Id6uyz6nhTpaCb3SVqYNqtTot9UBPl5YWOgL9u4hebZEP\neiYAP7lr9Ds8JQbAK6e7QTdvXLOoX3VTs4LuT4GkqJftk6XZunkvduZ998fceY1WBH3DbxbHln76\nqUq7HJ0o0cct4DSr1eaNa+b9nJsctjqG8JpZxiyol9/bH3tBt152vufaGUSeFmaYjKIRL/U3b4zr\nLHmppWQUSdXKMVUQuW7lb3CxsT1EedzvJ1NuM0w/EK5EXBw3xtwj6R3W2v9orf2ytfZncW9TO3Bq\nuLNWgTe6sKYOzPKUhO2itsj3FjsDHaD4yQeZAhAkp762v7uwqF91U3+oUciq/u6Cdt+4cV62+7o7\ndfd17w4075wMQSs4OdGcn36q0i7ve/Zl7dw8SB9Xo9E4opabHEY9hmDMAqf89hY7PdfOMPJERtGI\nl/pbLOQ554m20soxVRC5buVvMJ5vD1Ee9/vJlNsM0w+EKynTqu+X9DljzDrNTa/+V9bafTFvU+ZV\nGm5toVjdV9SSzpwe2DK0aNqJVtdXDmPqwCxPSdguKkX+5j37dHxyum4mW5lhwGs+yBSAIDn1tSt7\nuhb1q27qDzUKWZXLGa1b2aNHPn6Rjo5PaXSipHu////ps//41/X4tks1PVMOJO9ObZKTIXCjdtwa\n1HFS1vjppyrt8sCh47rr6WFtv2q9+rsLemdvUWcuX9LWfVyjcUTl393mMOoxBGMWOOX3ZGnWc+0M\nI09kFI14qb+9xYJ6iwXOeaJttHJMFUSuW/kbjOfbQ5TH/X4y5TbD9APhMtaGsxC9H8aYd0jaLOn3\nJZ1jrV0b1WsPDQ3Zffuyez2+XLYanSipNDOrzo6cOnJG0zNlHauZ7rV27QVJ1Z+n0bUkkg8tifmt\nzVy9DHnNJPmLRdvm16tzP/uU59959Y4rI3kdP/xsWwJFVjQaZdhprbp1K3vU0ZGICXxC16w/QF2J\nyG8cavNSLOQ1U7auLoy7zRlrjkcms2OIrNW0Vt9PEJ9HAttlYmqw02ezdmCZxianM5PDRrLW5iKQ\nmPzOzJQ1fHR83vH97hs26qzeJeotJmM/kq9ESsQYIsn1l9wmWmJqcBCcsuZm7JbknCZ522KW6vzO\nu9aQz2nkxFRk1xjIVGJ4+tCT8uR4xd+R9GuS/pYkplYPQLlsdWxiSienZvXKsQnd88xLGjkxpV3X\nDurObw9roKegRz5+kfI5s6jhDvR0xbz1SKtmg6RG3x/oWbLobihJGhmfooMBkEpdHTntuPoCLS3k\ndbI0q2Jn3lUNy8LgOoEXPBCCILNaWYfRS3a8/Cx3XqNVlYxmQas1Oojfr9SOVcu7Ap0lIksWjiO6\nOnKx5jDK8QnjiPQql61eGjmhL33v59XZIFb2dOmdZxRjuUG0Xm4lkS80FEb9DeKmNHKLKDTLWqNj\nqihz6qdNZWk8jzn1Mvfl6zfort97t85cvkRLu/Ja0d0V6oxJSchUFs4jRikRF8eNMXdKukbSy5K+\nLmmHtfZ4vFuVfvWKws7Ng7rr6WF9+psvaPtV67X1of06+Pq49m7blIgGjGwYnShVcyfNrelx8559\n1Zw1+35tFhn4A0iz0YmStjz440VTOTXrd7NS+5rVe6RfWFn1kh2vOUvKgSsQt1ZrdCu/n5V+Lmx+\nxxFhiXq/MY5Ir9p9952DRyXFl12n3K5a3kW+4CiM+htEDaUuIirNstbomCqqnDKeREW9zH3ikQPa\nftV6feobP9XebZsynwnag3dJmc/zZUmXWGs/YK39ChfGg1GvKNz+2Au69bLzdXhsUr3Fzuq/l2Zm\nPf3tctlqZHxKr42d1Mj4lMrl5EzPj/iVZmbnHUBI83PW7Pu1nAZUxyamQtp6AAhOs3rn1J861b7R\niVK0b6BFXuo90imsrDplZ3J6dtG4k5wh7eI6tmq17bTy+1np58Lm9TMOO0tR7zfqe3p52Xdx5Xay\nRL7gzO9xXCNB1FDqIqLSStaiyinjSVQ4Za632BlI9tJwLYz24F2sT44bY37NWvs/JP1E0jnGmHNq\nv2+t/Zt4tiwbGhWF1X1FHZ+cljR352OhI+/673IXCpopdOS1uq+46A7bSs6afb+WU45PTs2q3G3J\nHIBEa1TvGvWnWTnp4aXeI53CyqpTdl4+ekITUzPzxp3kDGkW57FVq22nld/PSj8XNi+fcRRZinq/\nUd/Ty+2+izO3s1bkC478Hsc1ym0QNZS6iKi0krXOjlzd3+0MeFkNxpOocMrr8cnplmtkWq6F0R68\ni/vJ8X91+n//tM7XXc1+2RjzoDHmqDHmxTrf+yNjjDXGrAhyg9OkUhRqre4r6mRpVruuHdR9z75c\nbcyV9Zbc4C4UNNPfXdADW4aq+VuYs2bfr+WU41eOTZA5AInXqN416k+dal/aTnp4qfdIp7CyWi87\nOzcP6p5nXlo07iRnSLM4j61abTut/H5W+rmwefmMo8hS1PuN+p5ebvddnLld0pkjX3Dk9ziukSBq\nKHURUWklax05o13XDs773V3XDqoj4AuJjCdR4XT+4LH9h1qukWm5FkZ78C7WJ8ettbec/t/3+vwT\nX5X0ZUl7av/RGLNG0hWS/lcr25d2laJQe1fL7hs26szeLhkZffn6DSp05NXfXfB0l0srd6GUy1aj\nEyWVZmZ9vTbSIZczWreqR3u3baq7r5t9v1Z/d0G7b9iorQ/vr+Z45+ZB3fX0sL58/YZAtpdcAghL\nLme0dmCZHt16iaZny+rM57Ry2dz6XI3607POKC7qw9N40sNLvUc61RtvBpHVSna+fsvFOjw2qeOT\n07rr6WEdODS3+lLtuDPtOWMc0t7ivMO/1bbTyu+HVTvqSXMbazSOWCiKLEW536T01/d2Vtl33/rE\nJk2WZjVrrZZ0up8pLorcruju0oruLvIFR/3LCnrk5ouUN0bFQl69xULT47iGfy+AGkpdRFRaydpk\naVZ3fntY269ar95ip45PTuvOb58+j9s99zNBjM+iHpcguRbm1RijvJG+eM2gq2w1ymNansimPXgX\n68XxCmPMUs09RX6OtfYWY8xaSeustU82+j1r7V8bY86t8627JX1G0hNBb2uaNO3Euv39Xb/TqqRl\nCgoEI5czGujp8v392p87q3eJdlx9gZYW8tWT4yMnpgK584lcAghTuWz10siJujWmUX+apZMebus9\n0inMrOZyRoWOvP7oGz9tOu5Ma84YhyDu6VFbbTt+fz+qfi7tbazROGLh9keRpTjGJ2mt75hz5K2p\nhvlNQm7JFxZy6jt6i3MXGPzmNqgaSl1EVPxmrdCR18iJKW19aH/132rbSFDjsyydN0Hr/Oa1WR7j\nPl5zi/bgXdzTqld8RVJJ0qWn//s1SX/i5w8ZY66W9Jq19qcBbVuqVYrC2X1LNdBT/w5zr/xOq5KW\nKSiQPL3Fgs48Y4n+6Bs/1daH9mvkxFRgdz6RSwBhalRjmvWnYfThQBjCzGrWp45kHIKsZ7yRKPq5\ntLcxL9sfVZYYn8AtN/klt0iiZtltJbdkEe2gWRsJcnxGm0Krwqz5UaM9eJOIJ8clnW+t/ZAx5g8k\nyVp70hjjec+dfgL932huSnU3P3+LpFsk6ZxzzvH6cm3L710oaZmCIi3aKb9h3vlELuOR5Pye+9mn\nMvU6CIfbDDeqMdzVibgkuQYvlPV2wjjEuzTl142sZzxuSWxjXjLsZfvJEqIQdH7JLaIUxDGcRG4R\nn7SMg5u1kSSOzxC+pOaXmt++knJxvGSMKUqykmSMOV/SlI+/c76k8yT99PS19dWS/sYYc6G19o2F\nP2ytvV/S/ZI0NDRkfW67oyDXNkvaOml+pqlIyxQUadFKflvNUxx5DGvaKHIZj7DrLxA2txluVmO8\n1LYwa2/SxhkIV1w12GvOFv78WWcUM5dLxiHepX0M4dQO3PQF1GrvktjGvGTY6/YHfcxE5pIhSfsh\njPzGOUV0kj7bepK+fWkT1DGc1Hpu2bfpkLT9lKZxsFMbKZetjDGJG58tlLR9nwVJzW8UNb9VzfJI\nXv1JysXxz0v6tqQ1xpi/kLRJ0oe9/hFr7X+XtLLy38aYVyUNWWuPBbOZ7gW5tlla10lb2Cj7ip16\nYMvQoveRxCkosqzVPKU1j04qU6O4ySUdDQCvvNSYRsKsvVmr60gmrzlrl1wGVSPCltYxUNK2u5Vc\nt0ubCFpa2pgTN9sfVs7JXDKkeT80ym8S6nPSP9ukb1+W1cvu7hs3qlwua2R8quW8sm/Tgf0UvMpn\nevd3h7Vz86Buf+yFRI7PsrLvk9DXpkHSjxea5TErea0n7Awba5Nxk4Yxpl/SxZKMpB+6uaBtjPlL\nSZdJWiHpiKTPW2v/vOb7r8rlxfGhoSG7b98+fxtfx8j4lK6597lFd5zs3bbJ810mQf6tqDg1yrUD\nyzQ2Od1ORTmSN+clv63mKY15bMZNoc1yR9NA4vIbBaY7l/5/9u4/TI7qvvP95/SMejQaDWg8DMKx\nhCEsmSwPK8zVGDB6HoeYi2EXbAcDi8Eg25hfV8YkXttAbsK1vTj7gLHjxNerlcDGtvBiY/MjJrCJ\n8bVhcxcDjhQCIXIULgGCMEjDWILRaJjWTJ/7h6aHnlH/qOqu6nNO1fv1PP1I0zPdfarqc751qk91\n1/M3num6CUnoWMdslOFy2er5sQm9MLZXS4pd2lua0dsHl+iIwb5YtSPN2pvFup4BXuQ3SXFzlqdc\n+v6mRYtjIOdjCB/Hbu3kOk99Imkt9DGvanCj9qeZczLnhxa2g/f5leRFffY94763L0XOxxDS/OzO\nlK2++MBWPbh1ZyJ5zfG2DUqL28mrGuyb6nV6/MpluvKUozTYV9RvLOvVYQct9uYYKAt91OdjOMm/\n/Pp8TN4sj1nIay2dyLAXnxw3xpwt6WfW2gdmf15mjPk9a+1fNHqctfaCJr8/IrlWxpPktTNCvA7H\n2ERJX/3JNl1/1jFa1rtIuyf36as/2aY/OXtV0J0yC9rNU6uP93knE+WrUcYmSnPFWNq/zJdt2hz8\njgZAusYmSlp72y9iD1IX1sw0xwIhjjMQnrg5y1MuXX9FWzOhjoF8bHecXHdyP5B1vvexZhq1P82c\nJ5k5n48FfRd636+V39HxKS/qc6N1Ozo+5TyvoW/70FWyu3DSIYm8Rt221E636IPJq16nT7y4W1fc\nvkWS9Mi1v+tVtptt+xD6po/HQj7z+XihWR59rlXt9JVOZNiLyXHt/8T3vZUfrLW7jTGfk9Rwctxn\nSV7bzMfrpDVTLpf1kZOPnPf1KDeds0rlctl103Kv3Ty18ngfP7kTl887GgD+aqV21KqZd1x6Ympj\ngRDHGQhPnJyVy1YzZUsuPRHqGMjHdkftB53eDyBcaeY8qfFBFo4FXcriOM2X+lxv3c6U7dxkqMu8\nZnHbhyiNvEbZttRO9+iDyQtlnTZqZyh905d9LdrXrN/42q/a7SudyHAhsWdqT612+DJx35LKtQpW\nDPRKUlvXKkjyuTplxmpuYlzaH9xr735KM358i3+utZunVh5f70yfsYlSm0vTOZUdTTUfdjQA/NZK\n7ahVM7/4wFZtvHh1KmOBEMcZCE+cnI1NlPTFB7bqpnNWzfv7jRevJpcOhDoG8u168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13+UnaVYoqOFIPDWLR3Z8MYsW/BQTt9nbFepELKYMML87pK1X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\n79SmS/u1fst2z2u3FF7p2Hzl6rKmoM13frPXvnSfhDd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M9hxlGPaQduYFp1pMKVabOY+qJcXMmdC8h+MaSZaTKTvlrF6KYL1y9Y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\nGcyfLPtvJ1pl1KWejnS004Ol7LLsUn+lTtzRbn5bqc9Zb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cv3ttRG/rn+nYvsettHBuj7q70rR/tFRpqv/S+Msdl71bvTOdT3aemUmTRUSWl+OWhYLV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\nbPnslKGR3KTXKk2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H1m6wo3nifqqdZ256onx/3G7Xc5qWKJVN584Hds3YcfrjgfscQw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Lu7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\nWMPXjX7c+eq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E1aR9bBlJ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\n2JkAVjAUmAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1980x1980 with 132 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2 = sns.pairplot(aggregate_2016_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "colab_type": "code",
    "id": "-J2YFGEViBlR",
    "outputId": "e9310329-cc5a-4f2f-bfc7-4bed14938e84"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3c0c7572e8>"
      ]
     },
     "execution_count": 63,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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k79q+oda5IYTQ7zo8iUZ3bv+4Ftin8kbSglXHbwK2ljSXpHmBLQBsvwpMkbR9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\nBUUnGkIIIayg/we8rwW1WyVH9QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create  correlation matrix\n",
    "corr1 = aggregate_2016_df.corr()\n",
    "\n",
    "sns.heatmap(corr1, xticklabels=corr1.columns, yticklabels=corr1.columns, cmap=\"Spectral\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "WXhUJiZmh3R1"
   },
   "source": [
    "Notice that the correlation between percentage of serious mental illness in states (%serious 18+) and number of incidents (n_incidents) and percentage of any mental illness in states (%any 18+) and number of incidents (n_incidents) are very close to 0. This means that there is little correlation between the number of reported mental health occurences within a state and the number of gun related incidents.\n",
    "\n",
    "Here are some interesting findings that we noticed:\n",
    "\n",
    "* There is a slight positive correlation between the percentage of people with mental health illnesses and the number of mental health facilities in each state. \n",
    "* There appears to be no correlation between the number of mental health facilities per capita and the number of gun incidents per capita.\n",
    "* There is a slight negative correlation between the number of people killed in gun violence incidents and the number of people who recieved mental health treatment. \n",
    "* There is a moderate negative correlation between the number of mental health facilities per capita and the number of gun-related incidents, number of people killed, and the number of injuries.  \n",
    "\n",
    "We will explore some of these findings in the following analyses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RTMrr2c3jDQX"
   },
   "source": [
    "##Analysis for treatment and number of individuals killed from a gun violence incident per capita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lU91FQ2SOc-0"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kLapBYzg3IHg"
   },
   "source": [
    "**Normalization Plots**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6asdiLGCpXNU"
   },
   "source": [
    "First, we normalized this data because data that is done by count rather than by percentage is likely not normal. When the outliers are removed from the set, it passes the p-value test, which means that we can assume approximate normality. \n",
    "\n",
    "We wanted to check normalization before further analyzing the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "5QuoKv08iBln",
    "outputId": "39234126-df31-405c-c4ea-c44079e7e692"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.841463115994358 2.0084516321745028e-07\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if it is normalized\n",
    "n_incident = aggregate_2016_df[\"n_incident_capita_2016\"].astype(float)\n",
    "n_incident.plot.hist()\n",
    "st_n_in, p_n_in = normaltest(n_incident)\n",
    "print(st_n_in, p_n_in) # not normalized because p < 0.01 = reject null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "uuJsNfvzk57t",
    "outputId": "d1138e2d-2db5-47ad-a312-c69d80b88a5f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.2883974154055702 0.8657157065874673\n"
     ]
    },
    {
     "data": {
      "image/png": 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fkhwEvBC4qeuCxrSBRf1J8jwGd+MfUVW/DLy7h7rGsYH7/o7eCfxlVT0FeNtwfhpsB06p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\nDAJJapxBIEmN+3/OfeOUsR7bjgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if treatment % by state is normalized\n",
    "treatment = stateMHPercent_df[\"%received 18+\"].astype(float)\n",
    "treatment.plot.hist()\n",
    "st_n_in, p_n_in = normaltest(treatment)\n",
    "print(st_n_in, p_n_in)  # normalized because p > 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "Y10Vcz5wnScV",
    "outputId": "55a67ed4-0331-43cc-c159-81a502330af0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.8753469564056713 0.39153769752874645\n"
     ]
    },
    {
     "data": {
      "image/png": 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DwDuHzjnP3+uFwPO76RcAFw+dc56s3wAO76aPB/56CeTcDzi0m94D+A6j27i8F1jTLV8D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\nU974M9fXHgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if it is normalized for % of any mental illness\n",
    "\n",
    "mh_any = stateMHPercent_df[\"%any 18+\"].astype(float)\n",
    "mh_any.plot.hist()\n",
    "st_n_in, p_n_in = normaltest(mh_any)\n",
    "print(st_n_in, p_n_in)  # normalized because p > 0.01"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "rkjNP9xbpR7D",
    "outputId": "82326761-a8c1-4ab9-d82d-05210e449271"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6454885779599001 0.7241590061323696\n"
     ]
    },
    {
     "data": {
      "image/png": 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fZfKp6oclecC8XT4MzFbVA4EN7PiLeFlJciqwpao2jV3L2Hr2RRPvi85vVNUaJqeAnpPk\nUWMXNKKF+uJy4L5V9SDg9cCHFtuAQTCiqvpf4FPAyfO2f6eqbutWzwUeMnRtAzkeOC3JZiaz0J6Q\n5B3z9vnJlCRJVgKHAd8ZssiBLNgXDb0vqKqbuu9bgAuYzFq8s15T1SwHC/VFVd2y/XRz9xmtA5Mc\nsZg2DIKBJZlJcni3fDBwEnDdvH12Ptd5GpOLh8tOVZ1dVauqapbJVCOfrKoz5+12IfD0bvnJ3T7L\n7ppKn75o5X2R5O5JDt2+DPwmcNW83S4E/qC7e+jhwPeq6psDlzp1ffoiyb23XzdL8jAmv9cX9ceS\ndw0N7z7Aed0Deg4A3ldVH0nyN8DGqroQeF6S04BtwHeBs0ardgTz+uLNwNuT3MCkL84YtbiBNfq+\nOBK4oPvdthJ4V1V9LMkfA1TVG5nMTPAE4Abgh8AzRqp12vr0xZOBP0myDfgRcMZi/1jyk8WS1DhP\nDUlS4wwCSWqcQSBJjTMIJKlxBoEkNc4gkKTGGQSS1DiDQJIa9/90PAJCSwI29QAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if it is normalized for % of serious mental illness\n",
    "\n",
    "mh_serious = stateMHPercent_df[\"%serious 18+\"].astype(float)\n",
    "mh_serious.plot.hist()\n",
    "st_n_in, p_n_in = normaltest(mh_serious)\n",
    "print(st_n_in, p_n_in)  # normalized because p > 0.01"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "di1uowTq3fGH"
   },
   "source": [
    "**OLS Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 425
    },
    "colab_type": "code",
    "id": "NQBgp7bsCeR4",
    "outputId": "4d4e483e-5637-4975-f73b-7b5a18e6e4d2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                             OLS Regression Results                             \n",
      "================================================================================\n",
      "Dep. Variable:     n_killed_capita_2016   R-squared:                       0.671\n",
      "Model:                              OLS   Adj. R-squared:                  0.664\n",
      "Method:                   Least Squares   F-statistic:                     99.80\n",
      "Date:                  Thu, 13 Jun 2019   Prob (F-statistic):           2.08e-13\n",
      "Time:                          01:54:21   Log-Likelihood:                 456.22\n",
      "No. Observations:                    50   AIC:                            -910.4\n",
      "Df Residuals:                        49   BIC:                            -908.5\n",
      "Df Model:                             1                                         \n",
      "Covariance Type:              nonrobust                                         \n",
      "=================================================================================\n",
      "                    coef    std err          t      P>|t|      [0.025      0.975]\n",
      "---------------------------------------------------------------------------------\n",
      "%received 18+   2.44e-06   2.44e-07      9.990      0.000    1.95e-06    2.93e-06\n",
      "==============================================================================\n",
      "Omnibus:                        3.941   Durbin-Watson:                   1.974\n",
      "Prob(Omnibus):                  0.139   Jarque-Bera (JB):                3.311\n",
      "Skew:                           0.629   Prob(JB):                        0.191\n",
      "Kurtosis:                       3.074   Cond. No.                         1.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# analysis x='%received 18+',y='n_killed_capita_2016\n",
    "aggregate_2016_df=aggregate_2016_df.apply(pd.to_numeric, errors='ignore')\n",
    "#outcome_1, predictors_1 = patsy.dmatrices(\"Q('%received 18+') ~ n_killed_capita_2016\", aggregate_2016_df)\n",
    "mod_1 = sm.OLS(aggregate_2016_df['n_killed_capita_2016'], aggregate_2016_df['%received 18+'])\n",
    "res_1 = mod_1.fit()\n",
    "print(res_1.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "so4wFaTMix7-"
   },
   "source": [
    "This is an OLS Regression test on the number of people killed per capita and the percentage of people who received mental health treatment. Because the p-value of 5.09e-13 is much smaller than 0.05, this relationship is statistically significant and we can reject the null hypothesis (null hypothesis suggests there is no relationship between number of people killed and mental health treatment)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jaUt0epl30m2"
   },
   "source": [
    "**Regression Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1038
    },
    "colab_type": "code",
    "id": "oCoNVru7iBli",
    "outputId": "a1ea8186-a511-4aec-d95e-6ae42c5b7c60"
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "ignored",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1163\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1164\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1165\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '12.9813.5411.8715.5411.5915.8617.0515.0612.7512.039.0316.6413.6415.3616.7514.217.9413.3519.3713.6618.8616.1219.0811.9815.716.4815.9311.6320.7212.6712.9514.4716.0114.4816.515.1317.5216.3518.9614.4214.9515.110.9715.7220.3916.5416.3717.0116.0713.98'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1166\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1167\u001b[0;31m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1168\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: complex() arg is a malformed string",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m    126\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 127\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    128\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnanmean\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m    478\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m     \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1169\u001b[0m                 raise TypeError('Could not convert {value!s} to numeric'\n\u001b[0;32m-> 1170\u001b[0;31m                                 .format(value=x))\n\u001b[0m\u001b[1;32m   1171\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Could not convert 12.9813.5411.8715.5411.5915.8617.0515.0612.7512.039.0316.6413.6415.3616.7514.217.9413.3519.3713.6618.8616.1219.0811.9815.716.4815.9311.6320.7212.6712.9514.4716.0114.4816.515.1317.5216.3518.9614.4214.9515.110.9715.7220.3916.5416.3717.0116.0713.98 to numeric",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1163\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1164\u001b[0;31m             \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1165\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: '12.9813.5411.8715.5411.5915.8617.0515.0612.7512.039.0316.6413.6415.3616.7514.217.9413.3519.3713.6618.8616.1219.0811.9815.716.4815.9311.6320.7212.6712.9514.4716.0114.4816.515.1317.5216.3518.9614.4214.9515.110.9715.7220.3916.5416.3717.0116.0713.98'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1166\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1167\u001b[0;31m                 \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcomplex\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1168\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: complex() arg is a malformed string",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-89-7a339b64268b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'%received 18+'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'n_killed_capita_2016'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maggregate_2016_df\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mfit_reg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_kws\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"color\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\"red\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Treatment vs. Killings\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"percentage of people who received mental health treatment\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"killings in a gun violence incident per capita\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mregplot\u001b[0;34m(x, y, data, x_estimator, x_bins, x_ci, scatter, fit_reg, ci, n_boot, units, order, logistic, lowess, robust, logx, x_partial, y_partial, truncate, dropna, x_jitter, y_jitter, label, color, marker, scatter_kws, line_kws, ax)\u001b[0m\n\u001b[1;32m    787\u001b[0m     \u001b[0mscatter_kws\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"marker\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmarker\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    788\u001b[0m     \u001b[0mline_kws\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mline_kws\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 789\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscatter_kws\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mline_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    790\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    791\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/seaborn/regression.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, scatter_kws, line_kws)\u001b[0m\n\u001b[1;32m    325\u001b[0m         \u001b[0;31m# Use the current color cycle state as a default\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    326\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolor\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 327\u001b[0;31m             \u001b[0mlines\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    328\u001b[0m             \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    329\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mremove\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36mstat_func\u001b[0;34m(self, axis, skipna, level, numeric_only, **kwargs)\u001b[0m\n\u001b[1;32m  10954\u001b[0m                                       skipna=skipna)\n\u001b[1;32m  10955\u001b[0m         return self._reduce(f, name, axis=axis, skipna=skipna,\n\u001b[0;32m> 10956\u001b[0;31m                             numeric_only=numeric_only)\n\u001b[0m\u001b[1;32m  10957\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m  10958\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mset_function_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstat_func\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36m_reduce\u001b[0;34m(self, op, name, axis, skipna, numeric_only, filter_type, **kwds)\u001b[0m\n\u001b[1;32m   3628\u001b[0m                                           'numeric_only.'.format(name))\n\u001b[1;32m   3629\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mall\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3630\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdelegate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3631\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3632\u001b[0m         \u001b[0;31m# TODO(EA) dispatch to Index\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     74\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     75\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrstate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minvalid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'ignore'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 76\u001b[0;31m                     \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     77\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     78\u001b[0m                 \u001b[0;31m# we want to transform an object array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mf\u001b[0;34m(values, axis, skipna, **kwds)\u001b[0m\n\u001b[1;32m    128\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    129\u001b[0m                 \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 130\u001b[0;31m                     \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0malt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mskipna\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    131\u001b[0m                 \u001b[0;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    132\u001b[0m                     \u001b[0;31m# we want to transform an object array\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36mnanmean\u001b[0;34m(values, axis, skipna, mask)\u001b[0m\n\u001b[1;32m    477\u001b[0m         \u001b[0mdtype_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    478\u001b[0m     \u001b[0mcount\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_get_counts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_count\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 479\u001b[0;31m     \u001b[0mthe_sum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype_sum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    480\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    481\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0maxis\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthe_sum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'ndim'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/nanops.py\u001b[0m in \u001b[0;36m_ensure_numeric\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m   1168\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1169\u001b[0m                 raise TypeError('Could not convert {value!s} to numeric'\n\u001b[0;32m-> 1170\u001b[0;31m                                 .format(value=x))\n\u001b[0m\u001b[1;32m   1171\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Could not convert 12.9813.5411.8715.5411.5915.8617.0515.0612.7512.039.0316.6413.6415.3616.7514.217.9413.3519.3713.6618.8616.1219.0811.9815.716.4815.9311.6320.7212.6712.9514.4716.0114.4816.515.1317.5216.3518.9614.4214.9515.110.9715.7220.3916.5416.3717.0116.0713.98 to numeric"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create regression plot for number of people killed per capita in 2016 in relation to \n",
    "# the percent of treatment received\n",
    "sns.regplot(x='%received 18+',y='n_killed_capita_2016',data=aggregate_2016_df,fit_reg=True, line_kws={\"color\": \"red\"}) \n",
    "plt.title(\"Treatment vs. Killings\")\n",
    "plt.xlabel(\"percentage of people who received mental health treatment\")\n",
    "plt.ylabel(\"killings in a gun violence incident per capita\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "-7whYYHPii1J"
   },
   "source": [
    "This regression plot suggests that as the percentage of people receiving mental health treatment increases, the number of killings in a gun-related incident decreases. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "JfGGpfU0Zltx"
   },
   "source": [
    "## Analysis for Distance and Number of Gun Incidents per Capita "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "PDDBEXk74LHI"
   },
   "source": [
    "**Normalization Plot**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "X76P2c2a4PSB"
   },
   "source": [
    "Once again, we wanted to prove that our data is normalized."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "fENJxY8riBl8",
    "outputId": "155d5e7c-6188-42a9-83dd-712eea783a3d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.841463115994358 2.0084516321745028e-07\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the incident data & check if it is normalized\n",
    "incident1 = aggregate_2016_df[\"n_incident_capita_2016\"].astype(float)\n",
    "gun_state_2016_df[\"n_incident_capita_2016\"] = incident1\n",
    "incident1.plot.hist()\n",
    "st_in, p_in = normaltest(incident1)\n",
    "print(st_in, p_in) # not normalized because p < 0.01 = reject null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "qRd8OrqUc3sy",
    "outputId": "2d4eccd2-6350-410c-8685-a8a1903b3d61"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "41.245719832485726 1.1056196208949758e-09\n"
     ]
    },
    {
     "data": {
      "image/png": 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gY8DbqurRWYtvAV5QVY8lWQt8HDhujtdZB6wDWLly5QgTS9LBrZc9jCSHMiiLK6vqmtnL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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if it is normalized for distance\n",
    "\n",
    "d = gun_state_2016_df[\"distance\"].astype(float)\n",
    "d.plot.hist()\n",
    "st_d, p_d = normaltest(d)\n",
    "print(st_d, p_d)  # normalized because p > 0.01 = not reject null hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ej6gssPg4ddZ"
   },
   "source": [
    "**OLS Regression**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "Qm7K3XNkiBmB",
    "outputId": "0f346e6b-444d-4395-9311-649e1eb345f4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                              \n",
      "==================================================================================\n",
      "Dep. Variable:     n_incident_capita_2016   R-squared:                       0.000\n",
      "Model:                                OLS   Adj. R-squared:                 -0.020\n",
      "Method:                     Least Squares   F-statistic:                   0.01626\n",
      "Date:                    Thu, 13 Jun 2019   Prob (F-statistic):              0.899\n",
      "Time:                            01:56:11   Log-Likelihood:                 395.38\n",
      "No. Observations:                      50   AIC:                            -786.8\n",
      "Df Residuals:                          48   BIC:                            -782.9\n",
      "Df Model:                               1                                         \n",
      "Covariance Type:                nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0002   2.22e-05      8.397      0.000       0.000       0.000\n",
      "distance    6.332e-07   4.96e-06      0.128      0.899   -9.35e-06    1.06e-05\n",
      "==============================================================================\n",
      "Omnibus:                       30.633   Durbin-Watson:                   2.035\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):               72.398\n",
      "Skew:                           1.723   Prob(JB):                     1.90e-16\n",
      "Kurtosis:                       7.783   Cond. No.                         8.00\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# analysis\n",
    "outcome_2, predictors_2 = patsy.dmatrices(\"n_incident_capita_2016 ~ distance\", gun_state_2016_df)\n",
    "mod_2 = sm.OLS(outcome_2, predictors_2)\n",
    "res_2 = mod_2.fit()\n",
    "print(res_2.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "EKVSuJlVp8zc"
   },
   "source": [
    "This is an OLS Regression test for the the number of gun violence incidents per capita and the distance from that incident to the closest mental health facility. Because the p-value of this is larger than 0.05, we do not reject the null hypothesis and assume that there is no relationship between the location of the incident and the location of the closest mental health facility."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "bra3KiSk4CCo"
   },
   "source": [
    "**Regression Plot**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 312
    },
    "colab_type": "code",
    "id": "Ckxz1qpziBl1",
    "outputId": "053d33a3-92e1-4dfa-8fda-f83a87170b3d"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Minimum distance to a mental health facility')"
      ]
     },
     "execution_count": 84,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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fKxnYTPLm5x/JU5bNHdvYZ7W1nPqSE7no5gd5pEi58rBEdeAAn73it9juVpZ0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\nvQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gun_state_2016_df[\"n_incident_capita_2016\"] = gun_state_2016_df['n_incidents'] / aggregate_df[\"population 2016\"]\n",
    "gun_state_2016_df=gun_state_2016_df.apply(pd.to_numeric, errors='ignore')\n",
    "sns.regplot(x='distance',y='n_incident_capita_2016',data=gun_state_2016_df,fit_reg=True, line_kws={\"color\": \"red\"})\n",
    "\n",
    "plt.title(\"Distance vs. Incidents\")\n",
    "plt.ylabel('Number of gun-related incidents per capita')\n",
    "plt.xlabel(\"Minimum distance to a mental health facility\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "oURdOA1Z5QEh"
   },
   "source": [
    "We hypothesized that as the minimum distance between a gun-related incident and a menth facility decreased, the number of gun-related incidents would also decrease.\n",
    "\n",
    "This regression plot suggests that as the distance between a mental health facility and a gun violence incident increases, the number of gun violence incidents per capita in 2016 decreases, suggesting our hypothesis was not correct. \n",
    "\n",
    "However, because the distance data did not look normal, we will normalize it and see how that affects the correlation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "Vny47_HsAbz4"
   },
   "source": [
    "**Regressions with normalized distance**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 286
    },
    "colab_type": "code",
    "id": "lopq_TBOAGwB",
    "outputId": "5792413c-8221-433d-8ca1-f8d0b741ef31"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.4172460680168297 0.49232164110221854\n"
     ]
    },
    {
     "data": {
      "image/png": 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vqJFLgP2S3Lef6pZvsX5V1We2ffZYwu+KtRwKXabLaPtU1W3AzcAv9VLd8ix1CpBTGP2F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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# normalize distance data\n",
    "gun_state_2016_df[\"logd\"] = d.apply(lambda x: math.log(x, 10))\n",
    "logd = gun_state_2016_df[\"logd\"]\n",
    "logd.plot.hist()\n",
    "st_lgd, p_lgd = normaltest(logd)\n",
    "print(st_lgd, p_lgd)  # normalized because p > 0.01 = not reject null hypothesis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 442
    },
    "colab_type": "code",
    "id": "dH5f294OApjJ",
    "outputId": "c3890323-dd57-4fe2-f42d-62fd9c5d3e9e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                              OLS Regression Results                              \n",
      "==================================================================================\n",
      "Dep. Variable:     n_incident_capita_2016   R-squared:                       0.061\n",
      "Model:                                OLS   Adj. R-squared:                  0.041\n",
      "Method:                     Least Squares   F-statistic:                     3.092\n",
      "Date:                    Thu, 13 Jun 2019   Prob (F-statistic):             0.0850\n",
      "Time:                            04:05:27   Log-Likelihood:                 303.79\n",
      "No. Observations:                      50   AIC:                            -603.6\n",
      "Df Residuals:                          48   BIC:                            -599.8\n",
      "Df Model:                               1                                         \n",
      "Covariance Type:                nonrobust                                         \n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.0006      0.000      3.645      0.001       0.000       0.001\n",
      "logd          -0.0006      0.000     -1.758      0.085      -0.001    7.91e-05\n",
      "==============================================================================\n",
      "Omnibus:                       60.304   Durbin-Watson:                   2.283\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):              351.604\n",
      "Skew:                           3.283   Prob(JB):                     4.47e-77\n",
      "Kurtosis:                      14.210   Cond. No.                         4.87\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "# analysis\n",
    "outcome_3, predictors_3 = patsy.dmatrices(\"n_incident_capita_2016 ~ logd\", gun_state_2016_df)\n",
    "mod_3 = sm.OLS(outcome_3, predictors_3)\n",
    "res_3 = mod_3.fit()\n",
    "print(res_3.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "n1HfX20sA1Nq"
   },
   "source": [
    "This is an OLS Regession for number of gun-related incidents and minimum distance from a mental health facility (normalized). While the p-value is smaller than when the distance is not normalized, it is still not smaller than 0.05, so we are unable to reject our null hypothesis. This means that the relationship between the number of gun-related incidents and distance from a mental health facility is not statistically significant. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 300
    },
    "colab_type": "code",
    "id": "6tJ7l32TV-fB",
    "outputId": "d2971691-6303-4b78-95c6-ce82d48b1362"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f3c078bf828>"
      ]
     },
     "execution_count": 78,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9YMSIFN27d8HMWjS2r18PS5bAsGHwzjuh51OvXjB6NOy2W0hYO+2Uu/fX4sXw\n1FO5B1CuWBE6G+y3X4hn5szQa+rNN7cP/Pz4Y+jRA66/HoYMadVX3PY9Z84MHRmaD+x0h+OPh4cf\nLp+ZnUWk45jZfHcfX3S/fAnGzL7n7v9mZreQozeZuxfsChIljDeBY4D3gXnAWe7+WtY+FwMHuvs3\nokb+09z9y2a2P6GhfgKhkf9RYB9CJ4Oc5zSzC4DzgGPcva7YF4e2JZhiMgmothb+8pdQOC9aFN7r\n02d799mDDgrJoFev7Ukhu5DeuBHmzm057gTCHcykSdu7K69aBd//PmzZEj6jsTGcd999w+u29GBb\ntiz/5y9dGr7n4MG5k49mdhbp3OImmELFwOvRY5tK4KhN5RLgYUKX4juiRHAdUOPuc4DbgTujRvy1\nhJ5jRPvdS+gQ0ABc7O7p6Iu1OGf0kT8H3gWei6pX7nP369oS+47IVOMsWxbuICDcueyyS3jepw+s\nXh1G/b/wAhx8MOy11/bjM20sZ5wRf2qYzZvhwAPDwM/Nm8P8aAMHhqTV1kGHhca9bNwYvtPeezfd\nrkGOIpKtUDflP0ePM9p68qhn1wPNtl2d9XwzkLNLhLv/P+D/xTlntL2srpkzBXRdHXTpEu4qNmwI\niWXTJli5MtxxdO/e9LhMIb1qVfxliTduDIM8mzW1ANury1o7ELPQuJfMLAK5ZD5PRCTOdP2PAGe4\n+0fR650JXYiPTzq4StK8AO/ZMxTQmSqw5ctDcunSJSSaTCGcNYZsm0whvcce8aaGKTYIsr4+tKe0\npr2k0OSagwaFhJbv8zpqZmcRKW9xrvoHZ5ILgLuvM7M2NBt3XrlG0vfuvX2235Urw2P//qEKa+ed\nQ/XZsmUtFxCDpoV0nGWJCyWD3r3DFDddu7ZuJczmveayE9PZZ4cG/lLO7Cwi5S9Ogkmb2R7uvgzA\nzPakwBQy1Wbz5rAWzJYtobdWpu0jM5PGunXhLmb9+nAn0qNHmKdswIDtMyM3b4NpbSFdKBmMGwdP\nP922SSoLTa4Zt/pORKpXnKLgKuBpM3uC0IvrSKJxJNVu7dqQXF54IVQbvftuSCbjx4cCfP360Pi+\ndWuoxso0wPfpEwrvt98O29qjkM6XDN58c8cmqcx3B1XKmZ1FpDLEma7/ITMbB3w62vRNd/97smGV\nv8waJVu2bF8TBkJhW1MTxqmkUuGvf//c3X179oRTTw2FcnsU0rmSQZKTVMapvhOR6hW3KOtB6Ebc\nFRhtZrj7k0WO6dQya5QMGRLGhWT06QN//3u4u0mnw8zKmXExudor9tgj2av+clgJU0TKQ9LLejQX\npxfZjcCZwGtAZnS8A1WdYDJaWs0iAAASmklEQVTdkAcODA3p2V13u3QJY12GDg0JZMCA0rVXqL1E\nRKA0S57HKV5OBT4ZzWwskUzVUyoV2lxqakJSSaVgzZowCDFTgJe6vaLUnx9XR19diVSL5suOZyS9\nXlWcU74NdAOUYLI0r3o66qhwhbB6dUgu//iPTce4lLq9otSfX0wprq5EqkWpljyPk2A2Aa+Y2aNk\nJZlic5F1dvmqnoYODdtzDaCU3Ep1dSVSLUq15Hmcn+2c6E+aaUvVk6qBWirV1ZVItejIJc+zxemm\n3Oa5yKpBa6qeqq0aKG4yLdXVlUi1KFVv0rwJxszujabOf5Xc0/WPSSakzqnaqoFak0xLdXUlO053\n5JWhVL1JC502s+TwpGQ+urpUUzVQa5NpZxirU40FbbXdkVe6UvQmLTRd/8ro8d3kPr56VFM1UGuT\naaWP1anGgrba7sg7i47uTar/BTpINVUDtSWZVspYneaqtaCtpjtyabsupQ6gWmRXA2WrpGqguNqa\nTDNXV/vtl/wUOu0lU9A2X3ahf/+wfcWK0sSVtGq6I5e2y5tgonEvmaliZAdlqoEyU/SvWBEe3cuz\nGqihIaxXs3hxeGxoiH9sNSXTai1oq+mOXNquULG2u5kdAZxsZrMIU/Vv4+4vJRpZJ1Qp1UA72qZQ\n6W0qrVGtBW1n6JghyTP33GuHmdmXgPOBzwA1zd52dz864dgSN378eK+paf7VKk979mBqaAjLK5u1\nLDjcW9emkImrnJPpjmrPf69KU42dGyQws/nuPr7YfoV6kc0GZpvZ9939+naNTtpNvh/58ceHxcxa\nm3Tas/G23Oc/aw/VdLfWXKXckUvpxBnJf72ZnQwcFW163N3nJhuWxJGvB9P778P3vw8HHADduzct\n8HbaqfDdTrW2KeyIai5oq+EiQtouznowNwATgLuiTZeZ2RHufmWikUlRue420umwTPKWLaGgGzw4\nbK+thXvuCUs6b9qUv0qjWtsUdpQKWpGW4nRTnggc6+53uPsdwAlodH9ZyHW3sXYt1NWFRLB58/bt\nffvC88+HRDNiRLjCHjEi3P3cf//2XmLFeoANGdL23mUiUl3i3sQPICyZDNC/0I7ScXLdbdTVhRU1\n0+mmSwasXQtbt0K3bk33b962UqhN4Ygj4Pe/b79G3WqcXkWkmsT5Od8AvGxmfyV0VT4KuDzRqCSW\nXF1Fe/UK7QADBzYt9OvqwmOudWqat63kalMYMiQkl/Yasa4eSCKdX5xG/plm9jhwaLTpn939g0Sj\nkoKyr/wPOgheemn73UZ9PfToAfvu27T6LLM9V+Gdq22leZvCsmXt17usWqdXEak2sX7G0cSXWnSs\nDOS68u/dGz7zmVD91acPfPnL8PDDTau4BgyAww4LSaktA+Pas3dZOc5jpeo6kfann1AFKXTlv2BB\n0yv/XN1m169v+3iN9uxdVm5doVVdJ5IMJZgK0por/1zdZndkvEZ7Tg1STl2hVV0nkpyC3ZTNLGVm\nf+uoYKSw9rjyb+uMxe05WWc5TYZZrbMhi3SEgsWCu6fN7A0z28Pdl3VUUJJbqa/822vEejlNr1Ju\n1XUinUmcn/LOwGtm9iKw7efm7icnFpXkVA4z2LbXiPVymV6l1ElbpDOL83P+fuJRSCzldOXfHsph\nepVySNoinVWccTBPmNmewD7u/hcz6w3kqVSQpJXLlX9n0dmStkg5KToXmZldCMwGfhFtGgb8Mc7J\nzeyEqA1niZm1GP1vZj3M7J7o/RfMbGTWe1dE298ws+OLndPMLom2uZntEie+SlWJSwuXs0zSnjQJ\njjoqPE6Zoi7KIjsqTtF0MWE25RcA3P0tMxtS7CAzSwE/BY4FlgPzzGyOuy/O2u18YJ27721mk4Eb\ngTPNbDQwGdgfGAr8xcz2jY7Jd85ngLnA4zG+U0XToMD2Vw7VdSKdTZxiaYu7bzULKyabWVcg9zKY\nTU0Alrj729Fxs4BTgOwEcwowPXo+G7jVwgedAsxy9y3AO2a2JDof+c7p7i9H22KEVrk0KFBEKkWc\n6fqfMLMrgV5mdizwe+DPMY4bBryX9Xp5tC3nPu7eANQCgwocG+ecBZnZNDOrMbOa1atXt+bQkms+\nKDDflPsiIuUgToK5HFgNvAp8HXgA+Jckg0qSu9/m7uPdffzgzGpcFSLOoMCGBq3XIiLlIU4vskYz\nm0Fog3HgDXePU0X2PpA9qcnwaFuufZZHVW/9gTVFji12zk6r2KDAFSvgiSdUfSYi5SFOL7KJwP8C\nPwFuBZaY2Ykxzj0P2MfMRplZd0KjffMZmecA50bPvwQ8FiWvOcDkqJfZKGAf4MWY5+y0Cg0KrK+H\n555T9ZmIlI84jfz/DnzO3ZcAmNlewP3Ag4UOcvcGM7sEeJgwbuYOd3/NzK4Datx9DnA7cGfUiL+W\nkDCI9ruX0CGgAbjY3dPR57c4Z7T9UuB7wG7AQjN7wN0vaMW/RdkrNCiwvj70hMpVffbee6G6rGtX\n9TwTkY5jxWq7zGyeux+a9dqAF7O3Varx48d7TU1NqcNolXy9yPbaC159NffI87feCssl77STqs5E\nZMeZ2Xx3H19sv7zXsGZ2WvS0xsweAO4ltMGcQaiqkhLIN5J/xQp45ZWW+6fTYa2YceM0Hb2IdKxC\nRcsXsp5/CPyf6PlqoFdiEUlRuQYF5qs+W7o0tMPsuWfT/fv2hUWL4PHHw/LKHVVlpkGiItUj70/b\n3b/WkYFI22UK7b33huefh3XroHv3cPdSVwdjxjTtfbZhA9TUwMqV4dg33+yYKjMNEhWpLkWvHaNe\nXP8EjMzeX9P1l4fmhXa3bqHBf/x4GDYsJJCHHtq+fzodkguEO5099oDBg5OvMtPKkSLVJ85P+o+E\n3l5/BhqTDUdao1ChvWQJHBp1w8iuOlu7NtzV9OoFvXtvv3PItexye2rNcs8i0jnESTCb3f0niUci\nrRa30M6ejv7DD+Gjj0KCGT++adVZkis4auVIkeoTJ8H82MyuAf4H2JLZ6O4vJRaVxBK30M7uefbW\nW6Ea7YADWh6b5AqOWjlSpPrESTAHAl8BjmZ7FZlHr6WEWlNoZ3qeZbo0b9zYsSs4auVIkeoTJ8Gc\nAXzC3bcmHYy0TlsK7VKt4KiVI0WqT5yf9SJgALAq4VikldpaaJdq2WUt9yxSXeL8tAcAfzOzeTRt\ng1E35TLQ1kK7VCs4auVIkeoRJ8Fck3gUskM6Q6GtEf4inU+c9WCe6IhApHpphL9I5xRnPZgNZrY+\n+ttsZmkzW98RwUnnp2WgRTqvognG3fu5+07uvhNhksvTgZ8lHplUhTjLQItIZSqaYLJ58Efg+ITi\nkSqjEf4inVecyS5Py3rZBRgPbE4sIqkqGuEv0nnF6aeTvS5MA7AUOCWRaKTqaIS/SOcVpxeZ1oWR\nxGiEv0jnVWjJ5KsLHOfufn0C8UgV0gh/kc6p0E84V/NqH+B8YBCgBCPtpjMMFhWRpgotmfzvmedm\n1g+4DPgaMAv493zHiYiIQJE2GDMbCHwbOBuYAYxz93UdEZiIiFS2Qm0wPwJOA24DDnT3jR0WlYiI\nVLxCAy2/AwwF/gVYkTVdzAZNFSMiIsUUaoNp1Sh/ERGRbEoiIiKSCCUYERFJhBKMiIgkQglGREQS\noQQjIiKJUIIREZFEKMGIiEgilGBERCQRiSYYMzvBzN4wsyVmdnmO93uY2T3R+y+Y2cis966Itr9h\nZscXO6eZjYrOsSQ6Z/ckv5uIiBSWWIIxsxTwU+BEYDQwxcxGN9vtfGCdu+8N3AzcGB07GpgM7A+c\nAPzMzFJFznkjcHN0rnXRuUVEpESSvIOZACxx97fdfSthmv/mSy2fQpilGWA2cIyZWbR9lrtvcfd3\ngCXR+XKeMzrm6OgcROc8NcHvJiIiRSSZYIYB72W9Xh5ty7mPuzcAtYTFzPIdm2/7IOCj6Bz5PktE\nRDpQ1S1Ka2bTgGnRy41m9kYp48ljF+DvpQ6iDSo1bqjc2BV3x6vU2Nsz7j3j7JRkgnkfGJH1eni0\nLdc+y82sK9AfWFPk2Fzb1wADzKxrdBeT67MAcPfbCGvclC0zq3H38aWOo7UqNW6o3NgVd8er1NhL\nEXeSVWTzgH2i3l3dCY32c5rtMwc4N3r+JeAxd/do++Sol9koYB/gxXznjI75a3QOonP+KcHvJiIi\nRSR2B+PuDWZ2CfAwkALucPfXzOw6oMbd5wC3A3ea2RJgLSFhEO13L7AYaAAudvc0QK5zRh/5z8As\nM/sB8HJ0bhERKRELF/9STsxsWlSVV1EqNW6o3NgVd8er1NhLEbcSjIiIJEJTxYiISCKUYMqEmY0w\ns7+a2WIze83MLit1TK0RzbTwspnNLXUsrWFmA8xstpn9zcxeN7PDSx1THGb2rej/k0VmNtPMepY6\npnzM7A4zW2Vmi7K2DTSzR8zsrehx51LGmEueuH8U/b+y0Mz+28wGlDLGfHLFnvXed8zMzWyXpONQ\ngikfDcB33H008Gng4hxT65Szy4DXSx1EG/wYeMjdPwUcRAV8BzMbBlwKjHf3AwgdXiaXNqqCfkOY\n8inb5cCj7r4P8Gj0utz8hpZxPwIc4O5jgDeBKzo6qJh+Q8vYMbMRwHHAso4IQgmmTLj7Snd/KXq+\ngVDQVcRsBGY2HJgI/KrUsbSGmfUHjiLqcejuW939o9JGFVtXoFc0fqw3sKLE8eTl7k8Seolmy54m\nqiyndsoVt7v/T9aMIc8TxtyVnTz/5hDmfPwe0CGN70owZSiaVXos8EJpI4ntPwn/0zaWOpBWGgWs\nBn4dVe/9ysz6lDqoYtz9feAmwlXoSqDW3f+ntFG12q7uvjJ6/gGwaymDaaPzgAdLHURcZnYK8L67\nL+ioz1SCKTNm1hf4A/BNd19f6niKMbNJwCp3n1/qWNqgKzAO+C93Hwt8THlW1TQRtVecQkiQQ4E+\nZnZOaaNqu2igdEV1ZzWzqwjV2neVOpY4zKw3cCVwdUd+rhJMGTGzboTkcpe731fqeGL6B+BkM1tK\nmN36aDP7XWlDim05sNzdM3eKswkJp9x9HnjH3Ve7ez1wH3BEiWNqrQ/NbHeA6HFVieOJzcy+CkwC\nzvbKGeexF+GCZEH0Wx0OvGRmuyX5oUowZSJacuB24HV3/49SxxOXu1/h7sPdfSShofkxd6+Iq2l3\n/wB4z8w+GW06hjB7RLlbBnzazHpH/98cQwV0Tmgme5qoipnaycxOIFQHn+zum0odT1zu/qq7D3H3\nkdFvdTkwLvoNJEYJpnz8A/AVwh3AK9HfSaUOqgr8E3CXmS0EDgb+tcTxFBXdcc0GXgJeJfyOy3Zk\nuZnNBJ4DPmlmy83sfOCHwLFm9hbhjuyHpYwxlzxx3wr0Ax6JfqM/L2mQeeSJvePjqJw7PBERqSS6\ngxERkUQowYiISCKUYEREJBFKMCIikgglGBERSYQSjEgeZrYx4fNPN7PvRs+vM7PPF9j31Aqb/FRE\nCUakHLj71e7+lwK7nAoowUhFUYIRaQUzG2lmj0XrgTxqZntE2/cys+fN7FUz+0G+ux8zu8rM3jSz\np4FPZm3/jZl9KXr+w2hdoIVmdpOZHQGcDPwoGty3l5ldaGbzzGyBmf0hmmsqc56fmNmzZvZ25pzR\ne/8cxbfAzH6YFfdDZjbfzJ4ys08l9o8nVadrqQMQqTC3ADPcfYaZnQf8hHB38WPgx+4+08y+ketA\nMzuEMJ3OwYTf3kvA/Gb7DAK+CHzK3d3MBrj7R2Y2B5jr7rOj/T5y919Gz38AnB/FBrA78BngU4Qp\nWWab2YmECTIPc/dNZjYw2vc24Bvu/paZHQb8DDh6R/+RREB3MCKtdThwd/T8TkJBntn+++j53c0P\nihwJ/Le7b4pmyp6TY59aYDNwu5mdBuSb7+qA6I7jVeBsYP+s9/7o7o3uvpjt0+B/Hvh1Zv4sd18b\nzdx9BPB7M3sF+AUhOYm0C93BiJQRd28wswmECSy/BFxC7juK3wCnuvuCaHbfz2a9tyXruRX4uC7A\nR+5+8I7ELJKP7mBEWudZti9PfDbwVPT8eeD06Hm+5YufBE41s15m1g/4QvMdoruK/u7+APAtwjLO\nABsIkyxm9ANWRks8nB0j7keAr2W11QyM7qLeMbMzom1mZgcVOolIayjBiOTXO5qJNvP3bcLsy1+L\nZl/+CnBZtO83gW9H2/cmVHU1ES2JfQ+wgLAS4rwcn9kPmBud52ng29H2WcD/tbDy5l7A9wkrnj4D\n/K3YF3H3hwhVcjVRddh3o7fOBs43swXAa4R2GpF2odmURdpBdGdQFzXMTwamuLsKa6lqaoMRaR+H\nALdGC4B9RFivXaSq6Q5GREQSoTYYERFJhBKMiIgkQglGREQSoQQjIiKJUIIREZFEKMGIiEgi/j8C\n/6TV9msGSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gun_state_2016_df['distance'], gun_state_2016_df['n_incident_capita_2016'], alpha=0.3, label='Data', s=50, color='b')\n",
    "plt.ylim([0, 0.0005])\n",
    "plt.yticks(np.arange(0, 0.0005, 0.0001))\n",
    "\n",
    "xs = np.arange(gun_state_2016_df['distance'].min(), gun_state_2016_df['distance'].max())\n",
    "ys = 6.332e-07 * xs + 0.0002\n",
    "plt.plot(xs, ys, '--k', linewidth=3, label='Model')\n",
    "\n",
    "plt.xlabel('Log distance')\n",
    "plt.ylabel('Number of incident per capita in 2016')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "_xLTaSepAptM"
   },
   "source": [
    "# Ethics & Privacy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0hG-WKqcAptM"
   },
   "source": [
    "When considering the question of the relationship between gun violence and the availability of mental health resources, we are aware that this may pose some ethical dilemmas that we will need to keep in mind. Firstly, the privacy of the victims of these gun-related fatalities and injuries would need to be protected. In terms of data, we plan to use data scraped from the gun violence archive, which is a non-profit organization that serves to provide data about gun-related violence and incidents in the US. This source does not include any private information about the victims and/or their families. In terms of biases, our dataset includes incidents where there are 0 fatalities, meaning there was still an act of violence that occurred even though no one died. In addition, our dataset also includes records of suicides with a gun, which can also lead to the immediate assumption that the suicide victim suffered from mental illness. Therefore, we further analyzed the suicide data to hopefully better understand these circumstances. \n",
    "\n",
    "\n",
    "Additionally, we understand that dealing with claims about mental health is always a complex issue. We do not attempt to insinuate that all individuals that suffer from mental illnesses will instigate gun violence, or that all perpetrators of gun violence are affected by mental health. We are looking at mental health resources and gun violence incidents to better understand the relationship between these two situations, in order to better understand trends and find solutions for the future"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "HpfUznW2AptN"
   },
   "source": [
    "# Conclusion & Discussion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "0mWcQb9BAptO"
   },
   "source": [
    "Based on our findings, we can conclude that there is little correlation between the number of mental health occurences (both serious and any mental health illnesses) with the number of gun related incidents within a state. This means that our first hypothesis was incorrect, since we assumed that there would be a correlation between these two areas. Additionally, we can conclude that there is a moderate negative correlation between the number of mental health facilities per capita and the number of gun violence incidents, number of people killed, and number of people injured which means that our initial hypothesis was kind of correct. The correlation is not super strong so we cannot say conclusively that states with more mental health facilities have decreased gun violence rates. We also found that there is no relationship between number of incidents and distance to a mental health facility; this means that there is no correlation between the number of gun-related incidents and the average minimum distance to a mental health facility for each state. This means that our hypothesis was also wrong for this claim as well, we thought that there would be lower gun violence incidents in places that are in closer proximity to mental health facilities, but we could not find a statistically important result which means that this is probably not the case and there may be other factors at play.\n",
    "\n",
    "Since we hypothesized that states with more mental health facilities will have a decreased rate of gun violence, our process consisted of finding the distance between the gun violence incidents and the closest mental health facility, and most importantly, normalizing it so that it would be relevant per capita. Because we had to normalize the data, this shows that population may be a confounding varaible. For example, in larger metropolitan areas with larger populations, it means that there would be more mental health facilities, as well as more gun violence incidents. Also, a higher population in general means that there will probably be more mental health facilities available for that area, along with more mental health occurences, and even more gun violence incidents as well. So population is something that we have to also consider when drawing conclusions from our analyses. \n",
    "\n",
    "\n",
    "What is interesting about our findings is that while there was little correlation between mental health occurences and gun violence incidents, but there was a more significant relationship between number of people receiving treatement and a decrease in violent gun violence. So even though mental health occurences may not be correlated with gun violence, this goes to show how important mental health treatment may be in helping combat gun violence. However, it is important to note about our data: it does not necessarily mean that having more mental health facilities automatically results in lower gun violence rates. The problem is far more complex than just painting it in black and white, and while a portion of mass shooters do have mental health disorders, \"the one-third who did get help 'carried out an attack anyway,\" according to Grant Duwe. This suggests that mental health treatment may not be the most effective form of reducing gun violence. \n",
    "\n",
    "For future evaluations of gun violence instances, it may be more constructive in this discourse to compare each state's gun laws in relation to gun violence incidents. Additionally, in relation to mental health facilities, it would be helpful to analyze how these laws affect the accessibility of buying guns in the first place, which would probably provide more information for how we can combat the issue of gun violence in the United States."
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
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   "collapsed_sections": [],
   "name": "FinalProject.ipynb",
   "provenance": [],
   "version": "0.3.2"
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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   "pygments_lexer": "ipython3",
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